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The role of MR Arterial-Spin-Labeling Cerebral Blood Flow maps (ASL-CBF) in the assessment of pediatric focal epilepsy is still debated. We aim to compare the Seizure Onset Zone (SOZ) detection rate of three methods of evaluation of ASL-CBF: 1) qualitative visual (qCBF), 2) z-score voxel-based quantitative analysis of index of asymmetry (AI-CBF), and 3) z-score voxel-based cluster analysis of the quantitative difference of patient’s CBF from the normative data of an age-matched healthy population (cCBF). Interictal ASL-CBF were acquired in 65 pediatric patients with focal epilepsy: 26 with focal brain lesions and 39 with a normal MRI. All hypoperfusion areas visible in at least 3 contiguous images of qCBF analysis were identified. In the quantitative evaluations, clusters with a significant z-score AI-CBF ≤ −1.64 and areas with a z-score cCBF ≤ −1.64 were considered potentially related to the SOZ. These areas were compared with the SOZ defined by the anatomo-electro-clinical data. In patients with a positive MRI, SOZ was correctly identified in 27% of patients using qCBF, 73% using AI-CBF, and 77% using cCBF. In negative MRI patients, SOZ was identified in 18% of patients using qCBF, in 46% using AI-CBF, and in 64% using cCBF (p < 0.001). Quantitative analyses of ASL-CBF maps increase the detection rate of SOZ compared to the qualitative method, principally in negative MRI patients.

In the pediatric age group, brain neoplasms are the second most common tumor category after leukemia, with an annual incidence of 6.13 per 100,000. Conventional MRI sequences, complemented by CT whenever necessary, are fundamental for the initial diagnosis and surgical planning as well as for post-operative evaluations, assessment of response to treatment, and surveillance; however, they have limitations, especially concerning histopathologic or biomolecular phenotyping and grading. In recent years, several advanced MRI sequences, including diffusion-weighted imaging, diffusion tensor imaging, arterial spin labelling (ASL) perfusion, and MR spectroscopy, have emerged as a powerful aid to diagnosis as well as prognostication; furthermore, other techniques such as diffusion kurtosis, amide proton transfer imaging, and MR elastography are being translated from the research environment to clinical practice. Molecular imaging, especially PET with amino-acid tracers, complement MRI in several aspects, including biopsy targeting and outcome prediction. Finally, radiomics with radiogenomics are opening entirely new perspectives for a quantitative approach aiming at identifying biomarkers that can be used for personalized, precision management strategies.

PurposeHerpes simplex virus 1 encephalitis (HSE) is the most common sporadic infectious encephalitis in Western countries, with a 70% mortality rate and only 9% of survivors free from neurological sequelae. While definitive diagnosis relies on cerebrospinal fluid testing, magnetic resonance imaging (MRI) plays a crucial role in identifying typical acute patterns and features. However, the imaging evolution of encephalitic lesions is not well understood. We aimed to identify and evaluate the prevalence and progression of cortical lesions, as well as the recurrence of these patterns in HSE and other encephalitic etiologies.MethodsAs a retrospective monocentric study, we included 40 patients with various etiological encephalitis from our institute. Each patient’s lesions were assessed, by three experienced neuroradiologists, in the acute phase and associated with specific evolution patterns in the chronic phase.Results10 out of 11 (91%) patients diagnosed with HSV-1 presented during chronic phase selective cortical liquefaction, identified as Cortical Melt Sign (CMS) (Fisher’s exact p-value < 0.001). Moreover, this pattern was then correlated with acute diffusion restriction—potentially explaining CMS as a chronic imaging biomarker for HSE as a result of the acute inflammation.ConclusionThese findings can aid in understanding the pathological mechanisms of herpetic encephalitis and guide differential diagnosis. Moreover, CMS could serve as a retrospective imaging marker in HSE.

