Recent advances and applications of human brain models.

Recent advances in human pluripotent stem cell (hPSC) technologies have prompted the emergence of new research fields and applications for human neurons and brain organoids. Brain organoids have gained attention as an in vitro model system that recapitulates the higher structure, cellular diversity and function of the brain to explore brain development, disease modeling, drug screening, and regenerative medicine. This progress has been accelerated by abundant interactions of brain organoid technology with various research fields. A cross-disciplinary approach with human brain organoid technology offers a higher-ordered advance for more accurately understanding the human brain. In this review, we summarize the status of neural induction in two- and three-dimensional culture systems from hPSCs and the modeling of neurodegenerative diseases using brain organoids. We also highlight the latest bioengineered technologies for the assembly of spatially higher-ordered neural tissues and prospects of brain organoid technology toward the understanding of the potential and abilities of the human brain.

Integrative analysis identifies region- and sex-specific gene networks and Mef2c as a mediator of anxiety-like behavior.

The molecular mechanisms underlying multi-brain region origins and sexual dimorphism of anxiety remain unclear. Here, we leverage large-scale transcriptomics from seven brain regions in mouse models of anxiety and extensive experiments to dissect brain-region- and sex-specific gene networks. We identify 4,840 genes with sex-specific expression alterations across seven brain regions, organized into ten network modules with sex-biased expression patterns. Modular analysis prioritizes 86 sex-specific mediators of anxiety susceptibility, including myocyte-specific enhancer factor 2c (Mef2c) in the CA3 region of male mice. Mef2c expression is decreased in the pyramidal neurons (PyNs) of susceptible male mice. Up-regulating Mef2c in CA3 PyNs significantly alleviates anxiety-like behavior, whereas down-regulating Mef2c induces anxiety-like behavior in male mice. The anxiolytic effect of Mef2c up-regulation is associated with enhanced neuronal excitability and synaptic transmission. In summary, this study uncovers brain-region- and sex-specific networks and identifies Mef2c in CA3 PyNs as a critical mediator of anxiety in male mice.

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network.

BACKGROUND: Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities. OBJECTIVE: This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals. METHODS: In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction. RESULTS: The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection. CONCLUSION: The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis.

Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier.

Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-ß (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.

Investigating the informative brain region in multiclass electroencephalography and near infrared spectroscopy based BCI system using band power based features.

In recent years, various brain imaging techniques have been used as input signals for brain-computer interface (BCI) systems. Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are two prominent techniques in this field, each with its own advantages and limitations. As a result, there is a growing tendency to integrate these methods in a hybrid within BCI systems. The primary aim of this study is to identify highly functional brain regions within an EEG + NIRS-based BCI system. To achieve this, the research focused on identifying EEG electrodes positioned in different brain lobes and then investigating the functionality of each lobe. The methodology involved segmenting the EEG + NIRS dataset into 2.4 s time windows, and then extracting band-power based features from these segmented signals. A classification algorithm, specifically the k-nearest neighbor algorithm, was then used to classify the features. The result was a remarkable classification accuracy (CA) of 95.54%±1.31 when using the active brain region within the hybrid model. These results underline the effectiveness of the proposed approach, as it outperformed both standalone EEG and NIRS modalities in terms of CA by 5.19% and 40.90%, respectively. Furthermore, the results confirm the considerable potential of the method in classifying EEG + NIRS signals recorded during tasks such as reading text while scrolling in different directions, including right, left, up and down. This research heralds a promising step towards enhancing the capabilities of BCI systems by harnessing the synergistic power of EEG and NIRS technologies.

Brain region changes following a spinal cord injury.

Brain-related complications are common in clinical practice after spinal cord injury (SCI); however, the molecular mechanisms of these complications are still unclear. Here, we reviewed the changes in the brain regions caused by SCI from three perspectives: imaging, molecular analysis, and electrophysiology. Imaging studies revealed abnormal functional connectivity, gray matter volume atrophy, and metabolic abnormalities in brain regions after SCI, leading to changes in the structure and function of brain regions. At the molecular level, chemokines, inflammatory factors, and damage-associated molecular patterns produced in the injured area were retrogradely transmitted through the corticospinal tract, cerebrospinal fluid, or blood circulation to the specific brain area to cause pathologic changes. Electrophysiologic recordings also suggested abnormal changes in brain electrical activity after SCI. Transcranial magnetic stimulation, transcranial direct current stimulation, and deep brain stimulation alleviated pain and improved motor function in patients with SCI; therefore, transcranial therapy may be a new strategy for the treatment of patients with SCI.

