referencias.bib

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@Article{Button2013,
  author    = {Katherine S. Button and John P. A. Ioannidis and Claire Mokrysz and Brian A. Nosek and Jonathan Flint and Emma S. J. Robinson and Marcus R. Munaf{\`{o}}},
  title     = {Power failure: why small sample size undermines the reliability of neuroscience},
  journal   = {Nature Reviews Neuroscience},
  year      = {2013},
  volume    = {14},
  number    = {5},
  pages     = {365--376},
  month     = apr,
  doi       = {10.1038/nrn3475},
  publisher = {Springer Nature},
}

@InProceedings{Duin2000,
  author    = {R. P. W. Duin},
  title     = {Classifiers in almost empty spaces},
  booktitle = {Proceedings of the 15th International Pattern Recognition Conference},
  year      = {2000},
  volume    = {2},
  pages     = {1--7},
  doi       = {10.1109/ICPR.2000.906006},
  issn      = {1051-4651},
  keywords  = {Hilbert spaces, decision theory, learning automata, object recognition, pattern classification, statistical analysis, Hilbert space, decision theory, dimensionality, dissimilarity, image recognition, kernel mapping, object recognition, pattern classification, statistical classifiers, support vector machines, training sets, Hilbert space, Image databases, Image recognition, NIST, Pattern recognition, Physics, Space technology, Support vector machine classification, Support vector machines, Testing},
}

@InProceedings{Kohavi1995,
  author    = {Kohavi, R},
  title     = {{A study of cross-validation and bootstrap for accuracy estimation and model selection.}},
  booktitle = {{Proceedings of International Joint Conference on AI}},
  year      = {1995},
  pages     = {1137--1145},
  owner     = {pakitochus},
  timestamp = {2011.12.09},
  url       = {http://citeseer.ist.psu.edu/kohavi95study.html},
}

@Article{Ashburner2000,
  author    = {Ashburner, John and Friston, Karl J},
  title     = {Voxel-based morphometry---the methods},
  journal   = {Neuroimage},
  year      = {2000},
  volume    = {11},
  number    = {6},
  pages     = {805--821},
  publisher = {Elsevier},
}

@Article{Chang2001,
  author    = {Chang, Chih-Chung and Lin, Chih-Jen},
  title     = {LIBSVM: a library for support vector machines},
  journal   = {ACM Transactions on Intelligent Systems and Technology},
  year      = {2011},
  volume    = {2},
  number    = {3},
  pages     = {1--27},
  month     = apr,
  doi       = {10.1145/1961189.1961199},
  issn      = {2157-6904},
  owner     = {paco},
  publisher = {Association for Computing Machinery (ACM)},
  timestamp = {2015.11.03},
}

@Article{haar2014anatomical,
  author    = {Haar, Shlomi and Berman, Sigal and Behrmann, Marlene and Dinstein, Ilan},
  title     = {{Anatomical Abnormalities in Autism?}},
  journal   = {Cerebral Cortex},
  year      = {2014},
  doi       = {10.1093/cercor/bhu242},
  owner     = {pakitochus},
  timestamp = {2015.07.06},
}

@Article{Mazziotta2001,
  author          = {Mazziotta, J and Toga, A and Evans, A and Fox, P and Lancaster, J and Zilles, K and Woods, R and Paus, T and Simpson, G and Pike, B and Holmes, C and Collins, L and Thompson, P and MacDonald, D and Iacoboni, M and Schormann, T and Amunts, K and Palomero-Gallagher, N and Geyer, S and Parsons, L and Narr, K and Kabani, N and Le Goualher, G and Boomsma, D and Cannon, T and Kawashima, R and Mazoyer, B},
  title           = {A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM).},
  journal         = {Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences},
  year            = {2001},
  volume          = {356},
  pages           = {1293--1322},
  month           = aug,
  issn            = {0962-8436},
  abstract        = {Motivated by the vast amount of information that is rapidly accumulating about the human brain in digital form, we embarked upon a program in 1992 to develop a four-dimensional probabilistic atlas and reference system for the human brain. Through an International Consortium for Brain Mapping (ICBM) a dataset is being collected that includes 7000 subjects between the ages of eighteen and ninety years and including 342 mono- and dizygotic twins. Data on each subject includes detailed demographic, clinical, behavioural and imaging information. DNA has been collected for genotyping from 5800 subjects. A component of the programme uses post-mortem tissue to determine the probabilistic distribution of microscopic cyto- and chemoarchitectural regions in the human brain. This, combined with macroscopic information about structure and function derived from subjects in vivo, provides the first large scale opportunity to gain meaningful insights into the concordance or discordance in micro- and macroscopic structure and function. The philosophy, strategy, algorithm development, data acquisition techniques and validation methods are described in this report along with database structures. Examples of results are described for the normal adult human brain as well as examples in patients with Alzheimer's disease and multiple sclerosis. The ability to quantify the variance of the human brain as a function of age in a large population of subjects for whom data is also available about their genetic composition and behaviour will allow for the first assessment of cerebral genotype-phenotype-behavioural correlations in humans to take place in a population this large. This approach and its application should provide new insights and opportunities for investigators interested in basic neuroscience, clinical diagnostics and the evaluation of neuropsychiatric disorders in patients.},
  citation-subset = {IM},
  completed       = {2001-12-07},
  country         = {England},
  created         = {2001-9-7},
  doi             = {10.1098/rstb.2001.0915},
  issn-linking    = {0962-8436},
  issue           = {1412},
  keywords        = {Adult; Brain, anatomy & histology; Brain Mapping, instrumentation, methods; Databases, Factual; Humans; Magnetic Resonance Imaging; Models, Statistical; Neuroanatomy, instrumentation, methods},
  nlm             = {PMC1088516},
  nlm-id          = {7503623},
  owner           = {NLM},
  pmc             = {PMC1088516},
  pmid            = {11545704},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2016-10-19},
}

@Article{Mazziotta2001a,
  author          = {Mazziotta, J and Toga, A and Evans, A and Fox, P and Lancaster, J and Zilles, K and Woods, R and Paus, T and Simpson, G and Pike, B and Holmes, C and Collins, L and Thompson, P and MacDonald, D and Iacoboni, M and Schormann, T and Amunts, K and Palomero-Gallagher, N and Geyer, S and Parsons, L and Narr, K and Kabani, N and Le Goualher, G and Feidler, J and Smith, K and Boomsma, D and Hulshoff Pol, H and Cannon, T and Kawashima, R and Mazoyer, B},
  title           = {A four-dimensional probabilistic atlas of the human brain.},
  journal         = {Journal of the American Medical Informatics Association : JAMIA},
  year            = {2001},
  volume          = {8},
  pages           = {401--430},
  abstract        = {The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype- phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders.},
  citation-subset = {IM},
  completed       = {2001-10-04},
  country         = {England},
  created         = {2001-8-27},
  issn            = {1067-5027},
  issn-linking    = {1067-5027},
  issue           = {5},
  keywords        = {Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Anatomy, Artistic; Anatomy, Cross-Sectional; Brain, anatomy & histology; Databases, Factual; Humans; Image Enhancement, methods; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Medical Illustration; Middle Aged; Neuroanatomy, methods; Probability; Schizophrenia, pathology},
  nlm             = {PMC131040},
  nlm-id          = {9430800},
  owner           = {NLM},
  pmc             = {PMC131040},
  pmid            = {11522763},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2016-10-19},
}

@Article{Reuter2010,
  author          = {Reuter, Martin and Rosas, H Diana and Fischl, Bruce},
  title           = {Highly accurate inverse consistent registration: a robust approach.},
  journal         = {NeuroImage},
  year            = {2010},
  volume          = {53},
  pages           = {1181--1196},
  month           = dec,
  abstract        = {The registration of images is a task that is at the core of many applications in computer vision. In computational neuroimaging where the automated segmentation of brain structures is frequently used to quantify change, a highly accurate registration is necessary for motion correction of images taken in the same session, or across time in longitudinal studies where changes in the images can be expected. This paper, inspired by Nestares and Heeger (2000), presents a method based on robust statistics to register images in the presence of differences, such as jaw movement, differential MR distortions and true anatomical change. The approach we present guarantees inverse consistency (symmetry), can deal with different intensity scales and automatically estimates a sensitivity parameter to detect outlier regions in the images. The resulting registrations are highly accurate due to their ability to ignore outlier regions and show superior robustness with respect to noise, to intensity scaling and outliers when compared to state-of-the-art registration tools such as FLIRT (in FSL) or the coregistration tool in SPM.},
  citation-subset = {IM},
  completed       = {2011-01-03},
  country         = {United States},
  created         = {2010-9-14},
  doi             = {10.1016/j.neuroimage.2010.07.020},
  issn            = {1095-9572},
  issn-linking    = {1053-8119},
  issue           = {4},
  keywords        = {Algorithms; Brain, anatomy & histology; Humans; Image Interpretation, Computer-Assisted, methods; Models, Theoretical},
  mid             = {NIHMS223420},
  nlm             = {PMC2946852},
  nlm-id          = {9215515},
  owner           = {NLM},
  pii             = {S1053-8119(10)00971-7},
  pmc             = {PMC2946852},
  pmid            = {20637289},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-22},
}

@Article{Smith2004,
  author          = {Smith, Stephen M and Jenkinson, Mark and Woolrich, Mark W and Beckmann, Christian F and Behrens, Timothy E J and Johansen-Berg, Heidi and Bannister, Peter R and De Luca, Marilena and Drobnjak, Ivana and Flitney, David E and Niazy, Rami K and Saunders, James and Vickers, John and Zhang, Yongyue and De Stefano, Nicola and Brady, J Michael and Matthews, Paul M},
  title           = {Advances in functional and structural MR image analysis and implementation as FSL.},
  journal         = {NeuroImage},
  year            = {2004},
  volume          = {23 Suppl 1},
  pages           = {S208--S219},
  abstract        = {The techniques available for the interrogation and analysis of neuroimaging data have a large influence in determining the flexibility, sensitivity, and scope of neuroimaging experiments. The development of such methodologies has allowed investigators to address scientific questions that could not previously be answered and, as such, has become an important research area in its own right. In this paper, we present a review of the research carried out by the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB). This research has focussed on the development of new methodologies for the analysis of both structural and functional magnetic resonance imaging data. The majority of the research laid out in this paper has been implemented as freely available software tools within FMRIB's Software Library (FSL).},
  citation-subset = {IM},
  completed       = {2005-01-19},
  country         = {United States},
  created         = {2004-10-25},
  doi             = {10.1016/j.neuroimage.2004.07.051},
  issn            = {1053-8119},
  issn-linking    = {1053-8119},
  keywords        = {Bayes Theorem; Brain, anatomy & histology, physiology; Databases, Factual; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging, statistics & numerical data; Models, Neurological; Models, Statistical; Software},
  nlm-id          = {9215515},
  owner           = {NLM},
  pii             = {S1053-8119(04)00393-3},
  pmid            = {15501092},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  references      = {42},
  revised         = {2006-11-15},
}

@Article{Reuter2012,
  author          = {Reuter, Martin and Schmansky, Nicholas J and Rosas, H Diana and Fischl, Bruce},
  title           = {Within-subject template estimation for unbiased longitudinal image analysis.},
  journal         = {NeuroImage},
  year            = {2012},
  volume          = {61},
  pages           = {1402--1418},
  month           = jul,
  abstract        = {Longitudinal image analysis has become increasingly important in clinical studies of normal aging and neurodegenerative disorders. Furthermore, there is a growing appreciation of the potential utility of longitudinally acquired structural images and reliable image processing to evaluate disease modifying therapies. Challenges have been related to the variability that is inherent in the available cross-sectional processing tools, to the introduction of bias in longitudinal processing and to potential over-regularization. In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, within-subject template creation, for automatic surface reconstruction and segmentation of brain MRI of arbitrarily many time points. We demonstrate that it is essential to treat all input images exactly the same as removing only interpolation asymmetries is not sufficient to remove processing bias. We successfully reduce variability and avoid over-regularization by initializing the processing in each time point with common information from the subject template. The presented results show a significant increase in precision and discrimination power while preserving the ability to detect large anatomical deviations; as such they hold great potential in clinical applications, e.g. allowing for smaller sample sizes or shorter trials to establish disease specific biomarkers or to quantify drug effects.},
  citation-subset = {IM},
  completed       = {2012-11-05},
  country         = {United States},
  created         = {2012-6-15},
  doi             = {10.1016/j.neuroimage.2012.02.084},
  issn            = {1095-9572},
  issn-linking    = {1053-8119},
  issue           = {4},
  keywords        = {Algorithms; Brain, anatomy & histology, physiology; Humans; Image Interpretation, Computer-Assisted, methods; Magnetic Resonance Imaging, methods},
  mid             = {NIHMS363223},
  nlm             = {PMC3389460},
  nlm-id          = {9215515},
  owner           = {NLM},
  pii             = {S1053-8119(12)00276-5},
  pmc             = {PMC3389460},
  pmid            = {22430496},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-22},
}

@Article{Jenkinson2001,
  author          = {Jenkinson, M and Smith, S},
  title           = {A global optimisation method for robust affine registration of brain images.},
  journal         = {Medical image analysis},
  year            = {2001},
  volume          = {5},
  pages           = {143--156},
  month           = jun,
  abstract        = {Registration is an important component of medical image analysis and for analysing large amounts of data it is desirable to have fully automatic registration methods. Many different automatic registration methods have been proposed to date, and almost all share a common mathematical framework - one of optimising a cost function. To date little attention has been focused on the optimisation method itself, even though the success of most registration methods hinges on the quality of this optimisation. This paper examines the assumptions underlying the problem of registration for brain images using inter-modal voxel similarity measures. It is demonstrated that the use of local optimisation methods together with the standard multi-resolution approach is not sufficient to reliably find the global minimum. To address this problem, a global optimisation method is proposed that is specifically tailored to this form of registration. A full discussion of all the necessary implementation details is included as this is an important part of any practical method. Furthermore, results are presented for inter-modal, inter-subject registration experiments that show that the proposed method is more reliable at finding the global minimum than several of the currently available registration packages in common usage.},
  citation-subset = {IM},
  completed       = {2001-10-25},
  country         = {Netherlands},
  created         = {2001-8-22},
  issn            = {1361-8415},
  issn-linking    = {1361-8415},
  issue           = {2},
  keywords        = {Brain Mapping, methods; Costs and Cost Analysis; Humans; Image Processing, Computer-Assisted, economics, methods; Magnetic Resonance Imaging; Mathematics},
  nlm-id          = {9713490},
  owner           = {NLM},
  pii             = {S1361841501000366},
  pmid            = {11516708},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2006-11-15},
}

@Book{spm_book,
  title     = {{Statistical Parametric Mapping: The Analysis of Functional Brain Images}},
  publisher = {Academic Press},
  year      = {2007},
  author    = {Friston, K.J. and Ashburner, J. and Kiebel, S.J. and Nichols, T.E. and Penny, W.D.},
}

@Misc{Talairach1988c,
  author    = {Talairach, J and Tournoux, P},
  title     = {Co-Planar Stereotactic Atlas ofthe Human Brain},
  year      = {1988},
  publisher = {Stuttgarr, Germany. Thieme Verlag},
}

@Article{Fischl2002,
  author          = {Fischl, Bruce and Salat, David H and Busa, Evelina and Albert, Marilyn and Dieterich, Megan and Haselgrove, Christian and van der Kouwe, Andre and Killiany, Ron and Kennedy, David and Klaveness, Shuna and Montillo, Albert and Makris, Nikos and Rosen, Bruce and Dale, Anders M},
  title           = {Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain.},
  journal         = {Neuron},
  year            = {2002},
  volume          = {33},
  pages           = {341--355},
  month           = jan,
  abstract        = {We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.},
  citation-subset = {IM},
  completed       = {2002-03-05},
  country         = {United States},
  created         = {2002-2-8},
  issn            = {0896-6273},
  issn-linking    = {0896-6273},
  issue           = {3},
  keywords        = {Aged; Alzheimer Disease, diagnosis, pathology; Brain, anatomy & histology, pathology; Brain Mapping; Female; Humans; Magnetic Resonance Imaging, methods; Male; Reproducibility of Results},
  nlm-id          = {8809320},
  owner           = {NLM},
  pii             = {S089662730200569X},
  pmid            = {11832223},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2007-11-14},
}

