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ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations Front. Neuroinform. (IF 3.5) Pub Date : 2024-04-12 Rene Miedema, Christos Strydis
IntroductionIn-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms
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Automatic segmentation of hemorrhagic transformation on follow-up non-contrast CT after acute ischemic stroke Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-30 Jiacheng Sun, Freda Werdiger, Christopher Blair, Chushuang Chen, Qing Yang, Andrew Bivard, Longting Lin, Mark Parsons
BackgroundHemorrhagic transformation (HT) following reperfusion therapies is a serious complication for patients with acute ischemic stroke. Segmentation and quantification of hemorrhage provides critical insights into patients’ condition and aids in prognosis. This study aims to automatically segment hemorrhagic regions on follow-up non-contrast head CT (NCCT) for stroke patients treated with endovascular
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suMRak: a multi-tool solution for preclinical brain MRI data analysis Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-26 Rok Ister, Marko Sternak, Siniša Škokić, Srećko Gajović
IntroductionMagnetic resonance imaging (MRI) is invaluable for understanding brain disorders, but data complexity poses a challenge in experimental research. In this study, we introduce suMRak, a MATLAB application designed for efficient preclinical brain MRI analysis. SuMRak integrates brain segmentation, volumetry, image registration, and parameter map generation into a unified interface, thereby
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Turbulent dynamics and whole-brain modeling: toward new clinical applications for traumatic brain injury Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-25 Noelia Martínez-Molina, Yonatan Sanz-Perl, Anira Escrichs, Morten L. Kringelbach, Gustavo Deco
Traumatic Brain Injury (TBI) is a prevalent disorder mostly characterized by persistent impairments in cognitive function that poses a substantial burden on caregivers and the healthcare system worldwide. Crucially, severity classification is primarily based on clinical evaluations, which are non-specific and poorly predictive of long-term disability. In this Mini Review, we first provide a description
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A computational model of Alzheimer's disease at the nano, micro, and macroscales Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-22 Éléonore Chamberland, Seyedadel Moravveji, Nicolas Doyon, Simon Duchesne
IntroductionMathematical models play a crucial role in investigating complex biological systems, enabling a comprehensive understanding of interactions among various components and facilitating in silico testing of intervention strategies. Alzheimer's disease (AD) is characterized by multifactorial causes and intricate interactions among biological entities, necessitating a personalized approach due
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Epileptic seizure prediction based on EEG using pseudo-three-dimensional CNN Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-19 Xin Liu, Chunyang Li, Xicheng Lou, Haohuan Kong, Xinwei Li, Zhangyong Li, Lisha Zhong
Epileptic seizures are characterized by their sudden and unpredictable nature, posing significant risks to a patient’s daily life. Accurate and reliable seizure prediction systems can provide alerts before a seizure occurs, as well as give the patient and caregivers provider enough time to take appropriate measure. This study presents an effective seizure prediction method based on deep learning that
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Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-15 Pragya Rai, Andrew Knight, Matias Hiillos, Csaba Kertész, Elizabeth Morales, Daniella Terney, Sidsel Armand Larsen, Tim Østerkjerhuus, Jukka Peltola, Sándor Beniczky
IntroductionAutomated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment.MethodsIn this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0–80 years), in terms of sensitivity
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A scoping review of mathematical models covering Alzheimer's disease progression Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-14 Seyedadel Moravveji, Nicolas Doyon, Javad Mashreghi, Simon Duchesne
Alzheimer's disease is a complex, multi-factorial, and multi-parametric neurodegenerative etiology. Mathematical models can help understand such a complex problem by providing a way to explore and conceptualize principles, merging biological knowledge with experimental data into a model amenable to simulation and external validation, all without the need for extensive clinical trials. We performed
