Resting State Networks and Their Associations with Cognitive Functions in Traumatic Brain Injury: An Integrative Review

Abstract

Background

Traumatic brain injury (TBI) is a significant global health concern resulting in persistent physical, cognitive, and social impairments that affect daily functioning and quality of life. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable neuroimaging modality offering novel insights into brain function at rest.

Summary

This integrative review synthesised evidence from studies indexed in PubMed, ScienceDirect, and Scopus on resting-state network (RSN) alterations and their association with cognitive dysfunction in TBI. Consistent alterations in functional connectivity were identified across multiple RSNs in TBI population, particularly within the default mode network (DMN), frontoparietal network (FPN), and salience network. DMN connectivity showed strong associations with attention, processing speed, and memory. FPN connectivity changes corresponded with executive function deficits, while sensorimotor network disruptions were linked to attentional impairments. Dynamic longitudinal changes in network connectivity further suggested underlying neuroplasticity mechanisms.

Key Message

Resting-state functional connectivity demonstrates as a potential sensitive biomarker for TBI diagnosis, prognosis, and rehabilitation monitoring. With implications for individualised rehabilitation, these findings highlight the need for a network oriented approach for understanding, diagnosing, and treating TBI. Future research should explore longitudinal changes in RSN connectivity and their implications for neuro-rehabilitation strategies.

Keywords

Traumatic brain injury cognitive functions resting state networks functional neuroimaging

Introduction

Traumatic brain injury (TBI) is a condition that is usually caused by road traffic accidents (RTA), falls or sports-related injury that results in a change in the brain’s functioning.¹ TBI is defined as an alteration in brain function, or other evidence of brain pathology, caused by an external mechanical force like a blow, jolt or penetrating injury to the head. It is classified by severity into mild (GCS 13–15), moderate (GCS 9–12) and severe (GCS ≤ 8) based on the Glasgow Coma Scale, loss of consciousness duration and post-traumatic amnesia.² Depending on the severity of injury, mild to severe TBI patients experience cognitive impairments, headaches, visual difficulties, sleep disturbances and post-traumatic epilepsy.³

As a result of TBI, the neurons and neural pathways get damaged, which leads to disruptions in cognitive processing. Acute axonal injury and contusions impact brain regions associated with cognition, particularly the frontal and temporal lobes.⁴ The impairments resulting from the injury frequently manifest as difficulties in attention, memory recall, reasoning and executive functioning.⁵ Research has identified structural damage associated with the cognitive deficits (working memory and reasoning), which highlights the importance of intact white matter connections.⁵

The wide use of functional magnetic resonance imaging (fMRI) helps us understand the neural mechanisms behind the alteration in brain connections due to TBI. These methods are used to correlate the results with neuropsychological outcomes like attention and memory deficits in chronic TBI patients.⁶ According to the neuroimaging results, TBI might accelerate the neurodegenerative process over time, resulting in cognitive decline and functional deficits.^{7, 8} This explains the importance of identifying neuroimaging biomarkers that can predict cognitive outcomes and help in framing individualised rehabilitation techniques.⁹

fMRI and diffusion tensor imaging (DTI) are used for analysing the functional and structural connections, to measure the brain’s activity and understand the underlying neurobiological mechanism. These methods provide insights by visualising the anatomical and functional changes in the brain due to TBI.¹⁰ For instance, DTI-based research has shown changes in the white matter integrity that are associated with cognitive dysfunction following TBI, which proves a significant connection for cognitive function.^{11, 12} Likewise, the neuroimaging studies have shown subtle changes in functional networks, impacting cognitive functions even when there is no visible structural damage to the brain. This may be due to compensatory mechanisms supporting one another.¹³

For individuals with TBI, identifying the neuroimaging biomarkers associated with cognitive impairments might be insightful in order to predict outcomes and guide rehabilitation programmes.¹⁴ As the field of neuroimaging develops further, implementing these findings in clinical practice would be very crucial, as it can help the treating physician and the therapist to address the challenges faced by them and for precise diagnosis.

