Network diffusion modeling predicts neurodegeneration in traumatic brain injury

Magnetic resonance imaging in traumatic brain injury: a survey of clinical practitioners' experiences and views on current practice and obstacles

INTRODUCTION

Magnetic resonance imaging (MRI) has revolutionized our capacity to examine brain alterations in traumatic brain injury (TBI). However, little is known about the level of implementation of MRI techniques in clinical practice in TBI and associated obstacles.

METHODS

A diverse set of health professionals completed 19 multiple choice and free text survey questions.

RESULTS

Of the 81 respondents, 73.4% reported that they acquire/order MRI scans in TBI patients, and 66% indicated they would prefer MRI be more often used with this cohort. The greatest impediment for MRI usage was scanner availability (57.1%). Less than half of respondents (42.1%) indicated that they perform advanced MRI analysis. Factors such as dedicated experts within the team (44.4%) and user-friendly MRI analysis tools (40.7%), were listed as potentially helpful to implement advanced MRI analyses in clinical practice.

CONCLUSION

Results suggest a wide variability in the purpose, timing, and composition of the scanning protocol of clinical MRI after TBI. Three recommendations are described to broaden implementation of MRI in clinical practice in TBI: 1) development of a standardized multimodal MRI protocol; 2) future directions for the use of advanced MRI analyses; 3) use of low-field MRI to overcome technical/practical issues with high-field MRI.

Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases

Neurodegenerative diseases are a group of disorders characterized by progressive degeneration or death of neurons. The complexity of clinical symptoms and irreversibility of disease progression significantly affects individual lives, leading to premature mortality. The prevalence of neurodegenerative diseases keeps increasing, yet the specific pathogenic mechanisms remain incompletely understood and effective treatment strategies are lacking. In recent years, convergent experimental evidence supports the "prion-like transmission" assumption that abnormal proteins induce misfolding of normal proteins, and these misfolded proteins propagate throughout the neural networks to cause neuronal death. To elucidate this dynamic process in vivo from a computational perspective, researchers have proposed three connectome-based biophysical models to simulate the spread of pathological proteins: the Network Diffusion Model, the Epidemic Spreading Model, and the agent-based Susceptible-Infectious-Removed model. These models have demonstrated promising predictive capabilities. This review focuses on the explanations of their fundamental principles and applications. Then, we compare the strengths and weaknesses of the models. Building upon this foundation, we introduce new directions for model optimization and propose a unified framework for the evaluation of connectome-based biophysical models. We expect that this review could lower the entry barrier for researchers in this field, accelerate model optimization, and thereby advance the clinical translation of connectome-based biophysical models.

Nanoparticle targeting strategies for traumatic brain injury

Nanoparticle (NP)-based drug delivery systems hold immense potential for targeted therapy and diagnosis of neurological disorders, overcoming the limitations of conventional treatment modalities. This review explores the design considerations and functionalization strategies of NPs for precise targeting of the brain and central nervous system. This review discusses the challenges associated with drug delivery to the brain, including the blood-brain barrier and the complex heterogeneity of traumatic brain injury. We also examine the physicochemical properties of NPs, emphasizing the role of size, shape, and surface characteristics in their interactions with biological barriers and cellular uptake mechanisms. The review concludes by exploring the options of targeting ligands designed to augment NP affinity and retention to specific brain regions or cell types. Various targeting ligands are discussed for their ability to mimic receptor-ligand interaction, and brain-specific extracellular matrix components. Strategies to mimic viral mechanisms to increase uptake are discussed. Finally, the emergence of antibody, antibody fragments, and antibody mimicking peptides are discussed as promising targeting strategies. By integrating insights from these scientific fields, this review provides an understanding of NP-based targeting strategies for personalized medicine approaches to neurological disorders. The design considerations discussed here pave the way for the development of NP platforms with enhanced therapeutic efficacy and minimized off-target effects, ultimately advancing the field of neural engineering.

Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi-Cohort Mega-Analysis

OBJECTIVE

The long-term consequences of traumatic brain injury (TBI) on brain structure remain uncertain. Given evidence that a single significant brain injury event increases the risk of dementia, brain-age estimation could provide a novel and efficient indexing of the long-term consequences of TBI. Brain-age procedures use predictive modeling to calculate brain-age scores for an individual using structural magnetic resonance imaging (MRI) data. Complicated mild, moderate, and severe TBI (cmsTBI) is associated with a higher predicted age difference (PAD), but the progression of PAD over time remains unclear. We sought to examine whether PAD increases as a function of time since injury (TSI) and if injury severity and sex interacted to influence this progression.

METHODS

Through the ENIGMA Adult Moderate and Severe (AMS)-TBI working group, we examine the largest TBI sample to date (n = 343), along with controls, for a total sample size of n = 540, to replicate and extend prior findings in the study of TBI brain age. Cross-sectional T1w-MRI data were aggregated across 7 cohorts, and brain age was established using a similar brain age algorithm to prior work in TBI.

RESULTS

Findings show that PAD widens with longer TSI, and there was evidence for differences between sexes in PAD, with men showing more advanced brain age. We did not find strong evidence supporting a link between PAD and cognitive performance.

INTERPRETATION

This work provides evidence that changes in brain structure after cmsTBI are dynamic, with an initial period of change, followed by relative stability in brain morphometry, eventually leading to further changes in the decades after a single cmsTBI. ANN NEUROL 2024;96:365-377.

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

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

On propagation in networks, promising models beyond network diffusion to describe degenerative brain diseases and traumatic brain injuries

Introduction: Connections among neurons form one of the most amazing and effective network in nature. At higher level, also the functional structures of the brain is organized as a network. It is therefore natural to use modern techniques of network analysis to describe the structures of networks in the brain. Many studies have been conducted in this area, showing that the structure of the neuronal network is complex, with a small-world topology, modularity and the presence of hubs. Other studies have been conducted to investigate the dynamical processes occurring in brain networks, analyzing local and large-scale network dynamics. Recently, network diffusion dynamics have been proposed as a model for the progression of brain degenerative diseases and for traumatic brain injuries. Methods: In this paper, the dynamics of network diffusion is re-examined and reaction-diffusion models on networks is introduced in order to better describe the degenerative dynamics in the brain. Results: Numerical simulations of the dynamics of injuries in the brain connectome are presented. Different choices of reaction term and initial condition provide very different phenomenologies, showing how network propagation models are highly flexible. Discussion: The uniqueness of this research lies in the fact that it is the first time that reaction-diffusion dynamics have been applied to the connectome to model the evolution of neurodegenerative diseases or traumatic brain injury. In addition, the generality of these models allows the introduction of non-constant diffusion and different reaction terms with non-constant parameters, allowing a more precise definition of the pathology to be studied.

Tau Phosphorylation Patterns in the Rat Cerebral Cortex After Traumatic Brain Injury and Sodium Selenate Effects: An Epibios4rx Project 2 Study

Sodium selenate (SS) activates protein phosphatase 2 (PP2A) and reduces phosphorylated tau (pTAU) and late post-traumatic seizures after lateral fluid percussion injury (LFPI). In EpiBioS4Rx Project 2, a multi-center international study for post-traumatic targets, biomarkers, and treatments, we tested the target relevance and modification by SS of pTAU forms and PP2A and in the LFPI model, at two sites: Einstein and Melbourne. In Experiment 1, adult male rats were assigned to LFPI and sham (both sites) and naïve controls (Einstein). Motor function was monitored by neuroscores. Brains were studied with immunohistochemistry (IHC), Western blots (WBs), or PP2A activity assay, from 2 days to 8 weeks post-operatively. In Experiment 2, LFPI rats received SS for 7 days (SS0.33: 0.33 mg/kg/day; SS1: 1 mg/kg/day, subcutaneously) or vehicle (Veh) post-LFPI and pTAU, PR55 expression, or PP2A activity were studied at 2 days and 1 week (on treatment), or 2 weeks (1 week off treatment). Plasma selenium and SS levels were measured. In Experiment 1 IHC, LFPI rats had higher cortical pTAU-Ser202/Thr205-immunoreactivity (AT8-ir) and pTAU-Ser199/202-ir at 2 days, and pTAU-Thr231-ir (AT180-ir) at 2 days, 2 weeks, and 8 weeks, ipsilaterally to LFPI, than controls. LFPI-2d rats also had higher AT8/total-TAU5-ir in cortical extracts ipsilateral to the lesion (WB). PP2A (PR55-ir) showed time- and region-dependent changes in IHC, but not in WB. PP2A activity was lower in LFPI-1wk than in sham rats. In Experiment 2, SS did not affect neuroscores or cellular AT8-ir, AT180-ir, or PR55-ir in IHC. In WB, total cortical AT8/total-TAU-ir was lower in SS0.33 and SS1 LFPI rats than in Veh rats (2 days, 1 week); total cortical PR55-ir (WB) and PP2A activity were higher in SS1 than Veh rats (2 days). SS dose dependently increased plasma selenium and SS levels. Concordant across-sites data confirm time and pTAU form-specific cortical increases ipsilateral to LFPI. The discordant SS effects may either suggest SS-induced reduction in the numbers of cells with increased pTAU-ir, need for longer treatment, or the involvement of other mechanisms of action.

