Neuroinformatics最新文献_第7页

DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images. DeepN4：学习 T1 加权图像的 N4ITK 偏场校正。

IF 3 4区医学

Neuroinformatics Pub Date : 2024-04-01 Epub Date: 2024-03-25 DOI: 10.1007/s12021-024-09655-9

Praitayini Kanakaraj, Tianyuan Yao, Leon Y Cai, Ho Hin Lee, Nancy R Newlin, Michael E Kim, Chenyu Gao, Kimberly R Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Lori L Beason-Held, Susan M Resnick, Eleftherios Garyfallidis, Adam Anderson, Kurt G Schilling, Bennett A Landman, Daniel Moyer

{"title":"DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images.","authors":"Praitayini Kanakaraj, Tianyuan Yao, Leon Y Cai, Ho Hin Lee, Nancy R Newlin, Michael E Kim, Chenyu Gao, Kimberly R Pechman, Derek Archer, Timothy Hohman, Angela Jefferson, Lori L Beason-Held, Susan M Resnick, Eleftherios Garyfallidis, Adam Anderson, Kurt G Schilling, Bennett A Landman, Daniel Moyer","doi":"10.1007/s12021-024-09655-9","DOIUrl":"10.1007/s12021-024-09655-9","url":null,"abstract":"T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network \"DeepN4\" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"193-205"},"PeriodicalIF":3.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11182041/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140289387","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}

引用次数: 0

Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability. 使用可见性图对 fMRI 数据进行网络表示：运动和测试-重测可靠性的影响

IF 3 4区医学

Neuroinformatics Pub Date : 2024-04-01 Epub Date: 2024-02-09 DOI: 10.1007/s12021-024-09652-y

Govinda R Poudel, Prabin Sharma, Valentina Lorenzetti, Nicholas Parsons, Ester Cerin

{"title":"Network Representation of fMRI Data Using Visibility Graphs: The Impact of Motion and Test-Retest Reliability.","authors":"Govinda R Poudel, Prabin Sharma, Valentina Lorenzetti, Nicholas Parsons, Ester Cerin","doi":"10.1007/s12021-024-09652-y","DOIUrl":"10.1007/s12021-024-09652-y","url":null,"abstract":"Visibility graphs provide a novel approach for analysing time-series data. Graph theoretical analysis of visibility graphs can provide new features for data mining applications in fMRI. However, visibility graphs features have not been used widely in the field of neuroscience. This is likely due to a lack of understanding of their robustness in the presence of noise (e.g., motion) and their test-retest reliability. In this study, we investigated visibility graph properties of fMRI data in the human connectome project (N = 1010) and tested their sensitivity to motion and test-retest reliability. We also characterised the strength of connectivity obtained using degree synchrony of visibility graphs. We found that strong correlation (r > 0.5) between visibility graph properties, such as the number of communities and average degrees, and motion in the fMRI data. The test-retest reliability (Intraclass correlation coefficient (ICC)) of graph theoretical features was high for the average degrees (0.74, 95% CI = [0.73, 0.75]), and moderate for clustering coefficient (0.43, 95% CI = [0.41, 0.44]) and average path length (0.41, 95% CI = [0.38, 0.44]). Functional connectivity between brain regions was measured by correlating the visibility graph degrees. However, the strength of correlation was found to be moderate to low (r < 0.35). These findings suggest that even small movement in fMRI data can strongly influence robustness and reliability of visibility graph features, thus, requiring robust motion correction strategies prior to data analysis. Further studies are necessary for better understanding of the potential application of visibility graph features in fMRI.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"107-118"},"PeriodicalIF":3.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11021232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139708380","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}

引用次数: 0

InSpectro-Gadget: A Tool for Estimating Neurotransmitter and Neuromodulator Receptor Distributions for MRS Voxels InSpectro-Gadget：估算 MRS 体素的神经递质和神经调节剂受体分布的工具

