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Genetic and phenotypic architecture of the ontogenetic determinants of severe mental illness (completed)

Mental illness is the largest contributor to years lived with disability worldwide.

About the project

Causal models implementing cognitive, biological, and genetic mechanisms are lacking, and identifying mechanisms is crucial to improve mental health care. Psychotic disorders are heritable, involving aberrant development of brain networks.

Objectives

In this large-scale multi-disciplinary and translational effort, we combine advanced multimodal fusion of brain imaging data, clinical information and genetics to (1) characterize the hierarchy of the structural and functional neuronal breakdown in psychosis, its developmental profiles and genetic modifiers, (2) develop multimodal classifiers to identify clinically predictive genotype-phenotype patterns, and (3) describe the genetic mapping and hierarchical clustering of the discriminative brain patterns based on phenotypic and genetic correlations to identify genetic pleiotropy between risk and sustainment of disease and brain networks in a lifespan perspective.

Outcomes

Our unique approach includes state-of-the art technology and tools for data integration, and builds on the joint efforts of a cross-disciplinary international team. Anecdotal observations and statistical evidence have suggested overlap between a range of traits and disorders. Novel biostatistical tools have finally enabled us to quantify the genetic correlations between traits using high-density genotype data, which have created an avenue for genetic dissection of complex traits, allowing for new discoveries and hypotheses. These methods provide a biostatistical platform for challenging historical conventions, paving the way toward a new nosology of complex disorders. We leverage these exciting new developments to propose a genetically informed hierarchical clustering of brain imaging and clinical traits, with a particular emphasis on neurodevelopmental markers. In addition to improving current brain-based models of mental illness, our genetic dissection provides novel clues about the fundamental architecture of the brain, the dynamic lifespan alterations, and the perturbations underlying neurodevelopmental disorders.

Financing

The project is financed by the Research Council of Norway (FRIMEDBIO - Young Researcher Talents) 2016 - 2020.

Cooperation

Department of Psychology and Norwegian Centre for Mental Disorders Research (NORMENT).

Publications

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  • Maximov, Ivan & Westlye, Lars Tjelta (2019). Variability of NODDI metrics between AMICO and original NODDI in UK Biobank data.
  • Moutal, Nicolas; Maximov, Ivan & Grebenkov, Denis (2019). Optimized diffusion gradient waveforms for estimating surface-to-volume ratio of an anisotropic medium.
  • Moutal, Nicolas; Maximov, Ivan & Grebenkov, Denis (2019). An accurate surface-to-volume ratio estimation by general diffusion gradient waveform.
  • Geier, Oliver; Viekilde Pfeiffer, Helle; Nguyen, Bac; Hagen Kersten, Åsmund; Moltu, Sissel Jenifer & Maximov, Ivan (2019). Survey of maturation state in premature neonates using diffusion weighted and multi-echo T2 imaging.
  • Voldsbekk, Irene; Roelfs, Daniël Thomas Heimen; Bjørnerud, Atle; Groote, Inge Rasmus & Maximov, Ivan (2019). White matter microstructure changes as a function of time-of-day: a biomarker of accumulating sleep pressure?
  • Maximov, Ivan; Alnæs, Dag & Westlye, Lars Tjelta (2019). Influence of diffusion pipeline on data analysis: UK Biobank example for age-diffusion dependences.
  • Sone, Daichi; Watanabe, Masako; Ota, Miho; Imabayashi, Etsuko; Rokicki, Jaroslav & Maikusa, Norihide [Show all 12 contributors for this article] (2018). Subtle abnormality in neurite dispersion in idiopathic generalized epilepsy detected by an advanced diffusion imaging technique. Epilepsy and Seizure. ISSN 1882-5567. 10(1), p. 33–43. doi: 10.3805/EANDS.10.33.

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Published Aug. 17, 2016 12:47 PM - Last modified Feb. 9, 2021 12:10 PM