Journal of Neuroscience Methods最新文献

{"title":"Corrigendum to “Isolation of ependymal cilia from mouse brain” [J. Neurosci. Method. 409 (2024) 110198]","authors":"Akihiro Mizuno , Kazuhito Takeuchi , Yuichi Nagata , Hideyuki Harada , Taiki Yamamoto , Takayuki Ishikawa , Sachi Maeda , Fumiharu Ohka , Hironori Ueno , Ryuta Saito","doi":"10.1016/j.jneumeth.2024.110293","DOIUrl":"10.1016/j.jneumeth.2024.110293","url":null,"abstract":"","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110293"},"PeriodicalIF":2.7,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289419","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":"Functional MRI study with conductivity signal changes during visual stimulation","authors":"Hyug-Gi Kim ,&nbsp;Youngeun Yoon ,&nbsp;Mun Bae Lee ,&nbsp;Jeongin Jeong ,&nbsp;Jiyoon Lee ,&nbsp;Oh In Kwon ,&nbsp;Geon-Ho Jahng","doi":"10.1016/j.jneumeth.2024.110288","DOIUrl":"10.1016/j.jneumeth.2024.110288","url":null,"abstract":"<div><h3>Background</h3><div>Although blood oxygen level-dependent (BOLD) functional MRI (fMRI) is a standard method, major BOLD signals primarily originate from intravascular sources. Magnetic resonance electrical properties tomography (MREPT)-based fMRI signals may provide additional insights into electrical activity caused by alterations in ion concentrations and mobilities.</div></div><div><h3>Purpose</h3><div>This study aimed to investigate the neuronal response of conductivity during visual stimulation and compare it with BOLD.</div></div><div><h3>Materials and methods</h3><div>A total of 30 young, healthy volunteers participated in two independent experiments using BOLD and MREPT techniques with a visual stimulation paradigm at 3 T MRI. The first set of MREPT fMRI data was obtained using a multi-echo spin-echo (SE) echo planar imaging (EPI) sequence from 14 participants. The second set of MREPT fMRI data was collected from 16 participants using both a single-echo SE-EPI and a single-echo three-dimensional (3D) balanced fast-field-echo (bFFE) sequence. We reconstructed the time-course Larmor frequency conductivity to evaluate hemodynamics.</div></div><div><h3>Results</h3><div>Conductivity values slightly increased during visual stimulation. Activation strengths were consistently stronger with BOLD than with conductivity for both SE-EPI MREPT and bFFE MREPT. Additionally, the activated areas were always larger with BOLD than MREPT. Some participants also exhibited decreased conductivity values during visual stimulations. In Experiment 1, conductivity showed significant differences between the fixation and visual stimulation blocks in the secondary visual cortex (SVC) and cuneus, with conductivity differences of 0.43 % and 0.47 %, respectively. No significant differences in conductivity were found in the cerebrospinal fluid (CSF) areas between the two blocks. In Experiment 2, significant conductivity differences were observed between the two blocks in the SVC, cuneus, and lingual gyrus for SE-EPI MREPT, with differences of 0.90 %, 0.67 %, and 0.24 %, respectively. Again, no significant differences were found in the CSF areas.</div></div><div><h3>Conclusion</h3><div>Conductivity values increased slightly during visual stimulation in the visual cortex areas but were much weaker than BOLD responses. The conductivity change during visual stimulation was less than 1 % compared to the fixation block. No significant differences in conductivity were observed between the primary visual cortex (PVC)-CSF and SVC-CSF during fixation and visual stimulations, suggesting that the observed conductivity changes may not be related to CSF changes in the visual cortex but rather to diffusion changes. Future research should explore the potential of MREPT to detect neuronal electrical activity and hemodynamic changes, with further optimization of the MREPT technique.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110288"},"PeriodicalIF":2.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289420","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":"Artificial intelligence-based analysis of behavior and brain images in cocaine-self-administered marmosets","authors":"Wonmi Gu ,&nbsp;Juhui Gim ,&nbsp;Dohyun Lee ,&nbsp;Heejong Eom ,&nbsp;Jae Jun Lee ,&nbsp;Seong Shoon Yoon ,&nbsp;Tae-Young Heo ,&nbsp;Jaesuk Yun","doi":"10.1016/j.jneumeth.2024.110294","DOIUrl":"10.1016/j.jneumeth.2024.110294","url":null,"abstract":"<div><h3>Background</h3><div>The sophisticated behavioral and cognitive repertoires of non-human primates (NHPs) make them suitable subjects for studies involving cocaine self-administration (SA) schedules. However, ethical considerations, adherence to the 3Rs principle (replacement, reduction and refinement), and other factors make it challenging to obtain NHPs individuals for research. Consequently, there is a need for methods that can comprehensively analyze small datasets using artificial intelligence (AI).</div></div><div><h3>New methods</h3><div>We employed AI to identify cocaine dependence patterns from collected data. First, we collected behavioral data from cocaine SA marmosets (<em>Callithrix jacchus</em>) to develop a dependence prediction model. SHapley Additive exPlanations (SHAP) values were used to demonstrate the importance of various variables. Additionally, we collected positron emission tomographic (PET) images showing dopamine transporter (DAT) binding potential and developed an algorithm for PET image segmentation.</div></div><div><h3>Results</h3><div>The prediction model indicated that the Random Forest (RF) algorithm performed best, with an area under the curve (AUC) of 0.92. The top five variables influencing the model were identified using SHAP values. The PET image segmentation model achieved an accuracy of 0.97, a mean squared error of 0.02, an intersection over union (IoU) of 0.845, and a Dice coefficient of 0.913.</div></div><div><h3>Comparison with existing methods and conclusion</h3><div>Utilizing data from the marmoset SA experiment, we developed an ML-based dependence prediction model and analyzed variable importance rankings using SHAP. AI-based imaging segmentation methods offer a valuable tool for evaluating DAT availability in NHPs following chronic cocaine administration.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110294"},"PeriodicalIF":2.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289418","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":"Generating and maintaining brain organoids at various levels of complexity","authors":"Floris G. Wouterlood","doi":"10.1016/j.jneumeth.2024.110291","DOIUrl":"10.1016/j.jneumeth.2024.110291","url":null,"abstract":"","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110291"},"PeriodicalIF":2.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289421","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 DRL-guided sparse voxel decoding model for reconstructing perceived images from brain activity","authors":"Xu Yin ,&nbsp;Zhengping Wu ,&nbsp;Haixian Wang","doi":"10.1016/j.jneumeth.2024.110292","DOIUrl":"10.1016/j.jneumeth.2024.110292","url":null,"abstract":"<div><h3>Background</h3><p>Due to the sparse encoding character of the human visual cortex and the scarcity of paired training samples for {images, fMRIs}, voxel selection is an effective means of reconstructing perceived images from fMRI. However, the existing data-driven voxel selection methods have not achieved satisfactory results.</p></div><div><h3>New method</h3><p>Here, a novel deep reinforcement learning-guided sparse voxel (DRL-SV) decoding model is proposed to reconstruct perceived images from fMRI. We innovatively describe voxel selection as a Markov decision process (MDP), training agents to select voxels that are highly involved in specific visual encoding.</p></div><div><h3>Results</h3><p>Experimental results on two public datasets verify the effectiveness of the proposed DRL-SV, which can accurately select voxels highly involved in neural encoding, thereby improving the quality of visual image reconstruction.</p></div><div><h3>Comparison with existing methods</h3><p>We qualitatively and quantitatively compared our results with the state-of-the-art (SOTA) methods, getting better reconstruction results. We compared the proposed DRL-SV with traditional data-driven baseline methods, obtaining sparser voxel selection results, but better reconstruction performance.</p></div><div><h3>Conclusions</h3><p>DRL-SV can accurately select voxels involved in visual encoding on few-shot, compared to data-driven voxel selection methods. The proposed decoding model provides a new avenue to improving the image reconstruction quality of the primary visual cortex.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110292"},"PeriodicalIF":2.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242920","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 magnetometers arrays and analysis pipelines for multivariate pattern analysis","authors":"Yulia Bezsudnova,&nbsp;Andrew J. Quinn,&nbsp;Ole Jensen","doi":"10.1016/j.jneumeth.2024.110279","DOIUrl":"10.1016/j.jneumeth.2024.110279","url":null,"abstract":"<div><h3>Background</h3><div>Multivariate pattern analysis (MVPA) has proven an excellent tool in cognitive neuroscience. It also holds a strong promise when applied to optically-pumped magnetometer-based magnetoencephalography.</div></div><div><h3>New method</h3><div>To optimize OPM-MEG systems for MVPA experiments this study examines data from a conventional MEG magnetometer array, focusing on appropriate noise reduction techniques for magnetometers. We determined the least required number of sensors needed for robust MVPA for image categorization experiments.</div></div><div><h3>Results</h3><div>We found that the use of signal space separation (SSS) without a proper regularization significantly lowered the classification accuracy considering a sub-array of 102 magnetometers or a sub-array of 204 gradiometers. We also found that classification accuracy did not improve when going beyond 30 sensors irrespective of whether SSS has been applied.</div></div><div><h3>Comparison with existing methods</h3><div>The power spectra of data filtered with SSS has a substantially higher noise floor that data cleaned with SSP or HFC. Consequently, MVPA decoding results obtained from the SSS-filtered data are significantly lower compared to all other methods employed.</div></div><div><h3>Conclusions</h3><div>When designing MEG system based on SQUID magnetometers optimized for multivariate analysis for image categorization experiments, about 30 magnetometers are sufficient. We advise against applying SSS filters without a proper regularization to data from MEG and OPM systems prior to performing MVPA as this method, albeit reducing low-frequency external noise contributions, also introduces an increase in broadband noise. We recommend employing noise reduction techniques that either decrease or maintain the noise floor of the data like signal-space projection, homogeneous field correction and gradient noise reduction.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110279"},"PeriodicalIF":2.7,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142289433","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":"Research on shared control of robots based on hybrid brain-computer interface","authors":"Ziqi Zhang ,&nbsp;Mengfan Li ,&nbsp;Ran Wei ,&nbsp;Wenzhe Liao ,&nbsp;Fuyong Wang ,&nbsp;Guizhi Xu","doi":"10.1016/j.jneumeth.2024.110280","DOIUrl":"10.1016/j.jneumeth.2024.110280","url":null,"abstract":"<div><h3>Background</h3><p>With the arrival of the new generation of artificial intelligence wave, new human-robot interaction technologies continue to emerge. Brain–computer interface (BCI) offers a pathway for state monitoring and interaction control between human and robot. However, the unstable mental state reduce the accuracy of human brain intent decoding, and consequently affects the precision of BCI control.</p></div><div><h3>New methods</h3><p>This paper proposes a hybrid BCI-based shared control (HB-SC) method for brain-controlled robot navigation. Hybrid BCI fuses electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and interactive control to output human perception and decision. The shared control based on multi-sensory fusion integrates the special obstacle information perceived by humans with the regular environmental information perceived by the robot. In this process, valid BCI commands are screened by mental state assessment and output to a layered costmap for fusion.</p></div><div><h3>Results</h3><p>Eight subjects participated in the navigation experiment with dynamically changing mental state levels to validate the effects of a hybrid brain-computer interface through two shared control modes. The results show that the proposed HB-SC reduces collisions by 37.50 %, improves the success rate of traversing obstacles by 25.00 %, and the navigation trajectory is more consistent with expectations.</p></div><div><h3>Conclusions</h3><p>The HB-SC method can dynamically and intelligently adjust command output according to different brain states, helping to reduce errors made by subjects in a unstable mental state, thereby greatly enhancing the system's safety.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110280"},"PeriodicalIF":2.7,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232870","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":"Strategies for the development of in vitro models of spinal cord ischemia-reperfusion injury: Oxygen-glucose deprivation and reoxygenation","authors":"Jiyoun Lee ,&nbsp;Sung-Hee Han ,&nbsp;Jin-Hee Kim ,&nbsp;Hyun-Jung Shin ,&nbsp;Jin-Woo Park ,&nbsp;Jin-Young Hwang","doi":"10.1016/j.jneumeth.2024.110278","DOIUrl":"10.1016/j.jneumeth.2024.110278","url":null,"abstract":"<div><h3>Background</h3><p>In vitro models tailored for spinal cord ischemia-reperfusion injury are pivotal for investigation of the mechanisms underlying spinal cord injuries. We conducted a two-phased study to identify the optimal conditions for establishing an in vitro model of spinal cord ischemia–reperfusion injury using primary rat spinal motor neurons.</p></div><div><h3>New method</h3><p>In the first phase, cell cultures were subjected to oxygen deprivation (OD) only, glucose deprivation (GD) only, or simultaneous deprivation of oxygen and glucose [oxygen-glucose deprivation (OGD)] for different durations (1, 2, and 6 h). In the second phase, different durations of re-oxygenation (1, 12, and 24 h) were applied after 1 h of OGD to determine the optimal duration simulating reperfusion injury.</p></div><div><h3>Results and comparison with existing method(s)</h3><p>GD for 6 h significantly reduced cell viability (91 % of control, P&lt;0.001) and increase cytotoxicity (111 % of control, P&lt;0.001). OGD for 1 h and 2 h, resulted in a significant decrease in cell viability (80 % of control P&lt;0.001, respectively), and increase in cytotoxicity (130 % of control, P&lt;0.001, respectively). Re-oxygenation for 1, 12, and 24 h worsened ischemic injury following 1 h of OGD (all P&lt;0.05).</p></div><div><h3>Conclusions</h3><p>Our results may provide a valuable guide to devise in vitro models of spinal cord ischemia–reperfusion injury using primary spinal motor neurons.