New version of Actflow Toolbox just released ( https://t.co/8PPqQQxWux), adding glasso FC functionality. Demo: https://t.co/S9L7LPAZYA We found that glasso FC is an improvement on the fMRI field-standard functional connectivity measure (correlation): https://t.co/h2Y6hGL3kM

@RaviMill This study is now peer reviewed and published at Nature Communications! Congratulations to first author @RaviMill Check out the paper here (open access):

@RaviMill This study is now peer reviewed and published at Nature Communications! Congratulations to first author @RaviMill Check out the paper here (open access):

Excited to see our paper with @TheColeLab finally out in peer-reviewed form @NatureComms ! Please check it out: “Dynamically shifting from compositional to conjunctive brain representations supports cognitive task learning”,

The graphical lasso functional connectivity approach that performed best in the paper can be implemented using the Activity Flow Toolbox: https://t.co/8PPqQQyuk5 also using the paper's code release: https://t.co/b7D8ykXOn7 [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 Ravi Mill! 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

In brief: Improvements to pairwise (standard) correlation: 1) reduced false connections (confounding), 2) reduced sensitivity to in-scanner motion, 3) better correspondence to task-related activity, and 4) more interpretable links with individual differences in behavior 2/n

Lab’s latest out in Imaging Neuroscience, led by K. Peterson: “Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding”, where we show a major improvement to standard fMRI func. connectivity (correlation) 1/n

The full peer-reviewed (and open access) article is available here: https://t.co/eqcTCY4Ubg "Distributed network flows generate localized category selectivity in human visual cortex" Thanks to coauthors @carrisa_cocuzza @RUBENSARO @taku_ito1 @BrianKeaneLab and @RaviMill!

While surprising, this is broadly consistent with recent convolutional neural network models of vision (featuring cross-region/layer vector transformations), though it is less compatible with recurrent neural network models (featuring within-region processing) [10/N]

Overall, we found that visual category selectivity – traditionally assumed to reflect localized within-region processes – is generated by distributed cross-region activity flows [9/N]

But is the visual hierarchy important for category selectivity? Yes, adding V1-indirect flows improved flow model accuracy. This suggests V1-direct flows generate category selectivity, but complex hierarchical flows add additional category selective neural activity [8/N]

We found that the model using direct connectivity from V1 to each category-selective visual region was effective at generating category selectivity, demonstrating activity flows directly from V1 are sufficient for generating visual category selectivity [7/N]