Pipeline Comparison for the Pre-Processing of Resting-State Data in Epilepsy

Noise removal is a critical step to recover the signal of interest from resting-state fMRI data. Several pre-processing pipelines have been developed mainly based on nuisance regression or independent component analysis. The aim of this work was to evaluate the ability in removing spurious non-BO LD signals of different cleaning pipelines when applied to a dataset of healthy controls and temporal lobe epilepsy patients. Increased tSNR and power spectral density in the resting-state frequency range (0.01-0.1 Hz) were found for all pre-processing pipelines with respect to the minimally pre-processed data, suggesting a positive gain in terms of temporal properties when optimal cleaning procedures are applied to the acquired fMRI data. All the pre-processing pipelines considered were able to recover the DMN through group ICA. By visually comparing this network across all the pipelines and groups, we found that AROMA, SPM12, FIX and FIXMC were able to better delineate the posterior cingulate cortex

Multi-parametric Imaging Using Hybrid PET/MR to Investigate the Epileptogenic Brain

Neuroimaging analysis has led to fundamental discoveries about the healthy and pathological human brain. Different imaging modalities allow garnering complementary information about brain metabolism, structure and function. To ensure that the integration of imaging data from these modalities is robust and reliable, it is fundamental to attain deep knowledge of each modality individually. Epilepsy, a neurological condition characterised by recurrent spontaneous seizures, represents a field in which applications of neuroimaging and multi-parametric imaging are particularly promising to guide diagnosis and treatment. In this PhD thesis, I focused on different imaging modalities and investigated advanced denoising and analysis strategies to improve their application to epilepsy. The first project focused on fluorodeoxyglucose (FDG) positron emission tomography (PET), a well-established imaging modality assessing brain metabolism, and aimed to develop a novel, semi-quantitative pipeline to analyse data in children with epilepsy, thus aiding presurgical planning. As pipelines for FDG-PET analysis in children are currently lacking, I developed age-appropriate templates to provide statistical parametric maps identifying epileptogenic areas on patient scans. The second and third projects focused on two magnetic resonance imaging (MRI) modalities: resting-state functional MRI (rs-fMRI) and arterial spin labelling (ASL), respectively. The aim was to i) probe the efficacy of different fMRI denoising pipelines, and ii) formally compare different ASL data acquisition strategies. In the former case, I compared different pre-processing methods and assessed their impact on fMRI signal quality and related functional connectivity analyses. In the latter case, I compared two ASL sequences to investigate their ability to quantify cerebral blood flow and interregional brain connectivity. The final project addressed the combination of rs-fMRI and ASL, and leveraged graph-theoretical analysis tools to i) compare metrics estimated via these two imaging modalities in healthy subjects and ii) assess topological changes captured by these modalities in a sample of temporal lobe epilepsy patients

Pipeline Comparison for the Pre-Processing of Resting-State Data in Epilepsy

Abstract\u2014Noise removal is a critical step to recover the signal of interest from resting-state fMRI data. Several pre-processing pipelines have been developed mainly based on nuisance regression or independent component analysis. The aim of this work was to evaluate the ability in removing spurious non- BOLD signals of different cleaning pipelines when applied to a dataset of healthy controls and temporal lobe epilepsy patients. Increased tSNR and power spectral density in the resting-state frequency range (0.01-0.1 Hz) were found for all pre-processing pipelines with respect to the minimally pre-processed data, suggesting a positive gain in terms of temporal properties when optimal cleaning procedures are applied to the acquired fMRI data. All the pre-processing pipelines considered were able to recover the DMN through group ICA. By visually comparing this network across all the pipelines and groups, we found that AROMA, SPM12, FIX and FIXMC were able to better delineate the posterior cingulate cortex