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There is a growing research interest to extract the temporal dependency between brain networks. Among several existing methods, functional network connectivity (FNC) is one of the widely used approaches to capture the intrinsic functional relationships among brain networks. In this study, we introduced a novel approach that uses FNC matrices of Adolescent Brain and Cognitive Development (ABCD) data to evaluate multiple overlapping brain functional change patterns (FCPs). Results show several highly structured FCPs that have a significant change over a two-year period and become stronger with age including brain functional connectivity between visual (VS) and sensorimotor (SM) domains. Our approach is a powerful tool to visualize and evaluate patterns of whole brain functional changes in longitudinal data.
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An Instrumental Variable Approach to Functional Network Discovery from Optogenetic Experiments
Understanding how networks of neurons transmit information is crucial to uncovering the underlying mechanisms of brain function. A common measure of communication in neuronal networks is functional connectivity. But, due to the presence of many latent confounding factors in existing experimental paradigms, functional connectivity estimates do not allow a direct interpretation of causal interactions in a network. Here, we aim at addressing this challenge using a quasi-experimental approach, namely Instrumental Variables, in a concurrent optogenetic stimulation of two-photon calcium imaging paradigm. We propose a methodology based on variational inference that allows estimating the spiking activity from blurred and noisy two-photon observations. We then use maximum likelihood estimation to construct a statistical testing framework that allows to distinguish between direct and confounding pairwise effects, by taking a set of random stimulation patterns as the instrumental variables. We demonstrate the utility of our approach using simulated data and compare its performance with existing work. Our results show that the proposed method can achieve high sensitivity and specificity in functional network discovery in presence of confounding effects and using a limited number of stimulation patterns and trials.
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- PAR ID:
- 10656776
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 234 to 238
- Format(s):
- Medium: X
- Location:
- 2024 58th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA
- Sponsoring Org:
- National Science Foundation
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Abstract Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents two tools to conduct valid inference and tests in the presence of invalid instruments. First, we propose a simple and general approach to construct confidence intervals based on taking unions of well‐known confidence intervals. Second, we propose a novel test for the null causal effect based on a collider bias. Our two proposals outperform traditional instrumental variable confidence intervals when invalid instruments are present and can also be used as a sensitivity analysis when there is concern that instrumental variables assumptions are violated. The new approach is applied to a Mendelian randomization study on the causal effect of low‐density lipoprotein on globulin levels.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IEEECONF60004.2024.10943097
