Cross-Frequency Coupling and Intelligent Neuromodulation

Post-Stroke Corticomuscular Coupling Assessment Based on Bilateral Cerebral Hemisphere Difference

Corticomuscular coupling (CMC) can quantify the information interaction between the brain and muscles during motor control. However, current research regarding changes in CMC after stroke is inconsistent. To address this, this paper propose a novel use of $\textit {CMC}_{\textit {dif}}$ as an indicator to assess motor function after stroke. This indicator include $\textit {WC}_{\textit {dif}}$ , derived from wavelet coherence analysis and $\textit {TSE}_{\textit {dif}}$ , derived from transfer spectral entropy analysis. Twelve stroke patients and twelve healthy controls were included in this study, with an experimental paradigm of upper limb isokinetic push-pull movements. The results revealed that $\textit {WC}_{\textit {dif}}$ were significantly higher in the stroke patient group compared to the healthy group. Moreover, the $\textit {TSE}_{\textit {dif}}$ of stroke group is higher than healthy group on the efferent pathway, but no difference on the afferent pathway. Utilizing the validated $\textit {CMC}_{\textit {dif}}$ indices, we developed a motor function assessment model that showed strong relation with clinical assessment outcomes ( ${R}^{{2}}={0}.{873}$ , ${p}={0}.{003}$ ). These findings provide a new insight to understand the mechanisms underlying CMC changes after stroke. The combined use of linear and nonlinear indicators enhances the potential of CMC for clinical motor function assessment in stroke patients.

Plasma loaded uniaxial chiral slab waveguide

In this study, numerical analysis has been carried out at plasma loaded uniaxial chiral slab waveguide. The formulation is based on differential form of Maxwell's equations and characteristics equation is obtained by employing boundary conditions at uniaxial chiral-plasma-uniaxial. Two cases of uniaxial chiral media i.e., axial permitivity grater than tranverse permitivity and axial permitivity less than tranverse permitivity are discussed to explore the characteristics for proposed waveguide structure. The influence of collsional frequencies, plasma frequencies, chirality and core width of waveguide on normalized propagation constant and propagation are analyzed. Numerical results indicate that physical parametrs of isotropic plasma (collisonal frequency and plasma frequency), chirality and core width play pivotal role to tune the normlized propagation constant, propagation length band gap and cut-off frequency in certain frequency range. The extraordinary traits of uniaxial chiral and plasma medium offer a wide range of research and development possibilities in diverse areas of optoelectronics.

Disrupted coordination between primary and high-order cognitive networks in Parkinson's disease based on morphological and functional analysis

Patients with Parkinson's disease (PD) exhibit structural and functional alterations in both primary and high-order cognitive networks, but the interactions within aberrant functional networks and relevant structural foundation remains unexplored. In this study, the functional networks (FN) and the morphometric similarity networks (MSN) were constructed respectively based on the time-series data and gray matter volume from the MRI data of PD patients and controls. The efficiency, average controllability and k-shell values of the FN and MSN were calculated to evaluate their ability of information transmission and identify structural and functional abnormalities in PD. The abnormal regions were categorized into five types: regions with MSN abnormalities, regions with FN abnormalities, regions with both MSN and FN abnormalities, regions with abnormalities only in MSN but not in FN and regions with abnormalities only in FN but not in MSN. Further, the dynamic causal model (DCM) was used to evaluate the causal relationship of information flow between the identified regions. In the network property analysis of the FN, PD patients showed decreased global efficiency and connectivity in the visual network (VIS) and increased global efficiency in higher-order cognitive networks, including the ventral attention network (VAN), default mode network (DMN), and the limbic network (LIM) but no difference in MSN. In the DCM analysis of the regions, PD patients exhibited increased excitatory transition from the visual areas to the superior frontal gyrus, whereas had disturbed information flow from the visual areas to the insula and the orbitofrontal cortex. These findings suggest changes in structural and functional brain of PD patients, and advance our understanding of PD pathogenesis from different neural dimensions.

Inorganic nanoparticles and blood-brain barrier modulation: Advancing targeted neurological therapies

The blood-brain barrier (BBB) is a protective barrier that complicates the treatment of neurological disorders. Pharmaceutical compounds encounter significant challenges in crossing the central nervous system (CNS). Nanoparticles (NPs) are promising candidates for treating neurological conditions as they help facilitate drug delivery. This review explores the diverse characteristics and mechanisms of inorganic NPs (INPs), including metal-based, ferric-oxide, and carbon-based nanoparticles, which facilitate their passage through the BBB. Emphasis is placed on the physicochemical properties of NPs such as size, shape, surface charge, and surface modifications and their role in enhancing drug delivery efficacy, reducing immune clearance, and improving BBB permeability. Specific synthesis approaches are demonstrated, with an emphasis on the influence of each one on NP property, biological activity and the capability of an NP for its intended application. As for the advances in the field, the review emphasizes those characterized the NP formulation and surface chemistry that conquered the BBB and tested the need for its alteration. Current findings indicate that NP therapy can in the future enable effective targeting of specific brain disorders and eventually evolve this drug delivery system, which would allow for lower doses with less side effects.

