Enhanced spatiotemporal resolution imaging of neuronal activity using joint electroencephalography and diffuse optical tomography

XCF-LSTMSATNet: A Classification Approach for EEG Signals Evoked by Dynamic Random Dot Stereograms

Stereovision is the visual perception of depth derived from the integration of two slightly different images from each eye, enabling understanding of the three-dimensional space. This capability is deeply intertwined with cognitive brain functions. To explore the impact of stereograms with varied motions on brain activities, we collected Electroencephalography (EEG) signals evoked by Dynamic Random Dot Stereograms (DRDS). To effectively classify the EEG signals induced by DRDS, we introduced a novel hybrid neural network model, XCF-LSTMSATNet, which integrates an XGBoost Channel Feature Optimization Module with the EEGNet and an LSTM Self-Attention Modules. Initially, in the channel selection phase, XGBoost is employed for preliminary classification and feature weight analysis, which can enhance our channel selection strategy. Following this, EEGNet employs deep convolutional layers to extract spatial features, while separable convolutions are subsequently used to derive high-dimensional spatial-temporal features. Meanwhile, the LSTMSAT Module, with its capability to learn long-term dependencies in time-series signals, is deployed to capture temporal continuity information. The incorporation of the self-attention mechanism further amplifies the model's ability to grasp long-distance dependencies and enables dynamic weight allocation to the extracted features. In the end, both temporal and spatial features are integrated into the classification module, enabling precise prediction across three categories of EEG signals. The proposed XCF-LSTMSATNet was extensively tested on both a custom dataset and the public datasets SRDA and SRDB. The results demonstrate that the model exhibits solid classification performance across all three datasets, effectively showcasing its robustness and generalization capabilities.

Native language background affects the perception of duration and pitch

Estonian is a quantity language with both a primary duration cue and a secondary pitch cue, whereas Chinese is a tonal language with a dominant pitch use. Using a mismatch negativity experiment and a behavioral discrimination experiment, we investigated how native language background affects the perception of duration only, pitch only, and duration plus pitch information. Chinese participants perceived duration in Estonian as meaningless acoustic information due to a lack of phonological use of duration in their native language; however, they demonstrated a better pitch discrimination ability than Estonian participants. On the other hand, Estonian participants outperformed Chinese participants in perceiving the non-speech pure tones that resembled the Estonian quantity (i.e., containing both duration and pitch information). Our results indicate that native language background affects the perception of duration and pitch and that such an effect is not specific to processing speech sounds.

Optimizing spatial accuracy in electroencephalography reconstruction through diffuse optical tomography priors in the auditory cortex

Diffuse optical tomography (DOT) enhances the localization accuracy of neural activity measured with electroencephalography (EEG) while preserving EEG's high temporal resolution. However, the spatial resolution of reconstructed activity diminishes for deeper neural sources. In this study, we analyzed DOT-enhanced EEG localization of neural sources modeled at depths ranging from 11-25 mm in simulations. Our findings reveal systematic biases in reconstructed depth related to DOT channel length. To address this, we developed a data-informed method for selecting DOT channels to improve the spatial accuracy of DOT-enhanced EEG reconstruction. Using our method, the average absolute reconstruction depth errors of DOT reconstruction across all depths are 0.9 ± 0.6 mm, 1.2 ± 0.9 mm, and 1.2 ± 1.1 mm under noiseless, low-level noise, and high-level noise conditions, respectively. In comparison, using fixed channel lengths resulted in errors of 2.6 ± 1.5 mm, 5.0 ± 2.6 mm, and 7.3 ± 4.5 mm under the same conditions. Consequently, our method improved the depth accuracy of DOT reconstructions and facilitated the use of more accurate spatial priors for EEG reconstructions, enhancing the overall precision of the technique.

EEG Source Imaging of Infarct Core and Penumbra for Ischemic Stroke Patients

A quantitative method of analyzing EEG signals after stroke onset can help monitor disease progression and tailor treatments. In this work, we present an EEG-based imaging algorithm to estimate the location and size of the stroke infarct core and penumbra tissues. Building on recent advancements in localizing neural silences, we develop an algorithm that utilizes known spectral properties of the infarct core and penumbra to separately localize them. Our algorithm uses these properties to estimate source contributions to the scalp EEG recordings in different frequency bands. Subsequently, it utilizes optimization techniques to search for the affected brain sources iteratively. We test our algorithm on simulated datasets using a realistic MRI head model, achieving center-of-mass error of 12.80mm and 17.24mm, and size estimation error of 21.78% and 36.62% for infarct core and penumbra respectively.

