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MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline. MaPPeRTrac:大规模并行、便携、可重现的痕量成像管道。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-04-01 Epub Date: 2024-03-06 DOI: 10.1007/s12021-024-09650-0
Lanya T Cai, Joseph Moon, Paul B Camacho, Aaron T Anderson, Won Jong Chwa, Bradley P Sutton, Amy J Markowitz, Eva M Palacios, Alexis Rodriguez, Geoffrey T Manley, Shivsundaram Shankar, Peer-Timo Bremer, Pratik Mukherjee, Ravi K Madduri

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

大规模弥散核磁共振成像束成像仍然是一项重大挑战。用户必须协调一连串复杂的指令,这就需要许多软件包,而这些软件包具有复杂的依赖性和高昂的计算成本。我们开发了MaPPeRTrac,这是一个以边缘为中心的牵引成像流水线,可在各种高性能计算(HPC)环境中简化并加速这一过程。它能从受试者的磁共振成像(MRI)数据(包括结构和弥散 MRI 图像)到其结构连接体的边缘密度图像(EDI),实现概率或确定性牵引成像的完全自动化。依赖项通过 Singularity(现称为 Apptainer)进行容器化,并与代码解耦,以实现快速原型设计和修改。数据衍生物采用脑成像数据结构(BIDS)进行组织,以确保它们可查找、可访问、可互操作,并遵循 FAIR 原则可重复使用。该管道使用 Parsl 并行编程框架,充分利用了高性能计算资源,从而创建了规模空前的连接组数据集。MaPPeRTrac 公开可用,并在商业和科学硬件上进行了测试,因此可以为更广泛的用户群加速大脑连接组研究。MaPPeRTrac可在以下网址获取:https://github.com/LLNL/mappertrac 。
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引用次数: 0
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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 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

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 .

T1 加权(T1w)磁共振成像因磁场不均匀而产生低频强度伪影。去除 T1w MRI 图像中的这些偏差是确保图像解读空间一致性的关键预处理步骤。目前最先进的 N4ITK 偏场校正技术的实现方式使其很难在不同的管道和工作流程之间移植,因此很难在本地、云和边缘平台上重新实现和复制结果。此外,N4ITK 在应用前后的优化是不透明的,这意味着方法论的发展必须围绕不均匀性校正步骤进行。鉴于偏场校正在结构预处理和灵活实施中的重要性,我们追求对 N4ITK 偏场校正进行深度学习近似/重新解释,以创建一种可移植、灵活且完全可微分的方法。在本文中,我们对来自 72 个不同扫描仪和年龄段的 8 个独立队列进行了深度学习网络 "DeepN4 "的训练,这些队列都具有 N4ITK 校正的 T1w MRI 和对数空间监督偏倚场。我们发现,我们可以用天真网络近似地进行 N4ITK 偏场校正。我们根据 N4ITK 校正图像评估了测试数据集的峰值信噪比(PSNR)。N4ITK 和 DeepN4 校正图像的 PSNR 中值为 47.96 dB。此外,我们还在另外八个外部数据集上评估了 DeepN4 模型,并展示了该方法的通用性。这项研究证明,不兼容的 N4ITK 预处理步骤可以用天真深度神经网络进行近似,从而提高了灵活性。所有代码和模型都发布在 https://github.com/MASILab/DeepN4 上。
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 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

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.

可见性图为分析时间序列数据提供了一种新方法。能见度图的图论分析可为 fMRI 的数据挖掘应用提供新的特征。然而,可见性图特征在神经科学领域尚未得到广泛应用。这很可能是由于人们对其在噪声(如运动)情况下的鲁棒性及其测试再测试的可靠性缺乏了解。在本研究中,我们调查了人类连接组项目(N = 1010)中 fMRI 数据的可见性图特性,并测试了它们对运动的敏感性和测试-再测可靠性。我们还利用可见度图的阶同步来描述连接强度。我们发现,可见性图的属性(如群落数和平均度数)与 fMRI 数据中的运动之间存在很强的相关性(r > 0.5)。图形理论特征的测试-再测可靠性(类内相关系数(ICC))在平均度数(0.74,95% CI = [0.73,0.75])方面较高,在聚类系数(0.43,95% CI = [0.41,0.44])和平均路径长度(0.41,95% CI = [0.38,0.44])方面中等。大脑区域之间的功能连通性是通过可见度图度的相关性来测量的。然而,研究发现相关性强度为中低水平(r
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-22 DOI: 10.1007/s12021-024-09654-w
Elizabeth McManus, Nils Muhlert, Niall W. Duncan

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.

磁共振光谱(MRS)被广泛用于估算活体人脑特定区域中谷氨酸和(γ)-氨基丁酸(GABA)的浓度。由于整个大脑的细胞结构特性各不相同,因此了解所研究的 MRS 区域的此类特性有助于解释这些测量结果。特别是,对可能的局部神经递质受体模式有所了解,就有可能深入了解 GABA 和谷氨酸能神经元运作的机制环境。在比较两个或更多区域时,这一点可能特别有用,因为不同区域的受体群可能有很大不同。同时,在研究来自多个参与者或时间点的 MRS 数据时,样本的同质性也变得非常重要,因为来自不同细胞结构区域的测量结果可能难以比较。为了深入了解用户定义的感兴趣区域可能存在的细胞结构环境,我们制作了一个易于使用的工具--InSpectro-Gadget,该工具可与艾伦人脑图谱的受体 mRNA 表达信息对接。这款 Python 工具允许用户输入掩码,并自动获得其中可能存在的受体群的图形概览。这包括在相关情况下对多个掩码或参与者进行比较。受体和受体亚基基因包括 GABA 和谷氨酸能类,以及多种神经调节剂。本文对该工具的功能进行了说明,并通过一组示例分析对其使用进行了演示。该工具可在 https://github.com/lizmcmanus/Inspectro-Gadget 上获取。
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-01 DOI: 10.1007/s12021-023-09649-z
John Darrell Van Horn
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 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

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.

国际神经科学界正在建立第一个全面的脑细胞类型地图集,以从一个比以往更高的分辨率和更综合的角度来理解大脑是如何运作的。为了建立这些图谱,神经元亚群(如血清素能神经元、前额皮质神经元等)通过沿树突和轴突放置点在个体大脑样本中进行追踪。然后,通过变换其点的位置将轨迹映射到公共坐标系,这忽略了变换如何弯曲中间的线段。在这项工作中,我们应用射流理论来描述如何保持神经元轨迹的导数到任何阶。我们提供了一个计算标准映射方法可能引入的误差的框架,其中涉及映射变换的雅可比矩阵。我们展示了我们的一阶方法如何在随机微分同态下提高模拟和真实神经元轨迹的映射精度。我们的方法可以在我们的开源Python包brainlit中免费获得。
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 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

Decisions made during the analysis or reporting of an fMRI study influence the eligibility of that study to be entered into a meta-analysis. In a meta-analysis, results of different studies on the same topic are combined. To combine the results, it is necessary that all studies provide equivalent pieces of information. However, in task-based fMRI studies we see a large variety in reporting styles. Several specific meta-analysis methods have been developed to deal with the reporting practices occurring in task-based fMRI studies, therefore each requiring a specific type of input. In this manuscript we provide an overview of the meta-analysis methods and the specific input they require. Subsequently we discuss how decisions made during the study influence the eligibility of a study for a meta-analysis and finally we formulate some recommendations about how to report an fMRI study so that it complies with as many meta-analysis methods as possible.

fMRI研究分析或报告过程中做出的决定会影响该研究进入荟萃分析的资格。在荟萃分析中,对同一主题的不同研究结果进行了组合。为了综合这些结果,所有研究都必须提供同等的信息。然而,在基于任务的功能磁共振成像研究中,我们看到了各种各样的报告风格。已经开发了几种特定的荟萃分析方法来处理基于任务的功能磁共振成像研究中出现的报告实践,因此每种方法都需要特定类型的输入。在这份手稿中,我们对荟萃分析方法及其所需的具体投入进行了概述。随后,我们讨论了研究过程中做出的决定如何影响研究的荟萃分析资格,最后,我们就如何报告功能磁共振成像研究提出了一些建议,使其符合尽可能多的荟萃分析方法。
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 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

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.

