Functional MRI Method Development

功能性 MRI 方法开发

• 批准号: 8556929
• 负责人: Peter Bandettini
• 金额: $ 150.82万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8556929
• 关键词: Address; Algorithms; Anatomy; Area; Biological Markers; Brain; Brain Mapping; Calibration; Categories; Characteristics; Classification; Cognitive; Communities; Cues; Data; Data Set; Dependence; Disease; Drops; Excision; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Grouping; Hour; Individual; Individual Differences; Knowledge; Left; Machine Learning; Magnetic Resonance Imaging; Maps; Measures; Methodology; Methods; Middle frontal gyrus structure; Mind; Modeling; Morphologic artifacts; Motion; Neurons; Neurosciences; Noise; Paper; Parietal Lobe; Patients; Pattern; Perfusion; Physiological Processes; Population; Postdoctoral Fellow; Process; Protons; Publishing; Reading; Relaxation; Reporting; Research; Rest; Sampling; Scanning; Seeds; Series; Signal Transduction; Slide; Solutions; Sorting - Cell Movement; Structure; Subject Headings; System; Technology; Time; Training; Variant; Work; Yang; abstracting; base; clinical application; clinically relevant; cohort; comparison group; density; design; graduate student; image processing; imaging modality; improved; interest; method development; novel; response; time use; usability

The Section on Functional Imaging Methods (SFIM) continues to develop cutting edge methods at the interface of functional MRI (fMRI) technology, image processing, interpretation of the signal, and neuroscience applications. The goal of SFIM is to fill the much-needed gap of improving fMRI from both a methodological and usability standpoint. We aim to increase the depth and breath of fMRI applications and bridge the gap to clinical applications on individual patients. This report describes progress in our research. In the past year, we have published 16 papers, making this one of our most prolific years. Feature Selection and classification accuracy. Carried out by my post doc Carlton Chu. A growing area in fMRI is the use of Machine Learning algorithms to characterize fMRI patterns of activity as they are associated with specific tasks. A central problem in this approach is regarding how to chose ahead of time, the appropriate region on which to perform these calculations. Many classification approaches simply use all voxels in the entire brain. While this works, it includes many voxels that are not involved with the task itself. This paper addresses this issue and determines that, in fact, feature selection is better, yet depends a bit on the algorithm being used. Multi-echo acquisition and Independent Component Sorting Carried out by my graduate student Prantik Kundu as well as E. Bullmore, N. Brenowitz, J. W. Evans, S. J. Inati, W.-M. Luh, V. Roopchansingh, Z. S. Saad. An ongoing challenge has been to remove the large fraction of non-neuronal fluctuations rom rs-fMRI time series. Work on characterizing and removing non-neuronal fluctuations has focused on time series modeling based on external measures of physiologic processes. In this study, we use the TE-dependence to separate BOLD from non-BOLD time signal. BOLD signal changes are manifest as changes in T2*, which can be characterized as showing a linear increase in fractional signal change with echo time (TE). Motion, system instabilities, and inflow effects can be manifest as changes in longitudinal relaxation, T1 or proton density, S0 but not typically T2*. Muti-echo for removal of systematic motion effects in group comparisons Carried out by my graduate student Prantik Kundu. It has been shown that in-scanner subject head motion leads to systematic patterns of false positives in population-level