Journal of Neuroscience Methods最新文献

{"title":"ER-detect: A pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data","authors":"Max A. van den Boom ,&nbsp;Nicholas M. Gregg ,&nbsp;Gabriela Ojeda Valencia ,&nbsp;Brian N. Lundstrom ,&nbsp;Kai J. Miller ,&nbsp;Dorien van Blooijs ,&nbsp;Geertjan J.M. Huiskamp ,&nbsp;Frans S.S. Leijten ,&nbsp;Gregory A. Worrell ,&nbsp;Dora Hermes","doi":"10.1016/j.jneumeth.2025.110389","DOIUrl":"10.1016/j.jneumeth.2025.110389","url":null,"abstract":"<div><h3>Background</h3><div>Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. However, the methods used for the detection of responses in cortico-cortical evoked potential (CCEP) data vary across studies, from visual inspection with manual annotation to a variety of automated methods.</div></div><div><h3>New method</h3><div>To provide a robust workflow to process CCEP data and detect early evoked responses in a fully automated and reproducible fashion, we developed the Early Response (ER)-detect toolbox. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three early response detection methods, which were validated against 14 manually annotated CCEP datasets from two different clinical sites by four independent raters.</div></div><div><h3>Results and comparison with existing methods</h3><div>ER-detect’s automated detection performed on par with the inter-rater reliability (Cohen’s Kappa of ∼0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations.</div></div><div><h3>Conclusion</h3><div>ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results for both research and clinical purposes.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"418 ","pages":"Article 110389"},"PeriodicalIF":2.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143425460","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":"Cross prior Bayesian attention with correlated inception and residual learning for brain tumor classification using MR images (CB-CIRL Net)","authors":"B. Vijayalakshmi ,&nbsp;S. Anand","doi":"10.1016/j.jneumeth.2025.110392","DOIUrl":"10.1016/j.jneumeth.2025.110392","url":null,"abstract":"<div><h3>Background</h3><div>Brain tumor classification from magnetic resonance (MR) images is crucial for early diagnosis and effective treatment planning. However, the homogeneity of tumors across different categories poses a challenge. Although, attention-based convolutional neural networks (CNNs) approaches have shown promising results in brain tumor classification, simultaneous consideration of both spatial and channel-specific features remains limited.</div></div><div><h3>Methods</h3><div>This study proposes a novel model that integrates Bi-FocusNet with correlated learning and CB-Attention. Bi-FocusNet is designed to concentrate on both spatial and channel-wise tumor features by using two complementary learning methodologies: correlated spatial inception learning and correlated channel residual learning. These learnings extract richer and more diverse feature representations from tumor lesions of varied sizes, significantly enhancing the model’s learning capacity. The CB-Attention mechanism works as a cross-learning module, facilitating interaction between the two learning methods to capture the missing information across spatial and channel-wise features.</div></div><div><h3>Results</h3><div>Ablation studies and experiments were conducted using the BT-large-2c, Figshare, and Kaggle datasets. The proposed model outperformed existing classification methods in accuracy and other metrics, demonstrating enhanced performance on all three datasets with accuracies of 99.02 %, 97.06 %, and 96.44 %, respectively. Additionally, the BT-Merged-4c dataset was used to assess the ability to handle class variation, and 96.28 % accuracy was achieved.</div></div><div><h3>Conclusion</h3><div>The CB-CIRL Net improves the extraction of spatial and channel-wise features through the utilization of Bi-FocusNet with correlated learning and CB-Attention, resulting in enhanced classification accuracy across various datasets. The model's outstanding performance demonstrates its capacity to improve brain tumor diagnosis and clinical application.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110392"},"PeriodicalIF":2.7,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414309","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":"Comparison of methods for the preparation of single-cell suspensions of rat retina and immunophenotyping by flow cytometry","authors":"Qingyao Wang ,&nbsp;Xiandan Zhu ,&nbsp;Xing Zhang ,&nbsp;Yong Xia ,&nbsp;Xuesong Tian","doi":"10.1016/j.jneumeth.2025.110384","DOIUrl":"10.1016/j.jneumeth.2025.110384","url":null,"abstract":"<div><h3>Background</h3><div>Although the eye has traditionally been considered an immune-privileged organ, progressive studies have re-evaluated the role of the immune response in retinopathy. The application of flow cytometry for immunophenotyping analysis of retinal single-cell suspensions has gradually attracted attention.