Nature Search

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals

Cardiovascular and metabolic risk factors (CVM) impact brain structure and increase dementia risk. Here, the authors developed and validated machine learning models to measure the neuroanatomical changes in people with cardiovascular and metabolic diseases that are cognitively unimpaired.

Sindhuja Tirumalai Govindarajan
Elizabeth Mamourian
Christos Davatzikos
ResearchOpen Access19 Mar 2025
Nature Communications

Volume: 16, P: 1-12
The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers

Kazerooni et al. explore the associations between tumor imaging and spatial characteristics with cancer gene mutations and the inferred sequence of mutational events. Radiogenomics reflects glioblastoma's molecular heterogeneity and imaging biomarkers and spatial patterns can reveal key oncogenic drivers.

Anahita Fathi Kazerooni
Hamed Akbari
Christos Davatzikos
ResearchOpen Access01 Mar 2025
Communications Medicine

Volume: 5, P: 1-15
Brain aging patterns in a large and diverse cohort of 49,482 individuals

Assessing brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies, a generative model identifies five dominant patterns of brain atrophy, with specific associations with biomedical, lifestyle and genetic factors.

Zhijian Yang
Junhao Wen
Christos Davatzikos
Research15 Aug 2024
Nature Medicine

Volume: 30, P: 3015-3026
The genetic architecture of multimodal human brain age

The biological basis of brain aging is not well understood, but it has implications for human health. Here, the authors explore the genetic basis of human brain aging, finding genetic variants, genes and potential causal relationships with disease.

Junhao Wen
Bingxin Zhao
Christos Davatzikos
ResearchOpen Access23 Mar 2024
Nature Communications

Volume: 15, P: 1-15
Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.

Zhijian Yang
Junhao Wen
Christos Davatzikos
ResearchOpen Access08 Jan 2024
Nature Communications

Volume: 15, P: 1-16
Quantification of tumor microenvironment acidity in glioblastoma using principal component analysis of dynamic susceptibility contrast enhanced MR imaging

Hamed Akbari
Anahita Fathi Kazerooni
Ali Nabavizadeh
ResearchOpen Access22 Jul 2021
Scientific Reports

Volume: 11, P: 1-8
The genetic architecture of biological age in nine human organ systems

Using machine learning techniques applied to multimodal UK Biobank data, Wen et al. characterize the genetic basis of the biological age gaps of individual organs, uncovering interorgan cross-talk and links between chronic diseases, lifestyle factors and biological age gaps.

Junhao Wen
Ye Ella Tian
Christos Davatzikos
Research28 Jun 2024
Nature Aging

Volume: 4, P: 1290-1307
Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer’s disease continuum

Junhao Wen
Zhijian Yang
Christos Davatzikos
ResearchOpen Access05 Oct 2024
Translational Psychiatry

Volume: 14, P: 1-14
Integrating imaging and genomic data for the discovery of distinct glioblastoma subtypes: a joint learning approach

Jun Guo
Anahita Fathi Kazerooni
Christos Davatzikos
ResearchOpen Access28 Feb 2024
Scientific Reports

Volume: 14, P: 1-11
Clinical measures, radiomics, and genomics offer synergistic value in AI-based prediction of overall survival in patients with glioblastoma

Anahita Fathi Kazerooni
Sanjay Saxena
Christos Davatzikos
ResearchOpen Access24 May 2022
Scientific Reports

Volume: 12, P: 1-13
The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Spyridon Bakas
Chiharu Sako
Christos Davatzikos
ResearchOpen Access29 Jul 2022
Scientific Data

Volume: 9, P: 1-12

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