Journal of Neuroscience Methods最新文献

{"title":"Probabilistic normative maps of the corticospinal and cortico-reticulospinal tracts in streamline and volumetric formats","authors":"Pierce Boyne ,&nbsp;Brady Williamson ,&nbsp;Josephine Buclez ,&nbsp;Steven C. Cramer ,&nbsp;David J. Lin","doi":"10.1016/j.jneumeth.2025.110549","DOIUrl":"10.1016/j.jneumeth.2025.110549","url":null,"abstract":"<div><h3>Background</h3><div>Normative maps of brain tracts are key tools for assessing the extent of tract injury and plasticity after brain lesions such as stroke. The conventional procedure for tract mapping reduces diffusion tractography streamlines to a volumetric image before warping them to a standard coordinate space. Unfortunately, this volumetric reduction discards information about tract connectivity across voxels, complicating estimation of tract injury from the resulting map. This issue could be addressed by using a streamline-based tract map, but prior efforts to do so using group-based tractography have not accounted for individual variability, and thus are at risk of underestimating tract extent.</div></div><div><h3>New Method</h3><div>Direct streamline normalization (DSN) directly warps individual tractography streamlines to a standard coordinate space, which can be pooled across individuals to account for individual variability. Here, DSN was used to create normative tract maps for the first time.</div></div><div><h3>Results</h3><div>Novel probabilistic maps of the corticospinal tract (CST) and cortico-reticulospinal tract (CRST) were generated in streamline format using DSN.</div></div><div><h3>Comparison with Existing Methods</h3><div>Compared with group-based tractography, tract maps generated with DSN had 3.1–5.2 times greater volume, 1.0–1.5 times greater mean diameter and 7.8–15.3 times greater cortical origin area.</div></div><div><h3>Conclusions</h3><div>DSN produces tract maps with more accurate representation of axonal geometry than the conventional procedure and greater incorporation of individual variability than group-based tractography, likely resulting in larger maps. Future studies can use the CST and CRST maps generated here to optimize tract injury and plasticity measurements, which may provide an improved understanding of motor impairment and recovery.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110549"},"PeriodicalIF":2.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842857","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":"Action sequence guidance with exposure trajectory technology improves performance of motor imagery-based brain-computer interface","authors":"Kai Kong ,&nbsp;Jie Wang ,&nbsp;Mengfan Li ,&nbsp;Tengyu Zhang ,&nbsp;Enming Qi ,&nbsp;Qi Zhao","doi":"10.1016/j.jneumeth.2025.110553","DOIUrl":"10.1016/j.jneumeth.2025.110553","url":null,"abstract":"<div><h3>Background</h3><div>The paradigms greatly influence the performance of motor imagery (MI)-based brain-computer interfaces (BCI) by guiding subjects to imagine. How to make the guidance clear and intuitive is important for MI-BCI to improve performance.</div></div><div><h3>New methods</h3><div>This study proposes a novel MI-BCI paradigm based on action sequence (AS) guidance, which visualizes and choreographs sequential actions to support motor imagery. In a drawing task, the action exposure trajectory technique presents a gray nib at the starting point of the next stroke while the current stroke is being drawn, highlighting the order and details of the movement. Ten subjects participated in offline and online experiments under both AS and traditional MI conditions. EEG activation regarding multiple frequencies and periods, and MI-BCI performance are evaluated.</div></div><div><h3>Results</h3><div>The AS paradigm evokes more significant ERD/ERS features, and improves offline and online BCI accuracies and information transfer rates to 85.69 %, 78.77 %, and 15.60 bits/min, which are 8.37 %, 7.95 %, and 7.13 bits/min higher than the traditional paradigm. In addition, the subjects are demonstrated more comfortable subjective feelings.</div></div><div><h3>Comparison with existing methods</h3><div>The AS paradigm offers clearer and more intuitive guidance, enhances EEG feature activation, and significantly improves MI-BCI performance in both offline and online experiments.</div></div><div><h3>Conclusions</h3><div>Dynamic action sequences action with exposure trajectory technique could enhance the subject’s brian activation by offering richer content and more intuitive guidance, providing a new way for prompting BCI performance.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110553"},"PeriodicalIF":2.3,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858355","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":"SST-DUNet: Smart Swin Transformer and Dense UNet for automated preclinical fMRI skull stripping","authors":"Sima Soltanpour ,&nbsp;Rachel Utama ,&nbsp;Arnold Chang ,&nbsp;Md Taufiq Nasseef ,&nbsp;Dan Madularu ,&nbsp;Praveen Kulkarni ,&nbsp;Craig F. Ferris ,&nbsp;Chris Joslin","doi":"10.1016/j.jneumeth.2025.110545","DOIUrl":"10.1016/j.jneumeth.2025.110545","url":null,"abstract":"<div><h3>Background:</h3><div>Skull stripping is a common preprocessing step in Magnetic Resonance Imaging (MRI) pipelines and is often performed manually. Automating this process is challenging for preclinical data due to variations in brain geometry, resolution, and tissue contrast. Existing methods for MRI skull stripping often struggle with the low resolution and varying slice sizes found in preclinical functional MRI (fMRI) data.