Investigation of SPECT rest and task brain networks in schizophrenia patients vs controls
Poster No:
484
Submission Type:
Abstract Submission
Authors:
Amritha Harikumar1, Maria Misiura1, Daniel Amen2, David Keator3, Vince Calhoun4
Institutions:
1TReNDS Center, Atlanta, GA, 2Change Your Brain Change Your Life Foundation, Costa Mesa, CA, 3Amen Clinics Inc., Costa Mesa, CA, 4GSU/GATech/Emory, Atlanta, GA
First Author:
Co-Author(s):
Introduction:
Single photon emission computerized tomography (SPECT) scans has emerged as a useful imaging modality. The advent of SPECT imaging has also led to applying this modality to various clinical populations (Amen et al., 2021), with advantages including the powerful ability to detect patterns of cerebral blood flow (CBF) that may be indicative of disrupted brain activity. SPECT imaging has been proven to be a cost effective and easy imaging method to utilize for clinical populations (Kalyoncu & Gonul, 2021). To date, little work has focused on data-driven analysis of SPECT data. We utilize SPECT data to compare group differences in patients with schizophrenia and healthy controls using fully automated, spatially constrained ICA approach (i.e., the NeuroMark pipeline; Du et al., 2020). We evaluate both the spatial regions as well as the whole brain SPECT connectome (assessed as covariation among subjects) to evaluate the neuroimaging links to schizophrenia. Additionally, we share updated task data and clinical results which inform our initial resting state findings.
Methods:
76 healthy controls, and 137 schizophrenia patient SPECT images were acquired from the Amen Clinic (https://www.amenclinics.com/), along with diagnostic information. Each patient participated in two SPECT brain scans, acquired during rest and while performing a Conners Continuous Performance Test (Conners Continuous Performance Test, CCPT-II, Multi-Health Systems, Toronto, Ontario) (Conners, 2000) across twelve clinical imaging sites. SPECT scans were acquired using Picker (Philips) Prism XP 3000 double-headed gamma cameras and PRISM3000 triple headed cameras with low energy high resolution fan beam collimators. Data acquisition yielded 120 images per scan with each image separated by three degrees, spanning 360 degrees. The resulting reconstructed image matrices were 128x128x78 with voxel sizes of 2.5mm^3. Scans were MNI space registered and raw count values were scaled by the maximum voxel. Preprocessed SPECT data were analyzed via spatially constrained ICA using the NeuroMark spatially constrained ICA pipeline. For this we used the NeuroMark_fMRI_1.0 template which includes 53 components reproduced from two largescale human fMRI datasets. The components are delineated into various domains including the subcortical (SC), auditory (AUD), visual (VS), sensorimotor (SM), cognitive control (CC), default mode network (DM), and cerebellar (CB) component regions which are referred to as component networks (ICNs). Following the analysis, pairwise correlations between the loading parameters for the SPECT components were analyzed for within and between group differences. Two sample t-tests were performed to identify group differences.
Results:
Results revealed significant differences between healthy controls (HC) and patient (SZ) SPECT data. Out of the 53 components, 15 were found to show significant differences between controls > patients, specifically across the auditory, subcortical and sensorimotor regions. Resting state group results (healthy-patients) showed decreased expression in the CB-CC, CC-VS, VS-SM areas, and increased expression in the CB-AUD, DM-SM, and DM-SC areas. Analysis of clinical variables showed relationships between hallucination variables and components related to the DM-CC network. Finally, task-based results comparing rest-task based ICN patterns showed that patients had markedly decreased loading parameter expression across most ICNs compared to healthy controls.
· Bar plots showing 15 resting state components (left) that were significant after FDR correction along with a comparison to the task data components (center) and a spatial montage map (right).
· Functional network connectivity (FNC) maps which show uncorrected results for controls (left), patients (center), and uncorrected/corrected group differences (right).
Conclusions:
Analyzing SPECT data using ICA revealed multiple significant group differences in HC vs SZ. This poses interesting clinical questions related to possible disruptions in schizophrenia, particularly in the superior temporal gyrus, default mode network, and subcortical networks. These results shed further light on patterns of functional dysconnectivity correlated with positive and negative symptoms in schizophrenia (Harikumar et al., 2023).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Task-Independent and Resting-State Analysis 2
Other Methods
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Psychiatric Disorders
Schizophrenia
Single Photon Emission Computed Tomography (SPECT)
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Provide references using APA citation style.
2. Conners CK (2000). Conners’ Continuous Performance Test. Multi-health systems North Tonawanda NY.
3. Du, Y. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 28, 102375.
4. Harikumar, A. (2023). Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia. Curr Neurol Neurosci Rep 23, 937–946. https://doi.org/10.1007/s11910-023-01325-8
5. Kalyoncu, A. (2021). The Emerging Role of SPECT Functional Neuroimaging in Schizophrenia and Depression. Frontiers in Psychiatry, 12, 716600.