Distinct clinical phenotypes and their neuroanatomic correlates in chronic traumatic brain injury

Abstract

Accumulating evidence of heterogeneous long-term outcomes after traumatic brain injury (TBI) has challenged longstanding approaches to TBI outcome classification that are largely based on global functioning. A lack of studies with clinical and biomarker data from individuals living with chronic (>1 year post-injury) TBI has precluded refinement of long-term outcome classification ontology. Multimodal data in well-characterized TBI cohorts is required to understand the clinical phenotypes and biological underpinnings of persistent symptoms in the chronic phase of TBI. The present cross-sectional study leveraged data from 281 participants with chronic complicated mild-to-severe TBI in the Late Effects of Traumatic Brain Injury (LETBI) Study. Our primary objective was to develop and validate clinical phenotypes using data from 41 TBI measures spanning a comprehensive cognitive battery, motor testing, and assessments of mood, health, and functioning. We performed a 70/30% split of training (n=195) and validation (n=86) datasets and performed principal components analysis to reduce the dimensionality of data. We used Hierarchical Cluster Analysis on Principal Components with k-means consolidation to identify clusters, or phenotypes, with shared clinical features. Our secondary objective was to investigate differences in brain volume in seven cortical networks across clinical phenotypes in the subset of 168 participants with brain MRI data. We performed multivariable linear regression models adjusted for age, age-squared, sex, scanner, injury chronicity, injury severity, and training/validation set. In the training/validation sets, we observed four phenotypes: 1) mixed cognitive and mood/behavioral deficits (11.8%; 15.1% in the training and validation set, respectively); 2) predominant cognitive deficits (20.5%; 23.3%); 3) predominant mood/behavioral deficits (27.7%; 22.1%); and 4) few deficits across domains (40%; 39.5%). The predominant cognitive deficit phenotype had lower cortical volumes in executive control, dorsal attention, limbic, default mode, and visual networks, relative to the phenotype with few deficits. The predominant mood/behavioral deficit phenotype had lower volumes in dorsal attention, limbic, and visual networks, compared to the phenotype with few deficits. Contrary to expectation, we did not detect differences in network-specific volumes between the phenotypes with mixed deficits versus few deficits. We identified four clinical phenotypes and their neuroanatomic correlates in a well-characterized cohort of individuals with chronic TBI. TBI phenotypes defined by symptom clusters, as opposed to global functioning, could inform clinical trial stratification and treatment selection. Individuals with predominant cognitive and mood/behavioral deficits had reduced cortical volumes in specific cortical networks, providing insights into sensitive, though not specific, candidate imaging biomarkers of clinical symptom phenotypes after chronic TBI and potential targets for intervention.

Keywords: Machine Learning; Neuroimaging; Phenotyping; Traumatic brain injury.

Fig 1:. Study Flow Diagram.

We provide details on the derivation of the phenotypic (primary objective) and neuroimaging analytic (secondary objective) samples.

Fig 2:. TBI Phenotyping Analysis Pipeline.

Includes pre-processing steps for clinical and neuroimaging MRI data. Last two steps were among the subgroup with MRI data. Visuals in the left column are not based on data from the present study but meant as theoretical depictions.

Fig 3:. Hierarchical cluster group assignment (training set).

Hierarchical cluster group assignment in the training set. The results are based on a Hierarchical Cluster on Principal Components (HCPC). Here, each participant in the training sample is depicted in the x-y coordinate space based on their PC1 vs. PC2 scores. The cluster membership of each participant is color coded.

Fig 4:. Heat map characterizing average values of neurobehavioral measures by cluster assignment (training set; n=195).

Heat map characterizing average values of neurobehavioral measures by cluster assignment in the training set. The measures have all been transformed such that darker colors represent greater impairment, and lighter colors represent less impairment. Based on the findings, we have assigned the following qualitative descriptors of each cluster: Cluster 1: Mixed deficits; Cluster 2) Predominant cognitive deficits; Cluster 3: Predominant mood and behavioral deficits; Cluster 4: Relatively few deficits.

Fig 5:. Dot plot of model-based estimated marginal mean cortical volume by cluster.

Model-based estimated marginal mean cortical volume (mm³) by cluster. The estimated marginal mean corresponds to the least-squares mean at each level of the cluster, adjusting for model covariates (age, age-squared, sex, scanner type, injury severity, injury chronicity, and training set). All volumes were standardized by network to have a mean of 0 and standard deviation of 1 for the sample. Therefore, values below 0 by cluster can be interpreted as below average volumes, and above 0 can be interpreted as above average in the sample.