Machine learning and individual variability in electric field characteristics predict tDCS treatment response

Effects of antagonistic network-targeted tDCS on brain co-activation patterns depends on the networks' electric field: a simultaneous tDCS-fMRI study

BACKGROUND

Brain networks should be ideal targets for non-invasive brain stimulation, as network dysfunction is a common feature of various neuropsychiatric disorders. Understanding the mechanisms of network-targeted stimulation is essential for advancing its clinical applications.

MATERIAL AND METHOD

The current study utilized simultaneous network-targeted transcranial direct current stimulation(tDCS) and functional magnetic resonance imaging (fMRI) to investigate the effects of tDCS targeting antagonistic networks on brain dynamics. A total of 143 healthy participants were recruited and assigned to receive central executive network (CEN)-targeted tDCS (C-targeted group), default mode network (DMN)-targeted tDCS (D-targeted group), or sham tDCS (sham group). fMRI data with three sections (pre-stimulation, during-stimulation, post-stimulation) were collected across all subjects. Individual electric field (EF) strength was simulated using individual head model. Six recurring brain patterns (co-activation patterns, CAPs) were identified. The temporal indices of these CAPs (occurrence, fraction time, persistence time) and their transition probabilities were calculated. This study first examined the effects of C-targeted / D-targeted / sham tDCS on temporal indices and further explored the contribution of brain networks' EF strength on the altered temporal indices.

RESULTS

C-targeted tDCS significantly increased the temporal indices of CAPs dominated by DMN and the transition probabilities from other CAPs to DMN-dominated CAPs during stimulation. Meanwhile, the decreased temporal indices of CAP dominated by CEN, and its transition probabilities to these CAPs were also found during C-targeted tDCS. In contrast, the D-targeted tDCS had only a slight effect on brain dynamics, while sham tDCS showed no significant impact. Further fusion analyses revealed that the EF strength in the salience network made a large contribution to the temporal indices of CAPs during stimulation, highlighting tight interactions within the triple networks. Moreover, integrating the EF strength of networks with large contributions and the pre-stimulation temporal indices effectively predicted the temporal indices of CAPs during stimulation. These findings suggest that C-targeted tDCS can modulate brain dynamics and emphasize the critical role of networks' EF during stimulation.

CONCLUSION

This study demonstrates the effectiveness and feasibility of network-targeted tDCS in modulating brain dynamics, providing a new choice for treating neuropsychiatric disorders characterized by aberrant brain dynamics.

Investigating the effects of brain stimulation on the neural substrates of inhibition in patients with OCD: A simultaneous tDCS - fMRI study

Inhibition deficits constitute a core characteristic of obsessive-compulsive disorder (OCD). There is evidence in healthy individuals that transcranial direct current stimulation (tDCS) of the pre-supplementary motor area (pre-SMA) leads to a significantly improved inhibition performance. Against this background we investigated the effects of pre-SMA tDCS on inhibition performance and the underlying neural correlates in patients with OCD. Using a double-blind, randomized, sham-controlled, cross-over design (i.e., tDCS sham vs. tDCS stimulation) we investigated the effects of 2 mA anodal tDCS stimulation of the right pre-SMA in a sample of 47 OCD patients. The present study is, to our best knowledge, the first study applying concurrent tDCS-fMRI in patients with OCD. tDCS was applied using the MRI-compatible NeuroConn DC-Stimulator which allowed for a concurrent stimulation, while patients performed an inhibition (i.e., Stroop) task in a 3 T MRI. Imaging data were analysed using a multivariate partial least squares (PLS) approach. tDCS stimulation (vs. sham) was associated with increased activation in a fronto-parieto-cerebellar network comprising, amongst others, the precentral, middle frontal and inferior frontal gyrus, the anterior cingulate and the superior parietal lobe. On the performance level, tDCS stimulation (vs. sham) was linked to an improved inhibition performance in terms of an increased percentage of correct responses in the Stroop task. Present results indicate that tDCS in patients with OCD goes along with an improved inhibition performance as well as activation increases in regions known to be involved in inhibition, motor, and cognitive control. Thus, our findings suggest that tDCS might be a promising method to improve specific impairments in OCD.

Non-invasive brain stimulation in cognitive sciences and Alzheimer's disease

Over the last four decades, non-invasive brain stimulation techniques (NIBS) have significantly gained interest in the fields of cognitive sciences and dementia care, including neurorehabilitation, for its emerging potential in increasing the insights over brain functions and in boosting residual cognitive functions. In the present paper, basic physiological and technical mechanisms and different applications of NIBS were reviewed and discussed to highlight the importance of NIBS in multidisciplinary and translational approaches in clinical and research settings of cognitive sciences and neurodegenerative diseases, especially in Alzheimer's disease. Indeed, NIBS strategies may represent a promising opportunity to increase the potential of neuromodulation as efficacious interventions for individualized patients care.

Working memory ımprovement after transcranial direct current stimulation paired with working memory training ın diabetic peripheral neuropathy

Association of cognitive deficits and diabetic peripheral neuropathy (DPN) is frequent. Working memory (WM) deficits result in impairment of daily activities, diminished functionality, and treatment compliance. Mounting evidence suggests that transcranial Direct Current Stimulation (tDCS) with concurrent working memory training (WMT) ameliorates cognitive deficits. Emboldening results of tDCS were shown in DPN. The study aimed to evaluate the efficacy of anodal tDCS over the left dorsolateral prefrontal cortex (DLPFC) coupled with cathodal right DLPFC with concurrent WMT in DPN for the first time. The present randomized triple-blind parallel-group sham-controlled study evaluated the efficacy of 5 sessions of tDCS over the DLPFC concurrent with WMT in 28 individuals with painful DPN on cognitive (primary) and pain-related, psychiatric outcome measures before, immediately after, and 1-month after treatment protocol. tDCS enhanced the efficacy of WMT on working memory and yielded lower anxiety levels than sham tDCS but efficacy was not superior to sham on other cognitive domains, pain severity, quality of life, and depression. tDCS with concurrent WMT enhanced WM and ameliorated anxiety in DPN without affecting other cognitive and pain-related outcomes. Further research scrutinizing the short/long-term efficacy with larger samples is accredited.

Native learning ability and not age determines the effects of brain stimulation

Healthy aging often entails a decline in cognitive and motor functions, affecting independence and quality of life in older adults. Brain stimulation shows potential to enhance these functions, but studies show variable effects. Previous studies have tried to identify responders and non-responders through correlations between behavioral change and baseline parameters, but results lack generalization to independent cohorts. We propose a method to predict an individual's likelihood of benefiting from stimulation, based on baseline performance of a sequential motor task. Our results show that individuals with less efficient learning mechanisms benefit from stimulation, while those with optimal learning strategies experience none or even detrimental effects. This differential effect, first identified in a public dataset and replicated here in an independent cohort, was linked to one's ability to integrate task-relevant information and not age. This study constitutes a further step towards personalized clinical-translational interventions based on brain stimulation.

Machine learning-optimized non-invasive brain stimulation and treatment response classification for major depression

BACKGROUND/OBJECTIVES

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation intervention that shows promise as a potential treatment for depression. However, the clinical efficacy of tDCS varies, possibly due to individual differences in head anatomy affecting tDCS dosage. While functional changes in brain activity are more commonly reported in major depressive disorder (MDD), some studies suggest that subtle macroscopic structural differences, such as cortical thickness or brain volume reductions, may occur in MDD and could influence tDCS electric field (E-field) distributions. Therefore, accounting for individual anatomical differences may provide a pathway to optimize functional gains in MDD by formulating personalized tDCS dosage.

METHODS

To address the dosing variability of tDCS, we examined a subsample of sixteen active-tDCS participants' data from the larger ELECT clinical trial (NCT01894815). With this dataset, individualized neuroimaging-derived computational models of tDCS current were generated for (1) classifying treatment response, (2) elucidating essential stimulation features associated with treatment response, and (3) computing a personalized dose of tDCS to maximize the likelihood of treatment response in MDD.

RESULTS

In the ELECT trial, tDCS was superior to placebo (3.2 points [95% CI, 0.7 to 5.5; P = 0.01]). Our algorithm achieved over 90% overall accuracy in classifying treatment responders from the active-tDCS group (AUC = 0.90, F1 = 0.92, MCC = 0.79). Computed precision doses also achieved an average response likelihood of 99.981% and decreased dosing variability by 91.9%.

CONCLUSION

These findings support our previously developed precision-dosing method for a new application in psychiatry by optimizing the statistical likelihood of tDCS treatment response in MDD.

