Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation.

Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at "very strong evidence" (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area - BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

The pathoconnectivity network analysis of the insular cortex: A morphometric fingerprinting.

Brain disorders tend to impact on many different regions in a typical way: alterations do not spread randomly; rather, they seem to follow specific patterns of propagation that show a strong overlap between different pathologies. The insular cortex is one of the brain areas more involved in this phenomenon, as it seems to be altered by a wide range of brain diseases. On these grounds we thoroughly investigated the impact of brain disorders on the insular cortices analyzing the patterns of their structural co-alteration. We therefore investigated, applying a network analysis approach to meta-analytic data, 1) what pattern of gray matter alteration is associated with each of the insular cortex parcels; 2) whether or not this pattern correlates and overlaps with its functional meta-analytic connectivity; and, 3) the behavioral profile related to each insular co-alteration pattern. All the analyses were repeated considering two solutions: one with two clusters and another with three. Our study confirmed that the insular cortex is one of the most altered cerebral regions among the cortical areas, and exhibits a dense network of co-alteration including a prevalence of cortical rather than sub-cortical brain regions. Regions of the frontal lobe are the most involved, while occipital lobe is the less affected. Furthermore, the co-alteration and co-activation patterns greatly overlap each other. These findings provide significant evidence that alterations caused by brain disorders are likely to be distributed according to the logic of network architecture, in which brain hubs lie at the center of networks composed of co-altered areas. For the first time, we shed light on existing differences between insula sub-regions even in the pathoconnectivity domain.

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders.

Numerous studies have investigated grey matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases are independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

Hubs of long-distance co-alteration characterize brain pathology.

It is becoming clearer that the impact of brain diseases is more convincingly represented in terms of co-alterations rather than in terms of localization of alterations. In this context, areas characterized by a long mean distance of co-alteration may be considered as hubs with a crucial role in the pathology. We calculated meta-analytic transdiagnostic networks of co-alteration for the gray matter decreases and increases, and we evaluated the mean Euclidean, fiber-length, and topological distance of its nodes. We also examined the proportion of co-alterations between canonical networks, and the transdiagnostic variance of the Euclidean distance. Furthermore, disease-specific analyses were conducted on schizophrenia and Alzheimer's disease. The anterodorsal prefrontal cortices appeared to be a transdiagnostic hub of long-distance co-alterations. Also, the disease-specific analyses showed that long-distance co-alterations are more able than classic meta-analyses to identify areas involved in pathology and symptomatology. Moreover, the distance maps were correlated with the normative connectivity. Our findings substantiate the network degeneration hypothesis in brain pathology. At the same time, they suggest that the concept of co-alteration might be a useful tool for clinical neuroscience.

Finding specificity in structural brain alterations through Bayesian reverse inference.

In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.

The Neural Correlates of Time: A Meta-analysis of Neuroimaging Studies.

During the last two decades, our inner sense of time has been repeatedly studied with the help of neuroimaging techniques. These investigations have suggested the specific involvement of different brain areas in temporal processing. At least two distinct neural systems are likely to play a role in measuring time: One is mainly constituted of subcortical structures and is supposed to be more related to the estimation of time intervals below the 1-sec range (subsecond timing tasks), and the other is mainly constituted of cortical areas and is supposed to be more related to the estimation of time intervals above the 1-sec range (suprasecond timing tasks). Tasks can then be performed in motor or nonmotor (perceptual) conditions, thus providing four different categories of time processing. Our meta-analytical investigation partly confirms the findings of previous meta-analytical works. Both sub- and suprasecond tasks recruit cortical and subcortical areas, but subcortical areas are more intensely activated in subsecond tasks than in suprasecond tasks, which instead receive more contributions from cortical activations. All the conditions, however, show strong activations in the SMA, whose rostral and caudal parts have an important role not only in the discrimination of different time intervals but also in relation to the nature of the task conditions. This area, along with the striatum (especially the putamen) and the claustrum, is supposed to be an essential node in the different networks engaged when the brain creates our sense of time.

The alteration landscape of the cerebral cortex.

