Journal of neural engineering最新文献

{"title":"Improved spatial memory for physical versus virtual navigation.","authors":"Shachar Maidenbaum, Vaclav Kremen, Vladimir Sladky, Kai Miller, Jamie Van Gompel, Gregory A Worrell, Joshua Jacobs","doi":"10.1088/1741-2552/ade6aa","DOIUrl":"10.1088/1741-2552/ade6aa","url":null,"abstract":"<p><p><i>Objective</i>. Virtual reality (VR) has become a key tool for researching spatial memory. Virtual environments offer many advantages for research in terms of logistics, neuroimaging compatibility etc. However, it is well established in animal models that the lack of physical movement in VR impairs some neural representations of space, and this is considered likely to be true in humans as well. Furthermore, it is unclear how big the disruptive effect stationary navigation is-how much does physical movement during encoding and recall affect human spatial memory and representations of space? What effect does the fatigue of actually walking during tasks have on participants-will physical movement decrease performance, or increase perception of difficulty?<i>Approach</i>. We utilize Augmented reality (AR) to enable participants to perform a spatial memory task while physically moving in the real world, compared to a matched VR task performed while stationary. Our task was performed by a group of healthy participants, by a group of stationary epilepsy patients, as they represent the population from which invasive human spatial signals are typically collected, and, in a case study, by a mobile epilepsy patient with an investigational chronic neural implant (Medtronic Summit RC + S^TM) streaming real-time continuous hippocampal local field potential data.<i>Main results</i>. Participants showed good performance in both conditions, but reported that the walking condition was significantly easier, more immersive, and more fun than the stationary condition. Importantly, memory performance was significantly better in walking vs. stationary in all groups, including epilepsy patients. We also found evidence for an increase in the amplitude of the theta oscillations associated with movement during the walking condition.<i>Significance</i>. Our findings highlight the importance of paradigms that include physical movement and suggest that integrating AR with movement in real environments can lead to improved techniques for spatial memory research.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12247154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144337387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards real time efficient and robust ECoG decoding for mobile brain-computer interface.","authors":"Zhanhui Lin, Xinyu Jiang, Chenyun Dai, Fumin Jia","doi":"10.1088/1741-2552/ade917","DOIUrl":"10.1088/1741-2552/ade917","url":null,"abstract":"<p><p><i>Objective</i>. Decoding locomotion-related brain activities from electrocorticographic (ECoG) signals is essential in brain-computer interfaces (BCIs). Most previous ECoG decoders are computationally demanding and sensitive to noises/outliers. Mobile and robust BCIs are particularly important for physically disabled patients to restore motor ability in outdoor scenarios, where the processing pipeline should be implemented efficiently using constrained computation resources. In this work, we aim to explore the optimal pipeline for mobile BCI decoding.<i>Approach</i>. We comprehensively evaluated the trade-off between the decoding precision, computational efficiency and robustness of diverse decoding algorithms on a combined ECoG dataset of 12 subjects conducting individual finger movement, including partial-least-square and their N-way variants, Bayesian ridge regression, least absolute shrinkage and selection operator, support vector regression, neural networks (NNs) with diverse architectures, and random forest (RF). We further explored the feature optimization technique for selected models by using their inherent model explainability. We also compared the decoding performance of updatable algorithms when the data is split into multiple batches and used sequentially.<i>Main results</i>. The RF model, not valued by previous studies, can achieve the best trade-off between precision and efficiency, achieving an average Pearson's correlation coefficient (<i>r</i>) of 0.466 with only 0.5 K floating-point operations per second (FLOPs) per inference and a model size of 900KiB. We also demonstrate the inherent superior robustness of RF model on corrupted ECoG electrodes, with a>2×decoding precision on noisy signals compared with all state-of-the-art deep NNs. The optimized RF processing pipeline was deployed on a STM32-based embedded platform with only a 15.2 ms computation delay.<i>Significance</i>. In this study, we systematically explored the performance and efficiency of ECoG decoding algorithms in finger movement analysis. The proposed decoding pipeline is implemented on a compact embedded platform to achieve low-latency, power-efficient real-time decoding. This research accelerates the translation of mobile BCI into real-life practices.