Functional MRI Method Development

功能性 MRI 方法开发

• 批准号: 8745702
• 负责人: Peter Bandettini
• 金额: $ 162.56万
• 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/8745702
• 关键词: Aerobic Exercise; Affect; Algorithms; Anatomy; Anesthesia procedures; Area; Back; Behavioral; Behavioral Symptoms; Biological; Biological Markers; Blood Volume; Brain; Calibration; Categories; Cerebrum; Characteristics; Childhood; Classification; Clinical; Cognitive; Collaborations; Communities; Data; Data Set; Detection; Development; Drug resistance; Frequencies; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Grouping; Growth; Heterogeneity; Hippocampus (Brain); Human; Individual; Intervention; Ketamine; Longevity; Magnetic Resonance Imaging; Maps; Measurement; Memory; Mental Depression; Mental Processes; Mental disorders; Methodology; Methods; Modeling; National Institute of Neurological Disorders and Stroke; Neurons; Noise; Paper; Participant; Patients; Pattern; Perfusion; Pharmaceutical Preparations; Postdoctoral Fellow; Process; Protocols documentation; Psyche structure; Reaction Time; Relative (related person); Reporting; Reproducibility; Research; Research Methodology; Resolution; Rest; Rodent; Scanning; Schizophrenia; Series; Severities; Signal Transduction; Slice; Subgroup; Symptoms; Technology; Time; Transcranial magnetic stimulation; Universities; Visual attention; Work; Yang; abstracting; arm; base; blood oxygen level dependent; blood oxygenation level dependent response; clinical Diagnosis; clinical application; clinically relevant; cognitive change; cohort; design; disease characteristic; early onset; fitness; graduate student; gray matter; hippocampal morphometry; imaging modality; improved; independent component analysis; interest; method development; myelination; neuroimaging; novel; novel strategies; research study; response

Protocol number 93M0170 The Section on Functional Imaging Methods (SFIM) has advanced functional MRI (fMRI) methodology through development of sophisticated processing and acquisition methods and research on the underlying mechanisms behind the fMRI signal. The ultimate goals are 1: a deeper understanding of healthy human brain and 2: clinical application of fMRI on an individual patient basis. SFIM aims to increase the depth and breadth of fMRI applications and bridge the gap to clinical applications on individual patients. Multi-echo fMRI to improve connectivity mapping Carried out primarily my graduate student, Prantik Kundu, in collaboration with Dr. Edward Bullmore of Cambridge University: The present work built upon the novel methodology, developed by us, that combines NMR measurements from multi-echo fMRI (T2* signal decay analysis) with statistical decomposition (independent components analysis, ICA) to differentiate functionally related blood oxygenation level dependent (BOLD) signals from the noise. This method is called multi-echo ICA (ME-ICA). This years work was directed towards applying ME-ICA to enhance resting state connectivity mapping, implementing ME-ICA for multi-echo multi-slice fMRI at 3T, denoising and characterizing 11.7T rodent multi-echo fMRI at varying anesthesia, and completing our study that introduced a new approach for cortical connectomics enabled by ME-ICA decomposition. Detection of low frequency task signals in fMRI after multi-echo denoising Carried out by my post doc, Jen Evans, as well as Prantik Kundu and in collaboration with Silvina Horvitz of NINDS: Assessment of slow BOLD changes is critical in studies of slow drug effects, transcranial magnetic stimulation (TMS) induced changes and other treatments that may involve slow cognitive changes. Conventional single echo fMRI cannot separate non-BOLD based signal drifts from neuronally-related changes limiting the types of paradigms that can be used. In this project we demonstrate that multi-echo independent components analysis (ME-ICA) can separate these two mixed low frequency signals. Biological significance of wide-spread fMRI activations at high TSNR Carried out by my post doc Javier