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Bene Ramirez

@benediktramirez.bsky.social

102 followers 154 following 9 posts

J.S.B. Ramirez PhD. Neuroscientist at the Masonic Institute for the Developing Brain, University of Minnesota. | Developmental Neuroscience | resting-state fMRI | Translational Research

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Bene Ramirez @benediktramirez.bsky.social · 23/08/2026

Why does all of this matter for neuromodulation? If the goal is to stimulate a specific functional network, false-positive assignments can mean stimulating the wrong network. PCM lets us prioritize regions we're most confident belong to the intended network.

Illustration of why network-assignment confidence matters for precision neuromodulation. An example stimulation site targets an individualized functional network. Without confidence thresholding, the target includes more false-positive territory belonging to other networks. PCM thresholding produces a smaller, more precise target with fewer false positives, at the cost of excluding some true-positive network territory.

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Bene Ramirez @benediktramirez.bsky.social · 23/08/2026

Could thresholding improve reliability simply by removing what makes each person unique and leaving a generic network “core”? That’s not what we found. PCM increased agreement with each person’s independent reference while preserving, and even increasing, within-vs-between-subject separation.

Test of whether PCM improves reliability while preserving individual-specific network organization. Action Mode Network maps from four participants are compared with each participant's independent reference and with the references of other participants. PCM thresholding increases within-person agreement while maintaining lower between-person agreement, increasing the separation between within- and between-subject similarity.

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Bene Ramirez @benediktramirez.bsky.social · 23/08/2026

We tested PCM across scan durations and confidence thresholds. Both more data and PCM thresholding increased targeting precision, but PCM provided its largest gains when data were limited. At 5 min, PPV increased from 0.47 → 0.66 with thresholding, vs 0.71 at 70 min.

Quantification of PCM performance across scan durations and confidence thresholds. Positive predictive value increases with both additional fMRI data and more stringent PCM thresholding, with the largest thresholding benefits at short scan durations. Additional panels show reductions in false positives, modest losses in true-positive coverage, and the resulting precision-versus-coverage tradeoff.

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Bene Ramirez @benediktramirez.bsky.social · 23/08/2026

Once uncertainty is quantified, it becomes actionable. Thresholding PCM maps removes unstable assignments. As confidence increases, off-network assignments are progressively removed and the retained network better matches an independent 70-min reference. The benefit is largest with limited data.

Example showing PCM confidence thresholding of the Action Mode Network using 5 versus 70 minutes of fMRI data. Removing progressively lower-confidence network assignments increasingly restricts the map to regions matching an independent 70-minute reference. The improvement is largest with limited data: in the 5-minute example, positive predictive value increases from 0.35 without thresholding to 0.57 at the 99% confidence threshold.

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Bene Ramirez @benediktramirez.bsky.social · 23/08/2026

So how can we know which parts of an individualized network map we can actually trust? PCM bootstraps the available fMRI time series, repeatedly reruns network detection, and asks how consistently each location is assigned to each network. The result: a map of assignment confidence.

Overview of the Precision Confidence Mapping (PCM) workflow. Resting-state fMRI data are divided into temporal partitions and repeatedly bootstrap resampled. Network detection is performed independently on each resampled dataset, producing many network assignment maps. These are aggregated to estimate how consistently each brain location is assigned to a network, generating individualized network confidence maps.

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Bene Ramirez @benediktramirez.bsky.social · 23/08/2026

Functional networks vary substantially across people, making individualized mapping attractive for neuromodulation. But precision comes at a cost: more time in the scanner. With limited data, maps contain more off-network assignments, increasing the risk of targeting the wrong network.

Schematic illustrating the motivation for Precision Confidence Mapping. Group-average functional network maps provide consistent anatomy but miss individual-specific network organization. Individualized mapping captures this variability, but maps generated from only 5 minutes of resting-state fMRI contain substantially more incorrect network assignments than maps generated from 70 minutes. Purple shows correctly assigned Action Mode Network territory, striped regions show misassignments, and yellow outlines indicate the independent reference network.

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