@Article{Salas-Gonzalez2009,
  author          = {Salas-Gonzalez, Diego and G\'orriz, Juan M and Ram\'irez, Javier and L\'opez, Miriam and Illan, Ignacio A and Segovia, Ferm\'in and Puntonet, Carlos G and G\'omez-R\'io, Manuel},
  title           = {Analysis of SPECT brain images for the diagnosis of Alzheimer's disease using moments and support vector machines.},
  journal         = {Neuroscience letters},
  year            = {2009},
  volume          = {461},
  pages           = {60--64},
  month           = sep,
  abstract        = {This paper presents a computer-aided diagnosis technique for improving the accuracy of diagnosing the Alzheimer's type dementia. The proposed methodology is based on the calculation of the skewness for each m-by-m-by-m sliding block of the SPECT brain images. The center pixel in this m-by-m-by-m block is replaced by the skewness value to build a new 3-D brain image which is used for classification purposes. After that, voxels which present a Welch's t-statistic between classes, Normal and Alzheimer's images, higher (or lower) than a threshold are selected. The mean, standard deviation, skewness and kurtosis are calculated for these selected voxels and they are subjected as features to linear kernel based support vector machine classifier. The proposed methodology reaches accuracy higher than 99\% in the classification task.},
  chemicals       = {Organotechnetium Compounds, Radiopharmaceuticals, technetium Tc 99m bicisate, Cysteine},
  citation-subset = {IM},
  completed       = {2009-08-24},
  country         = {Ireland},
  created         = {2009-7-6},
  doi             = {10.1016/j.neulet.2009.05.056},
  issn            = {1872-7972},
  issn-linking    = {0304-3940},
  issue           = {1},
  keywords        = {Alzheimer Disease, radionuclide imaging; Brain, radionuclide imaging; Cysteine, analogs & derivatives; Humans; Image Interpretation, Computer-Assisted; Organotechnetium Compounds; Radiopharmaceuticals; Tomography, Emission-Computed, Single-Photon},
  nlm-id          = {7600130},
  owner           = {NLM},
  pii             = {S0304-3940(09)00707-1},
  pmid            = {19477227},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-25},
}

@Article{Scherfler2005,
  author          = {Scherfler, Christoph and Seppi, Klaus and Donnemiller, Eveline and Goebel, Georg and Brenneis, Christian and Virgolini, Irene and Wenning, Gregor K and Poewe, Werner},
  title           = {Voxel-wise analysis of [123I]beta-CIT SPECT differentiates the Parkinson variant of multiple system atrophy from idiopathic Parkinson's disease.},
  journal         = {Brain},
  year            = {2005},
  volume          = {128},
  pages           = {1605--1612},
  month           = jul,
  chemicals       = {Dopamine Plasma Membrane Transport Proteins, Membrane Glycoproteins, Membrane Transport Proteins, Nerve Tissue Proteins, Radiopharmaceuticals, Receptors, Dopamine D2, SLC6A3 protein, human, RTI 55, Cocaine, Dopamine},
  citation-subset = {AIM, IM},
  completed       = {2005-07-18},
  country         = {England},
  created         = {2005-6-27},
  doi             = {10.1093/brain/awh485},
  issn            = {1460-2156},
  issn-linking    = {0006-8950},
  issue           = {Pt 7},
  keywords        = {Aged; Case-Control Studies; Caudate Nucleus, metabolism; Cocaine, analogs & derivatives; Corpus Striatum, metabolism; Diagnosis, Differential; Discriminant Analysis; Dopamine, metabolism; Dopamine Plasma Membrane Transport Proteins; Female; Humans; Linear Models; Male; Membrane Glycoproteins, metabolism; Membrane Transport Proteins, metabolism; Mesencephalon, metabolism; Middle Aged; Multiple System Atrophy, metabolism, radionuclide imaging; Nerve Tissue Proteins, metabolism; Parkinson Disease, metabolism, radionuclide imaging; Radiopharmaceuticals; Receptors, Dopamine D2, metabolism; Tomography, Emission-Computed, Single-Photon},
  nlm-id          = {0372537},
  owner           = {NLM},
  pii             = {awh485},
  pmid            = {15817519},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-24},
}

@Article{Arndt1996,
  author          = {Arndt, S and Cizadlo, T and O'Leary, D and Gold, S and Andreasen, N C},
  title           = {Normalizing counts and cerebral blood flow intensity in functional imaging studies of the human brain.},
  journal         = {NeuroImage},
  year            = {1996},
  volume          = {3},
  pages           = {175--184},
  month           = jun,
  abstract        = {Image intensity normalization is frequently applied to eliminate or adjust for subject or injection global blood flow (gCBF) and other sources of nuisance variation. Normalization has several other positive effects on the analysis of PET images. However, the choice of an intensity normalization technique affects the statistical and psychometric properties of the image data. We compared three normalization procedures, the ratio approach (regional (r)CBF/gCBF), histogram equalization, and ANCOVA, on both PET count and flow data sets. The ratio method presents the proportional increase of regions, the histogram equalization method offers the relative ranking of intensities over the image, and the ANCOVA method provides statistical deviations from an expected linear model of regional values from the subject's gCBF. The original study used 33 normal subjects in a standard subtraction paradigm. The normalization methods were evaluated on their ability to remove extraneous error variation, induce homogeneity of intersubject variation, and remove unwanted dependencies. In general, the normalization modified the subtraction image more than the individual condition images. All three methods worked well at removing the dependency of rCBF on gCBF in count and flow images. For count data, the three methods also reduced the amount of error variation equally well, improving the signal to noise ratio. For flow data, the histogram equalization and ratio methods worked best at reducing statistical error. All three methods dramatically stabilized the variance over the image.},
  citation-subset = {IM},
  completed       = {1998-01-06},
  country         = {United States},
  created         = {1998-1-6},
  doi             = {10.1006/nimg.1996.0019},
  issn            = {1053-8119},
  issn-linking    = {1053-8119},
  issue           = {3 Pt 1},
  keywords        = {Analysis of Variance; Blood Flow Velocity; Brain, anatomy & histology, physiology, radionuclide imaging; Cerebrovascular Circulation; Humans; Magnetic Resonance Imaging; Memory, physiology; Reference Values; Regional Blood Flow, physiology; Statistics, Nonparametric; Tomography, Emission-Computed},
  nlm-id          = {9215515},
  owner           = {NLM},
  pii             = {S1053-8119(96)90019-1},
  pmid            = {9345488},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2016-11-24},
}

@Article{Salas-Gonzalez2013,
  author          = {Salas-Gonzalez, Diego and G\'orriz, Juan M and Ram\'irez, Javier and Ill\'an, Ignacio A and Lang, Elmar W},
  title           = {Linear intensity normalization of FP-CIT SPECT brain images using the $\alpha$-stable distribution.},
  journal         = {NeuroImage},
  year            = {2013},
  volume          = {65},
  pages           = {449--455},
  month           = jan,
  abstract        = {In this work, a linear procedure to perform the intensity normalization of FP-CIT SPECT brain images is presented. This proposed methodology is based on the fact that the histogram of intensity values can be fitted accurately using a positive skewed $\alpha$-stable distribution. Then, the predicted $\alpha$-stable parameters and the location-scale property are used to linearly transform the intensity values in each voxel. This transformation is performed such that the new histograms in each image have a pre-specified $\alpha$-stable distribution with desired location and dispersion values. The proposed methodology is compared with a similar approach assuming Gaussian distribution and the widely used specific-to-nonspecific ratio. In this work, we show that the linear normalization method using the $\alpha$-stable distribution outperforms those existing methods.},
  chemicals       = {Radiopharmaceuticals, Tropanes, 2-carbomethoxy-8-(3-fluoropropyl)-3-(4-iodophenyl)tropane},
  citation-subset = {IM},
  completed       = {2013-06-28},
  country         = {United States},
  created         = {2012-12-3},
  doi             = {10.1016/j.neuroimage.2012.10.005},
  issn            = {1095-9572},
  issn-linking    = {1053-8119},
  keywords        = {Brain, radionuclide imaging; Brain Mapping, methods; Humans; Image Processing, Computer-Assisted, methods; Radiopharmaceuticals; Tomography, Emission-Computed, Single-Photon, methods; Tropanes},
  nlm-id          = {9215515},
  owner           = {NLM},
  pii             = {S1053-8119(12)00993-7},
  pmid            = {23063448},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-25},
}

@Article{Weiskopf2013,
  author    = {Weiskopf, Nikolaus and Suckling, John and Williams, Guy and Correia, Marta Morgado and Inkster, Becky and Tait, Roger and Ooi, Cinly and Bullmore, Edward T and Lutti, Antoine},
  title     = {Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation},
  journal   = {Frontiers in neuroscience},
  year      = {2013},
  volume    = {7},
  pages     = {95},
  publisher = {Frontiers},
}

@Article{DeMartino2007,
  author    = {{De Martino}, Federico and Gentile, Francesco and Esposito, Fabrizio and Balsi, Marco and {Di Salle}, Francesco and Goebel, Rainer and Formisano, Elia},
  title     = {{Classification of fMRI independent components using {IC}-fingerprints and support vector machine classifiers}},
  journal   = {Neuroimage},
  year      = {2007},
  volume    = {34},
  number    = {1},
  pages     = {177--194},
  month     = jan,
  abstract  = {We present a general method for the classification of independent components (ICs) extracted from functional MRI (fMRI) data sets. The method consists of two steps. In the first step, each fMRI-IC is associated with an IC-fingerprint, i.e., a representation of the component in a multidimensional space of parameters. These parameters are post hoc estimates of global properties of the ICs and are largely independent of a specific experimental design and stimulus timing. In the second step a machine learning algorithm automatically separates the IC-fingerprints into six general classes after preliminary training performed on a small subset of expert-labeled components. We illustrate this approach in a multisubject fMRI study employing visual structure-from-motion stimuli encoding faces and control random shapes. We show that: (1) IC-fingerprints are a valuable tool for the inspection, characterization and selection of fMRI-ICs and (2) automatic classifications of fMRI-ICs in new subjects present a high correspondence with those obtained by expert visual inspection of the components. Importantly, our classification procedure highlights several neurophysiologically interesting processes. The most intriguing of which is reflected, with high intra- and inter-subject reproducibility, in one IC exhibiting a transiently task-related activation in the [`]face' region of the primary sensorimotor cortex. This suggests that in addition to or as part of the mirror system, somatotopic regions of the sensorimotor cortex are involved in disambiguating the perception of a moving body part. Finally, we show that the same classification algorithm can be successfully applied, without re-training, to fMRI collected using acquisition parameters, stimulation modality and timing considerably different from those used for training.},
  issn      = {1053-8119},
  owner     = {imagenes2},
  timestamp = {2009.02.12},
}

@Article{Gorriz2010,
  author   = {G{\'o}rriz, J.~M. and Segovia, F. and Ram{\'i}rez, J. and Lassl, A. and Salas-Gonzalez, D.},
  title    = {{GMM based SPECT image classification for the diagnosis of Alzheimer's disease}},
  journal  = {Applied Soft Computing},
  year     = {2011},
  volume   = {11},
  number   = {2},
  pages    = {2313--2325},
  issn     = {1568-4946},
  doi      = {10.1016/j.asoc.2010.08.012},
  keywords = {SPECT},
}

@Article{Illan2011,
  author     = {Ill{\'a}n, I.~A. and G{\'o}rriz, J.~M. and Ram{\'i}rez, J. and Salas-Gonzalez, D. and L{\'o}pez, M. M. and Segovia, F. and Chaves, R. and G{\'o}mez-Rio, M. and Puntonet, C.~G.},
  title      = {{$^{18}${F-FDG PET} imaging analysis for computer aided {Alzheimer's} diagnosis}},
  journal    = {Information Sciences},
  year       = {2011},
  volume     = {181},
  pages      = {903--916},
  month      = feb,
  acmid      = {1897362},
  address    = {New York, NY, USA},
  doi        = {10.1016/j.ins.2010.10.027},
  issn       = {0020-0255},
  issue      = {4},
  issue_date = {February, 2011},
  keywords   = {Alzheimer's disease (AD); Computer aided diagnosis; FDG-PET; Independent component analysis (ICA); Principal component analysis (PCA); Supervised learning; Support vector machine (SVM)},
  numpages   = {14},
  publisher  = {Elsevier Science Inc.},
}

@Article{Benjamini2010,
  author    = {Benjamini, Yoav},
  title     = {Simultaneous and selective inference: current successes and future challenges},
  journal   = {Biometrical Journal},
  year      = {2010},
  volume    = {52},
  number    = {6},
  pages     = {708--721},
  publisher = {Wiley Online Library},
}

@InBook{Krishnaiah1982,
  chapter   = {Dimensionality and sample size considerations in pattern recognition practice},
  pages     = {825--855},
  title     = {{Handbook of Statistics}},
  publisher = {North-Holland},
  year      = {1982},
  editor    = {Krishnaiah, R. R. and Kanal, L. N.},
  owner     = {pakitochus},
  timestamp = {2010.09.02},
}

@Article{Friston1994,
  author    = {Friston, Karl J and Holmes, Andrew P and Worsley, Keith J and Poline, J-P and Frith, Chris D and Frackowiak, Richard SJ},
  title     = {Statistical parametric maps in functional imaging: a general linear approach},
  journal   = {Human brain mapping},
  year      = {1994},
  volume    = {2},
  number    = {4},
  pages     = {189--210},
  publisher = {Wiley Online Library},
}

@InCollection{Herman2009l,
  author    = {Herman, Gabor T.},
  title     = {Filtered Backprojection for Parallel Beams},
  booktitle = {Fundamentals of Computerized Tomography},
  publisher = {Springer},
  year      = {2009},
  month     = jan,
  isbn      = {978-1-85233-617-2},
  abstract  = {The most commonly used methods in CT for parallel beam projection data are the filtered backprojection (FBP) methods. (In some of the earlier literature these methods are also referred to as ``convolution methods.'') The reason for this is ease of implementation combined with good accuracy. These methods are transform methods, where the taking of the derivative and the Hilbert transform is approximated by the use of a single convolution.},
  doi       = {10.1007/978-1-84628-723-7_8},
}

@Article{Kelloff2005,
  author    = {Kelloff, Gary J and Hoffman, John M and Johnson, Bruce and Scher, Howard I and Siegel, Barry A and Cheng, Edward Y and Cheson, Bruce D and O'Shaughnessy, Joyce and Guyton, Kathryn Z and Mankoff, David A and others},
  title     = {Progress and promise of FDG-PET imaging for cancer patient management and oncologic drug development},
  journal   = {Clinical Cancer Research},
  year      = {2005},
  volume    = {11},
  number    = {8},
  pages     = {2785--2808},
  publisher = {AACR},
}

@InProceedings{Newberg2002,
  author       = {Newberg, Andrew and Alavi, Abass and Reivich, Martin},
  title        = {Determination of regional cerebral function with FDG-PET imaging in neuropsychiatric disorders},
  booktitle    = {Seminars in nuclear medicine},
  year         = {2002},
  volume       = {32},
  number       = {1},
  pages        = {13--34},
  organization = {Elsevier},
}

@Book{Carlson2016,
  title     = {Physiology of behavior},
  publisher = {Pearson},
  year      = {2016},
  author    = {Carlson, Neil R},
}

@InProceedings{Stoeckel04,
  author    = {Stoeckel, J. and Ayache, N. and Malandain, G. and Koulibaly, P. M. and Ebmeier, K. P. and Darcourt, J.},
  title     = {{Automatic Classification of {SPECT} Images of {Alzheimer's} Disease Patients and Control Subjects}},
  booktitle = {{Medical Image Computing and Computer-Assisted Intervention - MICCAI}},
  year      = {2004},
  volume    = {3217},
  series    = {{Lecture Notes in Computer Science}},
  pages     = {654--662},
  publisher = {Springer},
}

@Article{Xu2009,
  author    = {Xu, Lai and Groth, Karyn M and Pearlson, Godfrey and Schretlen, David J and Calhoun, Vince D},
  title     = {Source-based morphometry: The use of independent component analysis to identify gray matter differences with application to schizophrenia},
  journal   = {Human brain mapping},
  year      = {2009},
  volume    = {30},
  number    = {3},
  pages     = {711--724},
  publisher = {Wiley Online Library},
}

@Article{bossa2010tensor,
  author    = {Bossa, Matias and Zacur, Ernesto and Olmos, Salvador and Alzheimer's Disease Neuroimaging Initiative and others},
  title     = {Tensor-based morphometry with stationary velocity field diffeomorphic registration: application to ADNI},
  journal   = {Neuroimage},
  year      = {2010},
  volume    = {51},
  number    = {3},
  pages     = {956--969},
  publisher = {Elsevier},
}

@Article{Dixon2003,
  author          = {Dixon, Peter},
  title           = {The p-value fallacy and how to avoid it.},
  journal         = {Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale},
  year            = {2003},
  volume          = {57},
  pages           = {189--202},
  month           = sep,
  issn            = {1196-1961},
  abstract        = {Null hypothesis significance tests are commonly used to provide a link between empirical evidence and theoretical interpretation. However, this strategy is prone to the "p-value fallacy" in which effects and interactions are classified as either "noise" or "real" based on whether the associated p value is greater or less than .05. This dichotomous classification can lead to dramatic misconstruals of the evidence provided by an experiment. For example, it is quite possible to have similar patterns of means that lead to entirely different patterns of significance, and one can easily find the same patterns of significance that are associated with completely different patterns of means. Describing data in terms of an inventory of significant and nonsignificant effects can thus completely misrepresent the results. An alternative analytical technique is to identify competing interpretations of the data and then use likelihood ratios to assess which interpretation provides the better account. Several different methods of calculating the likelihood ratios are illustrated. It is argued that this approach satisfies a principle of "graded evidence," according to which similar data should provide similar evidence.},
  citation-subset = {IM},
  completed       = {2003-12-09},
  country         = {Canada},
  created         = {2003-11-04},
  issn-linking    = {1196-1961},
  issue           = {3},
  keywords        = {Humans; Models, Psychological; Research Design},
  nlm-id          = {9315513},
  owner           = {NLM},
  pmid            = {14596477},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2006-11-15},
}

