Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness

Characterizing functional connectivity (FC) in the human brain is crucial for understanding and supporting clinical decision making in disorders of consciousness. This study investigates FC using sliding window correlation (SWC) analysis of electroencephalogram (EEG) applied to three connectivity measures: phase-lag index (PLI) and weighted phase-lag index (wPLI), which quantify phase synchronization, and amplitude envelope correlation (AEC), which captures amplitude-based coactivation patterns between pairs of channels. SWC analysis is performed across the five canonical frequency bands (delta, theta, alpha, beta, gamma) of EEG data from four distinct groups: coma, unresponsive wakefulness syndrome, minimally conscious state, and healthy controls. The extracted SWC metrics, mean, reflecting the stability of connectivity, and standard deviation, indicating variability, are analyzed to discern FC differences at the group level. Multiclass classification is attempted using various models of artificial neural networks that include different multilayer perceptrons (MLP), recurrent neural networks, long-short-term memory networks, gated recurrent units, and a hybrid CNN-LSTM model that combines convolutional neural networks (CNN) and long-short-term memory network to validate the discriminative power of these FC features. The results show that MLP model 2 achieves a classification accuracy of 96.3% using AEC features obtained with a window length of 16s, highlighting the effectiveness of AEC. An evaluation of the model performance for different window sizes (16 to 20 s) shows that MLP model 2 consistently achieves high accuracy, ranging from 95.5% to 96.3%, using AEC features. When AEC and wPLI features are combined, the maximum accuracy increases to 96.9% for MLP model 2 and 96.7% for MLP model 3, with a window size of 17 seconds in both cases.

Clinical and neurophysiological predictors of the functional outcome in right-hemisphere stroke

OBJECTIVE

The aim of the present study is to examine the relationship between EEG measures and functional recovery in right-hemisphere stroke patients.

METHODS

Participants with stroke (PS) and neurologically unimpaired controls (UC) were enrolled. At enrolment, all participants were assessed for motor and cognitive functioning with specific scales (motricity index, trunk control test, Level of Cognitive Functioning, and Functional Independence Measure (FIM). Moreover, EEG data were recorded. At discharge, participants were re-tested with the FIM RESULTS: Powers in the delta, theta, alpha, and beta bands and connectivity within the fronto-parietal network were compared between groups. Then, the between-group discriminative EEG measures and the motor/cognitive scales were used to feed a machine learning algorithm to predict FIM scores at discharge and the length of hospitalization (LoH). Higher delta, theta, and beta and impaired connectivity were found in PS compared to UC. Moreover, motor/cognitive functioning, beta power, and fronto-parietal connectivity predicted the FIM score at discharge and the LoH (accuracy=73.2 % and 85.2 % respectively).

CONCLUSIONS

Results show that the integration of motor/cognitive scales and EEG measures can reveal the rehabilitative potentials of PS predicting their functional outcome and LoH.

SIGNIFICANCE

Synergistic clinical and electrophysiological models can support rehabilitative decision-making.

AI and Neurology

BACKGROUND

Artificial Intelligence is influencing medicine on all levels. Neurology, one of the most complex and progressive medical disciplines, is no exception. No longer limited to neuroimaging, where data-driven approaches were initiated, machine and deep learning methodologies are taking neurologic diagnostics, prognostication, predictions, decision making and even therapy to very promising potentials.

MAIN BODY

In this review, the basic principles of different types of Artificial Intelligence and the options to apply them to neurology are summarized. Examples of noteworthy studies on such applications are presented from the fields of acute and intensive care neurology, stroke, epilepsy, and movement disorders. Finally, these potentials are matched with risks and challenges jeopardizing ethics, safety and equality, that need to be heeded by neurologists welcoming Artificial Intelligence to their field of expertise.

CONCLUSION

Artificial intelligence is and will be changing neurology. Studies need to be taken to the prospective level and algorithms undergo federated learning to reach generalizability. Neurologists need to master not only the benefits but also the risks in safety, ethics and equity of such data-driven form of medicine.

Research hotspots and trends in the application of electroencephalography for assessment of disorders of consciousness: a bibliometric analysis

Objective

Disorders of consciousness (DoC) result from severe traumatic brain injury and hypoxia or ischemia of brain tissues, leading to impaired perceptual abilities. Electroencephalography (EEG) is a non-invasive and widely applicable technology used for assessing DoC. We aimed to identify the research hotspots in this field through a systematic analysis.

Methods

Relevant studies published from January 1, 2004 to December 31, 2023 were retrieved from the Web of Science Core Collection database. The data were analyzed and visualized using CiteSpace, VOSviewer, and SCImago Graphica.

Results

In total, 1,639 relevant publications were retrieved. The country with the highest number of publications was the United States, the most productive institution was Harvard University, the journal with the highest output was Clinical Neurophysiology, and the journal with the highest total number of citations was Neurology. The author with the most publications was Steven Laureys and the most common keyword was "vegetative state."

Conclusion

The field is undergoing rapid development, characterized by a proliferation of advanced technologies and an increased emphasis on international collaboration. The document offers an impartial perspective on the advancements of the research study for the benefit of the researchers.

Oxidative and Excitatory Neurotoxic Stresses in CRISPR/Cas9-Induced Kynurenine Aminotransferase Knockout Mice: A Novel Model for Despair-Based Depression and Post-Traumatic Stress Disorder

BACKGROUNDS

Memory and emotion are especially vulnerable to psychiatric disorders such as post-traumatic stress disorder (PTSD), which is linked to disruptions in serotonin (5-HT) metabolism. Over 90% of the 5-HT precursor tryptophan (Trp) is metabolized via the Trp-kynurenine (KYN) metabolic pathway, which generates a variety of bioactive molecules. Dysregulation of KYN metabolism, particularly low levels of kynurenic acid (KYNA), appears to be linked to neuropsychiatric disorders. The majority of KYNA is produced by the aadat (kat2) gene-encoded mitochondrial kynurenine aminotransferase (KAT) isotype 2. Little is known about the consequences of deleting the KYN enzyme gene.

METHODS

In CRISPR/Cas9-induced aadat knockout (kat2-/-) mice, we examined the effects on emotion, memory, motor function, Trp and its metabolite levels, enzyme activities in the plasma and urine of 8-week-old males compared to wild-type mice.

