Electrophysiological Biomarkers Research Articles

Therapeutic Outcomes and Electrophysiological Biomarkers in Anti-Myelin-Associated Glycoprotein Neuropathy: A Multicenter Cohort Study in South Korea.

Unlike other immune-mediated neuropathies, anti-myelin-associated glycoprotein (MAG) neuropathy is often refractory to immunotherapy. It is necessary to compare the relative efficacies of various immunotherapies and develop objective biomarkers in order to optimize its clinical management. This study recruited 91 patients with high anti-MAG antibody titers from 7 tertiary hospitals in South Korea. We analyzed the baseline characteristics, therapeutic outcomes, and nerve conduction study (NCS) findings of 68 patients and excluded 23 false positive cases. The rate of positive responses to treatment was highest using zanubrutinib (50%) and rituximab (36.4%), followed by corticosteroids (16.7%), immunosuppressants (9.5%), intravenous immunoglobulin (5%), and plasma exchange (0%). Disability and weakness were significantly associated with multiple NCS parameters at the time of diagnosis, especially distal compound muscle action potential (CMAP) amplitudes. Moreover, the longitudinal trajectory of the average CMAP amplitudes paralleled the clinical courses, with a 16.2 percentile decrease as an optimal cutoff for predicting a clinical exacerbation (area under the receiver operating characteristic curve=0.792). Our study supports the use of NCS as an objective marker for estimating disease burden and tracking clinical changes in patients with anti-MAG neuropathy. We have described the beneficial effects of rituximab and a new drug, zanubrutinib, compared with conventional immunotherapies.

Immediate and long-term electrophysiological biomarkers of antidepressant-like behavioral effects after subanesthetic ketamine and medial prefrontal cortex deep brain stimulation treatment.

Both ketamine (KET) and medial prefrontal cortex (mPFC) deep brain stimulation (DBS) are emerging therapies for treatment-resistant depression, yet our understanding of their electrophysiological mechanisms and biomarkers is incomplete. This study investigates aperiodic and periodic spectral parameters, and the signal complexity measure sample entropy, within mPFC local field potentials (LFP) in a chronic corticosterone (CORT) depression model after ketamine and/or mPFC DBS. Male rats were intraperitoneally administered CORT or vehicle for 21 days. Over the last 7 days, animals receiving CORT were treated with mPFC DBS, KET, both, or neither; then tested across an array of behavioral tasks for 9 days. We found that the depression-like behavioral and weight effects of CORT correlated with a decrease in aperiodic-adjusted theta power (5-10 Hz) and an increase in sample entropy during the administration phase, and an increase in theta peak frequency and a decrease in the aperiodic exponent once the depression-like phenotype had been induced. The remission-like behavioral effects of ketamine alone correlated with a post-treatment increase in the offset and exponent, and decrease in sample entropy, both immediately and up to eight days post-treatment. The remission-like behavioral effects of mPFC DBS alone correlated with an immediate decrease in sample entropy, an immediate and sustained increase in low gamma (20-50 Hz) peak width and aperiodic offset, and sustained improvements in cognitive function. Failure to fully induce remission-like behavior in the combinatorial treatment group correlated with a failure to suppress an increase in sample entropy immediately after treatment. Our findings therefore support the potential of periodic theta parameters as biomarkers of depression-severity; and periodic low gamma parameters and cognitive measures as biomarkers of mPFC DBS treatment efficacy. They also support sample entropy and the aperiodic spectral parameters as potential cross-modal biomarkers of depression severity and the therapeutic efficacy of mPFC DBS and/or ketamine. Study of these biomarkers is important as objective measures of disease severity and predictive measures of therapeutic efficacy can be used to personalize care and promote the translatability of research across studies, modalities, and species.

Changes in Mental Health and EEG Biomarkers of Undergraduates Under Different Patterns of Mindfulness.