Growing evidence is challenging the assumption that brain disorders are diagnostically clear-cut categories. Transdiagnostic studies show that a set of cerebral areas is frequently altered in a variety of psychiatric as well as neurological syndromes. In order to provide a map of the altered areas in the pathological brain we devised a metric, called alteration entropy (A-entropy), capable of denoting the "structural alteration variety" of an altered region. Using the whole voxel-based morphometry database of BrainMap, we were able to differentiate the brain areas exhibiting a high degree of overlap between different neuropathologies (or high value of A-entropy) from those exhibiting a low degree of overlap (or low value of A-entropy). The former, which are parts of large-scale brain networks with attentional, emotional, salience, and premotor functions, are thought to be more vulnerable to a great range of brain diseases; while the latter, which include the sensorimotor, visual, inferior temporal, and supramarginal regions, are thought to be more informative about the specific impact of brain diseases. Since low A-entropy areas appear to be altered by a smaller number of brain disorders, they are more informative than the areas characterized by high values of A-entropy. It is also noteworthy that even the areas showing low values of A-entropy are substantially altered by a variety of brain disorders. In fact, no cerebral area appears to be only altered by a specific disorder. Our study shows that the overlap of areas with high A-entropy provides support for a transdiagnostic approach to brain disorders but, at the same time, suggests that fruitful differences can be traced among brain diseases, as some areas can exhibit an alteration profile more specific to certain disorders than to others.

Brain structural alterations are distributed following functional, anatomic and genetic connectivity.

The pathological brain is characterized by distributed morphological or structural alterations in the grey matter, which tend to follow identifiable network-like patterns. We analysed the patterns formed by these alterations (increased and decreased grey matter values detected with the voxel-based morphometry technique) conducting an extensive transdiagnostic search of voxel-based morphometry studies in a large variety of brain disorders. We devised an innovative method to construct the networks formed by the structurally co-altered brain areas, which can be considered as pathological structural co-alteration patterns, and to compare these patterns with three associated types of connectivity profiles (functional, anatomical, and genetic). Our study provides transdiagnostical evidence that structural co-alterations are influenced by connectivity constraints rather than being randomly distributed. Analyses show that although all the three types of connectivity taken together can account for and predict with good statistical accuracy, the shape and temporal development of the co-alteration patterns, functional connectivity offers the better account of the structural co-alteration, followed by anatomic and genetic connectivity. These results shed new light on the possible mechanisms at the root of neuropathological processes and open exciting prospects in the quest for a better understanding of brain disorders.

The morphometric co-atrophy networking of schizophrenia, autistic and obsessive spectrum disorders.

By means of a novel methodology that can statistically derive patterns of co-alterations distribution from voxel-based morphological data, this study analyzes the patterns of brain alterations of three important psychiatric spectra-that is, schizophrenia spectrum disorder (SCZD), autistic spectrum disorder (ASD), and obsessive-compulsive spectrum disorder (OCSD). Our analysis provides five important results. First, in SCZD, ASD, and OCSD brain alterations do not distribute randomly but, rather, follow network-like patterns of co-alteration. Second, the clusters of co-altered areas form a net of alterations that can be defined as morphometric co-alteration network or co-atrophy network (in the case of gray matter decreases). Third, within this network certain cerebral areas can be identified as pathoconnectivity hubs, the alteration of which is supposed to enhance the development of neuronal abnormalities. Fourth, within the morphometric co-atrophy network of SCZD, ASD, and OCSD, a subnetwork composed of eleven highly connected nodes can be distinguished. This subnetwork encompasses the anterior insulae, inferior frontal areas, left superior temporal areas, left parahippocampal regions, left thalamus and right precentral gyri. Fifth, the co-altered areas also exhibit a normal structural covariance pattern which overlaps, for some of these areas (like the insulae), the co-alteration pattern. These findings reveal that, similarly to neurodegenerative diseases, psychiatric disorders are characterized by anatomical alterations that distribute according to connectivity constraints so as to form identifiable morphometric co-atrophy patterns.

Cognitive Pragmatic Rehabilitation Program in Schizophrenia: A Single Case fMRI Study.

Introduction. The present study was intended to evaluate the effects of a rehabilitative training, the Cognitive Pragmatic Treatment (CPT), aimed at improving communicative-pragmatic abilities and the related cognitive components, on the cerebral modifications of a single case patient diagnosed with schizophrenia. Methods. The patient underwent two functional magnetic resonance imaging (fMRI) sessions, before and after the treatment. In order to assess brain changes, we calculated the Amplitude of Low Frequency Fluctuation (ALFF) index of the resting-state fMRI signal, which is interpreted as reflecting the intensity of the spontaneous regional activity of the brain. Behavioural measures of the patient's communicative performance were also gathered before and after training and at follow-up. Results. The patient improved his communicative performance in almost all tests. Posttraining stronger ALFF signal emerged in the superior, inferior, and medial frontal gyri, as well as the superior temporal gyri. Conclusions. Even if based on a single case study, these preliminary results show functional changes at the cerebral level that seem to support the patient's behavioural improvements.

