Search

The connectome, generally defined as a comprehensive map of the structural and/or functional connections of a complete set of neural elements, has been recognized to condense the mechanistic architecture of the brain and capture meaningful individual differences. Increasing efforts are being invested in exploring the associations between connectomes and behavior/symptoms to piece together guidelines from a systems/cognitive neuroscience perspective for reforming mental health care. This editorial sketches a road to mental health with lifespan connectome gradients (LCG), highlighting unique perspectives, prospects, and priorities. [ABSTRACT FROM AUTHOR]

Optimal levels of intrinsic Blood-Oxygenation-Level-Dependent (BOLD) signal variability (variability hereafter) are important for normative brain functioning. However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027–0.073 Hz) and slow5 (0.01–0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum. [ABSTRACT FROM AUTHOR]

[Display omitted] • Our understanding is incomplete for high-altitude (HA) adaption. • A population neuro-geno-type gap exist due to HA immigrants. • HA-related neurotypes are proposed to decipher their genetic roles. • Population neuroscience provides a blueprint for future HA studies. • A Tibetans cohort will be the first step of imaging HA populations. [ABSTRACT FROM AUTHOR]

Connectomics has enhanced our understanding of neurocognitive development and decline by the integration of network sciences into studies across different stages of the human life span. However, these studies commonly occurred independently, missing the opportunity to test integrated models of the dynamical brain organization across the entire life span. In this review article, we survey empirical findings in life-span connectomics and propose a generative framework for computationally modeling the connectome over the human life span. This framework highlights initial findings that across the life span, the human connectome gradually shifts from an ‘anatomically driven’ organization to one that is more ‘topological’. Finally, we consider recent advances that are promising to provide an integrative and systems perspective of human brain plasticity as well as underscore the pitfalls and challenges. [ABSTRACT FROM AUTHOR]

Much effort has been made to understand the organizational principles of human brain function using functional magnetic resonance imaging (fMRI) methods, among which resting-state fMRI (rfMRI) is an increasingly recognized technique for measuring the intrinsic dynamics of the human brain. Functional connectivity (FC) with rfMRI is the most widely used method to describe remote or long-distance relationships in studies of cerebral cortex parcellation, interindividual variability, and brain disorders. In contrast, local or short-distance functional interactions, especially at a scale of millimeters, have rarely been investigated or systematically reviewed like remote FC, although some local FC algorithms have been developed and applied to the discovery of brain-based changes under neuropsychiatric conditions. To fill this gap between remote and local FC studies, this review will (1) briefly survey the history of studies on organizational principles of human brain function; (2) propose local functional homogeneity as a network centrality to characterize multimodal local features of the brain connectome; (3) render a neurobiological perspective on local functional homogeneity by linking its temporal, spatial, and individual variability to information processing, anatomical morphology, and brain development; and (4) discuss its role in performing connectome-wide association studies and identify relevant challenges, and recommend its use in future brain connectomics studies. [ABSTRACT FROM AUTHOR]

Background: Individuals with autism spectrum disorders (ASD) often exhibit symptoms of attention-deficit/hyperactivity disorder (ADHD). Across both disorders, observations of distributed functional abnormalities suggest aberrant large-scale brain network connectivity. Yet, common and distinct network correlates of ASD and ADHD remain unidentified. Here, we aimed to examine patterns of dysconnection in school-age children with ASD and ADHD and typically developing children who completed a resting state functional magnetic resonance imaging scan. Methods: We measured voxelwise network centrality, functional connectivity metrics indexing local (degree centrality [DC]) and global (eigenvector centrality) functional relationships across the entire brain connectome, in resting state functional magnetic resonance imaging data from 56 children with ASD, 45 children with ADHD, and 50 typically developing children. A one-way analysis of covariance, with group as fixed factor (whole-brain corrected), was followed by post hoc pairwise comparisons. Results: Cortical and subcortical areas exhibited centrality abnormalities, some common to both ADHD and ASD, such as in precuneus. Others were disorder-specific and included ADHD-related increases in DC in right striatum/pallidum, in contrast with ASD-related increases in bilateral temporolimbic areas. Secondary analyses differentiating children with ASD into those with or without ADHD-like comorbidity (ASD+ and ASD−, respectively) revealed that the ASD+ group shared ADHD-specific abnormalities in basal ganglia. By contrast, centrality increases in temporolimbic areas characterized children with ASD regardless of ADHD-like comorbidity. At the cluster level, eigenvector centrality group patterns were similar to DC. Conclusions: ADHD and ASD are neurodevelopmental disorders with distinct and overlapping clinical presentations. This work provides evidence for both shared and distinct underlying mechanisms at the large-scale network level. [Copyright &y& Elsevier]

