Brain networks with energy-efficient hubs might support the high cognitive performance of humans and a better understanding of their organization is likely of relevance for studying not only brain development and plasticity but also neuropsychiatric disorders. However, the distribution of hubs in the human brain is largely unknown due to the high computational demands of comprehensive analytical methods. Here we propose a 10 3 times faster method to map the distribution of the local functional connectivity density (lFCD) in the human brain. The robustness of this method was tested in 979 subjects from a large repository of MRI time series collected in resting conditions. Consistently across research sites, a region located in the posterior cingulate/ventral precuneus (BA 23/31) was the area with the highest lFCD, which suggest that this is the most prominent functional hub in the brain. In addition, regions located in the inferior parietal cortex (BA 18) and cuneus (BA 18) had high lFCD. The variability of this pattern across subjects was <36% and within subjects was 12%. The power scaling of the lFCD was consistent across research centers, suggesting that that brain networks have a "scale-free" organization.resting state functional MRI connectivity | functional connectomes | default mode networks | scale-free networks | consciousness T o support fast communication with minimal energy cost, cortical brain networks may have few nodes with dense local clustering (hubs) and numerous nodes with an average low number of connections (1-7). The energy-efficient regions (densely connected nodes) are thought to serve as the interconnection hubs, and neuropsychiatric diseases have been linked to abnormalities in their configuration (8, 9). However, the investigation of hubs in the brain has been hindered by the cumbersome computational requirements of comprehensive analytical methods.During the last decade, numerous studies have evaluated the functional connectivity among brain regions by using correlation analyses of spontaneous fluctuations of brain activity measured with MRI time series in resting conditions (10). A popular technique used for the analysis of resting-state time series is based on regions-of-interest (seed regions). This technique uses correlation analysis of blood oxygenation level-dependent (BOLD) signals for the identification of brain regions functionally connected to the seed regions (reviewed in ref. 11). Cluster analyses are also used to evaluate the degree of functional connectivity among multiple seed regions (12). These methods are constrained by the fact that they rely strongly on a priori selection of specific seed regions rather than allowing for the characteristics of the network to identify and locate the node regions; these methods are also computationally demanding. For these limitations previous studies assessing the topological organization of the human brain restricted their analysis to ∼10 2 seed regions (5, 13). More recently researchers have started to use data-driven approaches th...

Dopamine (DA) is considered crucial for the rewarding effects of drugs of abuse, but its role in addiction is much less clear. This review focuses on studies that used PET to characterize the brain DA system in addicted subjects. These studies have corroborated in humans the relevance of drug-induced fast DA increases in striatum [including nucleus accumbens (NAc)] in their rewarding effects but have unexpectedly shown that in addicted subjects, drug-induced DA increases (as well as their subjective reinforcing effects) are markedly blunted compared with controls. In contrast, addicted subjects show significant DA increases in striatum in response to drug-conditioned cues that are associated with self-reports of drug craving and appear to be of a greater magnitude than the DA responses to the drug. We postulate that the discrepancy between the expectation for the drug effects (conditioned responses) and the blunted pharmacological effects maintains drug taking in an attempt to achieve the expected reward. Also, whether tested during early or protracted withdrawal, addicted subjects show lower levels of D2 receptors in striatum (including NAc), which are associated with decreases in baseline activity in frontal brain regions implicated in salience attribution (orbitofrontal cortex) and inhibitory control (anterior cingulate gyrus), whose disruption results in compulsivity and impulsivity. These results point to an imbalance between dopaminergic circuits that underlie reward and conditioning and those that underlie executive function (emotional control and decision making), which we postulate contributes to the compulsive drug use and loss of control in addiction.prefrontal cortex | dorsal striatum | substance use disorders | stimulant drugs | brain imaging

Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the “default-mode” network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis we evaluated resting-state datasets corresponding to 913 healthy subjects from a public magnetic resonance imaging database using functional connectivity density mapping, a voxelwise and data-driven approach together with parallel computing. Aging was associated with pronounced long-range FCD decreases in DMN and dorsal attention network (DAN) and with increases in somatosensory and subcortical networks. Aging effects in these networks were stronger for long-range than for short-range FCD and were also detected at the level of the main functional hubs. Females had higher short- and long-range FCD in DMN and lower FCD in the somatosensory network than males, but the gender by age interaction effects were not significant for any of the networks or hubs. These findings suggest that long-range connections may be more vulnerable to aging effects than short-range connections and that in addition to the DMN the DAN is also sensitive to aging effects, which could underlie the deterioration of attention processes that occurs with aging.

Summary Drug addiction and obesity appear to share several properties. Both can be defined as disorders in which the saliency of a specific type of reward (food or drug) becomes exaggerated relative to, and at the expense of others rewards. Both drugs and food have powerful reinforcing effects, which are in part mediated by abrupt dopamine increases in the brain reward centres. The abrupt dopamine increases, in vulnerable individuals, can override the brain’s homeostatic control mechanisms. These parallels have generated interest in understanding the shared vulnerabilities between addiction and obesity. Predictably, they also engendered a heated debate. Specifically, brain imaging studies are beginning to uncover common features between these two conditions and delineate some of the overlapping brain circuits whose dysfunctions may underlie the observed deficits. The combined results suggest that both obese and drug-addicted individuals suffer from impairments in dopaminergic pathways that regulate neuronal systems associated not only with reward sensitivity and incentive motivation, but also with conditioning, self-control, stress reactivity and interoceptive awareness. In parallel, studies are also delineating differences between them that centre on the key role that peripheral signals involved with homeostatic control exert on food intake. Here, we focus on the shared neurobiological substrates of obesity and addiction.

