功能性近红外光谱技术是近期发展起来的脑功能成像的方法。它主要是通过监测大脑活动时相关生物组织对近红外光的吸收情况来反映氧合血红蛋白、脱氧血红蛋白的浓度变化,继而获得大脑皮层的激活情况。功能性近红外光谱技术具有高时间分别率、无创伤性、成本低、易便携、可用于低龄个体等特点,目前在认知神经科学领域中被广泛应用。部分功能性磁共振成像研究发现主运动区与辅助运动区在运动任务中存在功能性分离的现象,然而在另一些研究中没有观察到这一现象。另外,随着运动想象疗法在运动功能康复治疗领域的迅速发展,越来越多的人关注运动想象疗法的作用效果与内部机制。为了检测主运动区与辅助运动区在运动任务中是否存在功能性分离现象;比较运动执行任务与运动想象任务大脑皮质激活情况探究运动想象的加工过程;探索运动想象任务中是否也可够发现主运动区与辅助运动区的功能分离。本研究用近红外光谱技术监测20名健康大学生在不同运动任务中大脑皮质的激活情况。采用2任务类型(运动执行,运动想象)×2持续时间(2s,5s)的被试内设计,考察健康大学生在不同任务条件下激活脑区的血液动力学变化特征,以及激活程度的差异。结果发现:在运动执行任务中,主运动区的激活情况与任务的持续时间存在正相关关系,而辅助运动区与任务的持续时间没有相关。这表明在运动执行任务中主运动区与辅助运动区在功能上分离,辅助运动区更多的参与最初的运动准备计划阶段,而主运动区更多的参与运动执行阶段。另外在持续时间为5s的实验条件下,运动执行任务与运动想象任务的大脑皮质激活情况基本相同。这说明运动想象疗法是一种有效地康复治疗手段。

We generated probabilistic area maps and maximum probability maps (MPMs) for a set of 18 retinotopic areas previously mapped in individual subjects (Georgieva et al., 2009 and Kolster et al., 2010) using four different inter-subject registration methods. The best results were obtained using a recently developed multimodal surface matching method. The best set of MPMs had relatively smooth borders between visual areas and group average area sizes that matched the typical size in individual subjects. Comparisons between retinotopic areas and maps of estimated cortical myelin content revealed the following correspondences: (i) areas V1, V2, and V3 are heavily myelinated; (ii) the MT cluster is heavily myelinated, with a peak near the MT/pMSTv border; (iii) a dorsal myelin density peak corresponds to area V3D; (iv) the phPIT cluster is lightly myelinated; and (v) myelin density differs across the four areas of the V3A complex. Comparison of the retinotopic MPM with cytoarchitectonic areas, including those previously mapped to the fs_LR cortical surface atlas, revealed a correspondence between areas V1–3 and hOc1–3, respectively, but little correspondence beyond V3. These results indicate that architectonic and retinotopic areal boundaries are in agreement in some regions, and that retinotopy provides a finer-grained parcellation in other regions. The atlas datasets from this analysis are freely available as a resource for other studies that will benefit from retinotopic and myelin density map landmarks in human visual cortex. 61 Maximum probability maps for 18 retinotopic areas were generated. 61 Multimodal surface matching was used to compare with myelin and cytoarchitectonic maps. 61 Early areas V1–3 areas are heavily myelinated, as are V3D and most of areas MT/pMSTv. 61 The phPIT cluster is lightly myelinated compared to other retinotopic areas. 61 Early areas V1–3 correspond to areas hOc1–3, with little correspondence beyond V3. Maximum probability maps for 18 retinotopic areas were generated. Multimodal surface matching was used to compare with myelin and cytoarchitectonic maps. Early areas V1–3 areas are heavily myelinated, as are V3D and most of areas MT/pMSTv. The phPIT cluster is lightly myelinated compared to other retinotopic areas. Early areas V1–3 correspond to areas hOc1–3, with little correspondence beyond V3.

61The human frontopolar cortex consists of two cytoarchitectonically distinct areas.613D probabilistic maps of frontopolar area 1 and 2 (Fp1,Fp2) were created.61Quantitative inference showed involvement in cognition (Fp1) and emotion (Fp2).61Significant difference in task-based functional connectivity between Fp1 and Fp2.

