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Connectome reorganization associated with temporal lobe pathology and its surgical resection
与颞叶病理学及其手术切除相关的连接组重组

Sara Larivière, Bo-yong Park,, Jessica Royer, Jordan DeKraker, Alexander Ngo, Ella
Sara Larivière, Bo-yong Park,, Jessica Royer, Jordan DeKraker, Alexander Ngo, Ella

Sahlas, Judy Chen, Raúl Rodríguez-Cruces, Yifei Weng, Birgit Frauscher, Ruoting Liu, Zhengge Wang, Golia Shafiei, Bratislav Mišić, Andrea Bernasconi, Neda Bernasconi, , Michael D. Fox, Zhiqiang Zhang and Boris C. Bernhardt
Sahlas, Judy Chen, Raúl Rodríguez-Cruces, Weng Yifei, Birgit Frauscher, Ruoting Liu, Zhengge Wang, Golia Shafiei, Bratislav Mišić, Andrea Bernasconi, Neda Bernasconi, , Michael D.Fox, Zhiqiang Zhang and Boris C. Bernhardt

These authors contributed equally to this work.
这些作者对本研究做出了同等贡献。

Abstract 摘要

Network neuroscience offers a unique framework to understand the organizational principles of the human brain. Despite recent progress, our understanding of how the brain is modulated by focal lesions remains incomplete. Resection of the temporal lobe is the most effective treatment to control seizures in pharmaco-resistant temporal lobe epilepsy (TLE), making this syndrome a powerful model to study lesional effects on network organization in young and middle-aged adults. Here, we assessed the downstream consequences of a focal lesion and its surgical resection on the brain's structural connectome, and explored how this reorganization relates to clinical variables at the individual patient level.
网络神经科学为了解人脑的组织原理提供了一个独特的框架。尽管最近取得了一些进展，但我们对大脑如何受局灶病变调节的理解仍不全面。颞叶切除术是控制药物难治性颞叶癫痫（TLE）发作的最有效治疗方法，因此这种综合征成为研究病变对中青年网络组织影响的有力模型。在这里，我们评估了局灶性病变及其手术切除对大脑结构连接组的下游影响，并探讨了这种重组与患者个体水平的临床变量之间的关系。
We included adults with pharmaco-resistant TLE who underwent anterior temporal lobectomy between two imaging time points, as well as age- and sex-matched healthy controls who underwent comparable imaging ( ). Core to our analysis was the projection of high-dimensional structural connectome data-derived from diffusion MRI tractography from each subject-into lower-dimensional gradients. We then compared connectome gradients in patients relative to controls before surgery, tracked surgicallyinduced connectome reconfiguration from pre- to postoperative time points, and examined associations to patient-specific clinical and imaging phenotypes.
我们纳入了在两个成像时间点之间接受了前颞叶切除术的药物耐受性TLE成人 ，以及接受了类似成像的年龄和性别匹配的健康对照组（ ）。我们分析的核心是将来自每个受试者扩散磁共振成像束成像的高维结构连接组数据投影到低维梯度中。然后，我们比较了手术前患者与对照组的连接组梯度，追踪了手术诱导的连接组从术前到术后时间点的重构，并研究了与患者特定临床和成像表型的关联。
Before surgery, individuals with TLE presented with marked connectome changes in bilateral temporo-parietal regions, reflecting an increased segregation of the ipsilateral anterior temporal lobe from the rest of the brain. Surgery-induced connectome reorganization was localized to this temporo-parietal subnetwork, but primarily involved postoperative (C) The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site-for further information please contact journals.permissions@oup.com.
手术前，TLE患者双侧颞顶区的连接组发生明显变化，反映出同侧颞叶前部与大脑其他部分的分离程度增加。手术诱导的连接组重组被定位在这个颞顶叶子网络中，但主要涉及术后（C） 作者 2024。由牛津大学出版社代表《大脑》担保人出版。这是一篇根据知识共享署名-非商业性许可 ( https://creativecommons.org/licenses/by-nc/4.0/) 条款发布的开放获取文章，该许可允许在任何媒体上进行非商业性的再使用、分发和复制，前提是适当引用了原作。如需商业再利用，请联系 reprints@oup.com 以获取重印和转载的翻译权。所有其他许可均可通过我们网站文章页面上的 "许可 "链接，通过我们的 RightsLink 服务获得，如需更多信息，请联系 journals.permissions@oup.com。
integration of contralateral regions with the rest of the brain. Using a partial least-squares analysis, we uncovered a latent clinical-imaging signature underlying this pre- to postoperative connectome reorganization, showing that patients who displayed postoperative integration in bilateral fronto-occipital cortices also had greater preoperative ipsilateral hippocampal atrophy, lower seizure frequency, and secondarily generalized seizures.
对侧区域与大脑其他区域的整合。通过偏最小二乘法分析，我们发现了从术前到术后连接组重组背后潜在的临床成像特征，显示术后双侧前枕叶皮质出现整合的患者术前同侧海马萎缩程度更大，癫痫发作频率更低，并出现二次泛化癫痫发作。
Our results bridge the effects of focal brain lesions and their surgical resections with largescale network reorganization and inter-individual clinical variability, thus offering new avenues to examine the fundamental malleability of the human brain.
我们的研究结果将局灶性脑损伤及其手术切除的影响与大规模网络重组和个体间临床变异性联系起来，从而为研究人脑的基本可塑性提供了新的途径。

Author affiliations: 作者单位：

1 Multimodal Imaging and Connectome Analysis Laboratory, MeConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
1 加拿大蒙特利尔 H3A 2B4 麦克吉尔大学蒙特利尔神经研究所和医院 MeConnell 脑成像中心多模态成像和连接组分析实验室
2 Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA
2 美国马萨诸塞州波士顿市哈佛大学布里格姆妇女医院脑回路治疗中心 邮编：02115
3 Department of Data Science, Inha University, Incheon 22212, Republic of Korea
3 仁荷大学数据科学系，大韩民国仁川 22212
4 Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 34126, Republic of Korea
4 基础科学研究所神经科学成像研究中心，大韩民国水原 34126
5 Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
5 南京大学医学院附属金陵医院医学影像科，南京 210002
6 Analytical Neurophysiology Laboratory, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
6 加拿大蒙特利尔 H3A 2B4 麦克吉尔大学蒙特利尔神经研究所神经生理学分析实验室
7 Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
7 南京大学医学院附属鼓楼医院放射科，南京 210008
8 Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
8 美国宾夕法尼亚州费城 19104 宾夕法尼亚大学佩雷尔曼医学院精神病学系
9 McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
9 麦克吉尔大学蒙特利尔神经研究所和医院麦康奈尔脑成像中心，加拿大蒙特利尔，QC H3A 2B4
10 Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
10 加拿大蒙特利尔 H3A 2B4 麦克吉尔大学蒙特利尔神经研究所麦康奈尔脑成像中心癫痫神经成像实验室
Correspondence to: Boris C. Bernhardt, PhD
通讯作者：Boris C. Bernhardt 博士Boris C. Bernhardt 博士
Montreal Neurological Institute (NW-256)
蒙特利尔神经研究所（NW-256）
3801 University Street 大学街 3801 号
Montreal, Quebec, Canada H3A 2B4
加拿大魁北克省蒙特利尔 H3A 2B4
E-mail: boris.bernhardt@mcgill.ca
电子邮件： boris.bernhardt@mcgill.ca
Correspondence may also be addressed to: Sara Larivière,
来函也可寄往Sara Larivière、
Brigham and Women's Hospital
布里格姆妇女医院
60 Fenwood Rd
Boston, MA 02115, USA
美国马萨诸塞州波士顿 02115
E-mail: slariviere@bwh.harvard.edu
电子邮件： slariviere@bwh.harvard.edu
Running title: Surgery-induced connectome reorganization in TLE
运行标题：手术诱导的 TLE 连接组重组
Keywords: connectome; brain networks; focal lesion; surgery; longitudinal; gradients
关键词：连接组；大脑网络；病灶；手术；纵向；梯度

