Low-intensity ultrasound ameliorates brain organoid integration and rescues microcephaly deficits.

Human brain organoids represent a remarkable platform for modeling neurological disorders and a promising brain repair approach. However, the effects of physical stimulation on their development and integration remain unclear. Here, we report that low-intensity ultrasound significantly increases neural progenitor cell proliferation and neuronal maturation in cortical organoids. Histological assays and single-cell gene expression analyses reveal that low-intensity ultrasound improves the neural development in cortical organoids. Following organoid grafts transplantation into the injured somatosensory cortices of adult mice, longitudinal electrophysiological recordings and histological assays reveal that ultrasound-treated organoid grafts undergo advanced maturation. They also exhibit enhanced pain-related gamma-band activity and more disseminated projections into the host brain than the untreated groups. Finally, low-intensity ultrasound ameliorates neuropathological deficits in a microcephaly brain organoid model. Hence, low-intensity ultrasound stimulation advances the development and integration of brain organoids, providing a strategy for treating neurodevelopmental disorders and repairing cortical damage.

Effect of Phase Clustering Bias on Phase-Amplitude Coupling for Emotional EEG.

BACKGROUND: Emotions are thought to be related to distinct patterns of neural oscillations, but the interactions among multi-frequency neural oscillations during different emotional states lack full exploration. Phase-amplitude coupling is a promising tool for understanding the complexity of the neurophysiological system, thereby playing a crucial role in revealing the physiological mechanisms underlying emotional electroencephalogram (EEG). However, the non-sinusoidal characteristics of EEG lead to the non-uniform distribution of phase angles, which could potentially affect the analysis of phase-amplitude coupling. Removing phase clustering bias (PCB) can uniform the distribution of phase angles, but the effect of this approach is unknown on emotional EEG phase-amplitude coupling. This study aims to explore the effect of PCB on cross-frequency phase-amplitude coupling for emotional EEG. METHODS: The technique of removing PCB was implemented on a publicly accessible emotional EEG dataset to calculate debiased phase-amplitude coupling. Statistical analysis and classification were conducted to compare the difference in emotional EEG phase-amplitude coupling prior to and post the removal of PCB. RESULTS: Emotional EEG phase-amplitude coupling values are overestimated due to PCB. Removing PCB enhances the difference in coupling strength between fear and happy emotions in the frontal lobe. Comparable emotion recognition performance was achieved with fewer features after removing PCB. CONCLUSIONS: These findings suggest that removing PCB enhances the difference in emotional EEG phase-amplitude coupling patterns and generates features that contain more emotional information. Removing PCB may be advantageous for analyzing emotional EEG phase-amplitude coupling and recognizing human emotions.

Fusion of Multi-domain EEG Signatures Improves Emotion Recognition.

BACKGROUND: Affective computing has gained increasing attention in the area of the human-computer interface where electroencephalography (EEG)-based emotion recognition occupies an important position. Nevertheless, the diversity of emotions and the complexity of EEG signals result in unexplored relationships between emotion and multichannel EEG signal frequency, as well as spatial and temporal information. METHODS: Audio-video stimulus materials were used that elicited four types of emotions (sad, fearful, happy, neutral) in 32 male and female subjects (age 21-42 years) while collecting EEG signals. We developed a multidimensional analysis framework using a fusion of phase-locking value (PLV), microstates, and power spectral densities (PSDs) of EEG features to improve emotion recognition. RESULTS: An increasing trend of PSDs was observed as emotional valence increased, and connections in the prefrontal, temporal, and occipital lobes in high-frequency bands showed more differentiation between emotions. Transition probability between microstates was likely related to emotional valence. The average cross-subject classification accuracy of features fused by Discriminant Correlation Analysis achieved 64.69%, higher than that of single mode and direct-concatenated features, with an increase of more than 7%. CONCLUSIONS: Different types of EEG features have complementary properties in emotion recognition, and combining EEG data from three types of features in a correlated way, improves the performance of emotion classification.

Combining Network Pharmacology and Experimental Verification to Ascertain the Mechanism of Action of Asparagus officinalis Against the Brain Damage Caused by Fluorosis.