Phase-Amplitude Coupling in Response to Transcutaneous Vagus Nerve Stimulation: Focus on Regions Implicated in Mood and Memory

OBJECTIVE

This study aimed to investigate whether transcutaneous vagus nerve stimulation (tVNS) at different frequencies affects phase-amplitude coupling among regions of the brain linked to mood and memory disorders using simultaneous magnetoencephalography (MEG) in healthy participants.

MATERIALS AND METHODS

Phase-amplitude coupling was measured among brain areas in response to different stimulation frequencies of tVNS using concurrent MEG and tVNS in 17 healthy participants. The 4 protocols were: 24 Hz cymba concha, 1 Hz cymba concha, PFM cymba concha, and 24 Hz ear lobe. A driven autoregressive method was used to estimate the coupling among brain areas in different physiological frequency bands in response to these protocols.

RESULTS

Different tVNS stimulation protocols led to alterations in phase-amplitude coupling among multiple brain regions linked to mood and memory, notably the prefrontal cortex, hippocampus, and temporal pole. Stimulation delivered at 24 Hz was observed to decrease delta-gamma coupling within the temporal pole and cingulate cortex when contrasted with 24-Hz sham stimulation. Increased alpha-gamma coupling was observed between the hippocampus and prefrontal cortex when contrasting 24 Hz with pulse-frequency-modulated stimulation. Finally, a comparison of 24-Hz with low-frequency 1-Hz stimulation showed an increase in theta-gamma coupling within the prefrontal cortex.

SIGNIFICANCE

To our knowledge, this study represents the first attempt to quantify phase-amplitude coupling in response to tVNS and suggests that different stimulation frequencies can modulate coupling between different areas of the brain. Abnormal phase-amplitude coupling has been linked to multiple mood and memory disorders. Further investigations using different stimulation frequencies of tVNS to alter phase-amplitude coupling may lead to the development of tVNS as a therapeutic option for different medical conditions.

What can neurofeedback and transcranial alternating current stimulation reveal about cross-frequency coupling?

In recent years, the dynamics and function of cross-frequency coupling (CFC) in electroencephalography (EEG) have emerged as a prevalent area of investigation within the research community. One possible approach in studying CFC is to utilize non-invasive neuromodulation methods such as transcranial alternating current stimulation (tACS) and neurofeedback (NFB). In this study, we address (1) the potential applicability of single and multifrequency tACS and NFB protocols in CFC research; (2) the prevalence of CFC types, such as phase-amplitude or amplitude-amplitude CFC, in tACS and NFB studies; and (3) factors that contribute to inter- and intraindividual variability in CFC and ways to address them potentially. Here we analyzed research studies on CFC, tACS, and neurofeedback. Based on current knowledge, CFC types have been reported in tACS and NFB studies. We hypothesize that direct and indirect effects of tACS and neurofeedback can induce CFC. Several variability factors such as health status, age, fatigue, personality traits, and eyes-closed (EC) vs. eyes-open (EO)condition may influence the CFC types. Modifying the duration of the tACS and neurofeedback intervention and selecting a specific demographic experimental group could reduce these sources of CFC variability. Neurofeedback and tACS appear to be promising tools for studying CFC.

An integrated paper-based microfluidic platform for screening of early-stage Alzheimer's disease by detecting Aβ42

Alzheimer's disease (AD) is the leading cause of dementia worldwide, and the development of early screening methods can address its significant health and social consequences. In this paper, we present a rotary-valve assisted paper-based immunoassay device (RAPID) for early screening of AD, featuring a highly integrated on-chip rotary micro-valve that enables fully automated and efficient detection of the AD biomarker (amyloid beta 42, Aβ42) in artificial plasma. The microfluidic paper-based analytical device (μPAD) of the RAPID pre-stores the required assay reagents on a μPAD and automatically controls the liquid flow through a single valve. Once the test sample is added, the test reagents are sequentially transferred to the test area in the order set by the enzyme-linked immunosorbent assay (ELISA) protocol. In addition, the RAPID can remotely control the operation of the μPAD valve via a micro-servomotor, quantify the signals generated, display the results, and wirelessly transmit the data to a smartphone. To calibrate the RAPID, we performed a sandwich ELISA for Aβ42 in artificial plasma, and obtained a low limit of detection (LOD) of 9.6 pg mL-1, a coefficient of determination (COD) of 0.994, and an individual assay time of ∼30 minutes. In addition, we simulated 24 artificial samples to quantify Aβ42 protein concentrations in artificial plasma samples. The results show good consistency between the conventional ELISA and RAPID detection. The experimental results demonstrate that the RAPID is expected to promote further popularization of the screening of early-stage AD.