Multisession tDCS combined with intrastimulation training improves emotion recognition in adolescents with autism spectrum disorder

Previous studies indicate that transcranial direct current stimulation (tDCS) is a promising emerging treatment option for autism spectrum disorder (ASD) and its efficacy could be augmented using concurrent training. However, no intrastimulation social cognition training for ASD has been developed so far. The objective of this two-armed, double-blind, randomized, sham-controlled clinical trial is to investigate the effects of tDCS combined with a newly developed intrastimulation social cognition training on adolescents with ASD. Twenty-two male adolescents with ASD were randomly assigned to receive 10 sessions of either anodal or sham tDCS at F3/right supraorbital region together with online intrastimulation training comprising basic and complex emotion recognition tasks. Using baseline magnetic resonance imaging data, individual electric field distributions were simulated, and brain activation patterns of the training tasks were analyzed. Additionally, questionnaires were administered at baseline and following the intervention. Compared to sham tDCS, anodal tDCS significantly improved dynamic emotion recognition over the course of the sessions. This task also showed the highest activations in face processing regions. Moreover, the improvement was associated with electric field density at the medial prefrontal cortex and social awareness in exploratory analyses. Both groups showed high tolerability and acceptability of tDCS, and significant improvement in overall ASD symptoms. Taken together, multisession tDCS improved dynamic emotion recognition in adolescents with ASD using a task that activates brain regions associated with the social brain network. The variability in the electric field might diminish tDCS effects and future studies should investigate individualized approaches.

Behavioral, Functional Imaging, and Neurophysiological Outcomes of Transcranial Direct Current Stimulation and Speech-Language Therapy in an Individual with Aphasia

Speech-language therapy (SLT) is the most effective technique to improve language performance in persons with aphasia. However, residual language impairments remain even after intensive SLT. Recent studies suggest that combining transcranial direct current stimulation (tDCS) with SLT may improve language performance in persons with aphasia. However, our understanding of how tDCS and SLT impact brain and behavioral relation in aphasia is poorly understood. We investigated the impact of tDCS and SLT on a behavioral measure of scripted conversation and on functional connectivity assessed with multiple methods, both resting-state functional magnetic resonance imaging (rs-fMRI) and resting-state electroencephalography (rs-EEG). An individual with aphasia received 15 sessions of 20-min cathodal tDCS to the right angular gyrus concurrent with 40 min of SLT. Performance during scripted conversation was measured three times at baseline, twice immediately post-treatment, and at 4- and 8-weeks post-treatment. rs-fMRI was measured pre-and post-3-weeks of treatment. rs-EEG was measured on treatment days 1, 5, 10, and 15. Results show that both communication performance and left hemisphere functional connectivity may improve after concurrent tDCS and SLT. Results are in line with aphasia models of language recovery that posit a beneficial role of left hemisphere perilesional areas in language recovery.

Investigating the neural mechanisms of transcranial direct current stimulation effects on human cognition: current issues and potential solutions

Transcranial direct current stimulation (tDCS) has been studied extensively for its potential to enhance human cognitive functions in healthy individuals and to treat cognitive impairment in various clinical populations. However, little is known about how tDCS modulates the neural networks supporting cognition and the complex interplay with mediating factors that may explain the frequently observed variability of stimulation effects within and between studies. Moreover, research in this field has been characterized by substantial methodological variability, frequent lack of rigorous experimental control and small sample sizes, thereby limiting the generalizability of findings and translational potential of tDCS. The present manuscript aims to delineate how these important issues can be addressed within a neuroimaging context, to reveal the neural underpinnings, predictors and mediators of tDCS-induced behavioral modulation. We will focus on functional magnetic resonance imaging (fMRI), because it allows the investigation of tDCS effects with excellent spatial precision and sufficient temporal resolution across the entire brain. Moreover, high resolution structural imaging data can be acquired for precise localization of stimulation effects, verification of electrode positions on the scalp and realistic current modeling based on individual head and brain anatomy. However, the general principles outlined in this review will also be applicable to other imaging modalities. Following an introduction to the overall state-of-the-art in this field, we will discuss in more detail the underlying causes of variability in previous tDCS studies. Moreover, we will elaborate on design considerations for tDCS-fMRI studies, optimization of tDCS and imaging protocols and how to assure high-level experimental control. Two additional sections address the pressing need for more systematic investigation of tDCS effects across the healthy human lifespan and implications for tDCS studies in age-associated disease, and potential benefits of establishing large-scale, multidisciplinary consortia for more coordinated tDCS research in the future. We hope that this review will contribute to more coordinated, methodologically sound, transparent and reproducible research in this field. Ultimately, our aim is to facilitate a better understanding of the underlying mechanisms by which tDCS modulates human cognitive functions and more effective and individually tailored translational and clinical applications of this technique in the future.

The impact of a tDCS and cognitive training intervention on task-based functional connectivity

Declines in several cognitive domains, most notably processing speed, occur in non-pathological aging. Given the exponential growth of the older adult population, declines in cognition serve as a significant public health issue that must be addressed. Promising studies have shown that cognitive training in older adults, particularly using the useful field of view (UFOV) paradigm, can improve cognition with moderate to large effect sizes. Additionally, meta-analyses have found that transcranial direct current stimulation (tDCS), a non-invasive form of brain stimulation, can improve cognition in attention/processing speed and working memory. However, only a handful of studies have looked at concomitant tDCS and cognitive training, usually with short interventions and small sample sizes. The current study assessed the effect of a tDCS (active versus sham) and a 3-month cognitive training intervention on task-based functional connectivity during completion of the UFOV task in a large older adult sample (N = 153). We found significant increased functional connectivity between the left and right pars triangularis (the ROIs closest to the electrodes) following active, but not sham tDCS. Additionally, we see trending behavioral improvements associated with these functional connectivity changes in the active tDCS group, but not sham. Collectively, these findings suggest that tDCS and cognitive training can be an effective modulator of task-based functional connectivity above and beyond a cognitive training intervention alone.

Neuroanatomical Predictors of Transcranial Direct Current Stimulation (tDCS)-Induced Modifications in Neurocognitive Task Performance in Typically Developing Individuals

Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technique gaining more attention in neurodevelopmental disorders (NDDs). Due to the phenotypic heterogeneity of NDDs, tDCS is unlikely to be equally effective in all individuals. The present study aimed to establish neuroanatomical markers in typically developing (TD) individuals that may be used for the prediction of individual responses to tDCS. Fifty-seven male and female children received 2 mA anodal and sham tDCS, targeting the left dorsolateral prefrontal cortex (DLPFCleft), right inferior frontal gyrus, and bilateral temporoparietal junction. Response to tDCS was assessed based on task performance differences between anodal and sham tDCS in different neurocognitive tasks (N-back, flanker, Mooney faces detection, attentional emotional recognition task). Measures of cortical thickness (CT) and surface area (SA) were derived from 3 Tesla structural MRI scans. Associations between neuroanatomy and task performance were assessed using general linear models (GLM). Machine learning (ML) algorithms were employed to predict responses to tDCS. Vertex-wise estimates of SA were more closely linked to differences in task performance than measures of CT. Across ML algorithms, highest accuracies were observed for the prediction of N-back task performance differences following stimulation of the DLPFCleft, where 65% of behavioral variance was explained by variability in SA. Lower accuracies were observed for all other tasks and stimulated regions. This suggests that it may be possible to predict individual responses to tDCS for some behavioral measures and target regions. In the future, these models might be extended to predict treatment outcome in individuals with NDDs.

Advances in Non-Invasive Neuromodulation Techniques for Improving Cognitive Function: A Review

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition.

Predicting interindividual response to theta burst stimulation in the lower limb motor cortex using machine learning

Using theta burst stimulation (TBS) to induce neural plasticity has played an important role in improving the treatment of neurological disorders. However, the variability of TBS-induced synaptic plasticity in the primary motor cortex prevents its clinical application. Thus, factors associated with this variability should be explored to enable the creation of a predictive model. Statistical approaches, such as regression analysis, have been used to predict the effects of TBS. Machine learning may potentially uncover previously unexplored predictive factors due to its increased capacity for capturing nonlinear changes. In this study, we used our prior dataset (Katagiri et al., 2020) to determine the factors that predict variability in TBS-induced synaptic plasticity in the lower limb motor cortex for both intermittent (iTBS) and continuous (cTBS) TBS using machine learning. Validation of the created model showed an area under the curve (AUC) of 0.85 and 0.69 and positive predictive values of 77.7 and 70.0% for iTBS and cTBS, respectively; the negative predictive value was 75.5% for both patterns. Additionally, the accuracy was 0.76 and 0.72, precision was 0.82 and 0.67, recall was 0.82 and 0.67, and F1 scores were 0.82 and 0.67 for iTBS and cTBS, respectively. The most important predictor of iTBS was the motor evoked potential amplitude, whereas it was the intracortical facilitation for cTBS. Our results provide additional insights into the prediction of the effects of TBS variability according to baseline neurophysiological factors.

