Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry

Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18–87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20–88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78–0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74–0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders

Exploring the associations between physical activity level, cognitive performance, and response to computerized cognitive training among chronic stroke patients

Abstract Background Post‐stroke attentional and working memory deficits are common and represent relevant predictors of long‐term functional recovery and outcome. The individual responses to cognitive rehabilitation and interventions vary between patients and are influenced by multiple factors. Recently, a link between the level of engagement in physical activities and cognitive rehabilitation has been suggested. However, few previous studies have tested the predictive value of physical activity on cognitive performance and response to cognitive training among chronic stroke patients. There is also a lack of knowledge concerning the prognostic value of index stroke characteristics on physical activity in chronic phase. Method In this cross‐sectional and longitudinal study, including stroke survivors suffering mild‐to‐moderate strokes (n = 52, mean age = 70 years), we used Bayesian regression to test the association between cognitive performance and response to a 3‐week intervention with a commonly used computerized cognitive training (CCT) system and baseline physical activity level measured with International Physical Activity Questionnaire. We also tested the association between physical activity level in chronic phase and stroke characteristics, including stroke severity (National Institutes of Health Stroke Scale), ischemic stroke etiology (Trial of Org 10172 in Acute Stroke Treatment), and stroke location (n = 66, mean age = 68 years). For descriptive purposes, we included 104 sex‐ and age‐matched healthy controls (mean age = 69 years). Results The analyses revealed anecdotal evidence of a positive association between overall cognitive performance and daily minutes of sedentary behavior, indicating that better cognitive performance was associated with more daily hours of sitting still. We found no support for an association between cognitive performance and response to CCT with activity level. In addition, the analysis showed group differences in sedentary behavior between patients with small‐vessel disease (n = 20) and cardioembolism (n = 7), indicating more sedentary behavior in patients with small‐vessel disease. There was no further support for a predictive value of index stroke characteristics on physical activity level. Conclusion The results do not support that baseline physical activity level is a relevant predictor of the overall performance or response to CCT in this sample of chronic stroke patients. Similarly, the analyses revealed little evidence for an association between index stroke characteristics and future activity level in patients surviving mild‐to‐moderate stroke

No add‐on effect of tDCS on fatigue and depression in chronic stroke patients: A randomized sham‐controlled trial combining tDCS with computerized cognitive training

Abstract Background Fatigue and emotional distress rank high among self‐reported unmet needs in life after stroke. Transcranial direct current stimulation (tDCS) may have the potential to alleviate these symptoms for some patients, but the acceptability and effects for chronic stroke survivors need to be explored in randomized controlled trials. Methods Using a randomized sham‐controlled parallel design, we evaluated whether six sessions of 1 mA tDCS (anodal over F3, cathodal over O2) combined with computerized cognitive training reduced self‐reported symptoms of fatigue and depression. Among the 74 chronic stroke patients enrolled at baseline, 54 patients completed the intervention. Measures of fatigue and depression were collected at five time points spanning a 2 months period. Results While symptoms of fatigue and depression were reduced during the course of the intervention, Bayesian analyses provided evidence for no added beneficial effect of tDCS. Less severe baseline symptoms were associated with higher performance improvement in select cognitive tasks, and study withdrawal was higher in patients with more fatigue and younger age. Time‐resolved symptom analyses by a network approach suggested higher centrality of fatigue items (except item 1 and 2) than depression items. Conclusion The results reveal no add‐on effect of tDCS on fatigue or depression but support the notion of fatigue as a relevant clinical symptom with possible implications for treatment adherence and response

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Background Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. Methods We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18–94 years of age) and applied the models to the test sets including 648 patients with SZ (18–66 years of age), 185 patients with BD (18–64 years of age), and 990 HC subjects (17–68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen’s d = −0.29) and patients with BD (Cohen’s d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy–based models showed larger group differences than the models based on other DTI-derived metrics. Conclusions Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners

Genetic control of variability in subcortical and intracranial volumes

Sensitivity to external demands is essential for adaptation to dynamic environments, but comes at the cost of increased risk of adverse outcomes when facing poor environmental conditions. Here, we apply a novel methodology to perform genome-wide association analysis of mean and variance in ten key brain features (accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, intracranial volume, cortical surface area, and cortical thickness), integrating genetic and neuroanatomical data from a large lifespan sample (n = 25,575 individuals; 8-89 years, mean age 51.9 years). We identify genetic loci associated with phenotypic variability in thalamus volume and cortical thickness. The variance-controlling loci involved genes with a documented role in brain and mental health and were not associated with the mean anatomical volumes. This proof-of-principle of the hypothesis of a genetic regulation of brain volume variability contributes to establishing the genetic basis of phenotypic variance (i.e., heritability), allows identifying different degrees of brain robustness across individuals, and opens new research avenues in the search for mechanisms controlling brain and mental health

Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study

BACKGROUND: Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. METHODS: We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. RESULTS: Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. CONCLUSIONS: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners

Publisher Correction: Common brain disorders are associated with heritable patterns of apparent aging of the brain (Nature Neuroscience, (2019), 22, 10, (1617-1623), 10.1038/s41593-019-0471-7)

In the version of this article initially published, spaces were missing in the names of Stephanie Le Hellard and Pasquale Di Carlo. The errors have been corrected in the HTML and PDF versions of the article