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.

Massive modulation of brain areas after mechanical pain stimulation: a time-resolved FMRI study.

To date, relatively little is known about the spatiotemporal aspects of whole-brain blood oxygenation level-dependent (BOLD) responses to brief nociceptive stimuli. It is known that the majority of brain areas show a stimulus-locked response, whereas only some are characterized by a canonical hemodynamic response function. Here, we investigated the time course of brain activations in response to mechanical pain stimulation applied to participants' hands while they were undergoing functional magnetic resonance imaging (fMRI) scanning. To avoid any assumption about the shape of BOLD response, we used an unsupervised data-driven method to group voxels sharing a time course similar to the BOLD response to the stimulus and found that whole-brain BOLD responses to painful mechanical stimuli elicit massive activation of stimulus-locked brain areas. This pattern of activations can be segregated into 5 clusters, each with a typical temporal profile. In conclusion, we show that an extensive activity of multiple networks is engaged at different time latencies after presentation of a noxious stimulus. These findings aim to motivate research on a controversial topic, such as the temporal profile of BOLD responses, the variability of these response profiles, and the interaction between the stimulus-related BOLD response and ongoing fluctuations in large-scale brain networks.

Drawing lines while imagining circles: Neural basis of the bimanual coupling effect during motor execution and motor imagery.

When people simultaneously draw lines with one hand and circles with the other hand, both trajectories tend to assume an oval shape, showing that hand motor programs interact (the so-called "bimanual coupling effect"). The aim of the present study was to investigate how motor parameters (drawing trajectories) and the related brain activity vary during bimanual movements both in real execution and in motor imagery tasks. In the 'Real' modality, subjects performed right hand movements (lines) and, simultaneously, Congruent (lines) or Non-congruent (circles) left hand movements. In the 'Imagery' modality, subjects performed only right hand movements (lines) and, simultaneously, imagined Congruent (lines) or Non-congruent (circles) left hand movements. Behavioral results showed a similar interference of both the real and the imagined circles on the actually executed lines, suggesting that the coupling effect also pertains to motor imagery. Neuroimaging results showed that a prefrontal-parietal network, mostly involving the pre-Supplementary Motor Area (pre-SMA) and the posterior parietal cortex (PPC), was significantly more active in Non-congruent than in Congruent conditions, irrespective of task (Real or Imagery). The data also confirmed specific roles of the right superior parietal lobe (SPL) in mediating spatial interference, and of the left PPC in motor imagery. Collectively, these findings suggest that real and imagined Non-congruent movements activate common circuits related to the intentional and predictive operation generating bimanual coupling, in which the pre-SMA and the PPC play a crucial role.

Concordance of white matter and gray matter abnormalities in autism spectrum disorders: a voxel-based meta-analysis study.

There are at least two fundamental unanswered questions in the literature on autism spectrum disorders (ASD): Are abnormalities in white (WM) and gray matter (GM) consistent with one another? Are WM morphometric alterations consistent with alterations in the GM of regions connected by these abnormal WM bundles and vice versa? The aim of this work is to bridge this gap. After selecting voxel-based morphometry and diffusion tensor imaging studies comparing autistic and normally developing groups of subjects, we conducted an activation likelihood estimation (ALE) meta-analysis to estimate consistent brain alterations in ASD. Multidimensional scaling was used to test the similarity of the results. The ALE results were then analyzed to identify the regions of concordance between GM and WM areas. We found statistically significant topological relationships between GM and WM abnormalities in ASD. The most numerous were negative concordances, found bilaterally but with a higher prevalence in the right hemisphere. Positive concordances were found in the left hemisphere. Discordances reflected the spatial distribution of negative concordances. Thus, a different hemispheric contribution emerged, possibly related to pathogenetic factors affecting the right hemisphere during early developmental stages. Besides, WM fiber tracts linking the brain structures involved in social cognition showed abnormalities, and most of them had a negative concordance with the connected GM regions. We interpreted the results in terms of altered brain networks and their role in the pervasive symptoms dramatically impairing communication and social skills in ASD patients.

