Functional Neuroimaging in Traumatic Brain Injury: From Nodes to Networks

Integrating fractional amplitude of low-frequency fluctuation and functional connectivity to investigate the mechanism and prognosis of severe traumatic brain injury

Objective

Functional magnetic resonance imaging (fMRI) has been used for evaluating residual brain function and predicting the prognosis of patients with severe traumatic brain injury (sTBI). This study aimed to integrate the fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) to investigate the mechanism and prognosis of patients with sTBI.

Methods

Sixty-five patients with sTBI were included and underwent fMRI scanning within 14 days after brain injury. The patient's outcome was assessed using the Glasgow Outcome Scale-Extended (GOSE) at 6 months post-injury. Of the 63 patients who met fMRI data analysis standards, the prognosis of 18 patients was good (GOSE scores ≥ 5), and the prognosis of 45 patients was poor (GOSE scores ≤  4). First, we apply fALFF to identify residual brain functional differences in patients who present different prognoses and conjoined it in regions of interest (ROI)-based FC analysis to investigate the residual brain function of sTBI at the acute phase of sTBI. Then, the area under the curve (AUC) was used to evaluate the predictive ability of the brain regions with the difference of fALFF and FC values.

Results

Patients who present good outcomes at 6 months post-injury have increased fALFF values in the Brodmann area (7, 18, 31, 13, 39 40, 42, 19, 23) and decreased FC values in the Brodmann area (28, 34, 35, 36, 20, 28, 34, 35, 36, 38, 1, 2, 3, 4, 6, 13, 40, 41, 43, 44, 20, 28 35, 36, 38) at the acute phase of sTBI. The parameters of these alterations can be used for predicting the long-term outcomes of patients with sTBI, of which the fALFF increase in the temporal lobe, occipital lobe, precuneus, and middle temporal gyrus showed the highest predictive ability (AUC = 0.883).

Conclusion

We provide a compensatory mechanism that several regions of the brain can be spontaneously activated at the acute phase of sTBI in those who present with a good prognosis in the 6-month follow-up, that is, a destructive mode that increases its fALFF in the local regions and weakens its FC to the whole brain. These findings provide a theoretical basis for developing early intervention targets for sTBI patients.

Neural mechanisms of emotional health in traumatic brain injury patients undergoing rTMS treatment

Emotional dysregulation such as that seen in depression, are a long-term consequence of mild traumatic brain injury (TBI), that can be improved by using neuromodulation treatments such as repetitive transcranial magnetic stimulation (rTMS). Previous studies provide insights into the changes in functional connectivity related to general emotional health after the application of rTMS procedures in patients with TBI. However, these studies provide little understanding of the underlying neuronal mechanisms that drive the improvement of the emotional health in these patients. The current study focuses on inferring the effective (causal) connectivity changes and their association with emotional health, after rTMS treatment of cognitive problems in TBI patients (N = 32). Specifically, we used resting state functional magnetic resonance imaging (fMRI) together with spectral dynamic causal model (spDCM) to investigate changes in brain effective connectivity, before and after the application of high frequency (10 Hz) rTMS over left dorsolateral prefrontal cortex. We investigated the effective connectivity of the cortico-limbic network comprised of 11 regions of interest (ROIs) which are part of the default mode, salience, and executive control networks, known to be implicated in emotional processing. The results indicate that overall, among extrinsic connections, the strength of excitatory connections decreased while that of inhibitory connections increased after the neuromodulation. The cardinal region in the analysis was dorsal anterior cingulate cortex (dACC) which is considered to be the most influenced during emotional health disorders. Our findings implicate the altered connectivity of dACC with left anterior insula and medial prefrontal cortex, after the application of rTMS, as a potential neural mechanism underlying improvement of emotional health. Our investigation highlights the importance of these brain regions as treatment targets in emotional processing in TBI.

Feasibility of Acquiring Neuroimaging Data from Adults with Acquired Brain Injuries before and after a Yoga Intervention

BACKGROUND

To date, no one has prospectively evaluated yoga intervention-induced changes in brain structure or function in adults with acquired brain injuries (ABI). Thus, this study was conducted to test the feasibility of acquiring neuroimaging data from adults with ABI before and after a yoga intervention.

METHODS

This was a single-arm intervention feasibility study that included 12 adults with chronic (i.e., greater than 6 months post-injury) ABI and self-reported limitations in balance. Neuroimaging data were acquired before and after yoga. The yoga intervention was completed once per week for eight weeks. Feasibility objectives and benchmarks were established a priori.

RESULTS

Most feasibility objectives and benchmarks were achieved. The goal of recruiting 12 participants was successfully achieved, and 75% of participants were retained throughout the study (goal of 80%). All imaging feasibility benchmarks were met; rs-fMRI and fNIRS data were acquired safely, data were of acceptable quality, and data pre-processing procedures were successful. Additionally, improvements were detected in balance after yoga, as group-level balance was significantly better post-yoga compared to pre-yoga, p = 0.043.

CONCLUSIONS

These findings indicate it is feasible to acquire neuroimaging data from adults with ABI before and after a yoga intervention. Thus, future prospective studies are warranted.

