Elucidating a Magnetic Resonance Imaging-Based Neuroanatomic Biomarker for Psychosis: Classification Analysis Using Probabilistic Brain Atlas and Machine Learning Algorithms

[1]  R. Woods,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[2]  Stefan Pollmann,et al.  PyMVPA: a Python Toolbox for Multivariate Pattern Analysis of fMRI Data , 2009, Neuroinformatics.

[3]  Liana G. Apostolova,et al.  Automatic Subcortical Segmentation Using a Contextual Model , 2008, MICCAI.

[4]  A. Caprihan,et al.  Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements , 2008, NeuroImage.

[5]  Xiaoying Wu,et al.  Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study , 2008, NeuroImage.

[6]  J. Gallant,et al.  Identifying natural images from human brain activity , 2008, Nature.

[7]  J. Reiber,et al.  Ventricular shape biomarkers for Alzheimer's disease in clinical MR images , 2008, Magnetic resonance in medicine.

[8]  R. Gur,et al.  Unaffected Family Members and Schizophrenia Patients Share Brain Structure Patterns: A High-Dimensional Pattern Classification Study , 2008, Biological Psychiatry.

[9]  Sun I. Kim,et al.  Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia , 2007, NeuroImage.

[10]  Ferath Kherif,et al.  Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls , 2007, NeuroImage.

[11]  Manel Martínez-Ramón,et al.  fMRI pattern classification using neuroanatomically constrained boosting , 2006, NeuroImage.

[12]  B. Turetsky,et al.  Whole-brain morphometric study of schizophrenia revealing a spatially complex set of focal abnormalities. , 2005, Archives of general psychiatry.

[13]  Tyrone D. Cannon,et al.  The psychosis prodrome in adolescent patients viewed through the lens of DSM-IV. , 2005, Journal of child and adolescent psychopharmacology.

[14]  Lawrence Carin,et al.  Sparse multinomial logistic regression: fast algorithms and generalization bounds , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Kiralee M. Hayashi,et al.  Mapping cortical change in Alzheimer's disease, brain development, and schizophrenia , 2004, NeuroImage.

[16]  Karl J. Friston,et al.  Generative and recognition models for neuroanatomy , 2004, NeuroImage.

[17]  Jay N. Giedd,et al.  Motion Artifact in Magnetic Resonance Imaging: Implications for Automated Analysis , 2002, NeuroImage.

[18]  T. McGlashan,et al.  Prospective diagnosis of the initial prodrome for schizophrenia based on the Structured Interview for Prodromal Syndromes: preliminary evidence of interrater reliability and predictive validity. , 2002, The American journal of psychiatry.

[19]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[20]  Terri L. Moore,et al.  Regression Analysis by Example , 2001, Technometrics.

[21]  R. McCarley,et al.  A review of MRI findings in schizophrenia , 2001, Schizophrenia Research.

[22]  P. Thompson,et al.  Three-dimensional mapping of gyral shape and cortical surface asymmetries in schizophrenia: gender effects. , 2001, The American journal of psychiatry.

[23]  Alan C. Evans,et al.  Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI , 2000, NeuroImage.

[24]  A. Toga,et al.  Cortical variability and asymmetry in normal aging and Alzheimer's disease. , 1998, Cerebral cortex.

[25]  Michael F. Green,et al.  Training and quality assurance with the structured clinical interview for DSM-IV (SCID-I/P) , 1998, Psychiatry Research.

[26]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[27]  Karl J. Friston,et al.  Detecting Activations in PET and fMRI: Levels of Inference and Power , 1996, NeuroImage.

[28]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[29]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[30]  Vince D. Calhoun,et al.  A method to classify schizophrenia using inter-task spatial correlations of functional brain images , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  Dennis Velakoulis,et al.  Structural brain imaging evidence for multiple pathological processes at different stages of brain development in schizophrenia. , 2005, Schizophrenia bulletin.

[32]  T. McGlashan,et al.  Pre-onset detection and intervention research in schizophrenia psychoses: current estimates of benefit and risk. , 2001, Schizophrenia bulletin.

[33]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[34]  M. First,et al.  Structured Clinical Interview for DSM-IV Axis I Disorders , 1997 .

[35]  B. Avants,et al.  Evaluation of 14 Nonlinear Deformation Algorithms Applied to Human Brain Mri Registration , 2022 .

[36]  Stefan Pollmann,et al.  Neuroinformatics Original Research Article Pymvpa: a Unifying Approach to the Analysis of Neuroscientifi C Data , 2022 .