Independent component analysis: a reliable alternative to general linear model for task-based fMRI.

Background: Functional magnetic resonance imaging (fMRI) is a valuable tool for the presurgical evaluation of patients undergoing neurosurgeries. Although many pre-processing steps have been modified according to advances in recent years, statistical analysis has remained largely the same since the first days of fMRI. In this study, we examined the ability of Independent Component Analysis (ICA) to separate the activation of a language task in fMRI, and we compared it with the results of the General Lineal Model (GLM).

Frontal lobe epilepsy and mild malformation with oligodendroglial hyperplasia: Further observations on electroclinical and imaging phenotypes, and surgical perspectives.

Objective: Mild malformation with oligodendroglial hyperplasia (MOGHE) is a recently described clinicopathologic entity, associated with drug-resistant epilepsy and extensive epileptogenic networks. Knowledge is accumulating about particular electroclinical phenotypes, correlations with imaging, and potential prognostic significance for surgical outcomes. The study adds relevant information by documenting the presence of a hyperkinetic frontal lobe seizure phenotype in adolescents and an epileptic encephalopathy phenotype in young children.

Standardization of presurgical language fMRI in Greek population: Mapping of six critical regions.

Background: Mapping the language system has been crucial in presurgical evaluation especially when the area to be resected is near relevant eloquent cortex. Functional magnetic resonance imaging (fMRI) proved to be a noninvasive alternative of Wada test that can account not only for language lateralization but also for localization when appropriate tasks and MRI sequences are being used. The tasks utilized during the fMRI acquisition are playing a crucial role as to which areas will be activated.

ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia.

We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices.

Chemotherapy-Induced Brain Effects in Small-Cell Lung Cancer Patients: A Multimodal MRI Study.

The golden standard of treating Small Cell Lung Cancer (SCLC) entails application of platinum-based chemotherapy, is often accompanied by Prophylactic Cranial Irradiation (PCI), which have been linked to neurotoxic side-effects in cognitive functions. The related existing neuroimaging research mainly focuses on the effect of PCI treatment in life quality and expectancy, while little is known regarding the distinct adverse effects of chemotherapy. In this context, a multimodal MRI analysis based on structural and functional brain data is proposed in order to evaluate chemotherapy-specific effects on SCLC patients.

Altered Brain Functional Connectivity in Small-Cell Lung Cancer Patients after Chemotherapy Treatment: A Resting-State fMRI Study.

Previous studies in small-cell lung cancer (SCLC) patients have mainly focused on exploring neurocognitive deficits associated with prophylactic cranial irradiation (PCI). Little is known about functional brain alterations that might occur due to chemotherapy treatment in this population before PCI is administered. For this reason, we used resting-state functional Magnetic Resonance Imaging (fMRI) to examine potential functional connectivity disruptions in brain networks, including the Default Mode Network (DMN), the Sensorimotor Network, and the Task-Positive Network (TPN).