RESULTS

Transgenic mice showed more depressive-like behaviors in the forced swim test, but not in the tail suspension, anxiety, or memory tests. They also had fewer center field and corner entries, shorter walking distances, and fewer jumping counts in the open field test. Plasma metabolite levels are generally consistent with those of urine: antioxidant KYNs, 5-hydroxyindoleacetic acid, and indole-3-acetic acid levels were lower; enzyme activities in KATs, kynureninase, and monoamine oxidase/aldehyde dehydrogenase were lower, but kynurenine 3-monooxygenase was higher; and oxidative stress and excitotoxicity indices were higher. Transgenic mice displayed depression-like behavior in a learned helplessness model, emotional indifference, and motor deficits, coupled with a decrease in KYNA, a shift of Trp metabolism toward the KYN-3-hydroxykynurenine pathway, and a partial decrease in the gut microbial Trp-indole pathway metabolite.

CONCLUSIONS

This is the first evidence that deleting the aadat gene induces depression-like behaviors uniquely linked to experiences of despair, which appear to be associated with excitatory neurotoxic and oxidative stresses. This may lead to the development of a double-hit preclinical model in despair-based depression, a better understanding of these complex conditions, and more effective therapeutic strategies by elucidating the relationship between Trp metabolism and PTSD pathogenesis.

Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks

Introduction

Neurodegenerative diseases, including Parkinson's, Alzheimer's, and epilepsy, pose significant diagnostic and treatment challenges due to their complexity and the gradual degeneration of central nervous system structures. This study introduces a deep learning framework designed to automate neuro-diagnostics, addressing the limitations of current manual interpretation methods, which are often time-consuming and prone to variability.

Methods

We propose a specialized deep convolutional neural network (DCNN) framework aimed at detecting and classifying neurological anomalies in MRI data. Our approach incorporates key preprocessing techniques, such as reducing noise and normalizing image intensity in MRI scans, alongside an optimized model architecture. The model employs Rectified Linear Unit (ReLU) activation functions, the Adam optimizer, and a random search strategy to fine-tune hyper-parameters like learning rate, batch size, and the number of neurons in fully connected layers. To ensure reliability and broad applicability, cross-fold validation was used.

Results and discussion

Our DCNN achieved a remarkable classification accuracy of 98.44%, surpassing well-known models such as ResNet-50 and AlexNet when evaluated on a comprehensive MRI dataset. Moreover, performance metrics such as precision, recall, and F1-score were calculated separately, confirming the robustness and efficiency of our model across various evaluation criteria. Statistical analyses, including ANOVA and t-tests, further validated the significance of the performance improvements observed with our proposed method. This model represents an important step toward creating a fully automated system for diagnosing and planning treatment for neurological diseases. The high accuracy of our framework highlights its potential to improve diagnostic workflows by enabling precise detection, tracking disease progression, and supporting personalized treatment strategies. While the results are promising, further research is necessary to assess how the model performs across different clinical scenarios. Future studies could focus on integrating additional data types, such as longitudinal imaging and multimodal techniques, to further enhance diagnostic accuracy and clinical utility. These findings mark a significant advancement in applying deep learning to neuro-diagnostics, with promising implications for improving patient outcomes and clinical practices.

Artificial intelligence and machine learning in disorders of consciousness

PURPOSE OF REVIEW

As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness following acquired brain damage. We here review recent studies that utilized these techniques to reduce the diagnostic and prognostic uncertainty in disorders of consciousness, and to better characterize patients' response to novel therapeutic interventions.

RECENT FINDINGS

Most papers have focused on differentiating between unresponsive wakefulness syndrome and minimally conscious state, utilizing artificial intelligence to better analyze functional neuroimaging and electroencephalography data. They often proposed new features using conventional machine learning rather than deep learning algorithms. To better predict the outcome of patients with disorders of consciousness, recovery was most often based on the Glasgow Outcome Scale, and traditional machine learning techniques were used in most cases. Machine learning has also been employed to predict the effects of novel therapeutic interventions (e.g., zolpidem and transcranial direct current stimulation).

SUMMARY

Artificial intelligence and machine learning can assist in clinical decision-making, including the diagnosis, prognosis, and therapy for patients with disorders of consciousness. The performance of these models can be expected to be significantly improved by the use of deep learning techniques.

Utility of Electroencephalograms for Enhancing Clinical Care and Rehabilitation of Children with Acquired Brain Injury

The objective of this literature review was to present evidence from recent studies and applications focused on employing electroencephalogram (EEG) monitoring and methodological approaches during the rehabilitation of children with acquired brain injuries and their related effects. We describe acquired brain injury (ABI) as one of the most common reasons for cognitive and motor disabilities in children that significantly impact their safety, independence, and overall quality of life. These disabilities manifest as dysfunctions in cognition, gait, balance, upper-limb coordination, and hand dexterity. Rehabilitation treatment aims to restore and optimize these impaired functions to help children regain autonomy and enhance their quality of life. Recent advancements in monitoring technologies such as EEG measurements are increasingly playing a role in clinical diagnosis and management. A significant advantage of incorporating EEG technology in pediatric rehabilitation is its ability to provide continuous and objective quantitative monitoring of a child's neurological status. This allows for the real-time assessment of improvement or deterioration in brain function, including, but not limited to, a significant impact on motor function. EEG monitoring enables healthcare providers to tailor and adjust interventions-both pharmacological and rehabilitative-based on the child's current neurological status.

Assessment of residual awareness in patients with disorders of consciousness using functional near-infrared spectroscopy-based connectivity: a pilot study

Significance

The accurate assessment and classification of residual consciousness are crucial for optimizing therapeutic interventions in patients with disorders of consciousness (DOCs). However, there remains an absence of effective and definitive diagnostic methods for DOC in clinical practice.

Aim

The primary objective was to investigate the feasibility of utilizing resting state functional near-infrared spectroscopy (rs-fNIRS) for evaluating residual consciousness. The secondary objective was to explore the distinguishing characteristics that are more effective in differentiating between the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) and to identify the machine learning model that offers superior classification accuracy.

Approach

We utilized rs-fNIRS to evaluate the residual consciousness in patients with DOC. Specifically, rs-fNIRS was used to construct functional brain networks, and graph theory analysis was conducted to quantify the topological differences within these brain networks between MCS and UWS. After that, two classifiers were used to distinguish MCS from UWS.

Results

The graph theory results showed that the MCS group ( n = 8 ) exhibited significantly higher global efficiency (E g ) and smaller characteristic path length (L p ) than the UWS group ( n = 10 ). The functional connectivity results showed that the correlation within the left occipital cortex (L_OC) was significantly lower in the MCS group than in the UWS group. By using the indicators with significant differences as features for further classification, the accuracy for K -nearest neighbors and linear discriminant analysis classifiers was improved by 0.89 and 0.83, respectively.

