Resting-state fMRI Functional Connectivity Research Articles

The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and the underlying patterns of neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the amygdala of two male macaques. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5–14.5 Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-modal approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.

A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity

BackgroundFunctional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. AimsEvaluate discriminative performance and generalizability of functional connectivity markers for BPD. MethodWhole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain—global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity—and evaluated the generalizability of the classification in the left-out portion of non-matched data. ResultsFull-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61–70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70–75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. LimitationsThe accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. ConclusionsSpatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.

Assessment of the macrovascular contribution to resting-state fMRI functional connectivity at 3 Tesla

Abstract In resting-state functional magnetic resonance imaging (rs-fMRI) functional connectivity (FC) mapping, temporal correlation is widely assumed to reflect synchronized neural-related activity. Although a large number of studies have demonstrated the potential vascular effects on FC, little research has been conducted on FC resulting from macrovascular signal fluctuations. Previously, our study found (Tong, Yao, et al., 2019) a robust anti-correlation between the fMRI signals in the internal carotid artery and the internal jugular vein (and the sagittal sinus). The present study extends the previous study to include all detectable major veins and arteries in the brain in a systematic analysis of the macrovascular contribution to the functional connectivity of the whole-gray matter (GM). This study demonstrates that: (1) The macrovasculature consistently exhibited strong correlational connectivity among itself, with the sign of the correlations varying between arterial and venous connectivity; (2) GM connectivity was found to have a strong macrovascular contribution, stronger from veins than arteries; (3) FC originating from the macrovasculature displayed disproportionately high spatial variability compared to that associated with all GM voxels; and (4) macrovascular contributions to connectivity were still evident well beyond the confines of the macrovascular space. These findings highlight the extensive contribution to rs-fMRI blood-oxygenation level-dependent (BOLD) and FC predominantly by large veins, but also by large arteries. These findings pave the way for future studies aimed at more comprehensively modeling and thereby removing these macrovascular contributions.

Neurophysiological hallmarks of Huntington's disease progression: an EEG and fMRI connectivity study.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can provide corroborative data on neurophysiological alterations in Huntington's disease (HD). However, the alterations in EEG and fMRI resting-state functional connectivity (rsFC), as well as their interrelations, at different stages of HD remain insufficiently investigated. This study aimed to identify neurophysiological alterations in individuals with preclinical HD (preHD) and early manifest HD (EMHD) by analyzing EEG and fMRI rsFC and examining their interrelationships. We found significant differences in EEG power between preHD individuals and healthy controls (HC), with a decrease in power in a specific frequency range at the theta-alpha border and slow alpha activity. In EMHD patients, in addition to the decrease in power in the 7-9 Hz range, a reduction in power within the classic alpha band compared to HC was observed. The fMRI analysis revealed disrupted functional connectivity in various brain networks, particularly within frontal lobe, putamen-cortical, and cortico-cerebellar networks, in individuals with the HD mutation compared to HC. The analysis of the relationship between EEG and fMRI rsFC revealed an association between decreased alpha power, observed in individuals with EMHD, and increased connectivity in large-scale brain networks. These networks include putamen-cortical, DMN-related and cortico-hippocampal circuits. Overall, the findings suggest that EEG and fMRI provide valuable information for monitoring pathological processes during the development of HD. A decrease in inhibitory control within the putamen-cortical, DMN-related and cortico-hippocampal circuits, accompanied by a reduction in alpha and theta-alpha border oscillatory activity, could potentially contribute to cognitive decline in HD.