PurposeTo review recent advances in structural MRI post-processing for pediatric drug-resistant epilepsy, with emphasis on artificial intelligence–driven and quantitative techniques, including MELD-Graph, MAP18, FLAT1, and SUPR-FLAIR, and to evaluate their impact on lesion detection, epileptogenic zone localization, and presurgical planning.MethodsNovel post-processing approaches were examined with respect to their computational foundations, imaging requirements, and diagnostic performance. Techniques employing machine learning, deep learning, voxel-based morphometry, cortical surface projection, and FLAIR/T1 ratio mapping were assessed for their applicability in children and their integration into multimodal evaluation pathways alongside electrophysiology and functional imaging.ResultsAdvanced post-processing tools substantially increase sensitivity for detecting subtle cortical abnormalities, particularly in MRI-negative pediatric epilepsy. MELD-Graph identify features of focal cortical dysplasia through automated surface-based analysis and deep neural network classification, achieving notable lesion detection even when conventional MRI findings are normal. MAP18 provides complementary voxel-wise morphometric assessment, improving specificity and benefiting from optimized structural sequences. FLAT1 enhances lesion conspicuity by quantifying FLAIR/T1 signal relationships, while SUPR-FLAIR improves visualization of cortical signal abnormalities through normalized FLAIR intensity projection onto the cortical surface. When incorporated into multimodal diagnostic workflows, these methods refine epileptogenic zone localization, inform individualized surgical strategies, and can reduce reliance on invasive testing.ConclusionAdvanced structural MRI post-processing is transforming the neuroradiological evaluation of pediatric drug-resistant epilepsy. By revealing subtle cortical abnormalities not visible on conventional imaging, these tools support more precise lesion characterization and surgical planning. Ongoing efforts toward standardization, clinical validation, and workflow integration will be essential to ensure widespread adoption and maximize clinical impact within precision-medicine approaches to pediatric epilepsy.Advanced structural MRI post-processing tools significantly improve the detection of subtle epileptogenic lesions like focal cortical dysplasia in pediatric epilepsy.Accurate localization requires multimodal integration of structural, metabolic, and functional data, with electrical imaging.Imaging informs personalized surgical planning by mapping eloquent cortex and predicting post-surgical seizure and cognitive outcomes using virtual models

Background and purposeLow grade tumors (LGT) are the most frequent central nervous system lesions observed in children. Despite the high-throughput research, differentiating LGT from tumor- like lesions (TLL) and providing an accurate differential diagnosis based on conventional MRI remains a challenge. For this reason, advanced MR sequences are routinely investigated and applied in clinical practice. The aim of this study is to explore the potential of the amide proton transfer (APTw) sequence as a tool for discriminating LGT from TLL.Materials and methodsIn this single-center retrospective study, we recruited 35 patients (20 with a histologically confirmed LGT, and 15 with a TLL) with both conventional and APT MRI images obtained on a 3T clinical scanner at onset or prior to treatment/surgery. Two volumes of interest (VOI), namely the whole lesion and the normal appearing white matter (NAWM), were defined using the semi-automatic segmentation tool from Philips Intellispace portal for Windows (v. 8). The mean APTw (mAPTw) and difference between the mAPTw lesion and the NAWM (dAPTw) were measured and compared between the two groups.ResultsLower values were found in the TLL group compared to the LGT group for both the mAPTw (1.51 ± 0.64% vs. 2.87 ± 0.96%) and dAPTw (0.24 ± 0.72% vs. 1.53 ± 1.08%) (p-value < 0.001). Based on ROC curve analysis, optimal cut-offs value for mAPTw and dATPw were 1.79 and 0.53, respectively.ConclusionAPT imaging may prove useful to discriminate between LGT and TLL in pediatric patients.