Conclusions

The resting state functional connectivity and graph theory analysis based on fNIRS has the potential to enhance the classification accuracy, providing valuable insights into the diagnosis of patients with DOC.

DOCTer: a novel EEG-based diagnosis framework for disorders of consciousness

Objective. Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework.Approach. We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases.Main results. Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics.Significance. The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics.

The assessment of consciousness status in primary brainstem hemorrhage (PBH) patients can be achieved by monitoring changes in basic vital signs

The objective of the study was to explore the association between basic vital signs and consciousness status in patients with primary brainstem hemorrhage (PBH). Patients with PBH were categorized into two groups based on Glasgow Coma Scale (GCS) scores: disturbance of consciousness (DOC) group (GCS=3-8) and non-DOC group (GCS=15). Within DOC group, patients were further divided into behavioral (GCS=4-8) and non-behavioral (GCS=3) subgroups. Basic vital signs, such as body temperature, heart rate, and respiratory rate, were monitored every 3 hours during the acute bleeding phase (1st day) and the bleeding stable phase (7th day) of hospitalization. The findings revealed a negative correlation between body temperature and heart rate with GCS scores in DOC group at both time points. Moreover, basic vital signs were notably higher in the DOC group compared to non-DOC group. Specifically, the non-behavioral subgroup within DOC group exhibited significantly elevated heart rates on the 1st day of hospitalization and moderately increased respiratory rates on the 7th day compared to the control group. Scatter plots illustrated a significant relationship between body temperature and heart rate with consciousness status, while no significant correlation was observed with respiratory rate. In conclusion, the study suggests that monitoring basic vital signs, particularly body temperature and heart rate, can serve as valuable indicators for evaluating consciousness status in PBH patients. These basic vital signs demonstrated variations corresponding to lower GCS scores. Furthermore, integrating basic vital sign monitoring with behavioral assessment could enhance the assessment of consciousness status in PBH patients.

A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models

Objective

This study pursued a meta-analysis to evaluate the predictive accuracy of machine learning (ML) models in determining disorders of consciousness (DOC) among patients with traumatic brain injury (TBI).

Methods

A comprehensive literature search was conducted to identify ML applications in the establishment of a predictive model of DOC after TBI as of August 6, 2023. Two independent reviewers assessed publication eligibility based on predefined criteria. The predictive accuracy was measured using areas under the receiver operating characteristic curves (AUCs). Subsequently, a random-effects model was employed to estimate the overall effect size, and statistical heterogeneity was determined based on I2 statistic. Additionally, funnel plot asymmetry was employed to examine publication bias. Finally, subgroup analyses were performed based on age, ML type, and relevant clinical outcomes.

Results

Final analyses incorporated a total of 46 studies. Both the overall and subgroup analyses exhibited considerable statistical heterogeneity. Machine learning predictions for DOC in TBI yielded an overall pooled AUC of 0.83 (95% CI: 0.82-0.84). Subgroup analysis based on age revealed that the ML model in pediatric patients yielded an overall combined AUC of 0.88 (95% CI: 0.80-0.95); among the model subgroups, logistic regression was the most frequently employed, with an overall pooled AUC of 0.85 (95% CI: 0.83-0.87). In the clinical outcome subgroup analysis, the overall pooled AUC for distinguishing between consciousness recovery and consciousness disorders was 0.84 (95% CI: 0.82-0.85).

Conclusion

The findings of this meta-analysis demonstrated outstanding accuracy of ML models in predicting DOC among patients with brain injuries, which presented substantial research value and potential of ML application in this domain.

A Decade of Dedication: Pioneering Perspectives on Neurological Diseases and Mental Illnesses

Welcome to Biomedicines' 10th Anniversary Special Issue, a journey through the human mind's labyrinth and complex neurological pathways [...].

Innovation at the Intersection: Emerging Translational Research in Neurology and Psychiatry

Translational research in neurological and psychiatric diseases is a rapidly advancing field that promises to redefine our approach to these complex conditions [...].

The Impact of C-3 Side Chain Modifications on Kynurenic Acid: A Behavioral Analysis of Its Analogs in the Motor Domain

The central nervous system (CNS) is the final frontier in drug delivery because of the blood-brain barrier (BBB), which poses significant barriers to the access of most drugs to their targets. Kynurenic acid (KYNA), a tryptophan (Trp) metabolite, plays an important role in behavioral functions, and abnormal KYNA levels have been observed in neuropsychiatric conditions. The current challenge lies in delivering KYNA to the CNS owing to its polar side chain. Recently, C-3 side chain-modified KYNA analogs have been shown to cross the BBB; however, it is unclear whether they retain the biological functions of the parent molecule. This study examined the impact of KYNA analogs, specifically, SZR-72, SZR-104, and the newly developed SZRG-21, on behavior. The analogs were administered intracerebroventricularly (i.c.v.), and their effects on the motor domain were compared with those of KYNA. Specifically, open-field (OF) and rotarod (RR) tests were employed to assess motor activity and skills. SZR-104 increased horizontal exploratory activity in the OF test at a dose of 0.04 μmol/4 μL, while SZR-72 decreased vertical activity at doses of 0.04 and 0.1 μmol/4 μL. In the RR test, however, neither KYNA nor its analogs showed any significant differences in motor skills at either dose. Side chain modification affects affective motor performance and exploratory behavior, as the results show for the first time. In this study, we showed that KYNA analogs alter emotional components such as motor-associated curiosity and emotions. Consequently, drug design necessitates the development of precise strategies to traverse the BBB while paying close attention to modifications in their effects on behavior.

Advancements in non-invasive microwave brain stimulation: A comprehensive survey

This survey provides a comprehensive insight into the world of non-invasive brain stimulation and focuses on the evolving landscape of deep brain stimulation through microwave research. Non-invasive brain stimulation techniques provide new prospects for comprehending and treating neurological disorders. We investigate the methods shaping the future of deep brain stimulation, emphasizing the role of microwave technology in this transformative journey. Specifically, we explore antenna structures and optimization strategies to enhance the efficiency of high-frequency microwave stimulation. These advancements can potentially revolutionize the field by providing a safer and more precise means of modulating neural activity. Furthermore, we address the challenges that researchers currently face in the realm of microwave brain stimulation. From safety concerns to methodological intricacies, this survey outlines the barriers that must be overcome to fully unlock the potential of this technology. This survey serves as a roadmap for advancing research in microwave brain stimulation, pointing out potential directions and innovations that promise to reshape the field.

Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

Targeting Human Glucocorticoid Receptors in Fear Learning: A Multiscale Integrated Approach to Study Functional Connectivity

Fear extinction is a phenomenon that involves a gradual reduction in conditioned fear responses through repeated exposure to fear-inducing cues. Functional brain connectivity assessments, such as functional magnetic resonance imaging (fMRI), provide valuable insights into how brain regions communicate during these processes. Stress, a ubiquitous aspect of life, influences fear learning and extinction by changing the activity of the amygdala, prefrontal cortex, and hippocampus, leading to enhanced fear responses and/or impaired extinction. Glucocorticoid receptors (GRs) are key to the stress response and show a dual function in fear regulation: while they enhance the consolidation of fear memories, they also facilitate extinction. Accordingly, GR dysregulation is associated with anxiety and mood disorders. Recent advancements in cognitive neuroscience underscore the need for a comprehensive understanding that integrates perspectives from the molecular, cellular, and systems levels. In particular, neuropharmacology provides valuable insights into neurotransmitter and receptor systems, aiding the investigation of mechanisms underlying fear regulation and potential therapeutic targets. A notable player in this context is cortisol, a key stress hormone, which significantly influences both fear memory reconsolidation and extinction processes. Gaining a thorough understanding of these intricate interactions has implications in terms of addressing psychiatric disorders related to stress. This review sheds light on the complex interactions between cognitive processes, emotions, and their neural bases. In this endeavor, our aim is to reshape the comprehension of fear, stress, and their implications for emotional well-being, ultimately aiding in the development of therapeutic interventions.

Long-term clinical evolution of patients with prolonged disorders of consciousness due to severe anoxic brain injury: A meta-analytic study

BACKGROUND AND PURPOSE

The prognosis of prolonged (28 days to 3 months post-onset) disorders of consciousness (pDoC) due to anoxic brain injury is uncertain. The present study aimed to evaluate the long-term outcome of post-anoxic pDoC and identify the possible predictive value of demographic and clinical information.

METHOD

This is a systematic review and meta-analysis. The rates of mortality, any improvement in clinical diagnosis, and recovery of full consciousness at least 6 months after severe anoxic brain injury were evaluated. A cross-sectional approach searched for differences in baseline demographic and clinical characteristics between survivors and non-survivors, patients improved versus not improved, and patients who recovered full consciousness versus not recovered.

RESULTS

Twenty-seven studies were identified. The pooled rates of mortality, any clinical improvement and recovery of full consciousness were 26%, 26% and 17%, respectively. Younger age, baseline diagnosis of minimally conscious state versus vegetative state/unresponsive wakefulness syndrome, higher Coma Recovery Scale Revised total score, and earlier admission to intensive rehabilitation units were associated with a significantly higher likelihood of survival and clinical improvement. These same variables, except time of admission to rehabilitation, were also associated with recovery of full consciousness.

CONCLUSIONS

Patients with anoxic pDoC might improve over time up to full recovery of consciousness and some clinical characteristics can help predict clinical improvement. These new insights could support clinicians and caregivers in the decision-making on patient management.

Neurophysiological and Psychometric Outcomes in Minimal Consciousness State after Advanced Audio-Video Emotional Stimulation: A Retrospective Study

In the last ten years, technological innovations have led to the development of new, advanced sensory stimulation (SS) tools, such as PC-based rehabilitative programs or virtual reality training. These are meant to stimulate residual cognitive abilities and, at the same time, assess cognition and awareness, also in patients with a minimally conscious state (MCS). Our purpose was to evaluate the clinical and neurophysiological effects of multi-sensory and emotional stimulation provided by Neurowave in patients with MCS, as compared to a conventional SS treatment. The psychological status of their caregivers was also monitored. In this retrospective study, we have included forty-two MCS patients and their caregivers. Each MCS subject was included in either the control group (CG), receiving a conventional SS, or the experimental group (EG), who was submitted to the experimental training with the Neurowave. They were assessed before (T0) and after the training (T1) through a specific clinical battery, including both motor and cognitive outcomes. Moreover, in the EG, we also monitored the brain electrophysiological activity (EEG and P300). In both study groups (EG and CG), the psychological caregiver's aspects, including anxiety levels, were measured using the Zung Self-Rating Anxiety Scale (SAS). The intra-group analysis (T0-T1) of the EG showed statistical significances in all patients' outcome measures, while in the CG, we found statistical significances in consciousness and awareness outcomes. The inter-group analysis between the EG and the CG showed no statistical differences, except for global communication skills. In conclusion, the multi-sensory stimulation approach through Neurowave was found to be an innovative rehabilitation treatment, also allowing the registration of brain activity during treatment.

Emerging Translational Research in Neurological and Psychiatric Diseases: From In Vitro to In Vivo Models

Revealing the underlying pathomechanisms of neurological and psychiatric disorders, searching for new biomarkers, and developing novel therapeutics all require translational research [...].

Evaluation of consciousness rehabilitation via neuroimaging methods

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.

KRAS/NRAS Mutations Associated with Distant Metastasis and BRAF/PIK3CA Mutations Associated with Poor Tumor Differentiation in Colorectal Cancer

Background

The occurrence, progression, and prognosis of colorectal cancer (CRC) are regulated by EGFR-mediated signaling pathways. However, the relationship between the core genes (KRAS/NRAS/BRAF/PIK3CA) status in the signaling pathways and clinicopathological characteristics of CRC patients in Hakka population remains controversial.

Methods

Patients were genotyped for KRAS (codons 12, 13, 61, 117, and 146), NRAS (codons 12, 61, 117, and 146), BRAF (codons 600), and PIK3CA (codons 542, 545 and 1047) mutations. Clinical records were collected, and clinicopathological characteristic associations were analyzed together with mutations of studied genes.

