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CRCNS: Using Perturbations and Modeling to Study Connectivity for Decision-Making

Award Number: R01NS143140
ORGANIZATION: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 01/01/2025
PERIOD OF PERFORMANCE END DATE: 12/31/2027

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CRCNS: Using Perturbations and Modeling to Study Connectivity for Decision-Making - Much work over the past decades has identified brain areas that are necessary for decision-making and neural activity patterns that correlate with decision-making computations. These findings have inspired computational models that propose greatly differing computations (e.g., competition, amplification, and stabilization) for how the same cortical circuits contribute to decision-making, and thus distinct underlying connectivity mechanisms. While much experimental work has tested and refined these models, from neural activity measurements alone, it often remains unclear if a brain area generates its decision-related activity patterns, and how it might generate them, or merely inherits them from another brain area. Ultimately, a direct measure of the connectivity motifs, and the computations they perform, is needed to identify what and how cortical circuits contribute to decision-making. Here, we propose to use cellular-resolution optogenetics to measure a circuit's effective connectivity by perturbing small groups of neurons while measuring the effect on neural activity in surrounding neurons. In the first aim, we will measure the effective connectivity between excitatory and inhibitory neurons with identified activity patterns during a decision task in virtual reality. In the second aim, we will develop and test multiple classes of computational models in conjunction with the experimental results. We aim to discover the connectivity motifs that exist in cortex and reveal the decision-related computations that are performed by these motifs. We hypothesize that recurrent connections amplify choice selectivity through competition motifs mediated by inhibitory neurons and create choice-informative sequences of neural activity through inhibitory control of activity timing. We thus predict that inhibitory neurons play a critical role in these computations by having sharp choice selectivity and precise connectivity profiles. RELEVANCE (See instructions): The proposed work will reveal fundamental mechanisms of neural function for decision-making. The new methods, models, and concepts will provide a foundation to understand how decision-making is disrupted in neurological disorders and diseases.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $213,780 )
2026	2026	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	001	2	2/23/2026	NON-COMPETING CONTINUATION	$21,379
2026	2026	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	000	2	12/19/2025	NON-COMPETING CONTINUATION	$192,401
														Subtotal = $213,780

Issue Date FY: 2025 ( Subtotal = $216,689 )
2025	2025	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	000	1	12/24/2024	NEW	$216,689
														Subtotal = $216,689

Grand Total All Awards = $430,469

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CRCNS: Using Perturbations and Modeling to Study Connectivity for Decision-Making

Award Number: R01NS143140

ORGANIZATION: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 01/01/2025

PERIOD OF PERFORMANCE END DATE: 12/31/2027

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