@Article{Benjamini1995,
  author    = {Benjamini, Yoav and Hochberg, Yosef},
  title     = {Controlling the false discovery rate: a practical and powerful approach to multiple testing},
  journal   = {Journal of the royal statistical society. Series B (Methodological)},
  year      = {1995},
  pages     = {289--300},
  publisher = {JSTOR},
}

@Book{Theodoridis1999,
  title     = {Pattern Recognition},
  publisher = {Academic Press},
  year      = {1999},
  author    = {Sergios Theodoridis and Konstantinos Koutroumbas},
  address   = {New York},
  isbn      = {9781597492720},
  journal   = {New York},
}

@Article{Winkler2014,
  author    = {Winkler, Anderson M and Ridgway, Gerard R and Webster, Matthew A and Smith, Stephen M and Nichols, Thomas E},
  title     = {Permutation inference for the general linear model},
  journal   = {Neuroimage},
  year      = {2014},
  volume    = {92},
  pages     = {381--397},
  publisher = {Elsevier},
}

@Article{Anderson2001,
  author    = {Anderson, Marti J and Robinson, John},
  title     = {Permutation tests for linear models},
  journal   = {Australian \& New Zealand Journal of Statistics},
  year      = {2001},
  volume    = {43},
  number    = {1},
  pages     = {75--88},
  publisher = {Wiley Online Library},
}

@Article{Haralick73,
  author  = {Haralick, R. and Shanmugam, K. and Dinstein, I.},
  title   = {{Textural features for image classification}},
  journal = {IEEE Transactions on Systems, Man and Cybernetics},
  year    = {1973},
  volume  = {3},
  number  = {6},
  pages   = {610--621},
}

@Article{Towey2011,
  author   = {Towey, David J and Bain, Peter G and Nijran, Kuldip S},
  title    = {{Automatic classification of {123I-{FP}-CIT (DaTSCAN) SPECT} images}},
  journal  = {Nuclear Medicine Communications},
  year     = {2011},
  volume   = {32},
  number   = {8},
  pages    = {699--707},
  month    = aug,
  issn     = {1473-5628},
  note     = {{PMID:} 21659911},
  abstract = {{INTRODUCTION} We present a method of automatic classification of I-fluoropropyl-carbomethoxy-3\beta-4-iodophenyltropane {(FP-CIT)} images. This technique uses singular value decomposition {(SVD)} to reduce a training set of patient image data into vectors in feature space {(D} space). The automatic classification techniques use the distribution of the training data in D space to define classification boundaries. Subsequent patients can be mapped into D space, and their classification can be automatically given. {METHODS} The technique has been tested using 116 patients for whom the diagnosis of either Parkinsonian syndrome or {non-Parkinsonian} syndrome has been confirmed from post {I-FP-CIT} imaging follow-up. The first three components were used to define D space. Two automatic classification tools were used, na\"ive Bayes {(NB)} and group prototype. A leave-one-out cross-validation was performed to repeatedly train and test the automatic classification system. Four commercially available systems for the classification were tested using the same clinical database. {RESULTS} The proposed technique combining {SVD} and {NB} correctly classified 110 of 116 patients (94.8\%), with a sensitivity of 93.7\% and specificity of 97.3\%. The combination of {SVD} and an automatic classifier performed as well or better than the commercially available systems. {CONCLUSION} The combination of data reduction by {SVD} with automatic classifiers such as {NB} can provide good diagnostic accuracy and may be a useful adjunct to clinical reporting.},
  doi      = {10.1097/MNM.0b013e328347cd09},
  keywords = {Automation; Female; Humans; Image Interpretation; {Computer-Assisted}; Male; Middle Aged; Parkinsonian Disorders; Principal Component Analysis; Tomography; {Emission-Computed}; {Single-Photon}; Tropanes},
}

@Article{Khedher2015,
  author    = {Khedher, L. and Ram\'irez, J. and G\'orriz, J.M. and Brahim, A. and Segovia, F.},
  title     = {Early diagnosis of Alzheimer's disease based on partial least squares, principal component analysis and support vector machine using segmented {MRI} images},
  journal   = {Neurocomputing},
  year      = {2015},
  volume    = {151},
  pages     = {139--150},
  month     = mar,
  issn      = {0925-2312},
  doi       = {10.1016/j.neucom.2014.09.072},
  owner     = {paco},
  publisher = {Elsevier BV},
  timestamp = {2015.10.29},
}

@Article{Segovia2013,
  author    = {Segovia, Ferm{\'i}n and G{\'o}rriz, JM and Ram{\'i}rez, Javier and Salas-Gonzalez, Diego and {\'A}lvarez, Ignacio},
  title     = {Early diagnosis of Alzheimer's disease based on partial least squares and support vector machine},
  journal   = {Expert Systems with Applications},
  year      = {2013},
  volume    = {40},
  number    = {2},
  pages     = {677--683},
  publisher = {Elsevier},
}

@Article{Association2016,
  author    = {Alzheimer's Association and others},
  title     = {2016 Alzheimer's disease facts and figures},
  journal   = {Alzheimer's \& Dementia},
  year      = {2016},
  volume    = {12},
  number    = {4},
  pages     = {459--509},
  publisher = {Elsevier},
}

@Article{Sevigny2016,
  author    = {Sevigny, Jeff and Chiao, Ping and Bussi{\`e}re, Thierry and Weinreb, Paul H and Williams, Leslie and Maier, Marcel and Dunstan, Robert and Salloway, Stephen and Chen, Tianle and Ling, Yan and others},
  title     = {The antibody aducanumab reduces A$\beta$ plaques in Alzheimer's disease},
  journal   = {Nature},
  year      = {2016},
  volume    = {537},
  number    = {7618},
  pages     = {50--56},
  publisher = {Nature Research},
}

@Article{Philips2008,
  author    = {Philips, Carl and Li, Daniel and Raicu, Daniela and Furst, Jacob},
  title     = {{Directional Invariance of Co-occurrence Matrices within the Liver}},
  journal   = {International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies},
  year      = {2008},
  volume    = {0},
  pages     = {29--34},
  address   = {Los Alamitos, CA, USA},
  doi       = {10.1109/BIOTECHNO.2008.24},
  isbn      = {978-0-7695-3191-5},
  publisher = {IEEE Computer Society},
}

@Article{Ballard2011,
  author          = {Ballard, Clive and Gauthier, Serge and Corbett, Anne and Brayne, Carol and Aarsland, Dag and Jones, Emma},
  title           = {Alzheimer's disease.},
  journal         = {Lancet (London, England)},
  year            = {2011},
  volume          = {377},
  pages           = {1019--1031},
  month           = mar,
  issn            = {1474-547X},
  abstract        = {An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimer's disease. Thus, Alzheimer's disease represents a major public health concern and has been identified as a research priority. Although there are licensed treatments that can alleviate symptoms of Alzheimer's disease, there is a pressing need to improve our understanding of pathogenesis to enable development of disease-modifying treatments. Methods for improving diagnosis are also moving forward, but a better consensus is needed for development of a panel of biological and neuroimaging biomarkers that support clinical diagnosis. There is now strong evidence of potential risk and protective factors for Alzheimer's disease, dementia, and cognitive decline, but further work is needed to understand these better and to establish whether interventions can substantially lower these risks. In this Seminar, we provide an overview of recent evidence regarding the epidemiology, pathogenesis, diagnosis, and treatment of Alzheimer's disease, and discuss potential ways to reduce the risk of developing the disease.},
  chemicals       = {Amyloid beta-Peptides, Biomarkers, tau Proteins},
  citation-subset = {AIM, IM},
  completed       = {2011-04-20},
  country         = {England},
  created         = {2011-03-22},
  doi             = {10.1016/S0140-6736(10)61349-9},
  issn-linking    = {0140-6736},
  issue           = {9770},
  keywords        = {Alzheimer Disease, diagnosis, epidemiology, genetics, therapy; Amyloid beta-Peptides, metabolism; Biomarkers, blood, cerebrospinal fluid; Brain, metabolism, pathology; Delphi Technique; Diagnostic Imaging; Humans; Neurofibrillary Tangles, pathology; Neurologic Examination; Plaque, Amyloid, pathology; Prevalence; Risk Factors; tau Proteins, metabolism},
  nlm-id          = {2985213R},
  owner           = {NLM},
  pii             = {S0140-6736(10)61349-9},
  pmid            = {21371747},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-22},
}

@Article{Baron2001,
  author      = {Baron, J. C. and Ch{\'{e}}telat, G. and Desgranges, B. and Perchey, G. and Landeau, B. and {de la Sayette}, V. and Eustache, F.},
  title       = {In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease.},
  journal     = {Neuroimage},
  year        = {2001},
  volume      = {14},
  number      = {2},
  pages       = {298--309},
  month       = aug,
  abstract    = {Up till now, the study of regional gray matter atrophy in Alzheimer's disease (AD) has been assessed with regions of interest, but this method is time-consuming, observer dependent, and poorly reproducible (especially in terms of cortical regions boundaries) and in addition is not suited to provide a comprehensive assessment of the brain. In this study, we have mapped gray matter density by means of voxel-based morphometry on T1-weighted MRI volume sets in 19 patients with mild AD and 16 healthy subjects of similar age and gender ratio and report highly significant clusters of gray matter loss with almost symmetrical distribution, affecting mainly and in decreasing order of significance the medial temporal structures, the posterior cingulate gyrus and adjacent precuneus, and the temporoparietal association and perisylvian neocortex, with only little atrophy in the frontal lobe. The findings are discussed in light of previous studies of gray matter atrophy in AD based either on postmortem or neuroimaging data and in relation to PET studies of resting glucose consumption. The limitations of the method are also discussed in some detail, especially with respect to the segmentation and spatial normalization procedures as they apply to pathological brains. Some potential applications of voxel-based morphometry in the study of AD are also mentioned.},
  doi         = {10.1006/nimg.2001.0848},
  institution = {INSERM U320, University of Caen, Caen, France. jcb54@cam.ac.uk},
  keywords    = {Aged; Aged, 80 and over; Alzheimer Disease, diagnosis/pathology; Atrophy; Brain Mapping; Cerebral Cortex, pathology; Dominance, Cerebral, physiology; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Reference Values; Reproducibility of Results; Tomography, Emission-Computed},
  language    = {eng},
  medline-pst = {ppublish},
  owner       = {pakitochus},
  pii         = {S1053-8119(01)90848-1},
  pmid        = {11467904},
  timestamp   = {2015.01.22},
}

@Article{Dubois2007,
  author    = {Dubois, Bruno and Feldman, Howard H and Jacova, Claudia and DeKosky, Steven T and Barberger-Gateau, Pascale and Cummings, Jeffrey and Delacourte, Andr{\'{e}} and Galasko, Douglas and Gauthier, Serge and Jicha, Gregory and et al.},
  title     = {Research criteria for the diagnosis of Alzheimer{\textquoteright}s disease: revising the NINCDS--ADRDA criteria},
  journal   = {The Lancet Neurology},
  year      = {2007},
  volume    = {6},
  number    = {8},
  pages     = {734--746},
  month     = aug,
  issn      = {1474-4422},
  doi       = {10.1016/s1474-4422(07)70178-3},
  owner     = {pakitochus},
  publisher = {Elsevier BV},
  timestamp = {2015.01.22},
}

@Article{Misra2009,
  author    = {Misra, Chandan and Fan, Yong and Davatzikos, Christos},
  title     = {{Baseline and longitudinal patterns of brain atrophy in {MC}I patients, and their use in prediction of short-term conversion to AD: Results from ADNI}},
  journal   = {Neuroimage},
  year      = {2009},
  volume    = {44},
  number    = {4},
  pages     = {1415--1422},
  month     = feb,
  issn      = {1053-8119},
  abstract  = {High-dimensional pattern classification was applied to baseline and multiple follow-up MRI scans of the Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with mild cognitive impairment (MCI), in order to investigate the potential of predicting short-term conversion to Alzheimer's Disease (AD) on an individual basis. MCI participants that converted to AD (average follow-up 15 months) displayed significantly lower volumes in a number of grey matter (GM) regions, as well as in the white matter (WM). They also displayed more pronounced periventricular small-vessel pathology, as well as an increased rate of increase of such pathology. Individual person analysis was performed using a pattern classifier previously constructed from AD patients and cognitively normal (CN) individuals to yield an abnormality score that is positive for AD-like brains and negative otherwise. The abnormality scores measured from MCI non-converters (MCI-NC) followed a bimodal distribution, reflecting the heterogeneity of this group, whereas they were positive in almost all MCI converters (MCI-C), indicating extensive patterns of AD-like brain atrophy in almost all MCI-C. Both MCI subgroups had similar MMSE scores at baseline. A more specialized classifier constructed to differentiate converters from non-converters based on their baseline scans provided good classification accuracy reaching 81.5\%, evaluated via cross-validation. These pattern classification schemes, which distill spatial patterns of atrophy to a single abnormality score, offer promise as biomarkers of AD and as predictors of subsequent clinical progression, on an individual patient basis.},
  doi       = {10.1016/j.neuroimage.2008.10.031},
  keywords  = {Alzheimer's disease; AD; Early detection; Mild cognitive impairment; MCI; Pattern classification; Structural MRI; Imaging biomarker},
  owner     = {imagenes2},
  timestamp = {2009.02.12},
}

@Article{Pievani2013,
  author    = {Pievani, Michela and Bocchetta, Martina and Boccardi, Marina and Cavedo, Enrica and Bonetti, Matteo and Thompson, Paul M. and Frisoni, Giovanni B.},
  title     = {Striatal morphology in early-onset and late-onset Alzheimer{\textquoteright}s disease: a preliminary study},
  journal   = {Neurobiology of Aging},
  year      = {2013},
  volume    = {34},
  number    = {7},
  pages     = {1728--1739},
  month     = jul,
  issn      = {0197-4580},
  doi       = {10.1016/j.neurobiolaging.2013.01.016},
  owner     = {pakitochus},
  publisher = {Elsevier BV},
  timestamp = {2015.01.22},
}

@Article{Leon1983,
  author    = {{de Leon}, M~J and Ferris, S~H and George, A~E and Reisberg, B and Christman, D~R and Kricheff, I~I and Wolf, A~P},
  title     = {{Computed tomography and positron emission transaxial tomography evaluations of normal aging and {Alzheimer's} disease}},
  journal   = {Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism},
  year      = {1983},
  volume    = {3},
  number    = {3},
  pages     = {391--4},
  month     = sep,
  note      = {{PMID:} 6603463},
  abstract  = {Young normal subjects, old normal subjects, and patients with senile dementia of the {Alzheimer's} type {(SDAT)} were studied with both computed tomography {(CT)} and positron emission transaxial tomography {(PETT).} Increases in ventricular size with both aging and disease were measured. Regional glucose metabolic rate was not affected by age, but was markedly reduced in {SDAT} patients. These data indicate that in normal aging, structural brain changes may be more salient than biochemical changes. Although both structural and biochemical changes occur in {SDAT,} the biochemical changes are more marked. The results suggest that {PETT} is potentially more useful than {CT} in the in vivo diagnosis of {SDAT.}},
  keywords  = {{Adult; Aged; Aging; Alzheimer} {Disease; Cognition; Dementia; Humans; Tomography; } {Emission-Computed; Tomography; } {X-Ray} Computed},
  owner     = {pakitochus},
  timestamp = {2010.08.27},
}

@Article{Kogure2000,
  author    = {Kogure, D. and Matsuda, H. and Ohnishi, T. and Asada, T. and Uno, M. and Kunihiro, T. and Nakano, S. and Takasaki, M.},
  title     = {{Longitudinal Evaluation of Early {Alzheimer} Disease Using Brain Perfusion {SPECT}}},
  journal   = {The Journal of Nuclear Medicine},
  year      = {2000},
  volume    = {41},
  number    = {7},
  pages     = {1155--1162},
  owner     = {imagenes2},
  timestamp = {2009.02.11},
  url       = {http://jnm.snmjournals.org/cgi/content/abstract/41/7/1155},
}

@Article{Claus1994,
  author    = {Claus, J.~J. and van Harskamp, F. and Breteler, M.~M.~B. and Krenning, E.~P. and van der Cammen, I. de Koning abd J.~M. and Hofman, A. and Hasan, D.},
  title     = {{The diagnostic value of {SPECT} with Tc 99{m HMPAO} in Alzheimer's disease. A population-based study}},
  journal   = {Neurology},
  year      = {1994},
  volume    = {44},
  number    = {3},
  pages     = {454--461},
  doi       = {10.1159/000051283},
  owner     = {imagenes2},
  timestamp = {2009.02.11},
}

