Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

Nature Medicine, Journal Year: 2024, Volume and Issue: 30(12), P. 3646 - 3657

Published: Aug. 26, 2024

Abstract Brain clocks, which quantify discrepancies between brain age and chronological age, hold promise for understanding health disease. However, the impact of diversity (including geographical, socioeconomic, sociodemographic, sex neurodegeneration) on brain-age gap is unknown. We analyzed datasets from 5,306 participants across 15 countries (7 Latin American Caribbean (LAC) 8 non-LAC countries). Based higher-order interactions, we developed a deep learning architecture functional magnetic resonance imaging (2,953) electroencephalography (2,353). The comprised healthy controls individuals with mild cognitive impairment, Alzheimer disease behavioral variant frontotemporal dementia. LAC models evidenced older ages (functional imaging: mean directional error = 5.60, root square (r.m.s.e.) 11.91; electroencephalography: 5.34, r.m.s.e. 9.82) associated frontoposterior networks compared models. Structural socioeconomic inequality, pollution disparities were influential predictors increased gaps, especially in ( R ² 0.37, F 0.59, 6.9). An ascending to impairment was found. In LAC, observed larger gaps females control groups respective males. results not explained by variations signal quality, demographics or acquisition methods. These findings provide quantitative framework capturing accelerated aging.

Language: Английский

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Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples

EBioMedicine, Journal Year: 2023, Volume and Issue: 90, P. 104540 - 104540

Published: March 25, 2023

Dementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, non-harmonised pipelines.We implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied raw (unpreprocessed) 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, healthy controls; both male female as self-reported by participants). We tested our results demographically matched unmatched discard possible biases performed multiple out-of-sample validations.Robust classification across all groups were achieved 3T North, which also generalised Latin America. Moreover, non-standardised, routine 1.5T clinical images These generalisations robust heterogenous recordings not confounded demographics (i.e., samples, when incorporating variables multifeatured model). Model interpretability analysis occlusion sensitivity evidenced core pathophysiological regions for each (mainly hippocampus AD, insula bvFTD) demonstrating biological specificity plausibility.The generalisable approach outlined here could be used future aid clinician decision-making samples.The specific funding this article is provided acknowledgements section.

Language: Английский

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Brain health in diverse settings: How age, demographics and cognition shape brain function

NeuroImage, Journal Year: 2024, Volume and Issue: 295, P. 120636 - 120636

Published: May 21, 2024

Diversity in brain health is influenced by individual differences demographics and cognition. However, most studies on diseases have typically controlled for these factors rather than explored their potential to predict signals. Here, we assessed the role of (age, sex, education; n = 1,298) cognition (n 725) as predictors different metrics usually used case-control studies. These included power spectrum aperiodic (1/f slope, knee, offset) metrics, well complexity (fractal dimension estimation, permutation entropy, Wiener spectral structure variability) connectivity (graph-theoretic mutual information, conditional organizational information) from source space resting-state EEG activity a diverse sample global south north populations. Brain-phenotype models were computed using reflecting local (power components) dynamics interactions (complexity graph-theoretic measures). Electrophysiological modulated despite varied methods data acquisition assessments across multiple centers, indicating that results unlikely be accounted methodological discrepancies. Variations signals mainly age cognition, while education sex exhibited less importance. Power measures sensitive capturing differences. Older age, poorer being male associated with reduced alpha power, whereas older network integration segregation. Findings suggest basic impact core function are standard Considering variability diversity settings would contribute more tailored understanding function.

Language: Английский

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Model-based whole-brain perturbational landscape of neurodegenerative diseases

eLife, Journal Year: 2023, Volume and Issue: 12

Published: March 30, 2023

The treatment of neurodegenerative diseases is hindered by lack interventions capable steering multimodal whole-brain dynamics towards patterns indicative preserved brain health. To address this problem, we combined deep learning with a model reproducing functional connectivity in patients diagnosed Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability hippocampal insular signatures AD bvFTD, respectively. Using variational autoencoders, visualized different pathologies their severity the evolution trajectories low-dimensional latent space. Finally, perturbed reveal key AD- bvFTD-specific regions induce transitions from pathological healthy states. Overall, obtained novel insights on progression control means external stimulation, while identifying dynamical mechanisms that underlie alterations neurodegeneration.