Introduction 导言

Human brain organization is increasingly conceptualized and analyzed from a network perspective, allowing great strides in understanding both health and disease. In recent years, these approaches have been guided by advances in neuroimaging acquisition and complex data analytics. In particular, the advent of diffusion MRI tractography has enabled the approximation of structural connections in vivo, and the systematic characterization of brain connectivity-so called connectomes. In parallel, a series of multivariate analytics has been developed to capture organizational principles of large-scale brain connectivity. These range from graph theoretical assessments of network topology to modular decompositions that identify interacting systems. More recently, dimensionality reduction methods that derive compact and continuous embeddings from high dimensional connectomes have begun to reveal salient spatial axes of connectivity. In the current work, we adopted the latter
人们越来越多地从网络角度对人脑组织进行概念化和分析，从而在了解健康和疾病方面取得了长足进步。近年来，神经成像采集和复杂数据分析技术的进步为这些方法提供了指导。尤其是弥散核磁共振成像束成像技术的出现，使得体内结构连接的近似性和大脑连接性（即所谓的connectomes）的系统表征成为可能。 与此同时，还开发了一系列多元分析方法来捕捉大规模大脑连接的组织原理。这些方法包括对网络拓扑的图论评估 ，以及识别相互作用系统的模块分解。 最近，从高维连接组中推导出紧凑连续嵌入的降维方法开始揭示连接性的显著空间轴。 在目前的工作中，我们采用了后者
approach to establish how structural connectivity embeddings can be modulated by focal lesions.
的方法来确定病灶病变如何调节结构连接嵌入。
We specifically study patients with pharmaco-resistant temporal lobe epilepsy (TLE), who suffer from seizures originating from the temporal lobe despite anti-seizure medical treatment. Compared to patients with adequate seizure control, pharmaco-resistant patients present with elevated risk for comorbidity and mortality, impaired quality of life and wellbeing, cognitive dysfunction and psychiatric difficulties, as well as progressive brain damage. In these patients, randomized controlled trials have shown that resection of the affected temporal lobe is currently the most effective treatment to control seizures, to restore psychosocial functioning, and to improve quality of life. By applying dimensional decomposition approaches to structural connectivity data acquired in healthy individuals as well as patients before and after surgery, we can investigate the impact of focal lesions on connectome-level organization.
我们专门研究了药物耐药性颞叶癫痫（TLE）患者，这些患者尽管接受了抗癫痫药物治疗，但仍会出现源于颞叶的癫痫发作。 与癫痫发作得到充分控制的患者相比，药物耐药性患者的合并症和死亡风险较高， ，生活质量和幸福感受损， ，认知功能障碍和精神障碍， ，以及进行性脑损伤。 随机对照试验表明，对于这些患者，切除受影响的颞叶是目前控制癫痫发作、恢复社会心理功能和改善生活质量的最有效治疗方法。 通过对健康人以及患者手术前后获得的结构连通性数据进行维度分解，我们可以研究病灶病变对连通组水平组织的影响。
Our novel framework, thereby, complements previous investigations of structural connectivity alterations in TLE patients relative to controls before surgery, which have reported microstructural and architectural damage seldomly limited to the temporal lobe or to the hemisphere ipsilateral to the seizure focus. Similarly, prior work has assessed topological changes based on graph theoretical parameterization, and has accumulated evidence for a modular rearrangement in networks that extends beyond the temporal lobe. Such findings are compatible with the notion that "focal" pathologies generally affect large-scale brain connectivity, and that large-scale compromise contributes to patient presentation and clinically relevant outcomes. In line with these findings, studies assessing brain network changes following epilepsy surgery suggest that a focal resection does not only jimpact the primary lesioned site, but rather has effects that cascade along direct and indirect connections into mutually interconnected networks. As such, TLE represents a powerful human lesion model to assess how focal pathology and its resection affect wholebrain organization.
因此，我们的新框架补充了以往对TLE患者与对照组手术前结构连接性改变的研究，这些研究报告的微观结构和架构损伤很少局限于颞叶或癫痫病灶同侧半球。 同样，先前的工作也根据图论参数化评估了拓扑变化，并积累了网络模块化重新排列的证据，其范围超出了颞叶。 这些发现与 "局灶性 "病变通常会影响大脑大范围连接性，而大范围连接性受损会导致患者表现和临床相关结果的观点相吻合。 与这些发现相一致的是，评估癫痫手术后大脑网络变化的研究表明，病灶切除术不仅会影响原发病变部位，而且会沿着直接和间接连接串联成相互关联的网络。 因此，TLE 是一个强大的人类病变模型，可用于评估病灶病理及其切除如何影响整个脑组织。
Here, we leveraged pre- as well as postoperative diffusion MRI data acquired in a cohort of pharmaco-resistant TLE patients. Our analytical paradigm derived whole brain connectomes from diffusion tractographic modelling, followed by non-linear dimensionality reduction to derive a continuous coordinate system in which confluent spatial trends of structural connectivity variation can be investigated. We compared our patients to a group of healthy controls, to investigate preoperative alterations and we tracked connectome reorganization
在这里，我们利用了一组药物耐受性TLE患者术前和术后的弥散核磁共振成像数据。我们的分析范式从弥散牵引建模中得出全脑连接组，然后进行非线性降维，得出一个连续坐标系，在该坐标系中可以研究结构连接变化的汇合空间趋势。 我们将患者与一组健康对照组进行了比较，以调查术前改变，并跟踪了连接组的重组情况。
from pre-to postoperative time points in patients. Exploiting inter-patient heterogeneity, we furthermore examined associations of pre- to postoperative connectivity trends with clinical and other imaging phenotypes at the individual patient level.
从患者术前到术后的时间点。利用患者间的异质性，我们进一步研究了术前到术后的连通性趋势与单个患者的临床和其他影像表型之间的关联。

Materials and methods 材料和方法

Participants 与会者

We studied 37 consecutive people suffering from pharmaco-resistant TLE, who (i) underwent anterior temporal lobectomy as a treatment of their seizures at Jinling Hospitar between 2009 and 2018, (ii) had postoperative histological confirmation of hippocampal sclerosis, (iii) underwent a research-dedicated, high-resolution 3T MRI before and after surgery that included T1-weighted (T1w) and diffusion MRI scans, and (iv) had at least one year of postoperative follow-up. Patients were diagnosed according to the classification of the International League Against Epilepsy based on a comprehensive examination that includes clinical history, seizure semiology, continuous video-electroencephalographic (EEG) telemetry recordings, neuroimaging, and neuropsychology. No patient had encephalitis, malformations of cortical development (e.g., tumors, vascular malformations), or a history of traumatic brain injury. We also studied 31 age- and sex-matched healthy individuals who underwent identical 3T MRI at one time point. Demographic and clinical information on both cohorts is provided in Supplementary Table 1. Our study was approved by the research ethics board of Jinling Hospital, Nanjing University School of Medicine, and written informed consent was obtained from all participants.
我们连续研究了37名药物耐药性TLE患者，他们(i)在2009年至2018年期间在金陵医院接受了前颞叶切除术作为癫痫发作的治疗方法，(ii)术后组织学证实海马硬化、(iii) 手术前后均接受了研究专用的高分辨率 3T 磁共振成像，包括 T1 加权（T1w）和弥散磁共振成像扫描，以及 (iv) 术后随访至少一年。根据国际抗癫痫联盟（International League Against Epilepsy）的分类，对患者进行了全面检查，包括临床病史、癫痫发作半定量分析、连续视频脑电图（EEG）遥测记录、神经影像学检查和神经心理学检查。没有患者患有脑炎、大脑皮层发育畸形（如肿瘤、血管畸形）或脑外伤史。我们还研究了 31 名年龄和性别匹配的健康人，他们在一个时间点接受了相同的 3T MRI 检查。两组患者的人口统计学和临床信息见补充表 1。我们的研究获得了南京大学医学院附属金陵医院研究伦理委员会的批准，并获得了所有参与者的书面知情同意。

MRI acquisition and processing
磁共振成像采集和处理

All MRI data were obtained on a Siemens Trio 3T scanner (Siemens, Erlangen, Germany) and included: (i) high-resolution 3D T1w MRI using a magnetization-prepared rapid gradient-echo sequence (repetition time , echo time , flip angle , voxel size , field of view slices) and (ii) diffusion MRI using a spin echo-based echo planar imaging sequence with four b0 images (dMRI, TR , flip angle , voxel size , b-value , diffusion directions .
所有磁共振成像数据均在西门子 Trio 3T 扫描仪（西门子，德国埃尔兰根）上获得，包括(i) 使用磁化预处理快速梯度回波序列进行高分辨率三维 T1w MRI（重复时间 ，回波时间 ，翻转角 ，体素大小 ，视场 切片）；(ii) 使用基于自旋回波的回波平面成像序列进行弥散 MRI，包含四幅 b0 图像（dMRI，TR ，翻转角 ，体素大小 ，b 值 ，弥散方向 。
Multimodal MRI preprocessing was performed using micapipe, an open-access image processing and data fusion pipeline ( ; https://micapipe.readthedocs.io).. In brief, native T1w structural images were deobliqued, reoriented to standard neuroscience orientation, corrected for intensity nonuniformity, intensity normalized, skull-stripped, and submitted to FreeSurfer (v6.0) to extract models of the cortical surfaces. Subcortical structures were segmented using FSL FIRST. Native DWI images were denoised, underwent b0 intensity normalization, and were corrected for susceptibility distortion, head motion, and edfy currents.
多模态磁共振成像预处理是使用开放式图像处理和数据融合管道 micapipe ( ; https://micapipe.readthedocs.io)进行的。 简而言之，原始 T1w 结构图像经过去漂白、重新定向到标准神经科学方向、校正强度不均匀性、强度归一化、颅骨切片，并提交到 FreeSurfer (v6.0) 提取皮质表面模型。使用 FSL FIRST 对皮层下结构进行分割。 原始 DWI 图像经过去噪、b0 强度归一化处理，并对易感失真、头部运动和刃电流进行了校正。

Surgical cavity mapping 手术洞穴绘图

Patient-specific surgical cavities were automatically segmented by registering both pre- and postoperative T1w images to the MNI152 standard template through linear transformations and subtracting the postoperative scan from the preoperative scan. Segmented cavities were visually inspected and manually edited to ensure that the extent of the resections was correctly identified. Cavities were then mapped to the surface template, and a consensus label was generated as defined by the union of all segmentations.
通过线性变换将术前和术后的 T1w 图像注册到 MNI152 标准模板上，并从术前扫描中减去术后扫描，从而自动分割患者特定的手术腔。对分割后的腔隙进行目视检查和手动编辑，以确保正确识别切除范围。然后将腔隙映射到表面模板上，并根据所有分割结果的组合生成共识标签。

