Altered microstructural properties of grey matter in schizophrenia
Poster No:
534
Submission Type:
Abstract Submission
Authors:
Carolyn McNabb1, Marco Palombo1, Eirini Messaritaki1, Elin Roberts1, Vanessa Hyde1, Pedro Luque Laguna1, Emily Lambe2, Xavier Caseras1, Krish Singh1, Derek Jones1, James Walters1
Institutions:
1Cardiff University, Cardiff, United Kingdom, 2University of Bristol, Bristol, United Kingdom
First Author:
Co-Author(s):
Introduction:
Macrostructural grey matter changes are well established in schizophrenia, including alterations in cortical thickness, gyrification and volume. However, the microstructural properties of grey matter, e.g., relating to cell bodies (or soma) and neurite density, are less well characterised. This is mainly due to limitations in non-invasive imaging such as magnetic resonance imaging (MRI) that make these challenging to measure in the living human brain. Using state-of-the-art diffusion-weighted MRI hardware with ultra-strong magnetic gradients, we employed ultra-high diffusion weightings to investigate the properties of soma and neurite in the cortex of people with schizophrenia.
Methods:
Twenty-five people with schizophrenia and 23 healthy age- and sex-matched controls were recruited from Cardiff, UK and surrounding areas as part of the Welsh Advanced Neuroimaging Database (WAND; McNabb, 2024a, McNabb, 2024b) and WAND in Psychosis (WAND-P) studies. All participants underwent structural and diffusion-weighted MRI using an ultra-strong magnetic field gradient (300 mT/m) 3T Connectom MRI scanner (Siemens Healthcare, Erlangen, Germany). Diffusion MRI data were acquired in the anterior-posterior phase-encoding direction, using uniformly distributed diffusion encoding directions and b values ranging from 200 to 6000 s/mm2. Non-diffusion-weighted images were dispersed throughout. Preprocessing of diffusion data included correction for motion, eddy current distortions, field inhomogeneities/non-uniformities and Gibbs ringing. Apparent MR soma size, soma density, and neurite density were estimated using the SANDI model (Palombo, 2020). T1weighted structural data were registered to the first b0 image from diffusion data, followed by cortical segmentation in FreeSurfer, and parcellation using the Desikan-Killiany atlas in MRTrix. Mean values from each parcel were extracted using FSL's fslmeant and group comparisons were conducted using t-tests (corrected for multiple comparisons using the false discovery rate) in R. P values were further thresholded at p<.01 to reduce the likelihood of false positives.
Results:
Compared with healthy controls, people with schizophrenia exhibited larger apparent MR soma size (Figure 1a) and fraction (Figure 1b) both indicating a higher volume of larger cells (e.g., neurons), and intracellular diffusivity (Figure 2), indicating a reduction in dendritic undulation, spines or number, across several parietal, occipital and temporal regions. Apparent MR extracellular diffusivity and extracellular fraction were lower in those with schizophrenia across similar regions, indicating higher packing of cells in the cortex compared with healthy controls, however the extracellular values were notably high in the control group, potentially indicating partial volume effects or cerebrospinal fluid contamination.
Conclusions:
These findings are consistent with previous work showing increased cell packing density in a mouse model of schizophrenia (Gong, 2020), as well as reductions in grey matter density in people with schizophrenia (Bora, 2011), indicative of a reduction in the cortical neuropil, comprising the axons, dendrites, pre- and post-synaptic
terminals of cortical neurons (Glantz, 2006). Further work is planned to investigate the potential impact of partial volume effects on the extracellular findings, as well as the relationship between soma fraction and size, intracellular diffusivity and symptom severity.
terminals of cortical neurons (Glantz, 2006). Further work is planned to investigate the potential impact of partial volume effects on the extracellular findings, as well as the relationship between soma fraction and size, intracellular diffusivity and symptom severity.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Cortex
Neuron
Psychiatric Disorders
Schizophrenia
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
WAND: A multi-modal dataset integrating advanced MRI, MEG, and TMS for multi-scale brain analysis, Scientific Data (2024), DOI:10.1038/S41597-024-04154-7
McNabb CB, Driver ID, Hyde V, Hughes G, Chandler HL, Thomas H, Allen C, Messaritaki E, Hodgetts CJ, Hedge C, Engel M, Standen SF, Morgan EL, Stylianopoulou E, Manalova S, Reed L, Ploszajski M, Drakesmith M, Germuska M, Shaw AD, Mueller L, Rossiter H, Davies-Jenkins CW, Lancaster T, Evans CJ, Owen D, Perry G, Kusmia S, Lambe E, Partridge AM, Cooper A, Hobden P, Lu H, Graham KS, Lawrence AD, Wise RG, Walters JTR, Sumner P, Singh KD, Jones DK (2024) The Welsh Advanced Neuroimaging Database (WAND). G-Node. https://doi.org/10.12751/g-node.5mv3bf
Palombo, M., Ianus, A., Guerreri, M., Nunes, D., Alexander, D. C., Shemesh, N., & Zhang, H. (2020). SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage, 215, 116835.
Gong N-J, Dibb R, Pletnikov M, Benner E, Liu C. (2020). Imaging microstructure with diffusion and susceptibility MR: neuronal density correlation in Disrupted-in-Schizophrenia-1 mutant mice. NMR in Biomedicine. 33e4365. https://doi.org/10.1002/nbm.4365
Bora, E., Fornito, A., Radua, J., Walterfang, M., Seal, M., Wood, S. J., ... & Pantelis, C. (2011). Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophrenia research, 127(1-3), 46-57.
Glantz, L. A., Gilmore, J. H., Lieberman, J. A., & Jarskog, L. F. (2006). Apoptotic mechanisms and the synaptic pathology of schizophrenia. Schizophrenia research, 81(1), 47-63.