@Article{Ikonomovic2008,
  author          = {Ikonomovic, Milos D and Klunk, William E and Abrahamson, Eric E and Mathis, Chester A and Price, Julie C and Tsopelas, Nicholas D and Lopresti, Brian J and Ziolko, Scott and Bi, Wenzhu and Paljug, William R and Debnath, Manik L and Hope, Caroline E and Isanski, Barbara A and Hamilton, Ronald L and DeKosky, Steven T},
  title           = {Post-mortem correlates of in vivo PiB-PET amyloid imaging in a typical case of Alzheimer's disease.},
  journal         = {Brain : a journal of neurology},
  year            = {2008},
  volume          = {131},
  pages           = {1630--1645},
  month           = jun,
  issn            = {1460-2156},
  abstract        = {The positron emission tomography (PET) radiotracer Pittsburgh Compound-B (PiB) binds with high affinity to beta-pleated sheet aggregates of the amyloid-beta (Abeta) peptide in vitro. The in vivo retention of PiB in brains of people with Alzheimer's disease shows a regional distribution that is very similar to distribution of Abeta deposits observed post-mortem. However, the basis for regional variations in PiB binding in vivo, and the extent to which it binds to different types of Abeta-containing plaques and tau-containing neurofibrillary tangles (NFT), has not been thoroughly investigated. The present study examined 28 clinically diagnosed and autopsy-confirmed Alzheimer's disease subjects, including one Alzheimer's disease subject who had undergone PiB-PET imaging 10 months prior to death, to evaluate region- and substrate-specific binding of the highly fluorescent PiB derivative 6-CN-PiB. These data were then correlated with region-matched Abeta plaque load and peptide levels, [(3)H]PiB binding in vitro, and in vivo PET retention levels. We found that in Alzheimer's disease brain tissue sections, the preponderance of 6-CN-PiB binding is in plaques immunoreactive to either Abeta42 or Abeta40, and to vascular Abeta deposits. 6-CN-PiB labelling was most robust in compact/cored plaques in the prefrontal and temporal cortices. While diffuse plaques, including those in caudate nucleus and presubiculum, were less prominently labelled, amorphous Abeta plaques in the cerebellum were not detectable with 6-CN-PiB. Only a small subset of NFT were 6-CN-PiB positive; these resembled extracellular 'ghost' NFT. In Alzheimer's disease brain tissue homogenates, there was a direct correlation between [(3)H]PiB binding and insoluble Abeta peptide levels. In the Alzheimer's disease subject who underwent PiB-PET prior to death, in vivo PiB retention levels correlated directly with region-matched post-mortem measures of [(3)H]PiB binding, insoluble Abeta peptide levels, 6-CN-PiB- and Abeta plaque load, but not with measures of NFT. These results demonstrate, in a typical Alzheimer's disease brain, that PiB binding is highly selective for insoluble (fibrillar) Abeta deposits, and not for neurofibrillary pathology. The strong direct correlation of in vivo PiB retention with region-matched quantitative analyses of Abeta plaques in the same subject supports the validity of PiB-PET imaging as a method for in vivo evaluation of Abeta plaque burden.},
  chemicals       = {2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole, Amyloid beta-Peptides, Aniline Compounds, Carbon Radioisotopes, Thiazoles, tau Proteins},
  citation-subset = {AIM, IM},
  completed       = {2008-11-13},
  country         = {England},
  created         = {2008-06-04},
  doi             = {10.1093/brain/awn016},
  issn-linking    = {0006-8950},
  issue           = {Pt 6},
  keywords        = {Alzheimer Disease, diagnostic imaging, pathology; Amyloid beta-Peptides, analysis, metabolism; Aniline Compounds, metabolism; Autopsy; Brain, diagnostic imaging, pathology; Carbon Radioisotopes, metabolism; Enzyme-Linked Immunosorbent Assay, methods; Female; Humans; Image Interpretation, Computer-Assisted; Immunohistochemistry; Magnetic Resonance Imaging; Middle Aged; Neurofibrillary Tangles, diagnostic imaging, pathology; Plaque, Amyloid, diagnostic imaging, pathology; Positron-Emission Tomography, methods; Reproducibility of Results; Thiazoles, metabolism; tau Proteins, analysis, metabolism},
  nlm             = {PMC2408940},
  nlm-id          = {0372537},
  owner           = {NLM},
  pii             = {awn016},
  pmc             = {PMC2408940},
  pmid            = {18339640},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-11-24},
}

@Article{Eckert2007,
  author   = {Eckert, Thomas and Edwards, Christine},
  title    = {{The application of network mapping in differential diagnosis of parkinsonian disorders}},
  journal  = {Clinical Neuroscience Research},
  year     = {2007},
  volume   = {6},
  number   = {6},
  pages    = {359--366},
  issn     = {1566-2772},
  note     = {Neural Networks in the Imaging of Neuropsychiatric Diseases},
  abstract = {Although approximately 1-3\% of the population over age 65 have Parkinson's disease (PD), only about 75\% of the patients diagnosed with parkinsonism have PD. The differential diagnosis of parkinsonian disorders based on clinical symptoms alone is particularly difficult during the early stages of the disease. A number of imaging strategies have been developed to differentiate between these clinically similar conditions. The assessment of abnormal patterns of brain metabolism, either by visual inspection or using computer-assisted algorithms, can be used to discriminate between classical PD and atypical variant conditions such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP), or corticobasal ganglionic degeneration (CBGD). Recent advances in network quantification routines have created the basis for fully automated differential diagnosis. Using PET, investigators have identified specific disease-related spatial covariance patterns that are characteristic of PD and its variants. By computing pattern expression in individual patient scans, it has become possible to determine the likelihood of a specific diagnosis. In this review, we describe the various imaging techniques that have been used to diagnose PD with emphasis on the application of network tools. Analogous methods may have value in the assessment of other neurodegenerative and neuropsychiatric conditions.},
  doi      = {10.1016/j.cnr.2007.05.001},
  keywords = {Parkinsonism},
}

@Article{Christine2004,
  author   = {Christine, Chadwick W. and Aminoff, Michael J.},
  title    = {{Clinical differentiation of parkinsonian syndromes: Prognostic and therapeutic relevance}},
  journal  = {The American Journal of Medicine},
  year     = {2004},
  volume   = {117},
  number   = {6},
  pages    = {412--419},
  issn     = {0002-9343},
  abstract = {Parkinson disease is the most common cause of parkinsonism, but other causes should always be excluded because they have a different prognosis, respond differently to medical treatment, and should not be managed by surgical means. However, diagnosis, even by experts, is challenging; one autopsy series showed an error rate of 24\%. Distinction between various diagnostic possibilities depends on the history and examination findings. The use of certain medications, the rapid rate of disease progression, early onset of falling, the presence of certain dysautonomic symptoms, cognitive or behavioral changes, or a history of poor response to dopaminergic therapy may suggest an atypical form of parkinsonism. Postural hypotension, dementia, supranuclear ophthalmoparesis, or early postural instability should alert the examiner to consider an atypical cause of parkinsonism. Tests of autonomic function and brain imaging are often helpful in distinguishing these diseases.},
  doi      = {10.1016/j.amjmed.2004.03.032},
}

@InCollection{tatsch2008extrapyramidal,
  author    = {Tatsch, Klaus},
  title     = {{Extrapyramidal syndromes: {PET} and SPECT}},
  booktitle = {{Diseases of the Brain, Head \& Neck, Spine}},
  publisher = {Springer},
  year      = {2008},
  pages     = {234--239},
}

@Article{Winogrodzka2003,
  author   = {Winogrodzka, A and Bergmans, P and Booij, J and van Royen, E A and Stoof, J C and Wolters, E C},
  title    = {{{[123I]}beta-{CIT SPECT} is a useful method for monitoring dopaminergic degeneration in early stage {P}arkinson's disease}},
  journal  = {Journal of Neurology, Neurosurgery \& Psychiatry},
  year     = {2003},
  volume   = {74},
  number   = {3},
  pages    = {294--298},
  abstract = {Objectives:To examine the validity of {[123I]}-CIT {SPECT} for monitoring the progression of dopaminergic degeneration in Parkinson?s disease; to investigate the influence of short term treatment with D2receptor agonists on striatal {[123I]}?-CIT binding; and to determine the sample size and frequency of {SPECT} imaging required to demonstrate a significant effect of a putative neuroprotective agent.Methods:A group of 50 early stage Parkinson?s disease patients was examined. Two {SPECT} imaging series were obtained, 12 months apart. The mean annual change in the ratio of specific to non-specific {[123I]}?-CIT binding to the striatum, putamen, and caudate nucleus was used as the outcome measure.Results:A decrease in {[123I]}?-CIT binding ratios between the two images was found in all regions of interest. The average decrease in {[123I]}?-CIT binding ratios was about 8\% in the whole striatum, 8\% in the putaminal region, and 4\% in the caudate region. Comparison of scans done in nine patients under two different conditions?in the off state and while on drug treatment?showed no significant alterations in the expression of striatal dopamine transporters as measured using {[123I]}?-CIT {SPECT}. Power analysis indicated that to detect a significant (p < 0.05) effect of a neuroprotective agent with 0.80 power and 30\% of predicted protection within two years, 216 patients are required in each group when the effects are measured in the whole putamen.Conclusions:{[123I]}?-CIT {SPECT} seems to be a useful tool to investigate the progression of dopaminergic degeneration in Parkinson?s disease and may provide an objective method of measuring the effectiveness of neuroprotective treatments. Short term treatment with a D2agonist does not have a significant influence on {[123I]}?-CIT binding to dopamine transporters. If the latter finding is replicated in larger groups of patients, it supports the suitability of {[123I]}?-CIT {SPECT} for examining the progression of neurodegeneration in patients being treated with D2receptor agonists.},
  doi      = {10.1136/jnnp.74.3.294},
}

@Article{PunalRioboo2007,
  author   = {{Pu{\~n}al Riob{\'o}o}, J and {Varela Lema}, L and {Serena Puig}, A and {Ruano-Ravina}, A},
  title    = {{Effectiveness} of {123I-ioflupane (DaTSCAN)} in the diagnosis of Parkinsonian syndromes. A systematic review},
  journal  = {Revista Espa{\~n}ola De Medicina Nuclear},
  year     = {2007},
  volume   = {26},
  number   = {6},
  pages    = {375--384},
  month    = dec,
  issn     = {0212-6982},
  note     = {{PMID:} 18021694},
  abstract = {{BACKGROUND} Parkinson disease {(PD)} is the second most frequent neurodegenerative disease, affecting the 1-2 \% of the population over 65. Around 20-24 \% of diagnosed patients are estimated to be misdiagnosed. The aim of this paper is to assess the efficacy of {DaTSCAN} in the diagnosis of early {PD} and to determine the efficacy of {123I-FP} in the differential diagnosis of vascular parkinsonism, drug-induced parkinsonism, essential tremor, Lewy body dementia {(LBD)} and Alzheimer disease {(AD).} {METHODS} Systematic review. Two independent investigators reviewed and selected the papers according to predefined selection criteria. The quality of the original studies was assessed using one specifically designed scale. {RESULTS} Eleven original articles were included. No randomized clinical trials were found. Three papers assessed the effect of {DaTSCAN} in medication of patients and found that 17 to 69 \% of the patients treatment changed after {SPECT.} Six studies assessed the change in the diagnosis for patients with parkinsonian syndromes after {SPECT.} Four of them showed that {123I-FP} could be useful for the differential diagnosis between {PD} and non-degenerative disorders. One observed that ioflupane could help differentiate between {PD} and {AD} and between this last disease and {LBD.} The other investigation group showed that {DaTSCAN} could help in the differential diagnosis between {PD} and parkinsonian syndromes such as multisystem atrophy {(MSA)} and progressive supranuclear palsy {(PSP).} {CONCLUSIONS} The scientific evidence available indicates that {123I-FP} can be useful to differentiate {PD} from essential tremor and vascular and drug-induced parkinsonism, and also to differentiate {AD} from {LBD.}},
  keywords = {Humans; Iodine Radioisotopes; Nortropanes; Parkinsonian Disorders},
}

@InProceedings{Segovia2016a,
  author       = {Segovia, F and Gorriz, JM and Ram{\'i}rez, J and Salas-Gonzalez, D},
  title        = {Multiclass classification of 18 F-DMFP-PET data to assist the diagnosis of parkinsonism},
  booktitle    = {Pattern Recognition in Neuroimaging (PRNI), 2016 International Workshop on},
  year         = {2016},
  pages        = {1--4},
  organization = {IEEE},
}

@Article{Szatmari1999,
  author          = {Szatmari, P},
  title           = {Heterogeneity and the genetics of autism.},
  journal         = {Journal of psychiatry \& neuroscience : JPN},
  year            = {1999},
  volume          = {24},
  pages           = {159--165},
  month           = mar,
  issn            = {1180-4882},
  abstract        = {The objective of this review is to summarize recent data on the genetics of autism, highlight the evidence for genetic heterogeneity and extend the implications of these findings for the identification of susceptibility genes in this disorder. Family studies have shown that autism runs in families and twin studies indicate that the basis of that familial aggregation is genetic. As a result the prospects for the identification of susceptibility genes using either linkage or association studies are quite good. However, recent evidence is accumulating suggesting that the disorder is genetically heterogeneous; higher functioning individuals with autism may arise from separate genetic mechanisms that lower functioning ones. If true, this will make the detection of linkage and association much more difficult.},
  citation-subset = {IM},
  completed       = {1999-05-19},
  country         = {Canada},
  created         = {1999-05-19},
  issn-linking    = {1180-4882},
  issue           = {2},
  keywords        = {Autistic Disorder, epidemiology, genetics; Genetic Heterogeneity; Genetic Linkage; Humans},
  nlm             = {PMC1188998},
  nlm-id          = {9107859},
  owner           = {NLM},
  pmc             = {PMC1188998},
  pmid            = {10212560},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  references      = {44},
  revised         = {2014-06-17},
}

@Article{Ecker2014,
  author          = {Ecker, Christine and Murphy, Declan},
  title           = {Neuroimaging in autism--from basic science to translational research.},
  journal         = {Nature reviews. Neurology},
  year            = {2014},
  volume          = {10},
  pages           = {82--91},
  month           = feb,
  issn            = {1759-4766},
  abstract        = {Over the past decade, human neuroimaging studies have provided invaluable insights into the neural substrates that underlie autism spectrum disorder (ASD). Although observations from multiple neuroimaging approaches converge in suggesting that changes in brain structure, functioning and connectivity are associated with ASD, the neurobiology of this disorder is complex, and considerable aetiological and phenotypic heterogeneity exists among individuals on the autism spectrum. Characterization of the neurobiological alterations that underlie ASD and development of novel pharmacotherapies for ASD, therefore, requires multidisciplinary collaboration. Consequently, pressure is growing to combine neuroimaging data with information provided by other disciplines to translate research findings into clinically useful biomarkers. So far, however, neuroimaging studies in patients with ASD have mainly been conducted in isolation, and the low specificity of neuroimaging measures has hindered the development of biomarkers that could aid clinical trials and/or facilitate patient identification. Novel approaches to acquiring and analysing data on brain characteristics are currently being developed to overcome these inherent limitations, and to integrate neuroimaging into translational research. Here, we discuss promising new studies of cortical pathology in patients with ASD, and outline how the novel insights thereby obtained could inform diagnosis and treatment of ASD in the future.},
  citation-subset = {IM},
  completed       = {2014-08-06},
  country         = {England},
  created         = {2014-02-07},
  doi             = {10.1038/nrneurol.2013.276},
  issn-linking    = {1759-4758},
  issue           = {2},
  keywords        = {Animals; Autistic Disorder, diagnosis, therapy; Humans; Neuroimaging; Translational Medical Research},
  nlm-id          = {101500072},
  owner           = {NLM},
  pii             = {nrneurol.2013.276},
  pmid            = {24419683},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2014-02-07},
}

