Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment

Li, Fengfang; Liu, Yin; Lu, Liangliang; Shang, Shaobin; Chen, Huiyou; Haidari, Nasir Ahmad; Wang, Peng; Yin, Xindao; Chen, Yu‐Chen

doi:10.21037/qims-21-915

Cited by 10 publications

(7 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our findings are in line with prior studies reporting that alterations in FC in mTBI patients are more between-network than within-network indicating disruption of communication between brain network modules in this patient population. Taken collectively, these findings confirm that the hub organization and networks might interfere with multiple integrative roles such as executive function, social cognition, internal focus of attention, divided attention, vision, memory, and language ( Vakhtin et al, 2013 ; Madhavan et al, 2019 ; Meier et al, 2020 ; Li et al, 2022 ).…”

Section: Discussionsupporting

confidence: 59%

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

et al. 2023

View full text Add to dashboard Cite

Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67–100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42–93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level.Clinical trial registration[clinicaltrials.gov], identifier [NCT03241732].

show abstract

Section: Discussionsupporting

confidence: 59%

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Next, rs-fMRI time series were bandpass filtered (0.01–0.08 Hz) to improve the signal-to-noise ratio ( 8 ) and images were detrended for making strong instrumental stability. In this study, to avoid artificial local spatial correlations, no spatial smoothing was applied ( 26 ). To remove spurious sources of variance, the Friston 24 parameter obtained via head motion correction and signals from cerebrospinal fluid and white matter were regressed.…”

Section: Methodsmentioning

confidence: 99%

Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury

Liu¹,

Li²,

Shang³

et al. 2023

Quant Imaging Med Surg

Self Cite

View full text Add to dashboard Cite

Background: This study was conducted to investigate topological changes in large-scale functional connectivity (FC) and structural connectivity (SC) networks in acute mild traumatic brain injury (mTBI) and determine their potential relevance to cognitive impairment.Methods: Seventy-one patients with acute mTBI (29 males, 42 females, mean age 43.54 years) from Nanjing First Hospital and 57 matched healthy controls (HC) (33 males, 24 females, mean age 46.16 years) from the local community were recruited in this prospective study. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were acquired within 14 days (mean 3.29 days) after the onset of mTBI. Then, large-scale FC and SC networks with 116 regions from the automated anatomical labeling (AAL) brain atlas were constructed. Graph theory analysis was used to analyze global and nodal metrics. Finally, correlations were assessed between topological properties and neurocognitive performances evaluated by the Montreal Cognitive Assessment (MoCA). Bonferroni correction was performed out for multiple comparisons in all involved analyses.Results: Compared with HC, acute mTBI patients had a higher normalized clustering coefficient (γ) for FC (Cohen's d=4.076), and higher γ and small worldness (σ) for SC (Cohen's d=0.390 and Cohen's d=0.395).The mTBI group showed aberrant nodal degree (Dc), nodal efficiency (Ne), and nodal local efficiency (Nloc) for FC and aberrant Dc, nodal betweenness (Bc), nodal clustering coefficient (NCp) and Ne for SC mainly in the frontal and temporal, cerebellum, and subcortical areas. Acute mTBI patients also had higher functionalstructural coupling strength at both the group and individual levels (Cohen's d=0.415). These aberrant global and nodal topological properties at functional and structural levels were associated with attention, orientation, memory, and naming performances (all P<0.05).Conclusions: Our findings suggested that large-scale FC and SC network changes, higher correlation between FC and SC and cognitive impairment can be detected in the acute stage of mTBI. These network aberrances may be a compensatory mechanism for cognitive impairment in acute mTBI patients.

show abstract

Section: Discussionmentioning

confidence: 90%

“…Recent work using rsfMRI identified broad functional connectivity deficits in human subjects; these defects correlated with diminished choice-reaction scores, showing high sensitivity. Our work improves upon pre-clinical and clinical studies because we dissected more granular aspects of functional connectivity in a mouse model of rmTBI and at ultra-high field [25,39–41]. Functional graph metrics such as clustering coefficient, characteristic path length, centrality, and efficiency are quantifiable features that reveal how information flows throughout the network [42], and they were impaired following rmTBI (Fig.…”

Section: Discussionmentioning

confidence: 96%

Repetitive mild traumatic brain injury impairs resting state fMRI connectivity and alters protein profile signaling networks

Ravi

Criado‐Marrero

Barroso

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Repetitive mild traumatic brain injury (rmTBI) is a leading and severe threat to cognition that often goes undiagnosed. A major challenge in developing diagnostics and treatments for the consequences of rmTBI is the fundamental knowledge gaps that explain how TBI promotes brain dysfunction. It is both critical and urgent to understand the neuropathological and functional consequences of rmTBI to develop effective therapeutic strategies. In this study, we defined the extent of altered brain functional connectivity (FC) and expression of neuropathological markers after rmTBI. Methods: We performed two rmTBI (2x 0.6 J impacts 24 h apart) in male and female C57BL/6J wild-type (WT) (~2.5-3mo) mice using CHIMERA or sham procedures. At 5-6 days post-injury (dpi), we measured changes in brain volume and FC using T2-weighted images, resting-state functional MRI (rsfMRI) and graph theory analyses. We used diffusion tensor imaging (DTI) to assess microstructural changes in white matter tracts. In addition, at 7dpi, we measured changes in Iba1 and GFAP in specific brain regions to determine the extent of gliosis. The expression of disease-associated protein markers in grey and white matter (WM) regions were evaluated using the NanoString-GeoMx digital spatial protein profiling spatial profiling (DSP) platform. Results: The rsfMRI data revealed aberrant changes in connectivity such as node clustering coefficient, global and local efficiency, participation coefficient, eigenvector centrality, and betweenness centrality in thalamus and other key brain regions that process visual, auditory, and somatosensory information. Using DTI, we found that fractional anisotropy (FA) and axial diffusivity in the optic tract was significantly decreased. Also, mean, radial, and axial diffusivity (L1) were significantly increased in the hippocampus. DSP revealed that phospho-serine 199 tau (pS199) as well as glial markers such as GFAP, cathepsin-D, and Iba1 were significantly increased in the optic tract. In thalamic nuclei, the neuroinflammatory marker GPNMB was increased significantly, and the cell proliferation marker Ki-67 was significantly decreased in the rmTBI group. Our data suggest that rmTBI significantly alters brain functional connectivity and causes a profound inflammatory response in gray matter regions, beyond chronic white matter damage.

show abstract

Rich-club reorganization of functional brain networks in acute mild traumatic brain injury with cognitive impairment

Cited by 10 publications

References 64 publications

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

Functional-structural large-scale brain networks are correlated with neurocognitive impairment in acute mild traumatic brain injury

Repetitive mild traumatic brain injury impairs resting state fMRI connectivity and alters protein profile signaling networks

Contact Info

Product

Resources

About