Danilo Bzdok

Genetic and Causal Insights Into White Matter Hyperintensities Across the Brain‐Body Axis

Manpreet Singh

Kimia Shafighi

Flavie E. Detcheverry

Gabrielle Dagasso

Fanta Dabo

Ikrame Housni

Sridar Narayanan

Nils D. Forkert

Sarah A Gagliano Taliun

Danilo Bzdok

AmanPreet Badhwar

White matter hyperintensities (WMHs), visible as bright regions on T2‐weighted FLAIR MRI, are frequent with age and elevated in Alzheimer'… (see more)s disease (AD). Representing axonal damage, demyelination, and edema, WMHs are driven by vascular mechanisms, including endothelial dysfunction and impaired cerebrovascular autoregulation. WMHs also exhibit strong heritability (55–73%), with overlapping genetic pathways shared with AD. Emerging evidence suggests systemic factors across the brain‐body axis influence WMHs, yet these contributions and their genetic overlap with AD remain underexplored. Our study investigated genetic underpinnings specific to WMHs and those shared with AD by assessing partitioned heritability of WMHs and AD across the brain‐body axis with SNP level tissue‐ and cell‐specific annotations; identifying genes associated with WMHs and AD through integration of gene expression data, establishing causal links between SNP‐level findings and imaging‐derived phenotypes (IDPs), particularly structural variations in regional brain volumes. Partitioned heritability was assessed using stratified‐linkage disequilibrium score regression (sLDSC) on GWAS summary statistics ( N = 3 WMH studies; N = 6 AD studies) using human A1) tissue level annotations ( N = 10) and A2) continuous cell‐specific annotations ( N = 64). MAGMA and FUSION analyses highlighted genes associated with WMH and AD for further bioinformatics analysis (using human protein atlas (HPA) and STRING database). MACAW (Vigneshwaran et al, 2024) modeled causal relationships between WMH‐associated SNPs (from FUMA analysis) and IDPs ( N = 172), leveraging directed acyclic graphs to evaluate genetic effects while controlling for confounders (Figure 2). Tissue‐specific analysis revealed significant enrichment of WMH‐associated SNPs in the CNS, liver, cardiovascular system, and kidneys, while AD‐associated SNPs were enriched in the CNS, connective bone, liver, and immune tissues. (Figure 1). Cell‐specific analysis identified vascular endothelial cells as enriched across WMH‐enriched tissues. MAGMA analysis, combined with HPA analysis, corroborated sLDSC tissue‐level findings. MAGMA and FUSION analyses highlighted genes associated with WMHs ( N = 39 and 69) and AD ( N = 291 and 193). MACAW linked WMH‐associated SNP to 172 IDPs, consistently impacting WM hypointensities and regional brain volumes (e.g., left inferior temporal volume). Our findings highlight systemic multi‐tissue contributions (CNS, liver, cardiovascular system, and kidneys) to WMHs, driven by vascular endothelial dysfunction and shared AD genetics, with SNPs across the body also affecting brain imaging derived phenotypes.

2025-11-30

Alzheimer's & Dementia (published)

doi.org

Mitochondria‐nucleus crosstalk characterizes Alzheimer's disease across 1,5 million brain cells

