Many different research groups working with many different datasets have encountered the same problem. Multivariate brain patterns often lack specificity and tend to be associated with a wide range of system level variables. This non specific general background or neural psychopathology (P) factor poses a significant challenge when we try to uncover disorder specific signatures in the brain. It is of key importance to discuss ways to target this problem and move from non-specific patterns to one-on-one maping of specific pathology to specific brain alterations. Here we bring together four different researchers who encountered this problem in four different large datasets and addressed it in different ways.
Dr. Hajek will demonstrate on ENIGMA data from 2443 participants that the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis of BD or schizophrenia, age, obesity, and treatment with antipsychotic medications or lithium. Dr. Paus will document their analyses of over 25000 individuals across 6 psychiatric disorders. Using virtual histology and virtual ontogeny, they identified a number of commonalities in the neurobiological underpinnings of group differences between patients with different psychiatric disorders and their respective controls. Dr. Paus will illustrate the power of relating phenotypes derived from in vivo magnetic resonance imaging to those obtained post mortem with transcriptomics. Dr. Sprooten and her group developed genomic independent and principal component analysis (ICA and PCA) to decompose thousands of genome-wide associations of neuroimaging traits into fewer and more interpretable genome-wide components. She showed that mere 10 components explained ~40% of the total genome wide associations across >2,000 neuroimaging traits. Mrs. Hettwer will present multi-modal insights into brain organizational principles shaping transdiagnostic vulnerability. Combining meta-analytic cortical thickness maps of case control differences in 6 major mental disorders with multi-scale data, she characterized a transdiagnostic cortical coordinate space framed by connectomic, cytoarchitectonic, and functional dimensions, along which synchronized brain alterations. At the end of this session, the participant will gain an understanding of the neural P factor how it relates to genetic/functional/symptomatic overlaps and how to see through this non-specific background and uncover brain signatures that are more specific to disorders, symptoms or underlying biological mechanisms.
At the end of this session, participants will gain understanding of:
1) what is meant by the "neural P factor"
2) how the neural P factor relates to genetic/functional/symptomatic/histological similarities across major psychiatric disorders
3) new approaches to uncover brain signatures that are more specific to disorders, symptoms or underlying biological mechanisms.
This symposium will be of interest to researchers working with large datasets in general, those generally interested in brain organization and how it relates to pathology and especially to those interested in neural underpinnings of psychiatric disorders. In addition, the symposium would be valuable to people who encountered similar issue of non-specificity when studying genetic or cognitive signatures of different brain disorders.
Individual differences in brain structure and function, as measured using MRI, are heritable. Genome-wide association studies (GWAS) are conducted to gain an understanding of the molecular mechanisms driving this inheritance of brain variation. However, this mechanistic translation is challenging due to the high polygenicity and pleiotropy. The typical big data structure we have with neuroimaging genomics is a very large number of traits (across brain regions and MRI modalities) associated with millions of genomic variants, in a way that shows intricate cross-trait genetic correlation patterns. Taking advantage of this high-dimensional data structure, we developed genomic independent and principal component analysis (ICA and PCA) to decompose thousands of GWASs of neuroimaging traits into more interpretable genome-wide components. Our results show that 10 components explain ~40% of the total genetic associations across >2,000 neuroimaging traits, and improve the reproducibility of the genetic signal. Further, we show that several of these components index loci with shared biological functions and/or behavioural associations, and that they are mainly driven by distinct genetic underpinnings across tissues and MRI modalities. Currently, we are investigating how the different genomic components map to genetic risk for brain disorders. Taken together, these results encourage further applications of genomic ICA and PCA to high-dimensional GWAS data to improve interpretability, signal-to-noise ratio, and potentially individual stratification of healthy and clinical populations.
, Donders Institute for Brain, Cognition & Behaviour Amsterdam, N/A
Overlapping psychopathological spectra may reflect an underlying, general liability for mental illness linked to shared risk factors and common alterations in neurodevelopmental processes. In three studies, we investigated how multi-scale features of brain organization shape topologically heterogeneous levels of transdiagnostic vulnerability. Combining meta-analytic cortical thickness maps of 6 major mental disorders with multi-scale data, we characterized a transdiagnostic cortical coordinate space framed by connectomic, cytoarchitectonic, and functional dimensions, along which synchronized brain alterations are organized. We identified low-dimensional cortical axes of co-alteration networks, which indicated that the likelihood of two brain regions to display synchronized illness effects, regardless of their location on the cortical landscape, is related to the degree to which these regions share cytoarchitectonic profiles and are engaged in similar functional tasks. Moreover, fronto-temporal epicenters emerged as potential connectome anchors of shared cortical alterations. We extended these observations towards symptom domains and explored a continuous coordinate space in which individuals with mental disorders are embedded based on the degree to which they express a combination of pathological brain imaging patterns – partly crossing categorical diagnoses. Last, we studied adaptation to transdiagnostic environmental risk factors during adolescence and found that adolescents’ vulnerability or resilience to environmental stressors varies during development. This intra-individual variability was tied to maturational trajectories of cortical myelination of association cortices, as well as microstructural and functional network re-organization. In my contribution, I will present multi-modal insights into brain organizational principles shaping transdiagnostic vulnerability.
, Forschungszentrum Jülich Jülich, North Rhine-Westphalia
Using virtual histology (PMID: 32857118) and virtual ontogeny (PMID: 35489875), we have been able to identify a number of commonalities in the neurobiological underpinnings of group differences between patients with different psychiatric disorders (and their respective controls) in the radial (thickness) and tangential (surface area) expansion of the cerebral cortex. Using these two approaches, we have been also able to explore common neurobiological mechanisms underlying variations in cortical thickness across the lifespan (PMID: 33311571), and those in surface area in carriers of copy number variations across the genome and in selected clinical syndromes (unpublished). In my contribution, I will use some of these findings to illustrate the power of relating – in space and time – phenotypes derived in vivo with magnetic resonance imaging to those obtained post mortem with transcriptomics.
, Université de Montréal Montreal, Quebec
In this study, we applied principal component analyses and clustering to T1-weighted structural MRI data from 2443 participants with bipolar disorders (BD) and healthy controls and studied the associations between principal components and clinical and, demographic variables. The first principal component (PC) accounted for 42.7% of variance in cortical thickness across the whole brain and was associated with diagnosis of BD, BMI, treatment with antipsychotic medications and age. Lithium treatment was also associated with the first PC, but in opposite direction than the other factors. We could not find brain alterations specific to only a single system level variable. In other words, many different system level variables, were associated with cortical thickness in the same network of regions, a pattern reported also in other large studies. There appears to be a common common and difuse background of non-specific atrophy, which explains by far the largest proportion of variance across brain regions and is strongly associated with many different system level variables. This poses a significant challenge in our attempts to detect more specific brain signatures of specific pathologies.
, Dalhousie University Halifax, Nova Scotia