International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering最新文献

{"title":"Learning signatures of decision making from many individuals playing the same game.","authors":"Michael J Mendelson,&nbsp;Mehdi Azabou,&nbsp;Suma Jacob,&nbsp;Nicola Grissom,&nbsp;David Darrow,&nbsp;Becket Ebitz,&nbsp;Alexander Herman,&nbsp;Eva L Dyer","doi":"10.1109/ner52421.2023.10123846","DOIUrl":"10.1109/ner52421.2023.10123846","url":null,"abstract":"<p><p>Human behavior is incredibly complex and the factors that drive decision making-from instinct, to strategy, to biases between individuals-often vary over multiple timescales. In this paper, we design a predictive framework that learns representations to encode an individual's 'behavioral style', i.e. long-term behavioral trends, while simultaneously predicting future actions and choices. The model explicitly separates representations into three latent spaces: the recent past space, the short-term space, and the long-term space where we hope to capture individual differences. To simultaneously extract both global and local variables from complex human behavior, our method combines a multi-scale temporal convolutional network with latent prediction tasks, where we encourage embeddings across the entire sequence, as well as subsets of the sequence, to be mapped to similar points in the latent space. We develop and apply our method to a large-scale behavioral dataset from 1,000 humans playing a 3-armed bandit task, and analyze what our model's resulting embeddings reveal about the human decision making process. In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559224/pdf/nihms-1931965.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41171846","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":"Quantifying changes in local basal ganglia structural connectivity in the 6-hydroxydopamine model of Parkinson's Disease using correlational tractography.","authors":"Mikhail Moshchin,&nbsp;Kevin P Cheng,&nbsp;Susan Osting,&nbsp;Matthew Laluzerne,&nbsp;Samuel A Hurley,&nbsp;Ajay Paul Singh,&nbsp;James K Trevathan,&nbsp;Andrea Brzeczkowski,&nbsp;John-Paul J Yu,&nbsp;Wendell B Lake,&nbsp;Kip A Ludwig,&nbsp;Aaron J Suminski","doi":"10.1109/ner52421.2023.10123839","DOIUrl":"https://doi.org/10.1109/ner52421.2023.10123839","url":null,"abstract":"<p><p>In recent years, tractography based on diffusion magnetic resonance imaging (dMRI) has become a popular tool for studying microstructural changes resulting from brain diseases like Parkinson's Disease (PD). Quantitative anisotropy (QA) is a parameter that is used in deterministic fiber tracking as a measure of connection between brain regions. It remains unclear, however, if microstructural changes caused by lesioning the median forebrain bundle (MFB) to create a Parkinsonian rat model can be resolved using tractography based on ex-vivo diffusion MRI. This study aims to fill this gap and enable future mechanistic research on structural changes of the whole brain network rodent models of PD. Specifically, it evaluated the ability of correlational tractography to detect structural changes in the MFB of 6-hydroxydopamine (6-OHDA) lesioned rats. The findings reveal that correlational tractography can detect structural changes in lesioned MFB and differentiate between the 6-OHDA and control groups. Imaging results are supported by behavioral and histological evidence demonstrating that 6-OHDA lesioned rats were indeed Parkinsonian. The results suggest that QA and correlational tractography is appropriate to examine local structural changes in rodent models of neurodegenerative disease. More broadly, we expect that similar techniques may provide insight on how disease alters structure throughout the brain, and as a tool to optimize therapeutic interventions.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10484213/pdf/nihms-1928894.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10240887","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":"Detecting change points in neural population activity with contrastive metric learning.","authors":"Carolina Urzay,&nbsp;Nauman Ahad,&nbsp;Mehdi Azabou,&nbsp;Aidan Schneider,&nbsp;Geethika Atamkuri,&nbsp;Keith B Hengen,&nbsp;Eva L Dyer","doi":"10.1109/ner52421.2023.10123821","DOIUrl":"10.1109/ner52421.2023.10123821","url":null,"abstract":"<p><p>Finding points in time where the distribution of neural responses changes (change points) is an important step in many neural data analysis pipelines. However, in complex and free behaviors, where we see different types of shifts occurring at different rates, it can be difficult to use existing methods for change point (CP) detection because they can't necessarily handle different types of changes that may occur in the underlying neural distribution. Additionally, response changes are often sparse in high dimensional neural recordings, which can make existing methods detect spurious changes. In this work, we introduce a new approach for finding changes in neural population states across diverse activities and arousal states occurring in free behavior. Our model follows a contrastive learning approach: we learn a metric for CP detection based on maximizing the Sinkhorn divergences of neuron firing rates across two sides of a labeled CP. We apply this method to a 12-hour neural recording of a freely behaving mouse to detect changes in sleep stages and behavior. We show that when we learn a metric, we can better detect change points and also yield insights into which neurons and sub-groups are important for detecting certain types of switches that occur in the brain.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10559226/pdf/nihms-1931962.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41107632","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":"Inferring Pyramidal Neuron Morphology using EAP Data.","authors":"Ziao Chen,&nbsp;Matthew Carroll,&nbsp;Satish S Nair","doi":"10.1109/ner52421.2023.10123903","DOIUrl":"https://doi.org/10.1109/ner52421.2023.10123903","url":null,"abstract":"<p><p>We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10259830/pdf/nihms-1892244.