Resting‐state network connectivity changes in prodromal stage of Alzheimer’s disease

AbstractBackgroundAlzheimer Disease (AD), the most common type of dementia, is accompanied by a generalized loss of neurons and cause severe changes in brain’s structure and function. Mild Cognitive Impairment is associated with a higher risk of developing dementia. This clinical classification is particularly relevant because each subtype is linked to a presumed etiology, with the amnestic subtypes (aMCI) considered as a prodromal form of AD (Petersen et al., 2009, 2014). Resting state fMRI functional connectivity (FCrs‐fMRI) analysis is broadly used to detect brain network differences in neurodegenerative diseases such as AD.MethodForty‐five participants were evaluated: patients with aMCI (n = 17), patients with dementia due to AD (n = 14) and healthy controls (HC) (n = 14). All participants received an extensive neuropsychological battery and brain MRI scanning. Resting state functional scans consisted of 240 T2*‐weighted echo planar imaging volumes (repetition time 3000 ms; echo time 60 ms; ip angle 90°; 32 axial slices; matrix 64×64; voxel size 3.3×3.4×4 mm). Additionally, a high‐resolution T1‐weighted magnetization prepared rapid acquisition gradient echo image was acquired. Data analyses were performed using the Functional Connectivity Toolbox (CONN) for Matlab (https://www.nitrc.org/frs/) to identify large‐scale patterns of temporal signal‐intensity coherence, interpreted as functional connectivity (Beckmann et al., 2005). Regions of interests (ROIs) analysis was performed for each participants and among group. Spatial maps of the group independent component analysis were used in a linear model t against each individual fMRI data set.ResultThe Default Mode Network (DMN) showed two regions of lower functional connectivity (FC) in AD when comparing with HC within the precuneus (p<0.001 uncorrected). No regions of FC changes were found in the DMN when comparing aMCI with HC or AD.ConclusionThe changes in FC rsfMRI in adults with AD are specifically related to regions of the DMN.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47–0.63) than for ai-MC (0.24–0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

537. Baseline Resting-State fMRI Functional Connectivity and Plasma Proteins Improve Predictability of Remission Following Sertraline Pharmacotherapy in Depressed Patients

537. Baseline Resting-State fMRI Functional Connectivity and Plasma Proteins Improve Predictability of Remission Following Sertraline Pharmacotherapy in Depressed Patients

Resting-state fMRI functional connectivity and clinical correlates in Afro-descendants with schizophrenia and bipolar disorder

Schizophrenia (SCZ) and bipolar disorder (BD) exhibited altered activation in several brain areas, including the prefrontal and temporal cortex; however, a less explored topic is how brain connectivity and functional disturbances occur in non-Caucasian samples of SCZ and BD. Individuals with SCZ (n=20), BD (n=21), and healthy controls (HC, n=21) from indigenous and African ethnicity were submitted to clinical screening and functional assessments. Mood, compulsive and psychotic symptoms were also correlated to network dysfunction in each group. Two distinct networks' subcomponents demonstrated significant lower global efficiency (GE) in SCZ versus HC, corresponding to left posterior dorsal attention and medial left ventral attention (VA) networks. Lower GE was found in BD versus controls in four subcomponents, including the left medial and right VA. Higher compulsion scores correlated in BD with lower GE in the left VA, whereas increased report of alcohol abuse was associated with higher GE in left default mode network. Although preliminary, differences in the activation of specific networks, notably the left hemisphere, in SCZ versus controls, and lower activation in VA areas, in BD versus controls. Results highlight default mode and salient network as relevant for the emotional processing of SCZ and BD of indigenous and black ethnicity.Abstract: schizophrenia, bipolar disorder, functional neuroimaging, ethnicity, default network

Associations between insomnia symptoms and functional connectivity in the UK Biobank cohort (n=29,423).

An increasing number of studies harness resting-state fMRI functional connectivity analysis to investigate the neurobiological mechanisms of insomnia. The results to date are inconsistent and the detection of minor and widely distributed alterations in functional connectivity requires large sample sizes. The present study investigated associations between insomnia symptoms and resting-state functional connectivity at the whole-brain level in the largest sample to date. This cross-sectional analysis used resting-state imaging data from the UK Biobank, a large scale, population-based biomedical database. The analysis included 29,423 participants (age: 63.1 ± 7.5 years, 54.3% female), comprising 9210 with frequent insomnia symptoms and 20,213 controls without. Linear models were adjusted for relevant clinical, imaging, and socio-demographic variables. The Akaike information criterion was used for model selection. Multiple comparisons were corrected using the false discovery rate with a significance level of q < 0.05. Frequent insomnia symptoms were associated with increased connectivity within the default mode network and frontoparietal network, increased negative connectivity between the default mode network and the frontoparietal network, and decreased connectivity between the salience network and a node of the default mode network. Furthermore, frequent insomnia symptoms were associated with altered functional connectivity between nodes comprising sensory areas and the cerebellum. These functional alterations of brain networks may underlie dysfunctional affective and cognitive processing in insomnia and contribute to subjectively and objectively impaired sleep. However, it must be noted that the item that was used to assess frequent insomnia symptoms in this study did not assess all the characteristics of clinically diagnosed insomnia.

