- Email: [email protected]
1017. Molecular Targets from Schizophrenia GWAS Investigated Using a Computer Model of Hippocampus
Biological Psychiatry
Saturday Abstracts
Background: Striatum involves in diverse psychological process and plays a central role in the dysfunctions of schizophrenia. However, as a heterogeneous complex structure, the precise connectivity profile of human striatum remains unclear. Recently, Tziortzi (2014) subdivided the striatum into seven subregions and made a striatal structural connectivity atlas according to their DTI and PET study. Methods: We used the meta-analytic connectivity modeling (MACM) to determine consistent functional coactivation patterns across experiments and behaviors associated with the seven striatal subregions defined by Oxford–GSK:Imanova Striatal connectivity Atlas (Tziortzi, 2014), along with a cross validation using the same seven ROIs for restingstate FC (RSFC) analysis on 27 healthy subjects. Then we compared RSFC of those seven striatal subregions between 45 first-episode, treatment-naive patients with schizophrenia and those 27 healthy controls. Results: 1) MACM results showed good consistency between functional and structural connectivity maps in Limbic, Executive, Rostral-motor, Caudal-motor, and Parietal striatal subregions. For example, Str_limbic subregion showed strong FC with mPFC and IFG, and was involved mainly in cognition and emotion behavior domains and reward paradigms. 2) There was high concordance among the RSFC maps of and the MACM results on seven striatal subregions. 3) Comparing to HC, significantly reduced FC between the Str_Limic subregion and thalamus/mPFC/IFG/insula, and Str_Executive subregion and thalamus/SMA were identified in the FES group (FWE, p,0.05). Conclusions: We demonstrate consistent coactivation maps across experiments and behaviors for different anatomical striatal subregions, which further validated by the RSFC analysis. Those findings help us to explain the neural functional deficit of FES. Supported By: NSFC (31671144 , 61473221); NCET-12-0557 Keywords: striatum, first episode schizophrenia, Meta-analytic Connectivity Modeling (MACM), Functional connectivity, BOLD fMRI
1017. Molecular Targets from Schizophrenia GWAS Investigated Using a Computer Model of Hippocampus Mohamed Sherif1, Samuel Neymotin2, and William Lytton3 Yale University, 2SUNY Downstate Medical Center, 3SUNY Downstate Medical Center / Kings County Hospital Center 1
Background: Neural oscillations, which play roles in memory and attention, are abnormal in schizophrenia (SCZ). A genome wide association study (GWAS) in SCZ identified 108 loci of single nucleotide polymorphisms. We investigated two putative associated mutations with multiscale modeling of hippocampal area CA3: 1. HCN1 – coding the channel mediating the h-current (Ih); 2. GRIN2A – coding subunit 2A of the NMDA-type receptor (NMDAR). Methods: We used a multiscale network computer model comprised of 800 pyramidal (PYR), 200 basket (BAS) and 200 Oriens Lacunosum Moleculare (OLM) neurons. We evaluated the consequences of alterations in conductance of h channel
(gh) and NMDAR (gNMDAR) at each of the cell types separately and in combination, on gamma oscillations and information flow, focusing on increased gamma power, a putative signature for SCZ. Results: Consistent gamma increase was seen with either: 1) Decreased gNMDAR on OLM; this was associated with an inverted-U relationship between information transfer and gamma power; or 2) Increased gh on BAS and PYR; this was associated with negative correlation between information transfer and gamma power. Varying both gNMDAR and gh showed dominance of the gNMDAR effect. Conclusions: Our model demonstrated the oscillatory signatures of SCZ as a consequence of the mutations expected from the GWAS results. We propose that both HCN1 and GRIN2A are part of the same GWAS “clinical pathway” involved in generating oscillations (a potential biomarker) and that they are also involved in producing cognitive impairment due to anomalies in information transformation and transmission. Supported By: VA Connecticut Health Care System, R01EB02290301, U01EB017695, and R01MH086638 Keywords: GWAS, Schizophrenia, NMDAR, h current, Computer Model
1018. Modeling Hierarchical Heterogeneity of Cortical Circuits in Large-Scale Networks with Relevance to Functional Dysconnectivity in Schizophrenia Murat Demirtas- 1, Joshua B. Burt2, Genevieve Yang2, Lisa Ji2, Alan Anticevic2, and John Murray2 1
Yale University School of Medicine Department of Psychiatry, 2Yale University
Background: Computational models of large-scale brain networks provide a useful tool to study relationships between hypothesized microcircuit dysfunction in neuropsychiatric disorders, and systems-level observations from non-invasive neuroimaging techniques such as fMRI. Previously, we found that pattern of altered resting-state functional connectivity (rsFC) in schizophrenia shows heterogeneity related to cortical hierarchy (Yang GJ et al., 2016, PNAS). These findings highlight a need to extend computational frameworks, which typically assume microcircuit properties are homogenous across cortex, to incorporate hierarchical heterogeneity. Methods: We investigated effects of cortical heterogeneity on large-scale neuronal dynamics using a biophysically-based computational model and neuroimaging data from the Human Connectome Project. Specifically, we characterized roles of heterogeneity in NMDA conductance of local and long-range inputs along cortical hierarchy. We used the MRI-derived myelin map as a proxy measure for cortical hierarchy. Based on similarity between simulated and empirical rs-FC, we estimated optimal gradients for NMDA conductance across cortical hierarchy. Results: We found that microcircuit heterogeneity substantially increased the fit of the model to empirical rs-FC. The optimal heterogeneity gradients for the computational model revealed
