Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study

Enhanced network synchronization connectivity following transcranial direct current stimulation (tDCS) in bipolar depression: Effects on EEG oscillations and deep learning-based predictors of clinical remission

AIM

To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response.

METHODS

20 participants (14 women) with bipolar disorder, mean age 50.75 ± 10.46 years, in a depressive episode of severe severity (mean Montgomery-Åsberg Rating Scale (MADRS) score 24.60 ± 2.87) received home-based transcranial direct current stimulation (tDCS) treatment for 6 weeks. Clinical remission defined as MADRS score < 10. Resting-state EEG data were acquired at baseline, prior to the start of treatment, and at the end of treatment, using a portable 4-channel EEG device (electrode positions: AF7, AF8, TP9, TP10). EEG band power was extracted for each electrode and phase locking value (PLV) was computed as a functional connectivity measure of phase synchronization. Deep learning was applied to pre-treatment PLV features to examine potential predictors of clinical remission.

RESULTS

Following treatment, 11 participants (9 women) attained clinical remission. A significant positive correlation was observed with improvements in depressive symptoms and delta band PLV in frontal and temporoparietal regional channel pairs. An interaction effect in network synchronization was observed in beta band PLV in temporoparietal regions, in which participants who attained clinical remission showed increased synchronization following tDCS treatment, which was decreased in participants who did not achieve clinical remission. Main effects of clinical remission status were observed in several PLV bands: clinical remission following tDCS treatment was associated with increased PLV in frontal and temporal regions and in several frequency bands, including delta, theta, alpha and beta, as compared to participants who did not achieve clinical remission. The highest deep learning prediction accuracy 69.45 % (sensitivity 71.68 %, specificity 66.72 %) was obtained from PLV features combined from theta, beta, and gamma bands.

CONCLUSIONS

tDCS treatment enhances network synchronization, potentially increasing inhibitory control, which underscores improvement in depressive symptoms. Baseline EEG-based measures might aid predicting clinical response.

EEG microstates are associated with the improvement of obsessive-compulsive symptoms after transcranial direct current stimulation

BACKGROUND

Transcranial direct current stimulation (tDCS) is a safe, accessible, and promising therapeutic approach for obsessive-compulsive disorder (OCD).

AIMS

This study aimed to evaluate the effect of tDCS on electroencephalography (EEG) microstates and identify potential biomarkers to predict efficacy.

METHODS

A total of 24 individuals diagnosed with OCD underwent ten sessions of tDCS targeting the orbitofrontal cortex, while 27 healthy individuals were included as controls. Microstates A, B, C, and D were extracted before and after tDCS. A comparative analysis of microstate metrics was performed between the OCD and the healthy control groups, as well as within the OCD group before and after tDCS. Multiple linear regression analysis was performed to identify potential biomarkers of tDCS.

RESULTS

Comparison to healthy controls, the OCD group exhibited a significantly reduced duration of microstate A and increased occurrence of microstate D. The transition between microstates A and C was significantly different between patients with OCD and healthy controls and was no longer observed following tDCS. Multiple linear regression analysis revealed that the duration of microstate C was associated with an improvement OCD symptom after tDCS.

CONCLUSIONS

The results revealed an aberrant large-scale EEG brain network that could be modulated by tDCS. In particular, the duration of EEG microstate C may be a neurophysiological characteristic associated with the therapeutic effects of tDCS on OCD.

Influence of noninvasive brain stimulation on connectivity and local activation: a combined tDCS and fMRI study

The effect of transcranial direct current stimulation (tDCS) on neurobiological mechanisms underlying executive function in the human brain remains elusive. This study aims at examining the effect of anodal and cathodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) in comparison with sham stimulation on resting-state connectivity as well as functional activation and working memory performance. We hypothesized perturbed fronto-parietal resting-state connectivity during stimulation and altered working memory performance combined with modified functional working memory-related activation. We applied tDCS with 1 mA for 21 min over the DLPFC inside an fMRI scanner. During stimulation, resting-state fMRI was acquired and task-dependent fMRI during working memory task performance was acquired directly after stimulation. N = 36 healthy subjects were studied in a within-subject design with three different experimental conditions (anodal, cathodal and sham) in a double-blind design. Seed-based functional connectivity analyses and dynamic causal modeling were conducted for the resting-state fMRI data. We found a significant stimulation by region interaction in the seed-based ROI-to-ROI resting-state connectivity, but no effect on effective connectivity. We also did not find an effect of stimulation on task-dependent signal alterations in working memory activation in our regions of interest and no effect on working memory performance parameters. We found effects on measures of seed-based resting-state connectivity, while measures of effective connectivity and task-based connectivity did not show any stimulation effect. We could not replicate previous findings of tDCS stimulation effects on behavioral outcomes. We critically discuss possible methodological limitations and implications for future studies.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling

INTRODUCTION

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results.

METHODS

We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls).

RESULTS

Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings.

DISCUSSION

The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

Neuromodulation techniques - From non-invasive brain stimulation to deep brain stimulation

Over the past 30 years, the field of neuromodulation has witnessed remarkable advancements. These developments encompass a spectrum of techniques, both non-invasive and invasive, that possess the ability to both probe and influence the central nervous system. In many cases neuromodulation therapies have been adopted into standard care treatments. Transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), and transcranial ultrasound stimulation (TUS) are the most common non-invasive methods in use today. Deep brain stimulation (DBS), spinal cord stimulation (SCS), and vagus nerve stimulation (VNS), are leading surgical methods for neuromodulation. Ongoing active clinical trials using are uncovering novel applications and paradigms for these interventions.

Action video games and posterior parietal cortex neuromodulation enhance both attention and reading in adults with developmental dyslexia

The impact of action video games on reading performance has been already demonstrated in individuals with and without neurodevelopmental disorders. The combination of action video games and posterior parietal cortex neuromodulation by a transcranial random noise stimulation could enhance brain plasticity, improving attentional control and reading skills also in adults with developmental dyslexia. In a double blind randomized controlled trial, 20 young adult nonaction video game players with developmental dyslexia were trained for 15 h with action video games. Half of the participants were stimulated with bilateral transcranial random noise stimulation on the posterior parietal cortex during the action video game training, whereas the others were in the placebo (i.e. sham) condition. Word text reading, pseudowords decoding, and temporal attention (attentional blink), as well as electroencephalographic activity during the attentional blink, were measured before and after the training. The action video game + transcranial random noise stimulation group showed temporal attention, word text reading, and pseudoword decoding enhancements and P300 amplitude brain potential changes. The enhancement in temporal attention performance was related with the efficiency in pseudoword decoding improvement. Our results demonstrate that the combination of action video game training with parietal neuromodulation increases the efficiency of visual attention deployment, probably reshaping goal-directed and stimulus-driven fronto-parietal attentional networks interplay in young adults with neurodevelopmental conditions.