Recovery of clinical, cognitive and cortical activity measures following mild traumatic brain injury (mTBI): A longitudinal investigation

The mechanisms that underpin recovery following mild traumatic brain injury (mTBI) remain poorly understood. Identifying neurophysiological markers and their functional significance is necessary to develop diagnostic and prognostic indicators of recovery. The current study assessed 30 participants in the subacute phase of mTBI (10-31 days post-injury) and 28 demographically matched controls. Participants also completed 3 month (mTBI: N = 21, control: N = 25) and 6 month (mTBI: N = 15, control: N = 25) follow up sessions to track recovery. At each time point, a battery of clinical, cognitive, and neurophysiological assessments was completed. Neurophysiological measures included resting-state electroencephalography (EEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG). Outcome measures were analysed using mixed linear models (MLM). Group differences in mood, post-concussion symptoms and resting-state EEG resolved by 3 months, and recovery was maintained at 6 months. On TMS-EEG derived neurophysiological measures of cortical reactivity, group differences ameliorated at 3 months but re-emerged at 6 months, while on measures of fatigue, group differences persisted across all time points. Persistent neurophysiological changes and greater fatigue in the absence of measurable cognitive impairment may suggest the impact of mTBI on neuronal communication may leads to increased neural effort to maintain efficient function. Neurophysiological measures to track recovery may help identify both temporally optimal windows and therapeutic targets for the development of new treatments in mTBI.

Longitudinal Patterns of Functional Connectivity in Moderate-to-Severe Traumatic Brain Injury

Longitudinal neuroimaging studies aid our understanding of recovery mechanisms in moderate-to-severe traumatic brain injury (TBI); however, there is a dearth of longitudinal functional connectivity research. Our aim was to characterize longitudinal functional connectivity patterns in two clinically important brain networks, the frontoparietal network (FPN) and the default mode network (DMN), in moderate-to-severe TBI. This inception cohort study of prospectively collected longitudinal data used resting-state functional magnetic resonance imaging (fMRI) to characterize functional connectivity patterns in the FPN and DMN. Forty adults with moderate-to-severe TBI (mean ± standard deviation [SD]; age = 39.53 ± 16.49 years, education = 13.92 ± 3.20 years, lowest Glasgow Coma Scale score = 6.63 ± 3.24, sex = 70% male) were scanned at approximately 0.5, 1-1.5, and 3+ years post-injury. Seventeen healthy, uninjured participants (mean ± SD; age = 38.91 ± 15.57 years, education = 15.11 ± 2.71 years, sex = 29% male) were scanned at baseline and approximately 11 months afterwards. Group independent component analyses and linear mixed-effects modeling with linear splines that contained a knot at 1.5 years post-injury were employed to investigate longitudinal network changes, and associations with covariates, including age, sex, and injury severity. In patients with TBI, functional connectivity in the right FPN increased from approximately 0.5 to 1.5 years post-injury (unstandardized estimate = 0.19, standard error [SE] = 0.07, p = 0.009), contained a slope change in the opposite direction, from positive to negative at 1.5 years post-injury (estimate = -0.21, SE = 0.11, p = 0.009), and marginally declined afterwards (estimate = -0.10, SE = 0.06, p = 0.079). Functional connectivity in the DMN increased from approximately 0.5 to 1.5 years (estimate = 0.15, SE = 0.05, p = 0.006), contained a slope change in the opposite direction, from positive to negative at 1.5 years post-injury (estimate = -0.19, SE = 0.08, p = 0.021), and was estimated to decline from 1.5 to 3+ years (estimate = -0.04, SE = 0.04, p = 0.303). Similarly, the left FPN increased in functional connectivity from approximately 0.5 to 1.5 years post-injury (estimate = 0.15, SE = 0.05, p = 0.002), contained a slope change in the opposite direction, from positive to negative at 1.5 years post-injury (estimate = -0.18, SE = 0.07, p = 0.008), and was estimated to decline thereafter (estimate = -0.04, SE = 0.03, p = 0.254). At approximately 0.5 years post-injury, patients showed hypoconnectivity compared with healthy, uninjured participants at baseline. Covariates were not significantly associated in any of the models. Findings of early improvement but a tapering and possible decline in connectivity thereafter suggest that compensatory effects are time-limited. These later reductions in connectivity mirror growing evidence of behavioral and structural decline in chronic moderate-to-severe TBI. Targeting such declines represents a novel avenue of research and offers potential for improving clinical outcomes.

Aberrant brain functional hubs convergence in the acute severe traumatic brain injury patients with rapidly recovering

PURPOSE

We aimed to identify the aberrant functional hubs in patients with acute severe traumatic brain injury (sTBI) and investigate whether they could help inform prognosis.

METHODS

Twenty-eight sTBI patients and health controls underwent imaging scanning. The graph-theoretical measure of degree centrality (DC) was applied to identify the abnormal brain functional hubs and conjoined with regions of interest-based analysis to investigate their interaction and impact on whole-brain. We further split sTBI patients into two subgroups according to their recovery to explore whether the fractional amplitude of low-frequency fluctuation (fALFF) roles in functional connectivity (FC) differential areas to help inform the patients' long-term prognosis.