Beta oscillation modulations of the orienting attention network effect correlate with dopamine-dependent motor symptoms of Parkinson's disease

Attention impairment, a prevalent non-motor symptom in Parkinson's disease (PD), plays a crucial role in movement disorders. PD patients exhibit abnormalities in the attentional network related to alerting, orienting, and executive control. While dopamine medications have well-documented effects on motor function, their impact on attention networks and the underlying neural mechanisms involved in motor functions remain unclear. In this study, we utilized a modified attention network test to investigate the neural correlates underlying attention network effects measured by electroencephalography (EEG) in 29 PD patients, both on and off dopamine medication and examined their association with motor performance. Interestingly, we found that dopamine medication specifically modulated the orienting effect of the attention network. We analyzed event-related potential components, time-frequency oscillations, and brain network connectivity, as determined by the weighted phase lag index, within the orienting effect under different dopamine medication states. We observed that event-related desynchronization in the betalow, event-related synchronization in the betahigh, and functional connectivity of the betalow in the frontal, central, and parietal were regulated by dopamine medication in the orienting effect. We discovered an association between the attention network's orienting effect and motor performance alterations, which may be attributed to enhanced functional connectivity within the betalow-brain network. Enhanced weighted phase lag index of the betalow-brain network in the orienting effect may contribute to dopamine-dependent changes in motor performance. These preliminary findings provide insights into the EEG mechanisms that underlie the impact of the orienting effect in individuals with PD, shedding light on the influence of dopamine medication and its potential role in regulating top-down attention processes. These findings could help in the advancement of substitution strategies and may have the potential to address both motor and cognitive deficits in PD patients.

Examining the practical importance of nonstationary cardio-respiratory coupling detection in breathing training: a methodological appraisal

This study investigates changes in cardiorespiratory coupling during clinic breathing training and its impact on autonomic nervous functioning compared with heart rate variability (HRV). A total of 39 subjects undergoing dynamic electrocardiogram-recorded breathing training were analyzed. Subjects were divided into early- and late-training periods, and further categorized based on changes in HRV indexes. Subtypes were identified using time-frequency cardiorespiratory coupling diagrams. Significant differences were observed in the high-frequency (HF) index between training stages in the subgroup with increasing HF-HRV (p = 0.0335). Both unimodal and bimodal subtypes showed significant high-frequency coupling (HFC) in the mid-training period compared with early and late stages (both p < 0.0001), suggesting improved parasympathetic cardiac regulation or reduced sympathetic control. This study highlights the potential of nonstationary cardiorespiratory coupling analysis alongside traditional HRV in evaluating the therapeutic effect of breathing training on autonomic nervous function. Cardiorespiratory coupling analysis could provide valuable adjunctive information to HRV measures for assessing the impact of breathing training.

A simulation study of transcranial magnetoacoustic stimulation of the basal ganglia thalamic neural network to improve pathological beta oscillations in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a common neurodegenerative disease. Transcranial magnetoacoustic stimulation (TMAS) is a new therapy that combines a transcranial focused acoustic pressure field with a magnetic field to excite or inhibit neurons in targeted area, which suppresses the abnormally elevated beta band amplitude in PD states, with high spatial resolution and non-invasively.

OBJECTIVE

To study the effective stimulation parameters of TMAS mononuclear and multinuclear stimulation for the treatment of PD with reduced beta band energy, improved abnormal synchronization, and no thermal damage.

METHODS

The TMAS model is constructed based on the volunteer's computed tomography, 128 arrays of phase-controlled transducers, and permanent magnets. A basal ganglia-thalamic (BG-Th) neural network model of the PD state was constructed on the basis of the Izhikevich model and the acoustic model. An ultrasound stimulation neuron model is constructed based on the Hodgkin-Huxley model. Numerical simulations of transcranial focused acoustic pressure field, temperature field and induced electric field at single and dual targets were performed using the locations of STN, GPi, and GPe in the human brain as the main stimulation target areas. And the acoustic and electric parameters at the focus were extracted to stimulate mononuclear and multinuclear in the BG-Th neural network.

RESULTS

When the stimulating effect of ultrasound is ignored, TMAS-STN simultaneously inhibits the beta-band amplitude of the GPi nucleus, whereas TMAS-GPi fails to simultaneously have an inhibitory effect on the STN. TMAS-STN&GPi can reduce the beta band amplitude. TMAS-STN&GPi&GPe suppressed the PD pathologic beta band amplitude of each nucleus to a greater extent. When considering the stimulatory effect of ultrasound, lower sound pressures of ultrasound do not affect the neuronal firing state, but higher sound pressures may promote or inhibit the stimulatory effect of induced currents.

CONCLUSIONS

At 9 T static magnetic field, 0.5-1.5 MPa and 1.5-2.0 MPa ultrasound had synergistic effects on individual STN and GPi neurons. TMAS multinuclear stimulation with appropriate ultrasound intensity was the most effective in suppressing the amplitude of pathological beta oscillations in PD and may be clinically useful.

