Frontiers in Computational Neuroscience最新文献

{"title":"Challenges and limitations in computational prediction of protein misfolding in neurodegenerative diseases","authors":"Marios G. Krokidis, Georgios N. Dimitrakopoulos, Aristidis G. Vrahatis, T. Exarchos, Vlamos","doi":"10.3389/fncom.2023.1323182","DOIUrl":"https://doi.org/10.3389/fncom.2023.1323182","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381740","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":"Positional multi-length and mutual-attention network for epileptic seizure classification","authors":"Guokai Zhang, Aiming Zhang, Huan Liu, Jihao Luo, Jianqing Chen","doi":"10.3389/fncom.2024.1358780","DOIUrl":"https://doi.org/10.3389/fncom.2024.1358780","url":null,"abstract":"<p>The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139558972","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":"Editorial: Advances in Shannon-based communications and computations approaches to understanding information processing in the brain","authors":"James Tee, Giorgio M. Vitetta","doi":"10.3389/fncom.2023.1352772","DOIUrl":"https://doi.org/10.3389/fncom.2023.1352772","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386586","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":"Identifying distinctive brain regions related to consumer choice behaviors on branded foods using activation likelihood estimation and machine learning","authors":"Shinya Watanuki","doi":"10.3389/fncom.2024.1310013","DOIUrl":"https://doi.org/10.3389/fncom.2024.1310013","url":null,"abstract":"<sec><title>Introduction</title><p>Brand equity plays a crucial role in a brand’s commercial success; however, research on the brain regions associated with brand equity has had mixed results. This study aimed to investigate key brain regions associated with the decision-making of branded and unbranded foods using quantitative neuroimaging meta-analysis and machine learning.</p></sec><sec><title>Methods</title><p>Quantitative neuroimaging meta-analysis was performed using the activation likelihood method. Activation of the ventral medial prefrontal cortex (VMPFC) overlapped between branded and unbranded foods. The lingual and parahippocampal gyri (PHG) were activated in the case of branded foods, whereas no brain regions were characteristically activated in response to unbranded foods. We proposed a novel predictive method based on the reported foci data, referencing the multi-voxel pattern analysis (MVPA) results. This approach is referred to as the multi-coordinate pattern analysis (MCPA). We conducted the MCPA, adopting the sparse partial least squares discriminant analysis (sPLS-DA) to detect unique brain regions associated with branded and unbranded foods based on coordinate data. The sPLS-DA is an extended PLS method that enables the processing of categorical data as outcome variables.</p></sec><sec><title>Results</title><p>We found that the lingual gyrus is a distinct brain region in branded foods. Thus, the VMPFC might be a core brain region in food categories in consumer behavior, regardless of whether they are branded foods. Moreover, the connection between the PHG and lingual gyrus might be a unique neural mechanism in branded foods.</p></sec><sec><title>Discussion</title><p>As this mechanism engages in imaging the feature-self based on emotionally subjective contextual associative memories, brand managers should create future-oriented relevancies between brands and consumers to build valuable brands.</p></sec>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139482002","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":"Retraction: Cerebral microbleed detection via convolutional neural network and extreme learning machine.","authors":"","doi":"10.3389/fncom.2023.1358283","DOIUrl":"https://doi.org/10.3389/fncom.2023.1358283","url":null,"abstract":"<p><p>[This retracts the article DOI: 10.3389/fncom.2021.738885.].</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10782968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424552","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":"Exploring gene-drug interactions for personalized treatment of post-traumatic stress disorder","authors":"Konstantina Skolariki, Panagiotis Vlamos","doi":"10.3389/fncom.2023.1307523","DOIUrl":"https://doi.org/10.3389/fncom.2023.1307523","url":null,"abstract":"<sec><title>Introduction</title><p>Post-Traumatic Stress Disorder (PTSD) is a mental disorder that can develop after experiencing traumatic events. The aim of this work is to explore the role of genes and genetic variations in the development and progression of PTSD.</p></sec><sec><title>Methods</title><p>Through three methodological approaches, 122 genes and 184 Single Nucleotide Polymorphisms (SNPs) associated with PTSD were compiled into a single gene repository for PTSD. Using PharmGKB and DrugTargetor, 323 drug candidates were identified to target these 122 genes. The top 17 drug candidates were selected based on the statistical significance of the genetic associations, and their promiscuity (number of associated genestargets) and were further assessed for their suitability in terms of bioavailability and drug-like characteristics. Through functional analysis, insights were gained into the biological processes, cellular components, and molecular functions involved in PTSD. This formed the foundation for the next aspect of this study which was to propose an efficient treatment for PTSD by exploring drug repurposing methods.</p></sec><sec><title>Results</title><p>The main aim was to identify the drugs with the most favorable profile that can be used as a pharmacological approach for PTSD treatment. More in particular, according to the genetic variations present in each individual, the relevant biological pathway can be identified, and the drug candidate proposed will specifically target said pathway, accounting for the personalized aspect of this work. The results showed that the drugs used as off-label treatment for PTSD have favorable pharmacokinetic profiles and the potential drug candidates that arose from DrugTargetor were not very promising. Clozapine showed a promising pharmacokinetic profile and has been linked with decreased psychiatric symptoms. Ambrucin also showed a promising pharmacokinetic profile but has been mostly linked with cancer treatment.