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: The brain''s energy economy excessively favors intrinsic, spontaneous neural activity over extrinsic, evoked activity, presumably to maintain its internal organization. Emerging hypotheses capable of explaining such an investment posit that the brain''s intrinsic functional architecture encodes a blueprint for its repertoire of responses to the external world. Yet, there is little evidence directly linking intrinsic and extrinsic activity in the brain. Here we relate differences among individuals in the magnitude of task-evoked activity during performance of an Eriksen flanker task, to spontaneous oscillatory phenomena observed during rest. Specifically, we focused on the amplitude of low-frequency oscillations (LFO, 0.01–0.1Hz) present in the BOLD signal. LFO amplitude measures obtained during rest successfully predicted the magnitude of task-evoked activity in a variety of regions that were all activated during performance of the flanker task. In these regions, higher LFO amplitude at rest predicted higher task-evoked activity. LFO amplitude measures obtained during rest were also found to have robust predictive value for behavior. In midline cingulate regions, LFO amplitudes predicted not only the speed and consistency of performance but also the magnitude of the behavioral congruency effect embedded in the flanker task. These results support the emerging hypothesis that the brain''s repertoire of responses to the external world are represented and updated in the brain''s intrinsic functional 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]

Abstract: 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 (0.027–0.073 Hz) 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. [Copyright &y& Elsevier]

Abstract: The cognitive division of anterior cingulate cortex (ACC-cd) plays an important role in cognitive control via a distributed attention network. The structural hemispheric asymmetries of ACC have been revealed by several neuroimaging studies. However potential functional hemispheric asymmetries of ACC remain less clear. Investigating the functional hemispheric asymmetries of ACC helps for a better understanding of ACC function. The aim of this study was to use resting-state functional magnetic resonance imaging (fMRI) to examine hemispheric differences in the functional networks associated with ACC-cd in the two hemispheres. ROI-based functional connectivity analysis was performed on a group of 49 right-handed healthy volunteers. The left and right ACC-cd showed significant differences in their patterns of connectivity with a variety of brain regions, including the dorsolateral prefrontal cortex, inferior parietal lobule, superior parietal lobule and dorsal posterior cingulate cortex in their ipsilateral cerebral cortex, as well as cerebellar tonsil and inferior semilunar lobule in their contralateral cerebellar hemisphere. Specifically, for these areas, we found significantly greater connectivity strength with ACC-cd in the right hemisphere than the left, regardless of whether the connection was positive or negative. The current results highlight the presence of clear asymmetries in functional networks associated with ACC-cd. Future functional imaging studies are needed to give greater attention to the lateralized ACC functional networks which are observed. [Copyright &y& Elsevier]

Abstract: Recently, human brain activity during a resting-state has attracted increasing attention. Several studies have found that there are two networks: the default mode network and its anti-correlation network. Some studies have subsequently showed that the functions of brain areas within the default mode network are crucial in human mental activity. To further discern the brain default mode network as well as its anti-correlation network during resting-state, we used three methods to analyze resting-state functional magnetic resonance imaging (fMRI) data; regional homogeneity analysis, linear correlation and independent component analysis, on four groups of dataset. Our results showed the existence of these two networks prominently and consistently during a resting- and conscious-state across the three methods. This consistency was exhibited in four independent groups of normal adults. Moreover, the current results provided evidences that the brain areas within the two anti-correlated networks are highly integrated at both the intra- and inter-regional level. [Copyright &y& Elsevier]

Unraveling the altered brain-behavior relationships in autism spectrum disorder (ASD) has been challenging because of the limitations of sample size and methodologies that are still evolving. Recently, pediatric neuroimaging has undergone considerable advancement by harnessing resting-state functional magnetic resonance imaging (rfMRI),1 in which methodologies can be applied to quantify functional connectivity (FC) from spontaneous fluctuations of brain activity. Benefiting from relatively easy data collection from clinical samples and the ability to harmonize these samples, rfMRI has supported the emergence of open pediatric neuroimaging science (OPENS) through the pooling and sharing of large-scale neuroimaging data by and for the research community (eg, the Autism Brain Imaging Data Exchange [ABIDE]2,3). Big data OPENS ASD studies have revealed functional impairments in both sensory and cognitive brain networks. However, whether these impairments reflect a miswired connectome of network interplay in ASD remains to be elucidated. [ABSTRACT FROM AUTHOR]

