Journal of Neuroscience Methods最新文献

{"title":"Deep feature extraction and swarm-optimized enhanced extreme learning machine for motor imagery recognition in stroke patients","authors":"Vaishali R. Shirodkar ,&nbsp;Damodar Reddy Edla ,&nbsp;Annu Kumari ,&nbsp;Ramesh Dharavath","doi":"10.1016/j.jneumeth.2025.110565","DOIUrl":"10.1016/j.jneumeth.2025.110565","url":null,"abstract":"<div><h3>Background:</h3><div>Interpretation of motor imagery (MI) in brain–computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.</div></div><div><h3>New methods:</h3><div>To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification. The approach begins with subject-specific frequency band selection based on event-related desynchronization (ERD), aimed at reducing non-stationarity and improving signal relevance. Spatial and temporal features are then extracted using a combination of the scale-invariant feature transform (SIFT) and a one-dimensional convolutional neural network (1D CNN), providing a comprehensive representation of EEG signal dynamics. These features are fused and classified using an enhanced extreme learning machine (EELM), with hidden layer weights optimized using differential evolution (DE), particle swarm optimization (PSO), and dynamic multi-swarm PSO (DMS-PSO).</div></div><div><h3>Results:</h3><div>Experimental validation on a dataset of 50 stroke patients demonstrated an average classification accuracy of 97% using DMS-PSO with 10-fold cross-validation. Additional evaluation on the BCI Competition IV 1a dataset yielded 95% and 91.56% on IV 2a, indicating strong generalization performance.</div></div><div><h3>Comparison with existing methods:</h3><div>Unlike conventional BCI approaches, this method combines adaptive filtering, spatial–temporal hybrid feature learning, and metaheuristic optimization, resulting in a lightweight model with improved classification accuracy and robustness.</div></div><div><h3>Conclusion:</h3><div>These findings demonstrate the effectiveness of evolutionary optimization in dealing with the constraints provided by high-dimensional, non-stationary EEG data, making it a promising strategy for real-time MI classification in BCI-based stroke applications.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110565"},"PeriodicalIF":2.3,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027314","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":"Assessing adult zebrafish despair-like behaviors in the small vertical cylinder immobility test (VCIT)","authors":"Jiyi Wang ,&nbsp;Vea Bley ,&nbsp;Jiayou Jiang ,&nbsp;Yunqian Zhang ,&nbsp;Yixing Qin ,&nbsp;Haoyu Feng ,&nbsp;Yucheng Liu ,&nbsp;Ruiyu Li ,&nbsp;Chaoming Wang ,&nbsp;Shulei He ,&nbsp;Gan Wang ,&nbsp;Kai He ,&nbsp;Huiling Cai ,&nbsp;Yuxiang Jia ,&nbsp;Chongguang Zhao ,&nbsp;Yingze Wang ,&nbsp;Jiahao Cui ,&nbsp;Longen Yang ,&nbsp;Adam Michael Stewart ,&nbsp;Murilo S. de Abreu ,&nbsp;Allan V. Kalueff","doi":"10.1016/j.jneumeth.2025.110569","DOIUrl":"10.1016/j.jneumeth.2025.110569","url":null,"abstract":"<div><h3>Background</h3><div>Affective disorders represent a major global health burden. Animal models are widely used for modeling brain disorders and neuroactive drug discovery. A novel powerful tool in translational neuroscience research, zebrafish (<em>Danio rerio</em>) provide multiple behavioral assays relevant to anxiety-like and depression-related conditions (including despair-like behavior, a common feature in depression).</div></div><div><h3>New method</h3><div>Here, we introduce a novel behavioral paradigm for assessing zebrafish locomotion and despair-like phenotypes, the small 5-mL glass vertical cylinder immobility test (VCIT). Conceptually similar to rodent and other zebrafish ‘despair’-like models, the VCIT protocol is based on restricting fish locomotion vertically for 5 min in head-first position, to assess their locomotion and despair-like immobility.</div></div><div><h3>Results</h3><div>The test was sensitive to acute and chronic stressors that increased immobility duration (alarm pheromone, net chasing, chronic sleep deprivation and 12-week unpredictable stress), as well as to bidirectional modulation of zebrafish behavior by various acute and chronic neuroactive drugs. The VCIT immobility was reduced by psychostimulants nicotine and arecoline, as well as a conventional antidepressant fluoxetine. In contrast, the immobility in this test was increased by a pro-depressant dopamine-depleting drug reserpine, and remained unaltered by an anxiolytic agent ethanol or anxiogenic drugs caffeine and GBR-12909 (vanoxerine).</div></div><div><h3>Comparisons with existing method(s)</h3><div>The VCIT provides an easy-to-perform, minimally invasive, non-traumatic, and procedurally simpler and faster model of assessing zebrafish stress-evoked despair-like phenotypes.