Brain Regions Research Articles

Emerging Functions of Neuromodulation during Sleep.

Neuromodulators act on multiple timescales to affect neuronal activity and behavior. They function as synaptic fine-tuners and master coordinators of neuronal activity across distant brain regions and body organs. While much research on neuromodulation has focused on roles in promoting features of wakefulness and transitions between sleep and wake states, the precise dynamics and functions of neuromodulatory signaling during sleep have received less attention. This review discusses research presented at our minisymposium at the 2024 Society for Neuroscience meeting, highlighting how norepinephrine, dopamine, and acetylcholine orchestrate brain oscillatory activity, control sleep architecture and microarchitecture, regulate responsiveness to sensory stimuli, and facilitate memory consolidation. The potential of each neuromodulator to influence neuronal activity is shaped by the state of the synaptic milieu, which in turn is influenced by the organismal or systemic state. Investigating the effects of neuromodulator release across different sleep substates and synaptic environments offers unique opportunities to deepen our understanding of neuromodulation and explore the distinct computational opportunities that arise during sleep. Moreover, since alterations in neuromodulatory signaling and sleep are implicated in various neuropsychiatric disorders and because existing pharmacological treatments affect neuromodulatory signaling, gaining a deeper understanding of the less-studied aspects of neuromodulators during sleep is of high importance.

Associations between antagonistic SNPs for neuropsychiatric disorders and human brain structure

A previously published genome-wide association study (GWAS) meta-analysis across eight neuropsychiatric disorders identified antagonistic single-nucleotide polymorphisms (SNPs) at eleven genomic loci where the same allele was protective against one neuropsychiatric disorder and increased the risk for another. Until now, these antagonistic SNPs have not been further investigated regarding their link to brain structural phenotypes. Here, we explored their associations with cortical surface area and cortical thickness (in 34 brain regions and one global measure each) as well as the volumes of eight subcortical structures using summary statistics of large-scale GWAS of brain structural phenotypes. We assessed if significantly associated brain structural phenotypes were previously reported to be associated with major neuropsychiatric disorders in large-scale case-control imaging studies by the ENIGMA consortium. We further characterized the effects of the antagonistic SNPs on gene expression in brain tissue and their association with additional cognitive and behavioral phenotypes, and performed an exploratory voxel-based whole-brain analysis in the FOR2107 study (n = 754 patients with major depressive disorder and n = 847 controls). We found that eight antagonistic SNPs were significantly associated with brain structural phenotypes in regions such as anterior parts of the cingulate cortex, the insula, and the superior temporal gyrus. Case-control differences in implicated brain structural phenotypes have previously been reported for bipolar disorder, major depressive disorder, and schizophrenia. In addition, antagonistic SNPs were associated with gene expression changes in brain tissue and linked to several cognitive-behavioral traits. In our exploratory whole-brain analysis, we observed significant associations of gray matter volume in the left superior temporal pole and left superior parietal region with the variants rs301805 and rs1933802, respectively. Our results suggest that multiple antagonistic SNPs for neuropsychiatric disorders are linked to brain structural phenotypes. However, to further elucidate these findings, future case-control genomic imaging studies are required.

Impact of GLP-1 Agonists on Depression Treatment: A Literature Review

Introduction Depression is increasingly recognized as a major global health issue, driven by neurobiological, genetic, and environmental factors. Despite extensive research, understanding depression remains challenging due to its complexity and the lack of biomarkers. Recent studies highlight a link between depression and metabolic disturbances, emphasizing the need for alternative therapeutic approaches. Purpose This review aims to explore the therapeutic potential of GLP-1 (glucagon-like peptide-1) in treating depression, focusing on its impact on neurogenesis, neuroinflammation, neurotransmitter imbalances, and synaptic dysfunction. Materials and Methods A systematic review was conducted by analyzing literature from databases such as PubMed and Google Scholar, covering studies from 2003 to 2024. The review focused on keywords related to GLP-1, depression, and diabetes. Description of the State of Knowledge This section discusses the neurobiological mechanisms of depression, including neuroinflammation, neurotransmitter imbalances, and impaired neurogenesis. It highlights the potential of GLP-1 agonists to address these issues and improve mood and cognitive function. Conclusion The review concludes that GLP-1 holds promise as a therapeutic target for depression. Beyond its metabolic roles, GLP-1 can influence brain regions involved in mood regulation, enhance neurogenesis, reduce neuroinflammation, and improve neurotransmitter balance. These effects suggest that GLP-1-based therapies could offer new treatment options for depression, potentially improving outcomes beyond traditional antidepressants.

