Correction of low-frequency physiological noise from the resting state BOLD fMRI--Effect on ICA default mode analysis at 1.5 T

Default mode network mediates low-frequency fluctuations in brain activity and behavior during sustained attention

The low-frequency (<0.1 Hz) fluctuation in sustained attention attracts enormous interest in cognitive neuroscience and clinical research since it always leads to cognitive and behavioral lapses. What is the source of the spontaneous fluctuation in sustained attention in neural activity, and how does the neural fluctuation relate to behavioral fluctuation? Here, we address these questions by collecting and analyzing two independent fMRI and behavior datasets. We show that the neural (fMRI) fluctuation in a key brain network, the default-mode network (DMN), mediate behavioral (reaction time) fluctuation during sustained attention. DMN shows the increased amplitude of fluctuation, which correlates with the behavioral fluctuation in a similar frequency range (0.01-0.1 Hz) but not in the lower (<0.01 Hz) or higher (>0.1 Hz) frequency range. This was observed during both auditory and visual sustained attention and was replicable across independent datasets. These results provide a novel insight into the neural source of attention-fluctuation and extend the former concept that DMN was deactivated in cognitive tasks. More generally, our findings highlight the temporal dynamic of the brain-behavior relationship.

Impact of physiological noise in characterizing the functional MRI default-mode network in Alzheimer's disease

The functional connectivity of the default-mode network (DMN) monitored by functional magnetic resonance imaging (fMRI) in Alzheimer's disease (AD) patients has been found weaker than that in healthy participants. Since breathing and heart beating can cause fluctuations in the fMRI signal, these physiological activities may affect the fMRI data differently between AD patients and healthy participants. We collected resting-state fMRI data from AD patients and age-matched healthy participants. With concurrent cardiac and respiratory recordings, we estimated both physiological responses phase-locked and non-phase-locked to heart beating and breathing. We found that the cardiac and respiratory physiological responses in AD patients were 3.00 ± 0.51 s and 3.96 ± 0.52 s later (both p < 0.0001) than those in healthy participants, respectively. After correcting the physiological noise in the resting-state fMRI data by population-specific physiological response functions, the DMN estimated by seed-correlation was more localized to the seed region. The DMN difference between AD patients and healthy controls became insignificant after suppressing physiological noise. Our results indicate the importance of controlling physiological noise in the resting-state fMRI analysis to obtain clinically related characterizations in AD.

Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies.

Resting-State Blood Oxygen Level-Dependent Functional MRI: A Paradigm Shift in Preoperative Brain Mapping

Currently, functional magnetic resonance imaging (fMRI) facilitates a preoperative awareness of an association of an eloquent region with a tumor. This information gives the neurosurgeon helpful information that can aid in creating a surgical strategy. Typically, task-based fMRI has been employed to preoperatively localize speech and motor function. Task-based fMRI depends on the patient's ability to comply with the task paradigm, which often is impaired in the setting of a brain tumor. This problem is overcome by using resting-state fMRI (rs-fMRI) to localize function. rs-fMRI measures spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, representing the brain's functional organization. In a neurosurgical context, it allows noninvasive simultaneous assessment of multiple large-scale distributed networks. Compared with task-related fMRI, rs-fMRI provides more comprehensive information on the functional architecture of the brain and is applicable in settings where task-related fMRI may provide inadequate information or could not be performed. Taken together, rs-fMRI substantially expands the preoperative mapping capability in efficiency, effectiveness, and scope. In this article, a brief introduction into rs-fMRI processing methods is followed by a detailed discussion on the role rs-fMRI plays in presurgical planning.

Susceptibility to everyday cognitive failure is reflected in functional network interactions in the resting brain

The proneness to minor errors and slips in everyday life as assessed by the Cognitive Failures Questionnaire (CFQ) constitutes a trait characteristic and is reflected in stable features of brain structure and function. It is unclear, however, how dynamic interactions of large-scale brain networks contribute to this disposition. To address this question, we performed a high model order independent component analysis (ICA) with subsequent dual regression on resting-state fMRI data from 71 subjects to extract temporal time courses describing the dynamics of 17 resting-state networks (RSN). Dynamic network interactions between all 17 RSN were assessed by linear correlations between networks' time courses. On this basis, we investigated the relationship between subject-level RSN interactions and the susceptibility to everyday cognitive failure. We found that CFQ scores were significantly correlated with the interplay of the cingulo-opercular network (CON) and a posterior parietal network which unifies clusters in the posterior cingulate, precuneus, intraparietal lobules and middle temporal regions. Specifically, a higher positive functional connectivity between these two RSN was indicative of higher proneness to cognitive failure. Both the CON and posterior parietal network are implicated in cognitive functions, such as tonic alertness and executive control. Results indicate that proper checks and balances between the two networks are needed to protect against cognitive failure. Furthermore, we demonstrate that the study of temporal network dynamics in the resting state is a feasible tool to investigate individual differences in cognitive ability and performance.