Furthermore, improvements in neuroimaging and analytic techniques have led to the discovery of resting state networks (RSNs), where the fMRI data is broken down into discrete functional units identified as networks. This method is called independent component analysis (ICA).¹⁵ This has made it easier to comprehend how various brain regions interact while at rest and has created possibilities to investigate how these networks change in conditions like TBI.¹⁶

Studies in this field have identified the RSNs, namely the default mode network (DMN), dorsal attention network (DAN), ventral attention network (VAN), salience network (SN), sensorimotor network (SMN), visual network (VN) and executive control network (ECN). Each one of these networks has a distinct contribution to cognitive processing. Research on RSNs in the field of TBI is limited when compared to its findings in psychiatric illnesses.^{17, 18}

There are significant gaps in our knowledge of how exactly the RSNs and cognitive functions are correlated in TBI. Detailed research is required to provide comprehensive results and achieve a holistic understanding of the mechanism, though existing literature has focused only on specific cognitive functions. The integrative review aims to fill in the literature gap by compiling the existing studies in this field to give a better understanding on the relationship between functional connectivity and cognitive abilities like memory, attention, executive functioning and visuospatial skills, with the help of advanced neuroimaging techniques in TBI.

Methods

An integrative review of the literature was conducted using databases including PubMed, ScienceDirect and Scopus to identify studies examining resting-state functional MRI and cognitive outcomes in TBI. Relevant peer-reviewed articles focusing on RSN alterations and their associations with cognitive domains were examined. The literature was synthesised to identify recurring patterns, network-specific findings and conceptual links between functional connectivity and cognitive dysfunction in TBI. The information consolidation was carried out through a step-by-step process of data extraction, classification, integration and data analysis. The studies were categorised based on the cognitive functions and brain networks they examined. For each category, an overview of the research settings, study populations and experimental designs employed was noted. Additionally, the assessment utilised and the general conclusions drawn from these investigations were included. The time elapsed from injury to neuroimaging assessment was extracted where reported, as acute, sub-acute and chronic phases of TBI are associated with distinct changes in resting-state connectivity due to changing neurobiological processes such as oedema, diaschisis and neuroplasticity.^{10, 16} As this is an integrative review rather than a meta-analysis, formal pooling of effect sizes and confidence intervals was not performed.¹⁹ The strength of evidence is reflected narratively based on the consistency, replication and directionality of results across studies, and findings should be interpreted as exploratory rather than definitive quantitative findings. A formal risk of bias assessment using a standardised tool was not conducted, as integrative reviews do not mandate formal bias appraisal.²⁰ However, potential sources of bias were considered qualitatively, including small sample sizes, cross-sectional study designs, heterogeneity of TBI severity and variability in neuroimaging acquisition parameters.

Results

Integrative Synthesis of Resting State Networks and Cognitive Functions

Table 1 summarises the findings on indicating the RSNs associated with each cognitive domain.

Table 1.

Summary of Resting State Network-cognition Associations Reported in Traumatic Brain Injury Studies.