Emerging insight from stem cell research reinforces Alzheimer's disease (AD) to affect mitochondrial protein expression. Compelling new evid… (see more)ence points to mitochondrial reactive oxygen species (ROS) as potential driving player in Aβ toxicity, mediated through glial cells and ultimately impacting neuronal health. A comprehensive understanding of how oxidative phosphorylation variations relate to cell function remains largely unexplored, especially through a cell type lens. Leveraging today's largest single‐nucleus RNA sequencing dataset of AD, we unveil how cell‐type‐specific mitochondrial alterations reverberate in the nuclear transcriptome, in 424 AD patients and healthy controls from ROSMAP. By adopting a supervised latent factor modelling approach, we identified distinct gene modules capturing unique aspects of the mitochondrial crosstalk in 6 major brain cell types across 5,427 nuclear and 13 mitochondrial genes. We found that nuclear‐mitochondrial crosstalk varies distinctly with cell identity, reflecting metabolic demands and functional specialization. In neurons and oligodendrocytes, ATP synthase (complex V) takes a central role, whereas type 1 NADH dehydrogenase (complex I) is more prominent in astrocytes, microglia, and OPCs. Screening across >1 million gene expression profiles from ∼20,000 drug perturbations identified mitochondrial‐nuclear signatures that resemble those activated by parthenolide and niclosamide—two chemical compounds previously associated with oxidative stress and cytotoxicity via ubiquitination—as most predictive of AD. Microglia and OPCs achieved the highest overall classification accuracy, with stronger predictive performance observed in males than in females. Mapping gene module expressions to the Allen Human Brain Atlas revealed shared whole‐brain patterns highlighting the precuneus, which we implicated in ubiquitin‐cascade‐enriched modules. Clinical phenotyping revealed that males with higher AD risk, as indicated by their mitochondrial‐nuclear scores on glial gene modules, exhibited a greater pathological burden, including higher amyloid load, Parkinson's‐like symptoms, and neuroticism‐related traits. Finally, by comparing our findings with 2.5 million CRISPRi‐based perturbations, we identified neural signatures associated with female‐biased transcription factors and fatty acid biosynthesis, while glial signatures were linked to DNA damage and oxidative stress. By integrating multiple layers of biological data from established reference atlases, our analysis of mitochondria‐nuclear crosstalk revealed distinct transcriptional signatures associated with AD risk in glial and neural cells, with these associations exhibiting sex‐biased patterns.

2025-11-30

Alzheimer's & Dementia (published)

doi.org

Enhancing decision-making in glioblastoma surgery through an explainable human-Al collaboration: an international multicenter model development and external validation study

Julius M. Kernbach

Urte Schroeder

Karlijn Hakvoort

Jonas Ort

Hussam Hamou

Danilo Bzdok

Yasin Temel

Pieter Kubben

Charlotte Weyland

Martin Wiesmann

Victor Staartjes

Kevin Akeret

Moira Vieli

Carlo Serra

Luca Regli

Stefan Grau

Lasse Dührsen

Franz Ricklefs

Oliver Schnell

David Ryan Ormond … (see 9 more)

Alexander Grote

Matthias Simon

Hagen Meredig

Marianne Schell

Martin Bendszus

Georg Neuloh

Hans Clusmann

Dieter-Henrik Heiland

Daniel Delev

Surgical resection improves survival in glioblastoma, yet predicting the extent of resection (EOR) remains highly challenging. We developed … (see more)and externally validated an explainable AI model to generate personalized EOR estimates in 811 glioblastoma patients undergoing microsurgical resection. EOR was categorized into gross-total (GTR), near-total (NTR), and subtotal resections (STR). An interpretable framework provided model explanations and sensitivity analyses to assess the model’s strengths and limitations. To demonstrate clinical impact, we compared the performance of the human expert (gold standard) with our AI model and a combined human-AI approach. External validation confirmed generalizability (AUC 0.78, CI 0.73-0.82). Class-specific AUCs were 0.75 (0.67-0.82) for GTR, 0.59 (0.50-0.69) for NTR, and 0.69 (0.53-0.85) for STR. Key predictors included KPS and NANO scores, age, tumor volume, and unfavorable anatomical locations. A combined human-AI collaboration outperformed human experts, with higher overall accuracies (0.53 to 0.94), F1 scores (0.30 to 0.92), and Cohen’s κ (0.41 to 0.84). Enhancing predictive performance through the clinician-AI collaboration, our explainable model supports preoperative planning and highlights the value of integrating machine intelligence into surgical decision-making.

2025-11-26

NPJ Precision Oncology (published)

doi.org

Quantitative MRI of the hippocampus reveals microstructural trajectories of aging and Alzheimer’s disease pathology

Alfie Wearn

Christine L. Tardif

Ilana R. Leppert

Giulia Baracchini

Colleen Hughes

Jennifer Tremblay-Mercier

John Breitner

Judes Poirier

Sylvia Villeneuve

Boris C. Bernhardt

Gary R. Turner

R. Nathan Spreng

Sylvia Villeneuve

Judes Poirier

John Breitner

Sylvia Villeneuve

Andrée-Ann Baril

Pierre Bellec

Véronique Bohbot

Danilo Bzdok … (see 27 more)