Resting-state networks in healthy adult subjects: a comparison between a 32-element and an 8-element phased array head coil at 3.0 Tesla

BACKGROUND

Little research exists on the influence of a magnetic resonance imaging (MRI) head coil's channel count on measured resting-state functional connectivity.

PURPOSE

To compare a 32-element (32ch) and an 8-element (8ch) phased array head coil with respect to their potential to detect functional connectivity within resting-state networks.

MATERIAL AND METHODS

Twenty-six healthy adults (mean age, 21.7 years; SD, 2.1 years) underwent resting-state functional MRI at 3.0 Tesla with both coils using equal standard imaging parameters and a counterbalanced design. Independent component analysis (ICA) at different model orders and a dual regression approach were performed. Voxel-wise non-parametric statistical between-group contrasts were determined using permutation-based non-parametric inference.

RESULTS

Phantom measurements demonstrated a generally higher image signal-to-noise ratio using the 32ch head coil. However, the results showed no significant differences between corresponding resting-state networks derived from both coils (p < 0.05, FWE-corrected).

CONCLUSION

Using the identical standard acquisition parameters, the 32ch head coil does not offer any significant advantages in detecting ICA-based functional connectivity within RSNs.

Revisiting default mode network function in major depression: evidence for disrupted subsystem connectivity

BACKGROUND

Major depressive disorder (MDD) is characterized by alterations in brain function that are identifiable also during the brain's 'resting state'. One functional network that is disrupted in this disorder is the default mode network (DMN), a set of large-scale connected brain regions that oscillate with low-frequency fluctuations and are more active during rest relative to a goal-directed task. Recent studies support the idea that the DMN is not a unitary system, but rather is composed of smaller and distinct functional subsystems that interact with each other. The functional relevance of these subsystems in depression, however, is unclear.

METHOD

Here, we investigated the functional connectivity of distinct DMN subsystems and their interplay in depression using resting-state functional magnetic resonance imaging.

RESULTS

We show that patients with MDD exhibit increased within-network connectivity in posterior, ventral and core DMN subsystems along with reduced interplay from the anterior to the ventral DMN subsystems.

CONCLUSIONS

These data suggest that MDD is characterized by alterations of subsystems within the DMN as well as of their interactions. Our findings highlight a critical role of DMN circuitry in the pathophysiology of MDD, thus suggesting these subsystems as potential therapeutic targets.

Intrinsic brain activity of cognitively normal older persons resembles more that of patients both with and at risk for Alzheimer's disease than that of healthy younger persons

In Alzheimer's disease (AD), recent findings suggest that amyloid-β (Aβ)-pathology might start 20-30 years before first cognitive symptoms arise. To account for age as most relevant risk factor for sporadic AD, it has been hypothesized that lifespan intrinsic (i.e., ongoing) activity of hetero-modal brain areas with highest levels of functional connectivity triggers Aβ-pathology. This model induces the simple question whether in older persons without any cognitive symptoms intrinsic activity of hetero-modal areas is more similar to that of symptomatic patients with AD or to that of younger healthy persons. We hypothesize that due to advanced age and therefore potential impact of pre-clinical AD, intrinsic activity of older persons resembles more that of patients than that of younger controls. We tested this hypothesis in younger (ca. 25 years) and older healthy persons (ca. 70 years) and patients with mild cognitive impairment and AD-dementia (ca. 70 years) by the use of resting-state functional magnetic resonance imaging, distinct measures of intrinsic brain activity, and different hierarchical clustering approaches. Independently of applied methods and involved areas, healthy older persons' intrinsic brain activity was consistently more alike that of patients than that of younger controls. Our result provides evidence for larger similarity in intrinsic brain activity between healthy older persons and patients with or at-risk for AD than between older and younger ones, suggesting a significant proportion of pre-clinical AD cases in the group of cognitively normal older people. The observed link of aging and AD with intrinsic brain activity supports the view that lifespan intrinsic activity may contribute critically to the pathogenesis of AD.

Tracking whole-brain connectivity dynamics in the resting state

Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered.