Authors	Sample Size and Group	Neuroimaging Analysis Method Used	Resting State Networks Assessed	Cognitive Domains Tested	Correlation Analysis Method	RSNs—Cognitive Domains Correlation
Sharp et al.²¹	21 TBI (mild, moderate, severe)	ICA Dual regression	DMN	Information processing, attention, executive function	Spearman’s rank correlation, Spearman’s correlation	DMN correlated with attention DMN was strongly correlated with processing speed
Bonnelle et al.²⁴	28 26 TBI HC	ICA Voxel-based morphome try (VBM) DTI	DMN, ECN, SN	Simple choice reaction time task (CRT), matrix reasoning subtests, letter fluency, verbal fluency tests, set shifting, colour-word (Stroop) and trail making test (forms A and B), digit span, logical memory	Spearman correlation stepwise logistic regression	DMN: DMN (precuneus) correlated with sustained attention performance. Correlation between right cingulum bundle integrity and sustained attention
Bharath et al.²⁶	25 21 mTBI HC	ICA	Lingual gyrus network, FPN, SN, DMN, DAN, CN, BGN, VN	Attention, executive functions, learning and memory	Pearson’s correlation	Positive correlations: between lingual-DMN network and Auditory Verbal Learning Test (AVLT) Salience-pre-central network with verbal n back (VnB) hits salience-DMN network with AVLT Negative correlations: between posterior DMN-inferior frontal connectivity and complex figure tracing test Between SN with DMN and pre-central network with VnB errors Insula-precentral network with AVLT long-term potential retention Both positive and negative correlations: Insula-precentral network positively correlated with VnB
Shumskaya et al.²²	43 moderate/severe TBI 34 HC	ICA Dual regression multiple linear regression	Sensorimotor networks, auditory network, posterior DMN, anterior DMN, ECN, FPN, two cerebellar networks frontal pole network	Verbal and visuospatial episodic memory, executive functions, attention, language, premorbid verbal	Pearson correlation multiple linear regression	Significant relationship observed between sensorimotor network and attention while in HC this correlation was not observed Strong correlation between DMN and attention in both TBI patients and HC
Palacios et al.¹⁰	75 mTBI 47 HC ICA	Dual regression	DMN ECN FPN DAN VN SN Occipito-cerebellar Network BGN AN	Processing speed and attention, executive function, learning and memory, Rivermead Post-concussion Symptom Questionnaire (RPQ)	General linear model (GLM) FSLNets	mTBI CT/MRI positive group-positive correlation between DMN with SN and attention and processing speed tasks DAN correlated with TMT A mTBI CT/MRI negative group- positive correlation between orbitofrontal network and TMT A-B negative correlation observed between BGN with orbitofrontal network and TMT B-A Occipito-cerebellar network correlated with better learning performance
Li et al.²³	50 mTBI 43 HC	ICA Static functional network connectivity (FNC) analysis	DMN, ECN, SMN, AN, VN, SN, CN	MoCA	Independent t-tests, chi-square tests, and Pearson’s correlation coefficients	Positive correlation between right insula within SN and attention scores Positive correlation between right precuneus (VN) and executive function score Positive correlation between DMN and ECN connectivity and attention
D’Souza et al.	65 mTBI	ICA Dual regression	DMN (anterior, posterior) FPN (right, left) MN, CEN, VN, AN Cerebellar network (CN) and emotion network	Episodic memory (long-term, recent), WM span (forward, backward) immediate recall, mental balance, semantical word pairs (recall), visual retention, arbitrary word pairs (IR) recognition of objects	Pearson bivariate correlation analysis two-tailed bootstrapping version	Acute phase (within a week of sustaining injury) SMN (Somato Motor Network) negatively correlated with RPSQ (Rivermead Post-Concussion Symptoms Questionnaire) right anterior DMN negatively correlated with RPSQ In the chronic phase (6 months after injury) SMN negatively correlated with RPSQ scores SMN connectivity positively correlated with working memory score
Martínez-Molina et al.¹⁸	AB 15 TBI BA 8 (In 2 groups to provide intervention)	sMRI and rs-fMRI	DMN, SN, FPN, DAN, SMN, LN, VN, CN	General executive functions: Frontal Assessment Battery (FAB), Behavioural Rating Inventory of Executive Function-Adult version (BRIEF-A), Number-Letter Task (NLT), tests from WAIS IV, tests from WMS III, Sustained Attention to Response Task (SART), Simon Task.	Pearson’s bivariate correlation analysis	Positive correlation: within-network connectivity in the FPN correlated with BRIEF-A self-monitor scores. DMN-SM connectivity correlated with NLT switching cost errors Negative correlation: FPN connectivity and FAB scores. Between-network connectivity with DMN and SMN was negatively correlated with increased FAB scores.
Rahman et al.²⁷	19 non- severe TBI (GCS-8-15) 22 HC	rs-fMRI ROI analysis ICA	DMN, SMN, VN, SN, DAN, FPN, CN Language Network (LN)	General cognitive ability, verbal memory, visual memory and perception. Psychomotor speed and cognitive flexibility, executive functioning	Pearson’s correlation	DMN, language network (LN) correlated with general cognitive ability DMN with lower scores in verbal memory Higher FC within DMN and between DMN and LN was associated with better verbal memory performance Lower FC between DMN and visual network (VN) was associated with better performance in visual memory tests Higher FC between DMN nodes and nodes in the language network and frontoparietal network was associated with cognitive flexibility (CTMT) Lower FC within DMN (between
Amgalan et al.²⁵	136 mTBI 35 HC	rs-fMRI	VN, SMN, DAN, VAN, LN, FPN, DMN	Verbal fluency (VF) Inductive reasoning (IR) WM span (WMS) Episodic verbal memory immediate recall (EVMI) episodic verbal memory delayed recall (EVMD) Processing speed (PS)	partial correlation Pearson’s product moment correlation coefficient	VN and the middle temporal gyrus (MTG) were correlated with IR, EVMD, and PS DAN and superior temporal gyri were correlated with EVMD DAN and the precentral gyrus (PCG) were associated with PS VAN and lateral occipital cortex (LOC) correlated with EVMI and PS LN and bilateral precuneus correlated with EVMI, VF and PS

