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Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications. [ABSTRACT FROM AUTHOR]

Local functional homogeneity of the human cortex indicates the boundaries between functionally heterogeneous regions and varies remarkably across the cortical mantle. It is unclear whether these variations have the neurobiological and structural basis. We employed structural and resting-state functional magnetic resonance imaging scans from 482 healthy subjects and computed the vertex-wise regional homogeneity of low-frequency fluctuations (2dReHo) and five measures of cortical morphology. We then used these metrics to examine regional variation, morphological association and functional covariance network of 2dReHo. Within the ventral visual stream, increases of 2dReHo reflect reduced complexity of information processing or functional hierarchies. Along the divisions of the prefrontal cortex and posteromedial cortex, the gradients of 2dReHo revealed the hierarchical organization within the two association areas, respectively. Individual differences in 2dReHo are associated with those of cortical morphology, and their whole-brain inter-regional covariation is organized into a functional covariance network, comprising five hierarchically organized modules with hubs of both primary sensory and high-order association areas. These highly reproducible observations suggest that local functional homogeneity has neurobiological relevance that is likely determined by anatomical, developmental and neurocognitive factors and should serve as a neuroimaging marker to investigate the human brain function. [ABSTRACT FROM AUTHOR]

Although research has provided abundant evidence for Taichi-induced improvements in psychological and physiological well-being, little is known about possible links to brain structure of Taichi practice. Using high-resolution MRI of 22 Tai Chi Chuan (TCC) practitioners and 18 controls matched for age, sex and education, we set out to examine the underlying anatomical correlates of long-term Taichi practice at two different levels of regional specificity. For this purpose, parcel-wise and vertex-wise analyses were employed to quantify the difference between TCC practitioners and the controls based on cortical surface reconstruction. We also adopted the Attention Network Test (ANT) to explore the effect of TCC on executive control. TCC practitioners, compared with controls, showed significantly thicker cortex in precentral gyrus, insula sulcus and middle frontal sulcus in the right hemisphere and superior temporal gyrus and medial occipito-temporal sulcus and lingual sulcus in the left hemisphere. Moreover, we found that thicker cortex in left medial occipito-temporal sulcus and lingual sulcus was associated with greater intensity of TCC practice. These findings indicate that long-term TCC practice could induce regional structural change and also suggest TCC might share similar patterns of neural correlates with meditation and aerobic exercise. [ABSTRACT FROM AUTHOR]

Abstract: While researchers have extensively characterized functional connectivity between brain regions, the characterization of functional homogeneity within a region of the brain connectome is in early stages of development. Several functional homogeneity measures were proposed previously, among which regional homogeneity (ReHo) was most widely used as a measure to characterize functional homogeneity of resting state fMRI (R-fMRI) signals within a small region (Zang et al., 2004). Despite a burgeoning literature on ReHo in the field of neuroimaging brain disorders, its test–retest (TRT) reliability remains unestablished. Using two sets of public R-fMRI TRT data, we systematically evaluated the ReHo''s TRT reliability and further investigated the various factors influencing its reliability and found: 1) nuisance (head motion, white matter, and cerebrospinal fluid) correction of R-fMRI time series can significantly improve the TRT reliability of ReHo while additional removal of global brain signal reduces its reliability, 2) spatial smoothing of R-fMRI time series artificially enhances ReHo intensity and influences its reliability, 3) surface-based R-fMRI computation largely improves the TRT reliability of ReHo, 4) a scan duration of 5min can achieve reliable estimates of ReHo, and 5) fast sampling rates of R-fMRI dramatically increase the reliability of ReHo. Inspired by these findings and seeking a highly reliable approach to exploratory analysis of the human functional connectome, we established an R-fMRI pipeline to conduct ReHo computations in both 3-dimensions (volume) and 2-dimensions (surface). [Copyright &y& Elsevier]

