Poster No:
566
Submission Type:
Late-Breaking Abstract Submission
Authors:
wenyi xiao1, Rachel Woodham1, Yuhan Cui2, Junhao Wen2, Mathilde Antoniades2, Dhivya Srinivasan3, Yong Fan3, Guray Erus3, Jose Garcia3, Stephen Arnott4, Taolin Chen5, Ki Choi6, Cherise Fatt7, Benicio Frey8, Vibe Frokjaer9, Melanie Ganz9, Beata Godlewska10, Stefanie Hassel11, Keith Ho12, Andrew McIntosh13, Kun Qin5, Susan Rotzinger12, Matthew Sacchet14, Jonathan Savitz15, Haochang Shou3, Ashish Singh3, Aleks Stolicyn13, Irina Strigo16, Stephen Strother4, Duygu Tosun16, Dongtao Wei17, Roland Zahn18, Ian Anderson19, Edward Craighead20, William Deakin19, Boadie Dunlop20, Rebecca Elliott19, Qiyong Gong Qiyong Gong21, lan Gotlib22, Catherine Harmer10, Sidney Kennedy12, Gitte Knudsen23, Helen Mayberg6, Martin Paulus15, Jiang Qiu17, Madhukar Trivedi7, Heather Whalley13, Chao-Gan Yan24, Allan Young18, Christos Davatzikos3, Cynthia Fu25
Institutions:
1university of east london, London, 2Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 3University of Pennsylvania, Philadelphia, PA, 4Baycrest Centre, Toronto, Ontario, 5West China Hospital of Sichuan University, Chengdu, Sichuan, 6Icahn School of Medicine at Mount Sinai, New York, NY, 7University of Texas Southwestern Medical Center, Dallas, TX, 8McMaster University, Hamilton, Ontario, 9University of Copenhagen, Copenhagen, Copenhagen, 10University of Oxford, London, London, 11University of Calgary, Calgary, Alberta, 12University Health Network, Toronto, Ontario, 13Royal Infirmary Edinburgh, Edinburgh, Edinburgh, 14Harvard Medical School, Boston, MA, 15Laureate Institute for Brain Research, Tulsa, OK, 16University of California San Francisco, San Francisco, CA, 17Southwest University, Chongqing, Chongqing, 18King’s College London, London, London, 19University of Manchester, Manchester, Yorkshire, 20Emory University School of Medicine, Atlanta, GA, 21Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, chengdu, sichuan, 22Stanford University, Stanford, CA, 23University Hospital Rigshospitalet, Copenhagen, Copenhagen, 24Tsinghua University, Beijing, Beijing, 25university of east london, London, London
First Author:
Co-Author(s):
Yuhan Cui
Perelman School of Medicine, University of Pennsylvania
Philadelphia, PA
Junhao Wen
Perelman School of Medicine, University of Pennsylvania
Philadelphia, PA
Yong Fan
University of Pennsylvania
Philadelphia, PA
Guray Erus
University of Pennsylvania
Philadelphia, PA
Jose Garcia
University of Pennsylvania
Philadelphia, PA
Taolin Chen
West China Hospital of Sichuan University
Chengdu, Sichuan
Ki Choi
Icahn School of Medicine at Mount Sinai
New York, NY
Cherise Fatt
University of Texas Southwestern Medical Center
Dallas, TX
Melanie Ganz
University of Copenhagen
Copenhagen, Copenhagen
Keith Ho
University Health Network
Toronto, Ontario
Kun Qin
West China Hospital of Sichuan University
Chengdu, Sichuan
Irina Strigo
University of California San Francisco
San Francisco, CA
Duygu Tosun
University of California San Francisco
San Francisco, CA
Qiyong Gong Qiyong Gong
Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University
chengdu, sichuan
Gitte Knudsen
University Hospital Rigshospitalet
Copenhagen, Copenhagen
Helen Mayberg
Icahn School of Medicine at Mount Sinai
New York, NY
Jiang Qiu
Southwest University
Chongqing, Chongqing
Cynthia Fu
university of east london
London, London
Introduction:
Major depressive disorder (MDD) is a leading cause of disability worldwide and the most common precursor to suicide. It is characterised by a persistent low mood or loss of pleasure which is associated with neurovegetative symptoms. Currently, MDD diagnosis relies solely on clinical symptoms, with no established biomarkers to guide treatment (Fu et al., 2019). In our COORDINATE-MDD consortium, we had identified two neuroanatomical dimensions in medication-free individuals with first-episode or recurrent MDD, all in a current depressive episode (Fu et al., 2023, 2024). Dimension 1 (D1) was characterised by preserved grey and white matter volumes and a positive response to selective serotonin reuptake inhibitors (SSRIs) but not to placebo medication, while Dimension 2 (D2) showed reduced grey and white matter volumes and a poor response to both SSRIs and placebo. The present study aimed to externally validate these dimensions in a general population cohort and to investigate their associations with cognition, adverse life events, self-harm, metabolomics, and genetic variation.
