Biology subjects

Celius, E. G.

Publications and source records attributed to Celius, E. G..

3 recordsLinked to original sources

bioRxiv2019

The genetic architecture of human brainstem structures and their involvement in common brain disorders

Brainstem regions support critical bodily functions, yet their genetic architectures and involvement in brain disorders remain understudied. Here, we examined volumes of brainstem structures using magnetic resonance imaging in 43,353 individuals. In 27,034 genotyped healthy participants, we identified 16 genetic loci associated with whole brainstem volume and 10, 23, 3, and 9 loci associated with volumes of the midbrain, pons, superior cerebellar peduncle, and medulla oblongata, respectively. These loci were mapped to 305 genes, including genes linked to brainstem development and common brain disorders. We detected genetic overlap between the brainstem volumes and eight psychiatric and neurological disorders. Using imaging data from 16,319 additional individuals, we observed differential volume alterations in schizophrenia, bipolar disorder, multiple sclerosis, mild cognitive impairment, dementia, and Parkinsons disease. Together, our results provide new insights into the genetic underpinnings of brainstem structures and support their involvement in common brain disorders.

neuroscience↗

bioRxiv2019

Brain age estimation is a sensitive marker of processing speed in the early course of multiple sclerosis

Background and objectivesCognitive deficits in MS are common, also early in the disease course. We aimed to identify if estimated brain age from MRI could serve as an imaging marker for early cognitive symptoms in a longitudinal MS study.\n\nMethodsA group of 76 MS patients (mean age 34 years, 71% females, 96% relapsing-remitting) was examined 1, 2 and 5 years after diagnosis. A machine-learning model using Freesurfer-processed T1-weighted brain MRI data from 3208 healthy controls, was applied to develop a prediction model for brain age. The difference between estimated and chronological brain age was calculated (brain age gap). Tests of memory, attention and executive functions were performed. Associations between brain age gap and cognitive performance were assessed using linear mixed effects (LME) models and corrected for multiple testing.\n\nResultsLME models revealed a significant association between the Color Naming condition of Color-Word Interference Test and brain age gap (t=2.84, p=0.005).\n\nConclusionsIn this study, decreased information processing speed correlated with increased brain age gap. Our findings suggest that brain age estimation using MRI provides a useful semi-automated approach applying machine learning for individual level brain phenotyping and correlates with information processing speed in the early course of MS.

neuroscience↗

bioRxiv2018

Genetics of brain age suggest an overlap with common brain disorders

Numerous genetic and environmental factors contribute to psychiatric disorders and other brain disorders. Common risk factors likely converge on biological pathways regulating the optimization of brain structure and function across the lifespan. Here, using structural magnetic resonance imaging and machine learning, we estimated the gap between brain age and chronological age in 36,891 individuals aged 3 to 96 years, including individuals with different brain disorders. We show that several disorders are associated with accentuated brain aging, with strongest effects in schizophrenia, multiple sclerosis and dementia, and document differential regional patterns of brain age gaps between disorders. In 16,269 healthy adult individuals, we show that brain age gap is heritable with a polygenic architecture overlapping those observed in common brain disorders. Our results identify brain age gap as a genetically modulated trait that offers a window into shared and distinct mechanisms in different brain disorders.

neuroscience↗