Jingnan Du

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!

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/

⬇️ 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.

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!

Thank you, Rocco!

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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)

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...

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

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

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

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…

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... . 🧵

🚨 🧠
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.

As if there wasn’t enough to worry about…

It turns out the Salience (SAL) and Parietal Memory Networks (PMN) are the same thing!

😩😩😩

🧵

Intriguing work led by Jingnan! Task regressed functional connectivity is similar to resting state in defining individualized functional networks!

Within-Individual Precision Mapping of Brain Networks Exclusively Using Task Data https://www.biorxiv.org/content/10.1101/2025.02.25.640090v1

New preprint!!!

We can reliably detect brain changes in individuals in just a year by collecting several rapid 1-minute T1s at each time point - i.e. "cluster scanning". We discovered large individual differences, even in healthy adults

t.co/oxD9EBdbvu

Our results suggest that there is an underlying, stable network architecture that is idiosyncratic to the individual and persists across task states. For further discussion on the implications for task design and analysis, read the full preprint! (6/6)

Additionally, network estimates from task-regressed data predict functional response properties in independent contrasts similar to parallel analyses using traditional resting-state fixation data. (5/6)