Asparagus officinalis (ASP) has antioxidation, anti-inflammatory, antiaging, and immune system-enhancing effects. We explored the preventive and therapeutic consequences of ASP on the brain damage elicited by fluorosis through network pharmacology and in vivo experimental validation. We ascertained the pharmaceutically active ingredients and drug targets of ASP from the Traditional Chinese Medicine Systems Pharmacology database, predicted the disease targets of fluorosis-induced brain injury using GeneCards and Online Mendelian Inheritance in Man databases, obtained target protein-protein interaction networks in the Search Tool for the Retrieval of Interacting Genes/Proteins database, used Cytoscape to obtain key targets and active ingredients, and conducted enrichment analyses of key targets in the Database for Annotation, Visualization and Integrated Discovery. Enrichment analyses showed that "mitogen-activated protein kinase" (MAPK), "phosphoinositide 3-kinase/protein kinase B" (PI3K-Akt), "nuclear factor-kappa B" (NF-κB), and the "neurotrophin signaling pathway" were the most enriched biological processes and signaling pathways. ASP could alleviate fluorosis-based injury, improve brain-tissue damage, increase urinary fluoride content, and improve oxidation levels and inflammatory-factor levels in the body. ASP could also reduce dental fluorosis, bone damage, fluoride concentrations in blood and bone, and accumulation of lipid peroxide. Upon ASP treatment, expression of silent information regulator (SIRT)1, brain-derived neurotrophic factor (BDNF), tropomyosin receptor kinase B (TrkB), MAPK, NF-κB, PI3K, Akt, and B-cell lymphoma-2 in rat brain tissue increased gradually, whereas that of Bax, caspase-3, and p53 decreased gradually. We demonstrated that ASP could regulate the brain damage caused by fluorosis through the SIRT1/BDNF/TrkB signaling pathway, and reported the possible part played by ASP in preventing and treating fluorosis.

[Review of joint attention deficit intervention based on virtual reality for children with autism].

Joint attention deficit is one of the core disorders in children with autism, which seriously affects the development of multiple basic skills such as language and communication. Virtual reality scene intervention has great potential in improving joint attention skills in children with autism due to its good interactivity and immersion. This article reviewed the application of virtual reality based social and nonsocial scenarios in training joint attention skills for children with autism in recent years, summarized the problems and challenges of this intervention method, and proposed a new joint paradigm for social scenario assessment and nonsocial scenario training. Finally, it looked forward to the future development and application prospects of virtual reality technology in joint attention skill training for children with autism.

[Research progress of brain-computer interface application paradigms based on rapid serial visual presentation].

Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.

Enhancing SSVEP-BCI Performance Under Fatigue State Using Dynamic Stopping Strategy.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have emerged as a prominent technology due to their high information transfer rate, rapid calibration time, and robust signal-to-noise ratio. However, a critical challenge for practical applications is performance degradation caused by user fatigue during prolonged use. This work proposes novel methods to address this challenge by dynamically adjusting data acquisition length and updating detection models based on a fatigue-aware stopping strategy. Two 16-target SSVEP-BCIs were employed, one using low-frequency and the other using high-frequency stimulation. A self-recorded fatigue dataset from 24 subjects was utilized for extensive evaluation. A simulated online experiment demonstrated that the proposed methods outperform the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, irrespective of stimulation frequency. These findings suggest that the proposed approach can significantly improve SSVEP-BCI performance under fatigue conditions, leading to superior performance during extended use.

cTBS over primary motor cortex increased contralateral corticomuscular coupling and interhemispheric functional connection.

Objective.Transcranial magnetic stimulation is a non-invasive brain stimulation technique that changes the activity of the cerebral cortex. Contralesional continuous theta burst stimulation (cTBS) has been proposed and verified beneficial to stroke motor recovery. However, the underlying mechanism is still unclear.Approach.20 healthy right-handed subjects were recruited in this study, receiving real-cTBS over their left primary motor cortex or sham-cTBS. We designed the finger tapping task (FTT) before and after stimulation and recorded the accuracy and reaction time (RT) of the task. The electroencephalogram and surface electromyogram signals were recorded during the left finger pinching task (FPT) before and after stimulation. We calculated cortico-muscular coherence (CMC) in the contralateral hemisphere and cortico-cortical coherence (CCC) in the bilateral hemisphere. The two-way repeated measures analysis of variance was used to analyze the effect of cTBS.Main results.In the FTT, there was a significant main effect of 'time' on RT (F(1, 38) = 24.739,p< 0.001). In the FPT, the results showed that there was a significant interaction effect on the CMC peak and area in the beta band (peak:F(1, 38) = 8.562,p= 0.006; area:F(1, 38) = 5.273,p= 0.027), on the CCC peak in the alpha band (F(1, 38) = 4.815,p= 0.034) and area in the beta band (F(1, 38) = 4.822,p= 0.034). The post hoc tests showed that the CMC peak (W= 20,p= 0.002), the CMC area (W= 13,p= 0.003) and the CCC peak (t= -2.696,p= 0.014) increased significantly after real-cTBS. However, there was no significant decrease or increase after sham-cTBS.Significance.Our study found that cTBS can improve CMC of contralateral hemisphere and CCC of bilateral hemisphere, indicating that cTBS can strengthen cortico-muscular and cortico-cortical coupling.