Tensor response quantile regression with neuroimaging data.

Collecting neuroimaging data in the form of tensors (i.e. multidimensional arrays) has become more common in mental health studies, driven by an increasing interest in studying the associations between neuroimaging phenotypes and clinical disease manifestation. Motivated by a neuroimaging study of post-traumatic stress disorder (PTSD) from the Grady Trauma Project, we study a tensor response quantile regression framework, which enables novel analyses that confer a detailed view of the potentially heterogeneous association between a neuroimaging phenotype and relevant clinical predictors. We adopt a sensible low-rank structure to represent the association of interest, and propose a simple two-step estimation procedure which is easy to implement with existing software. We provide rigorous theoretical justifications for the intuitive two-step procedure. Simulation studies demonstrate good performance of the proposed method with realistic sample sizes in neuroimaging studies. We conduct the proposed tensor response quantile regression analysis of the motivating PTSD study to investigate the association between fMRI resting-state functional connectivity and PTSD symptom severity. Our results uncover non-homogeneous effects of PTSD symptoms on brain functional connectivity, which cannot be captured by existing tensor responsemethods.

Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting-State Functional MRI After Working Memory Training in People With HIV.

Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown. To test whether WMT affects PWH brain functional connectivity in resting-state fMRI (rsfMRI). Prospective. A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53 HIV-seronegative controls (SN, ages 49.5 ± 1.6 years, six women). Axial single-shot gradient-echo echo-planar imaging at 3.0 T was performed at baseline (TL1), at 1-month (TL2), and at 6-months (TL3), after WMT. All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training, n=58: 28 PWH, 30 SN; nonadaptive training, n=48: 25 PWH, 23 SN), 25 sessions over 5-8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2. Two-way analyses of variance (ANOVA) on GT metrics and two-sample t-tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set at P < 0.05 after false discovery rate correction. The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P=0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r=-0.62 at LD=50%) and vDMN eCent before training significantly correlated with memory performance changes (r=0.53 at LD=50%). ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH. 1 TECHNICAL EFFICACY: 1.

Resting-state fMRI functional connectivity of the left temporal parietal junction is associated with visual temporal order threshold

The study aimed to determine the relationship between the millisecond timing, measured by visual temporal order threshold (TOT), i.e. a minimum gap between two successive stimuli necessary to judge a before-after relation, and resting-state fMRI functional connectivity (rsFC). We assume that the TOT reflects a relatively stable feature of local internal state networks and is associated with rsFC of the temporal parietal junction (TPJ). Sixty five healthy young adults underwent the visual TOT, fluid intelligence (Gf) and an eyes-open resting-state fMRI examination. After controlling for the influence of gender, the higher the TOT, the stronger was the left TPJ’s rsFC with the left postcentral and the right precentral gyri, bilateral putamen and the right supplementary motor area. When the effects of Gf and TOT × Gf interaction were additionally controlled, the TOT—left TPJ’s rsFC relationship survived for almost all above regions with the exception of the left and right putamen. This is the first study demonstrating that visual TOT is associated with rsFC between the areas involved both in sub-second timing and motor control. Current outcomes indicate that the local neural networks are prepared to process brief, rapidly presented, consecutive events, even in the absence of such stimulation.

The Location Reliability of the Resting-State fMRI FC of Emotional Regions Towards rTMS Therapy.