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Intra-V1 functional networks and classification of observed stimuli Front. Neuroinform. (IF 3.5) Pub Date : 2024-03-11 Marlis Ontivero-Ortega, Jorge Iglesias-Fuster, Jhoanna Perez-Hidalgo, Daniele Marinazzo, Mitchell Valdes-Sosa, Pedro Valdes-Sosa
IntroductionPrevious studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject
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Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-29 Javier V. Juan, Rubén Martínez, Eduardo Iáñez, Mario Ortiz, Jesús Tornero, José M. Azorín
IntroductionIn recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms
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Long-range temporal correlations in resting state alpha oscillations in major depressive disorder and obsessive-compulsive disorder Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-21 Ekaterina Proshina, Olga Martynova, Galina Portnova, Guzal Khayrullina, Olga Sysoeva
IntroductionMental disorders are a significant concern in contemporary society, with a pressing need to identify biological markers. Long-range temporal correlations (LRTC) of brain rhythms have been widespread in clinical cohort studies, especially in major depressive disorder (MDD). However, research on LRTC in obsessive-compulsive disorder (OCD) is severely limited. Given the high co-occurrence
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Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-21 Wei Jing Fong, Hong Ming Tan, Rishabh Garg, Ai Ling Teh, Hong Pan, Varsha Gupta, Bernadus Krishna, Zou Hui Chen, Natania Yovela Purwanto, Fabian Yap, Kok Hian Tan, Kok Yen Jerry Chan, Shiao-Yng Chan, Nicole Goh, Nikita Rane, Ethel Siew Ee Tan, Yuheng Jiang, Mei Han, Michael Meaney, Dennis Wang, Jussi Keppo, Geoffrey Chern-Yee Tan
IntroductionPharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450
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Accelerating spiking neural network simulations with PymoNNto and PymoNNtorch Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-20 Marius Vieth, Ali Rahimi, Ashena Gorgan Mohammadi, Jochen Triesch, Mohammad Ganjtabesh
Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators and software frameworks for such simulations exist with different target application areas. Among these, PymoNNto is a recent Python-based toolbox for spiking neural network simulations that emphasizes the embedding of custom
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Empirical comparison of deep learning models for fNIRS pain decoding Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-14 Raul Fernandez Rojas, Calvin Joseph, Ghazal Bargshady, Keng-Liang Ou
IntroductionPain assessment is extremely important in patients unable to communicate and it is often done by clinical judgement. However, assessing pain using observable indicators can be challenging for clinicians due to the subjective perceptions, individual differences in pain expression, and potential confounding factors. Therefore, the need for an objective pain assessment method that can assist
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Multiscale co-simulation design pattern for neuroscience applications Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-12 Lionel Kusch, Sandra Diaz-Pier, Wouter Klijn, Kim Sontheimer, Christophe Bernard, Abigail Morrison, Viktor Jirsa
Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its
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The Locare workflow: representing neuroscience data locations as geometric objects in 3D brain atlases Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-09 Camilla H. Blixhavn, Ingrid Reiten, Heidi Kleven, Martin Øvsthus, Sharon C. Yates, Ulrike Schlegel, Maja A. Puchades, Oliver Schmid, Jan G. Bjaalie, Ingvild E. Bjerke, Trygve B. Leergaard
Neuroscientists employ a range of methods and generate increasing amounts of data describing brain structure and function. The anatomical locations from which observations or measurements originate represent a common context for data interpretation, and a starting point for identifying data of interest. However, the multimodality and abundance of brain data pose a challenge for efforts to organize
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In silico analyses of the involvement of GPR55, CB1R and TRPV1: response to THC, contribution to temporal lobe epilepsy, structural modeling and updated evolution Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-07 Amy L. Cherry, Michael J. Wheeler, Karolina Mathisova, Mathieu Di Miceli
IntroductionThe endocannabinoid (eCB) system is named after the discovery that endogenous cannabinoids bind to the same receptors as the phytochemical compounds found in Cannabis. While endogenous cannabinoids include anandamide (AEA) and 2-arachidonoylglycerol (2-AG), exogenous phytocannabinoids include Δ-9 tetrahydrocannabinol (THC) and cannabidiol (CBD). These compounds finely tune neurotransmission
