Subthalamic and pallidal oscillations and their couplings reflect dystonia severity and improvements by deep brain stimulation.

BACKGROUND: Deep brain stimulation (DBS) targeting the globus pallidus internus (GPi) and subthalamic nucleus (STN) is employed for the treatment of dystonia. Pallidal low-frequency oscillations have been proposed as a pathophysiological marker for dystonia. However, the role of subthalamic oscillations and STN-GPi coupling in relation to dystonia remains unclear. OBJECTIVE: We aimed to explore oscillatory activities within the STN-GPi circuit and their correlation with the severity of dystonia and efficacy achieved by DBS treatment. METHODS: Local field potentials were recorded simultaneously from the STN and GPi from 13 dystonia patients. Spectral power analysis was conducted for selected frequency bands from both nuclei, while power correlation and the weighted phase lag index were used to evaluate power and phase couplings between these two nuclei, respectively. These features were incorporated into generalized linear models to assess their associations with dystonia severity and DBS efficacy. RESULTS: The results revealed that pallidal theta power, subthalamic beta power and subthalamic-pallidal theta phase coupling and beta power coupling all correlated with clinical severity. The model incorporating all selected features predicts empirical clinical scores and DBS-induced improvements, whereas the model relying solely on pallidal theta power failed to demonstrate significant correlations. CONCLUSIONS: Beyond pallidal theta power, subthalamic beta power, STN-GPi couplings in theta and beta bands, play a crucial role in understanding the pathophysiological mechanism of dystonia and developing optimal strategies for DBS.

Quantifying local field potential dynamics with amplitude and frequency stability between ON and OFF medication and stimulation in Parkinson's disease.

Neural oscillations are critical to understanding the synchronisation of neural activities and their relevance to neurological disorders. For instance, the amplitude of beta oscillations in the subthalamic nucleus has gained extensive attention, as it has been found to correlate with medication status and the therapeutic effects of continuous deep brain stimulation in people with Parkinson's disease. However, the frequency stability of subthalamic nucleus beta oscillations, which has been suggested to be associated with dopaminergic information in brain states, has not been well explored. Moreover, the administration of medicine can have inverse effects on changes in frequency and amplitude. In this study, we proposed a method based on the stationary wavelet transform to quantify the amplitude and frequency stability of subthalamic nucleus beta oscillations and evaluated the method using simulation and real data for Parkinson's disease patients. The results suggest that the amplitude and frequency stability quantification has enhanced sensitivity in distinguishing pathological conditions in Parkinson's disease patients. Our quantification shows the benefit of combining frequency stability information with amplitude and provides a new potential feedback signal for adaptive deep brain stimulation.

Balance between pallidal neural oscillations correlated with dystonic activity and severity.

BACKGROUND AND OBJECTIVE: The balance between neural oscillations provides valuable insights into the organisation of neural oscillations related to brain states, which may play important roles in dystonia. We aim to investigate the relationship of the balance in the globus pallidus internus (GPi) with the dystonic severity under different muscular contraction conditions. METHODS: Twenty-one patients with dystonia were recruited. All of them underwent bilateral GPi implantation, and local field potentials (LFPs) from the GPi were recorded via simultaneous surface electromyography. The power spectral ratio between neural oscillations was computed as the measure of neural balance. This ratio was calculated under high and low dystonic muscular contraction conditions, and its correlation with the dystonic severity was assessed using clinical scores. RESULTS: The power spectral of the pallidal LFPs peaked in the theta and alpha bands. Within participant comparison showed that the power spectral of the theta oscillations significantly increased during high muscle contraction compared with that during low contraction. The power spectral ratios between the theta and alpha, theta and low beta, and theta and high gamma oscillations were significantly higher during high contraction than during low contraction. The total score and motor score were associated with the power spectral ratio between the low and high beta oscillations, which was correlated with the dystonic severity both during high and low contractions. The power spectral ratios between the low beta and low gamma and between the low beta and high gamma oscillations showed a significantly positive correlation with the total score during both high and low contractions; a correlation with the motor scale score was found only during high contraction. Meanwhile, the power spectral ratio between the theta and alpha oscillations during low contraction showed a significantly negative correlation with the total score. The power spectral ratios between the alpha and high beta, alpha and low gamma, and alpha and high gamma oscillations were significantly correlated with the dystonic severity only during low contraction. CONCLUSION: The balance between neural oscillations, as quantified by the power ratio between specific frequency bands, differed between the high and low muscular contraction conditions and was correlated with the dystonic severity. The balance between the low and high beta oscillations was correlated with the dystonic severity during both conditions, making this parameter a new possible biomarker for closed-loop deep brain stimulation in patients with dystonia.

