The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

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.

Single brain metastasis versus glioblastoma multiforme: a VOI-based multiparametric analysis for differential diagnosis.

PURPOSE: The authors' purpose was to create a valid multiparametric MRI model for the differential diagnosis between glioblastoma and solitary brain metastasis. MATERIALS AND METHODS: Forty-one patients (twenty glioblastomas and twenty-one brain metastases) were retrospectively evaluated. MRIs were analyzed with Olea Sphere® 3.0. Lesions' volumes of interest (VOIs) were drawn on enhanced 3D T1 MP-RAGE and projected on ADC and rCBV co-registered maps. Another two VOIs were drawn in the region of hyperintense cerebral edema, surrounding the lesion, respectively, within 5 mm around the enhancing tumor and into residual edema. Perfusion curves were obtained, and the value of signal recovery (SR) was reported. A two-sample T test was obtained to compare all parameters of GB and BM groups. Receiver operating characteristics (ROC) analysis was performed. RESULTS: According to ROC analysis, the area under the curve was 88%, 78% and 74%, respectively, for mean ADC VOI values of the solid component, the mean and max rCBV values in the perilesional edema and the PSR. The cumulative ROC curve of these parameters reached an area under the curve of 95%. Using perilesional max rCBV > 1.37, PSR > 75% and mean lesional ADC < 1 × 10-3 mm2 s-1 GB could be differentiated from solitary BM (sensitivity and specificity of 95% and 86%). CONCLUSION: Lower values of ADC in the enhancing tumor, a higher percentage of SR in perfusion curves and higher values of rCBV in the peritumoral edema closed to the lesion are strongly indicative of GB than solitary BM.

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.

Correction to: Venous pathologies in paediatric neuroradiology: from foetal to adolescent life.

The original version of this article unfortunately contained a referencing omission. Figure 11 is reused from the original publication of Figure 10 of Gunny and Lin [1].

Venous pathologies in paediatric neuroradiology: from foetal to adolescent life.

The interpretation of cerebral venous pathologies in paediatric practice is challenging as there are several normal anatomical variants, and the pathologies are diverse, involving the venous system through direct and indirect mechanisms. This paper aims to provide a comprehensive review of these entities, as their awareness can avoid potential diagnostic pitfalls. We also propose a practical classification system of paediatric cerebral venous pathologies, which will enable more accurate reporting of the neuroimaging findings, as relevant to the underlying pathogenesis of these conditions. The proposed classification system comprises of the following main groups: arterio-venous shunting-related disorders, primary venous malformations and veno-occlusive disorders. A multimodal imaging approach has been included in the relevant subsections, with a brief overview of the modality-specific pitfalls that can also limit interpretation of the neuroimaging. The article also summarises the current literature and international practices in terms of management options and outcomes in specific disease entities.

Dynamic intraoperative MRI in transsphenoidal resection of pituitary macroadenomas: a quantitative analysis.

PURPOSE: To compare intraoperative dynamic contrast-enhanced (dCE) sequences with conventional CE (cCE) in the evaluation of the surgical bed after transsphenoidal removal of pituitary macroadenomas. MATERIALS AND METHODS: Twenty-one patients with macroadenoma were selected. They all underwent intraoperative magnetic resonance imaging (iMRI) (1.5T) acquisitions during transsphenoidal resection of the tumor. For each patient, dCE and cCE images were acquired in the operating room after tumor removal. The mean values of surgical cavities volumes were measured and statistically compared through Student's t-test analysis. Informed consent to iMRI was obtained from the patients as a part of the surgical procedure. Institutional Review Board (IRB) approval was obtained. RESULTS: No patient showed recurrence within at least 1 year of follow-up. Two patients showed residual tumor in the iMRI. Intraoperative analysis of the remaining 19 demonstrated that the mean value of the surgical cavities was significantly bigger in dCE than in cCE images (2955 mm(3) vs. 1963 mm(3) , respectively, P = 0.022). CONCLUSION: This study demonstrated underestimation of surgical cavity by conventional iMRI, simulating residual tumor and potentially leading to unnecessary surgical revision.