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Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective. 识别白质纤维束的深度学习方法:最新进展和未来展望。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09636-4
Nayereh Ghazi, Mohammad Hadi Aarabi, Hamid Soltanian-Zadeh

Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.

从扩散磁共振成像(dMRI)数据中定量分析白质纤维束在健康和疾病方面具有重要意义。例如,术前和治疗计划中需要对与解剖意义纤维束相关的纤维束进行分析,手术结果取决于对所需纤维束的准确分割。目前,这一过程主要是通过神经解剖学专家进行耗时的人工鉴定来完成的。然而,人们对自动化管道有着广泛的兴趣,这样它就可以快速,准确,易于在临床环境中应用,并且还可以消除读取器内的可变性。随着使用深度学习技术的医学图像分析的进步,人们对使用这些技术进行通道识别的兴趣也越来越大。最近关于该应用的报告表明,基于深度学习的通道识别方法优于现有的最先进的方法。本文综述了目前基于深度神经网络的气道识别方法。首先,我们回顾了最近用于通道识别的深度学习方法。接下来,我们比较它们的性能、训练过程和网络属性。最后,我们对未来工作的开放挑战和可能的方向进行了批判性的讨论。
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引用次数: 2
Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing. 马来西亚大学的大大脑数据计划:数据管理到数据存储库和数据共享。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09637-3
Nurfaten Hamzah, Nurul Hashimah Ahamed Hassain Malim, Jafri Malin Abdullah, Putra Sumari, Ariffin Marzuki Mokhtar, Siti Nur Syamila Rosli, Sharifah Aida Shekh Ibrahim, Zamzuri Idris

The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.

在过去几年中,开放获取的神经成像数据的共享显著增加。共享神经成像数据对于加速科学进步至关重要,特别是在神经科学领域。目前正在开发一些将增加可用神经成像数据量的重大举措。大大脑数据倡议项目是由马来西亚理科大学发起的,是马来西亚第一个以数据共享为目的的神经成像数据存储平台。为了确保本项目神经成像数据的可访问性、可用性和安全性,并为用户提供可持续访问的高质量数据,我们首先提出了良好的数据管理实践。然后,我们开发了MyneuroDB,一个用于数据共享的在线存储数据库系统。在这里,我们描述了Big Brain Data Initiative和MyneuroDB, MyneuroDB是一个数据存储库,它提供了公开共享神经成像数据的能力,目前包括磁共振成像(MRI)、脑电图(EEG)和脑磁图(MEG),遵循FAIR数据共享原则。
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引用次数: 0
Single Neuron Modeling Identifies Potassium Channel Modulation as Potential Target for Repetitive Head Impacts. 单神经元建模确定钾通道调制是头部重复性撞击的潜在目标
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 Epub Date: 2023-06-09 DOI: 10.1007/s12021-023-09633-7
Daniel P Chapman, Stefano Vicini, Mark P Burns, Rebekah Evans

Traumatic brain injury (TBI) and repetitive head impacts can result in a wide range of neurological symptoms. Despite being the most common neurological disorder in the world, repeat head impacts and TBI do not have any FDA-approved treatments. Single neuron modeling allows researchers to extrapolate cellular changes in individual neurons based on experimental data. We recently characterized a model of high frequency head impact (HFHI) with a phenotype of cognitive deficits associated with decreases in neuronal excitability of CA1 neurons and synaptic changes. While the synaptic changes have been interrogated in vivo, the cause and potential therapeutic targets of hypoexcitability following repetitive head impacts are unknown. Here, we generated in silico models of CA1 pyramidal neurons from current clamp data of control mice and mice that sustained HFHI. We use a directed evolution algorithm with a crowding penalty to generate a large and unbiased population of plausible models for each group that approximated the experimental features. The HFHI neuron model population showed decreased voltage gated sodium conductance and a general increase in potassium channel conductance. We used partial least squares regression analysis to identify combinations of channels that may account for CA1 hypoexcitability after HFHI. The hypoexcitability phenotype in models was linked to A- and M-type potassium channels in combination, but not by any single channel correlations. We provide an open access set of CA1 pyramidal neuron models for both control and HFHI conditions that can be used to predict the effects of pharmacological interventions in TBI models.