Resting-state magnetic resonance imaging (RS-fMRI) studies indicated that the repetitive transcranial magnetic stimulation (rTMS) exerts antidepression effect through the functional connectivity (FC) of the DLPFC with the subgenual anterior cingulate cortex (sgACC), pregneual ACC (pgACC), or nucleus accumbens (NAc). It is proposed that the FC-guided individualized precise stimulation on the DLPFC would be more effective. The current study systematically investigated the reliability of the RS-fMRI FC location as well as the FC strength with multiple potential factors. We aimed to provide a stable stimulation target for future FC-guided TMS therapy for affective related disorders. Twenty-one subjects under RS-fMRI conditions with the first two times (V1, V2) scanned on a GE 3T scanner and the third visit (V3) on a Siemens 3T scanner. Then the FC strength and location reliability were assessed by using intra-class correlation (ICC) and intra-individual distance, respectively. The factors included deep seed ROIs (midline (mid-) sgACC, left pgACC, mid-pgACC, and left NAc), eyes closed (EC) vs eyes open (EO), frequency bands, FC algorithm (Pearson vs Spearman), scanning length (half a session vs whole session), and location method (FC peak vs center of gravity (COG)). The reliability of the voxel-wise FC strength was low to moderate. The intra-individual distances of the COG were 3.8-7.3mm across all factors, much smaller than that of FC peak (approximately 30mm). The COG of seed-based FC might be a potential rTMS stimulation target. Anyway, all potential stimulation targets should be tested in future rTMS treatment studies.

Variability of regional glucose metabolism and the topology of functional networks in the human brain

The brain consumes the most energy per relative mass amongst the organs in the human body. Theoretical and empirical studies have shown that behavioral processes are relatively inexpensive metabolically, and that most energy goes to maintaining the status quo, i.e., the balance of cell membranes’ resting potentials and subthreshold spontaneous activity. Spontaneous activity fluctuates across brain regions in a correlated fashion that defines multi-scale hierarchical networks called resting-state networks (RSNs). Different regions of the brain display different metabolic consumption, but the relationship between regional brain metabolism and RSNs is still under investigation.Here, we examine the variability of glucose metabolism across brain regions, measured with the relative standard uptake value (SUVR) using 18F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC).We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures.

Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.

Longitudinal Changes in Resting-State Functional Connectivity and Gray Matter Volume Are Associated with Conversion to Hearing Impairment in Older Adults.

Hearing loss was recently identified as a modifiable risk factor for dementia although the potential mechanisms explaining this relationship are unknown. The current study examined longitudinal change in resting-state fMRI functional connectivity and gray matter volume in individuals who developed a hearing impairment compared to those whose hearing remained normal. This study included 440 participants from the UK Biobank: 163 who had normal hearing at baseline and impaired hearing at follow-up (i.e., converters, mean age = 63.11±6.33, 53% female) and 277 who had normal hearing at baseline and maintained normal hearing at follow-up (i.e., non-converters, age = 63.31±5.50, 50% female). Functional connectivity was computed between a priori selected auditory seed regions (left and right Heschl's gyrus and cytoarchitectonic subregions Te1.0, Te1.1, and Te1.2) and select higher-order cognitive brain networks. Gray matter volume within these same regions was also obtained. Converters had increased connectivity from left Heschl's gyrus to left anterior insula and from right Heschl's gyrus to right anterior insula, and decreased connectivity between right Heschl's gyrus and right hippocampus, compared to non-converters. Converters also had reduced gray matter volume in left hippocampus and left lateral visual cortex compared to non-converters. These findings suggest that conversion to a hearing impairment is associated with altered brain functional connectivity and gray matter volume in the attention, memory, and visual processing regions that were examined in this study.

Covariance and Correlation Analysis of Resting State Functional Magnetic Resonance Imaging Data Acquired in a Clinical Trial of Mindfulness-Based Stress Reduction and Exercise in Older Individuals.