The effects of short-term mindfulness are associated with the different patterns (autonomic, audio guided, or experienced and certified mindfulness instructor guided mindfulness). However, robust evidence for reported the impacts of different patterns of mindfulness on mental health and EEG biomarkers of undergraduates is currently lacking. Therefore, we aimed to test the hypotheses that mindfulness training for undergraduates would improve mental health, and increase alpha power over frontal region and theta power over midline region at the single electrode level. We also describe the distinction among frequency bands patterns in different sites of frontal and midline regions. 70 participants were enrolled and assigned to either 5-day mindfulness or a waiting list group. Subjective questionnaires measured mental health and other psychological indicators, and brain activity was recorded during various EEG tasks before and after the intervention. The 5-day mindfulness training improved trait mindfulness, especially observing (p = 0.001, d = 0.96) and nonreactivity (p = 0.03, d = 0.56), sleep quality (p = 0.001, d = 0.91), and social support (p = 0.001, d = 0.95) while not in affect. Meanwhile, the expected increase in the alpha power of frontal sites (p < 0.017, d > 0.84) at the single electrode level was confirmed by the current data rather than the theta. Interestingly, the alteration of low-beta power over the single electrode of the midline (p < 0.05, d > 0.71) was difference between groups. Short-term mindfulness improves practitioners' mental health, and the potentially electrophysiological biomarkers of mindfulness on neuron oscillations were alpha activity over frontal sites and low-beta activity over midline sites.

The prioritization of biomarkers in the diagnostic workup of Lewy body disease

AbstractThe term Lewy body disease (LBD) encompasses a spectrum of neurodegenerative conditions, particularly Parkinson’s disease (PD) and Dementia with Lewy bodies (DLB), associated with abnormal deposits of alpha‐synuclein in the brain and representing up to 20% of all neurocognitive disorders in memory clinics. Mild cognitive impairment or mild dementia precede or concur with Parkinsonism in the early stages of DLB and in a third of patients with PD at diagnosis. However, when Parkinsonism is slightly detectable, the differential diagnosis from other dementing conditions, such as Alzheimer’s disease (AD), may be challenging. For this reason, neuroimaging and electrophysiological biomarkers, divided into indicative or supportive based on their diagnostic performance over or below 80%, assist the clinical reasoning to increase the likelihood that MCI/early dementia is associated with LB. The European Intersociety Consensus Diagnostic Task Force recommends prioritizing the Dopamine transporter (DaT)‐SPECT imaging to investigate nigro‐striatal degeneration as the first biomarker (sensitivity 76%, specificity 88%), and suggests subsequently [123I]‐MIBG cardiac scintigraphy to evaluate the postganglionic sympathetic denervation in those cases (about a quarter) where DaT‐SPECT is negative but DLB is still clinically possible (AD pathology is estimated to be underlying in about 86% of these cases). In the absence of well‐validated techniques to establish the presence of brain alpha‐synuclein aggregates in vivo, the proposed sequence of biomarkers will provide the most accurate and cost‐effective biomarker‐based diagnosis of LBD.

Assessment of Motor Unit Potentials Duration as the Biomarker of DT-DEC01 Cell Therapy Efficacy in Duchenne Muscular Dystrophy Patients up to 12 Months After Systemic–Intraosseous Administration

