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Significant ALE values related to GM increases were observed bilaterally in the cerebellum, in the middle temporal gyrus, in the right anterior cingulate cortex, caudate head, insula, fusiform gyrus, precuneus and posterior cingulate cortex, and in the left lingual gyrus. GM decreases were observed bilaterally in the cerebellar tonsil and inferior parietal lobule, in the right amygdala, insula, middle temporal gyrus, caudate tail and precuneus and in the left precentral gyrus.

BackgroundThe 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 FindingsFunctional 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.ConclusionIn 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.

Abstract■ This article investigates the functional connectivity patterns of the nucleus accumbens (NAcc) in 18 healthy participants using a resting state functional connectivity (rsFC) protocol. Also, a meta-analytic connectivity modeling (MACM) was used to characterize patterns of functional coactivations involving NAcc: The results of a structure-based meta-analyses of 57 fMRI and PET studies were submitted to activation likelihood estimation analysis to estimate consistent activation patterns across the different imaging studies. The results of the combined rsFC and MACM analyses show that spontaneous activity in NAcc predicts activity in regions implicated in reward circuitries, including orbitomedial prefrontal cortex, globus pallidus, thalamus, midbrain, amygdala, and insula. This confirms the key role of NAcc in the mesocorticolimbic system, which integrates inputs from limbic and cortical regions. We also detected activity in brain regions having few or no direct anatomical connections with NAcc, such as sensorimotor cortex, cerebellum, medial and posterior parietal cortex, and medial/inferior temporal cortex, supporting the view that not all functional connections can be explained by anatomical connections but can also result from connections mediated by third areas. Our rsFC findings are in line with the results of the structure-based meta-analysis: MACM maps are superimposable with NAcc rsFC results, and the reward paradigm class is the one that most frequently generates activation in NAcc. Our results overlap considerably with recently proposed schemata of the main neuron systems in the limbic forebrain and in the anterior part of the limbic midbrain in rodents and nonhuman primates. ■

Unawareness of deficits is a symptom of Alzheimer's disease that can be observed even in the early stages of the disease. The frontal hypoperfusion associated with reduced awareness of deficits has led to suggestions of the existence of a hypofunctioning prefrontal pathway involving the right dorsolateral prefrontal cortex, inferior parietal lobe, anterior cingulate gyri and limbic structures. Since this network plays an important role in response inhibition competence and patients with Alzheimer's disease who are unaware of their deficits exhibit impaired performance in response inhibition tasks, we predicted a relationship between unawareness of deficits and cingulate hypofunctionality. We tested this hypothesis in a sample of 29 patients with Alzheimer's disease (15 aware and 14 unaware of their disturbances), rating unawareness according to the Awareness of Deficit Questionnaire-Dementia scale. The cognitive domain was investigated by means of a wide battery including tests on executive functioning, memory and language. Neuropsychiatric aspects were investigated using batteries on behavioural mood changes, such as apathy and disinhibition. Cingulate functionality was assessed with functional magnetic resonance imaging, while patients performed a go/no-go task. In accordance with our hypotheses, unaware patients showed reduced task-sensitive activity in the right anterior cingulate area (Brodmann area 24) and in the rostral prefrontal cortex (Brodmann area 10). Unaware patients also showed reduced activity in the right post-central gyrus (Brodmann area 2), in the associative cortical areas such as the right parietotemporal-occipital junction (Brodmann area 39) and the left temporal gyrus (Brodmann areas 21 and 38), in the striatum and in the cerebellum. These findings suggest that the unawareness of deficits in early Alzheimer's disease is associated with reduced functional recruitment of the cingulofrontal and parietotemporal regions. Furthermore, in line with previous findings, we also found apathy and disinhibition to be prominent features of the first behavioural changes in unaware patients.

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.

As different areas within the PMC have different connectivity patterns with various cortical and subcortical regions, we hypothesized that distinct functional modules may be present within the PMC. Because the PMC appears to be the most active region during resting state, it has been postulated to play a fundamental role in the control of baseline brain functioning within the default mode network (DMN). Therefore one goal of this study was to explore which components of the PMC are specifically involved in the DMN. In a sample of seventeen healthy volunteers, we performed an unsupervised voxelwise ROI-based clustering based on resting state functional connectivity. Our results showed four clusters with different network connectivity. Each cluster showed positive and negative correlations with cortical regions involved in the DMN. Progressive shifts in PMC functional connectivity emerged from anterior to posterior and from dorsal to ventral ROIs. Ventral posterior portions of PMC were found to be part of a network implicated in the visuo-spatial guidance of movements, whereas dorsal anterior portions of PMC were interlinked with areas involved in attentional control. Ventral retrosplenial PMC selectively correlated with a network showing considerable overlap with the DMN, indicating that it makes essential contributions in self-referential processing, including autobiographical memory processing. Finally, ventral posterior PMC was shown to be functionally connected with a visual network.The paper represents the first attempt to provide a systematic, unsupervised, voxelwise clustering of the human posteromedial cortex (PMC), using resting-state functional connectivity data. Moreover, a ROI-based parcellation was used to confirm the results.

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