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Enabling uncertainty estimation in neural networks through weight perturbation for improved Alzheimer's disease classification Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-06 Matteo Ferrante, Tommaso Boccato, Nicola Toschi
BackgroundThe willingness to trust predictions formulated by automatic algorithms is key in a wide range of domains. However, a vast number of deep architectures are only able to formulate predictions without associated uncertainty.PurposeIn this study, we propose a method to convert a standard neural network into a Bayesian neural network and estimate the variability of predictions by sampling different
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Improving the detection of sleep slow oscillations in electroencephalographic data Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-05 Cristiana Dimulescu, Leonhard Donle, Caglar Cakan, Thomas Goerttler, Lilia Khakimova, Julia Ladenbauer, Agnes Flöel, Klaus Obermayer
Study objectivesWe aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection.MethodSOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built
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Discovering optimal features for neuron-type identification from extracellular recordings Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-02 Vergil R. Haynes, Yi Zhou, Sharon M. Crook
Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus
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Domain adaptation for EEG-based, cross-subject epileptic seizure prediction Front. Neuroinform. (IF 3.5) Pub Date : 2024-02-02 Imene Jemal, Lina Abou-Abbas, Khadidja Henni, Amar Mitiche, Neila Mezghani
The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complications. However, a major challenge in seizure prediction arises from the significant variability observed in patient data. Common patient-specific approaches, which apply to each patient independently, often perform poorly for other patients due to the data variability. The aim of this
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Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models Front. Neuroinform. (IF 3.5) Pub Date : 2024-01-30 Thomas Tveitstøl, Mats Tveter, Ana S. Pérez T., Christoffer Hatlestad-Hall, Anis Yazidi, Hugo L. Hammer, Ira R. J. Hebold Haraldsen
IntroductionA challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability
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SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation Front. Neuroinform. (IF 3.5) Pub Date : 2024-01-29 Ekaterina Mikhaylets, Alexandra M. Razorenova, Vsevolod Chernyshev, Nikolay Syrov, Lev Yakovlev, Julia Boytsova, Elena Kokurina, Yulia Zhironkina, Svyatoslav Medvedev, Alexander Kaplan
The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states
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Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression Front. Neuroinform. (IF 3.5) Pub Date : 2024-01-11 Geoffrey Chern-Yee Tan, Ziying Wang, Ethel Siew Ee Tan, Rachel Jing Min Ong, Pei En Ooi, Danan Lee, Nikita Rane, Sheryl Yu Xuan Tey, Si Ying Chua, Nicole Goh, Glynis Weibin Lam, Atlanta Chakraborty, Anthony Khye Loong Yew, Sin Kee Ong, Jin Lin Kee, Xin Ying Lim, Nawal Hashim, Sharon Huixian Lu, Michael Meany, Serenella Tolomeo, Christopher Asplund Lee, Hong Ming Tan, Jussi Keppo
IntroductionThe heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety
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The hemodynamic response function as a type 2 diabetes biomarker: a data-driven approach Front. Neuroinform. (IF 3.5) Pub Date : 2024-01-05 Pedro Guimarães, Pedro Serranho, João V. Duarte, Joana Crisóstomo, Carolina Moreno, Leonor Gomes, Rui Bernardes, Miguel Castelo-Branco
IntroductionThere is a need to better understand the neurophysiological changes associated with early brain dysfunction in Type 2 diabetes mellitus (T2DM) before vascular or structural lesions. Our aim was to use a novel unbiased data-driven approach to detect and characterize hemodynamic response function (HRF) alterations in T2DM patients, focusing on their potential as biomarkers.MethodsWe meshed
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The past, present and future of neuroscience data sharing: a perspective on the state of practices and infrastructure for FAIR Front. Neuroinform. (IF 3.5) Pub Date : 2024-01-05 Maryann E. Martone
Neuroscience has made significant strides over the past decade in moving from a largely closed science characterized by anemic data sharing, to a largely open science where the amount of publicly available neuroscience data has increased dramatically. While this increase is driven in significant part by large prospective data sharing studies, we are starting to see increased sharing in the long tail
