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

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

doi:10.1371/journal.pone.0206005

Cited by 3 publications

(1 citation statement)

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“…Time since injury is a critical factor to consider in understanding recovery trajectories and has been shown to be vital in the understanding of network plasticity. There is evidence that rsFC connectomics may change over time during the first year post injury, with increases in rsFC found at 6months, 1-year, and 18-months after injury in adult patients with mild, moderate, and severe TBI (Hillary et al, 2011;Sharp et al, 2011;Hillary et al, 2014;Dall'Acqua et al, 2017;de Souza et al, 2020;Roy et al, 2018). Other studies have shown limited changes in rsFC between 3 and 5 months after injury (Mayer et al, 2011), as well as heterogeneous patterns of altered whole-brain connectivity at the chronic stage (Palacios et al, 2013;Venkatesan et al, 2015).…”

Section: Account For Heterogeneity In Sample Characteristicsmentioning

confidence: 99%

ENIGMA’s Simple Seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Caeyenberghs,

Hillary

2024

Preprint

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Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients, such as (i) integrated network analyses to examine the direct relationship between alterations in structural and functional connectivity; (ii) dealing with heterogeneous lesions in the rsfMRI analyses; and (iii) single-subject profiling of functional connectivity for making inferences at the single subject level in relation to a reference population. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

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Section: Account For Heterogeneity In Sample Characteristicsmentioning

confidence: 99%

ENIGMA’s Simple Seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Caeyenberghs,

Hillary

2024

Preprint

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ENIGMA’s simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Caeyenberghs,

Imms,

Irimia

et al. 2024

NeuroImage: Clinical

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Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity

Hillary

Grafman

2017

Trends in Cognitive Sciences

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212

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A common finding in human functional brain-imaging studies is that damage to neural systems paradoxically results in enhanced functional connectivity between network regions, a phenomenon commonly referred to as 'hyperconnectivity'. Here, we describe the various ways that hyperconnectivity operates to benefit a neural network following injury while simultaneously negotiating the trade-off between metabolic cost and communication efficiency. Hyperconnectivity may be optimally expressed by increasing connections through the most central and metabolically efficient regions (i.e., hubs). While adaptive in the short term, we propose that chronic hyperconnectivity may leave network hubs vulnerable to secondary pathological processes over the life span due to chronically elevated metabolic stress. We conclude by offering novel, testable hypotheses for advancing our understanding of the role of hyperconnectivity in systems-level brain plasticity in neurological disorders.

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

Cited by 3 publications

References 1 publication

ENIGMA’s Simple Seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

ENIGMA’s Simple Seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

ENIGMA’s simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Injured Brains and Adaptive Networks: The Benefits and Costs of Hyperconnectivity

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