Investigating the variability of prefrontal tDCS effects on working memory: An individual E-field distribution study

Transcranial direct current stimulation (tDCS) over the prefrontal cortex has the potential to enhance working memory by means of a weak direct current applied to the scalp. However, its effects are highly variable and possibly dependent on individual variability in cortical architecture and head anatomy. Unveiling sources of heterogeneity might improve fundamental and clinical application of tDCS in the field. Therefore, we investigated sources of tDCS variability of prefrontal 1.5 mA tDCS, 3 mA tDCS and sham tDCS in 40 participants (67.5% women, mean age 24.7 years) by associating simulated electric field (E-field) magnitude in brain regions of interest (dorsolateral prefrontal cortex, anterior cingulate cortex (ACC) and subgenual ACC) and working memory performance. Emotional and non-emotional 3-back paradigms were used. In the tDCS protocol analysis, effects were only significant for the 3 mA group, and only for the emotional tasks. In the individual E-field magnitude analysis, faster responses in non-emotional, but not in the emotional task, were associated with stronger E-fields in all brain regions of interest. Concluding, individual E-field distribution might explain part of the variability of prefrontal tDCS effects on working memory performance and in clinical samples. Our results suggest that tDCS effects might be more consistent or improved by applying personalizing current intensity, although this hypothesis should be confirmed by further studies.

Electric field simulation and appropriate electrode positioning for optimized transcranial direct current stimulation of stroke patients: an in Silico model

Transcranial Direct Current Stimulation (tDCS) has benefits for motor rehabilitation in stroke patients, but its clinical application is limited due to inter-individual heterogeneous effects. Recently, optimized tDCS that considers individual brain structure has been proposed, but the utility thereof has not been studied in detail. We explored whether optimized tDCS provides unique electrode positions for each patient and creates a higher target electric field than the conventional approach. A comparative within-subject simulation study was conducted using data collected for a randomized controlled study evaluating the effect of optimized tDCS on upper extremity function in stroke patients. Using Neurophet tES LAB 3.0 software, individual brain models were created based on magnetic resonance images and tDCS simulations were performed for each of the conventional and optimized configurations. A comparison of electrode positions between conventional tDCS and optimized tDCS was quantified by calculation of Euclidean distances. A total of 21 stroke patients were studied. Optimized tDCS produced a higher electric field in the hand motor region than conventional tDCS, with an average improvement of 20% and a maximum of 52%. The electrode montage for optimized tDCS was unique to each patient and exhibited various configurations that differed from electrode placement of conventional tDCS. Optimized tDCS afforded a higher electric field in the target of a stroke patient compared to conventional tDCS, which was made possible by appropriately positioning the electrodes. Our findings may encourage further trials on optimized tDCS for motor rehabilitation after stroke.

Cognitive training and brain stimulation in patients with cognitive impairment: a randomized controlled trial

BACKGROUND

Repeated sessions of training and non-invasive brain stimulation have the potential to enhance cognition in patients with cognitive impairment. We hypothesized that combining cognitive training with anodal transcranial direct current stimulation (tDCS) will lead to performance improvement in the trained task and yield transfer to non-trained tasks.

METHODS

In our randomized, sham-controlled, double-blind study, 46 patients with cognitive impairment (60-80 years) were randomly assigned to one of two interventional groups. We administered a 9-session cognitive training (consisting of a letter updating and a Markov decision-making task) over 3 weeks with concurrent 1-mA anodal tDCS over the left dorsolateral prefrontal cortex (20 min in tDCS, 30 s in sham group). Primary outcome was trained task performance (letter updating task) immediately after training. Secondary outcomes included performance in tasks testing working memory (N-back task), decision-making (Wiener Matrices test) and verbal memory (verbal learning and memory test), and resting-state functional connectivity (FC). Tasks were administered at baseline, at post-assessment, and at 1- and 7-month follow-ups (FU). MRI was conducted at baseline and 7-month FU. Thirty-nine participants (85%) successfully completed the intervention. Data analyses are reported on the intention-to-treat (ITT) and the per-protocol (PP) sample.

RESULTS

For the primary outcome, no difference was observed in the ITT (β = 0.1, 95%-CI [- 1.2, 1.3, p = 0.93] or PP sample (β =  - 0.2, 95%-CI [- 1.6, 1.2], p = 0.77). However, secondary analyses in the N-back working memory task showed that, only in the PP sample, the tDCS outperformed the sham group (PP: % correct, β = 5.0, 95%-CI [- 0.1, 10.2], p = 0.06, d-prime β = 0.2, 95%-CI [0.0, 0.4], p = 0.02; ITT: % correct, β = 3.0, 95%-CI [- 3.9, 9.9], p = 0.39, d-prime β = 0.1, 95%-CI [- 0.1, 0.3], p = 0.5). Frontoparietal network FC was increased from baseline to 7-month FU in the tDCS compared to the sham group (pFDR < 0.05). Exploratory analyses showed a correlation between individual memory improvements and higher electric field magnitudes induced by tDCS (ρtDCS = 0.59, p = 0.02). Adverse events did not differ between groups, questionnaires indicated successful blinding (incidence rate ratio, 1.1, 95%-CI [0.5, 2.2]).

CONCLUSIONS

In sum, cognitive training with concurrent brain stimulation, compared to cognitive training with sham stimulation, did not lead to superior performance enhancements in patients with cognitive impairment. However, we observed transferred working memory benefits in patients who underwent the full 3-week intervention. MRI data pointed toward a potential intervention-induced modulation of neural network dynamics. A link between individual performance gains and electric fields suggested dosage-dependent effects of brain stimulation. Together, our findings do not support the immediate benefit of the combined intervention on the trained function, but provide exploratory evidence for transfer effects on working memory in patients with cognitive impairment. Future research needs to explore whether individualized protocols for both training and stimulation parameters might further enhance treatment gains.

TRIAL REGISTRATION

The study is registered on ClinicalTrials.gov (NCT04265378). Registered on 7 February 2020. Retrospectively registered.

HD-tDCS induced changes in resting-state functional connectivity: Insights from EF modeling

BACKGROUND

High-definition transcranial direct current stimulation (HD-tDCS) holds promise for therapeutic use in psychiatric disorders. One obstacle for the implementation into clinical practice is response variability. One way to tackle this obstacle is the use of Individualized head models.

OBJECTIVE

This study investigated the variability of HD-tDCS induced electric fields (EFs) and its impact on resting-state functional connectivity (rsFC) during different time windows.

METHODS

In this randomized, double-blind, and sham controlled study, seventy healthy males underwent 20 min of 1.5 mA HD-tDCS on the right inferior frontal gyrus (rIFG) while undergoing resting-state functional magnetic resonance imaging (rs-fMRI). Individual head models and EF simulations were created from anatomical images. The effects of HD-tDCS on rsFC were assessed using a seed-to-voxel analysis. A subgroup analysis explored the relationship between EF magnitude and rsFC during different stimulation time windows.

RESULTS

Results highlighted significant variability in HD-tDCS-induced EFs. Compared to the sham group, the active group showed increased rsFC between the rIFG and the left prefrontal cortex, during and after stimulation. During active stimulation, EF magnitude correlated positively with rsFC between the rIFG and the left hippocampus initially, and negatively during the subsequent period.

CONCLUSION

This study indicated an HD-tDCS induced increase of rsFC between left and right prefrontal areas. Furthermore, an interaction between the magnitude and the duration of HD-tDCS on rsFC was observed. Due to the high EF variability that was apparent, these findings highlight the need for individualized HD-tDCS protocols and the creation of head models to optimize effects and reduce response heterogeneity.