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification.","authors":"Michael Pritchard, Felipe Campelo, Harry Goldingay","doi":"10.1088/1741-2552/ade1f9","DOIUrl":"10.1088/1741-2552/ade1f9","url":null,"abstract":"<p><p><i>Objective</i>. Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG & EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings.<i>Approach</i>. We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection & Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature.<i>Main results</i>. EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems.<i>Significance</i>. To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies , enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A transformer-based network with second-order pooling for motor imagery EEG classification.","authors":"Jing Jin, Wei Liang, Ren Xu, Weijie Chen, Ruitian Xu, Xingyu Wang, Andrzej Cichocki","doi":"10.1088/1741-2552/adeae8","DOIUrl":"10.1088/1741-2552/adeae8","url":null,"abstract":"<p><p><i>Objective</i>. Electroencephalography (EEG) signals can reflect motor intention signals in the brain. In recent years, motor imagery (MI) based brain-computer interfaces (BCIs) have attracted the attention of neuroinformatics researchers. Numerous deep learning models have been developed to decode EEG signals. Although deep learning models, particularly those based on convolutional neural networks, have shown promise in decoding EEG signals, most existing methods focus on attention mechanisms while neglecting high-order statistical dependencies that are critical for accurately capturing the complex structure of EEG data.<i>Approach</i>. To address this limitation, we propose a neural network integrating a transpose-attention mechanism and second-order pooling (SecTNet). The proposed model tackles two fundamental challenges in EEG decoding. It metrics the covariance structure of EEG signals using Riemannian geometry on symmetric positive definite (SPD) matrices, and it enhances the discriminability of these SPD features by introducing attention mechanisms that adaptively model inter-channel dependencies. Specifically, SecTNet is composed of three key components. First, a multi-scale spatial-temporal convolution module extracts detailed local features. Second, a transpose-attention mechanism captures dependency information from the internal interactions between channels. Lastly, a second-order pooling layer captures high-order statistical correlations in the EEG feature space.<i>Main results</i>. SecTNet is evaluated on two publicly available EEG datasets, namely BCI competition IV 2a dataset and OpenBMI dataset. It achieves an average accuracy of 86.88% on the BCI competition IV dataset 2a and 74.99% on the OpenBMI dataset. Moreover, results show that SecTNet maintains competitive performance even when trained on only 50% of the data, demonstrating strong generalization under limited data conditions.<i>Significance</i>. These results demonstrate the broad applicability and effectiveness of SecTNet in enhancing MI-BCI performance. SecTNet provides a robust and generalizable framework for EEG decoding, supporting the development of BCI applications across diverse real-world scenarios.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sub-scalp EEG for sensorimotor brain-computer interface.","authors":"T B Mahoney, D B Grayden, S E John","doi":"10.1088/1741-2552/ade9f1","DOIUrl":"10.1088/1741-2552/ade9f1","url":null,"abstract":"<p><p><i>Objective</i>. To establish sub-scalp electroencephalography (EEG) as a viable option for brain-computer interface (BCI) applications, particularly for chronic use, by demonstrating its effectiveness in recording and classifying sensorimotor neural activity.<i>Approach</i>. Two experiments were conducted in this study. The first aim was to demonstrate the high spatial resolution of sub-scalp EEG through analysis of somatosensory evoked potentials in sheep models. The second focused on the practical application of sub-scalp EEG, classifying motor execution using data collected during a sheep behavioural experiment.<i>Main results</i>. We successfully demonstrated the recording of sensorimotor rhythms using sub-scalp EEG in sheep models. Important spatial, temporal, and spectral features of these signals were identified, and we were able to classify motor execution with above-chance performance. These results are comparable to previous work that investigated signal quality and motor execution classification using ECoG and endovascular arrays in sheep models.<i>Significance</i>. These results suggest that sub-scalp EEG may provide signal quality that approaches that of more invasive neural recording methods such as ECoG and endovascular arrays, and support the use of sub-scalp EEG for chronic BCI applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic parameter estimation in thalamo-cortical computational models: a novel approach for tracking anesthetic brain states.","authors":"Luxin Fan, Dihuan Wang, Xin Wen, Bo Xu, Xiaoling Chen, Xiaoli Li, Zhenhu Liang","doi":"10.1088/1741-2552/ade9f2","DOIUrl":"10.1088/1741-2552/ade9f2","url":null,"abstract":"<p><p><i>Objective.