</div></div><div><h3>Conclusions</h3><div>The VCIT is sensitive to various stress-related manipulations and bidirectional pharmacological modulation, hence emphasizing the growing relevance and potential of zebrafish in advancing neuropsychiatric research and identifying innovative treatments for neuropsychiatric disorders.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110569"},"PeriodicalIF":2.3,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145029447","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 comparison of transduction efficiency of medial septal neurons by adeno-associated viruses with three promoters","authors":"Alena A. Koryagina ,&nbsp;Yulia V. Dobryakova ,&nbsp;Konstantin A. Gerasimov ,&nbsp;Ghofran Alhalabi ,&nbsp;Alexandr A. Moshchenko ,&nbsp;Vsevolod V. Belousov ,&nbsp;Alexey P. Bolshakov","doi":"10.1016/j.jneumeth.2025.110570","DOIUrl":"10.1016/j.jneumeth.2025.110570","url":null,"abstract":"<div><h3>Background</h3><div>Most researchers rely on popular promoters like the synthetic CAG promoter or human synapsin promoter to transduce various brain neurons. However, their effectiveness in transducing forebrain cholinergic neurons remains unclear.</div></div><div><h3>New method</h3><div>We compared efficacy of transduction of cholinergic neurons and parvalbumin-positive neurons in the medial septal area of rats and mice by adeno-associated viruses (AAVs) carrying the green fluorescent protein (GFP) marker gene under three distinct promoters—CAG, synapsin, and the mouse choline acetyltransferase (<em>CHAT</em>) promoter.</div></div><div><h3>Results</h3><div>In mice and rats, the CAG and synapsin promoters demonstrated extremely low efficacy for GFP expression in CHAT+ neurons but were effective for transducing PV+ neurons. The <em>CHAT</em> promoter yielded moderate GFP expression in cholinergic neurons in mice, with negligible expression in PV+ neurons, though it also led to off-target expression in other cell types. In rats, the <em>CHAT</em> promoter produced moderate GFP expression in both cholinergic and PV+ neurons; however, the majority of GFP-expressing cells were unrelated to these specific neuronal subtypes.</div><div>Comparison with existing methods: Contrary to expectations, human synapsin and CAG promoters provided negligible expression in cholinergic septal neurons in both rats and mice.</div></div><div><h3>Conclusions</h3><div>The mouse <em>CHAT</em> promoter is significantly more effective for cholinergic neuron expression in rats and mice compared to universal neuronal promoters like CAG and synapsin promoter.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110570"},"PeriodicalIF":2.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015623","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":"Cortico-cortical evoked potentials: Automated localization and classification of early and late responses","authors":"Sahaj A. Patel ,&nbsp;Helen Brinyark ,&nbsp;Caila Coyne ,&nbsp;Noshin Tasnia ,&nbsp;Rebekah Chatfield ,&nbsp;Erin C. Conrad ,&nbsp;Benjamin Cox ,&nbsp;Arie Nakhmani ,&nbsp;Rachel J. Smith","doi":"10.1016/j.jneumeth.2025.110571","DOIUrl":"10.1016/j.jneumeth.2025.110571","url":null,"abstract":"<div><h3>Background</h3><div>Cortico-cortical evoked potentials (CCEPs), elicited via single-pulse electrical stimulation, are used to map brain networks. These responses comprise early (N1) and late (N2) components, which reflect direct and indirect cortical connectivity. Reliable identification of these components remains difficult due to substantial variability in amplitude, phase, and timing. Traditional statistical methods often struggle to localize N1 and N2 peaks under such conditions.</div></div><div><h3>New Method</h3><div>A deep learning framework based on You Only Look Once (YOLO v10) was developed. Each CCEP epoch was converted into a two-dimensional image using Matplotlib and subsequently analyzed by the YOLO model to localize and classify N1 and N2 components. Detected image coordinates were mapped back to corresponding time-series indices for clinical interpretation.</div></div><div><h3>Results</h3><div>The framework was trained and validated on intracranial EEG data from 9 patients with drug-resistant epilepsy (DRE) at the University of Alabama at Birmingham (UAB), achieving a mean average precision (mAP) of 0.928 at an Intersection over Union (IoU) threshold of 0.5 on the test dataset. Generalizability was assessed on more than 4000 unannotated epochs obtained from 5 additional UAB patients and 10 patients at the Hospital of the University of Pennsylvania.</div></div><div><h3>Comparison with existing methods</h3><div>To our knowledge, no existing deep learning methods localize and classify both N1 and N2 components, limiting comparison. Current approaches rely on manual identification within fixed windows, introducing inter-rater variability and often missing inter-individual differences.</div></div><div><h3>Conclusion</h3><div>The proposed framework accurately detects and classifies CCEP components, offering a robust, automated alternative to manual analysis.