Early alterations of cortical thickness and gyrification in migraine without aura: a retrospective MRI study in pediatric patients.

BACKGROUND: Migraine is the most common neurological disease, with high social-economical burden. Although there is growing evidence of brain structural and functional abnormalities in patients with migraine, few studies have been conducted on children and no studies investigating cortical gyrification have been conducted on pediatric patients affected by migraine without aura. METHODS: Seventy-two pediatric patients affected by migraine without aura and eighty-two controls aged between 6 and 18 were retrospectively recruited with the following inclusion criteria: MRI exam showing no morphological or signal abnormalities, no systemic comorbidities, no abnormal neurological examination. Cortical thickness (CT) and local gyrification index (LGI) were obtained through a dedicated algorithm, consisting of a combination of voxel-based and surface-based morphometric techniques. The statistical analysis was performed separately on CT and LGI between: patients and controls; subgroups of controls and subgroups of patients. RESULTS: Patients showed a decreased LGI in the left superior parietal lobule and in the supramarginal gyrus, compared to controls. Female patients presented a decreased LGI in the right superior, middle and transverse temporal gyri, right postcentral gyrus and supramarginal gyrus compared to male patients. Compared to migraine patients younger than 12 years, the ≥ 12-year-old subjects showed a decreased CT in the superior and middle frontal gyri, pre- and post-central cortex, paracentral lobule, superior and transverse temporal gyri, supramarginal gyrus and posterior insula. Migraine patients experiencing nausea and/or vomiting during headache attacks presented an increased CT in the pars opercularis of the left inferior frontal gyrus. CONCLUSIONS: Differences in CT and LGI in patients affected by migraine without aura may suggest the presence of congenital and acquired abnormalities in migraine and that migraine might represent a vast spectrum of different entities. In particular, ≥ 12-year-old pediatric patients showed a decreased CT in areas related to the executive function and nociceptive networks compared to younger patients, while female patients compared to males showed a decreased CT of the auditory cortex compared to males. Therefore, early and tailored therapies are paramount to obtain migraine control, prevent cerebral reduction of cortical thickness and preserve executive function and nociception networks to ensure a high quality of life.

Assessing cortical features in early stage ASD children.

Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental disorder largely investigated in the neurologic field. Recently, neuroimaging studies have been conducted in order to investigate cerebral morphologic alterations in ASD patients, demonstrating an atypical brain development before the clinical manifestations of the disorder. Cortical Thickness (CT) and Local Gyrification Index (LGI) distribution for ASD children were investigated in this study, with the aim to evaluate possible relationship between brain measures and individual characteristics (i.e., IQ and verbal ability). 3D T1-w sequences from 129 ASD and 58 age-matched Healthy Controls (HC) were acquired and processed in order to assess CT and LGI for each subject. Intergroup differences between ASD and HC were investigated, including analyses of 2 ASD subgroups, split according to patient verbal ability and IQ. When compared to HC, ASD showed increased CT and LGI within several brain areas, both as an overall group and as verbal ability an IQ subgroups. Moreover, when comparing language characteristics of the ASD subjects, those patients with verbal ability exhibit significant CT and LGI increase was found within the occipital lobe of right hemisphere. No significant results occurred when comparing ASD patients according to their IQ value. These results support the hypothesis of abnormal brain maturation in ASD since early childhood with differences among clinical subgroups suggesting different anatomical substrates underlying an aberrant connectivity.

Sex Differences in Autism Spectrum Disorder: Diagnostic, Neurobiological, and Behavioral Features.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder with a worldwide prevalence of about 1%, characterized by impairments in social interaction, communication, repetitive patterns of behaviors, and can be associated with hyper- or hypo-reactivity of sensory stimulation and cognitive disability. ASD comorbid features include internalizing and externalizing symptoms such as anxiety, depression, hyperactivity, and attention problems. The precise etiology of ASD is still unknown and it is undoubted that the disorder is linked to some extent to both genetic and environmental factors. It is also well-documented and known that one of the most striking and consistent finding in ASD is the higher prevalence in males compared to females, with around 70% of ASD cases described being males. The present review looked into the most significant studies that attempted to investigate differences in ASD males and females thus trying to shade some light on the peculiar characteristics of this prevalence in terms of diagnosis, imaging, major autistic-like behavior and sex-dependent uniqueness. The study also discussed sex differences found in animal models of ASD, to provide a possible explanation of the neurological mechanisms underpinning the different presentation of autistic symptoms in males and females.

In Vivo Brain GSH: MRS Methods and Clinical Applications.

Glutathione (GSH) is an important antioxidant implicated in several physiological functions, including the oxidation-reduction reaction balance and brain antioxidant defense against endogenous and exogenous toxic agents. Altered brain GSH levels may reflect inflammatory processes associated with several neurologic disorders. An accurate and reliable estimation of cerebral GSH concentrations could give a clear and thorough understanding of its metabolism within the brain, thus providing a valuable benchmark for clinical applications. In this context, we aimed to provide an overview of the different magnetic resonance spectroscopy (MRS) technologies introduced for in vivo human brain GSH quantification both in healthy control (HC) volunteers and in subjects affected by different neurological disorders (e.g., brain tumors, and psychiatric and degenerative disorders). Additionally, we aimed to provide an exhaustive list of normal GSH concentrations within different brain areas. The definition of standard reference values for different brain areas could lead to a better interpretation of the altered GSH levels recorded in subjects with neurological disorders, with insights into the possible role of GSH as a biomarker and therapeutic target.

COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal.

More than a year has passed since the report of the first case of coronavirus disease 2019 (COVID), and increasing deaths continue to occur. Minimizing the time required for resource allocation and clinical decision making, such as triage, choice of ventilation modes and admission to the intensive care unit is important. Machine learning techniques are acquiring an increasingly sought-after role in predicting the outcome of COVID patients. Particularly, the use of baseline machine learning techniques is rapidly developing in COVID mortality prediction, since a mortality prediction model could rapidly and effectively help clinical decision-making for COVID patients at imminent risk of death. Recent studies reviewed predictive models for SARS-CoV-2 diagnosis, severity, length of hospital stay, intensive care unit admission or mechanical ventilation modes outcomes; however, systematic reviews focused on prediction of COVID mortality outcome with machine learning methods are lacking in the literature. The present review looked into the studies that implemented machine learning, including deep learning, methods in COVID mortality prediction thus trying to present the existing published literature and to provide possible explanations of the best results that the studies obtained. The study also discussed challenging aspects of current studies, providing suggestions for future developments.

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation.

There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.