Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Test-retest reliability and time-of-day variations of perfusion imaging at rest and during a vigilance task

Arterial spin-labeled perfusion and blood oxygenation level-dependent functional MRI are indispensable tools for noninvasive human brain imaging in clinical and cognitive neuroscience, yet concerns persist regarding the reliability and reproducibility of functional MRI findings. The circadian rhythm is known to play a significant role in physiological and psychological responses, leading to variability in brain function at different times of the day. Despite this, test-retest reliability of brain function across different times of the day remains poorly understood. This study examined the test-retest reliability of six repeated cerebral blood flow measurements using arterial spin-labeled perfusion imaging both at resting-state and during the psychomotor vigilance test, as well as task-induced cerebral blood flow changes in a cohort of 38 healthy participants over a full day. The results demonstrated excellent test-retest reliability for absolute cerebral blood flow measurements at rest and during the psychomotor vigilance test throughout the day. However, task-induced cerebral blood flow changes exhibited poor reliability across various brain regions and networks. Furthermore, reliability declined over longer time intervals within the day, particularly during nighttime scans compared to daytime scans. These findings highlight the superior reliability of absolute cerebral blood flow compared to task-induced cerebral blood flow changes and emphasize the importance of controlling time-of-day effects to enhance the reliability and reproducibility of future brain imaging studies.

Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI

Objective.New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity (FC) between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms ofreproducibilityand theability to predict individual traitsin order to demonstrate these measures' internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits.Main results.The proposed new FC measure ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.

Regression models for partially localized fMRI connectivity analyses

Background

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. Analysis methods can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is an assumption that brain regions are functionally aligned across subjects; however, it is known that this functional alignment assumption is often violated.

Methods

In this paper, we use subject-level regression models to explain intra-subject variability in connectivity. Covariates can include factors such as geographic distance between two pairs of brain regions, whether the two regions are symmetrically opposite (homotopic), and whether the two regions are members of the same functional network. Additionally, a covariate for each brain region can be included, to account for the possibility that some regions have consistently higher or lower connectivity. This style of analysis allows us to characterize the fraction of variation explained by each type of covariate. Additionally, comparisons across subjects can then be made using the fitted connectivity regression models, offering a more parsimonious alternative to edge-at-a-time approaches.

Results

We apply our approach to Human Connectome Project data on 268 regions of interest (ROIs), grouped into eight functional networks. We find that a high proportion of variation is explained by region covariates and network membership covariates, while geographic distance and homotopy have high relative importance after adjusting for the number of predictors. We also find that the degree of data repeatability using our connectivity regression model-which uses only partial location information about pairs of ROI's-is comparably as high as the repeatability obtained using full location information.

Discussion

While our analysis uses data that have been transformed into a common template-space, we also envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.

Evaluating functional brain organization in individuals and identifying contributions to network overlap

Individual differences in the spatial organization of resting state networks have received increased attention in recent years. Measures of individual-specific spatial organization of brain networks and overlapping network organization have been linked to important behavioral and clinical traits and are therefore potential biomarker targets for personalized psychiatry approaches. To better understand individual-specific spatial brain organization, this paper addressed three key goals. First, we determined whether it is possible to reliably estimate weighted (non-binarized) resting state network maps using data from only a single individual, while also maintaining maximum spatial correspondence across individuals. Second, we determined the degree of spatial overlap between distinct networks, using test-retest and twin data. Third, we systematically tested multiple hypotheses (spatial mixing, temporal switching, and coupling) as candidate explanations for why networks overlap spatially. To estimate weighted network organization, we adopt the Probabilistic Functional Modes (PROFUMO) algorithm, which implements a Bayesian framework with hemodynamic and connectivity priors to supplement optimization for spatial sparsity/independence. Our findings showed that replicable individual-specific estimates of weighted resting state networks can be derived using high quality fMRI data within individual subjects. Network organization estimates using only data from each individual subject closely resembled group-informed network estimates (which was not explicitly modeled in our individual-specific analyses), suggesting that cross-subject correspondence was largely maintained. Furthermore, our results confirmed the presence of spatial overlap in network organization, which was replicable across sessions within individuals and in monozygotic twin pairs. Intriguingly, our findings provide evidence that network overlap is indicative of linear additive coupling. These results suggest that regions of network overlap concurrently process information from all contributing networks, potentially pointing to the role of overlapping network organization in the integration of information across multiple brain systems.

The robustness of persistent homology of brain networks to data acquisition-related non-neural variability in resting state fMRI

There is increasing interest in investigating brain function based on functional connectivity networks (FCN) obtained from resting-state functional magnetic resonance imaging (fMRI). FCNs, typically obtained using measures of time series association such as Pearson's correlation, are sensitive to data acquisition parameters such as sampling period. This introduces non-neural variability in data pooled from different acquisition protocols and MRI scanners, negating the advantages of larger sample sizes in pooled data. To address this, we hypothesize that the topology or shape of brain networks must be preserved irrespective of how densely it is sampled, and metrics which capture this topology may be statistically similar across sampling periods, thereby alleviating this source of non-neural variability. Accordingly, we present an end-to-end pipeline that uses persistent homology (PH), a branch of topological data analysis, to demonstrate similarity across FCNs acquired at different temporal sampling periods. PH, as a technique, extracts topological features by capturing the network organization across all continuous threshold values, as opposed to graph theoretic methods, which fix a discrete network topology by thresholding the connectivity matrix. The extracted topological features are encoded in the form of persistent diagrams that can be compared against one another using the earth-moving metric, also popularly known as the Wasserstein distance. We extract topological features from three data cohorts, each acquired at different temporal sampling periods and demonstrate that these features are statistically the same, hence, empirically showing that PH may be robust to changes in data acquisition parameters such as sampling period.

Test-retest reliability of fNIRS in resting-state cortical activity and brain network assessment in stroke patients

Resting-state functional near infrared spectroscopy (fNIRS) scanning has attracted considerable attention in stroke rehabilitation research in recent years. The aim of this study was to quantify the reliability of fNIRS in cortical activity intensity and brain network metrics among resting-state stroke patients, and to comprehensively evaluate the effects of frequency selection, scanning duration, analysis and preprocessing strategies on test-retest reliability. Nineteen patients with stroke underwent two resting fNIRS scanning sessions with an interval of 24 hours. The haemoglobin signals were preprocessed by principal component analysis, common average reference and haemodynamic modality separation (HMS) algorithm respectively. The cortical activity, functional connectivity level, local network metrics (degree, betweenness and local efficiency) and global network metrics were calculated at 25 frequency scales × 16 time windows. The test-retest reliability of each fNIRS metric was quantified by the intraclass correlation coefficient. The results show that (1) the high-frequency band has higher ICC values than the low-frequency band, and the fNIRS metric is more reliable than at the individual channel level when averaged within the brain region channel, (2) the ICC values of the low-frequency band above the 4-minute scan time are generally higher than 0.5, the local efficiency and global network metrics reach high and excellent reliability levels after 4 min (0.5 < ICC < 0.9), with moderate or even poor reliability for degree and betweenness (ICC < 0.5), (3) HMS algorithm performs best in improving the low-frequency band ICC values. The results indicate that a scanning duration of more than 4 minutes can lead to high reliability of most fNIRS metrics when assessing low-frequency resting brain function in stroke patients. It is recommended to use the global correction method of HMS, and the reporting of degree, betweenness and single channel level should be performed with caution. This paper provides the first comprehensive reference for resting-state experimental design and analysis strategies for fNIRS in stroke rehabilitation.

