F128. Transcriptomics of Brain Age Gap Estimate (BrainAGE): Association Analysis of Depressed and Healthy Individuals

2018 ◽  
Vol 83 (9) ◽  
pp. S287
Author(s):  
Trang Le ◽  
Masaya Misaki ◽  
Hideo Suzuki ◽  
Jonathan Savitz ◽  
Martin Paulus ◽  
...  
Keyword(s):  
Association Analysis ◽  
Healthy Individuals ◽  
Age Gap ◽  
Gap Estimate ◽  
Brain Age

scholarly journals Brain Age in Early Stages of Bipolar Disorders or Schizophrenia

2017 ◽  
Vol 45 (1) ◽  
pp. 190-198 ◽  
Author(s):  
Tomas Hajek ◽  
Katja Franke ◽  
Marian Kolenic ◽  
Jana Capkova ◽  
Martin Matejka ◽  
...  
Keyword(s):  
Bipolar Disorders ◽  
Structural Mri ◽  
Chronological Age ◽  
First Episode ◽  
Schizophrenia Spectrum Disorders ◽  
Early Stages ◽  
Age Gap ◽  
Gap Estimate ◽  
Brain Age ◽  
The Brain

Abstract Background The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.

scholarly journals Increased Brain Age Gap Estimate (BrainAGE) in Young Adults After Premature Birth

2021 ◽  
Vol 13 ◽  
Author(s):  
Dennis M. Hedderich ◽  
Aurore Menegaux ◽  
Benita Schmitz-Koep ◽  
Rachel Nuttall ◽  
Juliana Zimmermann ◽  
...  
Keyword(s):  
Premature Birth ◽  
Brain Aging ◽  
Structural Mri ◽  
Full Term ◽  
Intracranial Volume ◽  
Age Gap ◽  
Increased Risk ◽  
Aging Rates ◽  
Brain Age ◽  
Premature Born

Recent evidence suggests increased metabolic and physiologic aging rates in premature-born adults. While the lasting consequences of premature birth on human brain development are known, its impact on brain aging remains unclear. We addressed the question of whether premature birth impacts brain age gap estimates (BrainAGE) using an accurate and robust machine-learning framework based on structural MRI in a large cohort of young premature-born adults (n = 101) and full-term (FT) controls (n = 111). Study participants are part of a geographically defined population study of premature-born individuals, which have been followed longitudinally from birth until young adulthood. We investigated the association between BrainAGE scores and perinatal variables as well as with outcomes of physical (total intracranial volume, TIV) and cognitive development (full-scale IQ, FS-IQ). We found increased BrainAGE in premature-born adults [median (interquartile range) = 1.4 (−1.3–4.7 years)] compared to full-term controls (p = 0.002, Cohen’s d = 0.443), which was associated with low Gestational age (GA), low birth weight (BW), and increased neonatal treatment intensity but not with TIV or FS-IQ. In conclusion, results demonstrate elevated BrainAGE in premature-born adults, suggesting an increased risk for accelerated brain aging in human prematurity.

scholarly journals Reliable longitudinal brain age prediction in stroke patients: Associations with cognitive function and response to cognitive training

2019 ◽  
Author(s):  
Geneviève Richard ◽  
Knut Kolskår ◽  
Kristine M. Ulrichsen ◽  
Tobias Kaufmann ◽  
Dag Alnæs ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Outcome Prediction ◽  
Cognitive Intervention ◽  
Chronological Age ◽  
Double Blind Study ◽  
Stroke Patients ◽  
Brain Morphometry ◽  
Age Gap ◽  
Brain Age ◽  
Age Prediction

