Cognitive Science Archives

Mapping Chronic Pain Hotspots

This project aims to map and characterize “hot spots” of chronic pain using self-reported body-map data. The dataset includes about 200 community participants who meet chronic-pain inclusion criteria; each participant marks painful segments out of 74 body segments and assigns an intensity [0–10] to marked regions (0 used also for unmarked regions).

Goal

1) To characterize "hot spots" of pain within patterns of pain distribution and intensity (self-reported on body maps). 2) To identify clusters of people with chronic pain who share similar patterns of pain "hot spots" and/or similar distributions of pain intensity across the body. 3) To examine whether these clusters are meaningful with respect to demographic, clinical, and psychological measures, controlling for the known effect that a greater number of painful body regions is associated with worse pain outcomes.

Researchers

Gadi Gilam
Or Rappel-Kroyzer

What we did

In progress.

Results

Forthcoming.

The Role of Positive Emotions in Accelerated Experiential Dynamic Psychotherapy

This project studies whether positive emotions that patients feel during therapy sessions help with better session experiences and improvements afterwards. The study was done on 46 patients who each received 16 sessions of Accelerated Experiential Dynamic Psychotherapy (AEDP). Patients completed questionnaires after every session about the emotions they experienced, the session’s quality, and their functioning that week. Final reports were that session positive emotions relate to better session ratings and to improved functioning the following week, and that higher average positive emotions during treatment predicted some post-treatment gains.

Goal

To test whether the amount of positive emotion patients report during AEDP sessions predicts session quality, functioning the following week, and overall treatment outcomes at post-treatment and follow-up.

Researchers

What we did

We ran a naturalistic study of 46 patients, 16 AEDP sessions each. After every session patient completed short questionnaires about emotions, session quality, and weekly functioning. The researchers analyzed the session-level data with multilevel models and a cross-lagged panel model to test whether session positive emotions predicted next-week functioning, and used regression analyses to link average positive emotions across treatment to standard outcome measures.

Results

Sessions with more positive emotions were rated as deeper and had stronger patient–therapist ratings; more positive emotions in a session predicted better functioning the following week; and higher average positive emotion across treatment predicted improvements in depressive symptoms and interpersonal functioning at post-treatment, but those links were not significant at 6-month follow-up. The authors note limitations such as sample size and use of self-reports.

Artificial intelligence and model based multimodal approach for the detection, classification and mechanistic understanding of preclinical Alzheimer’s disease

Alzheimer’s disease (AD) is a growing public health challenge with no available cure. A major obstacle to effective treatment is the lack of understanding of complex inter-relations between the patients’ clinical manifestation and disease-specific mechanism(s) at the molecular and neuronal levels. The extremely long prodromal stage (preclinical AD), makes this understanding, and therefore early diagnosis, difficult. This has special importance as very recent evidence suggests that newly developed biological treatment modalities are effective only when introduced very early in the disease course. The research aims to address this challenge by developing a data-science based approach for the early identification and diagnosis of AD.

Goal

Developing new approach for the detection, classification and mechanistic understanding of preclinical Alzheimer’s disease, that may be also utilized in other disorders which are characterized by a long preclinical stage.