</p></sec>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139421062","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":"Neuro-environmental interactions: a time sensitive matter","authors":"Azzurra Invernizzi, Stefano Renzetti, Elza Rechtman, Claudia Ambrosi, Lorella Mascaro, Daniele Corbo, Roberto Gasparotti, Cheuk Y. Tang, Donald R. Smith, Roberto G. Lucchini, Robert O. Wright, Donatella Placidi, Megan K. Horton, Paul Curtin","doi":"10.3389/fncom.2023.1302010","DOIUrl":"https://doi.org/10.3389/fncom.2023.1302010","url":null,"abstract":"<sec><title>Introduction</title><p>The assessment of resting state (rs) neurophysiological dynamics relies on the control of sensory, perceptual, and behavioral environments to minimize variability and rule-out confounding sources of activation during testing conditions. Here, we investigated how temporally-distal environmental inputs, specifically metal exposures experienced up to several months prior to scanning, affect functional dynamics measured using rs functional magnetic resonance imaging (rs-fMRI).</p></sec><sec><title>Methods</title><p>We implemented an interpretable XGBoost-shapley additive explanation (SHAP) model that integrated information from multiple exposure biomarkers to predict rs dynamics in typically developing adolescents. In 124 participants (53% females, ages, 13–25 years) enrolled in the public health impact of metals exposure (PHIME) study, we measured concentrations of six metals (manganese, lead, chromium, copper, nickel, and zinc) in biological matrices (saliva, hair, fingernails, toenails, blood, and urine) and acquired rs-fMRI scans. Using graph theory metrics, we computed global efficiency (GE) in 111 brain areas (Harvard Oxford atlas). We used a predictive model based on ensemble gradient boosting to predict GE from metal biomarkers, adjusting for age and biological sex.</p></sec><sec><title>Results</title><p>Model performance was evaluated by comparing predicted versus measured GE. SHAP scores were used to evaluate feature importance. Measured versus predicted rs dynamics from our model utilizing chemical exposures as inputs were significantly correlated (<italic>p</italic> &lt; 0.001, <italic>r</italic> = 0.36). Lead, chromium, and copper contributed most to the prediction of GE metrics.</p></sec><sec><title>Discussion</title><p>Our results indicate that a significant component of rs dynamics, comprising approximately 13% of observed variability in GE, is driven by recent metal exposures. These findings emphasize the need to estimate and control for the influence of past and current chemical exposures in the assessment and analysis of rs functional connectivity.</p></sec>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139397338","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 lightweight mixup-based short texts clustering for contrastive learning","authors":"Qiang Xu, HaiBo Zan, ShengWei Ji","doi":"10.3389/fncom.2023.1334748","DOIUrl":"https://doi.org/10.3389/fncom.2023.1334748","url":null,"abstract":"<p>Traditional text clustering based on distance struggles to distinguish between overlapping representations in medical data. By incorporating contrastive learning, the feature space can be optimized and applies mixup implicitly during the data augmentation phase to reduce computational burden. Medical case text is prevalent in everyday life, and clustering is a fundamental method of identifying major categories of conditions within vast amounts of unlabeled text. Learning meaningful clustering scores in data relating to rare diseases is difficult due to their unique sparsity. To address this issue, we propose a contrastive clustering method based on mixup, which involves selecting a small batch of data to simulate the experimental environment of rare diseases. The contrastive learning module optimizes the feature space based on the fact that positive pairs share negative samples, and clustering is employed to group data with comparable semantic features. The module mitigates the issue of overlap in data, whilst mixup generates cost-effective virtual features, resulting in superior experiment scores even when using small batch data and reducing resource usage and time overhead. Our suggested technique has acquired cutting-edge outcomes and embodies a favorable strategy for unmonitored text clustering.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139421065","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":"Monitoring time domain characteristics of Parkinson’s disease using 3D memristive neuromorphic system","authors":"Md Abu Bakr Siddique, Yan Zhang, Hongyu An","doi":"10.3389/fncom.2023.1274575","DOIUrl":"https://doi.org/10.3389/fncom.2023.1274575","url":null,"abstract":"IntroductionParkinson’s disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices.MethodsIn this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13–35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms.ResultsSimulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%–25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage.DiscussionThis study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138687317","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":"Cortical field maps across human sensory cortex","authors":"Alyssa A. Brewer, Brian Barton","doi":"10.3389/fncom.2023.1232005","DOIUrl":"https://doi.org/10.3389/fncom.2023.1232005","url":null,"abstract":"Cortical processing pathways for sensory information in the mammalian brain tend to be organized into topographical representations that encode various fundamental sensory dimensions. Numerous laboratories have now shown how these representations are organized into numerous cortical field maps (CMFs) across visual and auditory cortex, with each CFM supporting a specialized computation or set of computations that underlie the associated perceptual behaviors. An individual CFM is defined by two orthogonal topographical gradients that reflect two essential aspects of feature space for that sense. Multiple adjacent CFMs are then organized across visual and auditory cortex into macrostructural patterns termed cloverleaf clusters. CFMs within cloverleaf clusters are thought to share properties such as receptive field distribution, cortical magnification, and processing specialization. Recent measurements point to the likely existence of CFMs in the other senses, as well, with topographical representations of at least one sensory dimension demonstrated in somatosensory, gustatory, and possibly olfactory cortical pathways. Here we discuss the evidence for CFM and cloverleaf cluster organization across human sensory cortex as well as approaches used to identify such organizational patterns. Knowledge of how these topographical representations are organized across cortex provides us with insight into how our conscious perceptions are created from our basic sensory inputs. In addition, studying how these representations change during development, trauma, and disease serves as an important tool for developing improvements in clinical therapies and rehabilitation for sensory deficits.","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138686964","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}