I still get chills
Meet Mike
*30+ years severe depression
*first hospitalized @ 13y
*20 meds
*3 rounds of ECT
*2 near-fatal suicide attempts
Mike felt joy for the first time in decades after we turned on his new brain pacemaker or PACE
see videos, read paper, follow thread
doi.org/10.31234/osf...
Big thanks to our team led by @zaixucui.bsky.social, with Guowei Wu, Yaoxin Li, @xiaoyuxuu.bsky.social, @jing-cong.bsky.social, Haoshu Xu, Yiyao Ma, Yang Li, @runsenchen.bsky.social, @pineurosci.bsky.social, @tingsterx.bsky.social, @valeriejsydnor.bsky.social, and @ted-satterthwaite.bsky.social.
In summary, we identified a stable and reproducible connectional axis of edge-level FC variability, which aligns with structural connectivity variability, evolves with development, and is associated with higher-order cognition /end.
Furthermore, our results remained robust across variations in data preprocessing methods, atlas selection, and analytical parameters 10/n.
Our findings were consistent across both the HCP-D and HCP-YA datasets. Additionally, we replicated these results in an independent youth cohort collected by our lab over the past two years, further demonstrating the robustness and generalizability of our findings 9/n.
We found that the connectional variability axis slope positively correlated with higher-order cognitive performance, controlling for age. This effect was driven by greater individual variability in association connections among higher-cognitive individuals 8/n.
To assess the development of the FC variability axis, we calculated its slope. During youth, the variability axis slope declines with age, primarily driven by reduced FC variability in connections between association networks 7/n.
Using diffusion MRI to construct individual structural connectomes, we find that the connectional axis of FC variability aligns with the spatial pattern of individual variability in structural communicability across edges, suggesting a potential structural basis 6/n.
Next, we ranked FC variability across all 21 within- and between-network connections to define the “connectional variability axis.” Association-association (A-A) connections occupy the top of the axis, while sensorimotor-association (S-A) connections anchor the base 5/n.
Ranking FC variability within and between networks revealed an axis of decreasing variability from within-network to between-network edges. For example, in the DMN, variability declines from edges linking association areas to those connecting with sensorimotor regions 4/n.
We analyzed edge-level FC variability and summarized within- and between-network averages using the Yeo atlas. FC variability was heterogeneously distributed across connectome edges and consistent between datasets 3/n.
Understanding the spatial variation of individual FC variability across human connectome edges offers insights into connections most susceptible to plasticity, influenced by insults and interventions, aiding the development of connectivity-guided interventions 2/n.
Regional FC variability is heterogeneously distributed across the cortex, decreasing progressively from higher-order association to primary sensorimotor cortices (Mueller et al., 2013, Neuron). However, edge-level details remain unclear 1/n.
Excited to share our work “Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition,” now out at @pnas.org www.pnas.org/doi/10.1073/....
👍👍👍
Structural and genetic determinants of zebrafish functional brain networks www.biorxiv.org/content/10.1101/2024.12....
Mapping neuropeptide signaling in the human brain | doi.org/10.1101/2024...
Neuropeptides are among the functionally diverse signaling molecules in the brain and body.
@cebric.bsky.social curates an atlas of neuropeptide receptors and relates it brain function 🧩 🧠 ⤵️
OHBM has confirmed that Dec 17 is a hard deadline for abstracts.
New paper in Imaging Neuroscience by Peiyu Chen, Zaixu Cui, et al:
Group-common and individual-specific effects of structure–function coupling in human brain networks with graph neural networks
doi.org/10.1162/imag...
Excited to share our latest work, 'Group-common and Individual-specific Effects of Structure-Function Coupling in Human Brain Networks Using Graph Neural Networks,' now published in Imaging Neuroscience @imagingneurosci.bsky.social
