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Boerwinkle VL, Manjón I, Sussman BL, McGary A, Mirea L, Gillette K, Broman-Fulks J, Cediel EG, Arhin M, Hunter SE, Wyckoff SN, Allred K, Tom D. Resting-State Functional Magnetic Resonance Imaging Network Association With Mortality, Epilepsy, Cognition, and Motor Two-Year Outcomes in Suspected Severe Neonatal Acute Brain Injury. Pediatr Neurol 2024; 152:41-55. [PMID: 38198979 DOI: 10.1016/j.pediatrneurol.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/14/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND AND OBJECTIVES In acute brain injury of neonates, resting-state functional magnetic resonance imaging (MRI) (RS) showed incremental association with consciousness, mortality, cognitive and motor development, and epilepsy, with correction for multiple comparisons, at six months postgestation in neonates with suspected acute brain injury (ABI). However, there are relatively few developmental milestones at six months to benchmark against, thus, we extended this cohort study to evaluate two-year outcomes. METHODS In 40 consecutive neonates with ABI and RS, ordinal scores of resting-state networks; MRI, magnetic resonance spectroscopy, and electroencephalography; and up to 42-month outcomes of mortality, general and motor development, Pediatric Cerebral Performance Category Scale (PCPC), and epilepsy informed associations between tests and outcomes. RESULTS Mean gestational age was 37.8 weeks, 68% were male, and 60% had hypoxic-ischemic encephalopathy. Three died in-hospital, four at six to 42 months, and five were lost to follow-up. Associations included basal ganglia network with PCPC (P = 0.0003), all-mortality (P = 0.005), and motor (P = 0.0004); language/frontoparietal network with developmental delay (P = 0.009), PCPC (P = 0.006), and all-mortality (P = 0.01); default mode network with developmental delay (P = 0.003), PCPC (P = 0.004), neonatal intensive care unit mortality (P = 0.01), and motor (P = 0.009); RS seizure onset zone with epilepsy (P = 0.01); and anatomic MRI with epilepsy (P = 0.01). CONCLUSION For the first time, at any age, resting state functional MRI in ABI is associated with long-term epilepsy and RSNs predicted mortality in neonates. Severity of RSN abnormality was associated with incrementally worsened neurodevelopment including cognition, language, and motor function over two years.
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Affiliation(s)
- Varina L Boerwinkle
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina.
| | - Iliana Manjón
- University of Arizona College of Medicine - Tucson, Tucson, Arizona
| | - Bethany L Sussman
- Division of Neuroscience Research, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona
| | - Alyssa McGary
- Department of Clinical Research, Phoenix Children's Hospital, Phoenix, Arizona
| | - Lucia Mirea
- Department of Clinical Research, Phoenix Children's Hospital, Phoenix, Arizona
| | - Kirsten Gillette
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Jordan Broman-Fulks
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Emilio G Cediel
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Martin Arhin
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Senyene E Hunter
- Division of Child Neurology, University of North Carolina Medical School, Chapel Hill, North Carolina
| | - Sarah N Wyckoff
- Division of Neuroscience Research, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, Arizona
| | - Kimberlee Allred
- Division of Neonatology, Phoenix Children's Hospital, Phoenix, Arizona
| | - Deborah Tom
- Division of Neonatology, Phoenix Children's Hospital, Phoenix, Arizona
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Kamboj P, Banerjee A, Boerwinkle VL, Gupta SKS. The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI. Front Neurol 2024; 14:1324461. [PMID: 38274868 PMCID: PMC10808636 DOI: 10.3389/fneur.2023.1324461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024] Open
Abstract
We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI was collected from 52 children with RE who had subsequently undergone ic-EEG and then, if indicated, surgery for seizure control (n = 25). The resting state functional connectomics data were previously independently classified by two expert epileptologists, as indicative of measurement noise, typical resting state network connectivity, or SOZ. An expert knowledge integrated deep network was trained on functional connectomics data to identify SOZ. Expert knowledge integrated with DL showed a SOZ localization accuracy of 84.8 ± 4.5% and F1 score, harmonic mean of positive predictive value and sensitivity, of 91.7 ± 2.6%. Conversely, a DL only model yielded an accuracy of <50% (F1 score 63%). Activations that initiate in gray matter, extend through white matter, and end in vascular regions are seen as the most discriminative expert-identified SOZ characteristics. Integration of expert knowledge of functional connectomics can not only enhance the performance of DL in localizing SOZ in RE but also lead toward potentially useful explanations of prevalent co-activation patterns in SOZ. RE with surgical outcomes and preoperative rs-fMRI studies can yield expert knowledge most salient for SOZ identification.