RESULTS

We identified the part of prefrontal cortex (PFC), precentral and postcentral gyrus (Pre-/Post-CG), cingulate gyrus (CgG), posterior medial cortex (PMC), and brainstem that could be core hubs whose DC was significantly increased in patients with acute sTBI. The interaction strength of the paired hubs could be enhanced (CG-PFC, CgG-PFC, CG-brainstem, CgG-brainstem, PMC-brainstem, and PFC-brainstem) and weakened (CG-CgG, CG-PMC, CgG-PMC, and PMC-PFC), compared with healthy controls. We also found abnormal FC in 5 hubs to whole-brain. The spontaneous brain activities in the FC differential regions [e.g., the fALFF and mean fALFF value] were valid to predict outcome at 6-month in patients with sTBI.

CONCLUSION

We demonstrated a compensatory mechanism that part of brain regions will converge into abnormal functional hubs in patients with acute sTBI, which provides a potential approach to objectively predicting patients' long-term outcome.

Activation Patterns of Functional Brain Network in Response to Action Observation-Induced and Non-Induced Motor Imagery of Swallowing: A Pilot Study

Action observation (AO) combined with motor imagery (MI) was verified as more effective in improving limb function than AO or MI alone, while the underlying mechanism of swallowing was ambiguous. The study aimed at exploring the efficacy of AO combined with MI in swallowing. In this study, twelve subjects performed the motor imagery of swallowing (MI-SW) during magnetoencephalography (MEG) scanning, and trials were divided into three groups: the non-induced group (control group, CG), male AO-induced group (M-AIG), and female AO-induced group (F-AIG). We used event-related spectral perturbations (ERSPs) and phase locking value (PLV) to assess the degree of activation and connectivity of the brain regions during MI-SW in the three groups. The results showed that compared to CG, F-AIG and M-AIG significantly activated more brain regions in the frontoparietal, attention, visual, and cinguloopercular systems. In addition, M-AIG significantly activated the sensorimotor cortex compared to CG and F-AIG. For the brain network, F-AIG and M-AIG increased the diffusion of non-hub hot spots and cold hubs to the bilateral hemispheres which enhanced interhemispheric functional connectivity and information transmission efficiency in the MI-SW task. This study provided supporting evidence that AO induction could enhance the effect of MI-SW and supported the application of AO-induced MI-SW in clinical rehabilitation.

A precise language network revealed by the independent component-based lesion mapping in post-stroke aphasia

Brain lesion mapping studies have provided the strongest evidence regarding the neural basis of cognition. However, it remained a problem to identify symptom-specific brain networks accounting for observed clinical and neuroanatomical heterogeneity. Independent component analysis (ICA) is a statistical method that decomposes mixed signals into multiple independent components. We aimed to solve this issue by proposing an independent component-based lesion mapping (ICLM) method to identify the language network in patients with moderate to severe post-stroke aphasia. Lesions were first extracted from 49 patients with post-stroke aphasia as masks applied to fMRI data in a cohort of healthy participants to calculate the functional connectivity (FC) within the masks and non-mask brain voxels. ICA was further performed on a reformatted FC matrix to extract multiple independent networks. Specifically, we found that one of the lesion-related independent components (ICs) highly resembled classical language networks. Moreover, the damaged level within the language-related lesioned network is strongly associated with language deficits, including aphasia quotient, naming, and auditory comprehension scores. In comparison, none of the other two traditional lesion mapping methods found any regions responsible for language dysfunction. The language-related lesioned network extracted with the ICLM method showed high specificity in detecting aphasia symptoms compared with the performance of resting ICs and classical language networks. In total, we detected a precise language network in patients with aphasia and proved its efficiency in the relationship with language symptoms. In general, our ICLM could successfully identify multiple lesion-related networks from complicated brain diseases, and be used as an effective tool to study brain-behavior relationships and provide potential biomarkers of particular clinical behavioral deficits.

A Review of Glymphatics and the Impact of Osteopathic Manipulative Treatment in Alzheimer's Disease, Concussions, and Beyond

Glymph is a fluid that circulates in the brain interstitium and, under pathological conditions, unusually accumulates and enhances the buildup of other noxious molecules. The study of this process of circulation, accumulation, and clearance is called glymphatics. We review the physiology of glymphatics and then dive into recent innovative research surrounding this neurological field of study and how it has applied to mainstream pathological processes, including Alzheimer's disease and spectrums of traumatic brain injury that range from a concussion to chronic traumatic encephalopathy (CTE). Furthermore, we explore the implications of glymphatics and a new and developing frontier of healthcare in space travel; with the advent of a Space Force and the introduction of space travel to consumer markets, this is an exciting time to develop novel techniques in enhancing its safety and optimizing human physiology for best outcomes. Therefore, we also propose that osteopathic manipulative treatment (OMT) plays an intuitive role in the treatment of abnormal glymphatics, as adjunctive therapy in Alzheimer's and CTE, and as a future staple before, during, and after space travel for the benefit of both enhancing healthcare in chronic conditions and advancing the capabilities of the human race in its shining new endeavor.