Exploring alternative approaches to precision medicine through genomics and artificial intelligence - a systematic review

The core idea behind precision medicine is to pinpoint the subpopulations that differ from one another in terms of disease risk, drug responsiveness, and treatment outcomes due to differences in biology and other traits. Biomarkers are found through genomic sequencing. Multi-dimensional clinical and biological data are created using these biomarkers. Better analytic methods are needed for these multidimensional data, which can be accomplished by using artificial intelligence (AI). An updated review of 80 latest original publications is presented on four main fronts-preventive medicine, medication development, treatment outcomes, and diagnostic medicine-All these studies effectively illustrated the significance of AI in precision medicine. Artificial intelligence (AI) has revolutionized precision medicine by swiftly analyzing vast amounts of data to provide tailored treatments and predictive diagnostics. Through machine learning algorithms and high-resolution imaging, AI assists in precise diagnoses and early disease detection. AI's ability to decode complex biological factors aids in identifying novel therapeutic targets, allowing personalized interventions and optimizing treatment outcomes. Furthermore, AI accelerates drug discovery by navigating chemical structures and predicting drug-target interactions, expediting the development of life-saving medications. With its unrivaled capacity to comprehend and interpret data, AI stands as an invaluable tool in the pursuit of enhanced patient care and improved health outcomes. It's evident that AI can open a new horizon for precision medicine by translating complex data into actionable information. To get better results in this regard and to fully exploit the great potential of AI, further research is required on this pressing subject.

Effects of transcranial direct current stimulation on lower limb function, balance and quality of life after stroke: a systematic review and meta-analysis

OBJECTIVE

This systematic review with meta-analysis aimed to evaluate the effectiveness of tDCS on lower limb function, balance and quality of life in stroke patients.

METHODS

The search included PubMed, CENTRAL, PEDro, Web of Science, SCOPUS, PsycINFO Ovid, CINAHL EBSCO, EMBASE, ScienceDirect, reference lists of relevant reviews, clinical trials registries and academic google, in June and July 2021. Randomized controlled trials were selected, which present the effect of tDCS on lower limb motor function recovery in stroke patients, comparing any type of active tDCS versus sham; parallel or crossover study design; adult patients; stimulation on the primary motor cortex; articles published in any language; without restriction of publication period.

RESULTS

Nineteen studies were included. The treatment with active tDCS did not improve motor function (Chi2 = 32,87, I2 = 76%, SMD = 0,36 e 95% CI -0,18-0,90). Subgroup analyzes showed a significant effect favorable to tDCS, in relation to motor function, in the acute and subacute post stroke phases. However, the quality of evidence for this outcome was very low. Regarding balance outcome, a meta-analysis showed a significant difference in favor of active tDCS, but the quality of the evidence was considered very low. As for the quality of life outcome, no statistically significant difference was found in favor of tDCS.

DISCUSSION

There is a lack of evidence in recommending the use of tDCS in isolation in the treatment of patients after stroke, aiming at improving motor function, balance and quality of life. However, it is possible that tDCS can be beneficial when associated with other therapies or interventions.

Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of clinical and research applications. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to time consuming and cost ineffective treatment development phases. We propose the combination of electroencephalography (EEG) and unsupervised learning for the stratification and prediction of individual responses to tDCS. A randomized, sham-controlled, double-blind crossover study design was conducted within a clinical trial for the development of pediatric treatments based on tDCS. The tDCS stimulation (sham and active) was applied either in the left dorsolateral prefrontal cortex or in the right inferior frontal gyrus. Following the stimulation session, participants performed 3 cognitive tasks to assess the response to the intervention: the Flanker Task, N-Back Task and Continuous Performance Test (CPT). We used data from 56 healthy children and adolescents to implement an unsupervised clustering approach that stratify participants based on their resting-state EEG spectral features before the tDCS intervention. We then applied a correlational analysis to characterize the clusters of EEG profiles in terms of participant's difference in the behavioral outcome (accuracy and response time) of the cognitive tasks when performed after a tDCS-sham or a tDCS-active session. Better behavioral performance following the active tDCS session compared to the sham tDCS session is considered a positive intervention response, whilst the reverse is considered a negative one. Optimal results in terms of validity measures was obtained for 4 clusters. These results show that specific EEG-based digital phenotypes can be associated to particular responses. While one cluster presents neurotypical EEG activity, the remaining clusters present non-typical EEG characteristics, which seem to be associated with a positive response. Findings suggest that unsupervised machine learning can be successfully used to stratify and eventually predict responses of individuals to a tDCS treatment.

Machine-learning defined precision tDCS for improving cognitive function

BACKGROUND

Transcranial direct current stimulation (tDCS) paired with cognitive training (CT) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that the level of benefit from tDCS paired with CT varies from person to person, likely due to individual differences in neuroanatomical structure.

OBJECTIVE

The current study aims to develop a method to objectively optimize and personalize current dosage to maximize the functional gains of non-invasive brain stimulation.

METHODS

A support vector machine (SVM) model was trained to predict treatment response based on computational models of current density in a sample dataset (n = 14). Feature weights of the deployed SVM were used in a weighted Gaussian Mixture Model (GMM) to maximize the likelihood of converting tDCS non-responders to responders by finding the most optimum electrode montage and applied current intensity (optimized models).

RESULTS

Current distributions optimized by the proposed SVM-GMM model demonstrated 93% voxel-wise coherence within target brain regions between the originally non-responders and responders. The optimized current distribution in original non-responders was 3.38 standard deviations closer to the current dose of responders compared to the pre-optimized models. Optimized models also achieved an average treatment response likelihood and normalized mutual information of 99.993% and 91.21%, respectively. Following tDCS dose optimization, the SVM model successfully predicted all tDCS non-responders with optimized doses as responders.

CONCLUSIONS

The results of this study serve as a foundation for a custom dose optimization strategy towards precision medicine in tDCS to improve outcomes in cognitive decline remediation for older adults.

The impact of gamma transcranial alternating current stimulation (tACS) on cognitive and memory processes in patients with mild cognitive impairment or Alzheimer's disease: A literature review

BACKGROUND

Transcranial alternating current stimulation (tACS)-a noninvasive brain stimulation technique that modulates cortical oscillations through entrainment-has been demonstrated to alter oscillatory activity and enhance cognition in healthy adults. TACS is being explored as a tool to improve cognition and memory in patient populations with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

OBJECTIVE

To review the growing body of literature and current findings obtained from the application of tACS in patients with MCI or AD, highlighting the effects of gamma tACS on brain function, memory, and cognition. Evidence on the use of brain stimulation in animal models of AD is also discussed. Important parameters of stimulation are underscored for consideration in protocols that aim to apply tACS as a therapeutic tool in patients with MCI/AD.

FINDINGS

The application of gamma tACS has shown promising results in the improvement of cognitive and memory processes that are impacted in patients with MCI/AD. These data demonstrate the potential for tACS as an interventional stand-alone tool or alongside pharmacological and/or other behavioral interventions in MCI/AD.

CONCLUSIONS

While the use of tACS in MCI/AD has evidenced encouraging results, the effects of this stimulation technique on brain function and pathophysiology in MCI/AD remains to be fully determined. This review explores the literature and highlights the need for continued research on tACS as a tool to alter the course of the disease by reinstating oscillatory activity, improving cognitive and memory processing, delaying disease progression, and remediating cognitive abilities in patients with MCI/AD.

Primary outcome from the augmenting cognitive training in older adults study (ACT): A tDCS and cognitive training randomized clinical trial

BACKGROUND

There is a need for effective interventions to stave off cognitive decline in older adults. Cognitive training has variably produced gains in untrained tasks and daily functioning. Combining cognitive training with transcranial direct current stimulation (tDCS) may augment cognitive training effects; however, this approach has yet to be tested on a large-scale.

OBJECTIVE

This paper will present the primary findings of the Augmenting Cognitive Training in Older Adults (ACT) clinical trial. We hypothesize that receiving active stimulation with cognitive training will result in greater improvements on an untrained fluid cognition composite compared to sham following intervention.

METHODS

379 older adults were randomized, and 334 were included in intent-to-treat analyses for a 12-week multidomain cognitive training and tDCS intervention. Active or sham tDCS was administered at F3/F4 during cognitive training daily for two weeks then weekly for 10 weeks. To assess the tDCS effect, we fitted regression models for changes in NIH Toolbox Fluid Cognition Composite scores immediately following intervention and one year from baseline controlling for covariates and baseline scores.

RESULTS

Across the entire sample, there were improvements in NIH Toolbox Fluid Cognition Composite scores immediately post-intervention and one year following baseline; however, there were no significant tDCS group effects at either timepoint.

CONCLUSIONS

The ACT study models rigorous, safe administration of a combined tDCS and cognitive training intervention in a large sample of older adults. Despite potential evidence of near-transfer effects, we failed to demonstrate an additive benefit of active stimulation. Future analyses will continue to assess the intervention's efficacy by examining additional measures of cognition, functioning, mood, and neural markers.

