Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations
Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions....
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923004512 |
| _version_ | 1827866654706499584 |
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| author | Ruben Sanchez-Romero Takuya Ito Ravi D. Mill Stephen José Hanson Michael W. Cole |
| author_facet | Ruben Sanchez-Romero Takuya Ito Ravi D. Mill Stephen José Hanson Michael W. Cole |
| author_sort | Ruben Sanchez-Romero |
| collection | DOAJ |
| description | Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists’ preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain. |
| first_indexed | 2024-03-12T15:08:33Z |
| format | Article |
| id | doaj.art-beadbee99a9540f88b0fe9068344aaa7 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-03-12T15:08:33Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-beadbee99a9540f88b0fe9068344aaa72023-08-12T04:33:51ZengElsevierNeuroImage1095-95722023-09-01278120300Causally informed activity flow models provide mechanistic insight into network-generated cognitive activationsRuben Sanchez-Romero0Takuya Ito1Ravi D. Mill2Stephen José Hanson3Michael W. Cole4Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA; Corresponding author.Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USACenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USARutgers University Brain Imaging Center (RUBIC), Rutgers University, Newark, NJ 07102, USACenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USABrain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists’ preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.http://www.sciencedirect.com/science/article/pii/S1053811923004512Predictive modelsCausal inferenceBrain networksFunctional connectivityNetwork neuroscienceActivity flow |
| spellingShingle | Ruben Sanchez-Romero Takuya Ito Ravi D. Mill Stephen José Hanson Michael W. Cole Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations NeuroImage Predictive models Causal inference Brain networks Functional connectivity Network neuroscience Activity flow |
| title | Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations |
| title_full | Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations |
| title_fullStr | Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations |
| title_full_unstemmed | Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations |
| title_short | Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations |
| title_sort | causally informed activity flow models provide mechanistic insight into network generated cognitive activations |
| topic | Predictive models Causal inference Brain networks Functional connectivity Network neuroscience Activity flow |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923004512 |
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