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Reposted by Jingnan Du

Rodrigo Braga @rodbraga.bsky.social · 1d

📣 New preprint from the Braga Lab! 📣

The ventral visual stream for reading converges on the transmodal language network

Congrats to Dr. Joe Salvo for this epic set of results

Big Q: What brain systems support the translation of writing to concepts and meaning?

Thread 🧵 ⬇️

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Jingnan Du @jingnandu.bsky.social · 8d

Thrilled to team up with @caterinagratton.bsky.social @ariellekeller.bsky.social and Chuck Lynch for a symposium at CNS 2026! Please consider voting for our session!

Caterina Gratton @caterinagratton.bsky.social · 8d

Are you a @cogneuronews.bsky.social member?
Consider voting for our symposium at #CNS2026:
Not Your Average Brain: Individual-Level fMRI as a Paradigm Shift for Cognitive Neuroscience
Led by @jingnandu.bsky.social & A Zamani w/ C Lynch
Vote: www.cogneurosociety.org/account-login/
By: 11:59pm Oct. 1

CNS Account Login - Cognitive Neuroscience Society

March 7 – 10, 2026 Submit a Symposium Submit a Poster Latest from Twitter

www.cogneurosociety.org

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Reposted by Jingnan Du

Heather L Kosakowski @kosakowski.bsky.social · 10d

My lab at USC is recruiting!
1) research coordinator: perfect for a recent graduate looking for research experience before applying to PhD programs: usccareers.usc.edu REQ20167829
2) PhD students: see FAQs on lab website dornsife.usc.edu/hklab/faq/

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Reposted by Jingnan Du

Anne Billot, PhD @annebillot.bsky.social · 9d

⬇️ Check out this great paper by Jingnan Du et al. It shows you can use fMRI task data to estimate networks as reliably as with rest data! A game changer to increase statistical power in regions with low SNR and leverage datasets that may only have task data.

Jingnan Du @jingnandu.bsky.social · 12d

Our new paper is out now in Neuron! 🎉 With @vaibhavtripathi.bsky.social @maxwellelliott.bsky.social Joanna Ladopoulou, Wendy Sun, Mark Eldaief, and Randy Buckner

Paper link: www.sciencedirect.com/science/arti...

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Reposted by Jingnan Du

Neuron @cp-neuron.bsky.social · 12d

Within-individual precision mapping of brain networks exclusively using task data

Du et al. demonstrate that task data alone can map the detailed organization of within-individual brain networks. Applications include reanalysis of existing data, pooling of task and resting-state data to increase statistical power, and studies that solely acquire task data to simultaneously estimate brain networks and extract the evoked response.

dlvr.it

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Reposted by Jingnan Du

Vaibhav Tripathi @vaibhavtripathi.bsky.social · 12d

Amazing work led by Jingnan! Pooling in task and rest data can give us a lot of discovery potential like discovering hard to find networks in the thalamus (bulk of my postdoc work with Randy). Task data can be used to define networks and activations from left out runs can be investigated. Cool work!

Jingnan Du @jingnandu.bsky.social · 12d

Our new paper is out now in Neuron! 🎉 With @vaibhavtripathi.bsky.social @maxwellelliott.bsky.social Joanna Ladopoulou, Wendy Sun, Mark Eldaief, and Randy Buckner

Paper link: www.sciencedirect.com/science/arti...

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Jingnan Du @jingnandu.bsky.social · 12d

Thank you, Rocco!

Jingnan Du @jingnandu.bsky.social · 12d

Looking forward, future data collections might consider acquiring only task data and using that data to both estimate networks and also to quantify the evoked task response from regions within those networks. (13/13)

Jingnan Du @jingnandu.bsky.social · 12d

Overall, our findings suggest that there is an underlying, stable network architecture that is unique to the individual and persists across task states. For existing datasets with both task and resting-state data, power can be increased by combining all available data. (12/13)

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Jingnan Du @jingnandu.bsky.social · 12d

A single MRI session of ∼1 h in length can thus be used both to estimate precision brain networks and quantify meaningful task responses. This strategy is particularly useful in translational studies seeking to minimize patient burden. (11/13)

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Jingnan Du @jingnandu.bsky.social · 12d

To demonstrate this, we obtained both networks and network-level task response in a new participant during revision of this work, using only NBACK task data from a single ∼1 h session. There was a strong preferential response in FPN-A as compared with other networks. (10/13)

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Jingnan Du @jingnandu.bsky.social · 12d

This finding suggests a novel and efficient strategy of using only task-based data to estimate networks within an individual’s own anatomy as well as estimate the task response within those networks. (9/13)

