Journal of Neuroscience Methods最新文献

{"title":"Reproducibility of HERMES-measured GABA+ and glutathione in the mesial temporal lobe","authors":"Marilena M. DeMayo ,&nbsp;Mary Botros ,&nbsp;Tiffany K. Bell ,&nbsp;Mark Mikkelsen ,&nbsp;Victoria Mosher ,&nbsp;Antis George ,&nbsp;Alexander McGirr ,&nbsp;Paolo Federico ,&nbsp;Ashley D. Harris","doi":"10.1016/j.jneumeth.2025.110542","DOIUrl":"10.1016/j.jneumeth.2025.110542","url":null,"abstract":"<div><h3>Background</h3><div>There is growing interest in using Hadamard Encoding and Reconstruction for MEGA-Edited Spectroscopy (HERMES) within the mesial temporal lobe (MTL). For cross-sectional group comparisons and longitudinal repeated measures designs, an understanding of the internal and test-retest validity of γ-aminobutyric acid (GABA+) and glutathione (GSH) is critical. We therefore evaluated the reproducibility of the consensus recommended semi-localization by adiabatic selective refocusing (sLASER) localization for edited-MRS acquisitions in a challenging region, the MTL.</div></div><div><h3>New method</h3><div>Data were acquired in 15 participants. Single voxel HERMES was collected in the left MTL (two acquisitions) and the right MTL (one acquisition). Participants were repositioned between the two left HERMES acquisitions. An ANOVA was used to assess differences between acquisitions. To assess measurement variation in the repeated left of GABA+ and GSH measures within the left MTL difference values and coefficients of variation (CVs) were calculated.</div></div><div><h3>Results</h3><div>There were no significant differences in metabolite values between any of the acquisitions. The mean difference between the metabolite measures from the repeated left acquisitions centred close to zero, and the average CVs were 14.09 % for GABA+ and 18.94 % for GSH.</div></div><div><h3>Comparison with existing methods</h3><div>The CVs of GABA+ and GSH in the MTL obtained from a HERMES acquisition were comparable to GABA+ or GSH-edited acquisitions in this region, and to data from cortical voxels using HERMES acquisitions.</div></div><div><h3>Conclusions</h3><div>This supports the use of HERMES in the MTL, a challenging region for MRS. However, larger samples and caution in interpretation may be required in repeated-measures designs.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110542"},"PeriodicalIF":2.7,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144713150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized methyl green-pyronin Y staining for layer visualization in frozen mouse cerebellum","authors":"Hyeju Kim,&nbsp;Soo-Jong Um","doi":"10.1016/j.jneumeth.2025.110540","DOIUrl":"10.1016/j.jneumeth.2025.110540","url":null,"abstract":"<div><h3>Background</h3><div>Conventional histological stains, such as hematoxylin and eosin (H&amp;E) or toluidine blue O (TBO), have a limited ability to clearly delineate the layered architecture of the cerebellar cortex.</div></div><div><h3>New method</h3><div>We applied methyl green–pyronin Y (MGP) staining, which is traditionally used for nucleic acid differentiation, to frozen mouse cerebellar sections to enhance visualization of cortical layers and neuronal subtypes.</div></div><div><h3>Results</h3><div>MGP staining yielded strong contrast between cell types: Purkinje cells stained distinctly pink, while granule cells appeared green. This enabled clear identification of cerebellar lamination and neuronal distribution.</div></div><div><h3>Comparison with existing methods</h3><div>In H&amp;E or TBO staining, Purkinje and granule cells are colored similarly, which obscures layer boundaries. Although immunohistochemistry is commonly used to distinguish these cell types, MGP staining provides a rapid, color-based distinction without the need for antibodies or fluorescence.</div></div><div><h3>Conclusions</h3><div>MGP staining provides a fast and cost-effective alternative for analyzing cerebellar tissue, enabling clear visualization of cortical layering and facilitating the morphological screening of cerebellar abnormalities.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110540"},"PeriodicalIF":2.7,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new gradient-based method for analyzing brain white matter fiber geometry","authors":"Jiaolong Qin ,&nbsp;Weihong Dong ,&nbsp;Huangjing Ni ,&nbsp;Ye Wu ,&nbsp;Haiyan Liu ,&nbsp;Zhijian Yao ,&nbsp;Qing Lu","doi":"10.1016/j.jneumeth.2025.110538","DOIUrl":"10.1016/j.jneumeth.2025.110538","url":null,"abstract":"<div><h3>Background</h3><div>Precise geometric and morphometric analyses of brain fiber pathways are crucial for unraveling brain organization and mechanisms underlying normal and pathological brain functions. However, existing methods for white matter (WM) fiber geometry analysis remain limited.