Abstract: Existing spatial independent component analysis (ICA) methods for multi-subject fMRI datasets have mainly focused on detecting common components across subjects, under the assumption that all the subjects in a group share the same (identical) components. However, as a data-driven approach, ICA could potentially serve as an exploratory tool at multi-subject level, and help us uncover inter-subject differences in patterns of connectivity (e.g., find subtypes in patient populations). In this work, we propose a methodology named gRAICAR that exploits the data-driven nature of ICA to allow discovery of sub-groupings of subjects based on reproducibility of their ICA components. This technique allows us not only to find highly reproducible common components across subjects but also to explore (without a priori subject groupings) components that could classify all subjects into sub-groups. gRAICAR generalizes the reproducibility framework previously developed for single subjects (Ranking and averaging independent component analysis by reproducibility—RAICAR—Yang et al., Hum Brain Mapp, 2008) to multiple-subject analysis. For each group-level component, gRAICAR generates its reproducibility matrix and further computes two metrics, inter-subject consistency and intra-subject reliability, to characterize inter-subject variability and reflect contributions from individual subjects. Nonparametric tests are employed to examine the significance of both the inter-subject consistency and the separation of subject groups reflected in the component. Our validations based on simulated and experimental resting-state fMRI datasets demonstrated the advantage of gRAICAR in extracting features reflecting potential subject groupings. It may facilitate discovery of the underlying brain functional networks with substantial potential to inform our understandings of development, neurodegenerative conditions, and psychiatric disorders. [Copyright &y& Elsevier]

Abstract: Schizophrenia has been increasingly conceptualized as a disorder of brain connectivity, in large part due to findings emerging from white matter and functional connectivity (FC) studies. This work has focused primarily on within-hemispheric connectivity, however some evidence has suggested abnormalities in callosal structure and interhemispheric interaction. Here we examined functional connectivity between homotopic points in the brain using a technique called voxel-mirrored homotopic connectivity (VMHC). We performed VMHC analyses on resting state fMRI data from 23 healthy controls and 25 patients with schizophrenia or schizoaffective disorder. We found highly significant reductions in VMHC in patients for a number of regions, particularly the occipital lobe, the thalamus, and the cerebellum. No regions of increased VMHC were detected in patients. VMHC in the postcentral gyrus extending into the precentral gyrus was correlated with PANSS Total scores. These results show substantial impairment of interhemispheric coordination in schizophrenia. [Copyright &y& Elsevier]

Background: Models of cocaine addiction emphasize the role of disrupted frontal circuitry supporting cognitive control processes. However, addiction-related alterations in functional interactions among brain regions, especially between the cerebral hemispheres, are rarely examined directly. Resting-state functional magnetic resonance imaging (fMRI) approaches, which reveal patterns of coherent spontaneous fluctuations in the fMRI signal, offer a means to quantify directly functional interactions between the hemispheres. We examined interhemispheric resting-state functional connectivity (RSFC) in cocaine dependence using a recently validated approach, voxel-mirrored homotopic connectivity. Methods: We compared interhemispheric RSFC between 25 adults (aged 35.0 ± 8.8) meeting DSM-IV criteria for cocaine dependence within the past 12 months but currently abstaining (>2 weeks) from cocaine and 24 healthy comparisons (35.1 ± 7.5), group-matched on age, sex, education, and employment status. Results: We observed reduced prefrontal interhemispheric RSFC in cocaine-dependent participants relative to control subjects. Further analyses demonstrated a striking cocaine-dependence-related reduction in interhemispheric RSFC among nodes of the dorsal attention network, comprising bilateral lateral frontal, medial premotor, and posterior parietal areas. Further, within the cocaine-dependent group, RSFC within the dorsal attention network was associated with self-reported attentional lapses. Conclusions: Our findings provide further evidence of an association between chronic exposure to cocaine and disruptions within large-scale brain circuitry supporting cognitive control. We did not detect group differences in diffusion tensor imaging measures, suggesting that alterations in the brain''s functional architecture associated with cocaine exposure can be observed in the absence of detectable abnormalities in the white matter microstructure supporting that architecture. [Copyright &y& Elsevier]

