Evidence map›Paper›PMID 40501683›Full record

ArticlebioRxiv : the preprint server for biology2025

Towards model-based design of causal manipulations of brain circuits with high spatiotemporal precision.

Anandita De, Roozbeh Kiani, Luca Mazzucato

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Anandita DeInstitute of Neuroscience, University of Oregon, Eugene, OR.
Roozbeh KianiCenter for Neural Science, New York University, New York, NY.
Luca MazzucatoInstitute of Neuroscience, University of Oregon, Eugene, OR.ORCID 0000-0002-8525-7539

Funding

Causal power of cortical neural ensembles: mechanisms and utility for brain perturbationsR01MH127375 · NIMH · NEW YORK UNIVERSITY · PI Roozbeh Kiani, Luca Mazzucato · 2022 to 2026
$3.0M
Brain States and Flexible BehaviorRF1NS118461 · NINDS · UNIVERSITY OF OREGON · PI JARAMILLO, SANTIAGO, MCCORMICK, DAVID · 2020 to 2020
$2.4M
CRCNS: A mechanistic theory of serotonergic modulation of cortical processingR01DA055439 · NIDA · UNIVERSITY OF OREGON · PI MAZZUCATO, LUCA, NIELL, CRISTOPHER M · 2021 to 2025
$1.6M
Brain States and Flexible BehaviorR01NS118461 · NINDS · UNIVERSITY OF OREGON · PI MCCORMICK, DAVID, NIELL, CRISTOPHER M · 2023 to 2024
$1.4M
NIDA NIH HHS R01 DA055439NIMH NIH HHS R01 MH127375NINDS NIH HHS R01 NS118461NINDS NIH HHS RF1 NS118461
6 · The paper itself

Abstract

Recent advancements in neurotechnology enable precise spatiotemporal patterns of microstimulations with single-cell resolution. The choice of perturbation sites must satisfy two key criteria: efficacy in evoking significant responses and selectivity for the desired target effects. This choice is currently based on laborious trial-and-error procedures, unfeasible for sequences of multi-site stimulations. Efficient methods to design complex perturbation patterns are urgently needed. Can we design a spatiotemporal pattern of stimulation to steer neural activity and behavior towards a desired target? We outline a method for achieving this goal in two steps. First, we identify the most effective perturbation sites, or hubs, only based on short observations of spontaneous neural activity. Second, we provide an efficient method to design multi-site stimulation patterns by combining approaches from nonlinear dynamical systems, control theory and data-driven methods. We demonstrate the feasibility of our approach using multi-site stimulation patterns in recurrent network models.

Identifiers

PMID40501683
PMCPMC12154595

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.