Structural connectome generation
结构连接组的生成

Structural connectomes were generated with MRtrix3 from pre-processed DWI data. Examples of the images used in our image processing pipeline are illustrated in Fig. 1A. We performed anatomically-constrained tractography using tissue types (cortical and subcortical grey matter, white matter, cerebrospinal fluid) segmented from each participant's preprocessed T1w images registered to native DWI space. We estimated multi-tissue response functions, performed constrained spherical-deconvolution and intensity normalization. Seeding from all white matter voxels, we generated a tractogram with streamlines (maximum tract length ; fractional anisotropy cut-off ) and applied spherical deconvolution informed filtering of tractograms (SIFT2) to reconstruct whole-brain streamlines weighted by cross-sectional multipliers. Reconstructed streamlines were mapped onto 400 similar-sized cortical parcels constrained within the boundaries of the DesikanKilliany atlas and 14 regions in the subcortex and the hippocampus to produce subjectspecific structural connectivity matrices. Group-average normative structural connectomes were defined using a distance-dependent thresholding procedure, which preserved the edge length distribution in individual patients, and were transformed to reduce connectivity strength variance. As such, structural connectivity was defined by the number of streamlines
使用 MRtrix3 根据预处理后的 DWI 数据生成结构连接组。 图 1A 展示了我们的图像处理流程中使用的图像示例。我们使用从每位受试者的预处理 T1w 图像中分割出的组织类型（皮质和皮质下灰质、白质、脑脊液），并将其注册到原始 DWI 空间，进行了解剖学约束的束成像。我们估算了多组织响应函数，进行了约束球形解卷积和强度归一化。从所有白质体素开始，我们生成了带有 流线的束图（最大束长 ；分数各向异性截断 ），并应用球形解卷积对束图进行知情滤波（SIFT2），以重建按横截面乘数加权的全脑流线。将重建的流线映射到 400 个类似大小的皮层区块上，这些区块限制在 DesikanKilliany 地图集 以及皮层下和海马的 14 个区域的边界内，以生成特定受试者的结构连接矩阵。使用距离相关阈值化程序定义了组平均标准结构连接矩阵，该程序保留了单个患者的边缘长度分布， ，并对 ，以减少连接强度方差。因此，结构连通性由流线的数量定义
between two regions (i.e., fiber density). Structural connectomes were thresholded to retain only the top of connections.
即纤维密度）。对结构连接组进行了阈值化处理，只保留顶部的连接 。

Connectome gradient estimation
连接组梯度估计

Cortex- and subcortex-wide structural connectome gradients were generated using BrainSpace (v0.1.10; https://brainspace.readthedocs.io). First, a group-level structural connectome (combining cortical and subcortical data) was obtained using an iterative, leaveone-out procedure (Fig. 1B). For both the group-level and the left-out subject-specific connectome, an affinity matrix was constructed with a normalized angle kernel, and eigenvectors were estimated via diffusion map embedding, an unsuperyised nonlinear dimensionality reduction technique that projects connectome features into low-dimensional gradients. This technique is only controlled by only féw parameters, computationally efficient, relatively robust to noise compared to other nonlinear techniques, and has been extensively used in the previous literature. Algorithm parameters were identical to those from prior applications, specifically and diffusion time to retain the global relations between data points in the embedded space and to emphasize direct connections. Interhemispheric connections were not included in the gradient computation; left and right gradients were generated separately and aligned with Procrustes. The eigenvectors estimated from this technique provide a connectivity coordinate system - a diffusion mapwhere Euclidean distances in the manifold correspond to diffusion times between the nodes of the network. In this gradient space, regions with similar connectivity profiles are closely located, whereas regions with different connectivity profiles are located farther apart. To analyze gradient changes within and across participants, each left-out subject-specific gradient was aligned to the group template gradient (estimated using data from all other participants) yia Procrustes alignment and scaled between 0 and 1 . To improve sensitivity, patients were sorted into ipsilateral/contralateral to the seizure focus. The scree plot describing the eigenvalue decay function revealed that four principal eigenvectors (i.e., gradients) corresponded to the cut-off point, after which eigenvalues leveled off at small values and only explained marginal information.
使用 BrainSpace（v0.1.10；https://brainspace.readthedocs.io）生成皮层和皮层下结构连接组梯度。 首先，使用迭代、剔除程序（图 1B）获得组级结构连接组（结合皮层和皮层下数据）。对于组级和剔除的特定受试者连接组，使用归一化角度核构建亲和矩阵，并通过扩散图嵌入估算特征向量，扩散图嵌入是一种非上乘的非线性降维技术，可将连接组特征投射到低维梯度中。 该技术仅受féw参数控制，计算效率高，与其他非线性技术相比对噪声具有相对的鲁棒性， ，并已在之前的文献中得到广泛应用。 算法参数与之前的应用相同，特别是 和扩散时间 ，以保留嵌入空间中数据点之间的全局关系，并强调直接连接。 大脑半球间的连接不包括在梯度计算中；左右梯度分别生成，并用 Procrustes 对齐。 这项技术估算出的特征向量提供了一个连接坐标系--一个扩散图，流形中的欧氏距离对应于网络节点之间的扩散时间。 在这个梯度空间中，具有相似连通性特征的区域距离较近，而具有不同连通性特征的区域距离较远。为了分析参与者内部和参与者之间的梯度变化，每个被排除在外的特定受试者的梯度都与组模板梯度（使用所有其他参与者的数据估算）进行了对齐，yia Procrustes alignment ，并在 0 和 1 之间进行缩放。为了提高灵敏度，将患者分为癫痫病灶的同侧/外侧。描述特征值衰减函数的scree图显示，四个主要特征向量（即梯度）与临界点相对应，之后特征值在小值时趋于平稳，只能解释边缘信息。

Quantifying pre- and postoperative changes in structural
量化手术前后的结构变化

gradients
We assessed cross-sectional connectome changes between individuals with TLE (preoperatively) and controls using univariate fixed effects models performed on each eigenvector independently. Eigenvector-specific score histograms were then generated across the entire cortex, and split into ipsilateral/contralateral as well as patients/controls to examine changes in the distribution of significant scores and to assess inter-hemispheric asymmetry. To signify an overall load of alterations, we also compared the aggregate of the first four eigenvectors in patients relative to controls using a multivariate fixéd effect linear model. By statistically combining multidimensional gradients, the latter approach leverages their covariance to obtain a substantial gain in sensitivity, and thus unveil subthreshold properties not readily identified in a single eigenvector. Both uni- and multivariate linear models tested for differences between groups while controlling for effects of age and sex using BrainStat ( https://brainstat.readthedocs.io). 49
我们使用对每个特征向量独立执行的单变量固定效应模型，评估了TLE患者（术前）和对照组之间的横断面连接组变化。 然后在整个皮层生成特征向量特定得分直方图，并将其分为同侧/对侧以及患者/对照组，以检查重要得分分布的变化，并评估半球间的不对称性。为了显示整体负荷的变化，我们还使用多变量固定效应线性模型比较了患者与对照组前四个特征向量的总和。通过对多维梯度进行统计组合，后一种方法利用了它们的协方差，大大提高了灵敏度，从而揭示了单一特征向量不易识别的亚阈值特性。单变量和多变量线性模型都测试了组间差异，同时使用 BrainStat ( https://brainstat.readthedocs.io) 控制年龄和性别的影响。49
To test for changes over time (i.e., pre- vs. postsurgery), we fitted univariate and multivariate linear mixed effects models, a flexible framework for the analysis of repeated measures. Our models controlled for effects of age and sex, and included a subject-specific random intercept to improve model fit in longitudinal designs. With this approach, we were able to test for pre- to postoperative within-subject individual and multivariate eigenvector changes.
为了检验随时间的变化（即手术前与手术后），我们建立了单变量和多变量线性混合效应模型，这是一种灵活的重复测量分析框架。 我们的模型控制了年龄和性别的影响，并加入了特定受试者的随机截距，以改善纵向设计中的模型拟合度。通过这种方法，我们能够检验从术前到术后受试者内部个体和多变量特征向量的变化。
Findings from all fixed and mixed effects models were corrected for multiple comparisons using the false discovery rate (FDR) procedure.
所有固定效应和混合效应模型的研究结果都使用误发现率（FDR）程序进行了多重比较校正。

4D gradient deformations
4D 梯度变形

To simplify multidimensional changes into a single scalar feature, we computed the Euclidean distance (also referred to as 'eccentricity') between the group template center and eaçh subject-specific data point in the 4D gradient space (Fig. 2B). Previous work from our group and others has shown an inverse relationship between a region's eccentricity and its level of integration across different networks. For instance, highly eccentric regions (i.e., those distant from the 4D group template center) are interpreted to be segregated from other networks in the brain, but tightly interconnected with regions of the same network. On the other hand, regions with lower eccentricity (i.e., those with close proximity to the template center) are thought to reflect greater covariance with the rest of the brain, and consequently,
为了将多维变化简化为单个标量特征，我们计算了组模板中心与 4D 梯度空间中特定受试者数据点之间的欧氏距离（也称为 "偏心率"）（图 2B）。我们的研究小组 和其他研究小组 以前的研究表明，一个区域的偏心率与其在不同网络中的整合程度成反比关系。例如，高偏心率区域（即远离 4D 组模板中心的区域）被解释为与大脑中的其他网络隔离，但与同一网络的区域紧密相连。 另一方面，偏心率较低的区域（即与模板中心距离较近的区域）被认为反映了与大脑其他区域的更大共变性，从而反映了与大脑其他区域的更大共变性、
be more structurally and functionally integrated. Collectively, these findings support the notion that changes in a region's segregation or integration can be assessed through variations in manifold eccentricity. In turn, we interpreted eccentricity reductions as connectome integration and eccentricity increases as connectome segregation. The template center was defined as the centroid of the first four eigenvectors. Shifts in structural connectivity patterns lead to a displacement of eigenvector scores in the 4D gradient space, which in turn affects their proximity to the center. Gradient contraction/expansion thus quantifies global brain reorganization in connectivity space. Gradient deformation was quantified only in regions of interest, as determined by areas that showed significant cross-sectional and longitudinal changes, respectively. The rationale was to determine whether preoperative and surgeryinduced 4D gradient deformations were observed in areas that showed significant eigenvector changes.
在结构和功能上更加一体化。总之，这些发现支持了这样一种观点，即一个区域的分离或整合的变化可以通过歧面偏心率的变化来评估。反过来，我们将偏心率降低解释为连接组整合，将偏心率增加解释为连接组分离。模板中心被定义为前四个特征向量的中心点。结构连接模式的变化会导致特征向量得分在四维梯度空间中发生位移，进而影响其与中心的距离。因此，梯度收缩/扩张可以量化连接空间中的全球大脑重组。梯度变形仅在感兴趣的区域进行量化，这些区域由分别显示出显著横截面和纵向变化的区域决定。这样做的目的是为了确定在出现显著特征向量变化的区域是否观察到术前和手术诱导的四维梯度变形。

