The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury

Roy, Arnab; Bernier, Rachel A.; Wang, Jianli; Benson, Monica A.; French, Jerry J.; Good, David C.; Hillary, Frank G.

doi:10.1371/journal.pone.0170541

Cited by 63 publications

(67 citation statements)

References 72 publications

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“…Cassidy and colleagues (2018) recently demonstrated that persistent homology, a technique from topological analysis, has the potential to quantify the similarity of individual functional connectomes across different parcellations. We believe that such directions hold promise for supporting reproducible findings in clinical RSFC studies (Craddock, James, Holtzheimer, Hu, & Mayberg, 2012;Roy et al, 2017;Schaefer et al, in press).…”

Section: Creating Comparable Network In Clinical Samplesmentioning

confidence: 72%

Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Hallquist

Hillary

2018

Preprint

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Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer's disease or depression have increasingly adapted tools from graph theory to characterize differences between healthy and patient populations.Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four key challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common across studies, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.

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Section: Creating Comparable Network In Clinical Samplesmentioning

confidence: 72%

Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Hallquist

Hillary

2018

Preprint

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“…The loss of nodal specificity may result in incorporation of additional resources or engagement of alternative auxiliary pathways [ 24 , 66 ] or hubs (see Fig 10 in TBI) resulting in less freedom for expression of network dynamics and greater susceptibility to neural noise [ 64 ]. This “hyperconnectivity” response observed in other samples [ 5 , 14 , 53 , 67 ] may operate to accommodate ongoing task demands in the context of reduced neural resources, but one consequence may be reduced network flexibility.…”

Section: Discussionmentioning

confidence: 99%

“…Consistent with Roy and colleagues [ 53 ], we define the cost of each functional edge as the product of the Euclidean distance between the ROI-pair it connects and the absolute weight of the connection. The overall network cost is determined as the total cost for all edges within the network.…”

Section: Methodsmentioning

confidence: 99%

Diminished neural network dynamics after moderate and severe traumatic brain injury

et al. 2018

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Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain’s subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network “states” that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics.

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“…[5][6][7][8][9][10][11][12] For example, a TBI disrupts the optimal balance between network integration and segregation, 8,9 modular organization, 6,10 and the efficiency of brain communication. 7,8,11,12 Neuroimaging tools have also provided evidence for training-dependent neuroplasticity in healthy adults. [13][14][15] Accordingly, the neurorehabilitation community seeks to identify training-induced neuroplasticity of the injured brain.…”

Section: Introductionmentioning

confidence: 99%

“…Although our previous studies [18][19][20][21] and those of others 22,23 demonstrated neuroplasticity after cognitive training in TBI, little is known about training-induced changes in the full-scale organization of the brain networks in individuals with TBI. Because the assessments of whole-brain networks in TBI utilizing rsfMRI and graph theory have provided insights into the brain systems disrupted by DAI, 3,5,6,[8][9][10][11][12] it would be informative to identify training-related neuroplasticity in TBI at the whole-brain level. Furthermore, assessing training-dependent plasticity of the network at the whole-brain level is advantageous, given that cognitive training is often multifaceted and addresses heterogeneous and complex patterns of cognitive dysfunction in TBI.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Training Reorganizes Network Modularity in Traumatic Brain Injury

Han

Chapman

Krawczyk

2019

Neurorehabil Neural Repair

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Background. Graph-theoretic approaches are increasingly popular for identifying the patterns of disrupted neural systems after traumatic brain injury (TBI). However, the patterns of neuroplasticity in brain organization after cognitive training in TBI are less well understood. Objective. We identified the patterns of training-induced neuroplasticity of the whole-brain network in TBI, using resting-state functional connectivity and graph theory. Methods. A total of 64 civilians and veterans with TBI were randomized into either a strategy-based cognitive training group (n = 33) or a knowledge-based training group (active control group; n = 31) for 8 weeks. The participants experienced mild to severe TBI without focal damage and persistent cognitive dysfunctions. A subset of participants complained of subclinical but residual psychiatric symptoms. We acquired their resting-state functional magnetic resonance imaging before training, immediately posttraining, and 3 months posttraining. From participants’ resting-state networks, we obtained the modularity, participation coefficient, within-module connectivity, global efficiency, and local efficiency over multiple network densities. We next performed longitudinal analyses on those measures corrected for multiple comparisons across network densities using false discovery rate (FDR). Results. Relative to the knowledge-based training group, the strategy-based cognitive training group had reduced modularity and increased participation coefficient, global efficiency, and local efficiency over time ( Pnodal < .05; qFDR < 0.05). Brain behavior analysis revealed that the participation coefficient and global efficiency within the strategy-based cognitive training group correlated with trail-making scores in the context of training ( Pnodal < .05; qFDR < 0.05). Conclusions. Cognitive training reorganized modular networks in TBI over the whole brain. Graph-theoretic approaches may be useful in identifying a potential brain-based marker of training efficacy in TBI.

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The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury

Cited by 63 publications

References 72 publications

Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Diminished neural network dynamics after moderate and severe traumatic brain injury

Cognitive Training Reorganizes Network Modularity in Traumatic Brain Injury

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