Brain network hypersensitivity underlies pain crises in sickle cell disease

Sickle cell disease (SCD) is a genetic disorder causing painful and unpredictable Vaso-occlusive crises (VOCs) through blood vessel blockages. In this study, we propose explosive synchronization (ES) as a novel approach to comprehend the hypersensitivity and occurrence of VOCs in the SCD brain network. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) as well as VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited lower alpha frequency than controls. SCD patients showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, stronger FDA was observed in SCD patients with a higher frequency of VOCs and EEG recording near VOC. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Our model demonstrated that a stronger FDA could be linked to increased sensitivity and frequency of VOCs. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

Rain may improve survival from direct lightning strikes to the human head

There is evidence that humans can survive a direct lightning strike to the head. Our question is: could water (rain) on the skin contribute to an increase in the survival rate? We measure the influence of rain during high-energy direct lightning strikes on a realistic three-compartment human head phantom. We find a lower number of perforations and eroded areas near the lightning strike impact points on the head phantom when rain was applied compared to no rain. Current amplitudes in the brain were lower with rain compared to no rain before a fully formed flashover. We conclude that rain on the scalp potentially contributes to the survival rate of 70-90% due to: (1) lower current exposition in the brain before a fully formed flashover, and (2) reduced mechanical and thermal damage.

Cathodal HD-tDCS and attention: A study on patients with intractable left lateral frontal lobe epilepsy

OBJECTIVE

Defects in the attentional network in patients with epilepsy are influenced by factors such as the location of epileptic foci. Examining the impact of cathodal high-definition transcranial direct current stimulation (HD-tDCS) on attention components could provide insights into potential attention-related side effects of tDCS. This study aimed to investigate the effect of cathodal HD-tDCS on interictal epileptiform discharges (IEDs), auditory/visual (A/V) attention components, and reaction time (RT) in patients with intractable focal left lateral frontal lobe epilepsy (LFLE).

METHODS

To control for variations in individual epilepsy syndrome, 12 adult participants diagnosed with drug-resistant left LFLE with focal cortical IEDs on C3 underwent repeated measurements at pretest, posttest, and follow-up steps. 4 × 1 ring electrodes (cathode on C3 and four anodes on F3, P3, T3, and Cz) delivered 2 mA DC for 20 min per session for 10 consecutive days. The integrated visual and auditory continuous performance test (IVA+) assessed the A/V attention components and RT. One-way repeated-measure ANOVA was used.

RESULTS

The findings suggest a significant effect in reducing IEDs. The IVA+ results showed a significant improvement in auditory divided attention and visual selective and focused attention (p < 0.05). In the follow-up, these changes demonstrated lasting efficacy. A/V speed scales increased (p < 0.05), showing a significant decrease in reaction time.

CONCLUSIONS

Cathodal HD-tDCS significantly reduced IEDs and improved the components of auditory divided attention, visual focused attention, and visual selective attention, with a reduction in patient reaction time. A significant lasting, side-effect-free positive effect was observed for up to one month after the intervention.

Optimised Multi-Channel Transcranial Direct Current Stimulation (MtDCS) Reveals Differential Involvement of the Right-Ventrolateral Prefrontal Cortex (rVLPFC) and Insular Complex in those Predisposed to Aberrant Experiences

Research has shown a prominent role for cortical hyperexcitability underlying aberrant perceptions, hallucinations, and distortions in human conscious experience - even in neurotypical groups. The rVLPFC has been identified as an important structure in mediating cognitive affective states / feeling conscious states. The current study examined the involvement of the rVLPFC in mediating cognitive affective states in those predisposed to aberrant experiences in the neurotypical population. Participants completed two trait-based measures: (i) the Cortical Hyperexcitability Index_II (CHi_II, a proxy measure of cortical hyperexcitability) and (ii) two factors from the Cambridge Depersonalisation Scale (CDS). An optimised 7-channel MtDCS montage for stimulation conditions (Anodal, Cathodal and Sham) was created targeting the rVLPFC in a single-blind study. At the end of each stimulation session, participants completed a body-threat task (BTAB) while skin conductance responses (SCRs) and psychological responses were recorded. Participants with signs of increasing cortical hyperexcitability showed significant suppression of SCRs in the Cathodal stimulation relative to the Anodal and sSham conditions. Those high on the trait-based measures of depersonalisation-like experiences failed to show reliable effects. Collectively, the findings suggest that baseline brain states can mediate the effects of neurostimulation which would be missed via sample level averaging and without appropriate measures for stratifying individual differences.

New approaches to recovery after stroke

After a stroke, several mechanisms of neural plasticity can be activated, which may lead to significant recovery. Rehabilitation therapies aim to restore surviving tissue over time and reorganize neural connections. With more patients surviving stroke with varying degrees of neurological impairment, new technologies have emerged as a promising option for better functional outcomes. This review explores restorative therapies based on brain-computer interfaces, robot-assisted and virtual reality, brain stimulation, and cell therapies. Brain-computer interfaces allow for the translation of brain signals into motor patterns. Robot-assisted and virtual reality therapies provide interactive interfaces that simulate real-life situations and physical support to compensate for lost motor function. Brain stimulation can modify the electrical activity of neurons in the affected cortex. Cell therapy may promote regeneration in damaged brain tissue. Taken together, these new approaches could substantially benefit specific deficits such as arm-motor control and cognitive impairment after stroke, and even the chronic phase of recovery, where traditional rehabilitation methods may be limited, and the window for repair is narrow.

Frontopolar multifocal transcranial direct current stimulation reduces conditioned fear reactivity during extinction training: A pilot randomized controlled trial

Exposure-based therapies for anxiety and related disorders are believed to depend on fear extinction learning and corresponding changes in extinction circuitry. Frontopolar multifocal transcranial direct current stimulation (tDCS) has been shown to improve therapeutic safety learning during in vivo exposure and may modulate functional connectivity of networks implicated in fear processing and inhibition. A pilot randomized controlled trial was completed to determine the effects of frontopolar tDCS on extinction learning and memory. Community volunteers (n = 35) completed a 3-day fear extinction paradigm with measurement of electrodermal activity. Participants were randomized (single-blind) to 20-min of sham (n = 17, 30 s. ramp in/out) or active (n = 18) frontopolar (anode over Fpz, 10-10 EEG) multifocal tDCS (20-min, 1.5 mA) prior to extinction training. Mixed ANOVAs revealed a significant group*trial effect on skin conductance response (SCR) to the conditioned stimulus (CS + ) during extinction training (p = 0.007, Cohen's d = 0.55). The effects of frontopolar tDCS were greatest during the first two extinction trials, suggesting that tDCS may have promoted fear inhibition prior to safety learning. Return of fear to the CS + during tests were comparable across conditions (ps > 0.50). These findings suggest that frontopolar tDCS may modulate the processing of threat cues and associated circuitry or promote the inhibition of fear. This has clear implications for the treatment of anxiety and related disorders with therapeutic exposure.