Abstract: Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA decomposition) is difficult to achieve for a large dataset. To overcome this, TC-GICA employs multiple-stage PCA data reduction. Such multiple-stage PCA data reduction, however, leads to variable outputs due to different subject concatenation orders. Consequently, the ICA algorithm uses the variable multiple-stage PCA outputs and generates variable decompositions. In this study, a rigorous theoretical analysis was conducted to prove the existence of such variability. Simulated and real fMRI experiments were used to demonstrate the subject-order-induced variability of TC-GICA results using multiple PCA data reductions. To solve this problem, we propose a new subject order-independent group ICA (SOI-GICA). Both simulated and real fMRI data experiments demonstrated the high robustness and accuracy of the SOI-GICA results compared to those of traditional TC-GICA. Accordingly, we recommend SOI-GICA for group ICA-based fMRI studies, especially those with large data sets. [Copyright &y& Elsevier]

Abstract: Recently, a great deal of interest has arisen in resting state fMRI as a measure of tonic brain function in clinical populations. Most studies have focused on the examination of temporal correlation between resting state fMRI low-frequency oscillations (LFOs). Studies on the amplitudes of these low-frequency oscillations are rarely reported. Here, we used amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF; the relative amplitude that resides in the low frequencies) to examine the amplitude of LFO in schizophrenia. Twenty-six healthy controls and 29 patients with schizophrenia or schizoaffective disorder participated. Our findings show that patients showed reduced low-frequency amplitude in proportion to the total frequency band investigated (i.e., fALFF) in the lingual gyrus, left cuneus, left insula/superior temporal gyrus, and right caudate and increased fALFF in the medial prefrontal cortex and the right parahippocampal gyrus. ALFF was reduced in patients in the lingual gyrus, cuneus, and precuneus and increased in the left parahippocampal gyrus. These results suggest LFO abnormalities in schizophrenia. The implication of these abnormalities for schizophrenic symptomatology is further discussed. [Copyright &y& Elsevier]

Abstract: Functional connectivity analyses of resting-state fMRI data are rapidly emerging as highly efficient and powerful tools for in vivo mapping of functional networks in the brain, referred to as intrinsic connectivity networks (ICNs). Despite a burgeoning literature, researchers continue to struggle with the challenge of defining computationally efficient and reliable approaches for identifying and characterizing ICNs. Independent component analysis (ICA) has emerged as a powerful tool for exploring ICNs in both healthy and clinical populations. In particular, temporal concatenation group ICA (TC-GICA) coupled with a back-reconstruction step produces participant-level resting state functional connectivity maps for each group-level component. The present work systematically evaluated the test–retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5–16 months). Additionally, to investigate the degree to which the components revealed by TC-GICA are detectable via single-session ICA, we investigated the reproducibility of TC-GICA findings. First, we found moderate-to-high short- and long-term test–retest reliability for ICNs derived by combining TC-GICA and dual regression. Exceptions to this finding were limited to physiological- and imaging-related artifacts. Second, our reproducibility analyses revealed notable limitations for template matching procedures to accurately detect TC-GICA based components at the individual scan level. Third, we found that TC-GICA component''s reliability and reproducibility ranks are highly consistent. In summary, TC-GICA combined with dual regression is an effective and reliable approach to exploratory analyses of resting state fMRI data. [Copyright &y& Elsevier]