Detection of dynamic changes of electrodermal activity to predict the classroom performance of college students.

It is emphasized in the Self-regulated learning (SRL) framework that self-monitoring of learning state is vital for students to keep effective in studying. However, it's still challenging to get an accurate and timely understanding of their learning states during classes. In this study, we propose to use electrodermal activity (EDA) signals which are deemed to be associated with physiological arousal state to predict the college student's classroom performance. Twenty college students were recruited to attend eight lectures in the classroom, during which their EDA signals were recorded simultaneously. For each lecture, the students should complete pre- and after-class tests, and a self-reported scale (SRS) on their learning experience. EDA indices were extracted from both time and frequency domains, and they were furtherly mapped to the student's learning efficiency. As a result, the indices relevant to the dynamic changes of EDA had significant positive correlations with the learning efficiency. Furthermore, compared with only using SRS, a combination with EDA indices had significantly higher accuracy in predicting the learning efficiency. In conclusion, our findings demonstrate that the EDA dynamics are sensitive to the changes in learning efficiency, suggesting a promising approach to predicting the classroom performance of college students.

Major depressive disorder recognition by quantifying EEG signal complexity using proposed APLZC and AWPLZC.

BACKGROUND: Seeking objective quantitative indicators is important for accurately recognizing major depressive disorder (MDD). Lempel-Ziv complexity (LZC), employed to characterize neurological disorders, faces limitations in tracking dynamic changes in EEG signals due to defects in the coarse-graining process, hindering its precision for MDD objective quantitative indicators. METHODS: This work proposed Adaptive Permutation Lempel-Ziv Complexity (APLZC) and Adaptive Weighted Permutation Lempel-Ziv Complexity (AWPLZC) algorithms by refining the coarse-graining process and introducing weight factors to effectively improve the precision of LZC in characterizing EEGs and further distinguish MDD patients better. APLZC incorporated the ordinal pattern, while False Nearest Neighbor and Mutual Information algorithms were introduced to determine and adjust key parameters adaptively. Furthermore, we proposed AWPLZC by assigning different weights to each pattern based on APLZC. Thirty MDD patients and 30 healthy controls (HCs) were recruited and their 64-channel resting EEG signals were collected. The complexities of gamma oscillations were then separately computed using LZC, APLZC, and AWPLZC algorithms. Subsequently, a multi-channel adaptive K-nearest neighbor model was constructed for identifying MDD patients and HCs. RESULTS: LZC, APLZC, and AWPLZC algorithms achieved accuracy rates of 78.29 %, 90.32 %, and 95.13 %, respectively. Sensitivities reached 67.96 %, 85.04 %, and 98.86 %, while specificities were 88.62 %, 95.35 %, and 89.92 %, respectively. Notably, AWPLZC achieved the best performance in accuracy and sensitivity, with a specificity limitation. LIMITATION: The sample size is relatively small. CONCLUSION: APLZC and AWPLZC algorithms, particularly AWPLZC, demonstrate superior effectiveness in differentiating MDD patients from HCs compared with LZC. These findings hold significant clinical implications for MDD diagnosis.

The scalp time-varying network of auditory spatial attention in "cocktail-party" situations.

Sound source localization in "cocktail-party" situations is a remarkable ability of the human auditory system. However, the neural mechanisms underlying auditory spatial attention are still largely unknown. In this study, the "cocktail-party" situations are simulated through multiple sound sources and presented through head-related transfer functions and headphones. Furthermore, the scalp time-varying network of auditory spatial attention is constructed using the high-temporal resolution electroencephalogram, and its network properties are measured quantitatively using graph theory analysis. The results show that the time-varying network of auditory spatial attention in "cocktail-party" situations is more complex and partially different than in simple acoustic situations, especially in the early- and middle-latency periods. The network coupling strength increases continuously over time, and the network hub shifts from the posterior temporal lobe to the parietal lobe and then to the frontal lobe region. In addition, the right hemisphere has a stronger network strength for processing auditory spatial information in "cocktail-party" situations, i.e., the right hemisphere has higher clustering levels, higher transmission efficiency, and more node degrees during the early- and middle-latency periods, while this phenomenon disappears and appears symmetrically during the late-latency period. These findings reveal different network patterns and properties of auditory spatial attention in "cocktail-party" situations during different periods and demonstrate the dominance of the right hemisphere in the dynamic processing of auditory spatial information.