</div></div><div><h3>New methods</h3><div>We dissociated the retinal tissue using trypsin digestion, papain digestion, mechanical grinding treatment and liberase + DNase Ⅰ digestion, respectively. Subsequently we assessed the quality of cell suspension (clumping rate, concentration and viability) with two different dyes, Trypan Blue (TPB) and Acridine Orange/Propidium Iodide (AO/PI). We distinguished T cells and their sub-populations by flow cytometry. Furthermore, we analyzed their immune responses to evaluate those four methods for preparing retinal single-cell suspension.</div></div><div><h3>Results</h3><div>The AO/PI staining method enables a rapid and precise evaluation of cell quality of retinal single cell suspensions, while TPB staining has limitations. Flow cytometric analysis showed that single cell suspensions dispersed with papain and trypsin exhibited reduced cell adhesion. However, trypsin digestion may affect antibody binding. The mechanical grinding treatment reduced cell yield and was prone to double-positivity. The number of cells within the cell-circle gate is significantly limited in the Liberase + DNase I digestion method.</div></div><div><h3>Comparison with existing methods</h3><div>The AO/PI staining method was employed to assess the quality of retinal single-cell suspensions. T cells and their subpopulations in retinal tissues were analyzed by flow cytometry. These results were integrated to evaluate the optimal preparation protocol for retinal single-cell suspensions.</div></div><div><h3>Conclusions</h3><div>Papain digestion is a superior method for preparing retinal single-cell suspensions.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"417 ","pages":"Article 110384"},"PeriodicalIF":2.7,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143382630","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":"Assessment of voluntary drug and alcohol intake in Drosophila melanogaster using a modified one-tube capillary feeding assay","authors":"Mallory Karr ,&nbsp;Arti Patel ,&nbsp;William Klipec ,&nbsp;Christopher L. Kliethermes","doi":"10.1016/j.jneumeth.2025.110388","DOIUrl":"10.1016/j.jneumeth.2025.110388","url":null,"abstract":"<div><h3>Background</h3><div>The capillary feeding assay measures intakes of solutions containing ethanol and other drugs of abuse in the fruit fly <em>Drosophila melanogaster</em>. Existing single-tube and two-choice variations of the assay conflate drug intake with nutrient intake and utilize conditions that decrease lifespan of flies, suggesting these conditions might serve as a physiological stressor.</div></div><div><h3>New method</h3><div>The current experiments used a novel variation of a single-tube capillary feeding assay with flies maintained on standard, semi-soft food throughout the experiment, and offered a drug in a moderately preferred sucrose solution. Preference or aversion for a drug-containing solution was assessed relative to a control sucrose solution.</div></div><div><h3>Results</h3><div>We found concentration-dependent preferences for solutions containing ethanol or fencamfamine, aversions to solutions containing amphetamine, caffeine, muscimol, nicotine, pentobarbital, or picrotoxin, and a nominal aversion to solutions containing methamphetamine. Some aversions were found only in male flies, although low overall intake by female flies likely contributed to this apparent sex-specific effect.</div></div><div><h3>Comparison with existing method(s)</h3><div>The continuous availability of semi-soft food in our assay to decouple the need for nutrients from intake of the drug-containing solution. In addition, our assay uses only a single capillary tube per vial of flies, making it less resource intensive than two-choice capillary feeding assays.</div></div><div><h3>Conclusions</h3><div>The availability of standard food in our modified, one-tube capillary feeding assay should prove to be useful modification of the capillary feeding assay for studies of drug intake.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"416 ","pages":"Article 110388"},"PeriodicalIF":2.