Several studies have attempted to characterize morphological brain changes due to chronic pain. Although it has repeatedly been suggested that longstanding pain induces gray matter modifications, there is still some controversy surrounding the direction of the change (increase or decrease in gray matter) and the role of psychological and psychiatric comorbidities. In this study, we propose a novel, network-oriented, meta-analytic approach to characterize morphological changes in chronic pain. We used network decomposition to investigate whether different kinds of chronic pain are associated with a common or specific set of altered networks. Representational similarity techniques, network decomposition and model-based clustering were employed: i) to verify the presence of a core set of brain areas commonly modified by chronic pain; ii) to investigate the involvement of these areas in a large-scale network perspective; iii) to study the relationship between altered networks and; iv) to find out whether chronic pain targets clusters of areas. Our results showed that chronic pain causes both core and pathology-specific gray matter alterations in large-scale networks. Common alterations were observed in the prefrontal regions, in the anterior insula, cingulate cortex, basal ganglia, thalamus, periaqueductal gray, post- and pre-central gyri and inferior parietal lobule. We observed that the salience and attentional networks were targeted in a very similar way by different chronic pain pathologies. Conversely, alterations in the sensorimotor and attention circuits were differentially targeted by chronic pain pathologies. Moreover, model-based clustering revealed that chronic pain, in line with some neurodegenerative diseases, selectively targets some large-scale brain networks. Altogether these findings indicate that chronic pain can be better conceived and studied in a network perspective.

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 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.