Brain Associations of Affinity Scores Across the Psychosis Continuum
Poster No:
575
Submission Type:
Late-Breaking Abstract Submission
Authors:
Tina Kristensen1, Louise Glenthøj2, Karen Ambrosen3, Cecilie Lemvigh3, Kirsten Bojesen3, Mette Nielsen4, Birte Glenthøj4, Bjørn Ebdrup5, Warda Syeda6
Institutions:
1Centre for Neuropsychiatric Schizophrenia Research, CNSR, Glostrup, Capital Region of Denmark, 2VIRTU Research Group, Mental Health Center Copenhagen, Hellerup, Denmark, 3Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Glostrup, 2600, 4Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, DK-2600 Glostrup, Glostrup, Denmark, 5Center for Neuropsychiatric Schizophrenia Research, Glostrup, 2600, 6Melbourne Brain Center Imaging Unit, Department of Radiology, University of Melbourne, Melbourne, Victoria
First Author:
Tina Kristensen, PhD
Centre for Neuropsychiatric Schizophrenia Research, CNSR
Glostrup, Capital Region of Denmark
Centre for Neuropsychiatric Schizophrenia Research, CNSR
Glostrup, Capital Region of Denmark
Co-Author(s):
Karen Ambrosen, PhD
Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup
Glostrup, 2600
Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup
Glostrup, 2600
Cecilie Lemvigh, PhD
Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup
Glostrup, 2600
Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup
Glostrup, 2600
Kirsten Bojesen
Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup
Glostrup, 2600
Center for Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup
Glostrup, 2600
Mette Nielsen, PhD
Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, DK-2600 Glostrup
Glostrup, Denmark
Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, DK-2600 Glostrup
Glostrup, Denmark
Birte Glenthøj
Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, DK-2600 Glostrup
Glostrup, Denmark
Centre for Clinical Intervention and Neuropsychiatric Schizophrenia Research, CINS, DK-2600 Glostrup
Glostrup, Denmark
Warda Syeda
Melbourne Brain Center Imaging Unit, Department of Radiology, University of Melbourne
Melbourne, Victoria
Melbourne Brain Center Imaging Unit, Department of Radiology, University of Melbourne
Melbourne, Victoria
Introduction:
Psychotic disorders exist along a continuum of symptoms and cognitive impairments that vary significantly between individuals. Despite advances in neuroimaging and computational psychiatry, associating clinical and cognitive features with brain structure remains challenging due to considerable heterogeneity within and across diagnostic categories and illness stages. Machine-learning methods often model population-level comparisons, overlooking individual variability. Here, we applied Affinity Scores: an individual-centric framework quantifying individuals affinity to different diagnostic groups and illness stages across the psychosis continuum1. We hypothesized that distinct brain regions would associate with affinity scores based on cognitive and clinical data, reflecting the neurobiological basis of individualized profiles.
Methods:
671 participants aged 18–60 was included: 284 healthy controls (HC), 201 individuals at ultra-high risk for psychosis (UHR), 128 patients with first-episode psychosis (FEP), and 57 individuals with established schizophrenia (SCZ). Clinical assessments included global functioning (SOFAS2); UHR symptoms (CAARMS3), and psychotic symptom severity (PANSS4). Cognitive functioning was assessed using WAIS-III5 for estimating IQ; BACS6 for verbal memory and working memory, fluency, and processing speed; and CANTAB7 for spatial working memory, planning, cognitive flexibility, reaction time, and sustained attention. T1-weighted brain scans were conducted on a subset (166 HC, 148 UHR, 104 FEP) using a Philips 3T whole-body MRI scanner. Images were segmented in FreeSurfer using Desikan-Killiany atlas and ENIGMA guidelines to estimate regional brain volumes.
Affinity Scores: For each individual and each clinical and cognitive variable, a 4-component scores vector quantified their affinity to HC, UHR, FEP, and SCZ. First, all data was standardized into z-scores. A variable-specific hop size was determined based on the average mean difference across groups. Each participant's distance from all others was calculated, and a neighborhood was defined around their z-score using the hopsize. Participants within this neighborhood contributed to the group-wise affinity vector.