</div></div><div><h3>New method</h3><div>We propose a novel Large-scale Gradient Feature (LsGF) metric to quantify the tangent direction change rate along fiber streamlines. Using intra-class correlation coefficients (ICC), we systematically evaluated the stability of LsGF maps under two key factors: streamline count and tractography algorithm. LsGF was then applied to investigate gender disparities in WM morphology, with sensitivity assessed by comparing LsGF maps against fiber length maps.</div></div><div><h3>Results</h3><div>Results showed that LsGF exhibited remarkable robustness to variations in streamline count (99 % of ICCs &gt; 0.8), but demonstrated significant dependency on tractography algorithm (less than 60 % of ICCs &gt; 0.6). Application of the LsGF method to gender dimorphism analysis uncovered distinct geometric patterns primarily in the thalamus, internal capsule, cerebellum, corpus callosum, lingual gyrus, fusiform gyrus, precuneus, gyrus rectus, orbitofrontal cortex, cingulate cortex, calcarine, and olfactory regions.</div></div><div><h3>Comparison with existing methods</h3><div>Comparative analysis indicated that LsGF outperformed fiber length metrics in detecting microstructural geometric complexity, whereas the latter more effectively characterized macroscale architecture features. These findings underscore the complementary value of LsGF and fiber length metric in WM analysis.</div></div><div><h3>Conclusions</h3><div>The LsGF map enables voxel-wise analysis of quantitative streamline metrics across the whole brain, highlighting the necessity of consistent tractography methods for reliable results.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110538"},"PeriodicalIF":2.7,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144675003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid BCI for upper limb rehabilitation: integrating MI with peripheral field SSVEP stimulation","authors":"Ruoqing Zhang ,&nbsp;Zhaohui Li ,&nbsp;Xiaohu Pan ,&nbsp;Hongyan Cui ,&nbsp;Xiaogang Chen","doi":"10.1016/j.jneumeth.2025.110537","DOIUrl":"10.1016/j.jneumeth.2025.110537","url":null,"abstract":"<div><h3>Background</h3><div>Rehabilitation systems based on brain-computer interfaces (BCIs) hold significant potential for stroke patients. Existing systems, predominantly relying on motor imagery (MI), have room for improvement in both performance and user comfort. This study aims to enhance these aspects by developing a hybrid BCI system integrating MI with steady-state visual evoked potentials (SSVEPs) elicited by peripheral visual field stimulation.</div></div><div><h3>New methods</h3><div>The system is coupled with a soft robotic hand for feedback, forming a closed-loop framework. The design incorporates concentric rings with 7° and 10° eccentricities as peripheral stimuli, flashing at frequencies of 34 Hz and 35 Hz for left and right sides, respectively, to evoke SSVEPs. A central video (304 ×304 pixels) of left-hand/right-hand grasping motions guides subjects in performing synchronized MI tasks simply by focusing on it, which could also complete the SSVEP task.</div></div><div><h3>Results</h3><div>The offline results of 11 subjects showed that the classification result of MI was 70.65 ± 3.38 %, and the SSVEP result was 96.04 ± 3.33 %, and the fusion result reached 96.23 ± 3.21 %, which confirmed the validity of the fusion method. The online experiment of 11 subjects achieved a result of 97.12 ± 2.09 %, validating the feasibility of the system.</div></div><div><h3>Comparison with existing methods</h3><div>The proposed system improves the comfort level while ensuring the performance of the system as compared to the existing systems.</div></div><div><h3>Conclusion</h3><div>The feasibility of the proposed system was verified by offline and online experiments to advance the clinical applications.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110537"},"PeriodicalIF":2.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network model.","authors":"D Deepika, G Rekha","doi":"10.1016/j.jneumeth.2025.110536","DOIUrl":"https://doi.org/10.1016/j.jneumeth.2025.110536","url":null,"abstract":"<p><strong>Background: </strong>Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.</p><p><strong>New method: </strong>To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).</p><p><strong>Results: </strong>The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3% and 99.56%, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.</p><p><strong>Comparison with existing methods: </strong>Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.