Resting State Networks Associated with Attention

Default Mode Network

Existing literature suggests that alterations in DMN connectivity are associated with attention-related measures in TBI.^{10, 21–24} Several studies have demonstrated an inverse relationship between DMN connectivity and processing speed, specifically in the posterior cingulate cortex, indicating higher DMN connectivity is associated with better performance.²¹ There was an association between precuneus (DMN), and sustained attention.²⁴ A trend was noted towards a significant effect of DMN connectivity on attention, with abnormally high connectivity correlating with more efficient response speed in TBI patients.²⁴ DMN and trail-making test scores were linked in mTBI patients with positive computed tomography/magnetic resonance imaging (CT/MRI) scans.¹⁰ A link was noted between DMN and ECN connection and attention scores, which suggests a strong relationship between DMN and attention in TBI patients.²³

Sensorimotor Network

A significant SMN FC on attention was reported.²⁴ Healthy controls showed an inverse relationship, while TBI patients showed a correspondence between FC and attention, which varied between groups.

Dorsal Attention Network

Precentral gyrus-DAN connectivity and processing speed, precentral gyrus and DAN were linked, which suggests faster processing speed related to attention.²⁵ A positive relationship between DAN and TMT-A, measuring attention and processing speed, was observed in mTBI with CT/MRI positive scans also.¹⁰

Salience Network

An increase in SN connectivity was linked with better task performance,¹⁰ and the right insula was linked with attention scores.²³

Visual Network

A functional connectivity exists between the VN and the middle temporal gyrus, which is correlated with processing speed.²⁵

Resting State Networks Associated with Executive Functions

Frontoparietal Network

A significant relationship between frontoparietal network (FPN) connectivity and executive functions and BRIEF-A self-monitor scores was noted, whereas frontal assessment battery (FAB) scores showed an inverse relationship.¹⁸

Default Mode Network

Significant relationships were reported between DMN connectivity and executive function, DMN and frontal assessment battery scores with number-letter task switching cost errors.¹⁸ A relationship between DMN and executive function was observed.²¹ Particularly, the FC was seen in relation to sustained attention and inhibition, as measured by the Stroop task. The reported results suggest that alterations in DMN connectivity may cause changes in overall executive functions.

Orbitofrontal Network

For mTBI patients with CT/MRI negative scans, an increase in connectivity in the orbitofrontal network, which was associated with better executive function performance, was observed.¹⁰

Basal Ganglia Network

A reduced connectivity between the basal ganglia and orbitofrontal networks was correlated with executive function in patients with mTBI.¹⁰

Visual Network

Right precuneus (within the VN) and executive scores positively correlated.²³

Resting State Networks Associated with Memory

Verbal learning and memory.

Default Mode Network. A positive association between lingual-DMN network connections and Auditory Verbal Learning Test (AVLT) scores.²⁶ SN-DMN network connection corresponds with AVLT scores, particularly AVLT4 scores. These findings suggest that enhanced connectivity within and between these networks is associated with improved verbal learning and memory performance.²⁷

Other Networks

A positive relationship between increased semi-acute connectivity within the occipito-cerebellar network and learning memory scores at 6 months after injury in the CT/MRI negative subgroup of mTBI patients.¹⁰ A link between LN and visual memory was also noted.²⁷

Visuospatial Memory

Default Mode Network

An inverse relationship between posterior DMN-inferior frontal connectivity and complex figure tracing test performance. This finding suggests that enhanced connectivity between these networks is associated with poor visuospatial memory performance.²⁶

Working Memory

Salience Network

Salience-pre central network connectivity was positively linked with verbal n back (VnB) hits. Connections of the SN with DMN and precentral network were negatively correlated with VnB errors. The increased connectivity between SN and other regions is suggestive of better working memory performance.²⁶

Somatomotor Network

There is a positive connection between intrinsic functional connectivity in SMN and working memory scores.²⁸