Language: Английский

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The BrainLat project, a multimodal neuroimaging dataset of neurodegeneration from underrepresented backgrounds

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Dec. 9, 2023

Abstract The Latin American Brain Health Institute (BrainLat) has released a unique multimodal neuroimaging dataset of 780 participants from American. includes 530 patients with neurodegenerative diseases such as Alzheimer’s disease (AD), behavioral variant frontotemporal dementia (bvFTD), multiple sclerosis (MS), Parkinson’s (PD), and 250 healthy controls (HCs). This (62.7 ± 9.5 years, age range 21–89 years) was collected through multicentric effort across five countries to address the need for affordable, scalable, available biomarkers in regions larger inequities. BrainLat is first regional collection clinical cognitive assessments, anatomical magnetic resonance imaging (MRI), resting-state functional MRI (fMRI), diffusion-weighted (DWI), high density electroencephalography (EEG) patients. In addition, it demographic information about harmonized recruitment assessment protocols. publicly encourage further research development tools health applications neurodegeneration based on neuroimaging, promoting variability inclusion underrepresented research.

Language: Английский

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Tau follows principal axes of functional and structural brain organization in Alzheimer’s disease

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 12, 2024

Abstract Alzheimer’s disease (AD) is a brain network disorder where pathological proteins accumulate through networks and drive cognitive decline. Yet, the role of connectivity in facilitating this accumulation remains unclear. Using in-vivo multimodal imaging, we show that distribution tau reactive microglia humans follows spatial patterns variation, so-called gradients organization. Notably, less distinct (“gradient contraction”) are associated with decline regions greater tau, suggesting an interaction between reduced differentiation on cognition. Furthermore, by modeling subject-specific gradient space, demonstrate frontoparietal temporo-occipital cortices baseline within their functionally structurally connected hubs, respectively. Our work unveils for both functional structural organization pathology AD, supports space as promising tool to map progression.

Language: Английский

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Harmonized multi‐metric and multi‐centric assessment of EEG source space connectivity for dementia characterization

Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring, Journal Year: 2023, Volume and Issue: 15(3)

Published: July 1, 2023

Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers.We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, multi-metric EEG source space connectomics analyses.Spline interpolations signals onto a head mesh model with 6067 virtual electrodes resulted effective method integrating layouts. Z-score transformations time series matrices high bilateral symmetry, reinforced long-range connections, diminished short-range interactions. A composite FC metric allowed accurate multicentric classifications Alzheimer's disease behavioral variant frontotemporal dementia.Harmonized analysis can data heterogeneities multi-centric studies, representing powerful tool accurately characterizing dementia.

Language: Английский

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Extracting interpretable signatures of whole-brain dynamics through systematic comparison

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 12, 2024

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for given application. Here, we address this limitation by systematically comparing diverse, interpretable features both intra-regional activity and inter-regional functional coupling from resting-state magnetic resonance imaging (rs-fMRI) data, demonstrating our method case-control comparisons four neuropsychiatric disorders. Our findings generally support use linear time-series analysis techniques rs-fMRI analyses, while also identifying new ways to quantify informative fMRI structures. While simple representations performed surprisingly well (e.g., within single brain region), combining with improved performance, underscoring distributed, multifaceted changes in comprehensive, data-driven introduced here enables systematic identification interpretation quantitative signatures multivariate applicability beyond neuroimaging diverse scientific problems involving time-varying systems.

Language: Английский

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Structural inequality and temporal brain dynamics across diverse samples

Clinical and Translational Medicine, Journal Year: 2024, Volume and Issue: 14(10)

Published: Oct. 1, 2024

Language: Английский

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A functional connectivity metric method for EEG time series via nonlinear symbolization

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 103, P. 107498 - 107498

Published: Jan. 6, 2025

Language: Английский

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