Mapping Large-Scale Networks Associated with Action, Behavioral Inhibition and Impulsivity

Fakhraei, Leila; Francoeur, Miranda J.; Balasubramani, Pragathi Priyadharsini; Tang, Tingdong; Hulyalkar, Sidharth; Buscher, Nathalie; Claros, C.; Terry, Alvin V.; Gupta, Abhishek; Xiong, Hainan; Zhang, Xu; Mishra, Jyoti; Ramanathan, Dhakshin

doi:10.1523/eneuro.0406-20.2021

Cited by 14 publications

(14 citation statements)

References 94 publications

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“…Participants accessed a game-like task, "Go Wait" 43,44 . The basic task framework was modeled after the standard test of variables of attention 45 .…”

Section: Assessment 1: Inhibitory Controlmentioning

confidence: 99%

Personalized machine learning of depressed mood using wearables

et al. 2021

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Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual’s best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

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“…Participants accessed a game-like task, "Go Wait" 43,44 . The basic task framework was modeled after the standard test of variables of attention 45 .…”

Section: Assessment 1: Inhibitory Controlmentioning

confidence: 99%

Personalized machine learning of depressed mood using wearables

et al. 2021

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“…One of our major goals was to develop a method for measuring brain activity that would be stable over time, thus allowing for a more sensitive way of measuring brain changes following injury or chronic manipulation. Although we previously examined spatio-temporal patterns time-locked to behavioral events ( 79 ), here we extend our investigation and further prove the utility of our probe design by looking at signal stability over a chronic period. We found a fair degree of stability in brain activity over time.…”

Section: Resultsmentioning

confidence: 56%

“…Comparing activity in two distinct frequency bands illustrates how there are different distributions of activity at different time-points/frequencies ( Figure 7A ). A full account of the relationship between brain activity and behavior on this task has been published recently and will not be described here ( 79 ). One of our major goals was to develop a method for measuring brain activity that would be stable over time, thus allowing for a more sensitive way of measuring brain changes following injury or chronic manipulation.…”

Section: Resultsmentioning

confidence: 99%

“…While this limits the specific localization, it does allow for broad generalization to be made (as is evidenced by the data above), in which certain regions clearly show more or less activity, which can provide support for where activity may be arising. Finally, in recent analyses we have utilized methods of functional connectivity like weighted phase-lagged index [see ( 79 )], which inherently suppress volume conduction artifacts, allowing us to better demonstrate that activity within and between regions are not simply a result of volume conduction (which would have strong 0-phase-lagged relationships).…”

Section: Discussionmentioning

confidence: 99%

“…The full details of our operant tasks and the accompanying electrophysiological results have been described previously ( 79 ). Here we briefly describe one of our behavioral tasks to demonstrate how our probes can be utilized to study cognitive behaviors in rodents and to show the quality of physiological signals captured.…”

Section: Methodsmentioning

confidence: 99%

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Chronic, Multi-Site Recordings Supported by Two Low-Cost, Stationary Probe Designs Optimized to Capture Either Single Unit or Local Field Potential Activity in Behaving Rats

et al. 2021

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Rodent models of cognitive behavior have greatly contributed to our understanding of human neuropsychiatric disorders. However, to elucidate the neurobiological underpinnings of such disorders or impairments, animal models are more useful when paired with methods for measuring brain function in awake, behaving animals. Standard tools used for systems-neuroscience level investigations are not optimized for large-scale and high-throughput behavioral battery testing due to various factors including cost, time, poor longevity, and selective targeting limited to measuring only a few brain regions at a time. Here we describe two different “user-friendly” methods for building extracellular electrophysiological probes that can be used to measure either single units or local field potentials in rats performing cognitive tasks. Both probe designs leverage several readily available, yet affordable, commercial products to facilitate ease of production and offer maximum flexibility in terms of brain-target locations that can be scalable (32–64 channels) based on experimental needs. Our approach allows neural activity to be recorded simultaneously with behavior and compared between micro (single unit) and more macro (local field potentials) levels of brain activity in order to gain a better understanding of how local brain regions and their connected networks support cognitive functions in rats. We believe our novel probe designs make collecting electrophysiology data easier and will begin to fill the gap in knowledge between basic and clinical research.

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