Kharabian Masouleh, S. | Search bioRxiv

bioRxiv2017

Association of Peripheral Blood Pressure with Grey Matter Volume in 19- to 40-Year-Old Adults

ObjectiveTo test whether elevated blood pressure (BP) relates to grey matter volume (GMV) changes in young adults who had not previously been diagnosed as hypertensive (systolic BP (SBP)/diastolic BP (DBP)[≥]140/90 mmHg).\n\nMethodsWe associated BP with GMV from structural 3 Tesla T1-weighted MRI of 423 healthy adults between 19-40 years (mean age=27.7{+/-}5.3 years, 177 women, SBP/DBP=123.2/73.4{+/-}12.2/8.5 mmHg). Data originated from four previously unpublished cross-sectional studies conducted in Leipzig, Germany. We performed voxel-based morphometry on each study separately and combined results in image-based meta-analyses (IBMA) to assess cumulative effects across studies. Resting BP was assigned to one of four categories: (1) SBP<120 and DBP<80 mmHg, (2) SBP 120-129 or DBP 80-84 mmHg, (3) SBP 130-139 or DBP 85-89 mmHg, (4) SBP[≥]140 or DBP[≥]90 mmHg.\n\nResultsIBMA yielded: (a) lower regional GMV was correlated with higher peripheral BP; (b) lower GMV with higher BP when comparing individuals in sub-hypertensive categories 3 and 2, respectively, to those in category 1; (c) lower BP-related GMV was found in regions including hippocampus, amygdala, thalamus, frontal and parietal structures (e.g. precuneus).\n\nConclusionsBP[≥]120/80 mmHg was associated with lower GMV in regions that have previously been related to GM decline in older individuals with manifest hypertension. Our study shows that BP-associated GM alterations emerge continuously across the range of BP and earlier in adulthood than previously assumed. This suggests that treating hypertension or maintaining lower BP in early adulthood might be essential for preventing the pathophysiological cascade of asymptomatic cerebrovascular disease to symptomatic end-organ damage, such as stroke or dementia.

neuroscience↗

bioRxiv2016

Predicting brain-age from multimodal imaging data captures cognitive impairment

The disparity between the chronological age of an individual and their brain-age measured based on biological information has the potential to offer clinically-relevant biomarkers of neurological syndromes that emerge late in the lifespan. While prior brain-age prediction studies have relied exclusively on either structural or functional brain data, here we investigate how multimodal brainimaging data improves age prediction. Using cortical anatomy and whole-brain functional connectivity on a large adult lifespan sample (N = 2354, age 19-82), we found that multimodal data improves brain-based age prediction, resulting in a mean absolute prediction error of 4.29 years. Furthermore, we found that the discrepancy between predicted age and chronological age captures cognitive impairment. Importantly, the brain-age measure was robust to confounding effects: head motion did not drive brain-based age prediction and our models generalized reasonably to an independent dataset acquired at a different site (N = 475). Generalization performance was increased by training models on a larger and more heterogeneous dataset. The robustness of multimodal brain-age prediction to confounds, generalizability across sites, and sensitivity to clinically-relevant impairments, suggests promising future application to the early prediction of neurocognitive disorders.\n\nHighlightsO_LIBrain-based age prediction is improved with multimodal neuroimaging data.\nC_LIO_LIParticipants with cognitive impairment show increased brain aging.\nC_LIO_LIAge prediction models are robust to motion and generalize to independent datasets from other sites.\nC_LI

neuroscience↗