HGGL-01 GLIOBLASTOMA MULTIFORM INVOLVING MULTIPLE BRAIN REGIONS ASSOCIATED WITH MUTATIONS IN EXON 4 OF ISOCITRATE DEHYDROGENASE 1 COMPLICATED BY ACUTE KIDNEY FAILURE: A CASE REPORT

Abstract Glioblastoma multiform (GBM), WHO grade 4, is the most aggressive primary brain tumor in adults associated with poor prognosis. We present a case of a 43-year-old female, Muyira, diagnosed with GBM involving multiple brain regions and harboring mutations in exon 4 of isocitrate dehydrogenase 1, who subsequently developed acute kidney failure. The patient initially presented with progressive neurological symptoms, including headache and cognitive decline. Multiplanar multiecho MRI of the brain was performed with IV contrast in orthogonal plane and revealed a moderate sized multilobulated heterogeneously and predominantly peripherally enhancing mass lesion with irregular margins and central non enhancing areas of necrosis arising from the right corona radiata, insular cortex, external capsule, lentiform nucleus, temporal, and frontal white matter with blood products. Perilesional edema was seen. Effacement of sulcal spaces. Right sylvian fissure, compression over right ventricle and midline shift of 8mm toward left side. There was dilatation of the left lateral ventricle with periventricular cerebrospinal fluid ooze. Uncal herniation was right side. On MR spectroscopy large choline, creatinine levels were seen in the lesion with reduced N-acetyl aspartate levels as compared to the normal parenchyma. Findings were suggestive of neoplastic lesion mostly high-grade glioma. Polymerase chain reaction sequencing confirmed the presence of missense mutation R132H in exon 4 of isocitrate dehydrogenase 1 gene. Despite maximal safe surgical resection, the patient developed acute kidney failure secondary to the nephrotoxic effects of gadolinium. Management of both the primary brain tumor and renal complication necessitated a multidisciplinary approach involving neurosurgery, oncology, nephrology, and supportive care. Despite aggressive interventions, the patient’s condition deteriorated, highlighting the challenges in managing concurrent life-threatening conditions in patients with advanced GBM.

Frustrative Nonreward: Behavior, Circuits, Neurochemistry, and Disorders.

The surprising omission or reduction of vital resources (food, fluid, social partners) can induce an aversive emotion known as frustrative nonreward (FNR), which can influence subsequent behavior and physiology. FNR is an integral mediator of irritability/aggression, motivation (substance use disorders, depression), anxiety/fear/threat, learning/conditioning, and social behavior. Despite substantial progress in the study of FNR during the twentieth century, research lagged in the later part of the century and into the early twenty-first century until the National Institute of Mental Health's Research Domain Criteria initiative included FNR and loss as components of the negative valence domain. This led to a renaissance of new research and paradigms relevant to basic and clinical science alike. The COVID-19 pandemic's extensive individual and social restrictions were correlated with increased drug and alcohol use, social conflict, irritability, and suicide, all potential consequences of FNR. This article highlights animal models related to these psychiatric disorders and symptoms and presents recent advances in identifying the brain regions and neurotransmitters implicated.

Network statistics of the whole-brain connectome of Drosophila

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections1–3, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations4,5, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex (https://codex.flywire.ai) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

Omeprazole affects the expression of serotonin-1A in the brain regions and alleviates anxiety in rat model of immobilization-induced stress.

