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Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been used to study the neural correlates of reward anticipation, but the interrelation of EEG and fMRI measures remains unknown. The goal of the present study was to investigate this relationship in response to a well established reward anticipation paradigm using simultaneous EEG-fMRI recording in healthy human subjects. Analysis of causal interactions between the thalamus (THAL), ventral-striatum (VS), and supplementary motor area (SMA), using both mediator analysis and dynamic causal modeling, revealed that (1) THAL fMRI blood oxygenation level-dependent (BOLD) activity is mediating intermodal correlations between the EEG contingent negative variation (CNV) signal and the fMRI BOLD signal in SMA and VS, (2) the underlying causal connectivity network consists of top-down regulation from SMA to VS and SMA to THAL along with an excitatory information flow through a THAL→VS→SMA route during reward anticipation, and (3) the EEG CNV signal is best predicted by a combination of THAL fMRI BOLD response and strength of top-down regulation from SMA to VS and SMA to THAL. Collectively, these findings represent a likely neurobiological mechanism mapping a primarily subcortical process, i.e., reward anticipation, onto a cortical signature.

The complex organization of connectivity in the human brain is incompletely understood. Recently, topological measures based on graph theory have provided a new approach to quantify large-scale cortical networks. These methods have been applied to anatomical connectivity data on nonhuman species, and cortical networks have been shown to have small-world topology, associated with high local and global efficiency of information transfer. Anatomical networks derived from cortical thickness measurements have shown the same organizational properties of the healthy human brain, consistent with similar results reported in functional networks derived from resting state functional magnetic resonance imaging (MRI) and magnetoencephalographic data. Here we show, using anatomical networks derived from analysis of inter-regional covariation of gray matter volume in MRI data on 259 healthy volunteers, that classical divisions of cortex (multimodal, unimodal, and transmodal) have some distinct topological attributes. Although all cortical divisions shared nonrandom properties of small-worldness and efficient wiring (short mean Euclidean distance between connected regions), the multimodal network had a hierarchical organization, dominated by frontal hubs with low clustering, whereas the transmodal network was assortative. Moreover, in a sample of 203 people with schizophrenia, multimodal network organization was abnormal, as indicated by reduced hierarchy, the loss of frontal and the emergence of nonfrontal hubs, and increased connection distance. We propose that the topological differences between divisions of normal cortex may represent the outcome of different growth processes for multimodal and transmodal networks and that neurodevelopmental abnormalities in schizophrenia specifically impact multimodal cortical organization.

Working memory (WM) is critically mediated by dopaminergic tuning of signal-to-noise in cortical neural assemblies. However, little is known about the distributed neuronal networks impacted by dopaminergic modulation in the component processes of WM. Here, we used the genotype of the Val158Met polymorphism in catechol-O-methyltransferase (COMT) as an index of relative cortical dopamine bioavailability and tuning efficiency, to examine the spatial and subprocess specificity by which dopaminergic modulation occurs within the prefrontal-parietal-striatal network during WM, thus empirically showing that dopamine plays key roles in updating and stabilizing new information at the neural systems level. In an event-related fMRI task dissociating component numerical WM subprocesses, baseline numerical size comparison engaged ventrolateral prefrontal cortical activation that correlated with COMT Val-allele load (COMT Val>Met), while performing arithmetic transformations further engaged this genotype effect in dorsolateral prefrontal cortex (DLPFC), as well as in parietal and striatal regions. Critically, additional temporal integration of information in WM disproportionately engaged greater COMTVal>Met effects only at DLPFC.COMTVal>Met effects were also observed in DLPFC during encoding of new information into WM, but not at its subsequent retrieval. Thus, temporal updating operations, but less so the retrieval of already encoded representations, engaged relatively specific dopaminergic tuning at the DLPFC. Manipulating and rapidly updating representations were sensitive to dopaminergic modulation of neural signaling in a larger prefrontal-parietal-striatal network. These findings add to the integration of dopaminergic signaling in basic cortical assemblies with their roles in specific human brain networks during the orchestration of information processing in WM. [ABSTRACT FROM AUTHOR]

A variation in the BDNF gene (val66met) affects the function of BDNF in neurons, predicts variation in human memory, and is associated with several neurological and psychiatric disorders. Here, we show that, in magnetic resonance imaging scans of a large sample of normal individuals, this polymorphism affects the anatomy of the hippocampus and prefrontal cortex, identifying a genetic mechanism of variation in brain morphology related to learning and memory. [ABSTRACT FROM AUTHOR]