Plasticity induced by pairing brain stimulation with motor-related states only targets a subset of cortical neurones

Ibáñez, Jaime; Fu, Lingdi; Rocchi, Lorenzo; Spanoudakis, Manos; Spampinato, Danny; Farina, Dario; Rothwell, John C.

doi:10.1016/j.brs.2019.12.014

Cited by 8 publications

(8 citation statements)

References 10 publications

(12 reference statements)

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“…This gave clear MEPs in the TA. In offline analysis, MEP onset latency was determined via visual inspection on a trial by trial basis from the raw EMG traces and the average latency was determined (Rocchi et al, 2018;Ibáñez et al, 2020).…”

Section: Recording Of Mep Latenciesmentioning

confidence: 99%

Only the Fastest Corticospinal Fibers Contribute to β Corticomuscular Coherence

et al. 2021

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Human corticospinal transmission is commonly studied using brain stimulation. However, this approach is biased to activity in the fastest conducting axons. It is unclear whether conclusions obtained in this context are representative of volitional activity in mild-to-moderate contractions. An alternative to overcome this limitation may be to study the corticospinal transmission of endogenously generated brain activity. Here, we investigate in humans (N = 19; of either sex), the transmission speeds of cortical b rhythms (;20 Hz) traveling to arm (first dorsal interosseous) and leg (tibialis anterior; TA) muscles during tonic mild contractions. For this purpose, we propose two improvements for the estimation of corticomuscular b transmission delays. First, we show that the cumulant density (cross-covariance) is more accurate than the commonly-used directed coherence to estimate transmission delays in bidirectional systems transmitting band-limited signals. Second, we show that when spiking motor unit activity is used instead of interference electromyography, corticomuscular transmission delay estimates are unaffected by the shapes of the motor unit action potentials (MUAPs). Applying these improvements, we show that descending corticomuscular b transmission is only 1-2 ms slower than expected from the fastest corticospinal pathways. In the last part of our work, we show results from simulations using estimated distributions of the conduction velocities for descending axons projecting to lower motoneurons (from macaque histologic measurements) to suggest two scenarios that can explain fast corticomuscular transmission: either only the fastest corticospinal axons selectively transmit b activity, or else the entire pool does. The implications of these two scenarios for our understanding of corticomuscular interactions are discussed.

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Section: Recording Of Mep Latenciesmentioning

confidence: 99%

Only the Fastest Corticospinal Fibers Contribute to β Corticomuscular Coherence

et al. 2021

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“…Specifically, precision below the order of tens of milliseconds is required to induce neural plasticity [48] as shown by different studies using paired associative stimulation (PAS) protocols. In these interventions PES stimulation is synchronized either with physiological events, such as motor activity [49] or tremor [20],…”

Section: Discussionmentioning

confidence: 99%

Prediction of Pathological Tremor Signals Using Long Short-Term Memory Neural Networks

Pascual-Valdunciel

Lopo-Martínez

Sendra-Arranz

et al. 2022

IEEE J. Biomed. Health Inform.

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Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long shortterm memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexionextension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32-56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.

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“…The authors aimed to induce plasticity by repetitively pairing a TMS pulse 30 ms prior to individuals moving their index finger (i.e., reaction time task). Here, the authors found that administering PA pulses at this specific time induced changes in cortical spinal excitability, whereas no effects were found when the current was reversed (Ibáñez et al 2019a ). This suggests that specific types of movements (i.e.…”

Section: Introductionmentioning

confidence: 91%

Dissecting two distinct interneuronal networks in M1 with transcranial magnetic stimulation

Spampinato

2020

Exp Brain Res

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Interactions from both inhibitory and excitatory interneurons are necessary components of cortical processing that contribute to the vast amount of motor actions executed by humans daily. As transcranial magnetic stimulation (TMS) over primary motor cortex is capable of activating corticospinal neurons trans-synaptically, studies over the past 30 years have provided how subtle changes in stimulation parameters (i.e., current direction, pulse width, and paired-pulse) can elucidate evidence for two distinct neuronal networks that can be probed with this technique. This article provides a brief review of some fundamental studies demonstrating how these networks have separable excitatory inputs to corticospinal neurons. Furthermore, the findings of recent investigations will be discussed in detail, illustrating how each network’s sensitivity to different brain states (i.e., rest, movement preparation, and motor learning) is dissociable. Understanding the physiological characteristics of each network can help to explain why interindividual responses to TMS exist, while also providing insights into the role of these networks in various human motor behaviors.

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Plasticity induced by pairing brain stimulation with motor-related states only targets a subset of cortical neurones

Cited by 8 publications

References 10 publications

Only the Fastest Corticospinal Fibers Contribute to β Corticomuscular Coherence

Only the Fastest Corticospinal Fibers Contribute to β Corticomuscular Coherence

Prediction of Pathological Tremor Signals Using Long Short-Term Memory Neural Networks

Dissecting two distinct interneuronal networks in M1 with transcranial magnetic stimulation

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