Purpose To validate a semiautomated method for segmenting vein of Galen aneurysmal malformations (VGAM) and to assess the relationship between VGAM volume and other angioarchitectural features, cardiological findings, and outcomes. Methods In this retrospective study, we selected all subjects with VGAM admitted to the Gaslini Children’s Hospital between 2009 and 2022. Clinical data were retrieved from electronic charts. We compared 3D-Slicer segmented VGAM volumes obtained by two independent observers using phase-contrast MR venography to those obtained with manual measurements performed on T2-weighted images. The relationship between VGAM volumes and clinical and neuroimaging features was then explored. Results Forty-three subjects with VGAM (22 males, mean age 6.56 days) were included in the study. Manual and semiautomated VGAM volumes were well correlated for both readers ( r  = 0.86 and 0.82, respectively). Regarding reproducibility, the inter-rater interclass correlation coefficients were 0.885 for the manual method and 0.992 for the semiautomated method ( p  < 0.001). The standard error for repeated measures was lower for the semiautomated method (0.04 versus 0.40 of manual method). Higher VGAM volume was associated with superior sagittal sinus narrowing, jugular bulb stenosis, and aqueductal stenosis ( p  < 0.05). A weak correlation was found between VGAM volume and straight sinus dilatation ( r  = 0.331) and superior sagittal sinus index ( r  =  − 0.325). No significant associations were found with cardiac findings, post-embolization complications, and outcome ( p  > 0.05). Conclusions Semiautomated VGAM volumetry is feasible and reliable with improved reproducibility compared to the manual method. VGAM volume is not a prognostic factor for clinical outcome, but it is related to other venous findings with potential hemodynamic effects.

The NeuroArtP3 (NET-2018-12366666) is a multicenter study funded by the Italian Ministry of Health. The aim of the project is to identify the prognostic trajectories of Alzheimer's disease (AD) through the application of artificial intelligence (AI). Only a few AI studies investigated the clinical variables associated with cognitive worsening in AD. We used Mini Mental State Examination (MMSE) scores as outcome to identify the factors associated with cognitive decline at follow up. A sample of = 126 patients diagnosed with AD (MMSE >19) were followed during 3 years in 4 time-points: T0 for the baseline and T1, T2 and T3 for the years of follow-ups. Variables of interest included demographics: age, gender, education, occupation; measures of functional ability: Activities of Daily Living (ADLs) and Instrumental (IADLs); clinical variables: presence or absence of comorbidity with other pathologies, severity of dementia (Clinical Dementia Rating Scale), behavioral symptoms; and the equivalent scores (ES) of cognitive tests. Logistic regression, random forest and gradient boosting were applied on the baseline data to estimate the MMSE scores (decline of at least >3 points) measured at T3. Patients were divided into multiple splits using different model derivation (training) and validation (test) proportions, and the optimization of the models was carried out through cross validation on the derivation subset only. The models predictive capabilities (balanced accuracy, AUC, AUPCR, F1 score and MCC) were computed on the validation set only. To ensure the robustness of the results, the optimization was repeated 10 times. A SHAP-type analysis was carried out to identify the predictive power of individual variables. The model predicted MMSE outcome at T3 with a mean AUC of 0.643. Model interpretability analysis revealed that the global cognitive state progression in AD patients is associated with: low spatial memory (Corsi block-tapping), verbal episodic long-term memory (Babcock's story recall) and working memory (Stroop Color) performances, the presence of hypertension, the absence of hypercholesterolemia, and functional skills inabilities at the IADL scores at baseline. This is the first AI study to predict cognitive trajectories of AD patients using routinely collected clinical data, while at the same time providing explainability of factors contributing to these trajectories. Also, our study used the results of single cognitive tests as a measure of specific cognitive functions allowing for a finer-grained analysis of risk factors with respect to the other studies that have principally used aggregated scores obtained by short neuropsychological batteries. The outcomes of this work can aid prognostic interpretation of the clinical and cognitive variables associated with the initial phase of the disease towards personalized therapies.

