Probabilistic maps of neocortical areas and subcortical fiber tracts, warped to a common reference brain, have been published using microscopic architectonic parcellations in ten human postmortem brains. The maps have been successfully applied as topographical references for the anatomical localization of activations observed in functional imaging studies. Here, for the first time, we present stereotaxic, probabilistic maps of the hippocampus, the amygdala and the entorhinal cortex and some of their subdivisions. Cytoarchitectonic mapping was performed in serial, cell-body stained histological sections. The positions and the extent of cytoarchitectonically defined structures were traced in digitized histological sections, 3-D reconstructed and warped to the reference space of the MNI single subject brain using both linear and non-linear elastic tools of alignment. The probability maps and volumes of all structures were calculated. The precise localization of the borders of the mapped regions cannot be predicted consistently by macroanatomical landmarks. Many borders, e.g. between the subiculum and entorhinal cortex, subiculum and Cornu ammonis, and amygdala and hippocampus, do not match sulcal landmarks such as the bottom of a sulcus. Only microscopic observation enables the precise localization of the borders of these brain regions. The superposition of the cytoarchitectonic maps in the common spatial reference system shows a considerably lower degree of intersubject variability in size and position of the allocortical structures and nuclei than the previously delineated neocortical areas. For the first time, the present observations provide cytoarchitectonically verified maps of the human amygdala, hippocampus and entorhinal cortex, which take into account the stereotaxic position of the brain structures as well as intersubject variability. We believe that these maps are efficient tools for the precise microstructural localization of fMRI, PET and anatomical MR data, both in healthy and pathologically altered brains.

Cognitive regulation of emotions is a fundamental prerequisite for intact social functioning which impacts on both well being and psychopathology. The neural underpinnings of this process have been studied intensively in recent years, without, however, a general consensus. We here quantitatively summarize the published literature on cognitive emotion regulation using activation likelihood estimation in fMRI and PET (23 studies/479 subjects). In addition, we assessed the particular functional contribution of identified regions and their interactions using quantitative functional inference and meta-analytic connectivity modeling, respectively. In doing so, we developed a model for the core brain network involved in emotion regulation of emotional reactivity. According to this, the superior temporal gyrus, angular gyrus and (pre) supplementary motor area should be involved in execution of regulation initiated by frontal areas. The dorsolateral prefrontal cortex may be related to regulation of cognitive processes such as attention, while the ventrolateral prefrontal cortex may not necessarily reflect the regulatory process per se, but signals salience and therefore the need to regulate. We also identified a cluster in the anterior middle cingulate cortex as a region, which is anatomically and functionally in an ideal position to influence behavior and subcortical structures related to affect generation. Hence this area may play a central, integrative role in emotion regulation. By focusing on regions commonly active across multiple studies, this proposed model should provide important a priori information for the assessment of dysregulated emotion regulation in psychiatric disorders.

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Autism spectrum disorders (ASD) are pervasive developmental disorders with characteristic core symptoms such as impairments in social interaction, deviance in communication, repetitive and stereotyped behavior, and impaired motor skills. Anomalies of brain structure have repeatedly been hypothesized to play a major role in the etiopathogenesis of the disorder. Our objective was to perform unbiased meta-analysis on brain structure changes as reported in the current ASD literature. We thus conducted a comprehensive search for morphometric studies by Pubmed query and literature review. We used a revised version of the activation likelihood estimation (ALE) approach for coordinate-based meta-analysis of neuroimaging results. Probabilistic cytoarchitectonic maps were applied to compare the localization of the obtained significant effects to histological areas. Each of the significant ALE clusters was analyzed separately for age effects on gray and white matter density changes. We found six significant clusters of convergence indicating disturbances in the brain structure of ASD patients, including the lateral occipital lobe, the pericentral region, the medial temporal lobe, the basal ganglia, and proximate to the right parietal operculum. Our study provides the first quantitative summary of brain structure changes reported in literature on autism spectrum disorders. In contrast to the rather small sample sizes of the original studies, our meta-analysis encompasses data of 277 ASD patients and 303 healthy controls. This unbiased summary provided evidence for consistent structural abnormalities in spite of heterogeneous diagnostic criteria and voxel-based morphometry (VBM) methodology, but also hinted at a dependency of VBM findings on the age of the patients.

This investigation points to state-dependent neurobiological correlates of cue-induced craving in alcoholic patients and suggests that these correlates can be influenced by therapeutic interventions. The presence of emotional aspects of craving is suggested by amygdala activation.

Emotional faces communicate both the emotional state and behavioral intentions of an individual. They also activate behavioral tendencies in the perceiver, namely approach or avoidance. Here, we compared more automatic motor to more conscious rating responses to happy, sad, angry and disgusted faces in a healthy student sample. Happiness was associated with approach and anger with avoidance. However, behavioral tendencies in response to sadness and disgust were more complex. Sadness produced automatic approach but conscious withdrawal, probably influenced by interpersonal relations or personality. Disgust elicited withdrawal in the rating task whereas no significant tendency emerged in the joystick task, probably driven by expression style. Based on our results it is highly relevant to further explore actual reactions to emotional expressions and to differentiate between automatic and controlled processes since emotional faces are used in various kinds of studies. Moreover, our results highlight the importance of gender of poser effects when applying emotional expressions as stimuli.

Predicting how the brain can be driven to specific states by means of internal or external control requires a fundamental understanding of the relationship between neural connectivity and activity. Network control theory is a powerful tool from the physical and engineering sciences that can provide insights regarding that relationship; it formalizes the study of how the dynamics of a complex system can arise from its underlying structure of interconnected units. Given the recent use of network control theory in neuroscience, it is now timely to offer a practical guide to methodological considerations in the controllability of structural brain networks. Here we provide a systematic overview of the framework, examine the impact of modeling choices on frequently studied control metrics, and suggest potentially useful theoretical extensions. We ground our discussions, numerical demonstrations, and theoretical advances in a dataset of high-resolution diffusion imaging with 730 diffusion directions acquired over approximately 1 hour of scanning from ten healthy young adults. Following a didactic introduction of the theory, we probe how a selection of modeling choices affects four common statistics: average controllability, modal controllability, minimum control energy, and optimal control energy. Next, we extend the current state of the art in two ways: first, by developing an alternative measure of structural connectivity that accounts for radial propagation of activity through abutting tissue, and second, by defining a complementary metric quantifying the complexity of the energy landscape of a system. We close with specific modeling recommendations and a discussion of methodological constraints. Our hope is that this accessible account will inspire the neuroimaging community to more fully exploit the potential of network control theory in tackling pressing questions in cognitive, developmental, and clinical neuroscience.Recent advances in network control theory offer a formal means to study how the temporal dynamics of a complex system emerges from its underlying network structure [9][10][11]. Applying this theory to the brain requires that one first builds a network model in which brain regions (nodes) are anatomically connected to one another (edges) [12,13]. The state of the brain network system is then reflected in the pattern of neurophysiological activity across network nodes, and state trajectories represent the temporal sequence of brain states that the system traverses [14,15]. With definitions of the network and its state in hand, we can consider the problem of network controllability, which in essence amounts to asking how the system can be driven to specific target states by means of internal or external control input [16]. In the context of the brain, such input can intuitively take the form of electrical stimulation [17-21], task modulation [22][23][24], or other perturbations from the world or from different portions of the body [25,26]. Practically, network control theory and its associated toolkit e...

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