Inclusion and exclusion of surgical cavities
纳入和排除手术空腔

To ensure a rigorous and balanced comparison between preoperative TLE patients and controls, we included all brain regions in the construction of subject-specific structural connectome gradients. This approach was adopted to preserve the integrity of the whole-brain analysis and facilitate accurate gradient construction. However, to maintain consistency in the number of brain regions, and facilitate a balanced statistical comparison between preoperative and postoperative time points, we subsequently excluded surgical cavities from the analysis. In preoperative patients, the surgical cavities were excluded after constructing the wholebrain gradients to avoid biasing the gradient construction process. For postoperative patients, the surgical cavities were excluded at an earlier step, specifically during the construction of the connectivity matrix by omitting the corresponding rows and columns.
为确保对术前TLE患者和对照组进行严格、平衡的比较，我们在构建特定受试者结构连接组梯度时纳入了所有脑区。采用这种方法是为了保持全脑分析的完整性，便于准确构建梯度。不过，为了保持脑区数量的一致性，并便于对术前和术后时间点进行均衡的统计比较，我们随后将手术腔排除在分析之外。对于术前患者，我们在构建全脑梯度后排除了手术腔，以避免梯度构建过程出现偏差。对于术后患者，我们在更早的步骤中排除了手术腔，特别是在构建连接矩阵时省略了相应的行和列。
To understand the effects of cavity removal on gradient alterations and ensure that reported findings reflect bona fide alterations in connectome reorganization post-surgery, we performed a twofold evaluation process. First, we excluded the cavity at two different stages in the generation of whole-brain gradients in controls and preoperative patients: (i) during the construction of the connectivity matrix and (ii) after constructing the whole-brain gradients. Using spatial correlation analyses, we then evaluated the consistency of gradient patterns obtained from both approaches at an individual level. Second, to assess robustness of our preto postoperative findings, we repeated our multivariate analysis with surgical cavities being
为了了解龋洞去除对梯度改变的影响，并确保报告的结果反映了手术后连接组重组的真正改变，我们进行了双重评估。首先，我们在对照组和术前患者生成全脑梯度的两个不同阶段排除了空腔：(i)在构建连接矩阵时；(ii)在构建全脑梯度后。然后，我们利用空间相关性分析评估了两种方法在个体水平上获得的梯度模式的一致性。其次，为了评估术前和术后研究结果的稳健性，我们重复了我们的多变量分析，将手术腔室作为术前和术后研究对象。
excluded during the connectivity matrix construction at both preoperative and postoperative time points.
术前和术后时间点的连接矩阵构建过程中都排除了这些数据。

Spatial associations between preoperative and surgery-induced reorganization
术前重组与手术诱导重组之间的空间关联

To assess whether preoperative alterations constrained surgery-induced changes, we performed spatial associations between cross-sectional (relative to controls) and longitudinal (pre- to postoperative) connectome findings. Statistical significance of spatial correlations was assessed using variogram-matching models that take into account the spatial dependencies in the data. This method generates surrogate brain maps with matched spatial autocorrelation to that of a target brain, and has previously been applied to cortical and subcortical structures. As recommended, surrogate maps were generated using surfacebased geodesic distance between cortical regions and three-dimensional Euclidean distance between subcortical and cortical/subcortical regions. Variogram-matching null distributions were then generated from randomly shuffling surrogate maps while preserving the distancedependent correlation between elements of the brain map. The empirical correlation coefficients were compared against the null distribution determined by the ensemble of spatially permuted correlation coefficients tó estimate a -value (termed ).
为了评估术前改变是否制约了手术引起的改变，我们在横断面（相对于对照组）和纵向（术前到术后）连接组结果之间建立了空间关联。空间相关性的统计意义使用变异图匹配模型进行评估，该模型考虑了数据的空间依赖性。 这种方法生成的代用脑图与目标脑图具有匹配的空间自相关性，以前曾应用于皮层和皮层下结构。根据建议， ，利用皮层区域之间基于表面的大地测量距离以及皮层下和皮层/皮层下区域之间的三维欧氏距离生成代脑图。然后，在保留脑图元素之间与距离有关的相关性的前提下，通过随机洗牌代用图生成变异图匹配空分布。将经验相关系数与空间包络相关系数集合确定的空分布进行比较，以估算 -值（称为 ）。

Clinical associations 临床关联

We used partial least squares (PLS) analysis to unravel within-patient relationships between surgery-induced 4D gradient deformations and standard neuroimaging (i.e., hippocampal atrophy ) and clinicál parameters (Fig. 4A). PLS analysis is a multivariate associative technique that maximizes the covariance between two sets of variables. Briefly, these two variable sets are correlated with each other across patients. The resulting correlation matrix is submitted to singular value decomposition, which identifies linear combinations of the original variables to generate new latent variables that have maximum covariance. We evaluated the significance of our model using nonparametric methods: (i) permutation tests were used to assess statistical significance of each latent variable ( 10,000 permutations; termed ), (ii) bootstrap ratios were used to assess the reliability of singular vector weights (analogous to -scores, such that confidence interval corresponds to a bootstrap ratio of and , respectively), and (iii) cross-validation was used to assess out-ofsample correlations between projected scores using a four-fold approach (100 randomized
我们使用偏最小二乘法（PLS）分析来揭示手术诱导的 4D 梯度变形与标准神经影像学（即海马萎缩 ）和临床参数之间的患者内部关系（图 4A）。PLS 分析是一种多元关联技术，可最大化两组变量之间的协方差。 简而言之，这两组变量在不同患者之间相互关联。由此产生的相关矩阵经过奇异值分解，找出原始变量的线性组合，生成具有最大协方差的新潜变量。 我们使用非参数方法评估了模型的显著性：(i) 使用置换检验来评估每个潜变量的统计意义（10,000 次置换；称为 ），(ii) 使用自举比率来评估奇异向量权重的可靠性（类似于 分数，因此 置信区间分别对应于 和 的自举比率），(iii) 使用交叉验证来评估预测分数之间的样本外相关性。
train-test splits of the original data, where of the data was treated as a training set and of the data was treated as an out-of-sample test set) and a leave-one-out approach. Mathematical details of the analysis and inferential methods are described elsewhere.
其中 数据被视为训练集，而 数据被视为样本外测试集）和 "留一弃一 "方法。分析和推论方法的数学细节在其他地方有详细描述。

Cortical morphology and microstructure
皮质形态和微观结构

It has been shown that grey and white matter is significantly altered following resective surgery in TLE. To improve our understanding of surgery-induced gradient changes at the morphological and microstructural levels, we aligned our longitudinal multivariate gradient analyses with corresponding alterations in grey and white matter measures. For grey matter assessments, subject-specific cortical thickness was estimated using FreeSurfer and was measured as the Euclidean distance between corresponding pial and white matter vertices. For white matter assessments, we studied the superficial white matter located beneath the cortex. This compartment harbors termination zones of long-range tracts and short-range U-fibers and is, therefore, pivotal in maintaining cortico-cortical connectivity. We used surface-based linear models to compare grey (cortical thickness) and white (multivariate measures of fractional anisotropy and mean diffusivity) matter changes before and after surgery, restricting our analysis to regions showing significant longitudinal gradient alterations.
研究表明，TLE患者在接受切除手术后，灰质和白质会发生显著改变。 为了更好地了解手术在形态学和微观结构层面引起的梯度变化，我们将纵向多变量梯度分析与灰质和白质测量的相应变化进行了比对。在灰质评估中，使用 FreeSurfer 估算受试者特定的皮质厚度，并以相应的髓质和白质顶点之间的欧氏距离进行测量。对于白质评估，我们研究了位于皮层下方 的表层白质。 该区域是长程束和短程U型纤维的终止区，因此在维持皮层与皮层之间的连接方面起着关键作用。 我们使用基于表面的线性模型来比较手术前后灰质（皮质厚度）和白质（分数各向异性和平均扩散率的多变量测量）的变化，并将我们的分析局限于显示出显著纵向梯度变化的区域。