Negative associations were reported between alterations in functional connectivity in DMN, DAN, VN, SMN, VAN and the working memory span. The finding suggests that the changes in functional connectivity are correlated with working memory performance in TBI patients.²⁵

Episodic Memory

Inverse relationships were also observed between alterations in functional connectivity in DAN and superior temporal gyri (STG) with episodic verbal memory. Limbic network and bilateral precuneus correlated with episodic verbal memory immediate recall (EVMI). DMN and precuneus associated with episodic verbal memory delayed recall (EVMD). Functional connectivity changes were observed between VAN and lateral occipital cortex (LOC) associated with episodic memory immediate recall (EVMI).²⁵

Discussion

This review explains the intricate association between the brain’s FC and cognitive domains from multiple studies on RSNs in TBI patients. DMN is the most widely explored RSN. The inverse relationship between DMN and FPN, as related to DMN and processing speed measures, indicates that task-positive network functions are interfered with by higher DMN connectivity. These varying relations are suggestive of compensatory mechanisms due to injury, influencing the DMN’s activity in both task-negative and task-positive performance. A positive pattern of association between FPN and executive functions was observed.

The contradictory patterns of links with different executive function tests, such as the Brief A and FAB, explain the complexity of executive functions and the need to assess cognitive functions from multiple aspects following TBI. The positive association between SMN and attention in TBI patients, and the negative association in healthy controls, indicates that alterations in FC are due to the injury.²⁴ The results suggest that FC, as a result of compensatory mechanisms, requires further research on maladaptive responses to injury.²⁴

The multiple network interactions emphasise the distributed nature of cognitive processing. The observed associations between RSN connectivity and cognitive performance suggest meaningful, though complex, brain behaviour relationships in TBI.²⁵ The consistent negative direction of these links is noteworthy and may indicate that reduced connectivity between certain regions correlates with better cognitive performance in TBI patients. The structural connectivity findings, particularly the negative links between right cingulum bundle integrity and sustained attention, present an intriguing and counterintuitive relationship.²⁴ This may suggest compensatory mechanisms where reduced white matter integrity leads to functional reorganisation, potentially improving cognitive performance. These findings collectively highlight the complexity of brain–behaviour relationships in TBI patients. The varying directions and strengths of associations across different networks and cognitive domains underscore the need for a nuanced understanding of post-TBI brain function. This integrative review synthesises conceptual and empirical patterns across neuroimaging studies rather than providing an exhaustive or systematic evaluation of the literature.

Implications for Future Research

Upcoming studies can focus on investigating the adaptive versus maladaptive nature of altered network connectivity in TBI patients, exploring the temporal dynamics of these relationships, considering both acute and chronic stages of TBI and examining how these network changes shift from group-level to individual-level analysis. Development of an individual connectome profile can be used to create an individualised rehabilitation module by identifying the disrupted network and target. The use of machine learning techniques for individualised data helps in predicting clinical outcomes. Additionally, multilayer and multiplex network models can be explored to simultaneously analyse the functional and structural connectivity.

Limitations

The available literature remains limited, which may affect the generalisability of the synthesised findings. Most available studies employed cross-sectional designs, limiting conclusions regarding longitudinal network changes and recovery trajectories. The heterogeneity of TBI severity across studies may have introduced variability in results, complicating the derivation of consistent conclusions. While cognitive domains like attention, working memory, verbal learning and executive functions were examined, the review may not have comprehensively addressed all cognitive functions affected by TBI. Despite all studies utilising rs-fMRI, variations in acquisition parameters, pre-processing and analysis methods could have affected the comparability of results. The review may not have explored the effects of age and sex on the disruptions of functional connectivity and its effects on cognitive impairment post-TBI. The neuropsychological tools used to assess cognitive functions may vary across research studies, which may affect the consistency of the findings reported. Furthermore, the absence of meta-analytic pooling means that confidence intervals and precise effect sizes cannot be reported. The certainty of findings varies across cognitive domains: DMN-attention and DMN-memory associations are supported by multiple independent studies and are therefore more reliable, while findings for networks such as the Basal Ganglia Network or Orbitofrontal Network rest on single studies and should be interpreted with caution. A significant limitation across included studies is the variability in the timing of neuroimaging assessments relative to injury onset. Acute, sub-acute and chronic TBI are each characterised by distinct neurobiological processes: early post-injury phases reflect oedema and diaschisis, while chronic phases reflect neuroplasticity and reorganisation.¹⁶ The absence of a formal bias assessment tool is an additional limitation of this review; future systematic reviews in this area should employ standardised instruments such as the JBI critical appraisal checklist to appraise study quality.²⁰ The heterogeneity in imaging time points across studies makes it difficult to draw conclusions about stage-specific RSN alterations and their differential impact on cognitive outcomes.¹⁰

