scholarly journals Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging

2020 ◽  
Author(s):  
Melis Anatürk ◽  
Tobias Kaufmann ◽  
James H. Cole ◽  
Sana Suri ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
Cognitive Age ◽  
Brain Age

scholarly journals Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging

2020 ◽  
Author(s):  
Melis Anatürk ◽  
Tobias Kaufmann ◽  
James H. Cole ◽  
Sana Suri ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
Cognitive Reserve ◽  
Older Age ◽  
Study Cohort ◽  
Cognitive Age ◽  
Novel Method ◽  
Brain Changes ◽  
Brain Maintenance ◽  
Normative Aging ◽  
No Gold Standard ◽  
Brain Age

The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no 'gold standard' for measuring these constructs. Using machine-learning, we estimated brain and cognitive maintenance based on deviations from normative aging patterns in the Whitehall II MRI sub-study cohort, and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to cognitive maintenance independent of the degree of brain maintenance. No strong evidence was found for associations between lifestyle trajectories and brain or cognitive maintenance. In conclusion, we present a novel method to characterize brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.

scholarly journals A history of previous childbirths is linked to women's white matter brain age in midlife and older age

2021 ◽  
Author(s):  
Irene Voldsbekk ◽  
Claudia Barth ◽  
Ivan I. Maximov ◽  
Tobias Kaufmann ◽  
Dani Beck ◽  
...  
Keyword(s):  
White Matter ◽  
Older Age ◽  
History Of ◽  
Brain Age

543 EEG-Based Deep Neural Network Model for Brain Age Prediction and Its Association with Patient Health Conditions

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A214-A214
Author(s):  
Yoav Nygate ◽  
Sam Rusk ◽  
Chris Fernandez ◽  
Nick Glattard ◽  
Jessica Arguelles ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Population Sample ◽  
Health Conditions ◽  
Apnea Hypopnea Index ◽  
Healthy Population ◽  
Brain Health ◽  
Patient Health ◽  
Drug Problems ◽  
Brain Age

Abstract Introduction Electroencephalogram (EEG) provides clinically relevant information for personalized patient health evaluation and comprehensive assessment of sleep. EEG-based indices have been associated with neurodegenerative conditions, psychiatric disorders, and metabolic and cardiovascular disease, and hold promise as a biomarker for brain health. Methods A deep neural network (DNN) model was trained to predict the age of patients using raw EEG signals recorded during clinical polysomnography (PSG). The DNN was trained on N=126,241 PSGs, validated on N=6,638, and tested on a holdout set of N=1,172. The holdout dataset included several categories of patient demographic and diagnostic parameters, allowing us to examine the association between brain age and a variety of medical conditions. Brain age was assessed by subtracting the individual’s chronological brain age from their EEG-predicted brain age (Brain Age Index; BAI), and then taking the absolute value of this variable (Absolute Brain Age Index; ABAI). We then constructed two regression models to test the relationship between BAI/ABAI and the following list of patient parameters: sex, BMI, depression, alcohol/drug problems, memory/concentration problems, epilepsy/seizures, diabetes, stroke, severe excessive daytime sleepiness (e.g., Epworth Sleepiness Scale ≥ 16; EDS), apnea-hypopnea index (AHI), arousal index (ArI), and sleep efficiency (SE). Results The DNN brain age model produced a mean absolute error of 4.604 and a Pearson’s r value of 0.933 which surpass the performance of prior research. In our regression analyses, we found a statistically significant relationship between the ABAI and: epilepsy and seizure disorders, stroke, elevated AHI, elevated ArI, and low SE (all p<0.05). This demonstrates these health conditions are associated with deviations of one’s predicted brain age from their chronological brain age. We also found patients with diabetes, depression, severe EDS, hypertension, and/or memory and concentration problems showed, on average, an elevated BAI compared to the healthy population sample (all p<0.05). Conclusion We show DNNs can accurately predict the brain age of healthy patients based on their raw, PSG derived, EEG recordings. Furthermore, we reveal indices, such as BAI and ABAI, display unique characteristics within different diseased populations, highlighting their potential value as novel diagnostic biomarker and potential “vital sign” of brain health. Support (if any):

scholarly journals Statistical Estimation of Accelerated Biological Brain Aging After Mild Traumatic Brain Injury in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 890-890
Author(s):  
Andrei Irimia ◽  
Jun Kim ◽  
Shania Wang ◽  
Hyung Jun Lee ◽  
Van Ngo ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Brain Aging ◽  
Rate Of Change ◽  
Time Period ◽  
Weighted Magnetic Resonance Imaging ◽  
Neurological Conditions ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Age

