Functional Connectome Dynamics After Mild Traumatic Brain Injury According to Age and Sex

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury: predictors of patient prognosis

JOURNAL/nrgr/04.03/01300535-202407000-00035/figure1/v/2023-11-20T171125Z/r/image-tiff

Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of follow-up data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning

Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age μ ± σ: 39.8 ± 15.5 years) and 471 adult mTBI patients (158 females, age μ ± σ: 38.4 ± 5.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age μ ± σ: 55.3 ± 12.1 years) and 126 patients with concussion (46 females, age μ ± σ: 39.0 ± 17.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral ("what") visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.

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

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. 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.

Transcutaneous auricular vagus nerve stimulation in the treatment of disorders of consciousness: mechanisms and applications

This review provides an in-depth exploration of the mechanisms and applications of transcutaneous auricular vagus nerve stimulation (taVNS) in treating disorders of consciousness (DOC). Beginning with an exploration of the vagus nerve's role in modulating brain function and consciousness, we then delve into the neuroprotective potential of taVNS demonstrated in animal models. The subsequent sections assess the therapeutic impact of taVNS on human DOC, discussing the safety, tolerability, and various factors influencing the treatment response. Finally, the review identifies the current challenges in taVNS research and outlines future directions, emphasizing the need for large-scale trials, optimization of treatment parameters, and comprehensive investigation of taVNS's long-term effects and underlying mechanisms. This comprehensive overview positions taVNS as a promising and safe modality for DOC treatment, with a focus on understanding its intricate neurophysiological influence and optimizing its application in clinical settings.

Structural-functional connectivity bandwidth predicts processing speed in mild traumatic brain Injury: A multiplex network analysis

An emerging body of work has revealed alterations in structural (SC) and functional (FC) brain connectivity following mild TBI (mTBI), with mixed findings. However, these studies seldom integrate complimentary neuroimaging modalities within a unified framework. Multilayer network analysis is an emerging technique to uncover how white matter organization enables functional communication. Using our novel graph metric (SC-FC Bandwidth), we quantified the information capacity of synchronous brain regions in 53 mild TBI patients (46 females; age mean = 40.2 years (y), σ = 16.7 (y), range: 18-79 (y). Diffusion MRI and resting state fMRI were administered at the acute and chronic post-injury intervals. Moreover, participants completed a cognitive task to measure processing speed (30 Seconds and Counting Task; 30-SACT). Processing speed was significantly increased at the chronic, relative to the acute post-injury intervals (p = <0.001). Nonlinear principal components of direct (t = -1.84, p = 0.06) and indirect SC-FC Bandwidth (t = 3.86, p = <0.001) predicted processing speed with a moderate effect size (R2 = 0.43, p < 0.001), while controlling for age. A subnetwork of interhemispheric edges with increased SC-FC Bandwidth was identified at the chronic, relative to the acute mTBI post-injury interval (pFDR = 0.05). Increased interhemispheric SC-FC Bandwidth of this network corresponded with improved processing speed at the chronic post-injury interval (partial r = 0.32, p = 0.02). Our findings revealed that mild TBI results in complex reorganization of brain connectivity optimized for maximum information flow, supporting improved cognitive performance as a compensatory mechanism. Moving forward, this measurement may complement clinical assessment as an objective marker of mTBI recovery.