Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults

Gallen, Courtney L.; Baniqued, Pauline L.; Chapman, Sandra B.; Aslan, Sina; Keebler, Molly; Didehbani, Nyaz; D’Esposito, Mark

doi:10.1371/journal.pone.0169015

Cited by 98 publications

(124 citation statements)

References 54 publications

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“…These studies have examined both structural and functional network organization in a wide variety of samples, including healthy young adults (Power et al, 2013;Zanto and Gazzaley, 2013), developmental cohorts (Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), older adults (Baniqued et al, 2018;Gallen et al, 2016), and a plethora of neurological and psychiatric populations (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). We have gained a better understanding of typical and atypical human brain organization from these efforts.…”

Section: Improved Sampling Of the Subcortex And Cerebellummentioning

confidence: 99%

“…These two ROI sets sample the cortex well, representing a diverse set of brain areas that can be organized into functional networks. Many investigators have used them to describe functional brain organization in a variety of healthy samples (Power et al, 2013;Zanto and Gazzaley, 2013), lifespan cohorts (Baniqued et al, 2018;Gallen et al, 2016;Gu et al, 2015;Nielsen et al, 2018;Rudolph et al, 2017), as well as populations with neurologic and psychiatric diseases (Gratton et al, 2018a;Greene et al, 2016;Sheffield et al, 2015;Siegel et al, 2018). However, the first set (264 volumetric ROIs) under-samples subcortical and cerebellar structures, as only 17 ROIs are non-cortical, and the second set (333 parcels) is restricted to the cortex only, similar to other popular ROI sets, e.g.…”

Section: Introductionmentioning

confidence: 99%

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A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

Seitzman

Gratton

Marek

et al. 2018

Preprint

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An important aspect of network-based analysis is robust node definition. This issue is critical for functional brain network analyses, as poor node choice can lead to spurious findings and misleading inferences about functional brain organization. Two sets of functional brain nodes from our group are well represented in the literature: (1) 264 volumetric regions of interest (ROIs) reported in Power et al., 2011 and (2) 333 cortical surface parcels reported in Gordon et al., 2016. However, subcortical and cerebellar structures are either incompletely captured or missing from these ROI sets. Therefore, properties of functional network organization involving the subcortex and cerebellum may be underappreciated thus far. Here, we apply a winner-take-all partitioning method to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum. We validate these ROIs in three datasets using several criteria, including agreement with existing literature and anatomical atlases. Further, we demonstrate that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization. This new set of ROIs is made publicly available for general use, including a full list of MNI coordinates and functional network labels.

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Section: Improved Sampling Of the Subcortex And Cerebellummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

Seitzman

Gratton

Marek

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…The decrease in modularity was most evident for methamphetamine withdrawal (three modules) and cocaine withdrawal (four modules), whereas nicotine withdrawal showed a smaller reduction of modularity (five modules). Such reductions of modularity are found in humans who suffer from dementia and traumatic brain injury and are associated with cognitive deficits [28–33]. Changes in network structure/functional connectivity [10–13] and cognitive function [34–36] have been observed after chronic drug use and withdrawal, suggesting that similar mechanisms may be active between these different neural disorders.…”

Section: Discussionmentioning

confidence: 99%

Characterization of the brain functional architecture of psychostimulant withdrawal using single-cell whole brain imaging

Kimbrough

Smith

Kallupi

et al. 2019

Preprint

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Numerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive. To address this question, we used male C57BL/6J mice and performed single-cell whole-brain imaging of neural activity during withdrawal from cocaine, methamphetamine, and nicotine. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical- to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway that leads to addiction.Significance StatementA key aspect of treating drug abuse is understanding similarities and differences of how drugs of abuse affect the brain. In the present study we examined how the brain is altered during withdrawal from psychostimulants. We found that each drug produced a unique pattern of activity in the brain, but that brains in withdrawal from cocaine and methamphetamine shared similar features. Interestingly, we found the major common link between withdrawal from all psychostimulants, when compared to controls, was a shift in the broad organization of the brain in the form of reduced modularity. Reduced modularity has been shown in several brain disorders, including traumatic brain injury, and dementia, and may be the common link between drugs of abuse.

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“…These methods decompose the network into building blocks or modules that are internally strongly connected, often corresponding to specialized functions. Importantly, brain modularity has been revealed to be related to behavior involved in learning (Bassett et al, 2011), remembering, attention and integrated reasoning (Gallen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Detecting M/EEG modular brain states in rest and task

Kabbara

Mohamad

O'Neill

et al. 2019

Preprint

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The human brain is a dynamic networked system that continually reconfigures its connectivity patterns over time. Thus, developing approaches able to adequately detect fast brain dynamics is critical. Of particular interest are the methods that analyze the modular structure of brain networks, i.e. the presence of clusters of regions that are densely inter-connected. In this paper, we propose a novel framework to identify fast modular states that dynamically fluctuate over time during rest and task. We validated our method using MEG data recorded during a finger movement task, identifying modular states linking somatosensory and primary motor regions.The algorithm was also validated on dense-EEG data recorded during picture naming task, revealing the sub-second transition between several modular states which relate to visual processing, semantic processing and language. Next, we validated our method on a dataset of resting state dense-EEG signals recorded from 124 patients with Parkinson's disease and different cognitive phenotypes. Results disclosed brain modular states that differentiate cognitively intact patients, patients with moderate cognitive deficits and patients with severe cognitive deficits. Our new approach tracks the brain modular states, in healthy subjects and patients, on an adequate task-specific timescale. KeywordsBrain network dynamics, functional connectivity, M/EEG source-space networks.Arenas, 2005; M. Guimerà and Amaral, 2005). between 0.2 and 0.5.

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Modular Brain Network Organization Predicts Response to Cognitive Training in Older Adults

Cited by 98 publications

References 54 publications

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

A set of functionally-defined brain regions with improved representation of the subcortex and cerebellum

Characterization of the brain functional architecture of psychostimulant withdrawal using single-cell whole brain imaging

Detecting M/EEG modular brain states in rest and task

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