Gonzalez-Castillo and post bac IRTA, Colin Hoy: We have previously demonstrated that with adequate temporal signal-to-noise ratio (TSNR) and sufficiently versatile response models, statistically significant BOLD responses time-locked with the experimental paradigm can be detected in over 95% of the brain for simple tasks. We also showed that these widespread responses cluster spatially in a functionally meaningful manner. In order to better evaluate the biological significance of these widespread activations, we conducted an additional experiment in which task load was modulated across participants. Despite lower spatial smoothness, TSNR, and contrast-to-noise ratio (CNR) in the present dataset relative to our original study, still over 80% of gray matter became significantly active at the highest available TSNR. Activation extent scaled with task load and followed the gray matter contour, suggesting biological significance for activations found outside of cortical areas commonly associated with the tasks. These results fundamentally challenge how typical activation processing - using a simple canonical model for expected response - is performed. More information is clearly in the signal and we are effectively extracting much of it using this method. Cognitive correlates of dynamic changes in connectivity Carried out by my post doc Javier Gonzalez-Castillo and post bac IRTA, Colin Hoy: A common assumption in most resting state fMRI (rsfMRI) studies is temporal stationarity for the duration of the scan. However, recent studies have shown that rsfMRI spatial connectivity patterns do change considerably across short periods of time. The potential correlation between connectivity changes and ongoing cognitive processing is not fully understood. The purpose here was to evaluate whether dynamic changes in whole brain fMRI connectivity can be used to reliably infer cognitive state at the single-subject level using unsupervised methods. Subjects have been scanned in a 7T MRI scanner continuously for approx. 25 mins as they performed and transitioned between four distinct mental tasks: undirected rest, 2-back memory task, simple math, visual attention. Each task was performed for 3 mins on two different occasions within the 25 mins of scanning. Our results show that connectivity patterns contain sufficient information to correctly classify time-periods according to ongoing mental processes well above chance for window durations longer than 30s. In these experiments we found that combining multidimensional scaling (feature reduction) and k-means (classification algorithm) provide the best results. Hippocampal Variability across subject and with intervention. Carried out by my graduate student, Adam Thomas in collaboration with Dr. Heidi Johansen-Berg at the University of Oxford. The first project furthers the labs continued interest in longitudinal methods and within-subject designs by looking at the affect of aerobic exercise on the hippocampus. This is the first finding from a large intervention experiment in which we have shown a volume growth in hippocampus that is dominated by increases in myelination. As second arm of this study is the exploration of the relationship between Cerebral Blood Volume (CBV), aerobic exercise and fitness. Our data show correlations between CBV and fitness levels in humans. Another project is looking at high resolution, 7T hippocampal morphometry. With over 30 subjects scanned, we have uncovered a high degree of individual variability in hippocampal morphometry previously not visible at 3T. Some participants show a high degree of convolution in the hippocampal sheath, while others are relatively smooth. We are currently devising methods to quantify this convolution and correlate it against behavioral variables such as fitness and memory. In collaboration with Dr. Carlos Zarate, we have also scanned a large cohort of patients suffering from drug-resistant depression before and after the administration of ketamine. We aim to explore whether the rapid behavioral effects of ketamine