Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.
IMPORTANCE Animal models suggest that reduced cerebral blood flow (CBF) is one of the most enduring physiological deficits following concussion. Despite this, longitudinal studies documenting serial changes in regional CBF following human concussion have yet to be performed. OBJECTIVE To longitudinally assess the recovery of CBF in a carefully selected sample of collegiate athletes and compare time course of CBF recovery with that of cognitive and behavioral symptoms. DESIGN, SETTING, AND PARTICIPANTS A cohort of collegiate football athletes (N = 44) participated in this mixed longitudinal and cross-sectional study at a private research institute specializing in neuroimaging between March 2012 and December 2013. Serial imaging occurred approximately 1 day, 1 week, and 1 month postconcussion for a subset of participants (n = 17). All athletes reported no premorbid mood disorders, anxiety disorders, substance abuse, or alcohol abuse. MAIN OUTCOMES AND MEASURES Arterial spin labeling magnetic resonance imaging was used to collect voxelwise relative CBF at each visit. Neuropsychiatric evaluations and a brief cognitive screen were also performed at all 3 points. Clinicians trained in sports medicine provided an independent measure of real-world concussion outcome (ie, number of days withheld from competition). RESULTS The results indicated both cognitive (simple reaction time) and neuropsychiatric symptoms at 1 day postinjury that resolved at either 1 week (cognitive; P < .005) or 1 month (neuropsychiatric; P < .005) postinjury. Imaging data suggested both cross-sectional (ie, healthy vs concussed athletes; P < .05) and longitudinal (1 day and 1 week vs 1 month postinjury; P < .001) evidence of CBF recovery in the right insular and superior temporal cortex. Importantly, CBF in the dorsal midinsular cortex was both decreased at 1 month postconcussion in slower-to-recover athletes (t 11 = 3.45; P = .005) and was inversely related to the magnitude of initial psychiatric symptoms (Hamilton Depression Scale: r = −0.64, P = .02; Hamilton Anxiety Scale: r = −0.56, P = .046), suggesting a potential prognostic indication for CBF as a biomarker. CONCLUSIONS AND RELEVANCE To our knowledge, these results provide the first prospective evidence of reduced CBF in human concussion and subsequent recovery. The resolution of CBF abnormalities closely mirrors previous reports from the animal literature and show real-world validity for predicting outcome following concussion.
Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the “Brain Age Gap Estimate” (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to “regression to the mean.” The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18–60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18–56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.
The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N=4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen’s d effect size (ES) 0.06–0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08–0.1) and fluid intelligence (ES: 0.04–0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.
Among a group of collegiate football athletes, there was a significant inverse relationship of concussion and years of football played with hippocampal volume. Years of football experience also correlated with slower reaction time. Further research is needed to determine the temporal relationships of these findings.
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