Modeling motor-evoked potentials from neural field simulations of transcranial magnetic stimulation.

OBJECTIVE: To develop a population-based biophysical model of motor-evoked potentials (MEPs) following transcranial magnetic stimulation (TMS). METHODS: We combined an existing MEP model with population-based cortical modeling. Layer 2/3 excitatory and inhibitory neural populations, modeled with neural-field theory, are stimulated with TMS and feed layer 5 corticospinal neurons, which also couple directly but weakly to the TMS pulse. The layer 5 output controls mean motoneuron responses, which generate a series of single motor-unit action potentials that are summed to estimate a MEP. RESULTS: A MEP waveform was generated comparable to those observed experimentally. The model captured TMS phenomena including a sigmoidal input-output curve, common paired pulse effects (short interval intracortical inhibition, intracortical facilitation, long interval intracortical inhibition) including responses to pharmacological interventions, and a cortical silent period. Changes in MEP amplitude following theta burst paradigms were observed including variability in outcome direction. CONCLUSIONS: The model reproduces effects seen in common TMS paradigms. SIGNIFICANCE: The model allows population-based modeling of changes in cortical dynamics due to TMS protocols to be assessed in terms of changes in MEPs, thus allowing a clear comparison between population-based modeling predictions and typical experimental outcome measures.

Investigating how electroencephalogram measures associate with delirium: A systematic review.

Delirium is a common neurocognitive disorder in hospital settings, characterised by fluctuating impairments in attention and arousal following an acute precipitant. Electroencephalography (EEG) is a useful method to understand delirium pathophysiology. We performed a systematic review to investigate associations between delirium and EEG measures recorded prior, during, and after delirium. A total of 1,655 articles were identified using PsycINFO, Embase and MEDLINE, 31 of which satisfied inclusion criteria. Methodological quality assessment was undertaken, resulting in a mean quality score of 4 out of a maximum of 5. Qualitative synthesis revealed EEG slowing and reduced functional connectivity discriminated between those with and without delirium (i.e. EEG during delirium); the opposite pattern was apparent in children, with cortical hyperexcitability. EEG appears to have utility in differentiating those with and without delirium, but delirium vulnerability and the long-term effects on brain function require further investigation. Findings provide empirical support for the theory that delirium is a disorder of reduced functional brain integration.

Characterization of Young and Old Adult Brains: An EEG Functional Connectivity Analysis.

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in delta, theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 93% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in delta, theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and more than half of connections involving occipital electrodes, showed decreased connectivity with older age. Slightly less than half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age were not significantly different in electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with aging during a resting state.

Towards Targeted Brain Stimulation in Stroke: Connectivity as a Biomarker of Response.

Stroke is a leading cause of adult disability. New treatments capable of assisting recovery hold significant potential to improve quality of life for many stroke survivors. Transcranial direct current stimulation is one technique that has received much attention due to its potential to promote neuroplasticity and enhance recovery. However, current evidence suggests this is not a one-size-fits-all treatment with indication that responses are highly variable. Using electroencephalography, Hordacre et al recently demonstrated that connectivity between the ipsilesional motor cortex, ipsilesional parietal cortex, and contralesional frontotemporal cortex was a strong predictor of the neurophysiological response to anodal transcranial direct current stimulation applied to the ipsilesional motor cortex in people with chronic ischemic stroke. Based on this outcome, we discuss the potential for connectivity to be used as a biomarker to target transcranial direct current stimulation. This includes identification of a connectivity threshold which could be used to select stroke survivors who are likely to respond to this potentially beneficial neuromodulatory treatment. Furthermore, we discuss treatment approaches for those identified as unlikely to benefit from ipsilesional anodal transcranial direct current stimulation based on connectivity profile. This represents an important progression towards targeting transcranial direct current stimulation for best treatment outcome based on individual connectivity characteristics.