AbstractCognitive deficits are important predictors for outcome, independence and quality of life after stroke, but often remain unnoticed and unattended because other impairments are more evident. Computerized cognitive training (CCT) is among the candidate interventions that may alleviate cognitive difficulties, but the evidence supporting its feasibility and effectiveness is scarce, partly due to the lack of tools for outcome prediction and monitoring. Magnetic resonance imaging (MRI) provides candidate markers for disease monitoring and outcome prediction. By integrating information not only about lesion extent and localization, but also regarding the integrity of the unaffected parts of the brain, advanced MRI provides relevant information for developing better prediction models in order to tailor cognitive intervention for patients, especially in a chronic phase.Using brain age prediction based on MRI based brain morphometry and machine learning, we tested the hypotheses that stroke patients with a younger-appearing brain relative to their chronological age perform better on cognitive tests and benefit more from cognitive training compared to patients with an older-appearing brain. In this randomized double-blind study, 54 patients who suffered mild stroke (>6 months since hospital admission, NIHSS<7 at hospital discharge) underwent 3-weeks CCT and MRI before and after the intervention. In addition, patients were randomized to one of two groups receiving either active or sham transcranial direct current stimulation (tDCS). We tested for main effects of brain age gap (estimated age – chronological age) on cognitive performance, and associations between brain age gap and task improvement. Finally, we tested if longitudinal changes in brain age gap during the intervention were sensitive to treatment response. Briefly, our results suggest that longitudinal brain age prediction based on automated brain morphometry is feasible and reliable in stroke patients. However, no significant association between brain age and both performance and response to cognitive training were found.

scholarly journals Multimodal Brain Age Gap as a Mediating Indicator in the Relation between Modifiable Dementia Risk Factors and Cognitive Functioning

2020 ◽  
Author(s):  
Chang-Le Chen ◽  
Pin-Yu Chen ◽  
Yu-Hung Tung ◽  
Yung-Chin Hsu ◽  
Wen-Yih Isaac Tseng
Keyword(s):  
Risk Factors ◽  
Cognitive Decline ◽  
Cognitive Aging ◽  
Prediction Models ◽  
Equation Modeling ◽  
Pathophysiological Mechanism ◽  
Cognitive Changes ◽  
Age Gap ◽  
Dementia Risk ◽  
Brain Age

AbstractIntroductionAs a structural proxy for evaluating brain health, neuroimaging-based brain age gap (BAG) is presumed to link the dementia risks to cognitive changes in the premorbid phase, but this remains unclear.MethodsBrain age prediction models were constructed and applied to a population-based cohort (N=371) to estimate their BAG. Further, structural equation modeling was employed to investigate the mediation effect of BAG between risk levels (assessed by 2 dementia-related risk scores) and cognitive changes (examined by 4 cognitive assessments).ResultsA higher burden of modifiable dementia risk factors was causally associated with a greater cognitive decline, and this was significantly mediated (P=0.017) by a larger multimodal BAG, which indicated an older brain. Moreover, a steeper slope (P=0.020) of association between cognitive decline and multimodal BAG was observed when individuals had higher dementia risks.DiscussionMultimodal BAG is a potential mediating indicator to reflect the changes in the pathophysiological mechanism of cognitive aging.

scholarly journals Evidence for a moderating effect of drinking severity but not HIV status on brain age in heavy episodic drinkers

2021 ◽  
Author(s):  
Jonathan C Ipser ◽  
John Joska ◽  
Tatum Sevenoaks ◽  
Hetta Gouse ◽  
Carla Freeman ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Health Centre ◽  
Brain Regions ◽  
Neurocognitive Impairment ◽  
Heavy Episodic Drinking ◽  
Hiv Status ◽  
Whole Brain ◽  
Age Gap ◽  
Brain Ageing ◽  
Brain Age

Background: Chronic HIV infection and alcohol use have been associated with brain changes and neurocognitive impairment. However, their combined effects are less well studied. We correlated measures of "brain age gap" (BAG) and neurocognitive impairment in participants with and without HIV/AIDS and heavy episodic drinking (HED). We predicted that BAG will be greater in PWH who engage in HED and that these effects will be particularly pronounced in frontoparietal and subcortical regions. Method: 69 participants were recruited from a community health centre in Cape Town: HIV-/HED- (N = 17), HIV+/HED- (N = 14), HIV-/HED+ (N = 21), and HIV+/HED+ (N = 17). Brain age gap (BAG) was derived from thickness, area and volumetric measurements from the whole brain or one of six brain regions. Linear regression models were employed to identify differences in BAG between patient groups and controls. Associations between BAG and clinical indicators of HIV and HED status were also tested using bivariate statistical methods. Results: Compared to controls, greater whole brain BAG was observed in HIV-/HED+ (Cohens d = 1.61, p < 0.001), and HIV+/HED+ (d = 1.52, p = 0.005) participants. Differences in BAG between patients and controls were observed subcortically, as for the cingulate and the parietal cortex. An exploratory analysis revealed that higher relative brain ageing in HED participants with the highest drinking scores (W = 66, p = 0.036) but did not vary as a function of nadir CD4 count or current HIV viral load. Conclusion: The association between heavy episodic drinking and BAG, independent of HIV status, points to the importance of screening for and targeting alcohol use disorders in primary care. Our findings also point to the utility of assessing the contribution of brain regions to the BAG.

