Longitudinal Diffusion Tensor Imaging Detects Recovery of Fractional Anisotropy Within Traumatic Axonal Injury Lesions

Diffusion Tensor Imaging as Neurologic Predictor in Patients Affected by Traumatic Brain Injury: Scoping Review

Background: Diffusion tensor imaging (DTI), a variant of Diffusion Weighted Imaging (DWI), enables a neuroanatomical microscopic-like examination of the brain, which can detect brain damage using physical parameters. DTI's application to traumatic brain injury (TBI) has the potential to reveal radiological features that can assist in predicting the clinical outcomes of these patients. What is the ongoing role of DTI in detecting brain alterations and predicting neurological outcomes in patients with moderate to severe traumatic brain injury and/or diffuse axonal injury? Methods: A scoping review of the PubMed, Scopus, EMBASE, and Cochrane databases was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The aim was to identify all potentially relevant studies concerning the role of DTI in TBI. From an initial pool of 3527 publications, 26 articles were selected based on relevance. These studies included a total of 729 patients with moderate to severe TBI and/or diffuse axonal injury. DTI parameters were analyzed to determine their relationship with neurological outcomes post-TBI, with assessments of several brain functions and regions. Results: The studies included various DTI parameters, identifying significant relationships between DTI variations and neurological outcomes following TBI. Multiple brain functions and regions were evaluated, demonstrating the capability of DTI to detect brain alterations with higher accuracy, sensitivity, and specificity than MRI alone. Conclusions: DTI is a valuable tool for detecting brain alterations in TBI patients, offering enhanced accuracy, sensitivity, and specificity compared to MRI alone. Recent studies confirm its effectiveness in identifying neurological impairments and predicting outcomes in patients following brain trauma, underscoring its utility in clinical settings for managing TBI.

Volumetric MRI Findings in Mild Traumatic Brain Injury (mTBI) and Neuropsychological Outcome

Region of interest (ROI) volumetric assessment has become a standard technique in quantitative neuroimaging. ROI volume is thought to represent a coarse proxy for making inferences about the structural integrity of a brain region when compared to normative values representative of a healthy sample, adjusted for age and various demographic factors. This review focuses on structural volumetric analyses that have been performed in the study of neuropathological effects from mild traumatic brain injury (mTBI) in relation to neuropsychological outcome. From a ROI perspective, the probable candidate structures that are most likely affected in mTBI represent the target regions covered in this review. These include the corpus callosum, cingulate, thalamus, pituitary-hypothalamic area, basal ganglia, amygdala, and hippocampus and associated structures including the fornix and mammillary bodies, as well as whole brain and cerebral cortex along with the cerebellum. Ventricular volumetrics are also reviewed as an indirect assessment of parenchymal change in response to injury. This review demonstrates the potential role and limitations of examining structural changes in the ROIs mentioned above in relation to neuropsychological outcome. There is also discussion and review of the role that post-traumatic stress disorder (PTSD) may play in structural outcome in mTBI. As emphasized in the conclusions, structural volumetric findings in mTBI are likely just a single facet of what should be a multimodality approach to image analysis in mTBI, with an emphasis on how the injury damages or disrupts neural network integrity. The review provides an historical context to quantitative neuroimaging in neuropsychology along with commentary about future directions for volumetric neuroimaging research in mTBI.

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.

Irritable Bowel Syndrome, Depression, and Neurodegeneration: A Bidirectional Communication from Gut to Brain

Patients with irritable bowel syndrome (IBS) are increasingly presenting with a wide range of neuropsychiatric symptoms, such as deterioration in gastroenteric physiology, including visceral hypersensitivity, altered intestinal membrane permeability, and gastrointestinal motor dysfunction. Functional imaging of IBS patients has revealed several abnormalities in various brain regions, such as significant activation of amygdala, thinning of insular and anterior cingulate cortex, and increase in hypothalamic gray matter, which results in poor psychiatric and cognitive outcomes. Interrelations between the enteric and central events in IBS-related gastrointestinal, neurological, and psychiatric pathologies have compelled researchers to study the gut-brain axis-a bidirectional communication that maintains the homeostasis of the gastrointestinal and central nervous system with gut microbiota as the protagonist. Thus, it can be disrupted by any alteration owing to the gut dysbiosis or loss of diversity in microbial composition. Available evidence indicates that the use of probiotics as a part of a balanced diet is effective in the management of IBS and IBS-associated neurodegenerative and psychiatric comorbidities. In this review, we delineate the pathogenesis and complications of IBS from gastrointestinal and neuropsychiatric standpoints while also discussing the neurodegenerative events in enteric and central nervous systems of IBS patients and the therapeutic potential of gut microbiota-based therapy established on clinical and preclinical data.

