Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation

The Brain's Topographical Organization Shapes Dynamic Interaction Patterns That Support Flexible Behavior Based on Rules and Long-Term Knowledge

Adaptive behavior relies both on specific rules that vary across situations and stable long-term knowledge gained from experience. The frontoparietal control network (FPCN) is implicated in the brain's ability to balance these different influences on action. Here, we investigate how the topographical organization of the cortex supports behavioral flexibility within the FPCN. Functional properties of this network might reflect its juxtaposition between the dorsal attention network (DAN) and the default mode network (DMN), two large-scale systems implicated in top-down attention and memory-guided cognition, respectively. Our study tests whether subnetworks of FPCN are topographically proximal to the DAN and the DMN, respectively, and how these topographical differences relate to functional differences: the proximity of each subnetwork is anticipated to play a pivotal role in generating distinct cognitive modes relevant to working memory and long-term memory. We show that FPCN subsystems share multiple anatomical and functional similarities with their neighboring systems (DAN and DMN) and that this topographical architecture supports distinct interaction patterns that give rise to different patterns of functional behavior. The FPCN acts as a unified system when long-term knowledge supports behavior but becomes segregated into discrete subsystems with different patterns of interaction when long-term memory is less relevant. In this way, our study suggests that the topographical organization of the FPCN and the connections it forms with distant regions of cortex are important influences on how this system supports flexible behavior.

A novel biomarker selection method using multimodal neuroimaging data

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

Regression and Alignment for Functional Data and Network Topology

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of pre-processing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Inter-regional correlation estimators for functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) functional connectivity between brain regions is often computed using parcellations defined by functional or structural atlases. Typically, some kind of voxel averaging is performed to obtain a single temporal correlation estimate per region pair. However, several estimators can be defined for this task, with various assumptions and degrees of robustness to local noise, global noise, and region size. In this paper, we systematically present and study the properties of 9 different functional connectivity estimators taking into account the spatial structure of fMRI data, based on a simple fMRI data spatial model. These include 3 existing estimators and 6 novel estimators. We demonstrate the empirical properties of the estimators using synthetic, animal, and human data, in terms of graph structure, repeatability and reproducibility, discriminability, dependence on region size, as well as local and global noise robustness. We prove analytically the link between regional intra-correlation and inter-region correlation, and show that the choice of estimator has a strong influence on inter-correlation values. Some estimators, including the commonly used correlation of averages (ca), are positively biased, and have more dependence to region size and intra-correlation than robust alternatives, resulting in spatially-dependent bias. We define the new local correlation of averages estimator with better theoretical guarantees, lower bias, significantly lower dependence on region size (Spearman correlation 0.40 vs 0.55, paired t-test T=27.2, p=1.1e-47), at negligible cost to discriminative power, compared to the ca estimator. The difference in connectivity pattern between the estimators is not distributed uniformly throughout the brain, but rather shows a clear ventral-dorsal gradient, suggesting that region size and intra-correlation plays an important role in shaping functional networks defined using the ca estimator, and leading to non-trivial differences in their connectivity structure. We provide an open source R package and equivalent Python implementation to facilitate the use of the new estimators, together with preprocessed rat time-series.

Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions

Understanding how the brain's macroscale dynamics are shaped by underlying microscale mechanisms is a key problem in neuroscience. In animal models, we can now investigate this relationship in unprecedented detail by directly manipulating cellular-level properties while measuring the whole-brain response using resting-state fMRI. Here, we focused on understanding how blood-oxygen-level-dependent (BOLD) dynamics, measured within a structurally well-defined striato-thalamo-cortical circuit in mice, are shaped by chemogenetically exciting or inhibiting D1 medium spiny neurons (MSNs) of the right dorsomedial caudate putamen (CPdm). We characterize changes in both the BOLD dynamics of individual cortical and subcortical brain areas, and patterns of inter-regional coupling (functional connectivity) between pairs of areas. Using a classification approach based on a large and diverse set of time-series properties, we found that CPdm neuromodulation alters BOLD dynamics within thalamic subregions that project back to dorsomedial striatum. In the cortex, changes in local dynamics were strongest in unimodal regions (which process information from a single sensory modality) and weakened along a hierarchical gradient towards transmodal regions. In contrast, a decrease in functional connectivity was observed only for cortico-striatal connections after D1 excitation. Our results show that targeted cellular-level manipulations affect local BOLD dynamics at the macroscale, such as by making BOLD dynamics more predictable over time by increasing its self-correlation structure. This contributes to ongoing attempts to understand the influence of structure-function relationships in shaping inter-regional communication at subcortical and cortical levels.