Enhancing Motor Sequence Learning via Transcutaneous Auricular Vagus Nerve Stimulation (taVNS): An EEG Study.

Motor learning plays a crucial role in human life, and various neuromodulation methods have been utilized to strengthen or improve it. Transcutaneous auricular vagus nerve stimulation (taVNS) has gained increasing attention due to its non-invasive nature, affordability and ease of implementation. Although the potential of taVNS on regulating motor learning has been suggested, its actual regulatory effect has yet been fully explored. Electroencephalogram (EEG) analysis provides an in-depth understanding of cognitive processes involved in motor learning so as to offer methodological support for regulation of motor learning. To investigate the effect of taVNS on motor learning, this study recruited 22 healthy subjects to participate a single-blind, sham-controlled, and within-subject serial reaction time task (SRTT) experiment. Every subject involved in two sessions at least one week apart and received a 20-minute active/sham taVNS in each session. Behavioral indicators as well as EEG characteristics during the task state, were extracted and analyzed. The results revealed that compared to the sham group, the active group showed higher learning performance. Additionally, the EEG results indicated that after taVNS, the motor-related cortical potential amplitudes and alpha-gamma modulation index decreased significantly and functional connectivity based on partial directed coherence towards frontal lobe was enhanced. These findings suggest that taVNS can improve motor learning, mainly through enhancing cognitive and memory functions rather than simple movement learning. This study confirms the positive regulatory effect of taVNS on motor learning, which is particularly promising as it offers a potential avenue for enhancing motor skills and facilitating rehabilitation.

Temporal hyper-connectivity and frontal hypo-connectivity within gamma band in schizophrenia: A resting state EEG study.

OBJECTIVE: The brain network serves as the physiological foundation for information processing of the brain. Many studies have reported abnormalities of gamma oscillations in Schizophrenia. The aim of this study was to investigate the gamma-band connectivity in Schizophrenia patients. METHODS: We recorded the resting state electroencephalogram (EEG) for 15 schizophrenia patients with refractory auditory hallucinations and 14 healthy controls, with eyes open and closed. The brain network was constructed based on weighted phase lag index for gamma band. Whole scalp metrics (clustering coefficient, global efficiency and local efficiency) and local region metrics (degree and betweenness centrality) in the frontal and temporal lobes were computed. Correlation analyses between network metrics and symptom scales were examined to find associations with symptom severity. RESULTS: Schizophrenia patients had larger global efficiency and local efficiency (p < 0.05) with eyes closed, probably representing greater brain activity and information exchange. For degree and betweenness centrality, schizophrenia patients showed an increase (p < 0.05) in the temporal lobe but a decrease (p < 0.05) in the frontal lobe with eyes closed and open, potentially account for the patients' symptoms such as hallucinations and thought disorders. Local efficiency and frontal lobe degree were positively and negatively correlated with the scales, respectively (both p < 0.05). CONCLUSIONS: Altered connectivity of the resting state brain network has been revealed and may be associated with the core symptoms of schizophrenia. Our study provides promising evidence for the investigation of the pathological basis of Schizophrenia and could aid in objective diagnosis.

In Vivo Transcranial Acoustoelectric Brain Imaging of Different Deep Brain Stimulation Currents.