Human MECP2 transgenic rats show increased anxiety, severe social deficits, and abnormal prefrontal neural oscillation stability.

Methylated CpG binding protein 2 (MeCP2) plays an important role in the development and normal function of the neural system. Abnormally high expression of MECP2 leads to a subtype of autism called MECP2 duplication syndrome and MECP2 is considered one of the key pathogenic genes for autism spectrum disorders. However, the effect of MECP2 overexpression on neural activity is still not fully understood. Thus, transgenic (TG) animals that abnormally overexpress MeCP2 are important disease models in research on neurological function and autism. To create an animal model with a stronger and more stable autism phenotype, this study established a human MECP2 TG rat model and evaluated its movement ability, anxiety, and social behavior through behavioral tests. The results showed that MECP2 TG rats had an abnormally increased anxiety phenotype and social deficits in terms of abnormal social approach and social novelty preference, but no movement disorder. These autism-like behavioral phenotypes suggest that human MECP2 TG rats are suitable models for studying autism as they show more severe social deficit phenotypes and without interference from movement disorders affecting other phenotypes, which is an issue for mouse models with MECP2 duplication. In addition, this study performed preliminary exploration of the influence of the human MECP2 transgene on neural oscillation stability of the medial prefrontal cortex (mPFC), which is an important brain region for social interactions. Oscillation stability in MECP2 TG rats showed abnormal responses to social conditions. Overall, the results of this study provide a new research tool for understanding the mechanism of social impairment and treatment of autism. The results also provide evidence for the influence of MECP2 duplication on mPFC neural activity.

Functional dynamics of thalamic local field potentials correlate with modulation of neuropathic pain.

Understanding the functional dynamics of neural oscillations in the sensory thalamus is essential for elucidating the perception and modulation of neuropathic pain. Local field potentials were recorded from the sensory thalamus of twelve neuropathic pain patients. Single and combinational neural states were defined by the activity state of a single or paired oscillations. Relationships between the duration or occurrence rate of neural state and pre-operative pain level or pain relief induced by deep brain stimulation were evaluated. Results showed that the occurrence rate of the single neural state of low-beta oscillation was significantly correlated with pain relief. The duration and occurrence rate of combinational neural states of the paired low-beta with delta, theta, alpha, high-beta or low-gamma oscillations were more significantly correlated with pain relief than the single neural states. Moreover, these significant combinational neural states formed a local oscillatory network with low-beta oscillation as a key node. The results also showed correlations between measures of combinational neural states and subjective pain level as well. The duration of combinational neural states of paired alpha with delta or theta oscillations and the occurrence rate of neural states of the paired delta with low-beta or low-gamma oscillations were significantly correlated with pre-operative pain level. In conclusion, this study revealed that the integration of oscillations and the functional dynamics of neural states were differentially involved in modulation and perception of neuropathic pain. The functional dynamics could be biomarkers for developing neural state-dependent deep brain stimulation for neuropathic pain.

Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

Amplitude Adaptive Modulation of Neural Oscillations Over Long-Term Dynamic Conditions: A Computational Study.

Closed-loop deep brain stimulation (DBS) shows great potential for precise neuromodulation of various neurological disorders, particularly Parkinson's disease (PD). However, substantial challenges remain in clinical translation due to the complex programming procedure of closed-loop DBS parameters. In this study, we proposed an online optimized amplitude adaptive strategy based on the particle swarm optimization (PSO) and proportional-integral-differential (PID) controller for modulation of the beta oscillation in a PD mean field model over long-term dynamic conditions. The strategy aimed to calculate the stimulation amplitude adapting to the fluctuations caused by circadian rhythm, medication rhythm, and stochasticity in the basal ganglia-thalamus-cortical circuit. The PID gains were optimized online using PSO, based on modulation accuracy, mean stimulation amplitude, and stimulation variation. The results showed that the proposed strategy optimized the stimulation amplitude and achieved beta power modulation under the influence of circadian rhythm, medication rhythm, and stochasticity of beta oscillations. This work offers a novel approach for precise neuromodulation with the potential for clinical translation.