Non-coding RNAs in Parkinson's disease: Regulating SNCA and alpha-synuclein aggregation

Parkinson's disease is one of the vital neurodegenerative ailments attributed to a rise in Alpha-synuclein proteins leading to the advancement of motor and cognitive deterioration. Interestingly, in PD lncRNAs, miRNAs and siRNAs are also key regulators of SNCA and alpha-synuclein aggregation. This review will focus on the roles of these three types of small RNAs in trebling the development of PD through regulating SNCA expression or alpha-synuclein protein mediating the RNA from acting. Parkinson's disease is defined by the build-up of alpha-synuclein protein resulting predominantly from the elevated expression level of the SNCA gene. Non-coding RNAs have gained broad appeal as fundamental modulators of gene expression and protein aggregation dynamics, with significant implications on the aetiology of PD. LncRNAs modulate SNCA transcription and edit epigenetic modifications, while miRNA target mRNA is involved in the stability and translation of count alpha-synuclein. Considering all these data, siRNAs can achieve the precise gene silencing effect that directly induces the downregulation of SNCA mRNA. This review also summarizes some recent reports about the interaction between these ncRNAs with the SNCA gene and alpha-synuclein protein, each through its independent in addition to synergistic mechanisms. This review highlights the possibility of therapeutic interventions to perturb SNCA expression to prevent alpha-synuclein aggregation via targeting ncRNAs that might be spun off novel drug development for PD.

Highly efficient electrocatalytic oxidation of levodopa as a Parkinson therapeutic drug based on modified screen-printed electrode

The current study presents the creation of a straightforward and sensitive sensor based on ZnO/Co3O4 nanocomposite modified screen-printed electrode (ZnO/Co3O4NC/SPE) for levodopa determination. At ZnO/Co3O4NC/SPE, an oxidative peak for levodopa solution in pH 6.0 phosphate buffer solution (PBS) were seen that were both more resolved and more enhanced. Levodopa was measured using differential pulse voltammetry (DPV), which showed an excellent linear range (0.001-800.0 μM) and detection limit (0.81 nM). The presence of interference did not affect the electrochemical response of levodopa at ZnO/Co3O4NC/SPE, demonstrating high selectivity. Levodopa in a real samples have been successfully detected using the manufactured sensor.

Amygdala and cognitive impairment in cerebral small vessel disease: structural, functional, and metabolic changes

Cerebral small vessel disease (CSVD) is a prevalent vascular disorder that has been consistently associated with vascular cognitive impairment (VCI). The diagnosis of CSVD continues to rely on magnetic resonance imaging (MRI). Epidemiological data indicate that the characteristic MRI features of CSVD, including white matter hyperintensity (WMH) and lacunar infarction, are very common among individuals over 40 years of age in community studies. This prevalence poses a significant burden on many low- and middle-income families. The amygdala plays a crucial role in integrating sensory and associative information to regulate emotional cognition. Although many previous studies have linked alterations in the amygdala to various diseases, such as depression, there has been little research on CSVD-associated alterations in the amygdala due to the complexity of CSVD. In this paper, we summarize the various imaging features of CSVD and discuss the correlation between amygdala changes and VCI. We also explore how new neuroimaging methods can assess amygdala changes early, laying a foundation for future comprehensive exploration of the pathogenesis of CSVD.

ncRNAs and their impact on dopaminergic neurons: Autophagy pathways in Parkinson's disease

Parkinson's Disease (PD) is a complex neurological illness that causes severe motor and non-motor symptoms due to a gradual loss of dopaminergic neurons in the substantia nigra. The aetiology of PD is influenced by a variety of genetic, environmental, and cellular variables. One important aspect of this pathophysiology is autophagy, a crucial cellular homeostasis process that breaks down and recycles cytoplasmic components. Recent advances in genomic technologies have unravelled a significant impact of ncRNAs on the regulation of autophagy pathways, thereby implicating their roles in PD onset and progression. They are members of a family of RNAs that include miRNAs, circRNA and lncRNAs that have been shown to play novel pleiotropic functions in the pathogenesis of PD by modulating the expression of genes linked to autophagic activities and dopaminergic neuron survival. This review aims to integrate the current genetic paradigms with the therapeutic prospect of autophagy-associated ncRNAs in PD. By synthesizing the findings of recent genetic studies, we underscore the importance of ncRNAs in the regulation of autophagy, how they are dysregulated in PD, and how they represent novel dimensions for therapeutic intervention. The therapeutic promise of targeting ncRNAs in PD is discussed, including the barriers that need to be overcome and future directions that must be embraced to funnel these ncRNA molecules for the treatment and management of PD.