Brain network hypersensitivity underlies pain crises in sickle cell disease

Sickle cell disease (SCD) is a genetic disorder causing blood vessel blockages and painful Vaso-occlusive crises (VOCs). VOCs, characterized by severe pain due to blocked blood flow, are recurrent and unpredictable, posing challenges for preventive strategies. In this study we propose explosive synchronization (ES), a phenomenon characterized by abrupt brain network phase transitions, as a novel approach to address this challenge. We hypothesized that the accumulated disruptions in the brain network induced by SCD might lead to strengthened ES and hypersensitivity. We explored ES's relationship with patient reported outcome measures (PROMs) and VOCs by analyzing EEG data from 25 SCD patients and 18 matched controls. SCD patients exhibited significantly lower alpha wave frequency than controls. SCD patients under painful pressure stimulation showed correlation between frequency disassortativity (FDA), an ES condition, and three important PROMs. Furthermore, patients who had a higher frequency of VOCs in the preceding 12 months presented with stronger FDA. The timing of VOC occurrence relative to EEG recordings was significantly associated to FDA. We also conducted computational modeling on SCD brain network to study FDA's role in network sensitivity. Stronger FDA correlated with higher responsivity and complexity in our model. Simulation under noisy environment showed that higher FDA could be linked to increased occurrence frequency of crisis. This study establishes connections between SCD pain and the universal network mechanism, ES, offering a strong theoretical foundation. This understanding will aid predicting VOCs and refining pain management for SCD patients.

Noise improves the association between effects of local stimulation and structural degree of brain networks

Stimulation to local areas remarkably affects brain activity patterns, which can be exploited to investigate neural bases of cognitive function and modify pathological brain statuses. There has been growing interest in exploring the fundamental action mechanisms of local stimulation. Nevertheless, how noise amplitude, an essential element in neural dynamics, influences stimulation-induced brain states remains unknown. Here, we systematically examine the effects of local stimulation by using a large-scale biophysical model under different combinations of noise amplitudes and stimulation sites. We demonstrate that noise amplitude nonlinearly and heterogeneously tunes the stimulation effects from both regional and network perspectives. Furthermore, by incorporating the role of the anatomical network, we show that the peak frequencies of unstimulated areas at different stimulation sites averaged across noise amplitudes are highly positively related to structural connectivity. Crucially, the association between the overall changes in functional connectivity as well as the alterations in the constraints imposed by structural connectivity with the structural degree of stimulation sites is nonmonotonically influenced by the noise amplitude, with the association increasing in specific noise amplitude ranges. Moreover, the impacts of local stimulation of cognitive systems depend on the complex interplay between the noise amplitude and average structural degree. Overall, this work provides theoretical insights into how noise amplitude and network structure jointly modulate brain dynamics during stimulation and introduces possibilities for better predicting and controlling stimulation outcomes.

Clinical relevance of animal models in aging-related dementia research

Alzheimer's disease (AD) and other, less prevalent dementias are complex age-related disorders that exhibit multiple etiologies. Over the past decades, animal models have provided pathomechanistic insight and evaluated countless therapeutics; however, their value is increasingly being questioned due to the long history of drug failures. In this Perspective, we dispute this criticism. First, the utility of the models is limited by their design, as neither the etiology of AD nor whether interventions should occur at a cellular or network level is fully understood. Second, we highlight unmet challenges shared between animals and humans, including impeded drug transport across the blood-brain barrier, limiting effective treatment development. Third, alternative human-derived models also suffer from the limitations mentioned above and can only act as complementary resources. Finally, age being the strongest AD risk factor should be better incorporated into the experimental design, with computational modeling expected to enhance the value of animal models.

A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.

Complex spatiotemporal oscillations emerge from transverse instabilities in large-scale brain networks

Spatiotemporal oscillations underlie all cognitive brain functions. Large-scale brain models, constrained by neuroimaging data, aim to trace the principles underlying such macroscopic neural activity from the intricate and multi-scale structure of the brain. Despite substantial progress in the field, many aspects about the mechanisms behind the onset of spatiotemporal neural dynamics are still unknown. In this work we establish a simple framework for the emergence of complex brain dynamics, including high-dimensional chaos and travelling waves. The model consists of a complex network of 90 brain regions, whose structural connectivity is obtained from tractography data. The activity of each brain area is governed by a Jansen neural mass model and we normalize the total input received by each node so it amounts the same across all brain areas. This assumption allows for the existence of an homogeneous invariant manifold, i.e., a set of different stationary and oscillatory states in which all nodes behave identically. Stability analysis of these homogeneous solutions unveils a transverse instability of the synchronized state, which gives rise to different types of spatiotemporal dynamics, such as chaotic alpha activity. Additionally, we illustrate the ubiquity of this route towards complex spatiotemporal activity in a network of next generation neural mass models. Altogehter, our results unveil the bifurcation landscape that underlies the emergence of function from structure in the brain.

Comparison between an exact and a heuristic neural mass model with second-order synapses

Neural mass models (NMMs) are designed to reproduce the collective dynamics of neuronal populations. A common framework for NMMs assumes heuristically that the output firing rate of a neural population can be described by a static nonlinear transfer function (NMM1). However, a recent exact mean-field theory for quadratic integrate-and-fire (QIF) neurons challenges this view by showing that the mean firing rate is not a static function of the neuronal state but follows two coupled nonlinear differential equations (NMM2). Here we analyze and compare these two descriptions in the presence of second-order synaptic dynamics. First, we derive the mathematical equivalence between the two models in the infinitely slow synapse limit, i.e., we show that NMM1 is an approximation of NMM2 in this regime. Next, we evaluate the applicability of this limit in the context of realistic physiological parameter values by analyzing the dynamics of models with inhibitory or excitatory synapses. We show that NMM1 fails to reproduce important dynamical features of the exact model, such as the self-sustained oscillations of an inhibitory interneuron QIF network. Furthermore, in the exact model but not in the limit one, stimulation of a pyramidal cell population induces resonant oscillatory activity whose peak frequency and amplitude increase with the self-coupling gain and the external excitatory input. This may play a role in the enhanced response of densely connected networks to weak uniform inputs, such as the electric fields produced by noninvasive brain stimulation.

Review of individualized current flow modeling studies for transcranial electrical stimulation

There is substantial intersubject variability of behavioral and neurophysiological responses to transcranial electrical stimulation (tES), which represents one of the most important limitations of tES. Many tES protocols utilize a fixed experimental parameter set disregarding individual anatomical and physiological properties. This one-size-fits-all approach might be one reason for the observed interindividual response variability. Simulation of current flow applying head models based on available anatomical data can help to individualize stimulation parameters and contribute to the understanding of the causes of this response variability. Current flow modeling can be used to retrospectively investigate the characteristics of tES effectivity. Previous studies examined, for example, the impact of skull defects and lesions on the modulation of current flow and demonstrated effective stimulation intensities in different age groups. Furthermore, uncertainty analysis of electrical conductivities in current flow modeling indicated the most influential tissue compartments. Current flow modeling, when used in prospective study planning, can potentially guide stimulation configurations resulting in individually effective tES. Specifically, current flow modeling using individual or matched head models can be employed by clinicians and scientists to, for example, plan dosage in tES protocols for individuals or groups of participants. We review studies that show a relationship between the presence of behavioral/neurophysiological responses and features derived from individualized current flow models. We highlight the potential benefits of individualized current flow modeling.

