The latest CTBN dives deep into the neural systems of fear and anxiety — from mitochondria to machine learning.
• Animal & human circuit models
• Risk, sex, and developmental factors
• Cutting-edge treatments (CBT, TMS, neuromodulation)
• Co-occurring disorders (PTSD, autism, schizophrenia)

A labor of love with my friend & long-time collaborator Murray B. Stein. Addressing Difficult-to-Treat Anxiety: A review integrating computational psychiatry, systems neuroscience, & novel interventions. Mechanistic insights for precision psychiatry. www.biologicalpsychiatrycnni.org/article/S245...

Great work by Wes Thompson, Evan White and Chun Chieh Fan at LIBR as well as others: The promise of population neuroscience: understanding brains across diverse groups.

A tour-de-force by Leandra Figueroa-Hall: A comparison of EV isolation methods from human serum & plasma reveals a trade-off: Precipitation methods (e.g., ExoQuick) yield more EVs, but size-exclusion (SmartSEC, qEV) offers greater purity.

📢 This is an interesting new preprint. 💡 The authors developed a framework using LLMs to boost creativity in scientific research. 🧪 The approach recombines existing knowledge to generate novel ideas, grounded in Boden's theory of combinatorial creativity. arxiv.org/abs/2412.14141

🚀 Interesting preprint about AI agent simulations? #LMAgent! A multimodal agent society powered by LLMs, simulating 10,000+ agents in e-commerce scenarios. Realistic behaviors, herd effects, & 40% efficiency boost—paving the way for next-gen social science! 🌐🛍️ #AI #Research arxiv.org/abs/2412.092...

Interesting piece about Illbeing ≠ Wellbeing: the study shows overlapping genetic influences but distinct environmental factors shaping mental health. The authors suggest that integration is key for future interventions. #MentalHealth #Wellbeing

OpenAI's o1-preview achieves superhuman performance in clinical reasoning: Diagnoses correct in 78.3% of cases, outperforming GPT-4 (88.6% vs 72.9%). Excels in diagnostic & management tasks, scoring 99% on reasoning tests.
#AIinMedicine
arxiv.org/abs/2412.108...

In a new preprint Van Calster et al. provides comprehensive guidance on evaluating predictive AI in healthcare. Recommends core metrics: AUROC, calibration plots, net benefit via decision curve analysis. Essential reading for robust AI model validation! 🔗 arxiv.org/abs/2412.10288 #AIHealth #MedAI

Woo-Young Ahn ccs-lab.github.io/team/young-a... gave a fantastic talk at LIBR www.laureateinstitute.org about his work on inverse reinforcement learning and the need for better model-based assessments to make a real difference in mental health. 2024.ccneuro.org/pdf/466_Pape...

For those that are still around at #ACNP2024 consider attending our study group on challenges and opportunities of population neuroscience

Want to thank Carolyn Rodriguez and all participants for a great mini panel at #ACNP2024

Create get together at #ACNP2024

Hello all at #ACNP2024 hope to see you at our mini-panel tomorrow, great presentations on how generative AI may change the way we study and treat mental health conditions:

I found this new review on Bayesian methods in cardiovascular clinical trials very helpful! 🫀📊 This paper breaks down the advantages of Bayesian analysis, from incorporating prior knowledge to interpreting results. doi.org/10.1016/j.cj...