Spatiotemporal profile of atrophy in the first year following moderate-severe traumatic brain injury

Traumatic brain injury (TBI) triggers progressive neurodegeneration resulting in brain atrophy that continues months-to-years following injury. However, a comprehensive characterization of the spatial and temporal evolution of TBI-related brain atrophy remains incomplete. Utilizing a sensitive and unbiased morphometry analysis pipeline optimized for detecting longitudinal changes, we analyzed a sample consisting of 37 individuals with moderate-severe TBI who had primarily high-velocity and high-impact injury mechanisms. They were scanned up to three times during the first year after injury (3 months, 6 months, and 12 months post-injury) and compared with 33 demographically matched controls who were scanned once. Individuals with TBI already showed cortical thinning in frontal and temporal regions and reduced volume in the bilateral thalami at 3 months post-injury. Longitudinally, only a subset of cortical regions in the parietal and occipital lobes showed continued atrophy from 3 to 12 months post-injury. Additionally, cortical white matter volume and nearly all deep gray matter structures exhibited progressive atrophy over this period. Finally, we found that disproportionate atrophy of cortex along sulci relative to gyri, an emerging morphometric marker of chronic TBI, was present as early as 3 month post-injury. In parallel, neurocognitive functioning largely recovered during this period despite this pervasive atrophy. Our findings demonstrate msTBI results in characteristic progressive neurodegeneration patterns that are divergent across regions and scale with the severity of injury. Future clinical research using atrophy during the first year of TBI as a biomarker of neurodegeneration should consider the spatiotemporal profile of atrophy described in this study.

Chronic motor performance following different traumatic brain injury severity-A systematic review

Introduction

Traumatic brain injury (TBI) is now known to be a chronic disease, causing ongoing neurodegeneration and linked to increased risk of neurodegenerative motor diseases, such as Parkinson's disease and amyotrophic lateral sclerosis. While the presentation of motor deficits acutely following traumatic brain injury is well-documented, however, less is known about how these evolve in the long-term post-injury, or how the initial severity of injury affects these outcomes. The purpose of this review, therefore, was to examine objective assessment of chronic motor impairment across the spectrum of TBI in both preclinical and clinical models.

Methods

PubMed, Embase, Scopus, and PsycINFO databases were searched with a search strategy containing key search terms for TBI and motor function. Original research articles reporting chronic motor outcomes with a clearly defined TBI severity (mild, repeated mild, moderate, moderate-severe, and severe) in an adult population were included.

Results

A total of 97 studies met the inclusion criteria, incorporating 62 preclinical and 35 clinical studies. Motor domains examined included neuroscore, gait, fine-motor, balance, and locomotion for preclinical studies and neuroscore, fine-motor, posture, and gait for clinical studies. There was little consensus among the articles presented, with extensive differences both in assessment methodology of the tests and parameters reported. In general, an effect of severity was seen, with more severe injury leading to persistent motor deficits, although subtle fine motor deficits were also seen clinically following repeated injury. Only six clinical studies investigated motor outcomes beyond 10 years post-injury and two preclinical studies to 18-24 months post-injury, and, as such, the interaction between a previous TBI and aging on motor performance is yet to be comprehensively examined.