Test-Retest Reliability of Resting Brain Small-World Network Properties across Different Data Processing and Modeling Strategies

Resting-state functional magnetic resonance imaging (fMRI) with graph theoretical modeling has been increasingly applied for assessing whole brain network topological organization, yet its reproducibility remains controversial. In this study, we acquired three repeated resting-state fMRI scans from 16 healthy controls during a strictly controlled in-laboratory study and examined the test-retest reliability of seven global and three nodal brain network metrics using different data processing and modeling strategies. Among the global network metrics, the characteristic path length exhibited the highest reliability, whereas the network small-worldness performed the poorest. Nodal efficiency was the most reliable nodal metric, whereas betweenness centrality showed the lowest reliability. Weighted global network metrics provided better reliability than binary metrics, and reliability from the AAL90 atlas outweighed those from the Power264 parcellation. Although global signal regression had no consistent effects on the reliability of global network metrics, it slightly impaired the reliability of nodal metrics. These findings provide important implications for the future utility of graph theoretical modeling in brain network analyses.

Quality control in resting-state fMRI: the benefits of visual inspection

Background

A variety of quality control (QC) approaches are employed in resting-state functional magnetic resonance imaging (rs-fMRI) to determine data quality and ultimately inclusion or exclusion of a fMRI data set in group analysis. Reliability of rs-fMRI data can be improved by censoring or "scrubbing" volumes affected by motion. While censoring preserves the integrity of participant-level data, including excessively censored data sets in group analyses may add noise. Quantitative motion-related metrics are frequently reported in the literature; however, qualitative visual inspection can sometimes catch errors or other issues that may be missed by quantitative metrics alone. In this paper, we describe our methods for performing QC of rs-fMRI data using software-generated quantitative and qualitative output and trained visual inspection.

Results

The data provided for this QC paper had relatively low motion-censoring, thus quantitative QC resulted in no exclusions. Qualitative checks of the data resulted in limited exclusions due to potential incidental findings and failed pre-processing scripts.

Conclusion

Visual inspection in addition to the review of quantitative QC metrics is an important component to ensure high quality and accuracy in rs-fMRI data analysis.

Regression models for partially localized fMRI connectivity analyses

Brain functional connectivity analysis of resting-state functional magnetic resonance imaging (fMRI) data is typically performed in a standardized template space assuming consistency of connections across subjects. This can come in the form of one-edge-at-a-time analyses or dimension reduction/decomposition methods. Common to these approaches is the assumption of complete localization (or spatial alignment) of brain regions across subjects. Alternative approaches completely eschew localization assumptions by treating connections as statistically exchangeable (for example, using the density of connectivity between nodes). Yet other approaches, such as hyperalignment, attempt to align subjects on function as well as structure, thereby achieving a different sort of template-based localization. In this paper, we propose the use of simple regression models to characterize connectivity. To that end, we build regression models on subject-level Fisher transformed regional connection matrices using geographic distance, homotopic distance, network labels, and region indicators as covariates to explain variation in connections. While we perform our analysis in template-space in this paper, we envision the method being useful in multi-atlas registration settings, where subject data remains in its own geometry and templates are warped instead. A byproduct of this style of analysis is the ability to characterize the fraction of variation in subject-level connections explained by each type of covariate. Using Human Connectome Project data, we found that network labels and regional characteristics contribute far more than geographic or homotopic relationships (considered non-parametrically). In addition, visual regions had the highest explanatory power (i.e., largest regression coefficients). We also considered subject repeatability and found that the degree of repeatability seen in fully localized models is largely recovered using our proposed subject-level regression models. Further, even fully exchangeable models retain a sizeable amount of repeatability information, despite discarding all localization information. These results suggest the tantalizing possibility that fMRI connectivity analysis can be performed in subject-space, using less aggressive registration, such as simple affine transformations, multi-atlas subject-space registration, or perhaps even no registration whatsoever.

Reduced emergent character of neural dynamics in patients with a disrupted connectome

High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.

Assessing the Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researcher's Choice Paths

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, Ι will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI (rs-fMRI) recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the rs-fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the rs-fMRI dataset containing 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers' choices that yield consistently brain networks with repeatable topologies, giving me the chance to recommend best practices over consistent topologies.

Test-retest reliability of modular-relevant analysis in brain functional network

Introduction

The human brain could be modeled as a complex network via functional magnetic resonance imaging (fMRI), and the architecture of these brain functional networks can be studied from multiple spatial scales with different graph theory tools. Detecting modules is an important mesoscale network measuring approach that has provided crucial insights for uncovering how brain organizes itself among different functional subsystems. Despite its successful application in a wide range of brain network studies, the lack of comprehensive reliability assessment prevents its potential extension to clinical trials.

Methods

To fill this gap, this paper, using resting-state test-retest fMRI data, systematically explored the reliabilities of five popular network metrics derived from modular structure. Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection.

Results

The results reported moderate-to-good reliability in modularity, intra- and inter-modular functional connectivities, within-modular degree and participation coefficient at both individual and group levels, indicating modular-relevant network metrics can provide robust evaluation results. Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results.

Discussion

This paper provides important guidance for choosing reliable modular-relevant network metrics and analysis strategies in future studies.