Fast fMRI provides high statistical power in the analysis of epileptic networks

EEG-fMRI is a unique method to combine the high temporal resolution of EEG with the high spatial resolution of MRI to study generators of intrinsic brain signals such as sleep grapho-elements or epileptic spikes. While the standard EPI sequence in fMRI experiments has a temporal resolution of around 2.5-3s a newly established fast fMRI sequence called MREG (Magnetic-Resonance-Encephalography) provides a temporal resolution of around 100ms. This technical novelty promises to improve statistics, facilitate correction of physiological artifacts and improve the understanding of epileptic networks in fMRI. The present study compares simultaneous EEG-EPI and EEG-MREG analyzing epileptic spikes to determine the yield of fast MRI in the analysis of intrinsic brain signals. Patients with frequent interictal spikes (>3/20min) underwent EEG-MREG and EEG-EPI (3T, 20min each, voxel size 3×3×3mm, EPI TR=2.61s, MREG TR=0.1s). Timings of the spikes were used in an event-related analysis to generate activation maps of t-statistics. (FMRISTAT, |t|>3.5, cluster size: 7 voxels, p<0.05 corrected). For both sequences, the amplitude and location of significant BOLD activations were compared with the spike topography. 13 patients were recorded and 33 different spike types could be analyzed. Peak T-values were significantly higher in MREG than in EPI (p<0.0001). Positive BOLD effects correlating with the spike topography were found in 8/29 spike types using the EPI and in 22/33 spikes types using the MREG sequence. Negative BOLD responses in the default mode network could be observed in 3/29 spike types with the EPI and in 19/33 with the MREG sequence. With the latter method, BOLD changes were observed even when few spikes occurred during the investigation. Simultaneous EEG-MREG thus is possible with good EEG quality and shows higher sensitivity in regard to the localization of spike-related BOLD responses than EEG-EPI. The development of new methods of analysis for this sequence such as modeling of physiological noise, temporal analysis of the BOLD signal and defining appropriate thresholds is required to fully profit from its high temporal resolution.

Tracking whole-brain connectivity dynamics in the resting state

Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.

The impact of "physiological correction" on functional connectivity analysis of pharmacological resting state fMRI

Growing interest in pharmacological resting state fMRI (RSfMRI) necessitates developing standardized and robust analytical approaches that are insensitive to spurious correlated physiological signals. However, in pharmacological experiments physiological variations constitute an important aspect of the pharmacodynamic/pharmacokinetic profile of drug action; therefore retrospective corrective methods that discard physiological signals as noise may not be suitable. Previously, we have shown that template-based dual regression analysis is a sensitive method for model-free and objective detection of drug-specific effects on functional brain connectivity. In the current study, the robustness of this standard approach to physiological variations in a placebo controlled, repeated measures pharmacological RSfMRI study of morphine and alcohol in 12 healthy young men is tested. The impact of physiology-related variations on statistical inferences has been studied by: 1) modeling average physiological rates in higher level group analysis; 2) Regressing out the instantaneous respiration variation (RV); 3) applying retrospective image correction (RETROICOR) in the preprocessing stage; and 4) performing combined RV and heart rate correction (RVHRCOR) by regressing out physiological pulses convolved with canonical respiratory and cardiac hemodynamic response functions. Results indicate regional sensitivity of the BOLD signal to physiological variations, especially in the vicinity of large vessels, plus certain brain structures that are reported to be involved in physiological regulation, such as posterior cingulate, precuneus, medial prefrontal and insular cortices, as well as the thalamus, cerebellum and the brainstem. The largest impact of "correction" on final statistical test outcomes resulted from including the average respiration frequency and heart rate in the higher-level group analysis. Overall, the template-based dual regression method seems robust against physical noise that is corrected by RV regression or RETROICOR. However, convolving the RV and HR with canonical hemodynamic response functions caused a notable change in the BOLD signal variance, and in resting state connectivity estimates. The impact of RVHRCOR on statistical tests was limited to elimination of both morphine and alcohol effects related to the somatosensory network that consists of insula and cingulate cortex-important structures for autonomic regulation. Although our data do not warrant speculations about neuronal or vascular origins of these effects, these observations raise caution about the implications of physiological 'noise' and the risks of introducing false positives (e.g. increased white matter connectivity) by using generalized physiological correction methods in pharmacological studies. The obvious sensitivity of the posterior part of the default mode network to different correction schemes, underlines the importance of controlling for physiological fluctuations in seed-based functional connectivity analyses.

Resting-state fMRI: a review of methods and clinical applications

SUMMARY

Resting-state fMRI measures spontaneous low-frequency fluctuations in the BOLD signal to investigate the functional architecture of the brain. Application of this technique has allowed the identification of various RSNs, or spatially distinct areas of the brain that demonstrate synchronous BOLD fluctuations at rest. Various methods exist for analyzing resting-state data, including seed-based approaches, independent component analysis, graph methods, clustering algorithms, neural networks, and pattern classifiers. Clinical applications of resting-state fMRI are at an early stage of development. However, its use in presurgical planning for patients with brain tumor and epilepsy demonstrates early promise, and the technique may have a future role in providing diagnostic and prognostic information for neurologic and psychiatric diseases.