Duchenne muscular dystrophy (DMD) is a lethal X-linked disease caused by mutations in the dystrophin gene, leading to muscle degeneration and wasting. Electromyography (EMG) is an objective electrophysiological biomarker of muscle fiber function in muscular dystrophies. A novel, DT-DEC01 therapy, consisting of Dystrophin Expressing Chimeric (DEC) cells created by fusing allogeneic myoblasts from normal donors with autologous myoblasts from DMD-affected patients, was assessed for safety and preliminary efficacy in boys of age 6–15 years old (n = 3). Assessments included EMG testing of selected muscles of upper (deltoideus, biceps brachii) and lower (rectus femoris and gastrocnemius) extremities at the screening visit and at 3, 6, and 12 months following systemic–intraosseous administration of a single low dose of DT-DEC01 therapy (Bioethics Committee approval no. 46/2019). No immunosuppression was administered. Safety of DT-DEC01 was confirmed by the lack of therapy-related Adverse Events or Serious Adverse Events up to 22 months following DT-DEC01 administration. EMG of selected muscles of both, ambulatory and non-ambulatory patients confirmed preliminary efficacy of DT-DEC01 therapy by an increase in motor unit potentials (MUP) duration, amplitudes, and polyphasic MUPs at 12 months. This study confirmed EMG as a reliable and objective biomarker of functional assessment in DMD patients after intraosseous administration of the novel DT-DEC01 therapy.

NCOG-21. TESTING AND VALIDATION OF A DIFFUSE GLIOMA COGNITIVE RESERVE MODEL

Abstract Behavioral and cognitive symptom severity varies widely across patients with glioma because of unknown mechanisms. The concept of cognitive reserve (CR) recognizes that there are individual differences in each person’s brain reserve capacity and this theory accounts for the common disconnection between the degree of brain injury and its symptom manifestations. CR is a fixed variable throughout an individual’s life and works by recruitment of dormant yet redundant neural networks, and by efficient information modulation. New strategies to treat cognitive impairments have been encumbered by a lack of models that accurately reflect human disease. Here, we measure the Cognitive Reserve Index (CRI) in a registry of 450 patients with WHO grade 2-4 glioma. Transient behavioral impairments were induced by anesthetic medications propofol, dexmedetomidine, and remifentanil during standard of clinical care. We administered and analyzed 48 picture naming (PN) and 32 auditory stimulus naming (AN) trials per patient both before and after drug administration with impairments defined as a decline in task performance by more than one standard deviation from the mean. We discovered that the CRI in patients with glioma follows a normal distribution, like the general US population. Next, we found a significant decline in task performance in a subgroup of patients in both PN and AN. Electrode arrays in vivo to record human local field potentials from glioma infiltrated cortex demonstrated electrophysiological biomarkers of cognitive decline. Further elucidation and validation of CR measures in patients may significantly influence the delivery and timing of cancer directed therapies.

NCOG-15. COMPARATIVE ANALYSIS OF GLIOMA INDUCED IMPAIRMENT MODELS

Abstract Speech is produced by the coordination of neural networks involved in language processing and motor control of vocal outputs, both of which can be disrupted by diffuse gliomas. Natural language is the instinctual written or spoken language profile that an individual has developed throughout his or her lifetime. Recent psycholinguistic analyses suggest that patterns of natural language may be an accurate measure for assessing clinical and behavioral conditions. One important feature of natural language is speech onset. Speech delays are common complaints among patients with glioma. Here, we assess the relationship between speech onset and histologic-molecular features of glioma. Speech onset was defined as acoustic measure of the interval of time between the release of the oral constriction in a stop consonant and the onset of subsequent voicing. Audio samples and video analysis of oral kinetics were analyzed from patients with newly diagnosed low- and high-grade glioma. Features were extracted by manual segmentation and aligned for each trial with high-density electrode arrays to record task specific responses from gliomas projecting to the cortical surface. A total of 1,120 stimuli (672 = picture naming (PN) and 448 auditory naming (AN) trials were presented. As expected, patients with glioblastoma had higher picture and auditory naming error rates (p &amp;lt; 0.001, Mann-Whitney U test) compared with diffuse low-grade gliomas. Assuming speech errors produce slower response times, we next measured only correct behavioral responses which demonstrated 7% slower speech initiation responses (p &amp;lt; 0.0001, Mann-Whitney U test) when comparing patients with glioblastoma and diffuse low-grade gliomas. Slowed speech responses did not require glioma infiltration into canonical cortical speech areas of the lateral prefrontal cortex and superior temporal gyrus. Finally, we identified electrophysiological biomarkers of speech errors. These results deepen our understanding of speech processing impairments, which are not based purely on coding errors.