Results

Four hundred and eight patients (256 men and 152 women) were included in the analysis. At least one mutation in the four genes was detected in 216 (52.9%) patients, while none was detected in 192 (47.1%) patients. KRAS, NRAS, BRAF, and PIK3CA mutation status were detected in 190 (46.6%), 11 (2.7%), 10 (2.5%), 34 (8.3%) samples, respectively. KRAS exon 2 had the highest proportion (62.5%). Age, tumor site, tumor size, lymphovascular invasion, and perineural invasion were not associated with gene mutations. KRAS mutations (adjusted OR 1.675, 95% CI 1.017-2.760, P=0.043) and NRAS mutations (adjusted OR 5.183, 95% CI 1.239-21.687, P=0.024) appeared more frequently in patients with distant metastasis. BRAF mutations (adjusted OR 7.224, 95% CI 1.356-38.488, P=0.021) and PIK3CA mutations (adjusted OR 3.811, 95% CI 1.268-11.455, P=0.017) associated with poorly differentiated tumor.

Conclusion

KRAS/NRAS mutations are associated with distant metastasis and BRAF/PIK3CA mutations are associated with poor tumor differentiation in CRC. And the results provided a better understanding between clinicopathological characteristics and gene mutations in CRC patients.

Optimizing Treatment for Major Depressive Disorder in Adolescents: The Impact of Intradermal Acupuncture - A Randomized Controlled Trial Protocol

Background

Major depressive disorder (MDD) exhibits a pronounced occurrence among adolescents, aligning closely with the lifetime prevalence rate of 16.6% observed in adults. It is difficult to treat and prone to recurrence. Acupuncture has shown potential in enhancing treatment effectiveness. Nonetheless, there is a lack of research on the use of intradermal acupuncture (IA) in treating adolescent MDD.

Methods

This study is a double-blind, randomized controlled trial. A cohort of 120 participants will be assigned randomly to three distinct groups, namely a Selective Serotonin Reuptake Inhibitors (SSRIs)-only group, a sham intradermal acupuncture combined with SSRIs (SIA) group, and an active intradermal acupuncture combined with SSRIs (AIA) group. Hamilton Depression Rating Scale will serve as the primary outcome, while Patient Health Questionnaire-9, Self-Rating Depression Scale, Pittsburgh Sleep Quality Index, and Short Form 36 Questionnaire will serve as secondary outcomes in assessing the amelioration of depressive symptoms in patients. These data will be analyzed using SPSS26.0 software.

Results

We will assess the efficacy and safety of IA for MDD using commonly employed clinical psychiatric scales.

Conclusion

The efficacy of IA in treating adolescent MDD may be demonstrated in this study, suggesting its potential for optimizing MDD treatment schemes.

Trial Registration

ClinicalTrials.gov Identifier: NCT05832619 (April 27, 2023).

The Role of Serotonin in Fear Learning and Memory: A Systematic Review of Human Studies

Fear is characterized by distinct behavioral and physiological responses that are essential for the survival of the human species. Fear conditioning (FC) serves as a valuable model for studying the acquisition, extinction, and expression of fear. The serotonin (5-hydroxytryptamine, 5-HT) system is known to play a significant role in emotional and motivational aspects of human behavior, including fear learning and expression. Accumulating evidence from both animal and human studies suggests that brain regions involved in FC, such as the amygdala, hippocampus, and prefrontal cortex, possess a high density of 5-HT receptors, implicating the crucial involvement of serotonin in aversive learning. Additionally, studies exploring serotonin gene polymorphisms have indicated their potential influence on FC. Therefore, the objective of this work was to review the existing evidence linking 5-HT with fear learning and memory in humans. Through a comprehensive screening of the PubMed and Web of Science databases, 29 relevant studies were included in the final review. These studies investigated the relationship between serotonin and fear learning using drug manipulations or by studying 5-HT-related gene polymorphisms. The results suggest that elevated levels of 5-HT enhance aversive learning, indicating that the modulation of serotonin 5-HT2A receptors regulates the expression of fear responses in humans. Understanding the role of this neurochemical messenger in associative aversive learning can provide insights into psychiatric disorders such as anxiety and post-traumatic stress disorder (PTSD), among others.

Sub-Scalp Implantable Telemetric EEG (SITE) for the Management of Neurological and Behavioral Disorders beyond Epilepsy

Sub-scalp Implantable Telemetric EEG (SITE) devices are under development for the treatment of epilepsy. However, beyond epilepsy, continuous EEG analysis could revolutionize the management of patients suffering from all types of brain disorders. This article reviews decades of foundational EEG research, collected from short-term routine EEG studies of common neurological and behavioral disorders, that may guide future SITE management and research. Established quantitative EEG methods, like spectral EEG power density calculation combined with state-of-the-art machine learning techniques applied to SITE data, can identify new EEG biomarkers of neurological disease. From distinguishing syncopal events from seizures to predicting the risk of dementia, SITE-derived EEG biomarkers can provide clinicians with real-time information about diagnosis, treatment response, and disease progression.

Characterizing endogenous delta oscillations in human MEG

Rhythmic activity in the delta frequency range (0.5-3 Hz) is a prominent feature of brain dynamics. Here, we examined whether spontaneous delta oscillations, as found in invasive recordings in awake animals, can be observed in non-invasive recordings performed in humans with magnetoencephalography (MEG). In humans, delta activity is commonly reported when processing rhythmic sensory inputs, with direct relationships to behaviour. However, rhythmic brain dynamics observed during rhythmic sensory stimulation cannot be interpreted as an endogenous oscillation. To test for endogenous delta oscillations we analysed human MEG data during rest. For comparison, we additionally analysed two conditions in which participants engaged in spontaneous finger tapping and silent counting, arguing that internally rhythmic behaviours could incite an otherwise silent neural oscillator. A novel set of analysis steps allowed us to show narrow spectral peaks in the delta frequency range in rest, and during overt and covert rhythmic activity. Additional analyses in the time domain revealed that only the resting state condition warranted an interpretation of these peaks as endogenously periodic neural dynamics. In sum, this work shows that using advanced signal processing techniques, it is possible to observe endogenous delta oscillations in non-invasive recordings of human brain dynamics.

Age, Dose, and Locomotion: Decoding Vulnerability to Ketamine in C57BL/6J and BALB/c Mice

Ketamine has been abused as a psychedelic agent and causes diverse neurobehavioral changes. Adolescence is a critical developmental stage but vulnerable to substances and environmental stimuli. Growing evidence shows that ketamine affects glutamatergic neurotransmission, which is important for memory storage, addiction, and psychosis. To explore diverse biological responses, this study was designed to assess ketamine sensitivity in mice of different ages and strains. Male C57BL/6J and BALB/c mice were studied in adolescence and adulthood separately. An open field test assessed motor behavioral changes. After a 30-min baseline habituation, mice were injected with ketamine (0, 25, and 50 mg/kg), and their locomotion was measured for 60 min. Following ketamine injection, the travelled distance and speed significantly increased in C57BL/6J mice between both age groups (p < 0.01), but not in BALB/c mice. The pattern of hyperlocomotion showed that mice were delayed at the higher dose (50 mg/kg) compared to the lower dose (25 mg/kg) of ketamine treatment. Ketamine accentuated locomotor activation in adolescent C57BL/6J mice compared to adults, but not in the BALB/c strain. Here, we show that ketamine-induced locomotor behavior is modulated by dose and age. The discrepancy of neurobehaviors in the two strains of mice indicates that sensitivity to ketamine is biologically determined. This study suggests that individual vulnerability to ketamine's pharmacological responses varies biologically.

