TY - JOUR
T1 - Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions
AU - Masulli, Paolo
AU - Galazka, Martyna
AU - Eberhard, David
AU - Johnels, Jakob Åsberg
AU - Gillberg, Christopher
AU - Billstedt, Eva
AU - Hadjikhani, Nouchine
AU - Andersen, Tobias S.
PY - 2021
Y1 - 2021
N2 - Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.
AB - Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.
KW - Face Perception
KW - Eye Tracking
KW - Principal Component Analysis
KW - Autism
KW - Depression
KW - Feature Selection
KW - Left Visual Field Bias
U2 - 10.1016/j.cortex.2021.11.011
DO - 10.1016/j.cortex.2021.11.011
M3 - Journal article
C2 - 34998084
SN - 0010-9452
VL - 147
SP - 9
EP - 23
JO - Cortex
JF - Cortex
ER -