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NeuroDecodeR: a package for neural decoding in R Front. Neuroinform. (IF 3.5) Pub Date : 2024-01-03 Ethan M. Meyers
Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the package is designed in a modular fashion which allows researchers to easily implement
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An interactive image segmentation method for the anatomical structures of the main olfactory bulb with micro-level resolution Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-22 Xin Liu, Anan Li, Yue Luo, Shengda Bao, Tao Jiang, Xiangning Li, Jing Yuan, Zhao Feng
The main olfactory bulb is the key element of the olfactory pathway of rodents. To precisely dissect the neural pathway in the main olfactory bulb (MOB), it is necessary to construct the three-dimensional morphologies of the anatomical structures within it with micro-level resolution. However, the construction remains challenging due to the complicated shape of the anatomical structures in the main
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AngoraPy: A Python toolkit for modeling anthropomorphic goal-driven sensorimotor systems Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-22 Tonio Weidler, Rainer Goebel, Mario Senden
Goal-driven deep learning increasingly supplements classical modeling approaches in computational neuroscience. The strength of deep neural networks as models of the brain lies in their ability to autonomously learn the connectivity required to solve complex and ecologically valid tasks, obviating the need for hand-engineered or hypothesis-driven connectivity patterns. Consequently, goal-driven models
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Establishing a nomogram to predict refracture after percutaneous kyphoplasty by logistic regression Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-21 Aiqi Zhang, Hongye Fu, Junjie Wang, Zhe Chen, Jiajun Fan
IntroductionSeveral studies have examined the risk factors for post-percutaneous kyphoplasty (PKP) refractures and developed many clinical prognostic models. However, no prior research exists using the Random Forest (RF) model, a favored tool for model development, to predict the occurrence of new vertebral compression fractures (NVCFs). Therefore, this study aimed to investigate the risk factors for
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Evaluation of an English language phoneme-based imagined speech brain computer interface with low-cost electroencephalography Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-18 John LaRocco, Qudsia Tahmina, Sam Lecian, Jason Moore, Cole Helbig, Surya Gupta
IntroductionParalyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain–computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls.MethodsAlthough virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations
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Factorized discriminant analysis for genetic signatures of neuronal phenotypes Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-14 Mu Qiao
Navigating the complex landscape of single-cell transcriptomic data presents significant challenges. Central to this challenge is the identification of a meaningful representation of high-dimensional gene expression patterns that sheds light on the structural and functional properties of cell types. Pursuing model interpretability and computational simplicity, we often look for a linear transformation
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Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-06 Qianfang Jia, Xiaofang Wang, Rongyi Zhou, Bingxiang Ma, Fangqin Fei, Hui Han
BackgroundArtificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications
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Few-shot EEG sleep staging based on transductive prototype optimization network Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-06 Jingcong Li, Chaohuang Wu, Jiahui Pan, Fei Wang
Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study, we propose a few-shot EEG sleep staging termed transductive prototype optimization network (TPON) method, which aims to improve the performance of
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Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus Front. Neuroinform. (IF 3.5) Pub Date : 2023-12-01 Nestor Timonidis, Rembrandt Bakker, Mario Rubio-Teves, Carmen Alonso-Martínez, Maria Garcia-Amado, Francisco Clascá, Paul H. E. Tiesinga