Simulation of electromyographic recordings following transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is a technique that enables noninvasive manipulation of neural activity and holds promise in both clinical and basic research settings. The effect of TMS on the motor cortex is often measured by electromyography (EMG) recordings from a small hand muscle. However, the details of how TMS generates responses measured with EMG are not completely understood. We aim to develop a biophysically detailed computational model to study the potential mechanisms underlying the generation of EMG signals following TMS. Our model comprises a feed-forward network of cortical layer 2/3 cells, which drive morphologically detailed layer 5 corticomotoneuronal cells, which in turn project to a pool of motoneurons. EMG signals are modeled as the sum of motor unit action potentials. EMG recordings from the first dorsal interosseous muscle were performed in four subjects and compared with simulated EMG signals. Our model successfully reproduces several characteristics of the experimental data. The simulated EMG signals match experimental EMG recordings in shape and size, and change with stimulus intensity and contraction level as in experimental recordings. They exhibit cortical silent periods that are close to the biological values and reveal an interesting dependence on inhibitory synaptic transmission properties. Our model predicts several characteristics of the firing patterns of neurons along the entire pathway from cortical layer 2/3 cells down to spinal motoneurons and should be considered as a viable tool for explaining and analyzing EMG signals following TMS. NEW & NOTEWORTHY A biophysically detailed model of EMG signal generation following transcranial magnetic stimulation (TMS) is proposed. Simulated EMG signals match experimental EMG recordings in shape and amplitude. Motor-evoked potential and cortical silent period properties match experimental data. The model is a viable tool to analyze, explain, and predict EMG signals following TMS.

Neuroplasticity and network connectivity of the motor cortex following stroke: A transcranial direct current stimulation study.

Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that has potential for clinical utility in neurorehabilitation. However, recent evidence indicates that the responses to tDCS are highly variable. This study investigated whether electroencephalographic (EEG) measures of functional connectivity of the target network were associated with the response to ipsilesional anodal tDCS in stroke survivors. Ten chronic stroke patients attended two experimental sessions in a randomized cross-over trial and received anodal or sham tDCS. Single-pulse transcranial magnetic stimulation was used to quantify change in corticospinal excitability following tDCS. At the beginning of each session, functional connectivity was estimated using the debiased-weighted phase lag index from EEG recordings at rest. Magnetic resonance imaging identified lesion location and lesion volume. Partial least squares regression identified models of connectivity which maximally accounted for variance in anodal tDCS responses. Stronger connectivity of a network with a seed approximating the stimulated ipsilesional motor cortex, and clusters of electrodes approximating the ipsilesional parietal cortex and contralesional frontotemporal cortex in the alpha band (8-13 Hz) was strongly associated with a greater increase of corticospinal excitability following anodal tDCS. This association was not observed following sham stimulation. Addition of a structural measure(s) of injury (lesion volume) provided an improved model fit for connectivity between the seed electrode and ipsilesional parietal cortex, but not the contralesional frontotemporal cortex. TDCS has potential to greatly assist stroke rehabilitation and functional connectivity appears a robust and specific biomarker of response which may assist clinical translation of this therapy.

Variability in neural excitability and plasticity induction in the human cortex: A brain stimulation study.

BACKGROUND: The potential of non-invasive brain stimulation (NIBS) for both probing human neuroplasticity and the induction of functionally relevant neuroplastic change has received significant interest. However, at present the utility of NIBS is limited due to high response variability. One reason for this response variability is that NIBS targets a diffuse cortical population and the net outcome to stimulation depends on the relative levels of excitability in each population. There is evidence that the relative excitability of complex oligosynaptic circuits (late I-wave circuits) as assessed by transcranial magnetic stimulation (TMS) is useful in predicting NIBS response. OBJECTIVE: Here we examined whether an additional marker of cortical excitability, MEP amplitude variability, could provide additional insights into response variability following application of the continuous theta burst stimulation (cTBS) NIBS protocol. Additionally we investigated whether I-wave recruitment was associated with MEP variability. METHODS: Thirty-four healthy subjects (15 male, aged 18-35 years) participated in two experiments. Experiment 1 investigated baseline MEP variability and cTBS response. Experiment 2 determined if I-wave recruitment was associated with MEP variability. RESULTS: Data show that both baseline MEP variability and late I-wave recruitment are associated with cTBS response, but were independent of each other; together, these variables predict 31% of the variability in cTBS response. CONCLUSIONS: This study provides insight into the physiological mechanisms underpinning NIBS plasticity responses and may facilitate development of more reliable NIBS protocols.