Conclusion

Further research is required to establish standardized motor assessment procedures to fully characterize chronic motor impairment across the spectrum of TBI with comprehensive outcomes and consistent protocols. Longitudinal studies investigating the same cohort over time are also a key for understanding the interaction between TBI and aging. This is particularly critical, given the risk of neurodegenerative motor disease development following TBI.

Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients

BACKGROUND AND PURPOSE

Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses.

MATERIALS AND METHODS

Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 - 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, M_age = 35.7y, age range 25 - 64y).

RESULTS

Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test-retest reliability of the fixel-wise metrics are warranted.

CONCLUSIONS

Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life.

The role of the microbiota-gut-brain axis in long-term neurodegenerative processes following traumatic brain injury

Traumatic brain injury (TBI) can be a devastating and debilitating disease to endure. Due to improvements in clinical practice, declining mortality rates have led to research into the long-term consequences of TBI. For example, the incidence and severity of TBI have been associated with an increased susceptibility of developing neurodegenerative disorders, such as Parkinson's or Alzheimer's disease. However, the mechanisms linking this alarming association are yet to be fully understood. Recently, there has been a groundswell of evidence implicating the microbiota-gut-brain axis in the pathogenesis of these diseases. Interestingly, survivors of TBI often report gastrointestinal complaints and animal studies have demonstrated gastrointestinal dysfunction and dysbiosis following injury. Autonomic dysregulation and chronic inflammation appear to be the main driver of these pathologies. Consequently, this review will explore the potential role of the microbiota-gut-brain axis in the development of neurodegenerative diseases following TBI.

Reaction-diffusion models in weighted and directed connectomes

Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional epiphenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our high-precision connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances.

Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway.

In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation were performed in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.

Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis

Objective

Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS.

Methods

We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks.

Results

The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models.

Interpretation

Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.

Modeling seeding and neuroanatomic spread of pathology in amyotrophic lateral sclerosis

The neurodegenerative disorder amyotrophic lateral sclerosis (ALS) is characterized by the progressive loss of upper and lower motor neurons, with pathological involvement of cerebral motor and extra-motor areas in a clinicopathological spectrum with frontotemporal dementia (FTD). A key unresolved issue is how the non-random distribution of pathology in ALS reflects differential network vulnerability, including molecular factors such as regional gene expression, or preferential spread of pathology via anatomical connections. A system of histopathological staging of ALS based on the regional burden of TDP-43 pathology observed in postmortem brains has been supported to some extent by analysis of distribution of in vivo structural MRI changes. In this paper, computational modeling using a Network Diffusion Model (NDM) was used to investigate whether a process of focal pathological 'seeding' followed by structural network-based spread recapitulated postmortem histopathological staging and, secondly, whether this had any correlation to the pattern of expression of a panel of genes implicated in ALS across the healthy brain. Regionally parcellated T1-weighted MRI data from ALS patients (baseline n=79) was studied in relation to a healthy control structural connectome and a database of associated regional cerebral gene expression. The NDM provided strong support for a structural network-based basis for regional pathological spread in ALS, but no simple relationship to the spatial distribution of ALS-related genes in the healthy brain. Interestingly, OPTN gene was identified as a significant but a weaker non-NDM contributor within the network-gene interaction model (LASSO). Intriguingly, the critical seed regions for spread within the model were not within the primary motor cortex but basal ganglia, thalamus and insula, where NDM recapitulated aspects of the postmortem histopathological staging system. Within the ALS-FTD clinicopathological spectrum, non-primary motor structures may be among the earliest sites of cerebral pathology.

Surface-based abnormalities of the executive frontostriatial circuit in pediatric TBI

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Cortical thickness of the dorsolateral prefrontal cortex is reduced in pediatric TBI.

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Shape abnormalities of the caudate and mediodorsal nucleus of the thalamus are a feature of pediatric TBI.

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Surface-based abnormalities of the dorsolateral prefrontal loop do not appear to relate to executive functioning.

