1Multimodal imaging enables sensitive measures of the architecture and integrity of the human 2 brain, but the high-dimensional nature of advanced brain imaging features poses inherent 3 challenges for the analyses and interpretations. Multivariate age prediction reduces the 4 dimensionality to one biologically informative summary measure with potential for assessing 5 deviations from normal lifespan trajectories. A number of studies documented remarkably 6 accurate age prediction, but the differential age trajectories and the cognitive sensitivity of 7 distinct brain tissue classes have to a lesser extent been characterized. 8Exploring differential brain age models driven by tissue-specific classifiers provides a 9 hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain 10 biology. We trained machine-learning models to estimate brain age using various 11 combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of 12 white matter microstructure in 612 healthy controls aged 18-87 years. To compare the tissue-13 specific brain ages and their cognitive sensitivity we applied each of the 11 models in an 14 independent and cognitively well-characterized sample (n=265, 20-88 years). Correlations 15 between true and estimated age in our test sample were highest for the most comprehensive 16 brain morphometry (r=0.83, CI:0.78-0.86) and white matter microstructure (r=0.79, CI:0.74-17 0.83) models, confirming sensitivity and generalizability. The deviance from the 18 chronological age were sensitive to performance on several cognitive tests for various models, 19including spatial Stroop and symbol coding, indicating poorer performance in individuals 20 with an over-estimated age. Tissue-specific brain age models provide sensitive measures of 21 brain integrity, with implications for the study of a range of brain disorders. 22 23 24 25 26 27 28 29 30 31 32 33 TISSUE-SPECIFIC BRAIN AGE PREDICTION 3 multiple sclerosis (Kaufmann et al. 2018) and cardiovascular risk factors (Franke et al. 2013; 58 Habes et al. 2016). Indeed, while individuals with brains estimated as younger than their 59 chronological age have been shown to be more physically active (Steffener et al. 2016), 60 augmented brain age has been associated with poor health (Ronan et al. 2016), poor cognitive 61 performance (Liem et al. 2017), early neurodegenerative diseases (Gaser et al. 2013), and 62 increased mortality (Cole et al. 2017a).Less is known about the regional heterogeneity, i.e. to 63 which degree different brain regions, systems or compartments show differential aging 64 patterns and sensitivity to cognitive performance. Brain gray and white matter compartments, 65 which can be assessed and quantified using T1-weighted imaging and diffusion tensor 66 TISSUE-SPECIFIC BRAIN AGE PREDICTION 4 imaging (DTI), respectively, comprise distinct tissue classes with largely differential 67 biological and environmental modifiers and age trajectories (Bennett et al. 2010; Cao et al. 68 2017; Fje...
The hippocampus is a heterogeneous structure, comprising histologically distinguishable subfields. These subfields are differentially involved in memory consolidation, spatial navigation and pattern separation, complex functions often impaired in individuals with brain disorders characterized by reduced hippocampal volume, including Alzheimer's disease (AD) and schizophrenia. Given the structural and functional heterogeneity of the hippocampal formation, we sought to characterize the subfields' genetic architecture. T1weighted brain scans (n=21297, 16 cohorts) were processed with the hippocampal subfields algorithm in FreeSurfer v6.0. We ran a genome-wide association analysis on each subfield, covarying for total hippocampal volume. We further calculated the single nucleotide polymorphism (SNP)-based heritability of twelve subfields, as well as their genetic correlation with each other, with other structural brain features, and with AD and schizophrenia. All outcome measures were corrected for age, sex, and intracranial volume. We found 15 unique genome-wide significant loci across six subfields, of which eight had not been previously linked to the hippocampus. Top SNPs were mapped to genes associated with neuronal differentiation, locomotor behaviour, schizophrenia and AD. The volumes of all the subfields were estimated to be heritable (h 2 from .14 to .27, all p< 1x10 -16 ) and clustered together based on their genetic correlations compared to other structural brain features. There was also evidence of genetic overlap of subicular subfield volumes with schizophrenia. We conclude that hippocampal subfields have partly distinct genetic determinants associated with specific biological processes and traits. Taking into account this specificity may increase our understanding of hippocampal neurobiology and associated pathologies.
words)Post-stroke fatigue (PSF) is a prevalent symptom among stroke patients. Its symptom burden is pervasive, persistent and associated with poor rehabilitation outcomes, though its mechanisms are poorly understood. Many patients with PSF experience cognitive difficulties, but studies aiming to identify cognitive correlates of PSF have been largely inconclusive. In contrast to conventional neuropsychological assessment, computational modeling of behavioral data allows for a dissection of specific cognitive processes associated with group or individual differences in fatigue. With the aim to zero in on the cognitive phenotype of PSF, we fitted a hierarchical drift diffusion model (hDDM) to response time data from Attention Network Test (ANT) obtained from 53 chronic stroke patients. The computational model accurately reconstructed the individual level response time distributions in the different ANT conditions, and hDDM regressions identified an interaction between trial number and fatigue symptoms on non-decision time, intuitively indicating that the cognitive phenotype of fatigue entails an increased vulnerability to sustained attentional effort. These novel results demonstrate the significance of considering the sustained nature of cognitive effort when defining the cognitive phenotype of post-stroke fatigue, and suggest that the use of computational approaches offers a further characterization of the specific processes underlying observed behavioral differences.
Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. By combining longitudinal MRI-based brain morphometry and brain age estimation using machine learning, we tested the hypothesis that MS patients have higher brain age relative to chronological age than healthy controls (HC) and that longitudinal rate of brain aging in MS patients is associated with clinical course.Seventy-six MS patients, 71 % females and mean age 34.8 years (range 21-49) at inclusion, were examined with brain MRI at three time points with a mean total follow up period of 4.4 years. A machine learning model was applied on an independent training set of 3208 HC, estimating individual brain age and calculating the difference between estimated brain age and chronological age, termed brain age gap (BAG). We also assessed the longitudinal change rate in BAG in MS individuals. We used additional cross-sectional MRI data from 235 HC for case-control comparison.MS patients showed increased BAG (4.4 ±6.6 years) compared to HC (Cohen's D = 0.69, p = 4.0 x 10 -6 ). Longitudinal estimates of BAG in MS patients suggested an accelerated rate of brain aging corresponding to an annual increase of 0.41 (±1.23) years compared to chronological aging for the MS patients (p = 0.008).On average, patients with MS have significantly higher BAG compared to HC and accelerated rate of brain aging compared to chronological aging. Brain age estimation represents a promising method for evaluation of brain changes in MS, with potential for predicting future outcome and guide treatment.
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