Modeling Electric Fields in Transcutaneous Spinal Direct Current Stimulation: A Clinical Perspective

Clinical findings suggest that transcutaneous spinal direct current stimulation (tsDCS) can modulate ascending sensitive, descending corticospinal, and segmental pathways in the spinal cord (SC). However, several aspects of the stimulation have not been completely understood, and realistic computational models based on MRI are the gold standard to predict the interaction between tsDCS-induced electric fields and anatomy. Here, we review the electric fields distribution in the SC during tsDCS as predicted by MRI-based realistic models, compare such knowledge with clinical findings, and define the role of computational knowledge in optimizing tsDCS protocols. tsDCS-induced electric fields are predicted to be safe and induce both transient and neuroplastic changes. This could support the possibility to explore new clinical applications, such as spinal cord injury. For the most applied protocol (2-3 mA for 20-30 min, active electrode over T10-T12 and the reference on the right shoulder), similar electric field intensities are generated in both ventral and dorsal horns of the SC at the same height. This was confirmed by human studies, in which both motor and sensitive effects were found. Lastly, electric fields are strongly dependent on anatomy and electrodes' placement. Regardless of the montage, inter-individual hotspots of higher values of electric fields were predicted, which could change when the subjects move from a position to another (e.g., from the supine to the lateral position). These characteristics underlines the need for individualized and patient-tailored MRI-based computational models to optimize the stimulation protocol. A detailed modeling approach of the electric field distribution might contribute to optimizing stimulation protocols, tailoring electrodes' configuration, intensities, and duration to the clinical outcome.

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.

Neural correlates of gait adaptation in younger and older adults

Mobility decline is a major concern for older adults. A key component of maintaining mobility with advancing age is the ability to learn and adapt to the environment. The split-belt treadmill paradigm is an experimental protocol that tests the ability to adapt to a dynamic environment. Here we examined the magnetic resonance imaging (MRI) derived structural neural correlates of individual differences in adaptation to split-belt walking for younger and older adults. We have previously shown that younger adults adopt an asymmetric walking pattern during split-belt walking, particularly in the medial-lateral (ML) direction, but older adults do not. We collected T[Formula: see text]-weighted and diffusion-weighted MRI scans to quantify brain morphological characteristics (i.e. in the gray matter and white matter) on these same participants. We investigated two distinct questions: (1) Are there structural brain metrics that are associated with the ability to adopt asymmetry during split-belt walking; and (2) Are there different brain-behavior relationships for younger and older adults? Given the growing evidence that indicates the brain has a critical role in the maintenance of gait and balance, we hypothesized that brain areas commonly associated with locomotion (i.e. basal ganglia, sensorimotor cortex, cerebellum) would be associated with ML asymmetry and that older adults would show more associations between split-belt walking and prefrontal brain areas. We identified multiple brain-behavior associations. More gray matter volume in the superior frontal gyrus and cerebellar lobules VIIB and VIII, more sulcal depth in the insula, more gyrification in the pre/post central gyri, and more fractional anisotropy in the corticospinal tract and inferior longitudinal fasciculus corresponded to more gait asymmetry. These associations did not differ between younger and older adults. This work progresses our understanding of how brain structure is associated with balance during walking, particularly during adaptation.

Transcranial direct current stimulation for balance rehabilitation in neurological disorders: A systematic review and meta-analysis

Postural instability is common in neurological diseases. Although transcranial direct current stimulation (tDCS) seems to be a promising complementary therapy, emerging evidence indicates mixed results and protocols' characteristics. We conducted a systematic review and meta-analysis on PubMed, EMBASE, Scopus, and Web of Science to synthesize key findings of the effectiveness of single and multiple sessions of tDCS alone and combined with other interventions on balance in adults with neurological disorders. Thirty-seven studies were included in the systematic review and 33 in the meta-analysis. The reviewed studies did not personalize the stimulation protocol to individual needs/characteristics. A random-effects meta-analysis indicated that tDCS alone (SMD = -0.44; 95%CI = -0.69/-0.19; p < 0.001) and combined with another intervention (SMD = -0.31; 95%CI = -0.51/-0.11; p = 0.002) improved balance in adults with neurological disorders (small to moderate effect sizes). Balance improvements were evidenced regardless of the number of sessions and targeted area. In summary, tDCS is a promising therapy for balance rehabilitation in adults with neurological disorders. However, further clinical trials should identify factors that influence responsiveness to tDCS for a more tailored approach, which may optimize the clinical use of tDCS.

tDCS induced GABA change is associated with the simulated electric field in M1, an effect mediated by grey matter volume in the MRS voxel

BACKGROUND AND OBJECTIVE

Transcranial direct current stimulation (tDCS) has wide ranging applications in neuro-behavioural and physiological research, and in neurological rehabilitation. However, it is currently limited by substantial inter-subject variability in responses, which may be explained, at least in part, by anatomical differences that lead to variability in the electric field (E-field) induced in the cortex. Here, we tested whether the variability in the E-field in the stimulated cortex during anodal tDCS, estimated using computational simulations, explains the variability in tDCS induced changes in GABA, a neurophysiological marker of stimulation effect.

METHODS

Data from five previously conducted MRS studies were combined. The anode was placed over the left primary motor cortex (M1, 3 studies, N = 24) or right temporal cortex (2 studies, N = 32), with the cathode over the contralateral supraorbital ridge. Single voxel spectroscopy was performed in a 2x2x2cm voxel under the anode in all cases. MRS data were acquired before and either during or after 1 mA tDCS using either a sLASER sequence (7T) or a MEGA-PRESS sequence (3T). sLASER MRS data were analysed using LCModel, and MEGA-PRESS using FID-A and Gannet. E-fields were simulated in a finite element model of the head, based on individual structural MR images, using SimNIBS. Separate linear mixed effects models were run for each E-field variable (mean and 95th percentile; magnitude, and components normal and tangential to grey matter surface, within the MRS voxel). The model included effects of time (pre or post tDCS), E-field, grey matter volume in the MRS voxel, and a 3-way interaction between time, E-field and grey matter volume. Additionally, we ran a permutation analysis using PALM to determine whether E-field anywhere in the brain, not just in the MRS voxel, correlated with GABA change.

RESULTS

In M1, higher mean E-field magnitude was associated with greater anodal tDCS-induced decreases in GABA (t(24) = 3.24, p = 0.003). Further, the association between mean E-field magnitude and GABA change was moderated by the grey matter volume in the MRS voxel (t(24) = -3.55, p = 0.002). These relationships were consistent across all E-field variables except the mean of the normal component. No significant relationship was found between tDCS-induced GABA decrease and E-field in the temporal voxel. No significant clusters were found in the whole brain analysis.

CONCLUSIONS

Our data suggest that the electric field induced by tDCS within the brain is variable, and is significantly related to anodal tDCS-induced decrease in GABA, a key neurophysiological marker of stimulation. These findings strongly support individualised dosing of tDCS, at least in M1. Further studies examining E-fields in relation to other outcome measures, including behaviour, will help determine the optimal E-fields required for any desired effects.

Machine learning approach identified clusters for patients with low cardiac output syndrome and outcomes after cardiac surgery

BACKGROUND

Low cardiac output syndrome (LCOS) is the most serious physiological abnormality with high mortality for patients after cardiac surgery. This study aimed to explore the multidimensional data of clinical features and outcomes to provide individualized care for patients with LCOS.

METHODS

The electronic medical information of the intensive care units (ICUs) was extracted from a tertiary hospital in South China. We included patients who were diagnosed with LCOS in the ICU database. We used the consensus clustering approach based on patient characteristics, laboratory data, and vital signs to identify LCOS subgroups. The consensus clustering method involves subsampling from a set of items, such as microarrays, and determines to cluster of specified cluster counts (k). The primary clinical outcome was in-hospital mortality and was compared between the clusters.

RESULTS

A total of 1,205 patients were included and divided into three clusters. Cluster 1 (n = 443) was defined as the low-risk group [in-hospital mortality =10.1%, odds ratio (OR) = 1]. Cluster 2 (n = 396) was defined as the medium-risk group [in-hospital mortality =25.0%, OR = 2.96 (95% CI = 1.97-4.46)]. Cluster 3 (n = 366) was defined as the high-risk group [in-hospital mortality =39.2%, OR = 5.75 (95% CI = 3.9-8.5)].

CONCLUSION

Patients with LCOS after cardiac surgery could be divided into three clusters and had different outcomes.