Omeprazole, a drug of choice for the management of gastric hyperacidity, influences serotonergic neurotransmission in brain regions and its long-term use is known to cause stress-related behavioral deficits including anxiety. Aim of the current study was to explore the effects of omeprazole treatment on immobilization-induced anxiety in rats, specifically on the role of serotonin (5-HT). In view of the role of serotonin-1A (5-HT1A) autoreceptor in the availability of 5-HT in brain regions, mRNA expression of this autoreceptor was performed in raphe nuclei. Similarly, because of the role of hippocampal 5-HT neurotransmission in anxiety-like disorders, expression of the 5-HT1A heteroreceptors was determined in this region. We found that the treatment with omeprazole reduces anxiety-like behavior in rats, increases the expression of 5-HT1A autoreceptor in the raphe and decreases the hippocampal expression of 5-HT1A heteroreceptor. This suggests a role of 5-HT1A receptor types in omeprazole-induced behavioral changes. It also indicates a potential role of omeprazole in the management of serotonergic disorders.

EEG spatial inter-channel connectivity analysis: A GCN-based dual stream approach to distinguish mental fatigue status

Mental fatigue is defined as a decline in the ability and efficiency of mental activities. A lot of research suggests that the transition from alertness to fatigue is accompanied by alterations in correlation patterns among various brain regions. However, conventional methods for detecting mental fatigue seldom emphases inter-channel connectivity in the spatial domain. To fill this gap, this paper explores the spatial inter-channel connectivity in alertness and fatigue, employing spectral graph convolutional networks (GCN) for mental fatigue detection. We utilized Pearson correlation coefficients (PCC) to establish temporal connections and magnitude-squared coherence (MSC) for spectral connections. Topological features of the brain network were then analysed. To enhance the learning of spatial inter-channel connectivity, a dual-graph strategy transforms edge features into node features, serving as inputs to the spectral GCN. By simultaneously learning PCC and MSC features, the model results indicate significant differences in some brain network characteristics between alert and fatigue states. It confirms that the synchronicity of brain operations differs in the alert state compared to mental fatigue, and indicates that fatigue states can influence correlation patterns among different brain regions. Our approach is evaluated on a self-designed experimental dataset containing 7 subjects, demonstrating a classification accuracy of 89.59 % in group-level experiments and 95.24 % at the subject level. Additionally, on the public dataset SEED-VIG containing 23 subjects, our method achieves an accuracy of 86.58 %. In summary, this paper proposes a neural network approach based on a dynamic functional connectivity network. The network integrates both temporal and spectral connections with the goal of simultaneously learning spatial inter-channel connectivity in time and frequency domains. This effectively accomplishes fatigue state detection, highlighting that fatigue significantly influences correlations among different brain regions.

Brain Functional Alterations in Patients With Benign Paroxysmal Positional Vertigo Demonstrate the Visual-Vestibular Interaction and Integration.

This study aimed to analyze the features of resting-state functional magnetic resonance imaging (rs-fMRI) and clinical relevance in patients with benign paroxysmal positional vertigo (BPPV) that have undergone repositioning maneuvers. A total of 38 patients with BPPV who have received repositioning maneuvers and 38 matched healthy controls (HCs) were enrolled in the present study from March 2018 to August 2021. Imaging analysis software was employed for functional image preprocessing and indicator calculation, mainly including the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), percent amplitude of fluctuation (PerAF), and seed-based functional connectivity (FC). Statistical analysis of the various functional indicators in patients with BPPV and HCs was also conducted, and correlation analysis with clinical data was performed. Patients with BPPV displayed decrease in ALFF, fALFF, and PerAF values, mainly in the bilateral occipital lobes in comparison with HCs. Additionally, their ALFF and fALFF values in the proximal vermis region of the cerebellum increased relative to HCs. The PerAF values in the bilateral paracentral lobules, the right supplementary motor area (SMA), and the left precuneus decreased in patients with BPPV and were negatively correlated with dizziness visual analog scale (VAS) scores 1 week after repositioning (W1). In addition, in the left fusiform gyrus and lingual gyrus, the PerAF values show a negative correlation with dizziness handicap inventory (DHI) scores at initial visit (W0). Seed-based FC analysis using the seeds from differential clusters of fALFF, ALFF, and PerAF showed reductions between the left precuneus and bilateral occipital lobe, the left precuneus and left paracentral lobule, and within the occipital lobes among patients with BPPV. The spontaneous activity of certain brain regions in the bilateral occipital and frontoparietal lobes of patients with BPPV was reduced, whereas the activity in the cerebellar vermis was increased. Additionally, there were reductions in FC between the precuneus and occipital cortex or paracentral lobule, as well as within the occipital cortex. The functional alterations in these brain regions may be associated with the inhibitory interaction and functional integration of visual, vestibular, and sensorimotor systems. The functional alterations observed in the visual cortex and precuneus may represent adaptive responses associated with residual dizziness.