Supplementing transcranial direct current stimulation to local infiltration series for refractory neuropathic craniocephalic pain: A randomized controlled pilot trial

Background

Some patients with neuralgia of cranial nerves with otherwise therapy-refractory pain respond to invasive therapy with local anesthetics. Unfortunately, pain regularly relapses despite multimodal pain management. Transcranial direct current stimulation (tDCS) may prolong pain response due to neuro-modulatory effects.

Methods

This controlled clinical pilot trial randomized patients to receive anodal, cathodal or sham-tDCS stimulation prior to local anesthetic infiltration. Pain attenuation, quality-of-life and side effects were assessed and compared with historic controls to estimate effects of tDCS stimulation setting.

Results

Altogether, 17 patients were randomized into three groups with different stimulation protocols. Relative reduction of pain intensity in per protocol treated patients were median 73%, 50% and 69% in anodal, cathodal and sham group, respectively (p = 0.726). Compared with a historic control group, a lower rate of responders with 50% reduction of pain intensity indicates probable placebo effects (OR 3.41 stimulation vs. non-stimulation setting, NNT 3.63). 76.9% (n = 10) of tDCS patients reported mild side-effects. Of all initially included 17 patients, 23.5% (n = 4) withdrew their study participation with highest proportion in the cathodal group (n = 3). A sample size calculation for a confirmatory trial revealed 120 patients using conservative estimations.

Discussion

This pilot trial does not support series of anodal tDCS as neuro-modulatory treatment to enhance pain alleviation of local anesthetic infiltration series. Notably, results may indicate placebo effects of tDCS settings. Feasibility of studies in this population was limited due to relevant drop-out rates. Anodal tDCS warrants further confirmation as neuro-modulatory pain treatment option.

The effect of external stimulation on functional networks in the aging healthy human brain

Understanding the brain changes occurring during aging can provide new insights for developing treatments that alleviate or reverse cognitive decline. Neurostimulation techniques have emerged as potential treatments for brain disorders and to improve cognitive functions. Nevertheless, given the ethical restrictions of neurostimulation approaches, in silico perturbation protocols based on causal whole-brain models are fundamental to gaining a mechanistic understanding of brain dynamics. Furthermore, this strategy could serve to identify neurophysiological biomarkers differentiating between age groups through an exhaustive exploration of the global effect of all possible local perturbations. Here, we used a resting-state fMRI dataset divided into middle-aged (N =310, <65 years) and older adults (N =310, $\geq $65) to characterize brain states in each group as a probabilistic metastable substate (PMS) space. We showed that the older group exhibited a reduced capability to access a metastable substate that overlaps with the rich club. Then, we fitted the PMS to a whole-brain model and applied in silico stimulations in each node to force transitions from the brain states of the older- to the middle-aged group. We found that the precuneus was the best stimulation target. Overall, these findings could have important implications for designing neurostimulation interventions for reversing the effects of aging on whole-brain dynamics.

Strength-dependent perturbation of whole-brain model working in different regimes reveals the role of fluctuations in brain dynamics

Despite decades of research, there is still a lack of understanding of the role and generating mechanisms of the ubiquitous fluctuations and oscillations found in recordings of brain dynamics. Here, we used whole-brain computational models capable of presenting different dynamical regimes to reproduce empirical data's turbulence level. We showed that the model's fluctuations regime fitted to turbulence more faithfully reproduces the empirical functional connectivity compared to oscillatory and noise regimes. By applying global and local strength-dependent perturbations and subsequently measuring the responsiveness of the model, we revealed each regime's computational capacity demonstrating that brain dynamics is shifted towards fluctuations to provide much-needed flexibility. Importantly, fluctuation regime stimulation in a brain region within a given resting state network modulates that network, aligned with previous empirical and computational studies. Furthermore, this framework generates specific, testable empirical predictions for human stimulation studies using strength-dependent rather than constant perturbation. Overall, the whole-brain models fitted to the level of empirical turbulence together with functional connectivity unveil that the fluctuation regime best captures empirical data, and the strength-dependent perturbative framework demonstrates how this regime provides maximal flexibility to the human brain.

Hallmarks of neurodegenerative disease: A systems pharmacology perspective

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

tDCS over the primary motor cortex contralateral to the trained hand enhances cross-limb transfer in older adults

Transferring a unimanual motor skill to the untrained hand, a phenomenon known as cross-limb transfer, was shown to deteriorate as a function of age. While transcranial direct current stimulation (tDCS) ipsilateral to the trained hand facilitated cross-limb transfer in older adults, little is known about the contribution of the contralateral hemisphere to cross-limb transfer. In the present study, we investigated whether tDCS facilitates cross-limb transfer in older adults when applied over the motor cortex (M1) contralateral to the trained hand. Furthermore, the study aimed at investigating short-term recovery of tDCS-associated cross-limb transfer. In a randomized, double-blinded, sham-controlled setting, 30 older adults (67.0 ± 4.6 years, 15 female) performed a short grooved-pegboard training using their left hand, while anodal (a-tDCS) or sham-tDCS (s-tDCS) was applied over right M1 for 20 min. Left (LH_trained) - and right-hand (RH_untrained) performance was tested before and after training and in three recovery measures 15, 30 and 45 min after training. LH_trained performance improved during both a-tDCS and s-tDCS and improvements persisted during recovery measures for at least 45 min. RH_untrained performance improved only following a-tDCS but not after s-tDCS and outlasted the stimulation period for at least 45 min. Together, these data indicate that tDCS over the M1 contralateral to the trained limb is capable of enhancing cross-limb transfer in older adults, thus showing that cross-limb transfer is mediated not only by increased bi-hemispheric activation.

Transcranial-Direct-Current-Stimulation Accelerates Motor Recovery After Cortical Infarction in Mice: The Interplay of Structural Cellular Responses and Functional Recovery

BACKGROUND

Transcranial direct current stimulation (tDCS) promotes recovery after stroke in humans. The underlying mechanisms, however, remain to be elucidated. Animal models suggest tDCS effects on neuroinflammation, stem cell proliferation, neurogenesis, and neural plasticity.

OBJECTIVE

In a longitudinal study, we employed tDCS in the subacute and chronic phase after experimental focal cerebral ischemia in mice to explore the relationship between functional recovery and cellular processes.