</div></div><div><h3>New method:</h3><div>This study proposes a novel method that integrates a Dense UNet-based architecture with a feature extractor based on the Smart Swin Transformer (SST), called SST-DUNet. The Smart Shifted Window Multi-Head Self-Attention (SSW-MSA) module in SST replaces the mask-based module in the Swin Transformer (ST), enabling the learning of distinct channel-wise features while focusing on relevant dependencies within brain structures. This modification allows the model to better handle the complexities of fMRI skull stripping, such as low resolution and variable slice sizes. To address class imbalance in preclinical data, a combined loss function using Focal and Dice loss is applied.</div></div><div><h3>Results:</h3><div>The model was trained on rat fMRI images and evaluated across three in-house datasets, achieving Dice similarity scores of 98.65%, 97.86%, and 98.04%.</div></div><div><h3>Comparison with existing methods:</h3><div>We compared our method with conventional and deep learning-based approaches, demonstrating its superiority over state-of-the-art methods.</div></div><div><h3>Conclusions:</h3><div>The fMRI results using SST-DUNet closely align with those from manual skull stripping for both seed-based and independent component analyses, indicating that SST-DUNet can effectively substitute manual brain extraction in rat fMRI analysis.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110545"},"PeriodicalIF":2.3,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144813986","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 novel paradigm based on radar-like scanning for directional recognition in event-related potentials based brain-computer interfaces","authors":"Xueqing Zhao ,&nbsp;Ren Xu ,&nbsp;Yutao Zhang ,&nbsp;Andrew Ty Lau ,&nbsp;Ruitian Xu ,&nbsp;Xingyu Wang ,&nbsp;Andrzej Cichocki ,&nbsp;Jing Jin","doi":"10.1016/j.jneumeth.2025.110546","DOIUrl":"10.1016/j.jneumeth.2025.110546","url":null,"abstract":"<div><h3>Background</h3><div>Event-related potentials (ERPs) based brain-computer interface (BCI) systems have shown significant potential for directional control applications. Existing paradigms are constrained by the limited scalability of directional commands that demand interface reconfiguration for varying target numbers.</div></div><div><h3>New method</h3><div>We propose a novel radar-like scanning (RS) paradigm for 32-directional recognition tasks to address these limitations. This paradigm continuously scans through directions using a sector-shaped visual stimulus, naturally evoking ERP responses without discrete directional indicators. During the online experiments, an early-stopping strategy is employed to enhance efficiency. Additionally, this study analyzes subjects' directional recognition performance using EEGNet under three sector rotation periods. Thirteen subjects participated in the experiments.</div></div><div><h3>Results</h3><div>The grand-averaged ERP amplitudes exhibited a stronger negative deflection in the parietal, occipital, and temporoparietal regions. The results demonstrated that, with a 2 s rotation period and early-stopping strategy, the best subject achieved an accuracy of 87.50 % with a mean absolute angle error of 1.64°. When the directional error tolerance was set to 11.25°, the subject-averaged accuracy reached 91.83 % under the same conditions. Longer rotation periods led to better subject-averaged recognition performance. When the rotation period was short (1 s), targets close to the scanning center were challenging to recognize.</div></div><div><h3>Comparison with existing methods</h3><div>Compared with others, the RS paradigm enables more fine-grained directional target recognition and is unaffected by the target numbers.</div></div><div><h3>Conclusions</h3><div>The proposed paradigm demonstrates significant potential for applications in ERP-BCI systems.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110546"},"PeriodicalIF":2.3,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773182","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":"DMFLN: A dynamic multi-scale focus learning framework for Alzheimer’s disease classification","authors":"Jikai Wang ,&nbsp;Mingfeng Jiang ,&nbsp;Wei Zhang ,&nbsp;Yang Li ,&nbsp;Tao Tan ,&nbsp;Yaming Wang ,&nbsp;Tie-qiang Li","doi":"10.1016/j.jneumeth.2025.110541","DOIUrl":"10.1016/j.jneumeth.2025.110541","url":null,"abstract":"<div><h3>Background:</h3><div>Magnetic resonance imaging (MRI) of gray matter plays a crucial role in the diagnosis of Alzheimer’s disease (AD). Recent advances in multiscale learning techniques have improved AD classification by capturing structural information at multiple scales. However, effectively balancing the contributions of these multiscale features remains a significant challenge.</div></div><div><h3>New Method:</h3><div>To address this issue, we propose a Dynamic Multiscale Feature Learning Network (DMFLN) for AD classification. The framework incorporates a pyramid self-attention mechanism to capture high-level global contextual features and model long-range dependencies. Additionally, a residual wavelet transform is utilized to extract fine-grained local structural features. The DMFLN adaptively adjusts the weights of features across different scales, enabling a balanced fusion of global topological representations and local morphological details.</div></div><div><h3>Results:</h3><div>We evaluate our approach on T1-weighted MRI scans from the ADNI dataset. The proposed method achieves classification accuracies of 96.32% <span><math><mo>±</mo></math></span> 0.51%, 94.62% <span><math><mo>±</mo></math></span> 0.39%, and 93.07% <span><math><mo>±</mo></math></span> 0.81% for AD vs. NC, AD vs. MCI, and NC vs. MCI tasks, respectively.