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143267532","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":"Optimization of permeabilized brain tissue preparation to improve the analysis of mitochondrial oxidative capacities in specific subregions of the rat brain","authors":"Léa Dorémus ,&nbsp;Emilie Dugast ,&nbsp;Arnaud Delafenêtre ,&nbsp;Morgane Delouche ,&nbsp;Thomas Aupy ,&nbsp;Olivier Bernard ,&nbsp;Stéphane Sebille ,&nbsp;Nathalie Thiriet ,&nbsp;Jérôme Piquereau","doi":"10.1016/j.jneumeth.2025.110387","DOIUrl":"10.1016/j.jneumeth.2025.110387","url":null,"abstract":"<div><h3>Background</h3><div>As the major energy producer of cerebral tissue, mitochondria play key roles in brain physiology and physiopathology. Yet, the fine details of the functioning of mitochondrial oxidative phosphorylation in this organ are still scattered with grey area. This is partly due to the heterogeneity of this tissue that challenges our abilities to study specific cerebral subregions. In the last decades, cerebral mitochondria have largely been studied as a single entity by isolating mitochondria from large sections of brain. Given the evidence that these organelles must adapt to brain areas functions, it seems crucial to develop technologies enabling study of the mitochondria in given subregions.</div></div><div><h3>New method</h3><div>A few years ago, a method allowing the investigation of mitochondrial functions in permeabilized brain subregions have been proposed by Holloway’s team. Although this protocol represented a significant advance, we propose improvements in the tissue permeabilization procedure and in the conditions for measuring oxidative capacity.</div></div><div><h3>Results and comparison with existing methods</h3><div>The present study demonstrates that adjustments enabled obtention of higher respiration values than Holloway’s protocol and might allow the detection of slight mitochondrial alterations. In a second part of this study, we showed that cortex, striatum, hippocampus and cerebellum displayed similar maximal oxidative capacities (under pyruvate, malate and succinate) while complex IV-driven respiration is significantly lower in cerebellum compared to cortex. These observations were supported by the measurement of citrate synthase and cytochrome oxidase activities.</div></div><div><h3>Conclusion</h3><div>The developed procedure improves the investigations of mitochondrial electron transfer chain in specific cerebral regions.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"416 ","pages":"Article 110387"},"PeriodicalIF":2.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143267536","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":"Discrete variational autoencoders BERT model-based transcranial focused ultrasound for Alzheimer's disease detection","authors":"Kaushika Reddy Thipparthy ,&nbsp;Archana Kollu ,&nbsp;Chaitanya Kulkarni ,&nbsp;Ashit Kumar Dutta ,&nbsp;Hardik Doshi ,&nbsp;Aditya Kashyap ,&nbsp;Kumari Priyanka Sinha ,&nbsp;Suresh Babu Kondaveeti ,&nbsp;Rupesh Gupta","doi":"10.1016/j.jneumeth.2025.110386","DOIUrl":"10.1016/j.jneumeth.2025.110386","url":null,"abstract":"<div><h3>Research background</h3><div>Alzheimer's Disease (AD) is a neurodegenerative condition marked by symptoms including aphasia and diminished verbal fluency. Researchers have employed phonetic attributes, fluency, pauses, and various paralinguistic traits, or derived aspects from transcribed text, to identify Alzheimer's disease.</div></div><div><h3>Methods and methodology</h3><div>Nevertheless, conventional acoustic feature-based detection techniques are constrained in their ability to capture semantic information, and the process of transcribing speech into text is both time-consuming and labour-intensive. Non-invasive brain stimulation (NBS), encompassing methods such as transcranial magnetic stimulation (TMS) and Transcranial focused ultrasound (tFUS), has been investigated as a potential intervention to enhance cognitive functions and communication in Alzheimer's patients, demonstrating efficacy in modulating brain activity and promoting neuroplasticity. This research utilises Discrete Variational Autoencoders to transform speech into pseudo-phoneme sequences, subsequently applying the BERT (Bidirectional Encoder Representations from Transformers) model to analyse the relationships among these pseudo-phoneme sequences. This research proposes a tFUS-BERT model to encapsulate the linguistic representations of audio.</div></div><div><h3>Result analysis</h3><div>The proposed tFUS-BERT model demonstrated its effectiveness with an accuracy of 76.06 % when combined with Wav2vec 2.0 and 71.83 % with Hu-BERT, outperforming the baseline by 5.63 % on the ADReSSo dataset. Additionally, the model exhibited superior performance in capturing linguistic representations compared to traditional acoustic methods, showcasing its potential for accurate and scalable Alzheimer's detection.</div></div><div><h3>Comparison with previous studies</h3><div>The model attains an accuracy of 70.42 % on the ADReSSo (Alzheimer's Dementia Recognition through Spontaneous Speech Only) dataset, reflecting a 5.63 % enhancement compared to the baseline system.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"416 ","pages":"Article 110386"},"PeriodicalIF":2.