Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction

Predictive values of pre-treatment brain age models to rTMS effects in neurocognitive disorder with depression: Secondary analysis of a randomised sham-controlled clinical trial

INTRODUCTION

One major challenge in developing personalised repetitive transcranial magnetic stimulation (rTMS) is that the treatment responses exhibited high inter-individual variations. Brain morphometry might contribute to these variations. This study sought to determine whether individual's brain morphometry could predict the rTMS responders and remitters.

METHODS

This was a secondary analysis of data from a randomised clinical trial that included fifty-five patients over the age of 60 with both comorbid depression and neurocognitive disorder. Based on magnetic resonance imaging scans, estimated brain age was calculated with morphometric features using a support vector machine. Brain-predicted age difference (brain-PAD) was computed as the difference between brain age and chronological age.

RESULTS

The rTMS responders and remitters had younger brain age. Every additional year of brain-PAD decreased the odds of relieving depressive symptoms by ∼25.7% in responders (Odd ratio [OR] = 0.743, p = .045) and by ∼39.5% in remitters (OR = 0.605, p = .022) in active rTMS group. Using brain-PAD score as a feature, responder-nonresponder classification accuracies of 85% (3rd week) and 84% (12th week), respectively were achieved.

CONCLUSION

In elderly patients, younger brain age appears to be associated with better treatment responses to active rTMS. Pre-treatment brain age models informed by morphometry might be used as an indicator to stratify suitable patients for rTMS treatment.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: ChiCTR-IOR-16008191.

Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study

OBJECTIVES

This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions.

METHODS

The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject.

RESULTS

MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers.

CONCLUSIONS

Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.

BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32x10-7; T1: β=-1.331, p=6.52x10-7; FW+T1: β=-1.476, p=2.53x10-10; executive function, FW: β=-1.276, p=1.46x10-9; T1: β=-1.337, p=2.52x10-7; FW+T1: β=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62x10-11; T1: β=-0.097, p=1.40x10-8; FW+T1: β=-0.101, p=1.35x10-11; executive function, FW: β=-0.125, p=1.20x10-10; T1: β=-0.163, p=4.25x10-12; FW+T1: β=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

Advanced structural brain aging in preclinical autosomal dominant Alzheimer disease

BACKGROUND

"Brain-predicted age" estimates biological age from complex, nonlinear features in neuroimaging scans. The brain age gap (BAG) between predicted and chronological age is elevated in sporadic Alzheimer disease (AD), but is underexplored in autosomal dominant AD (ADAD), in which AD progression is highly predictable with minimal confounding age-related co-pathology.

METHODS

We modeled BAG in 257 deeply-phenotyped ADAD mutation-carriers and 179 non-carriers from the Dominantly Inherited Alzheimer Network using minimally-processed structural MRI scans. We then tested whether BAG differed as a function of mutation and cognitive status, or estimated years until symptom onset, and whether it was associated with established markers of amyloid (PiB PET, CSF amyloid-β-42/40), phosphorylated tau (CSF and plasma pTau-181), neurodegeneration (CSF and plasma neurofilament-light-chain [NfL]), and cognition (global neuropsychological composite and CDR-sum of boxes). We compared BAG to other MRI measures, and examined heterogeneity in BAG as a function of ADAD mutation variants, APOE ε4 carrier status, sex, and education.

RESULTS

Advanced brain aging was observed in mutation-carriers approximately 7 years before expected symptom onset, in line with other established structural indicators of atrophy. BAG was moderately associated with amyloid PET and strongly associated with pTau-181, NfL, and cognition in mutation-carriers. Mutation variants, sex, and years of education contributed to variability in BAG.

CONCLUSIONS

We extend prior work using BAG from sporadic AD to ADAD, noting consistent results. BAG associates well with markers of pTau, neurodegeneration, and cognition, but to a lesser extent, amyloid, in ADAD. BAG may capture similar signal to established MRI measures. However, BAG offers unique benefits in simplicity of data processing and interpretation. Thus, results in this unique ADAD cohort with few age-related confounds suggest that brain aging attributable to AD neuropathology can be accurately quantified from minimally-processed MRI.

Brain age prediction using combined deep convolutional neural network and multi-layer perceptron algorithms

The clinical applications of brain age prediction have expanded, particularly in anticipating the onset and prognosis of various neurodegenerative diseases. In the current study, we proposed a deep learning algorithm that leverages brain structural imaging data and enhances prediction accuracy by integrating biological sex information. Our model for brain age prediction, built on deep neural networks, employed a dataset of 3004 healthy subjects aged 18 and above. The T1-weighted images were minimally preprocessed and analyzed using the convolutional neural network (CNN) algorithm. The categorical sex information was then incorporated using the multi-layer perceptron (MLP) algorithm. We trained and validated both a CNN-only algorithm (utilizing only brain structural imaging data), and a combined CNN-MLP algorithm (using both structural brain imaging data and sex information) for age prediction. By integrating sex information with T1-weighted imaging data, our proposed CNN-MLP algorithm outperformed not only the CNN-only algorithm but also established algorithms, such as brainageR, in prediction accuracy. Notably, this hybrid CNN-MLP algorithm effectively distinguished between mild cognitive impairment and Alzheimer's disease groups by identifying variances in brain age gaps between them, highlighting the algorithm's potential for clinical application. Overall, these results underscore the enhanced precision of the CNN-MLP algorithm in brain age prediction, achieved through the integration of sex information.

A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimer's disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10-32; T1: r=0.61, p=1.45×10-26, FW+T1: r=0.77, p=6.48×10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=-1.094, p=6.32×10-7; T1: β=-1.331, p=6.52×10-7; FW+T1: β=-1.476, p=2.53×10-10; executive function, FW: β=-1.276, p=1.46×10-9; T1: β=-1.337, p=2.52×10-7; FW+T1: β=-1.850, p=3.85×10-17) and longitudinal cognition (memory, FW: β=-0.091, p=4.62×10-11; T1: β=-0.097, p=1.40×10-8; FW+T1: β=-0.101, p=1.35×10-11; executive function, FW: β=-0.125, p=1.20×10-10; T1: β=-0.163, p=4.25×10-12; FW+T1: β=-0.158, p=1.65×10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.