Cortical timescales and the modular organization of structural and functional brain networks

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

Functional brain networks reflect spatial and temporal autocorrelation

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler, low-dimensional statistics is largely unknown. To explore this question, we examined resting-state functional magnetic resonance imaging (rs-fMRI) data using complex topology measures from network neuroscience. Here we show that spatial and temporal autocorrelation are reliable statistics that explain numerous measures of network topology. Surrogate time series with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely used complexity measures may help link them to neurobiology.

Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation

Introduction

Analysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform "prewhitening" to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates.

Methods

In this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI.

Results

We find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives.

Conclusion

Our analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects.

Altered gamma oscillations and beta-gamma coupling in drug-naive first-episode major depressive disorder: Association with sleep and cognitive disturbance

OBJECTIVE

Gamma oscillations contribute to the pathogenesis mechanisms of major depressive disorder (MDD) have been proposed, but gamma activity is not well characterized. This study is the first attempt to investigate the altered gamma oscillations in first-episode MDD, particularly the beta-gamma coupling, and to determine the potential symptomatic relationship with the identified gamma dysregulation.

METHODS

Resting electroencephalography was recorded for 43 drug-naive first-episode MDD and 57 healthy control (HC) subjects. Integrated analysis of relative spectral power, weighted phase lag index, and phase-amplitude coupling (PAC) were utilized to reveal the alterations of gamma activities. Pearson's correlation was implemented to identify the relationship between altered gamma activities and the clinical depressive symptoms, which were categorized into four factors: anxiety somatization, retardation, cognitive disturbance, and sleep disturbance.

RESULTS

Compared with HC subjects, MDD patients showed not only significantly decreased gamma powers in the left temporal and the bilateral occipital regions but also weakened gamma connectivity between the left hemisphere and the right frontal region. Furthermore, attenuated beta-gamma PAC of MDD patients was observed in the left temporal regions. Importantly, the suppression of left occipital mid- and high gamma oscillations were negatively correlated with sleep disturbance, while the deficits in left temporal beta-mid-gamma PAC and beta-high gamma PAC showed negative correlations with cognitive disturbance.

LIMITATIONS

Important limitations are the small sample size and the possible inclusion of bipolar depression in the MDD group.

CONCLUSIONS

Our findings provide the first evidence that in first-episode MDD, aberrant gamma powers and beta-gamma coupling are associated with sleep and cognitive impairments, respectively, deepening our understanding of the physiological mechanisms underlying sleep and cognitive symptoms in first-episode MDD. Altered gamma oscillations emerge as promising biomarkers for diagnosing MDD.

Statistical approaches to temporal and spatial autocorrelation in resting-state functional connectivity in mice measured with optical intrinsic signal imaging

Significance: Resting-state functional connectivity imaging in mice with optical intrinsic signal (OIS) imaging could provide a powerful translational tool for developing imaging biomarkers in preclinical disease models. However, statistical interpretation of correlation coefficients is hampered by autocorrelations in the data. Aim: We sought to better understand temporal and spatial autocorrelations in optical resting-state data. We then adapted statistical methods from functional magnetic resonance imaging to improve statistical inference. Approach: Resting-state data were obtained from mice using a custom-built OSI system. The autocorrelation time was calculated at each pixel, and z scores for correlation coefficients were calculated using Fisher transforms and variance derived from either Bartlett's method or xDF. The significance of each correlation coefficient was determined through control of the false discovery rate (FDR). Results: Autocorrelation was generally even across the cortex and parcellation reduced variance. Correcting variance with Bartlett's method resulted in a uniform reduction in z scores, with xDF preserving high z scores for highly correlated data. Control of the FDR resulted in reasonable thresholding of the correlation coefficient matrices. The use of Bartlett's method compared with xDF results in more conservative thresholding and fewer false positives under null hypothesis conditions. Conclusions: We developed streamlined methods for control of autocorrelation in OIS functional connectivity data in mice, and Bartlett's method is a reasonable compromise and simplification that allows for accurate autocorrelation correction. These results improve the rigor and reproducibility of functional neuroimaging in mice.

