Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes.

BACKGROUND: Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis athletes and identify specific brain network biomarkers. METHODS: A total of 48 healthy right-handed college students participated in this study, including 24 table tennis athletes and 24 controls with no exercise experience. Electroencephalogram data were collected using a 64-conductive active electrode system during eyes-closed resting conditions. The analysis involved examining the average power spectral density and constructing brain functional networks using the weighted phase-lag index. Network topological characteristics were then calculated. RESULTS: The results revealed that table tennis athletes exhibited significantly higher average power spectral density in the α band compared to the control group. Moreover, athletes not only demonstrated stronger functional connections, but they also exhibited enhanced transmission efficiency in the brain network, particularly at the local level. Additionally, a lateralization effect was observed, with more potent interconnected hubs identified in the left hemisphere of the athletes' brain. CONCLUSIONS: Our findings imply that the α band may be uniquely associated with table tennis athletes and their motor skills. The brain network characteristics of athletes during the resting state are worth further attention to gain a better understanding of adaptability of and changes in their brains during training and competition.

Evidence of Ongoing Cerebral Microstructural Reorganization in Children With Persisting Symptoms Following Mild Traumatic Brain Injury: A NODDI DTI Analysis.

Approximately 300-550 children per 100,000 sustain a mild traumatic brain injury (mTBI) each year, of whom ∼25-30% have long-term cognitive problems. Following mTBI, free water (FW) accumulation occurs in white matter (WM) tracts. Diffusion tensor imaging (DTI) can be used to investigate structural integrity following mTBI. Compared with conventional DTI, neurite orientation dispersion and density imaging (NODDI) orientation dispersion index (ODI) and fraction of isolated free water (FISO) metrics may allow a more advanced insight into microstructural damage following pediatric mTBI. In this longitudinal study, we used NODDI to explore whole-brain and tract-specific differences in ODI and FISO in children with persistent symptoms after mTBI (n = 80) and in children displaying clinical recovery (n = 32) at 1 and 2-3 months post-mTBI compared with healthy controls (HCs) (n = 21). Two-way repeated measures analysis of variance (ANOVA) and voxelwise two-sample t tests were conducted to compare whole-brain and tract-specific diffusion across groups. All results were corrected at positive false discovery rate (pFDR) <0.05. We also examined the association between NODDI metrics and clinical outcomes, using logistical regression to investigate the value of NODDI metrics in predicting future recovery from mTBI. Whole-brain ODI was significantly increased in symptomatic participants compared with HCs at both 1 and 2 months post-injury, where the uncinate fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF) were particularly implicated. Using region of interest (ROI) analysis in significant WM, bilateral IFOF and UF voxels, symptomatic participants had the highest ODI in all ROIs. ODI was lower in asymptomatic participants, and HCs had the lowest ODI in all ROIs. No changes in FISO were found across groups or over time. WM ODI was moderately correlated with a higher youth-reported post-concussion symptom inventory (PCSI) score. With 87% predictive power, ODI (1 month post-injury) and clinical predictors (age, sex, PCSI score, attention scores) were a more sensitive predictor of recovery at 2-3 months post-injury than fractional anisotropy (FA) and clinical predictors, or clinical predictors alone. FISO could not predict recovery at 2-3 months post-injury. Therefore, we found that ODI was significantly increased in symptomatic children following mTBI compared with HCs at 1 month post-injury, and progressively decreased over time alongside clinical recovery. We found no significant differences in FISO between groups or over time. WM ODI at 1 month was a more sensitive predictor of clinical recovery at 2-3 months post-injury than FA, FISO, or clinical measures alone. Our results show evidence of ongoing microstructural reorganization or neuroinflammation between 1 and 2-3 months post-injury, further supporting delayed return to play in children who remain symptomatic. We recommend future research examining the clinical utility of NODDI following mTBI to predict recovery or persistence of post-concussion symptoms and thereby inform management of mTBI.

Toward hippocampal volume measures on ultra-high field magnetic resonance imaging: a comprehensive comparison study between deep learning and conventional approaches.