Exploration of Hippocampal Functional Connectivity Alterations in Patients with High Myopia via Seed-Based Functional Connectivity Analysis.

Aim: The objective of this study was to examine changes in functional connectivity (FC) in the hippocampus among patients with high myopia (HM) compared to healthy controls (HCs) through the utilization of seed-based functional connectivity (FC) analysis. Methods: Resting-state functional magnetic resonance imaging (fMRI) was conducted on a sample of 82 patients diagnosed with high myopia (HM) and 59 HCs. The two groups were matched based on age, weight and other relevant variables. Using seed-based FC analysis to detect alterations in hippocampal FC patterns in HM patients and HCs. Furthermore, a correlation analysis was performed to examine the associations between the mean functional connectivity (FC) signals in various brain regions of patients with HM and their corresponding clinical manifestations. Results: The FC values in the left temporal pole-temporal gyrus (L-TPOsup), right hippocampus (R-HIP), left medial temporal gyrus (L-MTG) and left hippocampus in HM patients were significantly lower than those of healthy subjects. In the left temporal pole-superior temporal gyrus (L-TPOsup), right orbital part of middle frontal gyrus (RO-MFG), left fusiform gyrus (L-FG), left cerebellum superior (L-Cbe6), left middle temporal gyrus (L-MTG), right thalamus (R-THA), and right hippocampus, FC values were also significantly lower. Conclusion: Brain dysfunction was observed in various regions of the HM patients, suggesting the existence of neurobiological alterations that could lead to impairments in visual cognition, movement, emotional processing, and visual memory.

Analysis of the brain transcriptome for substance-associated genes: An update on large-scale genome-wide association studies.

Substance use disorder (SUD) arises from the initiation to subsequent regular, irregular and harmful use of substances such as alcohol, tobacco/nicotine and cannabis. While thousands of genetic variants have been identified from recent large-scale genome-wide association studies (GWAS), understanding their functions in substance involvement and investigating the mechanisms by which they act in the addiction circuits remains challenging. In this study, we re-analysed the brain regional transcriptome data from the most comprehensive postmortem transcriptomic study, with a focus on up- or down-regulation of substance-associated protein-coding genes in the addiction circuit-related brain regions (AddictRegions), including six cortical and 11 subcortical regions. We found that substance-associated genes were overrepresented in AddictRegions. Interestingly, we observed a greater degree of genetic overlap between initiation and use and between use and SUD than between initiation and SUD. Moreover, substance initiation, use and SUD-associated genes tend to shift their enriched AddictRegions from the cortical for initiation and, to a lesser extent, substance use to subcortical regions for SUD (e.g., thalamus, substantia nigra and ventral tegmental area). We also uncovered a pattern of coordinated cortical up-regulation and subcortical down-regulation for the genes associated with tobacco initiation and alcohol use. Moreover, the Gene Ontology terms of glutamate receptor activity and neurotransmitter binding were most significantly overrepresented in AddictRegion-upregulated, substance-associated genes, with the strongest enrichment for those involved in common substance use behaviours. Overall, our analysis provides a resource of AddictRegion-related transcriptomes for studying substance-associated genes and generates intriguing insights into the genetic control of substance initiation, use and SUD.

Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment.