Hippocampal dipeptidyl peptidase 9 bidirectionally regulates memory associated with synaptic plasticity

IntroductionSubtypes of the dipeptidyl peptidase (DPP) family, such as DPP4, are reportedly associated with memory impairment. DPP9 is widely distributed in cells throughout the body, including the brain. However, whether DPP9 regulates memory has not yet been elucidated. ObjectivesThis study aimed to elucidate the role of DPP9 in memory, as well as the underlying molecular mechanism. MethodsWe performed immunofluorescence on mouse brains to explore the distribution of DPP9 in different brain regions and used AAV vectors to construct knockdown and overexpression models. The effects of changing DPP9 expression on memory were demonstrated through behavioral experiments. Finally, we used electrophysiology, proteomics and affinity purification mass spectrometry (AP-MS) to study the molecular mechanism by which DPP9 affects memory. ResultsHere, we report that DPP9, which is found almost exclusively in neurons, is expressed and has enzyme activity in many brain regions, especially in the hippocampus. Hippocampal DPP9 expression increases after fear memory formation. Fear memory was impaired by DPP9 knockdown and enhanced by DPP9 protein overexpression in the hippocampus. According to subsequent hippocampal proteomics, multiple pathways, including the peptidase pathway, which can be bidirectionally regulated by DPP9. DPP9 directly interacts with its enzymatic substrate neuropeptide Y (NPY) in neurons. Hippocampal long-term potentiation (LTP) is also bidirectionally regulated by DPP9. Moreover, inhibiting DPP enzyme activity impaired both LTP and memory. In addition, AP-MS revealed that DPP9-interacting proteins are involved in the functions of dendritic spines and axons. By combining AP-MS and proteomics, DPP9 was shown to play a role in regulating actin functions. ConclusionTaken together, our findings reveal that DPP9 affects the CNS not only through enzymatic activity but also through protein–protein interactions. This study provides new insights into the molecular mechanisms of memory and DPP family functions.

Activation of ventral pallidum-projecting neurons in the nucleus accumbens via 5-HT2C receptor stimulation regulates motivation for wheel running in male mice

Rodents have a strong motivation for wheel running; however, the neural mechanisms that regulate their motivation remain unknown. We investigated the possible involvement of serotonin (5-HT) systems in regulating motivation for wheel running in male mice. Systemic administration of a 5-HT1A receptor antagonist (WAY100635) increased the number of wheel rotations, whereas administration of a 5-HT2A or 5-HT2C receptor antagonist (volinanserin or SB242084, respectively) decreased it. In the open field test, neither WAY100635 nor volinanserin affected locomotor activity, whereas SB242084 increased locomotor activity. To identify the brain regions on which these antagonists act, we locally injected these into the motivational circuitry, including the nucleus accumbens (NAc), dorsomedial striatum (DM-Str), and medial prefrontal cortex (mPFC). Injection of SB242084 into the NAc, but not the DM-Str or mPFC, reduced the number of wheel rotations without altering locomotor activity. The local administration of WAY100635 or volinanserin to these brain regions did not affect the number of wheel rotations. Immunohistochemical analyses revealed that wheel running increased the number of c-Fos-positive cells in the NAc medial shell (NAc-MS), which was reduced by systemic SB242084 administration. In vitro slice whole-cell recordings showed that bath application of the 5-HT2C receptor agonist lorcaserin increased the frequency of spontaneous excitatory and inhibitory postsynaptic currents in the ventral tegmental area (VTA)-projecting neurons, whereas it only increased the frequency of spontaneous excitatory postsynaptic currents in ventral pallidum (VP)-projecting neurons in the NAc-MS. These findings suggest that the activation of VP-projecting NAc-MS neurons via 5-HT2C receptor stimulation regulates motivation for wheel running.

