Studies have reported substantial variability in emotion recognition ability (ERA) – an important social skill – but possible neural underpinnings for such individual differences are not well understood. This functional magnetic resonance imaging (fMRI) study investigated neural responses during emotion recognition in young adults (N=49) who were selected for inclusion based on their performance (high or low) during previous testing of ERA. Participants were asked to judge brief video recordings in a forced-choice emotion recognition task, wherein stimuli were presented in visual, auditory and multimodal (audiovisual) blocks. Emotion recognition rates during brain scanning confirmed that individuals with high (vs. low) ERA received higher accuracy for all presentation blocks. fMRI-analyses focused on key regions of interest (ROIs) involved in the processing of multimodal emotion expressions, based on previous meta-analyses. In neural response to emotional stimuli contrasted with neutral stimuli, individuals with high (vs. low) ERA showed higher activation in the following ROIs during the multimodal condition: right middle superior temporal gyrus (mSTG), right posterior superior temporal sulcus (PSTS), and right inferior frontal cortex (IFC). Overall, results suggest that individual variability in ERA may be reflected across several stages of decisional processing, including extraction (mSTG), integration (PSTS) and evaluation (IFC) of emotional information.

Skill learning induces changes in estimates of gray matter volume (GMV) in the human brain, commonly detectable with magnetic resonance imaging (MRI). Rapid changes in GMV estimates while executing tasks may however confound between- and within-subject differences. Fluctuations in arterial blood flow are proposed to underlie this apparent task-related tissue plasticity. To test this hypothesis, we acquired multiple repetitions of structural T₁-weighted and functional blood-oxygen level-dependent (BOLD) MRI measurements from 51 subjects performing a finger-tapping task (FTT; á 2 min) repeatedly for 30–60 min. Estimated GMV was decreased in motor regions during FTT compared with rest. Motor-related BOLD signal changes did not overlap nor correlate with GMV changes. Nearly simultaneous BOLD signals cannot fully explain task-induced changes in T₁-weighted images. These sensitive and behavior-related GMV changes pose serious questions to reproducibility across studies, and morphological investigations during skill learning can also open new avenues on how to study rapid brain plasticity.

Background: Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of noise, temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes.

Methods: A total of 45 patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive behavioral therapy. Multivariate modeling and reliability-based cross-validation were used to perform brain-based prediction of treatment outcomes.

Results: Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total rACTUAL,PREDICTED = 0.77), outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient = 0.80), even with a task length less than 3 minutes long.

Conclusions: Rather than a source of undesirable noise, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

Background: Månsson et al., Biological Psychiatry, In press:

Biomarkers of psychiatric treatment response remain elusive. Functional magnetic resonance imaging (fMRI) has shown promise, but low reliability has limited the utility of typical fMRI measures (e.g., average brain signal) as harbingers of treatment success. Notably, although historically considered a source of “noise,” temporal brain signal variability continues to gain momentum as a sensitive and reliable indicator of individual differences in neural efficacy, yet has not been examined in relation to psychiatric treatment outcomes.

Methods: Forty-five patients with social anxiety disorder were scanned twice (11 weeks apart) using simple task-based and resting-state fMRI to capture moment-to-moment neural variability. After fMRI test-retest, patients underwent a 9-week cognitive-behavioral therapy. Multivariate modeling and reliability-based cross-validation were utilized to perform brain-based prediction of treatment outcomes.

Results: Task-based brain signal variability was the strongest contributor in a treatment outcome prediction model (total r[ACTUAL,PREDICTED]=.77) - outperforming self-reports, resting-state neural variability, and standard mean-based measures of neural activity. Notably, task-based brain signal variability showed excellent test-retest reliability (intraclass correlation coefficient=.80), even with a task length less than 3 minutes long.

Conclusions: Rather than a source of undesirable “noise”, moment-to-moment fMRI signal variability may instead serve as a highly reliable and efficient prognostic indicator of clinical outcome.

The objective of this study is to introduce a new quantitative data-driven analysis (QDA) framework for the analysis of resting-state fMRI (R-fMRI) and use it to investigate the effect of adult age on resting-state functional connectivity (RFC). Whole-brain R-fMRI measurements were conducted on a 3T clinical MRI scanner in 227 healthy adult volunteers (N = 227, aged 18–76 years old, male/female = 99/128). With the proposed QDA framework we derived two types of voxel-wise RFC metrics: the connectivity strength index and connectivity density index utilizing the convolutions of the cross-correlation histogram with different kernels. Furthermore, we assessed the negative and positive portions of these metrics separately. With the QDA framework we found age-related declines of RFC metrics in the superior and middle frontal gyri, posterior cingulate cortex (PCC), right insula and inferior parietal lobule of the default mode network (DMN), which resembles previously reported results using other types of RFC data processing methods. Importantly, our new findings complement previously undocumented results in the following aspects: (1) the PCC and right insula are anti-correlated and tend to manifest simultaneously declines of both the negative and positive connectivity strength with subjects’ age; (2) separate assessment of the negative and positive RFC metrics provides enhanced sensitivity to the aging effect; and (3) the sensorimotor network depicts enhanced negative connectivity strength with the adult age. The proposed QDA framework can produce threshold-free and voxel-wise RFC metrics from R-fMRI data. The detected adult age effect is largely consistent with previously reported studies using different R-fMRI analysis approaches. Moreover, the separate assessment of the negative and positive contributions to the RFC metrics can enhance the RFC sensitivity and clarify some of the mixed results in the literature regarding to the DMN and sensorimotor network involvement in adult aging.