Data-driven causal analysis of observational biological time series

Complex systems are challenging to understand, especially when they defy manipulative experiments for practical or ethical reasons. Several fields have developed parallel approaches to infer causal relations from observational time series. Yet, these methods are easy to misunderstand and often controversial. Here, we provide an accessible and critical review of three statistical causal discovery approaches (pairwise correlation, Granger causality, and state space reconstruction), using examples inspired by ecological processes. For each approach, we ask what it tests for, what causal statement it might imply, and when it could lead us astray. We devise new ways of visualizing key concepts, describe some novel pathologies of existing methods, and point out how so-called ‘model-free’ causality tests are not assumption-free. We hope that our synthesis will facilitate thoughtful application of methods, promote communication across different fields, and encourage explicit statements of assumptions. A video walkthrough is available (Video 1 or https://youtu.be/AlV0ttQrjK8).

Geometric effects of volume-to-surface mapping of fMRI data

In this work, we identify a problem with the process of volume-to-surface mapping of functional Magnetic Resonance Imaging (fMRI) data that emerges in local connectivity analysis. We show that neighborhood correlations on the surface of the brain vary spatially with the gyral structure, even when the underlying volumetric data are uncorrelated noise. This could potentially have impacted studies focusing upon local neighborhood connectivity. We explore the effects of this anomaly across varying data resolutions and surface mesh densities, and propose several measures to mitigate these unwanted effects.

Correction of global physiology in resting-state functional near-infrared spectroscopy

Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear. Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data. Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance. Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution. Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

Edge-based network analysis reveals frequency-specific network dynamics in aberrant anxiogenic processing in rats

Uncovering interactions between edges of brain networks can reveal the organizational principle of the networks and also their dysregulations underlying aberrant behaviours such as in neuropsychiatric diseases. In this study, we looked into the applicability of edge-based network analysis in uncovering possible network mechanisms of aberrant anxiogenic processing. Utilizing a rat model of prodromal Parkinson's disease we examined how a dorsomedial striatum-tied associative network (DSAN) may mediate context-based anxiogenic behaviour. Following dopamine depletion in the dorsomedial striatum, an exaggerated bottom-up signalling (posterior parietal-hippocampal-retrosplenial to anterior prefrontal-cingulate-amygdala regions) and gradient specific to the theta frequency in this network was observed. This change was accompanied by increased anxiety behaviour of the animals. By employing an edge-based approach in correlating informational flow (phase transfer entropy) with functional connectivity of all edges of this network, we further explore how the abnormal bottom-up signalling might be explained by alterations to the informational flow-connectivity motifs in the network. Our results demonstrate usage of edge-based network analysis in revealing concurrent informational processing and functional organization dynamics across multiple pathways in a brain network. This approach in unveiling network abnormalities and its impact on behavioural outcomes would be useful in probing the network basis of neuropsychiatric conditions.

From Phase Transition to Interdecadal Changes of ENSO, Altered by the Lower Stratospheric Ozone

This paper shows more evidence for the existing spatial–temporal synchronization of the air surface temperature and pressure within the lower stratospheric ozone, which is unevenly distributed over the globe. The focus of this study is put on the region of formation and manifestation of El Niño Southern Oscillation (ENSO). Statistical analysis of data (covering the period 1900–2019) displays a well pronounced covariance of ozone at 70 hPa with (i) Nino3.4 index, (ii) air surface temperature, and (iii) sea level pressure, in each grid-point with spatial resolution of 5° in latitude and longitude. The ozone impact could be found at different time scales—from interannual (altering the ENSO phase transition) to interdecadal. Moreover, the centers of action of ozone on the sea level temperature and pressure are positioned at different places, depending on the temporal scale of variability—from the tropical Central Pacific—at interannual and interdecadal, to extratropics—at subdecadal time scales. We show also that positive ozone anomalies at 70 hPa trigger a cooling of the sea surface, with a delay of 9 record’s time intervals. The ozone depletion, on the other side, is followed by a sea level warming with a delay of 1–2 record’s time intervals.

