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MULTIVARIATE BRAIN STRUCTURE-COGNITION SIGNATURES OF EARLY PSYCHOSIS

Poster No:

1971 

Submission Type:

Abstract Submission 

Authors:

Yoshito Saito1,2, Christos Pantelis1,3,4,2, Cassandra Wannan1,2, Warda Syeda1,2

Institutions:

1Melbourne Neuropsychiatry Centre, Carlton, Australia, 2The University of Melbourne, Parkville, Australia, 3MidWest Area Mental Health Service, St. Albans, Australia, 4The Florey Institute of Neuroscience and Mental Health, Parkville, Australia

First Author:

Yoshito Saito  
Melbourne Neuropsychiatry Centre|The University of Melbourne
Carlton, Australia|Parkville, Australia

Co-Author(s):

Christos Pantelis  
Melbourne Neuropsychiatry Centre|MidWest Area Mental Health Service|The Florey Institute of Neuroscience and Mental Health|The University of Melbourne
Carlton, Australia|St. Albans, Australia|Parkville, Australia|Parkville, Australia
Cassandra Wannan  
Melbourne Neuropsychiatry Centre|The University of Melbourne
Carlton, Australia|Parkville, Australia
Warda Syeda  
Melbourne Neuropsychiatry Centre|The University of Melbourne
Carlton, Australia|Parkville, Australia

Introduction:

Cognitive impairment is frequently observed in recent-onset psychosis, does not improve with medications, and predicts functional outcomes (Green et al., 2019). Schizophrenia presents widespread grey matter (GM) reductions and more widespread and subtle white matter (WM) abnormalities (van Erp et al., 2018; Kelly et al., 2018), but their relationship to cognitive impairment is unclear. This is the first study investigating multivariate correlations between GM-WM couplings and cognition in recent-onset psychosis individuals using a novel dimensionality technique, multiblock partial least squares correlation analysis (MB-PLS-C). Our previous MB-PLS-C studies on treatment-resistant schizophrenia individuals showed differential patterns between GM and cognitive abilities (Syeda et al., 2022). Thus, we hypothesised that MB-PLS-C would show a differential GM-WM pattern between recent-onset psychosis individuals and controls, and the differential pattern would be correlated with the cognitive abilities impaired in the patients.

Methods:

We used the Human Connectome Project for Early psychosis and the Human Connectome Project Development datasets, including cognitive assessments of the NIH Toolbox, T1 and diffusion-weighted MRI data from 71 nonaffective recent-onset psychosis individuals (age 22.1±3.1) and 71 matched healthy controls (age 22.1±3.2). We performed MB-PLS-C analyses using GM thickness (GMTH) and surface area (GMSA) (Desikan-Killiany atlas) and WM fractional anisotropy (WMFA) (JHU atlas) to identify multivariate GM-WM patterns. We analysed correlations between the GM-WM patterns and cognitive abilities, including cognitive flexibility, attention, working memory, episodic memory, processing speed, reading and vocabulary.

Results:

MB-PLS-C between GMTH and WMFA identified two significant GM-WM patterns explaining 29.3% of the sum-of-squares variance; one pattern (16.92%) predominantly reflected a pattern in controls: the other pattern (12.38%) comprised a differential GM-WM pattern positively and strongly mapped onto the recent-onset psychosis group (Figure 1). The differential pattern was associated with frontal and temporal regions and WM tracts, including the bilateral anterior limb of the internal capsule, left posterior thalamic radiation and retrolenticular limb of the internal capsule, and right corticospinal tract. MB-PLS-C between GMSA and WMFA demonstrated two significant GM-WM patterns explaining 72.18% of the sum-of-squares variance; one pattern (53.21%) described a widespread GM-WM pattern shared between groups: the other pattern (18.92%) showed a differential GM-WM pattern involving with frontal, temporal, and parietal regions and WM tracts including in the bilateral inferior cerebellar peduncle and posterior corona radiata and left superior corona radiata and superior longitudinal fasciculus (Figure 2). The differential GMTH-WMFA pattern was correlated with working memory (p = 0.024), episodic memory (p = 0.022), and processing speed (p = 0.006), and the differential GMSA-WMFA was correlated with word reading ability (p = 0.047) in recent-onset psychosis individuals. However, they were not significant after False Discovery Rate correction at 5%.
Supporting Image: Figure1.png
   ·Figure 1. The differential pattern of GM thickness and WM fractional anisotropy derived from MB-PLS-C
Supporting Image: Figure2.png
   ·Figure 2. The differential pattern of GM surface area and WM fractional anisotropy derived from MB-PLS-C
 

Conclusions:

MB-PLS-C demonstrated the differential GM-WM patterns between recent-onset psychosis individuals and controls, indicating a potential signature of brain alterations in early schizophrenia. The differential pattern with GMTH was correlated with fluid intelligence, whereas the pattern with GMSA was correlated with crystallised intelligence, suggesting the relationship between the two GM metrics and two types of cognitive impairments. Identifying GM-WM differential patterns across various clinical phases could provide important information about the changes in GM-WM interaction in schizophrenia and new strategies for treating cognitive impairments.

Disorders of the Nervous System:

Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2

Higher Cognitive Functions:

Executive Function, Cognitive Control and Decision Making

Modeling and Analysis Methods:

Multivariate Approaches 1

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Cortical Anatomy and Brain Mapping
White Matter Anatomy, Fiber Pathways and Connectivity

Keywords:

Cognition
Memory
Psychiatric
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC

1|2Indicates the priority used for review

Provide references using author date format

Green, M. F., Horan, W. P., & Lee, J. (2019). Nonsocial and social cognition in schizophrenia: current evidence and future directions. World Psychiatry: Official Journal of the World Psychiatric Association, 18(2), 146–161.
van Erp, T. G. M., Walton, E., Hibar, D. P., Schmaal, L., Jiang, W., Glahn, D. C., Pearlson, G. D., Yao, N., Fukunaga, M., Hashimoto, R., Okada, N., Yamamori, H., Bustillo, J. R., Clark, V. P., Agartz, I., Mueller, B. A., Cahn, W., de Zwarte, S. M. C., Hulshoff Pol, H. E., … Turner, J. A. (2018). Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biological Psychiatry, 84(9), 644–654.
Kelly, S., Jahanshad, N., Zalesky, A., Kochunov, P., Agartz, I., Alloza, C., Andreassen, O. A., Arango, C., Banaj, N., Bouix, S., Bousman, C. A., Brouwer, R. M., Bruggemann, J., Bustillo, J., Cahn, W., Calhoun, V., Cannon, D., Carr, V., Catts, S., … Donohoe, G. (2018). Widespread white matter microstructural differences in schizophrenia across 4322 individuals: results from the ENIGMA Schizophrenia DTI Working Group. Molecular Psychiatry, 23(5), 1261–1269.
Syeda, W. T., Wannan, C. M. J., Merritt, A. H., Raghava, J. M., Jayaram, M., Velakoulis, D., Kristensen, T. D., Soldatos, R. F., Tonissen, S., Thomas, N., Ambrosen, K. S., Sørensen, M. E., Fagerlund, B., Rostrup, E., Glenthøj, B. Y., Skafidas, E., Bousman, C. A., Johnston, L. A., Everall, I., … Pantelis, C. (2022). Cortico-cognition coupling in treatment resistant schizophrenia. NeuroImage. Clinical, 35, 103064.