Methods:
The model derived from COORDINATE-MDD structural neuroimaging data was externally validated in UK Biobank (UKB) participants (n = 37,235) (Wen et al., 2022). MRI scans underwent quality control, segmentation into 145 brain regions, and harmonisation for sex, age, and intracranial volume. HYDRA clustering (k = 2), trained on COORDINATE-MDD data (Fu et al., 2024), classified UKB participants into D1, D2, both D1 and D2, or neither D1 nor D2 based on expression scores. Cognitive function was evaluated across seven domains, with general linear models adjusted for age, sex, and D1/D2 membership. Associations with depressive symptoms, anxiety, neuroticism, adverse life events, self-harm, lifestyle factors, and metabolomics were analysed using chi-square tests or general linear regression. Metabolomic analysis included 68 biomarkers inlipid metabolism, inflammation, and glucose regulation. Genome-wide association studies (GWAS) identified distinct genetic loci for D1 andD2, adjusting for age, sex, intracranial volume, and 40 genetic principal components.
Results:
External validation confirmed distinct neurobiological profiles of D1 and D2. Structural MRI differences showed that D1 had preserved grey and white matter volumes, whereas D2 exhibited widespread reductions in both. D2 was associated with significantly higher rates of childhood maltreatment, including physical abuse, greater exposure to adult interpersonal violence, higher prevalence of self-harm and suicide attempts, a pro-atherogenic lipid profile in D2, with elevated systemic inflammation markers such as C-reactive protein, glycated haemoglobin, and white blood cell count, as well as metabolic dysregulation reflected in increased body fat percentage and reduced muscle mass, as compared to D1 Genetic analyses identified distinct loci associated with D1 and D2, with D2-linked variants showing associations with neurodegeneration, white matter microstructure, and brain ageing.
Conclusions:
These findings demonstrate external validation of two dimensions associated with differing cognitive, metabolic, and genetic profiles. D2 is associated with widespread structural deficits, cognitive impairments, exposure to early-life adversity, and a pro-atherogenic, inflammatory metabolic profile. Distinct genetic associations further support the biological differentiation of these dimensions, with D2 linked to neurodegeneration and white matter microstructure alterations. The external validation in a large general population cohort underscores the potential of biomarker-driven subtyping to delineate the neurobiological dimensions that comprise our current wholly symptom-based diagnosis of MDD diagnosis which can enhance precision medicine approaches for treatment.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetic Association Studies
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Neuroanatomy Other 2
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Machine Learning
Psychiatric Disorders
STRUCTURAL MRI
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
MUlti-atlas region Segmentation utilizing Ensembles (MUSE)
Provide references using APA citation style.
Fu, C.H.Y., Fan, Y., & Davatzikos, C. (2019). Addressing heterogeneity (and homogeneity) in treatment mechanisms in depression and the potential to develop diagnostic and predictive biomarkers. NeuroImage: Clinical, 24, 101997.
Fu, C. H., Erus, G., Fan, Y., Antoniades, M., Arnone, D., Arnott, S. R., ... & Davatzikos, C. (2023). AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale. BMC psychiatry, 23(1), 59.
Fu, C.H.Y., et al. (2024). Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo. Nature Mental Health, 2(2), 164-176.
James, S.L., et al. (2018). Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 392(10159), 1789-1858.
Varol, E., Sotiras, A., & Davatzikos, C. (2017). HYDRA: Revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. NeuroImage, 145, 346–364.
Wen, J., et al. (2022). Characterizing heterogeneity in neuroimaging, cognition, clinical symptoms, and genetics among patients with late-life depression. JAMA Psychiatry, 79(5), 464–474.
No