The brain's functional connectivity is complex, has high energetic cost, and requires efficient use of glucose, the brain's main energy source. It has been proposed that regions with a high degree of functional connectivity are energy efficient and can minimize consumption of glucose. However, the relationship between functional connectivity and energy consumption in the brain is poorly understood. To address this neglect, here we propose a simple model for the energy demands of brain functional connectivity, which we tested with positron emission tomography and MRI in 54 healthy volunteers at rest. Higher glucose metabolism was associated with proportionally larger MRI signal amplitudes, and a higher degree of connectivity was associated with nonlinear increases in metabolism, supporting our hypothesis for the energy efficiency of the connectivity hubs. Basal metabolism (in the absence of connectivity) accounted for 30% of brain glucose utilization, which suggests that the spontaneous brain activity accounts for 70% of the energy consumed by the brain. The energy efficiency of the connectivity hubs was higher for ventral precuneus, cerebellum, and subcortical hubs than for cortical hubs. The higher energy demands of brain communication that hinges upon higher connectivity could render brain hubs more vulnerable to deficits in energy delivery or utilization and help explain their sensitivity to neurodegenerative conditions, such as Alzheimer's disease. T he high energetic cost of human brain function, which is 10 times higher than what would be expected from its weight alone (1, 2), can only be maintained through a combination of strategies for efficient energy use (3-5). Neural communication accounts for a significant fraction of the energy consumed by the brain (6), most of which was termed "dark energy" because it reflects intrinsic activity of uncertain functional origin (7).Under normal physiological conditions, the brain derives most of its energy requirements from the metabolism of glucose and energy reserve glucose equivalents (8, 9). In vivo PET imaging studies with [ 18 F]fluorodeoxyglucose (FDG), a radiotracer used for measuring glucose metabolism, have demonstrated high baseline glucose metabolic rates in ventral posterior regions of the brain (10). The brain uses a large fraction of this energy to support synaptic transmission, which is associated with the hemodynamic responses (11,12) that are captured during stimulation studies with functional magnetic resonance imaging (fMRI) and the spontaneous oscillations captured during resting-state fMRI (R-fMRI), and also to sustain the resting potentials in neurons and glia (13,14). Thus, the high degree of connectivity of the ventral posterior regions of the brain with other brain regions (15) suggests an association between energy consumption and R-fMRI (16). However, the energy budget of the spontaneous R-fMRI signals remains largely unknown, and a better understanding of the relationship between energy consumption and functional connectivity could be v...

SignificanceThere has been an emerging interest in sleep and its association with β-amyloid burden as a risk factor for Alzheimer’s disease. Despite the evidence that acute sleep deprivation elevates β-amyloid levels in mouse interstitial fluid and in human cerebrospinal fluid, not much is known about the impact of sleep deprivation on β-amyloid burden in the human brain. Using positron emission tomography, here we show that acute sleep deprivation impacts β-amyloid burden in brain regions that have been implicated in Alzheimer’s disease. Our observations provide preliminary evidence for the negative effect of sleep deprivation on β-amyloid burden in the human brain.

Based on brain imaging findings, we present a model according to which addiction emerges as an imbalance in the information processing and integration among various brain circuits and functions. The dysfunctions reflect (a) decreased sensitivity of reward circuits, (b) enhanced sensitivity of memory circuits to conditioned expectations to drugs and drug cues, stress reactivity, and (c) negative mood, and a weakened control circuit. Although initial experimentation with a drug of abuse is largely a voluntary behavior, continued drug use can eventually impair neuronal circuits in the brain that are involved in free will, turning drug use into an automatic compulsive behavior. The ability of addictive drugs to co-opt neuro-transmitter signals between neurons (including dopamine, glutamate, and GABA) modifies the function of different neuronal circuits, which begin to falter at different stages of an addiction trajectory. Upon exposure to the drug, drug cues or stress this results in unrestrained hyperactivation of the motivation/drive circuit that results in the compulsive drug intake that characterizes addiction.

Functional networks are usually accessed with "resting-state" functional magnetic resonance imaging using preselected "seeds" regions. Frequently, however, the selection of the seed locations is arbitrary. Recently, we proposed local functional connectivity density mapping (FCDM), an ultrafast data-driven to locate highly connected brain regions (functional hubs). Here, we used the functional hubs obtained from local FCDM to determine the functional networks of the resting state in 979 healthy subjects without a priori hypotheses on seed locations. In addition, we computed the global functional connectivity hubs. Seven networks covering 80% of the gray matter volume were identified. Four major cortical hubs (ventral precuneus/posterior cingulate, inferior parietal cortex, cuneus, and postcentral gyrus) were linked to 4 cortical networks (default mode, dorsal attention, visual, and somatosensory). Three subcortical networks were associated to the major subcortical hubs (cerebellum, thalamus, and amygdala). The networks differed in their resting activity and topology. The higher coupling and overlap of subcortical networks was associated to higher contribution of short-range functional connectivity in thalamus and cerebellum. Whereas cortical local FCD hubs were also hubs of long-range connectivity, which corroborates the key role of cortical hubs in network architecture, subcortical hubs had minimal long-range connectivity. The significant variability among functional networks may underlie their sensitivity/resilience to neuropathology.