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Kurmukov A, Mussabaeva A, Denisova Y, Moyer D, Jahanshad N, Thompson PM, Gutman BA. Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering. Brain Connect 2020; 10:183-194. [PMID: 32264696 PMCID: PMC7247040 DOI: 10.1089/brain.2019.0722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors.
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Affiliation(s)
- Anvar Kurmukov
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Higher School of Economics, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
| | - Ayagoz Mussabaeva
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Yulia Denisova
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia
| | - Daniel Moyer
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California, USA
| | - Boris A Gutman
- Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow, Russia.,Department of Biomedical Engineering, Medical Imaging Research Center, Illinois Institute of Technology, Chicago, Illinois, USA
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Horwitz B, Hwang C, Alstott J. Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling. Front Hum Neurosci 2013; 7:649. [PMID: 24273500 PMCID: PMC3822330 DOI: 10.3389/fnhum.2013.00649] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 10/22/2013] [Indexed: 11/13/2022] Open
Abstract
Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.
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Affiliation(s)
- Barry Horwitz
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of HealthBethesda, MD, USA
| | - Chuhern Hwang
- Brain Imaging and Modeling Section, National Institute on Deafness and Other Communication Disorders, National Institutes of HealthBethesda, MD, USA
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of HealthBethesda, MD, USA
- Department of Biomedical Engineering, University of VirginiaCharlottesville, VA, USA
| | - Jeff Alstott
- Section on Critical Brain Dynamics, National Institute of Mental Health, National Institutes of HealthBethesda, MD, USA
- Brain Mapping Unit, Behavioural and Clinical Neuroscience Institute, University of CambridgeCambridgeshire, UK
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