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.

There are no two similar brains on earth. But the brain-wide mechanisms of individuality are largely unknown, especially in animal models. In a collaboration between Ariel Gilad’s and Oren Forkosh’s labs, we aim to study brain-wide networks of the mouse, during cognitive and social functions, on the basis of individuality. To do this, we combine state-of-the-art experimental methods along with BIG data computational analysis to compute cognitive and social networks in behaving mice. First, mice will be
monitored in a ‘social box’ and we will implement a computational framework to map the personality of each mouse. Next, using multi-fiber photometry we will simultaneously record from dozens of cortical and subcortical areas as mice interact within the social box and perform complex cognitive tasks. Using BIG data network analysis on dynamic spatiotemporal patterns we will extract network motifs related to the social and cognitive function of each individual mouse.

Goal

To make a substantial scientific impact by collaborating between experimental and computational disciplines

Researchers

• Ariel Gilad
• Oren Forkosh

What we did

In progress

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.

Researchers

• Shahar Arzy
• Yonatan Loewenstein
• Hermona Soreq

What we did

In progress