Comparing electrophysiological biomarkers and circulating cardiac biomarkers in predicting cardiovascular mortality and all-cause mortality in the akershus sleep apnea (ASAP) epidemiological cohort

Abstract Background Major cardiovascular events (MACE) and mortality in obstructive sleep apnea (OSA) patients have been frequently reported. However, there is an ongoing debate regarding biomarkers predicting MACE and mortality in OSA. The Apnoea Hypopnea Index (AHI) has been used to assess the severity of Obstructive Sleep Apnea (OSA) despite poor association with adverse outcomes in OSA patients. Novel electrophysiological biomarkers (EPBs) such as desaturation parameters have been suggested as an alternative, and have been reported to be stronger predictors of cardiovascular mortality and heart failure. However, circulating cardiac biomarkers (CCBs) remain strong predictors of mortality and cardiovascular disease in the general population. Purpose To detect the incidence of MACE and all-cause mortality after a median of 13.8 years follow-up in individuals with OSA, and to assess the ability of CCBs and EPBs to predict MACE and all-cause mortality. Methods Polysomnography and CCB analyses (cardiac troponin I (cTnI) and T (cTnT)) were conducted in 459 participants in the Akershus Sleep APnea study (ASAP) between 2006-2008. EPBs evaluated were the AHI, oxygen desaturation index (ODI), desaturation duration (DesDur) and desaturation severity (DesSev). Cox regression and cumulative incidence Kaplan-Meier curves were used for survival analysis. Comparison of EPBs and CCBs in predicting MACE and all-cause mortality was assessed by the area under the curve (AUC). Results 77 and 35 participants reached the endpoint of MACE and all-cause mortality, respectively. In the adjusted cox regression model, cTnI and cTnT were significantly associated with MACE with a hazard ratios of 1.84 [95% Confidence interval [CI], 1.43, 2.37] and 1.52 [1.05, 2.20], respectively. Association of these CCBs with all-cause mortality was with a hazard ratios of 2.02 [1.38, 2.96] and 1.75 [1.05, 2.92], respectively. Moreover, cTnI had the highest AUC of 0.77 and 0.75 in predicting MACE and all-cause mortality. Notably, high AHI, ODI, DesDur and DesSev (upper quartile) increased the cumulative incidence of MACE and all-cause mortality in the subgroup of patients with high CCBs (upper quartile) compared to the subgroup of patients with high CCBs and low EPBs (lower quartile). Conclusion CCBs were more associated with all-cause mortality and MACE than EPBs in ASAP epidemiological cohort. However, the tested EPBs are important biomarkers that increase CCBs prediction of both MACE and all-cause mortality.

Oscillatory network markers of subcallosal cingulate deep brain stimulation for depression

Identifying functional biomarkers related to treatment success can aid in expediting therapy optimization, as well as contribute to a better understanding of the neural mechanisms of the treatment-resistant depression (TRD) and subcallosal cingulate deep brain stimulation (SCC-DBS).Magnetoencephalography data were obtained from 16 individuals with SCC-DBS for TRD and 25 healthy subjects. The first objective of the study was to identify region-specific oscillatory modulations that both (i) discriminate individuals with TRD (with SCC-DBS OFF) from healthy controls, and (ii) discriminate TRD treatment responders from non-responders (with SCC-DBS ON). The second objective of this work was to further explore the effects of stimulation intensity and frequency on oscillatory activity in the identified brain regions of interest.Oscillatory power analyses led to the identification of brain regions that differentiated responders from non-responders based on modulations of increased alpha (8–12 Hz) and decreased gamma (32–116 Hz) power within nodes of the default mode, central executive, and somatomotor networks, Broca's area, and lingual gyrus. Within these nodes, it was also found that low stimulation frequency had stronger effects on oscillatory modulation than increased stimulation intensity.The identified functional network biomarkers implicate modulation of TRD-related activity in brain regions involved in emotional control/processing, motor control, and the interaction between speech, vision, and memory, which have all been implicated in depression. These electrophysiological biomarkers have the potential to be used as functional proxies for therapy optimization. Additional stimulation parameter analyses revealed that oscillatory modulations can be strengthened by increasing stimulation intensity or reducing frequency, which may represent potential avenues of direction in non-responders.

