The structural connectome constrains in vivo synaptic density loss in schizophrenia
Poster No:
553
Submission Type:
Abstract Submission
Authors:
Sidhant Chopra1, Patrick Worhunsky1, Mika Naganawa1, Gustavo Angarita1, Kelly Cosgrove1, Deepak D'Souza1, David Matuskey1, Nabeel Nabulsi1, Yiyun Huang1, Richard Carson1, Irina Esterlis1, Patrick Skosnik1, Avram Holmes2, Rajiv Radhakrishnan1
Institutions:
1Yale University, New Haven, CT, 2Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ
First Author:
Co-Author(s):
Introduction:
Converging genetic, post-mortem and neuroimaging evidence suggests the loss of synapses is fundamental to schizophrenia pathogenesis. The synaptic vesicle glycoprotein 2A radioligand [11C]UCB-J allows the examination of synaptic density in vivo using positron emission tomography (PET)[1], with recent studies showing large and widespread lower synaptic density in individuals with schizophrenia[2,3]. However, the mechanisms explaining the anatomical distribution of these alterations remain elusive. The brain's different regions are connected by a complex structural network of axonal fibers, responsible for propagating action potentials and transporting biological molecules[4]. They may also act as conduits for the progression of synaptic loss, such that illness processes originating in one area can propagate to affect other vulnerable areas[5]. Here, we investigate whether the brain's axonal fibers act as conduits for synaptic density loss in schizophrenia, as observed in other neurological syndromes.
Methods:
Using PET parametric images of [11C]UCB-J binding potential from 117 individuals (92 healthy controls and 25 individuals with schizophrenia), we generated brain-wide voxel-level group difference maps. We derived representative functional and structural connectivity patterns from an independent control group (N=100). General linear models were used to assess group differences in synaptic density adjusting for age and sex (Fig1). We derived representative inter-regional functional coupling and structural connectivity patterns from an independent age-matched healthy control group (N=323) using resting-state functional MRI and diffusion-weighted imaging. We used coordinated deformation models6 to predict the extent of brain change in each of 332 parcellated areas by the changes observed in areas to which the index region is either structurally connected or functionally coupled (Fig2). To locate potential focal sources of the observed lower synaptic density, we used a network diffusion model7, sequentially using each brain region as a seed (Fig3).
Results:
We found a widespread pattern of lower synaptic density in individuals with schizophrenia (p_FWE<.05; Fig1), with peaks located in temporal, frontal, cingulate cortices, and thalamic and striatal regions. Our results demonstrated that regional synaptic density differences were strongly correlated with estimates of lower synaptic density predicted using a model constrained by structural connectivity (r=.58; p_FWE<.01). Associations between empirical and predicted synaptic density estimates were much lower for models considering only binary structural connectivity or constrained by functional coupling. Finally, we identified the left temporal pole as putative epicenters of the pathological spread of synaptic loss (Fig3).
·Fig 1 - Wide-spread lower synaptic density in schizoprenia
·Fig 2 - Coordination Deformation Models (CDM) used to identify constraints and Network Diffusion Model (NDM) used to identify epicenters of pathology
Conclusions:
Our findings highlight a robust and central role of white matter fibers on the spread of pathology in schizophrenia, mirroring findings reported in other neurological conditions. They also align with volumetric findings in individuals with schizophrenia, suggesting that temporal regions may play a critical role in the origins of brain dysfunction and indicate that the structural connectome may represent a fundamental constraint on synaptic pathology.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
Schizophrenia
Other - structural connectivity
1|2Indicates the priority used for review
Provide references using author date format
2 Radhakrishnan, R. et al. In vivo evidence of lower synaptic vesicle density in schizophrenia. Molecular Psychiatry, doi:10.1038/s41380-021-01184-0 (2021).
3 Onwordi, E. C. et al. Synaptic density marker SV2A is reduced in schizophrenia patients and unaffected by antipsychotics in rats. Nature Communications 11, 246, doi:10.1038/s41467-019-14122-0 (2020).
4 Sporns, O., Tononi, G. & Kötter, R. The Human Connectome: A Structural Description of the Human Brain. PLOS Computational Biology 1, e42, doi:10.1371/journal.pcbi.0010042 (2005).
5 Vogel, J. W. et al. Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease. Nature Communications 11, 2612, doi:10.1038/s41467-020-15701-2 (2020).
6 Chopra, S. et al. Network-Based Spreading of Gray Matter Changes Across Different Stages of Psychosis. JAMA Psychiatry, doi:10.1001/jamapsychiatry.2023.3293 (2023).
7 Raj, A., Kuceyeski, A. & Weiner, M. A network diffusion model of disease progression in dementia. Neuron 73, 1204-1215, doi:10.1016/j.neuron.2011.12.040 (2012).