METHODS

Mice received photothrombosis in the right motor cortex, verified by Magnetic Resonance Imaging. A composite neuroscore quantified subsequent functional deficits. Mice received tDCS daily: either 5 sessions from day 5 to 9, or 10 sessions with days 12 to 16 in addition. TDCS with anodal or cathodal polarity was compared to sham stimulation. Further imaging to assess proliferation and neuroinflammation was performed by immunohistochemistry at different time points and Positron Emission Tomography at the end of the observation time of 3 weeks.

RESULTS

Cathodal tDCS at 198 kC/m² (220 A/m²) between days 5 and 9 accelerated functional recovery, increased neurogenesis, decreased microglial activation, and mitigated CD16/32-expression associated with M1-phenotype. Anodal tDCS exerted similar effects on neurogenesis and microglial polarization but not on recovery of function or microglial activation. TDCS on days 12 to 16 after stroke did not induce any further effects, suggesting that the therapeutic time window was closed by then.

CONCLUSION

Overall, data suggest that non-invasive neuromodulation by tDCS impacts neurogenesis and microglial activation as critical cellular processes influencing functional recovery during the early phase of regeneration from focal cerebral ischemia.

Repeated High-Definition Transcranial Direct Current Stimulation Modulated Temporal Variability of Brain Regions in Core Neurocognitive Networks Over the Left Dorsolateral Prefrontal Cortex in Mild Cognitive Impairment Patients

Background: Early intervention of amnestic mild cognitive impairment (aMCI) may be the most promising way for delaying or even preventing the progression to Alzheimer’s disease. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that has been recognized as a promising approach for the treatment of aMCI. Objective: In this paper, we aimed to investigate the modulating mechanism of tDCS on the core neurocognitive networks of brain. Methods: We used repeated anodal high-definition transcranial direct current stimulation (HD-tDCS) over the left dorsolateral prefrontal cortex and assessed the effect on cognition and dynamic functional brain network in aMCI patients. We used a novel method called temporal variability to depict the characteristics of the dynamic brain functional networks. Results: We found that true anodal stimulation significantly improved cognitive performance as measured by the Montreal Cognitive Assessment after simulation. Meanwhile, the Mini-Mental State Examination scores showed a clear upward trend. More importantly, we found significantly altered temporal variability of dynamic functional connectivity of regions belonging to the default mode network, central executive network, and the salience network after true anodal stimulation, indicating anodal HD-tDCS may enhance brain function by modulating the temporal variability of the brain regions. Conclusion: These results imply that ten days of anodal repeated HD-tDCS over the LDLPFC exerts beneficial effects on the temporal variability of the functional architecture of the brain, which may be a potential neural mechanism by which HD-tDCS enhances brain functions. Repeated HD-tDCS may have clinical uses for the intervention of brain function decline in aMCI patients.

Anodal transcranial direct current stimulation sustainably increases EEG alpha activity in patients with schizophrenia

AIMS

Transcranial direct current stimulation (tDCS) applied to the prefrontal cortex has been frequently used to elicit behavioral changes in patients with schizophrenia. However, the interaction between prefrontal tDCS and electrophysiological changes remains largely uncharted. The present study aimed to investigate cortical electrophysiological changes induced by tDCS in frontal areas by means of repeated electroencephalography (EEG) in patients with schizophrenia.

METHODS

In total, 20 patients with schizophrenia received 13 minutes of anodal tDCS (1 mA) applied to the left dorsolateral prefrontal cortex (DLPFC). Repeated resting EEG was recorded before (once) and following (at five follow-up time-bins) tDCS to trace post-tDCS effects. We used sLORETA for source reconstruction to preserve the localization of brain signals with a low variance and to analyze frequency changes.

RESULTS

We observed significant changes after the stimulation in areas highly connected with the stimulated DLPFC areas. The alpha 1 (8.5-10.0 Hz) activity showed a highly significant, long-lasting, increase for up to 1 hour after the stimulation in the postcentral gyrus (Brodmann area 2, 3, and 40). Significant yet unstable changes were also seen in the alpha-2 frequency band precentral at 10 minutes, in the beta-1 frequency band occipital at 20 minutes, and in the beta-3 frequency band temporal at 40 minutes.

CONCLUSION

We were able to show that anodal tDCS can induce stable EEG changes in patients with schizophrenia. The results underline the potential of tDCS to induce long-lasting neurophysiological changes in patients with schizophrenia showing the possibility to induce brain excitability changes in this population.

Mathematical Model Insights into EEG Origin under Transcranial Direct Current Stimulation (tDCS) in the Context of Psychosis

Schizophrenia is a psychotic disease that develops progressively over years with a transition from prodromal to psychotic state associated with a disruption in brain activity. Transcranial Direct Current Stimulation (tDCS), known to alleviate pharmaco-resistant symptoms in patients suffering from schizophrenia, promises to prevent such a psychotic transition. To understand better how tDCS affects brain activity, we propose a neural cortico-thalamo-cortical (CTC) circuit model involving the Ascending Reticular Arousal System (ARAS) that permits to describe major impact features of tDCS, such as excitability for short-duration stimulation and electroencephalography (EEG) power modulation for long-duration stimulation. To this end, the mathematical model relates stimulus duration and Long-Term Plasticity (LTP) effect, in addition to describing the temporal LTP decay after stimulus offset. This new relation promises to optimize future stimulation protocols. Moreover, we reproduce successfully EEG-power modulation under tDCS in a ketamine-induced psychosis model and confirm the N-methyl-d-aspartate (NMDA) receptor hypofunction hypothesis in the etiopathophysiology of schizophrenia. The model description points to an important role of the ARAS and the δ-rhythm synchronicity in CTC circuit in early-stage psychosis.

Effects of transcranial direct current stimulation on patients with post-stroke fatigue: a study protocol for a double-blind randomized controlled trial

Introduction

Post-stroke fatigue (PSF) is an abnormal, persistent, and unexplained physical and psychological tiredness in patients after stroke. It is a common symptom of stroke patients with poor quality of life and bleak prognosis, and the incidence rate is up to 39% to 72%. It has been widely reported that medicine treatments achieved a lot of progress, there still needs to develop more powerful new strategies to more powerful effect. The transcranial direct-current stimulation (tDCS) shows great potential for the treatment of PSF. This study proposes to apply a double-blind randomized controlled clinical trial to explore the effect and safety of tDCS combined with routine rehabilitation for PSF.

Methods and analysis

One hundred patients with PSF will be randomly divided into two groups. One of the groups will receive conventional rehabilitation therapy and active tDCS, whereas another group will receive conventional rehabilitation treatment and sham tDCS. Both groups will receive the intervention for 4 weeks, during which time they will undergo either active or sham tDCS 20 min a day, 6 days a week. Primary outcome: Fatigue Severity Scale (FSS) will be measured at baseline every weekend during the intervention period. Secondary results: Fatigue Impact Scale (FIS), Functional Assessment Chronic Illness Therapy (Fatigue) (FACIT-F), and Specialized Quality of Life Scale in Stroke (SS-QOL) will be measured at baseline and at the end of the intervention time of 4 weeks. Throughout the study, adverse events and adverse reactions will be measured during every treatment. The research study “Effects of transcranial direct current stimulation on patients with post-stroke fatigue” has been approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University: Clinical Medicine Ethics Review [2015]043 in Nov 2015.

