Sequential analysis of fMRI images: A new approach to study human epileptic networks

Abstract

SUMMARYPurpose: The aim of this study was to introduce a new approach for analysis of functional magnetic resonance imaging (fMRI) data in order to illustrate the temporal development of the blood oxygenation level-dependent (BOLD) signal changes induced by epileptic seizures. Method: In order to sequentially analyze the fMRI images acquired during epileptic seizures, a continuous series of echo planar imaging (EPI) scans covering the complete period of a seizure was acquired. Data were segmented into 10-s blocks. Each block, representing a unique experimental condition, was contrasted with a neutral (no seizure) baseline condition. Visual comparison of the activations from one block to the next highlighted the course of activations and deactivations during the seizure event. This analysis was applied to three independent seizures of one patient with perirolandic epilepsy secondary to chronic encephalitis: one seizure before epilepsy surgery and two after unsuccessful tailored resection. Observations were compared to results from invasive subdural electroencephalography (EEG) monitoring, singlephoton emission computed tomography (SPECT) coregistered to MRI (SISCOM), and independent component analysis (ICA), a model-free method of BOLD-signal analysis. Results: The initial increase in BOLD signal occurred 10-40 s before clinical onset in the same location compared to the seizure-onset zone determined by invasive subdural evaluation and SISCOM. Sequential involvement of cortical and subcortical structures was in agreement with SISCOM, intracranial EEG recordings, and ICA results. Discussion: In selected patients, sequential analysis of changes in BOLD signal induced by epileptic seizures might represent a useful approach for investigating the temporal development of brain activity during epileptic seizures, thereby allowing imaging of those cerebral structures involved in seizure generation and propagation.

A personalized history of EEG–fMRI integration

“…Comparable efforts were undertaken at the EEG level, also paying special attention to artifacts (Huiskamp, 2005;Siniatchkin et al, 2007a), or the accurate detection and classification of IED Flanagan et al, 2009;Jann et al, 2008;Liston et al, 2006a;Marques et al, 2009;SalekHaddadi et al, 2006;Vulliemoz et al, 2010b;Zijlmans et al, 2007), vigilance effects and ongoing spontaneous 'background' EEG (Moehring et al, 2008;Siniatchkin et al, 2007b;Tyvaert et al, 2008b). Finally, analysis methods found entry into EEG-fMRI which had already been established in 'classical' neuroimaging studies including data driven methods such as independent component analysis Moeller et al, 2011;Rodionov et al, 2007;Siniatchkin et al, 2007b) and others with and without using EEG information for the detection of epileptiform activity with fMRI (Donaire et al, 2009;Hamandi et al, 2005;Morgan et al, 2007;Morgan et al, 2004). Using this variety of analysis methods, mainly more recently, ictal activity itself was investigated with EEG-fMRI (Auer et al, 2008;Bai et al, 2010;Berman et al, 2010;Bonaventura et al, 2006;Detre et al, 1996;Di Bonaventura et al, 2006;Donaire et al, 2009;Federico et al, 2005a;Kobayashi et al, 2006c;LeVan et al, 2010;Liu et al, 2008;Marrosu et al, 2009;Moeller et al, 2010;Salek-Haddadi et al, 2009;SalekHaddadi et al, 2002;Tyvaert et al, 2008a).…”

“…Finally, analysis methods found entry into EEG-fMRI which had already been established in 'classical' neuroimaging studies including data driven methods such as independent component analysis Moeller et al, 2011;Rodionov et al, 2007;Siniatchkin et al, 2007b) and others with and without using EEG information for the detection of epileptiform activity with fMRI (Donaire et al, 2009;Hamandi et al, 2005;Morgan et al, 2007;Morgan et al, 2004). Using this variety of analysis methods, mainly more recently, ictal activity itself was investigated with EEG-fMRI (Auer et al, 2008;Bai et al, 2010;Berman et al, 2010;Bonaventura et al, 2006;Detre et al, 1996;Di Bonaventura et al, 2006;Donaire et al, 2009;Federico et al, 2005a;Kobayashi et al, 2006c;LeVan et al, 2010;Liu et al, 2008;Marrosu et al, 2009;Moeller et al, 2010;Salek-Haddadi et al, 2009;SalekHaddadi et al, 2002;Tyvaert et al, 2008a).…”

Update on neuroimaging in epilepsy

“…Occasionally, seizures have been captured during EEG-fMRI, showing networks of brain activity during the onset and evolution of the seizure [174], and in some instances these changes in fMRI BOLD occurred tens of seconds or even a few minutes prior to EEG seizure onset [175][176][177].…”

Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data

“…Physicians at epilepsy centers acquire combinations of various noninvasive or/and invasive brain signal modalities to diagnose the seizure onsets in patients, plan neurosurgical treatment, or fathom ictogenesis mechanisms and epilepsy symptom (Bragin et al, 2010; Donaire et al, 2009a; Donaire et al, 2009b; Engel, 1993; Engel et al, 2010; Fried, 1995; Rosenow and Luders, 2001; Sierra-Marcos et al, 2013; Staba and Bragin, 2011). These modalities include scalp electroencephalography (EEG), intracranial electroencephalography (iEEG), magnetoelectroencephalography (MEG), functional magnetic resonance imaging (fMRI), and other neuroimaging data.…”

“…Traditionally, clinicians resort to subjective manual procedures when screening iEEG or fMRI for epilepsy patient diagnoses. But signal processing techniques in pattern classification and recognition for fMRI and iEEG have become useful tools in epilepsy research to help clinicians more objectively discern differences between functional and dysfunctional brain regions, identifying diseased brain tissue for therapy (Ayoubian et al, 2012; Donaire et al, 2009a; Donaire et al, 2009b; Fernandez-Blanco et al, 2012; Gaspard et al, 2014; Gotman et al, 1995; Grewal and Gotman, 2005; Halford, 2009; Han et al, 2011; Keogh and Cordes, 2007; Lee et al, 2009; Navakatikyan et al, 2006; Osorio et al, 1995; Osorio et al, 1998; Qu and Gotman, 1997; Saab and Gotman, 2005; Tzallas et al, 2009; Tzallas et al, 2012; Wilson and Emerson, 2002; Worrell et al, 2012). With further development, validation, and acceptance across several research groups, such algorithms may translate to practical clinical use as decision-support tools for physicians.…”