Pattern classification based on the amygdala does not predict an individual's response to emotional stimuli

• Published in: Human brain mapping 2023-08, Vol.44 (12), p.4452-4466
• Main Authors: Varkevisser, Tim, Geuze, Elbert, Boom, Max A., Kouwer, Karlijn, Honk, Jack, Lutterveld, Remko
• Format: Article
• Language: English
• Subjects: Activity patterns; Amygdala; Amygdala - physiology; Biomarkers; Brain; Brain - physiology; Brain mapping; Brain Mapping - methods; Brain research; emotion; Emotions; Emotions - physiology; Feedback; fMRI; Functional magnetic resonance imaging; Group effects; Humans; machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; multi‐voxel pattern analysis (MVPA); Neuroimaging; Pattern analysis; Pattern classification; Pictures; Post traumatic stress disorder; Standard deviation; Statistical analysis; Stimuli; Support vector machines; task reactivity
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.26391

Functional magnetic resonance imaging (fMRI) studies have often recorded robust univariate group effects in the amygdala of subjects exposed to emotional stimuli. Yet it is unclear to what extent this effect also holds true when multi‐voxel pattern analysis (MVPA) is applied at the level of the individual participant. Here we sought to answer this question. To this end, we combined fMRI data from two prior studies (N = 112). For each participant, a linear support vector machine was trained to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region‐of‐interest analysis) or the whole‐brain (secondary exploratory analysis). The accuracy score of the amygdala‐based pattern classifications was statistically significant for only a handful of participants (4.5%) with a mean and standard deviation of 37% ± 5% across all subjects (range: 28–58%; chance‐level: 33%). In contrast, the accuracy score of the whole‐brain pattern classifications was statistically significant in roughly half of the participants (50.9%), and had an across‐subjects mean and standard deviation of 49% ± 6% (range: 33–62%). The current results suggest that the information conveyed by the emotional pictures was encoded by spatially distributed parts of the brain, rather than by the amygdala alone, and may be of particular relevance to studies that seek to target the amygdala in the treatment of emotion regulation problems, for example via real‐time fMRI neurofeedback training. Many emotion provocation functional magnetic resonance imaging (fMRI) tasks are known to induce a response inside the amygdala that is robust across participants, yet recent evidence suggests that the reproducibility of such task effects may actually be quite low at the single‐subject level. To further examine this issue, we trained a linear support vector machine to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region‐of‐interest analysis) or the whole‐brain (secondary exploratory analysis). The accuracy score of the amygdala‐based pattern classifications was statistically significant for only a handful of participants, while the accuracy score of the whole‐brain pattern classifications was statistically significant in roughly half of the participants. These findings add to a growing body of research highlighting the low intra‐subject reliability o