Varicella-zoster virus-related neurological complications: From infection to immunomodulatory therapies

The Varicella-zoster virus (VZV), classified as a neurotropic member of the Herpesviridae family, exhibits a characteristic pathogenicity, predominantly inducing varicella, commonly known as chickenpox, during the initial infectious phase, and triggering the reactivation of herpes zoster, more commonly recognized as shingles, following its emergence from a latent state. The pathogenesis of VZV-associated neuroinflammation involves a complex interplay between viral replication within sensory ganglia and immune-mediated responses that contribute to tissue damage and dysfunction. Upon primary infection, VZV gains access to sensory ganglia, establishing latent infection within neurons. During reactivation, the virus can spread along sensory nerves, triggering a cascade of inflammatory mediators, chemokines, and immune cell infiltration in the affected neural tissues. The role of both adaptive and innate immune reactions, including the contributions of T and B cells, macrophages, and dendritic cells, in orchestrating the immune-mediated damage in the central nervous system is elucidated. Furthermore, the aberrant activation of the natural defence mechanism, characterised by the dysregulated production of immunomodulatory proteins and chemokines, has been implicated in the pathogenesis of VZV-induced neurological disorders, such as encephalitis, myelitis, and vasculopathy. The intricate balance between protective and detrimental immune responses in the context of VZV infection emphasises the necessity for an exhaustive comprehension of the immunopathogenic mechanisms propelling neuroinflammatory processes. Despite the availability of vaccines and antiviral therapies, VZV-related neurological complications remain a significant concern, particularly in immunocompromised individuals and the elderly. Elucidating these mechanisms might facilitate the emergence of innovative immunomodulatory strategies and targeted therapies aimed at mitigating VZV-induced neuroinflammatory damage and improving clinical outcomes. This comprehensive understanding enhances our grasp of viral pathogenesis and holds promise for pioneering therapeutic strategies designed to mitigate the neurological ramifications of VZV infections.

Contribution of Cross-Phase-Amplitude Coupling to Relapse in Infantile Epileptic Spasms Syndrome

Relapse poses a significant challenge after the initial effectiveness of adrenocorticotropic hormone treatment in infantile epileptic spasms syndrome (IESS), involving complex neuronal oscillations interactions. Phase-amplitude coupling (PAC) extensively characterizes mutual interactions among rhythmic activities in pathological conditions. However, the relapse-related PACs between brain regions across various frequencies remain unclear. Here, noise-assisted multivariate empirical mode decomposition based cross-PAC was utilized to evaluate the long-term prognosis of IESS. Our results revealed that stronger cross-PACs at relatively lower frequencies in the relapse group compared to the nonrelapse group, while the trend was reversed for higher frequency cross-PACs. The hub regions within functional networks were identified as frontal and occipital lobes. Combining cross-PAC with the burden of amplitudes and epileptiform discharges score demonstrated optimal performance in identifying relapse in IESS. We suggested the significance of cross-PAC in assessing the outcome of IESS.

Global prevalence and incidence of Young Onset Parkinson's disease: A systematic review and meta-analysis

BACKGROUND

There is a lack of enough evidence regarding the epidemiology of Young-onset Parkinson's disease (YOPD) which is needed by clinicians and healthcare policymakers.

AIM

Herein, in this systematic review and meta-analysis, we aimed to estimate the global prevalence and incidence rates of YOPD.

METHODS

We searched the literature in PubMed, Scopus, and Web of Science in May 2022. We included retrospective, prospective, cross-sectional observational population-based studies that reported the prevalence or incidence of PD in individuals younger than 40 years with known diagnostic criteria.

RESULTS

After two-step screening, 50 studies were eligible to be included in our study. The age-standardized prevalence of YOPD was 10.2 per 100,000 persons globally while it was 14.7 per 100,000 population in European countries. Age-standardized prevalence estimates for 5-year age bands showed that the YOPD prevalence estimates varied from 6.1 per 100,000 population in the group aged 20-24 to 16.1 per 100,000 population in the group aged 35-39. Also, the age-standardized incidence of YOPD was 1.3 per 100,000 person-years population worldwide and 1.2 per 100,000 person-years in the European population.

CONCLUSION

Based on this systematic review and meta-analysis, the overall prevalence of YOPD is 10.2 per 100,000 population, although estimates of the prevalence and incidence in low-income countries remain scarce. To improve monitoring and certain diagnoses of YOPD, healthcare providers and policymakers should be aware that much more effective tools are required.