Characterizing the connectomic and morphological diversity of thalamic neurons is key for better understanding how the thalamus relays sensory inputs to the cortex. The recent public release of complete single-neuron morphological reconstructions enables the analysis of previously inaccessible connectivity patterns from individual neurons. Here we focus on the Ventral Posteromedial (VPM) nucleus and
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Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub Front. Neuroinform. (IF 3.5) Pub Date : 2023-11-01 Luca Leonardo Bologna, Antonino Tocco, Roberto Smiriglia, Armando Romani, Felix Schürmann, Michele Migliore
To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possible, and to turn the model into a useful framework for making predictions based on the principles governing the nature of neural cells. In such a
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Prognostic estimation for acute ischemic stroke patients undergoing mechanical thrombectomy within an extended therapeutic window using an interpretable machine learning model Front. Neuroinform. (IF 3.5) Pub Date : 2023-10-13 Lin Tong, Yun Sun, Yueqi Zhu, Hui Luo, Wan Wan, Ying Wu
BackgroundMechanical thrombectomy (MT) is effective for acute ischemic stroke with large vessel occlusion (AIS-LVO) within an extended therapeutic window. However, successful reperfusion does not guarantee positive prognosis, with around 40–50% of cases yielding favorable outcomes. Preoperative prediction of patient outcomes is essential to identify those who may benefit from MT. Although machine learning
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Web-based processing of physiological noise in fMRI: addition of the PhysIO toolbox to CBRAIN Front. Neuroinform. (IF 3.5) Pub Date : 2023-09-28 Darius Valevicius, Natacha Beck, Lars Kasper, Sergiy Boroday, Johanna Bayer, Pierre Rioux, Bryan Caron, Reza Adalat, Alan C. Evans, Najmeh Khalili-Mahani
Neuroimaging research requires sophisticated tools for analyzing complex data, but efficiently leveraging these tools can be a major challenge, especially on large datasets. CBRAIN is a web-based platform designed to simplify the use and accessibility of neuroimaging research tools for large-scale, collaborative studies. In this paper, we describe how CBRAIN’s unique features and infrastructure were
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oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data Front. Neuroinform. (IF 3.5) Pub Date : 2023-09-27 Tung Dang, Alan S. R. Fermin, Maro G. Machizawa
The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging
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PyDapsys: an open-source library for accessing electrophysiology data recorded with DAPSYS Front. Neuroinform. (IF 3.5) Pub Date : 2023-09-15 Peter Konradi, Alina Troglio, Ariadna Pérez Garriga, Aarón Pérez Martín, Rainer Röhrig, Barbara Namer, Ekaterina Kutafina
In the field of neuroscience, a considerable number of commercial data acquisition and processing solutions rely on proprietary formats for data storage. This often leads to data being locked up in formats that are only accessible by using the original software, which may lead to interoperability problems. In fact, even the loss of data access is possible if the software becomes unsupported, changed
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NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-31 Lei Wang, José Luis Ambite, Abhishek Appaji, Janine Bijsterbosch, Jerome Dockes, Rick Herrick, Alex Kogan, Howard Lander, Daniel Marcus, Stephen M. Moore, Jean-Baptiste Poline, Arcot Rajasekar, Satya S. Sahoo, Matthew D. Turner, Xiaochen Wang, Yue Wang, Jessica A. Turner
IntroductionOpen science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from
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Time to consider animal data governance: perspectives from neuroscience Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-30 Damian Eke, George Ogoh, William Knight, Bernd Stahl
IntroductionScientific research relies mainly on multimodal, multidimensional big data generated from both animal and human organisms as well as technical data. However, unlike human data that is increasingly regulated at national, regional and international levels, regulatory frameworks that can govern the sharing and reuse of non-human animal data are yet to be established. Whereas the legal and
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Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-24 Xiongtao Zhang, Shuai Dong, Qing Shen, Jie Zhou, Jingjing Min
IntroductionIntelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in EEG signals have a serious effect on recognition efficiency. Deep learning is capable of noise resistance
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Effect of subthalamic coordinated reset deep brain stimulation on Parkinsonian gait Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-24 Kai M. Bosley, Ziling Luo, Sana Amoozegar, Kit Acedillo, Kanon Nakajima, Luke A. Johnson, Jerrold L. Vitek, Jing Wang