Spatial variation of changes in test-retest reliability of functional connectivity after global signal regression: The effect of considering hemodynamic delay

Global signal regression (GSR) is a controversial analysis method, since its removal of signal has been observed to reduce the reliability of functional connectivity estimates. Here, we used test-retest reliability to characterize potential differences in spatial patterns between conventional, static GSR (sGSR) and a novel dynamic form of GSR (dGSR). In contrast with sGSR, dGSR models the global signal at a time delay to correct for blood arrival time. Thus, dGSR accounts for greater variation in global signal, removes blood-flow-related nuisance signal, and leaves higher quality neuronal signal remaining. We used intraclass correlation coefficients (ICCs) to estimate the reliability of functional connectivity in 462 healthy controls from the Human Connectome Project. We tested across two factors: denoising method used (control, sGSR, and dGSR), and interacquisition interval (between days, or within session while varying phase encoding direction). Reliability was estimated regionally to identify topographic patterns for each condition. sGSR and dGSR provided global reductions in reliability compared with the non-GSR control. Test-retest reliability was highest in the frontoparietal and default mode regions, and lowest in sensorimotor cortex for all conditions. dGSR provides more effective denoising in regions where both strategies greatly reduce reliability. Both GSR methods substantially reduced test-retest reliability, which was most evident in brain regions that had low reliability prior to denoising. These findings suggest that reliability of interregional correlation is likely inflated by the global signal, which is thought to primarily reflect dynamic blood flow.

The effect of general anesthesia on the test–retest reliability of resting-state fMRI metrics and optimization of scan length

Resting-state functional magnetic resonance imaging (rs-fMRI) has been known as a powerful tool in neuroscience. However, exploring the test–retest reliability of the metrics derived from the rs-fMRI BOLD signal is essential, particularly in the studies of patients with neurological disorders. Here, two factors, namely, the effect of anesthesia and scan length, have been estimated on the reliability of rs-fMRI measurements. A total of nine patients with drug-resistant epilepsy (DRE) requiring interstitial thermal therapy (LITT) were scanned in two states. The first scan was performed in an awake state before surgery on the same patient. The second scan was performed 2 weeks later under general anesthesia necessary for LITT surgery. At each state, two rs-fMRI sessions were obtained that each one lasted 15 min, and the effect of scan length was evaluated. Voxel-wise rs-fMRI metrics, including the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), functional connectivity (FC), and regional homogeneity (ReHo), were measured. Intraclass correlation coefficient (ICC) was calculated to estimate the reliability of the measurements in two states of awake and under anesthesia. Overall, it appeared that the reliability of rs-fMRI metrics improved under anesthesia. From the 15-min data, we found mean ICC values in awake state including 0.81, 0.51, 0.65, and 0.84 for ALFF, fALFF, FC, and ReHo, respectively, as well as 0.80, 0.59, 0.83, and 0.88 for ALFF, fALFF, FC, and ReHo, respectively, under anesthesia. Additionally, our findings revealed that reliability increases as the function of scan length. We showed that the optimized scan length to achieve less variability of rs-fMRI measurements was 3.1–7.5 min shorter in an anesthetized, compared to a wakeful state.

Correlates and predictors of the severity of suicidal ideation in adolescence: an examination of brain connectomics and psychosocial characteristics

BACKGROUND

Suicidal ideation (SI) typically emerges during adolescence but is challenging to predict. Given the potentially lethal consequences of SI, it is important to identify neurobiological and psychosocial variables explaining the severity of SI in adolescents.

METHODS

In 106 participants (59 female) recruited from the community, we assessed psychosocial characteristics and obtained resting-state fMRI data in early adolescence (baseline: aged 9-13 years). Across 250 brain regions, we assessed local graph theory-based properties of interconnectedness: local efficiency, eigenvector centrality, nodal degree, within-module z-score, and participation coefficient. Four years later (follow-up: ages 13-19 years), participants self-reported their SI severity. We used least absolute shrinkage and selection operator (LASSO) regressions to identify a linear combination of psychosocial and brain-based variables that best explain the severity of SI symptoms at follow-up. Nested-cross-validation yielded model performance statistics for all LASSO models.

RESULTS

A combination of psychosocial and brain-based variables explained subsequent severity of SI (R² = .55); the strongest was internalizing and externalizing symptom severity at follow-up. Follow-up LASSO regressions of psychosocial-only and brain-based-only variables indicated that psychosocial-only variables explained 55% of the variance in SI severity; in contrast, brain-based-only variables performed worse than the null model.

CONCLUSIONS

A linear combination of baseline and follow-up psychosocial variables best explained the severity of SI. Follow-up analyses indicated that graph theory resting-state metrics did not increase the prediction of the severity of SI in adolescents. Attending to internalizing and externalizing symptoms is important in early adolescence; resting-state connectivity properties other than local graph theory metrics might yield a stronger prediction of the severity of SI.

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation

There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.

Frequency-Resolved Connectome Hubs and Their Test-Retest Reliability in the Resting Human Brain

Functional hubs with disproportionately extensive connectivities play a crucial role in global information integration in human brain networks. However, most resting-state functional magnetic resonance imaging (R-fMRI) studies have identified functional hubs by examining spontaneous fluctuations of the blood oxygen level-dependent signal within a typical low-frequency band (e.g., 0.01-0.08 Hz or 0.01-0.1 Hz). Little is known about how the spatial distributions of functional hubs depend on frequency bands of interest. Here, we used repeatedly measured R-fMRI data from 53 healthy young adults and a degree centrality analysis to identify voxelwise frequency-resolved functional hubs and further examined their test-retest reliability across two sessions. We showed that a wide-range frequency band (0.01-0.24 Hz) accessible with a typical sampling rate (f_sample = 0.5 Hz) could be classified into three frequency bands with distinct patterns, namely, low-frequency (LF, 0.01-0.06 Hz), middle-frequency (MF, 0.06-0.16 Hz), and high-frequency (HF, 0.16-0.24 Hz) bands. The functional hubs were mainly located in the medial and lateral frontal and parietal cortices in the LF band, and in the medial prefrontal cortex, superior temporal gyrus, parahippocampal gyrus, amygdala, and several cerebellar regions in the MF and HF bands. These hub regions exhibited fair to good test-retest reliability, regardless of the frequency band. The presence of the three frequency bands was well replicated using an independent R-fMRI dataset from 45 healthy young adults. Our findings demonstrate reliable frequency-resolved functional connectivity hubs in three categories, thus providing insights into the frequency-specific connectome organization in healthy and disordered brains.

Altered brain network topology in children with auditory processing disorder: A resting-state multi-echo fMRI study

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A graph-theoretical approach was used to assess the functional topology in APD.

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Brain networks in APD are similarly integrated and segregated compared to HCs.

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Children with APD have different hub organization.

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Significant group differences were found in the PC measure in the bilateral STG.

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Regional differences observed within the DMN indicate multimodal roles in APD.