@Article{DiMartino2014,
  author          = {Di Martino, A and Yan, C-G and Li, Q and Denio, E and Castellanos, F X and Alaerts, K and Anderson, J S and Assaf, M and Bookheimer, S Y and Dapretto, M and Deen, B and Delmonte, S and Dinstein, I and Ertl-Wagner, B and Fair, D A and Gallagher, L and Kennedy, D P and Keown, C L and Keysers, C and Lainhart, J E and Lord, C and Luna, B and Menon, V and Minshew, N J and Monk, C S and Mueller, S and M\"uller, R-A and Nebel, M B and Nigg, J T and O'Hearn, K and Pelphrey, K A and Peltier, S J and Rudie, J D and Sunaert, S and Thioux, M and Tyszka, J M and Uddin, L Q and Verhoeven, J S and Wenderoth, N and Wiggins, J L and Mostofsky, S H and Milham, M P},
  title           = {The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.},
  journal         = {Molecular psychiatry},
  year            = {2014},
  volume          = {19},
  pages           = {659--667},
  month           = jun,
  issn            = {1476-5578},
  abstract        = {Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although the field of genetics has pioneered data sharing for these reasons, neuroimaging had not kept pace. In response, we introduce the Autism Brain Imaging Data Exchange (ABIDE)-a grassroots consortium aggregating and openly sharing 1112 existing resting-state functional magnetic resonance imaging (R-fMRI) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. We focused on whole-brain intrinsic functional connectivity and also survey a range of voxel-wise measures of intrinsic functional brain architecture. Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.},
  citation-subset = {IM},
  completed       = {2015-01-09},
  country         = {England},
  doi             = {10.1038/mp.2013.78},
  issn-linking    = {1359-4184},
  issue           = {6},
  keywords        = {Adolescent; Adult; Brain, pathology, physiopathology; Brain Mapping; Child; Child Development Disorders, Pervasive, pathology, physiopathology; Connectome; Humans; Information Dissemination; Internet; Magnetic Resonance Imaging; Male; Middle Aged; Neural Pathways, pathology, physiopathology; Neuroimaging; Phenotype; Signal Processing, Computer-Assisted; Young Adult},
  mid             = {NIHMS560309},
  nlm             = {PMC4162310},
  nlm-id          = {9607835},
  owner           = {NLM},
  pii             = {mp201378},
  pmc             = {PMC4162310},
  pmid            = {23774715},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2016-10-19},
}

@Article{Ecker2013,
  author      = {Ecker, Christine and Ginestet, Cedric and Feng, Yue and Johnston, Patrick and Lombardo, Michael V. and Lai, Meng-Chuan and Suckling, John and Palaniyappan, Lena and Daly, Eileen and Murphy, Clodagh M. and Williams, Steven C. and Bullmore, Edward T. and Baron-Cohen, Simon and Brammer, Michael and Murphy, Declan G M. and MRC AIMS Consortium},
  title       = {Brain surface anatomy in adults with autism: the relationship between surface area, cortical thickness, and autistic symptoms.},
  journal     = {JAMA Psychiatry},
  year        = {2013},
  volume      = {70},
  number      = {1},
  pages       = {59--70},
  month       = jan,
  abstract    = {Neuroimaging studies of brain anatomy in autism spectrum disorder (ASD) have mostly been based on measures of cortical volume (CV). However, CV is a product of 2 distinct parameters, cortical thickness (CT) and surface area (SA), that in turn have distinct genetic and developmental origins. To investigate regional differences in CV, SA, and CT as well as their relationship in a large and well-characterized sample of men with ASD and matched controls.Multicenter case-control design using quantitative magnetic resonance imaging.Medical Research Council UK Autism Imaging Multicentre Study.A total of 168 men, 84 diagnosed as having ASD and 84 controls who did not differ significantly in mean (SD) age (26 [7] years vs 28 [6] years, respectively) or full-scale IQ (110 [14] vs 114 [12], respectively).Between-group differences in CV, SA, and CT investigated using a spatially unbiased vertex-based approach; the degree of spatial overlap between the differences in CT and SA; and their relative contribution to differences in regional CV.Individuals with ASD differed from controls in all 3 parameters. These mainly consisted of significantly increased CT within frontal lobe regions and reduced SA in the orbitofrontal cortex and posterior cingulum. These differences in CT and SA were paralleled by commensurate differences in CV. The spatially distributed patterns for CT and SA were largely nonoverlapping and shared only about 3\% of all significantly different locations on the cerebral surface.Individuals with ASD have significant differences in CV, but these may be underpinned by (separable) variations in its 2 components, CT and SA. This is of importance because both measures result from distinct developmental pathways that are likely modulated by different neurobiological mechanisms. This finding may provide novel targets for future studies into the etiology of the condition and a new way to fractionate the disorder.},
  doi         = {10.1001/jamapsychiatry.2013.265},
  language    = {eng},
  medline-pst = {ppublish},
  owner       = {paco},
  pii         = {1393585},
  pmid        = {23404046},
  timestamp   = {2015.11.03},
}

@Article{Ecker2012,
  author    = {Ecker, Christine and Suckling, John and Deoni, Sean C and Lombardo, Michael V and Bullmore, Ed T and Baron-Cohen, Simon and Catani, Marco and Jezzard, Peter and Barnes, Anna and Bailey, Anthony J and others},
  title     = {Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study},
  journal   = {Archives of general psychiatry},
  year      = {2012},
  volume    = {69},
  number    = {2},
  pages     = {195--209},
  owner     = {pakitochus},
  publisher = {American Medical Association},
  timestamp = {2015.11.28},
}

@Article{Spetsieris2009,
  author    = {Spetsieris, Phoebe G. and Ma, Yilong and Dhawan, Vijay and Eidelberg, David},
  title     = {{Differential diagnosis of parkinsonian syndromes using functional {PCA}-based imaging features}},
  journal   = {Neuroimage},
  year      = {2009},
  volume    = {45},
  number    = {4},
  pages     = {1241--52},
  month     = may,
  issn      = {1053-8119},
  abstract  = {We describe methodologies for single subject differential diagnosis of degenerative brain disorders using multivariate principal component analysis (PCA) of functional imaging scans. An automated routine utilizing these methods is applied to positron emission tomography (PET) brain data to distinguish several discrete parkinsonian movement disorders with similar clinical manifestations. Disease specific expressions of voxel-based spatial covariance patterns are predetermined using the Scaled Subprofile Model (SSM/PCA) and a scalar measure of the manifestation of each pattern in prospective subject images is subsequently derived. Scores are automatically compared to reference values generated for each pathological condition in a corresponding set of patient and control scans. Diagnostic outcome is optimized using strategies such as the derivation of patterns in a voxel subspace that reflects contrasting image characteristics between conditions, or by using an independent patient population as controls. The prediction models for two, three and four way classification problems using direct scalar comparison as well as classical discriminant analysis are assessed in a composite training population comprised of three different patient classes and normal controls and validated in a similar independent test population. Results illustrate that highly accurate diagnosis can often be achieved by simple comparison of scores utilizing optimized patterns.},
  file      = {Spetsieris - Differences Functional Imaging - PKS.pdf:PKS/Spetsieris - Differences Functional Imaging - PKS.pdf:PDF},
  keywords  = {Differential diagnosis; Parkinson's disease; Functional neuroimaging; Brain networks; PET; PCA; CAD},
  owner     = {imagenes2},
  timestamp = {2009.02.12},
}

@Article{Dale1999,
  author          = {Dale, A M and Fischl, B and Sereno, M I},
  title           = {Cortical surface-based analysis. I. Segmentation and surface reconstruction.},
  journal         = {NeuroImage},
  year            = {1999},
  volume          = {9},
  pages           = {179--194},
  month           = feb,
  issn            = {1053-8119},
  abstract        = {Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging.},
  citation-subset = {IM},
  completed       = {1999-03-18},
  country         = {United States},
  created         = {1999-03-18},
  doi             = {10.1006/nimg.1998.0395},
  issn-linking    = {1053-8119},
  issue           = {2},
  keywords        = {Brain Mapping, instrumentation; Cerebral Cortex, anatomy & histology; Humans; Image Processing, Computer-Assisted, instrumentation; Magnetic Resonance Imaging, instrumentation; Reference Values; Software},
  nlm-id          = {9215515},
  owner           = {NLM},
  pii             = {S1053-8119(98)90395-0},
  pmid            = {9931268},
  pubmodel        = {Print},
  pubstatus       = {ppublish},
  revised         = {2004-11-17},
}

@Article{Lozano2007,
  author   = {Lozano, S.J. Ortega and Torres, M.D. Mart{\'i}nez del Valle and Garc{\'i}a, J.M. Jim{\'e}nez-Hoyuela and Cardo, A.L. Guti{\'e}rrez and Arillo, V. Campos},
  title    = {{Diagnostic accuracy of {FP}-CIT SPECT in patients with Parkinsonism}},
  journal  = {Revista Espa{\~n}ola de Medicina Nuclear (English Edition)},
  year     = {2007},
  volume   = {26},
  number   = {5},
  pages    = {277 -- 285},
  issn     = {1578-200X},
  abstract = {Objective To determine the diagnostic accuracy of FP-CIT SPECT in entities with and without presynaptic involvement of the nigral-striatal dopaminergic pathway in a large group of patients with movement disorders, evaluating the usefulness of quantitative analysis. Materials and methods A group of 183 consecutive patients clinically diagnosed as either having or not having degenerative Parkinsonism. These results were then contrasted with those of FP-CIT SPECT to determine the diagnostic accuracy of the procedure. The specific binding index was evaluated with ROC curves. Results FP-CIT SPECT was highly accurate in the diagnosis of neurodegenerative Parkinsonism (sensitivity: 95\%, specificity: 90\%). Most of the false positive results arose in patients with vascular Parkinsonism and the false negative results in patients with Parkinson disease. ROC curve analysis of semiquantitative evaluation had a sensitivity of 83\% and specificity of 82\% with an optimal cut-off of 1.44. The area under the curve was not significantly different between patients <60 and > 60 years (0.899 vs 0.884) of age. Conclusions FP-CIT SPECT has a high degree of diagnostic accuracy for striatal dopaminergic involvement. No significant changes in diagnostic accuracy were seen with respect to patient age.},
  doi      = {10.1016/S1578-200X(07)70062-1},
  keywords = {Parkinsonism},
}

@Article{Zhou2007,
  author    = {Zhou, Yun and Resnick, Susan M. and Ye, Weiguo and Fan, Hong and Holt, Daniel P. and Klunk, William E. and Mathis, Chester A. and Dannals, Robert and Wong, Dean F.},
  title     = {{Using a reference tissue model with spatial constraint to quantify [11{C}]Pittsburgh compound B {PET} for early diagnosis of Alzheimer's disease}},
  journal   = {Neuroimage},
  year      = {2007},
  volume    = {36},
  number    = {2},
  pages     = {298--312},
  month     = jun,
  issn      = {1053-8119},
  abstract  = {Introduction: Reference tissue model (RTM) is a compartmental modeling approach that uses reference tissue time activity curve (TAC) as input for quantification of ligand-receptor dynamic PET without blood sampling. There are limitations in applying the RTM for kinetic analysis of PET studies using [11C]Pittsburgh compound B ([11C]PIB). For region of interest (ROI) based kinetic modeling, the low specific binding of [11C]PIB in a target ROI can result in a high linear relationship between the output and input. This condition may result in amplification of errors in estimates using RTM. For pixel-wise quantification, due to the high noise level of pixel kinetics, the parametric images generated by RTM with conventional linear or nonlinear regression may be too noisy for use in clinical studies.Methods: We applied RTM with parameter coupling and a simultaneous fitting method as a spatial constraint for ROI kinetic analysis. Three RTMs with parameter coupling were derived from a classical compartment model with plasma input: an RTM of 4 parameters (R1, k'2R, k4, BP) (RTM4P); an RTM of 5 parameters (R1, k2R, NS, k6, BP) (RTM5P); and a simplified RTM (SRTM) of 3 parameters (R1, k'2R, BP) (RTM3P). The parameter sets [k'2R, k4], [k2R, NS, k6], and k'2R are coupled among ROIs for RTM4P, RTM5P, and RTM3P, respectively. A linear regression with spatial constraint (LRSC) algorithm was applied to the SRTM for parametric imaging. Logan plots were used to estimate the distribution volume ratio (DVR) (= 1 + BP (binding potential)) in ROI and pixel levels. Ninety-minute [11C]PIB dynamic PET was performed in 28 controls and 6 individuals with mild cognitive impairment (MCI) on a GE Advance scanner. ROIs of cerebellum (reference tissue) and 15 other regions were defined on coregistered MRIs.Results: The coefficients of variation of DVR estimates from RTM3P obtained by the simultaneous fitting method were lower by 77-89\% (in striatum, frontal, occipital, parietal, and cingulate cortex) as compared to that by conventional single ROI TAC fitting method. There were no significant differences in both TAC fitting and DVR estimates between the RTM3P and the RTM4P or RTM5P. The DVR in striatum, lateral temporal, frontal and cingulate cortex for MCI group was 25\% to 38\% higher compared to the control group (p <= 0.05), even in this group of individuals with generally low PIB retention. The DVR images generated by the SRTM with LRSC algorithm had high linear correlations with those from the Logan plot (R2 = 0.99).Conclusion: In conclusion, the RTM3P with simultaneous fitting method is shown to be a robust compartmental modeling approach that may be useful in [11C]PIB PET studies to detect early markers of Alzheimer's disease where specific ROIs have been hypothesized. In addition, the SRTM with LRSC algorithm may be useful in generating R1 and DVR images for pixel-wise quantification of [11C]PIB dynamic PET.},
  owner     = {imagenes2},
  timestamp = {2009.02.12},
}

@Article{Guyon03,
  author    = {Guyon, I. and Elisseeff, A.},
  title     = {{An Introduction to Variable and Feature Selection}},
  journal   = {Journal of Machine Learning Research},
  year      = {2003},
  volume    = {3},
  pages     = {1157--1182},
  owner     = {pakitochus},
  timestamp = {2011.08.30},
}

@Article{Gorriz2009,
  author  = {G{\'o}rriz, J.~M. and Lassl, A. and Ram{\'i}rez, J. and Salas-Gonzalez, D. and Puntonet, C.~G. and Lang, E.~W.},
  title   = {{Automatic selection of {ROIs} in functional imaging using {Gauss}ian mixture models}},
  journal = {Neuroscience Letters},
  year    = {2009},
  volume  = {460},
  number  = {2},
  pages   = {108--111},
}

@Article{Bradley1997,
  author    = {Bradley, Andrew P},
  title     = {The use of the area under the ROC curve in the evaluation of machine learning algorithms},
  journal   = {Pattern recognition},
  year      = {1997},
  volume    = {30},
  number    = {7},
  pages     = {1145--1159},
  publisher = {Elsevier},
}

@InBook{Hastie2009,
  chapter   = {7},
  title     = {The Elements of Statistical Learning},
  publisher = {Springer New York},
  year      = {2009},
  author    = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  isbn      = {978-0-387-84858-7},
  date      = {2009-08-26},
  ean       = {9780387848587},
}

@Article{DaelemansHosteMeulderEtAl2003,
  author    = {Daelemans, Walter and Hoste, Véronique and Meulder, Fien and Naudts, Bart},
  title     = {Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language},
  journal   = {Machine Learning: ECML 2003},
  year      = {2003},
  month     = jan,
  abstract  = {Comparative machine learning experiments have become an important methodology in empirical approaches to natural language processing (i) to investigate which machine learning algorithms have the ‘right bias’ to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology currently used in comparative machine learning experiments, the results may often not be reliable because of the role of and interaction between feature selection and algorithm parameter optimization. We propose genetic algorithms as a practical approach to achieve both higher accuracy within a single approach, and more reliable comparisons.},
  date      = {2003-01-01},
  doi       = {10.1007/978-3-540-39857-8_10},
  publisher = {Springer},
}

@Article{SaeysInzaLarranaga2007,
  author      = {Saeys, Yvan and Inza, I{\~{n}}aki and Larra{\~{n}}aga, Pedro},
  title       = {A review of feature selection techniques in bioinformatics.},
  journal     = {Bioinformatics},
  year        = {2007},
  volume      = {23},
  number      = {19},
  pages       = {2507--2517},
  month       = {Oct},
  abstract    = {Feature selection techniques have become an apparent need in many bioinformatics applications. In addition to the large pool of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to a wealth of newly proposed techniques. In this article, we make the interested reader aware of the possibilities of feature selection, providing a basic taxonomy of feature selection techniques, and discussing their use, variety and potential in a number of both common as well as upcoming bioinformatics applications.},
  doi         = {10.1093/bioinformatics/btm344},
  institution = {Department of Plant Systems Biology, VIB, B-9052 Ghent, Belgium. yvan.saeys@psb.ugent.be},
  keywords    = {Algorithms; Artificial Intelligence; Computational Biology, methods; Computer Simulation; Gene Expression Profiling, methods; Models, Biological; Pattern Recognition, Automated, methods; Sequence Analysis, methods},
  language    = {eng},
  medline-pst = {ppublish},
  pii         = {btm344},
  pmid        = {17720704},
}

@Article{Fay10,
  author    = {Fay, Michael P. and Proschan, Michael A.},
  title     = {{Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules}},
  journal   = {Statistics Surveys},
  year      = {2010},
  volume    = {4},
  pages     = {1--39},
  file      = {Fay10-Wilcoxon vs t-Test.pdf:Estadística/Hypothesis Testing/Fay10-Wilcoxon vs t-Test.pdf:PDF},
  owner     = {pakitochus},
  timestamp = {2011.08.30},
}

@Book{Harman73,
  title     = {{Modern Factor Analysis}},
  publisher = {University of Chicago Press},
  year      = {1976},
  author    = {Harman, H. H.},
  address   = {Chicago},
}