Hindbrain Networks: Exploring the Hidden Anxiety Circuits in Rodents.

Anxiety disorders are multifaceted conditions that engage numerous brain regions and circuits. While the hindbrain is pivotal in fundamental biological functions, its role in modulating emotions has been underappreciated. This review will uncover critical targets and circuits within the hindbrain that are essential for both anxiety and anxiolytic effects, expanding on research obtained through behavioral tests. The bidirectional neural pathways between the hindbrain and other brain regions, with a spotlight on vagal afferent signaling, provide a crucial framework for unraveling the neural mechanisms underlying anxiety. Exploring neural circuits within the hindbrain can help to unravel the neurobiological mechanisms of anxiety and elucidate differences in the expression of these circuits between genders, thereby providing valuable insights for the development of future anxiolytic drugs.

The effect and mechanism of motor imagery based on action observation treatment on dysphagia in Wallenberg Syndrome: a randomized controlled trial.

The effect of motor imagery applied to dysphagia patients with Wallenberg syndrome has not yet been reported. This trial aimed to investigate the effect and mechanism of motor imagery based on action observation treatment in the rehabilitation of patients with dysphagia in Wallenberg syndrome. A randomized controlled trial. The setting was in-patient. Thirty patients with dysphagia of Wallenberg syndrome. The patients were divided into the experimental group and the control group. Both groups received conventional dysphagia treatment, and the experimental group underwent the addition of motor imagery based on action observation treatment to the control group once a day for 14 days. Overall swallowing function was assessed with specific scales before and after intervention. Meanwhile, the functional near infrared spectroscopy was used to detect changes in cerebral hemodynamics during the execution of volitional swallowing task and swallowing motor imagery. The standardized swallowing assessment score (P=0.030), Murray secretion scale score (P=0.044) and swallowing quality of life score (P=0.011) of the experimental group improved better than those of the control group. In addition, multiple brain regions of the cortical presented extensive activation (P<0.05) during the execution of swallowing motor imagery. Moreover, there were significant differences (P<0.05) in brain regions pre-motor and supplementary motor cortex, right primary motor cortex, and right primary somatosensory cortex of the experimental group before and after treatment. The present study indicated that motor imagery based on action observation treatment could improve swallowing function for patients with dysphagia of Wallenberg syndrome as an add-on training. As a top-down rehabilitation training, the mechanism of this therapy may be related to the selective activation of mirror neuron system. Motor imagery based on action observation treatment can be implemented as part of the therapeutic for dysphagia of Wallenberg syndrome.

Factors associated with the distribution of brain metastases in lung cancer: a retrospective study.

The distribution of brain metastases (BMs) in patients with lung cancer may be associated with the primary tumor-related factors and cerebral small vascular diseases (CSVDs). The aim of this study was to investigate the potential effects of the above factors on the distribution of BMs. A total of 5,788 lesions in 823 patients with BMs from lung cancer were enrolled. The numbers of BMs and CSVDs in 15 brain regions were determined. CSVDs include recent small subcortical infarcts (RSSIs), perivascular spaces, and lacunes of presumed vascular origin (LPVOs). We collected the number of CSVDs, and primary tumor-related factors (including clinical and imaging features) in lung cancer patients with BMs. Univariate and multivariate linear regression were utilized to analyze the potential influence of the above factors on the number of BMs in 15 brain regions. In addition, we performed subgroup analyses of all patients with adenocarcinoma (AD), female patients with AD, male patients with AD, and patients with small cell lung cancer. Univariate linear regression analyses showed that bone metastasis, adrenal metastasis, RSSIs, and LPVOs were associated with the number of BMs in over half of the examined brain regions. Only the independent association of LVPOs persisted in the multivariate linear regression analyses, and similar phenomenon was found in the subgroup analyses. In conclusion, the distribution of BMs in lung cancer patients appears to be associated with the presence of LVPOs, while primary tumor-related factors have less influence.