Conclusion

In this review, the RSNs associated with attention, executive functions, visuospatial cognition and memory in TBI patients were explored. It is noteworthy that functional connectivity within and between DMN is a sensitive biomarker for early prognosis of TBI, as there are a notable number of studies available focusing on DMN. Further research is required to clarify the mechanisms underlying other RSN alterations, as well as their role in cognitive recovery following TBI. The evidence for neuroplasticity and recovery can be observed through the dynamic changes in the affected networks, but the exact reasons for hyperintensities of activation are unknown. Although additional analysis of studies is required to completely understand the alterations in these networks and their significance for cognitive performance to frame a rehabilitation strategy for recovery and reorganisation of disrupted connectivity, this review provides a foundational overview of the existing literature.

Footnotes

Acknowledgement

The authors gratefully acknowledge the support provided by Sri Ramachandra Institute of Higher Education and Research for facilitating this research.

Author’s Contribution

Ms Madhumathi P C: Conceptualisation, methodology, literature search, study selection, data extraction, formal analysis, writing; Dr Visvanathan K: Supervision, methodology, critical review of manuscript, review & editing; Dr Rajeswaran R: validation (fMRI—resting state networks aspect), critical review of manuscript, assistance in writing; Dr Rimjhim Agrawal: review of manuscript draft, validation (fMRI—RSNs aspects); Dr Vijayalakshmi: inputs on methodology, review & editing, inputs on cognitive functions aspects; Mr Varadharajan: guidance on methodology, assistance in study selection, review, validation (cognitive functions aspect).

Data Availability Statement

This study is a review of previously published literature. All data analysed in this review are available in the cited articles, which are included in the reference list.

Declaration of Conflict of Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Patient Consent

Patient consent was not required as this study is an integrative review of previously published literature and did not involve human participants or collection of primary data.

Statement of Ethics

Not applicable.

ORCID iDs

Madhumathi P C

Visvanathan Krishnaswamy

Rajeswaran Rangasami

Vijayalakshmi Purushothaman

Varadharajan Sivaraman

References

Nekkanti

, Shaik

, Mondem

, . Effect of right median nerve stimulation on level of consciousness in traumatic brain injury subjects. Asian J Pharm Res Health Care 2016; 8(3): 67. DOI: 10.18311/ajprhc/2016/751

Menon

, Schwab

, Wright

, . Position statement: definition of traumatic brain injury. Arch Phys Med Rehabil 2010; 91(11): 1637–1640.

Rauchman

, Zubair

, Jacob

, . Traumatic brain injury: mechanisms, manifestations, and visual sequelae. Front Neurosci 2023; 17: 1090672. DOI: 10.3389/fnins.2023.1090672

Wijdicks

EFM

, Rabinstein

, Hocker

, . What neurologists know about outcome in traumatic brain injury and what other physicians want to know. In: Neurocritical care. Oxford University Press , 2016, pp. 291–296.

Jolly

, Scott

, Sharp

, . Distinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injury. Brain 2020; 143(4): 1158–1176. DOI: 10.1093/brain/awaa067

Kim

, Whyte

, Patel

, . A perfusion fMRI study of the neural correlates of sustained-attention and working-memory deficits in chronic traumatic brain injury. Neurorehabil Neural Repair 2012; 26(7): 870–880. DOI: 10.1177/1545968311434553

Irimia

and Van Horn

Traumatic brain injury, neuroimaging, and neurodegeneration. Front Hum Neurosci 2013; 11: 395.

Bigler

ED.

Traumatic brain injury, neuroimaging, and neurodegeneration. Front Hum Neurosci 2013; 7: 395.

Silverberg

, Crane

, Dams-O’Connor

, . Developing a cognition endpoint for traumatic brain injury clinical trials. J Neurotrauma 2017; 34(2): 363–371. DOI: 10.1089/neu.2016.4443

10.