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Affiliation(s)
- Payal Kamboj
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Ayan Banerjee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Varina L. Boerwinkle
- Department of Neurology, Division of Child Neurology, University of North Carolina, Chapel Hill, NC, United States
| | - Sandeep K. S. Gupta
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
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Uruñuela E, Gonzalez-Castillo J, Zheng C, Bandettini P, Caballero-Gaudes C. Whole-brain multivariate hemodynamic deconvolution for functional MRI with stability selection. Med Image Anal 2024; 91:103010. [PMID: 37950937 PMCID: PMC10843584 DOI: 10.1016/j.media.2023.103010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 11/13/2023]
Abstract
Conventionally, analysis of functional MRI (fMRI) data relies on available information about the experimental paradigm to establish hypothesized models of brain activity. However, this information can be inaccurate, incomplete or unavailable in multiple scenarios such as resting-state, naturalistic paradigms or clinical conditions. In these cases, blind estimates of neuronal-related activity can be obtained with paradigm-free analysis methods such as hemodynamic deconvolution. Yet, current formulations of the hemodynamic deconvolution problem have three important limitations: (1) their efficacy strongly depends on the appropriate selection of regularization parameters, (2) being univariate, they do not take advantage of the information present across the brain, and (3) they do not provide any measure of statistical certainty associated with each detected event. Here we propose a novel approach that addresses all these limitations. Specifically, we introduce multivariate sparse paradigm free mapping (Mv-SPFM), a novel hemodynamic deconvolution algorithm that operates at the whole brain level and adds spatial information via a mixed-norm regularization term over all voxels. Additionally, Mv-SPFM employs a stability selection procedure that removes the need to select regularization parameters and also lets us obtain an estimate of the true probability of having a neuronal-related BOLD event at each voxel and time-point based on the area under the curve (AUC) of the stability paths. Besides, we present a formulation tailored for multi-echo fMRI acquisitions (MvME-SPFM), which allows us to better isolate fluctuations of BOLD origin on the basis of their linear dependence with the echo time (TE) and to assign physiologically interpretable units (i.e., changes in the apparent transverse relaxation ΔR2∗) to the resulting deconvolved events. Remarkably, we demonstrate that Mv-SPFM achieves comparable performance even when using a single-echo formulation. We demonstrate that this algorithm outperforms existing state-of-the-art deconvolution approaches, and shows higher spatial and temporal agreement with the activation maps and BOLD signals obtained with a standard model-based linear regression approach, even at the level of individual neuronal events. Furthermore, we show that by employing stability selection, the performance of the algorithm depends less on the selection of temporal and spatial regularization parameters λ and ρ. Consequently, the proposed algorithm provides more reliable estimates of neuronal-related activity, here in terms of ΔR2∗, for the study of the dynamics of brain activity when no information about the timings of the BOLD events is available. This algorithm will be made publicly available as part of the splora Python package.
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Affiliation(s)
- Eneko Uruñuela
- Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Spain; University of the Basque Country (EHU/UPV), Donostia-San Sebastián, Spain.
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20892, United States
| | - Charles Zheng
- Machine Learning Team, Functional Magnetic Resonance Imaging Facility, National Institute of Mental Health, Bethesda, MD 20892, United States
| | - Peter Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD 20892, United States
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Rangaprakash D, Barry RL, Deshpande G. The confound of hemodynamic response function variability in human resting-state functional MRI studies. Front Neurosci 2023; 17:934138. [PMID: 37521709 PMCID: PMC10375034 DOI: 10.3389/fnins.2023.934138] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/07/2023] [Indexed: 08/01/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.
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Affiliation(s)
- D. Rangaprakash
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, United States
| | - Gopikrishna Deshpande
- Department of Electrical and Computer Engineering, AU MRI Research Center, Auburn University, Auburn, AL, United States
- Department of Psychological Sciences, Auburn University, Auburn, AL, United States
- Center for Neuroscience, Auburn University, Auburn, AL, United States
- Alabama Advanced Imaging Consortium, Birmingham, AL, United States
- Key Laboratory for Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Brain Research, Indian Institute of Science, Bangalore, India
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Banerjee A, Kamboj P, Wyckoff SN, Sussman BL, Gupta SKS, Boerwinkle VL. Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy. FRONTIERS IN NEUROIMAGING 2023; 1:1007668. [PMID: 37555141 PMCID: PMC10406253 DOI: 10.3389/fnimg.2022.1007668] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 10/24/2022] [Indexed: 08/10/2023]
Abstract
OBJECTIVE Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE. METHODS EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex. RESULTS EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve a ~5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening. SIGNIFICANCE Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.