Resting state functional connectivity measures correlate with the response to anodal transcranial direct current stimulation.

Responses to non-invasive brain stimulation are highly variable between subjects. Resting state functional connectivity was investigated as a marker of plasticity induced by anodal transcranial direct current stimulation (tDCS). Twenty-six healthy adults (15 male, 26.4 ± 6.5 years) were tested. Experiment 1 investigated whether functional connectivity could predict modulation of corticospinal excitability following anodal tDCS. Experiment 2 determined test-retest reliability of connectivity measures. Three minutes of electroencephalography was recorded and connectivity was quantified with the debiased weighted phase lag index. Anodal (1 mA, 20 min) or sham tDCS was applied to the left primary motor cortex (M1), with a change in motor evoked potential amplitude recorded from the right first dorsal interosseous used as a marker of tDCS response. Connectivity in the high beta frequency (20-30 Hz) between an electrode approximating the left M1 (C3) and electrodes overlying the left parietal cortex was a strong predictor of tDCS response (cross-validated R2  = 0.69). Similar relationships were observed for alpha (8-13 Hz; R2  = 0.64), theta (4-7 Hz; R2  = 0.53), and low beta (14-19 Hz; R2  = 0.58) frequencies, however, test-retest reliability of connectivity measures was strongest for the high beta frequency model (ICC = 0.65; good reliability). Further investigation of the high beta model found that greater connectivity between C3 and a cluster of electrodes approximately overlying the left parietal cortex was associated with stronger responses to anodal (rho = 0.61, P = 0.03), but not sham tDCS (rho = 0.43, P = 0.14). Functional connectivity is a strong predictor of the neuroplastic response to tDCS and may be one important characteristic to assist targeted tDCS application.

Ion channel noise can explain firing correlation in auditory nerves.

Neural spike trains are commonly characterized as a Poisson point process. However, the Poisson assumption is a poor model for spiking in auditory nerve fibres because it is known that interspike intervals display positive correlation over long time scales and negative correlation over shorter time scales. We have therefore developed a biophysical model based on the well-known Meddis model of the peripheral auditory system, to produce simulated auditory nerve fibre spiking statistics that more closely match the firing correlations observed in empirical data. We achieve this by introducing biophysically realistic ion channel noise to an inner hair cell membrane potential model that includes fractal fast potassium channels and deterministic slow potassium channels. We succeed in producing simulated spike train statistics that match empirically observed firing correlations. Our model thus replicates macro-scale stochastic spiking statistics in the auditory nerve fibres due to modeling stochasticity at the micro-scale of potassium channels.

Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics.

We propose several modifications to an existing computational model of stochastic vesicle release in inner hair cell ribbon synapses, with the aim of producing simulated auditory nerve fiber spiking data that more closely matches empirical data. Specifically, we studied the inter-spike-interval (ISI) distribution, and long and short term ISI correlations in spontaneous spiking in post-synaptic auditory nerve fibers. We introduced short term plasticity to the pre-synaptic release probability, in a manner analogous to standard stochastic models of cortical short term synaptic depression. This modification resulted in a similar distribution of vesicle release intervals to that estimated from empirical data. We also introduced a biophysical stochastic model of calcium channel opening and closing, but showed that this model is insufficient for generating a match with empirically observed spike correlations. However, by combining a phenomenological model of channel noise and our short term depression model, we generated short and long term correlations in auditory nerve spontaneous activity that qualitatively match empirical data.