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143167454","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":"EEG-based fatigue state evaluation by combining complex network and frequency-spatial features","authors":"Kefa Wang,&nbsp;Xiaoqian Mao,&nbsp;Yuebin Song,&nbsp;Qiuyu Chen","doi":"10.1016/j.jneumeth.2025.110385","DOIUrl":"10.1016/j.jneumeth.2025.110385","url":null,"abstract":"<div><h3>Background</h3><div>The proportion of traffic accidents caused by fatigue driving is increasing year by year, which has aroused wide concerns for researchers. In order to rapidly and accurately detect drivers' fatigue, this paper proposed an electroencephalogram (EEG)-based fatigue state evaluation method by combining complex network and frequency-spatial features.</div></div><div><h3>New method</h3><div>First, this paper constructed a complex network model based on the relative wavelet entropy to characterize the correlation strength information between channels. Then, the differential entropy and symmetry quotient were respectively calculated to extract frequency and spatial features. Then, the brain heat map combined the complex network and frequency-spatial features with different dimensions together as the fusion features. Finally, a convolutional neural network-long short-term memory (CNN-LSTM) neural network was used to evaluate the three-class fatigue states of the EEG data in the Shanghai Jiao Tong University (SJTU) Emotion EEG Dataset (SEED)-VIG dataset, and it was validated on the dataset on the Mendeley Data website.</div></div><div><h3>Results</h3><div>The experimental results of SEED-VIG dataset show that the average classification accuracy of three-class fatigue states, namely, awake, tired and drowsy, reaches 96.57 %. The average classification accuracy on the dataset on the Mendeley Data website reaches 99.23 %.</div></div><div><h3>Comparison with existing methods</h3><div>This method has a best evaluation performance compared with the state-of-the-art methods for the three-class fatigue states recognition.</div></div><div><h3>Conclusions</h3><div>The experiment results validated the feasibility of the fatigue state evaluation method based on the correlations between channels and the frequency-spatial features, which is of great significance for developing a driver fatigue detection system.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"416 ","pages":"Article 110385"},"PeriodicalIF":2.7,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143255909","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":"Latency correction in sparse neuronal spike trains with overlapping global events","authors":"Arturo Mariani ,&nbsp;Federico Senocrate ,&nbsp;Jason Mikiel-Hunter ,&nbsp;David McAlpine ,&nbsp;Barbara Beiderbeck ,&nbsp;Michael Pecka ,&nbsp;Kevin Lin ,&nbsp;Thomas Kreuz","doi":"10.1016/j.jneumeth.2025.110378","DOIUrl":"10.1016/j.jneumeth.2025.110378","url":null,"abstract":"<div><h3>Background:</h3><div>In Kreuz et al., J Neurosci Methods 381, 109703 (2022) two methods were proposed that perform latency correction, i.e., optimize the spike time alignment of sparse neuronal spike trains with well-defined global spiking events. The first one based on direct shifts is fast but uses only partial latency information, while the other one makes use of the full information but relies on the computationally costly simulated annealing. Both methods reach their limits and can become unreliable when successive global events are not sufficiently separated or even overlap.</div></div><div><h3>New Method:</h3><div>Here we propose an iterative scheme that combines the advantages of the two original methods by using in each step as much of the latency information as possible and by employing a very fast extrapolation direct shift method instead of the much slower simulated annealing.</div></div><div><h3>Results:</h3><div>We illustrate the effectiveness and the improved performance, measured in terms of the relative shift error, of the new iterative scheme not only on simulated data with known ground truths but also on single-unit recordings from two medial superior olive neurons of a gerbil.</div></div><div><h3>Comparison with Existing Method(s):</h3><div>The iterative scheme outperforms the existing approaches on both the simulated and the experimental data. Due to its low computational demands, and in contrast to simulated annealing, it can also be applied to very large datasets.</div></div><div><h3>Conclusions:</h3><div>The new method generalizes and improves on the original method both in terms of accuracy and speed. Importantly, it is the only method that allows to disentangle global events with overlap.