Deep brain stimulation (DBS) is an effective treatment for neurologic disease and its clinical effect is highly dependent on the DBS leads localization and current stimulating state. However, standard human brain imaging modalities could not provide direct feedback on DBS currents spatial distribution and dynamic changes. Acoustoelectric brain imaging (AEBI) is an emerging neuroimaging method that can directly map current density distribution. Here, we investigate in vivo AEBI of different DBS currents to explore the potential of DBS visualization using AEBI. According to the typical DBS stimulus parameters, four types of DBS currents, including time pattern, waveform, frequency, and amplitude are designed to implement AEBI experiments in living rat brains. Based on acoustoelectric (AE) signals, the AEBI images of each type DBS current are explored and the resolution is quantitatively analyzed for performance evaluation. Furtherly, the AE signals are decoded to characterize DBS currents from multiple perspectives, including time-frequency domain, spatial distribution, and amplitude comparation. The results show that in vivo transcranial AEBI can accurately locate the DBS contact position with a millimeter spatial resolution (< 2 mm) and millisecond temporal resolution (< 10 ms). Besides, the decoded AE signal at DBS contact position is capable of describing the corresponding DBS current characteristics and identifying current pattern changes. This study first validates that AEBI can localize in vivo DBS contact and characterize different DBS currents. AEBI is expected to develop into a noninvasive DBS real-time monitoring technology with high spatiotemporal resolution.

Ultrasound Stimulation Attenuates CRS-Induced Depressive Behavior by Modulating Dopamine Release in the Prefrontal Cortex.

Depression is one of the most serious mental disorders affecting modern human life and is often caused by chronic stress. Dopamine system dysfunction is proposed to contribute to the pathophysiology of chronic stress, especially the ventral tegmental area (VTA) which mainly consists of dopaminergic neurons. Focused ultrasound stimulation (FUS) is a promising neuromodulation modality and multiple studies have demonstrated effective ultrasonic activation of cortical, subcortical, and related networks. However, the effects of FUS on the dopamine system and the potential link to chronic stress-induced depressive behaviors are relatively unknown. Here, we measured the effects of FUS targeting VTA on the improvement of depression-like behavior and evaluated the dopamine concentration in the downstream region - medial prefrontal cortex (mPFC). We found that targeting VTA FUS treatment alleviated chronic restraint stress (CRS) -induced anhedonia and despair behavior. Using an in vivo photometry approach, we analyzed the dopamine signal of mPFC and revealed a significant increase following the FUS, positively associated with the improvement of anhedonia behavior. FUS also protected the dopaminergic neurons in VTA from the damage caused by CRS exposure. Thus, these results demonstrated that targeting VTA FUS treatment significantly rescued the depressive-like behavior and declined dopamine level of mPFC induced by CRS. These beneficial effects of FUS might be due to protection in the DA neuron of VTA. Our findings suggest that FUS treatment could serve as a new therapeutic strategy for the treatment of stress-related disorders.

Dynamic prediction of goal location by coordinated representation of prefrontal-hippocampal theta sequences.

Prefrontal (PFC) and hippocampal (HPC) sequences of neuronal firing modulated by theta rhythms could represent upcoming choices during spatial memory-guided decision-making. How the PFC-HPC network dynamically coordinates theta sequences to predict specific goal locations and how it is interrupted in memory impairments induced by amyloid beta (Aß) remain unclear. Here, we detected theta sequences of firing activities of PFC neurons and HPC place cells during goal-directed spatial memory tasks. We found that PFC ensembles exhibited predictive representation of the specific goal location since the starting phase of memory retrieval, earlier than the hippocampus. High predictive accuracy of PFC theta sequences existed during successful memory retrieval and positively correlated with memory performance. Coordinated PFC-HPC sequences showed PFC-dominant prediction of goal locations during successful memory retrieval. Furthermore, we found that theta sequences of both regions still existed under Aß accumulation, whereas their predictive representation of goal locations was weakened with disrupted spatial representation of HPC place cells and PFC neurons. These findings highlight the essential role of coordinated PFC-HPC sequences in successful memory retrieval of a precise goal location.

A user-friendly visual brain-computer interface based on high-frequency steady-state visual evoked fields recorded by OPM-MEG.

Objective. Magnetoencephalography (MEG) shares a comparable time resolution with electroencephalography. However, MEG excels in spatial resolution, enabling it to capture even the subtlest and weakest brain signals for brain-computer interfaces (BCIs). Leveraging MEG's capabilities, specifically with optically pumped magnetometers (OPM-MEG), proves to be a promising avenue for advancing MEG-BCIs, owing to its exceptional sensitivity and portability. This study harnesses the power of high-frequency steady-state visual evoked fields (SSVEFs) to build an MEG-BCI system that is flickering-imperceptible, user-friendly, and highly accurate.Approach.We have constructed a nine-command BCI that operates on high-frequency SSVEF (58-62 Hz with a 0.5 Hz interval) stimulation. We achieved this by placing the light source inside and outside the magnetic shielding room, ensuring compliance with non-magnetic and visual stimulus presentation requirements. Five participants took part in offline experiments, during which we collected six-channel multi-dimensional MEG signals along both the vertical (Z-axis) and tangential (Y-axis) components. Our approach leveraged the ensemble task-related component analysis algorithm for SSVEF identification and system performance evaluation.Main Results.The offline average accuracy of our proposed system reached an impressive 92.98% when considering multi-dimensional conjoint analysis using data from both theZandYaxes. Our method achieved a theoretical average information transfer rate (ITR) of 58.36 bits min-1with a data length of 0.7 s, and the highest individual ITR reached an impressive 63.75 bits min-1.Significance.This study marks the first exploration of high-frequency SSVEF-BCI based on OPM-MEG. These results underscore the potential and feasibility of MEG in detecting subtle brain signals, offering both theoretical insights and practical value in advancing the development and application of MEG in BCI systems.

