AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included

Human Treg responses allow sustained recombinant adeno-associated virus-mediated transgene expression

Recombinant adeno-associated virus (rAAV) vectors have shown promise for the treatment of several diseases; however, immune-mediated elimination of transduced cells has been suggested to limit and account for a loss of efficacy. To determine whether rAAV vector expression can persist long term, we administered rAAV vectors expressing normal, M-type alpha-1 antitrypsin (M-AAT) to AAT-deficient subjects at various doses by multiple i.m. injections. M-specific AAT expression was observed in all subjects in a dose-dependent manner and was sustained for more than 1 year in the absence of immune suppression. Muscle biopsies at 1 year had sustained AAT expression and a reduction of inflammatory cells compared with 3 month biopsies. Deep sequencing of the TCR Vbeta region from muscle biopsies demonstrated a limited number of T cell clones that emerged at 3 months after vector administration and persisted for 1 year. In situ immunophenotyping revealed a substantial Treg population in muscle biopsy samples containing AAT-expressing myofibers. Approximately 10% of all T cells in muscle were natural Tregs, which were activated in response to AAV capsid. These results suggest that i.m. delivery of rAAV type 1-AAT (rAAV1-AAT) induces a T regulatory response that allows ongoing transgene expression and indicates that immunomodulatory treatments may not be necessary for rAAV-mediated gene therapy