Alterations in both local and remote connectivity were reported in amnestic mild cognitive impairment (aMCI) patients but rarely in the same group of patients. In the present study, we employed a novel resting-state functional magnetic resonance imaging (rfMRI) connectome index, regional functional homogeneity on the 2-dimensional cortical surface, to detect full-cortex vertex-wise changes of the local rfMRI connectivity in 32 aMCI patients compared with 40 healthy controls. We further used the seed-based functional connectivity to explore the remote rfMRI connectivity in aMCI. The results revealed significantly lower local connectivity in the default network and higher local connectivity in the somatomotor network in aMCI patients. Abnormal remote connectivity relevant to local connectivity was primarily detectable within the default network (decrease) and in the somatomotor and attention networks (increase). The abnormalities in the remote (not local) default network connectivity were significantly associated with episodic memory performance in patients. These distance-related connectivity profiles illustrated a dysfunctional pattern in aMCI, which extended our knowledge of this pathological aging process. [ABSTRACT FROM AUTHOR]

A thinner cortex has higher potential for binding GABA receptor A which is associated with larger amplitudes of intrinsic brain activity (iBA). However, the relationship between cortical thickness and iBA is unknown in intact and epileptic brains. To this end, we investigated the relationship between cortical thickness measured by high-resolution MRI and surface-based iBA derived from resting-state functional MRI in normal controls ( n = 82) and patients with generalized tonic-clonic seizures (GTCS) only ( n = 82). We demonstrated that the spatial distribution of cortical thickness negatively correlated with surface-based iBA amplitude at both whole-brain and within independent brain functional networks. In GTCS patients, spatial coupling between thickness and iBA amplitude decreased in the default mode, dorsal attention, and somatomotor networks. In addition, the vertex-wise across-subject thickness-iBA amplitude correspondence altered in the frontal and temporal lobes as well as in the precuneus in GTCS patients. The relationship between these two modalities can serve as a brain-based marker for detecting epileptogenic changes. [ABSTRACT FROM AUTHOR]

Normal aging is associated with cognitive decline and underlying brain dysfunction. Previous studies concentrated less on brain network changes at a systems level. Our goal was to examine these age-related changes of fMRI-derived activation with a common network parcellation of the human brain function, offering a systems-neuroscience perspective of healthy aging. We conducted a series of meta-analyses on a total of 114 studies that included 2035 older adults and 1845 young adults. Voxels showing significant age-related changes in activation were then overlaid onto seven commonly referenced neuronal networks. Older adults present moderate cognitive decline in behavioral performance during fMRI scanning, and hypo-activate the visual network and hyper-activate both the frontoparietal control and default mode networks. The degree of increased activation in frontoparietal network was associated with behavioral performance in older adults. Age-related changes in activation present different network patterns across cognitive domains. The systems neuroscience approach used here may be useful for elucidating the underlying network mechanisms of various brain plasticity processes during healthy aging. [ABSTRACT FROM AUTHOR]

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 (6-85 years of age). The CCS has been made freely available to the public via GitHub () and our laboratory's Web site () to facilitate progress in discovery science in the field of human brain connectomics. [ABSTRACT FROM AUTHOR]

Abstract: Resting-state functional MRI (R-fMRI) has emerged as a promising neuroimaging technique used to identify global hubs of the human brain functional connectome. However, most R-fMRI studies on functional hubs mainly utilize traditional R-fMRI data with relatively low sampling rates (e.g., repetition time [TR]=2s). R-fMRI data scanned with higher sampling rates are important for the characterization of reliable functional connectomes because they can provide temporally complementary information about functional integration among brain regions and simultaneously reduce the effects of high frequency physiological noise. Here, we employed a publicly available multiband R-fMRI dataset with a sub-second sampling rate (TR=645ms) to identify global hubs in the human voxel-wise functional networks, and further examined their test–retest (TRT) reliability over scanning time. We showed that the functional hubs of human brain networks were mainly located at the default-mode regions (e.g., medial prefrontal and parietal cortex as well as the lateral parietal and temporal cortex) and the sensorimotor and visual cortex. These hub regions were highly anatomically distance-dependent, where short-range and long-range hubs were primarily located at the primary cortex and the multimodal association cortex, respectively. We found that most functional hubs exhibited fair to good TRT reliability using intraclass correlation coefficients. Interestingly, our analysis suggested that a 6-minute scan duration was able to reliably detect these functional hubs. Further comparison analysis revealed that these results were approximately consistent with those obtained using traditional R-fMRI scans of the same subjects with TR=2500ms, but several regions (e.g., lateral frontal cortex, paracentral lobule and anterior temporal lobe) exhibited different TRT reliability. Finally, we showed that several regions (including the medial/lateral prefrontal cortex and lateral temporal cortex) were identified as brain hubs in a high frequency band (0.2–0.3Hz), which is beyond the frequency scope of traditional R-fMRI scans. Our results demonstrated the validity of multiband R-fMRI data to reliably detect functional hubs in the voxel-wise whole-brain networks, which motivated the acquisition of high temporal resolution R-fMRI data for the studies of human brain functional connectomes in healthy and diseased conditions. [Copyright &y& Elsevier]

