Sign in

Hang Yang (杨航)

@hangyang.bsky.social
39 followers 69 following 14 posts

Postdoc at Chinese Institute for Brain Research, Beijing

PostsRepliesMedia
Reposted by Hang Yang (杨航)
Damien Fair @drdamienfair.bsky.social · 10/08/2025
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...
16390136
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
020
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
Furthermore, our results remained robust across variations in data preprocessing methods, atlas selection, and analytical parameters 10/n.
120
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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.
100
Hang Yang (杨航) @hangyang.bsky.social · 20/03/2025
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/....
1123
Hang Yang (杨航) @hangyang.bsky.social · 15/01/2025
👍👍👍
010
Reposted by Hang Yang (杨航)
bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 21/12/2024
Structural and genetic determinants of zebrafish functional brain networks www.biorxiv.org/content/10.1101/202…
054
Reposted by Hang Yang (杨航)
Bratislav Misic @misicbata.bsky.social · 19/12/2024
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 🧩 🧠 ⤵️
38939
Reposted by Hang Yang (杨航)
Thomas Yeo @bttyeo.bsky.social · 12/12/2024
OHBM has confirmed that Dec 17 is a hard deadline for abstracts.
02616
Reposted by Hang Yang (杨航)
Imaging Neuroscience @imagingneurosci.bsky.social · 06/12/2024
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...
0166
Reposted by Hang Yang (杨航)
Zaixu Cui @zaixucui.bsky.social · 26/11/2024
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
082