Microstructural changes in normal-appearing white matter in male sleep apnea patients are reversible after treatment: A pilot study

Visually appreciable white matter (WM) changes have been described in obstructive sleep apnea (OSA). However, few data exist on the involvement of silent WM abnormalities. This prospective study investigated the microstructural integrity of normal-appearing white matter (NAWM) in male OSA patients before and after continuous positive airway pressure (CPAP) treatment, using a neuroimaging approach. Magnetic resonance imaging (MRI) was acquired from 32 participants (16 severe never-treated OSA and 16 controls). Diffusion tensor imaging (DTI) and Tract-Based Spatial Statistics (TBSS) were used to assess the microstructural NAWM changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). In order to evaluate the efficacy of the therapy, OSA patients underwent MRI evaluations at baseline and after 3 months of treatment (follow-up). CPAP treatment significantly increased the FA in NAWM of the brain stem, corpus callosum and bilateral internal capsule of OSA patients at follow-up compared to baseline (p < 0.05, TFCE-corrected). OSA patients also showed increases in AD in the corpus callosum, superior corona radiata, and internal capsule of the right hemisphere (p < 0.05, TFCE-corrected) after CPAP treatment. A significant negative correlation was found between the FA of the corona radiata, corpus callosum, internal capsule, limbic structures, and neuropsychological scores at follow-up evaluation. No significant differences were found in MD and RD of NAWM in our patients after treatment. Our results demonstrate that FA and AD of NAWM in major tracts such as the corpus callosum and the internal capsule increased significantly after CPAP treatment, as a potential beneficial effect of ventilatory therapy. The recovery of NAWM alterations might also be related to the improvement in the neurocognitive profile, suggesting that nonclearly visible WM alterations may contribute to the physiopathology of OSA-related cognitive impairment.

Neuroimaging for the Neurologist: Clinical MRI and Future Trends

MRI is a commonly used diagnostic tool in neurology, and all neurologists should possess a working knowledge of imaging fundamentals. An overview of current and impending MRI techniques is presented to help the referring clinician communicate better with the imaging department, understand the utility and limitations of current and emerging technology, improve specificity and appropriateness when ordering MRI studies, and recognize key findings.

Slow-gamma frequencies are optimally guarded against effects of neurodegenerative diseases and traumatic brain injuries

We introduce a computational model for the cellular level effects of firing rate filtering due to the major forms of neuronal injury, including demyelination and axonal swellings. Based upon experimental and computational observations, we posit simple phenomenological input/output rules describing spike train distortions and demonstrate that slow-gamma frequencies in the 38-41 Hz range emerge as the most robust to injury. Our signal-processing model allows us to derive firing rate filters at the cellular level for impaired neural activity with minimal assumptions. Specifically, we model eight experimentally observed spike train transformations by discrete-time filters, including those associated with increasing refractoriness and intermittent blockage. Continuous counterparts for the filters are also obtained by approximating neuronal firing rates from spike trains convolved with causal and Gaussian kernels. The proposed signal processing framework, which is robust to model parameter calibration, is an abstraction of the major cellular-level pathologies associated with neurodegenerative diseases and traumatic brain injuries that affect spike train propagation and impair neuronal network functionality. Our filters are well aligned with the spectrum of dynamic memory fields including working memory, visual consciousness, and other higher cognitive functions that operate in a frequency band that is - at a single cell level - optimally guarded against common types of pathological effects. In contrast, higher-frequency neural encoding, such as is observed with short-term memory, are susceptible to neurodegeneration and injury.

Characterizing Signals Within Lesions and Mapping Brain Network Connectivity After Traumatic Axonal Injury: A 7 Tesla Resting-State FMRI Study

Resting-state functional magnetic resonance imaging (RS-FMRI) has been widely used to map brain functional connectivity, but it is unclear how to probe connectivity within and around lesions. In this study, we characterize RS-FMRI signal time course properties and evaluate different seed placements within and around hemorrhagic traumatic axonal injury (hTAI) lesions. RS-FMRI was performed on a 7 Tesla scanner in a patient who recovered consciousness after traumatic coma and in three healthy controls. Eleven lesions in the patient were characterized in terms of (1) temporal signal-to-noise ratio (tSNR); (2) physiological noise, through comparison of noise regressors derived from the white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM); and (3) seed-based functional connectivity. Temporal SNR at the center of the lesions was 38.3% and 74.1% lower compared with the same region in the contralesional hemisphere of the patient and in the ipsilesional hemispheres of the controls, respectively. Within the lesions, WM noise was more prominent than CSF and GM noise. Lesional seeds did not produce discernable networks, but seeds in the contralesional hemisphere revealed networks whose nodes appeared to be shifted or obscured due to overlapping or nearby lesions. Single-voxel seed analysis demonstrated that placing a seed within a lesion's periphery was necessary to identify networks associated with the lesion region. These findings provide evidence of resting-state network changes in the human brain after recovery from traumatic coma. Furthermore, we show that seed placement within a lesion's periphery or in the contralesional hemisphere may be necessary for network identification in patients with hTAI.