</p><p><strong>Conclusion: </strong>The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110536"},"PeriodicalIF":2.7,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144667771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep clustering of polysomnography data to characterize sleep structure in healthy sleep and non-rapid eye movement parasomnias","authors":"I.A.M. Huijben ,&nbsp;R.J.G. van Sloun ,&nbsp;A. Pijpers ,&nbsp;S. Overeem ,&nbsp;M.M. van Gilst","doi":"10.1016/j.jneumeth.2025.110516","DOIUrl":"10.1016/j.jneumeth.2025.110516","url":null,"abstract":"<div><h3>Background:</h3><div>The clinical standard to interpret polysomnography (PSG) data is to categorize sleep in five stages, which omits information. SOM-CPC is an unsupervised method that extracts features through contrastive predictive coding (CPC), and visualizes them in two dimensions using a self-organizing map (SOM). We propose various visualizations and analyses for pattern recognition in PSG data through SOM-CPC.</div></div><div><h3>New method:</h3><div>We used SOM-CPC to learn a representation of 30-s multi-channel epochs from two datasets of healthy sleepers (<span><math><mrow><mi>n</mi><mo>=</mo><mn>52</mn></mrow></math></span> and <span><math><mrow><mi>n</mi><mo>=</mo><mn>22</mn></mrow></math></span> in the test sets). SOM-CPC was, additionally, used to further characterize awakenings from slow wave sleep (SWS) in non-rapid eye movement (NREM) parasomnias. For the latter, SOM-CPC was trained on 5-s single-channel EEG windows of non-rapid eye movement parasomnias and matched healthy controls (test set: <span><math><mrow><mi>n</mi><mo>=</mo><mn>67</mn></mrow></math></span>).</div></div><div><h3>Results:</h3><div>SOM-CPC organized epochs of healthy sleepers such that it separated sleep stages, and also encoded age of the subjects and time in the night. Parasomnia episodes, compared to non-behavioral SWS awakenings, exhibited higher SWS-specificity prior to transition to wakefulness, higher Wake-specificity post-transition, and longer durations.</div></div><div><h3>Comparison with existing methods:</h3><div>The learned representations were compared against gold-standard sleep stage labels and variables known to impact sleep structure.</div></div><div><h3>Conclusions:</h3><div>SOM-CPC seems a useful model for pattern discovery in PSG data, as it enables observation of state changes that are more intricate than full sleep stage transitions. It, moreover, provided further evidence for signal level differences in the EEG between SWS awakenings with and without parasomnia episodes.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110516"},"PeriodicalIF":2.7,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144649736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CSCE: Cross Supervising and Confidence Enhancement pseudo-labels for semi-supervised subcortical brain structure segmentation","authors":"Yuan Sui,&nbsp;Yujie Zhang,&nbsp;Chengan Liu","doi":"10.1016/j.jneumeth.2025.110522","DOIUrl":"10.1016/j.jneumeth.2025.110522","url":null,"abstract":"<div><div>Robust and accurate segmentation of subcortical structures in brain MR images lays the foundation for observation, analysis and treatment planning of various brain diseases. Deep learning techniques based on Deep Neural Networks (DNNs) have achieved remarkable results in medical image segmentation by using abundant labeled data. However, due to the time-consuming and expensive of acquiring high quality annotations of brain subcortical structures, semi-supervised algorithms become practical in application. In this paper, we propose a novel framework for semi-supervised subcortical brain structure segmentation, based on pseudo-labels <strong>C</strong>ross <strong>S</strong>upervising and <strong>C</strong>onfidence <strong>E</strong>nhancement (CSCE). Our framework comprises dual student-teacher models, specifically a U-Net and a TransUNet. For unlabeled data training, the TransUNet teacher generates pseudo-labels to supervise the U-Net student, while the U-Net teacher generates pseudo-labels to supervise the TransUNet student. This mutual supervision between the two models promotes and enhances their performance synergistically. We have designed two mechanisms to enhance the confidence of pseudo-labels to improve the reliability of cross-supervision: a) Using information entropy to describe uncertainty quantitatively; b) Design an auxiliary detection task to perform uncertainty detection on the pseudo-labels output by the teacher model, and then screened out reliable pseudo-labels for cross-supervision. Finally, we construct an end-to-end deep brain structure segmentation network only using one teacher network (U-Net or TransUNet) for inference, the segmentation results are significantly improved without increasing the parameters amount and segmentation time compared with supervised U-Net or TransUNet based segmentation algorithms. Comprehensive experiments are performed on two public benchmark brain MRI datasets. The proposed method achieves the best Dice scores and MHD values on both datasets compared to several recent state-of-the-art semi-supervised segmentation methods.