We describe and apply novel methodology for whole-brain analysis of resting state fMRI functional connectivity data, combining conventional multi-channel Pearson correlation with covariance analysis. Unlike correlation, covariance analysis preserves signal amplitude information, which feature of fMRI time series may carry physiological significance. Additionally, we demonstrate that dimensionality reduction of the fMRI data offers several computational advantages including projection onto a space of manageable dimension, enabling linear operations on functional connectivity measures and exclusion of variance unrelated to resting state network structure. We show that group-averaged, dimensionality reduced, covariance and correlation matrices are related, to reasonable approximation, by a single scalar factor. We apply this methodology to the analysis of a large, resting state fMRI data set acquired in a prospective, controlled study of mindfulness training and exercise in older, sedentary participants at risk for developing cognitive decline. Results show marginally significant effects of both mindfulness training and exercise in both covariance and correlation measures of functional connectivity.

Characterization of Hemodynamic Alteration in Parkinson's Disease and Their Effect on Resting-State fMRI Functional Connectivity

Characterization of Hemodynamic Alteration in Parkinson's Disease and Their Effect on Resting-State fMRI Functional Connectivity

Characterization of Hemodynamic Alterations in Schizophrenia and Bipolar Disorder and Their Effect on Resting-State fMRI Functional Connectivity.

Common and distinct neural bases of Schizophrenia (SZ) and bipolar disorder (BP) have been explored using resting-state fMRI (rs-fMRI) functional connectivity (FC). However, fMRI is an indirect measure of neural activity, which is a convolution of the hemodynamic response function (HRF) and latent neural activity. The HRF, which models neurovascular coupling, varies across the brain within and across individuals, and is altered in many psychiatric disorders. Given this background, this study had three aims: quantifying HRF aberrations in SZ and BP, measuring the impact of such HRF aberrations on FC group differences, and exploring the genetic basis of HRF aberrations. We estimated voxel-level HRFs by deconvolving rs-fMRI data obtained from SZ (N = 38), BP (N = 19), and matched healthy controls (N = 35). We identified HRF group differences (P < .05, FDR corrected) in many regions previously implicated in SZ/BP, with mediodorsal, habenular, and central lateral nuclei of the thalamus exhibiting HRF differences in all pairwise group comparisons. Thalamus seed-based FC analysis revealed that ignoring HRF variability results in false-positive and false-negative FC group differences, especially in insula, superior frontal, and lingual gyri. HRF was associated with DRD2 gene expression (P < .05, 1.62 < |Z| < 2.0), as well as with medication dose (P < .05, 1.75 < |Z| < 3.25). In this first study to report HRF aberrations in SZ and BP, we report the possible modulatory effect of dopaminergic signalling on HRF, and the impact that HRF variability can have on FC studies in clinical samples. To mitigate the impact of HRF variability on FC group differences, we suggest deconvolution during data preprocessing.

White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter.

A comprehensive characterization of the spatiotemporal organization in the whole brain is critical to understand both the function and dysfunction of the human brain. Resting-state functional connectivity (FC) of gray matter (GM) has helped in uncovering the inherent baseline networks of brain. However, the white matter (WM), which composes almost half of brain, has been largely ignored in this characterization despite studies indicating that FC in WM does change during task and rest functional magnetic resonance imaging (fMRI). In this study, we identify 9 white matter functional networks (WM-FNs) and 9 gray matter functional networks (GM-FNs) of resting fMRI. Intraclass correlation coefficient (ICC) was calculated on multirun fMRI data to estimate the reliability of static functional connectivity (SFC) and dynamic functional connectivity (DFC). Associations between SFC, DFC, and their respective ICCs are estimated for GM-FNs, WM-FNs, and GM-WM-FNs. SFC of GM-FNs were stronger than that of WM-FNs, but the corresponding DFC of GM-FNs was lower, indicating that WM-FNs were more dynamic. Associations between SFC, DFC, and their ICCs were similar in both GM- and WM-FNs. These findings suggest that WM fMRI signal contains rich spatiotemporal information similar to that of GM and may hold important cues to better establish the functional organization of the whole brain.