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.

Psychopathy is a personality disorder that is strongly linked to criminal behavior. Using [18F]fallypride PET and BOLD fMRI, we show that impulsive-antisocial psychopathic traits selectively predict nucleus accumbens dopamine release and reward anticipation-related neural activity in response to pharmacological and monetary reinforcers, respectively. These findings suggest that neurochemical and neurophysiological hyperreactivity of the dopaminergic reward system may comprise a neural substrate for impulsivity, antisocial behavior and substance abuse in psychopathy.

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.

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