We hope it can be used in other conditions to identify the best timing for intervention.🧠

Thanks to co-authors, participants of the #ALSPAC #CO90s #ABCDStudy and #MCS, and to funders @ukri.org MRC and @wellcometrust.bsky.social 🌟

This is the first application of network temperature 🌡️ in mental health and provides a network-wide alternative to node-focussed measures (centrality estimates) which lack applicability in mental health conditions...

Our findings highlight the criticality of early intervention in young people when symptoms are more flexible and responsive to intervention or treatment 🩺

Depression is not just about the type of treatment - but the timing! ⏳...

In 3 large longitudinal adolescent cohorts (@CO90s, @theabcdstudy MCS @CLScohorts) we found that temperature decreased across adolescence, meaning symptoms became more aligned and stable with age 📉🧊 ...

We applied this to depression symptoms:

In a high temperature network, symptoms are less stable and fluctuate 🔥
In a low temperature network, symptoms are stable and harder to shift 🧊 ...

Think of the transition from solid>liquid>gas: As temperature increases, particles become more random and less stable 🥵. As temperature decreases, the system becomes more rigid and fixed 🥶...

Depression is dynamic - symptoms interact over time. We can model depression using networks which capture symptom-symptom relationships. 🕸️

We applied a concept from statistical thermodynamics - temperature 🌡️ ...