Automated Classification of Resting-State fMRI ICA Components Using a Deep Siamese Network

Manual classification of functional resting state networks (RSNs) derived from Independent Component Analysis (ICA) decomposition can be labor intensive and requires expertise, particularly in large multi-subject analyses. Hence, a fully automatic algorithm that can reliably classify these RSNs is desirable. In this paper, we present a deep learning approach based on a Siamese Network to learn a discriminative feature representation for single-subject ICA component classification. Advantages of this supervised framework are that it requires relatively few training data examples and it does not require the number of ICA components to be specified. In addition, our approach permits one-shot learning, which allows generalization to new classes not seen in the training set with only one example of each new class. The proposed method is shown to out-perform traditional convolutional neural network (CNN) and template matching methods in identifying eleven subject-specific RSNs, achieving 100% accuracy on a holdout data set and over 99% accuracy on an outside data set. We also demonstrate that the method is robust to scan-rescan variation. Finally, we show that the functional connectivity of default mode and salience networks identified by the proposed technique is altered in a group analysis of mild traumatic brain injury (TBI), severe TBI, and healthy subjects.

The Influence of Intraocular Lens Implantation and Alterations in Blue Light Transmittance Level on the Brain Functional Network Architecture Reorganization in Cataract Patients

BACKGROUND

Cataract is one of the most common age-related vision deteriorations, leading to opacification of the lens and therefore visual impairment as well as blindness. Both cataract extraction and the implantation of blue light filtering lens are believed to improve not only vision but also overall functioning.

METHODS

Thirty-four cataract patients were subject to resting-state functional magnetic resonance imaging before and after cataract extraction and intraocular lens implantation (IOL). Global and local graph metrics were calculated in order to investigate the reorganization of functional network architecture associated with alterations in blue light transmittance. Psychomotor vigilance task (PVT) was conducted.

RESULTS

Graph theory-based analysis revealed decreased eigenvector centrality after the cataract extraction and IOL replacement in inferior occipital gyrus, superior parietal gyrus and many cerebellum regions as well as increased clustering coefficient in superior and inferior parietal gyrus, middle temporal gyrus and various cerebellum regions. PVT results revealed significant change between experimental sessions as patients responded faster after IOL replacement. Moreover, a few regions were correlated with the difference in blue light transmittance and the time reaction in PVT.

CONCLUSION

Current study revealed substantial functional network architecture reorganization associated with cataract extraction and alteration in blue light transmittance.

Biomarkers for Traumatic Brain Injury: Data Standards and Statistical Considerations

Recent biomarker innovations hold potential for transforming diagnosis, prognostic modeling, and precision therapeutic targeting of traumatic brain injury (TBI). However, many biomarkers, including brain imaging, genomics, and proteomics, involve vast quantities of high-throughput and high-content data. Management, curation, analysis, and evidence synthesis of these data are not trivial tasks. In this review, we discuss data management concepts and statistical and data sharing strategies when dealing with biomarker data in the context of TBI research. We propose that application of biomarkers involves three distinct steps-discovery, evaluation, and evidence synthesis. First, complex/big data has to be reduced to useful data elements at the stage of biomarker discovery. Second, inferential statistical approaches must be applied to these biomarker data elements for assessment of biomarker clinical utility and validity. Last, synthesis of relevant research is required to support practice guidelines and enable health decisions informed by the highest quality, up-to-date evidence available. We focus our discussion around recent experiences from the International Traumatic Brain Injury Research (InTBIR) initiative, with a specific focus on four major clinical projects (Transforming Research and Clinical Knowledge in TBI, Collaborative European NeuroTrauma Effectiveness Research in TBI, Collaborative Research on Acute Traumatic Brain Injury in Intensive Care Medicine in Europe, and Approaches and Decisions in Acute Pediatric TBI Trial), which are currently enrolling subjects in North America and Europe. We discuss common data elements, data collection efforts, data-sharing opportunities, and challenges, as well as examine the statistical techniques required to realize successful adoption and use of biomarkers in the clinic as a foundation for precision medicine in TBI.

Improved neuropathological identification of traumatic brain injury through quantitative neuroimaging and neural network analyses: Some practical approaches for the neurorehabilitation clinician

BACKGROUND

Quantitative neuroimaging analyses have the potential to provide additional information about the neuropathology of traumatic brain injury (TBI) that more thoroughly informs the neurorehabilitation clinician.

OBJECTIVE

Quantitative neuroimaging is typically not covered in the standard radiological report, but often can be extracted via post-processing of clinical neuroimaging studies, provided that the proper volume acquisition sequences were originally obtained.

METHODS

Research and commercially available quantitative neuroimaging methods provide region of interest (ROI) quantification metrics, lesion burden volumetrics and cortical thickness measures, degree of focal encephalomalacia, white matter (WM) abnormalities and residual hemorrhagic pathology. If present, diffusion tensor imaging (DTI) provides a variety of techniques that aid in evaluating WM integrity. Using quantitatively identified structural and ROI neuropathological changes are most informative when done from a neural network approach.