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Jingnan Du @jingnandu.bsky.social · 12d

We further showed that between-individual differences in task responses can be obtained from network estimates derived from only task data, without acquiring separate resting-state data. (8/13)

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Jingnan Du @jingnandu.bsky.social · 12d

Closely positioned seed regions in the thalamus recapitulate spatially distinct cortical networks. For instance, seeds in the small FPN-A thalamic subregion produced a correlation map closely matching the cortical FPN-A network boundaries. (7/13)

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Jingnan Du @jingnandu.bsky.social · 12d

By pooling extensive resting-state and task data, we were able to triple the amount of data available for analysis within each individual, enabling precise mapping of five higher-order association networks within the thalamus. (6/13)

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Jingnan Du @jingnandu.bsky.social · 12d

We then quantitatively demonstrated that pooling resting-state data with motor task data stabilizes the similarity of correlation matrices between test and retest datasets. This suggests that we can pool all resting-state and task data to increase statistical power. (5/13)

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Jingnan Du @jingnandu.bsky.social · 12d

Furthermore, networks estimated solely from task data predicted functional specializations across multiple higher-order cognitive domains in independent task datasets just as well as traditional resting-state network estimates did. (4/13)

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Jingnan Du @jingnandu.bsky.social · 12d

Direct comparisons of network estimates from both datasets reveal a convergent functional architecture of the brain. While the fine-grained spatial details of these networks varied across individuals, they were largely preserved within each individual. (3/13)

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Jingnan Du @jingnandu.bsky.social · 12d

Using only task data, we derived a 15-network multi-session hierarchical Bayesian model (MS-HBM) estimate, and the results were remarkably similar to those derived from traditional resting-state data. (2/13)

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Jingnan Du @jingnandu.bsky.social · 12d

Precision mapping of brain networks within individuals typically relies on functional connectivity from resting-state data. In this study, we asked: can task data generate precision network maps that are practical equivalents to those generated from resting-state data? (1/13)

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Jingnan Du @jingnandu.bsky.social · 12d

Our new paper is out now in Neuron! 🎉 With @vaibhavtripathi.bsky.social @maxwellelliott.bsky.social Joanna Ladopoulou, Wendy Sun, Mark Eldaief, and Randy Buckner

Paper link: www.sciencedirect.com/science/arti...

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Reposted by Jingnan Du

Golia Shafiei @goliashf.bsky.social · 16d

Excited to share that our work introducing the Reproducible Brain Charts (RBC) data resource is now published in Neuron!! 🎉

📚 Read the paper: authors.elsevier.com/c/1lpaF3BtfH...
🧠 Explore the RBC dataset: reprobrainchart.github.io

Golia Shafiei @goliashf.bsky.social · Feb 27

(1/18) Now out on BioRxiv‼️ Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health | doi.org/10.1101/2025...

Funded by National Institute of Mental Health (NIMH)

Reproducible Brain Charts: An open data resource for mapping brain development and its associations with mental health

Major mental disorders are increasingly understood as disorders of brain development. Large and heterogeneous samples are required to define generalizable links between brain development and psychopat...

doi.org

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Reposted by Jingnan Du

Caterina Gratton @caterinagratton.bsky.social · Aug 14

The lateral prefrontal cortex 🧠— which we think of as critical for goal driven behavior + is a target for psychiatric treatments— is fundamentally different in individuals relative to the group averages we’ve often studied.

👇see preprint and thread, led by Zach Ladwig
#neuroskyence #PsychSciSky

Image of brain networks in a group average and individual LPFC. Individuals show:
1) smaller FP network
2) more interdigitation
3) conserved motifs
4) idiosyncratic features
This was validated with task and rest fMRI

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Reposted by Jingnan Du

Cory Shain @coryshain.bsky.social · Mar 31

New brain/language study w/ @evfedorenko.bsky.social! We applied task-agnostic individualized functional connectomics (iFC) to the entire history of fMRI scanning in the Fedorenko lab, parcellating nearly 1200 brains into networks based on activity fluctuations alone. doi.org/10.1101/2025... . 🧵

A language network in the individualized functional connectomes of over 1,000 human brains doing arbitrary tasks

A century and a half of neuroscience has yielded many divergent theories of the neurobiology of language. Two factors that likely contribute to this situation include (a) conceptual disagreement…

doi.org

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Reposted by Jingnan Du

Rodrigo Braga @rodbraga.bsky.social · Feb 28

🚨 🧠
We have a new preprint out where we studied which brain networks are engaged during mental imagery and self-generated thought.

We used a precision fMRI approach along with multidimensional experience sampling (mDES) to get trialwise self-reports from each participant about what they imagined.

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