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110522"},"PeriodicalIF":2.7,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of histological sectioning techniques for the cochlear implanted inner ear in miniature swine","authors":"Shuolong Yuan ,&nbsp;Liangwei Xu ,&nbsp;Wenjie Huang ,&nbsp;Wei Chen ,&nbsp;Weiwei Guo","doi":"10.1016/j.jneumeth.2025.110527","DOIUrl":"10.1016/j.jneumeth.2025.110527","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Cochlear pathological sectioning is essential for studying inner ear structural changes. Traditional methods like paraffin embedding, frozen sectioning, and collodion embedding are limited to decalcified tissues. For cochlear specimens with rigid implants (e.g., cochlear implants), the implant must be separated before decalcification and sectioning. This process often damages the delicate cochlear architecture, compromising pathological integrity. Thus, there is a need for a precise and efficient method for examining cochlear tissues with implants.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;New method&lt;/h3&gt;&lt;div&gt;We introduce a novel pathological method. First, the cochlea is sectioned and dissected. Then, micro-computed tomography (Micro CT) is used for three-dimensional imaging. Both normal and implant-bearing tissues undergo dehydration, embedding, and staining for histological analysis.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;This method produces high-quality sections with uniform thickness, preserves cochlear architecture, and maintains fine structural details. It also enables precise implant localization within the cochlea.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Comparison with existing methods&lt;/h3&gt;&lt;div&gt;Our approach allows for dynamic pathological change investigation, three-dimensional mapping of the implant-tissue interface, and micro-damage assessment in implant-bearing cochleae.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusions&lt;/h3&gt;&lt;div&gt;This histopathological sectioning method for cochlear-implanted porcine inner ears overcomes previous limitations. It provides a robust method for electrode positioning verification and a standardized framework for evaluating the mechanical-biocompatibility of new electrode designs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Summary&lt;/h3&gt;&lt;div&gt;Cochlear pathological sectioning serves as a critical technique for investigating structural alterations within the inner ear, with conventional methodologies including paraffin embedding, frozen sectioning, and collodion embedding. These techniques, however, are exclusively applicable to decalcified cochlear tissues. The preparation of histopathological sections from cochlear specimens containing rigid implants, such as cochlear implants, necessitates the preliminary separation of the implant from the cochlear tissue, followed by decalcification and subsequent sectioning. This separation process often results in mechanical disruption of the delicate cochlear architecture, thereby compromising the integrity of the inner ear's pathological structure. Consequently, there is a pressing need to develop a precise and efficient methodology for the pathological examination of cochlear tissues with implants. Given that traditional approaches involve prolonged decalcification, existing techniques are inadequate for addressing the challenges associated with implant-bearing cochlear specimens. To address this limitation, we propose a novel, rapid, and efficient pathological method. Initially, the cochlea is sectioned and dissected, foll","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110527"},"PeriodicalIF":2.7,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144600732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative evaluation of cerebral tissue pretreatment strategies for metabolomics using UHPLC-high-resolution mass spectrometry","authors":"Fang Guo ,&nbsp;Xiaoxue Zhou ,&nbsp;Yudie Ning ,&nbsp;Tingli Qv ,&nbsp;Jieping Lv ,&nbsp;Tao Wang ,&nbsp;Zhiwen Wei","doi":"10.1016/j.jneumeth.2025.110524","DOIUrl":"10.1016/j.jneumeth.2025.110524","url":null,"abstract":"<div><h3>Background</h3><div>The cerebrum, as the most complex human organ, contains diverse metabolites vital for understanding brain functions, diseases, drug effects, and addiction. Its liquefiable nature makes swift preparation crucial to preserve metabolic profiles, with physical homogenization needed to release metabolites. To determine the optimal preparation procedures for cerebrum samples for high-resolution mass spectrometry in metabolomics, we evaluated four homogenization techniques and three extraction protocols using UHPLC-HRMS.