Background: There is increasing evidence that patients with retinal detachment (RD) have aberrant brain activity. However, neuroimaging investigations remain focused on static changes in brain activity among RD patients. There is limited knowledge regarding the characteristics of dynamic brain activity in RD patients. Aim: This study evaluated changes in dynamic brain activity among RD patients, using a dynamic amplitude of low-frequency fluctuation (dALFF), k-means clustering method and support vector machine (SVM) classification approach. Methods: We investigated inter-group disparities of dALFF indices under three different time window sizes using resting-state functional magnetic resonance imaging (rs-fMRI) data from 23 RD patients and 24 demographically matched healthy controls (HCs). The k-means clustering method was performed to analyze specific dALFF states and related temporal properties. Additionally, we selected altered dALFF values under three distinct conditions as classification features for distinguishing RD patients from HCs using an SVM classifier. Results: RD patients exhibited dynamic changes in local intrinsic indicators of brain activity. Compared with HCs, RD patients displayed increased dALFF in the bilateral middle frontal gyrus, left putamen (Putamen_L), left superior occipital gyrus (Occipital_Sup_L), left middle occipital gyrus (Occipital_Mid_L), right calcarine (Calcarine_R), right middle temporal gyrus (Temporal_Mid_R), and right inferior frontal gyrus (Frontal_Inf_Tri_R). Additionally, RD patients showed significantly decreased dALFF values in the right superior parietal gyrus (Parietal_Sup_R) and right paracentral lobule (Paracentral_Lobule_R) [two-tailed, voxel-level p < 0.05, Gaussian random field (GRF) correction, cluster-level p < 0.05]. For dALFF, we derived 3 or 4 states of ALFF that occurred repeatedly. There were differences in state distribution and state properties between RD and HC groups. The number of transitions between the dALFF states was higher in the RD group than in the HC group. Based on dALFF values in various brain regions, the overall accuracies of SVM classification were 97.87, 100, and 93.62% under three different time windows; area under the curve values were 0.99, 1.00, and 0.95, respectively. No correlation was found between hamilton anxiety (HAMA) scores and regional dALFF. Conclusion: Our findings offer important insights concerning the neuropathology that underlies RD and provide robust evidence that dALFF, a local indicator of brain activity, may be useful for clinical diagnosis.

Investigation of Altered Spontaneous Brain Activity Patterns in Herpes Zoster Keratitis Using the Percent Amplitude of Fluctuation Method: A Resting-State Functional Magnetic Resonance Imaging Study.

Purpose: The purpose of this study was to use the percent amplitude of fluctuation (PerAF) to study the changes in brain activity and nerve function of herpes zoster keratitis (HZK) patients. Methods: We recruited 20 HZK patients and 20 healthy controls (HCs). Each of these groups included ten males and ten females and were matched in weight and age. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI). The percent amplitude of fluctuation (PerAF) method was used for analysis and detected differences between the two groups in the neurological function of brain areas. We also applied the receiver operating characteristic (ROC) curve to analyze the two groups and did a correlation analysis between the PerAF value, anxiety and depression score, and visual acuity. Results: The PerAF signal at the right putamen and right precentral gyrus was significantly higher in patients than in HCs. However, the PerAF value of the left inferior temporal was lower in patients than in HCs. In addition, the HZK patients' anxiety and depression score (HADS) and visual acuity (V.A.) Log MAR negatively correlated with the PerAF value at the left inferior temporal gyrus. Conclusion: HZK patients had some changes in brain regions, and the changes were also related to their mood and visual acuity. These findings might contribute to other studies on the potential pathological mechanism, disease development, prognosis, and brain function in HZK patients.

TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning.

In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.

Depression Detection Based on Analysis of EEG Signals in Multi Brain Regions.

BACKGROUND: As an objective method to detect the neural electrical activity of the brain, electroencephalography (EEG) has been successfully applied to detect major depressive disorder (MDD). However, the performance of the detection algorithm is directly affected by the selection of EEG channels and brain regions. METHODS: To solve the aforementioned problems, nonlinear feature Lempel-Ziv complexity (LZC) and frequency domain feature power spectral density (PSD) were extracted to analyze the EEG signals. Additionally, effects of different brain regions and region combinations on detecting MDD were studied with eyes closed and opened in a resting state. RESULTS: The mean LZC of patients with MDD was higher than that of the control group, and the mean PSD of patients with MDD was generally lower than that of the control group. The temporal region is the best brain region for MDD detection with a detection accuracy of 87.4%. The best multi brain regions combination had a detection accuracy of 92.4% and was made up of the frontal, temporal, and central brain regions. CONCLUSIONS: This paper validates the effectiveness of multiple brain regions in detecting MDD. It provides new ideas for exploring the pathology of MDD and innovative methods of diagnosis and treatment.