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Affiliation(s)
- Ayan Banerjee
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Payal Kamboj
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Sarah N. Wyckoff
- Division of Neuroscience, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Bethany L. Sussman
- Division of Neuroscience, Barrow Neurological Institute at Phoenix Children's Hospital, Phoenix, AZ, United States
| | - Sandeep K. S. Gupta
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, United States
| | - Varina L. Boerwinkle
- Division of Child Neurology, University of North Carolina Department of Neurology, Chapel Hill, NC, United States
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Emara HM, Elwekeil M, Taha TE, El-Fishawy AS, El-Rabaie ESM, El-Shafai W, El Banby GM, Alotaiby T, Alshebeili SA, Abd El-Samie FE. Efficient Frameworks for EEG Epileptic Seizure Detection and Prediction. ANNALS OF DATA SCIENCE 2022; 9:393-428. [DOI: 10.1007/s40745-020-00308-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 06/19/2020] [Accepted: 07/19/2020] [Indexed: 09/02/2023]
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Association of network connectivity via resting state functional MRI with consciousness, mortality, and outcomes in neonatal acute brain injury. Neuroimage Clin 2022; 34:102962. [PMID: 35152054 PMCID: PMC8851268 DOI: 10.1016/j.nicl.2022.102962] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 02/05/2022] [Accepted: 02/07/2022] [Indexed: 01/07/2023]
Abstract
Basal ganglia and seizure onset zone networks were associated with motor outcomes. Broad language/cognitive region networks were associated with developmental delay. Discharge with mortality was linked to default mode and language/cognitive networks. Exams were not linked to networks after multiple testing corrections. Lack of detection of all studied networks only occurred in those who did not survive.
Background An accurate and comprehensive test of integrated brain network function is needed for neonates during the acute brain injury period to inform on morbidity. This retrospective cohort study assessed whether integrated brain network function acquired by resting state functional MRI during the acute period in neonates with brain injury, is associated with acute exam, neonatal mortality, and 6-month outcomes. Methods Study subjects included 40 consecutive neonates with resting state functional MRI acquired within 31 days after suspected brain insult from March 2018 to July 2019 at Phoenix Children’s Hospital. Acute-period exam and test results were assigned ordinal scores based on severity as documented by respective treating specialists. Analyses (Fisher exact, Wilcoxon-rank sum test, ordinal/multinomial logistic regression) examined association of resting state networks with demographics, presentation, neurological exam, electroencephalogram, anatomical MRI, magnetic resonance spectroscopy, passive task functional MRI, and outcomes of discharge condition, outpatient development, motor tone, seizure, and mortality. Results Subjects had a mean (standard deviation) gestational age of 37.8 (2.6) weeks, a majority were male (63%), with a diagnosis of hypoxic ischemic encephalopathy (68%). Findings at birth included mild distress (48%), moderately abnormal neurological exam (33%), and consciousness characterized as awake but irritable (40%). Significant associations after multiple testing corrections were detected for resting state networks: basal ganglia with outpatient developmental delay (odds ratio [OR], 14.5; 99.4% confidence interval [CI], 2.00–105; P < .001) and motor tone/weakness (OR, 9.98; 99.4% CI, 1.72–57.9; P < .001); language/frontoparietal network with discharge condition (OR, 5.13; 99.4% CI, 1.22–21.5; P = .002) and outpatient developmental delay (OR, 4.77; 99.4% CI, 1.21–18.7; P=.002); default mode network with discharge condition (OR, 3.72; 99.4% CI, 1.01–13.78; P=.006) and neurological exam (P = .002 (FE); OR, 11.8; 99.4% CI, 0.73–191; P = .01 (OLR)); and seizure onset zone with motor tone/weakness (OR, 3.31; 99.4% CI, 1.08–10.1; P=.003). Resting state networks were not detected in three neonates, who died prior to discharge. Conclusions This study provides level 3 evidence (OCEBM Levels of Evidence Working Group) demonstrating that in neonatal acute brain injury, the degree of abnormality of resting state networks is associated with acute exam and outcomes. Total lack of brain network detection was only found in patients who did not survive.
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Hütel M, Antonelli M, Melbourne A, Ourselin S. Hemodynamic matrix factorization for functional magnetic resonance imaging. Neuroimage 2021; 231:117814. [PMID: 33549748 PMCID: PMC8210649 DOI: 10.1016/j.neuroimage.2021.117814] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 01/10/2021] [Accepted: 01/24/2021] [Indexed: 11/30/2022] Open
Abstract
The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.
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Affiliation(s)
- Michael Hütel
- Department of Medical Physics and Biomedical Engineering, UCL, United Kingdom; School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom.
| | - Michela Antonelli
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
| | - Andrew Melbourne
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, KCL, United Kingdom
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Two-Dimensional Temporal Clustering Analysis for Patients with Epilepsy: Detecting Epilepsy-Related Information in EEG-fMRI Concordant, Discordant and Spike-Less Patients. Brain Topogr 2017; 31:322-336. [PMID: 29022116 DOI: 10.1007/s10548-017-0598-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 09/26/2017] [Indexed: 10/18/2022]
Abstract
EEG acquired simultaneously with fMRI (EEG-fMRI) is a multimodal method that has shown promise in mapping the seizure onset zone in patients with focal epilepsy. However, there are many instances when this method is unsuccessful or not applicable, and other data driven fMRI methods may be utilized. One such method is the two-dimensional temporal clustering analysis (2dTCA). In this study we compared the classic EEG-fMRI and 2dTCA performance in mapping regions related to the seizure onset region in 18 focal epilepsy patients (12 presenting interictal epileptiform discharges (IEDs), during EEG-fMRI acquisition) with Engel I or II surgical outcome. Activation maps of both 2dTCA timing outputs (positive and negative histograms) and EEG detected IEDs were computed and compared to the region of epilepsy surgical resection. Patients were evaluated in three categories based on frequency of EEG detected spiking during the MRI. EEG-fMRI maps were concordant to the epilepsy region in 5/12 subjects, four with frequent IEDs on EEG. The 2dTCA was successful in mapping 13/18 patients including 3/6 with no IEDs detected (10/12 with IEDs detected). The epilepsy-related activities were successfully mapped by both methods in only 4/12 patients. This work suggests that the epilepsy-related information detected by each method may be different: while EEG-fMRI is more accurate in patients with high rather than lower numbers of EEG detected IEDs; 2dTCA can be useful in evaluating patients even when no concurrent EEG spikes are detected or EEG-fMRI is not effective. Therefore, our results support that 2dTCA might be an alternative for mapping epilepsy-related BOLD activity in negative EEG-fMRI (6/7 patients) and spike-less patients.