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110571"},"PeriodicalIF":2.3,"publicationDate":"2025-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145015672","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":"Automated EEG signal processing: A comprehensive investigation into preprocessing techniques and sub-band extraction for enhanced brain-computer interface applications","authors":"Venkata Phanikrishna Balam","doi":"10.1016/j.jneumeth.2025.110561","DOIUrl":"10.1016/j.jneumeth.2025.110561","url":null,"abstract":"<div><div>The Electroencephalogram (EEG) is a vital physiological signal for monitoring brain activity and understanding neurological capacities, disabilities, and cognitive processes. Analyzing and classifying EEG signals are key to assessing an individual’s reactions to various stimuli. Manual EEG analysis is time-consuming and labor-intensive, necessitating automated tools for efficiency. Machine learning techniques often rely on preprocessing and segmentation methods to integrate automated classification into EEG signal processing, with EEG sub-band components (<em>δ</em>,<em>θ</em>,<em>α</em>,<em>β</em> and <em>γ</em>) playing a crucial role. This paper presents a comprehensive exploration of EEG preprocessing methods, with a specific focus on sub-band extraction techniques used in Brain-Computer Interface (BCI) applications. Various methods—including Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters, and wavelet transforms (DWT, WPT)—are evaluated through qualitative and quantitative parametric analysis, along with a review of their practical applicability. The study also includes an application-based evaluation using an open-access EEG dataset for drowsiness detection.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110561"},"PeriodicalIF":2.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008387","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 protocols for the simultaneous isolation of primary brain microvascular endothelial cells and primary neurons with high purity and functional maturation from individual newborn mice","authors":"Fating Zhou ,&nbsp;Rui Huang ,&nbsp;Jia Xie ,&nbsp;Junyu Jiang ,&nbsp;Xuemei Jiang ,&nbsp;Yunfei Xiang ,&nbsp;Guoxiang Zhang ,&nbsp;Hao Li ,&nbsp;Shunjie Zhang ,&nbsp;Shanmu Ai ,&nbsp;Yu Ma","doi":"10.1016/j.jneumeth.2025.110568","DOIUrl":"10.1016/j.jneumeth.2025.110568","url":null,"abstract":"<div><h3>Background</h3><div>Current neurovascular unit isolation requires processing brain microvascular endothelial cells (BMECs) and neurons from separate animals, preventing concurrent analysis of neurovascular crosstalk within identical genetic/physiological contexts.</div></div><div><h3>New methods</h3><div>We developed an enzymatic digestion/bovine serum albumin density gradient technique that enabled the simultaneous isolation of neural tissue and microvascular segments from individual mice. The neural tissue was filtered and centrifuged for primary cortical neuron culture on poly-L-lysine-coated plates. Microvascular segments were subjected to collagenase/dispase digestion and Percoll gradient centrifugation for BMEC culture on fibronectin-coated plates. Cellular purity was quantified via immunofluorescence, and BMEC functionality was assessed by tight junction expression, transendothelial electrical resistance (TEER), tubulogenesis, and secretory function. Neuronal characteristics were evaluated using morphometric analysis, detection of neurotransmitter secretion, and sensitivity to oxygen-glucose deprivation (OGD).</div></div><div><h3>Results</h3><div>High-purity BMECs and primary cortical neurons were successfully isolated by enzymatic digestion combined with density-gradient centrifugation. Primary BMECs exhibited fibronectin-dependent adhesion during initial plating, with a significantly enhanced adhesive capacity observed in passages 2 and 3. Tubulogenesis assays demonstrated superior tube-forming capacity of primary BMECs compared b.<em>E</em>nd3 cells. TEER and nitric oxide (NO) secretion decreased by 38.31 % and 26.1 %, respectively, following OGD. Primary cortical neurons displayed a characteristic somatic morphology with extensive neurite arborization and heightened sensitivity to OGD. The GABA level in the OGD group was 2.01 times higher than that in the control group and decreased by 52.5 % after reoxygenation.</div></div><div><h3>Comparison with existing methods</h3><div>Unlike conventional multi-animal protocols that introduce inter-individual variability, our single-mouse approach eliminates genetic confounders while reducing processing time by 40–60 % and yielding higher purity. Furthermore, primary BMECs and neurons maintained their original characteristics, including morphology, angiogenic capacity, and secretory function.