Childhood traumatic brain injury (TBI) is one of the most common causes of acquired disability and has significant implications for executive functions (EF), such as impaired attention, planning, and initiation that are predictive of everyday functioning. Evidence has suggested attentional features of executive functioning require behavioral flexibility that is dependent on frontostriatial circuitry. The purpose of this study was to evaluate surface-based deformation of a specific frontostriatial circuit in pediatric TBI and its role in EF. Regions of interest included: the dorsolateral prefrontal cortex (DLPFC), caudate nucleus, globus pallidus, and the mediodorsal nucleus of the thalamus (MD). T1-weighted magnetic resonance images were obtained in a sample of children ages 8–13 with complicated mild, moderate, or severe TBI (n = 32) and a group of comparison children with orthopedic injury (OI; n = 30). Brain regions were characterized using high-dimensional surface-based brain mapping procedures. Aspects of EF were assessed using select subtests from the Test of Everyday Attention for Children (TEA-Ch). General linear models tested group and hemisphere differences in DLPFC cortical thickness and subcortical shape of deep-brain regions; Pearson correlations tested relationships with EF. Main effects for group were found in both cortical thickness of the DLPFC (F_1,60 = 4.30, p = 0.042) and MD mean deformation (F_1,60 = 6.50, p = 0.01) all with lower values in the TBI group. Statistical surface maps revealed significant inward deformation on ventral-medial aspects of the caudate in TBI relative to OI, but null results in the globus pallidus. No significant relationships between EF and any region of interest were observed. Overall, findings revealed abnormalities in multiple aspects of a frontostriatial circuit in pediatric TBI, which may reflect broader pathophysiological mechanisms. Increased consideration for the role of deep-brain structures in pediatric TBI can aid in the clinical characterization of anticipated long-term developmental effects of these individuals.

Network-constrained technique to characterize pathology progression rate in Alzheimer's disease

Current methods for measuring the chronic rates of cognitive decline and degeneration in Alzheimer’s disease rely on the sensitivity of longitudinal neuropsychological batteries and clinical neuroimaging, particularly structural magnetic resonance imaging of brain atrophy, either at a global or regional scale. There is particular interest in approaches predictive of future disease progression and clinical outcomes using a single time point. If successful, such approaches could have great impact on differential diagnosis, therapeutic treatment and clinical trial inclusion. Unfortunately, it has proven quite challenging to accurately predict clinical and degeneration progression rates from baseline data. Specifically, a key limitation of the previously proposed approaches for disease progression based on the brain atrophy measures has been the limited incorporation of the knowledge from disease pathology progression models, which suggest a prion-like spread of disease pathology and hence the neurodegeneration. Here, we present a new metric for disease progression rate in Alzheimer that uses only MRI-derived atrophy data yet is able to infer the underlying rate of pathology transmission. This is enabled by imposing a spread process driven by the brain networks using a Network Diffusion Model. We first fit this model to each patient’s longitudinal brain atrophy data defined on a brain network structure to estimate a patient-specific rate of pathology diffusion, called the pathology progression rate. Using machine learning algorithms, we then build a baseline data model and tested this rate metric on data from longitudinal Alzheimer’s Disease Neuroimaging Initiative study including 810 subjects. Our measure of disease progression differed significantly across diagnostic groups as well as between groups with different genetic risk factors. Remarkably, hierarchical clustering revealed 3 distinct clusters based on CSF profiles with >90% accuracy. These pathological clusters exhibit progressive atrophy and clinical impairments that correspond to the proposed rate measure. We demonstrate that a subject’s degeneration speed can be best predicted from baseline neuroimaging volumetrics and fluid biomarkers for subjects in the middle of their degenerative course, which may be a practical, inexpensive screening tool for future prognostic applications.

Quantitative multimodal imaging in traumatic brain injuries producing impaired cognition

PURPOSE OF REVIEW

Cognitive impairments are a devastating long-term consequence following traumatic brain injury (TBI). This review provides an update on the quantitative mutimodal neuroimaging studies that attempt to elucidate the mechanism(s) underlying cognitive impairments and their recovery following TBI.

RECENT FINDINGS

Recent studies have linked individual specific behavioural impairments and their changes over time to physiological activity and structural changes using EEG, PET and MRI. Multimodal studies that combine measures of physiological activity with knowledge of neuroanatomical and connectivity damage have also illuminated the multifactorial function-structure relationships that underlie impairment and recovery following TBI.