P2X7 receptors modulate acquisition of cue fear extinction and contextual background memory generalization in male mice

The purinergic P2X7 receptors (P2X7R) are activated by adenosine triphosphate (ATP) in several brain regions, particularly those involved with emotional control and the regulation of fear-related memories. Here, we investigate the role of P2X7R in fear learning memory, specifically in the acquisition and consolidation phases of the cued fear conditioning paradigm. C57Bl/6 wildtype (WT) male mice that received a single i.p. injection of the selective P2X7R antagonist A438079 prior the conditioning session showed generalization of cued fear memory and impaired fear extinction recall in the test session, while those treated prior the extinction session exhibited a similar behavior profile accompanied by resistance in the extinction learning. However, no effects were observed when this drug was administered immediately after the conditioning, extinction, or before the test session. Our results with P2X7R knockout (P2X7 KO) mice showed a behavioral profile that mirrored the collective effects observed across all pharmacological treatment conditions. This suggests that the P2X7R KO model effectively replicates the behavioral changes induced by the pharmacological interventions, demonstrating that we have successfully isolated the role of P2X7R in the fear and extinction phases of memory. These findings highlight the role of P2X7R in the acquisition and recall of extinction memory and supports P2X7R as a promising candidate for controlling abnormal fear processing, with potential applications for stress exposure-related disorders such as post-traumatic stress disorder (PTSD).

CalTrig: A GUI-Based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca2+ imaging using miniatured microscopes have enabled researchers to study single-neuron activity in freely moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca2+ visual signals to numerical information, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.

Differential involvement of central and peripheral catecholamines between Alzheimer's disease and vascular dementia

Background and aimThe important role of catecholamines has been gradually emphasized in the pathogenesis of neurodegenerative process. As the most prevalent form of cognitive dysfunction, Alzheimer's disease (AD) and vascular dementia (VaD) have the distinct pathological features and pathogenic mechanisms, however, the differential involvement of central and peripheral catecholamines between AD and VaD was still unclear. MethodsTriple-transgenic AD (3 × Tg-AD) mice and chronic cerebral hypoperfusion (CCH) in rats induced by two-vessel occlusion (2VO) were used as the AD and VaD model in this study, respectively. The concentrations of catecholamines (dopamine, epinephrine and norepinephrine) and their metabolites (3-methoxytyramine, metanephrine and normetanephrine) in serum and five brain regions (hippocampus, cortex, corpus striatum, thalamus and pons) from 3 × Tg-AD mice and 2VO rats were quantitatively determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay. ResultsHigh expression and distribution of hippocampal dopamine, and epinephrine and norepinephrine in the cortex and thalamus were found in the early 3 × Tg-AD model, whereas chronic cerebral hypoperfusion induced by 2VO mainly affected the central noradrenergic and noradrenergic system, but not dopaminergic system. The increased serum levels of catecholamines were investigated in the 2VO rats, but not in the 3 × Tg-AD mice. ConclusionThe differential expression and distribution of central catecholamines and their metabolites suggests the distinct catecholamines-related pathogenesis between AD and VaD. Peripheral catecholamine surge may be involved in the development of VaD, and the treatment strategy to prevent or reverse the effects of peripheral catecholamines may be protective for VaD.

Frequency-specific dual-attention based adversarial network for blood oxygen level-dependent time series prediction.