Dissecting Relations between Depression Severity, Antidepressant Use, and Metabolic Syndrome Components in the NHANES 2005-2020

We aimed to dissect the complex relations between depressive symptoms, antidepressant use, and constituent metabolic syndrome (MetS) components in a representative U.S. population sample. A total of 15,315 eligible participants were included from 2005 to March 2020. MetS components were defined as hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, central obesity, and elevated blood glucose. Depressive symptoms were classified as mild, moderate, or severe. Logistic regression was used to evaluate the relationship between depression severity, antidepressant use, individual MetS components and their degree of clustering. Severe depression was associated with the number of MetS components in a graded fashion. ORs for severe depression ranged from 2.08 [95%CI, 1.29-3.37] to 3.35 [95%CI, 1.57-7.14] for one to five clustered components. Moderate depression was associated with hypertension, central obesity, raised triglyceride, and elevated blood glucose (OR = 1.37 [95%CI, 1.09-1.72], 1.82 [95%CI, 1.21-2.74], 1.63 [95%CI, 1.25-2.14], and 1.37 [95%CI, 1.05-1.79], respectively). Antidepressant use was associated with hypertension (OR = 1.40, 95%CI [1.14-1.72]), raised triglyceride (OR = 1.43, 95%CI [1.17-1.74]), and the presence of five MetS components (OR = 1.74, 95%CI [1.13-2.68]) after adjusting for depressive symptoms. The depression severity and antidepressant use were associated with individual MetS components and their graded clustering. Metabolic abnormalities in patients with depression need to be recognized and treated.

Outcome Evaluation in Social Comparison: When You Deviate from Others

Individuals often measure their performance through social comparison. With the increase in the deviation degree between the self and others, the outcome evaluation of individuals' abilities in the social comparison context is still unknown. In the current study, we used a two self-outcomes × three others' outcomes within-participant design to investigate the effect of the deviation degree of the self versus others in the social comparison context. Event-related potentials (ERPs) were measured while participants performed a three-person dot estimation task with two other people. When participants received positive results, the amplitudes of feedback-related negativity (FRN) and P300 showed a significant gradient change in the degree of deviation between the self and others (even win vs. better win vs. best win conditions). However, we did not find a similar progressive effect when participants received negative results (even loss vs. worse loss vs. worst loss conditions). These findings suggest that the deviation degree affects the primary and later processing stages of social comparison outcomes only when individuals received positive outcomes, which may reflect how people develop an empathic response to others. In contrast, people tended to avoid deeper social comparison that threatened their self-esteem when they received negative outcomes.

Periodic and Aperiodic EEG Features as Potential Markers of Developmental Dyslexia

Developmental Dyslexia (DD) is a neurobiological condition affecting the ability to read fluently and/or accurately. Analyzing resting-state electroencephalographic (EEG) activity in DD may provide a deeper characterization of the underlying pathophysiology and possible biomarkers. So far, studies investigating resting-state activity in DD provided limited evidence and did not consider the aperiodic component of the power spectrum. In the present study, adults with (n = 26) and without DD (n = 31) underwent a reading skills assessment and resting-state EEG to investigate potential alterations in aperiodic activity, their impact on the periodic counterpart and reading performance. In parieto-occipital channels, DD participants showed a significantly different aperiodic activity as indexed by a flatter and lower power spectrum. These aperiodic measures were significantly related to text reading time, suggesting a link with individual differences in reading difficulties. In the beta band, the DD group showed significantly decreased aperiodic-adjusted power compared to typical readers, which was significantly correlated to word reading accuracy. Overall, here we provide evidence showing alterations of the endogenous aperiodic activity in DD participants consistently with the increased neural noise hypothesis. In addition, we confirm alterations of endogenous beta rhythms, which are discussed in terms of their potential link with magnocellular-dorsal stream deficit.

Biomarkers of Neurodegeneration in Post-Traumatic Stress Disorder: An Integrative Review

Post-Traumatic Stress Disorder (PTSD) is a chronic psychiatric disorder that occurs following exposure to traumatic events. Recent evidence suggests that PTSD may be a risk factor for the development of subsequent neurodegenerative disorders, including Alzheimer's dementia and Parkinson's disease. Identification of biomarkers known to be associated with neurodegeneration in patients with PTSD would shed light on the pathophysiological mechanisms linking these disorders and would also help in the development of preventive strategies for neurodegenerative disorders in PTSD. With this background, the PubMed and Scopus databases were searched for studies designed to identify biomarkers that could be associated with an increased risk of neurodegenerative disorders in patients with PTSD. Out of a total of 342 citations retrieved, 29 studies were identified for inclusion in the review. The results of these studies suggest that biomarkers such as cerebral cortical thinning, disrupted white matter integrity, specific genetic polymorphisms, immune-inflammatory alterations, vitamin D deficiency, metabolic syndrome, and objectively documented parasomnias are significantly associated with PTSD and may predict an increased risk of subsequent neurodegenerative disorders. The biological mechanisms underlying these changes, and the interactions between them, are also explored. Though requiring replication, these findings highlight a number of biological pathways that plausibly link PTSD with neurodegenerative disorders and suggest potentially valuable avenues for prevention and early intervention.

A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping

The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light-electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.