Three multivariate metrics were calculated: composite affinity scores, representing mean group affinity across all variables; neighbour-based scores, derived from the neighbourhood connectome, where nodes represented participants and edge weights were determined by shared variables within their neighbourhood; and community-based scores, identified using a modularity-based community detection algorithm.
Linear regression was used to identify brain regions where volumetric differences significantly contributed to variations in clinical and cognitive affinity scores for each group. Regional volumes were independent variables, while group-wise affinity scores were the dependent variables. Statistical significance was set at p < 0.05.
Affinity Scores: For each individual and each clinical and cognitive variable, a 4-component scores vector quantified their affinity to HC, UHR, FEP, and SCZ. First, all data was standardized into z-scores. A variable-specific hop size was determined based on the average mean difference across groups. Each participant's distance from all others was calculated, and a neighborhood was defined around their z-score using the hopsize. Participants within this neighborhood contributed to the group-wise affinity vector.
Three multivariate metrics were calculated: composite affinity scores, representing mean group affinity across all variables; neighbour-based scores, derived from the neighbourhood connectome, where nodes represented participants and edge weights were determined by shared variables within their neighbourhood; and community-based scores, identified using a modularity-based community detection algorithm.
Linear regression was used to identify brain regions where volumetric differences significantly contributed to variations in clinical and cognitive affinity scores for each group. Regional volumes were independent variables, while group-wise affinity scores were the dependent variables. Statistical significance was set at p < 0.05.
Results:
The individualized profiles revealed unique patterns of heterogeneity across the psychosis continuum, highlighting subgroup-specific cognitive and clinical variations, see Figure 1A. Regional brain volumes contributed significantly to multivariate affinity scores of diagnostic groups (p < 2e-16) (Figure 2A). Frontal, temporal, and parietal regions associated with affinity to HCs, while fusiform, inferior parietal, orbitofrontal, lingual, temporal, paracentral, and precuneus regions (Figure 2B), associated with affinity to UHR, FEP and SCZ, highlighting their role in psychosis.
Conclusions:
We found that structural brain alterations effectively predicted affinity scores across the psychosis continuum, particularly the frontal, temporal, and parietal regions. These results align with established neuroanatomical abnormalities in psychosis and highlight the potential of Affinity Scores as a neurobiological marker for individualized assessment in schizophrenia and related disorders.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Methods Development
Multivariate Approaches 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Cognition
Computational Neuroscience
Machine Learning
MRI
Psychiatric Disorders
Schizophrenia
1|2Indicates the priority used for review
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2. Hilsenroth MJ, Ackerman SJ, Blagys MD, et al. Reliability and validity of DSM-IV Axis V. Am J Psychiatry. 2000;157(11):1858-1863. doi:10.1176/appi.ajp.157.11.1858
3. Yung AR, Yuen HP, Phillips LJ, Francey S, McGorry PD. Mapping the onset of psychosis: The comprehensive assessment of at risk mental states (CAARMS). Schizophr Res. 2003;60(1):30-31. doi:10.1016/S0920-9964(03)80090-7
4. Kay SR, Fiszbein A, Opler LA. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr Bull. 1987;13(2):261-276. doi:10.1093/schbul/13.2.261
5. Tulsky DS, Chiaravalloti ND, Palmer BW, Chelune GJ. The Wechsler Memory Scale, Third Edition.; 2003. doi:10.1016/b978-012703570-3/50007-9
6. Keefe RSE, Poe M, Walker TM, Kang JW, Harvey PD. The schizophrenia cognition rating scale: An interview-based assessment and its relationship to cognition, real-world functioning, and functional capacity. Am J Psychiatry. 2006;163(3):426-432. doi:10.1176/appi.ajp.163.3.426
7. Sahakian BJ, Owen AM. Computerized assessment in neuropsychiatry using CANTAB: Discussion paper. J R Soc Med. 1992;85(7):399-402. doi:10.1177/014107689208500711