A flexible Bayesian framework for unbiased estimation of timescales

Timescales characterize the pace of change for many dynamic processes in nature. Timescales are usually estimated by fitting the exponential decay of data autocorrelation in the time or frequency domain. Here we show that this standard procedure often fails to recover the correct timescales due to a statistical bias arising from the finite sample size. We develop an alternative approach which estimates timescales by fitting the sample autocorrelation or power spectrum with a generative model based on a mixture of Ornstein-Uhlenbeck (OU) processes using adaptive approximate Bayesian computations (aABC). Our method accounts for finite sample size and noise in data and returns a posterior distribution of timescales that quantifies the estimation uncertainty and can be used for model selection. We demonstrate the accuracy of our method on synthetic data and illustrate its application to recordings from primate cortex. We provide a customizable Python package implementing our framework with different generative models suitable for diverse applications.

Transcriptional Landscape of Enhancer RNAs in Peripheral Blood Mononuclear Cells from Patients with Systemic Lupus Erythematosus

Objective

Enhancer RNAs (eRNAs), a class of non-coding RNAs, play indispensable roles in regulating target gene transcription and maintaining cell identity in cooperation with promoters. In this study, we investigated the transcriptional landscape and potential functions of eRNAs in peripheral blood mononuclear cells (PBMCs) from patients with systemic lupus erythematosus (SLE).

Methods

PBMCs from five patients with stable SLE, five patients with active SLE, and ten healthy individuals (HCs) were subjected to RNA-sequencing. Putative regulators, differential expression, and pathways were analyzed. eRNAs that were significantly upregulated were first validated by RT-qPCR in 12 samples. Then, candidate eRNAs were confirmed in a validation cohort of 45 samples. We conducted comprehensive pathway analyses to explore the correlations between the candidate eRNAs and SLE pathology.

Results

By analyzing eRNA transcript data from PBMCs from SLE patients and HCs, we identified various eRNAs and functional super-enhancers potentially related with SLE. The SLE-specificity of eRNAs seemed to be largely driven by SLE-specific transcription factors (TFs). A Venn diagram of eRNAs differentially expressed in stable, active, and total SLE vs HCs revealed that 13 and 23 eRNAs were commonly upregulated and downregulated, respectively, in patients with stable SLE and those with active SLE. The commonly upregulated eRNAs participate in regulating SLE-related pathways. Only eRNA TCONS_00034326 was significantly (P < 0.05) upregulated in PBMCs of patients with SLE when compared with those of HCs as indicated by RT-qPCR. The area under the receiver-operating curve of TCONS_00034326 for distinguishing SLE patients from HCs was 0.691. Through its putative SLE-related master TF, TCONS_00034326 is involved in multiple SLE-relevant signaling pathways, especially tumor necrosis factor signaling.

Conclusion

This study unraveled the transcriptional landscape of eRNAs, eRNA-related TFs, and super-enhancers in PBMCs from SLE patients and HCs. We identified a panel of SLE-relevant eRNAs, providing potential targets in SLE pathogenesis.

Three dimensional radial echo planar imaging for functional MRI

PURPOSE

To demonstrate a novel 3D radial echo planar imaging (3D REPI) sequence for flexible, rapid, and motion-robust sampling in fMRI.

METHODS

The 3D REPI method expands on the recently described golden angle rotated EPI trajectory using radial batched internal navigator echoes (TURBINE) approach by exploiting the unused perpendicular direction in the EPI readout to form fast analogues of rotated stack of stars or spirals trajectories that cover all 3 dimensions of k-space. An iterative conjugate gradient algorithm with SENSE reconstruction and time-segmented non-uniform fast Fourier transform (FFT) was used for parallel imaging acceleration and to account for the effects of B₀ inhomogeneity. The golden angle rotation allowed for sliding window reconstruction schemes to be applied in brain BOLD fMRI experiments.

