Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions.

For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types' contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in 13 neurodegenerative conditions, including early- and late-onset Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and 3 clinical variants of frontotemporal lobar degeneration (behavioral variant, semantic and non-fluent primary progressive aphasia) along with associated three-repeat and four-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorder pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.

MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics.

Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.

Single-nucleus RNA velocity reveals critical synaptic and cell-cycle dysregulations in neuropathologically confirmed Alzheimer's disease.

Typical differential single-nucleus gene expression (snRNA-seq) analyses in Alzheimer's disease (AD) provide fixed snapshots of cellular alterations, making the accurate detection of temporal cell changes challenging. To characterize the dynamic cellular and transcriptomic differences in AD neuropathology, we apply the novel concept of RNA velocity to the study of single-nucleus RNA from the cortex of 60 subjects with varied levels of AD pathology. RNA velocity captures the rate of change of gene expression by comparing intronic and exonic sequence counts. We performed differential analyses to find the significant genes driving both cell type-specific RNA velocity and expression differences in AD, extensively compared these two transcriptomic metrics, and clarified their associations with multiple neuropathologic traits. The results were cross-validated in an independent dataset. Comparison of AD pathology-associated RNA velocity with parallel gene expression differences reveals sets of genes and molecular pathways that underlie the dynamic and static regimes of cell type-specific dysregulations underlying the disease. Differential RNA velocity and its linked progressive neuropathology point to significant dysregulations in synaptic organization and cell development across cell types. Notably, most of the genes underlying this synaptic dysregulation showed increased RNA velocity in AD subjects compared to controls. Accelerated cell changes were also observed in the AD subjects, suggesting that the precocious depletion of precursor cell pools might be associated with neurodegeneration. Overall, this study uncovers active molecular drivers of the spatiotemporal alterations in AD and offers novel insights towards gene- and cell-centric therapeutic strategies accounting for dynamic cell perturbations and synaptic disruptions.

The role of the primary sensorimotor system in generalized epilepsy: Evidence from the cerebello-cerebral functional integration.

The interaction between cerebellum and cerebrum participates widely in function from motor processing to high-level cognitive and affective processing. Because of the motor symptom, idiopathic generalized epilepsy (IGE) patients with generalized tonic-clonic seizure have been recognized to associate with motor abnormalities, but the functional interaction in the cerebello-cerebral circuit is still poorly understood. Resting-state functional magnetic resonance imaging data were collected for 101 IGE patients and 106 healthy controls. The voxel-based functional connectivity (FC) between cerebral cortex and the cerebellum was contacted. The functional gradient and independent components analysis were applied to evaluate cerebello-cerebral functional integration on the voxel-based FC. Cerebellar motor components were further linked to cerebellar gradient. Results revealed cerebellar motor functional modules were closely related to cerebral motor components. The altered mapping of cerebral motor components to cerebellum was observed in motor module in patients with IGE. In addition, patients also showed compression in cerebello-cerebral functional gradient between motor and cognition modules. Interestingly, the contribution of the motor components to the gradient was unbalanced between bilateral primary sensorimotor components in patients: the increase was observed in cerebellar cognitive module for the dominant hemisphere primary sensorimotor, but the decrease was found in the cerebellar cognitive module for the nondominant hemisphere primary sensorimotor. The present findings suggest that the cerebral primary motor system affects the hierarchical architecture of cerebellum, and substantially contributes to the functional integration evidence to understand the motor functional abnormality in IGE patients.

Resolving inter-regional communication capacity in the human connectome.

Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.

A machine learning-based score for precise echocardiographic assessment of cardiac remodelling in hypertensive young adults.