SUMMARY

The combined use of multiple neuroimaging modalities, with focus on individual longitudinal studies, has the potential to accurately classify impairments, enhance sensitivity of prognoses, inform targets for interventions and precisely track spontaneous and intervention-driven recovery.

Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis

Background: Graph theory and connectomics are new techniques for uncovering disease-induced changes in the brain's structural network. Most prior studied have focused on network statistics as biomarkers of disease. However, an emerging body of work involves exploring how the network serves as a conduit for the propagation of disease factors in the brain and has successfully mapped the functional and pathological consequences of disease propagation. In Alzheimer's disease (AD), progressive deposition of misfolded proteins amyloid and tau is well-known to follow fiber projections, under a "prion-like" trans-neuronal transmission mechanism, through which misfolded proteins cascade along neuronal pathways, giving rise to network spread. Methods: In this review, we survey the state of the art in mathematical modeling of connectome-mediated pathology spread in AD. Then we address several open questions that are amenable to mathematically precise parsimonious modeling of pathophysiological processes, extrapolated to the whole brain. We specifically identify current formal models of how misfolded proteins are produced, aggregate, and disseminate in brain circuits, and attempt to understand how this process leads to stereotyped progression in Alzheimer's and other related diseases. Conclusion: This review serves to unify current efforts in modeling of AD progression that together have the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico. Impact statement Graph theory is a powerful new approach that is transforming the study of brain processes. There do not exist many focused reviews of the subfield of graph modeling of how Alzheimer's and other dementias propagate within the brain network, and how these processes can be mapped mathematically. By providing timely and topical review of this subfield, we fill a critical gap in the community and present a unified view that can serve as an in silico test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.

Modelling the Anatomic Distribution of Neurologic Events in Patients with COVID-19: A Systematic Review of MRI Findings

BACKGROUND

Neurologic events have been reported in patients with coronavirus disease 2019 (COVID-19). However, a model-based evaluation of the spatial distribution of these events is lacking.

PURPOSE

Our aim was to quantitatively evaluate whether a network diffusion model can explain the spread of small neurologic events.

DATA SOURCES

The MEDLINE, EMBASE, Scopus, and LitCovid data bases were searched from January 1, 2020, to July 19, 2020.

STUDY SELECTION

Thirty-five case series and case studies reported 317 small neurologic events in 123 unique patients with COVID-19.

DATA ANALYSIS

Neurologic events were localized to gray or white matter regions of the Illinois Institute of Technology (gray-matter and white matter) Human Brain Atlas using radiologic images and descriptions. The total proportion of events was calculated for each region. A network diffusion model was implemented, and any brain regions showing a significant association (P < .05, family-wise error-corrected) between predicted and measured events were considered epicenters.

DATA SYNTHESIS

Within gray matter, neurologic events were widely distributed, with the largest number of events (∼10%) observed in the bilateral superior temporal, precentral, and lateral occipital cortices, respectively. Network diffusion modeling showed a significant association between predicted and measured gray matter events when the spread of pathology was seeded from the bilateral cerebellum (r = 0.51, P < .001, corrected) and putamen (r = 0.4, P = .02, corrected). In white matter, most events (∼26%) were observed within the bilateral corticospinal tracts.

LIMITATIONS

The risk of bias was not considered because all studies were either case series or case studies.

CONCLUSIONS

Transconnectome diffusion of pathology via the structural network of the brain may contribute to the spread of neurologic events in patients with COVID-19.

Network model of pathology spread recapitulates neurodegeneration and selective vulnerability in Huntington's Disease