RESULTS

Combined whole brain visual and motor fMRI experiments were successfully carried out on a clinical 3T scanner at 2 mm isotropic and 1 × 1 × 2 mm³ resolutions using the 3D REPI design. Improved sampling characteristics and image quality were observed for twisted trajectories at the expense of prolonged readout times and off-resonance effects. The ability to correct for rigid motion correction was also demonstrated.

CONCLUSIONS

3D REPI presents a flexible approach for segmented volumetric fMRI with motion correction and high in-plane spatial resolutions. Improved BOLD fMRI brain activation maps were obtained using a sliding window reconstruction.

Using Network Parcels and Resting-State Networks to Estimate Correlates of Mood Disorder and Related Research Domain Criteria Constructs of Reward Responsiveness and Inhibitory Control

BACKGROUND

Resting-state graph-based network edges can be powerful tools for identification of mood disorders. We address whether these edges can be integrated with Research Domain Criteria (RDoC) constructs for accurate identification of mood disorder-related markers, while minimizing active symptoms of disease.

METHODS

We compared 132 individuals with currently remitted or euthymic mood disorder with 65 healthy comparison participants, ages 18-30 years. Subsets of smaller brain parcels, combined into three prominent networks and one network of parcels overlapping across these networks, were used to compare edge differences between groups. Consistent with the RDoC framework, we evaluated individual differences with performance measure regressors of inhibitory control and reward responsivity. Within an omnibus regression model, we predicted edges related to diagnostic group membership, performance within both RDoC domains, and relevant interactions.

RESULTS

There were several edges of mood disorder group, predominantly of greater connectivity across networks, different than those related to individual differences in inhibitory control and reward responsivity. Edges related to diagnosis and inhibitory control did not align well with prior literature, whereas edges in relation to reward responsivity constructs showed greater alignment with prior literature. Those edges in interaction between RDoC constructs and diagnosis showed a divergence for inhibitory control (negative interactions in default mode) relative to reward (positive interactions with salience and emotion network).

CONCLUSIONS

In conclusion, there is evidence that prior simple network models of mood disorders are currently of insufficient biological or diagnostic clarity or that parcel-based edges may be insufficiently sensitive for these purposes.

rsHRF: A toolbox for resting-state HRF estimation and deconvolution

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.

Neuropeptide Y Variation Is Associated With Altered Static and Dynamic Functional Connectivity of the Salience Network

Neuropeptide Y (NPY) is a neurotransmitter that has been implicated in the development of anxiety and mood disorders. Low levels of NPY have been associated with risk for these disorders, and high levels with resilience. Anxiety and depression are associated with altered intrinsic functional connectivity of brain networks, but the effect of NPY on functional connectivity is not known. Here, we test the hypothesis that individual differences in NPY expression affect resting functional connectivity of the default mode and salience networks. We evaluated static connectivity using graph theoretical techniques and dynamic connectivity with Leading Eigenvector Dynamics Analysis (LEiDA). To increase our power of detecting NPY effects, we genotyped 221 individuals and identified 29 healthy subjects at the extremes of genetically predicted NPY expression (12 high, 17 low). Static connectivity analysis revealed that lower levels of NPY were associated with shorter path lengths, higher global efficiency, higher clustering, higher small-worldness, and average higher node strength within the salience network, whereas subjects with high NPY expression displayed higher modularity and node eccentricity within the salience network. Dynamic connectivity analysis showed that the salience network of low-NPY subjects spent more time in a highly coordinated state relative to high-NPY subjects, and the salience network of high-NPY subjects switched between states more frequently. No group differences were found for static or dynamic connectivity of the default mode network. These findings suggest that genetically driven individual differences in NPY expression influence risk of mood and anxiety disorders by altering the intrinsic functional connectivity of the salience network.

A prediction model of aquaculture water quality based on multiscale decomposition

In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.