Changes in EEG alpha-band power during prehension indicates neural motor drive inhibition.

Changes in alpha band activity (8-12 Hz) indicate the downregulation of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the object's weight or surface friction are introduced. The results suggest that alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas) but not in error processing areas (i.e., frontoparietal network) during unexpected weight changes. This suggests that oscillations over motor areas reflect the reduction of motor drive related to motor error correction, thus, being a potential cortical electrophysiological biomarker for the process and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces.NEW & NOTEWORTHY We demonstrated that alpha suppression, an EEG phenomenon with high temporal resolution, occurs over the primary sensorimotor area during error correction during lift movements. Interpretations of alpha activity are often attributed to high cognitive demands, thus recognizing that it is also influenced by motor processes is important in situations where cognitive demands are paired with movement errors. This could further have application as a biomarker for error correction in human-machine interfaces, such as neuroprostheses.

CORRELATION BETWEEN SUBTHALAMIC ALPHA RHYTHMIC ACTIVITY AND MOTOR CONTROL IMPAIRMENT IN PARKINSONIAN PATIENTS

Increased oscillatory beta activity in basal ganglia is one of the main electrophysiological biomarkers of impaired motor control in Parkinson’s disease. However, the functional role of dynamical rhythmic processes during movements is still not investigated in detail. We used microelectrode recordings to investigate the subthalamic nucleus neuronal activity in 9 patients with Parkinson’s disease during deep brain stimulation surgery. We have shown that the amplitude of the subthalamic nucleus alpha (7–12 Hz) and low-beta (12–20 Hz) activity correlated with motor impairment indices in patients with Parkinson’s disease. Also, we have shown that, in contrast to beta rhythmic activity, the magnitude of alpha activity decreased significantly during motor tests execution. Moreover, the degree of rhythmic activity suppression in the alpha band correlated with the severity of bradykinesia. These results allow us to suppose that, along with beta activities, subthalamic nucleus neuronal alpha activity also participate in motor control of Parkinson’s disease patients.

Local Field Potentials Predict Motor Performance in Deep Brain Stimulation for Parkinson's Disease.

Deep brain stimulation (DBS) is an effective treatment option for patients with Parkinson's disease (PD). However, clinical programming remains challenging with segmented electrodes. Using novel sensing-enabled neurostimulators, we investigated local field potentials (LFPs) and their modulation by DBS to assess whether electrophysiological biomarkers may facilitate clinical programming in chronically implanted patients. Sixteen patients (31 hemispheres) with PD implanted with segmented electrodes in the subthalamic nucleus and a sensing-enabled neurostimulator were included in this study. Recordings were conducted 3 months after DBS surgery following overnight withdrawal of dopaminergic medication. LFPs were acquired while stimulation was turned OFF and during a monopolar review of both directional and ring contacts. Directional beta power and stimulation-induced beta power suppression were computed. Motor performance, as assessed by a pronation-supination task, clinical programming and electrode placement were correlated to directional beta power and stimulation-induced beta power suppression. Better motor performance was associated with stronger beta power suppression at higher stimulation amplitudes. Across directional contacts, differences in directional beta power and the extent of stimulation-induced beta power suppression predicted motor performance. However, within individual hemispheres, beta power suppression was superior to directional beta power in selecting the contact with the best motor performance. Contacts clinically activated for chronic stimulation were associated with stronger beta power suppression than non-activated contacts. Our results suggest that stimulation-induced β power suppression is superior to directional β power in selecting the clinically most effective contact. In sum, electrophysiological biomarkers may guide programming of directional DBS systems in PD patients. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness.

Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology. In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC. Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%). Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Electrophysiological insights into deep brain stimulation of the network disorder dystonia.

Deep brain stimulation (DBS), a treatment for modulating the abnormal central neuronal circuitry, has become the standard of care nowadays and is sometimes the only option to reduce symptoms of movement disorders such as dystonia. However, on the one hand, there are still open questions regarding the pathomechanisms of dystonia and, on the other hand, the mechanisms of DBS on neuronal circuitry. That lack of knowledge limits the therapeutic effect and makes it hard to predict the outcome of DBS for individual dystonia patients. Finding electrophysiological biomarkers seems to be a promising option to enable adapted individualised DBS treatment. However, biomarker search studies cannot be conducted on patients on a large scale and experimental approaches with animal models of dystonia are needed. In this review, physiological findings of deep brain stimulation studies in humans and animal models of dystonia are summarised and the current pathophysiological concepts of dystonia are discussed.

Microwave-Based Dielectric Properties as an Electrophysiological Biomarker: Future Perspectives

Electrophysiology is the study of the electrical properties of biological tissues, which involves the movement of ions across cell membranes. The analysis of the movement of electrical charges through the body has a wide range of biomedical applications, such as diagnosing and planning treatment in cardiovascular, nervous systems, muscular, and gastrointestinal disorders. The dielectric properties of biological tissues change according to the water content in the tissue and are measured as permittivity and conductivity relative to the frequency of the electrical field. This principle has been applied in diagnostics and therapeutics using microwave energysuch as imaging and ablation, etc. This review article summarizes the potential use of measuring dielectric properties using microwave imaging and how it can augment electrophysiological studies in medicine.

Initial case series of a novel sensing deep brain stimulation device in drug-resistant epilepsy and consistent identification of alpha/beta oscillatory activity: A feasibility study.

Here, we report a retrospective, single-center experience with a novel deep brain stimulation (DBS) device capable of chronic local field potential (LFP) recording in drug-resistant epilepsy (DRE) and explore potential electrophysiological biomarkers that may aid DBS programming and outcome tracking. Five patients with DRE underwent thalamic DBS, targeting either the bilateral anterior (n = 3) or centromedian (n = 2) nuclei. Postoperative electrode lead localizations were visualized in Lead-DBS software. Local field potentials recorded over 12-18 months were tracked, and changes in power were associated with patient events, medication changes, and stimulation. We utilized a combination of lead localization, in-clinic broadband LFP recordings, real-time LFP response to stimulation, and chronic recordings to guide DBS programming. Four patients (80%) experienced a >50% reduction in seizure frequency, whereas one patient had no significant reduction. Peaks in the alpha and/or beta frequency range were observed in the thalamic LFPs of each patient. Stimulation suppressed these LFP peaks in a dose-dependent manner. Chronic timeline data identified changes in LFP amplitude associated with stimulation, seizure occurrences, and medication changes. We also noticed a circadian pattern of LFP amplitudes in all patients. Button-presses during seizure events via a mobile application served as a digital seizure diary and were associated with elevations in LFP power. We describe an initial cohort of patients with DRE utilizing a novel sensing DBS device to characterize potential LFP biomarkers of epilepsy that may be associated with seizure control after DBS in DRE. We also present a new workflow utilizing the Percept device that may optimize DBS programming using real-time and chronic LFP recording.

RTMS in mental health disorders.

