Electrophysiological Correlates of Rodent Default-Mode Network Suppression Revealed by Large-Scale Local Field Potential Recordings

Fakhraei, Leila; Francoeur, Miranda J.; Balasubramani, Pragathi Priyadharsini; Tang, Tingdong; Hulyalkar, Sidharth; Buscher, Nathalie; Mishra, Jyoti; Ramanathan, Dhakshin

doi:10.1093/texcom/tgab034

Cited by 16 publications

(14 citation statements)

References 76 publications

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“…Convergent with our findings at the anterior RSC, Ferrier et al recently used single-slice functional ultrasound imaging in the mouse brain and reported that somatosensory sensory stimulation induces cerebral blood volume suppression in the anterior RSC (51). Our results also parallel findings from Fakhraei et al who measured local field potentials in multiple rat brain areas and found suppression of anterior RSC during a behavioral inhibition task (50). Interestingly, suppression of the anterior RSC was linked with improved task performance, similar to findings previously reported in humans (52).…”

Section: Discussionsupporting

confidence: 92%

“…Notably, these regions have been the focus of investigations of the functional organization of the rodent SN and DMN. Based on prior reports of functional heterogeneity within the RSC (16, 25, 50), we divided this node into six subdivisions, spaced in 1 mm intervals along the anterior-to-posterior axis, allowing us to determine which subdivisions were most strongly coupled or decoupled with the AI during stimulation. Additionally, this facilitated identification of heterogeneous responses and connectivity along the AP axis of the RSC.…”

Section: Resultsmentioning

confidence: 99%

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Optogenetic stimulation of anterior insular cortex neurons reveals causal mechanisms underlying suppression of the default mode network by the salience network

Menon

Cerri

Lee³

et al. 2022

Preprint

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The salience network (SN) and default mode network (DMN) are among the most widely investigated networks in the human brain. The SN, anchored in the anterior insular cortex (AI), is thought to play a critical role in stimulus-driven suppression of DMN nodes. However, the causal neural mechanisms underlying DMN suppression and SN-DMN interactions are poorly understood. Here we combine feedforward optogenetic AI stimulation with fMRI and computational modeling to dissect the causal role of AI in dynamic functional interactions between SN and DMN nodes in the rat brain. Optogenetic stimulation of Chronos-expressing AI neurons in rats suppressed DMN activity and decreased AI-DMN and intra-DMN functional connectivity. Our findings demonstrate that feedforward optogenetic stimulation of AI neurons induces dynamic suppression and decoupling of the DMN and elucidates previously unknown features of rodent brain network organization. Our study advances foundational knowledge of causal mechanisms underlying dynamic cross-network interactions and brain network switching.

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Section: Discussionsupporting

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Optogenetic stimulation of anterior insular cortex neurons reveals causal mechanisms underlying suppression of the default mode network by the salience network

Menon

Cerri

Lee³

et al. 2022

Preprint

View full text Add to dashboard Cite

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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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“…Although multiple nodes of the rodent DMN has not been investigated in awake, behaving conditions, a few studies have examined the RSC node of the DMN, specifically, under those conditions. Ferrier et al observed whisker-stimulation-induced blood volume reduction in the RSC using single-slice functional ultrasound imaging in the mouse brain (Ferrier et al, 2020), and Fakhraei et al reported suppression of local field potentials in RSC during an externally oriented visual task in the rat brain (Fakhraei et al, 2021). Our study fills a critical gap by concurrently measuring neuronal activity from the three major putative DMN nodes – RSC, Cg, and PrL.…”

Section: Discussionmentioning

confidence: 99%

Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli

Chao

Lee

Hsu

et al. 2022

Preprint

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The default mode network (DMN) is closely associated with self-referential mental functions and its dysfunction is implicated in many neuropsychiatric disorders. However, the neurophysiological properties and task-based functional organization of the rodent DMN are poorly understood, limiting its translational utility. Here, we combine fiber-photometry with fMRI and computational modeling to characterize dynamics of putative rodent DMN nodes and their interactions with the anterior insular cortex (AI) of the salience network. We reveal neuronal activity changes in AI and DMN nodes prior to fMRI-derived DMN activations and uncover cyclical transition patterns between spatiotemporal neuronal activity states. Finally, we demonstrate that salient oddball stimuli suppress the DMN and enhance AI neuronal activity, and that the AI causally inhibits the retrosplenial cortex, a prominent DMN node. These findings elucidate previously unknown properties regarding the neurobiological foundations of the rodent DMN and its modulation by salient stimuli, paving the way for future translational studies.

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Electrophysiological Correlates of Rodent Default-Mode Network Suppression Revealed by Large-Scale Local Field Potential Recordings

Cited by 16 publications

References 76 publications

Optogenetic stimulation of anterior insular cortex neurons reveals causal mechanisms underlying suppression of the default mode network by the salience network

Optogenetic stimulation of anterior insular cortex neurons reveals causal mechanisms underlying suppression of the default mode network by the salience network

Personalized machine learning of depressed mood using wearables

Neuronal dynamics of the default mode network and anterior insular cortex: Intrinsic properties and modulation by salient stimuli

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