David M. Cole

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.

Nicotine replacement in abstinent smokers improves cognitive withdrawal symptoms with modulation of resting brain network dynamics

Symptoms of cognitive impairment during smoking withdrawal can be ameliorated by nicotine replacement. To define brain mechanisms contributing to this therapeutic effect, we conducted a functional connectivity analysis of resting-state fMRI in 17 abstinent smokers following nicotine replacement in a double-blind, placebo-controlled, crossover design. We found that individual differences in cognitive withdrawal symptom improvements after nicotine replacement were associated with increased inverse coupling between executive control and default mode brain networks. Furthermore, improvements in withdrawal symptoms were negatively correlated with altered functional connectivity within the default mode network, and with connectivity between the executive control network and regions implicated in reward processing. These findings demonstrate that nicotine administration in abstinent smokers modulates dynamic interactions between large-scale cognitive brain networks in the resting state. We specifically highlight the role of midline and prefrontal network regions in the neurocognitive response to nicotine pharmacotherapy and suggest that altered functional connectivity patterns of these networks reflect their engagement in reward and salience processing during smoking withdrawal. Individual differences in resting brain functional connectivity may predict therapeutic outcomes in nicotine addiction and other conditions associated with cognitive impairments.

Structural substrates for resting network disruption in temporal lobe epilepsy

Magnetic resonance imaging methods that measure interregional brain signalling at rest have been advanced as powerful tools to probe organizational properties of functional networks. In drug-resistant temporal lobe epilepsy, resting functional magnetic resonance imaging studies have primarily employed region of interest approaches that preclude a comprehensive evaluation of large-scale functional interactions. In line with the distributed nature of structural damage in this condition, we set out to quantify connectivity across the entire range of resting networks. Furthermore, we assessed whether connectivity is driven by co-localized structural pathology. We obtained resting state, diffusion tensor and anatomical imaging data in 35 patients with temporal lobe epilepsy and 20 healthy subjects on a 3 T scanner. Resting state networks were identified using independent component analysis, which allows an objective whole-brain quantification of functional connectivity. We performed group comparisons before and after correcting for voxel-wise grey matter density. In addition, we identified voxel-wise associations between resting connectivity and white matter coherence indexed by fractional anisotropy. Compared with controls, patients showed altered (typically reduced) functional connectivity between the hippocampus, anterior temporal, precentral cortices and the default mode and sensorimotor networks. Reduced network integration of the hippocampus was explained by variations in grey matter density, while functional connectivity of the parahippocampus, and frontal and temporal neocortices showed atypical associations with white matter coherence within pathways carrying connections of these regions. Our multimodal imaging study suggests that in temporal lobe epilepsy, cortical atrophy and microstructural white matter damage impact functional resting connectivity.

The effects of nicotine replacement on cognitive brain activity during smoking withdrawal studied with simultaneous fMRI/EEG.

Impaired attention (‘difficulty concentrating’) is a cognitive symptom of nicotine withdrawal that may be an important contributor to smoking relapse. However, the neurobiological basis of this effect and the potentially beneficial effects of nicotine replacement therapy both remain unclear. We used functional MRI with simultaneous electroencephalogram (EEG) recording to define brain activity correlates of cognitive impairment with short-term smoking cessation in habitual smokers and the effects of nicotine replacement. We found that irrespective of treatment (ie nicotine or placebo) EEG α power was negatively correlated with increased activation during performance of a rapid visual information processing (RVIP) task in dorsolateral prefrontal, dorsal anterior cingulate, parietal, and insular cortices, as well as, caudate, and thalamus. Relative to placebo, nicotine replacement further increased the α-correlated activation across these regions. We also found that EEG α power was negatively correlated with RVIP-induced deactivation in regions comprising the ‘default mode’ network (ie angular gyrus, cuneus, precuneus, posterior cingulate, and ventromedial prefrontal cortex). These α-correlated deactivations were further reduced by nicotine. These findings confirm that effects of nicotine on cognition during short-term smoking cessation occur with modulation of neuronal sources common to the generation of both the blood oxygen-level-dependent and α EEG signals. Our observations thus demonstrate that nicotine replacement in smokers has direct pharmacological effects on brain neuronal activity modulating cognitive networks.

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.

Resting-state networks

Resting-state networks (RSNs) in fMRI are ‘activation-like’, spatially structured maps of grey matter brain areas exhibiting temporally correlated signal changes, which are believed to reflect neuronal activities of the ‘resting’ brain and which robustly and consistently appear in both resting and task data. They are purported to reflect the intrinsic energy demands of neuron populations that fire together with a common functional purpose. This chapter introduces the concept of RSNs and why they are of interest to neuroscience, describes their characteristics, reviews the methods used for their analysis, and discusses a few areas of application.

Dynamic imaging of cognitive impairment in nicotine-deprived subjects using simultaneous EEG/FMRI

Imaging experiments involving combined Electroencephalography (EEG) and functional MRI (fMRI) acquisitions are becoming increasingly popular primarily because of their ability to offer complementary information about the underlying mechanisms of brain function. In this communication we describe a methodology that combines time-locked EEG and fMRI timeseries with the objective of providing improved characterization of cognitive impairment in nicotine-deprived subjects over conventional independently acquired fMRI.