Superior temporal gyrus functional connectivity predicts transcranial direct current stimulation response in Schizophrenia: A machine learning study

Transcranial direct current stimulation (tDCS) is a promising adjuvant treatment for persistent auditory verbal hallucinations (AVH) in Schizophrenia (SZ). Nonetheless, there is considerable inter-patient variability in the treatment response of AVH to tDCS in SZ. Machine-learned models have the potential to predict clinical response to tDCS in SZ. This study aims to examine the feasibility of identifying SZ patients with persistent AVH (SZ-AVH) who will respond to tDCS based on resting-state functional connectivity (rs-FC). Thirty-four SZ-AVH patients underwent resting-state functional MRI at baseline followed by add-on, twice-daily, 20-min sessions with tDCS (conventional/high-definition) for 5 days. A machine learning model was developed to identify tDCS treatment responders based on the rs-FC pattern, using the left superior temporal gyrus (LSTG) as the seed region. Functional connectivity between LSTG and brain regions involved in auditory and sensorimotor processing emerged as the important predictors of the tDCS treatment response. L1-regularized logistic regression model had an overall accuracy of 72.5% in classifying responders vs. non-responders. This model outperformed the state-of-the-art convolutional neural networks (CNN) model-both without (59.41%) and with pre-training (68.82%). It also outperformed the L1-logistic regression model trained with baseline demographic features and clinical scores of SZ patients. This study reports the first evidence that rs-fMRI-derived brain connectivity pattern can predict the clinical response of persistent AVH to add-on tDCS in SZ patients with 72.5% accuracy.

Applications of open-source software ROAST in clinical studies: A review

BACKGROUND

Transcranial electrical stimulation (TES) is broadly investigated as a therapeutic technique for a wide range of neurological disorders. The electric fields induced by TES in the brain can be estimated by computational models. A realistic and volumetric approach to simulate TES (ROAST) has been recently released as an open-source software package and has been widely used in TES research and its clinical applications. Rigor and reproducibility of TES studies have recently become a concern, especially in the context of computational modeling.

METHODS

Here we reviewed 94 clinical TES studies that leveraged ROAST for computational modeling. When reviewing each study, we pay attention to details related to the rigor and reproducibility as defined by the locations of stimulation electrodes and the dose of stimulating current. Specifically, we compared across studies the electrode montages, stimulated brain areas, achieved electric field strength, and the relations between modeled electric field and clinical outcomes.

RESULTS

We found that over 1800 individual heads have been modeled by ROAST for more than 30 different clinical applications. Similar electric field intensities were found to be reproducible by ROAST across different studies at the same brain area under same or similar stimulation montages.

CONCLUSION

This article reviews the use cases of ROAST and provides an overview of how ROAST has been leveraged to enhance the rigor and reproducibility of TES research and its applications.

Diagnosis and Nursing Intervention of Gynecological Ovarian Endometriosis with Magnetic Resonance Imaging under Artificial Intelligence Algorithm

This research was aimed to study the application value of the magnetic resonance imaging (MRI) diagnosis under artificial intelligence algorithms and the effect of nursing intervention on patients with gynecological ovarian endometriosis. 116 patients with ovarian endometriosis were randomly divided into a control group (routine nursing) and an experimental group (comprehensive nursing), with 58 cases in each group. The artificial intelligence fuzzy C-means (FCM) clustering algorithm was proposed and used in the MRI diagnosis of ovarian endometriosis. The application value of the FCM algorithm was evaluated through the accuracy, Dice, sensitivity, and specificity of the imaging diagnosis, and the nursing satisfaction and the incidence of adverse reactions were used to evaluate the effect of nursing intervention. The results showed that, compared with the traditional hard C-means (HCM) algorithm, the artificial intelligence FCM algorithm gave a significantly higher partition coefficient, and its partition entropy and running time were significantly reduced, with significant differences (P < 0.05). The average values of Dice, sensitivity, and specificity of patients' MRI images were 0.77, 0.73, and 0.72, respectively, which were processed by the traditional HCM algorithm, while those values obtained by the improved artificial intelligence FCM algorithm were 0.92, 0.90, and 0.93, respectively; all the values were significantly improved (P < 0.05). In addition, the accuracy of MRI diagnosis based on the artificial intelligence FCM algorithm was 94.32 ± 3.05%, which was significantly higher than the 81.39 ± 3.11% under the HCM algorithm (P < 0.05). The overall nursing satisfaction of the experimental group was 96.5%, which was significantly better than the 87.9% of the control group (P < 0.05). The incidence of postoperative adverse reactions in the experimental group (7.9%) was markedly lower than that in the control group (24.1%), with a significant difference (P < 0.05). In short, MRI images under the artificial intelligence FCM algorithm could greatly improve the clinical diagnosis of ovarian endometriosis, and the comprehensive nursing intervention would also improve the prognosis and recovery of patients.

Differences in electric field strength between clinical and non-clinical populations induced by prefrontal tDCS: A cross-diagnostic, individual MRI-based modeling study

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MDD and SCZ showed lower prefrontal tDCS-induced e-field strengths compared to HC.

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Average e-field strengths did not significantly differ between MDD and SCZ patients.

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Inter-individual variability of e-field intensities and distribution was prominent.

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Inter-rater variability emphasizes the importance of standardized positioning.

Introduction

Prefrontal cortex (PFC) regions are promising targets for therapeutic applications of non-invasive brain stimulation, e.g. transcranial direct current stimulation (tDCS), which has been proposed as a novel intervention for major depressive disorder (MDD) and negative symptoms of schizophrenia (SCZ). However, the effects of tDCS vary inter-individually, and dose–response relationships have not been established. Stimulation parameters are often tested in healthy subjects and transferred to clinical populations. The current study investigates the variability of individual MRI-based electric fields (e-fields) of standard bifrontal tDCS across individual subjects and diagnoses.

Method

The study included 74 subjects, i.e. 25 patients with MDD, 24 patients with SCZ, and 25 healthy controls (HC). Individual e-fields of a common tDCS protocol (i.e. 2 mA stimulation intensity, bifrontal anode-F3/cathode-F4 montage) were modeled by two investigators using SimNIBS (2.0.1) based on structural MRI scans.

Result

On a whole-brain level, the average e-field strength was significantly reduced in MDD and SCZ compared to HC, but MDD and SCZ did not differ significantly. Regions of interest (ROI) analysis for PFC subregions showed reduced e-fields in Sallet areas 8B and 9 for MDD and SCZ compared to HC, whereas there was again no difference between MDD and SCZ. Within groups, we generally observed high inter-individual variability of e-field intensities at a higher percentile of voxels.

Conclusion

MRI-based e-field modeling revealed significant differences in e-field strengths between clinical and non-clinical populations in addition to a general inter-individual variability. These findings support the notion that dose–response relationships for tDCS cannot be simply transferred from healthy to clinical cohorts and need to be individually established for clinical groups. In this respect, MRI-based e-field modeling may serve as a proxy for individualized dosing.

Anodal Transcranial Direct Current Stimulation Over Prefrontal Cortex Slows Sequence Learning in Older Adults

Aging is associated with declines in sensorimotor function. Several studies have demonstrated that transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, can be combined with training to mitigate age-related cognitive and motor declines. However, in some cases, the application of tDCS disrupts performance and learning. Here, we applied anodal tDCS either over the left prefrontal cortex (PFC), right PFC, supplementary motor complex (SMC), the left M1, or in a sham condition while older adults (n = 63) practiced a Discrete Sequence Production (DSP), an explicit motor sequence, task across 3 days. We hypothesized that stimulation to either the right or left PFC would enhance motor learning for older adults, based on the extensive literature showing increased prefrontal cortical activity during motor task performance in older adults. Contrary to our predictions, stimulation to the right and left PFC resulted in slowed motor learning, as evidenced by a slower reduction rate of reduction of reaction time and the number of sequence chunks across trials relative to sham in session one and session two, respectively. These findings suggest an integral role of the right PFC early in sequence learning and a role of the left PFC in chunking in older adults, and contribute to mounting evidence of the difficultly of using tDCS in an aging population.

Can Transcranial Electrical Stimulation Facilitate Post-stroke Cognitive Rehabilitation? A Systematic Review and Meta-Analysis

Background

Non-invasive brain stimulation methods have been widely utilized in research settings to manipulate and understand the functioning of the human brain. In the last two decades, transcranial electrical stimulation (tES) has opened new doors for treating impairments caused by various neurological disorders. However, tES studies have shown inconsistent results in post-stroke cognitive rehabilitation, and there is no consensus on the effectiveness of tES devices in improving cognitive skills after the onset of stroke.