To investigate the impact of adult age on the brain functional connectivity, whole-brain resting-state functional magnetic resonance imaging (R-fMRI) data were acquired on a 3T clinical MRI scanner in a cohort of 227, right-handed, native Swedish-speaking, healthy adult volunteers (N=227, aged 18-74 years old, male/female=99/128). The dataset is mainly consisted of a younger (18-30 years old n=124, males/females=51/73) and elderly adult (n=76, 60-76 years old, males/females=35/41) subgroups. The dataset was analyzed using a new data-driven analysis (QDA) framework. With QDA two types of threshold-free voxel-wise resting-state functional connectivity (RFC) metrics were derived: the connectivity strength index (CSI) and connectivity density index (CDI), which can be utilized to assess the brain changes in functional connectivity associated with adult age. The dataset can also be useful as a reference to identify abnormal changes in brain functional connectivity resulted from neurodegenerative or neuropsychiatric disorders.

Gender identity is a core aspect of self-identity and is usually congruent with birth-assigned sex and own body sex-perception. The neuronal circuits underlying gender identity are unknown, but greater awareness of transgenderism has sparked interest in studying these circuits. We did this by comparing brain activation and connectivity in transgender individuals (for whom gender identity and birth-assigned sex are incongruent) with that in cisgender controls (for whom they are congruent) when performing a body self-identification task during functional magnetic resonance imaging. Thirty transgender and 30 cisgender participants viewed images of their own bodies and bodies morphed in sex toward or opposite to birth-assigned sex, rating each image to the degree they identified with it. While controls identified with images of themselves, transgender individuals identified with images morphed opposite to their birth-assigned sex. After covarying out the effect of self-similarity ratings, both groups activated similar self- and body-processing systems when viewing bodies that aligned with their gender identity rather than birth-assigned sex. Additionally, transgender participants had greater limbic involvement when viewing ambiguous, androgynous images of themselves morphed toward their gender identity. These results shed light on underlying self-processing networks specific to gender identity and uncover additional involvement of emotional processing in transgender individuals.

Normal adult aging is associated with decline in some socioemotional abilities, such as the ability to recognize emotions in others, and age-related neurobiological processes may contribute to these deficits. There is increasing evidence that the neuropeptide oxytocin plays a key role in social cognition, including emotion recognition. The mechanisms through which oxytocin promotes emotion recognition are not well understood yet, and particularly in aging. In a randomized, double-blind, placebo-controlled within-subjects design, we investigated the extent to which a single dose of 40 IU of intranasal oxytocin facilitates emotion recognition in 40 younger (M = 24.90 yrs., SD = 2.97, 48% women) and 40 older (M = 69.70 yrs., SD = 2.99, 55% women) men and women. During two fMRI sessions, participants viewed dynamic positive and negative emotional displays. Preliminary analyses show that younger participants recognized positive and negative emotions more accurately than older participants (p < .001), with this behavioral effect not modulated by oxytocin. In the brain data, however, we found an age x treatment interaction in medial prefrontal cortex (xyz [14, 14, 6], p = .007) and superior temporal gyrus (xyz [53, 9, 2], p = .031). In particular, oxytocin (vs. placebo) reduced activity in these regions for older participants, while it enhanced activity in these regions for younger participants. In line with previous research, these findings support the notion that the effects of oxytocin vary by context and individual factors (e.g., social proficiency, age).

The neuropeptide oxytocin plays a prominent role in social and emotional cognition. Findings suggest that intranasal oxytocin administration facilitates emotion recognition in humans, but individual and contextual differences may have moderating effects. A major caveat in this line of work is its predominant focus on young males, which limits current knowledge and generalizability across gender. To uncover potential gender effects, the present study included 32 men (mean age 45.78, sd. 22.87) and 39 women (mean 47.87, sd. 22.59). Utilizing a randomized, double-blind, placebo-controlled, within-subjects design, participants self-administered a single-dose of 40 IUs intranasal oxytocin 40 minutes prior to completion of a dynamic emotion recognition task in the MRI scanning. The task paradigm used positive and negative stimuli from the Geneva Multimodal Emotion Portrayals Core Set. Preliminary analyses show that oxytocin induced dorsomedial prefrontal cortex (dmPFC) activity reductions during exposure to negative (relative to positive) stimuli in women, while dmPCF activity was increased under this condition in men. We observed no effect of sex in the behavioral data, however, the results show a similar trend as in brain data. We speculate that the effects of oxytocin on brain activity during emotion recognition may be related to emotion-regulatory and mentalization processes. The observed gender-differential modulatory role of oxytocin raises concern of a bias in the previous oxytocin literature on emotion recognition and associated brain activity by neglecting women in the examination. Next, we will determine the role of age effects on gender-bytreatment interactions, as well as consider modality of the emotion stimulus presentation.  

This study presents findings about the effectiveness of two computerized training-programs for emotion recognition accuracy that were evaluated in a double-blind randomized controlled study with repeated measures design. Both trainings are effective in training emotion recognition accuracy. The trainings and results are presented in detail and practical implications are discussed.