Multi-organ AI Endophenotypes Chart the Heterogeneity of Pan-disease in the Brain, Eye, and Heart.

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease entities and overlook shared mechanisms across conditions. Inspired by pan-cancer and multi-organ research, we introduce the concept of "pan-disease" to investigate the heterogeneity and shared etiology in brain, eye, and heart diseases. Leveraging individual-level data from 129,340 participants, as well as summary-level data from the MULTI consortium, we applied a weakly-supervised deep learning model (Surreal-GAN) to multi-organ imaging, genetic, proteomic, and RNA-seq data, identifying 11 AI-derived biomarkers - called Multi-organ AI Endophenotypes (MAEs) - for the brain (Brain 1-6), eye (Eye 1-3), and heart (Heart 1-2), respectively.

Sleep chart of biological aging clocks across organs and omics.

Optimal sleep plays a vital role in promoting healthy aging and enhancing longevity. This study proposes a Sleep Chart to assess the relationship between sleep duration and 23 biological aging clocks across 17 organ systems or tissues and 3 omics data types (imaging, proteomics, and metabolomics). First, a systemic, U-shaped pattern shows that both short (<6 hours) and long (>8 hours) sleep duration are linked to elevated biological age gaps (BAGs) across 9 brain and body systems and 3 omics types, with optimal sleep time varying by organ and sex ([6.

Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease.

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies to slow disease progression and onset. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information.

The PREVENT-AD cohort: accelerating Alzheimer's disease research and treatment in Canada and beyond.

The PREVENT-AD is an investigator-driven study that was created in 2011 and enrolled cognitively normal older adults with a family history of sporadic AD. Participants are deeply phenotyped and have now been followed annually for more than 12 years [median follow-up 8.0 years,SD 3.

Plasma GDF15 affects long-term dementia risk and alters neuro-immune signaling.

Growth/differentiation factor-15 (GDF15) is a secreted peptide hormone and cytokine that is strongly associated with dementia risk. However, the extent to which plasma GDF15 represents a biomarker and driver of dementia risk remains unclear. Across multiple cohorts, we demonstrated that plasma GDF15 is associated with greater dementia risk over 15-to 25-year follow-up periods when measured in midlife, with stronger associations observed for vascular dementia compared to Alzheimer's disease (AD).

Intra-tumoral Preservation of the Stem-like Malignant Cell Proportion in Glioblastoma, Prognostic Impact, and its Radiomethylomic Signatures.

Background: Glioblastoma (GBM) exhibits significant intra-tumoral heterogeneity. However, the presence and extent of intra-tumoral heterogeneity of stem-like and differentiated cell components based on methylation profiles remain poorly understood. Furthermore, the utility of integrating methylation profiles with radiomic features (radiomethylomics) for predicting these cellular states has not been explored.

Multi-organ MRI digitizes biological aging clocks across proteomics, metabolomics, and genetics.

Leveraging clinical phenotypes, neuroimaging, proteomics, metabolomics, and epigenetics, biological aging clocks across organ systems and tissues have advanced our understanding of human aging and disease. In this study, we expand this biological aging clock framework to multi-organ magnetic resonance imaging (MRI) by developing 7 organ-specific MRI-based biological age gaps (MRIBAGs), including the brain, heart, liver, adipose tissue, spleen, kidney, and pancreas. Leveraging imaging, genetic, proteomic, and metabolomic data from 313,645 individuals curated by the MULTI consortium, we link the 7 MRIBAGs to 2,923 plasma proteins, 327 metabolites, and 6,477,810 common genetic variants.

Precision Neuromodulation with Real-Time Brain Decoding for Working Memory Enhancement.

Unlabelled: Transcranial magnetic stimulation (TMS) has transformed non-invasive brain therapies but faces challenges due to variability in outcomes, likely stemming from inter-individual differences in brain function. This study aimed to address this challenge by integrating personalized functional networks (PFNs) derived from functional magnetic resonance imaging (fMRI) with a neural network-based decoder to optimize stimulation in real time during a working memory (WM) task. After identification of individualized stimulation targets, participants completed a TMS/fMRI session, performing a WM task while receiving rTMS at randomized frequencies.

Advancing Fair and Explainable Machine Learning for Neuroimaging Dementia Pattern Classification in Multi-Ethnic Populations.

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective treatment and improved quality of life. However, current diagnostic approaches frequently demonstrate inconsistent precision and impartiality, particularly among diverse cultural groups.

A Pan-Organ Vision-Language Model for Generalizable 3D CT Representations.

Generalizable foundation models for computed tomographic (CT) medical imaging data are emerging AI tools anticipated to vastly improve clinical workflow efficiency. However, existing models are typically trained within narrow imaging contexts, including limited anatomical coverage, contrast settings, and clinical indications. These constraints reduce their ability to generalize across the broad spectrum of real-world presentations encountered in volumetric CT imaging data.

Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge.

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites.

Brain-heart-eye axis revealed by multi-organ imaging, genetics and proteomics.

Multi-organ research investigates interconnections among multiple human organ systems, enhancing our understanding of human aging and disease mechanisms. Here, we used multi-organ imaging (=105,433), individual- and summary-level genetics, and proteomics (=53,940) from the UK Biobank, Baltimore Longitudinal Study of Aging, FinnGen, and Psychiatric Genomics Consortium to delineate a brain-heart-eye axis via 2003 brain patterns of structural covariance (PSC), 82 heart imaging-derived phenotypes (IDP) and 84 eye IDPs. Cross-organ phenotypic associations highlight the central autonomic network between the brain and heart and the central visual pathway between the brain and eye.