@InCollection{Hyvaerinen2003,
  author    = {Hyv{\"a}rinen, Aapo and Kano, Yutaka},
  title     = {Independent component analysis for non-normal factor analysis},
  booktitle = {New developments in psychometrics},
  publisher = {Springer},
  year      = {2003},
  pages     = {649--656},
}

@Book{Rice2006,
  title     = {Mathematical statistics and data analysis},
  publisher = {Nelson Education},
  year      = {2006},
  author    = {Rice, John},
}

@Article{Hyvarinen2000,
  author   = {Hyv{\"a}rinen, A. and Oja, E.},
  title    = {{Independent component analysis: algorithms and applications}},
  journal  = {Neural Networks},
  year     = {2000},
  volume   = {13},
  number   = {4-5},
  pages    = {411 -- 430},
  issn     = {0893-6080},
  abstract = {A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject.},
  doi      = {10.1016/S0893-6080(00)00026-5},
  keywords = {Independent component analysis},
}

@Article{Oja1997,
  author    = {Hyv{\"a}rinen, A. and Oja., E.},
  title     = {{A fast fixed-point algorithm for independent component analysis}},
  journal   = {Neural Computation},
  year      = {1997},
  volume    = {9},
  pages     = {1483--1492},
  owner     = {imagenes2},
  timestamp = {2009.02.11},
}

@Article{FastICA99,
  author    = {Hyv{\"a}rinen, A.},
  title     = {{Fast and Robust Fixed-Point Algorithms for Independent Component Analysis}},
  journal   = {IEEE Transactions on Neural Networks},
  year      = {1999},
  volume    = {10},
  number    = {3},
  pages     = {626--634},
  owner     = {pakitochus},
  timestamp = {2011.08.30},
}

@Article{Vapnik1997,
  author    = {Vapnik, Vladimir and Golowich, Steven E and Smola, Alex and others},
  title     = {Support vector method for function approximation, regression estimation, and signal processing},
  journal   = {Advances in neural information processing systems},
  year      = {1997},
  pages     = {281--287},
  publisher = {Morgan Kaufmann Publishers},
}

@Article{kovalev2001three,
  author    = {Kovalev, Vassili A and Kruggel, Frithjof and Gertz, H-J and von Cramon, D Yves},
  title     = {Three-dimensional texture analysis of {MRI} brain datasets},
  journal   = {Medical Imaging, IEEE Transactions on},
  year      = {2001},
  volume    = {20},
  number    = {5},
  pages     = {424--433},
  publisher = {IEEE},
}

@Article{sikio2015mr,
  author    = {Siki{\"o}, Minna and Holli-Helenius, Kirsi K and Harrison, Lara CV and Ryymin, Pertti and Ruottinen, Hanna and Saunam{\"a}ki, Tiia and Eskola, Hannu J and Elovaara, Irina and Dastidar, Prasun},
  title     = {MR image texture in Parkinson{\textquoteright}s disease: a longitudinal study},
  journal   = {Acta Radiologica},
  year      = {2015},
  volume    = {56},
  number    = {1},
  pages     = {97--104},
  publisher = {SAGE Publications},
}

@Article{Wang2009,
  author    = {Wang, Li and Li, Chunming and Sun, Quansen and Xia, Deshen and Kao, Chiu-Yen},
  title     = {Active contours driven by local and global intensity fitting energy with application to brain MR image segmentation},
  journal   = {Computerized Medical Imaging and Graphics},
  year      = {2009},
  volume    = {33},
  number    = {7},
  pages     = {520--531},
  publisher = {Elsevier},
}

@Article{Saeed2002,
  author    = {Saeed, N and Puri, BK},
  title     = {Cerebellum segmentation employing texture properties and knowledge based image processing: applied to normal adult controls and patients},
  journal   = {Magnetic resonance imaging},
  year      = {2002},
  volume    = {20},
  number    = {5},
  pages     = {425--429},
  publisher = {Elsevier},
}

@Article{Alejo2003,
  author    = {de Alejo, Rigoberto P{\'e}rez and Ruiz-Cabello, Jes{\'u}s and Cortijo, Manuel and Rodriguez, Ignacio and Echave, Imanol and Regadera, Javier and Arrazola, Juan and Avil{\'e}s, Pablo and Barreiro, Pilar and Gargallo, Domingo and others},
  title     = {Computer-assisted enhanced volumetric segmentation magnetic resonance imaging data using a mixture of artificial neural networks},
  journal   = {Magnetic resonance imaging},
  year      = {2003},
  volume    = {21},
  number    = {8},
  pages     = {901--912},
  publisher = {Elsevier},
}

@InProceedings{Srinivasan2008,
  author    = {Srinivasan, GN and Shobha, G},
  title     = {Statistical texture analysis},
  booktitle = {Proceedings of world academy of science, engineering and technology},
  year      = {2008},
  volume    = {36},
  pages     = {1264--1269},
}

@Misc{Haralick1992a,
  author    = {Haralick, RM and Shapiro, LG},
  title     = {Robot and Computer Vision (vols. 1 and 2)},
  year      = {1992},
  publisher = {Addison-Wesley, Reading, MA},
}

@Article{Segovia2012,
  author  = {Segovia, F. and G{\'o}rriz, J. M. and Ram{\'i}rez, J. and {\'A}lvarez, I. and Jim{\'e}nez-Hoyuela, J. M. and Ortega, S. J.},
  title   = {{Improved Parkinsonism diagnosis using a partial least squares based approach}},
  journal = {Medical physics},
  year    = {2012},
  volume  = {39},
  number  = {7},
  pages   = {4395--4403},
}

@Article{Illan2012,
  author          = {Ill{\'a}n, I.A.a and G{\'o}rriz, J.M.a and Ram{\'i}rez, J.a and Segovia, F.a and Jim{\'e}nez-Hoyuela, J.M.b and {Ortega Lozano}, S.J.b},
  title           = {{Automatic assistance to Parkinsons disease diagnosis in DaTSCAN SPECT imaging}},
  journal         = {Medical Physics},
  year            = {2012},
  volume          = {39},
  number          = {10},
  pages           = {5971--5980},
  abstract        = {Purpose: In this work, an approach to computer aided diagnosis (CAD) system is proposed as a decision-making aid in Parkinsonian syndrome (PS) detection. This tool, intended for physicians, entails fully automatic preprocessing, normalization, and classification procedures for brain single-photon emission computed tomography images. Methods: Ioflupane[ 123I]FP-CIT images are used to provide in vivo information of the dopamine transporter density. These images are preprocessed using an automated template-based registration followed by two proposed approaches for intensity normalization. A support vector machine (SVM) is used and compared to other statistical classifiers in order to achieve an effective diagnosis using whole brain images in combination with voxel selection masks. Results: The CAD system is evaluated using a database consisting of 208 DaTSCAN images (100 controls, 108 PS). SVM-based classification is the most efficient choice when masked brain images are used. The generalization performance is estimated to be 89.02 (90.41-87.62) sensitivity and 93.21 (92.24-94.18) specificity. The area under the curve can take values of 0.9681 (0.9641-0.9722) when the image intensity is normalized to a maximum value, as derived from the receiver operating characteristics curves. Conclusions: The present analysis allows to evaluate the impact of the design elements for the development of a CAD-system when all the information encoded in the scans is considered. In this way, the proposed CAD-system shows interesting properties for clinical use, such as being fast, automatic, and robust. � 2012 American Association of Physicists in Medicine.},
  affiliation     = {Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain; Nuclear Medicine Department, Hospital Universitario Virgen de la Victoria, 29010 M�laga, Spain},
  author_keywords = {computer aided diagnosis; Parkinsonian syndromes; supervised learning; support vector machine},
  document_type   = {Article},
  source          = {Scopus},
}

@Article{Rojas2012,
  author   = {Rojas, A. and G{\'o}rriz, J.M. and Ram{\'i}rez, J. and Ill{\'a}n, I.A. and Mart{\'i}nez-Murcia, F.J. and Ortiz, A. and R{\'i}o, M. G{\'o}mez and Moreno-Caballero, M.},
  title    = {{Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease}},
  journal  = {Expert Systems with Applications},
  year     = {2013},
  volume   = {40},
  number   = {7},
  pages    = {2756 -- 2766},
  issn     = {0957-4174},
  abstract = {Parkinsonism is the second most common neurodegenerative disorder. It includes several pathologies with similar symptoms, what makes the diagnosis really difficult. I-ioflupane allows to obtain in vivo images of the brain that can be used to assist the PS diagnosis and provides a way to improve its accuracy. In this paper a new method for brain SPECT image feature extraction is shown. This novel Computer Aided Diagnosis (CAD) system is based on the Empirical Mode Decomposition (EMD), which decomposes any non-linear and non-stationary time series into a small number of oscillatory Intrinsic Mode Functions (IMF) a monotonous Residuum. A 80-DaTSCAN image database from the 'Virgen de las Nieves' Hospital in Granada (Spain) was used to evaluate this method, yielding up to 95\% accuracy, which greatly improves the baseline Voxel-As-Feature (VAF) approach.},
  doi      = {10.1016/j.eswa.2012.11.017},
  keywords = {Parkinsonian Syndrome (PS)},
}

@Article{Shaffer1995,
  author    = {Shaffer, Juliet Popper},
  title     = {Multiple hypothesis testing},
  journal   = {Annual review of psychology},
  year      = {1995},
  volume    = {46},
  number    = {1},
  pages     = {561--584},
  publisher = {Annual Reviews 4139 El Camino Way, PO Box 10139, Palo Alto, CA 94303-0139, USA},
}

@Article{Zhao2007,
  author     = {Zhao, Guoying and Pietikainen, Matti},
  title      = {Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions},
  journal    = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year       = {2007},
  volume     = {29},
  number     = {6},
  pages      = {915--928},
  month      = jun,
  issn       = {0162-8828},
  acmid      = {1263469},
  address    = {Washington, DC, USA},
  doi        = {10.1109/TPAMI.2007.1110},
  issue_date = {June 2007},
  keywords   = {Temporal texture, Temporal texture, motion, facial image analysis, facial expression, local binary pattern., facial expression, facial image analysis, local binary pattern., motion},
  numpages   = {14},
  owner      = {pakitochus},
  publisher  = {IEEE Computer Society},
  timestamp  = {2015.02.20},
}

@Article{Ojala1996,
  author               = {Ojala, Timo and Pietik\"{a}inen, Matti and Harwood, David},
  title                = {{A comparative study of texture measures with classification based on featured distributions}},
  journal              = {Pattern Recognition},
  year                 = {1996},
  volume               = {29},
  number               = {1},
  pages                = {51--59},
  month                = jan,
  issn                 = {00313203},
  abstract             = {{This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently. For classification a method based on Kullback discrimination of sample and prototype distributions is used. The classification results for single features with one-dimensional feature value distributions and for pairs of complementary features with two-dimensional distributions are presented}},
  citeulike-article-id = {8254898},
  citeulike-linkout-0  = {http://dx.doi.org/10.1016/0031-3203(95)00067-4},
  doi                  = {10.1016/0031-3203(95)00067-4},
  keywords             = {classification, lbp, texture},
  owner                = {pakitochus},
  posted-at            = {2010-11-16 16:12:31},
  timestamp            = {2015.02.20},
}

@Article{Chen2008,
  author    = {Chen, H.C. and Goldberg, M. and Magdon-Ismail, M. and Wallace, W.A.},
  title     = {Reverse Engineering a Social Agent-Based Hidden {Markov} Model - ViSAGE},
  journal   = {International Journal of Neural Systems},
  year      = {2008},
  volume    = {18},
  number    = {06},
  pages     = {491--526},
  owner     = {pakitochus},
  timestamp = {2015.08.04},
}

@Article{soh1999texture,
  author    = {Soh, L-K and Tsatsoulis, Costas},
  title     = {Texture analysis of {SAR} sea ice imagery using gray level co-occurrence matrices},
  journal   = {Geoscience and Remote Sensing, IEEE Transactions on},
  year      = {1999},
  volume    = {37},
  number    = {2},
  pages     = {780--795},
  publisher = {IEEE},
}

@Article{clausi2002analysis,
  author    = {Clausi, David A},
  title     = {An analysis of co-occurrence texture statistics as a function of grey level quantization},
  journal   = {Canadian Journal of remote sensing},
  year      = {2002},
  volume    = {28},
  number    = {1},
  pages     = {45--62},
  publisher = {NRC Research Press Ottawa, Canada},
}

@Article{han2006reliability,
  author    = {Han, Xiao and Jovicich, Jorge and Salat, David and van der Kouwe, Andre and Quinn, Brian and Czanner, Silvester and Busa, Evelina and Pacheco, Jenni and Albert, Marilyn and Killiany, Ronald and others},
  title     = {Reliability of {MRI}-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner upgrade and manufacturer},
  journal   = {Neuroimage},
  year      = {2006},
  volume    = {32},
  number    = {1},
  pages     = {180--194},
  publisher = {Elsevier},
}

@Article{Fischl2004,
  author    = {Fischl, Bruce and van der Kouwe, Andr{\'e} and Destrieux, Christophe and Halgren, Eric and S{\'e}gonne, Florent and Salat, David H and Busa, Evelina and Seidman, Larry J and Goldstein, Jill and Kennedy, David and others},
  title     = {Automatically parcellating the human cerebral cortex},
  journal   = {Cerebral cortex},
  year      = {2004},
  volume    = {14},
  number    = {1},
  pages     = {11--22},
  publisher = {Oxford Univ Press},
}

@Article{Grana2011,
  author    = {Gra\~na, M. and Termenon, M. and Savio, A. and Gonzalez-Pinto, A. and Echeveste, J. and P\'erez, J.M. and Besga, A.},
  title     = {Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson{\textquoteright}s correlation},
  journal   = {Neuroscience Letters},
  year      = {2011},
  volume    = {502},
  number    = {3},
  pages     = {225–229},
  month     = sep,
  issn      = {0304-3940},
  doi       = {10.1016/j.neulet.2011.07.049},
  owner     = {pakitochus},
  publisher = {Elsevier BV},
  timestamp = {2015.01.26},
}

@Article{Medina2008,
  author      = {Medina, David A. and Gaviria, Moises},
  title       = {Diffusion tensor imaging investigations in Alzheimer's disease: the resurgence of white matter compromise in the cortical dysfunction of the aging brain.},
  journal     = {Neuropsychiatric Disease and Treatment},
  year        = {2008},
  volume      = {4},
  number      = {4},
  pages       = {737--742},
  month       = aug,
  abstract    = {Diffusion tensor imaging (DTI) is a sophisticated MRI-based neuroimaging technique that enables in vivo quantification of differences in molecular diffusion at the cellular level. Owing to the highly directional architecture of white matter (WM), DTI is providing important clues of the structure and geometric organization of this neural compartment. Since DTI can detect changes even in the case of radiologically "normal" appearing WM, researchers are using the technique for the study of WM integrity at the initial stages of the most common neurodegenerative disorders. Along with a well characterized cortical pathology (neuritic plaques and intracellular neurofibrillary tangles), WM changes have been also demonstrated in Alzheimer's disease (AD). However, these changes had been for years found nonliable in the onset and progress of AD, basically due to lack of incriminatory evidence. The use of novel tools such as DTI has enabled the anatomical distribution of WM microstructural damage in the prodromal stages of AD to be gauged and determined, granting a long-delayed protagonic role to WM in the natural history of this highly prevalent neurodegenerative condition.},
  institution = {Department of Psychiatry, Case Western Reserve University, 11100 Euclid Avenue, Cleveland, OH 44106, USA. davimedin@hotmail.com},
  language    = {eng},
  medline-pst = {ppublish},
  owner       = {pakitochus},
  pmid        = {19043518},
  timestamp   = {2015.01.26},
}

@Article{Tzourio-Mazoyer2002,
  author      = {Tzourio-Mazoyer, N. and Landeau, B. and Papathanassiou, D. and Crivello, F. and Etard, O. and Delcroix, N. and Mazoyer, B. and Joliot, M.},
  title       = {Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI {MRI} single-subject brain.},
  journal     = {Neuroimage},
  year        = {2002},
  volume      = {15},
  number      = {1},
  pages       = {273--289},
  month       = jan,
  abstract    = {An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.},
  doi         = {10.1006/nimg.2001.0978},
  institution = {Groupe d'Imagerie Neurofonctionnelle, UMR 6095 CNRS CEA, Université de Caen, Université de Paris 5, France.},
  keywords    = {Brain Mapping; Brain, anatomy /&/ histology; Computer Graphics; Dominance, Cerebral, physiology; Humans; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Reference Values},
  medline-pst = {ppublish},
  owner       = {pakitochus},
  pii         = {S1053811901909784},
  pmid        = {11771995},
  timestamp   = {2015.01.22},
}

