Effect of High-Frequency rTMS Combined with Bilateral Arm Training on Brain Functional Network in Patients with Chronic Stroke: An fNIRS study

Exploring Effects of Mental Stress with Data Augmentation and Classification Using fNIRS

Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge. In this study, we employ a classification strategy to explore stress and its impact on spatial activation patterns and brain connectivity caused by the Stroop color-word task (SCWT). To improve our results and increase our dataset, we use data augmentation with a deep convolutional generative adversarial network (DCGAN). The study is carried out at two separate times of day (morning and evening) and involves 21 healthy participants. Additionally, we introduce binaural beats (BBs) stimulation to investigate its potential for stress reduction. The morning session includes a control phase with 10 SCWT trials, whereas the afternoon session is divided into three phases: stress, mitigation (with 16 Hz BB stimulation), and post-mitigation, each with 10 SCWT trials. For a comprehensive evaluation, the acquired fNIRS data are classified using a variety of machine-learning approaches. Linear discriminant analysis (LDA) showed a maximum accuracy of 60%, whereas non-augmented data classified by a convolutional neural network (CNN) provided the highest classification accuracy of 73%. Notably, after augmenting the data with DCGAN, the classification accuracy increases dramatically to 96%. In the time series data, statistically significant differences were noticed in the data before and after BB stimulation, which showed an improvement in the brain state, in line with the classification results. These findings illustrate the ability to detect changes in brain states with high accuracy using fNIRS, underline the need for larger datasets, and demonstrate that data augmentation can significantly help when data are scarce in the case of brain signals.

Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke

Persistent motor deficits are highly prevalent among post-stroke survivors, contributing significantly to disability. Despite the prevalence of these deficits, the precise mechanisms underlying motor recovery after stroke remain largely elusive. The exploration of motor system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue of research. Quantitative electroencephalography (qEEG) parameters, including the power ratio index, brain symmetry index, and phase synchrony index, have emerged as potential prognostic markers for overall motor recovery post-stroke. Current evidence suggests a correlation between qEEG parameters and functional motor outcomes in stroke recovery. However, accurately identifying the source activity poses a challenge, prompting the integration of EEG with other neuroimaging modalities, such as functional near-infrared spectroscopy (fNIRS). fNIRS is nowadays widely employed to investigate brain function, revealing disruptions in the functional motor network induced by stroke. Combining these two methods, referred to as integrated fNIRS-EEG, neural activity and hemodynamics signals can be pooled out and offer new types of neurovascular coupling-related features, which may be more accurate than the individual modality alone. By harnessing integrated fNIRS-EEG source localization, brain connectivity analysis could be applied to characterize cortical reorganization associated with stroke, providing valuable insights into the assessment and treatment of post-stroke motor recovery.

Priming transcranial direct current stimulation for improving hemiparetic upper limb in patients with subacute stroke: study protocol for a randomised controlled trial

INTRODUCTION

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that modulates brain states by applying a weak electrical current to the brain cortex. Several studies have shown that anodal stimulation of the ipsilesional primary motor cortex (M1) may promote motor recovery of the affected upper limb in patients with stroke; however, a high-level clinical recommendation cannot be drawn in view of inconsistent findings. A priming brain stimulation protocol has been proposed to induce stable modulatory effects, in which an inhibitory stimulation is applied prior to excitatory stimulation to a brain area. Our recent work showed that priming theta burst magnetic stimulation demonstrated superior effects in improving upper limb motor function and neurophysiological outcomes. However, it remains unknown whether pairing a session of cathodal tDCS with a session of anodal tDCS will also capitalise on its therapeutic effects.

METHODS AND ANALYSIS

This will be a two-arm double-blind randomised controlled trial involving 134 patients 1-6 months after stroke onset. Eligible participants will be randomly allocated to receive 10 sessions of priming tDCS+robotic training, or 10 sessions of non-priming tDCS+robotic training for 2 weeks. The primary outcome is the Fugl-Meyer Assessment-upper extremity, and the secondary outcomes are the Wolf Motor Function Test and Modified Barthel Index. The motor-evoked potentials, regional oxyhaemoglobin level and resting-state functional connectivity between the bilateral M1 will be acquired and analysed to investigate the effects of priming tDCS on neuroplasticity.

ETHICS AND DISSEMINATION

The study has been approved by the Research Ethics Committee of the Shanghai Yangzhi Rehabilitation Center (reference number: Yangzhi2023-022) and will be conducted in accordance with the Declaration of Helsinki of 1964, as revised in 2013.

TRIAL REGISTRATION NUMBER

ChiCTR2300074681.