RESULTS

Viewing quantitatively identifiable damage from a neural network perspective provides the neurorehabilitation clinician with an additional tool for linking brain pathology to understand symptoms, problems and deficits as well as aid neuropsychological test interpretation. All of these analyses can be displayed in graphic form, including3-D image analysis. A case study approach is used to demonstrate the utility of quantitative neuroimaging and network analyses in TBI.

CONCLUSIONS

Quantitative neuroimaging may provide additional useful information for the neurorehabilitation clinician.

Graph-theoretical analysis of EEG functional connectivity during balance perturbation in traumatic brain injury: A pilot study

Traumatic brain injury (TBI) often results in balance impairment, increasing the risk of falls, and the chances of further injuries. However, the underlying neural mechanisms of postural control after TBI are not well understood. To this end, we conducted a pilot study to explore the neural mechanisms of unpredictable balance perturbations in 17 chronic TBI participants and 15 matched healthy controls (HC) using the EEG, MRI, and diffusion tensor imaging (DTI) data. As quantitative measures of the functional integration and segregation of the brain networks during the postural task, we computed the global graph-theoretic network measures (global efficiency and modularity) of brain functional connectivity derived from source-space EEG in different frequency bands. We observed that the TBI group showed a lower balance performance as measured by the center of pressure displacement during the task, and the Berg Balance Scale (BBS). They also showed reduced brain activation and connectivity during the balance task. Furthermore, the decrease in brain network segregation in alpha-band from baseline to task was smaller in TBI than HC. The DTI findings revealed widespread structural damage. In terms of the neural correlates, we observed a distinct role played by different frequency bands: theta-band modularity during the task was negatively correlated with the BBS in the TBI group; lower beta-band network connectivity was associated with the reduction in white matter structural integrity. Our future studies will focus on how postural training will modulate the functional brain networks in TBI.

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group's short-term, intermediate, and long-term goals.

Long-term changes in the small-world organization of brain networks after concussion

There is a growing body of literature using functional MRI to study the acute and long-term effects of concussion on functional brain networks. To date, studies have largely focused on changes in pairwise connectivity strength between brain regions. Less is known about how concussion affects whole-brain network topology, particularly the “small-world” organization which facilitates efficient communication at both local and global scales. The present study addressed this knowledge gap by measuring local and global efficiency of 26 concussed athletes at acute injury, return to play (RTP) and one year post-RTP, along with a cohort of 167 athletic controls. On average, concussed athletes showed no alterations in local efficiency but had elevated global efficiency at acute injury, which had resolved by RTP. Athletes with atypically long recovery, however, had reduced global efficiency at 1 year post-RTP, suggesting long-term functional abnormalities for this subgroup. Analyses of nodal efficiency further indicated that global network changes were driven by high-efficiency visual and sensorimotor regions and low-efficiency frontal and subcortical regions. This study provides evidence that concussion causes subtle acute and long-term changes in the small-world organization of the brain, with effects that are related to the clinical profile of recovery.

Slacklining: An explanatory multi-dimensional model considering classical mechanics, biopsychosocial health and time

This paper aims to overcome slacklining's limited formulated explanatory models. Slacklining is an activity with increasing recreational use, but also has progressive adoption into prehabilitation and rehabilitation. Slacklining is achieved through self-learned strategies that optimize energy expenditure without conceding dynamic stability, during the neuromechanical action of balance retention on a tightened band. Evolved from rope-walking or 'Funambulus', slacklining has an extensive history, yet limited and only recent published research, particularly for clinical interventions and in-depth hypothesized multi-dimensional models describing the neuromechanical control strategies. These 'knowledge-gaps' can be overcome by providing an, explanatory model, that evolves and progresses existing standards, and explains the broader circumstances of slacklining's use. This model details the individual's capacity to employ control strategies that achieve stability, functional movement and progressive technical ability. The model considers contributing entities derived from: Self-learned control of movement patterns; subjected to classical mechanical forces governed by Newton's physical laws; influenced by biopsychosocial health factors; and within time's multi-faceted perspectives, including as a quantified unit and as a spatial and cortical experience. Consequently, specific patient and situational uses may be initiated within the framework of evidence based medicine that ensures a multi-tiered context of slacklining applications in movement, balance and stability. Further research is required to investigate and mathematically define this proposed model and potentially enable an improved understanding of human functional movement. This will include its application in other diverse constructed and mechanical applications in varied environments, automation levels, robotics, mechatronics and artificial-intelligence factors, including machine learning related to movement phenotypes and applications.

Osteopathic Manipulative Treatment to Optimize the Glymphatic Environment in Severe Traumatic Brain Injury Measured With Optic Nerve Sheath Diameter, Intracranial Pressure Monitoring, and Neurological Pupil Index

Background

Traumatic brain injury (TBI) has a complex pathophysiology that has historically been poorly understood. New evidence on the pathophysiology, molecular biology, and diagnostic studies involved in TBI have shed new light on optimizing rehabilitation and recovery. The goal of this study was to assess the effect of osteopathic manipulative treatment (OMT) on peripheral and central glial lymphatics in patients with severe TBI, brain edema, and elevated intracranial pressure (ICP) by measuring changes in several parameters regularly used in management.