Neuroprotective potential of berberine against acetamiprid induced toxicity in rats: Implication of oxidative stress, mitochondrial alterations, and structural changes in brain regions.

Acetamiprid (ACMP) is an extensively used neonicotinoid pesticide to control sucking and chewing insects and is known to cause nontarget toxicity. The present study aimed to evaluate the ameliorative potential of berberine (BBR)-a polyphenolic alkaloid- on ACMP-induced oxidative stress, mitochondrial dysfunctioning, and structural changes in different rat brain regions. The male Wistar rats were divided into four groups, that is, control, BBR-treated (150 mg/kg b.wt), ACMP-exposed (21.7 mg/kg b.wt) and BBR + ACMP co-treated; and were dosed intragastrically for 21 consecutive days. Results of the biochemical analysis showed that BBR significantly ameliorated ACMP-induced oxidative stress by decreasing lipid peroxidation and protein oxidation along with a marked increase in endogenous antioxidants and lowered AChE activity in rat brain regions. Inside mitochondria, BBR significantly attenuated the toxic effects of ACMP by increasing the activity of mitochondrial complexes. Findings of polymerase chain reaction also demonstrated the modulatory effects of BBR against ACMP-mediated downregulation of ND1, ND2, COX1, and COX4 subunits of mitochondrial complexes. The histopathological and ultrastructural examination also validated the biochemical and transcriptional alterations following toxicity of ACMP exposure and the protective potential of BBR against ACMP-induced neurotoxicity. Thus, the present study indicates the promising ameliorative potential of BBR against ACMP-induced neurotoxicity via its antioxidative and modulatory activities.

Spatiotemporal tracking of gold nanorods after intranasal administration for brain targeting.

Intranasal administration is becoming increasingly more attractive as a fast delivery route to the brain for therapeutics circumventing the blood-brain barrier (BBB). Gold nanorods (AuNRs) demonstrate unique optical and biological properties compared to other gold nanostructures due to their high aspect ratio. In this study, we investigated for the first time the brain region-specific distribution of AuNRs and their potential as a drug delivery platform for central nervous system (CNS) therapy following intranasal administration to mice using a battery of analytical and imaging techniques. AuNRs were functionalized with a fluorescent dye (Cyanine5, Cy5) or a metal chelator (diethylenetriaminepentaacetic dianhydride, DTPA anhydride) to complex with Indium-111 via a PEG spacer for optical and nuclear imaging, respectively. Direct quantification of gold was achieved by inductively coupled plasma mass spectrometry. Rapid AuNRs uptake in mice brains was observed within 10 min following intranasal administration which gradually reduced over time. This was confirmed by the 3 imaging/analytical techniques. Autoradiography of sagittal brain sections suggested entry to the brain via the olfactory bulb followed by diffusion to other brain regions within 1 h of administration. The presence of AuNR in glioblastoma (GBM) tumors following intranasal administration was also proven which opens doors for AuNRs applications, as nose-to-brain drug delivery carriers, for treatment of a range of CNS diseases.

Abnormal activation of brain regions in idiopathic trigeminal neuralgia patients by fMRI: An activation likelihood estimation meta-analysis.

BACKGROUND: Idiopathic trigeminal neuralgia (ITN) is one of the most common types of neuropathic pain, severely affecting the physiological and psychological wellbeing of patients. Recently, fMRI has been used to examine abnormal activation of brain regions in patients with ITN. However, sample sizes have been small in these few studies, and the abnormally activated brain regions remain unclear. Therefore, in the present study, we retrieved and analyzed literature on the brain areas with abnormal or reduced activation in ITN patients, with the aim of providing insight into the neuropathological basis of the disease and to provide new targets for treatment. METHODS: We retrieved resting state fMRI studies on trigeminal neuralgia patients from PubMed, the Web of Science and Scopus databases until November 2022, and we extracted the coordinates of the sites with increased or decreased activation. We used activation likelihood estimation (ALE) meta-analysis to identify regions of abnormal activation in ITN patients. RESULTS: ALE meta-analysis revealed that the left caudate nucleus and right anterior ventral nucleus of the thalamus are abnormally hyperactivated in ITN patients. Moreover, ITN patients showed reduced activation in the left precuneus, middle temporal gyrus, lingual gyrus, and medial frontal gyrus. CONCLUSION: ALE meta-analysis identified several brain regions with abnormally high or decreased activation in ITN patients. Sites with altered activation may be potential targets for non-invasive brain stimulation as adjunct therapy for ITN.

