Mol Psychiatry

Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.IDDRC U54 HD090255/U.S. Department of Health & Human ServicesNational Institutes of Health (NIH)/S10 OD025111/OD/NIH HHS/United StatesFoundation Fellowship/Royal College of Psychiatrists (RCPsych)/WT_/Wellcome Trust/United KingdomNARSAD Distinguished Investigator award/Brain and Behavior Research Foundation (Brain & Behavior Research Foundation)/R44 MH086984/MH/NIMH NIH HHS/United StatesR01 EB019483/EB/NIBIB NIH HHS/United StatesU54 HD090255/HD/NICHD NIH HHS/United StatesR01 NS079788/NS/NINDS NIH HHS/United StatesFellowship/Foulkes Foundation/S10 OD025111/CD/ODCDC CDC HHS/United States2021-11-06T00:00:00Z32372008PMC76446228645vault:3615

Improved fidelity of brain microstructure mapping from single-shell diffusion MRI

Diffusion Weighted Imaging (DWI) is sensitive to alterations in the diffusion of water molecules caused by microstructural barriers. Different microstructural compartments are characterized by differences in DWI signal. Diffusion tensor imaging conflates the signal from these compartments into a single tensor, which poorly represents multiple white matter fascicles and extra-axonal space. Diffusion compartment imaging (DCI) models overcome this limitation by providing parametric representations for the signal contribution of each compartment, thereby improving the fidelity of brain microstructure mapping. However, current approaches fail to identify DCI model parameters from conventional single-shell DWI with the desired accuracy. It has been demonstrated that part of this inaccuracy is due to the ill-posedness of the estimation of DCI model parameters from conventional single-shell acquisitions. In this paper, we propose to regularize the estimation problem for single-shell DWI by learning a prior distribution of DCI model parameters from DWI acquired at multiple b-values in an external population of subjects. We demonstrate that this population-informed prior enables, for the first time, accurate estimation of DCI models from single-shell DWI typically acquired in clinical practice. We validated our approach on synthetic and in vivo data of healthy subjects and patients with autism spectrum disorder. We applied the approach to population studies of brain microstructure in autism and found that introducing a population-informed prior lead to reliable detection of group differences. Our algorithm enables novel investigation from large existing DWI datasets in normal development and in disease and injury

Microstructural imaging of the human spinal cord with advanced diffusion MRI

The aim of this PhD thesis is to advance the state-of-the-art of spinal cord magnetic resonance imaging (MRI) in multiple sclerosis (MS), a demyelinating, inflammatory and neurodegenerative disease of the central nervous system. Neurite orientation dispersion and density imaging (NODDI) is a recent diffusion-weighted (DW) MRI technique that provides indices of density and orientation dispersion of neuronal processes. These could be new useful biomarkers for the spinal cord, since they could better characterise overall, widespread MS pathology than conventional metrics. In this thesis, we test innovative clinically feasible acquisitions as well as signal analysis methods to study the potential of NODDI for the spinal cord. We also design and run computer simulations that corroborate our in vivo findings. Furthermore, we compare NODDI metrics to quantitative histological features, with the aim of validating their specificity. The thesis is divided in two parts. In the first part, in vivo experiments are described. Specific objectives are: i) to demonstrate the feasibility of performing NODDI in the spinal cord and in clinical settings; ii) to study the possibility of extracting with new approaches such as NODDI more specific microstructural information from standard DW acquisitions; iii) to assess how features typical of spinal cord microstructure, such as presence of large axons, influence NODDI metrics. In the second part of the thesis, ex vivo experiments are discussed. Their objective is the validation of the specificity of NODDI metrics via comparison to quantitative histology in post mortem spinal cord tissue. The experiments required the implementation of high-field DW scans as well as histological procedures and complex analysis pipelines. The results of this thesis contribute to current scientific knowledge. They prove that NODDI offers new opportunities to study how neurodegenerative diseases such as MS alter neural tissue complexity. We showed for the first time that NODDI can be performed in the spinal cord in vivo and in clinical scans. We also demonstrated that NODDI analysis of standard DW data is challenging, and quantified how the presence of large axons in the spinal cord influences NODDI metrics. Lastly, our ex vivo data highlight that unlike routine DW MRI methods, NODDI can detect reliably pathological variations of neurite orientation dispersion. NODDI is also sensitive to the density of axons and dendrites, but can not fully resolve axonal loss and demyelination in MS. We believe that the technique is a key element of a more general multi-modal MRI approach, which is necessary to obtain a complete description of complex diseases such as MS