Transcranial magnetic stimulation (TMS) is an innovative and non-invasive technique used in the diagnosis and treatment of psychiatric and neurological disorders. Repetitive TMS (rTMS) can modulate neuronal activity, neuroplasticity and arousal of the waking and sleeping brain, and, more generally, overall mental health. Numerous studies have examined the predictors of the efficacy of rTMS on clinical outcome variables in various psychiatric disorders. These predictors often encompass the stimulated brain region's location, electroencephalogram (EEG) activity patterns, potential morphological and neurophysiological anomalies, and individual patient's response to treatment. Most commonly, rTMS is used in awake patients with depression, catatonia, and tinnitus. Interestingly, rTMS has also shown promise in inducing slow-wave oscillations in insomnia patients, opening avenues for future research into the potential beneficial effects of these oscillations on reports of non-restorative sleep. Furthermore, neurophysiological measures emerge as potential, disease-specific biomarkers, aiding in predicting treatment response and monitoring post-treatment changes. The study posits the convergence of neurophysiological biomarkers and individually tailored rTMS treatments as a gateway to a new era in psychiatric care. The potential of rTMS to induce slow-wave activity also surfaces as a significant contribution to personalized treatment approaches. Further investigations are called for to validate the imaging and electrophysiological biomarkers associated with rTMS. In conclusion, the potential for rTMS to significantly redefine treatment strategies through personalized approaches could enhance the outcomes in neuropsychiatric disorders.

Clustering-Fusion Feature Selection Method in Identifying Major Depressive Disorder Based on Resting State EEG Signals

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

New developments in diagnostics and treatment of adult-onset focal dystonia.

The aim of this review is to showcase the recent developments in the field of diagnosis and treatment of adult-onset focal dystonia. Accurate phenotyping of focal dystonia is essential in the process of finding an underlying cause, including acquired, genetic, and idiopathic causes. Motor symptoms as well as the associated nonmotor symptoms and their detrimental impact on quality of life have received increased interest over the last years. The diagnostic process is complicated by the steadily increasing numbers of newly discovered genes associated with dystonia. Recent efforts have been aimed at further developing recommendations and algorithms to aid in diagnosis and in navigating the use of diagnostic tools. In terms of treatment, research on DBS is advancing towards a better understanding of the most effective stimulation locations within the globus pallidus. Moreover, with the introduction of the LFP-recording devices, the search continues for an accurate electrophysiological biomarker for dystonia. Accurate phenotyping and (sub)classification of patients with dystonia is important for improving diagnosis, subsequent treatment effect and population-based study outcomes in research. Medical practitioners should be attentive to the presence of nonmotor symptoms in dystonia.

Spotlight on Sleep Stage Classification Based on EEG.

The recommendations for identifying sleep stages based on the interpretation of electrophysiological signals (electroencephalography [EEG], electro-oculography [EOG], and electromyography [EMG]), derived from the Rechtschaffen and Kales manual, were published in 2007 at the initiative of the American Academy of Sleep Medicine, and regularly updated over years. They offer an important tool to assess objective markers in different types of sleep/wake subjective complaints. With the aims and advantages of simplicity, reproducibility and standardization of practices in research and, most of all, in sleep medicine, they have overall changed little in the way they describe sleep. However, our knowledge on sleep/wake physiology and sleep disorders has evolved since then. High-density electroencephalography and intracranial electroencephalography studies have highlighted local regulation of sleep mechanisms, with spatio-temporal heterogeneity in vigilance states. Progress in the understanding of sleep disorders has allowed the identification of electrophysiological biomarkers better correlated with clinical symptoms and outcomes than standard sleep parameters. Finally, the huge development of sleep medicine, with a demand for explorations far exceeding the supply, has led to the development of alternative studies, which can be carried out at home, based on a smaller number of electrophysiological signals and on their automatic analysis. In this perspective article, we aim to examine how our description of sleep has been constructed, has evolved, and may still be reshaped in the light of advances in knowledge of sleep physiology and the development of technical recording and analysis tools. After presenting the strengths and limitations of the classification of sleep stages, we propose to challenge the "EEG-EOG-EMG" paradigm by discussing the physiological signals required for sleep stages identification, provide an overview of new tools and automatic analysis methods and propose avenues for the development of new approaches to describe and understand sleep/wake states.