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9629248","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":"A brain-computer typing interface using finger movements.","authors":"Nishal P Shah, Matthew S Willsey, Nick Hahn, Foram Kamdar, Donald T Avansino, Leigh R Hochberg, Krishna V Shenoy, Jaimie M Henderson","doi":"10.1109/ner52421.2023.10123912","DOIUrl":"10.1109/ner52421.2023.10123912","url":null,"abstract":"<p><p>Intracortical brain computer interfaces (iBCIs) decode neural activity from the cortex and enable motor and communication prostheses, such as cursor control, handwriting and speech, for people with paralysis. This paper introduces a new iBCI communication prosthesis using a 3D keyboard interface for typing using continuous, closed loop movement of multiple fingers. A participant-specific BCI keyboard prototype was developed for a BrainGate2 clinical trial participant (T5) using neural recordings from the hand-knob area of the left premotor cortex. We assessed the relative decoding accuracy of flexion/extension movements of individual single fingers (5 degrees of freedom (DOF)) vs. three groups of fingers (thumb, index-middle, and ring-small fingers, 3 DOF). Neural decoding using 3 independent DOF was more accurate (95%) than that using 5 DOF (76%). A virtual keyboard was then developed where each finger group moved along a flexion-extension arc to acquire targets that corresponded to English letters and symbols. The locations of these letter/symbols were optimized using natural language statistics, resulting in an approximately a 2× reduction in distance traveled by fingers on average compared to a random keyboard layout. This keyboard was tested using a simple real-time closed loop decoder enabling T5 to type with 31 symbols at 90% accuracy and approximately 2.3 sec/symbol (excluding a 2 second hold time) on average.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10353344/pdf/nihms-1915827.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9903823","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":"Linear feedback control of spreading dynamics in stochastic nonlinear network models: epileptic seizures.","authors":"S A Moosavi,&nbsp;W Truccolo","doi":"10.1109/ner52421.2023.10123896","DOIUrl":"https://doi.org/10.1109/ner52421.2023.10123896","url":null,"abstract":"<p><p>The development of models and approaches for controlling the spreading dynamics of epileptic seizures is an essential step towards new therapies for people with pharmacologically resistant epilepsy. Beyond resective neurosurgery, in which epileptogenic zones (EZs) are the target of surgery, closed-loop control based on intracranial electrical stimulation, applied at the very early stage of seizure evolution, has been the main alternative, e.g. the RNS system from NeuroPace (Mountain View, CA). In this approach the electrical stimulation is delivered to target brain areas after detection of seizure initiation in the EZ. Here, we examined, on model simulations, some of the closed-loop control aspects of the problem. Seizure dynamics and spread are typically modeled with highly nonlinear dynamics on complex brain networks. Despite the nonlinearity and complexity, currently available optimal feedback control approaches are mostly based on linear approximations. Alternative machine learning control approaches might require amounts of data beyond what is commonly available in the intended application. We thus examined how standard linear feedback control approaches perform when applied to nonlinear models of neural dynamics of seizure generation and spread. In particular, we considered patient-specific epileptor network models for seizure onset and spread. The models incorporate network connectivity derived from (diffusion MRI) white-matter tractography, have been shown to capture the qualitative dynamics of epileptic seizures and can be fit to patient data. For control, we considered simple linear quadratic Gaussian (LQG) regulators. The LQG control was based on a discrete-time state-space model derived from the linearization of the patient-specific epileptor network model around a stable fixed point in the regime of preictal dynamics. We show in simulations that LQG regulators acting on the EZ node during the initial seizure period tend to be unstable. The LQG solution for the control law in this case leads to global feedback to the EZ-node actuator. However, if the LQG solution is constrained to depend on only local feedback originating from the EZ node itself, the controller is stable. In this case, we demonstrate that localized LQG can easily terminate the seizure at the early stage and prevent spread. In the context of optimal feedback control based on linear approximations, our results point to the need for investigating in more detail feedback localization and additional relevant control targets beyond epileptogenic zones.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10433950/pdf/nihms-1923539.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10047026","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":"Construction of semi-supervised spatial projections to identify the source of beta- and high frequency oscillations in Parkinson's disease.","authors":"Luciano R F Branco,&nbsp;Ashwin Viswanathan,&nbsp;Arjun Tarakad,&nbsp;Nuri F Ince","doi":"10.1109/ner52421.2023.10123890","DOIUrl":"https://doi.org/10.1109/ner52421.2023.10123890","url":null,"abstract":"<p><p>Traditional deep brain stimulation (DBS) treatment for Parkinson's disease (PD) targets the placement of DBS leads into subthalamic nucleus (STN). Extraction of neurobiomarkers from STN local field potential activity can be used for the optimization of DBS. Beta (12-30 Hz) and high frequency oscillations (200-450 Hz, HFO) of STN and their phase-amplitude coupling have been previously correlated with symptom severity in PD. The typical approach is to take bipolar derivations of electrode contacts in order to enhance recordings of local brain activity and suppress noise levels. This approach can often cancel the signals in correlated neighboring contacts and create ambiguity in which monopolar contact to select for the identification of the main source of the oscillatory signal. To improve local specificity and help identify the source of beta and HFO in terms of electrode contact, we propose a semi supervised blind-source separation method. This approach presents a novel perspective to investigate electrophysiology by projecting the recorded channels into a subspace of virtual channels. We show the contribution of each channel to the identified source and correlate the spatial information with imaging and postoperative programming parameters. We anticipate such a source identification strategy can be used in the future to investigate the distribution of beta and HFO on individual contacts of the DBS lead and can improve the interpretation of these signals.