Aims: Accurate staging of hypertension-related cardiac changes, before the development of significant left ventricular hypertrophy, could help guide early prevention advice. We evaluated whether a novel semi-supervised machine learning approach could generate a clinically meaningful summary score of cardiac remodelling in hypertension. Methods and results: A contrastive trajectories inference approach was applied to data collected from three UK studies of young adults. Low-dimensional variance was identified in 66 echocardiography variables from participants with hypertension (systolic ≥160 mmHg) relative to a normotensive group (systolic < 120 mmHg) using a contrasted principal component analysis. A minimum spanning tree was constructed to derive a normalized score for each individual reflecting extent of cardiac remodelling between zero (health) and one (disease). Model stability and clinical interpretability were evaluated as well as modifiability in response to a 16-week exercise intervention. A total of 411 young adults (29 ± 6 years) were included in the analysis, and, after contrastive dimensionality reduction, 21 variables characterized >80% of data variance. Repeated scores for an individual in cross-validation were stable (root mean squared deviation = 0.1 ± 0.002) with good differentiation of normotensive and hypertensive individuals (area under the receiver operating characteristics 0.98). The derived score followed expected hypertension-related patterns in individual cardiac parameters at baseline and reduced after exercise, proportional to intervention compliance (P = 0.04) and improvement in ventilatory threshold (P = 0.01). Conclusion: A quantitative score that summarizes hypertension-related cardiac remodelling in young adults can be generated from a computational model. This score might allow more personalized early prevention advice, but further evaluation of clinical applicability is required.

Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease.

Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.

Revealing the combined roles of Aß and tau in Alzheimer's disease via a pathophysiological activity decoder.

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, evidence in humans remains scarce, necessitating improved non-invasive techniques and integrative mechanistic models. Here, we introduce personalized brain activity models incorporating functional MRI, amyloid-ß (Aß) and tau-PET from AD-related participants (N=132). Within the model assumptions, electrophysiological activity is mediated by toxic protein deposition. Our integrative subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aß and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP). Furthermore, our results reproduce hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aß-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

Fornix degeneration in risk factors of Alzheimer's disease, possible trigger of cognitive decline.

Risk factors of late-onset Alzheimer's disease (AD) such as aging, type 2 diabetes, obesity, heart failure, and traumatic brain injury can facilitate the appearance of cognitive decline and dementia by triggering cerebrovascular pathology and neuroinflammation. White matter (WM) microstructure and function are especially vulnerable to these conditions. Microstructural WM changes, assessed with diffusion weighted magnetic resonance imaging, can already be detected at preclinical stages of AD, and in the presence of the aforementioned risk factors. Particularly, the limbic system and cortico-cortical association WM tracts, which myelinate late during brain development, degenerate at the earliest stages. The fornix, a C-shaped WM tract that originates from the hippocampus, is one of the limbic tracts that shows early microstructural changes. Fornix integrity is necessary for ensuring an intact executive function and memory performance. Thus, a better understanding of the mechanisms that cause fornix degeneration is critical in the development of therapeutic strategies aiming to prevent cognitive decline in populations at risk. In this literature review, i) we deepen the idea that partial loss of forniceal integrity is an early event in AD, ii) we describe the role that common risk factors of AD can play in the degeneration of the fornix, and iii) we discuss some potential cellular and physiological mechanisms of WM degeneration in the scenario of cerebrovascular disease and inflammation.

CAPTURE ALS: the comprehensive analysis platform to understand, remedy and eliminate ALS.

The absence of disease modifying treatments for amyotrophic lateral sclerosis (ALS) is in large part a consequence of its complexity and heterogeneity. Deep clinical and biological phenotyping of people living with ALS would assist in the development of effective treatments and target specific biomarkers to monitor disease progression and inform on treatment efficacy. The objective of this paper is to present the Comprehensive Analysis Platform To Understand Remedy and Eliminate ALS (CAPTURE ALS), an open and translational platform for the scientific community currently in development. CAPTURE ALS is a Canadian-based platform designed to include participants' voices in its development and through execution. Standardized methods will be used to longitudinally characterize ALS patients and healthy controls through deep clinical phenotyping, neuroimaging, neurocognitive and speech assessments, genotyping and multisource biospecimen collection. This effort plugs into complementary Canadian and international initiatives to share common resources. Here, we describe in detail the infrastructure, operating procedures, and long-term vision of CAPTURE ALS to facilitate and accelerate translational ALS research in Canada and beyond.

Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer's disease progression and heterogeneity.

Alzheimer's disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.

Neuroimaging signatures predicting motor improvement to focused ultrasound subthalamotomy in Parkinson's disease.

