My latest post to Neural Flows!
“Reorienting neuroscience around brain flows”
A perspective shift to enhance both activity-focused (e.g., manifold, representations) and connectivity-focused (e.g., connectome) neuroscience
open.substack.com/pub/neuralfl...
I think we finally made really significant progress on the biggest unsolved "developmental AI" problem: learning from human-scale data. Key idea: zero-shot world models that support concept extraction via approximate causal inference. amazing collab w/ @mcxfrank.bsky.social @khaiaw.bsky.social
Today I am relaunching my newsletter as Neural Flows, inspired by my forthcoming book "Brain Flows"
The newsletter asks: How does the improvised symphony of flows inside our heads generate our minds?
Check out the intro post to the new newsletter here: open.substack.com/pub/neuralfl...
Quick bit of advice for everyone on Bluesky:
We should switch to using the For You feed as our default feed. It will make things better
Inspired by Edinburgh’s legacy of big ideas (e.g., Hume), on a train leaving the city, I crafted a proposal for a book about big ideas in network neuroscience—how brain activity flows generate representations that together compose our minds. The rest is history, and this new book: shorturl.at/3p100
Inspired by Edinburgh’s legacy of big ideas (e.g., Hume), on a train leaving the city, I crafted a proposal for a book about big ideas in network neuroscience—how brain activity flows generate representations that together compose our minds. The rest is history, and this new book: shorturl.at/3p100
Schematic of theta waves and gamma packets recorded from the mouse visual cortex
How does the visual cortex coordinate neural activity over spatial and temporal scales? We found broad θ waves organize local γ bursts and spiking, forming a flexible spatiotemporal code to multiplex feedforward/feedback signals. Now out in full @natcomms.nature.com: doi.org/10.1038/s414... 🧵
Thanks for the clarifications. I have been uncertain if there's really a distinction here – I think it's an empirical question. Does the activity-level-dependent decreased variability that drives reduced covariance also result in reduced interareal coupling? Does your study look at that at all?
Cool results! I'm curious, how does this relate to the common observation that noise correlations in neural recordings go down as spiking activity goes up? We found this effect also for fMRI functional connectivity here: Ito et al. (2020) PLOS Comp Bio www.colelab.org/pubs/2020_It...
Our recent study, "Distributed cortical network dynamics of binocular convergent eye movements in humans" is out in Network Neuroscience, where we move beyond simple brain mapping to show that binocular convergence is driven by the flow of activity across distributed neural networks. 1/8
This paper had a pretty shocking headline result (40% of voxels!), so I dug into it, and I think it is wrong. Essentially: they compare two noisy measures and find that about 40% of voxels have different sign between the two. I think this is just noise!
10) Please check out the full paper here: “Dynamically shifting from compositional to conjunctive brain representations supports cognitive task learning”, doi.org/10.1038/s414...
Schematic depicting cortical-subcortical interactions during multi-task learning
Excited to see our paper with @mwcole.bsky.social finally out in peer-reviewed form @natcomms.nature.com! We examine how the human brain learns new tasks and optimizes representations over practice…1/n
2/3 Imagine if NFL coaches were hired because they were friends with the White House. You’d end up with bad football teams pretty fast. Same deal with NIH IC Directors and science.
Wanna better understand what society has gained from research? This website crowd-sources societal benefits that stem from US government-funded research publicusaresearchbenefits.com
Yes! bsky.app/profile/mwco...
I think both of those explanations are plausible. I co-authored a paper on functional/effective connectivity in 2019 that may be helpful: Reid et al. (2019). "Advancing functional connectivity research from association to causation". Nature Neuroscience. www.colelab.org/pubs/Reid201...
Ideally, regularized partial correlation would have become the default back then. Instead, 90%+ of studies have continued to use pairwise correlations, especially with fMRI. I think one reason is that the advantages of the new approach hadn't been shown clearly, which is what we try to do here.
The graphical lasso functional connectivity approach that performed best in the paper can be implemented using the Activity Flow Toolbox: colelab.github.io/ActflowToolb...
And also using the paper's code release: github.com/ColeLab/Reli... [12/n]
Together, results demonstrated vast improvements in fMRI functional connectivity estimation using regularized partial correlation. Thanks to first author Kirsten Peterson, and coauthors Ruben Sanchez-Romero and
@ravimill.bsky.social!
doi.org/10.1162/IMAG... #neuroscience #neuroimaging [11/n]
And regularization improved prediction of individual differences in demographics (age) and behavior/cognition (general intelligence) relative to standard partial correlation. The glasso results were more interpretable than pairwise correlation (fewer false connections) 10/n
Also empirical, prediction of task-evoked activity (via activity flow modeling) was better with regularized partial correlation 9/n
As another empirical validation, regularized partial correlation was much less susceptible to motion artifacts than pairwise correlation. Percent connections linked to motion = Pairwise correlation FC: 56.4% vs. graphical lasso FC: 0.01% 8/n
First empirical validation: regularized partial correlation was much closer to structural connectivity, which doesn’t have the causal confounding problem (despite other issues) 7/n
This pattern of results was mirrored in empirical resting-state fMRI data across 4 validation measures. Regularization was key to estimating individual subject-level networks with reduced confounding. 6/n
In simulations, pairwise (standard) correlation led to many false connections, but so did partial correlation. Regularized partial correlation (glasso) better recovered the true network organization 5/n
We hypothesized that low reliability of partial correlation is due to overfitting to noise, with regularization (model simplification) improving reliability. 4/n
Pairwise correlations are known to be susceptible to false positives in theory. For example, region A causing activity in unconnected regions B and C (B<-A->C) can lead to a false B-C connection. Partial correlation can correct for this error, but not reliably 3/n
