Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium

McNamara, Colin G.; Rothwell, Max; Sharott, Andrew

doi:10.1016/j.celrep.2022.111616

Cited by 20 publications

(24 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The state space method is easily translated to a real-time framework (Wodeyar et al, 2021). Finally, similar to what we showed in the in vivo analyses, measures of uncertainty can help guide closed-loop stimulation to times of greater certainty in the phase estimate (Schatza et al, 2022;McNamara et al, 2022).…”

Section: Discussionmentioning

confidence: 79%

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Wodeyar

Marshall

Chu

et al. 2023

Preprint

View full text Add to dashboard Cite

Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically non-sinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods.

show abstract

Section: Discussionmentioning

confidence: 79%

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Wodeyar

Marshall

Chu

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Nevertheless, more experimental work is required to validate our framework, in particular with regards to the relationship between the characteristics of the ARC of the fast population and the optimal PAC-enhancing waveform. The ARC of the fast population of interest could be measured experimentally using phase-locked stimulation as in previous studies [42, 43]. Recent advances in continuous real-time phase estimation with zero filter delay [62] (also see link to code in [43]) make phase-locking to fast oscillations feasible (robust phase-locking was achieved at 40 Hz in [43]).…”

Section: Discussionmentioning

confidence: 99%

How to design optimal brain stimulation to modulate phase-amplitude coupling?

Duchet,

Bogacz

2024

Preprint

View full text Add to dashboard Cite

Objective. Phase-amplitude coupling (PAC), the coupling of the amplitude of a faster brain rhythm to the phase of a slower brain rhythm, plays a significant role in brain activity and has been implicated in various neurological disorders. For example, in Parkinson's disease, PAC between the beta (13-30 Hz) and gamma (50-200 Hz) rhythms in the motor cortex is exaggerated, while in Alzheimer's disease, PAC between the theta (4-8 Hz) and gamma rhythms is diminished. Modulating PAC (i.e. reducing or enhancing PAC) using brain stimulation could therefore open new therapeutic avenues. However, while it has been previously reported that phase-locked stimulation can increase PAC, it is unclear what the optimal stimulation strategy to modulate PAC might be. Here, we provide a theoretical framework to narrow down the experimental optimisation of stimulation aimed at modulating PAC, which would otherwise rely on trial and error. Approach. We make analytical predictions using a Stuart-Landau model, and confirm these predictions in a more realistic model of coupled neural populations. Main results. Our framework specifies the critical Fourier coefficients of the stimulation waveform which should be tuned to optimally modulate PAC. Depending on the characteristics of the amplitude response curve of the fast population, these components may include the slow frequency, the fast frequency, combinations of these, as well as their harmonics. We also show that the optimal balance of energy between these Fourier components depends on the relative strength of the endogenous slow and fast rhythms, and that the alignment of fast components with the fast rhythm should change throughout the slow cycle. Furthermore, we identify the conditions requiring to phase-lock stimulation to the fast and/or slow rhythms. Significance. Together, our theoretical framework lays the foundation for guiding the development of innovative and more effective brain stimulation aimed at modulating PAC for therapeutic benefit.

show abstract

“…This was performed using a closed-loop system to deliver in real time 561-nm light pulses in CA1 Calb1-cFos ::ArchT mice and CA1 Calb1-cFos ::EYFP mice using dynamic tracking of ongoing theta phase (Figure 4C,D). The real-time phase estimate was obtained using the OscillTrack algorithm (https://colinmcn.github.io/OscillTrack/) [28] implemented in the field-programmable gate array of the Intan Technologies interface board. Phase detection was obtained by continuously operating on the data stream coming from an input channel containing the CA1 LFPs.…”

Section: Methodsmentioning

confidence: 99%

Organizing the coactivity structure of the hippocampus from robust to flexible memory

Gava,

Lefèvre,

Broadbelt

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

New memories are integrated into prior knowledge of the world. But what if consecutive memories exert opposing demands on the host brain network? We report that acquiring a robust (food-context) memory constrains the hippocampus within a population activity space of highly correlated spike trains that prevents subsequent computation of a flexible (object-location) memory. This densely correlated firing structure developed over repeated mnemonic experience, gradually coupling neurons of the superficial CA1pyramidalesublayer to whole population activity. Applying hippocampal theta-driven closed-loop optogenetic suppression to mitigate this neuronal recruitment during (food-context) memory formation relaxed the topological constraint on hippocampal coactivity and restored subsequent flexible (object-location) memory. These findings uncover an organizational principle for the peer-to-peer coactivity structure of the hippocampal cell population to successfully meet memory demands.

show abstract

Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium

Cited by 20 publications

References 54 publications

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

How to design optimal brain stimulation to modulate phase-amplitude coupling?

Organizing the coactivity structure of the hippocampus from robust to flexible memory

Contact Info

Product

Resources

About