Correlations between cortical gyrification and schizophrenia symptoms with and without comorbid hostility symptoms

Introduction: Interest in identifying the clinical implications of the neuropathophysiological background of schizophrenia is rising, including changes in cortical gyrification that may be due to neurodevelopmental abnormalities. Inpatients with schizophrenia can show abnormal gyrification of cortical regions correlated with the symptom severity. Methods: Our study included 36 patients that suffered an acute episode of schizophrenia and have undergone structural magnetic resonance imaging (MRI) to calculate the local gyrification index (LGI). Results: In the whole sample, the severity of symptoms significantly correlated with higher LGI in different cortical areas, including bilateral frontal, cingulate, parietal, temporal cortices, and right occipital cortex. Among these areas, patients with low hostility symptoms (LHS) compared to patients with high hostility symptoms (HHS) showed significantly lower LGI related to the severity of symptoms in bilateral frontal and temporal lobes. Discussion: The severity of psychopathology correlated with higher LGI in large portions of the cerebral cortex, possibly expressing abnormal neural development in schizophrenia. These findings could pave the way for further studies and future tailored diagnostic and therapeutic strategies

Development of micro-engineered substrates for co-cultures of skeletal muscle cells and neurons

The thesis work is focused on the development of a bio-hybrid actuator featured by enhanced performances. Particularly, the aim of this study is to test the combination of multiple mechanical, topographical, chemical and biological cues on skeletal muscle cells development, in order to produce bio-hybrid actuators with enhanced contraction performances

Morphometric Analysis of Brain in Newborn with Congenital Diaphragmatic Hernia

Congenital diaphragmatic hernia (CDH) is a severe pediatric disorder with herniation of abdominal viscera into the thoracic cavity. Since neurodevelopmental impairment constitutes a common outcome, we performed morphometric magnetic resonance imaging (MRI) analysis on CDH infants to investigate cortical parameters such as cortical thickness (CT) and local gyrification index (LGI). By assessing CT and LGI distributions and their correlations with variables which might have an impact on oxygen delivery (total lung volume, TLV), we aimed to detect how altered perfusion affects cortical development in CDH. A group of CDH patients received both prenatal (i.e., fetal stage) and postnatal MRI. From postnatal high-resolution T2-weighted images, mean CT and LGI distributions of 16 CDH were computed and statistically compared to those of 13 controls. Moreover, TLV measures obtained from fetal MRI were further correlated to LGI. Compared to controls, CDH infants exhibited areas of hypogiria within bilateral fronto-temporo-parietal labels, while no differences were found for CT. LGI significantly correlated with TLV within bilateral temporal lobes and left frontal lobe, involving language- and auditory-related brain areas. Although the causes of neurodevelopmental impairment in CDH are still unclear, our results may suggest their link with altered cortical maturation and possible impaired oxygen perfusion

Reliability on multiband diffusion NODDI models: A test retest study on children and adults

Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI diffusion MRI models are nowadays very well-known models in the field of diffusion MRI as they represent powerful tools for the estimation of brain microstructure. In order to efficiently translate NODDI imaging findings into the diagnostic clinical practice, a test-retest approach would be useful to assess reproducibility and reliability of NODDI biomarkers, thus providing validation on precision of different fitting toolboxes. In this context, we conducted a test-retest study with the aim to assess the effects of different factors (i.e. fitting algorithms, multiband acceleration, shell configuration, age of subject and hemispheric side) on diffusion models reliability, assessed in terms of Intra-class Correlation Coefficient (ICC) and Variation Factor (VF). To this purpose, data from pediatric and adult subjects were acquired with Simultaneous-MultiSlice (SMS) imaging method with two different acceleration factor (AF) and four b-values, subsequently combined in seven shell configurations. Data were then fitted with two different GPU-based algorithms to speed up the analysis. Results show that each factor investigated had a significant effect on reliability of several diffusion parameters. Particularly, both datasets reveal very good ICC values for higher AF, suggesting that faster acquisitions do not jeopardize the reliability and are useful to decrease motion artifacts. Although very small reliability differences appear when comparing shell configurations, more extensive diffusion parameters variability results when considering shell configuration with lower b-values, especially for simple model like NODDI. Also fitting tools have a significant effect on reliability, but their difference occurs in both datasets and AF, so it appears to be independent from either misalignment and motion artifacts, or noise and SNR. The main achievement of the present study is to show how 10 min multi-shell diffusion MRI acquisition for NODDI acquisition can have reliable results in WM. More complex models do not appear to be more prone to less data acquisition as well as noisier data thus stressing the idea of Bingham-NODDI having greater sensitivity to true subject variability

