Consistent Causal Inference from Time Series with PC Algorithm and its Time-Aware Extension

Biswas, R.; Mukherjee, Somabha

doi:10.48550/arxiv.2210.09038

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The TPC Algorithm finds CFC between brain regions from time series based on DGM (Spirtes et al, 2000 ; Pearl, 2009 ; Biswas and Mukherjee, 2022 ; Biswas and Shlizerman, 2022a , b ). While traditional DGM applies to static data, TPC extends the applicability of DGM to CFC inference in time series by first implementing the Directed Markov Property to model causal spatial and temporal interactions in the time series by an unrolled Directed Acyclic Graph (DAG) of the time series.…”

Section: Methodsmentioning

confidence: 99%

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data

Biswas,

Sripada

2023

Front. Comput. Neurosci.

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Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.

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Section: Methodsmentioning

confidence: 99%

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data

Biswas,

Sripada

2023

Front. Comput. Neurosci.

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Dissecting the heterogeneity of “in the wild” stress from multimodal sensor data

Nagaraj,

Goodday,

Hartvigsen

et al. 2023

npj Digit. Med.

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Stress is associated with numerous chronic health conditions, both mental and physical. However, the heterogeneity of these associations at the individual level is poorly understood. While data generated from individuals in their day-to-day lives “in the wild” may best represent the heterogeneity of stress, gathering these data and separating signals from noise is challenging. In this work, we report findings from a major data collection effort using Digital Health Technologies (DHTs) and frontline healthcare workers. We provide insights into stress “in the wild”, by using robust methods for its identification from multimodal data and quantifying its heterogeneity. Here we analyze data from the Stress and Recovery in Frontline COVID-19 Workers study following 365 frontline healthcare workers for 4–6 months using wearable devices and smartphone app-based measures. Causal discovery is used to learn how the causal structure governing an individual’s self-reported symptoms and physiological features from DHTs differs between non-stress and potential stress states. Our methods uncover robust representations of potential stress states across a population of frontline healthcare workers. These representations reveal high levels of inter- and intra-individual heterogeneity in stress. We leverage multiple stress definitions that span different modalities (from subjective to physiological) to obtain a comprehensive view of stress, as these differing definitions rarely align in time. We show that these different stress definitions can be robustly represented as changes in the underlying causal structure on and off stress for individuals. This study is an important step toward better understanding potential underlying processes generating stress in individuals.

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Consistent Causal Inference from Time Series with PC Algorithm and its Time-Aware Extension

Cited by 2 publications

References 15 publications

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data

Dissecting the heterogeneity of “in the wild” stress from multimodal sensor data

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