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

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.

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Brain Age in Early Stages of Bipolar Disorders or Schizophrenia

Schizophrenia Bulletin ◽

10.1093/schbul/sbx172 ◽

2017 ◽

Vol 45 (1) ◽

pp. 190-198 ◽

Cited By ~ 26

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.

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Discriminating schizophrenia and schizo-obsessive disorder: a structural MRI study combining VBM and machine learning methods

Neural Computing and Applications ◽

10.1007/s00521-016-2451-0 ◽

2016 ◽

Vol 29 (2) ◽

pp. 377-387 ◽

Cited By ~ 7

Author(s):

Cumhur Tas ◽

Hazal Mogulkoc ◽

Gul Eryilmaz ◽

Isıl Gogcegoz-Gul ◽

Turker Tekin Erguzel ◽

...

Keyword(s):

Machine Learning ◽

Structural Mri ◽

Learning Methods ◽

Machine Learning Methods ◽

Mri Study

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Increased hippocampal volumes in lithium treated adolescents with bipolar disorders: A structural MRI study

Journal of Affective Disorders ◽

10.1016/j.jad.2011.12.047 ◽

2012 ◽

Vol 138 (3) ◽

pp. 433-439 ◽

Cited By ~ 16

Author(s):

Burak Baykara ◽

Neslihan Inal-Emiroglu ◽

Nuri Karabay ◽

Handan Çakmakçı ◽

Nagihan Cevher ◽

...

Keyword(s):

Bipolar Disorders ◽

Structural Mri ◽

Mri Study

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

10.1101/687079 ◽

2019 ◽

Cited By ~ 1

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.

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