Children with auditory processing disorder (APD) experience hearing difficulties, particularly in the presence of competing sounds, despite having normal audiograms. There is considerable debate on whether APD symptoms originate from bottom-up (e.g., auditory sensory processing) and/or top-down processing (e.g., cognitive, language, memory). A related issue is that little is known about whether functional brain network topology is altered in APD. Therefore, we used resting-state functional magnetic resonance imaging data to investigate the functional brain network organization of 57 children from 8 to 14 years old, diagnosed with APD (n = 28) and without hearing difficulties (healthy control, HC; n = 29). We applied complex network analysis using graph theory to assess the whole-brain integration and segregation of functional networks and brain hub architecture. Our results showed children with APD and HC have similar global network properties –i.e., an average of all brain regions– and modular organization. Still, the APD group showed different hub architecture in default mode-ventral attention, somatomotor and frontoparietal-dorsal attention modules. At the nodal level –i.e., single-brain regions–, we observed decreased participation coefficient (PC – a measure quantifying the diversity of between-network connectivity) in auditory cortical regions in APD, including bilateral superior temporal gyrus and left middle temporal gyrus. Beyond auditory regions, PC was also decreased in APD in bilateral posterior temporo-occipital cortices, left intraparietal sulcus, and right posterior insular cortex. Correlation analysis suggested a positive association between PC in the left parahippocampal gyrus and the listening-in-spatialized-noise -sentences task where APD children were engaged in auditory perception. In conclusion, our findings provide evidence of altered brain network organization in children with APD, specific to auditory networks, and shed new light on the neural systems underlying children's listening difficulties.

Static and Dynamic Topological Organizations of the Costly Punishment Network Predict Individual Differences in Punishment Propensity

Human costly punishment plays a vital role in maintaining social norms. Recently, a brain network model is conceptually proposed indicating that the implement of costly punishment depends on a subset of nodes in three high-level networks. This model, however, has not yet been empirically examined from an integrated perspective of large-scale brain networks. Here, we conducted comprehensive graph-based network analyses of resting-state functional magnetic resonance imaging data to explore system-level characteristics of intrinsic functional connectivity among 18 regions related to costly punishment. Nontrivial organizations (small-worldness, connector hubs, and high flexibility) were found that were qualitatively stable across participants and over time but quantitatively exhibited low test-retest reliability. The organizations were predictive of individual costly punishment propensities, which was reproducible on independent samples and robust against different analytical strategies and parameter settings. Moreover, the prediction was specific to system-level network organizations (rather than interregional functional connectivity) derived from positive (rather than negative or combined) connections among the specific (rather than randomly chosen) subset of regions from the three high-order (rather than primary) networks. Collectively, these findings suggest that human costly punishment emerges from integrative behaviors among specific regions in certain functional networks, lending support to the brain network model for costly punishment.

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.

Sex Differences in Cortical Morphometry and White Matter Microstructure During Brain Aging and Their Relationships to Cognition

Changes in brain structure are associated with aging, and accompanied by the gradual deterioration of cognitive functions, which manifests differently in males and females. Here, we quantify the age-related spatial aging patterns of brain gray and white matter structures, their volume reduction rate, their relationships with specific cognitive functions, as well as differences between males and females in a cross-sectional nondementia dataset. We found that both males and females showed extensive age-related decreases in the volumes of most gray matter and white matter regions. Females have larger regions where the volume decreases with age and a greater slope (females: 0.199%, males: 0.183%) of volume decrease in gray matter. For white matter, no significant sex differences were found in age-related regions, and the slope of volume decrease. More significant associations were identified between brain structures and cognition in males during aging than females. This study explored the age-related regional variations in gray matter and white matter, as well as the sex differences in a nondemented elderly population. This study helps to further understand the aging of the brain structure and sex differences in the aging of brain structures and provides new evidence for the aging of nondemented individuals.

Principles and open questions in functional brain network reconstruction

Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.

Disrupted Small-world Networks are Associated with Decreased Vigilant Attention after Total Sleep Deprivation

Sleep deprivation critically affects vigilant attention. Previous neuroimaging studies have revealed altered inter-regional functional connectivity after sleep deprivation, which may disrupt topological properties of brain functional networks. However, little is known about alterations in the topology of intrinsic connectivity and its involvement in attention performance after sleep deprivation. In the current study, we investigated the topological properties of brain networks derived from resting-state functional magnetic resonance imaging of 26 healthy men in rested wakefulness (RW) state and after 36 h of total sleep deprivation (TSD). In the predefined sparsity threshold range, both global and nodal network properties were evaluated based on graph theory analysis. Vigilant attention was assessed using the psychomotor vigilance test (PVT) before and after TSD. Furthermore, Pearson's correlation analyses were conducted to explore the association between altered network properties and changed PVT performance after TSD. At the global level, the brain functional networks in the TSD state showed a significantly lower small-world coefficient than RW, with decreased global efficiency. At the nodal level, the altered regions were selectively distributed in frontoparietal networks, sensorimotor networks, temporal regions, and salience networks. More specifically, the altered clustering coefficient in the posterior superior temporal sulcus (pSTS) and insula, and altered local efficiency in pSTS were further associated with PVT performance after TSD. Our results suggest that the topological properties of brain functional networks are disrupted, and aberrant topology of temporal networks and salience networks may act as neural signatures underlying the vigilant attention impairments after TSD.

Brain network integration dynamics are associated with loss and recovery of consciousness induced by sevoflurane

The dynamic interplay of integration and segregation in the brain is at the core of leading theoretical accounts of consciousness. The human brain dynamically alternates between a sub-state where integration predominates, and a predominantly segregated sub-state, with different roles in supporting cognition and behaviour. Here, we combine graph theory and dynamic functional connectivity to compare resting-state functional MRI data from healthy volunteers before, during, and after loss of responsiveness induced with different concentrations of the inhalational anaesthetic, sevoflurane. We show that dynamic states characterised by high brain integration are especially vulnerable to general anaesthesia, exhibiting attenuated complexity and diminished small-world character. Crucially, these effects are reversed upon recovery, demonstrating their association with consciousness. Higher doses of sevoflurane (3% vol and burst-suppression) also compromise the temporal balance of integration and segregation in the human brain. Additionally, we demonstrate that reduced anticorrelations between the brain's default mode and executive control networks dynamically reconfigure depending on the brain's state of integration or segregation. Taken together, our results demonstrate that the integrated sub-state of brain connectivity is especially vulnerable to anaesthesia, in terms of both its complexity and information capacity, whose breakdown represents a generalisable biomarker of loss of consciousness and its recovery.