IntroductionCoordinated Reset Deep Brain Stimulation (CR DBS) is a novel DBS approach for treating Parkinson's disease (PD) that uses lower levels of burst stimulation through multiple contacts of the DBS lead. Though CR DBS has been demonstrated to have sustained therapeutic effects on rigidity, tremor, bradykinesia, and akinesia following cessation of stimulation, i.e., carryover effect, its effect
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Synaptic network structure shapes cortically evoked spatio-temporal responses of STN and GPe neurons in a computational model Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-22 Justus A. Kromer, Hemant Bokil, Peter A. Tass
IntroductionThe basal ganglia (BG) are involved in motor control and play an essential role in movement disorders such as hemiballismus, dystonia, and Parkinson's disease. Neurons in the motor part of the BG respond to passive movement or stimulation of different body parts and to stimulation of corresponding cortical regions. Experimental evidence suggests that the BG are organized somatotopically
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Decision trees to evaluate the risk of developing multiple sclerosis Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-16 Manuela Pasella, Fabio Pisano, Barbara Cannas, Alessandra Fanni, Eleonora Cocco, Jessica Frau, Francesco Lai, Stefano Mocci, Roberto Littera, Sabrina Rita Giglio
IntroductionMultiple sclerosis (MS) is a persistent neurological condition impacting the central nervous system (CNS). The precise cause of multiple sclerosis is still uncertain; however, it is thought to arise from a blend of genetic and environmental factors. MS diagnosis includes assessing medical history, conducting neurological exams, performing magnetic resonance imaging (MRI) scans, and analyzing
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versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-11 Alex Valcourt Caron, Amir Shmuel, Ziqi Hao, Maxime Descoteaux
The lack of “gold standards” in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be
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Microglial morphometric analysis: so many options, so little consistency Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-10 Jack Reddaway, Peter Eulalio Richardson, Ryan J. Bevan, Jessica Stoneman, Marco Palombo
Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist’s toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated
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Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-02 Karissa Chan, Pejman Jabehdar Maralani, Alan R. Moody, April Khademi
IntroductionAcquisition and pre-processing pipelines for diffusion-weighted imaging (DWI) volumes are resource- and time-consuming. Generating synthetic DWI scalar maps from commonly acquired brain MRI sequences such as fluid-attenuated inversion recovery (FLAIR) could be useful for supplementing datasets. In this work we design and compare GAN-based image translation models for generating DWI scalar
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CACTUS: a computational framework for generating realistic white matter microstructure substrates Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-01 Juan Luis Villarreal-Haro, Remy Gardier, Erick J. Canales-Rodríguez, Elda Fischi-Gomez, Gabriel Girard, Jean-Philippe Thiran, Jonathan Rafael-Patiño
Monte-Carlo diffusion simulations are a powerful tool for validating tissue microstructure models by generating synthetic diffusion-weighted magnetic resonance images (DW-MRI) in controlled environments. This is fundamental for understanding the link between micrometre-scale tissue properties and DW-MRI signals measured at the millimetre-scale, optimizing acquisition protocols to target microstructure
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Perfusion-weighted software written in Python for DSC-MRI analysis Front. Neuroinform. (IF 3.5) Pub Date : 2023-08-01 Sabela Fernández-Rodicio, Gonzalo Ferro-Costas, Ana Sampedro-Viana, Marcos Bazarra-Barreiros, Alba Ferreirós, Esteban López-Arias, María Pérez-Mato, Alberto Ouro, José M. Pumar, Antonio J. Mosqueira, María Luz Alonso-Alonso, José Castillo, Pablo Hervella, Ramón Iglesias-Rey
IntroductionDynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved
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NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use Front. Neuroinform. (IF 3.5) Pub Date : 2023-07-24 Satya S. Sahoo, Matthew D. Turner, Lei Wang, Jose Luis Ambite, Abhishek Appaji, Arcot Rajasekar, Howard M. Lander, Yue Wang, Jessica A. Turner
BackgroundDespite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability.