Benefits From Different Modes of Slow and Deep Breathing on Vagal Modulation

Slow and deep breathing (SDB) is a relaxation technique that can increase vagal activity. Respiratory sinus arrhythmia (RSA) serves as an index of vagal function usually quantified by the high-frequency power of heart rate variability (HRV). However, the low breathing rate during SDB results in deviations when estimating RSA by HRV. Besides, the impact of the inspiration-expiration (I: E) ratio and guidelines ways (fixed breathing rate or intelligent guidance) on SDB is not yet clear. In our study, 30 healthy people (mean age = 26.5 years, 17 females) participated in three SDB modes, including 6 breaths per minute (bpm) with an I:E ratio of 1:1/ 1:2, and intelligent guidance mode (I:E ratio of 1:2 with guiding to gradually lower breathing rate to 6 bpm). Parameters derived from HRV, multimodal coupling analysis (MMCA), Poincaré plot, and detrended fluctuation analysis were introduced to examine the effects of SDB exercises. Besides, multiple machine learning methods were applied to classify breathing patterns (spontaneous breathing vs. SDB) after feature selection by max-relevance and min-redundancy. All vagal-activity markers, especially MMCA-derived RSA, statistically increased during SDB. Among all SDB modes, breathing at 6 bpm with a 1:1 I:E ratio activated the vagal function the most statistically, while the intelligent guidance mode had more indicators that still significantly increased after training, including SDRR and MMCA-derived RSA, etc. About the classification of breathing patterns, the Naive Bayes classifier has the highest accuracy (92.2%) with input features including LFn, CPercent, pNN50, [Formula: see text], SDRatio, [Formula: see text], and LF. Our study proposed a system that can be applied to medical devices for automatic SDB identification and real-time feedback on the training effect. We demonstrated that breathing at 6 bpm with an I:E ratio of 1:1 performed best during the training phase, while intelligent guidance mode had a more long-lasting effect.

Fast Fractional Fourier Transform-Aided Novel Graphical Approach for EEG Alcoholism Detection

Given its detrimental effect on the brain, alcoholism is a severe disorder that can produce a variety of cognitive, emotional, and behavioral issues. Alcoholism is typically diagnosed using the CAGE assessment approach, which has drawbacks such as being lengthy, prone to mistakes, and biased. To overcome these issues, this paper introduces a novel paradigm for identifying alcoholism by employing electroencephalogram (EEG) signals. The proposed framework is divided into various steps. To begin, interference and artifacts in the EEG data are removed using a multiscale principal component analysis procedure. This cleaning procedure contributes to information quality improvement. Second, an innovative graphical technique based on fast fractional Fourier transform coefficients is devised to visualize the chaotic character and complexities of the EEG signals. This elucidates the properties of regular and alcoholic EEG signals. Third, thirty-four graphical features are extracted to interpret the EEG signals' haphazard behavior and differentiate between regular and alcoholic trends. Fourth, we propose an ensembled feature selection method for obtaining an effective and reliable feature group. Following that, we study many neural network classifiers to choose the optimal classifier for building an efficient framework. The experimental findings show that the suggested method obtains the best classification performance by employing a recurrent neural network (RNN), with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the sixteen selected features. The proposed framework can aid physicians, businesses, and product designers to develop a real-time system.

Auditory cues modulate the short timescale dynamics of STN activity during stepping in Parkinson's disease

BACKGROUND

Gait impairment has a major impact on quality of life in patients with Parkinson's disease (PD). It is believed that basal ganglia oscillatory activity at β frequencies (15-30 Hz) may contribute to gait impairment, but the precise dynamics of this oscillatory activity during gait remain unclear. Additionally, auditory cues are known to lead to improvements in gait kinematics in PD. If the neurophysiological mechanisms of this cueing effect were better understood they could be leveraged to treat gait impairments using adaptive Deep Brain Stimulation (aDBS) technologies.

OBJECTIVE

We aimed to characterize the dynamics of subthalamic nucleus (STN) oscillatory activity during stepping movements in PD and to establish the neurophysiological mechanisms by which auditory cues modulate gait.

METHODS

We studied STN local field potentials (LFPs) in eight PD patients while they performed stepping movements. Hidden Markov Models (HMMs) were used to discover transient states of spectral activity that occurred during stepping with and without auditory cues.

RESULTS

The occurrence of low and high β bursts was suppressed during and after auditory cues. This manifested as a decrease in their fractional occupancy and state lifetimes. Interestingly, α transients showed the opposite effect, with fractional occupancy and state lifetimes increasing during and after auditory cues.

CONCLUSIONS

We show that STN oscillatory activity in the α and β frequency bands are differentially modulated by gait-promoting oscillatory cues. These findings suggest that the enhancement of α rhythms may be an approach for ameliorating gait impairments in PD.

Autophagy-associated non-coding RNAs: Unraveling their impact on Parkinson's disease pathogenesis

BACKGROUND

Parkinson's disease (PD) is a degenerative neurological condition marked by the gradual loss of dopaminergic neurons in the substantia nigra pars compacta. The precise etiology of PD remains unclear, but emerging evidence suggests a significant role for disrupted autophagy-a crucial cellular process for maintaining protein and organelle integrity.

METHODS

This review focuses on the role of non-coding RNAs (ncRNAs) in modulating autophagy in PD. We conducted a comprehensive review of recent studies to explore how ncRNAs influence autophagy and contribute to PD pathophysiology. Special attention was given to the examination of ncRNAs' regulatory impacts in various PD models and patient samples.