Discussion

This study will provide insight into the efficacy of transcranial direct-current stimulation for post-stroke fatigue. This is a double-blind randomized controlled trial whose aim is to assess the effects of tDCS on PSF. This study can provide more information about the treatment of PSF. This study has a period of follow-up, which allows for greater accuracy. It is a single-center trial, and this may be a limitation. The other limitation of this study is the relatively small number of participants; thus, the influence of chance on experimental results cannot be completely ruled out.

Trial registration

Chinese Clinical Trial Registry ChiCTR2000031120. Registered on March 22, 2020. This protocol version number is V1.1.

The Effect and Mechanism of Transcranial Direct Current Stimulation on Episodic Memory in Patients With Mild Cognitive Impairment

OBJECTIVE

This study aimed to investigate the efficacy of transcranial direct current stimulation (tDCS) on episodic memory in patients with mild cognitive impairment (MCI) and analyze the neural mechanism of tDCS therapy from the perspective of neuroelectrophysiological parameters.

METHODS

Forty MCI patients were recruited and randomly divided into a sham group (n = 20) and a tDCS group (n = 20). Patients in the tDCS group were treated with a tDCS instrument for 20 min, once a day, for 5 days. Patients in the sham group were treated with sham stimulus. Montreal Cognitive Assessment Scale (MoCA), Wechsler Memory Scale (WMS), and event-related potential (ERP) (amplitude and latency of P300 wave) were comparatively assessed between the two groups at pre-treatment, 5 days and 4 weeks post-treatment points.

RESULTS

The two groups showed no significant difference in any of the assessed parameters at pre-treatment (P > 0.05). At 5 days post-treatment, memory quotient (MQ) score in the tDCS group significantly increased (P < 0.05), scores of picture memory, visual regeneration, logical memory, memory span, visual regeneration-delay, and logical memory-delay were significantly increased compared to pre-treatment (P < 0.01). The P300 amplitude significantly increased, and its latency significantly shortened (P < 0.01). Four weeks post-treatment, the scores of MQ and visual regeneration-delay in the tDCS group increased, compared to pre-treatment (P < 0.05); picture memory, visual regeneration, logical memory, memory span, and logical memory-delay improved (P < 0.01); the P300 amplitude increased, and its latency shortened (P < 0.01). At 5 days and 4 weeks post-treatment points, the tDCS group, compared with the sham group (P < 0.01), exhibited greater scores of MQ, picture memory, visual regeneration, logical memory, memory span, visual regeneration-delay, and logical memory-delay, increased P300 amplitude, and shortened P300 latency. Similarly, the tDCS group showed higher MQ scores at 5 days post-treatment (P < 0.05) and 4 weeks post-treatment (P < 0.01). Before treatment and after 5 days of treatment, P300 amplitude and latency difference were positively correlated with MQ difference (P < 0.05).

CONCLUSION

tDCS improved episodic memory in MCI patients, and the effect lasted for 4 weeks. Changes in ERP (P300) suggested that tDCS could promote changes in brain function.

Effect of Transcranial Direct Current Stimulation on the Mismatch Negativity Features of Deviated Stimuli in Children With Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a devastating mental disorder in children. Currently, there is no effective treatment for ASD. Transcranial direct current stimulation (tDCS), which is a non-invasive brain stimulation neuromodulation technology, is a promising method for the treatment of ASD. However, the manner in which tDCS changes the electrophysiological process in the brain is still unclear. In this study, we used tDCS to stimulate the dorsolateral prefrontal cortex area of children with ASD (one group received anode tDCS, and the other received sham tDCS) and investigated the changes in evoked EEG signals and behavioral abilities before and after anode and sham stimulations. In addition to tDCS, all patients received conventional rehabilitation treatment. Results show that although conventional treatment can effectively improve the behavioral ability of children with ASD, the use of anode tDCS with conventional rehabilitation can boost this improvement, thus leading to increased treatment efficacy. By analyzing the electroencephalography pre- and post-treatment, we noticed a decrease in the mismatch negativity (MMN) latency and an increase in the MMN amplitude in both groups, these features are considered similar to MMN features from healthy children. However, no statistical difference between the two groups was observed after 4 weeks of treatment. In addition, the MMN features correlate well with the aberrant behavior checklist (ABC) scale, particularly the amplitude of MMN, thus suggesting the feasibility of using MMN features to assess the behavioral ability of children with ASD.

Modulation of Brain Networks during MR-Compatible Transcranial Direct Current Stimulation

Transcranial direct current stimulation (tDCS) can influence performance on behavioral tasks and improve symptoms of brain conditions. Yet, it remains unclear precisely how tDCS affects brain function and connectivity. Here, we measured changes in functional connectivity (FC) metrics in blood-oxygenation-level-dependent (BOLD) fMRI data acquired during MR-compatible tDCS in a whole-brain analysis with corrections for false discovery rate. Volunteers (n=64) received active tDCS, sham tDCS, and rest (no stimulation), using one of three previously established electrode tDCS montages targeting left dorsolateral prefrontal cortex (DLPFC, n=37), lateral temporoparietal area (LTA, n=16), or superior temporal cortex (STC, n=11). In brain networks where simulated E field was highest in each montage, connectivity with remote nodes decreased during active tDCS. During active DLPFC-tDCS, connectivity decreased between a fronto-parietal network and subgenual ACC, while during LTA-tDCS connectivity decreased between an auditory-somatomotor network and frontal operculum. Active DLPFC-tDCS was also associated with increased connectivity within an orbitofrontal network overlapping subgenual ACC. Irrespective of montage, FC metrics increased in sensorimotor and attention regions during both active and sham tDCS, which may reflect the cognitive-perceptual demands of tDCS. Taken together, these results indicate that tDCS may have both intended and unintended effects on ongoing brain activity, stressing the importance of including sham, stimulation-absent, and active comparators in basic science and clinical trials of tDCS.

Transcranial direct current stimulation targeting the medial prefrontal cortex modulates functional connectivity and enhances safety learning in obsessive-compulsive disorder: Results from two pilot studies

BACKGROUND

Exposed-based psychotherapy is a mainstay of treatment for obsessive-compulsive disorder (OCD) and anxious psychopathology. The medial prefrontal cortex (mPFC) and the default mode network (DMN), which is anchored by the mPFC, promote safety learning. Neuromodulation targeting the mPFC might augment therapeutic safety learning and enhance response to exposure-based therapies.