rs-fMRI analysis. This artifact is worst for group contrasts of cohorts with different levels of in-scanner motion, and is a potential roadblock to further resting state fMRI - based research. Here we show that me-ICA enables elimination the problem of spurious functional connectivity (FC) patterns caused by motion and other artifacts. After decomposing multi-echo data with ICA and separating BOLD from non-BOLD components based on echo-time (TE) dependence, the BOLD-based ICA components are used to compute FC. Computing an individual-subject me-ICA functional connectivity map for a region of interest (ROI) involves computing the correlation of its ICA series with the ICA component series of all other voxels. In contrast, the typical approach to computing FC maps involves motion regression, band pass filtering, and then Pearson correlation. The me-ICA seed-based FC approach was compared to standard seed-based FC approach at the population level. Thirty-three subjects were scanned for 10 minutes EPI during rest (Siemens 3T Tim Trio, 32-channel). No increase in me-ICA FC false positive rate is found as groupings are increasingly biased according to motion. We conclude that me-ICA FC is a principled and straightforward solution to the problem of spurious FC due to motion artifact. Clustering of resting state correlations. Carried out by Prantik Kundu. A major goal in rs-fMRI is the whole-brain mapping of functional cortical networks down to the scale of voxels from a single dataset. Promising results have been obtained on highly subject-averaged data, however, our ultimate goal is to use the characteristics of these networks as individual biomarkers. Towards this goal, we have developed an approach, based on our me-ICA method that converges on stable and consistent areas with very few averages. The maps that we produce can also predict subject-specific activation patterns associated with activation-based paradigms. This novel implementation of hierarchical clustering was able to create a community structure of brain organization, showing how large modules at low levels of clustering are related to smaller modules at high levels of clustering. We found that the 30-50 BOLD components from individual subject high dimensional me-ICA have localization to specific, finely delineated functional areas. The ability to consistently identify functional areas without functional localization tasks could also be of value as a biomarker for individual differences or disorder characterization. Determining most and least stable cortical networks using resting state fMRI Carried out by my post doc Javier Gonzalez-Castillo. An assumption has been that connectivity patterns are stable for the duration of the scan. The purpose of this work is to characterize the temporal variation of resting state connectivity within a continuous 1 hour resting state scan. From this we are able to determine the most and least stable networks. We compared pairs of matrices that were created by averaging the signal over varying amounts of time ranging from 2 to 30 minutes. The correlations begin to show decreasing similarity below 10 minutes, and continue to drop sharply down to 2 minutes. We used a sliding window correlation analysis with a window duration of 1 min and a window step of 1 s. We then sorted the pairwise connections from most to least variable. While some connections remain quite stable across time, others seem to vary considerably. It can be observed that stable connections tend to by inter-hemispheric and symmetric. The unstable connectivity patterns correspond to these between subcortical regions and high order cognitive regions in frontal and predominantly