Background: Entropy trajectories remain unclear for the aging process of human brain system due to the lacking of longitudinal neuroimaging resource. Results: We used open data from an accelerated longitudinal cohort (PREVENT-AD) that included 24 healthy aging participants followed by 4 years with 5 visits per participant to establish cortical entropy aging curves and distinguish with the effects of age and cohort. This reveals that global cortical entropy decreased with aging, while a significant cohort effect was detectable that people who were born earlier showed higher cortical entropy. Such entropy reductions were also evident for large-scale cortical networks, although with different rates of reduction for different networks. Specifically, the primary and intermediate networks reduce their entropy faster than the higher-order association networks. Conclusions: Our study confirmed that cortical entropy decreases continually in the aging process, both globally and regionally, and we conclude two specific characteristics of the entropy of the human cortex with aging: the shift of the complexity hierarchy and the diversity of complexity strengthen. [ABSTRACT FROM AUTHOR]

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health. [ABSTRACT FROM AUTHOR]

The cognitive and behavioral development of children and adolescents is closely related to the maturation of brain morphology. Although the trajectory of brain development has been depicted in detail, the underlying biological mechanism of normal cortical morphological development in childhood and adolescence remains unclear. By combining the Allen Human Brain Atlas dataset with two single‐site magnetic resonance imaging data including 427 and 733 subjects from China and the United States, respectively, we performed partial least squares regression and enrichment analysis to explore the relationship between the gene transcriptional expression and the development of cortical thickness in childhood and adolescence. We found that the spatial model of normal cortical thinning during childhood and adolescence is associated with genes expressed predominantly in astrocytes, microglia, excitatory and inhibitory neurons. Top cortical development‐related genes are enriched for energy‐related and DNA‐related terms and are associated with psychological and cognitive disorders. Interestingly, there is a great deal of similarity between the findings derived from the two single‐site datasets. This fills the gap between early cortical development and transcriptomes, which promotes an integrative understanding of the potential biological neural mechanisms. [ABSTRACT FROM AUTHOR]

Working memory (WM) deficit is an important component of impaired cognition in schizophrenia. However, between-studies inconsistencies as to the specific functional substrate imply that inter-individual variability (IIV) in the WM performance is associated with IIV in brain activity in schizophrenia. To examine the neural substrate of this WM IIV, we studied whether the neural mechanisms that underlie individual differences in WM capacity are the same in schizophrenia patients and healthy people. We correlated the IIV of the task-evoked brain activity and task performance during an n-back WM task with the IIV of the moment-to-moment variability in intrinsic resting-state activity, as measured by the amplitude of low-frequency fluctuations (ALFFs) and further compared this relationship between 17 patients with first-episode schizophrenia (FES) and 18 healthy controls. Between-group comparisons of the correlation patterns indicated aberrant ALFF-WM activation correlations and ALFF-WM performance correlations in the FES patients, but no significant changes were detected in any single measurement of these three characteristics. Specifically, we found increased positive ALFF-WM activation correlations in the bilateral lateral prefrontal cortices, posterior parietal cortices and fusiform gyri in the FES patients. We also observed significant increases in positive ALFF-WM performance correlations in the bilateral ventromedial prefrontal cortices in the FES patients. This hyper-coupling between the ALFF and fMRI measures during a WM task may indicate that it was difficult for the patients to detach themselves from one state to transition to another and suggests that the inefficient cortical function in schizophrenia stems from the intrinsic functional architecture of the brain. [ABSTRACT FROM AUTHOR]