Palacios

, Yuh

, Y-S

Chang

, . Resting-state functional connectivity alterations associated with six-month outcomes in mild traumatic brain injury. J Neurotrauma 2017; 34(8): 1546–1557. DOI: 10.1089/neu.2016.4752

11.

Wang

, Yang

, Chen

, . White matter integrity correlates with residual consciousness in patients with severe brain injury. Brain Imaging Behav 2018; 12(6): 1669–1677. DOI: 10.1007/s11682-018-9832-1

12.

Sorg

, Luc

, Hanson

, . White matter integrity in veterans with mild traumatic brain injury. J Head Trauma Rehabil 2014; 29(1): 21–32.

13.

Cole

, Leech

, and Sharp

DJ.

Alzheimer’s disease neuroimaging initiative. Prediction of brain age suggests accelerated atrophy after traumatic brain injury: brain age prediction after TBI. Ann Neurol 2015; 77(4): 571–581. DOI: 10.1002/ana.24367

14.

Galetto

and Sacco

Neuroplastic changes induced by cognitive rehabilitation in traumatic brain injury: a review. Neurorehabil Neural Repair 2017; 31: 800–813.

15.

Lee

, Smyser

, and Shimony

JS.

Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol 2013; 34(10): 1866–1872. DOI: 10.3174/ajnr.a3263

16.

Venkatesan

, Dennis

, and Hillary

FG.

Chronology and chronicity of altered resting-state functional connectivity after traumatic brain injury. J Neurotrauma 2015; 32(4): 252–264. DOI: 10.1089/neu.2013.3318

17.

, Lu

, Li

, . Disrupted resting-state functional connectivity and network topology in mild traumatic brain injury: an arterial spin labelling study. Brain Commun 2023; 5(5): fcad254. DOI: 10.1093/braincomms/fcad254

18.

Martínez-Molina

, Siponkoski

S-T

, Kuusela

, . Resting-state network plasticity induced by music therapy after traumatic brain injury. Neural Plast 2021; 2021: 6682471. DOI: 10.1155/2021/6682471

19.

Whittemore

and Knafl

The integrative review: updated methodology. J Adv Nurs 2005; 52(5): 546–553.

20.

Aromataris

and Munn

(eds). JBI manual for evidence synthesis. Adelaide: JBI; 2020. https://synthesismanual.jbi.global

21.

Sharp

, Beckmann

, Greenwood

, . Default mode network functional and structural connectivity after traumatic brain injury. Brain 2011; 134(Pt 8): 2233–2247. DOI: 10.1093/brain/awr175

22.

Shumskaya

, van Gerven

MAJ

, Norris

, . Abnormal connectivity in the sensorimotor network predicts attention deficits in traumatic brain injury. Exp Brain Res 2017; 235(3): 799–807. DOI: 10.1007/s00221-016-4841-z

23.

, Lu

, Shang

, Hu

, . Disrupted functional network connectivity predicts cognitive impairment after acute mild traumatic brain injury. CNS Neurosci Ther 2020; 26(10): 1083–1091. DOI: 10.1111/cns.13430

24.

Bonnelle

, Leech

, Kinnunen

, . Default mode network connectivity predicts sustained attention deficits after traumatic brain injury. J Neurosci 2011; 31(38): 13442–13451. DOI: 10.1523/JNEUROSCI.1163-11.2011

25.

Amgalan

, Maher

, Imms

, . Functional connectome dynamics after mild traumatic brain injury according to age and sex. Front Aging Neurosci 2022; 14: 852990. DOI: 10.3389/fnagi.2022.852990

26.

Bharath

, Munivenkatappa

, Gohel

, . Recovery of resting brain connectivity ensuing mild traumatic brain injury. Front Hum Neurosci 2015; 9: 513. DOI: 10.3389/fnhum.2015.00513

27.

Rahman

, Sapiai

, Noh

, . Alteration in the functional organization of the default mode network following closed non-severe traumatic brain injury. Front Neurosci 2022; 16: 833320.

28.

D’Souza

, Kumar

, Choudhary

, . Alterations of connectivity patterns in functional brain networks in patients with mild traumatic brain injury: a longitudinal resting-state functional magnetic resonance imaging study. Neuroradiol J 2020; 33(2): 186–197. DOI: 10.1177/1971400920901706