Kragel, P. | bioRxiv Science

bioRxiv2023

Sensory encoding of emotion conveyed by the face and visual context

Humans rapidly detect and interpret sensory signals that have emotional meaning. The posterior temporal sulcus (pSTS) and amygdala are known to be critical for this ability, but their precise contributions--whether specialized for facial features or sensory information more generally--remain contentious. Here we investigate how these structures process visual emotional cues using artificial neural networks (ANNs) to model fMRI signal acquired as participants view complex, naturalistic stimuli. Characterizing data from two archival studies (Ns = 20, 45), we evaluated whether representations from ANNs optimized to recognize emotion from either facial expressions alone or the broader visual context differ in their ability to predict responses in human pSTS and amygdala. Across studies, we found that representations of facial expressions were more robustly encoded in pSTS compared to the amygdala, whereas representations related to visual context were encoded in both regions. These findings demonstrate how the pSTS operates on abstract representations of facial expressions such as fear and joy to a greater extent than the amygdala, which more strongly encodes the emotional significance of visual information more broadly, depending on the context.

neuroscience↗

bioRxiv2021

Effect sizes and test-retest reliability of the fMRI-based Neurologic Pain Signature

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent shortterm (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with [≥]60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions. Significance statementCurrent efforts towards translating brain biomarkers require identifying brain measures that can strongly and reliably predict outcomes of interest. We systematically examined the performance of a well-established brain activity pattern, the Neurological Pain Signature (NPS), in a large and diverse sample of participants. The NPS showed excellent reliability, and the reliability scaled with the number of trials within-person. The NPS responses showed strong correlations with pain reports at the within-person level but only modest correlations at the between-person level. The findings suggest that the NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of a pain-related physiological target. Author NoteThis project was supported by grants R01MH076136 (T.D.W.), R01DA046064, R01EB026549, and R01DA035484. Elizabeth A. Reynolds Losin was supported by a Mentored Research Scientist Development award from National Institute On Drug Abuse of the National Institutes of Health (K01DA045735). Lauren Y. Atlas was supported in part by funding from the Intramural Research Program of the National Center for Complementary and Integrative Health. Yoni K. Ashar was supported by NCATS Grant # TL1-TR-002386. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Code for all analyses and figures is available at https://github.com/XiaochunHan/NPS_measurement_properties. Data for all analyses and figures is available at https://osf.io/v9px7/.

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