The hippocampus is a complex brain structure that plays an important role in various cognitive aspects such as memory, intelligence, executive function, and path integration. The volume of this highly plastic structure is identified as one of the most important biomarkers of specific neuropsychiatric and neurodegenerative diseases. It has also been extensively investigated in numerous aging studies. However, recent studies on aging show that the performance of conventional approaches in measuring the hippocampal volume is still far from satisfactory, especially in terms of delivering longitudinal measures from ultra-high field magnetic resonance images (MRIs), which can visualize more boundary details. The advancement of deep learning provides an alternative solution to measuring the hippocampal volume. In this work, we comprehensively compared a deep learning pipeline based on nnU-Net with several conventional approaches including Freesurfer, FSL and DARTEL, for automatically delivering hippocampal volumes: (1) Firstly, we evaluated the segmentation accuracy and precision on a public dataset through cross-validation. Results showed that the deep learning pipeline had the lowest mean (L = 1.5%, R = 1.7%) and the lowest standard deviation (L = 5.2%, R = 6.2%) in terms of volume percentage error. (2) Secondly, sub-millimeter MRIs of a group of healthy adults with test-retest 3T and 7T sessions were used to extensively assess the test-retest reliability. Results showed that the deep learning pipeline achieved very high intraclass correlation coefficients (L = 0.990, R = 0.986 for 7T; L = 0.985, R = 0.983 for 3T) and very small volume percentage differences (L = 1.2%, R = 0.9% for 7T; L = 1.3%, R = 1.3% for 3T). (3) Thirdly, a Bayesian linear mixed effect model was constructed with respect to the hippocampal volumes of two healthy adult datasets with longitudinal 7T scans and one disease-related longitudinal dataset. It was found that the deep learning pipeline detected both the subtle and disease-related changes over time with high sensitivity as well as the mild differences across subjects. Comparison results from the aforementioned three aspects showed that the deep learning pipeline significantly outperformed the conventional approaches by large margins. Results also showed that the deep learning pipeline can better accommodate longitudinal analysis purposes.

A cross-comparative analysis of in vivo versus ex vivo MRI indices in a mouse model of concussion.

BACKGROUND: We present a cross-sectional, case-matched, and pair-wise comparison of structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI) measures in vivo and ex vivo in a mouse model of concussion, thus aiming to establish the concordance of structural and diffusion imaging findings in living brain and after fixation. METHODS: We allocated 28 male mice aged 3-4 months to sham injury and concussion (CON) groups. CON mice had received a single concussive impact on day 0 and underwent MRI at day 2 (n = 9) or 7 (n = 10) post-impact, and sham control mice likewise underwent imaging at day 2 (n = 5) or 7 (n = 4). Immediately after the final scanning, we collected the perfusion-fixed brains, which were stored for imaging ex vivo 6-12 months later. We then compared the structural imaging, DTI, and NODDI results between different methods. RESULTS: In vivo to ex vivo structural and DTI/NODDI findings were in notably poor agreement regarding the effects of concussion on structural integrity of the brain. COMPARISON WITH EXISTING METHODS: ex vivo imaging was frequently done to study the effects of diseases and treatments, but our results showed that ex vivo and in vivo imaging can detect completely opposite and contradictory results. This is also the first study that compares in vivo and ex vivo NODDI. CONCLUSION: Our findings call for caution in extrapolating translational capabilities obtained ex vivo to physiological measurements in vivo. The divergent findings may reflect fixation artefacts and the contribution of the glymphatic system changes.

Hippocampal demyelination is associated with increased magnetic susceptibility in a mouse model of concussion.

Structural and functional deficits in the hippocampus are a prominent feature of moderate-severe traumatic brain injury (TBI). In this work, we investigated the potential of Quantitative Susceptibility Imaging (QSM) to reveal the temporal changes in myelin integrity in a mouse model of concussion (mild TBI). We employed a cross-sectional design wherein we assigned 43 mice to cohorts undergoing either a concussive impact or a sham procedure, with QSM imaging at day 2, 7, or 14 post-injury, followed by Luxol Fast Blue (LFB) myelin staining to assess the structural integrity of hippocampal white matter (WM). We assessed spatial learning in the mice using the Active Place Avoidance Test (APA), recording their ability to use visual cues to locate and avoid zone-dependent mild electrical shocks. QSM and LFB staining indicated changes in the stratum lacunosum-molecular layer of the hippocampus in the concussion groups, suggesting impairment of this key relay between the entorhinal cortex and the CA1 regions. These imaging and histology findings were consistent with demyelination, namely increased magnetic susceptibility to MR imaging and decreased LFB staining. In the APA test, sham animals showed fewer entries into the shock zone compared to the concussed cohort. Thus, we present radiological, histological, and behavioral findings that concussion can induce significant and alterations in hippocampal integrity and function that evolve over time after the injury.