LSD alters dynamic integration and segregation in the human brain

Investigating changes in brain function induced by mind-altering substances such as LSD is a powerful method for interrogating and understanding how mind interfaces with brain, by connecting novel psychological phenomena with their neurobiological correlates. LSD is known to increase measures of brain complexity, potentially reflecting a neurobiological correlate of the especially rich phenomenological content of psychedelic-induced experiences. Yet although the subjective stream of consciousness is a constant ebb and flow, no studies to date have investigated how LSD influences the dynamics of functional connectivity in the human brain. Focusing on the two fundamental network properties of integration and segregation, here we combined graph theory and dynamic functional connectivity from resting-state functional MRI to examine time-resolved effects of LSD on brain networks properties and subjective experiences. Our main finding is that the effects of LSD on brain function and subjective experience are non-uniform in time: LSD makes globally segregated sub-states of dynamic functional connectivity more complex, and weakens the relationship between functional and anatomical connectivity. On a regional level, LSD reduces functional connectivity of the anterior medial prefrontal cortex, specifically during states of high segregation. Time-specific effects were correlated with different aspects of subjective experiences; in particular, ego dissolution was predicted by increased small-world organisation during a state of high global integration. These results reveal a more nuanced, temporally-specific picture of altered brain connectivity and complexity under psychedelics than has previously been reported.

Cortical thinning and associated connectivity changes in patients with anorexia nervosa

Structural brain abnormalities are a consistent finding in anorexia nervosa (AN) and proposed as a state biomarker of the disorder. Yet little is known about how regional structural changes affect intrinsic resting-state functional brain connectivity (rsFC). Using a cross-sectional, multimodal imaging approach, we investigated the association between regional cortical thickness abnormalities and rsFC in AN. Twenty-two acute AN patients and twenty-six age- and gender-matched healthy controls underwent a resting-state functional magnetic resonance imaging scan and cognitive tests. We performed group comparisons of whole-brain cortical thickness, seed-based rsFC, and network-based statistical (NBS) analyses. AN patients showed cortical thinning in the precuneus and inferior parietal lobules, regions involved in visuospatial memory and imagery. Cortical thickness in the precuneus correlated with nutritional state and cognitive functions in AN, strengthening the evidence for a critical role of this region in the disorder. Cortical thinning was accompanied by functional connectivity reductions in major brain networks, namely default mode, sensorimotor and visual networks. Similar to the seed-based approach, the NBS analysis revealed a single network of reduced functional connectivity in patients, comprising mainly sensorimotor- occipital regions. Our findings provide evidence that structural and functional brain abnormalities in AN are confined to specific regions and networks involved in visuospatial and somatosensory processing. We show that structural changes of the precuneus are linked to nutritional and functional states in AN, and future longitudinal research should assess how precuneus changes might be related to the evolution of the disorder.

Combining network topology and information theory to construct representative brain networks

Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain's network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologies, known as the portrait divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional, and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functional networks based on efficiency-cost optimization-though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.

Oxytocin modulates local topography of human functional connectome in healthy men at rest

Oxytocin has recently received remarkable attention for its role as a modulator of human behaviour. Here, we aimed to expand our knowledge of the neural circuits engaged by oxytocin by investigating the effects of intranasal and intravenous oxytocin on the functional connectome at rest in 16 healthy men. Oxytocin modulates the functional connectome within discrete neural systems, but does not affect the global capacity for information transfer. These local effects encompass key hubs of the oxytocin system (e.g. amygdala) but also regions overlooked in previous hypothesis-driven research (i.e. the visual circuits, temporal lobe and cerebellum). Increases in levels of oxytocin in systemic circulation induce broad effects on the functional connectome, yet we provide indirect evidence supporting the involvement of nose-to-brain pathways in at least some of the observed changes after intranasal oxytocin. Together, our results suggest that oxytocin effects on human behaviour entail modulation of multiple levels of brain processing distributed across different systems.

Biomarkers in Panic Disorder

Background:

Panic disorder (PD) is a kind of anxiety disorder that impacts the life quality and functional perspectives in patients. However, the pathophysiological study of PD seems still inadequate and many unresolved issues need to be clarified.

Objectives:

In this review article of biomarkers in PD, the investigator will focus on the findings of magnetic resonance imaging (MRI) of the brain in the pathophysiology study. The MRI biomarkers would be divided into several categories, on the basis of structural and functional perspectives.

Methods:

The structural category would include the gray matter and white matter tract studies. The functional category would consist of functional MRI (fMRI), resting-state fMRI (Rs-fMRI), and magnetic resonance spectroscopy (MRS). The PD biomarkers revealed by the above methodologies would be discussed in this article.

Results:

For the gray matter perspectives, the PD patients would have alterations in the volumes of fear network structures, such as the amygdala, parahippocampal gyrus, thalamus, anterior cingulate cortex, insula, and frontal regions. For the white matter tract studies, the PD patients seemed to have alterations in the fasciculus linking the fear network regions, such as the anterior thalamic radiation, uncinate fasciculus, fronto-occipital fasciculus, and superior longitudinal fasciculus. For the fMRI studies in PD, the significant results also focused on the fear network regions, such as the amygdala, hippocampus, thalamus, insula, and frontal regions. For the Rs-fMRI studies, PD patients seemed to have alterations in the regions of the default mode network and fear network model. At last, the MRS results showed alterations in neuron metabolites of the hippocampus, amygdala, occipital cortex, and frontal regions.

Conclusion:

The MRI biomarkers in PD might be compatible with the extended fear network model hypothesis in PD, which included the amygdala, hippocampus, thalamus, insula, frontal regions, and sensory-related cortex.

Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease

While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.

Reliability and Individual Specificity of EEG Microstate Characteristics

Electroencephalography (EEG) microstates (MSs) are defined as quasi-stable topographies that represent global coherent activation. Alternations in EEG MSs have been reported in numerous neuropsychiatric disorders. Transferring the results of these studies into clinical practice requires not only high reliability but also sufficient individual specificity. Nevertheless, whether the amount of data used in microstate analysis influences reliability and how much individual information is provided by EEG MSs are unclear. In the current study, we aimed to assess the within-subject consistency and between-subject differences in the characteristics of EEG MSs. Two sets of eyes-closed resting-state EEG recordings were collected from 54 young, healthy participants on two consecutive days. The Raven Advanced Progressive Matrices test was conducted to assess general fluid intelligence (gF). We obtained four MSs (labeled A, B, C and D) through EEG microstate analysis. EEG MS characteristics including traditional features (the global explained variances, mean durations, coverages, occurrences and transition probabilities), the Hurst exponents and temporal dynamic features (the autocorrelation functions and the partial autocorrelation functions) were calculated and evaluated. The data with a duration greater than 2 min showed moderate to high reliability and individual specificity. The mean duration and coverage of MS C were significantly correlated with the gF score. The dynamic features showed a higher identification accuracy and were more significantly correlated with gF than the traditional MS features. These findings reveal that EEG microstate characteristics are reliably unique in single subjects and possess abundant inter-individual variability.