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Khoo HM, Hao Y, von Ellenrieder N, Zazubovits N, Hall J, Olivier A, Dubeau F, Gotman J. The hemodynamic response to interictal epileptic discharges localizes the seizure-onset zone. Epilepsia 2017; 58:811-823. [DOI: 10.1111/epi.13717] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/07/2017] [Indexed: 01/14/2023]
Affiliation(s)
- Hui Ming Khoo
- Montreal Neurological Institute and Hospital; McGill University; Montreal Quebec Canada
- Department of Neurosurgery; Osaka University Graduate School of Medicine; Suita Japan
| | - Yongfu Hao
- Montreal Neurological Institute and Hospital; McGill University; Montreal Quebec Canada
| | | | - Natalja Zazubovits
- Montreal Neurological Institute and Hospital; McGill University; Montreal Quebec Canada
| | - Jeffery Hall
- Montreal Neurological Institute and Hospital; McGill University; Montreal Quebec Canada
| | - André Olivier
- Montreal Neurological Institute and Hospital; McGill University; Montreal Quebec Canada
| | - François Dubeau
- Montreal Neurological Institute and Hospital; McGill University; Montreal Quebec Canada
| | - Jean Gotman
- Montreal Neurological Institute and Hospital; McGill University; Montreal Quebec Canada
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Besseling RMH, Jansen JFA, de Louw AJA, Vlooswijk MCG, Hoeberigs MC, Aldenkamp AP, Backes WH, Hofman PAM. Abnormal Profiles of Local Functional Connectivity Proximal to Focal Cortical Dysplasias. PLoS One 2016; 11:e0166022. [PMID: 27861502 PMCID: PMC5115673 DOI: 10.1371/journal.pone.0166022] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2015] [Accepted: 10/21/2016] [Indexed: 11/18/2022] Open
Abstract
Introduction Focal cortical dysplasia (FCD) is a congenital malformation of cortical development that often leads to medically refractory epilepsy. Focal resection can be an effective treatment, but is challenging as the surgically relevant abnormality may exceed the MR-visible lesion. The aim of the current study is to develop methodology to characterize the profile of functional connectivity around FCDs using resting-state functional MRI and in the individual patient. The detection of aberrant connectivity may provide a means to more completely delineate the clinically relevant lesion. Materials and Methods Fifteen FCD patients (age, mean±SD: 31±11 years; 11 males) and 16 matched healthy controls (35±9 years; 7 males) underwent structural and functional imaging at 3 Tesla. The cortical surface was reconstructed from the T1-weighted scan and the registered functional MRI data was spatially normalized to a common anatomical standard space employing the gyral pattern. Seed-based functional connectivity was determined in all subjects for all dysplasia locations. A single patient was excluded based on an aberrant FCD seed time series. Functional connectivity as a function of geodesic distance (along the cortical surface) was compared between the individual patients and the homotopic normative connectivity profiles derived from the controls. Results In 12/14 patients, aberrant profiles of functional connectivity were found, which demonstrated both hyper- and hypoconnectivity as well as combinations. Abnormal functional connectivity was typically found (also) beyond the lesion visible on structural MRI, while functional connectivity profiles not related to a lesion appeared normal in patients. Conclusion This novel functional MRI technique has potential for delineating functionally aberrant from normal cortex beyond the structural lesion in FCD, which remains to be confirmed in future research.
