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). Methods: Sixty patients undergoing evaluation for brain surgery due to various brain lesions and/or epilepsy and 20 control subjects completed an fMRI language mapping protocol that included three tasks, resulting in 259 fMRI scans. Depending on brain lesion characteristics, patients were allocated to (1) static/chronic not-expanding lesions (Group 1) and (2) progressive/expanding lesions (Group 2). GLM and ICA statistical maps were evaluated by fMRI experts to assess the performance of each technique. Results: In the control group, ICA and GLM maps were similar without any superiority of either technique. In Group 1 and Group 2, ICA performed statistically better than GLM, with a p-value of < 0.01801 and < 0.0237, respectively. This indicated that ICA performs as well as GLM when the subjects are able to cooperate well (less movement, good task performance), but ICA could outperform GLM in the patient groups. When both techniques were combined, 240 out of 259 scans produced reliable results, showing that the sensitivity of task-based fMRI can be increased when both techniques are integrated with the clinical setup. Conclusion: ICA may be slightly more advantageous, compared to GLM, in patients with brain lesions, across the range of pathologies included in our population and independent of symptoms chronicity. Our findings suggest that GLM analysis may be more susceptible to brain activity perturbations induced by a variety of lesions or scanner-induced artifacts due to motion or other factors. In our research, we demonstrated that ICA is able to provide fMRI results that can be used in surgery, taking into account patient and task-wise aspects that differ from those when fMRI is used in research.

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. METHODS: Five cases were subjected to a structured presurgical evaluation protocol, including EEG-FMRI, chronic and acute invasive EEG, subjected to frontal lobe surgery with postoperative follow-up between 15 months and 7 years. RESULTS: In the two adult cases, surface EEG demonstrated lateralized widespread frontal lobe epileptogenicity and hyperkinetic semiological features. MRI demonstrated cortical white matter blurring and deeper white matter abnormalities. EEG-FMRI suggested concordant frontal lobe involvement. iEEG demonstrated a widespread frontal lobe epilepsy network. The three young children demonstrated a diffuse epileptic encephalopathy phenotype, with nonlocalizing, nonlateralizing surface EEG, and "spasms" as the main seizure type. MRI demonstrated extensive frontal lobe subcortical gray and white matter abnormalities, consistent with MOGHE literature for this age, while EEG-FMRI, in 2/3, demonstrated concordant frontal lobe involvement. They did not undergo chronic iEEG, and the resection was assisted by acute intraoperative ECoG. All cases were subjected to extensive frontal lobectomies with Engel class IA (2/5), IB (1/5), and IIB (2/5) outcomes. SIGNIFICANCE: The study confirms the presence of frontal lobe epilepsy and epileptic encephalopathy phenotypes, in accordance with epilepsy phenotypes already described in MOGHE literature. Presurgical evaluation studies, including EEG-FMRI, can provide strong lateralizing and localizing evidence of the epileptogenic networks involved. All responded favorably to extensive frontal lobe resections, despite widespread epileptic activity recorded by surface and intracranial EEG pre- and postoperatively; an epileptic encephalopathy phenotype, in the first years of life, should not discourage such a resection.

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. Recent studies demonstrated that key language regions exist outside the classical model of "Wernicke-Lichtheim-Geschwind," but sensitive tasks must take place in order to be revealed. On top of that, the tasks should be in mother tongue for appropriate language mapping to be possible. METHODS: For that reason, in this study, we adopted an English protocol that can reveal six language critical regions even in clinical setups and we translated it into Greek to prove its efficacy in Greek population. Twenty healthy right-handed volunteers were recruited and performed the fMRI acquisition in a standardized manner. RESULTS: Results demonstrated that all six language critical regions were activated in all subjects as well as the group mean map. Furthermore, activations were found in the thalamus, the caudate, and the contralateral cerebellum. CONCLUSION: In this study, we standardized an fMRI protocol in Greek and proved that it can reliably activate six language critical regions. We have validated its efficacy for presurgical language mapping in Greek patients capable to be adopted in clinical setup.

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. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.

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. Data from 20 patients (after chemotherapy and before PCI) and 14 healthy controls who underwent structural MRI, DTI and resting state fMRI were selected in this study. From a structural aspect, the proposed analysis included volumetry and thickness measurements on structural MRI data for assessing gray matter dissimilarities, as well as deterministic tractography and Tract-Based Spatial Statistics (TBSS) on DTI data, aiming to investigate potential white matter abnormalities. Functional data were also processed on the basis of connectivity analysis, evaluating brain network parameters to identify potential manifestation of functional inconsistencies. By comparing patients to healthy controls, the obtained results revealed statistically significant differences, with the patients' brains presenting reduced volumetry/thickness and fractional anisotropy values, accompanied by prominent differences in functional connectivity measurements. All above mentioned findings were observed in patients that underwent chemotherapy.

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). Nineteen SCLC patients after platinum-based chemotherapy treatment and thirteen controls were recruited in the current study. ROI-to-ROI and Seed-to-Voxel analyses were carried out and revealed functional connectivity deficits in patients within all the networks investigated demonstrating the possible negative effect of chemotherapy in cognitive functions in SCLC populations.