Methodology

This was a retrospective study at a level II trauma center that occurred in 2018. The study enrolled patients with TBI, increased ICP, or brain edema who had an external ventricular drain placed. Patients previously underwent a 51-minute treatment with OMT with an established protocol. Patients received 51 minutes of OMT to the head, neck, and peripheral lymphatics. The ICP, cerebrospinal fluid (CSF) drainage, optic nerve sheath diameter (ONSD) measured by ultrasonography, and Neurological Pupil Index (NPi) measured by pupillometer were recorded before, during, and after receiving OMT.

Results

A total of 11 patients were included in the study, and 21 points of data were collected from the patients meeting inclusion criteria who received OMT. There was a mean decrease in the ONSD of 0.62 mm from 6.24 mm to 5.62 mm (P = 0.0001). The mean increase in NPi was 0.18 (P = 0.01). The mean decrease in ICP was 3.33 mmHg (P= 0.0001). There was a significant decrease in CSF output after treatment (P = 0.0001). Each measurement of ICP, ONSD, and NPi demonstrated a decrease in overall CSF volume and pressure after OMT compared to CSF output and ICP prior to OMT.

Conclusions

This study demonstrates that OMT may help optimize glial lymphatic clearance of CSF and improve brain edema, interstitial waste product removal, NPi, ICP, CSF volume, and ONSD. A holistic approach including OMT may be considered to enhance management in TBI patients. As TBI is a spectrum of disease, utilizing similar techniques may be considered for all forms of TBI including concussions and other diseases with brain edema. The results of this study can better inform future trials to specifically study the effectiveness of OMT in post-concussive treatment and in those with mild-to-moderate TBI.

Individual-fMRI-approaches reveal cerebellum and visual communities to be functionally connected in obsessive compulsive disorder

There is significant interest in understanding the pathophysiology of Obsessive-Compulsive Disorder (OCD) using resting-state fMRI (rsfMRI). Previous studies acknowledge abnormalities within and beyond the fronto-striato-limbic circuit in OCD that require further clarifications. However, limited information could be inferred from the conventional way of investigating the functional connectivity differences between OCD and healthy controls. Here, we identified altered brain organization in patients with OCD by applying individual-based approaches to maximize the identification of underlying network-based features specific to the OCD group. rsfMRI of 20 patients with OCD and 22 controls were preprocessed, and individual-fMRI-subspace was derived for each subject within each group. We evaluated group differences in functional connectivity using individual-fMRI-subspace and established its advantage over conventional-fMRI methodology. We applied prediction-based approaches to highlight the group differences by evaluating the differences in functional connections that predicted the clinical scores (namely, the Obsessive-Compulsive Inventory-Revised (OCI-R) and Hamilton Anxiety Rating Scale). Then, we explored the brain network organization of both groups by estimating the subject-specific communities within each group. Lastly, we evaluated associations between the inter-individual variation of nodes in the communities to clinical measures using linear regression. Functional connectivity analysis using individual-fMRI-subspace detected 83 connections that were different between OCD and control groups, compared to none found using conventional-fMRI methodology. Connectome-based prediction analysis did not show significant overlap between the two groups in the functional connections that predicted the clinical scores. This suggests that the functional architecture in patients with OCD may be different compared to controls. Seven communities were found in both groups. Interestingly, within the OCD group but not controls, we observed functional connectivity between cerebellar and visual regions, and lack of connectivity between striato-limbic and frontal areas. Inter-individual variations in the community-size of these two communities were also associated with the OCI-R score (p < .005). Due to our small sample size, we further validated our results by (i) accounting for head motion, (ii) applying global signal regression (GSR) in data processing, and (iii) using an alternate atlas for parcellation. While the main results were consistently observed with accounting for head motion and using another atlas, the key findings were not reproduced with GSR application. The study demonstrated the existence of disconnectedness in fronto-striato-limbic community and connectedness between cerebellar and visual areas in OCD patients, which was also related to the clinical symptomatology of OCD.

Brain hothubs and dark functional networks: correlation analysis between amplitude and connectivity for Broca's aphasia

Source localization and functional brain network modeling are methods of identifying critical regions during cognitive tasks. The first activity estimates the relative differences of the signal amplitudes in regions of interest (ROI) and the second activity measures the statistical dependence among signal fluctuations. We hypothesized that the source amplitude–functional connectivity relationship decouples or reverses in persons having brain impairments. Five Broca’s aphasics with five matched cognitively healthy controls underwent overt picture-naming magnetoencephalography scans. The gamma-band (30–45 Hz) phase-locking values were calculated as connections among the ROIs. We calculated the partial correlation coefficients between the amplitudes and network measures and detected four node types, including hothubs with high amplitude and high connectivity, coldhubs with high connectivity but lower amplitude, non-hub hotspots, and non-hub coldspots. The results indicate that the high-amplitude regions are not necessarily highly connected hubs. Furthermore, the Broca aphasics utilized different hothub sets for the naming task. Both groups had dark functional networks composed of coldhubs. Thus, source amplitude–functional connectivity relationships could help reveal functional reorganizations in patients. The amplitude–connectivity combination provides a new perspective for pathological studies of the brain’s dark functional networks.