创伤性脑损伤(TBI)和重复性头部撞击可导致多种神经系统症状。尽管重复性头部撞击和创伤性脑损伤是世界上最常见的神经系统疾病,但没有任何治疗方法获得美国食品及药物管理局的批准。单个神经元建模允许研究人员根据实验数据推断单个神经元的细胞变化。我们最近鉴定了一种高频头部撞击(HFHI)模型,其认知障碍表型与 CA1 神经元兴奋性下降和突触变化有关。虽然突触变化已在体内进行了研究,但重复性头部撞击后兴奋性降低的原因和潜在治疗目标尚不清楚。在这里,我们从对照组小鼠和持续性高频头痛小鼠的电流钳数据中生成了 CA1 锥体神经元的硅学模型。我们使用了一种带有拥挤惩罚的定向进化算法,为每组小鼠生成了大量无偏的近似实验特征的可信模型。HFHI 神经元模型群显示电压门控钠电导降低,钾通道电导普遍升高。我们使用偏最小二乘法回归分析来确定可能导致 CA1 在 HFHI 后兴奋性降低的通道组合。模型中的低兴奋表型与 A 型和 M 型钾通道组合有关,但与任何单一通道无关。我们提供了一组对照和高频手震条件下的 CA1 锥体神经元开放存取模型,可用于预测药物干预对创伤性脑损伤模型的影响。
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引用次数: 0
Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data. Funcmasker-flex:用于人类胎儿功能MRI数据脑分割的自动bids应用程序。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09629-3
Emily S Nichols, Susana Correa, Peter Van Dyken, Jason Kai, Tristan Kuehn, Sandrine de Ribaupierre, Emma G Duerden, Ali R Khan

Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics ≥ 0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. Funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.

胎儿功能磁共振成像(fMRI)提供了对发育中的大脑的关键洞察,并有助于预测发育结果。由于胎儿大脑被异质组织包围,不可能使用基于成人或儿童的分割工具箱。手工分割口罩可用于提取胎儿大脑;然而,这需要花费大量的时间。在这里,我们提出了一个新的用于掩盖胎儿fMRI的BIDS应用程序,funcmasker-flex,它通过在可扩展和透明的snake makake工作流程中实现的鲁棒3D卷积神经网络(U-net)架构克服了这些问题。使用159个胎儿(1103个总容积)的人工脑罩的开放获取胎儿fMRI数据来训练和测试U-net模型。我们还使用来自19个胎儿的82个局部获得的功能扫描来测试模型的泛化性,其中包括2300多个手动分割的体积。使用Dice指标比较funcmasker-flex与地面真实手动分割的体积的性能,并且分割始终是鲁棒的(所有Dice指标≥0.74)。该工具是免费提供的,可以应用于任何包含胎儿粗体序列的BIDS数据集。Funcmasker-flex减少了人工分割的需要,即使应用于新的胎儿功能数据集,也大大节省了进行胎儿功能磁共振成像分析的时间成本。
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引用次数: 1
De-Identification Technique with Facial Deformation in Head CT Images. 基于面部变形的头部CT图像去识别技术。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09631-9
Tatsuya Uchida, Taichi Kin, Toki Saito, Naoyuki Shono, Satoshi Kiyofuji, Tsukasa Koike, Katsuya Sato, Ryoko Niwa, Ikumi Takashima, Hiroshi Oyama, Nobuhito Saito

Head CT, which includes the facial region, can visualize faces using 3D reconstruction, raising concern that individuals may be identified. We developed a new de-identification technique that distorts the faces of head CT images. Head CT images that were distorted were labeled as "original images" and the others as "reference images." Reconstructed face models of both were created, with 400 control points on the facial surfaces. All voxel positions in the original image were moved and deformed according to the deformation vectors required to move to corresponding control points on the reference image. Three face detection and identification programs were used to determine face detection rates and match confidence scores. Intracranial volume equivalence tests were performed before and after deformation, and correlation coefficients between intracranial pixel value histograms were calculated. Output accuracy of the deep learning model for intracranial segmentation was determined using Dice Similarity Coefficient before and after deformation. The face detection rate was 100%, and match confidence scores were < 90. Equivalence testing of the intracranial volume revealed statistical equivalence before and after deformation. The median correlation coefficient between intracranial pixel value histograms before and after deformation was 0.9965, indicating high similarity. Dice Similarity Coefficient values of original and deformed images were statistically equivalent. We developed a technique to de-identify head CT images while maintaining the accuracy of deep-learning models. The technique involves deforming images to prevent face identification, with minimal changes to the original information.