The intricate neuroanatomical structure of the cerebellum is of longstanding interest in epilepsy, but has been poorly characterized within the current cortico-centric models of this disease. We quantified cross-sectional regional cerebellar lobule volumes using structural MRI in 1,602 adults with epilepsy and 1,022 healthy controls across twenty-two sites from the global ENIGMA-Epilepsy working group. A state-of-the-art deep learning-based approach was employed that parcellates the cerebellum into 28 neuroanatomical subregions. Linear mixed models compared total and regional cerebellar volume in i) all epilepsies; ii) temporal lobe epilepsy with hippocampal sclerosis (TLE-HS); iii) non-lesional temporal lobe epilepsy (TLE-NL); iv) genetic generalised epilepsy; and (v) extra-temporal focal epilepsy (ETLE). Relationships were examined for cerebellar volume versus age at seizure onset, duration of epilepsy, phenytoin treatment, and cerebral cortical thickness. Across all epilepsies, reduced total cerebellar volume was observed ( =0.42). Maximum volume loss was observed in the corpus medullare ( =0.49) and posterior lobe grey matter regions, including bilateral lobules VIIB ( = 0.47), Crus I/II ( = 0.39), VIIIA ( =0.45) and VIIIB ( =0.40). Earlier age at seizure onset ( =0.05) and longer epilepsy duration ( =0.06) correlated with reduced volume in these regions. Findings were most pronounced in TLE-HS and ETLE with distinct neuroanatomical profiles observed in the posterior lobe. Phenytoin treatment was associated with reduced posterior lobe volume. Cerebellum volume correlated with cerebral cortical thinning more strongly in the epilepsy cohort than in controls. We provide robust evidence of deep cerebellar and posterior lobe subregional grey matter volume loss in patients with chronic epilepsy. Volume loss was maximal for posterior subregions implicated in non-motor functions, relative to motor regions of both the anterior and posterior lobe. Associations between cerebral and cerebellar changes, and variability of neuroanatomical profiles across epilepsy syndromes argue for more precise incorporation of cerebellum subregions into neurobiological models of epilepsy.

COVID-19 outbreak imposed rapid and severe public policies that consistently impacted the lifestyle habits and mental health of the general population. Despite vaccination, lockdown restrictions are still considered as potential measures to contrast COVID-19 variants spread in several countries. Recent studies have highlighted the impacts of lockdowns on the population’s mental health; however, the role of the indoor housing environment where people spent most of their time has rarely been considered. Data from 8177 undergraduate and graduate students were collected in a large, cross-sectional, web-based survey, submitted to a university in Northern Italy during the first lockdown period from 1 April to 1 May 2020. Logistic regression analysis showed significant associations between moderate and severe depression symptomatology (PHQ-9 scores ≥ 15), and houses with both poor indoor quality and small dimensions (OR = 4.132), either medium dimensions (OR = 3.249) or big dimensions (OR = 3.522). It was also found that, regardless of housing size, poor indoor quality is significantly associated with moderate–severe depressive symptomatology. Further studies are encouraged to explore the long-term impact of built environment parameter modifications on mental health, and therefore support housing and public health policies.

The Coronavirus-19 (COVID-19) pandemic has many psychological consequences for the population, ranging from anxious-depressive symptoms and insomnia to complex post-traumatic syndromes. This study aimed to evaluate the impact of the Covid-19 pandemic on the mental well-being of healthcare workers, focusing on the association between hopelessness, death anxiety, and post-traumatic symptomatology. Eight hundred forty-two healthcare workers were recruited between 21 March 2020 and 15 May 2020. A specific questionnaire was administered to assess socio-demographic and clinical characteristics, together with psychometric scales: Beck Hopelessness Scale, Death Anxiety Scale (DAS), and Davidson Trauma Scale (DTS). Respondents with hopelessness scored higher in the DAS and DTS than respondents without hopelessness. Furthermore, death anxiety was identified as a potential mediator of the significant association between hopelessness and post-traumatic symptomatology. The impact of death anxiety should be recognized in vulnerable populations, such as frontline healthcare workers. Therefore, pharmacological and non-pharmacological strategies could be useful to attenuate the negative psychological consequences and reduce the burden worldwide.