Identifying shared resting state phenotypes in psychosis: a cross-sectional functional magnetic resonance imaging study of healthy controls and patients diagnosed with schizophrenia, bipolar disorder, and depression with psychotic features Nicholas Neufeld 1 , Joseph Geraci 2,3 , Jonathan Downar 1,3 , Aristotle Voineskos 1,4 (1) Department of Psychiatry, University of Toronto, Ontario, Canada, (2) Department of Pathology and Molecular Medicine, Queen’s University, Kingston, Ontario, Canada, (3) MRI-guided rTMS clinic, University Health Network, Toronto, Ontario, Canada, (4) Kimel Family Translational Imaging-Genetics Laboratory, Centre for Addiction and Mental Health, Toronto, Ontario, Canada • The evolution of psychiatric classification systems has increased the reliability of diagnoses, including diagnoses with psychosis as a feature. Yet modern psychiatric nosologies have fallen into disrepute for favouring diagnostic reliability at the expense of validity (Insel, 2013). • The validity of psychiatric diagnoses is thought to improve with efforts like the Research Domain Criteria (RDoC) framework which emphasizes a multimodal, multilevel integration of research findings to steer a new psychiatric nosology. • Resting state functional magnetic resonance imaging (rs- fMRI) is critical within such frameworks. Emerging evidence suggests rs-fMRI captures functional connectivity influenced by genetics (Glahn et al., 2010) and may relate to clinically apparent phenomena (Fornito and Bullmore, 2010). This raises the possibility of rs-fMRI as a biomarker for genetically predisposed individuals and a means of tracking response to therapeutic interventions. • To our knowledge there are no rs-fMRI studies of psychotic depression (Busatto, 2013) and psychotically depressed patients tend to be excluded from such studies (i.e. Dunlop et al., 2012). • Contrastingly, rs-fMRI studies of schizophrenics and bipolars have revealed shared and diagnosis-specific resting state networks (RSNs). • Across diagnoses, there are clinically apparent shared and differentiating symptoms in psychosis. We anticipate rs- fMRI of patients with psychotic depression will reveal RSNs that are shared with other patients. A pipeline is being developed that allows us to characterize these RSNs using graph theoretical methods. Participants We are currently recruiting healthy controls (n=16, target=30) as well as schizophrenic (n=17, target=30), bipolar (n=23, target=30), and previously psychotically depressed (n=14, target=30) patients at the Centre for Addiction and Mental Health (CAMH) in Toronto. Scanning 2D axial rs-fMRI scans are being acquired at CAMH using a spiral GRE pulse sequence on a GE Discovery MR750 3.0T scanner (TR/TE/Flip angle = 2000ms/30ms/60 o ; matrix = 64 x 64; 31 axial slices, 5mm slice thickness) Data analysis Quality control checks of our rs-fMRI are first conducted. A pipeline will then be used to preprocess images and generate/analyze graphs: • Behzadi, Y., Restom, K., Liau, J., & Liu, T.T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage, 37, 90-101. • Boost Graph Library. Retrieved from http://www.mathworks.com/matlabcentral/fileexchange/10922-matlabbgl • Brain Connectivity Toolbox. Retrieved from http://www.brain-connectivity-toolbox.net • Busatto, G.F. (2013). Structural and functional neuroimaging studies in major depressive disorder with psychotic features: a critical review. Schizophr Bull, 39, 776-786. • Chai, X.J., Castanon, A.N., Ongur, D., & Whitfield-Gabrieli, S. (2012). Anticorrelations in resting state networks without global signal regression. Neuroimage, 59, 1420-1428. • Craddock, R.C., James, G.A., Holtzheimer, P.E., Hu, X.P., & Mayberg, H.S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33, 1914-1928. • Dunlop, B.W., Binder, E.B., Cubells, J.F., Goodman, M.M., Kelley, M.E., Kinkea, B., Kutner, M., Nemeroff, C.B., Newport, D.J., Owens, M.J., Pace, T.W.W., Ritchie, J.C., Rivera, V.A., Western, D., Craighead, W.E., & Mayberg, H.S. (2012). Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial. Trials, 13, 1-18. • Insel, T. (2013). Transforming diagnosis. Retrieved from http://www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml • Fornito, A., & Bullmore, E.T. (2010). What can spontaneous fluctuations of the blood oxygenation-level-dependent signal tell us about psychiatric disorders? Curr Opin Psychiatry, 23, 239-249. Introduction Methods Anticipated results References Preprocessing 1. Images are realigned, normalized, and smoothed in FSL. 2. aCompCor is then used to correct for physiological noise (Behzadi et al., 2007; Chai et al., 2012). Graph generation (nodes and edges) 3. Generation of nodes: Spatially Constrained Parcellation is used to separate the rs-fMRI data into regions with homogeneous functional connectivity (Craddock et al., 2012). 4. Generation of edges: (a) Ledoit and Wolf (2004) algorithm constructs a covariance matrix; (b) partial correlations are computed through inversion of this covariance matrix. These partial correlations define the edges via an adjacency matrix. Graph analysis 5. The Brain Connectivity Toolbox and Matlab Boost Graph Library are used to compute measures centrality, including: degree, closeness, betweenness, eigenvector, and information centrality. Cluster coefficients and rich club measures will also be pursued (Rubinov and Sporns, 2010). • Fornito, A., & Bullmore, E.T. (2012). Connectomic intermediate phenotypes for psychiatric disorders. Front Psychiatry, 3, 1-15. • Glahn, D.C., Winkler, A.M., Kochunov, P., Almasy, L., Duggirala, R., Carless, M.A., Curran, J.C., Olvera, R.L., Laird, A.R., Smith, S.M., Beckmann, C.F., Fox, P.T., & Blangero, J. (2010). Genetic control over the resting brain. Proc Natl Acad Sci USA, 107, 1223-1228. • Khadka, S., Meda, S.A., Stevens, M.C., Glahn, D.C., Calhoun, V.D., Sweeney, J.A., Tamminga, C.A., Keshavan, M.S., O’Neil, K., Schretlen, D., & Pearlson, G.D. (2013). Is aberrant functional connectivity a psychosis endophenotype? A resting state functional magnetic resonance imaging study. Biol Psychiatry, 74, 458-466. • Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. J Multivar Anal, 88, 365-411. • Meda, S.A., Gill, A., Stevens, M.C., Lorenzoni, R.P., Glahn, D.C., Calhoun, V.D., Sweeney, J.A., Tamminga, C.A., Keshavan, M.S., Thaker, G., & Pearlson, G.D. (2012). Differences in resting-state functional magnetic resonance imaging functional network connectivity between schizophrenia and psychotic bipolar probands and their unaffected first-degree relatives. Biol Psychiatry, 71, 881-889. • Rubinov, M. & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52, 1059-69. • Based on the results of Khadka and colleagues (2013), we hypothesize that patients share networks in the meso/paralimbic and posterior default mode networks that differ from healthy controls. Alternatively, we hypothesize a more distributed network topology that connects fronto/occipital and anterior default mode/prefrontal networks that differs from controls (Meda et al., 2012). • Thinking nodally, we anticipate graph theoretical measures, particularly betweenness centrality and rich club topology, to identify key shared nodes in the prefrontal cortex, insula, and/or hippocampus in patients. These key nodes can be thought of as imaging phenotypes (Bussatto, 2013). A caveat to this interpretation is whether there are uni or bidirectional causal relationships between genetics, imaging phenotypes, and clinical symptoms. Yet situating this study within an RDoC framework allows us to draw on the strengths of rs-fMRI and graph theory while providing new, topology-driven hypotheses that can be explored in different modalities and levels of analysis. • For neuroscience: The identification of shared nodes provides targets/seed regions for multimodal studies that include genetic and structural imaging (Fornito and Bullmore, 2012). • For nosology: The identification of shared nodes may be thought of as common diatheses. We can then explore connections that originate at shared nodes and project to other topologies in the brain and ask how these connections alter clinical presentations. • For translational and clinical research: These nodes may be studied as candidate biomarkers for disorder severity and response to psychopharmacology or brain stimulation interventions.