Self-reported Fatigue was Associated with Increased White-matter Alterations in Long-term Traumatic Brain Injury and Posttraumatic Stress Disorder Patients.

Fatigue is a long-lasting problem in traumatic brain injury (TBI) and post-traumatic stress disorder (PTSD), with limited research that investigated the fatigue-related white-matter changes within TBI and/or PTSD cohorts. This exploratory cross-sectional study used diffusion tensor imaging (DTI) and neuropsychological data collected from 153 male Vietnam War veterans, as part of the Alzheimer's Disease Neuroimaging Initiative - Department of Defense, and were divided clinically into control veterans, PTSD, TBI, and with both TBI and PTSD (TBI + PTSD). The existence of fatigue was defined by the question "Do you often feel tired, fatigued, or sleepy during the daytime?". DTI data were compared between fatigue and non-fatigue subgroups in each clinical group using tract-based spatial statistics voxel-based differences. Fatigue was reported in controls (29.55%), slightly higher in TBI (52.17%, PBenf = 0.06), and significantly higher in both TBI + PTSD (66.67%, PBenf = 0.001) and PTSD groups (79.25%, PBenf < 0.001). Compared to non-fatigued subgroups, no white-matter differences were observed in the fatigued subgroups of control or TBI, while the fatigued PTSD subgroup only showed increased diffusivity measures (i.e., radial and axial), and the fatigued TBI + PTSD subgroup showed decreased fractional anisotropy and increased diffusivity measures (PFWE ≤ 0.05). The results act as preliminary findings suggesting fatigue to be significantly reported in TBI + PTSD and PTSD decades post-trauma with a possible link to white-matter microstructural differences in both PTSD and TBI + PTSD. Future studies with larger cohorts and detailed fatigue assessments would be required to identify the white-matter changes associated with fatigue in these cohorts.

Diffusion tensor imaging and plasma immunological biomarker panel in a rat traumatic brain injury (TBI) model and in human clinical TBI.

Introduction: Neuroinflammatory reactions play a significant role in the pathology and long-term consequences of traumatic brain injury (TBI) and may mediate salutogenic processes that white matter integrity. This study aimed to investigate the relationship between inflammatory markers and white matter integrity following TBI in both a rat TBI model and clinical TBI cases. Methods: In the rat model, blood samples were collected following a controlled cortical impact (CCI) to assess a panel of inflammatory markers; MR-based diffusion tensor imaging (DTI) was employed to evaluate white matter integrity 60 days post-injury. 15 clinical TBI patients were similarly assessed for a panel of inflammatory markers and DTI post-intensive care unit discharge. Blood samples from healthy controls were used for comparison of the inflammatory markers. Results: Time-dependent elevations in immunological markers were observed in TBI rats, with a correlation to preserved fractional anisotropy (FA) in white matter. Specifically, TBI-induced increased plasma levels of IL-1ß, IL-6, G-CSF, CCL3, CCL5, and TNF-α were associated with higher white matter integrity, as measured by FA. Clinical cases had similar findings: elevated inflammatory markers (relative to controls) were associated with preservation of FA in vulnerable white matter regions. Discussion: Inflammatory markers in post-TBI plasma samples are ambivalent with respect to prediction of favourable outcome versus a progression to more pervasive pathology and morbidity.

Escalation of Tau Accumulation after a Traumatic Brain Injury: Findings from Positron Emission Tomography.

