Parcellation of the human amygdala using recurrence quantification analysis

Several previous attempts have been made to divide the human amygdala into smaller subregions based on the unique functional properties of the subregions. Although these attempts have provided valuable insight into the functional heterogeneity in this structure, the possibility that spatial patterns of functional characteristics can quickly change over time has been neglected in previous studies. In the present study, we explicitly account for the dynamic nature of amygdala activity. Our goal was not only to develop another parcellation method but also to augment existing methods with novel information about amygdala subdivisions. We performed state-specific amygdala parcellation using resting-state fMRI (rsfMRI) data and recurrence quantification analysis (RQA). RsfMRI data from 102 subjects were acquired with a 3T Trio Siemens scanner. We analyzed values of several RQA measures across all voxels in the amygdala and found two amygdala subdivisions, the ventrolateral (VL) and dorsomedial (DM) subdivisions, that differ with respect to one of the RQA measures, Shannon’s entropy of diagonal lines. Compared to the DM subdivision, the VL subdivision can be characterized by a higher value of entropy. The results suggest that VL activity is determined and influenced by more brain structures than is DM activity. To assess the biological validity of the obtained subdivisions, we compared them with histological atlases and currently available parcellations based on structural connectivity patterns (Anatomy Probability Maps) and cytoarchitectonic features (SPM Anatomy toolbox). Moreover, we examined their cortical and subcortical functional connectivity. The obtained results are similar to those previously reported on parcellation performed on the basis of structural connectivity patterns. Functional connectivity analysis revealed that the VL subdivision has strong connections to several cortical areas, whereas the DM subdivision is mainly connected to subcortical regions. This finding suggests that the VL subdivision corresponds to the basolateral subdivision of the amygdala (BLA), while the DM subdivision has some characteristics typical of the centromedial amygdala (CMA). The similarity in functional connectivity patterns between the VL subdivision and BLA, as well as between the DM subdivision and CMA, confirm the utility of our parcellation method. Overall, the study shows that parcellation based on BOLD signal dynamics is a powerful tool for identifying distinct functional systems within the amygdala. This tool might be useful for future research on functional brain organization. Highlights A new method for parcellation of the human amygdala was developed The ventrolateral and dorsomedial subdivisions of the amygdala were revealed The two subdivisions correspond to the anatomically defined regions of the amygdala The two subdivisions differ with respect to values of entropy A new parcellation method provides novel information about amygdala subdivisions

[1]  Joseph E LeDoux.  Emotion Circuits in the Brain , 2000 .

[2]  L. Swanson,et al.  What is the amygdala? , 1998, Trends in Neurosciences.

[3]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

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

[5]  Tianzi Jiang,et al.  Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation , 2016, NeuroImage.

[6]  J. Detre,et al.  Brain Entropy Mapping Using fMRI , 2014, PloS one.

[7]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[8]  R. Adolphs,et al.  Annals of the New York Academy of Sciences What Does the Amygdala Contribute to Social Cognition? , 2022 .

[9]  A. Izquierdo,et al.  The basolateral amygdala in reward learning and addiction , 2015, Neuroscience & Biobehavioral Reviews.

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

[11]  K. Tye,et al.  From circuits to behaviour in the amygdala , 2015, Nature.

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

[13]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[14]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[15]  Joel Nothman,et al.  SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.

[16]  Travis E. Oliphant,et al.  Guide to NumPy , 2015 .

[17]  Karl J. Friston,et al.  The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..

[18]  John C Gore,et al.  Functional connectivity‐based parcellation of amygdala using self‐organized mapping: A data driven approach , 2014, Human brain mapping.

[19]  Charles L. Webber,et al.  Recurrence Quantification Analysis , 2015 .

[20]  K. Zilles,et al.  An investigation of the structural, connectional, and functional subspecialization in the human amygdala , 2012, Human brain mapping.

[21]  Douglas H. Schultz,et al.  Resting-state connectivity of the amygdala is altered following Pavlovian fear conditioning , 2012, Front. Hum. Neurosci..

[22]  Wolfgang Grodd,et al.  Functional parcellation using time courses of instantaneous connectivity , 2017, NeuroImage.

[23]  Hesheng Liu,et al.  State-dependent variability of dynamic functional connectivity between frontoparietal and default networks relates to cognitive flexibility , 2016, Neuroscience.

[24]  Miriam C. Klein-Flügge,et al.  Anatomically precise relationship between specific amygdala connections and selective markers of mental well-being in humans , 2020, bioRxiv.

[25]  Bradford C. Dickerson,et al.  A reliable protocol for the manual segmentation of the human amygdala and its subregions using ultra-high resolution MRI , 2012, NeuroImage.

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

[27]  S. Rauch,et al.  Neurobiology of emotion perception II: implications for major psychiatric disorders , 2003, Biological Psychiatry.

[28]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[29]  Cataldo Guaragnella,et al.  A Novel Synchronization-Based Approach for Functional Connectivity Analysis , 2017, Complex..

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

[31]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[32]  Michael Davis,et al.  The amygdala: vigilance and emotion , 2001, Molecular Psychiatry.

[33]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

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

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

[36]  Klaus Scheffler,et al.  Functional anatomy of the human thalamus at rest , 2017, NeuroImage.

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

[38]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.

[39]  Serafim Rodrigues,et al.  Metastable Resting State Brain Dynamics , 2019, Front. Comput. Neurosci..

[40]  Bradford C. Dickerson,et al.  The amygdala as a hub in brain networks that support social life , 2014, Neuropsychologia.

[41]  Alessandro Giuliani,et al.  Model-free analysis of brain fMRI data by recurrence quantification , 2007, NeuroImage.

[42]  Bruce W. Smith,et al.  Modulation of emotion by cognition and cognition by emotion , 2007, NeuroImage.

[43]  Kerry J Ressler,et al.  How the neurocircuitry and genetics of fear inhibition may inform our understanding of PTSD. , 2010, The American journal of psychiatry.

[44]  E. Murray.  The amygdala, reward and emotion , 2007, Trends in Cognitive Sciences.

[45]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[46]  Eugene M. Izhikevich,et al.  Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting , 2006 .

[47]  D. Bach,et al.  Deconstructing white matter connectivity of human amygdala nuclei with thalamus and cortex subdivisions in vivo , 2017, Human brain mapping.

[48]  Marcel Falkiewicz,et al.  Functional organization of the human amygdala in appetitive learning. , 2017, Acta neurobiologiae experimentalis.

[49]  Jan Vyhnánek,et al.  Analysis of fMRI time-series by entropy measures. , 2012, Neuro endocrinology letters.

[50]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[51]  Charles L. Webber,et al.  Mathematical and Computational Foundations of Recurrence Quantifications , 2015 .

[52]  H. Gu,et al.  Graph theory reveals amygdala modules consistent with its anatomical subdivisions , 2017, Scientific Reports.

[53]  Wolfgang M Pauli,et al.  In vivo delineation of subdivisions of the human amygdaloid complex in a high‐resolution group template , 2016, Human brain mapping.

[54]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .