Journal of Neuroscience Methods最新文献

{"title":"Establishing In-vivo brain microdialysis for comparing concentrations of a variety of cortical neurotransmitters in the awake rhesus macaque between different cognitive states","authors":"Stella Mayer ,&nbsp;Pankhuri Saxena ,&nbsp;Max Arwed Crayen ,&nbsp;Stefan Treue","doi":"10.1016/j.jneumeth.2025.110361","DOIUrl":"10.1016/j.jneumeth.2025.110361","url":null,"abstract":"<div><h3>Background</h3><div>Neuronal activity is modulated by behavior and cognitive processes. The combination of several neurotransmitter systems, acting directly or indirectly on specific populations of neurons, underlie such modulations. Most studies with non-human primates (NHPs) fail to capture this complexity, partly due to the lack of adequate methods for reliably and simultaneously measuring a broad spectrum of neurotransmitters while the animal engages in behavioral tasks.</div></div><div><h3>New Method</h3><div>To address this gap, we introduce a novel implementation of brain microdialysis (MD), employing semi-chronically implanted guides and probes in awake, behaving NHPs facilitated by removable insets within a standard recording chamber over extrastriate visual cortex (here, the visual middle temporal area (MT)). This approach allows flexible access to diverse brain regions, including areas deep within the sulcus.</div></div><div><h3>Results</h3><div>Reliable concentration measurements of GABA, glutamate, norepinephrine, epinephrine, dopamine, serotonin, and choline were achieved from small sample volumes (&lt;20 µl) using ultra-performance liquid chromatography with electrospray ionization-mass spectrometry (UPLC-ESI-MS). Comparing two behavioral states – ‘active’ and ‘inactive’, we observe subtle concentration variations between the two behavioral states and a greater variability of concentrations in the active state. Additionally, we find positively and negatively correlated concentration changes for neurotransmitter pairs between the behavioral states.</div></div><div><h3>Conclusions</h3><div>Therefore, this MD setup allows insights into the neurochemical dynamics in awake primates, facilitating comprehensive investigations into the roles and the complex interplay of neurotransmitters in cognitive and behavioral functions.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110361"},"PeriodicalIF":2.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971275","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":"An EEG-based emotion recognition method by fusing multi-frequency-spatial features under multi-frequency bands","authors":"Qiuyu Chen,&nbsp;Xiaoqian Mao,&nbsp;Yuebin Song,&nbsp;Kefa Wang","doi":"10.1016/j.jneumeth.2025.110360","DOIUrl":"10.1016/j.jneumeth.2025.110360","url":null,"abstract":"<div><h3>Background</h3><div>Recognition of emotion changes is of great significance to a person’s physical and mental health. At present, EEG-based emotion recognition methods are mainly focused on time or frequency domains, but rarely on spatial information. Therefore, the goal of this study is to improve the performance of emotion recognition by integrating frequency and spatial domain information under multi-frequency bands.</div></div><div><h3>New methods</h3><div>Firstly, EEG signals of four frequency bands are extracted, and then three frequency-spatial features of differential entropy (DE) symmetric difference (SD) and symmetric quotient (SQ) are separately calculated. Secondly, according to the distribution of EEG electrodes, a series of brain maps are constructed by three frequency-spatial features for each frequency band. Thirdly, a Multi-Parallel-Input Convolutional Neural Network (MPICNN) uses the constructed brain maps to train and obtain the emotion recognition model. Finally, the subject-dependent experiments are conducted on DEAP and SEED-IV datasets.</div></div><div><h3>Results</h3><div>The experimental results of DEAP dataset show that the average accuracy of four-class emotion recognition, namely, high-valence high-arousal, high-valence low-arousal, low-valence high-arousal and low-valence low-arousal, reaches 98.71 %. The results of SEED-IV dataset show the average accuracy of four-class emotion recognition, namely, happy, sad, neutral and fear reaches 92.55 %.</div></div><div><h3>Comparison with existing methods</h3><div>This method has a best classification performance compared with the state-of-the-art methods on both four-class emotion recognition datasets.</div></div><div><h3>Conclusions</h3><div>This EEG-based emotion recognition method fused multi-frequency-spatial features under multi-frequency bands, and effectively improved the recognition performance compared with the existing methods.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110360"},"PeriodicalIF":2.7,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142950262","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":"Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans","authors":"Anoop B.N. ,&nbsp;Karl Li ,&nbsp;Nicolas Honnorat ,&nbsp;Tanweer Rashid ,&nbsp;Di Wang ,&nbsp;Jinqi Li ,&nbsp;Elyas Fadaee ,&nbsp;Sokratis Charisis ,&nbsp;Jamie M. Walker ,&nbsp;Timothy E. Richardson ,&nbsp;David A. Wolk ,&nbsp;Peter T. Fox ,&nbsp;José E. Cavazos ,&nbsp;Sudha Seshadri ,&nbsp;Laura E.M. Wisse ,&nbsp;Mohamad Habes","doi":"10.1016/j.jneumeth.2024.110359","DOIUrl":"10.1016/j.jneumeth.2024.110359","url":null,"abstract":"<div><h3>Background:</h3><div>The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer’s disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming.</div></div><div><h3>New Method:</h3><div>In this study, we explore the use of fully automated methods relying on state-of-the-art Deep Learning approaches to produce these annotations. More specifically, we propose a new segmentation framework made of a set of encoder–decoder blocks embedding self-attention mechanisms and atrous spatial pyramidal pooling to produce better maps of the hippocampus and identify four hippocampal regions: the dentate gyrus, the hippocampal head, the hippocampal body, and the hippocampal tail.</div></div><div><h3>Results:</h3><div>Trained using slices extracted from 15 postmortem T1-weighted, T2-weighted, and susceptibility-weighted MRI scans, our new approach produces hippocampus parcellations that are better aligned with the manually delineated parcellations provided by neuroradiologists.</div></div><div><h3>Comparison with Existing Methods:</h3><div>Four standard deep learning segmentation architectures: UNet, Double UNet, Attention UNet, and Multi-resolution UNet have been utilized for the qualitative and quantitative comparison of the proposed hippocampal region segmentation model.</div></div><div><h3>Conclusions:</h3><div>Postmortem MRI serves as a highly valuable neuroimaging technique for examining the effects of neurodegenerative diseases on the intricate structures within the hippocampus. This study opens the way to large sample-size postmortem studies of the hippocampal substructures.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110359"},"PeriodicalIF":2.7,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927256","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":"An adaptive protocol to assess physiological responses as a function of task demand in speech-in-noise testing","authors":"Edoardo Maria Polo ,&nbsp;Davide Simeone ,&nbsp;Maximiliano Mollura ,&nbsp;Alessia Paglialonga ,&nbsp;Riccardo Barbieri","doi":"10.1016/j.jneumeth.2024.110348","DOIUrl":"10.1016/j.jneumeth.2024.110348","url":null,"abstract":"<div><h3>Background:</h3><div>Acoustic challenges impose demands on cognitive resources, known as listening effort (LE), which can substantially influence speech perception and communication. Standardized assessment protocols for monitoring LE are lacking, hindering the development of adaptive hearing assistive technology.</div></div><div><h3>New Method:</h3><div>We employed an adaptive protocol, including a speech-in-noise test and personalized definition of task demand, to assess LE and its physiological correlates. Features extracted from electroencephalogram, galvanic skin response, electrocardiogram, respiration, pupil dilation, and blood volume pulse responses were analyzed as a function of task demand in 21 healthy participants with normal hearing.</div></div><div><h3>Results:</h3><div>Heightened sympathetic response was observed with higher task demand, evidenced by increased heart rate, blood pressure, and breath amplitude. Blood volume amplitude and breath amplitude exhibited higher sensitivity to changes in task demand.</div></div><div><h3>Comparison with Existing Methods:</h3><div>Notably, galvanic skin response showed higher amplitude during low task demand phases, indicating increased attention and engagement, aligning with findings from electroencephalogram signals and Lacey’s attention theory.</div></div><div><h3>Conclusions:</h3><div>The analysis of a range of physiological signals, spanning cardiovascular, central, and autonomic domains, demonstrated effectiveness in comprehensively examining LE. Future research should explore additional levels and manipulations of task demand, as well as the influence of individual motivation and hearing sensitivity, to further validate these outcomes and enhance the development of adaptive hearing assistive technology.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110348"},"PeriodicalIF":2.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920571","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":"Emotion recognition based on EEG source signals and dynamic brain function network","authors":"He Sun ,&nbsp;Hailing Wang ,&nbsp;Raofen Wang ,&nbsp;Yufei Gao","doi":"10.1016/j.jneumeth.2024.110358","DOIUrl":"10.1016/j.jneumeth.2024.110358","url":null,"abstract":"<div><h3>Background</h3><div>Brain network features contain more emotion-related information and can be more effective in emotion recognition. However, emotions change continuously and dynamically, and current function brain network features using the sliding window method cannot explore dynamic characteristics of different emotions, which leads to the serious loss of functional connectivity information.</div></div><div><h3>New method</h3><div>In the study, we proposed a new framework based on EEG source signals and dynamic function brain network (dyFBN) for emotion recognition. We constructed emotion-related dyFBN with dynamic phase linearity measurement (dyPLM) at every time point and extracted the second-order feature Root Mean Square (RMS) based on of dyFBN. In addition, a multiple feature fusion strategy was employed, integrating sensor frequency features with connection information.</div></div><div><h3>Results</h3><div>The recognition accuracy of subject-independent and subject-dependent is 83.50 % and 88.93 %, respectively. The selected optimal feature subset of fused features highlighted the interplay between dynamic features and sensor features and showcased the crucial brain regions of the right superiortemporal, left isthmuscingulate, and left parsorbitalis in emotion recognition.</div></div><div><h3>Comparison with existing methods</h3><div>Compared with current methods, the emotion recognition accuracy of subject-independent and subject-dependent is improved by 11.46 % and 10.19 %, respectively. In addition, recognition accuracy of the fused features of RMS and sensor features is also better than the fused features of existing methods.</div></div><div><h3>Conclusions</h3><div>These findings prove the validity of the proposed framework, which leads to better emotion recognition.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110358"},"PeriodicalIF":2.7,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177174","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 spatial contrast coding approach for SSVEP-based BCIs","authors":"Hui Zhong ,&nbsp;Gege Ming ,&nbsp;Weihua Pei ,&nbsp;Xiaorong Gao ,&nbsp;Yijun Wang","doi":"10.1016/j.jneumeth.2024.110357","DOIUrl":"10.1016/j.jneumeth.2024.110357","url":null,"abstract":"<div><h3>Background</h3><div>Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems mainly adopt the frequency, phase, and hybrid coding approaches in previous studies. This study proposes a new encoding approach based on spatial contrast, which is one of the spatial properties of visual stimuli.</div></div><div><h3>New method</h3><div>First, this study designed checkerboard-like stimuli with 11 kinds of background contrast to explore the effect of background contrast on stimulus-response characteristics of SSVEPs. Based on the spatial contrast related modulation of responses, this study conducted offline simulations to evaluate the feasibility of a multi-target contrast coding approach. Finally, this study designed a four-target SSVEP-BCI system to demonstrate the contrast coding approach.</div></div><div><h3>Results</h3><div>Checkerboard-like stimuli with the same frequency and initial phase but different background contrasts have different SSVEP responses in terms of amplitude, topography, and phase. Taking advantage of the characteristics, both offline simulations and online verifications indicated that the proposed BCI system achieved good classification performance. Online BCI experiments found that the four-target SSVEP-BCI system achieved averaged information transfer rates of 59.58 ± 0.42 bits/min at the 15 Hz condition and 52.54 ± 2.32 bits/min at the 30 Hz condition, respectively.</div></div><div><h3>Comparison with existing method</h3><div>Different from previous frequency, phase, and spatial coding approaches, this study adopts a background contrast-based coding approach to achieve a four-target BCI system.</div></div><div><h3>Conclusion</h3><div>This study proposes a new spatial contrast coding approach, which will enrich the encoding approach of the SSVEP-BCI systems and promote the applications of the SSVEP-BCI systems in more scenarios.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110357"},"PeriodicalIF":2.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177175","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":"Estimation of fiber orientation distributions in superficial white matter using an asymmetric constrained spherical deconvolution method","authors":"Jingxin Meng,&nbsp;Jianglin He,&nbsp;Yuanjun Wang","doi":"10.1016/j.jneumeth.2024.110353","DOIUrl":"10.1016/j.jneumeth.2024.110353","url":null,"abstract":"<div><h3>Background</h3><div>Superficial white matter is an important component of white matter. Estimation of fiber orientation distributions based on diffusion magnetic resonance imaging is a critical step in white matter tractography imaging. However, due to the complex structure of superficial white matter, existing models for estimating fiber orientation distributions are ineffective in reconstructing superficial white matter and even reconstruct incorrect orientation distributions.</div></div><div><h3>New method</h3><div>In this paper, we improve the traditional constrained spherical deconvolution method and propose a novel asymmetric constrained spherical deconvolution method. The method takes into account that the displacement profile of the water molecules in brain tissue are non-Gaussian diffusion and the core parameter kurtosis might characterize tissue structure better than diffusivity coefficients. So diffusion kurtosis imaging model is used to estimate the white matter response function. The proposed method applies the diffusion kurtosis imaging model response function to the asymmetric fiber orientation distributions, and this is the first attempt to obtain more accurate fiber orientation distributions. Furthermore, the Gaussian-Distribution distance weight and Watson-Distribution angle weight are used for asymmetric regularization.</div></div><div><h3>Results</h3><div>We evaluate the method using FiberCup phantom, ISMRM 2015 data and in vivo data provided CHCP dataset. The results show that our proposed method can more accurately reconstruct the complex fiber structure of superficial white matter with more accurate fiber orientation, fewer pseudo-peaks, and mitigate gyral bias.</div></div><div><h3>Comparison with existing methods</h3><div>Our proposed method has higher accuracy in estimating the fiber orientation distributions and can reconstruct highly curved fiber voxels.</div></div><div><h3>Conclusion</h3><div>This proposed method provides new insights into the estimation of the orientation distribution of superficial white matter fibers.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110353"},"PeriodicalIF":2.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143177173","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":"Periodicity-based multi-dimensional interaction convolution network with multi-scale feature fusion for motor imagery EEG classification","authors":"Yunshuo Dai,&nbsp;Xiao Deng,&nbsp;Xiuli Fu,&nbsp;Yixin Zhao","doi":"10.1016/j.jneumeth.2024.110356","DOIUrl":"10.1016/j.jneumeth.2024.110356","url":null,"abstract":"<div><h3>Background</h3><div>The Motor Imagery (MI)-based Brain-Computer Interface (BCI) has vast potential in fields such as medical rehabilitation and control engineering. In recent years, MI decoding methods based on deep learning have gained extensive attention. However, capturing the complex dynamic changes in EEG signals remains a challenge, and the decoding performance still needs further improvement.</div></div><div><h3>New methods</h3><div>The paper proposes a novel method, Periodicity-based Multi-Dimensional Interaction Convolution Network with Multi-Scale Feature Fusion (PMD-MSNet), for MI-EEG signal classification. It converts 1D EEG signals into multi-period 2D tensors to capture intra-period and inter-period variations and enables cross-dimensional interaction based on periodic features. Subsequently, parallel multi-scale convolution is utilized to adaptively extract temporal, frequency, and time-frequency features.</div></div><div><h3>Results</h3><div>Experimental results on the BCI IV-2a dataset demonstrate that the PMD-MSNet model achieves a classification accuracy of 82.25 % on average and a kappa value of 0.763, which significantly outperforms seven other deep learning-based EEG decoding models. The model attained the highest classification accuracy and kappa value among the seven subjects, showcasing its superior performance and robustness.</div></div><div><h3>Conclusions</h3><div>The PMD-MSNet model incorporates periodic features, multi-dimensional interaction mechanisms, multi-scale convolutions to achieve efficient feature extraction and classification of EEG signals, significantly enhancing the performance of MI classification tasks.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110356"},"PeriodicalIF":2.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143176075","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":"Human pluripotent stem cell-derived microglia shape neuronal morphology and enhance network activity in vitro","authors":"L.M.L. Kok ,&nbsp;K. Helwegen ,&nbsp;N.F. Coveña ,&nbsp;V.M. Heine","doi":"10.1016/j.jneumeth.2024.110354","DOIUrl":"10.1016/j.jneumeth.2024.110354","url":null,"abstract":"<div><h3>Background</h3><div>Microglia, the resident immune cells of the central nervous system, play a critical role in maintaining neuronal health, but are often overlooked in traditional neuron-focused <em>in vitro</em> models.</div></div><div><h3>New method</h3><div>In this study, we developed a novel co-culture system of human pluripotent stem cell (hPSC)-derived microglia and neurons to investigate how hPSC-derived microglia influence neuronal morphology and network activity. Using high-content morphological analysis and multi-electrode arrays (MEA), we demonstrate that these microglia successfully incorporate into neuronal networks and modulate key aspects of neuronal function.</div></div><div><h3>Results</h3><div>hPSC-derived microglia significantly reduced cellular debris and altered neuronal morphology by decreasing axonal and dendritic segments and reducing synapse density. Interestingly, despite the decrease in synapse density, neuronal network activity increased.</div></div><div><h3>Conclusion</h3><div>Our findings underscore the importance of including hPSC-derived microglia in <em>in vitro</em> models to better simulate <em>in vivo</em> neuroglial interactions and provide a platform for investigating neuron-glia dynamics in health and disease.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110354"},"PeriodicalIF":2.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895532","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":"IDyOMpy: A new Python-based model for the statistical analysis of musical expectations","authors":"Guilhem Marion ,&nbsp;Fei Gao ,&nbsp;Benjamin P. Gold ,&nbsp;Giovanni M. Di Liberto ,&nbsp;Shihab Shamma","doi":"10.1016/j.jneumeth.2024.110347","DOIUrl":"10.1016/j.jneumeth.2024.110347","url":null,"abstract":"<div><h3>Background</h3><div>: IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.</div></div><div><h3>New method</h3><div>: IDyOMpy is a new Python re-implementation and extension of IDyOM. This new model allows for computing the information content and entropy for each melody note after training on a corpus of melodies. In addition to those features, two new features are presented: probability estimation of silences and enculturation modeling.</div></div><div><h3>Results</h3><div>: We first describe the mathematical details of the implementation. We extensively compare the two models and show that they generate very similar outputs. We also support the validity of IDyOMpy by using its output to replicate previous EEG and behavioral results that relied on the original Lisp version (Gold, 2019; Di Liberto, 2020; Marion, 2021). Finally, it reproduced the computation of cultural distances between two different datasets as described in previous studies (Pearce, 2018).</div></div><div><h3>Comparison with existing methods and Conclusions</h3><div>: Our model replicates the previous behaviors of IDyOM in a modern and easy-to-use language -Python. In addition, more features are presented. We deeply think this new version will be of great use to the community of neuroscience of music.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"415 ","pages":"Article 110347"},"PeriodicalIF":2.7,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872035","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}