The Role of Non-coding RNAs in Methamphetamine-Induced Neurotoxicity.

Methamphetamine (METH) is an amphetamine-type stimulant that is highly toxic to the central nervous system (CNS). Repeated intake of METH can lead to addiction, which has become a globalized problem, resulting in multiple public health and safety problems. Recently, the non-coding RNA (ncRNA) has been certified to play an essential role in METH addiction through various mechanisms. Herein, we mainly focused on three kinds of ncRNAs including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), which are involved in neurotoxicity effects such as cognitive impairment, behavioral abnormalities, and psychiatric disorders due to METH abuse. In addition, differential expression (DE) ncRNAs also suggest that specific responses and sensitivity to METH neurotoxicity exist in different brain regions and cells. We summarized the relationships between the ncRNAs and METH-induced neurotoxicity and psychiatric disturbances, respectively, hoping to provide new perspectives and strategies for the prevention and treatment of METH abuse. Schematic diagram of the non-coding RNAs (ncRNAs) was involved in methamphetamine (METH)-induced neurotoxicity. The ncRNAs were involved in METH-induced blood-brain barrier disruption, neuronal, astrocyte, and microglial damage, and synaptic neurotransmission impairment. The study of ncRNAs is a hot spot in the future to further understand the neurotoxicity of METH and provide more favorable scientific support for clinical diagnosis and innovation of related treatments.

BDNF is altered in a brain-region specific manner and rescues deficits in Spinocerebellar Ataxia Type 1.

Spinocerebellar ataxia type 1 (SCA1) is an adult-onset, dominantly inherited neurodegenerative disease caused by the expanded polyQ tract in the protein ATAXIN1 (ATXN1) and characterized by progressive motor and cognitive impairments. There are no disease-modifying treatments or cures for SCA1. Brain-derived neurotrophic factor (BDNF) plays important role in cerebellar physiology and has shown therapeutic potential for cerebellar pathology in the transgenic mouse model of SCA1, ATXN1[82Q] line that overexpress mutant ATXN1 under a cerebellar Purkinje-cell-specific promoter. Here we demonstrate decreased expression of brain derived neurotrophic factor (BDNF) in the cerebellum and medulla of patients with SCA1. Early stages of disease seem most amenable to therapy. Thus, we next quantified Bdnf expression in Atxn1154Q/2Q mice, a knock-in mouse model of SCA1, during the early symptomatic disease stage in four clinically relevant brain regions: cerebellum, medulla, hippocampus and motor cortex. We found that during the early stages of disease, Bdnf mRNA expression is reduced in the hippocampus and cerebellum, while it is increased in the cortex and brainstem. Importantly, we observed that pharmacological delivery of recombinant BDNF improved motor and cognitive performance, and mitigated pathology in the cerebellum and hippocampus of Atxn1154Q/2Q mice. Our findings demonstrate brain-region specific deficiency of BDNF in SCA1 and show that reversal of low BDNF levels offers the potential for meaningful treatment of motor and cognitive deficits in SCA1.

Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress.

BACKGROUND: Prenatal stress (PS) is considered as a risk factor for many mental disorders. PS-induced transcriptomic alterations may contribute to the functional dysregulation during brain development. Here, we used RNA-seq to explore changes of gene expression in the mouse fetal brain after prenatal exposure to chronic unpredictable mild stress (CUMS). RESULTS: We compared the stressed brains to the controls and identified groups of significantly differentially expressed genes (DEGs). GO analysis on up-regulated DEGs revealed enrichment for the cell cycle pathways, while down-regulated DEGs were mostly enriched in the neuronal pathways related to synaptic transmission. We further performed cell-type enrichment analysis using published scRNA-seq data from the fetal mouse brain and revealed cell-type-specificity for up- and down-regulated DEGs, respectively. The up-regulated DEGs were highly enriched in the radial glia, while down-regulated DEGs were enriched in different types of neurons. Cell deconvolution analysis further showed altered cell fractions in the stressed brain, indicating accumulation of neuroblast and impaired neurogenesis. Moreover, we also observed distinct brain-region expression pattern when mapping DEGs onto the developing Allen brain atlas. The up-regulated DEGs were primarily enriched in the dorsal forebrain regions including the cortical plate and hippocampal formation. Surprisingly, down-regulated DEGs were found excluded from the cortical region, but highly expressed on various regions in the ventral forebrain, midbrain and hindbrain. CONCLUSION: Taken together, we provided an unbiased data source for transcriptomic alterations of the whole fetal brain after chronic PS, and reported differential cell-type and brain-region vulnerability of the developing brain in response to environmental insults during the pregnancy.