Abstract Estimating biological brain age (BA) has the potential of identifying individuals at relatively high risk for accelerated neurodegeneration. This study compares the brain’s chronological age (CA) to its BA and reveals the BA rate of change after mild traumatic brain injury (mTBI) in an aging cohort. Using T1-weighted magnetic resonance imaging (MRI) volumes and cortical thickness, volume, surface area, and Gaussian curvature obtained using FreeSurfer software; we formulated a multivariate linear regression to determine the rate of BA increase associated with mTBI. 95 TBI patients (age in years (y): μ = 41 y, σ = 17 y; range = 18 to 83) were compared to 462 healthy controls (HCs) (age: μ = 69 y, σ = 18 y; range = 25 to 95) over a 6-month time period following mTBI. Across the initial ~6 months following injury, patients’ BAs increased by ~3.0 ± 1.2 years due to their mTBIs alone, i.e., above and beyond typical brain aging. The superior temporal and parahippocampal gyri, two structures involved in memory formation and retrieval, exhibited the fastest rates of TBI-related BA. In both hemispheres, the volume of the hippocampus decreased (left: μ=0.28%, σ=4.40%; right: μ=0.12%, σ=4.84%). These findings illustrate BA estimation techniques’ potential to identify TBI patients with accelerated neurodegeneration, whose rate is strongly associated with the risk for dementia and other aging-related neurological conditions.

Multi-channel Attention-Fusion Neural Network for Brain Age Estimation: Accuracy, Generality, and Interpretation with 16,705 Healthy MRIs across Lifespan

2021 ◽  
pp. 102091
Author(s):  
Sheng He ◽  
Diana Pereira ◽  
Juan David Perez ◽  
Randy L. Gollub ◽  
Shawn N. Murphy ◽  
...  
Keyword(s):  
Neural Network ◽  
Age Estimation ◽  
Estimation Accuracy ◽  
Brain Age

scholarly journals Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter?

2021 ◽  
Vol 310 ◽  
pp. 111270
Author(s):  
Won Hee Lee ◽  
Mathilde Antoniades ◽  
Hugo G Schnack ◽  
Rene S. Kahn ◽  
Sophia Frangou
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Brain Age ◽  
Age Prediction

scholarly journals Association of epileptiform brain activity and specific language impairment (SLI) in preschool children

2021 ◽  
Vol 57 (1) ◽  
Author(s):  
Ahmed Esmael ◽  
Sara Elsherbeny ◽  
Mohammed Abbas
Keyword(s):  
Preschool Children ◽  
Language Impairment ◽  
Significant Relationship ◽  
Specific Language Impairment ◽  
Intelligence Quotient ◽  
Epileptiform Activity ◽  
Control Group ◽  
Specific Language ◽  
Cognitive Age ◽  
The Impact

Abstract Background Epileptiform activities can cause transient or permanent deficits that affect the children during development and may be accompanied by neurodevelopmental disorders like specific language impairment. Objectives The objective of this study was to find if there is a possible association and the impact of epilepsy and epileptiform activity in children with specific language impairment. Patients and methods The study was conducted on 80 children suffering from specific language impairment and 80 age and sex match healthy control children. Computed tomography brain was performed and electroencephalography was recorded for children. Intelligence quotient level, cognitive age, social, and phoniatric assessment were done for all patients. Results Eighty children with specific language impairment (51 males and 29 females) with a mean age of 4.11 ± 1.93. Patients with specific language impairment showed significantly higher rates of abnormal electroencephalography (P = 0.006) and epilepsy (P < 0.001) compared to the control group. Spearman correlation demonstrated a highly negative significant relationship linking the language, intelligence quotient with abnormal electroencephalography and epilepsy (r = − 0.91, P < 0.01 and r = − 0.91, P < 0.01 respectively). Also, there was a moderately inverse significant relationship linking the cognitive age, social with abnormal electroencephalography, and epilepsy (r = − 0.70, P < 0.05 and r = − 0.65, P < 0.05 respectively). Conclusion Epileptiform activities even without epilepsy in preschool children may alter normal language function. Specific language impairment was associated with lower intelligence quotient levels, social, and cognitive age. Trial registration ClinicalTrials.gov ID: NCT04141332

scholarly journals Brain age prediction: A comparison between machine learning models using region‐ and voxel‐based morphometric data

2021 ◽  
Author(s):  
Lea Baecker ◽  
Jessica Dafflon ◽  
Pedro F. Costa ◽  
Rafael Garcia‐Dias ◽  
Sandra Vieira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Morphometric Data ◽  
Brain Age ◽  
Machine Learning Models ◽  
Age Prediction
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