left parietal cortex. Decoding "yes" from "no" answers. Carried out by Z. Yang. Towards the goal of determining the limits of sensitivity of the fMRI decoding approach, our goal was to determine of we could differentiate a simple yes or no response or subjective correctness, based on subjects fMRI activation patterns as they responded to simple common-knowledge questions. In each trial, we present a cue to instruct the subject to either honestly or dishonestly answer the following questions. After reading the questions, subjects have to keep their final answers in mind until a button-press prompt appears several seconds after the question are removed from the screen. Looking at the BA9 portion of the left middle frontal gyrus. We extracted five time points of the voxels within this region, starting at the onset of the questions. Treating each voxel as a feature and each time point as a sample, we trained Gaussian Nave Bayesian (GNB) classifiers to predict the truthful Yes/No answers to the questions. The mean prediction accuracy achieved 90% when we averaged all 20 trials. These findings indicate that the truthful answers are encoded in brain activity independently from intentions, and multivariate pattern analysis is able to decode them from fMRI signal.

功能成像方法部分 (SFIM) 继续开发功能 MRI (fMRI) 技术、图像处理、信号解释和神经科学应用领域的前沿方法。 SFIM 的目标是从方法论和可用性的角度填补改进 fMRI 急需的空白。我们的目标是增加功能磁共振成像应用的深度和广度，并缩小与个体患者临床应用的差距。本报告描述了我们研究的进展。去年，我们发表了 16 篇论文，是我们最多产的一年。 特征选择和分类准确性。 由我的博士后 Carlton Chu 执行。 fMRI 的一个不断发展的领域是使用机器学习算法来表征与特定任务相关的 fMRI 活动模式。这种方法的一个核心问题是如何提前选择执行这些计算的适当区域。许多分类方法只是使用整个大脑中的所有体素。虽然这有效，但它包含许多与任务本身无关的体素。本文解决了这个问题，并确定实际上特征选择更好，但在一定程度上取决于所使用的算法。 多回波采集和独立分量排序 由我的研究生 Prantik Ku​​ndu 以及 E. Bullmore、N. Brenowitz、J. W. Evans、S. J. Inati、W.-M. 进行。 Luh，V. Roopchansingh，Z. S. Saad。一个持续的挑战是消除 rs-fMRI 时间序列中的大部分非神经元波动。表征和消除非神经元波动的工作重点是基于生理过程的外部测量的时间序列建模。在本研究中，我们使用 TE 依赖性将 BOLD 与非 BOLD 时间信号分开。 BOLD 信号变化表现为 T2* 的变化，其特点是显示分数信号变化随回波时间 (TE) 线性增加。运动、系统不稳定性和流入效应可表现为纵向弛豫 T1 或质子密度 S0 的变化，但通常不表现为 T2*。 多回波用于消除组比较中的系统运动效应 由我的研究生 Prantik Ku​​ndu 进行。研究表明，扫描仪内受试者头部运动会导致群体水平 rs-fMRI 分析出现系统性误报模式。这种伪影对于具有不同扫描仪内运动水平的群组的组对比来说是最差的，并且是进一步基于静息态功能磁共振成像的研究的潜在障碍。在这里，我们展示了 me-ICA 能够消除由运动和其他伪影引起的虚假功能连接 (FC) 模式的问题。使用 ICA 分解多回波数据并根据回波时间 (TE) 依赖性将 BOLD 与非 BOLD 分量分离后，基于 BOLD 的 ICA 分量用于计算 FC。 计算感兴趣区域 (ROI) 的个体受试者 me-ICA 功能连接图涉及计算其 ICA 系列与所有其他体素的 ICA 分量系列的相关性。相比之下，计算 FC 图的典型方法包括运动回归、带通滤波，然后是皮尔逊相关。 在群体水平上，将基于 me-ICA 种子的 FC 方法与基于种子的标准 FC 方法进行了比较。 33 名受试者在休息时接受了 10 分钟的 EPI 扫描（Siemens 3T Tim Trio，32 通道）。没有发现 me-ICA FC 误报率增加，因为分组根据运动越来越有偏见。 我们得出的结论是，me-ICA FC 是针对运动伪影导致的虚假 FC 问题的原则性且简单的解决方案。 静息状态相关性的聚类。 由 Prantik Ku​​ndu 执行。 rs-fMRI 的一个主要目标是将功能性皮层网络的全脑映射到单个数据集的体素尺度。在高度受试者平均数据上获得了有希望的结果，然而，我们的最终目标是使用这些网络的特征作为个体生物标志物。为了实现这一目标，我们开发了一种基于 me-ICA 方法的方法，该方法收敛于平均值很少的稳定且一致的区域。我们生成的地图还可以预测与基于激活的范式相关的特定于主题的激活模式。 这种层次聚类的新颖实现能够创建大脑组织的社区结构，显示低聚类水平的大模块与高聚类水平的较小模块如何相关。我们发现，来自单个主题高维 me-ICA 的 30-50 个 BOLD 组件已定位到特定的、精细描绘的功能区域。在没有功能定位任务的情况下一致识别功能区域的能力也可能作为个体差异或疾病特征的生物标志物有价值。 使用静息态功能磁共振成像确定最稳定和最不稳定的皮质网络 由我的博士后哈维尔·冈萨雷斯-卡斯蒂略 (Javier Gonzalez-Castillo) 执行。一个假设是连接模式在扫描期间保持稳定。这项工作的目的是表征连续 1 小时静息状态扫描内静息状态连接的时间变化。由此我们能够确定最稳定和最不稳定的网络。我们比较了通过对 2 到 30 分钟的不同时间段内的信号进行平均而创建的矩阵对。相关性在 10 分钟以下开始表现出相似性下降，并继续急剧下降到 2 分钟。 我们使用滑动窗口相关性分析，窗口持续时间为 1 分钟，窗口步长为 1 秒。然后，我们将成对连接从变量最多到变量最小进行排序。虽然有些联系在一段时间内保持相当稳定，但其他联系似乎变化很大。可以看出，稳定的连接倾向于半球间和对称的。不稳定的连接模式对应于额叶皮层和主要是左顶叶皮层的皮层下区域和高阶认知区域之间的连接模式。 从“否”答案中解码“是”。 由Z. Yang 执行。为了确定 fMRI 解码方法的灵敏度限制，我们的目标是根据受试者回答简单常识问题时的 fMRI 激活模式，确定我们是否可以区分简单的是或否响应或主观正确性。在每次试验中，我们都会提出一个提示，指导受试者诚实或不诚实地回答以下问题。阅读问题后，受试者必须记住他们的最终答案，直到问题从屏幕上删除几秒钟后出现按下按钮的提示。 观察左侧额中回的 BA9 部分。从问题出现时开始，我们提取了该区域内体素的五个时间点。将每个体素视为一个特征，将每个时间点视为一个样本，我们训练高斯朴素贝叶斯（GNB）分类器来预测问题的真实的是/否答案。当我们对所有 20 次试验进行平均时，平均预测准确度达到了 90%。这些发现表明，真实的答案被编码在独立于意图的大脑活动中，并且多变量模式分析能够从功能磁共振成像信号中解码它们。