METHODS

To characterize the effects of mPFC neuromodulation on functional connectivity, 17 community volunteers completed resting-state functional magnetic resonance imaging scans before and after 20 min of frontopolar anodal multifocal transcranial direct current stimulation (tDCS). To examine the effects of tDCS on therapeutic safety learning, 24 patients with OCD completed a pilot randomized clinical trial; they were randomly assigned (double-blind, 50:50) to receive active or sham frontopolar tDCS before completing an in vivo exposure and response prevention (ERP) challenge. Changes in subjective emotional distress during the ERP challenge were used to index therapeutic safety learning.

RESULTS

In community volunteers, frontal pole functional connectivity with the middle and superior frontal gyri increased, while connectivity with the anterior insula and basal ganglia decreased (ps < .001, corrected) after tDCS; functional connectivity between DMN and salience network also decreased after tDCS (ps < .001, corrected). OCD patients who received active tDCS exhibited more rapid therapeutic safety learning (ps < .05) during the ERP challenge than patients who received sham tDCS.

CONCLUSIONS

Frontopolar tDCS may modulate mPFC and DMN functional connectivity and can accelerate therapeutic safety learning. Though limited by small samples, these findings motivate further exploration of the effects of frontopolar tDCS on neural and behavioral targets associated with exposure-based psychotherapies.

Attention network modulation via tRNS correlates with attention gain

Transcranial random noise stimulation (tRNS) can enhance vision in the healthy and diseased brain. Yet, the impact of multi-day tRNS on large-scale cortical networks is still unknown. We investigated the impact of tRNS coupled with behavioral training on resting-state functional connectivity and attention. We trained human subjects for 4 consecutive days on two attention tasks, while receiving tRNS over the intraparietal sulci, the middle temporal areas, or Sham stimulation. We measured resting-state functional connectivity of nodes of the dorsal and ventral attention network (DVAN) before and after training. We found a strong behavioral improvement and increased connectivity within the DVAN after parietal stimulation only. Crucially, behavioral improvement positively correlated with connectivity measures. We conclude changes in connectivity are a marker for the enduring effect of tRNS upon behavior. Our results suggest that tRNS has strong potential to augment cognitive capacity in healthy individuals and promote recovery in the neurological population.

A randomized controlled trial to explore the efficacy and safety of transcranial direct current stimulation on patients with post-stroke fatigue

BACKGROUND

Post-stroke fatigue seriously affects the quality of life for stroke patients. There is no effective treatment at present. transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation which may have therapeutic effect on post-stroke fatigue. This study will explore about this.

METHOD

A total of 60 patients with post-stroke fatigue were randomly divided into the control group and the treatment group with 30 patients each by minimization randomization. Both groups received basic treatment and conventional rehabilitation. In the treatment group, patients were treated with active tDCS, while in the control group, sham tDCS. Both active and sham tDCS were administered 6 times a week for 4 weeks. Before and after the trial, the Fatigue Severity Scale (FSS), Fugl-Meyer Assessment (FMA) and Modified Barthel Index (MBI) were evaluated and analyzed. And comparisons were made among groups. And there were an 8-week follow-up after the intervention.

RESULT

Before the intervention, there were no significant differences in baseline data and assessment scores between the groups (P > 0.05). After 4 weeks of intervention, FSS scores in the treatment group were significantly lower than those in the control group (P = 0.012), and FMA and BMI scores were significantly higher than those in the control group (P < 0.05). There was no significant change in FSS scores after 8 months of follow-up (P > 0.05).

DISCUSSION

TDCS is a safe treatment that can effectively reduce the degree of fatigue after stroke, improve the motor function and daily activity ability of patients after stroke, and the efficacy is better than only routine rehabilitation training.

TRIAL REGISTRATION NUMBER

Chinese Clinical Trial Registry, ChiCTR2000031120. Registered on March 22, 2020.

neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study, we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular, we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along with the example of diffusion-weighted magnetic resonance imaging (dMRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e., the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

Contributions of the Right Prefrontal and Parietal Cortices to the Attentional Blink: A tDCS Study

The AB refers to the performance impairment that occurs when visual selective attention is overloaded through the very rapid succession of two targets (T1 and T2) among distractors by using the rapid serial visual presentation task (RSVP). Under these conditions, performance is typically impaired when T2 is presented within 200–500 ms from T1 (AB). Based on neuroimaging studies suggesting a role of top-down attention and working memory brain hubs in the AB, here we potentiated via anodal or sham tDCS the activity of the right DLPFC (F4) and of the right PPC (P4) during an AB task. The findings showed that anodal tDCS over the F4 and over P4 had similar effects on the AB. Importantly, potentiating the activity of the right frontoparietal network via anodal tDCS only benefitted poor performers, reducing the AB, whereas in good performers it accentuated the AB. The contribution of the present findings is twofold: it shows both top-down and bottom-up contributions of the right frontoparietal network in the AB, and it indicates that there is an optimal level of excitability of this network, resulting from the individual level of activation and the intensity of current stimulation.

Glial cells have more important role in tDCS-induced brain activities rather than neurons

Transcranial Direct Current Stimulation (tDCS) is a non-invasive and sub-threshold method for altering the excitability of neurons to achieve target conditions. Many clinical applications of tDCS are in progress including treatment of neurological disease. Despite the therapeutic and practical potentials of tDCS, the precise mechanism of its impact is still unclear to a large extent. The initial perspective about the effect of tDCS on synaptic plasticity was that electric stimulation of the brain degrades the threshold of neural firing, thus facilitating electrical transmission and improving conductivity between neurons and ultimately leading to changes in the processing of information. However, new results of some experimental studies have changed the initial perspective. Recently, calcium imaging has shown sudden calcium fluctuations, followed by a change in cortical metaplasticity under tDCS. These pieces of evidence pinpoint the important role of astrocytes in describing the effect of tDCS. Astrocytes are glial cells that are traditionally viewed with only a supportive and nutritional role, but recent research expresses that they are active and contribute to processing and regulating synaptic information. We hypothesize that considering astrocytes as an important player in tDCS effect instead of the direct effect on voltage membrane of neurons is closer to the reality of tDCS effect mechanism. This hypothesis is examined here based on the experimental evidence published so far, which indicates astroglia involvement in tDCS-induced activities and results of our proposed computational model.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

The intervention, the patient and the illness - Personalizing non-invasive brain stimulation in psychiatry

Current hypotheses on the therapeutic action of non-invasive brain stimulation (NIBS) in psychiatric disorders build on the abundant data from neuroimaging studies. This makes NIBS a very promising tool for developing personalized interventions within a precision medicine framework. NIBS methods fundamentally vary in their neurophysiological properties. They comprise repetitive transcranial magnetic stimulation (rTMS) and its variants (e.g. theta burst stimulation - TBS) as well as different types of transcranial electrical stimulation (tES), with the largest body of evidence for transcranial direct current stimulation (tDCS). In the last two decades, significant conceptual progress has been made in terms of NIBS targets, i.e. from single brain regions to neural circuits and to functional connectivity as well as their states, recently leading to brain state modulating closed-loop approaches. Regarding structural and functional brain anatomy, NIBS meets an individually unique constellation, which varies across normal and pathophysiological states. Thus, individual constitutions and signatures of disorders may be indistinguishable at a given time point, but can theoretically be parsed along course- and treatment-related trajectories. We address precision interventions on three levels: 1) the NIBS intervention, 2) the constitutional factors of a single patient, and 3) the phenotypes and pathophysiology of illness. With examples from research on depressive disorders, we propose solutions and discuss future perspectives, e.g. individual MRI-based electrical field strength as a proxy for NIBS dosage, and also symptoms, their clusters, or biotypes instead of disorder focused NIBS. In conclusion, we propose interleaved research on these three levels along a general track of reverse and forward translation including both clinically directed research in preclinical model systems, and biomarker guided controlled clinical trials. Besides driving the development of safe and efficacious interventions, this framework could also deepen our understanding of psychiatric disorders at their neurophysiological underpinnings.

