Search

Supplemental material, sj-docx-1-jop-10.1177_02698811231158233 for Temporal profile of intranasal oxytocin in the human autonomic nervous system at rest: An electrocardiography and pupillometry study by Gonçalo Cosme, Patrícia Arriaga, Pedro J. Rosa, Mitul A. Mehta and Diana Prata in Journal of Psychopharmacology

© The Author(s), 2022. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited., Background: Given psychotic illnesses' high heritability and associations with brain structure, numerous neuroimaging-genetics findings have been reported in the last two decades. However, few findings have been replicated. In the present independent sample we aimed to replicate any psychosis-implicated SNPs (single nucleotide polymorphisms), which had previously shown at least two main effects on brain volume. Methods: A systematic review for SNPs showing a replicated effect on brain volume yielded 25 studies implicating seven SNPs in five genes. Their effect was then tested in 113 subjects with either schizophrenia, bipolar disorder, 'at risk mental state' or healthy state, for whole-brain and region-of-interest (ROI) associations with grey and white matter volume changes, using voxel-based morphometry. Results: We found FWER-corrected (Family-wise error rate) (i.e. statistically significant) associations of: (1) CACNA1C-rs769087-A with larger bilateral hippocampus and thalamus white matter, across the whole brain; and (2) CACNA1C-rs769087-A with larger superior frontal gyrus, as ROI. Higher replication concordance with existing literature was found, in decreasing order, for: (1) CACNA1C-rs769087-A, with larger dorsolateral-prefrontal/superior frontal gyrus and hippocampi (both with anatomical and directional concordance); (2) ZNF804A-rs11681373-A, with smaller angular gyrus grey matter and rectus gyri white matter (both with anatomical and directional concordance); and (3) BDNF-rs6265-T with superior frontal and middle cingulate gyri volume change (with anatomical and allelic concordance). Conclusions: Most literature findings were not herein replicated. Nevertheless, high degree/likelihood of replication was found for two genome-wide association studies- and one candidate-implicated SNPs, supporting their involvement in psychosis and brain structure., VT was supported by a Fundação para a Ciência e Tecnologia (FCT) PhD fellowship (PD/BD/114460/2016) and hired on the FCT DSAIPA/DS/0065/2018 grant. DP was supported, during this work, by the European Commission Seventh Framework Programme Marie Curie Career Integration Grant FP7-PEOPLE-2013-CIG-631952, the 2016 Bial Foundation Psychophysiology Grant – Ref. 292/16, and the FCT IF/00787/2014, LISBOA-01-0145-FEDER-030907, DSAIPA/DS/0065/2018 and UIDB/00645/2020 grants, and the Instituto de Medicina Molecular (iMM) Lisboa Director's Fund Breakthrough Idea Grant 2016.

Additional file 1. Supplementary Information for “Early diagnosis of Alzheimer’s disease using machine learning: a multi-diagnostic, generalizable approach”.

Funding Information: Acknowledgments: We thank the United Kingdom Brain Expression Consortium (UKBEC) investigators for BrainEAC access, whose work was supported by the MRC through the MRC Sudden Death Brain Bank (G0901254), a Training Fellowship (G0802462), and by the King Faisal Specialist Hospital and Research Centre, Saudi Arabia. We also thank the investigators of the Genotype-Tissue Expression (GTEx) Project, which was supported by the Common Fund of the Office of the Director of the National Institutes of Health (NIH) and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. Finally, we thank the CommonMind Consortium, supported by funding from Takeda Pharmaceuticals Company Limited, F. Hoffmann-La Roche Ltd, and NIH grants R01MH085542, R01MH093725, P50MH066392, P50MH080405, R01MH097276, RO1-MH-075916, P50M096891, P50MH084053S1, R37MH057881, AG02219, AG05138, MH06692, R01MH110921, R01MH109677, R01MH109897, U01MH103392, and contract HHSN271201300031C through IRP NIMH. Brain tissue for the study was obtained from the following brain bank collections: the Mount Sinai NIH Brain and Tissue Repository, the University of Pennsylvania Alzheimer’s Disease Core Center, the University of Pittsburgh NeuroBioBank and Brain and Tissue Repositories, and the NIMH Human Brain Collection Core. CMC Leadership: Panos Roussos, Joseph Buxbaum, Andrew Chess, Schahram Akbarian, Vahram Haroutunian (Icahn School of Medicine at Mount Sinai), Bernie Devlin, David Lewis (University of Pittsburgh), Raquel Gur, Chang-Gyu Hahn (University of Pennsylvania), Enrico Domenici (University of Trento), Mette A. Peters, Solveig Sieberts (Sage Bionetworks), Thomas Lehner, Stefano Marenco, and Barbara K. Lipska (NIMH). This paper represents independent research partly funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre in South London, the Maudsley NHS Foundation Trust, and King’s College, London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. Funding Information: Funding: For this work, V.T. received support from a Fundação para a Ciência e a Tecnologia (FCT) PhD fellowship (PD/BD/114460/2016) and DSAIPA/DS/0065/2018 grant. D.P. received support from FCT grants FCT-IF/00787/2014, LISBOA-01–0145-FEDER-030907, and DSAIPA/DS/0065/2018; a European Commission Marie Curie Career Integration Grant (FP7-PEOPLE-2013-CIG 631952); a Breakthrough Idea Grant from the Director’s Fund of iMM Lisboa (2016); and a Bial Foundation Psychophysiology Grant (Ref. 292/16). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC. publishersversion published