Hierarchical psychophysiological pathways subtend perceptual asymmetries in Neglect

Stroke patients with left Hemispatial Neglect (LHN) show deficits in perceiving left contralesional stimuli with biased visuospatial perception towards the right hemifield. However, very little is known about the functional organization of the visuospatial perceptual neural network and how this can account for the profound reorganization of space representation in LHN. In the present work, we aimed at (1) identifying EEG measures that discriminate LHN patients against controls and (2) devise a causative neurophysiological model between the discriminative EEG measures. To these aims, EEG was recorded during exposure to lateralized visual stimuli which allowed for pre-and post-stimulus activity investigation across three groups: LHN patients, lesioned controls, and healthy individuals. Moreover, all participants performed a standard behavioral test assessing the perceptual asymmetry index in detecting lateralized stimuli. The between-groups discriminative EEG patterns were entered into a Structural Equation Model for the identification of causative hierarchical associations (i.e., pathways) between EEG measures and the perceptual asymmetry index. The model identified two pathways. A first pathway showed that the combined contribution of pre-stimulus frontoparietal connectivity and individual-alpha-frequency predicts post-stimulus processing, as measured by visual-evoked N100, which, in turn, predicts the perceptual asymmetry index. A second pathway directly links the inter-hemispheric distribution of alpha-amplitude with the perceptual asymmetry index. The two pathways can collectively explain 83.1% of the variance in the perceptual asymmetry index. Using causative modeling, the present study identified how psychophysiological correlates of visuospatial perception are organized and predict the degree of behavioral asymmetry in LHN patients and controls.

The Tryptophan-Kynurenine Metabolic System Is Suppressed in Cuprizone-Induced Model of Demyelination Simulating Progressive Multiple Sclerosis

Progressive multiple sclerosis (MS) is a chronic disease with a unique pattern, which is histologically classified into the subpial type 3 lesions in the autopsy. The lesion is also homologous to that of cuprizone (CPZ) toxin-induced animal models of demyelination. Aberration of the tryptophan (TRP)-kynurenine (KYN) metabolic system has been observed in patients with MS; nevertheless, the KYN metabolite profile of progressive MS remains inconclusive. In this study, C57Bl/6J male mice were treated with 0.2% CPZ toxin for 5 weeks and then underwent 4 weeks of recovery. We measured the levels of serotonin, TRP, and KYN metabolites in the plasma and the brain samples of mice at weeks 1, 3, and 5 of demyelination, and at weeks 7 and 9 of remyelination periods by ultra-high-performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) after body weight measurement and immunohistochemical analysis to confirm the development of demyelination. The UHPLC-MS/MS measurements demonstrated a significant reduction of kynurenic acid, 3-hydoxykynurenine (3-HK), and xanthurenic acid in the plasma and a significant reduction of 3-HK, and anthranilic acid in the brain samples at week 5. Here, we show the profile of KYN metabolites in the CPZ-induced mouse model of demyelination. Thus, the KYN metabolite profile potentially serves as a biomarker of progressive MS and thus opens a new path toward planning personalized treatment, which is frequently obscured with immunologic components in MS deterioration.

Rosiglitazone Mitigates Dexamethasone-Induced Depression in Mice via Modulating Brain Glucose Metabolism and AMPK/mTOR Signaling Pathway

Major depressive disorder (MDD) is a common, complex disease with poorly understood pathogenesis. Disruption of glucose metabolism is implicated in the pathogenesis of depression. AMP-activated protein kinase (AMPK) has been shown to regulate the activity of several kinases, including pAKT, p38MAPK, and mTOR, which are important signaling pathways in the treatment of depression. This study tested the hypothesis that rosiglitazone (RGZ) has an antidepressant impact on dexamethasone (DEXA)-induced depression by analyzing the function of the pAKT/p38MAPK/mTOR pathway and NGF through regulation of AMPK. MDD-like pathology was induced by subcutaneous administration of DEXA (20 mg/kg) for 21 days in all groups except in the normal control group, which received saline. To investigate the possible mechanism of RGZ, the protein expression of pAMPK, pAKT, p38MAPK, and 4EBP1 as well as the levels of hexokinase, pyruvate kinase, and NGF were assessed in prefrontal cortex and hippocampal samples. The activities of pAMPK and NGF increased after treatment with RGZ. The administration of RGZ also decreased the activity of mTOR as well as downregulating the downstream signaling pathways pAKT, p38MAPK, and 4EBP1. Here, we show that RGZ exerts a potent inhibitory effect on the pAKT/p38MAPK/mTOR/4EBP1 pathway and causes activation of NGF in brain cells. This study has provided sufficient evidence of the potential for RGZ to ameliorate DEXA-induced depression. A new insight has been introduced into the critical role of NGF activation in brain cells in depression. These results suggest that RGZ is a promising antidepressant for the treatment of MDD.

Functional Activity in the Effect of Transcranial Magnetic Stimulation Therapy for Patients with Depression: A Meta-Analysis

Depression is a long-lasting mental disorder that affects more than 264 million people worldwide. Transcranial magnetic stimulation (TMS) can be a safe and effective choice for the treatment of depression. Functional neuroimaging provides unique insights into the neuropsychiatric effects of antidepressant TMS. In this meta-analysis, we aimed to assess the functional activity of brain regions caused by TMS for depression. A literature search was conducted from inception to 5 January 2022. Studies were then selected according to predetermined inclusion and exclusion criteria. Activation likelihood estimation was applied to analyze functional activation. Five articles were ultimately included after selection. The main analysis results indicated that TMS treatment for depression can alter the activity in the right precentral gyrus, right posterior cingulate, left inferior frontal gyrus and left middle frontal gyrus. In resting-state studies, increased activation was shown in the right precentral gyrus, right posterior cingulate, left inferior frontal gyrus and left superior frontal gyrus associated with TMS treatment. In task-related studies, clusters in the right middle frontal gyrus, left sub-gyrus, left middle frontal gyrus and left posterior cingulate were hyperactivated post-treatment. Our study offers an overview of brain activity changes in patients with depression after TMS treatment.

Dimensions of the Hamilton Depression Rating Scale Correlate with Impulsivity and Personality Traits among Youth Patients with Depression

The heterogeneity of symptoms in young patients with major depression disorder makes it difficult to properly identify and diagnose. Therefore, the appropriate evaluation of mood symptoms is important in early intervention. The aim of this study was to (a) establish dimensions of the Hamilton Depression Rating Scale (HDRS-17) in adolescents and young adults and (b) perform correlations between the identified dimensions and psychological variables (impulsivity, personality traits). This study enrolled 52 young patients with major depression disorder (MDD). The severity of the depressive symptoms was established using the HDRS-17. The factor structure of the scale was studied using the principal component analysis (PCA) with varimax rotation. The patients completed the self-reported Barratt Impulsiveness Scale (BIS-11) and Temperament and Character Inventory (TCI). The three dimensions of the HDRS-17 identified as core in adolescent and young patients with MDD were (1) psychic depression/motor retardation, (2) disturbed thinking, and (3) sleep disturbances/anxiety. In our study, dimension 1 correlated with reward dependence and cooperativeness; dimension 2 correlated with non-planning impulsivity, harm avoidance, and self-directedness; and dimension 3 correlated with reward dependence. Conclusions: Our study supports the previous findings, which indicate that a certain set of clinical features (including the HDRS-17 dimensions, not only total score) may represent a vulnerability pattern that characterizes patients with depression.