• Demonstrate use of BrainMap or Neurosynth as a cognition dictionary. • Less than 1% of brain imaging studies have utilized this dictionary approach. • This approach can be especially valuable for clinical research and practice. Characterizing the functional involvement of specific brain regions has long been a central challenge in cognitive neuroscience. Functional magnetic resonance imaging (fMRI) techniques have offered solutions for mapping functional neural networks. The complex nature of structure-function correspondence makes an elaborate task design difficult to fully capture higher-order cognitive function. Other research practices, such as brain-behavior association or between-group comparisons, are thus widely used to explore cognitive correlations with specific brain regions. However, interpreting the results derived from a specific brain region with their underlying cognitive functions has been too general in publications. Here, we use two examples, i.e., a brain-intelligence correlation study and a depression-control comparison meta-study, to demonstrate use of two neuroimaging online databases, BrainMap and Neurosynth. One key utility of the two databases is collecting results from massive cognitive task-based fMRI (tb-fMRI) studies, i.e., coordinates in standard brain space. Just like looking up a "coordinate-based cognition dictionary", researchers can receive a plethora of related tb-fMRI activation information characterized by cognitive domains, specific cognitive functions, cognitive task paradigms, and related publications. Surprisingly, we found that only less than 1% of brain-behavior association or between-group comparison studies have utilized this dictionary approach. We encourage the community to further engage with the existing databases for specific and comprehensive interpretation of neuroimaging as well as guidance of future experimental tb-fMRI design. [ABSTRACT FROM AUTHOR]

Background: Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of synucleinopathies. Patients with synucleinopathies frequently display eye movement abnormalities. However, whether patients with iRBD have eye movement abnormalities remains unknown. Objective: The aim of this study was to assess eye movement abnormalities and related gray matter alterations and explore whether such abnormalities can serve as biomarkers to indicate phenoconversion to synucleinopathies in iRBD. Methods: Forty patients with iRBD with early disease progression and 35 healthy control subjects participated in a 15‐minute ocular‐tracking task that evaluated their control of eye movement abilities. They also underwent clinical assessments for olfactory function, nonmotor symptoms, and autonomic symptoms, all of which are biomarkers to predict phenoconversion to synucleinopathies in iRBD. A subgroup of the participants (20 patients with iRBD and 20 healthy control subjects) also participated in structural magnetic resonance imaging. Results: The ocular‐tracking ability in patients with iRBD was inferior to that of healthy control subjects in two aspects: pursuit initiation and steady‐state tracking. Cortical thinning in the right visual area V4 in patients with iRBD is coupled with impaired pursuit initiation. Furthermore, prolonged pursuit initiation in patients with iRBD exhibits a trend of correlation with olfactory loss, the earliest biomarker that develops prior to other prodromal biomarkers. Conclusions: We found ocular‐tracking abnormalities in patients with iRBD even early in their disease progression that have not been reported before. These abnormalities are coupled with atrophy of brain areas involved in the perception of object motion and might indicate phenoconversion to synucleinopathies in iRBD. © 2022 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Abstract: As researchers increase their efforts to characterize variations in the functional connectome across studies and individuals, concerns about the many sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow. Although substantial within-site variation can exist, efforts to aggregate data across multiple sites such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) datasets amplify these concerns. The present work draws upon standardization approaches commonly used in the microarray gene expression literature, and to a lesser extent recent imaging studies, and compares them with respect to their impact on relationships between common R-fMRI measures and nuisance variables (e.g., imaging site, motion), as well as phenotypic variables of interest (age, sex). Standardization approaches differed with regard to whether they were applied post-hoc vs. during pre-processing, and at the individual vs. group level; additionally they varied in whether they addressed additive effects vs. additive+multiplicative effects, and were parametric vs. non-parametric. While all standardization approaches were effective at reducing undesirable relationships with nuisance variables, post-hoc approaches were generally more effective than global signal regression (GSR). Across approaches, correction for additive effects (global mean) appeared to be more important than for multiplicative effects (global SD) for all R-fMRI measures, with the exception of amplitude of low frequency fluctuations (ALFF). Group-level post-hoc standardizations for mean-centering and variance-standardization were found to be advantageous in their ability to avoid the introduction of artifactual relationships with standardization parameters; though results between individual and group-level post-hoc approaches were highly similar overall. While post-hoc standardization procedures drastically increased test–retest (TRT) reliability for ALFF, modest reductions were observed for other measures after post-hoc standardizations—a phenomena likely attributable to the separation of voxel-wise from global differences among subjects (global mean and SD demonstrated moderate TRT reliability for these measures). Finally, the present work calls into question previous observations of increased anatomical specificity for GSR over mean centering, and draws attention to the near equivalence of global and gray matter signal regression. [Copyright &y& Elsevier]