Traumatic brain injury (TBI) has come to be recognized as a risk factor for Alzheimer's disease (AD), with poorly understood underlying mechanisms. We hypothesized that a history of TBI would be associated with greater tau deposition in elders with high-risk for dementia. A Groups of 20 participants with self-reported history of TBI and 100 without any such history were scanned using [18F]-AV1451 positron emission tomography as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Scans were stratified into four groups according to TBI history, and by clinical dementia rating scores into cognitively normal (CDR = 0) and those showing cognitive decline (CDR ≥ 0.5). We pursued voxel-based group comparison of [18F]-AV1451 uptake to identify the effect of TBI history on brain tau deposition, and for voxel-wise correlation analyses between [18F]-AV1451 uptake and different neuropsychological measures and cerebrospinal fluid (CSF) biomarkers. Compared to the TBI-/CDR ≥ 0.5 group, the TBI+/CDR ≥ 0.5 group showed increased tau deposition in the temporal pole, hippocampus, fusiform gyrus, and inferior and middle temporal gyri. Furthermore, the extent of tau deposition in the brain of those with TBI history positively correlated with the extent of cognitive decline, CSF-tau, and CSF-amyloid. This might suggest TBI to increase the risk for tauopathies and Alzheimer's disease later in life.

A mice resting-state functional magnetic resonance imaging dataset on the effects of medetomidine dosages and prior-stimulation on functional connectivity.

Nine 8 C57Bl6 mice (9 ± 0.5 months) were utilised for this dataset. Each animal was scanned twice on a 9.4T Bruker Magnetic Resonance Imaging (MRI) scanner using a cryogenically cooled coil with 0.1 mg/kg body weight/h (low) or 0.3 mg/kg body weight/h (high) medetomidine doses; 0.5% isoflurane was used in conjunction with both doses. The scans were one week apart, and the first session's dose was decided randomly. In each session, the animal had a pre-stimulation resting-state functional Magnetic Resonance Imaging (rs-fMRI) scan followed by 10 min where mild, constant electrical stimulation to the forepaw was applied, and a post-stimulation rs-fMRI scan. Each fMRI scan lasted 10 min, and there was 5 min break between fMRI scans. The dataset included, for each animal, a pair of forward-phase and reverse-phase gradient echo Echo-Planar-Imaging (EPI) images for EPI distortion correction purpose and three (unprocessed) functional MRI images acquired using the same EPI sequence: prior, during, and post-stimulation. The MRI data was saved in compressed NIFTI format converted from Bruker DICOMs. The dataset also included the pre-processed functional MRI images, with the following pre-processing steps: slice-timing correction, temporal despiking, motion correction, distortion correction, band-pass filtration at 0.01-0.2 Hz, and spatial normalisation. This dataset adds to the publicly available collection of resting-state functional MRI in the mice and facilitates reproducibility and validation of functional imaging and its analysis.

Traumatic brain injury alterations in the functional connectome are associated with neuroinflammation but not tau in a P30IL tauopathy mouse model.

INTRODUCTION: Traumatic Brain Injury (TBI) is often associated with long-term cognitive deficits and altered brain networks which have been linked with accumulation of neurofibrillary tau tangles and neuroinflammation. In this work, we investigated the changes in the brain post-TBI in an Alzheimer's disease pR5 tauopathy model and evaluated the contribution of tauopathy and neuroinflammation to connectivity alterations using resting-state functional Magnetic Resonance Imaging (rs-fMRI). METHOD: 26 P301L tau transgenic mice of 8-9 months of age (21-35 g) expressing the human tau isoform carrying the pathogenic P301L mutation were used for the study. Animals were assessed at day 1 and 7 post-injury/craniotomy and were randomly divided into four groups. All animals underwent an MRI scan on a 9.4T Bruker system where rsfMRI was acquired. Following imaging, brains were stained with pSer (396 + 404), glial fibrillary acidic protein (GFAP), and ionised calcium-binding adaptor molecule-1 (Iba-1). Group-information-guided Independent Component Analysis (GIG-ICA) and region-of-interest (ROI)-based network connectivity approaches were applied. Principal Component Regression was applied to predict connectivity network strength from the corresponding ROIs. RESULTS: TBI mice showed decreased functional connectivity in the dentate gyrus, thalamus, and other areas compared to sham animals at day 1 post-injury with the majority of changes resolving at day 7. Principal Component Regression showed only the contralateral CA1 network strength was correlated with the CA1's astrocyte and microglia cell density and the ipsilateral thalamus network strength was correlated with the ipsilateral thalamus' astrocyte and microglia cell density. CONCLUSION: We present the first report on the temporal alterations in functional connectivity in a P30IL mouse model following TBI. Connectivity between key regions known to be affected in Alzheimer's disease were short-term and reversible following injury. Connectivity strength in CA1 and thalamus showed significant correlation with astrocyte and microglial cell density but not tau density.