Exploring Novel Therapeutic Targets in the Common Pathogenic Factors in Migraine and Neuropathic Pain

Migraine and neuropathic pain (NP) are both painful, disabling, chronic conditions which exhibit some symptom similarities and are thus considered to share a common etiology. The calcitonin gene-related peptide (CGRP) has gained credit as a target for migraine management; nevertheless, the efficacy and the applicability of CGRP modifiers warrant the search for more effective therapeutic targets for pain management. This scoping review focuses on human studies of common pathogenic factors in migraine and NP, with reference to available preclinical evidence to explore potential novel therapeutic targets. CGRP inhibitors and monoclonal antibodies alleviate inflammation in the meninges; targeting transient receptor potential (TRP) ion channels may help prevent the release of nociceptive substances, and modifying the endocannabinoid system may open a path toward discovery of novel analgesics. There may exist a potential target in the tryptophan-kynurenine (KYN) metabolic system, which is closely linked to glutamate-induced hyperexcitability; alleviating neuroinflammation may complement a pain-relieving armamentarium, and modifying microglial excitation, which is observed in both conditions, may be a possible approach. Those are several potential analgesic targets which deserve to be explored in search of novel analgesics; however, much evidence remains missing. This review highlights the need for more studies on CGRP modifiers for subtypes, the discovery of TRP and endocannabinoid modulators, knowledge of the status of KYN metabolites, the consensus on cytokines and sampling, and biomarkers for microglial function, in search of innovative pain management methods for migraine and NP.

Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that interferes with normal brain development. Brain connectivity may serve as a biomarker for ASD in this respect. This study enrolled a total of 179 children aged 3-10 years (90 typically developed (TD) and 89 with ASD). We used a weighted phase lag index and a directed transfer function to investigate the functional and effective connectivity in children with ASD and TD. Our findings indicated that patients with ASD had local hyper-connectivity of brain regions in functional connectivity and simultaneous significant decrease in effective connectivity across hemispheres. These connectivity abnormalities may help to find biomarkers of ASD.

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.

Altered Functional Connectivity and Complexity in Major Depressive Disorder after Musical Stimulation

Major depressive disorder (MDD) is a common mental illness. This study used electroencephalography (EEG) to explore the effects of music therapy on brain networks in MDD patients and to elucidate changes in functional brain connectivity in subjects before and after musical stimulation. EEG signals were collected from eight MDD patients and eight healthy controls. The phase locking value was adopted to calculate the EEG correlation of different channels in different frequency bands. Correlation matrices and network topologies were studied to analyze changes in functional connectivity between brain regions. The results of the experimental analysis found that the connectivity of the delta and beta bands decreased, while the connectivity of the alpha band increased. Regarding the characteristics of the EEG functional network, the average clustering coefficient, characteristic path length and degree of each node in the delta band decreased significantly after musical stimulation, while the characteristic path length in the beta band increased significantly. Characterized by the average clustering coefficient and characteristic path length, the classification of depression and healthy controls reached 93.75% using a support vector machine.

Relationships of Physical Activity, Depression, and Sleep with Cognitive Function in Community-Dwelling Older Adults

This cross-sectional, observational study aimed to integrate the analyses of relationships of physical activity, depression, and sleep with cognitive function in community-dwelling older adults using a single model. To this end, physical activity, sleep, depression, and cognitive function in 864 community-dwelling older adults from the Suwon Geriatric Mental Health Center were assessed using the International Physical Activity Questionnaire, Montgomery-Asberg Depression Rating Scale, Pittsburgh Sleep Quality Index, and Mini-Mental State Examination for Dementia Screening, respectively. Their sociodemographic characteristics were also recorded. After adjusting for confounders, multiple linear regression analysis was performed to investigate the effects of physical activity, sleep, and depression on cognitive function. Models 4, 5, 7, and 14 of PROCESS were applied to verify the mediating and moderating effects of all variables. Physical activity had a direct effect on cognitive function (effect = 0.97, p < 0.01) and indirect effect (effect = 0.36; confidence interval: 0.18, 0.57) through depression. Moreover, mediated moderation effects of sleep were confirmed in the pathways where physical activity affects cognitive function through depression (F-coeff = 13.37, p < 0.001). Furthermore, these relationships differed with age. Thus, the associations among physical activity, depression, and sleep are important in interventions for the cognitive function of community-dwelling older adults. Such interventions should focus on different factors depending on age.

Integrating Armchair, Bench, and Bedside Research for Behavioral Neurology and Neuropsychiatry: Editorial

"To learning much inclined, who went to see the Elephant (though all of them were blind) that each by observation might satisfy the mind" [...].

Early prediction of Alzheimer's disease using convolutional neural network: a review

In this paper, a comprehensive review on Alzheimer's disease (AD) is carried out, and an exploration of the two machine learning (ML) methods that help to identify the disease in its initial stages. Alzheimer's disease is a neurocognitive disorder occurring in people in their early onset. This disease causes the person to suffer from memory loss, unusual behavior, and language problems. Early detection is essential for developing more advanced treatments for AD. Machine learning (ML), a subfield of Artificial Intelligence (AI), uses various probabilistic and optimization techniques to help computers learn from huge and complicated data sets. To diagnose AD in its early stages, researchers generally use machine learning. The survey provides a broad overview of current research in this field and analyses the classification methods used by researchers working with ADNI data sets. It discusses essential research topics such as the data sets used, the evaluation measures employed, and the machine learning methods used. Our presentation suggests a model that helps better understand current work and highlights the challenges and opportunities for innovative and useful research. The study shows which machine learning method holds best for the ADNI data set. Therefore, the focus is given to two methods: the 18-layer convolutional network and the 3D convolutional network. Hence, CNNs with multi-layered fetch more accurate results as compared to 3D CNN. The work also contributes to the use of the ADNI data set, where the classification of training and testing samples is divided with such a number that brings the highest accuracy achieved with 18-layer CNN. The work concentrates on the early prediction of Alzheimer's disease with machine learning methods. Thus, the accuracy achieved is 98% for 18-layer CNN.