Support Vector Machine for Analyzing Contributions of Brain Regions During Task-State fMRI

The mainstream method used for the analysis of task functional Magnetic Resonance Imaging (fMRI) data, is to obtain task-related active brain regions based on generalized linear models. Machine learning as a data-driven technical method is increasingly used in fMRI data analysis. The language task data, including math task and story task, of the Human Connectome Project (HCP) was used in this work. We chose a linear support vector machine as a classifier to classify math and story tasks and compared them with the activated brain regions of a SPM statistical analysis. As a result, 13 of the 25 regions used for classification in SVM were activated regions, and 12 were non-activated regions. In particular, the right Paracentral Lobule and right Rolandic Operculum which belong to non-activated regions, contributed most to the classification. Therefore, the differences found in machine learning can provide a new understanding of the physiological mechanisms of brain regions under different tasks.

[1]  Yong He,et al.  Hemisphere- and gender-related differences in small-world brain networks: A resting-state functional MRI study , 2011, NeuroImage.

[2]  P. Goldman-Rakic,et al.  Sustained Mnemonic Response in the Human Middle Frontal Gyrus during On-Line Storage of Spatial Memoranda , 2002, Journal of Cognitive Neuroscience.

[3]  Swann Pichon,et al.  Classification of autistic individuals and controls using cross-task characterization of fMRI activity , 2015, NeuroImage: Clinical.

[4]  H Goodglass,et al.  Language function, foot of the third frontal gyrus, and rolandic operculum. , 1981, Archives of neurology.

[5]  F. Tong,et al.  Decoding the visual and subjective contents of the human brain , 2005, Nature Neuroscience.

[6]  Norbert Schuff,et al.  Locally linear embedding (LLE) for MRI based Alzheimer's disease classification , 2013, NeuroImage.

[7]  Declan Murphy,et al.  Neuroimaging in autism—from basic science to translational research , 2014, Nature Reviews Neurology.

[8]  Tianyi Yan,et al.  The Role of Configural Processing in Face Classification by Race: An ERP Study , 2015, Front. Hum. Neurosci..

[9]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[10]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[11]  R. Malach,et al.  When the Brain Loses Its Self: Prefrontal Inactivation during Sensorimotor Processing , 2006, Neuron.

[12]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[13]  Bernhard Schölkopf,et al.  The Kernel Trick for Distances , 2000, NIPS.

[14]  Stephen M. Smith,et al.  General multilevel linear modeling for group analysis in FMRI , 2003, NeuroImage.

[15]  P. Tibbetts.  :Cognitive Neuroscience: The Biology of the Mind , 2009 .

[16]  Michal Mikl,et al.  Mask_explorer: A tool for exploring brain masks in fMRI group analysis , 2016, Comput. Methods Programs Biomed..

[17]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[18]  Yong He,et al.  GRETNA: a graph theoretical network analysis toolbox for imaging connectomics , 2015, Front. Hum. Neurosci..

[19]  Rainer Goebel,et al.  Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns , 2008, NeuroImage.

[20]  Nina Kraus,et al.  Relating Structure to Function: Heschl's Gyrus and Acoustic Processing , 2009, The Journal of Neuroscience.

[21]  Jinglong Wu,et al.  Development of a wide‐view visual presentation system for visual retinotopic mapping during functional MRI , 2011, Journal of magnetic resonance imaging : JMRI.

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

[23]  Karl J. Friston,et al.  Generalisability, Random Effects & Population Inference , 1998, NeuroImage.

[24]  Slobodan Malobabic,et al.  Morphology and digitally aided morphometry of the human paracentral lobule. , 2013, Folia morphologica.

[25]  L. Baxter.  Random Fields on a Network: Modeling, Statistics, and Applications , 1996 .

[26]  Qiyong Guo,et al.  Retinotopic mapping of the peripheral visual field to human visual cortex by functional magnetic resonance imaging , 2012, Human brain mapping.

[27]  M. Brammer,et al.  Pattern Classification of Sad Facial Processing: Toward the Development of Neurobiological Markers in Depression , 2008, Biological Psychiatry.

[28]  Michael J Brammer,et al.  Functional magnetic resonance imaging of verbal fluency and confrontation naming using compressed image acquisition to permit overt responses , 2003, Human brain mapping.

[29]  Kurt E. Weaver,et al.  Mapping anterior temporal lobe language areas with fMRI: A multicenter normative study , 2011, NeuroImage.

[30]  Günter Mayer,et al.  From selection to caged aptamers: identification of light-dependent ssDNA aptamers targeting cytohesin. , 2009, Bioorganic & medicinal chemistry letters.

[31]  Stephen M. Smith,et al.  Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.

[32]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[33]  Benson Mwangi,et al.  A Review of Feature Reduction Techniques in Neuroimaging , 2013, Neuroinformatics.

[34]  Joy Hirsch,et al.  Mathematical and Linguistic Processing Differs Between Native and Second Languages: An fMRI Study , 2007, Brain Imaging and Behavior.

[35]  Changming Wang,et al.  Positive Classification Advantage: Tracing the Time Course Based on Brain Oscillation , 2018, Front. Hum. Neurosci..

[36]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[37]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  K. Amunts,et al.  Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps , 2005, Anatomy and Embryology.

[39]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[40]  S. Deb,et al.  Neuroimaging in autism , 1998, British Journal of Psychiatry.

[41]  Rainer Goebel,et al.  Classification of fMRI independent components using IC-fingerprints and support vector machine classifiers , 2007, NeuroImage.

[42]  Stephen C. Strother,et al.  Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA. , 2009, Magnetic resonance imaging.

[43]  Kewei Chen,et al.  Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease , 2018, Theranostics.

[44]  Janaina Mourão Miranda,et al.  Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach , 2010, NeuroImage.

[45]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[46]  Xiaoying Tang,et al.  Theta Oscillations Related to Orientation Recognition in Unattended Condition: A vMMN Study , 2017, Front. Behav. Neurosci..

[47]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[48]  Mary E. Meyerand,et al.  Support vector machine classification and characterization of age-related reorganization of functional brain networks , 2012, NeuroImage.

[49]  Michael J. Martinez,et al.  Music and language side by side in the brain: a PET study of the generation of melodies and sentences , 2006, The European journal of neuroscience.

[50]  Luyao Wang,et al.  Beta-Band Functional Connectivity Influences Audiovisual Integration in Older Age: An EEG Study , 2017, Front. Aging Neurosci..

[51]  Richard Bellman,et al.  Adaptive Control Processes: A Guided Tour , 1961, The Mathematical Gazette.