目前的中尺度连接性图谱提供了关于小鼠大脑中丘脑皮质投射组织的有限信息。标记丘脑中空间受限神经元群的投影可以提供功能相关水平的连接组分析,但这些需要整合在同一个公共参考空间内。在这里,我们提供了一个用于多通道跟踪实验的分割、配准、集成和分析的管道。与其他工作流的关键区别在于,数据经过转换以适应参考模板。作为一个测试案例,我们研究了丘脑腹侧后内侧核(VPM)的七个神经元群体的轴突投射和核内排列,我们用顺行示踪剂对其进行了标记。它们的胞体位置从背侧到腹侧与胡须、鼻子和嘴巴的皮层特征相对应。他们强烈针对第4层,大多数只针对一个皮层区域,而腹外侧VPM中的区域则分支到多个体感区域。我们发现,我们的实验在拓扑上比艾伦研究所的类似实验更精确,对主要体感区域的投影与公开数据库中的单神经元形态重建一致。这项试点研究为共享的虚拟连接图谱奠定了基础,该图谱可以丰富额外的数据,用于研究不同丘脑核的地形组织。通过Jupyter笔记本,只需最少的编程技能即可访问该管道,并提供多种可视化工具,如皮质平面图、皮质下图和3D渲染,可用于自定义解剖轮廓。
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 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

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.

为了仅用几个方程模拟全脑动力学,局部神经元种群的生物物理、介观模型可以使用经验神经束成像数据连接起来。神经群体的中观平均场模型的发展,特别是自适应指数(AdEx)平均场模型,成功地总结了神经元尺度现象,导致与意识(异步和快速动态)和无意识(同步慢波,上下状态动态)脑状态相关的全局脑动力学的出现。基于在细胞尺度上运作的生物物理机制(例如,睡眠-觉醒周期或麻醉剂期间对尖峰频率适应的神经调节调节)。使用虚拟脑(TVB)环境连接平均场AdEx模型,我们之前已经模拟了大脑状态的一般特性,发挥了尖峰频率适应作用,但尚未对可能也调节不同大脑状态之间脑尺度动态转换的其他参数进行详细分析。我们利用高性能计算对TVB-AdEx模型进行了密集的网格参数探索。我们报告了适应诱导同步慢波活动的显著鲁棒性。此外,慢波的发生往往与功能和结构连通性之间的密切关系并行。我们发现超极化也可以产生类似无意识的同步上下状态,这可能是麻醉药作用的一种机制。我们的结论是,TVB-AdEx模型揭示了在睡眠和麻醉中实验确定的大规模特性。
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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 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 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

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.

基于BOLD的fMRI是研究大脑功能最广泛使用的方法。BOLD信号虽然很有价值,但却充满了独特的漏洞。其中最值得注意的是适度的信噪比,以及相对较低的时间和空间分辨率。然而,BOLD信号的高维复杂性也为功能发现提供了独特的机会。拓扑数据分析(TDA)是数学的一个分支,它被优化为在高维数据中搜索特定类别的结构,可以提供特别有价值的应用。在这项研究中,我们使用基本的运动控制范式获得了前扣带皮层(ACC)的fMRI数据。然后,对于每个参与者和三种任务条件中的每一种,使用两种方法总结ACC中的fMRI信号:a)基于TDA的持久同源性和持久性景观的方法,以及b)使用标准矢量化方案的基于非TDA的方法。最后,使用机器学习(使用支持向量分类器),在参与者中测试TDA和非TDA矢量化数据的分类准确性。在每个参与者中,基于TDA的分类优于基于非TDA的对应分类,这表明我们的TDA分析管道更好地描述了ACC中fMRI数据中任务和条件诱导的结构。我们的结果强调了TDA在表征区域fMRI信号中任务和情况诱导的结构方面的价值。除了为其他用户提供可供效仿的分析工具外,我们还讨论了基于TDA的方法在研究健康和临床大脑中功能性脑信号结构的个体差异中可以发挥的独特作用。
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