Results 成果

Connectome gradient analysis at baseline
基线连接组梯度分析

For every participant, we generated cortex- and subcortex-wide structural connectome gradients using non-linear dimensionality reduction. The first four gradients explained approximately (baseline/preoperatively) and (postoperatively) of variance across participants, with each gradient representing a different axis of connectivity variation (Fig. 1). Across both time points and in both groups, gradients generally depicted smooth differentiation in structural connectivity profiles of frontal vs temporo-occipital regions (Gradient 1), orbitofrontal vs sensorimotor regions (Gradient 2), medial occipital vs anterior temporal lobe regions (Gradient 3), and sensory vs. transmodal regions (Gradient 4).
我们使用非线性降维方法为每位参与者生成了皮层和皮层下范围的结构连接组梯度。 前四个梯度分别解释了大约 （基线/术前）和 （术后）参与者的方差，每个梯度代表不同的连接变异轴（图 1）。在两个时间点和两个组别中，梯度总体上描述了额叶与颞枕叶区域（梯度 1）、眶额叶与感觉运动区域（梯度 2）、内侧枕叶与前颞叶区域（梯度 3）以及感觉与跨模态区域（梯度 4）的结构连接特征的平滑分化。
Compared to healthy controls, presurgical TLE patients showed significant gradient changes in bilateral temporo-parietal ( ) and orbitofrontal ( ) cortices, together with the ipsilateral hippocampus ( ) and the contralateral amygdala ; Fig. 2A). Considering each gradient separately, global histogram analysis revealed significant asymmetry between ipsilateral and contralateral gradient alterations in TLE. Most notably, ipsilateral mesiotemporal lobe regions were shifted toward the most extreme, segregated positions along Gradients 2, 3, and 4, whereas their contralateral homologues were shifted to a more central, integrated position (Supplementary Fig. 1). This ipsilateral mesiotemporal lobe segregation was further confirmed by quantifying their positional change in the 4D gradient space relative to controls. Constraining this analysis to areas of significant preoperative gradient changes, individuals with TLE showed highest deformation values (i.e., depicting increased expansion/segregation) in ipsilateral anterior temporal lobe ( 0.005 ) and parahippocampal gyrus ( ; Fig. 2B).
与健康对照组相比，手术前的 TLE 患者双侧颞顶叶（ ）和眶额叶（ ）皮层，以及同侧海马（ ）和对侧杏仁核（ ；图 2A）均显示出明显的梯度变化。分别考虑每个梯度，全局直方图分析表明，在 TLE 中，同侧和对侧梯度的改变明显不对称。最值得注意的是，同侧中颞叶区域沿梯度 2、3 和 4 向最极端、最分离的位置移动，而其对侧同源区域则向更中心、更整合的位置移动（补充图 1）。通过量化它们在四维梯度空间中相对于对照组的位置变化，进一步证实了这种同侧颞中叶分离现象。将这一分析限制在术前梯度变化显著的区域，TLE 患者同侧颞叶前部 ( 0.005 ) 和海马旁回 ( ; 图 2B) 的变形值最高（即显示扩展/分离增加）。

Tracking surgery-induced deformations
跟踪手术引起的变形

Directly comparing structural connectome changes before and after anterior temporal lobectomy (Fig. 3A), patients showed significant multivariate gradient changes in posterior temporal regions near the resection site and contralateral temporo-parietal cortices ( ; Fig. 3B). As evidenced in both univariate and 4D gradient deformation analyses, contralateral temporo-parietal regions were shifted towards more integrated positions in the 4D gradient space after surgery, particularly along the axis of Gradient 1 ( ; Supplementary Fig. 2). Findings remained significant after controlling for time after surgery (spatial correlation with main multivariate findings: ; Supplementary Fig. 3). Similarly, comparing the effects of removing the surgical cavity at different stages in our analysis pipeline revealed minimal impact on baseline/preoperative connectome gradients (all ; Supplementary Fig. 4A) and yielded similar pre- to postoperative gradient findings ; Supplementary Fig. 4B).
直接比较颞叶前部切除术前后的结构连接组变化（图 3A），患者在切除部位附近的颞后部区域 和对侧颞叶皮质 ( ; 图 3B)显示出显著的多变量梯度变化。单变量和四维梯度变形分析表明，术后对侧颞顶叶区域向四维梯度空间中更整合的位置移动，尤其是沿梯度 1 轴 ( ; 补充图 2)。在控制术后时间后，研究结果仍有意义（与主要多变量研究结果的空间相关性： ；附图 3）。同样，在我们的分析管道中比较不同阶段切除手术腔的影响，发现对基线/术前连通组梯度的影响极小（所有 ；补充图 4A），术前与术后梯度结果相似 ；补充图 4B）。
As all regions showing pre- to postoperative gradient changes fell within the boundaries of significant preoperative changes, we next assessed the extent to which preoperative connectome reorganization relates to surgery-induced changes. The degree of preoperative multivariate gradient changes was correlated with pre- to postoperative multivariate gradient changes across all regions ( ), as well as when considering only areas of significant preoperative ( ) and pre- to postoperative
由于所有显示术前到术后梯度变化的区域都在显著术前变化的范围内，我们接下来评估了术前连接组重组与手术引起的变化的相关程度。术前多变量梯度变化的程度与术前至术后所有区域的多变量梯度变化相关( )，当仅考虑术前显著变化的区域( )和术前至术后的梯度变化时也是如此。
0.05) gradient changes (Supplementary Fig. 3). In other words, regions with profound connectome alterations before surgery (relative to controls) corresponded to regions that also underwent the largest pre- to postoperative reorganization. We also found a significant, albeit weaker, negative spatial associations between whole-brain preoperative and surgery-induced 4D gradient deformations ( ). In this case, a negative correlation indicates that regions that were initially segregated from the rest of the brain before surgery subsequently shifted towards a more integrated position in the connectome space after surgery.
0.05）的梯度变化（补充图 3）。换句话说，手术前连接组发生严重改变的区域（相对于对照组）对应的区域也经历了最大的术前至术后重组。我们还发现，全脑术前和手术诱导的四维梯度变形之间存在明显的负空间关联（ ），尽管这种关联较弱。在这种情况下，负相关表明，术前最初与大脑其他部分分离的区域在术后向连接组空间中更一体化的位置转移。

Associations with clinical variables
与临床变量的关联

Multivariate PLS analysis identified one statistically significant latent variable (LV-1 accounting for of covariance, ) that maximized the covariance between whole-brain 4D surgery-induced deformation patterns and standard neuroimaging parameters (e.g., hippocampal atrophy) and clinical measures (Fig. 4A). Loadings (i.e., correlations of individual clinical measures with LV-1) revealed ipsilateral hippocampal atrophy in Cornu Ammonis (CA1-3) and dentate gyrus subfields (all ), lower seizure frequency ( , and presence of secondarily generalized seizures as the strongest contributors of LV-1. Patients presenting with these clinical features showed increased postoperative connectome integration in bilateral fronto-occipital cortices, ipsilateral postcentral gyrus, and contralateral mesiotemporal cortex (all bootstrap ratio < confidence interval). Clinical measures and 4D surgery-induced gradient deformations were correlated at the individual patient level ( ), while out-of-sample correlations averaged (four-fold cross-validation) and (leave-one-out cross-validation), both of which exceeded mean permuted out-of-sample correlations (four-fould: , leaveone-out: ; Fig. 4B).
多变量 PLS 分析确定了一个具有统计意义的潜在变量（LV-1，占协方差的 ， ），它最大化了全脑 4D 手术诱导的变形模式与标准神经影像学参数（如海马萎缩）和临床测量之间的协方差（图 4A）。载荷（即单个临床指标与 LV-1 的相关性）显示，Cornu Ammonis (CA1-3) 和齿状回亚区（均为 ）的同侧海马萎缩、较低的癫痫发作频率( ，以及存在二次全身性癫痫发作 是 LV-1 的最强贡献因素。具有这些临床特征的患者术后在双侧前枕叶皮层、同侧中央后回和对侧颞叶中叶皮层的连接组整合增加（所有bootstrap ratio < 置信区间）。临床测量和 4D 手术诱导的梯度变形在单个患者水平上具有相关性 ( )，而样本外相关性的平均值为 （四倍交叉验证）和 （留空交叉验证），两者都超过了平均 permuted 样本外相关性（四倍： ，留空： ；图 4B）。

Underlying cortical morphology and microstructure
基本皮质形态和微观结构

Consistent with previous studies, we found moderate alterations in grey and white matter in areas of significant longitudinal gradient changes (see Fig. 3B and 4B). Directly comparing progressive atrophy changes before and after resective surgery, patients showed less postsurgical thinning in contralateral temporo-parietal and frontal regions ( ), but increased thinning in areas adjacent to the resection margins ( ; Supplementary Fig. 6A). In contrast, increased postsurgical white matter perturbations were observed across
与之前的研究一致， ，我们发现在纵向梯度变化显著的区域，灰质和白质发生了中度改变（见图 3B 和 4B）。直接比较切除手术前后的进行性萎缩变化，患者对侧颞顶叶和额叶区域的手术后变薄程度较轻 ( ) ，但切除边缘邻近区域的变薄程度增加 ( ；补充图 6A)。与此相反，手术后白质扰动在整个
most regions of significant gradient change but predominantly affected contralateral temporoparietal regions ( ; Supplementary Fig. 6B).
大多数梯度变化明显的区域，但主要影响对侧颞顶叶区域 ( ；补充图 6B)。