</i>Accurate tracking of brain states during general anesthesia remains challenging due to the complex neurophysiological dynamics involved.<i>Approach.</i>This study developed a thalamo-cortical neural mass model (TC-NMM) and a mean-field model (MFM) incorporating shared thalamic nuclei, both integrated with a particle filtering (PF) algorithm, to characterize consciousness transitions during sevoflurane- and protocol-induced anesthesia. The PF algorithm was employed to dynamically estimate model parameters, including excitatory/inhibitory postsynaptic potential (EPSP/IPSP), and the time constant rate of EPSP/IPSP, along with the coupling coefficients of the thalamic and cortical modules.<i>Main results.</i>The PF-based TC-NMM and MFM accurately tracked frontal data obtained during sevoflurane anesthesia and thalamo-cortical data acquired during protocol-induced anesthesia, respectively. Parameter estimation results revealed that both sevoflurane and protocol anesthesia reduced thalamo-cortical connectivity, with the thalamo-cortical coupling coefficients reliably distinguishing between distinct consciousness states. Notably, the EPSP parameters and coupling coefficients from the TC-NMM hold potential as clinically viable indicators for monitoring anesthesia depth.<i>Significance.</i>These findings not only advance our understanding of anesthetic mechanisms from a model perspective, but also suggest novel, physiologically interpretable indicators for assessing anesthesia depth.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The construction of spatio-temporal functional brain network based on Ising model for EEG classification.","authors":"Lingling Wei, Taorong Qiu, Wenjie Mei, Jiaxin Liu, Zhaohua Wang","doi":"10.1088/1741-2552/ade56b","DOIUrl":"https://doi.org/10.1088/1741-2552/ade56b","url":null,"abstract":"<p><p><i>Objective.</i>Functional brain networks (FBN) are important tools for understanding, classifying and analyzing the brain. However, the multi-term features and temporal correlation of individuals are not adequately represented in single-layer and single-scale FBNs, resulting in room for improvement in the classification accuracy and generalizability of FBNs.<i>Approach.</i>Based on the temporal variability and spatial distribution of electroencephalography (EEG), a multi-scale spatio-temporal FBN is constructed on both temporal and spatial scales. Firstly, brain field data aggregation computation. Based on Ising model design the method of brain field data aggregation, represent whole characteristics of brain field with a symbol, and map multiple time series into a symbol sequence. Secondly, autocorrelation calculation between symbol subsequences. Divide sequence into multiple non-overlapping subsequences, compute the autocorrelation between subsequences based on Kronecker Delta, and represent the relationships between the states of the brain over time. Thirdly, spatio-temporal FBN construction. Subsequence are taken as nodes, and symbol sequence correlations are used as link weights, temporal FBN is constructed. Within each node of the temporal FBN, channels are taken as nodes, and functional connectivities of inter-channel time series are used as link weights, spatial FBN is constructed. Finally, the spatio-temporal FBN is applied for EEG classification.<i>Main results</i>. The classification accuracies of the spatio-temporal FBN are up to 99% on fatigue detection, emotion recognition, Parkinson's diagnosis and motor imagery datasets. Thereby, it is verified that the spatio-temporal FBN possesses satisfactory effectiveness, efficiency and generalizability.<i>Significance</i>. The advantages of the spatio-temporal FBN are that the short-term and long-term features of individuals and categories are represented, while enabling universal recognition among different individuals and distinction among different categories.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":"22 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposition of retinal ganglion cell electrical images for cell type and functional inference.","authors":"Eric G Wu, Andra M Rudzite, Martin O Bohlen, Peter H Li, Alexandra Kling, Sam Cooler, Colleen Rhoades, Nora Brackbill, Alex R Gogliettino, Nishal P Shah, Sasidhar S Madugula, Alexander Sher, Alan M Litke, Greg D Field, E J Chichilnisky","doi":"10.1088/1741-2552/ade344","DOIUrl":"10.1088/1741-2552/ade344","url":null,"abstract":"<p><p><i>Objective.</i>Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and for the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision.<i>Approach.</i>The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose EI into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments.<i>Main results.</i>The decomposition was evaluated using large-scale multi-electrode recordings from the macaque retina. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells) substantially more accurately than previous approaches.<i>Significance.</i>These findings contribute to more accurate inference of RGC types and their original light responses based purely on their electrical features, with potential implications for vision restoration technology.