Neuroimaging-Guided Transcranial Magnetic and Direct Current Stimulation in MCI: Toward an Individual, Effective and Disease-Modifying Treatment

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.

BDNF as a biomarker for neuropathic pain: Consideration of mechanisms of action and associated measurement challenges

INTRODUCTION

The primary objective of this paper is to (1) provide a summary of human studies that have used brain derived neurotrophic factor (BDNF) as a biomarker, (2) review animal studies that help to elucidate the mechanistic involvement of BDNF in the development and maintenance of neuropathic pain (NP), and (3) provide a critique of the existing measurement techniques to highlight the limitations of the methods utilized to quantify BDNF in different biofluids in the blood (i.e., serum and plasma) with the intention of presenting a case for the most reliable and valid technique. Lastly, this review also explores potential moderators that can influence the measurement of BDNF and provides recommendations to standardize its quantification to reduce the inconsistencies across studies.

METHODS

In this manuscript we examined the literature on BDNF, focusing on its role as a biomarker, its mechanism of action in NP, and critically analyzed its measurement in serum and plasma to identify factors that contribute to the discrepancy in results between plasma and serum BDNF values.

RESULTS

A large heterogenous literature was reviewed that detailed BDNF's utility as a potential biomarker in healthy volunteers, patients with chronic pain, and patients with neuropsychiatric disorders but demonstrated inconsistent findings. The literature provides insight into the mechanism of action of BDNF at different levels of the central nervous system using animal studies. We identified multiple factors that influence the measurement of BDNF in serum and plasma and based on current evidence, we recommend assessing serum BDNF levels to quantify peripheral BDNF as they are more stable and sensitive to changes than plasma BDNF.

CONCLUSION

Although mechanistic studies clearly explain the role of BDNF, results from human studies are inconsistent. More studies are needed to evaluate the methodological challenges in using serum BDNF as a biomarker in NP.

Predicting the Effects of Repetitive Transcranial Magnetic Stimulation on Cognitive Functions in Patients With Alzheimer's Disease by Automated EEG Analysis

Alzheimer's disease (AD) is a progressive, neurodegenerative brain disorder that generally affects the elderly. Today, after the limited benefit of the pharmacological treatment strategies, numerous noninvasive brain stimulation techniques have been developed. Transcranial magnetic stimulation (TMS), based on electromagnetic stimulation, is one of the most widely used methods. The main problem in the use of TMS is the existence of large individual variability in the results. This causes a waste of money, time, and more importantly, a burden for delicate patients. Hence, it is a necessity to form an efficient and personalized TMS application protocol. In this paper, we performed a machine-learning analysis to see whether it is possible to predict the responses of patients with AD to TMS by analyzing their electroencephalography (EEG) signals. For that purpose, we analyzed both the EEG signals collected before and after the TMS application (EEG1 and EEG2, respectively). Through correlating EEG1 and repetitive transcranial magnetic stimulation (rTMS) outcomes, we tried to see whether it is possible to predict patients' responses before the treatment application. On the other hand, by EEG2 analysis, we investigated TMS impacts on EEG, more importantly if this impact is correlated with patients' response to the treatment. We used the support vector machine (SVM) classifier due to its multiple advantages for the current task with feature selection processes by stepwise linear discriminant analysis (SWLDA) and SVM. However, to justify our numerical analysis framework, we examined and compared the performances of different feature selection and classification techniques. Since we have a limited sample number, we used the leave-one-out method for the validation with the Monte Carlo technique to eliminate bias by a small sample size. In the conclusion, we observed that the correlation between rTMS outcomes and EEG2 is stronger than EEG1, since we observed, respectively, 93 and 79% of accuracies during our data analysis. Besides the informative features of EEG2 are focused on theta band, it indicates that TMS is characterizing the theta band signals in patients with AD in direct relation to patients' response to rTMS. This shows that it is more possible to determine patients' benefit from the TMS at the early stages of the treatment, which would increase the efficiency of rTMS applications in patients with Alzheimer's disease.