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Affiliation(s)
- René M. H. Besseling
- Epilepsy center Kempenhaeghe, Heeze, the Netherlands
- Research School for Mental Health & Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Jacobus F. A. Jansen
- Research School for Mental Health & Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | - Mariëlle C. G. Vlooswijk
- Research School for Mental Health & Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | - Albert P. Aldenkamp
- Epilepsy center Kempenhaeghe, Heeze, the Netherlands
- Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Walter H. Backes
- Research School for Mental Health & Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Paul A. M. Hofman
- Epilepsy center Kempenhaeghe, Heeze, the Netherlands
- Research School for Mental Health & Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
- * E-mail:
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Pizarro R, Nair V, Meier T, Holdsworth R, Tunnell E, Rutecki P, Sillay K, Meyerand ME, Prabhakaran V. Delineating potential epileptogenic areas utilizing resting functional magnetic resonance imaging (fMRI) in epilepsy patients. Neurocase 2016; 22:362-8. [PMID: 27362339 PMCID: PMC4979575 DOI: 10.1080/13554794.2016.1195845] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Seizure localization includes neuroimaging like electroencephalogram, and magnetic resonance imaging (MRI) with limited ability to characterize the epileptogenic network. Temporal clustering analysis (TCA) characterizes epileptogenic network congruent with interictal epileptiform discharges by clustering together voxels with transient signals. We generated epileptogenic areas for 12 of 13 epilepsy patients with TCA, congruent with different areas of seizure onset. Resting functional MRI (fMRI) scans are noninvasive, and can be acquired quickly, in patients with different levels of severity and function. Analyzing resting fMRI data using TCA is quick and can complement clinical methods to characterize the epileptogenic network.
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Affiliation(s)
- Ricardo Pizarro
- a Department of Biomedical Engineering , UW-Madison , Madison , WI , USA
| | - Veena Nair
- b Department of Radiology , UW-Madison , Madison , WI , USA
| | - Timothy Meier
- b Department of Radiology , UW-Madison , Madison , WI , USA
| | | | - Evelyn Tunnell
- c Department of Neurology , UW-Madison , Madison , WI , USA
| | - Paul Rutecki
- c Department of Neurology , UW-Madison , Madison , WI , USA
| | - Karl Sillay
- d Department of Neurosurgery , UW-Madison , Madison , WI , USA
| | - Mary E Meyerand
- a Department of Biomedical Engineering , UW-Madison , Madison , WI , USA.,e Department of Medical Physics , UW-Madison , Madison , WI , USA
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He T, Cao L, Balas VE, McCauley P, Shi F. Curvature manipulation of the spectrum of Valence-Arousal-related fMRI dataset using Gaussian-shaped Fast Fourier Transform and its application to fuzzy KANSEI adjectives modeling. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.10.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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14
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Fergus P, Hussain A, Hignett D, Al-Jumeily D, Abdel-Aziz K, Hamdan H. A machine learning system for automated whole-brain seizure detection. APPLIED COMPUTING AND INFORMATICS 2016. [DOI: 10.1016/j.aci.2015.01.001] [Citation(s) in RCA: 81] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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A prospective fMRI-based technique for localising the epileptogenic zone in presurgical evaluation of epilepsy. Neuroimage 2015; 113:329-39. [DOI: 10.1016/j.neuroimage.2015.03.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Revised: 03/02/2015] [Accepted: 03/06/2015] [Indexed: 11/17/2022] Open
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Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques. BIOMED RESEARCH INTERNATIONAL 2015; 2015:986736. [PMID: 25710040 PMCID: PMC4325968 DOI: 10.1155/2015/986736] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 12/09/2014] [Accepted: 12/23/2014] [Indexed: 11/17/2022]
Abstract
The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis of epilepsy is usually made by a neurologist but can be difficult to be made in the early stages. Supporting paraclinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and investigate treatment earlier. However, electroencephalogram capture and interpretation are time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity may be a solution. In this paper, we present a supervised machine learning approach that classifies seizure and nonseizure records using an open dataset containing 342 records. Our results show an improvement on existing studies by as much as 10% in most cases with a sensitivity of 93%, specificity of 94%, and area under the curve of 98% with a 6% global error using a k-class nearest neighbour classifier. We propose that such an approach could have clinical applications in the investigation of patients with suspected seizure disorders.
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17
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Hunyadi B, Tousseyn S, Dupont P, Van Huffel S, Van Paesschen W, De Vos M. Automatic selection of epileptic independent fMRI components. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:3853-6. [PMID: 25570832 DOI: 10.1109/embc.2014.6944464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
EEG-correlated fMRI analysis has proven to be useful in localizing regions of BOLD activation related to epileptic activity. However, as EEG does not always provide reliable information, purely fMRI-based data-driven techniques are invaluable. Recently, we have shown that independent component analysis (ICA) can extract sources related to the epileptic network even in such EEG-negative cases [1]. Moreover, these sources were shown to be informative with respect to the seizure onset zone (SOZ). In order to utilize this concept in clinical practice in a prospective manner, this work aims at developing an automatic technique for selecting the epileptic sources. The proposed approach applies a cascade of two classifiers. In the first step artifact related sources are discarded. In the second step the sources are characterized by four discriminative features and epileptic sources are selected from among other BOLD-related components. Our technique reaches a promising 77% specificity and provides concordant sources with the EEG-correlated fMRI activation maps or with the SOZ in 71% of the cases.
