Multivariate pattern classification reveals differential brain activation during emotional processing in individuals with psychosis proneness.
Among the general population, individuals with subthreshold psychotic-like experiences, or psychosis proneness (PP), can be psychometrically identified and are thought to have a 10-fold increased risk of psychosis. They also show impairments in measures of emotional functioning parallel to schizophrenia. Whilst previous studies have revealed altered brain activation in patients with schizophrenia during emotional processing, it is unclear whether these alterations are also expressed in individuals with high PP. Here we used Support Vector Machine (SVM) to perform multivariate pattern classification based on brain activation during emotional processing in 20 individuals with high PP and 20 comparison subjects (low PP). In addition, we performed a standard univariate analysis based on the General Linear Model (GLM) on the same data for comparison. The experimental task involved passively viewing negative and neutral pictures from the International Affective Picture System (IAPS). SVM allowed classification of the two groups with statistically significant accuracy (p=0.017) and identified group differences within an emotional circuitry including the amygdala, insula, anterior cingulate and medial prefrontal cortex. In contrast, the standard univariate analysis did not detect any significant between-group differences. Our results reveal a distributed and subtle set of alterations in brain function within the emotional circuitry of individuals with high PP, providing neurobiological support for the notion of dysfunctional emotional circuitry in this group. In addition, these alterations are best detected using a multivariate approach rather than standard univariate methods. Further application of this approach may aid in characterising people at clinical and genetic risk of developing psychosis.
Department of Psychosis Studies, Institute of Psychiatry, King's College London, United Kingdom. gemma.modinos@kcl.ac.uk
Gemma
G
Modinos
William
W
Pettersson-Yeo
Paul
P
Allen
Philip K
PK
McGuire
André
A
Aleman
Andrea
A
Mechelli
D000339:Affect / Q000502:physiology; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D004198:Disease Susceptibility / Q000145:classification / Q000503:physiopathology; D004644:Emotions / Q000502:physiology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008959:Models, Neurological; D015999:Multivariate Analysis; D009415:Nerve Net / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D010775:Photic Stimulation; D011597:Psychomotor Performance / Q000502:physiology; D011618:Psychotic Disorders / Q000145:classification / Q000503:physiopathology*; D018570:Risk Assessment; D012559:Schizophrenia / Q000503:physiopathology; D060388:Support Vector Machine; D055815:Young Adult