Mallar Chakravarty

D. Louis Collins

Mahsa Dadar

Simon Ducharme

Alan Evans

Claudine Gauthier

Maiya R. Geddes

Rick Hoge

Yasser Ituria-Medina

Gerhard Multhaup

Lisa-Marie Münter

Alexa Pichet Binette

Natasha Rajah

Pedro Rosa-Neto

Taylor Schmitz

Jean-Paul Soucy

R. Nathan Spreng

Christine L. Tardif

Etienne Vachon-Presseau

Christine L. Tardif

Maxime Descoteaux

Robert Laforce

Pierre Etienne

Serge Gauthier

Vasavan Nair

Judes Poirier

Daniel Auld

Hippocampal atrophy, typically measured using volumetry, is a hallmark feature of both normal aging and Alzheimer’s disease (AD). However,… (see more) the earliest stages of atrophy manifest as microstructural changes in tissue composition rather than macroscopic volume loss. We conducted longitudinal in vivo mapping of hippocampal microstructure in healthy aging and incipient AD, highlighting demyelination, iron deposition, and changes in water content as markers of age and AD risk. A combination of macrostructural and microstructural measures provides a more comprehensive picture of brain health and disease, unlocking unique insights into the pathological state of brain tissue and the impact of AD at a point where therapeutic rescue of the tissue is most likely to be efficacious.

2025-10-26

Proceedings of the National Academy of Sciences (published)

doi.org

Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort

Avram J. Holmes

Sarah W. Yip

Danilo Bzdok

As an increasing realization, many behavioral relationships are interwoven with inherent variations in human populations. Presently, there i… (see more)s no clarity in the biomedical community on which sources of population variation are most dominant. The recent advent of population-scale cohorts like the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) are now offering unprecedented depth and width of phenotype profiling that potentially explains interfamily differences. Here, we leveraged a deep learning framework (conditional variational autoencoder) on the totality of the ABCD Study® phenome (8,902 candidate phenotypes in 11,875 participants) to identify and characterize major sources of population stratification. 80% of the top 5 sources of explanatory stratifications were driven by distinct combinations of 202 available socioeconomic status (SES) measures; each in conjunction with a unique set of non-overlapping social and environmental factors. Several sources of variation across this cohort flagged geographies marked by material poverty interlocked with mental health and behavioral correlates. Deprivation emerged in another top stratification in relation to urbanicity and its ties to immigrant and racial and ethnic minoritized groups. Conversely, two other major sources of population variation were both driven by indicators of privilege: one highlighted measures of access to educational opportunity and income tied to healthy home environments and good behavior, the other profiled individuals of European ancestry leading advantaged lifestyles in desirable neighborhoods in terms of location and air quality. Overall, the disclosed social stratifications underscore the importance of treating SES as a multidimensional construct and recognizing its ties into social determinants of health.

2025-08-12

PLOS One (published)

doi.org

Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort

Avram J. Holmes

Sarah W. Yip

Danilo Bzdok

As an increasing realization, many behavioral relationships are interwoven with inherent variations in human populations. Presently, there i… (see more)s no clarity in the biomedical community on which sources of population variation are most dominant. The recent advent of population-scale cohorts like the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) are now offering unprecedented depth and width of phenotype profiling that potentially explains interfamily differences. Here, we leveraged a deep learning framework (conditional variational autoencoder) on the totality of the ABCD Study® phenome (8,902 candidate phenotypes in 11,875 participants) to identify and characterize major sources of population stratification. 80% of the top 5 sources of explanatory stratifications were driven by distinct combinations of 202 available socioeconomic status (SES) measures; each in conjunction with a unique set of non-overlapping social and environmental factors. Several sources of variation across this cohort flagged geographies marked by material poverty interlocked with mental health and behavioral correlates. Deprivation emerged in another top stratification in relation to urbanicity and its ties to immigrant and racial and ethnic minoritized groups. Conversely, two other major sources of population variation were both driven by indicators of privilege: one highlighted measures of access to educational opportunity and income tied to healthy home environments and good behavior, the other profiled individuals of European ancestry leading advantaged lifestyles in desirable neighborhoods in terms of location and air quality. Overall, the disclosed social stratifications underscore the importance of treating SES as a multidimensional construct and recognizing its ties into social determinants of health.