Molecular understanding of the therapeutic potential of melanin inhibiting natural products.

With the development of society and the improvement of people's living standards, there is an increasing demand for melanin-inhibiting products that prioritize health, safety, and efficacy. Therefore, the development of natural products that can safely and efficiently inhibit melanin synthesis is of great social significance and has significant market potential. In this paper, by reviewing the literature reported in recent years, we summarized the natural products with inhibition of melanin synthesis effects that have been put into or not yet put into the market, and classified them according to the chemical groups of their compounds or the extraction methods of the natural products. Through the summary analysis, we found that these compounds mainly include terpenoids, phenylpropanoids, flavonoids and so on, while the natural product extracts mainly include methanol extracts, ethanol extracts, and aqueous extracts. Their main inhibition of melanin synthesis mechanisms include: (1) direct inhibition of tyrosinase activity; (2) down-regulation of the α-MSH-MC1R, Wnt, NO, PI3K/Akt and MAPK pathways through the expression of MITF and its downstream genes TYR, TRP-1, and TRP-2; (3) antioxidant; (4) inhibition of melanocyte growth through cytotoxicity; (5) inhibition of melanosome production and transport. This paper provides an in-depth discussion on the research progress of whitening natural products and their market value. The aim is to offer guidance for future research and development of natural skin whitening products.

Photobiomodulation therapy mitigates depressive-like behaviors by remodeling synaptic links and mitochondrial function.

Depression, a multifactorial mental disorder, characterized by cognitive slowing, anxiety, and impaired cognitive function, imposes a significant burden on public health. Photobiomodulation (PBM), involving exposure to sunlight or artificial light at a specific intensity and wavelength for a determined duration, influences brain activity, functional connectivity, and plasticity. It is recognized for its therapeutic efficacy in treating depression, yet its molecular and cellular underpinnings remain obscure. Here, we investigated the impact of PBM with 468 nm light on depression-like behavior and neuronal damage in the chronic unpredictable mild stress (CUMS) murine model, a commonly employed animal model for studying depression. Our results demonstrate that PBM treatment ameliorated behavioral deficits, inhibited neuroinflammation and apoptosis, and notably rejuvenates the hippocampal synaptic function in depressed mice, which may be mainly attributed to the up-regulation of brain-derived neurotrophic factor signaling pathways. In addition, in vitro experiments with a corticosterone-induced hippocampal neuron injury model demonstrate reduced oxidative stress and improved mitochondrial function, further validating the therapeutic potential of PBM. In summary, these findings suggest PBM as a promising, non-invasive treatment for depression, offering insights into its biological mechanisms and potential for clinical application.

A Male Patient with Hydrocephalus via Multimodality Diagnostic Approaches: A Case Report.

Introduction: Idiopathic normal pressure hydrocephalus (iNPH) is a kind of hydrocephalus that is easily to be misdiagnosed with brain atrophy due to the similarity of ventricular dilation and cognitive impairment. In this case, we present an old male patient who was diagnosed with iNPH by multimodality approaches. Outcomes: A 68-year-old male patient, with deteriorated gait, declined cognitive function for at least 3 years and urinary incontinence for 3 months. The doctors suspected him a patient with hydrocephalus or Alzheimer's disease based on his symptoms. We used multimodality diagnostic approaches including brain imaging, cerebrospinal fluid tap test, continuous intracranial pressure monitoring, and infusion study to make the final diagnosis of iNPH. He underwent ventriculoperitoneal shunt surgery and was well recovered. Conclusion: This case demonstrates the efficacy of using multimodality approaches for iNPH diagnosis, which saves patient time and clinical cost, worthy of further promotion.