@Article{Jong2008,
  author      = {de Jong, L. W. and van der Hiele, K. and Veer, I. M. and Houwing, J. J. and Westendorp, R. G. J. and Bollen, E. L. E. M. and de Bruin, P. W. and Middelkoop, H. A. M. and van Buchem, M. A. and van der Grond, J.},
  title       = {Strongly reduced volumes of putamen and thalamus in Alzheimer{\textquoteright}s disease: an {MRI} study},
  journal     = {Brain},
  year        = {2008},
  volume      = {131},
  number      = {12},
  pages       = {3277–3285},
  month       = nov,
  issn        = {1460-2156},
  abstract    = {Atrophy is regarded a sensitive marker of neurodegenerative pathology. In addition to confirming the well-known presence of decreased global grey matter and hippocampal volumes in Alzheimer's disease, this study investigated whether deep grey matter structure also suffer degeneration in Alzheimer's disease, and whether such degeneration is associated with cognitive deterioration. In this cross-sectional correlation study, two groups were compared on volumes of seven subcortical regions: 70 memory complainers (MCs) and 69 subjects diagnosed with probable Alzheimer's disease. Using 3T 3D T1 MR images, volumes of nucleus accumbens, amygdala, caudate nucleus, hippocampus, pallidum, putamen and thalamus were automatically calculated by the FMRIB's Integrated Registration and Segmentation Tool (FIRST)--algorithm FMRIB's Software Library (FSL). Subsequently, the volumes of the different regions were correlated with cognitive test results. In addition to finding the expected association between hippocampal atrophy and cognitive decline in Alzheimer's disease, volumes of putamen and thalamus were significantly reduced in patients diagnosed with probable Alzheimer's disease. We also found that the decrease in volume correlated linearly with impaired global cognitive performance. These findings strongly suggest that, beside neo-cortical atrophy, deep grey matter structures in Alzheimer's disease suffer atrophy as well and that degenerative processes in the putamen and thalamus, like the hippocampus, may contribute to cognitive decline in Alzheimer's disease.},
  doi         = {10.1093/brain/awn278},
  institution = {Department of Radiology, University Medical Center, Leiden, The Netherlands. L.W.de_Jong@lumc.nl},
  keywords    = {Aged; Aged, 80 and over; Alzheimer Disease, pathology/psychology; Atrophy, pathology/psychology; Cognition Disorders, etiology/pathology; Cross-Sectional Studies; Female; Hippocampus, pathology; Humans; Magnetic Resonance Imaging, methods; Male; Middle Aged; Neuropsychological Tests; Putamen, pathology; Thalamus, pathology},
  language    = {eng},
  medline-pst = {ppublish},
  owner       = {pakitochus},
  pii         = {awn278},
  pmid        = {19022861},
  publisher   = {Oxford University Press (OUP)},
  timestamp   = {2015.07.22},
}

@Article{Ortiz2013,
  author    = {Ortiz, Andr{\'e}s and G{\'o}rriz, Juan M and Ram{\'i}rez, Javier and Mart{\'i}nez-Murcia, Francisco Jes{\'u}s},
  title     = {LVQ-{SVM} Based {CAD} tool applied to structural {MRI} for the diagnosis of the Alzheimer's disease},
  journal   = {Pattern Recognition Letters},
  year      = {2013},
  volume    = {34},
  number    = {14},
  pages     = {1725--1733},
  month     = oct,
  issn      = {0167-8655},
  doi       = {10.1016/j.patrec.2013.04.014},
  owner     = {pakitochus},
  publisher = {North-Holland},
  timestamp = {2014.12.27},
}

@Article{Illan2014,
  author      = {Ill\'an, Ignacio A. and G{\'o}rriz, Juan M. and Ram{\'i}rez, Javier and Meyer-Base, Anke},
  title       = {Spatial component analysis of {MRI} data for Alzheimer's disease diagnosis: a {Bayesian} network approach.},
  journal     = {Frontiers in Computational Neuroscience},
  year        = {2014},
  volume      = {8},
  pages       = {156},
  abstract    = {This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is used to model the dependencies between affected regions of AD. The structure of relations between affected regions allows to detect neurodegeneration with an estimated performance of 88\% on more than 400 subjects and predict neurodegeneration with 80\% accuracy, supporting the conclusion that modeling the dependencies between components increases the recognition of different patterns of brain degeneration in AD.},
  doi         = {10.3389/fncom.2014.00156},
  institution = {Department of Scientific Computing, Florida State University Tallahassee, FL, USA.},
  language    = {eng},
  medline-pst = {epublish},
  owner       = {pakitochus},
  pmid        = {25505408},
  timestamp   = {2016.02.02},
}

@Article{Botev2010,
  author    = {Botev, ZI and Grotowski, JF and Kroese, DP and others},
  title     = {Kernel density estimation via diffusion},
  journal   = {The Annals of Statistics},
  year      = {2010},
  volume    = {38},
  number    = {5},
  pages     = {2916--2957},
  publisher = {Institute of Mathematical Statistics},
}

@Article{KwanEvansPike1999,
  author    = {Kwan, Remi KS and Evans, Alan C and Pike, G Bruce},
  title     = {{MRI} simulation-based evaluation of image-processing and classification methods},
  journal   = {Medical Imaging, IEEE Transactions on},
  year      = {1999},
  volume    = {18},
  number    = {11},
  pages     = {1085--1097},
  publisher = {IEEE},
}

@Article{Ma1993,
  author    = {Ma, Y and Kamber, M and Evans, AC},
  title     = {3D simulation of PET brain images using segmented MRI data and positron tomograph characteristics},
  journal   = {Computerized medical imaging and graphics},
  year      = {1993},
  volume    = {17},
  number    = {4-5},
  pages     = {365--371},
  publisher = {Elsevier},
}

@Article{friedman2006report,
  author    = {Friedman, Lee and Glover, Gary H},
  title     = {Report on a multicenter fMRI quality assurance protocol},
  journal   = {Journal of Magnetic Resonance Imaging},
  year      = {2006},
  volume    = {23},
  number    = {6},
  pages     = {827--839},
  owner     = {pakitochus},
  publisher = {Wiley Online Library},
  timestamp = {2015.07.06},
}

@Article{Jovicich2006,
  author    = {Jovicich, Jorge and Czanner, Silvester and Greve, Douglas and Haley, Elizabeth and van der Kouwe, Andre and Gollub, Randy and Kennedy, David and Schmitt, Franz and Brown, Gregory and MacFall, James and others},
  title     = {Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data},
  journal   = {Neuroimage},
  year      = {2006},
  volume    = {30},
  number    = {2},
  pages     = {436--443},
  publisher = {Elsevier},
}

@Article{Stonnington2008,
  author    = {Stonnington, Cynthia M and Tan, Geoffrey and Kl{\"o}ppel, Stefan and Chu, Carlton and Draganski, Bogdan and Jack, Clifford R and Chen, Kewei and Ashburner, John and Frackowiak, Richard SJ},
  title     = {Interpreting scan data acquired from multiple scanners: a study with Alzheimer's disease},
  journal   = {Neuroimage},
  year      = {2008},
  volume    = {39},
  number    = {3},
  pages     = {1180--1185},
  publisher = {Elsevier},
}

@Article{Turner2013,
  author    = {Turner, Jessica A and Damaraju, Eswar and Van Erp, Theo GM and Mathalon, Daniel H and Ford, Judith M and Voyvodic, James and Mueller, Bryon A and Belger, Aysenil and Bustillo, Juan and McEwen, Sarah Christine and others},
  title     = {A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia},
  journal   = {Frontiers in neuroscience},
  year      = {2013},
  volume    = {7},
  pages     = {137},
  publisher = {Frontiers},
}

@Article{Clementz2015,
  author    = {Clementz, Brett A and Sweeney, John A and Hamm, Jordan P and Ivleva, Elena I and Ethridge, Lauren E and Pearlson, Godfrey D and Keshavan, Matcheri S and Tamminga, Carol A},
  title     = {Identification of distinct psychosis biotypes using brain-based biomarkers},
  journal   = {American Journal of Psychiatry},
  year      = {2015},
  volume    = {173},
  number    = {4},
  pages     = {373--384},
  publisher = {Am Psychiatric Assoc},
}

@Article{Wang2015,
  author    = {Wang, Zheng and Meda, Shashwath A and Keshavan, Matcheri S and Tamminga, Carol A and Sweeney, John A and Clementz, Brett A and Schretlen, David J and Calhoun, Vince D and Lui, Su and Pearlson, Godfrey D},
  title     = {Large-scale fusion of gray matter and resting-state functional MRI reveals common and distinct biological markers across the psychosis spectrum in the B-SNIP cohort},
  journal   = {Frontiers in psychiatry},
  year      = {2015},
  volume    = {6},
  pages     = {174},
  publisher = {Frontiers},
}

@Article{VanHorn2009,
  author    = {Van Horn, John Darrell and Toga, Arthur W},
  title     = {Multi-site neuroimaging trials},
  journal   = {Current opinion in neurology},
  year      = {2009},
  volume    = {22},
  number    = {4},
  pages     = {370},
  publisher = {NIH Public Access},
}

@Misc{Pearlson2009,
  author    = {Pearlson, Godfrey},
  title     = {Multisite collaborations and large databases in psychiatric neuroimaging: advantages, problems, and challenges},
  year      = {2009},
  publisher = {MPRC},
}

@Article{Ashburner2007,
  author    = {Ashburner, John},
  title     = {A fast diffeomorphic image registration algorithm},
  journal   = {Neuroimage},
  year      = {2007},
  volume    = {38},
  number    = {1},
  pages     = {95--113},
  publisher = {Elsevier},
}

@Article{Avants2010,
  author    = {Avants, Brian B and Yushkevich, Paul and Pluta, John and Minkoff, David and Korczykowski, Marc and Detre, John and Gee, James C},
  title     = {The optimal template effect in hippocampus studies of diseased populations},
  journal   = {Neuroimage},
  year      = {2010},
  volume    = {49},
  number    = {3},
  pages     = {2457--2466},
  publisher = {Elsevier},
}

@Article{deoni2008standardized,
  author    = {Deoni, Sean CL and Williams, Steven CR and Jezzard, Peter and Suckling, John and Murphy, Declan GM and Jones, Derek K},
  title     = {Standardized structural magnetic resonance imaging in multicentre studies using quantitative T 1 and T 2 imaging at 1.5~{T}},
  journal   = {Neuroimage},
  year      = {2008},
  volume    = {40},
  number    = {2},
  pages     = {662--671},
  owner     = {pakitochus},
  publisher = {Academic Press},
  timestamp = {2015.11.28},
}

@Article{Suckling2014,
  author    = {Suckling, John and Henty, Julian and Ecker, Christine and Deoni, Sean C and Lombardo, Michael V and Baron-Cohen, Simon and Jezzard, Peter and Barnes, Anna and Chakrabarti, Bhismadev and Ooi, Cinly and others},
  title     = {Are power calculations useful? A multicentre neuroimaging study},
  journal   = {Human brain mapping},
  year      = {2014},
  volume    = {35},
  number    = {8},
  pages     = {3569--3577},
  publisher = {Wiley Online Library},
}

@Article{Brown2009,
  author    = {Brown, James Dean},
  title     = {Principal components analysis and exploratory factor analysis—Definitions, differences, and choices Definitions, differences, and choices},
  journal   = {Shiken: JALT Testing \& Evaluation SIG Newsletter},
  year      = {2009},
  volume    = {13},
  number    = {1},
  pages     = {26 - 30},
  month     = {January},
  publisher = {Citeseer},
}

@Article{Freedman1983,
  author    = {Freedman, David and Lane, David},
  title     = {A nonstochastic interpretation of reported significance levels},
  journal   = {Journal of Business \& Economic Statistics},
  year      = {1983},
  volume    = {1},
  number    = {4},
  pages     = {292--298},
  publisher = {Taylor \& Francis Group},
}

@Article{Ecker2015,
  author    = {Ecker, Christine and Bookheimer, Susan Y and Murphy, Declan GM},
  title     = {Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan},
  journal   = {The Lancet Neurology},
  year      = {2015},
  volume    = {14},
  number    = {11},
  pages     = {1121--1134},
  publisher = {Elsevier},
}

@Article{Hernandez2015,
  author    = {Hernandez, Leanna M and Rudie, Jeffrey D and Green, Shulamite A and Bookheimer, Susan and Dapretto, Mirella},
  title     = {Neural signatures of autism spectrum disorders: insights into brain network dynamics},
  journal   = {Neuropsychopharmacology},
  year      = {2015},
  volume    = {40},
  number    = {1},
  pages     = {171--189},
  publisher = {Nature Publishing Group},
}

@Article{Lenroot2013,
  author = {Lenroot, Rhoshel K and KaYeung, Pui},
  title  = {Heterogeneity within autism spectrum disorders: what have we learned from neuroimaging studies?},
  year   = {2013},
}

@Article{Zuercher2015,
  author    = {Z{\"u}rcher, Nicole R and Bhanot, Anisha and McDougle, Christopher J and Hooker, Jacob M},
  title     = {A systematic review of molecular imaging (PET and SPECT) in autism spectrum disorder: current state and future research opportunities},
  journal   = {Neuroscience \& Biobehavioral Reviews},
  year      = {2015},
  volume    = {52},
  pages     = {56--73},
  publisher = {Elsevier},
}

@Article{Suckling2012,
  author    = {Suckling, John and Barnes, Anna and Job, Dominic and Brennan, David and Lymer, Katherine and Dazzan, Paola and Marques, Tiago Reis and MacKay, Clare and McKie, Shane and Williams, Steve R and others},
  title     = {The neuro/PsyGRID calibration experiment},
  journal   = {Human brain mapping},
  year      = {2012},
  volume    = {33},
  number    = {2},
  pages     = {373--386},
  publisher = {Wiley Online Library},
}

@InProceedings{Kriegel2008,
  author       = {Kriegel, Hans-Peter and Kr{\"o}ger, Peer and Schubert, Erich and Zimek, Arthur},
  title        = {A general framework for increasing the robustness of PCA-based correlation clustering algorithms},
  booktitle    = {International Conference on Scientific and Statistical Database Management},
  year         = {2008},
  pages        = {418--435},
  organization = {Springer},
}

@InProceedings{tavoli2013,
  author       = {Tavoli, Reza and Kozegar, Ehsan and Shojafar, Mohammad and Soleimani, Hossein and Pooranian, Zahra},
  title        = {Weighted {PCA} for improving Document Image Retrieval System based on keyword spotting accuracy},
  booktitle    = {Telecommunications and Signal Processing (TSP), 2013 36\textsuperscript{th} International Conference on},
  year         = {2013},
  pages        = {773--777},
  organization = {IEEE},
}

@Book{vinzi2010,
  title     = {Handbook of partial least squares},
  publisher = {Springer},
  year      = {2010},
  author    = {Vinzi, V and Chin, Wynne W and Henseler, J{\"o}rg and Wang, Huiwen and others},
}

@Article{Leek2007,
  author    = {Leek, Jeffrey T and Storey, John D},
  title     = {Capturing heterogeneity in gene expression studies by surrogate variable analysis},
  journal   = {PLoS Genet},
  year      = {2007},
  volume    = {3},
  number    = {9},
  pages     = {e161},
  publisher = {Public Library of Science},
}

@Article{Nishitani2005,
  author    = {Nishitani, Nobuyuki and Sch{\"u}rmann, Martin and Amunts, Katrin and Hari, Riitta},
  title     = {Broca’s region: from action to language},
  journal   = {Physiology},
  year      = {2005},
  volume    = {20},
  number    = {1},
  pages     = {60--69},
  publisher = {Am Physiological Soc},
}

@Article{Dapretto2006,
  author      = {Dapretto, Mirella and Davies, Mari S. and Pfeifer, Jennifer H. and Scott, Ashley A. and Sigman, Marian and Bookheimer, Susan Y. and Iacoboni, Marco},
  title       = {Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders.},
  journal     = {Nature Neuroscience},
  year        = {2006},
  volume      = {9},
  number      = {1},
  pages       = {28--30},
  month       = jan,
  abstract    = {To examine mirror neuron abnormalities in autism, high-functioning children with autism and matched controls underwent fMRI while imitating and observing emotional expressions. Although both groups performed the tasks equally well, children with autism showed no mirror neuron activity in the inferior frontal gyrus (pars opercularis). Notably, activity in this area was inversely related to symptom severity in the social domain, suggesting that a dysfunctional 'mirror neuron system' may underlie the social deficits observed in autism.},
  doi         = {10.1038/nn1611},
  institution = {Ahmanson-Lovelace Brain Mapping Center, Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, 90095, USA. mirella@loni.ucla.edu},
  keywords    = {Autistic Disorder, physiopathology/psychology; Brain Mapping; Child; Emotions, physiology; Empathy; Facial Expression; Female; Humans; Magnetic Resonance Imaging; Male; Neurons, physiology; Social Perception},
  language    = {eng},
  medline-pst = {ppublish},
  owner       = {paco},
  pii         = {nn1611},
  pmid        = {16327784},
  timestamp   = {2015.09.02},
}