Manipulating brain connectivity with δ⁹-tetrahydrocannabinol: a pharmacological resting state FMRI study

Resting state-functional magnetic resonance imaging (RS-FMRI) is a neuroimaging technique that allows repeated assessments of functional connectivity in resting state. While task-related FMRI is limited to indirectly measured drug effects in areas affected by the task, resting state can show direct CNS effects across all brain networks. Hence, RS-FMRI could be an objective measure for compounds affecting the CNS. Several studies on the effects of cannabinoid receptor type 1 (CB(1))-receptor agonist δ(9)-tetrahydrocannabinol (THC) on task-dependent FMRI have been performed. However, no studies on the effects of cannabinoids on resting state networks using RS-FMRI have been published. Therefore, we investigated the effects of THC on functional brain connectivity using RS-FMRI. Twelve healthy volunteers (9 male, 3 female) inhaled 2, 6 and 6 mg THC or placebo with 90-minute intervals in a randomized, double blind, cross-over trial. Eight RS-FMRI scans of 8 min were obtained per occasion. Subjects rated subjective psychedelic effects on a visual analog scale after each scan, as pharmacodynamic effect measures. Drug-induced effects on functional connectivity were examined using dual regression with FSL software (FMRIB Analysis Group, Oxford). Eight maps of voxel-wise connectivity throughout the entire brain were provided per RS-FMRI series with eight predefined resting-state networks of interest. These maps were used in a mixed effects model group analysis to determine brain regions with a statistically significant drug-by-time interaction. Statistical images were cluster-corrected, and results were Bonferroni-corrected across multiple contrasts. THC administration increased functional connectivity in the sensorimotor network, and was associated with dissociable lateralized connectivity changes in the right and left dorsal visual stream networks. The brain regions showing connectivity changes included the cerebellum and dorsal frontal cortical regions. Clear increases were found for feeling high, external perception, heart rate and cortisol, whereas prolactin decreased. This study shows that THC induces both increases and (to a lesser extent) decreases in functional brain connectivity, mainly in brain regions with high densities of CB(1)-receptors. Some of the involved regions could be functionally related to robust THC-induced CNS-effects that have been found in previous studies (Zuurman et al., 2008), such as postural stability, feeling high and altered time perception.

Nicotine effects on default mode network during resting state

RATIONALE

The default mode network (DMN), one of several resting-state networks (RSN) in the brain, is thought to be involved in self-referential thought, awareness, and episodic memories. Nicotine improves cognitive performance, in part by improving attention. Nicotinic agonists have been shown to decrease activity in regions within DMN and increase activity in regions involved in visual attention during effortful processing of external stimuli. It is unknown if these pharmacological effects also occur in the absence of effortful processing.

OBJECTIVES

This study aims to determine if nicotine suppresses activity in default mode and enhances activity in extra-striate RSNs in the absence of an external visual task.

METHODS

Within-subject, single-blinded, counterbalanced study of 19 non-smoking subjects who had resting functional MRI scans after 7 mg nicotine or placebo patch. Group independent component analysis was performed. The DMN component was identified by spatial correlation with a reference DMN mask. A visual attention component was identified by spatial correlation with an extra-striate mask. Analyses were conducted using statistical parametric mapping.

RESULTS

Nicotine was associated with decreased activity in regions within the DMN and increased activity in extra-striate regions.

CONCLUSIONS

Suppression of DMN and enhancement of extra-striate resting-state activity in the absence of visual stimuli or effortful processing suggest that nicotine's cognitive effects may involve a shift in activity from networks that process internal to those that process external information. This is a potential mechanism by which cholinergic agonists may have a beneficial effect in diseases associated with altered resting-state activity.

A baseline for the multivariate comparison of resting-state networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

Age-Related Differences in Functional Nodes of the Brain Cortex - A High Model Order Group ICA Study

Functional MRI measured with blood oxygen dependent (BOLD) contrast in the absence of intermittent tasks reflects spontaneous activity of so-called resting state networks (RSN) of the brain. Group level independent component analysis (ICA) of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age-related effects from primary motor and sensory, and, higher level control RSNs. One hundred sixty-eight healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 years), 59 young adults (YA, 22.2 ± 0.6 years), and 54 older adults (OA, 42.7 ± 0.5 years), all with normal IQ. High model order group probabilistic ICA components (70) were calculated and dual-regression analysis was used to compare 21 RSN's spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN's reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs.

Advances and pitfalls in the analysis and interpretation of resting-state FMRI data

The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.