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18
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Tousseyn S, Dupont P, Robben D, Goffin K, Sunaert S, Van Paesschen W. A reliable and time-saving semiautomatic spike-template-based analysis of interictal EEG-fMRI. Epilepsia 2014; 55:2048-58. [PMID: 25377892 DOI: 10.1111/epi.12841] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2014] [Indexed: 11/26/2022]
Abstract
OBJECTIVE A prerequisite for the implementation of interictal electroencephalography-correlated functional magnetic resonance imaging (EEG-fMRI) in the presurgical work-up for epilepsy surgery is straightforward processing. We propose a new semi-automatic method as alternative for the challenging and time-consuming visual spike identification. METHODS Our method starts from a patient-specific spike-template, built by averaging spikes recorded on the EEG outside the scanner. Spatiotemporal cross-correlations between the template and the EEG measured during fMRI were calculated. To minimize false-positive detections, this time course of cross-correlations was binarized by means of a spike-template-specific threshold determined in healthy controls. To inform our model for statistical parametric mapping, this binarized regressor was convolved with the canonical hemodynamic response function. We validated our "template-based" method in 21 adult patients with refractory focal epilepsy with a well-defined epileptogenic zone and interictal spikes during EEG-fMRI. Sensitivity and specificity for detecting the epileptogenic zone were calculated and represented in receiver operating characteristic (ROC) curves. Our approach was compared with a previously proposed semiautomatic "topography-based" method that used the topographic amplitude distribution of spikes as a starting point for correlation-based fitting. RESULTS Good diagnostic performance could be reached with our template-based method. The optimal area under the ROC curve was 0.77. Diagnostic performance of the topography-based method was overall low. SIGNIFICANCE Our new template-based method is more standardized and time-saving than visual spike identification on intra-scanner EEG recordings, and preserves good diagnostic performance for detecting the epileptogenic zone.
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Affiliation(s)
- Simon Tousseyn
- Laboratory for Epilepsy Research, UZ Leuven & KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven & KU Leuven, Leuven, Belgium
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van Houdt PJ, Ossenblok PPW, Colon AJ, Hermans KHM, Verdaasdonk RM, Boon PAJM, de Munck JC. Are Epilepsy-Related fMRI Components Dependent on the Presence of Interictal Epileptic Discharges in Scalp EEG? Brain Topogr 2014; 28:606-18. [PMID: 25315607 DOI: 10.1007/s10548-014-0407-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2014] [Accepted: 09/30/2014] [Indexed: 01/27/2023]
Abstract
Spatial independent component analysis (ICA) is increasingly being used to extract resting-state networks from fMRI data. Previous studies showed that ICA also reveals independent components (ICs) related to the seizure onset zone. However, it is currently unknown how these epileptic ICs depend on the presence of interictal epileptic discharges (IEDs) in the EEG. The goal of this study was to explore the relation between ICs obtained from fMRI epochs during the occurrence of IEDs in the EEG and those without IEDs. fMRI data sets with co-registered EEG were retrospectively selected of patients from whom the location of the epileptogenic zone was confirmed by outcome of surgery (n = 8). The fMRI data were split into two epochs: one with IEDs visible in scalp EEG and one without. Spatial ICA was applied to the fMRI data of each part separately. The maps of all resulting components were compared to the resection area and the EEG-fMRI correlation pattern by computing a spatial correlation coefficient to detect the epilepsy-related component. For all patients, except one, there was a remarkable resemblance between the epilepsy-related components selected during epochs with IEDs and those without IEDs. These findings suggest that epilepsy-related ICs are not dependent on the presence of IEDs in scalp EEG. Since these epileptic ICs showed partial overlap with resting-state networks of healthy volunteers (n = 10), our study supports the need for new ways to classify epileptic ICs.
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Affiliation(s)
- Petra J van Houdt
- Department of Research and Development, Kempenhaeghe, Heeze, The Netherlands
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Lopes R, Moeller F, Besson P, Ogez F, Szurhaj W, Leclerc X, Siniatchkin M, Chipaux M, Derambure P, Tyvaert L. Study on the Relationships between Intrinsic Functional Connectivity of the Default Mode Network and Transient Epileptic Activity. Front Neurol 2014; 5:201. [PMID: 25346721 PMCID: PMC4193009 DOI: 10.3389/fneur.2014.00201] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2014] [Accepted: 09/24/2014] [Indexed: 12/04/2022] Open
Abstract
Rationale: Simultaneous recording of electroencephalogram and functional MRI (EEG–fMRI) is a powerful tool for localizing epileptic networks via the detection of hemodynamic changes correlated with interictal epileptic discharges (IEDs). fMRI can be used to study the long-lasting effect of epileptic activity by assessing stationary functional connectivity during the resting-state period [especially, the connectivity of the default mode network (DMN)]. Temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) are associated with low responsiveness and disruption of DMN activity. A dynamic functional connectivity approach might enable us to determine the effect of IEDs on DMN connectivity and to better understand the correlation between DMN connectivity changes and altered consciousness. Method: We studied dynamic changes in DMN intrinsic connectivity and their relation to IEDs. Six IGE patients (with generalized spike and slow-waves) and 6 TLE patients (with unilateral left temporal spikes) were included. Functional connectivity before, during, and after IEDs was estimated using a sliding window approach and compared with the baseline period. Results: No dependence on window size was observed. The baseline DMN connectivity was decreased in the left hemisphere (ipsilateral to the epileptic focus) in TLEs and was less strong but remained bilateral in IGEs. We observed an overall increase in DMN intrinsic connectivity prior to the onset of IEDs in both IGEs and TLEs. After IEDs in TLEs, we found that DMN connectivity increased before it returned to baseline values. Most of the DMN regions with increased connectivity before and after IEDs were lateralized to the left hemisphere in TLE (i.e., ipsilateral to the epileptic focus). Conclusion: Results suggest that DMN connectivity may facilitate IED generation and may be affected at the time of the IED. However, these results need to be confirmed in a larger independent cohort.