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110278"},"PeriodicalIF":2.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142229386","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":"Validating a novel paradigm for simultaneously assessing mismatch response and frequency-following response to speech sounds","authors":"Tzu-Han Zoe Cheng ,&nbsp;Tian Christina Zhao","doi":"10.1016/j.jneumeth.2024.110277","DOIUrl":"10.1016/j.jneumeth.2024.110277","url":null,"abstract":"<div><h3>Background</h3><p>Speech sounds are processed in the human brain through intricate and interconnected cortical and subcortical structures. Two neural signatures, one largely from cortical sources (mismatch response, MMR) and one largely from subcortical sources (frequency-following response, FFR) are critical for assessing speech processing as they both show sensitivity to high-level linguistic information. However, there are distinct prerequisites for recording MMR and FFR, making them difficult to acquire simultaneously</p></div><div><h3>New method</h3><p>Using a new paradigm, our study aims to concurrently capture both signals and test them against the following criteria: (1) replicating the effect that the MMR to a native speech contrast significantly differs from the MMR to a nonnative speech contrast, and (2) demonstrating that FFRs to three speech sounds can be reliably differentiated.</p></div><div><h3>Results</h3><p>Using EEG from 18 adults, we observed a decoding accuracy of 72.2 % between the MMR to native vs. nonnative speech contrasts. A significantly larger native MMR was shown in the expected time window. Similarly, a significant decoding accuracy of 79.6 % was found for FFR. A high stimulus-to-response cross-correlation with a 9 ms lag suggested that FFR closely tracks speech sounds.</p></div><div><h3>Comparison with existing method(s)</h3><p>These findings demonstrate that our paradigm reliably captures both MMR and FFR concurrently, replicating and extending past research with much fewer trials (MMR: 50 trials; FFR: 200 trials) and shorter experiment time (12 minutes).</p></div><div><h3>Conclusions</h3><p>This study paves the way to understanding cortical-subcortical interactions for speech and language processing, with the ultimate goal of developing an assessment tool specific to early development.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"412 ","pages":"Article 110277"},"PeriodicalIF":2.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154394","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 method for sparse dynamic functional connectivity analysis from resting-state fMRI","authors":"Houxiang Wang ,&nbsp;Jiaqing Chen ,&nbsp;Zihao Yuan ,&nbsp;Yangxin Huang ,&nbsp;Fuchun Lin","doi":"10.1016/j.jneumeth.2024.110275","DOIUrl":"10.1016/j.jneumeth.2024.110275","url":null,"abstract":"<div><h3>Background:</h3><p>There is growing interest in understanding the dynamic functional connectivity (DFC) between distributed brain regions. However, it remains challenging to reliably estimate the temporal dynamics from resting-state functional magnetic resonance imaging (rs-fMRI) due to the limitations of current methods.</p></div><div><h3>New methods:</h3><p>We propose a new model called HDP-HSMM-BPCA for sparse DFC analysis of high-dimensional rs-fMRI data, which is a temporal extension of probabilistic principal component analysis using Bayesian nonparametric hidden semi-Markov model (HSMM). Specifically, we utilize a hierarchical Dirichlet process (HDP) prior to remove the parametric assumption of the HMM framework, overcoming the limitations of the standard HMM. An attractive superiority is its ability to automatically infer the state-specific latent space dimensionality within the Bayesian formulation.</p></div><div><h3>Results:</h3><p>The experiment results of synthetic data show that our model outperforms the competitive models with relatively higher estimation accuracy. In addition, the proposed framework is applied to real rs-fMRI data to explore sparse DFC patterns. The findings indicate that there is a time-varying underlying structure and sparse DFC patterns in high-dimensional rs-fMRI data.</p></div><div><h3>Comparison with existing methods:</h3><p>Compared with the existing DFC approaches based on HMM, our method overcomes the limitations of standard HMM. The observation model of HDP-HSMM-BPCA can discover the underlying temporal structure of rs-fMRI data. Furthermore, the relevant sparse DFC construction algorithm provides a scheme for estimating sparse DFC.</p></div><div><h3>Conclusion:</h3><p>We describe a new computational framework for sparse DFC analysis to discover the underlying temporal structure of rs-fMRI data, which will facilitate the study of brain functional connectivity.</p></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"411 ","pages":"Article 110275"},"PeriodicalIF":2.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142145807","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}