RESULTS

Findings reveal that ncRNAs are pivotal in regulating key processes associated with PD progression, including autophagy, α-synuclein aggregation, mitochondrial dysfunction, and neuroinflammation. Dysregulation of specific ncRNAs appears to be closely linked to these pathogenic processes.

CONCLUSION

ncRNAs hold significant therapeutic potential for addressing autophagy-related mechanisms in PD. The review highlights innovative therapeutic strategies targeting autophagy-related ncRNAs and discusses the challenges and prospective directions for developing ncRNA-based therapies in clinical practice. The insights from this study underline the importance of ncRNAs in the molecular landscape of PD and their potential in novel treatment approaches.

Spatiotemporal convolution sleep network based on graph attention mechanism with automatic feature extraction

BACKGROUND AND OBJECTIVE

Graph neural networks (GNNs) are widely used for automatic sleep staging. However, the majority of GNNs are based on spectral approaches, as far as we know, which heavily depend on the Laplacian eigenbasis determined by the graph structure with a large computing cost.

METHODS

We introduced a non-spectral approach named graph attention networks v2 (GATv2) as the core of our network to extract spatial information (S-GATv2 in our work), which is more flexible and intuitive than the routined spectral method. Meanwhile, to resolve the issue of weak generalization of using traditional feature extraction, the multi-convolutional layers are implemented to automatically extract features. In this work, the proposed spatiotemporal convolution sleep network (ST-GATv2) consists of multi-convolution layers and a GATv2 block. Of note, the graph attention technique to the time domain was applied to construct temporal GATv2 (T-GATv2), which intends to capture the connection between two channels in the adjacent sleep stages. Besides, the modified function is further proposed to capture the hidden changing trend information by the difference in the feature's value of the two adjacent stages.

RESULTS

In our experiment, we used the SS3 datasets in the MASS as our test datasets to compare with other advanced models. Our result reveals our model achieves the highest accuracy at 89.0 %. Besides, the proposed T-GATv2 block and modified function bring an approximate 0.5 % improvement in Kappa and F1-score.

CONCLUSIONS

Our results support the potential of graph attention mechanisms and creative blocks (T-GATv2 and modified function) in sleep classification. We suggest the proposed ST-GATv2 model as an effective tool in sleep staging in either healthy or diseased states.

Quantifying Sharpness and Nonlinearity in Neonatal Seizure Dynamics

The integration of multiple electrophysiological biomarkers is crucial for monitoring neonatal seizure dynamics. The present study aimed to characterize the temporal dynamics of neonatal seizures by analyzing intrinsic waveforms of epileptic electroencephalogram (EEG) signals. We proposed a complementary set of methods considering envelope power, focal sharpness changes, and nonlinear patterns of EEG signals of 79 neonates with seizures. Features derived from EEG signals were used as input to the machine learning classifier. All three characteristics were significantly elevated during seizure events, as agreed upon by all viewers (P < 0.0001). Envelope power was elevated in the entire seizure period, and the degree of nonlinearity rose at the termination of a seizure event. Epileptic sharpness effectively characterizes an entire seizure event, complementing the role of envelope power in identifying its onset. However, the degree of nonlinearity showed superior discriminability for the termination of a seizure event. The proposed computational methods for intrinsic sharp or nonlinear EEG patterns evolving during neonatal seizure could share some features with envelope power. Current findings may be helpful in developing strategies to improve neonatal seizure monitoring.

Recent Advancements and SAR Studies of Synthetic Coumarins as MAO-B Inhibitors: An Updated Review

BACKGROUND

The oxidative deamination of a wide range of endogenous and exogenous amines is catalyzed by a family of enzymes known as monoamine oxidases (MAOs), which are reliant on flavin-adenine dinucleotides. Numerous neurological conditions, such as Parkinson's disease (PD) and Alzheimer's disease (AD), are significantly correlated with changes in the amounts of biogenic amines in the brain caused by MAO. Hydrogen peroxide, reactive oxygen species, and ammonia, among other toxic consequences of this oxidative breakdown, can harm brain cells' mitochondria and cause oxidative damage.

OBJECTIVE

The prime objective of this review article was to highlight and conclude the recent advancements in structure-activity relationships of synthetic derivatives of coumarins for MAO-B inhibition, published in the last five years' research articles.

METHODS

The literature (between 2019 and 2023) was searched from platforms like Science Direct, Google Scholar, PubMed, etc. After going through the literature, we have found a number of coumarin derivatives being synthesized by researchers for the inhibition of MAO-B for the management of diseases associated with the enzyme such as Alzheimer's Disease and Parkinson's Disease. The effect of these coumarin derivatives on the enzyme depends on the substitutions associated with the structure. The structure-activity relationships of the synthetic coumarin derivatives that are popular nowadays have been described and summarized in the current study.

RESULTS

The results revealed the updated review on SAR studies of synthetic coumarins as MAO-B inhibitors, specifically for Alzheimer's Disease and Parkinson's Disease. The patents reported on coumarin derivatives as MAO-B inhibitors were also highlighted.