Brain age gap as a potential biomarker for schizophrenia: A multi-site structural MRI study

2021 ◽  
Author(s):  
Weiqi Man ◽  
Hao Ding ◽  
Chao Chai ◽  
Xingwei An ◽  
Feng Liu ◽  
...  
Keyword(s):  
Structural Mri ◽  
Age Gap ◽  
Potential Biomarker ◽  
Mri Study ◽  
Brain Age

scholarly journals The Speed of Development of Adolescent Brain Age Depends on Sex and Is Genetically Determined

2020 ◽  
Author(s):  
Rachel M Brouwer ◽  
Jelle Schutte ◽  
Ronald Janssen ◽  
Dorret I Boomsma ◽  
Hilleke E Hulshoff Pol ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Developmental State ◽  
Brain Morphology ◽  
Age Gap ◽  
Males And Females ◽  
The Difference ◽  
Pass Through ◽  
Genetically Determined ◽  
Brain Age ◽  
The Brain

Abstract Children and adolescents show high variability in brain development. Brain age—the estimated biological age of an individual brain—can be used to index developmental stage. In a longitudinal sample of adolescents (age 9–23 years), including monozygotic and dizygotic twins and their siblings, structural magnetic resonance imaging scans (N = 673) at 3 time points were acquired. Using brain morphology data of different types and at different spatial scales, brain age predictors were trained and validated. Differences in brain age between males and females were assessed and the heritability of individual variation in brain age gaps was calculated. On average, females were ahead of males by at most 1 year, but similar aging patterns were found for both sexes. The difference between brain age and chronological age was heritable, as was the change in brain age gap over time. In conclusion, females and males show similar developmental (“aging”) patterns but, on average, females pass through this development earlier. Reliable brain age predictors may be used to detect (extreme) deviations in developmental state of the brain early, possibly indicating aberrant development as a sign of risk of neurodevelopmental disorders.

scholarly journals Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms

2021 ◽  
Author(s):  
Martina J. Lund ◽  
Dag Alnæs ◽  
Ann-Marie de Lange ◽  
Ole A. Andreassen ◽  
Lars T. Westlye ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Psychiatric Symptoms ◽  
Model Performance ◽  
Symptom Burden ◽  
Brain Network ◽  
Brain Maturation ◽  
Age Gap ◽  
Age Range ◽  
Brain Age ◽  
Age Prediction

AbstractObjectiveMagnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health.MethodsWe used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n=1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance.ResultsOur model was able to predict age in the independent test samples, with a model performance of r=0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN.DiscussionOur findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.

scholarly journals Deep learning-based brain age prediction in normal aging and dementia

2021 ◽  
Author(s):  
Jeyeon Lee ◽  
Brian Burkett ◽  
Hoon-Ki Min ◽  
Matthew Senjem ◽  
Emily Lundt ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cognitive Impairment ◽  
Brain Aging ◽  
Lewy Body Dementia ◽  
Normal Aging ◽  
Specific Pattern ◽  
Normal Brain ◽  
Age Gap ◽  
Brain Age ◽  
Age Prediction

Abstract Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging at respective age ranges. Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative cognitive syndromes including mild cognitive impairment, AD, frontotemporal dementia, and Lewy body dementia. Occlusion analysis, performed to facilitate interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap in dementia cohorts was highly correlated with the cognitive impairment and AD biomarker. However, regions generating brain age gaps were different for each diagnosis group of which the AD continuum showed similar patterns to normal aging in the CU.

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