@Article{Hadjikhani2006,
  author    = {Hadjikhani, Nouchine and Joseph, Robert M and Snyder, Josh and Tager-Flusberg, Helen},
  title     = {Anatomical differences in the mirror neuron system and social cognition network in autism},
  journal   = {Cerebral cortex},
  year      = {2006},
  volume    = {16},
  number    = {9},
  pages     = {1276--1282},
  publisher = {Oxford Univ Press},
}

@Article{Lopez-Hurtado2008,
  author  = {Lopez-Hurtado, Edith and Prieto, Jorge J},
  title   = {A microscopic study of language-related cortex in autism},
  journal = {Am J Biochem Biotechnol},
  year    = {2008},
  volume  = {4},
  pages   = {130--145},
}

@Article{Verly2014,
  author    = {Verly, Marjolein and Verhoeven, Judith and Zink, Inge and Mantini, Dante and Peeters, Ronald and Deprez, Sabine and Emsell, Louise and Boets, Bart and Noens, Ilse and Steyaert, Jean and others},
  title     = {Altered functional connectivity of the language network in ASD: role of classical language areas and cerebellum},
  journal   = {NeuroImage: Clinical},
  year      = {2014},
  volume    = {4},
  pages     = {374--382},
  publisher = {Elsevier},
}

@Article{Barnea-Goraly2004,
  author    = {Barnea-Goraly, Naama and Kwon, Hower and Menon, Vinod and Eliez, Stephan and Lotspeich, Linda and Reiss, Allan L},
  title     = {White matter structure in autism: preliminary evidence from diffusion tensor imaging},
  journal   = {Biological psychiatry},
  year      = {2004},
  volume    = {55},
  number    = {3},
  pages     = {323--326},
  publisher = {Elsevier},
}

@Article{Lombardo2011,
  author    = {Lombardo, Michael V and Chakrabarti, Bhismadev and Bullmore, Edward T and Baron-Cohen, Simon and MRC AIMS Consortium and others},
  title     = {Specialization of right temporo-parietal junction for mentalizing and its relation to social impairments in autism},
  journal   = {Neuroimage},
  year      = {2011},
  volume    = {56},
  number    = {3},
  pages     = {1832--1838},
  publisher = {Elsevier},
}

@Article{Ecker2010,
  author    = {Ecker, Christine and Rocha-Rego, Vanessa and Johnston, Patrick and Mourao-Miranda, Janaina and Marquand, Andre and Daly, Eileen M and Brammer, Michael J and Murphy, Clodagh and Murphy, Declan G and MRC AIMS Consortium and others},
  title     = {Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach},
  journal   = {Neuroimage},
  year      = {2010},
  volume    = {49},
  number    = {1},
  pages     = {44--56},
  publisher = {Elsevier},
}

@Article{Segovia2014,
  author    = {Segovia, Ferm{\'i}n and Holt, Rosemary and Spencer, Michael and G{\'o}rriz, Juan M and Ram{\'i}rez, Javier and Puntonet, Carlos G and Phillips, Christophe and Chura, Lindsay and Baron-Cohen, Simon and Suckling, John},
  title     = {Identifying endophenotypes of autism: a multivariate approach},
  journal   = {Frontiers in computational neuroscience},
  year      = {2014},
  volume    = {8},
  pages     = {60},
  publisher = {Frontiers},
}

@Article{Lord1994,
  author    = {Lord, Catherine and Rutter, Michael and Le Couteur, Ann},
  title     = {Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders},
  journal   = {Journal of Autism and Developmental Disorders},
  year      = {1994},
  volume    = {24},
  number    = {5},
  pages     = {659--685},
  issn      = {0162-3257},
  doi       = {10.1007/BF02172145},
  language  = {English},
  owner     = {pakitochus},
  publisher = {Kluwer Academic Publishers-Plenum Publishers},
  timestamp = {2015.11.28},
}

@Article{Lord2000,
  author    = {Lord, Catherine and Risi, Susan and Lambrecht, Linda and Cook, Edwin H and Leventhal, Bennett L and DiLavore, Pamela C and Pickles, Andrew and Rutter, Michael},
  title     = {The Autism Diagnostic Observation Schedule—Generic: A standard measure of social and communication deficits associated with the spectrum of autism},
  journal   = {Journal of autism and developmental disorders},
  year      = {2000},
  volume    = {30},
  number    = {3},
  pages     = {205--223},
  publisher = {Springer},
}

@Book{Wechsler1999a,
  title     = {Wechsler abbreviated scale of intelligence},
  publisher = {Psychological Corporation},
  year      = {1999},
  author    = {Wechsler, David},
}

@Article{Lai2012,
  author    = {Lai, Meng-Chuan and Lombardo, Michael V and Chakrabarti, Bhismadev and Ecker, Christine and Sadek, Susan A and Wheelwright, Sally J and Murphy, Declan GM and Suckling, John and Bullmore, Edward T and Baron-Cohen, Simon and others},
  title     = {Individual differences in brain structure underpin empathizing--systemizing cognitive styles in male adults},
  journal   = {Neuroimage},
  year      = {2012},
  volume    = {61},
  number    = {4},
  pages     = {1347--1354},
  publisher = {Elsevier},
}

@Book{Vapnik1998,
  title     = {{Statistical Learning Theory}},
  publisher = {John Wiley and Sons, Inc., New York},
  year      = {1998},
  author    = {Vapnik, V.~N.},
  owner     = {imagenes2},
  timestamp = {2009.02.11},
}

@Article{PPMI,
  author   = {Marek, Kenneth and Jennings, Danna and Lasch, Shirley and Siderowf, Andrew and Tanner, Caroline and Simuni, Tanya and Coffey, Chris and Kieburtz, Karl and Flagg, Emily and Chowdhury, Sohini and Poewe, Werner and Mollenhauer, Brit and Klinik, {Paracelsus-Elena} and Sherer, Todd and Frasier, Mark and Meunier, Claire and Rudolph, Alice and Casaceli, Cindy and Seibyl, John and Mendick, Susan and Schuff, Norbert and Zhang, Ying and Toga, Arthur and Crawford, Karen and Ansbach, Alison and {De Blasio}, Pasquale and Piovella, Michele and Trojanowski, John and Shaw, Les and Singleton, Andrew and Hawkins, Keith and Eberling, Jamie and Brooks, Deborah and Russell, David and Leary, Laura and Factor, Stewart and Sommerfeld, Barbara and Hogarth, Penelope and Pighetti, Emily and Williams, Karen and Standaert, David and Guthrie, Stephanie and Hauser, Robert and Delgado, Holly and Jankovic, Joseph and Hunter, Christine and Stern, Matthew and Tran, Baochan and Leverenz, Jim and Baca, Marne and Frank, Sam and Thomas, {Cathi-Ann} and Richard, Irene and Deeley, Cheryl and Rees, Linda and Sprenger, Fabienne and Lang, Elisabeth and Shill, Holly and Obradov, Sanja and Fernandez, Hubert and Winters, Adrienna and Berg, Daniela and Gauss, Katharina and Galasko, Douglas and Fontaine, Deborah and Mari, Zoltan and Gerstenhaber, Melissa and Brooks, David and Malloy, Sophie and Barone, Paolo and Longo, Katia and Comery, Tom and Ravina, Bernard and Grachev, Igor and Gallagher, Kim and Collins, Michelle and Widnell, Katherine L. and Ostrowizki, Suzanne and Fontoura, Paulo and Ho, Tony and Luthman, Johan and Brug, Marcel van der and Reith, Alastair D. and Taylor, Peggy},
  title    = {{The Parkinson Progression Marker Initiative {(PPMI)}}},
  journal  = {Progress in Neurobiology},
  year     = {2011},
  volume   = {95},
  number   = {4},
  pages    = {629--635},
  month    = dec,
  issn     = {0301-0082},
  abstract = {The Parkinson Progression Marker Initiative {(PPMI)} is a comprehensive observational, international, multi-center study designed to identify {PD} progression biomarkers both to improve understanding of disease etiology and course and to provide crucial tools to enhance the likelihood of success of {PD} modifying therapeutic trials. The {PPMI} cohort will comprise 400 recently diagnosed {PD} and 200 healthy subjects followed longitudinally for clinical, imaging and biospecimen biomarker assessment using standardized data acquisition protocols at twenty-one clinical sites. All study data will be integrated in the {PPMI} study database and will be rapidly and publically available through the {PPMI} web site- www.ppmi-info.org. Biological samples including longitudinal collection of blood, cerebrospinal fluid {(CSF)} and urine will be available to scientists by application to an independent {PPMI} biospecimen review committee also through the {PPMI} web site. {PPMI} will rely on a partnership of government, {PD} foundations, industry and academics working cooperatively. This approach is crucial to enhance the potential for success of this ambitious strategy to develop {PD} progression biomarkers that will accelerate research in disease modifying therapeutics.},
  doi      = {10.1016/j.pneurobio.2011.09.005},
  file     = {PPMI.pdf:Databases/PPMI - The Parkinson Progression Marker Initiative (PPMI).pdf:PDF;PPMI-2.pdf:Databases/PPMI-2.pdf:PDF;PPMI-Data-Use-Agreement.pdf:Databases/PPMI-Data-Use-Agreement.pdf:PDF;PPMI-Publication-Policy.pdf:Databases/PPMI-Publication-Policy.pdf:PDF;PPMI - Imaging-Manual.pdf:Databases/PPMI - Imaging-Manual.pdf:PDF},
  keywords = {alpha synuclein; biomarker; cerebrospinal fluid; Diffusion tensor imaging; Dopamine transporter imaging},
}

@Article{Ramirez2009,
  author    = {Ram{\'i}rez, J. and G{\'o}rriz, J.~M. and Chaves, R. and L{\'o}pez, M. and Salas-Gonz{\'a}lez, D. and {\'A}lvarez, I. and Segovia, F.},
  title     = {{SPECT image classification using random forests}},
  journal   = {Electronics Letters},
  year      = {2009},
  volume    = {45},
  pages     = {604--605},
  doi       = {10.1049/el.2009.1111},
  owner     = {pakitochus},
  timestamp = {2010.10.12},
}

@Manual{Inititative2010,
  title     = {{PPMI. Imaging Technical Operations Manual}},
  author    = {Initiative, The Parkinson Progression Markers},
  edition   = {2},
  month     = jun,
  year      = {2010},
  owner     = {pakitochus},
  timestamp = {2012.03.22},
}

@InProceedings{Alvarez2009,
  author    = {Ill{\'a}n, I.~{\'A}lvarez and G{\'o}rriz, J.~M. and Ram{\'i}rez, J. and Salas-Gonzalez, D. and L{\'o}pez, M. and Puntonet, C.~G. and Segovia, F.},
  title     = {{Independent Component Analysis of {SPECT} Images to Assist the {Alzheimer's}Disease Diagnosis}},
  booktitle = {{Advances in Neural Networks Research, ISNN 2009. Advances in Intelligentand Soft Computing}},
  year      = {2009},
  month     = may,
  note      = {Wuhan (China)},
  owner     = {pakitochus},
  timestamp = {2010.08.27},
}

@InProceedings{Evans1993,
  author       = {Evans, Alan C and Collins, D Louis and Mills, SR and Brown, ED and Kelly, RL and Peters, Terry M},
  title        = {{3D} statistical neuroanatomical models from 305 {MRI} volumes},
  booktitle    = {Nuclear Science Symposium and Medical Imaging Conference, 1993., 1993 IEEE Conference Record.},
  year         = {1993},
  pages        = {1813--1817},
  organization = {IEEE},
}

@Article{Yen1995,
  author   = {Jui-Cheng Yen and Fu-Juay Chang and Shyang Chang},
  title    = {A new criterion for automatic multilevel thresholding},
  journal  = {Image Processing, IEEE Transactions on},
  year     = {1995},
  volume   = {4},
  number   = {3},
  pages    = {370--378},
  month    = mar,
  issn     = {1057-7149},
  abstract = {A new criterion for multilevel thresholding is proposed. The criterion is based on the consideration of two factors. The first one is the discrepancy between the thresholded and original images and the second one is the number of bits required to represent the thresholded image. Based on a new maximum correlation criterion for bilevel thresholding, the discrepancy is defined and then a cost function that takes both factors into account is proposed for multilevel thresholding. By minimizing the cost function, the classification number that the gray-levels should be classified and the threshold values can be determined automatically. In addition, the cost function is proven to possess a unique minimum under very mild conditions. Computational analyses indicate that the number of required mathematical operations in the implementation of our algorithm is much less than that of maximum entropy criterion. Finally, simulation results are included to demonstrate their effectiveness},
  doi      = {10.1109/83.366472},
  keywords = {correlation methods;image processing;automatic multilevel thresholding;bilevel thresholding;cost function;image classification;image processing;mathematical operations;maximum correlation criterion;simulation results;threshold values;thresholded image;Algorithm design and analysis;Analytical models;Application software;Computational complexity;Computational modeling;Computer industry;Cost function;Entropy;Image processing;Target recognition},
}

@Book{Simonoff2012,
  title     = {Smoothing methods in statistics},
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@Article{Shepp82,
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  number  = {2},
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@Article{Vardi1985,
  author    = {Vardi, Y. and Shepp, L.~A. and Kaufman, L.},
  title     = {{A Statistical Model for Positron Emission Tomography}},
  journal   = {Journal of the American Statistical Association},
  year      = {1985},
  volume    = {80},
  number    = {389},
  pages     = {8--20},
  owner     = {imagenes2},
  timestamp = {2009.02.11},
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@Article{jobst_accurate_1998,
  author    = {{K.~A. Jobst}, L.~P. Barnetson B.~J. Shepstone},
  title     = {{Accurate prediction of histologically confirmed alzheimer's disease and the differential diagnosis of dementia: the use of {NINCDS-ADRDA} and {DSM-III-R} criteria, {SPECT,} x-ray {CT,} and apo e4 in medial temporal lobe dementias. Oxford project to investigate memory and aging,}},
  journal   = {International Psychogeriatrics},
  year      = {1998},
  volume    = {10},
  number    = {3},
  pages     = {271--302},
  month     = sep,
  owner     = {pakitochus},
  timestamp = {2012.03.12},
}

@Article{dougall_systematic_2004,
  author    = {{N.~J. Dougall}, S.~Bruggink K.~P. Ebmeier},
  title     = {{Systematic review of the diagnostic accuracy of {99{mT}c-HMPAO-SPECT} in dementia}},
  journal   = {The American Journal of Geriatric Psychiatry: Official Journal of the American Association for Geriatric Psychiatry},
  year      = {2004},
  volume    = {12},
  number    = {6},
  pages     = {554--570},
  owner     = {pakitochus},
  timestamp = {2012.03.12},
}

@Article{vbm_ref,
  author    = {Luders, E and Gaser, C and Jancke, L and Schlaug, G},
  title     = {A voxel-based approach to gray matter asymmetries},
  journal   = {Neuroimage},
  year      = {2004},
  volume    = {22},
  number    = {2},
  pages     = {656--664},
  month     = jun,
  issn      = {1053-8119},
  doi       = {10.1016/j.neuroimage.2004.01.032},
  owner     = {pakitochus},
  publisher = {Elsevier BV},
  timestamp = {2015.07.06},
}

@Article{Salas-Gonzalez2015,
  author    = {Salas-Gonzalez, Diego and G{\'o}rriz, Juan M and Ram{\'i}rez, Javier and Ill{\'a}n, Ignacio A and Padilla, Pablo and Mart{\'i}nez-Murcia, Francisco J and Lang, Elmar W},
  title     = {Building a FP-CIT SPECT Brain Template Using a Posterization Approach},
  journal   = {Neuroinformatics},
  year      = {2015},
  volume    = {13},
  number    = {4},
  pages     = {391--402},
  publisher = {Springer},
}

@Article{Lin2008,
  author  = {Lin, Chih-Jen and Weng, Ruby C and Keerthi, S Sathiya},
  title   = {Trust region newton method for logistic regression},
  journal = {Journal of Machine Learning Research},
  year    = {2008},
  volume  = {9},
  number  = {Apr},
  pages   = {627--650},
}

@Article{Caspi2014,
  author        = {Caspi, Avshalom and Houts, Renate M and Belsky, Daniel W and Goldman-Mellor, Sidra J and Harrington, HonaLee and Israel, Salomon and Meier, Madeline H and Ramrakha, Sandhya and Shalev, Idan and Poulton, Richie and others},
  title         = {The p factor: one general psychopathology factor in the structure of psychiatric disorders?},
  journal       = {Clinical Psychological Science},
  year          = {2014},
  volume        = {2},
  number        = {2},
  pages         = {119--137},
  __markedentry = {[pakitochus:]},
  publisher     = {SAGE Publications Sage CA: Los Angeles, CA},
}

@Book{Association2013,
  title         = {Diagnostic and statistical manual of mental disorders (DSM-5{\textregistered})},
  publisher     = {American Psychiatric Pub},
  year          = {2013},
  author        = {American Psychiatric Association and others},
  __markedentry = {[pakitochus:6]},
}

@Comment{jabref-meta: databaseType:bibtex;}