Dual electrical stimulation at spinal-muscular interface reconstructs spinal sensorimotor circuits after spinal cord injury.

The neural signals produced by varying electrical stimulation parameters lead to characteristic neural circuit responses. However, the characteristics of neural circuits reconstructed by electrical signals remain poorly understood, which greatly limits the application of such electrical neuromodulation techniques for the treatment of spinal cord injury. Here, we develop a dual electrical stimulation system that combines epidural electrical and muscle stimulation to mimic feedforward and feedback electrical signals in spinal sensorimotor circuits. We demonstrate that a stimulus frequency of 10-20 Hz under dual stimulation conditions is required for structural and functional reconstruction of spinal sensorimotor circuits, which not only activates genes associated with axonal regeneration of motoneurons, but also improves the excitability of spinal neurons. Overall, the results provide insights into neural signal decoding during spinal sensorimotor circuit reconstruction, suggesting that the combination of epidural electrical and muscle stimulation is a promising method for the treatment of spinal cord injury.

Subthalamic dynamic neural states correlate with motor symptoms in Parkinson's Disease.

OBJECTIVE: This study aims to discriminate the dynamic synchronization states from the subthalamic local field potentials and investigate their correlations with the motor symptoms in Parkinson's Disease (PD). METHODS: The resting-state local field potentials of 10 patients with PD were recorded from the subthalamic nucleus. The dynamic neural states of multiple oscillations were discriminated and analyzed. The Spearman correlation was used to investigate the correlations between occurrence rate or duration of dynamic neural states and the severity of motor symptoms. RESULTS: The proportion of long low-beta and theta synchronized state was significantly correlated with the general motor symptom and tremor, respectively. The duration of combined low/high-beta state was significantly correlated with rigidity, and the duration of combined alpha/high-beta state was significantly correlated with bradykinesia. CONCLUSIONS: This study provides evidence that motor symptoms are associated with the neural states coded with multiple oscillations in PD. SIGNIFICANCE: This study may advance the understanding of the neurophysiological mechanisms of the motor symptoms and provide potential biomarkers for closed-loop deep brain stimulation in PD.

Real-time removal of stimulation artifacts in closed-loop deep brain stimulation.

Objective.Closed-loop deep brain stimulation (DBS) with neural feedback has shown great potential in improving the therapeutic effect and reducing side effects. However, the amplitude of stimulation artifacts is much larger than the local field potentials, which remains a bottleneck in developing a closed-loop stimulation strategy with varied parameters.Approach.We proposed an irregular sampling method for the real-time removal of stimulation artifacts. The artifact peaks were detected by applying a threshold to the raw recordings, and the samples within the contaminated period of the stimulation pulses were excluded and replaced with the interpolation of the samples prior to and after the stimulation artifact duration. This method was evaluated with both simulation signals andin vivoclosed-loop DBS applications in Parkinsonian animal models.Main results. The irregular sampling method was able to remove the stimulation artifacts effectively with the simulation signals. The relative errors between the power spectral density of the recovered and true signals within a wide frequency band (2-150 Hz) were 2.14%, 3.93%, 7.22%, 7.97% and 6.25% for stimulation at 20 Hz, 60 Hz, 130 Hz, 180 Hz, and stimulation with variable low and high frequencies, respectively. This stimulation artifact removal method was verified in real-time closed-loop DBS applicationsin vivo, and the artifacts were effectively removed during stimulation with frequency continuously changing from 130 Hz to 1 Hz and stimulation adaptive to beta oscillations.Significance.The proposed method provides an approach for real-time removal in closed-loop DBS applications, which is effective in stimulation with low frequency, high frequency, and variable frequency. This method can facilitate the development of more advanced closed-loop DBS strategies.

EasyMEG: An easy-to-use toolbox for MEG analysis.

