Ricardo Faustino

Introduction: Gliomas are primary Central Nervous System glial tumors common in dogs and humans. Human glioblastomas are the most aggressive subtype, with an average survival of 15 months after diagnosis. Brachycephalic dogs are predisposed to gliomas and share biological and clinical features with human glioblastomas. This study explored radiomics applied to T2 weigthed Fluid Attenuated Inversion Recovery (T2-FLAIR) magnetic resonance imaging (MRI) sequences to characterize canine glioma lesions and non-lesioned tissues, aiming to identify imaging biomarkers for diagnosis and prognosis. Methods: MRI scans from canine gliomas and human glioblastomas were analyzed. Regions of Interest (ROIs) were segmented for extraction of radiomic features including texture, shape, and intensity. Descriptive and non-parametric statistical analyses, ANOVA, and Receiver Operating Characteristic (ROC) curves were used to evaluate feature discrimination capacity, particularly features with area under the curve (AUC) values above 80%, such as “jointaverage” and “autocorrelation”. Results: Volumetric analysis revealed significant differences between groups ( p < 0.001). Selected radiomic features discriminated tumoral from non-lesioned tissues, with ROC AUC values exceeding 80% ( p < 0.001). The Support Vector Machine (SVM) model achieved 80.33% accuracy in distinguishing gliomas and glioblastomas from non-lesioned brain tissue across canine and human datasets. Discussion: Radiomic features revealed comparable texture patterns between canine gliomas and human glioblastomas, enabling cross-species discrimination between tumoral and non-lesioned tissue with AUC values above 80% and SVM accuracy of 80.33%. These findings support canine gliomas as a valuable translational model for human glioblastoma research and precision medicine. However, the limited sample size and population diversity require validation in larger cohorts. Conclusion: Consistent imaging biomarkers across canine gliomas and human glioblastomas support advances in comparative and translational medicine. Radiomic features extracted from T2-FLAIR MRI enabled non-invasive discrimination between tumoral and non-lesioned brain tissue across species, reinforcing the relevance of canine gliomas as a natural model for human glioblastoma and the potential of comparative neuroimaging for precision medicine.

Background/Objectives: Pediatric multiple sclerosis (MS) is a rare and complex neuroinflammatory disease characterized by demyelination and neurological dysfunction in individuals under 18 years of age. This systematic review and activation likelihood estimation (ALE) meta-analysis aimed to synthesize the existing literature on functional and structural brain alterations in pediatric MS patients. Methods: Following the PRISMA guidelines, we analyzed 21 studies involving 917 pediatric MS patients and 320 healthy controls, assessing brain structure and function using MRI and fMRI techniques. Results: The results reveal consistent alterations in brain regions critical for cognitive and motor functions, including reduced brain volume, increased lesion load, and disrupted functional connectivity, particularly in the thalamus, cerebellum, and hippocampus. The ALE meta-analysis identified significant activation clusters in the dorsal anterior cingulate cortex, angular gyrus, and superior parietal lobes, regions associated with cognition, attention, and working memory. Conclusions: These findings suggest that pediatric MS uniquely affects brain development, contributing to cognitive impairments that differ from those observed in adult MS. Our study underscores the importance of early diagnosis and tailored therapeutic interventions to mitigate neurodevelopmental disruptions and improve long-term outcomes in pediatric MS patients.

The default mode network (DMN) is a complex network that plays a significant and active role during naturalistic stimulation. Previous studies that have used naturalistic stimuli, such as real-life stories or silent or sonorous films, have found that the information processing involved a complex hierarchical set of brain regions, including the DMN nodes. The DMN is not involved in low-level features and is only associated with high-level content-related incoming information. The human sexual experience involves a complex set of processes related to both external context and inner processes. Since the DMN plays an active role in the integration of naturalistic stimuli and aesthetic perception with beliefs, thoughts, and episodic autobiographical memories, we aimed at quantifying the involvement of the nodes of the DMN during visual sexual stimulation. After a systematic search in the principal electronic databases, we selected 83 fMRI studies, and an ALE meta-analysis was calculated. We performed conjunction analyses to assess differences in the DMN related to stimulus modalities, sex differences, and sexual orientation. The results show that sexual stimulation alters the topography of the DMN and highlights the DMN’s active role in the integration of sexual stimuli with sexual schemas and beliefs.

Introduction: Multiple sclerosis (MS), a chronic inflammatory immune-mediated disease of the central nervous system (CNS), is a common condition in young adults, but it can also affect children. The aim of this study was to construct radiomic models of lesions based on magnetic resonance imaging (MRI, T2-weighted-Fluid-Attenuated Inversion Recovery), to understand the correlation between extracted radiomic features, brain and lesion volumetry, demographic, clinical and laboratorial data.Methods: The neuroimaging data extracted from eleven scans of pediatric MS patients were analyzed. A total of 60 radiomic features based on MR T2-FLAIR images were extracted and used to calculate gray level co-occurrence matrix (GLCM). The principal component analysis and ROC analysis were performed to select the radiomic features, respectively. The realized classification task by the logistic regression models was performed according to these radiomic features.Results: Ten most relevant features were selected from data extracted. The logistic regression applied to T2-FLAIR radiomic features revealed significant predictor for multiple sclerosis (MS) lesion detection. Only the variable “contrast” was statistically significant, indicating that only this variable played a significant role in the model. This approach enhances the classification of lesions from normal tissue.Discussion and conclusion: Our exploratory results suggest that the radiomic models based on MR imaging (T2-FLAIR) may have a potential contribution to characterization of brain tissues and classification of lesions in pediatric MS.