Abstract: Spatial smoothing is typically used to denoise magnetic resonance imaging (MRI) data. Gaussian smoothing kernels, associated with heat equations or isotropic diffusion (ISD), are widely adopted for this purpose because of their easy implementation and efficient computation, but despite these advantages, Gaussian smoothing kernels blur the edges, curvature and texture of images. To overcome these issues, researchers have proposed anisotropic diffusion (ASD) and non-local means [i.e., diffusion (NLD)] kernels. However, these new filtering paradigms are rarely applied to MRI analyses. In the current study, using real degraded MRI data, we demonstrated the effect of denoising using ISD, ASD and NLD kernels. Furthermore, we evaluated their impact on three common preprocessing steps of MRI data analysis: brain extraction, segmentation and registration. Results suggest that NLD-based spatial smoothing is most effective at improving the quality of MRI data preprocessing and thus should become the new standard method of smoothing in MRI data processing. [Copyright &y& Elsevier]

Background: Models of autism spectrum disorders (ASD) as neural disconnection syndromes have been predominantly supported by examinations of abnormalities in corticocortical networks in adults with autism. A broader body of research implicates subcortical structures, particularly the striatum, in the physiopathology of autism. Resting state functional magnetic resonance imaging has revealed detailed maps of striatal circuitry in healthy and psychiatric populations and vividly captured maturational changes in striatal circuitry during typical development. Methods: Using resting state functional magnetic resonance imaging, we examined striatal functional connectivity (FC) in 20 children with ASD and 20 typically developing children between the ages of 7.6 and 13.5 years. Whole-brain voxelwise statistical maps quantified within-group striatal FC and between-group differences for three caudate and three putamen seeds for each hemisphere. Results: Children with ASD mostly exhibited prominent patterns of ectopic striatal FC (i.e., functional connectivity present in ASD but not in typically developing children), with increased functional connectivity between nearly all striatal subregions and heteromodal associative and limbic cortex previously implicated in the physiopathology of ASD (e.g., insular and right superior temporal gyrus). Additionally, we found striatal functional hyperconnectivity with the pons, thus expanding the scope of functional alterations implicated in ASD. Secondary analyses revealed ASD-related hyperconnectivity between the pons and insula cortex. Conclusions: Examination of FC of striatal networks in children with ASD revealed abnormalities in circuits involving early developing areas, such as the brainstem and insula, with a pattern of increased FC in ectopic circuits that likely reflects developmental derangement rather than immaturity of functional circuits. [Copyright &y& Elsevier]

Abstract: Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel''s BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity–distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship that has attracted much recent interest. Next, these profile functions are regressed on predictors of interest, whether categorical (e.g., acquisition site or diagnostic group) or continuous (e.g., age). This procedure can provide insight into the roles of multiple sources of variation, and detect large-scale patterns not easily available from conventional analyses. We illustrate the proposed methods with a resting state data set pooled across four imaging sites. [Copyright &y& Elsevier]