Functional connectivity‐based parcellation of amygdala using self‐organized mapping: A data driven approach

The overall goal of this work is to demonstrate how resting state functional magnetic resonance imaging (fMRI) signals may be used to objectively parcellate functionally heterogeneous subregions of the human amygdala into structures characterized by similar patterns of functional connectivity. We hypothesize that similarity of functional connectivity of subregions with other parts of the brain can be a potential basis to segment and cluster voxels using data driven approaches. In this work, self‐organizing map (SOM) was implemented to cluster the connectivity maps associated with each voxel of the human amygdala, thereby defining distinct subregions. The functional separation was optimized by evaluating the overall differences in functional connectivity between the subregions at group level. Analysis of 25 resting state fMRI data sets suggests that SOM can successfully identify functionally independent nuclei based on differences in their inter subregional functional connectivity, evaluated statistically at various confidence levels. Although amygdala contains several nuclei whose distinct roles are implicated in various functions, our objective approach discerns at least two functionally distinct volumes comparable to previous parcellation results obtained using probabilistic tractography and cytoarchitectonic analysis. Association of these nuclei with various known functions and a quantitative evaluation of their differences in overall functional connectivity with lateral orbital frontal cortex and temporal pole confirms the functional diversity of amygdala. The data driven approach adopted here may be used as a powerful indicator of structure–function relationships in the amygdala and other functionally heterogeneous structures as well. Hum Brain Mapp 35:1247–1260, 2014. © 2013 Wiley Periodicals, Inc.

[1]  Joseph E LeDoux,et al.  Extinction Learning in Humans Role of the Amygdala and vmPFC , 2004, Neuron.

[2]  S. Rauch,et al.  A functional magnetic resonance imaging study of amygdala and medial prefrontal cortex responses to overtly presented fearful faces in posttraumatic stress disorder. , 2005, Archives of general psychiatry.

[3]  J. Manjón,et al.  Schizophrenia with auditory hallucinations: A voxel-based morphometry study , 2008, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[4]  J. Grafman,et al.  The Human Amygdala: An Evolved System for Relevance Detection , 2003, Reviews in the neurosciences.

[5]  Tom Heskes,et al.  Self-organizing maps, vector quantization, and mixture modeling , 2001, IEEE Trans. Neural Networks.

[6]  Andreas Schulze-Bonhage,et al.  Response Properties of Human Amygdala Subregions: Evidence Based on Functional MRI Combined with Probabilistic Anatomical Maps , 2007, PloS one.

[7]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[8]  E. Murray,et al.  The amygdala and reward , 2002, Nature Reviews Neuroscience.

[9]  Christopher S. Monk,et al.  Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders , 2010, Brain Research.

[10]  G. Frisoni,et al.  Clinical and medial temporal features in a family with mood disorders , 2010, Neuroscience Letters.

[11]  Bruce Fischl,et al.  Connectivity-based segmentation of human amygdala nuclei using probabilistic tractography , 2011, NeuroImage.

[12]  Lourens J. Waldorp,et al.  Functional connectivity analysis of fMRI data using parameterized regions-of-interest , 2011, NeuroImage.

[13]  Rupert Lanzenberger,et al.  Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies , 2009, NeuroImage.

[14]  M. Kringelbach.  The human orbitofrontal cortex: linking reward to hedonic experience , 2005, Nature Reviews Neuroscience.

[15]  A. Aleman,et al.  Reduced Connectivity in the Self-Processing Network of Schizophrenia Patients with Poor Insight , 2012, PloS one.

[16]  Daniel S. Margulies,et al.  Functional connectivity of the human amygdala using resting state fMRI , 2009, NeuroImage.

[17]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[18]  David West,et al.  A comparison of SOM neural network and hierarchical clustering methods , 1996 .

[19]  Andrew Simmons,et al.  Human attachment security is mediated by the amygdala: Evidence from combined fMRI and psychophysiological measures , 2006, Human brain mapping.

[20]  John Patrick Aggleton,et al.  The Amygdala : a functional analysis , 2000 .

[21]  Qin Yang,et al.  Analysis of fMRI Data Using Improved Self-Organizing Mapping and Spatio-Temporal Metric Hierarchical Clustering , 2008, IEEE Transactions on Medical Imaging.

[22]  J. Gore,et al.  Fine-scale functional connectivity in somatosensory cortex revealed by high-resolution fMRI. , 2011, Magnetic resonance imaging.