Huntington's Disease (HD), an autosomal dominant genetic disorder caused by a mutation in the Huntingtin gene (HTT), displays a stereotyped topography in the human brain and a stereotyped progression, initially appearing in the striatum. Like other degenerative diseases, spatial topography of HD is divorced from where implicated genes are expressed, a dissociation whose mechanistic underpinning is not currently understood. Cell autonomous molecular factors characterized by gene expression signatures, including proteolytic and post translational modifications, play a role in vulnerability to disease. Non-autonomous mechanisms, likely involving the brain's anatomic or functional connectivity patterns, might also be responsible for selective vulnerability in HD. Leveraging a large dataset of 635 subjects from a multinational study, this paper tests various cell-autonomous and non-autonomous models that can explain HD topography. We test whether the expression patterns of implicated genes is sufficient to explain regional HD atrophy, or whether the network transmission of protein products is required to explain them. We find that network models are capable of predicting, to a high degree, observed atrophy in human subjects. Lastly, we propose a model of anterograde network transmission, and show that it is the most parsimonious yet most likely to explain observed atrophy patterns in HD. Collectively, these data indicate that pathology spread in HD may be mediated by the brain's intrinsic structural network organization. This is the first study to systematically and quantitatively test multiple hypotheses of pathology spread in living human subjects with HD.

Navigating the link between processing speed and network communication in the human brain

Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like 'navigation efficiency' have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan-Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the speed of cognitive processing and the structural constraints of the human brain network.

Longitudinal fixel-based analysis reveals restoration of white matter alterations following balance training in young brain-injured patients

BACKGROUND AND OBJECTIVES

Traumatic brain injury (TBI) is one of the leading causes of death and disability in children and adolescents. Young TBI patients suffer from gross motor deficits, such as postural control deficits, which can severely compromise their daily life activities. However, little attention has been devoted to uncovering the underlying white matter changes in response to training in TBI. In this study, we used longitudinal fixel-based analysis (FBA), an advanced diffusion imaging analysis technique, to investigate the effect of a balance training program on white matter fibre density and morphology in a group of young TBI patients.

METHODS

Young patients with moderate-to-severe TBI (N = 17, 10 females, mean age = 13 ± 3 years) and age-matched controls (N = 17) underwent a home-based balance training program. Diffusion MRI scans together with gross motor assessments, including the gross motor items of the Bruininks-Oseretsky Test of Motor Proficiency, the Activities-Specific Balance Confidence (ABC) Scale, and the Sensory Organization Test (SOT) were administered before and at completion of 8-weeks of training. We used FBA to compare microstructural differences in fibre density (FD), macrostructural (morphological) changes in fibre cross-section (FC), and the combined FD and FC (FDC) metric across the whole brain. We then performed a longitudinal analysis to test whether training restores the white matter in the regions found to be damaged before treatment.

RESULTS

Whole-brain fixel-based analysis revealed lower FD and FC in TBI patients compared to the control group across several commissural tracts, association fibres and projection fibres, with FD reductions of up to 50%. Following training, TBI patients showed a significant interaction effect between Group and Time for the SOT test, as well as significant increases in macrostructural white matter (i.e., FC & FDC) in left sensorimotor tracts. The amount of change in FC and FDC over time was, however, not associated with behavioural changes.

DISCUSSION

Our fixel-based findings identified both microstructural and macrostructural abnormalities in young TBI patients. The longitudinal results provide a deeper understanding of the neurobiological mechanisms underlying balance training, which will allow clinicians to make more effective treatment decisions in everyday clinical practice with brain-injured patients.

Diffuse axonal injury predicts neurodegeneration after moderate-severe traumatic brain injury