BACKGROUND AND OBJECTIVE: Magnetoencephalography (MEG) is an advanced magnetic source imaging technology that measures the magnetic fields produced by neural activities. It has been extensively used in scientific research and clinical diagnosis due to its high temporal and spatial resolution. Considering the special nature of MEG data, it needs to perform a series of processes and analysis to obtain valuable information. Therefore, the identification of data processing is a key point of MEG studies. At present, the software for MEG analysis such as FieldTrip has no Graphic User Interface (GUI) and users must write their own script to perform concrete analysis. It brings the difficulties to researchers like the doctors without experience in programming or newcomers to MEG. Thus, an open-sourced software-EasyMEG was developed. It has friendly interface with highly functions-integration. METHODS: The functions of EasyMEG are developed based on MATLAB language to ensure the consistency of the user interface under different operating systems. EasyMEG is a highly integrated software that contains a set of functions for preprocessing, time-lock analysis, time-frequency analysis, source analysis, and plotting. EasyMEG provides a friendly GUI and allows users to complete analyses through a simple and clean interface. RESULTS: This toolbox has been released as an open-source software on GitHub under the GNU General Public License: https://tonywu2018.github.io/EasyMEG/. CONCLUSIONS: We hope to improve this toolbox by the power of community and wish to make EasyMEG a simple and powerful toolbox for further MEG studies.

Alterations of Graphic Properties and Related Cognitive Functioning Changes in Mild Alzheimer's Disease Revealed by Individual Morphological Brain Network.

Alzheimer's disease (AD) is one of the most common forms of dementia that has slowly negative impacts on memory and cognition. With the assistance of multimodal brain networks and graph-based analysis approaches, AD-related network disruptions support the hypothesis that AD can be identified as a dysconnectivity syndrome. However, as the recent emerging of individual-based morphological network research of AD, the utilization of multiple morphometric features may provide a broader horizon for locating the lesions. Therefore, the present study applied the newly proposed individual morphological brain network with five commonly used morphometric features (cortical thickness, regional volume, surface area, mean curvature, and fold index) to explore the topological aberrations and their relationship with cognitive functioning alterations in the early stage of AD. A total of 40 right-handed participants were selected from Open Access Series of Imaging Studies Database with 20 AD patients (age ranged from 70 to 79, CDR = 0.5) and 20 age/gender-matched healthy controls. The significantly affected connections (p < 0.05 with FDR correction) were observed across multiple regions, both enhanced and attenuated correlations, primarily related to the left entorhinal cortex (ENT). In addition, profoundly changed Mini Mental State Examination (MMSE) score and global efficiency (p < 0.05) were noted in the AD patients, as well as the pronounced inter-group distinctions of betweenness centrality, global and local efficiency (p < 0.05) in the higher MMSE score zone (28-30), which indicating the potential role of graphic properties in determination of early-stage AD patients. Moreover, the reservations (regions in the occipital and frontal lobes) and alterations (regions in the right temporal lobe and cingulate cortex) of hubs were also detected in the AD patients. Overall, the findings further confirm the selective AD-related disruptions in morphological brain networks and also suggest the feasibility of applying the morphological graphic properties in the discrimination of early-stage AD patients.

Construction of Individual Morphological Brain Networks with Multiple Morphometric Features.

In recent years, researchers have increased attentions to the morphological brain network, which is generally constructed by measuring the mathematical correlation across regions using a certain morphometric feature, such as regional cortical thickness and voxel intensity. However, cerebral structure can be characterized by various factors, such as regional volume, surface area, and curvature. Moreover, most of the morphological brain networks are population-based, which has limitations in the investigations of individual difference and clinical applications. Hence, we have extended previous studies by proposing a novel method for realizing the construction of an individual-based morphological brain network through a combination of multiple morphometric features. In particular, interregional connections are estimated using our newly introduced feature vectors, namely, the Pearson correlation coefficient of the concatenation of seven morphometric features. Experiments were performed on a healthy cohort of 55 subjects (24 males aged from 20 to 29 and 31 females aged from 20 to 28) each scanned twice, and reproducibility was evaluated through test-retest reliability. The robustness of morphometric features was measured firstly to select the more reproducible features to form the connectomes. Then the topological properties were analyzed and compared with previous reports of different modalities. Small-worldness was observed in all the subjects at the range of the entire network sparsity (20-40%), and configurations were comparable with previous findings at the sparsity of 23%. The spatial distributions of the hub were found to be significantly influenced by the individual variances, and the hubs obtained by averaging across subjects and sparsities showed correspondence with previous reports. The intraclass coefficient of graphic properties (clustering coefficient = 0.83, characteristic path length = 0.81, betweenness centrality = 0.78) indicates the robustness of the present method. Results demonstrate that the multiple morphometric features can be applied to form a rational reproducible individual-based morphological brain network.