Effects of Chronic Ephedrine Toxicity on Functional Connections, Cell Apoptosis, and CREB-Related Proteins in the Prefrontal Cortex of Rhesus Monkeys

Ephedrine abuse has spread in many parts of the world, severely threatening human health. The mechanism of ephedrine toxicity is still unclear. To explore the possible neural mechanisms of ephedrine toxicity, this study established a non-human primate model of ephedrine exposure, analyzed the functional connectivity changes in its prefrontal cortex through resting state BOLD-fMRI, and then inspected the pathophysiological changes as well as the expression of the cyclic adenosine monophosphate response element-binding protein (CREB), phosphorylated CREB (P-CREB), and CREB target proteins (c-fos and fosB) in the prefrontal cortex. After ephedrine toxicity, we found that the prefrontal cortex of monkeys strengthened its functional connectivity with the brain regions that perform motivation, drive, reward, and learning and memory functions and weakened its functional connectivity with the brain regions that perform cognitive control. These results suggest that ephedrine toxicity causes abnormal neural circuits that lead to the amplification and enhancement of drug-related cues and the weakening and damage of cognitive control function. Histology showed that the neurocytotoxicity of ephedrine can cause neuronal degeneration and apoptosis. Real-time PCR and Western blot showed increased expression of CREB mRNA and CREB/P-CREB/c-fos/fosB protein in the prefrontal cortex after ephedrine toxicity. Collectively, the present study indicates that the enhancement of drug-related cues and the weakening of cognitive control caused by abnormal neural circuits after drug exposure may be a major mechanism of brain function changes caused by ephedrine. These histological and molecular changes may be the pathophysiological basis of brain function changes caused by ephedrine.

Twin MRI studies on genetic and environmental determinants of brain morphology and function in the early lifespan

Neurodevelopment represents a period of increased opportunity and vulnerability, during which a complex confluence of genetic and environmental factors influences brain growth trajectories, cognitive and mental health outcomes. Recently, magnetic resonance imaging (MRI) studies on twins have increased our knowledge of the extent to which genes, the environment and their interactions shape inter-individual brain variability. The present review draws from highly salient MRI studies in young twin samples to provide a robust assessment of the heritability of structural and functional brain changes during development. The available studies suggest that (as with many other traits), global brain morphology and network organization are highly heritable from early childhood to young adulthood. Conversely, genetic correlations among brain regions exhibit heterogeneous trajectories, and this heterogeneity reflects the progressive, experience-related increase in brain network complexity. Studies also support the key role of environment in mediating brain network differentiation via changes of genetic expression and hormonal levels. Thus, rest- and task-related functional brain circuits seem to result from a contextual and dynamic expression of heritability.

Scanning Conditions in Functional Connectivity Magnetic Resonance Imaging: How to Standardise Resting-State for Optimal Data Acquisition and Visualisation?

Functional connectivity magnetic resonance imaging (fcMRI), performed during resting wakefulness without tasks or stimulation, is a non-invasive technique to assess and visualise functional brain networks in vivo. Acquisition of resting-state imaging data has become increasingly common in longitudinal studies to investigate brain health and disease. However, the scanning protocols vary considerably across different institutions creating challenges for comparability especially for the interpretation of findings in patient cohorts and establishment of diagnostic or prognostic imaging biomarkers. The aim of this chapter is to discuss the effect of two experimental conditions (i.e. a low cognitive demand paradigm and a pure resting-state fcMRI) on the reproducibility of brain networks between a baseline and a follow-up session, 30 (±5) days later, acquired from 12 right-handed volunteers (29 ± 5 yrs). A novel method was developed and used for a direct statistical comparison of the test-retest reliability using 28 well-established functional brain networks. Overall, both scanning conditions produced good levels of test-retest reliability. While the pure resting-state condition showed higher test-retest reliability for 18 of the 28 analysed networks, the low cognitive demand paradigm produced higher test-retest reliability for 8 of the 28 brain networks (i.e. visual, sensorimotor and frontal areas); in 2 of the 28 brain networks no significant changes could be detected. These results are relevant to planning of longitudinal studies, as higher test-retest reliability generally increases statistical power. This work also makes an important contribution to neuroimaging where optimising fcMRI experimental scanning conditions, and hence data visualisation of brain function, remains an on-going topic of interest. In this chapter, we provide a full methodological explanation of the two paradigms and our analysis so that readers can apply them to their own scanning protocols.

Exploring the functional connectome in white matter

A major challenge in neuroscience is understanding how brain function emerges from the connectome. Most current methods have focused on quantifying functional connectomes in gray-matter (GM) signals obtained from functional magnetic resonance imaging (fMRI), while signals from white-matter (WM) have generally been excluded as noise. In this study, we derived a functional connectome from WM resting-state blood-oxygen-level-dependent (BOLD)-fMRI signals from a large cohort (n = 488). The WM functional connectome exhibited weak small-world topology and nonrandom modularity. We also found a long-term (i.e., over 10 months) topological reliability, with topological reproducibility within different brain parcellation strategies, spatial distance effect, global and cerebrospinal fluid signals regression or not. Furthermore, the small-worldness was positively correlated with individuals' intelligence values (r = .17, pcorrected = .0009). The current findings offer initial evidence using WM connectome and present additional measures by which to uncover WM functional information in both healthy individuals and in cases of clinical disease.

Detecting resting-state brain activity using OEF-weighted imaging

Traditional resting-state functional magnetic resonance imaging (fMRI) is mainly based on the blood oxygenation level-dependent (BOLD) contrast. The oxygen extraction fraction (OEF) represents an important parameter of brain metabolism and is a key biomarker of tissue viability, detecting the ratio of oxygen utilization to oxygen delivery. Investigating spontaneous fluctuations in the OEF-weighted signal is crucial for understanding the underlying mechanism of brain activity because of the immense energy budget during the resting state. However, due to the poor temporal resolution of OEF mapping, no studies have reported using OEF contrast to assess resting-state brain activity. In this fMRI study, we recorded brain OEF-weighted fluctuations for 10 min in healthy volunteers across two scanning visits, using our recently developed pulse sequence that can acquire whole-brain voxel-wise OEF-weighted signals with a temporal resolution of 3 s. Using both group-independent component analysis and seed-based functional connectivity analysis, we robustly identified intrinsic brain networks, including the medial visual, lateral visual, auditory, default mode and bilateral executive control networks, using OEF contrast. Furthermore, we investigated the resting-state local characteristics of brain activity based on OEF-weighted signals using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). We demonstrated that the gray matter regions of the brain, especially those in the default mode network, showed higher ReHo and fALFF values with the OEF contrast. Moreover, voxel-wise test-retest reliability comparisons across the whole brain demonstrated that the reliability of resting-state brain activity based on the OEF contrast was moderate for the network indices and high for the local activity indices, especially for ReHo. Although the reliabilities of the OEF-based indices were generally lower than those based on BOLD, the reliability of OEF-ReHo was slightly higher than that of BOLD-ReHo, with a small effect size, which indicated that OEF-ReHo could be used as a reliable index for characterizing resting-state local brain activity as a complement to BOLD. In conclusion, OEF can be used as an effective contrast to study resting-state brain activity with a medium to high test-retest reliability.