Discussion 讨论

Contemporary systems neuroscience is increasingly exploring advanced dimensionality reduction techniques to delineate the main organizational axes of the human connectome, and to derive potential clinical predictors. Here, we used connectome gradient mapping-an established dimensionality reduction method-to investigate local-global brain interactions in individuals with pharmaco-resistant TLE. We capitalized on this condition as a human lesion model to deepen our understanding of network-level consequences of focal lesions and their resection. We found that, before surgery, individuals with TLE showed an increased segregation of the ipsilateral temporal lobe (i.e., the disease epicenter) from the rest of the brain. By tracking pre- to postoperative connectome alterations, we showed that resection of the ipsilateral temporal lobe led to brain reorganization involving areas near the resection site, but also marked re-integration of contralateral temporo-parietal regions with the rest of the brain. We further provided proof-of-concept evidence of an association between interindividual variations in connectome reorganization following surgery and clinically salient features. Taken together, our results demonstrate the potential of gradient mapping to bridge different scales of human brain organization and allow the study of the brain's structural plasticity in response to focal lesions and their eventual resection in young adults.
当代系统神经科学正越来越多地探索先进的降维技术，以划定人类连接组的主要组织轴，并得出潜在的临床预测指标。 在这里，我们利用连接组梯度映射--一种成熟的降维方法--研究了药物耐受性TLE患者的局部-全局脑相互作用。我们将这种情况作为人类病变模型，以加深我们对局灶病变及其切除的网络水平后果的理解。我们发现，在手术前，TLE 患者同侧颞叶（即疾病震中）与大脑其他部分的分离程度增加。通过追踪术前到术后连接组的改变，我们发现同侧颞叶切除术导致了大脑重组，涉及切除部位附近的区域，同时也明显导致对侧颞顶叶区域与大脑其他区域的重新整合。我们进一步提供了概念性证据，证明手术后连接组重组的个体间差异与临床显著特征之间存在关联。总之，我们的研究结果证明了梯度绘图在沟通人类大脑组织的不同尺度方面所具有的潜力，并允许研究大脑结构的可塑性，以应对年轻成年人的局灶性病变及其最终切除。
Core to our analytical framework was the estimation of connectome gradients. As opposed to more conventional graph theoretical analysis, gradient techniques offer a low-dimensional perspective on connectome reconfigurations in a data-driven and spatially unconstrained manner. In addition, these techniques capture multiple, potentially overlapping gradients along continuous cortico-subcortical connectivity axes, which can represent both subregional heterogeneity and multiplicity within brain areas. Beyond these methodological advantages, gradients can further be used as a meaningful coordinate system to contextualize different MRI-derived measures with other markers of neural organization. For instance, such gradient approaches have been used to investigate the associations between macroscale brain organization and transcriptomic, synaptic, molecular, microstructural, and structurefunction factors. By situating these properties along a low-dimensional and continuous coordinate system, gradients have captured, and unified, core principles of brain organization
我们分析框架的核心是估计连接组梯度。 与更传统的图论分析不同，梯度技术以数据驱动和不受空间限制的方式，从低维度透视了连接组的重构。此外，这些技术还能沿着连续的皮层-皮层下连接轴捕捉到多个可能重叠的梯度，这既能代表亚区域的异质性，也能代表脑区内部的多重性。 除了这些方法学上的优势外，梯度还可进一步用作一种有意义的坐标系统，将不同的 MRI 衍生测量结果与其他神经组织标记联系起来。例如，这种梯度方法已被用于研究宏观大脑组织与转录组、 突触、 分子、 微结构、 和结构功能 等因素之间的关联。通过沿着低维连续坐标系定位这些属性，梯度捕捉并统一了大脑组织的核心原则
across multiple scales. Gradient mapping studies aiming to better understand brain changes observed in psychiatric and neurological conditions-including epilepsy-are also starting to emerge. Indeed, recent stepwise functional connectivity studies and applications of dimensional decompositions have described an imbalance of functional integration and segregation between sensory and transmodal systems in TLE. Our study applied gradient mapping for the first time to structural connectivity data in epilepsy. Using diffusion MRI, we demonstrated shifts in macroscale connectome organization implicating strategic regions such as the disease epicenter itself, as well as distant, yet direct, temporo-parietal connections, reflecting an increased segregation of these regions from the rest of the brain. Our findings further extend previous diffusion MRI studies that have focused on specific characteristics of white matter tracts either locally or g1obally, 25,75,76 and instead simultaneously evaluated structural connectome changes both at the lesion site and in distant regions. It is important to note, however, that this retrospective study used single-shell diffusion-weighted imaging with anisotropic voxels. Such data may contribute to inaccuracies in tractographic reconstructions, particularly in areas with kissing and/or crossing fibers. Moving forward, acquisitions are generally recommended to use isotropic voxels, with dimensions of or less. Similarly, inferring white matter connectivity from DWI-derived local orientation fields presents with challenges, particularly when pathways overlap, cross, branch, or have complex configurations. While our analysis employed streamline counts - a common metric for quantifying connectivity-emerging algorithms increasingly focus on advanced diffusion microstructure modeling. These novel approaches, such as g-ratio and neurite orientation dispersion and density imaging (NODDI), can provide more nuanced characterizations of white matter microstructure and may help to overcome limitations of current tractography methods. Nevertheless, our approach provides evidence that a condition with a focal lesion to the mesiotemporal lobe presents with changes that extend well beyond the site of primary pathology, affecting directly and indirectly structurally connected macroscale networks. Debate persists over the precise mechanisms that underlie changes in these structural networks, but such changes may reflect excitotoxic effects from seizure activity or deafferentation of mesiotemporal connections. In this context, a possible explanation for the observed segregation of the ipsilateral anterior temporal cortex may relate to hyperconnectivity within the epileptogenic mesiotemporal network, together with hypoconnectivity of the epileptogenic mesiotemporal network to the rest of the brain, or a combination of both. Under this account, topological "isolation" of the ipsilateral temporal lobe may represent a core connectional change that may
跨多个尺度。 旨在更好地理解精神和神经疾病（包括癫痫）中观察到的大脑变化的梯度图研究也开始出现。事实上，最近的逐步功能连接研究和维度分解的应用已经描述了TLE患者感觉系统和跨模态系统之间功能整合和分离的不平衡。 我们的研究首次将梯度绘图应用于癫痫的结构连接数据。通过使用弥散核磁共振成像，我们证实了宏观连接组组织的变化牵涉到战略区域，如疾病震中本身，以及遥远但直接的颞顶叶连接，这反映出这些区域与大脑其他部分的分离增加。我们的研究结果进一步扩展了之前的弥散核磁共振成像研究，这些研究侧重于白质束在局部或整体上的特定特征，25,75,76 而不是同时评估病变部位和远处区域的结构连接组变化。但需要注意的是，这项回顾性研究使用的是各向异性体素的单壳扩散加权成像。这些数据可能会导致束重建的不准确性，尤其是在纤维接吻和/或交叉的区域。 今后，一般建议使用各向同性体素进行采集，其尺寸应为 或更小。 同样，从 DWI 导出的局部定向场推断白质连通性也面临挑战，尤其是当通路重叠、交叉、分支或具有复杂配置时。 我们的分析采用了流线计数--量化连通性的常用指标--但新出现的算法越来越多地侧重于先进的扩散微结构建模。这些新方法，如 g 比率 和神经元定向弥散和密度成像（NODDI）， ，可以提供更细致的白质微观结构特征，并可能有助于克服当前束流成像方法的局限性。 尽管如此，我们的方法还是提供了证据，证明中颞叶局灶性病变的病变范围远远超出了原发病变的部位，直接或间接地影响了结构相连的宏观网络。关于这些结构性网络变化的确切机制仍存在争议，但这种变化可能反映了癫痫发作活动或中颞叶连接失代偿的兴奋毒性效应。 在这种情况下，对观察到的同侧前颞皮层分离现象的一种可能解释可能与致痫中颞网络内部的超连接性，以及致痫中颞网络与大脑其他部分的低连接性，或两者的结合有关。根据这种观点，同侧颞叶的拓扑 "隔离 "可能代表了一种核心连接变化，这种变化可能
incur locally sustained hyperexcitability and increased susceptibility to seizure activity. On the other hand, hypoconnectivity between ipsilateral temporal lobe regions and all other regions may be considered as a compensatory mechanism through which epileptogenic activity is contained, abrogated, and prevented from spreading.
这可能会导致局部持续过度兴奋，并增加对癫痫发作活动的易感性。 另一方面，同侧颞叶区域与所有其他区域之间的低连通性可被视为一种代偿机制，通过这种机制，致痫活动得到控制、减弱并防止扩散。