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM

Isocitrate dehydrogenase (IDH) mutant and wildtype glioblastoma multiforme (GBM) often show overlapping features on magnetic resonance imaging (MRI), representing a diagnostic challenge. Deep learning showed promising results for IDH identification in mixed low/high grade glioma populations; however, a GBM-specific model is still lacking in the literature. Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI. We selected 100 adult patients with pathologically demonstrated WHO grade IV gliomas and IDH testing. MRI sequences included: MPRAGE, T1, T2, FLAIR, rCBV and ADC. The model consisted of a 4-block 2D CNN, applied to each MRI sequence. Probability of IDH mutation was obtained from the last dense layer of a softmax activation function. Model performance was evaluated in the test cohort considering categorical cross-entropy loss (CCEL) and accuracy. Calculated performance was: rCBV (accuracy 83%, CCEL 0.64), T1 (accuracy 77%, CCEL 1.4), FLAIR (accuracy 77%, CCEL 1.98), T2 (accuracy 67%, CCEL 2.41), MPRAGE (accuracy 66%, CCEL 2.55). Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps. Highest predictivity achieved on perfusion images possibly reflects the known link between IDH and neoangiogenesis through the hypoxia inducible factor

Epileptic seizures heralding a relapse in high grade gliomas

Abstract PURPOSE: Seizures are a common clinical symptom in high-grade gliomas (HGG). The aim of the study was to investigate the relationship between seizures and HGG relapse (HGG-R). METHODS: We retrospectively evaluated 145 patients who were surgically treated for HGG-R. By analyzing clinical characteristics in these patients (all operated and treated by the same protocol), we identified 37 patients with seizures during follow-up. This cohort was divided into four subgroups according to a) presence or absence of seizures at the time of diagnosis and b) temporal relationship between seizure occurrence and HGG-R during follow-up: subgroup A (25pts) had seizures at follow-up but not at onset, subgroup B (12pts) had seizures both at follow-up and onset, subgroup C (30pts) had seizures before MRI-documented HGG-R, and subgroup D (7pts) had seizures after MRI-documented HGG-R. RESULTS: Although the datum was not statistically significant, survival was longer in patients with seizures during follow-up than in those without seizures (59.3% vs 51.4% alive at 2 years). In 30 patients (subgroup C) seizures heralded HGG-R. In a correlation analysis for this last subgroup, the time interval between seizure and the HGG-R was significantly associated with the number of chemotherapy cycles (r=0.470; p=0.009) and follow-up duration (r=0.566; p=0.001). A linear regression model demonstrated a reciprocal association between the above factors and that it may be possible to estimate the timing of HGG-R by combining these data. CONCLUSIONS: Seizures may herald HGG-R before MRI detection of relapse, thus suggesting that seizures should always be considered a red flag during follow-up

Ictal EEG/fMRI study of vertiginous seizures

Vertigo and dizziness are extremely common complaints, related to either peripheral or central nervous system disorders. Among the latter, epilepsy has to be taken into consideration: indeed, vertigo may be part of the initial aura of a focal epileptic seizure in association with other signs/symptoms, or represent the only ictal manifestation, a rare phenomenon known as \u201cvertiginous\u201d or \u201cvestibular\u201d seizure. These ictal symptoms are usually related to a discharge arising from/involving temporal or parietal areas, which are supposed to be a crucial component of the so-called \u201cvestibular cortex\u201d. In this paper, we describe three patients suffering from drug-resistant focal epilepsy, symptomatic of malformations of cortical development or perinatal hypoxic/ischemic lesions located in the posterior regions, who presented clusters of vertiginous seizures. The high recurrence rate of such events, recorded during video-EEG monitoring sessions, offered the opportunity to perform an ictal EEG/fMRI study to identify seizure-related hemodynamic changes. The ictal EEG/fMRI revealed the main activation clusters in the temporo-parieto-occipital regions, which are widely recognized to be involved in the processing of vestibular information. Interestingly, ictal deactivation was also detected in the ipsilateral cerebellar hemisphere, suggesting the ictal involvement of cortical\u2013subcortical structures known to be part of the vestibular integration network

Cortical Thickness and Clinical Findings in Prescholar Children With Autism Spectrum Disorder

: The term autism spectrum disorder (ASD) includes a wide variability of clinical presentation, and this clinical heterogeneity seems to reflect a still unclear multifactorial etiopathogenesis, encompassing different genetic risk factors and susceptibility to environmental factors. Several studies and many theories recognize as mechanisms of autism a disruption of brain development and maturation time course, suggesting the existence of common neurobiological substrates, such as defective synaptic structure and aberrant brain connectivity. Magnetic resonance imaging (MRI) plays an important role in both assessment of region-specific structural changes and quantification of specific alterations in gray or white matter, which could lead to the identification of an MRI biomarker. In this study, we performed measurement of cortical thickness in a selected well-known group of preschool ASD subjects with the aim of finding correlation between cortical metrics and clinical scores to understand the underlying mechanism of symptoms and to support early clinical diagnosis. Our results confirm that recent brain MRI techniques combined with clinical data can provide some useful information in defining the cerebral regions involved in ASD although large sample studies with homogeneous analytical and multisite approaches are needed