IF 3 4区医学

Neuroinformatics Pub Date : 2024-02-22 DOI: 10.1007/s12021-024-09654-w

Elizabeth McManus, Nils Muhlert, Niall W. Duncan

{"title":"InSpectro-Gadget: A Tool for Estimating Neurotransmitter and Neuromodulator Receptor Distributions for MRS Voxels","authors":"Elizabeth McManus, Nils Muhlert, Niall W. Duncan","doi":"10.1007/s12021-024-09654-w","DOIUrl":"https://doi.org/10.1007/s12021-024-09654-w","url":null,"abstract":"Magnetic resonance spectroscopy (MRS) is widely used to estimate concentrations of glutamate and (gamma)-aminobutyric acid (GABA) in specific regions of the living human brain. As cytoarchitectural properties differ across the brain, interpreting these measurements can be assisted by having knowledge of such properties for the MRS region(s) studied. In particular, some knowledge of likely local neurotransmitter receptor patterns can potentially give insights into the mechanistic environment GABA- and glutamatergic neurons are functioning in. This may be of particular utility when comparing two or more regions, given that the receptor populations may differ substantially across them. At the same time, when studying MRS data from multiple participants or timepoints, the homogeneity of the sample becomes relevant, as measurements taken from areas with different cytoarchitecture may be difficult to compare. To provide insights into the likely cytoarchitectural environment of user-defined regions-of-interest, we produced an easy to use tool - InSpectro-Gadget - that interfaces with receptor mRNA expression information from the Allen Human Brain Atlas. This Python tool allows users to input masks and automatically obtain a graphical overview of the receptor population likely to be found within. This includes comparison between multiple masks or participants where relevant. The receptors and receptor subunit genes featured include GABA- and glutamatergic classes, along with a wide range of neuromodulators. The functionality of the tool is explained here and its use is demonstrated through a set of example analyses. The tool is available at https://github.com/lizmcmanus/Inspectro-Gadget.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"1 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139926522","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}

引用次数: 0

Editorial: On the Economics of Neuroscientific Data Sharing. 社论:关于神经科学数据共享的经济学。

IF 3 4区医学

Neuroinformatics Pub Date : 2024-01-01 DOI: 10.1007/s12021-023-09649-z

John Darrell Van Horn

引用次数: 0

Preserving Derivative Information while Transforming Neuronal Curves. 神经元曲线变换时导数信息的保持。

IF 3 4区医学

Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-11-30 DOI: 10.1007/s12021-023-09648-0

Thomas L Athey, Daniel J Tward, Ulrich Mueller, Laurent Younes, Joshua T Vogelstein, Michael I Miller

{"title":"Preserving Derivative Information while Transforming Neuronal Curves.","authors":"Thomas L Athey, Daniel J Tward, Ulrich Mueller, Laurent Younes, Joshua T Vogelstein, Michael I Miller","doi":"10.1007/s12021-023-09648-0","DOIUrl":"10.1007/s12021-023-09648-0","url":null,"abstract":"The international neuroscience community is building the first comprehensive atlases of brain cell types to understand how the brain functions from a higher resolution, and more integrated perspective than ever before. In order to build these atlases, subsets of neurons (e.g. serotonergic neurons, prefrontal cortical neurons etc.) are traced in individual brain samples by placing points along dendrites and axons. Then, the traces are mapped to common coordinate systems by transforming the positions of their points, which neglects how the transformation bends the line segments in between. In this work, we apply the theory of jets to describe how to preserve derivatives of neuron traces up to any order. We provide a framework to compute possible error introduced by standard mapping methods, which involves the Jacobian of the mapping transformation. We show how our first order method improves mapping accuracy in both simulated and real neuron traces under random diffeomorphisms. Our method is freely available in our open-source Python package brainlit.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"63-74"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917852/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138463976","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}

引用次数: 0

Improving the Eligibility of Task-Based fMRI Studies for Meta-Analysis: A Review and Reporting Recommendations. 提高基于任务的功能磁共振成像研究用于荟萃分析的资格：综述和报告建议。

IF 3 4区医学

Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-11-04 DOI: 10.1007/s12021-023-09643-5

Freya Acar, Camille Maumet, Talia Heuten, Maya Vervoort, Han Bossier, Ruth Seurinck, Beatrijs Moerkerke

引用次数: 0

Analyzing Thalamocortical Tract-Tracing Experiments in a Common Reference Space. 在共同参考空间中分析丘脑皮质束追踪实验。

IF 3 4区医学

Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-10-21 DOI: 10.1007/s12021-023-09644-4

Nestor Timonidis, Mario Rubio-Teves, Carmen Alonso-Martínez, Rembrandt Bakker, María García-Amado, Paul Tiesinga, Francisco Clascá

{"title":"Analyzing Thalamocortical Tract-Tracing Experiments in a Common Reference Space.","authors":"Nestor Timonidis, Mario Rubio-Teves, Carmen Alonso-Martínez, Rembrandt Bakker, María García-Amado, Paul Tiesinga, Francisco Clascá","doi":"10.1007/s12021-023-09644-4","DOIUrl":"10.1007/s12021-023-09644-4","url":null,"abstract":"Current mesoscale connectivity atlases provide limited information about the organization of thalamocortical projections in the mouse brain. Labeling the projections of spatially restricted neuron populations in thalamus can provide a functionally relevant level of connectomic analysis, but these need to be integrated within the same common reference space. Here, we present a pipeline for the segmentation, registration, integration and analysis of multiple tract-tracing experiments. The key difference with other workflows is that the data is transformed to fit the reference template. As a test-case, we investigated the axonal projections and intranuclear arrangement of seven neuronal populations of the ventral posteromedial nucleus of the thalamus (VPM), which we labeled with an anterograde tracer. Their soma positions corresponded, from dorsal to ventral, to cortical representations of the whiskers, nose and mouth. They strongly targeted layer 4, with the majority exclusively targeting one cortical area and the ones in ventrolateral VPM branching to multiple somatosensory areas. We found that our experiments were more topographically precise than similar experiments from the Allen Institute and projections to the primary somatosensory area were in agreement with single-neuron morphological reconstructions from publicly available databases. This pilot study sets the basis for a shared virtual connectivity atlas that could be enriched with additional data for studying the topographical organization of different thalamic nuclei. The pipeline is accessible with only minimal programming skills via a Jupyter Notebook, and offers multiple visualization tools such as cortical flatmaps, subcortical plots and 3D renderings and can be used with custom anatomical delineations.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"23-43"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49684237","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}