Subthalamotomy using transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) is a novel and promising treatment for Parkinson's Disease (PD). In this study, we investigate if baseline brain imaging features can be early predictors of tcMRgFUS-subthalamotomy efficacy, as well as which are the post-treatment brain changes associated with the clinical outcomes. Towards this aim, functional and structural neuroimaging and extensive clinical data from thirty-five PD patients enrolled in a double-blind tcMRgFUS-subthalamotomy clinical trial were analyzed. A multivariate cross-correlation analysis revealed that the baseline multimodal imaging data significantly explain (P < 0.005, FWE-corrected) the inter-individual variability in response to treatment. Most predictive features at baseline included neural fluctuations in distributed cortical regions and structural integrity in the putamen and parietal regions. Additionally, a similar multivariate analysis showed that the population variance in clinical improvements is significantly explained (P < 0.001, FWE-corrected) by a distributed network of concurrent functional and structural brain changes in frontotemporal, parietal, occipital, and cerebellar regions, as opposed to local changes in very specific brain regions. Overall, our findings reveal specific quantitative brain signatures highly predictive of tcMRgFUS-subthalamotomy responsiveness in PD. The unanticipated weight of a cortical-subcortical-cerebellar subnetwork in defining clinical outcome extends the current biological understanding of the mechanisms associated with clinical benefits.

Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease.

Amyloid-beta deposition is one of the hallmark pathologies in both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, the latter of which is caused by mutations in genes involved in amyloid-beta processing. Despite amyloid-beta deposition being a centrepiece to both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, some differences between these Alzheimer's disease subtypes have been observed with respect to the spatial pattern of amyloid-beta. Previous work has shown that the spatial pattern of amyloid-beta in individuals spanning the sporadic Alzheimer's disease spectrum can be reproduced with high accuracy using an epidemic spreading model which simulates the diffusion of amyloid-beta across neuronal connections and is constrained by individual rates of amyloid-beta production and clearance. However, it has not been investigated whether amyloid-beta deposition in the rarer autosomal-dominant Alzheimer's disease can be modelled in the same way, and if so, how congruent the spreading patterns of amyloid-beta across sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease are. We leverage the epidemic spreading model as a data-driven approach to probe individual-level variation in the spreading patterns of amyloid-beta across three different large-scale imaging datasets (2 sporadic Alzheimer's disease, 1 autosomal-dominant Alzheimer's disease). We applied the epidemic spreading model separately to the Alzheimer's Disease Neuroimaging initiative (n = 737), the Open Access Series of Imaging Studies (n = 510) and the Dominantly Inherited Alzheimer's Network (n = 249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately and further identified the most likely subject-specific epicentre of amyloid-beta spread. Using epicentres defined in previous work in sporadic Alzheimer's disease, the epidemic spreading model provided moderate prediction of the regional pattern of amyloid-beta deposition across all three datasets. We further find that, whilst the most likely epicentre for most amyloid-beta-positive subjects overlaps with the default mode network, 13% of autosomal-dominant Alzheimer's disease individuals were best characterized by a striatal origin of amyloid-beta spread. These subjects were also distinguished by being younger than autosomal-dominant Alzheimer's disease subjects with a default mode network amyloid-beta origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most autosomal-dominant Alzheimer's disease patients express amyloid-beta spreading patterns similar to those of sporadic Alzheimer's disease, but that there may be a subset of autosomal-dominant Alzheimer's disease patients with a separate, striatal phenotype.

Multivariate genomic and transcriptomic determinants of imaging-derived personalized therapeutic needs in Parkinson's disease.

Due to the marked interpersonal neuropathologic and clinical heterogeneity of Parkinson's disease (PD), current interventions are not personalized and fail to benefit all patients. Furthermore, we continue to lack well-established methods and clinical tests to tailor interventions at the individual level in PD. Here, we identify the genetic determinants of individual-tailored treatment needs derived from longitudinal multimodal neuroimaging data in 294 PD patients (PPMI data). Advanced multivariate statistical analysis revealed that both genomic and blood transcriptomic data significantly explain (P < 0.01, FWE-corrected) the interindividual variability in therapeutic needs associated with dopaminergic, functional, and structural brain reorganization. We confirmed a high overlap between the identified highly predictive molecular pathways and determinants of levodopa clinical responsiveness, including well-known (Wnt signaling, angiogenesis, dopaminergic activity) and recently discovered (immune markers, gonadotropin-releasing hormone receptor) pathways/components. In addition, the observed strong correspondence between the identified genomic and baseline-transcriptomic determinants of treatment needs/response supports the genome's active role at the time of patient evaluation (i.e., beyond individual genetic predispositions at birth). This study paves the way for effectively combining genomic, transcriptomic and neuroimaging data for implementing successful individually tailored interventions in PD and extending our pathogenetic understanding of this multifactorial and heterogeneous disorder.