The anterior insula has been hypothesized to provide a link between attention-related problem solving and salience systems during the coordination and evaluation of task performance. Here, we test the hypothesis that the anterior insula/medial frontal operculum (aI/fO) provides linkage across systems supporting task demands and attention systems by examining the patterns of functional connectivity during word recognition and spatial attention functional imaging tasks. A shared set of frontal regions (right aI/fO, right dorsolateral prefrontal cortex, bilateral anterior cingulate) were engaged, regardless of perceptual domain (auditory or visual) or mode of response (word production or button press). We present novel evidence that: (1) the right aI/fO is functionally connected with other frontal regions implicated in executive function and not just brain regions responsive to stimulus salience; and (2) that the aI/fO, but not the ACC, exhibits significantly correlated activity with other brain regions specifically engaged by tasks with varying perceptual and behavioral demands. These results support the hypothesis that the right aI/fO aids in the coordination and evaluation of task performance across behavioral tasks with varying perceptual and response demands. Hum Brain Mapp 2009. 2008 Wiley-Liss, Inc.

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ingerprint of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.

Many components of resting-state (RS) FMRI show non-random structure that has little to do with neural connectivity but can covary over multiple brain structures. Some of these signals originate in physiology and others are hardware-related. One artifact discussed herein may be caused by defects in the receive coil array or the RF amplifiers powering it. During a scan, this artifact results in small image intensity shifts in parts of the brain imaged by the affected array components. These shifts introduce artifactual correlations in RS time series on the spatial scale of the coil's sensitivity profile, and can markedly bias RS connectivity results. We show that such a transient artifact can be substantially removed from RS time series by using locally formed regressors from white matter tissue. This is particularly important in arrays with larger numbers of coils, which may generate smaller artifact zones. In such a case, brain-wide average noise estimates would fail to capture the artifact. We also examine the anatomical structure of artifactual variance in RS FMRI time series, by identifying sources that contribute to these signals and where in the brain are they manifested. We consider current methods for reducing confounding sources (or noises) and their effects on connectivity maps, and offer an improved approach (ANATICOR) that can also reduce hardware artifacts. The methods described herein are currently available with AFNI, in addition to tools for rapid, interactive generation of seed-based correlation maps at single-subject and group levels.

61Reviews post-2011 research on motion artifact in resting state fMRI61Explains analyses to detect and quantify motion artifact61Presents evidence for removal of artifact by various processing strategies

Here we develop a measure of functional connectivity describing the degree of covariability between a brain region and the rest of the brain. This measure is based on previous formulas for the mutual information (MI) between clusters of regions in the frequency domain. Under the current scenario, the MI can be given as a simple monotonous function of the multiple coherence and it leads to an easy visual representation of connectivity patterns. Computationally efficient formulas, adequate for short time series, are presented and applied to functional magnetic resonance imaging (fMRI) data measured in subjects (02=0234) performing a working memory task or being at rest. While resting state coherence in high (0.17–0.25 Hz) and middle (0.08–0.17 Hz) frequency intervals is bilaterally salient in several limbic and temporal areas including the insula, the amygdala, and the primary auditory cortex, low frequencies (

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information 0, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.

Resting state-fMRI studies have found that the inter-areal correlations in cortical networks concentrate within ultra-low frequencies (0.01–0.04 Hz) while long-distance connections within subcortical networks distribute over a wider frequency range (0.01–0.14 Hz). However, the frequency characteristics of regional homogeneity (ReHo) in different areas are still unclear. To examine the ReHo properties in different frequency bands, a data-driven method, Empirical Mode Decomposition (EMD), was adopted to decompose the time series of each voxel into several components with distinct frequency bands. ReHo values in each of the components were then calculated. Our results showed that ReHo in cortical areas were higher and more frequency-dependent than those in the subcortical regions. BOLD oscillations of 0.02–0.04 Hz mainly contributed to the cortical ReHo, whereas the ReHo in limbic areas involved a wider frequency range and were dominated by higher-frequency BOLD oscillations (>0.08 Hz). The frequency characteristics of ReHo are distinct between different parts of the striatum, with the frequency band of 0.04–0.1 Hz contributing the most to ReHo in caudate nucleus, and oscillations lower than 0.02 Hz contributing more to ReHo in putamen. The distinct frequency-specific ReHo properties of different brain areas may arise from the assorted cytoarchitecture or synaptic types in these areas. Our work may advance the understanding of the neural-physiological basis of local BOLD activities and the functional specificity of different brain regions.

Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have been shown to be associated with electroencephalography (EEG) power fluctuations in multiple brain networks within predefined frequency bands. However, it remains unclear whether frequency-specific characteristics exist in the resting-state fMRI signal. In this study, fMRI signals in five functional brain networks (sensorimotor, ‘default mode’, visual, amygdala, and hippocampus) were decomposed into various frequency bands within a low-frequency range (0–0.2402Hz). Results show that the correlations in cortical networks concentrate within ultra-low frequencies (0.01–0.0602Hz) while connections within limbic networks distribute over a wider frequency range (0.01–0.1402Hz), suggesting distinct frequency-specific features in the resting-state fMRI signal within these functional networks. Moreover, the connectivity decay rates along the frequency bands are positively correlated with the physical distances between connected brain regions and seed points. This distance-frequency relationship might be attributed to a larger attenuation of synchrony of brain regions separated with longer distance and/or connected with more synaptic steps.

