Biology subjects

Penninx, B.

Publications and source records attributed to Penninx, B..

5 recordsLinked to original sources

bioRxiv2023

Accelerated brain change in healthy adults is associated with genetic risk for Alzheimer's disease and uncovers adult lifespan memory decline

Across healthy adult life our brains undergo gradual structural change in a pattern of atrophy that resembles accelerated brain changes in Alzheimers disease (AD). Here, using four polygenic risk scores for AD (PRS-AD) in a longitudinal adult lifespan sample aged 30 to 89 years (2-7 timepoints), we show that healthy individuals who lose brain volume faster than expected for their age, have a higher genetic AD risk. We first demonstrate PRS-AD associations with change in early Braak regions, namely hippocampus, entorhinal cortex, and amygdala, and find evidence these extend beyond that predicted by APOE genotype. Next, following the hypothesis that brain changes in ageing and AD are largely shared, we performed machine learning classification on brain change trajectories conditional on age in longitudinal AD patient-control data, to obtain a list of AD-accelerated features and model change in these in adult lifespan data. We found PRS-AD was associated with a multivariate marker of accelerated change in many of these features in healthy adults, and that most individuals above [~]50 years of age are on an accelerated change trajectory in AD-accelerated brain regions. Finally, high PRS-AD individuals also high on a multivariate marker of change showed more adult lifespan memory decline, compared to high PRS-AD individuals with less brain change. Our results support a dimensional account linking normal brain ageing with AD, suggesting AD risk genes speed up the shared pattern of ageing- and AD-related neurodegeneration that starts early, occurs along a continuum, and tracks memory change in healthy adults.

neuroscience↗

bioRxiv2022

A Large-Scale ENIGMA Multisite Replication Study of Brain Age in Depression

BackgroundSeveral studies have evaluated whether depressed persons have older appearing brains than their nondepressed peers. However, the estimated neuroimaging-derived "brain age gap" has varied from study to study, likely driven by differences in training and testing sample (size), age range, and used modality/features. To validate our previously developed ENIGMA brain age model and the identified brain age gap, we aim to replicate the presence and effect size estimate previously found in the largest study in depression to date (N=2,126 controls & N=2,675 cases; +1.08 years [SE 0.22], Cohens d=0.14, 95% CI: 0.08-0.20), in independent cohorts that were not part of the original study. MethodsA previously trained brain age model (www.photon-ai.com/enigma_brainage) based on 77 FreeSurfer brain regions of interest was used to obtain unbiased brain age predictions in 751 controls and 766 persons with depression (18-75 years) from 13 new cohorts collected from 20 different scanners. ResultsOur ENIGMA MDD brain age model generalized reasonably well to controls from the new cohorts (predicted age vs. age: r = 0.73, R2=0.47, MAE=7.50 years), although the performance varied from cohort to cohort. In these new cohorts, on average, depressed persons showed a significantly higher brain age gap of +1 year (SE 0.35) (Cohens d{square}={square}{square}.15, 95% CI: 0.05-0.25) compared with controls, highly similar to our previous finding. ConclusionsThis study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. Thus, two large-scale independent mega-analyses across in total 32 cohorts and >3,400 patients and >2,800 controls worldwide show reliable but subtle effects of brain aging in adult depression.

neuroscience↗

bioRxiv2022

Brain aging differs with cognitive ability regardless of education

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20-88 years, followed-up up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.

neuroscience↗

bioRxiv2020

Education and income show heterogeneous relationships to lifespan brain and cognitive differences across European and US cohorts

Socio-economic status (SES) has been proposed to have facilitating and protective effects on brain and cognition. Here we show that relationships between SES, brain volumes and general cognitive ability differ significantly across European and US cohorts (4-97 years, N {approx} 500,000; 54,000 with brain imaging). Education was positively related to intracranial (ICV) and total brain gray matter (GM) volume. Income was related to ICV, but not GM. Relationships varied significantly across samples, and SES was more strongly related to brain and cognition in US than European cohorts. Differences in neuroanatomical volumes explained part of the SES-cognition relationships. SES was more strongly related to ICV than to GM, implying that SES-cognition relations in adulthood are less likely grounded in neuroprotective effects on GM volume in aging. Rather, a relationship may be established early in life. The findings underscore that SES has no uniform association with, or impact on, brain and cognition.

neuroscience↗

bioRxiv2018

Evaluating the evidence for biotypes of depression: attempted replication of Drysdale et.al. 2017

BackgroundPsychiatric disorders are highly heterogeneous, defined based on symptoms with little connection to potential underlying biological mechanisms. A possible approach to dissect biological heterogeneity is to look for biologically meaningful subtypes. A recent study Drysdale et al. (2017) showed promising results along this line by simultaneously using resting state fMRI and clinical data and identified four distinct subtypes of depression with different clinical profiles and abnormal resting state fMRI connectivity. These subtypes were predictive of treatment response to transcranial magnetic stimulation therapy.\n\nObjectiveHere, we attempted to replicate the procedure followed in the Drysdale et al. study and their findings in an independent dataset of a clinically more heterogeneous sample of 187 participants with depression and anxiety. We aimed to answer the following questions: 1) Using the same procedure, can we find a statistically significant and reliable relationship between brain connectivity and clinical symptoms? 2) Is the observed relationship similar to the one found in the original study? 3) Can we identify distinct and reliable subtypes? 4) Do they have similar clinical profiles as the subtypes identified in the original study?\n\nMethodsWe followed the original procedure as closely as possible, including a canonical correlation analysis to find a low dimensional representation of clinically relevant resting state fMRI features, followed by hierarchical clustering to identify subtypes. We extended the original procedure using additional statistical tests, to test the statistical significance of the relationship between resting state fMRI and clinical data, and the existence of distinct subtypes. Furthermore, we examined the stability of the whole procedure using resampling.\n\nResults and ConclusionWe were not able to replicate the findings of the original study. Relationships between brain connectivity and clinical symptoms were not statistically significant and we also did not find clearly distinct subtypes of depression. We argue, that based on our rigorous approach and in-depth review of the original results, that the evidence for the existence of the distinct resting state connectivity based subtypes of depression is weak and should be interpreted with caution.

neuroscience↗