Deep Neural Networks Predict the Need for CT in Pediatric Mild Traumatic Brain Injury: A Corroboration of the PECARN Rule.

PURPOSE: Only 10% of CT scans unveil positive findings in mild traumatic brain injury, raising concerns of its overuse in this population. A number of clinical rules have been developed to address this issue, but they still suffer limitations in their specificity. Machine learning models have been applied in limited studies to mimic clinical rules; however, further improvement in terms of balanced sensitivity and specificity is still needed. In this work, the authors applied a deep artificial neural networks (DANN) model and an instance hardness threshold algorithm to reproduce the Pediatric Emergency Care Applied Research Network (PECARN) clinical rule in a pediatric population collected as a part of the PECARN study between 2004 and 2006. METHODS: The DANN model was applied using 14,983 patients younger than 18 years with Glasgow Coma Scale scores ≥ 14 who had head CT reports. The clinical features of the PECARN rules, PECARN-A (group A, age < 2 years) and PECARN-B (group B, age ≤ 2 years), were used to directly evaluate the model. The average accuracy, sensitivity, precision, and specificity were calculated by comparing the model's prediction outcome to that reported by the PECARN investigators. The instance hardness threshold and DANN model were applied to predict the need for CT in pediatric patients using 5-fold cross-validation. RESULTS: In the first phase, the DANN model resulted in 98.6% sensitivity and 99.7% specificity for predicting the need for CT using the predictors of the two PECARN clinical rules combined to train the model. In the second phase, the DANN model was superior to both the PECARN-A and PECARN-B rules using the predictors for each age group separately to train the model. Compared with the clinical rule, for group A, the model achieved an average sensitivity (93.7% versus 100%) and specificity (97.5% versus 53.6%); for group B, the average sensitivity of the model was 99.2% versus 98.6%, and the specificity was 98.8% versus 58.2%. CONCLUSIONS: In this study, a DANN model achieved comparable sensitivity and outstanding specificity for replicating the PECARN clinical rule and predicting the need for CT in pediatric patients after mild traumatic brain injury compared with the original statistically derived clinical rule.

Subacute cytokine changes after a traumatic brain injury predict chronic brain microstructural alterations on advanced diffusion imaging in the male rat.

INTRODUCTION: The process of neuroinflammation occurring after traumatic brain injury (TBI) has received significant attention as a potential prognostic indicator and interventional target to improve patients' outcomes. Indeed, many of the secondary consequences of TBI have been attributed to neuroinflammation and peripheral inflammatory changes. However, inflammatory biomarkers in blood have not yet emerged as a clinical tool for diagnosis of TBI and predicting outcome. The controlled cortical impact model of TBI in the rodent gives reliable readouts of the dynamics of post-TBI neuroinflammation. We now extend this model to include a panel of plasma cytokine biomarkers measured at different time points post-injury, to test the hypothesis that these markers can predict brain microstructural outcome as quantified by advanced diffusion-weighted magnetic resonance imaging (MRI). METHODS: Fourteen 8-10-week-old male rats were randomly assigned to sham surgery (n = 6) and TBI (n = 8) treatment with a single moderate-severe controlled cortical impact. We collected blood samples for cytokine analysis at days 1, 3, 7, and 60 post-surgery, and carried out standard structural and advanced diffusion-weighted MRI at day 60. We then utilized principal component regression to build an equation predicting different aspects of microstructural changes from the plasma inflammatory marker concentrations measured at different time points. RESULTS: The TBI group had elevated plasma levels of IL-1ß and several neuroprotective cytokines and chemokines (IL-7, CCL3, and GM-CSF) compared to the sham group from days 3 to 60 post-injury. The plasma marker panels obtained at day 7 were significantly associated with the outcome at day 60 of the trans-hemispheric cortical map transfer process that is a frequent finding in unilateral TBI models. DISCUSSION: These results confirm and extend prior studies showing that day 7 post-injury is a critical temporal window for the reorganisation process following TBI. High plasma level of IL-1ß and low plasma levels of the neuroprotective IL-7, CCL3, and GM-CSF of TBI animals at day 60 were associated with greater TBI pathology.

