SPECT brain networks and their functional connectivity in schizophrenia patients vs controls.
Poster No:
661
Submission Type:
Abstract Submission
Authors:
Amritha Harikumar1,2, Maria Misiura1,2, Daniel Amen3,4, David Keator5,6,7, Vince Calhoun8,9
Institutions:
1TReNDS Center , Atlanta, GA, 2 Georgia State University, N/A, 3Change Your Brain Change Your Life Foundation , Costa Mesa, CA, 4 Amen Clinics Inc., N/A, 5Psychiatry and Human Behavior, University of California,, Irvine, CA, 6Amen Clinics Inc., Costa Mesa, CA, 7Change Your Brain Change Your Life Foundation, Costa Mesa, CA, 8GSU/GATech/Emory, Decatur, GA, 9Georgia State University, Atlanta, GA
First Author:
Co-Author(s):
David Keator, PhD
Psychiatry and Human Behavior, University of California,|Amen Clinics Inc.|Change Your Brain Change Your Life Foundation
Irvine, CA|Costa Mesa, CA|Costa Mesa, CA
Psychiatry and Human Behavior, University of California,|Amen Clinics Inc.|Change Your Brain Change Your Life Foundation
Irvine, CA|Costa Mesa, CA|Costa Mesa, CA
Introduction:
In the last decade, single photon emission computerized tomography (SPECT) scans have emerged as a useful imaging modality. Much like fMRI, the proliferation of SPECT imaging has led to applying this modality to various clinical populations, with advantages including the powerful ability to detect patterns of cerebral blood flow (CBF) that may be indicative of disrupted brain activity. Additionally, unlike fMRI, SPECT imaging has been proven to be a cost effective and easy imaging method to utilize for clinical populations. To date, little work has focused on data-driven analysis of SPECT data. Here we utilize SPECT data to compare group differences in patients with schizophrenia and healthy controls using fully automated, spatially constrained ICA (i.e., the Neuromark pipeline). 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.
Methods:
76 healthy controls, and 138 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 and Staff n.d.) across eleven clinical imaging sites. SPECT scans were acquired using Picker (Philips) Prism XP 3000 triple-headed gamma 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 ICA template. The template included 53 components reproduced from two large scale human fMRI datasets. The components are delineated into various domains including the subcortical (SC), auditory (AUD), visual (VIS), sensorimotor (SM), cognitive control (CC), default mode network (DMN), and cerebellar (CB) component regions. Following the analysis, pairwise correlations between the loading parameters for the SPECT components were analyzed for within and between group differences. Additionally, two sample t-tests on loading parameter values between both groups were performed to identify group differences.
Preprocessed SPECT data were analyzed via spatially constrained ICA using the Neuromark ICA template. The template included 53 components reproduced from two large scale human fMRI datasets. The components are delineated into various domains including the subcortical (SC), auditory (AUD), visual (VIS), sensorimotor (SM), cognitive control (CC), default mode network (DMN), and cerebellar (CB) component regions. Following the analysis, pairwise correlations between the loading parameters for the SPECT components were analyzed for within and between group differences. Additionally, two sample t-tests on loading parameter values between both groups were performed to identify group differences.
Results:
Results revealed significant differences between healthy controls and patient SPECT data. Out of the 53 components, 21 were found to show significant differences.
·Two spatial maps, one with 19 significant components (HC > SZ) and one with 2 significant components (SZ>HC) were created.
·FNC plot displaying significant components clustered by the Neuromark template delineated networks with the full dataset (left), healthy controls – patients (middle), and a connectogram were made.
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 identified in various studies relating disruption in these networks correlated with positive and negative symptoms in schizophrenia. Taken together with clinical data, we hope to further analyze the SPECT data to see how group differences emerge across a variety of neuropsychiatric disorders. Doing so will allow us to see if disrupted brain activity through analyzing components pose a similar pattern across disorders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Other Methods
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
DISORDERS
FUNCTIONAL MRI
MRI
NORMAL HUMAN
Psychiatric Disorders
Schizophrenia
Single Photon Emission Computed Tomography (SPECT)
Other - Translational Neuroscience
1|2Indicates the priority used for review
Provide references using author date format
2 Du, Y. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 28, 102375.
3. Kalyoncu, A (2021). The Emerging Role of SPECT Functional Neuroimaging in Schizophrenia and Depression. Frontiers in Psychiatry, 12, 716600.
4 Harikumar A. (2023). Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia. Curr Neurol Neurosci Rep. 2023 Nov 24. doi: 10.1007/s11910-023-01325-8. Epub ahead of print. PMID: 37999830.