Biology subjects

Meschia, J. F.

Publications and source records attributed to Meschia, J. F..

3 recordsLinked to original sources

bioRxiv2025

Neuroanatomical Basis of Coma in Acute Ischemic Stroke

BackgroundAcute ischemic stroke (AIS) can lead to profound disturbances in consciousness, including coma, which is associated with poor prognosis and increased mortality. Clarifying the lesion patterns that precipitate loss of consciousness can refine pathophysiological models and guide prognosis. ObjectivesIn this study, we aim to identify the brain regions most commonly affected in comatose AIS and determine whether specific combinations of lesions are necessary and sufficient to produce coma. MethodsWe retrospectively analyzed 476 AIS patients (52 comatose) using diffusion-weighted imaging. Infarcts were automatically segmented, manually verified, and normalized to MNI space. Support vector regression lesion-symptom mapping (SVR-LSM) quantified voxel-wise associations with coma, controlling for lesion volume. To assess the necessity and sufficiency of lesion combinations, we employed permutation-based nested logistic regression models comparing all subsets of four anatomical predictors: brainstem, thalamus, cerebellum, and the rest of brain lesions. ResultsSVR-LSM revealed that coma was strongly associated with lesions involving the brainstem, thalamus, and cerebellum, whereas non-comatose patients exhibited predominantly cortical infarcts. Nested model comparisons showed that concurrent lesions to both the brainstem and thalamus were necessary and sufficient for coma. Additional involvement of the cerebellum or cerebral cortex did not improve predictive performance. ConclusionsComa after AIS results from a dual-node subcortical lesion pattern involving both the brainstem and thalamus. Cerebellar and cortical lesions, even when extensive, did not induce coma in the absence of the dual-brainstem and thalamic lesions. These observations emphasize the predominant role of lesion location over lesion volume in the pathogenesis of coma. They also support mechanistic models that position the brainstem and thalamic hubs as central to the neural circuitry underlying arousal. Furthermore, these findings delineate a specific anatomical substrate that may serve as a strategic target for circuit-based neuroprotective and neuromodulatory therapies.

neuroscience↗

bioRxiv2024

Plasma Proteomics of Genetic Brain Arteriosclerosis and Dementia Syndrome Identifies Signatures of Fibrosis, Angiogenesis, and Metabolic Alterations

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common monogenic form of vascular cognitive impairment and dementia. A genetic arteriolosclerotic disease, the molecular mechanisms driving vascular brain degeneration and decline remain unclear. With the goal of driving discovery of disease-relevant biological perturbations in CADASIL, we used machine learning approaches to extract proteomic disease signatures from large-scale proteomics generated from plasma collected from three distinct cohorts in US and Colombia: CADASIL-Early (N = 53), CADASIL-Late (N = 45), and CADASIL-Colombia (N = 71). We extracted molecular signatures with high predictive value for early and late-stage CADASIL and performed robust cross- and external-validation. We examined the biological and clinical relevance of our findings through pathway enrichment analysis and testing of associations with clinical outcomes. Our study represents a model for unbiased discovery of molecular signatures and disease biomarkers, combining non-invasive plasma proteomics with clinical data. We report on novel disease-associated molecular signatures for CADASIL, derived from the accessible plasma proteome, with relevance to vascular cognitive impairment and dementia.

neuroscience↗

bioRxiv2019

White Matter Hyperintensity Quantification in Large-Scale Clinical Acute Ischemic Stroke Cohorts - The MRI-GENIE Study

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95) and Pearson correlation of total brain volume (r=0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N=2783) and identify a decrease in total brain volume of -2.4cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2,533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.

neuroscience↗