Diffuse Optical Tomography Spatial Prior for EEG Source Localization in Human Visual Cortex

Electroencephalography (EEG) and diffuse optical tomography (DOT) are imaging methods which are widely used for neuroimaging. While the temporal resolution of EEG is high, the spatial resolution is typically limited. DOT, on the other hand, has high spatial resolution, but the temporal resolution is inherently limited by the slow hemodynamics it measures. In our previous work, we showed using computer simulations that when using the results of DOT reconstruction as the spatial prior for EEG source reconstruction, high spatio-temporal resolution could be achieved. In this work, we experimentally validate the algorithm by alternatingly flashing two visual stimuli at a speed that is faster than the temporal resolution of DOT. We show that the joint reconstruction using both EEG and DOT clearly resolves the two stimuli temporally, and the spatial confinement is drastically improved in comparison to reconstruction using EEG alone.

Altered Functional Brain Network Structure between Patients with High and Low Generalized Anxiety Disorder

To investigate the differences in functional brain network structures between patients with a high level of generalized anxiety disorder (HGAD) and those with a low level of generalized anxiety disorder (LGAD), a resting-state electroencephalogram (EEG) was recorded in 30 LGAD patients and 21 HGAD patients. Functional connectivity between all pairs of brain regions was determined by the Phase Lag Index (PLI) to construct a functional brain network. Then, the characteristic path length, clustering coefficient, and small world were calculated to estimate functional brain network structures. The results showed that the PLI values of HGAD were significantly increased in alpha2, and significantly decreased in the theta and alpha1 rhythms, and the small-world attributes for both HGAD patients and LGAD patients were less than one for all the rhythms. Moreover, the small-world values of HGAD were significantly lower than those of LGAD in the theta and alpha2 rhythms, which indicated that the brain functional network structure would deteriorate with the increase in generalized anxiety disorder (GAD) severity. Our findings may play a role in the development and understanding of LGAD and HGAD to determine whether interventions that target these brain changes may be effective in treating GAD.

Methodological issues of the central mechanism of two classic acupuncture manipulations based on fNIRS: suggestions for a pilot study

Background: Acupuncture reinforcing-reducing manipulation (ARRM) is a necessary procedure of traditional Chinese acupuncture and an essential factor affecting the therapeutic effect of acupuncture. Shaoshanhuo reinforcing method (SSH) and Toutianliang reducing method (TTL) are the most representative ARRMs. They integrate six single ARRMs and pose distinguished therapeutic effects of acupuncture. However, due to the complexity, diversity, and variation, investigating the mechanism of these two classic manipulations is insufficient. The neuroimaging technique is an important method to explore the central mechanism of SSH and TTL. This study attempted to design a randomized crossover trial based on functional near-infrared spectroscopy (fNIRS) to explore the mechanism of SSH and TTL, meanwhile, provide valuable methodological references for future studies. Methods: A total of 30 healthy subjects were finally included and analyzed in this study. fNIRS examination was performed to record the neural responses during the two most representative ARRMs. The cortical activation and the inter-network functional connectivity (FC) were explored. Results: The results found that SSH and TTL could elicit significant cerebral responses, respectively, but there was no difference between them. Conclusion: Neuroimaging techniques with a higher spatiotemporal resolution, combinations of therapeutic effects, and strict quality control are important to neuroimaging studies on SSH and TTL.

Functional Near-Infrared Spectroscopy as a Target Navigator for rTMS Modulation in Patients with Hemiplegia: A Randomized Control Study

Introduction

Although repetitive transcranial magnetic stimulation (rTMS) is efficacious for motor neuromodulation in stroke survivors, high interindividual variability for responsiveness remains a concern. Target probing on the skull using a proper brain-mapping technique may help overcome this challenge. This study assessed the feasibility of functional near-infrared spectroscopy (fNIRS) as a target navigator in rTMS treatment for motor facilitation in patients with stroke.

Methods

Fifty-one patients with stroke were enrolled in this randomized controlled study. The patients were assigned to three groups: fNIRS-guided rTMS treatment (fNIRS group, n = 20), motor evoked potential (MEP)-guided rTMS treatment (MEP group, n = 16), and sham (n = 15) group. Motor assessments, including Fugl–Meyer Assessment (FMA), Wolf Motor Function Test (WMFT), and muscle strength, were conducted at baseline and after the 10-session rTMS treatment.

Results

The fNIRS-guided hotspot (fNIRS-HS) was obtained for each patient, even those for whom the MEP-guided hotspot was undetectable. Both intervention groups exhibited significant improvements in muscle strength, FMA, and WMFT scores (P < 0.001) compared with the sham group. The fNIRS group achieved significantly greater improvement in elbow function (P = 0.001) than the MEP group.

Conclusion

fNIRS can be a reliable tool for hotspot navigation for motor neuromodulation in patients with stroke. With high sensitivity to cortical oxygenation changes, this navigation system achieved a superior outcome to the traditional MEP-based method in patients with stroke. fNIRS-based systems may also facilitate the integration of machine learning, thus enabling precision medicine for neuromodulation.

Trial Registration

https://clinicaltrials.gov; Unique identifier: NCT02006615.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40120-021-00300-0.