Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach

Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological limitations in previous studies, a consistent picture of the neural correlates related to the individual differences in their habitual use remains elusive. To address these issues, the present study applied a combination of unsupervised and supervised machine learning algorithms to the structural MRI scans of 128 individuals. First, unsupervised machine learning was used to separate the brain into naturally grouping grey matter circuits. Then, supervised machine learning was applied to predict individual differences in the use of different strategies of emotion regulation. Two predictive models, including structural brain features and psychological ones, were tested. Results showed that a temporo-parahippocampal-orbitofrontal network successfully predicted the individual differences in the use of reappraisal. Differently, insular and fronto-temporo-cerebellar networks successfully predicted suppression. In both predictive models, anxiety, the opposite strategy, and specific emotional intelligence factors played a role in predicting the use of reappraisal and suppression. This work provides new insights regarding the decoding of individual differences from structural features and other psychologically relevant variables while extending previous observations on the neural bases of emotion regulation strategies.

Therapeutic Alliance Between Dietitians and Patients With Head and Neck Cancer: The Effect of Training in a Health Behavior Change Intervention

BACKGROUND

The relationship between a clinician and their client-the "therapeutic alliance" is a robust predictor of outcome in healthcare settings; yet, few interventions to improve alliance have been tested. Motivational interviewing is a client-centered approach that embodies many principles and strategies consistent with a strong therapeutic alliance.

PURPOSE

To examine whether alliance is enhanced by training dietitians to deliver a motivational interviewing informed health behavior change intervention ("Eating as Treatment"; EAT) as part of routine consultations with patients with head and neck cancer. The predictive ability of motivational interviewing techniques was also assessed.

METHODS

A secondary analysis of the EAT stepped-wedge cluster-randomized controlled trial was conducted. Patients with head and neck cancer undergoing radiotherapy (n = 307) were treated by radiotherapy dietitians (n = 29) during the control (Treatment as Usual) or intervention (EAT) phase. Alliance was rated during the first and final weeks of radiotherapy, and again 4 and 12 weeks post-radiotherapy. Dietetic sessions were audiotaped. Week one sessions were objectively rated for dietitians' use of motivational interviewing techniques.

RESULTS

Generalized linear-mixed effects regressions found no effect of EAT on dietitian-rated alliance (p = .237). After excluding outliers, patient-rated alliance was 0.29 points lower after EAT training (p = .016). Post hoc analyses revealed lower patient ratings on perceived support and dietitian confidence. Hierarchical multiple regressions found that no specific motivational interviewing techniques predicted patient-rated alliance. Dietitian acknowledgment of patient challenges was related to dietitian-rated alliance (β =.15, p =.035).

CONCLUSIONS

Patient and dietitian ratings of alliance were high after EAT training, but not significantly improved. Further research is needed to better understand the differential impact of intervention training and delivery on patient and clinician ratings of therapeutic alliance.

CLINICAL TRIAL INFORMATION

Trial registration number ACTRN12613000320752.

Validation of a Brief Form of the Supervisory Working Alliance Inventory

The Supervisory Working Alliance Inventory–Trainee Form (SWAI-T) is among the most frequently used instruments for measuring the quality of supervisor–supervisee relationships within counselor supervision. Although the full-scale SWAI-T instrument has proven utility, there are instances when a shorter form may be useful for research and field applications. The current study used secondary data from a pair of cross-sectional studies to test the utility of a brief form of the SWAI-T in a two-step process: (a) reduction of the SWAI-T based on item analyses from an electronic survey of 87 rehabilitation counselors working in a Western state vocational rehabilitation agency and (b) an initial validation study of the instrument using electronic survey responses from a national sample of 228 rehabilitation counselors working in private rehabilitation. The resultant 5-item scale showed evidence of high internal consistency, convergent validity, and minimal differences in psychometric properties relative to the full-scale instrument. An abbreviated supervisory working alliance scale offers practical advantages for select research purposes and for continuous evaluation of supervisory relationships in field environments.