Reproducible sex differences in personalised functional network topography in youth.

Background: A key step toward understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organisation at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organisation of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex.

Aims: We aimed to evaluate the impact of sex on the spatial organisation of person-specific functional brain networks.

White Matter Abnormalities and Cognition in Aging and Alzheimer Disease.

Importance: There has yet to be a large-scale study quantifying the association between white matter microstructure and cognitive performance and decline in aging and Alzheimer disease (AD).

Objective: To investigate the associations between tract-specific white matter microstructure and cognitive performance and decline in aging and AD-related cognitive impairment.

Design Setting And Participants: This prognostic study of aging and AD, a secondary data analysis of multisite cohort studies, acquired data from 9 cohorts between September 2002 and November 2022.

SLR: A Modified Logistic Regression Model with Sinkhorn Divergence for Alzheimer's Disease Classification.

Logistic regression is a widely used model in machine learning, particularly as a baseline for binary classification tasks due to its simplicity, effectiveness, and interpretability. It is especially powerful when dealing with categorical features. Despite its advantages, standard logistic regression fails to capture the distributional and geometric structure of data, especially when features are derived from structured spaces like brain imaging.

Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and Generative Adversarial Networks (GANs).

We present a preliminary analysis of a GAN-based normative modeling technique for capturing individual-level deviations in brain measures, addressing heterogeneity in neurological disorders. By leveraging self-supervised training on pseudo-synthetically simulated patient data, our method detects disease-related effects without the need for large, disease-specific datasets. We demonstrate the versatility of this approach by applying it to structural MRI and resting-state fMRI data, identifying neuroanatomical and functional connectivity deviations in Alzheimer's disease (AD) and Traumatic Brain Injury (TBI).

Neuroimaging endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders.

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotypes, called dimensional neuroimaging endophenotypes (DNEs). We advance the argument that these DNEs capture the degree of expression of respective neuroanatomical patterns measured, offering a dimensional neuroanatomical representation for studying disease heterogeneity and similarities of neurologic and neuropsychiatric diseases. We investigate the presence of nine DNEs derived from independent yet harmonized studies on Alzheimer's disease, autism spectrum disorder, late-life depression and schizophrenia in the UK Biobank study.

Genetic architecture of the limbic white matter microstructure in aging and Alzheimer's Disease.

Background: Limbic white matter (WM) abnormalities are prevalent in aging and Alzheimer's disease (AD), yet their underlying biological mechanisms remain unclear. This study aims to identify the genetic architecture of limbic WM microstructure in older adults by leveraging harmonized data from multiple cohorts, including those enriched for cognitively impaired individuals.

Methods: We analyzed diffusion MRI (dMRI) data from 2,614 non-Hispanic White older adults (mean age = 73.

Assessing the Global Impact of Brain Small Vessel Disease on Cognition: The Multi-Ethnic Study of Atherosclerosis.

Introduction: We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance.

Methods: In 892 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery.

Proteomic signatures of corona and herpes viral antibodies identify IGDCC4 as a mediator of neurodegeneration.

Mechanisms underlying the dynamic relationships of viral infections and neurodegeneration warrant examination. Using a community-based cohort of older adults, the current study characterized the neurocognitive (cognitive functioning, brain volumes, Alzheimer's disease positron emission tomography, and plasma biomarkers) and plasma proteomic (7268 proteins) profiles of four common coronavirus and six herpesvirus antibody titers. Genetic inference techniques demonstrated the associations between viral antibody titers and neurocognitive outcomes may be attributed to altered expression in a subset of mechanistically relevant proteins in plasma.

Radiomic and proteomic signatures of body mass index on brain ageing and Alzheimer's-like patterns of brain atrophy.

Background: The impact of high body mass index (BMI) states and associated proteomic factors on brain ageing and Alzheimer's disease (AD) remains unclear.

Methods: We sought to evaluate machine learning (ML)-based neuroimaging markers of brain age and AD-like brain atrophy in participants with obesity or overweight without diagnosed cognitive impairment (WODCI), in a harmonised study of 46,288 participants in 15 studies (the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) consortium). We also assessed the association between cognition, serum proteins, and brain ageing indices.

Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk.

Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective treatment planning. However, existing methods are not well-suited for identifying Mild Cognitive Impairment (MCI), a critical transitional stage between normal aging and AD. This inadequacy is primarily due to label imbalance and bias from different sensitve attributes in MCI classification.

Development of Simple Risk Scores for Prediction of Brain β-Amyloid and Tau Status in Older Adults With Mild Cognitive Impairment: A Machine Learning Approach.

Objectives: The aim of this work is to use a machine learning framework to develop simple risk scores for predicting β-amyloid (Aβ) and tau positivity among individuals with mild cognitive impairment (MCI).

Methods: Data for 657 individuals with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set were used. A modified version of AutoScore, a machine learning-based software tool, was used to develop risk scores based on hierarchical combinations of predictor categories, including demographics, neuropsychological assessments, APOE4 status, and imaging biomarkers.

Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning.

The brain's multi-scale hierarchical organization supports functional segregation and integration. Characterizing the hierarchy of individualized multi-scale functional networks (FNs) is crucial for understanding these fundamental brain processes. It provides promising opportunities for both basic neuroscience and translational research in neuropsychiatric illness.