Computational Models in Electroencephalography

Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments and test hypotheses in silico. The final purpose of computational models of EEG is to obtain a comprehensive understanding of the mechanisms that underlie the EEG signal. This is crucial for an accurate interpretation of EEG measurements that may ultimately serve in the development of novel clinical applications.

An updated meta-analysis: repetitive transcranial magnetic stimulation for treating tinnitus

Objective

To undertake an updated meta-analysis to obtain more evidence from randomized controlled trials (RCTs) to assess the effect of repetitive transcranial magnetic stimulation (rTMS) for the treatment of tinnitus.

Methods

PubMed®, Embase®, Web of Science, Cochrane Database of Systematic Reviews, CBM, CNKI and Wanfang were searched for RCTs from inception up to March 2020. Studies meeting the eligibility criteria were included in the meta-analysis. The mean difference was calculated and the effect size was evaluated using a Z test.

Results

The analysis included 12 randomized sham-controlled clinical trials with a total of 717 participants. Active rTMS was superior to sham rTMS in terms of the short-term and long-term effects (6 months) on the tinnitus handicap inventory scores, but an immediate effect was not significant. There was no significant immediate effect on the tinnitus questionnaire (TQ) and Beck depression inventory (BDI) scores.

Conclusions

This meta-analysis demonstrated that rTMS improved tinnitus-related symptoms, but the TQ and BDI scores demonstrated little immediate benefit. Future research should be undertaken on large samples in multi-centre settings with longer follow-up durations.

In silico exploration of mouse brain dynamics by focal stimulation reflects the organization of functional networks and sensory processing

Resting-state functional networks such as the default mode network (DMN) dominate spontaneous brain dynamics. To date, the mechanisms linking brain structure and brain dynamics and functions in cognition, perception, and action remain unknown, mainly due to the uncontrolled and erratic nature of the resting state. Here we used a stimulation paradigm to probe the brain’s resting behavior, providing insights on state-space stability and multiplicity of network trajectories after stimulation. We performed explorations on a mouse model to map spatiotemporal brain dynamics as a function of the stimulation site. We demonstrated the emergence of known functional networks in brain responses. Several responses heavily relied on the DMN and were suggestive of the DMN playing a mechanistic role between functional networks. We probed the simulated brain responses to the stimulation of regions along the information processing chains of sensory systems from periphery up to primary sensory cortices. Moreover, we compared simulated dynamics against in vivo brain responses to optogenetic stimulation. Our results underwrite the importance of anatomical connectivity in the functional organization of brain networks and demonstrate how functionally differentiated information processing chains arise from the same system.

We demonstrate how functionally differentiated information processing chains arise from the same anatomical network. The main result of the in-silico mouse brain simulations is the emergence of specific functional networks based on structural data from the mouse brain. When the brain is stimulated, for example, by sensory inputs or direct electrical stimulation, the brain initially responds with activities in specific regions. The brain’s anatomical connectivity constrains the subsequent pattern formation. We built a high-resolution mouse brain network model. The model structure originated from experimental data. We systematically explored the mouse model and investigated the simulated brain dynamics after stimulation. Known functional networks emerged in the simulated brain responses. The default mode network occurred in almost all characteristic response patterns. Simulated brain response dynamics and in-vivo response dynamics of the mouse brain to optogenetic stimulation showed similarities even without parameter tuning. Anatomical connectivity and dynamics shape the functional organization of brain networks.

Neurobiological Mechanisms of Transcranial Direct Current Stimulation for Psychiatric Disorders; Neurophysiological, Chemical, and Anatomical Considerations

Backgrounds: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique for the treatment of several psychiatric disorders, e.g., mood disorders and schizophrenia. Therapeutic effects of tDCS are suggested to be produced by bi-directional changes in cortical activities, i.e., increased/decreased cortical excitability via anodal/cathodal stimulation. Although tDCS provides a promising approach for the treatment of psychiatric disorders, its neurobiological mechanisms remain to be explored. Objectives: To review recent findings from neurophysiological, chemical, and brain-network studies, and consider how tDCS ameliorates psychiatric conditions. Findings: Enhancement of excitatory synaptic transmissions through anodal tDCS stimulation is likely to facilitate glutamate transmission and suppress gamma-aminobutyric acid transmission in the cortex. On the other hand, it positively or negatively modulates the activities of dopamine, serotonin, and acetylcholine transmissions in the central nervous system. These neural events by tDCS may change the balance between excitatory and inhibitory inputs. Specifically, multi-session tDCS is thought to promote/regulate information processing efficiency in the cerebral cortical circuit, which induces long-term potentiation (LTP) by synthesizing various proteins. Conclusions: This review will help understand putative mechanisms underlying the clinical benefits of tDCS from the perspective of neurotransmitters, network dynamics, intracellular events, and related modalities of the brain function.

Noise-driven multistability vs deterministic chaos in phenomenological semi-empirical models of whole-brain activity

An outstanding open problem in neuroscience is to understand how neural systems are capable of producing and sustaining complex spatiotemporal dynamics. Computational models that combine local dynamics with in vivo measurements of anatomical and functional connectivity can be used to test potential mechanisms underlying this complexity. We compared two conceptually different mechanisms: noise-driven switching between equilibrium solutions (modeled by coupled Stuart-Landau oscillators) and deterministic chaos (modeled by coupled Rossler oscillators). We found that both models struggled to simultaneously reproduce multiple observables computed from the empirical data. This issue was especially manifested in the case of noise-driven dynamics close to a bifurcation, which imposed overly strong constraints on the optimal model parameters. In contrast, the chaotic model could produce complex behavior over a range of parameters, thus being capable of capturing multiple observables at the same time with good performance. Our observations support the view of the brain as a non-equilibrium system able to produce endogenous variability. We presented a simple model capable of jointly reproducing functional connectivity computed at different temporal scales. Besides adding to our conceptual understanding of brain complexity, our results inform and constrain the future development of biophysically realistic large-scale models.