have any structural correlates in hippocampus. Finally, we are exploring a similar collaboration with Dr. Judy Rapoport to use the same sequence to study hippocampal morphometry in childhood-onset schizophrenia. Data-driven methodology for patient subtype discovery Carried out by my post doc, Zhi Yang. An obstacle in searching for neuroimaging biomarkers for mental disorders is that the existing definitions of patient groups are based on behavioral symptoms and may not reflect the pathophysiological characteristics of the disease. This fact leads to high heterogeneity and poor reproducibility in psychiatric studies. Potential pathophysiological subtypes can be overlooked in studies grouping patients based on clinical diagnosis. We are exploring a data-driven approach, gRAICAR, to search for highly homogeneous patient subgroups based on their intrinsic brain connectivity patterns. gRAICAR is unique in that it reveals inter-subject variability in brain connectivity patterns. Based on the brain network variability, patient subgroups suggesting potential pathophysiological subtypes can be identified using community detection algorithms. Two novel clinical findings demonstrated the power of gRAICAR: Using gRAICAR, we separated a precuneus network from the default mode network according to their different cross-lifespan trajectories. We also uncovered an association between a frontotemporal network and the severity of positive and negative symptoms in early-onset schizophrenia.

协议号 93M0170 功能成像方法部分 (SFIM) 通过开发复杂的处理和采集方法以及对 fMRI 信号背后的潜在机制的研究，拥有先进的功能 MRI (fMRI) 方法。最终目标是 1：更深入地了解健康人脑；2：功能磁共振成像在个体患者的临床应用。 SFIM 旨在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。 多回波功能磁共振成像可改善连接映射 主要由我的研究生 Prantik Ku​​ndu 与剑桥大学的 Edward Bullmore 博士合作进行：目前的工作建立在我们开发的新颖方法之上，该方法结合了多回波 fMRI（T2* 信号衰减分析）的 NMR 测量通过统计分解（独立成分分析，ICA）来区分功能相关的血氧水平依赖性（BOLD）信号与噪声。这种方法称为多回波 ICA (ME-ICA)。今年的工作旨在应用 ME-ICA 增强静息态连接映射，在 3T 下实施 ME-ICA 进行多回波多切片 fMRI，在不同麻醉下对 11.7T 啮齿动物多回波 fMRI 进行去噪和表征，并完成我们的研究引入了一种通过 ME-ICA 分解实现的皮质连接组学新方法。 多回波去噪后功能磁共振成像中低频任务信号的检测 由我的博士后 Jen Evans 以及 Prantik Ku​​ndu 并与 NINDS 的 Silvina Horvitz 合作进行：评估缓慢的 BOLD 变化对于缓慢药物作用、经颅磁刺激 (TMS) 诱导的变化和其他治疗的研究至关重要。可能涉及缓慢的认知变化。传统的单回波功能磁共振成像无法将基于非 BOLD 的信号漂移与神经元相关的变化分开，限制了可以使用的范例类型。在这个项目中，我们证明多回波独立分量分析（ME-ICA）可以分离这两个混合低频信号。 高 TSNR 下广泛的 fMRI 激活的生物学意义 由我的博士后 Javier Gonzalez-Castillo 和 IRTA 博士后 Colin Hoy 进行：我们之前已经证明，通过足够的时间信噪比 (TSNR) 和足够通用的响应模型，统计上显着的 BOLD 响应与时间锁定对于简单的任务，实验范式可以在超过 95% 的大脑中被检测到。我们还表明，这些广泛的反应以具有功能意义的方式在空间上聚集。为了更好地评估这些广泛激活的生物学意义，我们进行了一项额外的实验，其中任务负荷在参与者之间进行调节。尽管与我们最初的研究相比，当前数据集中的空间平滑度、TSNR 和对比度噪声比 (CNR) 较低，但在最高可用 TSNR 下，仍有超过 80% 的灰质变得显着活跃。激活程度随任务负荷变化并遵循灰质轮廓，这表明在通常与任务相关的皮质区域之外发现的激活具有生物学意义。这些结果从根本上挑战了典型的激活处理（使用简单的规范模型来实现预期响应）的执行方式。信号中清楚地包含了更多信息，我们正在使用这种方法有效地提取其中的大部分信息。 连接动态变化的认知关联 由我的博士后 Javier Gonzalez-Castillo 和 IRTA 后 Colin Hoy 进行：大多数静息态功能磁共振成像 (rsfMRI) 研究的一个常见假设是扫描期间的时间平稳性。然而，最近的研究表明，rsfMRI 空间连接模式确实在短时间内发生了相当大的变化。连接变化和持续认知处理之间的潜在关联尚未完全了解。这里的目的是评估全脑功能磁共振成像连接的动态变化是否可以用于使用无监督方法可靠地推断单个受试者水平的认知状态。受试者已在 7T MRI 扫描仪中连续扫描约 20 分钟。他们在 25 分钟内执行并在四个不同的心理任务之间进行转换：无方向的休息、2-back 记忆任务、简单的数学、视觉注意力。在 25 分钟的扫描时间内，每个任务在两个不同的场合执行 3 分钟。我们的结果表明，连接模式包含足够的信息，可以根据持续的心理过程正确地对时间段进行分类，远高于窗口持续时间超过 30 秒的机会。在这些实验中，我们发现结合多维缩放（特征缩减）和 k 均值（分类算法）可提供最佳结果。 不同受试者和干预下的海马变异性。 由我的研究生 Adam Thomas 与牛津大学的 Heidi Johansen-Berg 博士合作完成。第一个项目通过研究有氧运动对海马体的影响，进一步加深了实验室对纵向方法和受试者内设计的持续兴趣。这是大型干预实验的第一个发现，在该实验中，我们发现海马体积的增长主要是髓鞘形成的增加。这项研究的第二部分是探索脑血量 (CBV)、有氧运动和健身之间的关系。我们的数据显示了人类 CBV 和健康水平之间的相关性。 另一个项目正在研究高分辨率 7T 海马形态测量。通过对 30 多名受试者进行扫描，我们发现海马形态测量存在高度的个体差异，而这在 3T 中是不可见的。一些参与者的海马鞘表现出高度的卷积，而另一些则相对平滑。我们目前正在设计方法来量化这种卷积并将其与健康和记忆等行为变量相关联。我们还与 Carlos Zarate 博士合作，对一大群患有耐药性抑郁症的患者在服用氯胺酮前后进行了扫描。我们的目的是探讨氯胺酮的快速行为效应是否与海马体有任何结构相关性。最后，我们正在探索与 Judy Rapoport 博士进行类似的合作，以使用相同的序列来研究儿童期精神分裂症的海马形态测量。 用于发现患者亚型的数据驱动方法 由我的博士后杨志进行。寻找精神障碍的神经影像生物标志物的一个障碍是，现有的患者群体定义是基于行为症状，可能无法反映疾病的病理生理学特征。这一事实导致精神病学研究的高度异质性和重复性差。在根据临床诊断对患者进行分组的研究中，可能会忽略潜在的病理生理学亚型。 我们正在探索一种数据驱动的方法 gRAICAR，根据患者内在的大脑连接模式来搜索高度同质的患者亚组。 gRAICAR 的独特之处在于它揭示了大脑连接模式的受试者间差异。根据大脑网络的变异性，可以使用社区检测算法来识别表明潜在病理生理学亚型的患者亚组。 两项新的临床发现证明了 gRAICAR 的强大功能：使用 gRAICAR，我们根据不同的跨寿命轨迹将楔前叶网络与默认模式网络分开。我们还发现了额颞叶网络与早发性精神分裂症阳性和阴性症状的严重程度之间的关联。