CONCLUSION

Recently, coumarins, a large class of chemicals with both natural and synthetic sources, have drawn a lot of attention because of the vast range of biological actions they have that are linked to neurological problems. Numerous studies have demonstrated that chemically produced and naturally occurring coumarin analogs both exhibited strong MAO-B inhibitory action. Coumarins bind to MAO-B reversibly thereby preventing the breakdown of neurotransmitters like dopamine leading to the inhibition of the enzyme A number of MAO-B blockers have been proven to be efficient therapies for treating neurological diseases like Alzheimer's Disease and Parkinson's Disease. To combat these illnesses, there is still an urgent need to find effective treatment compounds.

Unveiling the Multifaceted Problems Associated with Dysrhythmia

Dysrhythmia is a term referring to the occurrence of spontaneous and repetitive changes in potentials with parameters deviating from those considered normal. The term refers to heart anomalies but has a broader meaning. Dysrhythmias may concern the heart, neurological system, digestive system, and sensory organs. Ion currents conducted through ion channels are a universal phenomenon. The occurrence of channel abnormalities will therefore result in disorders with clinical manifestations depending on the affected tissue, but phenomena from other tissues and organs may also manifest themselves. A similar problem concerns the implementation of pharmacotherapy, the mechanism of which is related to the impact on various ion currents. Treatment in this case may cause unfavorable effects on other tissues and organs. Drugs acting through the modulation of ion currents are characterized by relatively low tissue specificity. To assess a therapy's efficacy and safety, the risk of occurrences in other tissues with similar mechanisms of action must be considered. In the present review, the focus is shifted prominently onto a comparison of abnormal electrical activity within different tissues and organs. This review includes an overview of the types of dysrhythmias and the basic techniques of clinical examination of electrophysiological disorders. It also presents a concise overview of the available pharmacotherapy in particular diseases. In addition, the authors review the relevant ion channels and their research technique based on patch clumping.

A novel dynamic cardiorespiratory coupling quantification method reveals the effect of aging on the autonomic nervous system

Traditional cardiopulmonary coupling (CPC) based on the Fourier transform shares an inherent trade-off between temporal and frequency resolutions with fixed window designs. Therefore, a cross-wavelet cardiorespiratory coupling (CRC) method was developed to highlight interwave cardiorespiratory dynamics and applied to evaluate the age effect on the autonomic regulation of cardiorespiratory function. The cross-wavelet CRC visualization successfully reflected dynamic alignments between R-wave interval signal (RR intervals) and respiration. Strong and continuous CRC was shown if there was perfect temporal coordination between consecutive R waves and respiration, while CRC becomes weaker and intermittent without such coordination. Using real data collected on electrocardiogram (ECG) and respiratory signals, the heart rate variability (HRV) and CRC were calculated. Subsequently, comparisons were conducted between young and elderly individuals. Young individuals had significantly higher partial time and frequency HRV indices than elderly individuals, indicating stronger control of parasympathetic regulation. The overall coupling strength of the CRC of young individuals was higher than that of elderly individuals, especially in high-frequency power, which was significantly lower in the elderly group than in the young group, achieving better results than the HRV indices in terms of statistical significance. Further analyses of the time-frequency dynamics of CRC indices revealed that the coupling strength was consistently higher in the high-frequency (HF) band (0.15-0.4 Hz) in young participants compared to elderly individuals. The dynamic CRC between respiration and HRV indices was accessible by integrating the cross-wavelet spectrum and coherence. Young participants had a significantly higher level of CRC in the HF band, indicating that aging reduces vagus nerve modulation.

A Novel Sleep Staging Method Based on EEG and ECG Multimodal Features Combination

Accurate sleep staging evaluates the quality of sleep, supporting the clinical diagnosis and intervention of sleep disorders and related diseases. Although previous attempts to classify sleep stages have achieved high classification performance, little attention has been paid to integrating the rich information in brain and heart dynamics during sleep for sleep staging. In this study, we propose a generalized EEG and ECG multimodal feature combination to classify sleep stages with high efficiency and accuracy. Briefly, a hybrid features combination in terms of multiscale entropy and intrinsic mode function are used to reflect nonlinear dynamics in multichannel EEGs, along with heart rate variability measures over time/frequency domains, and sample entropy across scales are applied for ECGs. For both the max-relevance and min-redundancy method and principal component analysis were used for dimensionality reduction. The selected features were classified by four traditional machine learning classifiers. Macro-F1 score, macro-geometric mean, and Cohen kappa value are adopted to evaluate the classification performance of each class in an imbalanced dataset. Experimental results show that EEG features contribute more to wake stage classification while ECG features contribute more to deep sleep stages. The proposed combination achieves the highest accuracy of 84.3% and the highest kappa value of 0.794 on the support vector machine in the ISRUC-S3 dataset, suggesting the proposed multimodal features combination is promising in accuracy and efficiency compared to other state-of-the-art methods.