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Editorial: Bringing together data- and knowledge-driven solutions for a better understanding and effective diagnostics of neurological disorders. Front. Neuroinform. (IF 3.5) Pub Date : 2023-07-20 Dmitrii Kaplun,Mikhail Bogachev,Pawan Kumar Singh,Ram Sarkar
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Efficient simulation of neural development using shared memory parallelization Front. Neuroinform. (IF 3.5) Pub Date : 2023-07-20 Erik De Schutter
The Neural Development Simulator, NeuroDevSim, is a Python module that simulates the most important aspects of brain development: morphological growth, migration, and pruning. It uses an agent-based modeling approach inherited from the NeuroMaC software. Each cycle has agents called fronts execute model-specific code. In the case of a growing dendritic or axonal front, this will be a choice between
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NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query Front. Neuroinform. (IF 3.5) Pub Date : 2023-07-18 Nazek Queder, Vivian B. Tien, Sanu Ann Abraham, Sebastian Georg Wenzel Urchs, Karl G. Helmer, Derek Chaplin, Theo G. M. van Erp, David N. Kennedy, Jean-Baptiste Poline, Jeffrey S. Grethe, Satrajit S. Ghosh, David B. Keator
The biomedical research community is motivated to share and reuse data from studies and projects by funding agencies and publishers. Effectively combining and reusing neuroimaging data from publicly available datasets, requires the capability to query across datasets in order to identify cohorts that match both neuroimaging and clinical/behavioral data criteria. Critical barriers to operationalizing
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Editorial: Physical neuromorphic computing and its industrial applications. Front. Neuroinform. (IF 3.5) Pub Date : 2023-07-17 Toshiyuki Yamane,Akira Hirose,Bert Jan Offrein
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Differential processing of intrinsic vs. extrinsic coordinates in wrist movement: connectivity and chronometry perspectives Front. Neuroinform. (IF 3.5) Pub Date : 2023-07-10 Laura Alejandra Martinez-Tejada, Yuji Imakura, Ying-Tung Cho, Ludovico Minati, Natsue Yoshimura
This study explores brain-network differences between the intrinsic and extrinsic motor coordinate frames. A connectivity model showing the coordinate frames difference was obtained using brain fMRI data of right wrist isometric flexions and extensions movements, performed in two forearm postures. The connectivity model was calculated by machine-learning-based neural representation and effective functional
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Automated detection of cerebral microbleeds on MR images using knowledge distillation framework Front. Neuroinform. (IF 3.5) Pub Date : 2023-07-10 Vaanathi Sundaresan, Christoph Arthofer, Giovanna Zamboni, Andrew G. Murchison, Robert A. Dineen, Peter M. Rothwell, Dorothee P. Auer, Chaoyue Wang, Karla L. Miller, Benjamin C. Tendler, Fidel Alfaro-Almagro, Stamatios N. Sotiropoulos, Nikola Sprigg, Ludovica Griffanti, Mark Jenkinson
IntroductionCerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated
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A scalable implementation of the recursive least-squares algorithm for training spiking neural networks Front. Neuroinform. (IF 3.5) Pub Date : 2023-06-27 Benjamin J. Arthur, Christopher M. Kim, Susu Chen, Stephan Preibisch, Ran Darshan
Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive
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Alternative patterns of deep brain stimulation in neurologic and neuropsychiatric disorders Front. Neuroinform. (IF 3.5) Pub Date : 2023-06-21 Ricardo A. Najera, Anil K. Mahavadi, Anas U. Khan, Ujwal Boddeti, Victor A. Del Bene, Harrison C. Walker, J. Nicole Bentley
Deep brain stimulation (DBS) is a widely used clinical therapy that modulates neuronal firing in subcortical structures, eliciting downstream network effects. Its effectiveness is determined by electrode geometry and location as well as adjustable stimulation parameters including pulse width, interstimulus interval, frequency, and amplitude. These parameters are often determined empirically during