头部CT包括面部区域,可以使用3D重建来可视化面部,这引起了人们对个人可能被识别的担忧。我们开发了一种新的去识别技术,使头部CT图像的人脸失真。将被扭曲的头部CT图像标记为“原始图像”,将其他图像标记为“参考图像”。在面部表面建立400个控制点,重建两者的面部模型。根据移动到参考图像上相应控制点所需的变形向量,对原始图像中的所有体素位置进行移动和变形。使用三个人脸检测和识别程序来确定人脸检测率和匹配置信度得分。变形前后进行颅内容积等效检验,计算颅内像素值直方图之间的相关系数。利用变形前后的Dice相似系数确定深度学习模型颅内分割的输出精度。人脸检测率为100%,匹配置信度评分< 90。颅内容积等效性检验显示变形前后的统计等效性。变形前后颅内像素值直方图的中位数相关系数为0.9965,相似度较高。原始图像和变形图像的骰子相似系数值在统计上是相等的。我们开发了一种去识别头部CT图像的技术,同时保持了深度学习模型的准确性。该技术通过变形图像来防止人脸识别,对原始信息的改变很小。
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引用次数: 0
Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space. 二维组织学小鼠大脑图像在三维图集空间中的定位和配准。
IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 Epub Date: 2023-06-26 DOI: 10.1007/s12021-023-09632-8
Maryam Sadeghi, Arnau Ramos-Prats, Pedro Neto, Federico Castaldi, Devin Crowley, Pawel Matulewicz, Enrica Paradiso, Wolfgang Freysinger, Francesco Ferraguti, Georg Goebel

To accurately explore the anatomical organization of neural circuits in the brain, it is crucial to map the experimental brain data onto a standardized system of coordinates. Studying 2D histological mouse brain slices remains the standard procedure in many laboratories. Mapping these 2D brain slices is challenging; due to deformations, artifacts, and tilted angles introduced during the standard preparation and slicing process. In addition, analysis of experimental mouse brain slices can be highly dependent on the level of expertise of the human operator. Here we propose a computational tool for Accurate Mouse Brain Image Analysis (AMBIA), to map 2D mouse brain slices on the 3D brain model with minimal human intervention. AMBIA has a modular design that comprises a localization module and a registration module. The localization module is a deep learning-based pipeline that localizes a single 2D slice in the 3D Allen Brain Atlas and generates a corresponding atlas plane. The registration module is built upon the Ardent python package that performs deformable 2D registration between the brain slice to its corresponding atlas. By comparing AMBIA's performance in localization and registration to human ratings, we demonstrate that it performs at a human expert level. AMBIA provides an intuitive and highly efficient way for accurate registration of experimental 2D mouse brain images to 3D digital mouse brain atlas. Our tool provides a graphical user interface and it is designed to be used by researchers with minimal programming knowledge.

要准确探索大脑神经回路的解剖组织,必须将大脑实验数据映射到标准化的坐标系统上。研究二维组织学小鼠脑切片仍然是许多实验室的标准程序。由于在标准制备和切片过程中会产生变形、伪影和倾斜角度,因此绘制这些二维脑切片具有挑战性。此外,对实验小鼠脑切片的分析还高度依赖于操作人员的专业知识水平。在这里,我们提出了一种用于精确小鼠脑图像分析(AMBIA)的计算工具,只需极少的人工干预就能将二维小鼠脑切片映射到三维脑模型上。AMBIA 采用模块化设计,包括定位模块和配准模块。定位模块是一个基于深度学习的管道,可定位三维艾伦脑图谱中的单个二维切片,并生成相应的图谱平面。配准模块建立在 Ardent python 软件包的基础上,可在大脑切片与其对应的地图集之间执行可变形的二维配准。通过将 AMBIA 的定位和配准性能与人类评分进行比较,我们证明它的性能达到了人类专家的水平。AMBIA 提供了一种直观、高效的方法,可将实验性二维小鼠脑图像精确配准到三维数字小鼠脑图谱。我们的工具提供了图形用户界面,研究人员只需具备最低限度的编程知识即可使用。
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引用次数: 0
CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations. cell䲟:一个工具箱转换,选择,切片三维细胞结构的道路上形态详细星形胶质细胞模拟。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-07-01 DOI: 10.1007/s12021-023-09627-5
Laura Keto, Tiina Manninen

Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.

通过建立和模拟能够捕捉星形胶质细胞形态细节的计算模型,可以大大增强对星形胶质细胞功能的理解。新的计算工具能够利用星形胶质细胞的现有形态学数据和建立具有适当细节水平的模型,用于特定的模拟目的。除了分析现有的用于构建、转换和评估星形胶质细胞形态的计算工具外,我们在这里提出了cell䲟工具包,作为Blender的附加组件,这是一个3D建模平台,因其在操纵3D生物数据方面的效用而日益得到认可。据我们所知,cell䲟是第一个将星形胶质细胞形态从多边形表面网格转换为可调表面点云的工具包,反之亦然,精确选择纳米工艺,并将形态切片成具有相同表面积或体积的片段。cellmurph是一个基于GNU通用公共许可证的开源工具包,可以通过直观的图形用户界面轻松访问。cell懊悔将是一个有价值的附加到其他搅拌机附加组件,提供新颖的功能,促进创建现实的星形胶质细胞形态不同类型的形态详细模拟阐明星形胶质细胞在健康和疾病中的作用。
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引用次数: 0
NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data. NiftyPAD -用于动态PET数据定量分析的新颖Python包。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-04-01 DOI: 10.1007/s12021-022-09616-0
Jieqing Jiao, Fiona Heeman, Rachael Dixon, Catriona Wimberley, Isadora Lopes Alves, Juan Domingo Gispert, Adriaan A Lammertsma, Bart N M van Berckel, Casper da Costa-Luis, Pawel Markiewicz, David M Cash, M Jorge Cardoso, Sebastién Ourselin, Maqsood Yaqub, Frederik Barkhof

Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved [Formula: see text] correlation with PPET, with absolute difference [Formula: see text] for linearised Logan and MRTM2 methods, and [Formula: see text] correlation with QModeling, with absolute difference [Formula: see text] for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential ([Formula: see text]), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available ( https://github.com/AMYPAD/NiftyPAD ), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines.

当前的PET数据集越来越大,从而增加了对快速和可重复处理管道的需求。本文介绍了一个免费的、开源的、基于python的软件包NiftyPAD,用于静态、全时间或双时间窗动态脑PET数据的通用分析。NiftyPAD的关键创新之处在于,通过参考输入处理分析双时间窗口扫描,通过结合动脉自旋标记(ASL)衍生的相对灌注测量,缩短PET采集的药代动力学建模,以及可选的基于PET数据的运动校正。用NiftyPAD得到的结果与完善的软件包pet和QModeling进行了一系列动力学模型的比较。使用四种不同淀粉样蛋白示踪剂扫描的8名受试者的临床数据来验证计算性能。对于线性化的Logan和MRTM2方法,NiftyPAD实现了与pet的[公式:见文]相关性,具有绝对差异[公式:见文];对于基于基函数的SRTM和SRTM2模型,NiftyPAD与QModeling的[公式:见文]相关性,具有绝对差异[公式:见文]。对于最近发表的SRTM ASL方法,该方法在现有软件包中不可用,在不可置换结合电位方面,与全扫描SRTM观察到高度相关性,偏差可忽略不计([公式:见文本]),表明在NiftyPAD中可靠的模型实现。总之,这些发现表明,NiftyPAD是通用的、灵活的,并且可以与现有的用于定量动态PET数据的软件包产生可比较的结果。它是免费的(https://github.com/AMYPAD/NiftyPAD),并允许多平台使用。模块化的设置使得添加新功能变得容易,并且该包是轻量级的,具有最小的依赖关系,使其易于使用和集成到现有的处理管道中。
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引用次数: 0
Correction to: Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition. 修正:使用块项分解对小鼠视觉通路中的功能性超声响应进行反卷积。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-04-01 DOI: 10.1007/s12021-022-09619-x
Aybüke Erol, Chagajeg Soloukey, Bastian Generowicz, Nikki van Dorp, Sebastiaan Koekkoek, Pieter Kruizinga, Borbála Hunyadi
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引用次数: 0
Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects. 重性抑郁症的共改变网络结构:大规模疾病效应的多模态神经影像学评估。
IF 3 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-04-01 DOI: 10.1007/s12021-022-09614-2
Jodie P Gray, Jordi Manuello, Aaron F Alexander-Bloch, Cassandra Leonardo, Crystal Franklin, Ki Sueng Choi, Franco Cauda, Tommaso Costa, John Blangero, David C Glahn, Helen S Mayberg, Peter T Fox

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

重度抑郁症(MDD)表现出多种症状,神经影像学研究报告了大脑关键区域的广泛破坏。许多支持网络退化假说(NDH)的理论认为,神经精神疾病通过有意义的网络机制选择性地靶向大脑区域,而不是作为模糊的疾病效应。本研究在结构和功能上验证了重度抑郁症是一种基于网络的疾病的假设。基于坐标的荟萃分析和激活似然估计(CBMA-ALE)用于评估92项先前发表的抑郁症研究结果的收敛性。然后使用CBMA-ALE的扩展来生成一个节点和边缘网络模型,该模型表示受MDD影响的大脑区域的共同改变。对图论网络架构的标准化措施进行了评估。然后在独立的临床t1加权结构磁共振成像(MRI)和静息状态功能(rs-fMRI)数据中测试meta分析MDD节点之间的共改变模式。评估了MDD患者与健康对照者之间,以及对照者与MDD患者临床亚组之间共改变谱的差异。建立了MDD的65节点144边共变网络模型。使用MDD节点测试复制数据中的共变概况,可以区分结构数据中的MDD和健康对照。然而,在rs-fMRI数据中,共改变谱在患者和对照组之间没有区别。在T1数据中,在临床均质MDD亚组中观察到患者和健康对照之间的差异有所改善。MDD异常表现出结构和功能网络结构,尽管只有结构网络表现出组间差异。我们的研究结果表明,结构共改变网络对正在进行的生物标志物开发的效用有所改善。
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引用次数: 0
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Neuroinformatics
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