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Affiliation(s)
- Renaud Lopes
- UMR 1046, University of Lille 2 , Lille , France ; In vivo Imaging Core Facility, IMPRT-IFR114, Lille University Medical Center , Lille , France
| | - Friederike Moeller
- Department of Neuropaediatrics, Christian-Albrechts-University , Kiel , Germany
| | - Pierre Besson
- UMR 1046, University of Lille 2 , Lille , France ; Department of Clinical Neurophysiology, Lille University Medical Center , Lille , France
| | | | - William Szurhaj
- UMR 1046, University of Lille 2 , Lille , France ; Department of Clinical Neurophysiology, Lille University Medical Center , Lille , France
| | - Xavier Leclerc
- UMR 1046, University of Lille 2 , Lille , France ; In vivo Imaging Core Facility, IMPRT-IFR114, Lille University Medical Center , Lille , France
| | - Michael Siniatchkin
- Department of Neuropaediatrics, Christian-Albrechts-University , Kiel , Germany
| | - Mathilde Chipaux
- Department of Pediatric Neurosurgery, Fondation Ophtalmologique A. de Rothschild , Paris , France
| | - Philippe Derambure
- UMR 1046, University of Lille 2 , Lille , France ; Department of Clinical Neurophysiology, Lille University Medical Center , Lille , France
| | - Louise Tyvaert
- UMR 1046, University of Lille 2 , Lille , France ; Department of Clinical Neurophysiology, Lille University Medical Center , Lille , France
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Zhang CH, Lu Y, Brinkmann B, Welker K, Worrell G, He B. Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI. Clin Neurophysiol 2014; 126:27-38. [PMID: 24856460 DOI: 10.1016/j.clinph.2014.04.011] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2014] [Revised: 03/09/2014] [Accepted: 04/16/2014] [Indexed: 12/01/2022]
Abstract
OBJECTIVE The aim was to develop a method for the purpose of localizing epilepsy related hemodynamic foci for patients suffering intractable focal epilepsy using task-free fMRI alone. METHODS We studied three groups of subjects: patients with intractable focal epilepsy, healthy volunteers performing motor tasks, and healthy volunteers in resting state. We performed spatial independent component analysis (ICA) on the fMRI alone data and developed a set of IC selection criteria to identify epilepsy related ICs. The method was then tested in the two healthy groups. RESULTS In seven out of the nine surgery patients, identified ICs were concordant with surgical resection. Our results were also consistent with presurgical evaluation of the remaining one patient without surgery and may explain why she was not suitable for resection treatment. In the motor task study of ten healthy subjects, our method revealed components with concordant spatial and temporal features as expected from the unilateral motor tasks. In the resting state study of seven healthy subjects, the method successfully rejected all components in four out of seven subjects as non-epilepsy related components. CONCLUSION These results suggest the lateralization and localization value of fMRI alone in presurgical evaluation for patients with intractable unilateral focal epilepsy. SIGNIFICANCE The proposed method is noninvasive in nature and easy to implement. It has the potential to be incorporated in current presurgical workup for treating intractable focal epilepsy patients.
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Affiliation(s)
| | - Yunfeng Lu
- Department of Biomedical Engineering, University of Minnesota, USA
| | - Benjamin Brinkmann
- Department of Neurology, Mayo Clinic, USA; Mayo Systems Electrophysiology Laboratory, Mayo Clinic, USA
| | | | - Gregory Worrell
- Department of Neurology, Mayo Clinic, USA; Mayo Systems Electrophysiology Laboratory, Mayo Clinic, USA
| | - Bin He
- Department of Biomedical Engineering, University of Minnesota, USA; Institute for Engineering in Medicine, University of Minnesota, USA.