Neuroanatomical correlates of extraversion: a test-retest study implicating gray matter volume in the caudate nucleus

Extraversion is a propensity for optimism, positive emotion, and sociability. Many studies have explored the brain correlates of extraversion, and the results have mainly emphasized the role of reward-related brain regions. However, it is unclear whether imaging studies of the brain's reward system can show good test-retest reliability. We aim to investigate the neuroanatomical correlates of extraversion and to examine the test-retest reliability of the results. Voxel-based morphometry derived from MRI and the Revised NEO Personality Inventory were performed in a sample of 382 subjects (dataset 1), and multiple regression was used to analyze the relation between regional gray matter volume (GMV) and extraversion scores. We found that bilateral GMV in the caudate region was positively associated with extraversion. Two years later, 133 subjects from dataset 1 were re-examined with MRI and the Revised NEO Personality Inventory. A conjunction analysis showed that the body of left caudate nucleus was consistently associated with extraversion. The bilateral GMV of the caudate nucleus, which may be related to sensitivity to rewards, may be a critical brain structure underlying extraverted behavior. This study is the first to use two different time points to assess the consistency of the association of brain structures with extraversion.

Test-retest reproducibility of a multi-atlas automated segmentation tool on multimodality brain MRI

INTRODUCTION

The increasing use of large sample sizes for population and personalized medicine requires high-throughput tools for imaging processing that can handle large amounts of data with diverse image modalities, perform a biologically meaningful information reduction, and result in comprehensive quantification. Exploring the reproducibility of these tools reveals the specific strengths and weaknesses that heavily influence the interpretation of results, contributing to transparence in science.

METHODS

We tested-retested the reproducibility of MRICloud, a free automated method for whole-brain, multimodal MRI segmentation and quantification, on two public, independent datasets of healthy adults.

RESULTS

The reproducibility was extremely high for T1-volumetric analysis, high for diffusion tensor images (DTI) (however, regionally variable), and low for resting-state fMRI.

CONCLUSION

In general, the reproducibility of the different modalities was slightly superior to that of widely used software. This analysis serves as a normative reference for planning samples and for the interpretation of structure-based MRI studies.

Reliability of Magnetoencephalography and High-Density Electroencephalography Resting-State Functional Connectivity Metrics

Resting-state connectivity, for example, based on magnetoencephalography (MEG) or electroencephalography (EEG), is a widely used method for characterizing brain networks and a promising imaging biomarker. However, there is no established standard as to which method, modality, and analysis variant is preferable and there is only limited knowledge on the reproducibility, an important prerequisite for clinical application. We conducted an MEG-/high-density (hd)-EEG-study on 22 young healthy adults, who were measured twice in a scan/rescan design after 7 ± 2 days. Reliability of resting-state (15 min, eyes-closed) connectivity in source space was calculated via intraclass correlation coefficient (ICC) in classical frequency bands (delta-gamma). We investigated the reliability of two commonly used connectivity metrics, namely the imaginary part of coherency and the weighted phase-lag index and the influence of frequency band, vigilance, and the number of trials. We found a strong increase of reliability with more trials and relatively mild effects of vigilance. Reliability was excellent in the alpha band for MEG, as well as hd-EEG (ICC >0.85); in the theta band, reliability was good for MEG and poor for EEG. Other frequency bands showed lower reliability, with delta band being the worst. Furthermore, we investigated the spatial reliability of resting-state connectivity in a vertex-based approach, which reached fair to good reliability (ICC up to 0.67) with 5 min of data. Our results indicate that excellent reliability of global connectivity is achievable in alpha band, and vertex-based connectivity was still fair to good. Moreover, electrophysiological resting-state studies could benefit from more data than used previously. MEG and hd-EEG were similar in their overall performance but showed frequency band-specific differences.

Aberrant brain network topology in fronto-limbic circuitry differentiates euthymic bipolar disorder from recurrent major depressive disorder

INTRODUCTION

Previous studies have established graph theoretical analysis of functional network connectivity (FNC) as a potential tool to detect neurobiological underpinnings of psychiatric disorders. Despite the promising outcomes in studies that examined FNC aberrancies in bipolar disorder (BD) and major depressive disorder (MDD), there is still a lack of research comparing both mood disorders, especially in a nondepressed state. In this study, we used graph theoretical network analysis to compare brain network properties of euthymic BD, euthymic MDD and healthy controls (HC) to evaluate whether these groups showed distinct features in FNC.

METHODS

We collected resting-state functional magnetic resonance imaging (fMRI) data from 20 BD patients, 15 patients with recurrent MDD as well as 30 age- and gender-matched HC. Graph theoretical analyses were then applied to investigate functional brain networks on a global and regional network level.

RESULTS

Global network analysis revealed a significantly higher mean global clustering coefficient in BD compared to HC. We further detected frontal, temporal and subcortical nodes in emotion regulation areas such as the limbic system and associated regions exhibiting significant differences in network integration and segregation in BD compared to MDD patients and HC. Participants with MDD and HC only differed in frontal and insular network centrality.

CONCLUSION

In conclusion, our findings indicate that a significantly altered brain network topology in the limbic system might be a trait marker specific to BD. Brain network analysis in these regions may therefore be used to differentiate euthymic BD not only from HC but also from patients with MDD.

Variability of Resting-State Functional MRI Graph Theory Metrics across 3T Platforms

BACKGROUND AND PURPOSE

Graph theory analysis of brain connectivity data is a promising tool for studying the function of the healthy and diseased brain. The consistency of resting-state functional MRI (rsfMRI) connectivity measures across multiple scanner types is an important factor in designing multi-institutional research studies and has important implications for the potential use of this technique in a heterogeneous clinical setting. We sought to quantitatively study the interscanner variability of rsfMRI graph theory metrics obtained from healthy volunteers scanned on three different scanner platforms.

METHODS

In this prospective Institutional Review Board approved study, 9 healthy volunteers were enrolled for brain MRI on three 3T scanners (Magnetom Prisma, Skyra, and Trio, Siemens, Erlangen, Germany) in three separate scan sessions within approximately 1 week. Standard preprocessing of rsfMRI was performed with SPM12. Subject scans were normalized to Montreal Neurologic Institute (MNI) space, and connectivity of 116 regions-of-interests based on the automated anatomic labeling (AAL) atlas was calculated using Conn toolbox. Whole-network graph theory metrics were calculated using Brain Connectivity Toolbox, and intraclass correlation (ICC) across three scan sessions was assessed.

