A statistical mechanical problem?

The problem of deriving the processes of perception and cognition or the modes of behaviour from states of the brain appears to be unsolvablein view of the huge numbers of units involved. However, neural activities are not random, but, rather, form spatio-temporal patterns, and thank to these restrictions, which in turn are due to connections among neurons, the problem can at least be approached.The situation is similar to what happens in large physical ensembles, whereglobal behaviour is derived by microscopic properties. Despite the obvious differences between neural and physical systems a statistical mechanics approach is almost inescapable, since dynamics of the brain as a whole are clearly determined by the outputs of single neurons. In this paper it will be shown how, starting from very simple systems, connectivity engenders levels of increasing complexity in thefunctions of the brain depending on specific constraints.Correspondingly levels of explanations must take into account the fundamental role of constraints and assign at each level proper model structures and variables, that, on one hand, emerge from outputs of the lower levels, and yet are specific, in that they ignore irrelevant details

Temporal and spatial neural dynamics in the perception of basic emotions from complex scenes

The different temporal dynamics of emotions are critical to understand their evolutionary role in the regulation of interactions with the surrounding environment. Here, we investigated the temporal dynamics underlying the perception of four basic emotions from complex scenes varying in valence and arousal (fear, disgust, happiness and sadness) with the millisecond time resolution of Electroencephalography (EEG). Event-related potentials were computed and each emotion showed a specific temporal profile, as revealed by distinct time segments of significant differences from the neutral scenes. Fear perception elicited significant activity at the earliest time segments, followed by disgust, happiness and sadness. Moreover, fear, disgust and happiness were characterized by two time segments of significant activity, whereas sadness showed only one long-latency time segment of activity. Multidimensional scaling was used to assess the correspondence between neural temporal dynamics and the subjective experience elicited by the four emotions in a subsequent behavioral task. We found a high coherence between these two classes of data, indicating that psychological categories defining emotions have a close correspondence at the brain level in terms of neural temporal dynamics. Finally, we localized the brain regions of time-dependent activity for each emotion and time segment with the low-resolution brain electromagnetic tomography. Fear and disgust showed widely distributed activations, predominantly in the right hemisphere. Happiness activated a number of areas mostly in the left hemisphere, whereas sadness showed a limited number of active areas at late latency. The present findings indicate that the neural signature of basic emotions can emerge as the byproduct of dynamic spatiotemporal brain networks as investigated with millisecond-range resolution, rather than in time-independent areas involved uniquely in the processing one specific emotion. Keywords: basic emotions, EEG, LORETA, ERP, IAPS, time, rapid perceptio

Node detection using high-dimensional fuzzy parcellation applied to the insular cortex

Several functional connectivity approaches require the definition of a set of regions of interest (ROIs) that act as network nodes. Different methods have been developed to define these nodes and to derive their functional and effective connections, most of which are rather complex. Here we aim to propose a relatively simple “one-step” border detection and ROI estimation procedure employing the fuzzy c-mean clustering algorithm. To test this procedure and to explore insular connectivity beyond the two/three-region model currently proposed in the literature, we parcellated the insular cortex of 20 healthy right-handed volunteers scanned in a resting state. By employing a high-dimensional functional connectivity-based clustering process, we confirmed the two patterns of connectivity previously described. This method revealed a complex pattern of functional connectivity where the two previously detected insular clusters are subdivided into several other networks, some of which are not commonly associated with the insular cortex, such as the default mode network and parts of the dorsal attentional network. Furthermore, the detection of nodes was reliable, as demonstrated by the confirmative analysis performed on a replication group of subjects

Meta-analytic clustering of the insular cortex: Characterizing the meta-analytic connectivity of the insula when involved in active tasks

The human insula has been parcellated on the basis of resting state functional connectivity and diffusion tensor imaging. Little is known about the organization of the insula when involved in active tasks. We explored this issue using a novel meta-analytic clustering approach. We queried the BrainMap database asking for papers involving normal subjects that recorded activations in the insular cortex, retrieving 1305 papers, involving 22,872 subjects and a total of 2957 foci. Data were analyzed with several different methodologies, some of which expressly designed for this work. We used meta-analytic connectivity modeling and meta-analytic clustering of data obtained from the BrainMap database. We performed cluster analysis to subdivide the insula in areas with homogeneous connectivity, and density analysis of the activated foci using Voronoi tessellation. Our results confirm and extend previous findings obtained investigating the resting state connectivity of the anterior–posterior and left–right insulae. They indicate, for the first time, that some blocks of the anterior insula play the role of hubs between the anterior and the posterior insulae, as confirmed by their activation in several different paradigms. This finding supports the view that the network to which the anterior insula belongs is related to saliency detection. The insulae of both sides can be parcellated in two clusters, the anterior and the posterior: the anterior is characterized by an attentional pattern of connectivity with frontal, cingulate, parietal, cerebellar and anterior insular highly connected areas, whereas the posterior is characterized by a more local connectivity pattern with connections to sensorimotor, temporal and posterior cingulate areas. This antero–posterior subdivision, better characterized on the right side, results sharper with the connectivity based clusterization than with the behavioral based clusterization. The circuits belonging to the anterior insula are very homogeneous and their blocks in multidimensional scaling of MACM-based profiles are in central position, whereas those belonging to the posterior insula, especially on the left, are located at the periphery and sparse, thus suggesting that the posterior circuits bear a more heterogeneous connectivity. The anterior cluster is mostly activated by cognition, whereas the posterior is mostly activated by interoception, perception and emotion