TY - JOUR
T1 - The brain activation-based sexual image classifier (BASIC)
T2 - a sensitive and specific fMRI activity pattern for sexual image processing
AU - Van'T Hof, Sophie R.
AU - Oudenhove, Lukas Van
AU - Janssen, Erick
AU - Klein, Sanja
AU - Reddan, Marianne C.
AU - Kragel, Philip A.
AU - Stark, Rudolf
AU - Wager, Tor D.
N1 - Publisher Copyright:
© The Author(s) 2021.
PY - 2022/7/15
Y1 - 2022/7/15
N2 - Previous studies suggest there is a complex relationship between sexual and general affective stimulus processing, which varies across individuals and situations. We examined whether sexual and general affective processing can be distinguished at the brain level. In addition, we explored to what degree possible distinctions are generalizable across individuals and different types of sexual stimuli, and whether they are limited to the engagement of lower-level processes, such as the detection of visual features. Data on sexual images, nonsexual positive and negative images, and neutral images from Wehrum et al. (2013) (N = 100) were reanalyzed using multivariate support vector machine models to create the brain activation-based sexual image classifier (BASIC) model. This model was tested for sensitivity, specificity, and generalizability in cross-validation (N = 100) and an independent test cohort (N = 18; Kragel et al. 2019). The BASIC model showed highly accurate performance (94-100%) in classifying sexual versus neutral or nonsexual affective images in both datasets with forced choice tests. Virtual lesions and tests of individual large-scale networks (e.g., visual or attention networks) show that individual networks are neither necessary nor sufficient to classify sexual versus nonsexual stimulus processing. Thus, responses to sexual images are distributed across brain systems.
AB - Previous studies suggest there is a complex relationship between sexual and general affective stimulus processing, which varies across individuals and situations. We examined whether sexual and general affective processing can be distinguished at the brain level. In addition, we explored to what degree possible distinctions are generalizable across individuals and different types of sexual stimuli, and whether they are limited to the engagement of lower-level processes, such as the detection of visual features. Data on sexual images, nonsexual positive and negative images, and neutral images from Wehrum et al. (2013) (N = 100) were reanalyzed using multivariate support vector machine models to create the brain activation-based sexual image classifier (BASIC) model. This model was tested for sensitivity, specificity, and generalizability in cross-validation (N = 100) and an independent test cohort (N = 18; Kragel et al. 2019). The BASIC model showed highly accurate performance (94-100%) in classifying sexual versus neutral or nonsexual affective images in both datasets with forced choice tests. Virtual lesions and tests of individual large-scale networks (e.g., visual or attention networks) show that individual networks are neither necessary nor sufficient to classify sexual versus nonsexual stimulus processing. Thus, responses to sexual images are distributed across brain systems.
KW - erotic images
KW - machine learning prediction model
KW - multivariate analysis
KW - neuroimaging
KW - sexual stimuli processing
KW - support vector machine classification
UR - http://www.scopus.com/inward/record.url?scp=85134721494&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134721494&partnerID=8YFLogxK
U2 - 10.1093/cercor/bhab397
DO - 10.1093/cercor/bhab397
M3 - Article
C2 - 34905775
AN - SCOPUS:85134721494
SN - 1047-3211
VL - 32
SP - 3014
EP - 3030
JO - Cerebral Cortex
JF - Cerebral Cortex
IS - 14
ER -