</div></div><div><h3>Conclusion</h3><div>This novel platform reliably co-isolated functional primary BMECs and cortical neurons from individual mice, providing unprecedented fidelity for modeling neurovascular interactions in disease contexts.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110568"},"PeriodicalIF":2.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008315","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":"High efficiency labeling of nerve fibers in cleared tissue for light-sheet microscopy","authors":"Marta Rojas-Rodríguez ,&nbsp;Elisa Imbimbo ,&nbsp;Claudia Capitini ,&nbsp;Irene Costantini ,&nbsp;Giacomo Mazzamuto ,&nbsp;Francesco Saverio Pavone ,&nbsp;Ludovico Silvestri ,&nbsp;Martino Calamai","doi":"10.1016/j.jneumeth.2025.110567","DOIUrl":"10.1016/j.jneumeth.2025.110567","url":null,"abstract":"<div><h3>Background</h3><div>Tissue clearing techniques combined with light-sheet fluorescence microscopy (LSFM) enable high-resolution 3D imaging of biological structures without physical sectioning. While widely used in neuroscience to determine brain architecture and connectomics, their application for spinal cord mapping remains more limited, posing challenges for studying demyelinating diseases like multiple sclerosis. Myelin visualization in cleared tissues is particularly difficult due to the lipid-removal nature of most clearing protocols, and alternative immunolabeling approaches failed to reach satisfying results.</div></div><div><h3>New method</h3><div>To overcome these limitations, we developed a novel protocol named HELF -High Efficiency Labeling of Fibers- which takes advantage of a fluorescently labeled aminosterol, trodusquemine, which displays a strong affinity for cholesterol-rich membranes, and a supplementary round of fixation with glutaraldehyde.</div></div><div><h3>Results and comparison with existing methods</h3><div>The labeling with trodusquemine was tested in combination with various established tissue clearing techniques and compared with HELF, which resulted to be the best approach for providing high-brightness myelin staining in mouse spinal cord and brain, and in human brain samples. Finally, we demonstrated that HELF can be used to stain and image with LSFM a whole cleared mouse spinal cord.</div></div><div><h3>Conclusions</h3><div>Our data support the potential use of HELF coupled to LSFM as a practical tool for the evaluation of novel therapeutics for remyelination in preclinical models of CNS diseases.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110567"},"PeriodicalIF":2.3,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145008354","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":"Reconstructing time-domain data from discontinuous Percept™ PC and RC output using external data acquisition and linear filtering","authors":"Jinxin Chen ,&nbsp;Mandy M. Koop ,&nbsp;Kenneth B. Baker ,&nbsp;Jay L. Alberts ,&nbsp;James Y. Liao","doi":"10.1016/j.jneumeth.2025.110566","DOIUrl":"10.1016/j.jneumeth.2025.110566","url":null,"abstract":"<div><h3>Background</h3><div>The Medtronic Percept™ PC and RC are deep brain stimulation (DBS) systems with recording capability. However, when the stimulation frequency is changed, the recordings were segmented, introducing interruptions that shift each segment in the time domain.</div></div><div><h3>New method</h3><div>Ex-vivo, stimulation frequency was changed while local field potential was being recorded in both leads. One lead captured stimulation artifacts from the DBS system, and another captured stimulation artifacts from the DBS system plus a 0.5 Hz impulse train from an external stimulator. Timing errors were assessed by comparing Percept™-recorded impulses to the gold-standard external stimulator impulses. The Percept™ recordings were then time-shifted to match the external system’s timing, based on the time difference between the two systems when stimulation frequency change was indicated.</div></div><div><h3>Results</h3><div>For both PC and RC, the sawtooth pattern occurred. Timing errors were noted to have linear ramps interrupted by sudden drops, which were used to develop an algorithm to correct, leveraging occasions where the Percept™ happens to record the true moments of stimulated frequency change. Errors ranged from -400 to 400 ms for PC, and from -1 to 1 s for RC. The timing reconstruction algorithm reduced the error to -10.07 ± 45.06 ms (mean ± std) for PC, and -23.52 ± 17.32 ms (mean ± std) for RC.</div></div><div><h3>Comparison with existing methods</h3><div>We measure and characterize the timing errors of each recorded segment, using ex-vivo DBS hardware, and propose a strategy to correct them.</div></div><div><h3>Conclusion</h3><div>This approach can be applied in-vivo using electroencephalogram to correct timing errors that are significant with long recordings, enabling accurate time synchronization.