Hungry brains: A meta-analytical review of brain activation imaging studies on food perception and appetite in obese individuals

The dysregulation of food intake in chronic obesity has been explained by different theories. To assess their explanatory power, we meta-analyzed 22 brain-activation imaging studies. We found that obese individuals exhibit hyper-responsivity of the brain regions involved in taste and reward for food-related stimuli. Consistent with a Reward Surfeit Hypothesis, obese individuals exhibit a ventral striatum hyper-responsivity in response to pure tastes, particularly when fasting. Furthermore, we found that obese subjects display more frequent ventral striatal activation for visual food cues when satiated: this continued processing within the reward system, together with the aforementioned evidence, is compatible with the Incentive Sensitization Theory. On the other hand, we did not find univocal evidence in favor of a Reward Deficit Hypothesis nor for a systematic deficit of inhibitory cognitive control. We conclude that the available brain activation data on the dysregulated food intake and food-related behavior in chronic obesity can be best framed within an Incentive Sensitization Theory. Implications of these findings for a brain-based therapy of obesity are briefly discussed

The Brain in (Willed) Action: A Meta-Analytical Comparison of Imaging Studies on Motor Intentionality and Sense of Agency

Voluntary actions can be fractionated in different phenomena: from the emergence of intentions and the ensuing motor plans and actions, to the anticipation and monitoring of their outcomes, to the appreciation of their congruency with intentions and to the eventual emergence of a sense of agency. It follows that motor intention and the sense of agency should occur at different stages in the normal generation of willed actions. Both these processes have been associated with a fronto-parietal motor network, but no study has investigated to what extent the two experiences can be dissociated for the brain regions involved. To this end, we assessed the PET/fMRI literature on agency and intentionality using a meta-analytic technique based on a hierarchical clustering algorithm. Beside a shared brain network involving the meso-frontal and prefrontal regions, the middle insula and subcortical structures, we found that motor intention and the sense of agency are functionally underpinned by separable sets of brain regions: an “intentionality network,” involving the rostral area of the mesial frontal cortex (middle cingulum and pre-supplementary motor area), the anterior insula and the parietal lobules, and a “self-agency network,” which involves the posterior areas of the mesial frontal cortex (the SMA proper), the posterior insula, the occipital lobe and the cerebellum. We were then able to confirm this functional organization by a subsequent seed-based fMRI resting-state functional connectivity analysis, with seeds derived from the intentionality/sense of agency specific clusters of the medial wall of the frontal lobe. Our results suggest the existence of a rostro-caudal gradient within the mesial frontal cortex, with the more anterior regions linked to the concept of motor intentionality and the brain areas located more posteriorly associated with the direct monitoring between the action and its outcome. This suggestion is reinforced by the association between the sense of agency and the activation of the occipital lobes, to suggest a direct comparison between the movement and its external (e.g., visual) consequences. The shared network may be important for the integration of intentionality and agency in a coherent appreciation of self-generated actions

INVESTIGATIONS ON COGNITIVE COMPUTATION AND COMPUTATIONAL COGNITION

This Thesis describes our work at the boundary between Computer Science and Cognitive (Neuro)Science. In particular, (1) we have worked on methodological improvements to clustering-based meta-analysis of neuroimaging data, which is a technique that allows to collectively assess, in a quantitative way, activation peaks from several functional imaging studies, in order to extract the most robust results in the cognitive domain of interest. Hierarchical clustering is often used in this context, yet it is prone to the problem of non-uniqueness of the solution: a different permutation of the same input data might result in a different clustering result. In this Thesis, we propose a new version of hierarchical clustering that solves this problem. We also show the results of a meta-analysis, carried out using this algorithm, aimed at identifying specific cerebral circuits involved in single word reading. Moreover, (2) we describe preliminary work on a new connectionist model of single word reading, named the two-component model because it postulates a cascaded information flow from a more cognitive component that computes a distributed internal representation for the input word, to an articulatory component that translates this code into the corresponding sequence of phonemes. Output production is started when the internal code, which evolves in time, reaches a sufficient degree of clarity; this mechanism has been advanced as a possible explanation for behavioral effects consistently reported in the literature on reading, with a specific focus on the so called serial effects. This model is here discussed in its strength and weaknesses. Finally, (3) we have turned to consider how features that are typical of human cognition can inform the design of improved artificial agents; here, we have focused on modelling concepts inspired by emotion theory. A model of emotional interaction between artificial agents, based on probabilistic finite state automata, is presented: in this model, agents have personalities and attitudes that can change through the course of interaction (e.g. by reinforcement learning) to achieve autonomous adaptation to the interaction partner. Markov chain properties are then applied to derive reliable predictions of the outcome of an interaction. Taken together, these works show how the interplay between Cognitive Science and Computer Science can be fruitful, both for advancing our knowledge of the human brain and for designing more and more intelligent artificial systems

A Novel Approach to the Problem of Non-uniqueness of the Solution in Hierarchical Clustering

Abstract—The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the definition of an equivalence relation over dendrograms that allows developing all and only the significantly different dendrograms for the same dataset, thus reducing to polynomial the computational complexity from the exponential one obtained when all possible dendrograms are considered. Experimental results in the neuroimaging and bioinformatics domains show the effectiveness of the proposed method. Index Terms—Hierarchical clustering, dendrogram equivalence relation, bioinformatics, neuroimaging I