Abstract: The resting brain exhibits coherent patterns of spontaneous low-frequency BOLD fluctuations. These so-called resting-state functional connectivity (RSFC) networks are posited to reflect intrinsic representations of functional systems commonly implicated in cognitive function. Yet, the direct relationship between RSFC and the BOLD response induced by task performance remains unclear. Here we examine the relationship between a region''s pattern of RSFC across participants and that same region''s level of BOLD activation during an Eriksen Flanker task. To achieve this goal we employed a voxel-matched regression method, which assessed whether the magnitude of task-induced activity at each brain voxel could be predicted by measures of RSFC strength for the same voxel, across 26 healthy adults. We examined relationships between task-induced activation and RSFC strength for six different seed regions [Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. U. S. A. 102, 9673–9678.], as well as the “default mode” and “task-positive” resting-state networks in their entirety. Our results indicate that, for a number of brain regions, inter-individual differences in task-induced BOLD activity were predicted by one of two resting-state properties: (1) the region''s positive connectivity strength with the task-positive network, or (2) its negative connectivity with the default mode network. Strikingly, most of the regions exhibiting a significant relationship between their RSFC properties and task-induced BOLD activity were located in transition zones between the default mode and task-positive networks. These results suggest that a common mechanism governs many brain regions'' neural activity during rest and its neural activity during task performance. [Copyright &y& Elsevier]

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations—such as measurement error—as compared to systematic deviations—such as individual differences—are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individual's samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error. Author summary: In recent decades, the size and complexity of data has grown exponentially. Unfortunately, the increased scale of modern datasets brings many new challenges. At present, we are in the midst of a replicability crisis, in which scientific discoveries fail to replicate to new datasets. Difficulties in the measurement procedure and measurement processing pipelines coupled with the influx of complex high-resolution measurements, we believe, are at the core of the replicability crisis. If measurements themselves are not replicable, what hope can we have that we will be able to use the measurements for replicable scientific findings? We introduce the "discriminability" statistic, which quantifies how discriminable measurements are from one another, without limitations on the structure of the underlying measurements. We prove that discriminable strategies tend to be strategies which provide better accuracy on downstream scientific questions. We demonstrate the utility of discriminability over competing approaches in this context on two disparate datasets from both neuroimaging and genomics. Together, we believe these results suggest the value of designing experimental protocols and analysis procedures which optimize the discriminability. [ABSTRACT FROM AUTHOR]

P values and confidence intervals (CIs) are the most widely used statistical indices in scientific literature. Several surveys have revealed that these two indices are generally misunderstood. However, existing surveys on this subject fall under psychology and biomedical research, and data from other disciplines are rare. Moreover, the confidence of researchers when constructing judgments remains unclear. To fill this research gap, we surveyed 1,479 researchers and students from different fields in China. Results reveal that for significant (i.e., p <.05, CI does not include zero) and non-significant (i.e., p >.05, CI includes zero) conditions, most respondents, regardless of academic degrees, research fields and stages of career, could not interpret p values and CIs accurately. Moreover, the majority were confident about their (inaccurate) judgements (see osf.io/mcu9q/ for raw data, materials, and supplementary analyses). Therefore, as misinterpretations of p values and CIs prevail in the whole scientific community, there is a need for better statistical training in science. [ABSTRACT FROM AUTHOR]

A disruption in the connectivity between brain regions may underlie the core pathology in schizophrenia. One of the most consistent observations in human functional imaging is a network of brain regions referred to as the default network (DMN) that contains core subsystem, the dorsomedial prefrontal cortex (dMPFC) subsystem and the medial temporal lobe (MTL) subsystem, with differential contributions. The goal of this study was to examine abnormalities of different DMN subsystems in first episode schizophrenia and associations between these abnormalities and individual psychopathology. We recruited 203 patients and 131 healthy controls. A seed-based resting-state functional connectivity (RSFC) analysis on the 2D surface was conducted. Individual DMN functional connectivity matrices were then obtained by calculating spatial correlations between pairs of RSFC maps, characterizing the functional fractionation of the DMN. Patients showed patterns similar to controls but markedly reduced strength of DMN fractionation, with the degree centrality of the MTL subsystem significantly reduced, including the posterior inferior parietal lobule (pIPL), parahippocampal cortex (PHC) and lateral temporal cortex (LTC). Patients also exhibited hypo-connectivity within the MTL subsystem and between the MTL and dMPFC subsystems. Clinical symptoms were negatively correlated with degree centrality of LTC, pIPL and PHC in patients. Hyper-fractionation of different DMN components implied that communication and coordination throughout the dissociated components of the DMN are functionally over-segregated in schizophrenia. The associations between the hyper-fractionation with clinical symptoms suggest a role of the high fractionation in the DMN in the abnormal neuropathology observed in schizophrenia. [ABSTRACT FROM AUTHOR]