Data-driven staging of genetic frontotemporal dementia using multi-modal MRI.

Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high-dimensional large-scale population datasets to obtain individual scores of disease stage. We used cross-sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting-state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI-obtained disease scores to the estimated years to onset (age-mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre-dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data-driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics.

Impact of long- and short-range fibre depletion on the cognitive deficits of fronto-temporal dementia.

Recent studies suggest a framework where white-matter (WM) atrophy plays an important role in fronto-temporal dementia (FTD) pathophysiology. However, these studies often overlook the fact that WM tracts bridging different brain regions may have different vulnerabilities to the disease and the relative contribution of grey-matter (GM) atrophy to this WM model, resulting in a less comprehensive understanding of the relationship between clinical symptoms and pathology. Using a common factor analysis to extract a semantic and an executive factor, we aimed to test the relative contribution of WM and GM of specific tracts in predicting cognition in the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI). We found that semantic symptoms were mainly dependent on short-range WM fibre disruption, while damage to long-range WM fibres was preferentially associated to executive dysfunction with the GM contribution to cognition being predominant for local processing. These results support the importance of the disruption of specific WM tracts to the core cognitive symptoms associated with FTD. As large-scale WM tracts, which are particularly vulnerable to vascular disease, were highly associated with executive dysfunction, our findings highlight the importance of controlling for risk factors associated with deep WM disease, such as vascular risk factors, in patients with FTD in order not to potentiate underlying executive dysfunction.

Personalized brain models identify neurotransmitter receptor changes in Alzheimer's disease.

Alzheimer's disease involves many neurobiological alterations from molecular to macroscopic spatial scales, but we currently lack integrative, mechanistic brain models characterizing how factors across different biological scales interact to cause clinical deterioration in a way that is subject-specific or personalized. As important signalling molecules and mediators of many neurobiological interactions, neurotransmitter receptors are promising candidates for identifying molecular mechanisms and drug targets in Alzheimer's disease. We present a neurotransmitter receptor-enriched multifactorial brain model, which integrates spatial distribution patterns of 15 neurotransmitter receptors from post-mortem autoradiography with multiple in vivo neuroimaging modalities (tau, amyloid-ß and glucose PET, and structural, functional and arterial spin labelling MRI) in a personalized, generative, whole-brain formulation. In a heterogeneous aged population (n = 423, ADNI data), models with personalized receptor-neuroimaging interactions showed a significant improvement over neuroimaging-only models, explaining about 70% (±20%) of the variance in longitudinal changes to the six neuroimaging modalities. In Alzheimer's disease patients (n = 25, ADNI data), receptor-imaging interactions explained up to 39.7% (P < 0.003, family-wise error-rate-corrected) of inter-individual variability in cognitive deterioration, via an axis primarily affecting executive function. Notably, based on their contribution to the clinical severity in Alzheimer's disease, we found significant functional alterations to glutamatergic interactions affecting tau accumulation and neural activity dysfunction and GABAergic interactions concurrently affecting neural activity dysfunction, amyloid and tau distributions, as well as significant cholinergic receptor effects on tau accumulation. Overall, GABAergic alterations had the largest effect on cognitive impairment (particularly executive function) in our Alzheimer's disease cohort (n = 25). Furthermore, we demonstrate the clinical applicability of this approach by characterizing subjects based on individualized 'fingerprints' of receptor alterations. This study introduces the first robust, data-driven framework for integrating several neurotransmitter receptors, multimodal neuroimaging and clinical data in a flexible and interpretable brain model. It enables further understanding of the mechanistic neuropathological basis of neurodegenerative progression and heterogeneity, and constitutes a promising step towards implementing personalized, neurotransmitter-based treatments.

Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury.

Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the "one-treatment fits all" approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex "-omics" data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.