Double Blast Wave Primary Effect on Synaptic, Glymphatic, Myelin, Neuronal and Neurovascular Markers.

Explosive blasts are associated with neurological consequences as a result of blast waves impact on the brain. Yet, the neuropathologic and molecular consequences due to blast waves vs. blunt-TBI are not fully understood. An explosive-driven blast-generating system was used to reproduce blast wave exposure and examine pathological and molecular changes generated by primary wave effects of blast exposure. We assessed if pre- and post-synaptic (synaptophysin, PSD-95, spinophilin, GAP-43), neuronal (NF-L), glymphatic (LYVE1, podoplanin), myelin (MBP), neurovascular (AQP4, S100ß, PDGF) and genomic (DNA polymerase-ß, RNA polymerase II) markers could be altered across different brain regions of double blast vs. sham animals. Twelve male rats exposed to two consecutive blasts were compared to 12 control/sham rats. Western blot, ELISA, and immunofluorescence analyses were performed across the frontal cortex, hippocampus, cerebellum, and brainstem. The results showed altered levels of AQP4, S100ß, DNA-polymerase-ß, PDGF, synaptophysin and PSD-95 in double blast vs. sham animals in most of the examined regions. These data indicate that blast-generated changes are preferentially associated with neurovascular, glymphatic, and DNA repair markers, especially in the brainstem. Moreover, these changes were not accompanied by behavioral changes and corroborate the hypothesis for which an asymptomatic altered status is caused by repeated blast exposures.

Exploration of static functional connectivity and dynamic functional connectivity alterations in the primary visual cortex among patients with high myopia via seed-based functional connectivity analysis.

Aim: This study was conducted to explore differences in static functional connectivity (sFC) and dynamic functional connectivity (dFC) alteration patterns in the primary visual area (V1) among high myopia (HM) patients and healthy controls (HCs) via seed-based functional connectivity (FC) analysis. Methods: Resting-state functional magnetic resonance imaging (fMRI) scans were performed on 82 HM patients and 59 HCs who were closely matched for age, sex, and weight. Seed-based FC analysis was performed to identify alterations in the sFC and dFC patterns of the V1 in HM patients and HCs. Associations between mean sFC and dFC signal values and clinical symptoms in distinct brain areas among HM patients were identified via correlation analysis. Static and dynamic changes in brain activity in HM patients were investigated by assessments of sFC and dFC via calculation of the total time series mean and sliding-window analysis. Results: In the left anterior cingulate gyrus (L-ACG)/left superior parietal gyrus (L-SPG) and left V1, sFC values were significantly greater in HM patients than in HCs. In the L-ACG and right V1, sFC values were also significantly greater in HM patients than in HCs [two-tailed, voxel-level P < 0.01, Gaussian random field (GRF) correction, cluster-level P < 0.05]. In the left calcarine cortex (L-CAL) and left V1, dFC values were significantly lower in HM patients than in HCs. In the right lingual gyrus (R-LING) and right V1, dFC values were also significantly lower in HM patients than in HCs (two-tailed, voxel-level P < 0.01, GRF correction, cluster-level P < 0.05). Conclusion: Patients with HM exhibited significantly disturbed FC between the V1 and various brain regions, including L-ACG, L-SPG, L-CAL, and R-LING. This disturbance suggests that patients with HM could exhibit impaired cognitive and emotional processing functions, top-down control of visual attention, and visual information processing functions. HM patients and HCs could be distinguished from each other with high accuracy using sFC and dFC variabilities. These findings may help to identify the neural mechanism of decreased visual performance in HM patients.