Generalized diffusion spectrum magnetic resonance imaging (GDSI) for model-free reconstruction of the ensemble average propagator

Diffusion spectrum MRI (DSI) provides model-free estimation of the diffusion ensemble average propagator (EAP) and orientation distribution function (ODF) but requires the diffusion data to be acquired on a Cartesian q-space grid. Multi-shell diffusion acquisitions are more flexible and more commonly acquired but have, thus far, only been compatible with model-based analysis methods. Here, we propose a generalized DSI (GDSI) framework to recover the EAP from multi-shell diffusion MRI data. The proposed GDSI approach corrects for q-space sampling density non-uniformity using a fast geometrical approach. The EAP is directly calculated in a preferable coordinate system by multiplying the sampling density corrected q-space signals by a discrete Fourier transform matrix, without any need for gridding. The EAP is demonstrated as a way to map diffusion patterns in brain regions such as the thalamus, cortex and brainstem where the tissue microstructure is not as well characterized as in white matter. Scalar metrics such as the zero displacement probability and displacement distances at different fractions of the zero displacement probability were computed from the recovered EAP to characterize the diffusion pattern within each voxel. The probability averaged across directions at a specific displacement distance provides a diffusion property based image contrast that clearly differentiates tissue types. The displacement distance at the first zero crossing of the EAP averaged across directions orthogonal to the primary fiber orientation in the corpus callosum is found to be larger in the body (5.65 ± 0.09 μm) than in the genu (5.55 ± 0.15 μm) and splenium (5.4 ± 0.15 μm) of the corpus callosum, which corresponds well to prior histological studies. The EAP also provides model-free representations of angular structure such as the diffusion ODF, which allows estimation and comparison of fiber orientations from both the model-free and model-based methods on the same multi-shell data. For the model-free methods, detection of crossing fibers is found to be strongly dependent on the maximum b-value and less sensitive compared to the model-based methods. In conclusion, our study provides a generalized DSI approach that allows flexible reconstruction of the diffusion EAP and ODF from multi-shell diffusion data and data acquired with other sampling patterns.

Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks

The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular level in vivo. We simulate the effects of neurodegenerative disease and TBI using convolutional neural networks (CNNs) as our model of cognition. We utilize biophysically relevant statistical data on FAS to damage the connections in CNNs in a functionally relevant way. We incorporate energy constraints on the brain by pruning the CNNs to be less over-engineered. Qualitatively, we demonstrate that damage leads to human-like mistakes. Our experiments also provide quantitative assessments of how accuracy is affected by various types and levels of damage. The deficit resulting from a fixed amount of damage greatly depends on which connections are randomly injured, providing intuition for why it is difficult to predict impairments. There is a large degree of subjectivity when it comes to interpreting cognitive deficits from complex systems such as the human brain. However, we provide important insight and a quantitative framework for disorders in which FAS are implicated.

Quantitative analysis in peritumoral volumes of brain metastases treated with stereotactic radiotherapy

PURPOSE

The purpose of this study was to verify changes in diffusion tensor imaging (DTI) factors in patients with brain metastases treated with stereotactic radiotherapy (SRT). We also investigated the impact of SRT on peritumoral volumes though the use of DTI.

METHODS

A total of 28 patients with brain metastases who had undergone SRT between March 2014 and December 2015 were enrolled. Magnetic resonance imaging with DTI factors, such as fractional anisotropy (FA) and apparent diffusion tensor (ADC) value, was performed 1 day before the procedure and 3 months after the procedure. DTI data from tumor lesions, edema volumes, and the volumes that received 12Gy were measured.

RESULTS

Tumor volume (P=0.001) and ADC values in the volumes that received 12Gy (P=0.018) and the edema volumes (P=0.003) significantly decreased after the procedure. Decreases in tumor volume were only correlated with decreases in edema volumes (P<0.001). Decreases in edema volumes were correlated with increases in FA values and decreases in ADC values of the volumes that received 12Gy [P=0.019 (FA)/0.002 (ADC)] and the edema volumes [P=0.011 (FA)/0.002 (ADC)].