[23]  Bryon A. Mueller,et al.  Altered resting state complexity in schizophrenia , 2012, NeuroImage.

[24]  Angela D. Friederici,et al.  Diffusion tensor imaging segments the human amygdala in vivo , 2010, NeuroImage.

[25]  Karl J. Friston,et al.  A neuromodulatory role for the human amygdala in processing emotional facial expressions. , 1998, Brain : a journal of neurology.

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

[27]  Panu Somervuo,et al.  How to make large self-organizing maps for nonvectorial data , 2002, Neural Networks.

[28]  Edward T. Bullmore,et al.  Overall brain connectivity maps show cortico subcortical abnormalities in schizophrenia , 2011 .

[29]  S. Floresco,et al.  Amygdala-prefrontal cortical circuitry regulates effort-based decision making. , 2006, Cerebral cortex.

[30]  L. K. Hansen,et al.  On Clustering fMRI Time Series , 1999, NeuroImage.

[31]  Michael Davis,et al.  The amygdala , 2000, Current Biology.

[32]  A. Falini,et al.  Emotional reactivity in chronic schizophrenia: structural and functional brain correlates and the influence of adverse childhood experiences , 2010, Psychological Medicine.

[33]  Angus W. MacDonald,et al.  The functional neuroanatomy of symptom dimensions in schizophrenia: A qualitative and quantitative review of a persistent question , 2010, Neuroscience & Biobehavioral Reviews.

[34]  Adam Gazzaley,et al.  Measuring functional connectivity during distinct stages of a cognitive task , 2004, NeuroImage.

[35]  Scott J Peltier,et al.  Detecting low‐frequency functional connectivity in fMRI using a self‐organizing map (SOM) algorithm , 2003, Human brain mapping.

[36]  David G. Amaral,et al.  Neuroanatomy of the primate amygdala. , 2009 .

[37]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[38]  Robert C. Welsh,et al.  Using a self-organizing map algorithm to detect age-related changes in functional connectivity during rest in autism spectrum disorders , 2011, Brain Research.

[39]  A. Kelley.  Ventral striatal control of appetitive motivation: role in ingestive behavior and reward-related learning , 2004, Neuroscience & Biobehavioral Reviews.

[40]  X Hu,et al.  Analysis of functional magnetic resonance imaging data using self‐organizing mapping with spatial connectivity , 1999, Magnetic resonance in medicine.

[41]  Timothy E. J. Behrens,et al.  Deep and Superficial Amygdala Nuclei Projections Revealed In Vivo by Probabilistic Tractography , 2011, The Journal of Neuroscience.

[42]  Gabriele Lohmann,et al.  Parcellation of human amygdala in vivo using ultra high field structural MRI , 2011, NeuroImage.

[43]  Anke Meyer-Bäse,et al.  Model-free functional MRI analysis based on unsupervised clustering , 2004, J. Biomed. Informatics.

[44]  Sheng He,et al.  Dispositional fear, negative affectivity, and neuroimaging response to visually suppressed emotional faces , 2012, NeuroImage.

[45]  J L Lancaster,et al.  Automated Talairach Atlas labels for functional brain mapping , 2000, Human brain mapping.

[46]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[47]  Alan C. Evans,et al.  Specific Involvement of Human Parietal Systems and the Amygdala in the Perception of Biological Motion , 1996, The Journal of Neuroscience.

[48]  Alex Martin,et al.  Modulation of human medial temporal lobe activity by form, meaning, and experience , 1997, Hippocampus.

[49]  Andreas Schulze-Bonhage,et al.  Anatomical specificity of functional amygdala imaging of responses to stimuli with positive and negative emotional valence , 2009, Journal of Neuroscience Methods.

[50]  Joseph E LeDoux.  The Emotional Brain, Fear, and the Amygdala , 2003, Cellular and Molecular Neurobiology.

[51]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[52]  Joseph E LeDoux,et al.  Contributions of the Amygdala to Emotion Processing: From Animal Models to Human Behavior , 2005, Neuron.

[53]  A. Fleisher,et al.  Altered default mode network connectivity in alzheimer's disease—A resting functional MRI and bayesian network study , 2011, Human brain mapping.

[54]  Christian Büchel,et al.  Different amygdala subregions mediate valence-related and attentional effects of oxytocin in humans , 2010, Proceedings of the National Academy of Sciences.

[55]  L. Heimer,et al.  Chapter II – The human basal forebrain. Part II , 1999 .