</div></div><div><h3>New method</h3><div>A combination of dry grinding and monophasic extraction was identified as an optimized sample preparation approach for cerebrum metabolomics.</div></div><div><h3>Results</h3><div>The number of feature peaks, coefficient of variation (CV) of peak areas, and peak areas were compared to evaluate the coverage, reproducibility, and sensitivity of the methods. The dry grinding method detected the most feature peaks among four methods of homogenization, and the peak areas obtained were superior to other homogenization methods. In terms of extraction methods, there was no significant difference in peak areas (except for tyrosine) between monophasic and two-step extraction methods (p &gt; 0.05), both of which were higher than the biphasic extraction method (p &lt; 0.05). Additionally, the reproducibility of the monophasic extraction method was significantly better than the other two methods.</div></div><div><h3>Comparison</h3><div>The dry grinding and monophasic extraction approach demonstrated excellent compatibility with UHPLC-HRMS, offering broader metabolite coverage and superior performance compared to existing methods.</div></div><div><h3>Conclusions</h3><div>The dry grinding and monophasic extraction method is recommended for cerebrum metabolomics, providing enhanced metabolite coverage, reproducibility, and adaptability for high-resolution mass spectrometry studies.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"422 ","pages":"Article 110524"},"PeriodicalIF":2.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144567488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A closed-loop approach to monitor and manipulate the neural control of blood pressure","authors":"Caitlin Baumer-Harrison ,&nbsp;Jesus D. Peñaloza Aponte ,&nbsp;Khalid Elsaafien ,&nbsp;Dominique N. Johnson ,&nbsp;Karen A. Scott ,&nbsp;Eric G. Krause ,&nbsp;Annette D. de Kloet","doi":"10.1016/j.jneumeth.2025.110528","DOIUrl":"10.1016/j.jneumeth.2025.110528","url":null,"abstract":"<div><h3>Background</h3><div>Arterial baroreceptors are mechanosensitive nerve endings that detect blood pressure deviations and transmit this information to the central nervous system via vagal afferent neurons. Vagal afferent neuron cell bodies reside in the nodose ganglion (NG) and they terminate in the nucleus of the solitary tract (NTS) within the brainstem, thus serving as a critical component of the baroreflex circuitry. We previously found that specific angiotensin-sensitive vagal afferent nerve terminals within the NTS (referred to as NTS^AT1aR afferents) are sufficient to initiate baroreflex responses in both normotensive and hypertensive conditions.</div></div><div><h3>New method</h3><div>Here, we developed a closed-loop system in mice to monitor blood pressure and target NTS^AT1aR afferents with optogenetic stimulation. To determine the efficacy of the system, mice were subjected to acute pressor stimuli, including restraint or subcutaneous delivery of Ang-II, and delivered optical stimulation by the system until blood pressure returned below threshold.</div></div><div><h3>Results</h3><div>The closed-loop system is effective in attenuating acute elevations in blood pressure induced by physiological or psychological stressors. by initiating compensatory mechanisms to reduce heart rate and blood pressure. However, blood pressure did return to below threshold levels within similar time frames in stimulated and in stimulation-paired control mice.</div></div><div><h3>Comparison with existing methods</h3><div>While some existing approaches that lower blood pressure target similar neural pathways, they do not take such a closed-loop tactic.</div></div><div><h3>Conclusion</h3><div>The implication is that this closed-loop system, coupled with the optogenetic targeting of NTS^AT1aR afferents, may be exploited to understand and alleviate hypertension.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"422 ","pages":"Article 110528"},"PeriodicalIF":2.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144575676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}