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"416 ","pages":"Article 110378"},"PeriodicalIF":2.7,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating neuroscience and artificial intelligence: EEG analysis using ensemble learning for diagnosis Alzheimer’s disease and frontotemporal dementia","authors":"Amir Hossein Hachamnia ,&nbsp;Ali Mehri ,&nbsp;Maryam Jamaati","doi":"10.1016/j.jneumeth.2025.110377","DOIUrl":"10.1016/j.jneumeth.2025.110377","url":null,"abstract":"<div><h3>Background:</h3><div>Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are both progressive neurological disorders that affect the elderly. Distinguishing between individuals suffering from these two diseases in the early stages can be quite challenging, and due to their different treatments, it has become an important problem. Machine learning (ML) algorithms can be helpful in this matter due to their high ability to manage large data and deliver high-quality diagnostic results.</div></div><div><h3>New method:</h3><div>In this research, we integrate multiple ML algorithms into 10 ensemble learning techniques, utilizing 7 distinct features: 3 from the time domain and 4 from the frequency domain.</div></div><div><h3>Results:</h3><div>They are used to achieve a higher diagnostic accuracy level in binary and multiclass classification of samples from electroencephalography (EEG) signals of elderly patients with AD, FTD, and healthy age-matching controls (CN), during the eye resting state.</div></div><div><h3>Comparison with existing methods:</h3><div>The best results in carrying out binary AD/CN, FTD/CN, and AD/FTD classifications with significant accuracy<span><math><mo>&gt;</mo></math></span>95% have been obtained with the help of the light gradient boosting machine (LGBM) method applying the wavelet transform feature.</div></div><div><h3>Conclusion:</h3><div>This combination (LGBM&amp;wavelet) also displays the best performance in the AD/FTD/CN multiclass classification process with accuracy<span><math><mo>&gt;</mo></math></span>93%.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"416 ","pages":"Article 110377"},"PeriodicalIF":2.7,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080357","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":"Developing a predictive value for predicting stroke recovery based on transcranial doppler ultrasound parameters","authors":"Liu Yang,&nbsp;Xinyi Cai,&nbsp;Yanhong Yan,&nbsp;Pinjing Hui","doi":"10.1016/j.jneumeth.2025.110383","DOIUrl":"10.1016/j.jneumeth.2025.110383","url":null,"abstract":"<div><h3>Background</h3><div>One of the leading causes of disability and death is acute ischemic stroke (AIS) brought on by middle cerebral artery (MCA) obstruction. For the best patient care, it is essential to accurately anticipate the functional prognosis in the early stages of stroke. The ability of conventional clinical evaluations and imaging methods to deliver precise and timely prognostic information is frequently limited.</div></div><div><h3>New method</h3><div>In this work, a predictive value for predicting functional outcome in patients with acute ischemic stroke caused by MCA blockage was developed utilizing transcranial Doppler (TCD) ultrasonography characteristics. Within 24 h after intravenous thrombolysis (IVT), TCD measures such as pulsatility index (PI), mean flow velocity (Vm), end-diastolic velocity (EDV), and peak systolic velocity (PSV) were assessed. Independent determinants of functional outcome, as determined by the modified Rankin Scale (mRS), were found using logistic regression analysis. These important factors were used to create a prediction model.</div></div><div><h3>Comparison with existing methods</h3><div>Favorable functional outcomes were substantially correlated with a number of TCD characteristics, such as the ratio of pulsatility index to mean flow velocity (rPI) and peak systolic velocity to end-diastolic velocity (rPSV). At three months after a stroke, a logistic regression model that included these measures together with additional clinical indicators showed excellent accuracy in predicting functional prognosis.</div></div><div><h3>Conclusion</h3><div>In individuals who have experienced an acute ischemic stroke as a result of MCA blockage, TCD ultrasonography parameters—in particular, rPSV and rPI—are useful prognostic indicators for forecasting functional prognosis. Early risk classification and individualized treatment plans can benefit from the creation of a quantitative model based on these criteria. Validating and improving this model in bigger and more varied patient groups should be the goal of future research.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"416 ","pages":"Article 110383"},"PeriodicalIF":2.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074816","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}