CONCLUSIONS

It was possible to quantify changes in peritumoral volumes in patients with brain metastases after SRT by using DTI. ADC values of peritumoral volumes decreased significantly after SRT. Therefore, it was confirmed through DTI that performing SRT on tumor lesions has a positive effect on the structure and function of peritumoral volumes.

Revisiting Grade 3 Diffuse Axonal Injury: Not All Brainstem Microbleeds are Prognostically Equal

BACKGROUND

Recovery of functional independence is possible in patients with brainstem traumatic axonal injury (TAI), also referred to as "grade 3 diffuse axonal injury," but acute prognostic biomarkers are lacking. We hypothesized that the extent of dorsal brainstem TAI measured by burden of traumatic microbleeds (TMBs) correlates with 1-year functional outcome more strongly than does ventral brainstem, corpus callosal, or global brain TMB burden. Further, we hypothesized that TMBs within brainstem nuclei of the ascending arousal network (AAN) correlate with 1-year outcome.

METHODS

Using a prospective outcome database of patients treated for moderate-to-severe traumatic brain injury at an inpatient rehabilitation hospital, we retrospectively identified 39 patients who underwent acute gradient-recalled echo (GRE) magnetic resonance imaging (MRI). TMBs were counted on the acute GRE scans globally and in the dorsal brainstem, ventral brainstem, and corpus callosum. TMBs were also mapped onto an atlas of AAN nuclei. The primary outcome was the disability rating scale (DRS) score at 1 year post-injury. Associations between regional TMBs, AAN TMB volume, and 1-year DRS score were assessed by calculating Spearman rank correlation coefficients.

RESULTS

Mean ± SD number of TMBs was: dorsal brainstem = 0.7 ± 1.4, ventral brainstem = 0.2 ± 0.6, corpus callosum = 1.8 ± 2.8, and global = 14.4 ± 12.5. The mean ± SD TMB volume within AAN nuclei was 6.1 ± 18.7 mm3. Increased dorsal brainstem TMBs and larger AAN TMB volume correlated with worse 1-year outcomes (R = 0.37, p = 0.02, and R = 0.36, p = 0.02, respectively). Global, callosal, and ventral brainstem TMBs did not correlate with outcomes.

CONCLUSIONS

These findings suggest that dorsal brainstem TAI, especially involving AAN nuclei, may have greater prognostic utility than the total number of lesions in the brain or brainstem.

Preventing Neurodegenerative Memory Loss in Hopfield Neuronal Networks Using Cerebral Organoids or External Microelectronics

Developing technologies have made significant progress towards linking the brain with brain-machine interfaces (BMIs) which have the potential to aid damaged brains to perform their original motor and cognitive functions. We consider the viability of such devices for mitigating the deleterious effects of memory loss that is induced by neurodegenerative diseases and/or traumatic brain injury (TBI). Our computational study considers the widely used Hopfield network, an autoassociative memory model in which neurons converge to a stable state pattern after receiving an input resembling the given memory. In this study, we connect an auxiliary network of neurons, which models the BMI device, to the original Hopfield network and train it to converge to its own auxiliary memory patterns. Injuries to the original Hopfield memory network, induced through neurodegeneration, for instance, can then be analyzed with the goal of evaluating the ability of the BMI to aid in memory retrieval tasks. Dense connectivity between the auxiliary and Hopfield networks is shown to promote robustness of memory retrieval tasks for both optimal and nonoptimal memory sets. Our computations estimate damage levels and parameter ranges for which full or partial memory recovery is achievable, providing a starting point for novel therapeutic strategies.

Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurological diseases

The presence of diffuse Focal Axonal Swellings (FAS) is a hallmark cellular feature in many neurological diseases and traumatic brain injury. Among other things, the FAS have a significant impact on spike-train encodings that propagate through the affected neurons, leading to compromised signal processing on a neuronal network level. This work merges, for the first time, three fields of study: (i) signal processing in excitatory-inhibitory (EI) networks of neurons via population codes, (ii) decision-making theory driven by the production of evidence from stimulus, and (iii) compromised spike-train propagation through FAS. As such, we demonstrate a mathematical architecture capable of characterizing compromised decision-making driven by cellular mechanisms. The computational model also leads to several novel predictions and diagnostics for understanding injury level and cognitive deficits, including a key finding that decision-making reaction times, rather than accuracy, are indicative of network level damage. The results have a number of translational implications, including that the level of network damage can be characterized by the reaction times in simple cognitive and motor tests.