RESULTS

A total of 25 rsfMRI exams were completed in 9 subjects with a median-intersession time of 3 days. Among all three sessions, there was good to excellent agreement in characteristic path length and global efficiency (ICC: .79, .79) and good agreement in the transitivity, local efficiency, and clustering coefficient (ICC = .72, .69, .62).

CONCLUSIONS

There was high consistency of graph theory metrics of rsfMRI connectivity networks among healthy volunteers scanned on three different generation 3T MRI scanners.

The neural markers of MRI to differentiate depression and panic disorder

Depression and panic disorder (PD) share the common pathophysiology from the perspectives of neurotransmitters. The relatively high comorbidity between depression and PD contributes to the substantial obstacles to differentiate from depression and PD, especially for the brain pathophysiology. There are significant differences in the diagnostic criteria between depression and PD. However, the paradox of similar pathophysiology and different diagnostic criteria in these two disorders were still the issues needing to be addressed. Therefore the clarification of potential difference in the field of neuroscience and pathophysiology between depression and PD can help the clinicians and scientists to understand more comprehensively about significant differences between depression and PD. The researchers should be curious about the underlying difference of pathophysiology beneath the significant distinction of clinical symptoms. In this review article, I tried to find some evidences for the differences between depression and PD, especially for neural markers revealed by magnetic resonance imaging (MRI). The distinctions of structural and functional alterations in depression and PD are reviewed. From the structural perspectives, PD seems to have less severe gray matter alterations in frontal and temporal lobes than depression. The study of white matter microintegrity reveals more widespread alterations in fronto-limbic circuit of depression patients than PD patients, such as the uncinate fasciculus and anterior thalamic radiation. PD might have a more restrictive pattern of structural alterations when compared to depression. For the functional perspectives, the core site of depression pathophysiology is the anterior subnetwork of resting-state network, such as anterior cingulate cortex, which is not significantly altered in PD. A possibly emerging pattern of fronto-limbic distinction between depression and PD has been revealed by these explorative reports. The future trend for machine learning and pattern recognition might confirm the differentiation pattern between depression and PD based on the explorative results.

Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity

Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced by participant motion. This report provides a systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths (ages 8-22 years). Each strategy was evaluated according to a number of benchmarks, including (a) the residual association between participant motion and edge dispersion, (b) distance-dependent effects of motion on edge dispersion, (c) the degree to which functional subnetworks could be identified by multilayer modularity maximization, and (d) measures of module reconfiguration, including node flexibility and node promiscuity. Results indicate variability in the effectiveness of the evaluated pipelines across benchmarks. Methods that included global signal regression were the most consistently effective de-noising strategies.

Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

Generalized anxiety disorder (GAD) has excessive anxiety and uncontrollable worry as core symptoms. Abnormal cerebral functioning underpins the expression and perhaps pathogenesis of GAD:. Studies implicate impaired communication between the amygdala and the pre-frontal cortex (PFC). Our aim was to longitudinally investigate whether such network abnormalities are spatially restricted to this circuit or if the integrity of functional brain networks is globally disrupted in GAD. We acquired resting-state functional magnetic resonance imaging data from 16 GAD patients and 16 matched controls at baseline and after 1 year. Using network modeling and graph-theory, whole-brain connectivity was characterized from local and global perspectives. Overall lower global efficiency, indicating sub-optimal brain-wide organization and integration, was present in patients with GAD compared to controls. The amygdala and midline cortices showed higher betweenness centrality, reflecting functional dominance of these brain structures. Third, lower betweenness centrality and lower degree emerged for PFC, suggesting weakened inhibitory control. Overall, network organization showed impairments consistent with neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) and further pointed to an involvement of temporal regions. Such impairments tended to progress over time and predict anxiety symptoms. A graph-analytic approach represents a powerful approach to deepen our understanding of GAD.

Test-retest Reliability of Functional Connectivity and Graph Metrics in the Resting Brain Network

The combination of graph theoretical approaches and neuroimaging data provides a powerful way to explore the characteristics of brain network. Recently, the temporal variability of spontaneous brain activity and functional connectivity has attracted wide attention. Thus, it is essential to evaluate the reliability of functional network connectivity and properties from the dynamic perspective. However, previous test-retest (TRT) studies have explored this reliability with a static point of view. In this study, using a large rs-fMRI dataset from Human Connectome Project (HCP), we investigated TRT reliability of functional connectivity and graph metrics derived from the most commonly used method- sliding window at three time intervals (short: 72 seconds, middle: 15 minutes and long: >24 hours). The results revealed that reliable connectivities and related brain regions are mainly distributed in primary cortex, such as visual area and sensorimotor area and default mode network. Notably, connectivity strength and global efficiency have better reliability than other metrics. Finally, short scan time interval and long scan duration can increase the TRT reliability of metrics. Findings of present study provide important guidance for searching reliable network markers in future research.

Within-subject test-retest reliability of the atlas-based cortical volume measurement in the rat brain: A voxel-based morphometry study

BACKGROUND

Various neurological and psychological disorders are related to cortical volume changes in specific brain regions, which can be measured in vivo using structural magnetic resonance imaging (sMRI). There is an increasing interest in MRI studies using rat models, especially in longitudinal studies of brain disorders and pharmacologic interventions. However, morphometric changes observed in sMRI are only meaningful if the measurements are reliable. To date, a systematic evaluation of the test-retest reliability of the morphometric measures in the rat brain is still lacking.

NEW METHOD

We rigorously evaluated the test-retest reliability of morphometric measures derived from the voxel-based morphometry (VBM) analysis. 37 Sprague-Dawley rats were scanned twice at an interval of six hours and the gray matter volume was estimated using the VBM-DARTEL method. The intraclass coefficient, percent volume change and Pearson correlation coefficient were used to evaluate the reliability in 96 subregions of the rat brain.

RESULTS

Most subregions showed excellent test-retest reliabilities within an interval of 6 h while a few regions demonstrated lower reliability, especially in the retrosplenial granular cortex. The results were consistent between different methods of reliability assessment.

COMPARISON WITH EXISTING METHOD

To the best of our knowledge, this is the first study to quantify the test-retest reliability of the VBM measurements of the rat brain.

CONCLUSION

Atlas-based cortical volume of the rat brain can be reliably estimated using the VBM-DARTEL method in most subregions. However, findings in subregions with lower reliability must be interpreted with caution.