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The idea that complex systems have a hierarchical modular organization originated in the early 1960s and has recently attracted fresh support from quantitative studies of large scale, real-life networks. Here we investigate the hierarchical modular (or “modules-within-modules”) decomposition of human brain functional networks, measured using functional magnetic resonance imaging in 18 healthy volunteers under no-task or resting conditions. We used a customized template to extract networks with more than 1800 regional nodes, and we applied a fast algorithm to identify nested modular structure at several hierarchical levels. We used mutual information, 0 < I < 1, to estimate the similarity of community structure of networks in different subjects, and to identify the individual network that is most representative of the group. Results show that human brain functional networks have a hierarchical modular organization with a fair degree of similarity between subjects, I = 0.63. The largest five modules at the highest level of the hierarchy were medial occipital, lateral occipital, central, parieto-frontal and fronto-temporal systems; occipital modules demonstrated less sub-modular organization than modules comprising regions of multimodal association cortex. Connector nodes and hubs, with a key role in inter-modular connectivity, were also concentrated in association cortical areas. We conclude that methods are available for hierarchical modular decomposition of large numbers of high resolution brain functional networks using computationally expedient algorithms. This could enable future investigations of Simon's original hypothesis that hierarchy or near-decomposability of physical symbol systems is a critical design feature for their fast adaptivity to changing environmental conditions.

Addiction to drugs is a major contemporary public health issue, characterized by maladaptive behavior to obtain and consume an increasing amount of drugs at the expense of the individual's health and social and personal life. We discovered abnormalities in fronto-striatal brain systems implicated in self-control in both stimulant-dependent individuals and their biological siblings who have no history of chronic drug abuse; these findings support the idea of an underlying neurocognitive endophenotype for stimulant drug addiction.

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A growing body of preclinical evidence indicates that addiction to cocaine is associated with neuroadaptive changes in frontostriatal brain systems. Human studies in cocaine-dependent individuals have shown alterations in brain structure, but it is less clear how these changes may be related to the clinical phenotype of cocaine dependence characterized by impulsive behaviours and compulsive drug-taking. Here we compared self-report, behavioural and structural magnetic resonance imaging data on a relatively large sample of cocaine-dependent individuals (n = 60) with data on healthy volunteers (n = 60); and we investigated the relationships between grey matter volume variation, duration of cocaine use, and measures of impulsivity and compulsivity in the cocaine-dependent group. Cocaine dependence was associated with an extensive system of abnormally decreased grey matter volume in orbitofrontal, cingulate, insular, temporoparietal and cerebellar cortex, and with a more localized increase in grey matter volume in the basal ganglia. Greater duration of cocaine dependence was correlated with greater grey matter volume reduction in orbitofrontal, cingulate and insular cortex. Greater impairment of attentional control was associated with reduced volume in insular cortex and increased volume of caudate nucleus. Greater compulsivity of drug use was associated with reduced volume in orbitofrontal cortex. Cocaine-dependent individuals had abnormal structure of corticostriatal systems, and variability in the extent of anatomical changes in orbitofrontal, insular and striatal structures was related to individual differences in duration of dependence, inattention and compulsivity of cocaine consumption.

BackgroundGenetic factors have been implicated in the development of substance abuse disorders, but the role of pre-existing vulnerability in addiction is still poorly understood. Personality traits of impulsivity and sensation-seeking are highly prevalent in chronic drug users and have been linked with an increased risk for substance abuse. However, it has not been clear whether these personality traits are a cause or an effect of stimulant drug dependence.MethodWe compared self-reported levels of impulsivity and sensation-seeking between 30 sibling pairs of stimulant-dependent individuals and their biological brothers/sisters who did not have a significant drug-taking history and 30 unrelated, nondrug-taking control volunteers.ResultsSiblings of chronic stimulant users reported significantly higher levels of trait-impulsivity than control volunteers but did not differ from control volunteers with regard to sensation-seeking traits. Stimulant-dependent individuals reported significantly higher levels of impulsivity and sensation-seeking compared with both their siblings and control volunteers.ConclusionsThese data indicate that impulsivity is a behavioral endophenotype mediating risk for stimulant dependence that may be exacerbated by chronic drug exposure, whereas abnormal sensation-seeking is more likely to be an effect of stimulant drug abuse.

The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by methods such as linear regression of head movement parameters. However, a number of recent independent studies have demonstrated that these techniques are insufficient to remove motion confounds, and that even small movements can spuriously bias estimates of functional connectivity. Here we propose a new data-driven, spatially-adaptive, wavelet-based method for identifying, modeling, and removing non-stationary events in fMRI time series, caused by head movement, without the need for data scrubbing. This method involves the addition of just one extra step, the Wavelet Despike, in standard pre-processing pipelines. With this method, we demonstrate robust removal of a range of different motion artifacts and motion-related biases including distance-dependent connectivity artifacts, at a group and single-subject level, using a range of previously published and new diagnostic measures. The Wavelet Despike is able to accommodate the substantial spatial and temporal heterogeneity of motion artifacts and can consequently remove a range of high and low frequency artifacts from fMRI time series, that may be linearly or non-linearly related to physical movements. Our methods are demonstrated by the analysis of three cohorts of resting-state fMRI data, including two high-motion datasets: a previously published dataset on children (N = 22) and a new dataset on adults with stimulant drug dependence (N = 40). We conclude that there is a real risk of motion-related bias in connectivity analysis of fMRI data, but that this risk is generally manageable, by effective time series denoising strategies designed to attenuate synchronized signal transients induced by abrupt head movements. The Wavelet Despiking software described in this article is freely available for download at www.brainwavelet.org.

We review drug addiction from the perspective of the hypothesis that drugs of abuse interact with distinct brain memory systems. We focus on emotional and procedural forms of memory, encompassing Pavlovian and instrumental conditioning, both for action-outcome and for stimulus-response associations. Neural structures encompassed by these systems include the amygdala, hippocampus, nucleus accumbens, and dorsal striatum. Additional influences emanate from the anterior cingulate and prefrontal cortex, which are implicated in the encoding and retrieval of drug-related memories that lead to drug craving and drug use. Finally, we consider the ancillary point that chronic abuse of many drugs may impact directly on neural memory systems via neuroadaptive and neurotoxic effects that lead to cognitive impairments in which memory dysfunction is prominent.