• The first study to detect abnormalities by surface-based ReHo in BD patients. • BD patients showed reduced surface-based ReHo in the left ventral visual stream. • Abnormal activity in the left VVS cortex may contribute to the pathogenesis of BD. • Surface-based ReHo may be a useful index to explore the pathophysiology of BD. Surface-based, two-dimensional regional homogeneity (2dReHo) was used in the current study to compare local functional synchronization of spontaneous neuronal activity between patients with bipolar disorder (BD) and healthy controls (HC), rather than volume-based, three-dimensional regional homogeneity (3dReHo) methods that have been previously described. Seventy-one BD patients and 113 HC participated in structural and resting-state fMRI scans. Participants ranged in age from 12 to 54 years. All subjects were rated with the Young Mania Rating Scale and the Hamilton Depression Rating Scale. BD patients showed reduced surface-based ReHo across the cortical surface, both at the global level and in the left ventral visual stream (VVS). Additionally, ReHo value across the cortical surface showed a significant negative correlation with age in both groups at the global level. Abnormal activity in the left VVS cortex may contribute to the pathogenesis of BD. Therefore, surface-based ReHo may be a useful index to explore the pathophysiology of BD. [ABSTRACT FROM AUTHOR]

Background: Growing evidence indicates both shared and distinct features of emotional perception in schizophrenia, bipolar disorder and major depressive disorder. In these disorders, alterations in spontaneous low-frequency fluctuations have been reported in the neural system for emotional perception, but the similarities and differences in the amplitude of low-frequency fluctuation (ALFF) across the 3 disorders are unknown. Methods: We compared ALFF and its signal balance in the neural system for emotional perception at 2 frequency bands (slow-5 and slow-4) in 119 participants with schizophrenia, 100 with bipolar disorder, 123 with major depressive disorder and 183 healthy controls. We performed exploratory Pearson partial correlation analyses to determine the relationship between ALFF signal balance and clinical variables. Results: We observed commonalities in ALFF change patterns across the 3 disorders for emotional perception neural substrates, such as increased ALFF in the anterior cerebrum (including subcortical, limbic, paralimbic and heteromodal cortical regions) and decreased ALFF in the posterior visual cortices. Schizophrenia, bipolar disorder and major depressive disorder showed significantly decreased ALFF signal balance in the neural system for emotional perception at both slow-5 and slow-4 frequency bands, with the greatest alterations for schizophrenia, followed by bipolar disorder and major depressive disorder. We found a negative correlation between ALFF signal balance and negative/disorganized symptoms in slow-4 across the 3 disorders. Limitations: The relatively broad age range in our sample and the cross-sectional study design may not account for our findings. Conclusion: The extent of the commonalities we observed further support the concept of core neurobiological disruptions shared among the 3 disorders; ALFF signal balance could be an important neuroimaging marker for the diagnosis and treatment of schizophrenia, bipolar disorder and major depressive disorder. [ABSTRACT FROM AUTHOR]

The human brain can be described as a complex network of anatomical connections between distinct areas, referred to as the human connectome. Fundamental characteristics of connectome organization can be revealed using the tools of network science and graph theory. Of particular interest is the network’s community structure, commonly identified by modularity maximization, where communities are conceptualized as densely intra-connected and sparsely inter-connected. Here we adopt a generative modeling approach called weighted stochastic block models (WSBM) that can describe a wider range of community structure topologies by explicitly considering patterned interactions between communities. We apply this method to the study of changes in the human connectome that occur across the life span (between 6-85 years old). We find that WSBM communities exhibit greater hemispheric symmetry and are spatially less compact than those derived from modularity maximization. We identify several network blocks that exhibit significant linear and non-linear changes across age, with the most significant changes involving subregions of prefrontal cortex. Overall, we show that the WSBM generative modeling approach can be an effective tool for describing types of community structure in brain networks that go beyond modularity. [ABSTRACT FROM AUTHOR]