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10440159/pdf/nihms-1923508.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10040339","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":"A Diagnostic Circuit for Crosstalk Detection in Microelectrode Arrays.","authors":"Morgan McNamara,&nbsp;Alpaslan Ersöz,&nbsp;Martin Han","doi":"10.1109/ner49283.2021.9441164","DOIUrl":"https://doi.org/10.1109/ner49283.2021.9441164","url":null,"abstract":"<p><p>Current leakage between channels in microelectrode arrays is a sign of device failure and can lead to shorting of neural signals. The purpose of this project is to detect crosstalk between 32 channels of electrodes. We designed an embedded crosstalk detection system that can stimulate each electrode individually with a constant-current pulse and record voltage transients of the stimulated and adjacent electrodes to generate a matrix of crosstalk values. Charge injection in a phosphate buffered saline solution was used to check the condition of each electrode. A semi-wet condition was then used to determine the percent crosstalk between the channels. The analysis showed that there was minimal crosstalk between the electrodes, except for a known physical defect on the probe. The measurement technique enabled by the electronics circuit has the potential to be used in functional testing and screening of implantable devices.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":" ","pages":"544-547"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ner49283.2021.9441164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39739969","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":"Fuzzy Logic Control of Heartrate by Electrical Block of Vagus Nerve.","authors":"Shane A Bender,&nbsp;David B Green,&nbsp;Robert J Daniels,&nbsp;Kevin L Kilgore,&nbsp;Niloy Bhadra,&nbsp;Tina L Vrabec","doi":"10.1109/ner49283.2021.9441092","DOIUrl":"https://doi.org/10.1109/ner49283.2021.9441092","url":null,"abstract":"<p><p>Although vagus nerve stimulation (VNS) can be used to reduce heartrate by enhancing parasympathetic activity, a fully controllable intervention would also require a method for downregulating parasympathetic activity. A direct current (DC) block can be applied to a nerve to block its action potential conduction. This nerve block can be used to downregulate parasympathetic activity by blocking afferent reflexes. The damaging effects of reactions that occur at the electrode-nerve interface using conventional platinum electrodes can be avoided by separating the electrode from the nerve. Using a biocompatible, ionically conducting medium, the electrode and the damaging reactions can be isolated in a vessel away from the nerve. This type of electrode has been called the Separated Interface Nerve Electrode (SINE). Fuzzy logic control (FLC) is a controller approach that is well suited to physiological systems. The SINE, controlled by an FLC, was utilized to block a stimulated vagus nerve and regulate heart rate. The FLC was able to maintain the heartrate at a pre-determined setpoint while still achieving instant recovery when the block was removed.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":" ","pages":"1083-1086"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ner49283.2021.9441092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39839043","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":"A segmented forearm model of hand pronation-supination approximates joint moments for real time applications.","authors":"Matthew G Yough,&nbsp;Russell L Hardesty,&nbsp;Sergiy Yakovenko,&nbsp;Valeriya Gritsenko","doi":"10.1109/ner49283.2021.9441405","DOIUrl":"https://doi.org/10.1109/ner49283.2021.9441405","url":null,"abstract":"<p><p>Musculoskeletal modeling is a new computational tool to reverse engineer human control systems, which require efficient algorithms running in real-time. Human hand pronation-supination movement is accomplished by movement of the radius and ulna bones relative to each other via the complex proximal and distal radioulnar joints, each with multiple degrees of freedom (DOFs). Here, we report two simplified models of this complex kinematic transformation implemented as a part of a 20 DOF model of the hand and forearm. The pronation/supination DOF was implemented as a single rotation joint either within the forearm segment or separating proximal and distal parts of the forearm segment. Torques produced by the inverse dynamic simulations with anatomical architecture of the forearm (OpenSim model) were used as the \"gold standard\" in the comparison of two simple models. Joint placement was iteratively optimized to achieve the closest representation of torques during realistic hand movements. The model with a split forearm segment performed better than the model with a solid forearm segment in simulating pronation/supination torques. We conclude that simplifying pronation/supination DOF as a single-axis rotation between arm segments is a viable strategy to reduce the complexity of multi-DOF dynamic simulations.</p>","PeriodicalId":73414,"journal":{"name":"International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering","volume":" ","pages":"751-754"},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ner49283.2021.9441405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39140915","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}