Alterations in Network Connectivity after Traumatic Brain Injury in Mice

Victims of mild traumatic brain injury (mTBI) usually do not display clear morphological brain defects, but frequently have long-lasting cognitive deficits, emotional difficulties, and behavioral disturbances. In the present study we used diffusion magnetic resonance imaging (dMRI) combined with graph theory measurements to investigate the effects of mTBI on brain network connectivity. We employed a non-invasive closed-head weight-drop mouse model to produce mTBI. Mice were scanned at two time points, 24 h before the injury and either 7 or 30 days following the injury. Connectivity matrices were computed for each animal at each time point, and these were subsequently used to extract graph theory measures reflecting network integration and segregation, on both the global (i.e., whole brain) and local (i.e., single regions) levels. We found that cluster coefficient, reflecting network segregation, decreased 7 days post-injury and then returned to baseline level 30 days following the injury. Global efficiency, reflecting network integration, demonstrated opposite patterns in the left and right hemispheres, with an increase of right hemisphere efficiency at 7 days and then a decrease in efficiency following 30 days, and vice versa in the left hemisphere. These findings suggest a possible compensation mechanism acting to moderate the influence of mTBI on the global network. Moreover, these results highlight the importance of tracking the dynamic changes in mTBI over time, and the potential of structural connectivity as a promising approach for studying network integrity and pathology progression in mTBI.

Imaging Guidance for Therapeutic Delivery: The Dawn of Neuroenergetics

Modern neurocritical care relies on ancillary diagnostic testing in the form of multimodal monitoring to address acute changes in the neurological homeostasis. Much of our armamentarium rests upon physiological and biochemical surrogates of organ or regional level metabolic activity, of which a great deal is invested at the metabolic-hemodynamic-hydrodynamic interface to rectify the traditional intermediaries of glucose consumption. Despite best efforts to detect cellular neuroenergetics, current modalities cannot appreciate the intricate coupling between astrocytes and neurons. Invasive monitoring is not without surgical complication, and noninvasive strategies do not provide an adequate spatial or temporal resolution. Without knowledge of the brain's versatile behavior in specific metabolic states (glycolytic vs oxidative), clinical practice would lag behind laboratory empiricism. Noninvasive metabolic imaging represents a new hope in delineating cellular, nigh molecular level energy exchange to guide targeted management in a diverse array of neuropathology.

Disrupted brain functional hub and causal connectivity in acute mild traumatic brain injury

There have been an increasing number of functional magnetic resonance imaging (fMRI) reports on brain abnormalities in mild traumatic brain injury (mTBI) at different phases. However, the neural bases and cognitive impairment after acute mTBI are unclear. This study aimed to identify brain functional hubs and connectivity abnormalities in acute mTBI patients and their correlations with deficits in cognitive performance. Within seven days after brain injury, mTBI patients (n=55) and age-, sex-, and educational -matched healthy controls (HCs) (n=41) underwent resting-state fMRI scans and cognitive assessments. We derived functional connectivity (FC) strength of the whole-brain network using degree centrality (DC) and performed Granger causality analysis (GCA) to analyze causal connectivity patterns in acute mTBI. Compared with HCs, acute mTBI patients had significantly decreased network centrality in the left middle frontal gyrus (MFG). Additionally, acute mTBI showed decreased inflows from the left MFG to bilateral middle temporal gyrus (MTG), left medial superior frontal gyrus (mSFG), and left anterior cingulate cortex (ACC). Correlation analyses revealed that changes in network centrality and causal connectivity were associated with deficits in cognitive performance in mTBI. Our findings may help to provide a new perspective for understanding the neuropathophysiological mechanism of acute cognitive impairment after mTBI.

Why Does Brain Trauma Research Fail?

Traumatic brain injury (TBI) represents a major health care problem and a significant social and economic issue worldwide. Considering the generalized failure in introducing effective drugs and clinical protocols, there is an urgent need for efficient treatment modalities, able to improve devastating posttraumatic morbidity and mortality. In this work, the status of brain trauma research is analyzed in all its aspects, including basic and translational science and clinical trials. Implicit and explicit challenges to different lines of research are discussed and clinical trial structures and outcomes are scrutinized, along with possible explanations for systematic therapeutic failures and their implications for future development of drug and clinical trials. Despite significant advances in basic and clinical research in recent years, no specific therapeutic protocols for TBI have been shown to be effective. New potential therapeutic targets have been identified, following a better understanding of pathophysiologic mechanisms underlying TBI, although with disappointing results. Several reasons can be pinpointed at different levels, from inaccurate animal models of disease to faulty preclinical and clinical trials, with poor design and subjective outcome measures. Distinct strategies can be delineated to overcome specific shortcomings of research studies. Identifying and contextualizing the failures that have dominated TBI research is mandatory. This review analyzes current approaches and discusses possible strategies for improving outcomes.

A proposed amendment to the current guidelines for mild traumatic brain injury: reducing computerized tomographies while maintaining safety

PURPOSE

Head trauma is a common complaint in emergency departments. Identifying patients with serious injuries can be difficult and generates many computerized tomographies. Reducing the number of computerized tomographies decreases both cost and radiation exposure. The aim of this study was to evaluate whether the current Scandinavian Neurotrauma Committee guidelines could be revised in such a way that would enable hospitals to perform fewer computerized tomographies while maintaining the ability to identify all patients requiring neurological intervention.