Abstract Modularity is an important topological attribute for functional brain networks. Recent human fMRI studies have reported that modularity of functional networks varies not only across individuals being related to demographics and cognitive performance, but also within individuals co-occurring with fluctuations in network properties of functional connectivity, estimated over short time intervals. However, characteristics of these time-resolved functional networks during periods of high and low modularity have remained largely unexplored. In this study we investigate basic spatiotemporal properties of time-resolved networks in the high and low modularity periods during rest, with a particular focus on their spatial connectivity patterns, temporal homogeneity and test-retest reliability. We show that spatial connectivity patterns of time-resolved networks in the high and low modularity periods are represented by increased and decreased dissociation of the default mode network module from task-positive network modules, respectively. We also find that the instances of time-resolved functional connectivity sampled from within the high (respectively, low) modularity period are relatively homogeneous (respectively, heterogeneous) over time, indicating that during the low modularity period the default mode network interacts with other networks in a variable manner. We confirmed that the occurrence of the high and low modularity periods varies across individuals with moderate inter-session test-retest reliability and that it is correlated with previously-reported individual differences in the modularity of functional connectivity estimated over longer timescales. Our findings illustrate how time-resolved functional networks are spatiotemporally organized during periods of high and low modularity, allowing one to trace individual differences in long-timescale modularity to the variable occurrence of network configurations at shorter timescales. Highlights • Time-resolved functional connectivity in high/low modularity periods is investigated. • High modularity is tied to increased dissociation of task-positive/negative networks. • Low modularity is associated with heterogeneous connectivity patterns over time. • Frequency of high/low modularity periods has moderate inter-session reproducibility. • An interpretation of individual differences in long-timescale modularity is provided. [ABSTRACT FROM AUTHOR]

Background: Local functional connectivity (FC) indicates local or short-distance functional interactions and may serve as a neuroimaging marker to investigate the human brain connectome. Local FC alterations suggest a disrupted balance in the local functionality of the whole brain network and are increasingly implicated in schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD).Methods: We aim to examine the similarities and differences in the local FC across SZ, BD, and MDD. In total, 537 participants (SZ, 126; BD, 97; MDD, 126; and healthy controls, 188) completed resting-state functional magnetic resonance imaging at a single site. The local FC at resting state was calculated and compared across SZ, BD, and MDD.Results: The local FC increased across SZ, BD, and MDD within the bilateral orbital frontal cortex (OFC) and additional region in the left OFC extending to putamen and decreased in the primary visual, auditory, and motor cortices, right supplemental motor area, and bilateral thalami. There was a gradient in the extent of alterations such that SZ > BD > MDD.Limitations: This cross-sectional study cannot consider medications and other clinical variables.Conclusions: These findings indicate a disrupted balance between network integration and segregation in SZ, BD, and MDD, including over-integration via increased local FC in the OFC and diminished segregation of neural processing with the weakening of the local FC in the primary sensory cortices and thalamus. The shared local FC abnormalities across SZ, BD, and MDD may shed new light on the potential biological mechanisms underlying these disorders. [ABSTRACT FROM AUTHOR]

Structural white matter connections are thought to facilitate integration of neural information across functionally segregated systems. Recent studies have demonstrated that changes in the balance between segregation and integration in brain networks can be tracked by time-resolved functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) data and that fluctuations between segregated and integrated network states are related to human behavior. However, how these network states relate to structural connectivity is largely unknown. To obtain a better understanding of structural substrates for these network states, we investigated how the relationship between structural connectivity, derived from diffusion tractography, and functional connectivity, as measured by rs-fMRI, changes with fluctuations between segregated and integrated states in the human brain. We found that the similarity of edge weights between structural and functional connectivity was greater in the integrated state, especially at edges connecting the default mode and the dorsal attention networks. We also demonstrated that the similarity of network partitions, evaluated between structural and functional connectivity, increased and the density of direct structural connections within modules in functional networks was elevated during the integrated state. These results suggest that, when functional connectivity exhibited an integrated network topology, structural connectivity and functional connectivity were more closely linked to each other and direct structural connections mediated a larger proportion of neural communication within functional modules. Our findings point out the possibility of significant contributions of structural connections to integrative neural processes underlying human behavior. [ABSTRACT FROM AUTHOR]