Traumatic brain injury is associated with elevated rates of neurodegenerative diseases such as Alzheimer's disease and chronic traumatic encephalopathy. In experimental models, diffuse axonal injury triggers post-traumatic neurodegeneration, with axonal damage leading to Wallerian degeneration and toxic proteinopathies of amyloid and hyperphosphorylated tau. However, in humans the link between diffuse axonal injury and subsequent neurodegeneration has yet to be established. Here we test the hypothesis that the severity and location of diffuse axonal injury predicts the degree of progressive post-traumatic neurodegeneration. We investigated longitudinal changes in 55 patients in the chronic phase after moderate-severe traumatic brain injury and 19 healthy control subjects. Fractional anisotropy was calculated from diffusion tensor imaging as a measure of diffuse axonal injury. Jacobian determinant atrophy rates were calculated from serial volumetric T1 scans as a measure of measure post-traumatic neurodegeneration. We explored a range of potential predictors of longitudinal post-traumatic neurodegeneration and compared the variance in brain atrophy that they explained. Patients showed widespread evidence of diffuse axonal injury, with reductions of fractional anisotropy at baseline and follow-up in large parts of the white matter. No significant changes in fractional anisotropy over time were observed. In contrast, abnormally high rates of brain atrophy were seen in both the grey and white matter. The location and extent of diffuse axonal injury predicted the degree of brain atrophy: fractional anisotropy predicted progressive atrophy in both whole-brain and voxelwise analyses. The strongest relationships were seen in central white matter tracts, including the body of the corpus callosum, which are most commonly affected by diffuse axonal injury. Diffuse axonal injury predicted substantially more variability in white matter atrophy than other putative clinical or imaging measures, including baseline brain volume, age, clinical measures of injury severity and microbleeds (>50% for fractional anisotropy versus <5% for other measures). Grey matter atrophy was not predicted by diffuse axonal injury at baseline. In summary, diffusion MRI measures of diffuse axonal injury are a strong predictor of post-traumatic neurodegeneration. This supports a causal link between axonal injury and the progressive neurodegeneration that is commonly seen after moderate/severe traumatic brain injury but has been of uncertain aetiology. The assessment of diffuse axonal injury with diffusion MRI is likely to improve prognostic accuracy and help identify those at greatest neurodegenerative risk for inclusion in clinical treatment trials.

Cortical morphometry and neural dysfunction in Huntington's disease: a review

Numerous neuroimaging techniques have been used to identify biomarkers of disease progression in Huntington's disease (HD). To date, the earliest and most sensitive of these is caudate volume; however, it is becoming increasingly evident that numerous changes to cortical structures, and their interconnected networks, occur throughout the course of the disease. The mechanisms by which atrophy spreads from the caudate to these cortical regions remains unknown. In this review, the neuroimaging literature specific to T1-weighted and diffusion-weighted magnetic resonance imaging is summarized and new strategies for the investigation of cortical morphometry and the network spread of degeneration in HD are proposed. This new avenue of research may enable further characterization of disease pathology and could add to a suite of biomarker/s of disease progression for patient stratification that will help guide future clinical trials.

Modelling the Anatomical Distribution of Neurological Events in COVID-19 Patients: A Systematic Review

Background

Neuropathology caused by the coronavirus disease 2019 (COVID-19) has been reported across several studies. The characterisation of the spatial distribution of these pathology remains critical to assess long and short-term neurological sequelae of COVID-19. To this end, Mathematical models can be used to characterise the location and aetiologies underlying COVID-19-related neuropathology.

Method

We performed a systematic review of the literature to quantify the locations of small neurological events identified with magnetic resonance imaging (MRI) among COVID-19 patients. Neurological events were localised into the Desikan-Killiany grey and white matter atlases. A mathematical network diffusion model was then used to test whether the spatial distribution of neurological events could be explained via a linear spread through the structural connectome of the brain.

Findings

We identified 35 articles consisting of 123 patients that assessed the spatial distribution of small neurological events among COVID-19 patients. Of these, 91 patients had grey matter changes, 95 patients had white matter changes and 72 patients had confirmed cerebral microbleeds. White matter events were observed within 14 of 42 white matter bundles from the IIT atlas. The highest proportions (26%) of events were observed within the bilateral corticospinal tracts. The splenium and middle of the corpus callosum were affected in 14% and 9% of the cases respectively. Grey matter events were spatially distributed in the 41 brain regions within the Desikan-Killiany atlas. The highest proportions (∼10%) of the events were observed in areas including the bilateral superior temporal, precentral, and lateral occipital cortices. Sub-cortical events were most frequently identified in the Pallidum. The application of a mathematical network diffusion model suggested that the spatial pattern of the small neurological events in COVID-19 can be modelled with a linear diffusion of spread from epicentres in the bilateral cerebellum and basal ganglia (Pearson's r =0.41, p <0.001, corrected).

Interpretation

To our knowledge, this is the first study to systematically characterise the spatial distribution of small neurological events in COVID-19 patients and test whether the spatial distribution of these events can be explained by a linear diffusion spread model. The location of neurological events is consistent with commonly identified neurological symptoms including alterations in conscious state among COVID-19 patients that require brain imaging. Given the prevalence and severity of these manifestations, clinicians should carefully monitor neurological symptoms within COVID-19 patients and their potential long-term sequelae .