Functional magnetic resonance imaging (fMRI) is currently one of the most popular technologies for measuring brain activity in both research and clinical contexts. However, clinical constraints often result in short fMRI scan durations, limiting the diagnostic performance for brain disorders. To address this limitation, we developed an end-to-end frequency-specific dual-attention-based adversarial network (FDAA-Net) to extend the time series of existing blood oxygen level-dependent (BOLD) data, enhancing their diagnostic utility. Our approach leverages the frequency-dependent nature of fMRI signals using variational mode decomposition (VMD), which adaptively tracks brain activity across different frequency bands. We integrated the generative adversarial network (GAN) with a spatial-temporal attention mechanism to fully capture relationships among spatially distributed brain regions and temporally continuous time windows. We also introduced a novel loss function to estimate the upward and downward trends of each frequency component. We validated FDAA-Net on the Human Connectome Project (HCP) database by comparing the original and predicted time series of brain regions in the default mode network (DMN), a key network activated during rest. FDAA-Net effectively overcame linear frequency-specific challenges and outperformed other popular prediction models. Test-retest reliability experiments demonstrated high consistency between the functional connectivity of predicted outcomes and targets. Furthermore, we examined the clinical applicability of FDAA-Net using short-term fMRI data from individuals with autism spectrum disorder (ASD) and major depressive disorder (MDD). The model achieved a maximum predicted sequence length of 40% of the original scan durations. The prolonged time series improved diagnostic performance by 8.0% for ASD and 11.3% for MDD compared with the original sequences. These findings highlight the potential of fMRI time series prediction to enhance diagnostic power of brain disorders in short fMRI scans.

Social network size, empathy, and white matter: A diffusion tensor imaging (DTI) study.

Social networks are fundamental for social interactions, with the social brain hypothesis positing that the size of the neocortex evolved to meet social demands. However, the role of fractional anisotropy (FA) in white matter (WM) tracts relevant to mentalizing, empathy, and social networks remains unclear. In this study, we investigated the relationships between FA in brain regions associated with social cognition (superior longitudinal fasciculus (SLF), cingulum (CING), uncinate fasciculus, inferior fronto-occipital fasciculus), social network characteristics (diversity, size, complexity), and empathy (cognitive, affective). We employed diffusion tensor imaging, tract-based spatial statistics, and mediation analyses to examine these associations. Our findings revealed that increased social network size was positively correlated with FA in the left SLF. Further, our mediation analysis showed that lower FA in left CING was associated with increased social network size, mediated by cognitive empathy. In summary, our findings suggest that WM tracts involved in social cognition play distinct roles in social network size and empathy, potentially implicating affective brain regions. In conclusion, our findings offer new perspectives on the cognitive mechanisms involved in understanding others' mental states and experiencing empathy within supportive social networks, with potential implications for understanding individual differences in social behavior and mental health.

MAS-DGAT-Net: A Dynamic Graph Attention Network with Multibranch Feature Extraction and Staged Fusion for EEG Emotion Recognition

In recent years, with the rise of deep learning technologies, EEG-based emotion recognition has garnered significant attention. However, most existing methods tend to focus on the spatiotemporal information of EEG signals while overlooking the potential topological information of brain regions. To address this issue, this paper proposes a dynamic graph attention network with multi-branch feature extraction and staged fusion (MAS-DGAT-Net), which integrates graph convolutional neural networks (GCN) for EEG emotion recognition. Specifically, the differential entropy (DE) features of EEG signals are first reconstructed into a correlation matrix using the Spearman correlation coefficient. Then, the brain-region connectivity-feature extraction (BCFE) module is employed to capture the brain connectivity features associated with emotional activation states. Meanwhile, this paper introduces a dual-branch cross-fusion feature extraction (CFFE) module, which consists of an attention-based cross-fusion feature extraction branch (A-CFFEB) and a cross-fusion feature extraction branch (CFFEB). A-CFFEB efficiently extracts key channel-frequency information from EEG features by using an attention mechanism and then fuses it with the output features from the BCFE. The fused features are subsequently input into the proposed dynamic graph attention module with a broad learning system (DGAT-BLS) to mine the brain connectivity feature information further. Finally, the deep features output by DGAT-BLS and CFFEB are combined for emotion classification. The proposed algorithm has been experimentally validated on SEED, SEED-IV, and DEAP datasets in subject-dependent and subject-independent settings, with the results confirming the model's effectiveness. The source code is publicly available at: https://github.com/cvmdsp/MAS-DGAT-Net