Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

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Effect of antipsychotics on community structure in functional brain networks

Journal of Complex Networks ◽

10.1093/comnet/cnz013 ◽

2019 ◽

Vol 7 (6) ◽

pp. 932-960 ◽

Cited By ~ 2

Author(s):

Ryan Flanagan ◽

Lucas Lacasa ◽

Emma K Towlson ◽

Sang Hoon Lee ◽

Mason A Porter

Keyword(s):

Community Structure ◽

Network Effects ◽

Brain Networks ◽

Relevant Information ◽

Control Group ◽

Functional Brain ◽

Structure Changes ◽

Structural Abnormalities ◽

Functional Brain Networks ◽

Network Properties

AbstractSchizophrenia, a mental disorder that is characterized by abnormal social behaviour and failure to distinguish one’s own thoughts and ideas from reality, has been associated with structural abnormalities in the architecture of functional brain networks. In this article, we (1) investigate whether mesoscale network properties give relevant information to distinguish groups of patients from controls in different scenarios and (2) use this lens to examine network effects of different antipsychotic treatments. Using various methods of network analysis, we examine the effect of two classical therapeutic antipsychotics—Aripiprazole and Sulpiride—on the architecture of functional brain networks of both controls (i.e., a set of people who were deemed to be healthy) and patients (who were diagnosed with schizophrenia). We compare community structures of functional brain networks of different individuals using mesoscopic response functions, which measure how community structure changes across different scales of a network. Our approach does a reasonably good job of distinguishing patients from controls, and the distinction is sharper for patients and controls who have been treated with Aripiprazole. Unexpectedly, we find that this increased separation between patients and controls is associated with a change in the control group, as the functional brain networks of the patient group appear to be predominantly unaffected by this drug. This suggests that Aripiprazole has a significant and measurable effect on community structure in healthy individuals but not in individuals who are diagnosed with schizophrenia, something that conflicts with the naive assumption that the drug alters the mesoscale network properties of the patients (rather than the controls). By contrast, we are less successful at separating the networks of patients from those of controls when the subjects have been given the drug Sulpiride. Taken together, in our results, we observe differences in the effects of the drugs (and a placebo) on community structure in patients and controls and also that this effect differs across groups. From a network-science perspective, we thereby demonstrate that different types of antipsychotic drugs selectively affect mesoscale properties of brain networks, providing support that structures such as communities are meaningful functional units in the brain.

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Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

10.1101/2020.06.29.178152 ◽

2020 ◽

Author(s):

Joshua M. Mueller ◽

Laura Pritschet ◽

Tyler Santander ◽

Caitlin M. Taylor ◽

Scott T. Grafton ◽

...

Keyword(s):

Menstrual Cycle ◽

Community Detection ◽

Brain Networks ◽

Brain Network ◽

Functional Brain ◽

Default Mode ◽

Hormone Interactions ◽

Dynamic Community Detection ◽

Functional Brain Networks ◽

Dynamic Community

AbstractSex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intra-network increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that ovulation results in a temporary, localized patterns of brain network reorganization.Author SummarySex steroid hormones influence the central nervous system across multiple spatiotemporal scales. Estrogen and progesterone concentrations rise and fall throughout the menstrual cycle, but it remains poorly understood how day-to-day fluctuations in hormones shape human brain dynamics. Here, we assessed the structure and stability of resting-state brain network activity in concordance with serum hormone levels from a female who underwent fMRI and venipuncture for 30 consecutive days. Our results reveal that while network structure is largely stable over the menstrual cycle, there is temporary reorganization of several largescale functional brain networks during the ovulatory window. In particular, a default mode subnetwork exhibits increased connectivity with itself and with regions from temporoparietal and limbic networks, providing novel perspective into brain-hormone interactions.

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Non-assortative community structure in resting and task-evoked functional brain networks

10.1101/355016 ◽

2018 ◽

Cited By ~ 8

Author(s):

Richard F. Betzel ◽

Maxwell A. Bertolero ◽

Danielle S. Bassett

Keyword(s):

Community Structure ◽

Community Organization ◽

Brain Networks ◽

Single Class ◽

Worst Case ◽

Functional Brain ◽

Functional Roles ◽

Functional Brain Networks ◽

New Perspective ◽

Cognitively Demanding Tasks

Brain networks exhibit community structure that reconfigures during cognitively demanding tasks. Extant work has emphasized a single class of communities: those that are assortative, or internally dense and externally sparse. Other classes that may play key functional roles in brain function have largely been ignored, leading to an impoverished view in the best case and a mischaracterization in the worst case. Here, we leverage weighted stochastic blockmodeling, a community detection method capable of detecting diverse classes of communities, to study the community structure of functional brain networks while subjects either rest or perform cognitively demanding tasks. We find evidence that the resting brain is largely assortative, although higher order association areas exhibit non-assortative organization, forming cores and peripheries. Surprisingly, this assortative structure breaks down during tasks and is supplanted by core, periphery, and disassortative communities. Using measures derived from the community structure, we show that it is possible to classify an individual’s task state with an accuracy that is well above average. Finally, we show that inter-individual differences in the composition of assortative and non-assortative communities is correlated with subject performance on in-scanner cognitive tasks. These findings offer a new perspective on the community organization of functional brain networks and its relation to cognition.

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Detecting State Changes in Community Structure of Functional Brain Networks Using a Markov-Switching Stochastic Block Model

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) ◽

10.1109/isbi.2019.8759405 ◽

2019 ◽

Author(s):

S. Balqis Samdin ◽

Chee-Ming Ting ◽

Hernando Ombao

Keyword(s):

Community Structure ◽

Markov Switching ◽

Brain Networks ◽

Block Model ◽

Stochastic Block Model ◽

Functional Brain ◽

Functional Brain Networks ◽

State Changes

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The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability

NeuroImage ◽

10.1016/j.neuroimage.2019.07.003 ◽

2019 ◽

Vol 202 ◽

pp. 115990 ◽

Cited By ~ 21

Author(s):

Richard F. Betzel ◽

Maxwell A. Bertolero ◽

Evan M. Gordon ◽

Caterina Gratton ◽

Nico U.F. Dosenbach ◽

...

Keyword(s):

Community Structure ◽

Brain Networks ◽

Functional Brain ◽

Subject Variability ◽

Functional Brain Networks

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Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle

Network Neuroscience ◽

10.1162/netn_a_00169 ◽

2020 ◽

pp. 1-20 ◽

Cited By ~ 1

Author(s):

Joshua M. Mueller ◽

Laura Pritschet ◽

Tyler Santander ◽

Caitlin M. Taylor ◽

Scott T. Grafton ◽

...

Keyword(s):

Menstrual Cycle ◽

Community Detection ◽

Large Scale ◽

Brain Networks ◽

Functional Brain ◽

Default Mode ◽

Detection Techniques ◽

Dynamic Community Detection ◽

Functional Brain Networks ◽

Dynamic Community

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain–hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.

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