Abstract Various resting-state fMRI (R-fMRI) measures have been developed to characterize intrinsic brain activity. While each of these measures has gained a growing presence in the literature, questions remain regarding the common and unique aspects these indices capture. The present work provided a comprehensive examination of inter-individual variation and intra-individual temporal variation for commonly used measures, including fractional amplitude of low frequency fluctuations, regional homogeneity, voxel-mirrored homotopic connectivity, network centrality and global signal correlation. Regardless of whether examining intra-individual or inter-individual variation, we found that these definitionally distinct R-fMRI indices tend to exhibit a relatively high degree of covariation, which doesn't exist in phase randomized surrogate data. As a measure of intrinsic brain function, concordance for R-fMRI indices was negatively correlated with age across individuals (i.e., concordance among functional indices decreased with age). To understand the functional significance of concordance, we noted that higher concordance was generally associated with higher strengths of R-fMRI indices, regardless of whether looking through the lens of inter-individual (i.e., high vs. low concordance participants) or intra-individual (i.e., high vs. low concordance states identified via temporal dynamic analyses) differences. We also noted a linear increase in functional concordance together with the R-fMRI indices through the scan, which may suggest a decrease in arousal. The current study demonstrated an enriched picture regarding the relationship among the R-fMRI indices, as well as provided new insights in examining dynamic states within and between individuals. [ABSTRACT FROM AUTHOR]

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]

• We extended the Schaefer parcellations to a new set of homotopic, areal-level parcellations. • The resulting homotopic parcellations are as homogeneous as the Schaefer parcellations and more homogeneous than 5 publicly available parcellations, across 4 datasets. • The homotopic parcellations agree with the boundaries of cortical areas estimated from histology and visuotopic fMRI. • Homotopic correlation patterns are associated with lateralization in resting network organization, as well as lateralization in language and motor task activation. Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic). [ABSTRACT FROM AUTHOR]

• We extracted pre-adulthood urbanicity of 5,390 participants from remote sensing satellite data based on annual residential address of each participant. • Pre-adulthood urbanicity is positively correlated with volumes of bilateral fimbria and left subiculum body. • Pre-adulthood urbanicity has beneficial effects on neurocognitive abilities. • The volumes of left fimbria and left subiculum body mediated the relations between urbanicity and visuospatial memory. Urbanicity refers to the conditions that are particular to urban areas and is a growing environmental challenge that may affect hippocampus and neurocognition. This study aimed to investigate the effects of the average pre-adulthood urbanicity on hippocampal subfield volumes and neurocognitive abilities as well as the sensitive age windows of the urbanicity effects. We included 5,390 CHIMGEN participants (3,538 females; age: 23.69 ± 2.26 years, range: 18–30 years). Pre-adulthood urbanicity of each participant was defined as the average value of annual night-time light (NL) or built-up% from age 0–18, which were extracted from remote-sensing satellite data based on annual residential coordinates of the participants. The hippocampal subfield volumes were calculated based on structural MRI and eight neurocognitive measures were assessed. The linear regression was applied to investigate the associations of pre-adulthood NL with hippocampal subfield volumes and neurocognitive abilities, mediation models were used to find the underlying pathways among urbanicity, hippocampus and neurocognition, and distributed lag models were used to identify sensitive age windows of urbanicity effect. Higher pre-adulthood NL was associated with greater volumes in the left (β = 0.100, 95%CI: [0.075, 0.125]) and right (0.078, [0.052, 0.103]) fimbria and left subiculum body (0.045, [0.020, 0.070]) and better neurocognitive abilities in information processing speed (-0.212, [-0.240, −0.183]), working memory (0.085, [0.057, 0.114]), episodic memory (0.107, [0.080, 0.135]), and immediate (0.094, [0.065, 0.123]) and delayed (0.087, [0.058, 0.116]) visuospatial recall, and hippocampal subfield volumes and visuospatial memory showed bilateral mediations for the urbanicity effects. Urbanicity effects were greatest on the fimbria in preschool and adolescence, on visuospatial memory and information processing from childhood to adolescence and on working memory after 14 years. These findings improve our understanding of the impact of urbanicity on hippocampus and neurocognitive abilities and will benefit for designing more targeted intervention for neurocognitive improvement. [ABSTRACT FROM AUTHOR]