Objectives

We aim to systematically investigate the efficacy of tES in improving post-stroke global cognition, attention, working memory, executive functions, visual neglect, and verbal fluency. Furthermore, we aim to provide a pathway to an effective use of stimulation paradigms in future studies.

Methods

Preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed. Randomized controlled trials (RCTs) were systematically searched in four different databases, including Medline, Embase, Pubmed, and PsychInfo. Studies utilizing any tES methods published in English were considered for inclusion. Standardized mean difference (SMD) for each cognitive domain was used as the primary outcome measure.

Results

The meta-analysis includes 19 studies assessing at least one of the six cognitive domains. Five RCTs studying global cognition, three assessing visual neglect, five evaluating working memory, three assessing attention, and nine studies focusing on aphasia were included for meta-analysis. As informed by the quantitative analysis of the included studies, the results favor the efficacy of tES in acute improvement in aphasic deficits (SMD = 0.34, CI = 0.02-0.67, p = 0.04) and attention deficits (SMD = 0.59, CI = -0.05-1.22, p = 0.07), however, no improvement was observed in any other cognitive domains.

Conclusion

The results favor the efficacy of tES in an improvement in aphasia and attentive deficits in stroke patients in acute, subacute, and chronic stages. However, the outcome of tES cannot be generalized across cognitive domains. The difference in the stimulation montages and parameters, diverse cognitive batteries, and variable number of training sessions may have contributed to the inconsistency in the outcome. We suggest that in future studies, experimental designs should be further refined, and standardized stimulation protocols should be utilized to better understand the therapeutic effect of stimulation.

Predictions of tDCS treatment response in PTSD patients using EEG based classification

Transcranial direct current stimulation (tDCS) is an emerging therapeutic tool for treating posttraumatic stress disorder (PTSD). Prior studies have shown that tDCS responses are highly individualized, thus necessitating the individualized optimization of treatment configurations. To date, an effective tool for predicting tDCS treatment outcomes in patients with PTSD has not yet been proposed. Therefore, we aimed to build and validate a tool for predicting tDCS treatment outcomes in patients with PTSD. Forty-eight patients with PTSD received 20 min of 2 mA tDCS stimulation in position of the anode over the F3 and cathode over the F4 region. Non-responders were defined as those with less than 50% improvement after reviewing clinical symptoms based on the Clinician-Administered DSM-5 PTSD Scale (before and after stimulation). Resting-state electroencephalograms were recorded for 3 min before and after stimulation. We extracted power spectral densities (PSDs) for five frequency bands. A support vector machine (SVM) model was used to predict responders and non-responders using PSDs obtained before stimulation. We investigated statistical differences in PSDs before and after stimulation and found statistically significant differences in the F8 channel in the theta band (p = 0.01). The SVM model had an area under the ROC curve (AUC) of 0.93 for predicting responders and non-responders using PSDs. To our knowledge, this study provides the first empirical evidence that PSDs can be useful biomarkers for predicting the tDCS treatment response, and that a machine learning model can provide robust prediction performance. Machine learning models based on PSDs can be useful for informing treatment decisions in tDCS treatment for patients with PTSD.

Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm

The purpose of this study is to explore the application value of artificial intelligence algorithm in multimodal MRI image diagnosis of cervical cancer. Based on the traditional convolutional neural network (CNN), an artificial intelligence 3D-CNN algorithm is designed according to the characteristics of cervical cancer. 70 patients with cervical cancer were selected as the experimental group, and 10 healthy people were selected as the reference group. The 3D-CNN algorithm was applied to the diagnosis of clinical cervical cancer multimodal MRI images. The value of the algorithm was comprehensively evaluated by the image quality and diagnostic accuracy. The results showed that compared with the traditional CNN algorithm, the convergence rate of the loss curve of the artificial intelligence 3D-CNN algorithm was accelerated, and the segmentation accuracy of whole-area tumors (WT), core tumor areas (CT), and enhanced tumor areas (ET) was significantly improved. In addition, the clarity of the multimodal MRI image and the recognition performance of the lesion were significantly improved. Under the artificial intelligence 3D-CNN algorithm, the Dice values of WT, ET, and CT regions were 0.78, 0.71, and 0.64, respectively. The sensitivity values were 0.92, 0.91, and 0.88, respectively. The specificity values were 0.93, 0.92, and 0.9 l, respectively. The Hausdorff (Haus) distances were 0.93, 0.92, and 0.90, respectively. The data of various indicators were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of the artificial intelligence 3D-CNN algorithm was 93.11 ± 4.65%, which was also significantly higher than that of the traditional CNN algorithm (82.45 ± 7.54%) (P < 0.05). In summary, the recognition and segmentation ability of multimodal MRI images based on artificial intelligence 3D-CNN algorithm for cervical cancer lesions were significantly improved, which can significantly enhance the clinical diagnosis rate of cervical cancer.

Standard Non-Personalized Electric Field Modeling of Twenty Typical tDCS Electrode Configurations via the Computational Finite Element Method: Contributions and Limitations of Two Different Approaches

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation procedure to modulate cortical excitability and related brain functions. tDCS can effectively alter multiple brain functions in healthy humans and is suggested as a therapeutic tool in several neurological and psychiatric diseases. However, variability of results is an important limitation of this method. This variability may be due to multiple factors, including age, head and brain anatomy (including skull, skin, CSF and meninges), cognitive reserve and baseline performance level, specific task demands, as well as comorbidities in clinical settings. Different electrode montages are a further source of variability between tDCS studies. A procedure to estimate the electric field generated by specific tDCS electrode configurations, which can be helpful to adapt stimulation protocols, is the computational finite element method. This approach is useful to provide a priori modeling of the current spread and electric field intensity that will be generated according to the implemented electrode montage. Here, we present standard, non-personalized model-based electric field simulations for motor, dorsolateral prefrontal, and posterior parietal cortex stimulation according to twenty typical tDCS electrode configurations using two different current flow modeling software packages. The resulting simulated maximum intensity of the electric field, focality, and current spread were similar, but not identical, between models. The advantages and limitations of both mathematical simulations of the electric field are presented and discussed systematically, including aspects that, at present, prevent more widespread application of respective simulation approaches in the field of non-invasive brain stimulation.

Impact of Transcranial Direct Current Stimulation and Cognitive Training on Frontal Lobe Neurotransmitter Concentrations

Objective: This study examines the impact of transcranial direct current stimulation (tDCS) combined with cognitive training on neurotransmitter concentrations in the prefrontal cortex. Materials and Methods: Twenty-three older adults were randomized to either active-tDCS or sham-tDCS in combination with cognitive training for 2 weeks. Active-tDCS was delivered over F3 (cathode) and F4 (anode) electrode placements for 20 min at 2 mA intensity. For each training session, 40-min of computerized cognitive training were applied with active or sham stimulation delivered during the first 20-min. Glutamine/glutamate (Glx) and gamma-aminobutyric acid (GABA) concentrations via proton magnetic resonance spectroscopy were evaluated at baseline and at the end of 2-week intervention. Results: Glx concentrations increased from pre- to post-intervention (p = 0.010) in the active versus sham group after controlling for age, number of intervention days, MoCA scores, and baseline Glx concentration. No difference in GABA concentration was detected between active and sham groups (p = 0.650) after 2-week intervention. Conclusion: Results provide preliminary evidence suggesting that combining cognitive training and tDCS over the prefrontal cortex elicits sustained increase in excitatory neurotransmitter concentrations. Findings support the combination of tDCS and cognitive training as a potential method for altering neurotransmitter concentrations in the frontal cortices, which may have implications for neuroplasticity in the aging brain.