Traumatic brain injury fast-forwards Alzheimer's pathology: evidence from amyloid positron emission tomorgraphy imaging.

PURPOSE: Traumatic brain injury (TBI) has been proposed as a risk factor for Alzheimer's disease (AD), although the mechanisms underlying the putative association are poorly understood. We investigated elderly individuals with a remote history of TBI, aiming to understand how this may have influenced amyloidosis, neurodegeneration, and clinical expression along the AD continuum. METHODS: Total of 241 individual datasets including amyloid beta (Aß) positron emission tomography ([18F]-AV45), structural MRI, and neuropsychological measures, were obtained from the Alzheimer's Disease Neuroimaging Initiative. The data were stratified into groups with (TBI +) or without (TBI -) history of head injury, and by clinical dementia rating (CDR) scores, into subgroups with normal cognition (CDR = 0) and those with symptomatic cognitive decline (CDR ≥ 0.5). We contrasted the TBI + and TBI - subgroups with respect to the onset age and extent of cognitive decline, cortical thickness changes, and Aß standard uptake value (SUVr). RESULTS: Compared to the TBI -/CDR ≥ 0.5 subgroup, the TBI + /CDR ≥ 0.5 subgroup showed a 3-4 year earlier age of cognitive impairment onset (ACIO, p = 0.005). Among those participants on the AD continuum (Aß + , as defined by a cortical SUVr ≥ 1.23), irrespective of current CDR, a TBI + history was associated with greater Aß deposition and more pronounced cortical thinning. When matched for severity of cognitive status, the TBI + /CDR ≥ 0.5 group showed greater Aß burden, but earlier ACIO as compared to the TBI -/CDR ≥ 0.5, suggesting a more indolent clinical AD progression in those with TBI history. CONCLUSION: Remote TBI history may alter the AD onset trajectory, with approximately 4 years earlier ACIO, greater amyloid deposition, and cortical thinning.

Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL.

BACKGROUND: A trend in the development of resting-state fMRI (rsfMRI) data analysis is the drive towards more data-driven methods. Group Independent Component Analysis (GICA) is a well-proven data-driven method for performing fMRI group analysis, though not without issues, especially the back-reconstruction from group-level independent components to individual-level components. Group information-guided ICA (GIG-ICA) and Independent Vector Analysis (IVA) are recent extensions of GICA that were shown to outperform GICA in the identification of unique rsfMRI biomarkers in psychiatric conditions. NEW METHOD: In this work, GICA, GIG-ICA, and IVA-GL analysis methods were applied to rsfMRI data acquired from 9 mice under different doses of medetomidine (0.1 - 0.3 mg/kg/h) in the before and after forepaw stimulation, and their performance was compared to determine whether GIG-ICA and IVA-GL outperform GICA in identifying robust and reliable resting-state networks in the rodent brain. RESULTS: Our results showed IVA-GL method had certain desirable performance characteristics over the other two methods under minimal data pre-processing and data-driven assumptions in application to analysis of mouse resting-state functional MRI. COMPARISON WITH EXISTING METHODS: IVA-GL provides better stability towards detecting group differences at different model order assumptions and performed better at separating data well-defined and functionally reasonable components in mouse resting-state fMRI. At higher model order and more likely functional component assumptions, GIG-ICA and IVA-GL were found to have greater sensitivity at detecting functional connectivity changes due to physiological challenges compared to GICA. CONCLUSIONS: This study indicates that IVA-GL yields better detection of resting-state networks in the rodent brain compared to other ICA methods and a promising data-driven analysis method for rodent rsfMRI.

Association of sub-acute changes in plasma amino acid levels with long-term brain pathologies in a rat model of moderate-severe traumatic brain injury.