</div></div><div><h3>Comparison with existing methods:</h3><div>Compared to state-of-the-art approaches, the DMFLN framework offers improved performance by effectively addressing the challenge of multiscale feature weighting, which is often a bottleneck in multiscale fusion-based AD classification.</div></div><div><h3>Conclusions:</h3><div>The DMFLN framework demonstrates significant improvements in AD classification by adaptively integrating global and local structural information from gray matter. These results highlight the potential of dynamic multiscale feature learning in advancing neuroimaging-based AD diagnosis.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110541"},"PeriodicalIF":2.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144768689","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":"Effects of focused ultrasound stimulation on various in vitro neurological cell models","authors":"Iqra Bano ,&nbsp;Jaison Jeevanandam ,&nbsp;Grygoriy Tsenov","doi":"10.1016/j.jneumeth.2025.110544","DOIUrl":"10.1016/j.jneumeth.2025.110544","url":null,"abstract":"<div><div>Focused ultrasound stimulation (FUS) is rapidly gaining attention as a non-invasive and highly precise neuromodulatory technique with broad therapeutic potential in neurological and psychiatric disorders. While most reviews to date have emphasized in vivo and clinical studies, the cellular mechanisms underlying FUS remain underexplored. This study presents an innovative and thorough synthesis of FUS effects in in vitro neurological cell models, including SH-SY5Y, PC12, BV2 microglia, NSC-34 motor neurons, and human iPSC-derived neurons and astrocytes. These models offer essential insights into the mechanisms by which FUS influences intracellular calcium dynamics, mitigates oxidative stress, modulates inflammatory responses, and stimulates autophagy, thus facilitating neuroprotection and synaptic resilience in various disease contexts, including Parkinson’s disease, Alzheimer’s disease, schizophrenia, epilepsy, multiple sclerosis, OCD, and traumatic brain injury. Mapping disease-specific results with comprehensive FUS sonication parameters, this evaluation only focuses on cell-based systems, which is a fundamental advance. Additionally, it emphasizes the incorporation of new technology into FUS, such as acoustically responsive biomaterials, microbubble-assisted gene transfection, and nanoparticle-mediated medication delivery. The study highlights the increasing importance of AI in directing real-time FUS targeting and optimizing parameters, which is leading to tailored neuromodulation treatments. This study establishes a solid groundwork for the advancement of FUS in preclinical research by connecting the dots between cellular bioeffects and translational potential. It highlights the critical need for multidisciplinary methods, standardization, and the use of 3D organoid systems for next-generation brain treatments that fully use FUS.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110544"},"PeriodicalIF":2.3,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722687","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":"Closed-loop phase-targeted stimulation during sleep: Open-source benchmarking of methods and a novel algorithm for the epileptic brain","authors":"Vicki Li ,&nbsp;Simeon M. Wong ,&nbsp;George M. Ibrahim","doi":"10.1016/j.jneumeth.2025.110543","DOIUrl":"10.1016/j.jneumeth.2025.110543","url":null,"abstract":"<div><h3>Background</h3><div>Phase-targeted auditory stimulation (PTAS) during sleep has been shown to enhance slow oscillations (SOs) and improve memory consolidation through closed-loop delivery of auditory stimuli at the up-phase of SOs. However, clinical translation of PTAS therapy has been hindered by challenges in the estimation of real-time phase. Our scoping review of 53 PTAS studies identified substantial variability in phase estimation methods and therapeutic outcomes. In particular, there were no validated methods for clinical populations with pathological electroencephalography (EEG) features, such as persons with epilepsy, where interictal epileptiform discharges (IEDs) compromise the performance of real-time PTAS delivery.</div></div><div><h3>New method</h3><div>To address critical limitations in the application of existing approaches to the epileptic brain, we developed TWave, a real-time algorithm that integrates wavelet-based phase estimation with predictive modelling and multi-feature validation. TWave is designed to maintain SO phase estimation performance while rejecting pathological EEG artifacts to achieve the temporal precision required for effective PTAS.</div></div><div><h3>Results</h3><div>TWave achieved high phase estimation accuracy and precision in healthy adult (mean error=0.11 radians; SD=1.23 radians) and paediatric epilepsy (mean error=0.26 radians; SD=1.22 radians) EEG recordings. Importantly, TWave successfully rejected 83 % of IEDs while maintaining sensitivity to SOs.</div></div><div><h3>Comparison with existing algorithms</h3><div>Benchmarking against four commonly used algorithms demonstrated TWave’s superior performance in maintaining phase estimation precision across normative and epilepsy EEG recordings.</div></div><div><h3>Conclusion</h3><div>The current work accelerates clinical translation of PTAS by providing a validated approach to real-time phase estimation and providing an open-source toolbox to increase reproducibility in sleep modulation research.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"423 ","pages":"Article 110543"},"PeriodicalIF":2.3,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144717967","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":"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}