Time-varying measures of cerebral network centrality correlate with visual saliency during movie watching

The extensive development of graph-theoretic analysis for functional connectivity has revealed the multifaceted characteristics of brain networks. Network centralities identify the principal functional regions, individual differences, and hub structure in brain networks. Neuroimaging studies using movie-watching have investigated brain function under naturalistic stimuli. Visual saliency is one of the promising measures for revealing cognition and emotions driven by naturalistic stimuli. This study investigated whether the visual saliency in movies was associated with network centrality. The study examined eigenvector centrality (EC), which is a measure of a region's influence in the brain network, and the participation coefficient (PC), which reflects the hub structure in the brain, was used for comparison. Static and time-varying EC and PC were analyzed by a parcel-based technique. While EC was correlated with brain activity in parcels in the visual and auditory areas during movie-watching, it was only correlated with parcels in the visual areas in the retinotopy task. In addition, high PC was consistently observed in parcels in the putative hub both during the tasks and the resting-state condition. Time-varying EC in the parietal parcels and time-varying PC in the primary sensory parcels significantly correlated with visual saliency in the movies. These results suggest that time-varying centralities in brain networks are distinctively associated with perceptual processing and subsequent higher processing of visual saliency.

Tiagabine induced modulation of oscillatory connectivity and activity match PET-derived, canonical GABA-A receptor distributions

As the most abundant inhibitory neurotransmitter in the mammalian brain, γ-aminobutyric acid (GABA) plays a crucial role in shaping the frequency and amplitude of oscillations, which suggests a role for GABA in shaping the topography of functional connectivity and activity. This study explored the effects of pharmacologically blocking the reuptake of GABA (increasing local concentrations) using the GABA transporter 1 (GAT1) blocker, tiagabine (15 mg). In a placebo-controlled crossover design, we collected resting magnetoencephalography (MEG) recordings from 15 healthy individuals prior to, and at 1-, 3- and 5- hours post, administration of tiagabine and placebo. We quantified whole brain activity and functional connectivity in discrete frequency bands. Drug-by-session (2 × 4) analysis of variance in connectivity revealed interaction and main effects. Post-hoc permutation testing of each post-drug recording vs. respective pre-drug baseline revealed consistent reductions of a bilateral occipital network spanning theta, alpha and beta frequencies, across 1- 3- and 5- hour recordings following tiagabine only. The same analysis applied to activity revealed significant increases across frontal regions, coupled with reductions in posterior regions, across delta, theta, alpha and beta frequencies. Crucially, the spatial distribution of tiagabine-induced changes overlap with group-averaged maps of the distribution of GABA_A receptors, from flumazenil (FMZ-V_T) PET, demonstrating a link between GABA availability, GABA_A receptor distribution, and low-frequency network oscillations. Our results indicate that the relationship between PET receptor distributions and MEG effects warrants further exploration, since elucidating the nature of this relationship may uncover electrophysiologically-derived maps of oscillatory activity as sensitive, time-resolved, and targeted receptor-mapping tools for pharmacological imaging.

The modulation of brain network integration and arousal during exploration

There is growing interest in how neuromodulators shape brain networks. Recent neuroimaging studies provide evidence that brainstem arousal systems, such as the locus coeruleus-norepinephrine system (LC-NE), influence functional connectivity and brain network topology, suggesting they have a role in flexibly reconfiguring brain networks in order to adapt behavior and cognition to environmental demands. To date, however, the relationship between brainstem arousal systems and functional connectivity has not been assessed within the context of a task with an established relationship between arousal and behavior, with most prior studies relying on incidental variations in arousal or pharmacological manipulation and static brain networks constructed over long periods of time. These factors have likely contributed to a heterogeneity of effects across studies. To address these issues, we took advantage of the association between LC-NE-linked arousal and exploration to probe the relationships between exploratory choice, arousal—as measured indirectly via pupil diameter—and brain network dynamics. Exploration in a bandit task was associated with a shift toward fewer, more weakly connected modules that were more segregated in terms of connectivity and topology but more integrated with respect to the diversity of cognitive systems represented in each module. Functional connectivity strength decreased, and changes in connectivity were correlated with changes in pupil diameter, in line with the hypothesis that brainstem arousal systems influence the dynamic reorganization of brain networks. More broadly, we argue that carefully aligning dynamic network analyses with task designs can increase the temporal resolution at which behaviorally- and cognitively-relevant modulations can be identified, and offer these results as a proof of concept of this approach.

Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization

In neuroscience, the functional magnetic resonance imaging (fMRI) is a vital tool to non-invasively access brain activity. Using fMRI, the functional connectivity (FC) between brain regions can be inferred, which has contributed to a number of findings of the fundamental properties of the brain. As an important clinical application of FC, clustering of subjects based on FC recently draws much attention, which can potentially reveal important heterogeneity in subjects such as subtypes of psychiatric disorders. In particular, a multiple clustering method is a powerful analytical tool, which identifies clustering patterns of subjects depending on their FC in specific brain areas. However, when one applies an existing multiple clustering method to fMRI data, there is a need to simplify the data structure, independently dealing with elements in a FC matrix, i.e., vectorizing a correlation matrix. Such a simplification may distort the clustering results. To overcome this problem, we propose a novel multiple clustering method based on Wishart mixture models, which preserves the correlation matrix structure without vectorization. The uniqueness of this method is that the multiple clustering of subjects is based on particular networks of nodes (or regions of interest, ROIs), optimized in a data-driven manner. Hence, it can identify multiple underlying pairs of associations between a subject cluster solution and a ROI sub-network. The key assumption of the method is independence among sub-networks, which is effectively addressed by whitening correlation matrices. We applied the proposed method to synthetic and fMRI data, demonstrating the usefulness and power of the proposed method.

Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data

Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene-gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox.

Large-scale nonlinear Granger causality for inferring directed dependence from short multivariate time-series data

A key challenge to gaining insight into complex systems is inferring nonlinear causal directional relations from observational time-series data. Specifically, estimating causal relationships between interacting components in large systems with only short recordings over few temporal observations remains an important, yet unresolved problem. Here, we introduce large-scale nonlinear Granger causality (lsNGC) which facilitates conditional Granger causality between two multivariate time series conditioned on a large number of confounding time series with a small number of observations. By modeling interactions with nonlinear state-space transformations from limited observational data, lsNGC identifies casual relations with no explicit a priori assumptions on functional interdependence between component time series in a computationally efficient manner. Additionally, our method provides a mathematical formulation revealing statistical significance of inferred causal relations. We extensively study the ability of lsNGC in inferring directed relations from two-node to thirty-four node chaotic time-series systems. Our results suggest that lsNGC captures meaningful interactions from limited observational data, where it performs favorably when compared to traditionally used methods. Finally, we demonstrate the applicability of lsNGC to estimating causality in large, real-world systems by inferring directional nonlinear, causal relationships among a large number of relatively short time series acquired from functional Magnetic Resonance Imaging (fMRI) data of the human brain.

Variability in atlas registration of optical intrinsic signal imaging and its effect on functional connectivity analysis

To compare neuroimaging data between subjects, images from individual sessions need to be aligned to a common reference or "atlas." Atlas registration of optical intrinsic signal imaging of mice, for example, is commonly performed using affine transforms with parameters determined by manual selection of canonical skull landmarks. Errors introduced by such procedures have not previously been investigated. We quantify the variability that arises from this process and consequent errors from misalignment that affect interpretation of functional neuroimaging data. We propose an improved method, using separately acquired high-resolution images and demonstrate improvements in variability and alignment using this method.

Origin of the Time Lag Phenomenon and the Global Signal in Resting-State fMRI

The global mean signal of resting-state fMRI (rs-fMRI) shows a characteristic spatiotemporal pattern that is closely related to the pattern of vascular perfusion. Although being increasingly adopted in the mapping of the flow of neural activity, the mechanism that gives rise to the BOLD signal time lag remains controversial. In the present study, we compared the time lag of the global mean signal with those of the local network components obtained by applying temporal independent component analysis to the resting-state fMRI data, as well as by using simultaneous wide-field visual stimulation, and demonstrated that the time lag patterns are highly similar across all types of data. These results suggest that the time lag of the rs-fMRI signal reflects the local variance of the hemodynamic responses rather than the arrival or transit time of the stimulus, whether the trigger is neuronal or non-neuronal in origin as long as it is mediated by local hemodynamic responses. Examinations of the internal carotid artery signal further confirmed that the arterial signal is tightly inversely coupled with the global mean signal in accordance with previous studies, presumably reflecting the blood flow or blood pressure changes that are occurring almost simultaneously in the internal carotid artery and the cerebral pial/capillary arteries, within the low-frequency component in human rs-fMRI.