2025-08-12

Public Library of Science ONE (published)

doi.org

Latent brain subtypes of chronotype reveal unique behavioral and health profiles: an across-cohort validation

Julie Carrier

Kai-Florian Storch

Robin Dunbar

Danilo Bzdok

Chronotype is shaped by the complex interplay of endogenous and exogenous factors. This trait ties into various behaviors in the wider socie… (see more)ty and is linked to the prevalence of psychiatric and metabolic conditions. Despite its multifaceted nature, prior research has treated chronotype as a monolithic trait across the population, risking overlooking substantial heterogeneity in neural and behavioral fingerprints of both early risers and night owls. To test for such hidden subgroups, we developed a supervised pattern-learning framework for trait subtyping, integrating three complementary brain-imaging modalities with deep behavior, diagnosis, and drug prescription profiling from 27,030 UK Biobank participants. We identified and characterized five distinct biologically valid chronotype subtypes: (1) typical eveningness, (2) depression-associated eveningness, (3) typical morningness, (4) morningness with greater expression in females, and (5) eveningness with greater expression in males. Each uncovered subtype showed unique patterns across brain, behavioral and health profiles. We finally externally validated these subtypes in 10,550 US children from the ABCD Study® cohort, which revealed reversed age distributions and replicated sex-associated brain-behavioral patterns, underscoring the fact that potential divergences between chronotype traits observed throughout adulthood may begin to emerge early in life. These findings highlight underappreciated sources of population variation that echo the rhythm of people’s inner clock.

2025-07-28

Research Square (preprint)

doi.org

A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B. T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Danilo Bzdok

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging-genetics field has st… (see more)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, brain architecture, environment, human health and daily living skills. Here we interrogate the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and their corresponding implications on cognitive, behavioural, psychosocial and socioeconomic traits. We design a holistic pattern-learning framework that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 938 behavioural variables spanning 20 categories in 7,338 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks and specific parameters of the family wellbeing, including increased parental and child stress, anxiety and depression, or neighbourhood dynamics such as decreased safety. We thus find effects extending beyond the impairment of cognitive ability or language capacity which have been previously reported. Our investigation spotlights the interplay between genetic variation and subjective life quality in adolescents and their families.

2025-07-17

Nature Biomedical Engineering (published)

doi.org

A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort

Kuldeep Kumar

Zohra Saci

Martineau Jean-Louis

Xiaoqian J. Chai

Tian Ge

B. T. Thomas Yeo

Paul M. Thompson

Carrie E. Bearden

Ole A. Andreassen

Sébastien Jacquemont

Danilo Bzdok

Our genetic makeup, together with environmental and social influences, shape our brain's development. Yet, the imaging-genetics field has st… (see more)ruggled to integrate all these modalities to investigate the interplay between genetic blueprint, brain architecture, environment, human health and daily living skills. Here we interrogate the Adolescent Brain Cognitive Development (ABCD) cohort to outline the effects of rare high-effect genetic variants on brain architecture and their corresponding implications on cognitive, behavioural, psychosocial and socioeconomic traits. We design a holistic pattern-learning framework that quantitatively dissects the impacts of copy number variations (CNVs) on brain structure and 938 behavioural variables spanning 20 categories in 7,338 adolescents. Our results reveal associations between genetic alterations, higher-order brain networks and specific parameters of the family wellbeing, including increased parental and child stress, anxiety and depression, or neighbourhood dynamics such as decreased safety. We thus find effects extending beyond the impairment of cognitive ability or language capacity which have been previously reported. Our investigation spotlights the interplay between genetic variation and subjective life quality in adolescents and their families.

2025-07-17

Nature Biomedical Engineering (published)

doi.org

Brain Age Prediction: Deep Models Need a Hand to Generalize

Reza Rajabli

Mahdie Soltaninejad

Vladimir S. Fonov

Danilo Bzdok

D. Louis Collins

Predicting brain age from T1‐weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep le… (see more)arning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns about robust and accurate generalization in new data limit their clinical applicability. The large number of trainable parameters, combined with limited medical imaging training data, contributes to this challenge, often resulting in a generalization gap where there is a significant discrepancy between model performance on training data versus unseen data. In this study, we assess a deep model, SFCN‐reg, based on the VGG‐16 architecture, and address the generalization gap through comprehensive preprocessing, extensive data augmentation, and model regularization. Using training data from the UK Biobank, we demonstrate substantial improvements in model performance. Specifically, our approach reduces the generalization MAE by 47% (from 5.25 to 2.79 years) in the Alzheimer's Disease Neuroimaging Initiative dataset and by 12% (from 4.35 to 3.75 years) in the Australian Imaging, Biomarker and Lifestyle dataset. Furthermore, we achieve up to 13% reduction in scan‐rescan error (from 0.80 to 0.70 years) while enhancing the model's robustness to registration errors. Feature importance maps highlight anatomical regions used to predict age. These results highlight the critical role of high‐quality preprocessing and robust training techniques in improving accuracy and narrowing the generalization gap, both necessary steps toward the clinical use of brain age prediction models. Our study makes valuable contributions to neuroimaging research by offering a potential pathway to improve the clinical applicability of deep learning models.