A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi-dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved. The final reconstruction of the corresponding intrinsic mode function (IMF) is based on a comparable minimal scale combination principle. For two-dimensional spatial data or images, f(x,y), we consider the data (or image) as a collection of one-dimensional series in both x-direction and y-direction. Each of the one-dimensional slices is decomposed through EEMD with the slice of the similar scale reconstructed in resulting two-dimensional pseudo-IMF-like components. This new two-dimensional data is further decomposed, but the data is considered as a collection of one-dimensional series in y-direction along locations in x-direction. In this way, we obtain a collection of two-dimensional components. These directly resulted components are further combined into a reduced set of final components based on a minimal-scale combination strategy. The approach for two-dimensional spatial data can be extended to multi-dimensional data. EEMD is applied in the first dimension, then in the second direction, and then in the third direction, etc., using the almost identical procedure as for the two-dimensional spatial data. A similar comparable minimal-scale combination strategy can be applied to combine all the directly resulted components into a small set of multi-dimensional final components. For multi-dimensional temporal-spatial data, EEMD is applied to time series of each spatial location to obtain IMF-like components of different time scales. All the ith IMF-like components of all the time series of all spatial locations are arranged to obtain ith temporal-spatial multi-dimensional IMF-like component. The same approach to the one used in temporal-spatial data decomposition is used to obtain the resulting two-dimensional IMF-like components. This approach could be extended to any higher dimensional temporal-spatial data.

Much like genomics, brain connectomics has rapidly become a core component of most national brain projects around the world. Beyond the ambitious aims of these projects, a fundamental challenge is the need for an efficient, robust, reliable and easy-to-use pipeline to mine such large neuroscience datasets. Here, we introduce a computational pipeline—namely the Connectome Computation System (CCS)—for discovery science of human brain connectomes at the macroscale with multimodal magnetic resonance imaging technologies. The CCS is designed with a three-level hierarchical structure that includes data cleaning and preprocessing, individual connectome mapping and connectome mining, and knowledge discovery. Several functional modules are embedded into this hierarchy to implement quality control procedures, reliability analysis and connectome visualization. We demonstrate the utility of the CCS based upon a publicly available dataset, the NKI–Rockland Sample, to delineate the normative trajectories of well-known large-scale neural networks across the natural life span (6–8502years of age). The CCS has been made freely available to the public via GitHub ( https://github.com/zuoxinian/CCS ) and our laboratory’s Web site ( http://lfcd.psych.ac.cn/ccs.html ) to facilitate progress in discovery science in the field of human brain connectomics.

In children with attention deficit hyperactivity disorder (ADHD), functional neuroimaging studies have revealed abnormalities in various brain regions, including prefrontal-striatal circuit, cerebellum, and brainstem. In the current study, we used a new marker of functional magnetic resonance imaging (fMRI), amplitude of low-frequency (0.01 0.08 Hz) fluctuation (ALFF) to investigate the baseline brain function of this disorder. Thirteen boys with ADHD (13.0 1.4 years) were examined by resting-state fMRI and compared with age-matched controls. As a result, we found that patients with ADHD had decreased ALFF in the right inferior frontal cortex, left sensorimotor cortex, and bilateral cerebellum and the vermis as well as increased ALFF in the right anterior cingulated cortex, left sensorimotor cortex, and bilateral brainstem. This resting-state fMRI study suggests that the changed spontaneous neuronal activity of these regions may be implicated in the underlying pathophysiology in children with ADHD.

The human brain is a complex dynamic system capable of generating a multitude of oscillatory waves in support of brain function. Using fMRI, we examined the amplitude of spontaneous low-frequency oscillations (LFO) observed in the human resting brain and the test–retest reliability of relevant amplitude measures. We confirmed prior reports that gray matter exhibits higher LFO amplitude than white matter. Within gray matter, the largest amplitudes appeared along mid-brain structures associated with the “default-mode” network. Additionally, we found that high-amplitude LFO activity in specific brain regions was reliable across time. Furthermore, parcellation-based results revealed significant and highly reliable ranking orders of LFO amplitudes among anatomical parcellation units. Detailed examination of individual low frequency bands showed distinct spatial profiles. Intriguingly, LFO amplitudes in the slow-4 (0.027–0.07302Hz) band, as defined by Buzsáki et al., were most robust in the basal ganglia, as has been found in spontaneous electrophysiological recordings in the awake rat. These results suggest that amplitude measures of LFO can contribute to further between-group characterization of existing and future “resting-state” fMRI datasets.