Evidence for modulation of EEG microstate sequence by vigilance level

The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain's underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

Static and dynamic aspects of cerebro-cerebellar functional connectivity are associated with self-reported measures of impulsivity: A resting-state fMRI study

Human and animal brain studies bring converging evidence of a possible role for the cerebellum and the cerebro-cerebellar system in impulsivity. However, the precise nature of the relation between cerebro-cerebellar coupling and impulsivity is far from understood. Characterizing functional connectivity (FC) patterns between large-scale brain networks that mediate different forms of impulsivity, and the cerebellum may improve our understanding of this relation. Here, we analyzed static and dynamic features of cerebro-cerebellar FC using a highly sampled resting-state functional magnetic resonance imaging (rs-fMRI) dataset and tested their association with two widely used self-reports of impulsivity: the UPPS-P impulsive behavior scale and the behavioral inhibition/approach systems (BIS/BAS) in a large group of healthy subjects (N = 134, ≈ 1 hr of rs-fMRI/subject). We employed robust data-driven techniques to identify cerebral and cerebellar resting-state networks and extract descriptive summary measures of static and dynamic cerebro-cerebellar FC. We observed evidence linking BIS, BAS, sensation seeking, and lack of premeditation to the total strength and temporal variability of FC within networks connecting regions of the prefrontal cortex, precuneus, posterior cingulate cortex, basal ganglia, and thalamus with the cerebellum. Overall, our findings improve the existing knowledge of the neural correlates of impulsivity and the behavioral correlates of the cerebro-cerebellar system.

Evaluating global brain connectivity as an imaging marker for depression: influence of preprocessing strategies and placebo-controlled ketamine treatment

Major depressive disorder (MDD) is associated with altered global brain connectivity (GBC), as assessed via resting-state functional magnetic resonance imaging (rsfMRI). Previous studies found that antidepressant treatment with ketamine normalized aberrant GBC changes in the prefrontal and cingulate cortices, warranting further investigations of GBC as a putative imaging marker. These results were obtained via global signal regression (GSR). This study is an independent replication of that analysis using a separate dataset. GBC was analyzed in 28 individuals with MDD and 22 healthy controls (HCs) at baseline, post-placebo, and post-ketamine. To investigate the effects of preprocessing, three distinct pipelines were used: (1) regression of white matter (WM)/cerebrospinal fluid (CSF) signals only (BASE); (2) WM/CSF + GSR (GSR); and (3) WM/CSF + physiological parameter regression (PHYSIO). Reduced GBC was observed in individuals with MDD only at baseline in the anterior and medial cingulate cortices, as well as in the prefrontal cortex only after regressing the global signal. Ketamine had no effect compared to baseline or placebo in either group in any pipeline. PHYSIO did not resemble GBC preprocessed with GSR. These results concur with several studies that used GSR to study GBC. Further investigations are warranted into disease-specific components of global fMRI signals that may drive these results and of GBCr as a potential imaging marker in MDD.

Temporal non-local means filtering for studies of intrinsic brain connectivity from individual resting fMRI

Characterizing functional brain connectivity using resting functional magnetic resonance imaging (fMRI) is challenging due to the relatively small Blood-Oxygen-Level Dependent contrast and low signal-to-noise ratio. Denoising using surface-based Laplace-Beltrami (LB) or volumetric Gaussian filtering tends to blur boundaries between different functional areas. To overcome this issue, a time-based Non-Local Means (tNLM) filtering method was previously developed to denoise fMRI data while preserving spatial structure. The kernel and parameters that define the tNLM filter need to be optimized for each application. Here we present a novel Global PDF-based tNLM filtering (GPDF) algorithm that uses a data-driven kernel function based on a Bayes factor to optimize filtering for spatial delineation of functional connectivity in resting fMRI data. We demonstrate its performance relative to Gaussian spatial filtering and the original tNLM filtering via simulations. We also compare the effects of GPDF filtering against LB filtering using individual in-vivo resting fMRI datasets. Our results show that LB filtering tends to blur signals across boundaries between adjacent functional regions. In contrast, GPDF filtering enables improved noise reduction without blurring adjacent functional regions. These results indicate that GPDF may be a useful preprocessing tool for analyses of brain connectivity and network topology in individual fMRI recordings.

Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping

Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.