2025-07-15

Human Brain Mapping (published)

doi.org

Longer scans boost prediction and cut costs in brain-wide association studies

Leon Qi Rong Ooi

Csaba Orban

Shaoshi Zhang

Thomas E. Nichols

Trevor Wei Kiat Tan

Ru Kong

Scott Marek

Nico U. F. Dosenbach

Timothy O. Laumann

Evan M. Gordon

Kwong Hsia Yap

Fang Ji

Joanna Su Xian Chong

Christopher Chen

Lijun An

Nicolai Franzmeier

Sebastian N. Roemer-Cassiano

Qingyu Hu

Jianxun Ren

Hesheng Liu … (see 9 more)

Sidhant Chopra

Carrisa V. Cocuzza

Justin T. Baker

Juan Helen Zhou

Danilo Bzdok

Simon B. Eickhoff

Avram J. Holmes

B. T. Thomas Yeo

Clifford R. Jack Jr

A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional MRI (fMRI) scan time or sample size. We der… (see more)ive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies extremely well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R2 = 0.89), spanning a wide range of scanners, acquisitions, racial groups, disorders and ages. For scans ≤20 mins, prediction accuracy increases linearly with the logarithm of total scan duration, suggesting interchangeability of sample size and scan time. However, sample size is ultimately more important than scan time in determining prediction accuracy. Nevertheless, when accounting for overhead costs associated with each participant (e.g., recruitment costs), to boost prediction accuracy, longer scans can yield substantial cost savings over larger sample size. To achieve high prediction performance, 10-min scans are highly cost inefficient. In most scenarios, the optimal scan time is ≥20 mins. On average, 30-min scans are the most cost-effective, yielding 22% cost savings over 10-min scans. Overshooting is cheaper than undershooting the optimal scan time, so we recommend aiming for ≥30 mins. Compared with resting-state whole-brain BWAS, the most cost-effective scan time is shorter for task-fMRI and longer for subcortical-cortical BWAS. Standard power calculations maximize sample size at the expense of scan time. Our study demonstrates that optimizing both sample size and scan time can boost prediction power while cutting costs. Our empirically informed reference is available for future study planning: WEB_APPLICATION_LINK

2025-07-15

Nature (published)

doi.org

Longitudinal changes in brain asymmetry track lifestyle and disease

Karin Saltoun

B. T. Thomas Yeo

Lynn Paul

Jörn Diedrichsen

Danilo Bzdok

Human beings may have evolved the largest asymmetries of brain organization in the animal kingdom. Hemispheric left-vs-right specialization … (see more)is especially pronounced in species-unique capacities, including emotional processing such as facial judgments, language-based feats such as reading books, and creativity such as musical performances. We hence chart the largest longitudinal brain-imaging resource, and provide evidence that brain asymmetry changes continuously in a manner suggestive of neural plasticity throughout adulthood. In the UK Biobank population cohort, we demonstrate that whole-brain patterns of asymmetry changes show robust phenome-wide associations across 959 distinct variables spanning 11 categories. We also find that changes in brain asymmetry over years co-occur with changes among specific lifestyle markers. We uncover specific brain asymmetry changes which systematically co-occur with entering a new phase of life, namely retirement. Finally, we reveal relevance of evolving brain asymmetry within subjects to major disease categories across ~4500 total medical diagnoses. Our findings speak against the idea that asymmetrical neural systems are conserved throughout adulthood.

2025-06-30

Nature Communications (published)

doi.org

Mila Techaide 2026

Venture Scientist Bootcamp

AI Advantage: Productivity in Public Service

Danilo Bzdok

Biography

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Mila Techaide 2026

Venture Scientist Bootcamp

AI Advantage: Productivity in Public Service

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Danilo Bzdok

Biography

Current Students

Blog Posts

Publications