Grey-box modeling and hypothesis testing of functional near-infrared spectroscopy-based cerebrovascular reactivity to anodal high-definition tDCS in healthy humans

Transcranial direct current stimulation (tDCS) has been shown to evoke hemodynamics response; however, the mechanisms have not been investigated systematically using systems biology approaches. Our study presents a grey-box linear model that was developed from a physiologically detailed multi-compartmental neurovascular unit model consisting of the vascular smooth muscle, perivascular space, synaptic space, and astrocyte glial cell. Then, model linearization was performed on the physiologically detailed nonlinear model to find appropriate complexity (Akaike information criterion) to fit functional near-infrared spectroscopy (fNIRS) based measure of blood volume changes, called cerebrovascular reactivity (CVR), to high-definition (HD) tDCS. The grey-box linear model was applied on the fNIRS-based CVR during the first 150 seconds of anodal HD-tDCS in eleven healthy humans. The grey-box linear models for each of the four nested pathways starting from tDCS scalp current density that perturbed synaptic potassium released from active neurons for Pathway 1, astrocytic transmembrane current for Pathway 2, perivascular potassium concentration for Pathway 3, and voltage-gated ion channel current on the smooth muscle cell for Pathway 4 were fitted to the total hemoglobin concentration (tHb) changes from optodes in the vicinity of 4x1 HD-tDCS electrodes as well as on the contralateral sensorimotor cortex. We found that the tDCS perturbation Pathway 3 presented the least mean square error (MSE, median <2.5%) and the lowest Akaike information criterion (AIC, median -1.726) from the individual grey-box linear model fitting at the targeted-region. Then, minimal realization transfer function with reduced-order approximations of the grey-box model pathways was fitted to the ensemble average tHb time series. Again, Pathway 3 with nine poles and two zeros (all free parameters), provided the best Goodness of Fit of 0.0078 for Chi-Square difference test of nested pathways. Therefore, our study provided a systems biology approach to investigate the initial transient hemodynamic response to tDCS based on fNIRS tHb data. Future studies need to investigate the steady-state responses, including steady-state oscillations found to be driven by calcium dynamics, where transcranial alternating current stimulation may provide frequency-dependent physiological entrainment for system identification. We postulate that such a mechanistic understanding from system identification of the hemodynamics response to transcranial electrical stimulation can facilitate adequate delivery of the current density to the neurovascular tissue under simultaneous portable imaging in various cerebrovascular diseases.

Individualized tDCS modeling predicts functional connectivity changes within the working memory network in older adults

BACKGROUND

Working memory decline has been associated with normal aging. The frontal brain structure responsible for this decline is primarily located in the prefrontal cortex (PFC). Our previous neuroimaging study demonstrated a significant change in functional connectivity between the left dorsolateral PFC (DLPFC) and left ventrolateral PFC (VLPFC) when applying 2 mA tDCS in MRI scanner during an N-Back task. These regions were part of the working memory network. The present study is the first study that utilizes individualized finite element models derived from older adults' MRI to predict significant changes of functional connectivity observed from an acute tDCS application.

METHODS

Individualized head models from 15 healthy older adults (mean age = 71.3 years) were constructed to create current density maps. Each head model was segmented into 11 tissue types: white matter, gray matter, CSF, muscle, blood vessels, fat, eyes, air, skin, cancellous, and cortical bone. Electrodes were segmented from T1-weighted images and added to the models. Computed median and maximum current density values in the left DLPFC and left VLPFC regions of interest (ROIs) were correlated with beta values as functional connectivity metrics measured in different timepoint (baseline, during stimulation) and stimulation condition (active and sham).

MAIN RESULTS

Positive significant correlations (R2 = 0.523 for max J, R2 = 0.367 for median J, p < 0.05) were found between the beta values and computed current densities in the left DLPFC ROIs for active stimulation, but no significant correlation was found during sham stimulation. We found no significant correlation between connectivity and current densities computed in the left VLPFC for both active and sham stimulation.

CONCLUSIONS

The amount of current within the left DLPFC ROIs was found positively correlated with changes in functional connectivity between left DLPFC and left VLPFC during active 2 mA stimulation. Future work may include expansion of number of participants to further test the accuracy of tDCS models used to predict tDCS-induced functional connectivity changes within the working memory network.

Identifying regions in prefrontal cortex related to working memory improvement: A novel meta-analytic method using electric field modeling

Altering cortical activity using transcranial direct current stimulation (tDCS) has been shown to improve working memory (WM) performance. Due to large inter-experimental variability in the tDCS montage configuration and strength of induced electric fields, results have been mixed. Here, we present a novel meta-analytic method relating behavioral effect sizes to electric field strength to identify brain regions underlying largest tDCS-induced WM improvement. Simulations on 69 studies targeting left prefrontal cortex showed that tDCS electric field strength in lower dorsolateral prefrontal cortex (Brodmann area 45/47) relates most strongly to improved WM performance. This region explained 7.8 % of variance, equaling a medium effect. A similar region was identified when correlating WM performance and electric field strength of right prefrontal tDCS studies (n = 18). Maximum electric field strength of five previously used tDCS configurations were outside of this location. We thus propose a new tDCS montage which maximizes the tDCS electric field strength in that brain region. Our findings can benefit future tDCS studies that aim to affect WM function.

Impact of COMT val158met on tDCS-induced cognitive enhancement in older adults

BACKGROUND

Previous studies suggest that genetic polymorphisms and aging modulate inter-individual variability in brain stimulation-induced plasticity. However, the relationship between genetic polymorphisms and behavioral modulation through transcranial direct current stimulation (tDCS) in older adults remains poorly understood.

OBJECTIVE

Link individual tDCS responsiveness, operationalized as performance difference between tDCS and sham condition, to common genetic polymorphisms in healthy older adults.

METHODS

106 healthy older participants from five tDCS-studies were re-invited to donate blood for genotyping of apoliproprotein E (APOE: ε4 carriers and ε4 non-carriers), catechol-O-methyltransferase (COMT: val/val, val/met, met/met), brain-derived neurotrophic factor (BDNF: val/val, val/met, met/met) and KIdney/BRAin encoding gene (KIBRA: C/C, C/T, T/T). Studies had assessed cognitive performance during tDCS and sham in cross-over designs. We now asked whether the tDCS responsiveness was related to the four genotypes using a linear regression models.

RESULTS

We found that tDCS responsiveness was significantly associated with COMT polymorphism; i.e., COMT val carriers (compared to met/met) showed higher tDCS responsiveness. No other significant associations emerged.

CONCLUSION

Using data from five brain stimulation studies conducted in our group, we showed that only individual variation of COMT genotypes modulated behavioral response to tDCS. These findings contribute to the understanding of inherent factors that explain inter-individual variability in functional tDCS effects in older adults, and might help to better stratify participants for future clinical trials.

White matter hyperintensities affect transcranial electrical stimulation in the aging brain

BACKGROUND

White matter hyperintensities (WMH) are estimated to occur in greater than 63% of older adults over the age of 60 years. WMH identified in the T2-weighted FLAIR images can be combined with T1-weighted images to enhance individualized current flow models of older adults by accounting for the presence of WMH and its effects on delivered tES current in the aging brain.

METHODS

Individualized head models were derived from T1-weighted images of 130 healthy older adults (mean = 71 years). Lesions segmented from FLAIR acquisition were added to individualized models. Current densities were computed in the brain and compared between models with and without lesions.

MAIN RESULTS

Integrating WMH into the models resulted in an overall decrease (up to 7%) in median current densities in the brain outside lesion regions. Changes in current density and total lesion volume was positively correlated (R2 = 0.31, p < 0.0001).

CONCLUSIONS

Incorporating WMH into individualized models may increase the accuracy of predicted tES current flow in the aging brain.

A Systematic Review and Meta-Analysis of Transcranial Direct Current Stimulation to Remediate Age-Related Cognitive Decline in Healthy Older Adults

BACKGROUND

Transcranial direct current stimulation (tDCS) has been proposed as a possible method for remediating age-associated cognitive decline in the older adult population. While tDCS has shown potential for improving cognitive functions in healthy older adults, stimulation outcomes on various cognitive domains have been mixed.

METHODS

A systematic search was performed in four databases: PubMed, EMBASE, Web of Science, and PsychInfo. Search results were then screened for eligibility based on inclusion/exclusion criteria to only include studies where tDCS was applied to improve cognition in healthy older adults 65 years and above. Eligible studies were reviewed and demographic characteristics, tDCS dose parameters, study procedures, and cognitive outcomes were extracted. Reported effect sizes for active compared to sham group in representative cognitive domain were converted to Hedges' g.

MAIN RESULTS

A total of thirteen studies involving healthy older adults (n=532, mean age=71.2+5.3 years) were included in the meta-analysis. The majority of included studies (94%) targeted the prefrontal cortex with stimulation intensity 1-2 mA using various electrode placements with anodes near the frontal region. Across all studies, we found Hedges' g values ranged from -0.31 to 1.85 as reported group effect sizes of active stimulation compared to sham.

CONCLUSION

While observed outcomes varied, overall findings indicated promising effects of tDCS to remediate cognitive aging and thus deserves further exploration. Future characterization of inter-individual variability in tDCS dose response and applications in larger cohorts are warranted to further validate benefits of tDCS for cognition in healthy older adults.