A common surgical procedure to alleviate seizures in patients with medically-intractable TLE is anterior temporal lobectomy; i.e., the resection of of the temporal lobe (or up to in case of non-dominant TLE). Here, we carried out longitudinal neuroimaging to track connectome reorganization before and after anterior temporal lobectomy. In our study, we showed that surgery-induced changes occur primarily in regions that were also affected preoperatively, specifically near the resection site and in contralateral temporo-parietal cortices. The observed postsurgical changes in these specific regions could be attributed to a pre-existing vulnerability which consequently makes them more vulnerable to the effects of surgery. This hypothesis is supported by our finding showing a significant positive correlation between the degree of preoperative and pre- to postoperative changes. This suggests that the regions most affected postoperatively were likely predisposed to changes due to inherent preoperative vulnerabilities and may shed light on the underlying mechanisms of brain plasticity in response to surgical interventions. Albeit marginal, we also showed evidence of postoperative connectome segregation of ipsilateral posterior temporal lobe regions, possibly pointing to ongoing Wallerian degeneration in nerve bundles disconnected after surgery. This process generally occurs in two phases, with acute dying back of axons within the first week postsurgery, followed by chronic myelin degradation lasting several months. With respect to the latter, there is ample evidence that ipsilateral temporal lobe fibers undergo irreversible Wallerian degeneration, with diffusion changes being apparent throughout the first year postsurgery. In line with these findings, a possible explanation for our observation of contralateral postoperative changes is the degeneration of commissural fibers connecting the two hippocampi and their afferent pathways. In contrast to preoperative alterations, changes in contralateral temporo-parietal cortices, which are expected to incur minimal downstream microstructural damage with surgery, were predominantly characterized by increased postoperative connectome integration. This shift in their connectivity profiles may be related to structural plasticity of the white matter in an active attempt to compensate for the resected grey matter and ongoing axonal/myelin degradation in homologous regions. Although such a relationship remains speculative, previous data have demonstrated that diffusion changes in contralateral fiber tracts fail to normalize following surgery. The absence of longitudinal
缓解药物难治性TLE患者癫痫发作的常见手术方法是颞叶前部切除术，即切除 （非优势性TLE患者则最多切除 ）。 在此，我们开展了纵向神经影像学研究，以追踪颞叶前部切除术前后的连接组重组情况。我们的研究表明，手术引起的变化主要发生在术前也受到影响的区域，特别是切除部位附近和对侧颞叶皮层。在这些特定区域观察到的术后变化可归因于术前存在的脆弱性，从而使这些区域更容易受到手术的影响。我们的研究结果表明，术前和术前到术后的变化程度之间存在显著的正相关，这也支持了我们的假设。这表明，术后受影响最大的区域很可能由于术前固有的脆弱性而容易发生变化，这也可能揭示了大脑可塑性对手术干预做出反应的潜在机制。尽管微不足道，但我们也发现了同侧后颞叶区域术后连接组分离的证据，这可能表明术后断开的神经束正在发生沃勒变性。这一过程一般分为两个阶段，术后第一周内轴突急性坏死， ，随后是持续数月的慢性髓鞘退化。 关于后者，有充分证据表明，同侧颞叶纤维会发生不可逆的沃勒里变性，术后第一年内扩散变化明显。 根据这些发现，我们观察到的对侧术后变化的一个可能解释是，连接两个海马及其传入通路的神经纤维发生了变性。 与术前的变化不同，对侧颞顶叶皮质的变化主要表现为术后连接组整合的增加，而这些皮质在手术中受到的下游微结构损伤应该是最小的。其连通性特征的这种变化可能与白质结构的可塑性有关，白质积极试图补偿切除的灰质和同源区域正在发生的轴突/髓鞘降解。尽管这种关系仍然是推测性的，但之前的数据已经证明，手术后对侧纤维束的弥散变化无法恢复正常。 没有纵向
data in controls in the current study may have hindered our ability to assess whether connectivity reorganization stabilizes, heals, or reorganizes over time relative to controls. Test-retest reliability studies of both diffusion MRI-derived connectomes and gradientbased frameworks have nevertheless shown high reliability in healthy adults. Another limitation of our approach is that we did not investigate how similar pre- to postoperative changes might present if the resection cavity was applied to healthy controls. Although we performed several additional analyses to evaluate the impact of removing the surgical cavity across different analysis steps, we did not compare the changes that a virtual resection would induce in controls. Future studies could benefit from employing computational simulations or other indirect approaches to model resection effects in control populations, and consequently, ascertain the specificity of the observed surgery-induced connectome reorganization to drugresistant epilepsy.
当前研究中对照组的数据可能会妨碍我们评估连通性重组是否会随着时间的推移而稳定、愈合或重组。尽管如此，对弥散 MRI 导出的连接组 和基于梯度的框架 的测试-重测可靠性研究显示，在健康成年人中，这两种方法都具有很高的可靠性。我们的方法还有一个局限性，那就是我们没有研究如果切除腔适用于健康对照组，术前和术后的变化会有多相似。虽然我们进行了几项额外的分析，以评估去除手术腔对不同分析步骤的影响，但我们没有比较虚拟切除术在对照组中引起的变化。未来的研究可能会受益于采用计算模拟或其他间接方法来模拟对照组人群的切除效果，从而确定观察到的手术诱导的连接组重组对耐药性癫痫的特异性。
Mining inter-individual variability in MRI data has been a cornerstone concept in neuroscience research. As a proof-of-concept, here we translated knowledge about connectome reorganization into individualized and clinically-meaningful taxonomies using latent clinical-imaging dimensions that link pre- to postsurgical shifts in connectome organization with heterogenous clinical measures. Our findings captured unique associations between connectome re-integration following surgery, primarily in bilateral fronto-occipital cortices, ipsilateral postcentral gyrus, and contralateral mesiotemporal regions, and greater ipsilateral hippocampal atrophy, lower seizure frequency, and presence of secondarily generalized seizures, As postsurgical seizure outcomes did not emerge as a significant contributor in our model, it is possible that a combination of both preoperative and postoperative features may better reflect seizure outcome, as suggested by a range of machine learning studies reporting associations between network measures and outcomes. Alternatively, outcome predictors may be identified from lower order latent variables, ideally with associated patterns of connectivity shifts centered on contralateral temporo-parietal and default-mode regions. Future studies aiming to identify predictive features will ideally include multi-site data to assess predictive generalizability, enroll patients with longer-term seizure outcomes, and include both seizure and cognitive outcome measures to comprehensively assess prognostication of overall functioning and wellbeing. Nevertheless, the present results complement recent efforts to identify latent imaging-derived disease factors that not only variably co-express across TLE patients, but also have potential for individualized predictions. Our work provides a robust cross-validation framework that can
挖掘核磁共振成像数据中的个体差异一直是神经科学研究的基石概念。 作为概念验证，我们利用潜在的临床成像维度将连通组重组的知识转化为个体化的、具有临床意义的分类标准，这些维度将连通组组织的术前术后变化与不同的临床指标联系起来。我们的研究结果捕捉到了手术后连接组重新整合（主要是在双侧前枕叶皮层、同侧中央后回和对侧颞叶中区）与同侧海马萎缩程度加重、癫痫发作频率降低和出现二次广泛性癫痫发作之间的独特关联、在我们的模型中，手术后的癫痫发作结果并不是一个重要的影响因素，因此有可能术前和术后特征的组合能更好地反映癫痫发作结果，正如一系列机器学习研究报告的网络测量和结果之间的关联所表明的那样。 另外，也可以从低阶潜变量中识别结果预测因子，最好是以对侧颞叶和缺省模式区域为中心的相关连通性移动模式。 未来旨在确定预测特征的研究最好包括多站点数据，以评估预测的普遍性，招募有较长期发作结果的患者，并包括发作和认知结果测量，以全面评估整体功能和福祉的预后。尽管如此，目前的研究结果补充了最近为确定潜伏的成像衍生疾病因素所做的努力，这些因素不仅在TLE患者中共同表达各不相同，而且还具有进行个体化预测的潜力。 我们的工作提供了一个稳健的交叉验证框架，可以
be adopted by subsequent research studies with larger and more heterogeneous samples. This framework will not only quantify patient-specific pre- to postoperative imaging-clinical relationships, but will enable the prediction of clinical features, further advancing the field of individualized patient care.
随后的研究将采用更大、更多的异质性样本。这一框架不仅能量化患者术前到术后的成像与临床关系，还能预测临床特征，进一步推动患者个体化治疗领域的发展。
Taken together, these findings offer a new framework that can enrich our understanding of the effects of focal lesions and their eventual surgical resection on the macroscale organization of the human brain connectome. We dichotomized macroscale connectivity shifts before and after surgery in TLE, with the ipsilateral anterior temporal lobe-the target area for resection-being segregated from the rest of the brain prior to surgery, Secondary to surgery, contralateral temporo-parietal regions revealed connectivity shifts in a diametrically opposite direction, likely reflecting a compensatory response to restore or maintain structural connectivity. This data-driven connectome reorganization further accounted for a broad spectrum of clinical factors, potentially opening up new avenues for future neuroimaging studies to parse inter-individual differences in epilepsy and, consequently, may be translatable to individualized patient care. More broadly, a human Yesion model, like the one described herein, offers a rare window into the building blocks of brain organization in both healthy and diseased states, and can ultimately allow causality to be inferred based on the effects of focal brain lesions in TLE.
总之，这些发现提供了一个新的框架，可以丰富我们对局灶病变及其最终手术切除对人类大脑连接组宏观组织的影响的理解。我们对TLE手术前后的宏观连通性变化进行了二分，同侧前颞叶--手术切除的目标区域--在手术前与大脑其他区域分离，而在手术后，对侧颞顶叶区域显示出方向截然相反的连通性变化，这可能反映了恢复或维持结构连通性的代偿反应。这种数据驱动的连接组重组进一步解释了广泛的临床因素，可能为未来的神经影像学研究开辟了新的途径，以解析癫痫的个体间差异，进而转化为对患者的个体化治疗。更广泛地说，像本文所述的人类Yesion模型为了解健康和患病状态下大脑组织的构件提供了一个难得的窗口，最终可以根据TLE中局灶性脑损伤的影响来推断因果关系。