Biological Psychiatry May 15, 2017; 81:S277–S413 www.sobp.org/journal
S411
Saturday Abstracts
Background: Striatum involves in diverse psychological process and plays a central role in the dysfunctions of schizophrenia. However, as a heterogeneous complex structure, the precise connectivity profile of human striatum remains unclear. Recently, Tziortzi (2014) subdivided the striatum into seven subregions and made a striatal structural connectivity atlas according to their DTI and PET study. Methods: We used the meta-analytic connectivity modeling (MACM) to determine consistent functional coactivation patterns across experiments and behaviors associated with the seven striatal subregions defined by Oxford–GSK:Imanova Striatal connectivity Atlas (Tziortzi, 2014), along with a cross validation using the same seven ROIs for restingstate FC (RSFC) analysis on 27 healthy subjects. Then we compared RSFC of those seven striatal subregions between 45 first-episode, treatment-naive patients with schizophrenia and those 27 healthy controls. Results: 1) MACM results showed good consistency between functional and structural connectivity maps in Limbic, Executive, Rostral-motor, Caudal-motor, and Parietal striatal subregions. For example, Str_limbic subregion showed strong FC with mPFC and IFG, and was involved mainly in cognition and emotion behavior domains and reward paradigms. 2) There was high concordance among the RSFC maps of and the MACM results on seven striatal subregions. 3) Comparing to HC, significantly reduced FC between the Str_Limic subregion and thalamus/mPFC/IFG/insula, and Str_Executive subregion and thalamus/SMA were identified in the FES group (FWE, p,0.05). Conclusions: We demonstrate consistent coactivation maps across experiments and behaviors for different anatomical striatal subregions, which further validated by the RSFC analysis. Those findings help us to explain the neural functional deficit of FES. Supported By: NSFC (31671144 , 61473221); NCET-12-0557 Keywords: striatum, first episode schizophrenia, Meta-analytic Connectivity Modeling (MACM), Functional connectivity, BOLD fMRI
1017. Molecular Targets from Schizophrenia GWAS Investigated Using a Computer Model of Hippocampus Mohamed Sherif1, Samuel Neymotin2, and William Lytton3 Yale University, 2SUNY Downstate Medical Center, 3SUNY Downstate Medical Center / Kings County Hospital Center 1
Background: Neural oscillations, which play roles in memory and attention, are abnormal in schizophrenia (SCZ). A genome wide association study (GWAS) in SCZ identified 108 loci of single nucleotide polymorphisms. We investigated two putative associated mutations with multiscale modeling of hippocampal area CA3: 1. HCN1 – coding the channel mediating the h-current (Ih); 2. GRIN2A – coding subunit 2A of the NMDA-type receptor (NMDAR). Methods: We used a multiscale network computer model comprised of 800 pyramidal (PYR), 200 basket (BAS) and 200 Oriens Lacunosum Moleculare (OLM) neurons. We evaluated the consequences of alterations in conductance of h channel
(gh) and NMDAR (gNMDAR) at each of the cell types separately and in combination, on gamma oscillations and information flow, focusing on increased gamma power, a putative signature for SCZ. Results: Consistent gamma increase was seen with either: 1) Decreased gNMDAR on OLM; this was associated with an inverted-U relationship between information transfer and gamma power; or 2) Increased gh on BAS and PYR; this was associated with negative correlation between information transfer and gamma power. Varying both gNMDAR and gh showed dominance of the gNMDAR effect. Conclusions: Our model demonstrated the oscillatory signatures of SCZ as a consequence of the mutations expected from the GWAS results. We propose that both HCN1 and GRIN2A are part of the same GWAS “clinical pathway” involved in generating oscillations (a potential biomarker) and that they are also involved in producing cognitive impairment due to anomalies in information transformation and transmission. Supported By: VA Connecticut Health Care System, R01EB02290301, U01EB017695, and R01MH086638 Keywords: GWAS, Schizophrenia, NMDAR, h current, Computer Model
1018. Modeling Hierarchical Heterogeneity of Cortical Circuits in Large-Scale Networks with Relevance to Functional Dysconnectivity in Schizophrenia Murat Demirtas- 1, Joshua B. Burt2, Genevieve Yang2, Lisa Ji2, Alan Anticevic2, and John Murray2 1
Yale University School of Medicine Department of Psychiatry, 2Yale University
Background: Computational models of large-scale brain networks provide a useful tool to study relationships between hypothesized microcircuit dysfunction in neuropsychiatric disorders, and systems-level observations from non-invasive neuroimaging techniques such as fMRI. Previously, we found that pattern of altered resting-state functional connectivity (rsFC) in schizophrenia shows heterogeneity related to cortical hierarchy (Yang GJ et al., 2016, PNAS). These findings highlight a need to extend computational frameworks, which typically assume microcircuit properties are homogenous across cortex, to incorporate hierarchical heterogeneity. Methods: We investigated effects of cortical heterogeneity on large-scale neuronal dynamics using a biophysically-based computational model and neuroimaging data from the Human Connectome Project. Specifically, we characterized roles of heterogeneity in NMDA conductance of local and long-range inputs along cortical hierarchy. We used the MRI-derived myelin map as a proxy measure for cortical hierarchy. Based on similarity between simulated and empirical rs-FC, we estimated optimal gradients for NMDA conductance across cortical hierarchy. Results: We found that microcircuit heterogeneity substantially increased the fit of the model to empirical rs-FC. The optimal heterogeneity gradients for the computational model revealed
Biological Psychiatry May 15, 2017; 81:S277–S413 www.sobp.org/journal
S411