引用次数: 0

High-Density Exploration of Activity States in a Multi-Area Brain Model. 多区域脑模型中活动状态的高密度探索。

IF 3 4区医学

Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-11-20 DOI: 10.1007/s12021-023-09647-1

David Aquilué-Llorens, Jennifer S Goldman, Alain Destexhe

{"title":"High-Density Exploration of Activity States in a Multi-Area Brain Model.","authors":"David Aquilué-Llorens, Jennifer S Goldman, Alain Destexhe","doi":"10.1007/s12021-023-09647-1","DOIUrl":"10.1007/s12021-023-09647-1","url":null,"abstract":"To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e.g. neuromodulatory regulation of spike-frequency adaptation during sleep-wake cycles or anesthetics). Using the Virtual Brain (TVB) environment to connect mean-field AdEx models, we have previously simulated the general properties of brain states, playing on spike-frequency adaptation, but have not yet performed detailed analyses of other parameters possibly also regulating transitions in brain-scale dynamics between different brain states. We performed a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing. We report a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the TVB-AdEx model reveals large-scale properties identified experimentally in sleep and anesthesia.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"75-87"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138048308","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}

引用次数: 0

A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images. 基于深度学习的MRI图像早期诊断阿尔茨海默病集成方法。

IF 3 4区医学

Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-12-02 DOI: 10.1007/s12021-023-09646-2

Sina Fathi, Ali Ahmadi, Afsaneh Dehnad, Mostafa Almasi-Dooghaee, Melika Sadegh

{"title":"A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.","authors":"Sina Fathi, Ali Ahmadi, Afsaneh Dehnad, Mostafa Almasi-Dooghaee, Melika Sadegh","doi":"10.1007/s12021-023-09646-2","DOIUrl":"10.1007/s12021-023-09646-2","url":null,"abstract":"Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"89-105"},"PeriodicalIF":3.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10917836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138479108","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}

引用次数: 0

Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence. 拓扑数据分析捕捉个体参与者的任务驱动功能磁共振成像档案：基于持久性的分类管道。

IF 2.7 4区医学

Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-11-04 DOI: 10.1007/s12021-023-09645-3

Michael J Catanzaro, Sam Rizzo, John Kopchick, Asadur Chowdury, David R Rosenberg, Peter Bubenik, Vaibhav A Diwadkar

{"title":"Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence.","authors":"Michael J Catanzaro, Sam Rizzo, John Kopchick, Asadur Chowdury, David R Rosenberg, Peter Bubenik, Vaibhav A Diwadkar","doi":"10.1007/s12021-023-09645-3","DOIUrl":"10.1007/s12021-023-09645-3","url":null,"abstract":"BOLD-based fMRI is the most widely used method for studying brain function. The BOLD signal while valuable, is beset with unique vulnerabilities. The most notable of these is the modest signal to noise ratio, and the relatively low temporal and spatial resolution. However, the high dimensional complexity of the BOLD signal also presents unique opportunities for functional discovery. Topological Data Analyses (TDA), a branch of mathematics optimized to search for specific classes of structure within high dimensional data may provide particularly valuable applications. In this investigation, we acquired fMRI data in the anterior cingulate cortex (ACC) using a basic motor control paradigm. Then, for each participant and each of three task conditions, fMRI signals in the ACC were summarized using two methods: a) TDA based methods of persistent homology and persistence landscapes and b) non-TDA based methods using a standard vectorization scheme. Finally, using machine learning (with support vector classifiers), classification accuracy of TDA and non-TDA vectorized data was tested across participants. In each participant, TDA-based classification out-performed the non-TDA based counterpart, suggesting that our TDA analytic pipeline better characterized task- and condition-induced structure in fMRI data in the ACC. Our results emphasize the value of TDA in characterizing task- and condition-induced structure in regional fMRI signals. In addition to providing our analytical tools for other users to emulate, we also discuss the unique role that TDA-based methods can play in the study of individual differences in the structure of functional brain signals in the healthy and the clinical brain.","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"45-62"},"PeriodicalIF":2.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11268454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71488107","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}

引用次数: 0