Schizophrenia is a brain disorder leading to detached mind’s normally integrated processes. Hence, the exploration of the symptoms in relation to functional connectivity (FC) had great relevance in the field. Connectivity can be investigated on different levels, going from global features to single edges between pairs of regions, revealing diffuse and localized dysconnection patterns. In this context, schizophrenia is characterized by a different global integration with reduced connectivity in specific areas of the brain, part of the Default Mode Network (DMN). However, the assessment of FC presents various sources of uncertainty. This study proposes a multi-level approach for more robust group-comparison. FC data between 74 AAL brain areas of 15 healthy controls (HC) and 12 subjects with chronic schizophrenia (SZ) were used. Multi-level analyses were carried out by the previously published SPIDER-NET tool. Graph topological indexes were evaluated to assess global abnormalities. Robustness was augmented by bootstrapped (BOOT) data and the stability was evaluated by removing one (RST1) or two subjects (RST2). The DMN subgraph was extracted and specifically evaluated. Changes relevant to the overall local indexes were also analyzed. Finally, the connection weights were explored to enhance common strongest activations/deactivations. At a global level, expected trends of the indexes were found and the significance of modularity (p = 0.043) was not confirmed by BOOT (p = 0.133). The robustness assessment tests (both RST1 and RST2) highlighted more stable results for BOOT compared to the direct data testing. Conversely, significant results were found in the analysis at lower levels. The DMN highlighted reduced connectivity and strength as well as increased deactivation in the SZ group. At local level, 13 areas were found to be significantly different (p < 0.05) in the groups, highlighting a greater divergence in the frontal lobe. These results were confirmed analyzing the single negative edges, suggesting inverted connectivity between prefronto-temporal areas. In conclusion, multi-level analysis supported by BOOT is highly recommended when analyzing FC, especially when diffuse and localized dysconnections must be investigated in limited samples.

Surgical planning of percutaneous interventions has a crucial role to guarantee the success of minimally invasive surgeries. In the last decades, many methods have been proposed to reduce clinician work load related to the planning phase and to augment the information used in the definition of the optimal trajectory. In this survey, we include 113 articles related to computer assisted planning (CAP) methods and validations obtained from a systematic search on three databases. First, a general formulation of the problem is presented, independently from the surgical field involved, and the key steps involved in the development of a CAP solution are detailed. Secondly, we categorized the articles based on the main surgical applications, which have been object of study and we categorize them based on the type of assistance provided to the end-user.

The Virtual Navigator (VN) technology was used for the fusion of transcranial Ultrasound (US) and brain Magnetic Resonance Images (MRI), with a repeatability error under 0.1 cm. The superimposition of US to the previously acquired MRI volume consisted of external point-based registration, that was subsequently refined with image-based registration of internal brain structures. The common registration procedure with the usage of external fiducial markers acquired with the two modalities was improved using facial anatomical landmarks, with a reduction of the internal targeted structure residual shift (maximum 0.7 cm in the cranio-caudal direction). This allowed the investigation of Deep Cerebral Veins and dural sinuses insonated from the condyloid process of the mandible, a recently introduced US window. The fusion of these vessels to the MRI volume provided their anatomical position and helped excluding false Doppler signal sources.

The paper stresses the importance of model-based signal processing in the analysis of the cardiovascular regulation mechanisms. It is remarked that even traditional signal processing implicitly assumes a model and interprets data according to it. Therefore, traditional signal processing is here referred to as implicit model-based signal processing in contrast with explicit model-based signal processing directly stemming from modeling considerations. The paper points out the advantages that can be achieved by rendering explicit the underlying model structure when it is implicit and by jointly applying implicit and explicit model-based signal processing approaches when dealing with complex, interacting,closed-loop biomedical systems. These advantages are rendered practical over several examples derived from the study of the short-term cardiovascular regulation performed by the autonomic nervous system.