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144268269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards subject-centered co-adaptive brain-computer interfaces based on backward optimal transport.","authors":"Victoria Peterson, Valeria Spagnolo, Catalina M Galván, Nicolás Nieto, Rubén D Spies, Diego H Milone","doi":"10.1088/1741-2552/addb7a","DOIUrl":"10.1088/1741-2552/addb7a","url":null,"abstract":"<p><p><i>Objective</i>. Controlling a motor imagery brain-computer interface (MI-BCI) can be challenging, requiring several sessions of practice. Electroencephalography (EEG)-based BCIs are particularly affected by cross-session variability. In this scenario, it is crucial to implement co-adaptive systems, where the machine adapts the decoding algorithm while the user learns how to control the BCI. To support the user learning process, it is essential to measure and provide real-time feedback on self-modulation skills. This study aims to develop a method for online assessment of MI modulation capability to build co-adaptive BCIs that improve both user performance and system accuracy.<i>Approach</i>. Backward optimal transport for domain adaptation allows across-session MI-BCI usage without classifier retraining. Using the cued label to guide the adaptation, a supportive backward adaptation (SBA) method is defined. The required model effort to perform a trial adaptation is proposed as an online metric of MI modulation skills. We conducted experiments on both real and simulated data to demonstrate that this metric effectively informs about the the discriminability and stability of the EEG patterns related to the MI task. The proposed metric is validated by means of Riemannian distinctiveness metrics.<i>Main Results</i>. Our findings show that the associated effort when applying SBA provides a meaningful way of evaluating EEG patterns discriminability, being significantly correlated with Riemannian distinctiveness metrics.<i>Significance</i>. This study introduces a novel framework for co-adaptive BCI learning that performs data adaptation while assessing the MI-BCI skills of the user. The proposed SBA approach can enhance BCI performance by facilitating session-to-session adaptation and empowering users with valuable feedback based on their current MI modulation strategy. This framework represents a significant advancement in developing user-centered, co-adaptive MI-BCIs that effectively support and enhance user capabilities.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing changes in whole-brain structural connectivity in the unilateral 6-hydroxydopamine rat model of Parkinson's disease using diffusion imaging and tractography.","authors":"Mikhail Moshchin, Roger J Schultz, Kevin P Cheng, Susan Osting, James Koeper, Matthew Laluzerne, James K Trevathan, Andrea Brzeczkowski, Cuong P Luu, John-Paul J Yu, Richard F Betzel, Wendell B Lake, Samuel A Hurley, Kip A Ludwig, Aaron J Suminski","doi":"10.1088/1741-2552/ade567","DOIUrl":"10.1088/1741-2552/ade567","url":null,"abstract":"<p><p><i>Objective.</i>Parkinson's disease (PD) is a multifactorial, progressive neurodegenerative disease that has a profound impact on those it afflicts. Its hallmark pathophysiology is characterized by degeneration of dopaminergic (DA) neurons in the midbrain which trigger a host of motor and non-motor symptoms. Many preclinical research efforts utilize unilateral lesion models to assess the neural mechanisms of PD and explore new therapeutic approaches because these models produce similar motor symptoms to those of PD patients. The goal of this work is to examine changes in brain structure resulting from a unilateral lesion both within the nigrostriatal system, where DA neurons are lost, and throughout the brain.<i>Methods.</i>Using multi-shell diffusion magnetic resonance imaging and correlational tractography, we assessed microstructural changes throughout the brain resulting from unilateral injection of 6-hydroxydopamine in the median forebrain bundle.<i>Resutls.</i>Following lesioning, the PD phenotype was confirmed using behavioral and histological assessment. Correlational tractography found networks of fiber tracts that were either positively or negatively correlated with lesion status throughout the brain. Analyzing patterns of intra- and inter-hemispheric connectivity between the positively and negatively correlated fiber tracts revealed two separate neural networks. The first contained only negatively correlated fibers in the lesioned hemisphere consistent with the local effects of the lesion (i.e. DA depletion in the nigrostriatal system). The second contained systematically overlapping fiber tracts in the lesioned and non-lesioned hemispheres including the olfactory system and cerebellum, which we suggest are indicative of adaptive mechanisms to compensate for the lesion.<i>Conclusion.</i>Taken together, these results suggest that correlational tractography is a reasonable tool to examine whole brain structural changes in rodent models of neurodegenerative disease, and may have future translational value as a diagnostic tool for patients with PD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}