Introduction: Traumatic brain injury (TBI) induces a cascade of cellular alterations that are responsible for evolving secondary brain injuries. Changes in brain structure and function after TBI may occur in concert with dysbiosis and altered amino acid fermentation in the gut. Therefore, we hypothesized that subacute plasma amino acid levels could predict long-term microstructural outcomes as quantified using neurite orientation dispersion and density imaging (NODDI). Methods: Fourteen 8-10-week-old male rats were randomly assigned either to sham (n = 6) or a single moderate-severe TBI (n = 8) procedure targeting the primary somatosensory cortex. Venous blood samples were collected at days one, three, seven, and 60 post-procedure and NODDI imaging were carried out at day 60. Principal Component Regression analysis was used to identify time dependent plasma amino acid concentrations after in the subacute phase post-injury that predicted NODDI metric outcomes at day 60. Results: The TBI group had significantly increased plasma levels of glutamine, arginine, alanine, proline, tyrosine, valine, isoleucine, leucine, and phenylalanine at days three-seven post-injury. Higher levels of several neuroprotective amino acids, especially the branched-chain amino acids (valine, isoleucine, leucine) and phenylalanine, as well as serine, arginine, and asparagine at days three-seven post-injury were also associated with lower isotropic diffusion volume fraction measures in the ventricles and thus lesser ventricular dilation at day 60. Discussion: In the first such study, we examined the relationship between the long-term post-TBI microstructural outcomes across whole brain and the subacute changes in plasma amino acid concentrations. At days three to seven post-injury, we observed that increased plasma levels of several amino acids, particularly the branched-chain amino acids and phenylalanine, were associated with lesser degrees of ventriculomegaly and hydrocephalus TBI neuropathology at day 60 post-injury. The results imply that altered amino acid fermentation in the gut may mediate neuroprotection in the aftermath of TBI.

Neurogenic-dependent changes in hippocampal circuitry underlie the procognitive effect of exercise in aging mice.

We have shown that the improvement in hippocampal-based learning in aged mice following physical exercise observed is dependent on neurogenesis in the dentate gyrus (DG) and is regulated by changes in growth hormone levels. The changes in neurocircuitry, however, which may underlie this improvement, remain unclear. Using in vivo multimodal magnetic resonance imaging to track changes in aged mice exposed to exercise, we show the improved spatial learning is due to enhanced DG connectivity, particularly the strengthening of the DG-Cornu Ammonis 3 and the DG-medial entorhinal cortex connections in the dorsal hippocampus. Moreover, we provide evidence that these changes in circuitry are dependent on neurogenesis since they were abrogated by ablation of newborn neurons following exercise. These findings identify the specific changes in hippocampal circuitry that underlie the cognitive improvements resulting from physical activity and show that they are dependent on the activation of neurogenesis in aged animals.

Effect of proprioceptive stimulation using a soft robotic glove on motor activation and brain connectivity in stroke survivors.

OBJECTIVE: Soft-robotic-assisted training may improve motor function during post-stroke recovery, but the underlying physiological changes are not clearly understood. We applied a single-session of intensive proprioceptive stimulation to stroke survivors using a soft robotic glove to delineate its short-term influence on brain functional activity and connectivity. APPROACH: In this study, we utilized task-based and resting-state functional magnetic resonance imaging (fMRI) to characterize the changes in different brain networks following a soft robotic intervention. Nine stroke patients with hemiplegic upper limb engaged in resting-state and motor-task fMRI. The motor tasks comprised two conditions: active movement of fingers (active task) and glove-assisted active movement using a robotic glove (glove-assisted task), both with visual instruction. Each task was performed using bilateral hands simultaneously or the affected hand only. The same set of experiments was repeated following a 30-minute treatment of continuous passive motion (CPM) using a robotic glove. MAIN RESULTS: On simultaneous bimanual movement, increased activation of supplementary motor area (SMA) and primary motor area (M1) were observed after CPM treatment compared to the pre-treatment condition, both in active and glove-assisted task. However, when performing the tasks solely using the affected hand, the phenomena of increased activity were not observed either in active or glove-assisted task. The comparison of the resting-state fMRI between before and after CPM showed the connectivity of the supramarginal gyrus and SMA was increased in the somatosensory network and salience network. SIGNIFICANCE: This study demonstrates how passive motion exercise activates M1 and SMA in the post-stroke brain. The effective proprioceptive motor integration seen in bimanual exercise in contrast to the unilateral affected hand exercise suggests that the unaffected hemisphere might reconfigure connectivity to supplement damaged neural networks in the affected hemisphere. The somatosensory modulation rendered by the intense proprioceptive stimulation would affect the motor learning process in stroke survivors.