METHODS

A retrospective study of the medical records of adult patients suffering a traumatic brain injury was performed. A total of 1671 patients over a period of 365 days were included, and 25 parameters were extracted. Multitrauma patients managed with ATLS™ were excluded. The Scandinavian Neurotrauma Committee guidelines were amended with the previously derived "low-risk proposal" and applied retrospectively to the cohort.

RESULTS

Incidence of intracranial hemorrhage was 5.6% (93/1671). Application of the current Scandinavian Neurotrauma Committee guidelines would have resulted in 860 computerized tomographies and would have missed 11 intracranial hemorrhages. The proposed amendment with the low-risk proposal would have resulted in 748 CT scans and would have missed 19 intracranial hemorrhages (a relative reduction of 13%). None of the missed intracranial hemorrhages required neurological intervention.

CONCLUSION

For patients with mild and moderate traumatic brain injuries, application of the Scandinavian Neurotrauma Committee guidelines amended with the low-risk proposal may result in a significant reduction of computerized tomographies without missing any patients in need of neurological intervention.

Contrast Agents Delivery: An Up-to-Date Review of Nanodiagnostics in Neuroimaging

Neuroimaging is a highly important field of neuroscience, with direct implications for the early diagnosis and progression monitoring of brain-associated diseases. Neuroimaging techniques are categorized into structural, functional and molecular neuroimaging, each possessing advantages and disadvantages in terms of resolution, invasiveness, toxicity of contrast agents and costs. Nanotechnology-based approaches for neuroimaging mostly involve the development of nanocarriers for incorporating contrast agents or the use of nanomaterials as imaging agents. Inorganic and organic nanoparticles, liposomes, micelles, nanobodies and quantum dots are some of the most studied candidates for the delivery of contrast agents for neuroimaging. This paper focuses on describing the conventional modalities used for imaging and the applications of nanotechnology for developing novel strategies for neuroimaging. The aim is to highlight the roles of nanocarriers for enhancing and/or overcome the limitations associated with the most commonly utilized neuroimaging modalities. For future directions, several techniques that could benefit from the increased contrast induced by using imaging probes are presented.

Traumatic brain injury and frontal lobe plasticity

Over 1.4 million people in the United States experience traumatic brain injury (TBI) each year and approximately 52,000 people die annually due to complications related to TBI. Traditionally, TBI has been viewed as a static injury with significant consequences for frontal lobe functioning that plateaus after some window of recovery, remaining relatively stable thereafter. However, over the past decade there has been growing consensus that the consequences of TBI are dynamic, with unique characteristics expressed at the individual level and over the life span. This chapter first discusses the pathophysiology of TBI in order to understand its dynamic process and then describes the behavioral changes that are the result of injury with focus on frontal lobe functions. It integrates a historical perspective on structural and functional brain-imaging approaches used to understand how TBI impacts the frontal lobes, as well as more recent approaches to examine large-scale network changes after TBI. The factors most useful for outcome prediction are surveyed, along with how the theoretical frameworks used to predict recovery have developed over time. In this chapter, the authors argue for the need to understand outcome after TBI as a dynamic process with individual trajectories, taking a network theory perspective to understand the consequences of disrupting frontal systems in TBI. Within this framework, understanding frontal lobe dysfunction within a larger coordinated neural network to study TBI may provide a novel perspective in outcome prediction and in developing individualized treatments.

Diminished neural network dynamics after moderate and severe traumatic brain injury

Over the past decade there has been increasing enthusiasm in the cognitive neurosciences around using network science to understand the system-level changes associated with brain disorders. A growing literature has used whole-brain fMRI analysis to examine changes in the brain's subnetworks following traumatic brain injury (TBI). Much of network modeling in this literature has focused on static network mapping, which provides a window into gross inter-nodal relationships, but is insensitive to more subtle fluctuations in network dynamics, which may be an important predictor of neural network plasticity. In this study, we examine the dynamic connectivity with focus on state-level connectivity (state) and evaluate the reliability of dynamic network states over the course of two runs of intermittent task and resting data. The goal was to examine the dynamic properties of neural networks engaged periodically with task stimulation in order to determine: 1) the reliability of inter-nodal and network-level characteristics over time and 2) the transitions between distinct network states after traumatic brain injury. To do so, we enrolled 23 individuals with moderate and severe TBI at least 1-year post injury and 19 age- and education-matched healthy adults using functional MRI methods, dynamic connectivity modeling, and graph theory. The results reveal several distinct network "states" that were reliably evident when comparing runs; the overall frequency of dynamic network states are highly reproducible (r-values>0.8) for both samples. Analysis of movement between states resulted in fewer state transitions in the TBI sample and, in a few cases, brain injury resulted in the appearance of states not exhibited by the healthy control (HC) sample. Overall, the findings presented here demonstrate the reliability of observable dynamic mental states during periods of on-task performance and support emerging evidence that brain injury may result in diminished network dynamics.

Diminished neural network dynamics in amnestic mild cognitive impairment

Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain "states," or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI.