Ant Colony Clustering for ROI Identification in Functional Magnetic Resonance Imaging

Brain network analysis using functional magnetic resonance imaging (fMRI) is a widely used technique. The first step of brain network analysis in fMRI is to detect regions of interest (ROIs). The signals from these ROIs are then used to evaluate neural networks and quantify neuronal dynamics. The two main methods to identify ROIs are based on brain atlas registration and clustering. This work proposes a bioinspired method that combines both paradigms. The method, dubbed HAnt, consists of an anatomical clustering of the signal followed by an ant clustering step. The method is evaluated empirically in both in silico and in vivo experiments. The results show a significantly better performance of the proposed approach compared to other brain parcellations obtained using purely clustering-based strategies or atlas-based parcellations.

[1]  Chang-Dong Wang,et al.  Ultra-Scalable Spectral Clustering and Ensemble Clustering , 2019, IEEE Transactions on Knowledge and Data Engineering.

[2]  Wu Bin,et al.  CSIM: a document clustering algorithm based on swarm intelligence , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[3]  Luis Hernandez-Garcia,et al.  Neuronal event detection in fMRI time series using iterative deconvolution techniques. , 2011, Magnetic resonance imaging.

[4]  G. Glover.  Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.

[5]  Arno Klein,et al.  101 Labeled Brain Images and a Consistent Human Cortical Labeling Protocol , 2012, Front. Neurosci..

[6]  Chang-Dong Wang,et al.  Enhanced Ensemble Clustering via Fast Propagation of Cluster-Wise Similarities , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[7]  R Cameron Craddock,et al.  A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.

[8]  Julien Doyon,et al.  The Richness of Task-Evoked Hemodynamic Responses Defines a Pseudohierarchy of Functionally Meaningful Brain Networks. , 2015, Cerebral cortex.

[9]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[10]  Lei Zhang,et al.  A novel ant-based clustering algorithm using the kernel method , 2011, Inf. Sci..

[11]  Baldo Faieta,et al.  Diversity and adaptation in populations of clustering ants , 1994 .

[12]  Krzysztof Pancerz,et al.  Ant Based Clustering of Time Series Discrete Data - A Rough Set Approach , 2011, SEMCCO.

[13]  Simon B Eickhoff,et al.  Organizational principles of human visual cortex revealed by receptor mapping. , 2008, Cerebral cortex.

[14]  Krzysztof J. Gorgolewski,et al.  The human voice areas: Spatial organization and inter-individual variability in temporal and extra-temporal cortices , 2015, NeuroImage.

[15]  Polina Golland,et al.  Search for patterns of functional specificity in the brain: A nonparametric hierarchical Bayesian model for group fMRI data , 2011, NeuroImage.

[16]  Dimitri Van De Ville,et al.  Total activation: fMRI deconvolution through spatio-temporal regularization , 2013, NeuroImage.

[17]  Chang-Dong Wang,et al.  Robust Ensemble Clustering Using Probability Trajectories , 2016, IEEE Transactions on Knowledge and Data Engineering.

[18]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[19]  W. Art Chaovalitwongse,et al.  Brain response pattern identification of fMRI data using a particle swarm optimization-based approach , 2016, Brain Informatics.

[20]  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.

[21]  Vince D. Calhoun,et al.  An approach to directly link ICA and seed-based functional connectivity: Application to schizophrenia , 2018, NeuroImage.

[22]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[23]  Chang-Dong Wang,et al.  Locally Weighted Ensemble Clustering , 2016, IEEE Transactions on Cybernetics.

[24]  Nicholas Ayache,et al.  Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique , 2002, MICCAI.

[25]  Angela R. Laird,et al.  Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation , 2011, NeuroImage.

[26]  Stephen M. Smith,et al.  Resting-State FMRI Single Subject Cortical Parcellation Based on Region Growing , 2012, MICCAI.

[27]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[28]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[29]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[30]  Jean-Baptiste Poline,et al.  Which fMRI clustering gives good brain parcellations? , 2014, Front. Neurosci..

[31]  Krzysztof Pancerz,et al.  Classification of Speech Signals through Ant Based Clustering of Time Series , 2012, ICCCI.

[32]  Luke J. Chang,et al.  Connectivity-Based Parcellation of the Human Orbitofrontal Cortex , 2012, The Journal of Neuroscience.

[33]  Leland McInnes,et al.  UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction , 2018, ArXiv.

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