Traumatic brain injury augurs ill for prolonged deficits in the brain's structural and functional integrity following controlled cortical impact injury.

Previous neuroimaging studies in rodents investigated effects of the controlled cortical impact (CCI) model of traumatic brain injury (TBI) within one-month post-TBI. This study extends this temporal window to monitor the structural-functional alterations from two hours to six months post-injury. Thirty-seven male Sprague-Dawley rats were randomly assigned to TBI and sham groups, which were scanned at two hours, 1, 3, 7, 14, 30, 60 days, and six months following CCI or sham surgery. Structural MRI, diffusion tensor imaging, and resting-state functional magnetic resonance imaging were acquired to assess the dynamic structural, microstructural, and functional connectivity alterations post-TBI. There was a progressive increase in lesion size associated with brain volume loss post-TBI. Furthermore, we observed reduced fractional anisotropy within 24 h and persisted to six months post-TBI, associated with acutely reduced axial diffusivity, and chronic increases in radial diffusivity post-TBI. Moreover, a time-dependent pattern of altered functional connectivity evolved over the six months' follow-up post-TBI. This study extends the current understanding of the CCI model by confirming the long-term persistence of the altered microstructure and functional connectivity, which may hold a strong translational potential for understanding the long-term sequelae of TBI in humans.

Multi-modal magnetic resonance imaging in a mouse model of concussion.

This data collection contains Magnetic Resonance Imaging (MRI) data, including structural, diffusion, stimulus-evoked, and resting-state functional MRI and behavioural assessment results, including acute post-impact Loss-of-Righting Reflex time and acute, subacute, and longer-term Neural Severity Score, and Open Field Behaviour obtained from a mouse model of concussion. Four cohorts with 43 3-4 months old male mice in total were used: Sham (n = 14, n = 6 day 2, n = 3 day 7, n = 5 day 14), concussion day 2 (CON 2; n = 9), concussion day 7 (CON 7; n = 10), concussion day 14 (CON 14; n = 10). The data collection contains the aforementioned MRI data in compressed NIFTI format, data sheets on animal's backgrounds and behavioural outcomes and is made publicly available from a data repository. The available data are intended to facility cross-study comparisons, meta-analysis, and science reproducibility.

The detection of mild traumatic brain injury in paediatrics using artificial neural networks.

Head computed tomography (CT) is the gold standard in emergency departments (EDs) to evaluate mild traumatic brain injury (mTBI) patients, especially for paediatrics. Data-driven models for successfully classifying head CT scans that have mTBI will be valuable in terms of timeliness and cost-effectiveness for TBI diagnosis. This study applied two different machine learning (ML) models to diagnose mTBI in a paediatric population collected as part of the paediatric emergency care applied research network (PECARN) study between 2004 and 2006. The models were conducted using 15,271 patients under the age of 18 years with mTBI and had a head CT report. In the conventional model, random forest (RF) ranked the features to reduce data dimensionality and the top ranked features were used to train a shallow artificial neural network (ANN) model. In the second model, a deep ANN applied to classify positive and negative mTBI patients using the entirety of the features available. The dataset was divided into two subsets: 80% for training and 20% for testing using five-fold cross-validation. Accuracy, sensitivity, precision, and specificity were calculated by comparing the model's prediction outcome to the actual diagnosis for each patient. RF ranked ten clinical demographic features and twelve CT-findings; the hybrid RF-ANN model achieved an average specificity of 99.96%, sensitivity of 95.98%, precision of 99.25%, and accuracy of 99.74% in identifying positive mTBI from negative mTBI subjects. The deep ANN proved its ability to carry out the task efficiently with an average specificity of 99.9%, sensitivity of 99.2%, precision of 99.9%, and accuracy of 99.9%. The performance of the two proposed models demonstrated the feasibility of using ANN to diagnose mTBI in a paediatric population. This is the first study to investigate deep ANN in a paediatric cohort with mTBI using clinical and non-imaging data and diagnose mTBI with balanced sensitivity and specificity using shallow and deep ML models. This method, if validated, would have the potential to reduce the burden of TBI evaluation in EDs and aide clinicians in the decision-making process.