ArticleFrontiers in computational neuroscience2020
A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- Frequency-Resolved Cortical Functional Connectivity Across the Adult Lifespan.Human brain mapping · 2026Article
- Frequency-resolved cortical functional connectivity across the adult lifespan.bioRxiv : the preprint server for biology · 2026Article
- Integrated Classification of Cortical Cells and Quantitative Projectomic Mapping Unveil Organizational Principles of Brain-Wide Connectomes at Single Cell Level.bioRxiv : the preprint server for biology · 2025Article
- Article
- Secondary thalamic dysfunction underlies abnormal large-scale neural dynamics in chronic stroke.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Exploring global and local processes underlying alterations in resting-state functional connectivity and dynamics in schizophrenia.Frontiers in psychiatry · 2024Article
- Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models.Brain sciences · 2023Review
- Cortico-thalamic development and disease: From cells, to circuits, to schizophrenia.Frontiers in neuroanatomy · 2023Review
- Whole-Brain Network Models: From Physics to Bedside.Frontiers in computational neuroscience · 2022Review
- Inter-regional BOLD signal variability is an organizational feature of functional brain networks.NeuroImage · 2021Article
- A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity.Frontiers in computational neuroscience · 2020Article
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3 authors at 3 institutions in 1 country.
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Abstract
Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.
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