Evolutionary appearance of von Economo's neurons in the mammalian cerebral cortex.

von Economo’s neurons (VENs) are large, spindle-shaped projection neurons in layer V of the frontoinsular (FI) cortex, and the anterior cingulate cortex. During human ontogenesis, the VENs can first be differentiated at late stages of gestation, and increase in number during the first eight postnatal months. VENs have been identified in humans, chimpanzee, bonobos, gorillas, orangutan and, more recently, in the macaque. Their distribution in great apes seems to correlate with human-like social cognitive abilities and self-awareness. VENs are also found in whales, in a number of different cetaceans, and in the elephant. This phylogenetic distribution may suggest a correlation among the VENs, brain size and the “social brain.” VENs may be involved in the pathogenesis of specific neurological and psychiatric diseases, such as autism, callosal agenesis and schizophrenia. VENs are selectively affected in a behavioral variant of frontotemporal dementia in which empathy, social awareness and self-control are seriously compromised, thus associating VENs with the social brain. However, the presence of VENs has also been related to special functions such as mirror self-recognition. Areas containing VENs have been related to motor awareness or sense-of-knowing, discrimination between self and other, and between self and the external environment. Along this line, VENs have been related to the “global Workspace” architecture: in accordance the VENs have been correlated to emotional and interoceptive signals by providing fast connections (large axons = fast communication) between salience-related insular and cingulate and other widely separated brain areas. Nevertheless, the lack of a characterization of their physiology and anatomical connectivity allowed only to infer their functional role based on their location and on the functional magnetic resonance imaging data. The recent finding of VENs in the anterior insula of the macaque opens the way to new insights and experimental investigations

Functional anatomy of cortical areas characterized by Von Economo neurons

Von Economo’s neurons (VENs) are large, bipolar or corkscrew-shaped neurons located in layers III and V of the frontoinsular and the anterior cingulate cortices. VENs are reported to be altered in pathologies such as frontotemporal dementia and autism, in which the individual’s self control is seriously compromised. We have recently reviewed the evolutionary appearance of VENs and we are currently studying their distribution in different neurodegenerative diseases. To investigate the role of VENs in the active human brain, we have explored the functional connectivity of brain areas containing VENs by analyzing resting state functional connectivity (rsFC) in 20 healthy volunteers. Our results show that cortical areas containing VENs form a network of frontoparietal functional connectivity. With the use of fuzzy clustering techniques, we find that this network comprises four sub-networks: the first network cluster resembles a “saliency detection” attentional network, which includes superior frontal cortex (Brodmann’s Area, BA 10), inferior parietal lobe, anterior insula, and dorsal anterior cingulate cortex; the second cluster, part of a “sensory-motor network”, comprises the superior temporal, precentral and postcentral areas; the third cluster consists of frontal ventromedial and ventrodorsal areas constituted by parts of the “anterior default mode network”; and the fourth cluster encompasses dorsal anterior cingulate cortex, dorsomedial prefrontal, and superior frontal (BA 10) areas, resembling the anterior part of the “dorsal attentional network”. Thus, the network that emerges from analyzing functional connectivity among areas that are known to contain VENs is primarily involved in functions of saliency detection and self-regulation. In addition, parts of this network constitute sub-networks that partially overlap with the default mode, the sensory-motor and the dorsal attentional networks.This work was supported by grants from PROGETTO TRASLAZIONALE, Department of Neuroscience, University of Torino

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

Breast cancer survival in the US and Europe: a CONCORD high-resolution study.

Breast cancer survival is reportedly higher in the US than in Europe. The first worldwide study (CONCORD) found wide international differences in age-standardized survival. The aim of this study is to explain these survival differences. Population-based data on stage at diagnosis, diagnostic procedures, treatment and follow-up were collected for about 20,000 women diagnosed with breast cancer aged 15-99 years during 1996-98 in 7 US states and 12 European countries. Age-standardized net survival and the excess hazard of death up to 5 years after diagnosis were estimated by jurisdiction (registry, country, European region), age and stage with flexible parametric models. Breast cancers were generally less advanced in the US than in Europe. Stage also varied less between US states than between European jurisdictions. Early, node-negative tumors were more frequent in the US (39%) than in Europe (32%), while locally advanced tumors were twice as frequent in Europe (8%), and metastatic tumors of similar frequency (5-6%). Net survival in Northern, Western and Southern Europe (81-84%) was similar to that in the US (84%), but lower in Eastern Europe (69%). For the first 3 years after diagnosis the mean excess hazard was higher in Eastern Europe than elsewhere: the difference was most marked for women aged 70-99 years, and mainly confined to women with locally advanced or metastatic tumors. Differences in breast cancer survival between Europe and the US in the late 1990s were mainly explained by lower survival in Eastern Europe, where low healthcare expenditure may have constrained the quality of treatment

Altered Resting State in Diabetic Neuropathic Pain

BACKGROUND: The spontaneous component of neuropathic pain (NP) has not been explored sufficiently with neuroimaging techniques, given the difficulty to coax out the brain components that sustain background ongoing pain. Here, we address for the first time the correlates of this component in an fMRI study of a group of eight patients suffering from diabetic neuropathic pain and eight healthy control subjects. Specifically, we studied the functional connectivity that is associated with spontaneous neuropathic pain with spatial independent component analysis (sICA). PRINCIPAL FINDINGS: Functional connectivity analyses revealed a cortical network consisting of two anti-correlated patterns: one includes the left fusiform gyrus, the left lingual gyrus, the left inferior temporal gyrus, the right inferior occipital gyrus, the dorsal anterior cingulate cortex bilaterally, the pre and postcentral gyrus bilaterally, in which its activity is correlated negatively with pain and positively with the controls; the other includes the left precuneus, dorsolateral prefrontal, frontopolar cortex (both bilaterally), right superior frontal gyrus, left inferior frontal gyrus, thalami, both insulae, inferior parietal lobuli, right mammillary body, and a small area in the left brainstem, in which its activity is correlated positively with pain and negatively with the controls. Furthermore, a power spectra analyses revealed group differences in the frequency bands wherein the sICA signal was decomposed: patients' spectra are shifted towards higher frequencies. CONCLUSION: In conclusion, we have characterized here for the first time a functional network of brain areas that mark the spontaneous component of NP. Pain is the result of aberrant default mode functional connectivity