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110566"},"PeriodicalIF":2.3,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006243","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":"Optimizing tissue clearing methods for improved imaging of whole-mount retinas and optic nerves","authors":"Aubin Mutschler ,&nbsp;Volha V. Malechka ,&nbsp;Nikita Bagaev ,&nbsp;Petr Baranov ,&nbsp;Jonathan R. Soucy","doi":"10.1016/j.jneumeth.2025.110563","DOIUrl":"10.1016/j.jneumeth.2025.110563","url":null,"abstract":"<div><h3>Background</h3><div>Gene and cell therapies hold promise for restoring vision in hereditary and advanced optic neuropathies. Accurate evaluation of these therapies requires advanced imaging methods that can visualize transplanted cells within intact retinal tissue.</div></div><div><h3>New method</h3><div>We present a whole-mount tissue-clearing workflow optimized for the mouse retina and optic nerve to improve visualization of donor neuron integration following cell transplantation. Five clearing methods were evaluated, and a modified protocol, ScaleH, was developed by adding polyvinyl alcohol to ScaleS to improve fluorescence preservation.</div></div><div><h3>Results</h3><div>ScaleS yielded the highest transparency (46 % increase) and immunohistochemical clarity (89 % increase) in the retina among the tested methods. ScaleH retained comparable clarity while significantly reducing fluorescence decay over time (32 % less decay). ScaleH was compatible with endogenous reporters and immunolabeling, enabling detailed imaging of transplanted human stem cell-derived retinal neurons in the retinal ganglion cell layer, as well as visualization of neurites, microglia, and cell nuclei in the optic nerve.</div></div><div><h3>Comparison with existing methods</h3><div>Compared to other clearing protocols, ScaleH provided superior fluorescence retention and stability without compromising optical clarity. Its compatibility with both immunostaining and endogenous fluorescent proteins supports broad application in imaging.</div></div><div><h3>Conclusions</h3><div>ScaleH is a reliable and high-resolution clearing method for imaging whole-mount retinas and optic nerves. It facilitates robust assessment of donor cell integration in regenerative ophthalmology and may be broadly applicable in neurobiological research.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110563"},"PeriodicalIF":2.3,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989726","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":"Advanced brain diffusion MRI and image texture measures have the potential to predict multi-domain functional outcomes in multiple sclerosis","authors":"Olayinka Oladosu ,&nbsp;Yunyan Zhang","doi":"10.1016/j.jneumeth.2025.110562","DOIUrl":"10.1016/j.jneumeth.2025.110562","url":null,"abstract":"<div><h3>Background</h3><div>Multiple sclerosis (MS) causes diverse functional impairments that require early accurate characterization, but pertinent methods are scarce. This study aimed to develop new imaging-driven approaches for predicting MS functions.</div></div><div><h3>New method</h3><div>19 women with MS [10 relapsing-remitting (RRMS) and 9 secondary progressive (SPMS) subtypes] and 19 matched controls were examined including 3 T imaging. Advanced measures of nerve tract integrity were derived using diffusion MRI and anatomical MRI texture analysis with phase congruency, respectively. Imaging analysis focused on three tract regions critical in MS: the corpus callosum, corticospinal tracts, and optic radiations. Top-ranked tract measures sensitive to MS severity were employed to predict physical, neurocognitive, and affective functions facilitated by Ridge regression.</div></div><div><h3>Results</h3><div>Top predictors included diffusion apparent fiber density and fractional anisotropy, and phase congruency measures across tracts. The predictions were mostly strong for physical functions including Timed 25-Foot walk, Nine-Hole Peg Test, and neurological disability, strong-to-moderate for neurocognitive functions led by the symbol digit modality test, and relatively weak for affective functions. Further, the normal-appearing white matter (NAWM) models were superior or similar to NAWM+Lesion models based on either imaging type, and the best phase congruency models outperformed the best diffusion-based models.</div></div><div><h3>Comparison with existing methods</h3><div>Few studies attempted to derive novel measures of nerve integrity using clinical MRI, and virtually no study modelled the utility of these measures for predicting multi-domain MS functions.</div></div><div><h3>Conclusion</h3><div>Advanced imaging models could predict MS functions for early intervention, especially phase congruency NAWM models for physical functions.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"424 ","pages":"Article 110562"},"PeriodicalIF":2.3,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144957593","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}