Data availability 数据可用性

Codes to conduct multimodal preprocessing (micapipe ), hippocampal segmentation (HippUnfold ), and statistical analysis (BrainStat ) are openly available. Discovery cohort data are available upon reasonable request from the respective team of investigators. Data sharing will be subject to the policies and procedures of the host institution as well as the laws of the country where the dataset was collected.
进行多模态预处理（micapipe ）、海马分割（HippUnfold ）和统计分析（BrainStat ）的代码可公开获取。发现队列数据可向相关研究团队提出合理要求后提供。数据共享将遵守所在机构的政策和程序以及数据集收集国的法律。

Acknowledgements 致谢

The authors would like to thank all patients and control participants who took part in this study.
作者衷心感谢所有参与本研究的患者和对照组参与者。

1 Funding 1 资金

2 S.L. acknowledges funding from Fonds de la Recherche du Québec-Santé (FRQ-S), the
2 S.L. 感谢 Fonds de la Recherche du Québec-Santé (FRQ-S), the
3 Canadian Institutes of Health Research (CIHR), and the Richard and Ann Sievers
3 加拿大卫生研究院（CIHR）、理查德和安-西弗斯基金会（Richard and Ann Sievers
4 Neuroscience Award. B.Y.P. was supported by the National Research Foundation of Korea 5 (NRF-2021R1F1A1052303; NRF-2022R1A5A7033499), Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Koreá Government (MSIT; No. 2022-0-00448, Deep Total Recall: Continual Learning for HumanLike Recall of Artificial Neural Networks; No. RS-2022-00155915, Artificial Intelligence Convergence Innovation Human Resources Development [Inha University]; No. 2021-002068, Artificial Intelligence Innovation Hub), and Institute for Basic Science (IBS-R015D1). J.R. received support from the Canadian Open Neuróscience Platform (CONP) and CIHR. B.F. receives funding from FRQ-S (Chercheur-Boursier clinician Senior). A.N. acknowledges funding from FRQ-S. E.S. acknowledges funding from FRQ-S. G.S. acknowledges support from CIHR. B.M. acknowledges support from NSERC (Discovery Grant RGPIN 017-04265) and from the Cánada Research Chairs Program (CRC). A.B. and N.B. were supported by FRQ-S and CIHR (MOP-57840, MOP-123520). Z.Z. was supported by the National Science Foundation of China (NSFC: 81422022; 863 project: 2014BAI04B05 and 2015AA020505) and the China Postdoctoral Science Foundation (2016M603064). B.C.B. acknowledges support from CIHR (FDN-154298, PJT-174995), SickKids Foundation (NI17-039), NSERC (Discovery-1304413), Azrieli Center for Autism Research of the Montreal Neurological Institute (ACAR), BrainCanada, FRQ-S, the Helmholtz International BigBrain Analytics and Learning Laboratory (Hiball), and the Canada Research Chairs program.
4 神经科学奖。B.Y.P.得到了韩国国家研究基金会5（NRF-2021R1F1A1052303；NRF-2022R1A5A7033499）、韩国政府资助的信息和通信技术规划与评估研究所（IIPP）（MSIT；No.2022-0-00448，Deep Total Recall：RS-2022-00155915，人工智能融合创新人力资源开发[仁荷大学]；No.2021-002068，人工智能创新枢纽），以及基础科学研究所（IBS-R015D1）。J.R.获得了加拿大开放神经科学平台（CONP）和加拿大高级研究学院（CIHR）的资助。B.F.获得了 FRQ-S (Chercheur-Boursier clinician Senior) 的资助。A.N. 感谢 FRQ-S 的资助。E.S. 感谢 FRQ-S 的资助。G.S. 感谢 CIHR 的资助。B.M.鳴謝NSERC (Discovery Grant RGPIN 017-04265)和Cánada Research Chairs Program (CRC)的資助。A.B.和N.B.得到了FRQ-S和CIHR (MOP-57840, MOP-123520)的支持。Z.Z.得到了中国国家自然科学基金（NSFC：81422022；863项目：2014BAI04B05和2015AA020505）和中国博士后科学基金（2016M603064）的资助。B.C.B.感谢CIHR（FDN-154298、PJT-174995）、SickKids基金会（NI17-039）、NSERC（Discovery-1304413）、蒙特利尔神经研究所Azrieli自闭症研究中心（ACAR）、BrainCanada、FRQ-S、亥姆霍兹国际大脑分析和学习实验室（Hiball）以及加拿大研究教席计划的支持。

Competing interests 竞争利益

M.D.F. reports personal fees from Magnus Medical, Solterix, and nonfinancial support from Boston Scientific; in addition, M.D.F. has a patent for use of brain connectivity imaging to guide brain stimulation issued with no royalties and a patent for lesion network mapping pending with no royalties, all of which are unrelated to the present work.
M.D.F.报告了从Magnus Medical和Solterix获得的个人酬金，以及从Boston Scientific获得的非财政支持；此外，M.D.F.还拥有一项关于使用脑连接成像指导脑刺激的专利，该专利已获批准，但未支付专利使用费；还有一项关于病变网络映射的专利正在申请中，但未支付专利使用费，所有这些都与本研究无关。

Supplementary material 补充材料

Supplementary material is available at Brain online.
补充材料可在 Brain online 上查阅。

佂

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Figure legends 图例

Figure 1 Construction of structural connectome gradients. (A) An example of whole brain tractography reconstruction is shown for a healthy control (left) and an individual with TLE (right). (B) Subject-specific structural connectome gradients were generated from diffusion MRI data and iteratively aligned to a group template (using data from all other participants). The first four gradients were retained.
图 1 构建结构连通组梯度。(A）健康对照组（左）和TLE患者（右）的全脑束成像重建示例。(B) 从弥散核磁共振成像数据中生成特定受试者的结构连通组梯度，并与组模板（使用所有其他参与者的数据）迭代对齐。保留了前四个梯度。
Figure 2 Preoperative structural connectome gradient changes in TLE. (A) Univariate (left) and multivariate (right) fixed effect models compared gradient values in individuals with TLE (before surgery) and healthy controls. Significant gradient changes in patients relative to controls were observed in bilateral orbitofrontal and temporoparietal cortices, ipsilateral hippocampus, and contralateral amygdala (all ). (B) Multivariate gradient changes were simplified into a scalar feature of connectome deformations by computing the Euclidean distance between the group template center and each subject-specific data point in the 4D gradient space. Restricting this analysis to areas of significant gradient changes identified in (A) revealed increased segregation of temporoparietal regions in TLE relative to controls.
图 2 TLE 术前结构连通组梯度变化。(A）单变量（左）和多变量（右）固定效应模型比较了TLE患者（术前）和健康对照组的梯度值。在双侧眶额叶和颞顶叶皮层、同侧海马和对侧杏仁核（均为 ）观察到患者相对于对照组的显著梯度变化。(B）通过计算组模板中心 与 4D 梯度空间中每个特定受试者数据点之间的欧氏距离 ，将多变量梯度变化简化为连接组变形的标量特征。将这一分析局限于（A）中确定的显著梯度变化区域，发现相对于对照组，TLE患者颞顶叶区域的分离增加了。
Figure 3 Surgery-induced structural connectome gradient changes in TLE. (A) Surgical cavities were automatically segmented from pre- and postoperative T1w MRIs. Cavity masks were excluded a posteriori in preoperative analysis and a priori in postoperative analyses. (B) Multivariate mixed effects models revealed a pattern of surgery-induced gradient changes encompassing ipsilateral posterior temporal and contralateral temporo-parietal regions (all ). (C) Analysis of 4D gradient deformations constrained to areas of multivariate gradient change revealed segregation of ipsilateral temporal regions but increased integration of contralateral temporo-parietal regions after surgery.
图 3 手术引起的 TLE 连接组结构梯度变化。(A）从术前和术后的 T1w MRI 图像中自动分割出手术腔。在术前分析和术后分析中，分别在术后和术前排除了腔隙掩膜。(B) 多变量混合效应模型显示了手术诱导的梯度变化模式，包括同侧后颞区和对侧颞顶区（所有 ）。(C) 对多变量梯度变化区域的 4D 梯度变形进行分析，发现同侧颞区分离，但手术后对侧颞侧区域的整合增加。
1 Figure clinical-imaging signature of surgery-induced connectome reorganization.
1 图 手术诱导的连接组重组的临床成像特征。
2 (A) Partial least-squares (PLS) analysis was used to relate patient-specific pre- to 3 postoperative 4D gradient deformation with clinical variables. These two sets of variables are 4 correlated across patients and subjected to singular value decomposition, yielding multiple 5 latent variables. Bootstrap resampling evaluated the contribution of individual variables to the surgery-induced deformation patterns and clinical variables. (B) The first latent variable (LV8 1) accounted for of the covariance between the imaging and clinical data ( ; top left). The contribution of individual clinical measures is shown using correlations between patient-specific clinical scores and scores on the multivariate pattern (loadings; top right). Error bars indicate bootstrap-estimated SEs. The contribution of individual regions is shown using bootstrap ratios (interpretable as -scores; bottom left). Patients who displayed increased postoperative connectome integration had greater ipsilateral hippocampal atrophy in CA1-3 and DG subfields, lower seizure frequency, and secondarily generalized seizures. This association was confirmed by projecting individual patient data onto the weighted patterns ( ; bottom middle). Correlations between 4D surgery-induced deformations and clinical scores in cross-validated held-out dáta (mean ) were higher than those from the permuted null model (mean ; bottom right).
2 (A) 部分最小二乘法（PLS）分析用于将患者术前到术后的 4D 梯度变形与临床变量联系起来。这两组变量在患者间相互关联，并进行奇异值分解，从而产生多个潜变量。Bootstrap 重采样评估了单个变量对手术诱导的变形模式和临床变量的贡献。(B) 第一个潜变量（LV8 1）占成像和临床数据之间协方差的 ( ; 左上角)。患者特异性临床评分与多变量模式评分之间的相关性（载荷；右上角）显示了单个临床指标的贡献。误差条表示引导估计的 SE。单个区域的贡献用 bootstrap 比率（可解释为 -scores；左下）表示。术后连接组整合增加的患者同侧海马CA1-3和DG亚区萎缩程度更大，癫痫发作频率更低，且为继发性全身性癫痫发作。将患者的个体数据投射到加权模式上（ ；底部中间）证实了这种关联。4D 手术诱导的变形与交叉验证的保持不变 dáta 的临床评分之间的相关性（平均值 ）高于置换无效模型的相关性（平均值 ；右下）。
A Whole-brain tractography reconstruction
全脑束学重建
selly 臭
B Construction of structural connectome gradients
B 结构连接体梯度的构建
Figure 1 图 1

A Preoperative gradient changes in TLE
A TLE 术前梯度变化

Hotelling's TLE:
Hotelling's TLE：

B 4D gradient deformations
B 四维梯度变形

Figure 2 DPI) 图 2 DPI)

A Surgical cavity mapping
A 外科腔隙图

Cavity exclusion in connectomes
连接体中的空腔排斥
B Pre- to postoperative gradient changes
B 术前到术后梯度变化
Hotelling's  霍特灵
C Surgery-induced gradient deformations
C 手术引起的梯度变形
Deformations in areas of significant gradient changes in TLE ( -values)
TLE 中梯度变化明显区域的变形情况 ( -values)
Figure 3 图 3
DPI)

A Partial least-squares (PLS) approach
局部最小二乘法（PLS）方法

Postop - preop 4D gradient deformation values
术后 - 术前 4D 梯度变形值
Hippocampal 海马体
asymmetry 不对称
Sub
CA1
CA2
CA3
CA4
DG
Seizure frequency 发作频率
Age of epile sy onset
癫痫发病年龄
Duration of epile sy
癫痫持续时间
Engel outcome 恩格尔成果
Secondary seizure generalization
继发性发作泛化
Clinical 临床
variables 变量
Volume of tissue resected
切除组织的体积
Deformation values (postop - preop)
变形值（术后 - 术前）
Clinical 临床
variables 变量
Singular value  奇异值

B Clinical associations of surgery-induced gradient deformations
B 手术引起的梯度变形与临床的联系

LV-1 surgery-induced gradient deformations
LV-1 手术引起的 梯度变形
Figure 4 图 4
(x DPI)