Behavioral and neuroplastic effects of low-frequency rTMS of the unaffected hemisphere in a chronic stroke patient: a concomitant TMS and fMRI study.

Repetitive Transcranial Magnetic Stimulation (rTMS) ameliorates motor and neuropsychological deficits following stroke, but little is known about the underlying neuroplasticity. We investigated neuroplastic changes following 5 days of low-frequency rTMS on the intact motor cortex to promote motor recovery in a chronic patient with subcortical stroke. The feasibility of administering multiple treatments was also assessed 6 months later by applying the same protocol over the patient's parietal cortex to improve visuospatial disorders. Behavioral improvements and no adverse events were observed. Neuroimaging findings indicated that motor symptoms amelioration was associated with downregulation and cortical reorganization of hyperactive contralesional hemisphere.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research.

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Can PSMA-Targeting PET/CT Be a Valuable Instrument to Assess the Presence of Brain Metastases in Lung Cancer Patients?: A Case Report.

ABSTRACT: A 78-year-old man with synchronous diagnosis of prostate cancer and lung adenocarcinoma was referred to our institute for prostate cancer staging with [ 18 F]F-prostate-specific membrane antigen (PSMA) 1007 PET/CT. In addition to the previously known lesion of the right lung, PSMA-targeted PET/CT highlighted 2 areas of abnormal uptake in the brain, in the left frontal and temporal lobes. A subsequent MRI confirmed the lesions observed on PET/CT. Because PSMA-targeting radiopharmaceuticals do not accumulate in healthy brain parenchyma, and recent literature reported promising performances of PSMA-targeted PET/CT in gliomas and metastases from tumors other than prostate cancer, this employment of PSMA radioligands needs to be further explored.

A Minimum Bayes Factor Based Threshold for Activation Likelihood Estimation.

Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical p-value selected. However, this is not informative in terms of probabilities of the validity of the hypotheses. Here, we describe an innovative thresholding procedure based on the concept of minimum Bayes factor (mBF). The use of the Bayesian framework allows to consider different levels of probability, each of these being equally significant. In order to simplify the translation between the common ALE practice and the proposed approach, we analised six task-fMRI/VBM datasets and determined the mBF values equivalent to the currently recommended frequentist thresholds based on Family Wise Error (FWE). Sensitivity and robustness toward spurious findings were also analyzed. Results showed that the cutoff log10(mBF) = 5 is equivalent to the FWE threshold, often referred as voxel-level threshold, while the cutoff log10(mBF) = 2 is equivalent to the cluster-level FWE (c-FWE) threshold. However, only in the latter case voxels spatially far from the blobs of effect in the c-FWE ALE map survived. Therefore, when using the Bayesian thresholding the cutoff log10(mBF) = 5 should be preferred. However, being in the Bayesian framework, lower values are all equally significant, while suggesting weaker level of force for that hypothesis. Hence, results obtained through less conservative thresholds can be legitimately discussed without losing statistical rigor. The proposed technique adds therefore a powerful tool to the human-brain-mapping field.

Re-learning mental representation of walking after a brain lesion. Effects of a cognitive-motor training with a robotic orthosis.

Introduction: Stroke-related deficits often include motor impairments and gait dysfunction, leading to a limitation of social activities and consequently affecting the quality of life of stroke survivors. Neurorehabilitation takes advantage of the contribution of different techniques in order to achieve more benefits for patients. Robotic devices help to improve the outcomes of physical rehabilitation. Moreover, motor imagery seems to play a role in neurological rehabilitation since it leads to the activation of the same brain areas as actual movements. This study investigates the use of a combined physical and cognitive protocol for gait rehabilitation in stroke patients. Methods: Specifically, we tested the efficacy of a 5-week training program using a robotic orthosis (P.I.G.R.O.) in conjunction with motor imagery training. Twelve chronic stroke patients participated in the study. We evaluated balance and gait performance before and after the training. Six of them underwent fMRI examination before and after the training to assess the effects of the protocol on brain plasticity mechanisms in motor and imagery tasks. Results: Our results show that the rehabilitation protocol can effectively improve gait performance and balance and reduce the risk of falls in stroke patients. Furthermore, the fMRI results suggest that rehabilitation is associated with cerebral plastic changes in motor networks. Discussion: The present findings, if confirmed by future research, have the potential to advance the development of new, more effective rehabilitation approaches for stroke patients, improving their quality of life and reducing the burden of stroke-related disability.

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.