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Pittau F, Grouiller F, Spinelli L, Seeck M, Michel CM, Vulliemoz S. The role of functional neuroimaging in pre-surgical epilepsy evaluation. Front Neurol 2014. [PMID: 24715886 DOI: 10.3389/fneur.2014.00031.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The prevalence of epilepsy is about 1% and one-third of cases do not respond to medical treatment. In an eligible subset of patients with drug-resistant epilepsy, surgical resection of the epileptogenic zone is the only treatment that can possibly cure the disease. Non-invasive techniques provide information for the localization of the epileptic focus in the majority of cases, whereas in others invasive procedures are required. In the last years, non-invasive neuroimaging techniques, such as simultaneous recording of functional magnetic resonance imaging and electroencephalogram (EEG-fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), electric and magnetic source imaging (MSI, ESI), spectroscopy (MRS), have proved their usefulness in defining the epileptic focus. The combination of these functional techniques can yield complementary information and their concordance is crucial for guiding clinical decision, namely the planning of invasive EEG recordings or respective surgery. The aim of this review is to present these non-invasive neuroimaging techniques, their potential combination, and their role in the pre-surgical evaluation of patients with pharmaco-resistant epilepsy.
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Affiliation(s)
- Francesca Pittau
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
| | - Frédéric Grouiller
- Department of Radiology and Medical Informatics, University Hospital of Geneva , Geneva , Switzerland
| | - Laurent Spinelli
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
| | - Margitta Seeck
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva , Geneva , Switzerland
| | - Serge Vulliemoz
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
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Pittau F, Grouiller F, Spinelli L, Seeck M, Michel CM, Vulliemoz S. The role of functional neuroimaging in pre-surgical epilepsy evaluation. Front Neurol 2014; 5:31. [PMID: 24715886 PMCID: PMC3970017 DOI: 10.3389/fneur.2014.00031] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2013] [Accepted: 03/06/2014] [Indexed: 12/25/2022] Open
Abstract
The prevalence of epilepsy is about 1% and one-third of cases do not respond to medical treatment. In an eligible subset of patients with drug-resistant epilepsy, surgical resection of the epileptogenic zone is the only treatment that can possibly cure the disease. Non-invasive techniques provide information for the localization of the epileptic focus in the majority of cases, whereas in others invasive procedures are required. In the last years, non-invasive neuroimaging techniques, such as simultaneous recording of functional magnetic resonance imaging and electroencephalogram (EEG-fMRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), electric and magnetic source imaging (MSI, ESI), spectroscopy (MRS), have proved their usefulness in defining the epileptic focus. The combination of these functional techniques can yield complementary information and their concordance is crucial for guiding clinical decision, namely the planning of invasive EEG recordings or respective surgery. The aim of this review is to present these non-invasive neuroimaging techniques, their potential combination, and their role in the pre-surgical evaluation of patients with pharmaco-resistant epilepsy.
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Affiliation(s)
- Francesca Pittau
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
| | - Frédéric Grouiller
- Department of Radiology and Medical Informatics, University Hospital of Geneva , Geneva , Switzerland
| | - Laurent Spinelli
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
| | - Margitta Seeck
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, University of Geneva , Geneva , Switzerland
| | - Serge Vulliemoz
- Presurgical Epilepsy Evaluation Unit, Neurology Department, University Hospital of Geneva , Geneva , Switzerland
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Modern Techniques of Epileptic Focus Localization. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2014; 114:245-78. [DOI: 10.1016/b978-0-12-418693-4.00010-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Aso T. [MRI in epilepsy and migraine]. Rinsho Shinkeigaku 2013; 53:1097-9. [PMID: 24291890 DOI: 10.5692/clinicalneurol.53.1097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
In vivo observation of the ictal events are thought to help understanding the etiology and pathology in both epilepsy and migraine. While simultaneous recording of EEG and fMRI is actively conducted for the former, in some cases, epileptogenic activity is undetectable by EEG. Attempts to detect such abnormal brain activity by using fMRI are underway. Analysis methods for resting-state fMRI can be applicable for such purposes. For migraine, fMRI is also highly valuable in detecting series of ictal events. However, since the disease is suspected to involve abnormal neuro-vascular coupling, it is not always straightforward how to interpret the observation by vascular-dependent methods. Therefore development of non-vascular methods is critical for future advances.
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Affiliation(s)
- Toshihiko Aso
- Human Brain Research Center, Kyoto University Graduate School of Medicine
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Hunyadi B, Tousseyn S, Mijović B, Dupont P, Van Huffel S, Van Paesschen W, De Vos M. ICA extracts epileptic sources from fMRI in EEG-negative patients: a retrospective validation study. PLoS One 2013; 8:e78796. [PMID: 24265717 PMCID: PMC3827107 DOI: 10.1371/journal.pone.0078796] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2013] [Accepted: 09/22/2013] [Indexed: 11/18/2022] Open
Abstract
Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.
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Affiliation(s)
- Borbála Hunyadi
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- iMinds Future Health Department, Leuven, Belgium
- * E-mail:
| | - Simon Tousseyn
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
- Medical Imaging Research Centre, KU Leuven, Leuven, Belgium
| | - Bogdan Mijović
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- iMinds Future Health Department, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
- Medical Imaging Research Centre, KU Leuven, Leuven, Belgium
- Laboratory for Cognitive Neurology, KU Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- iMinds Future Health Department, Leuven, Belgium
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, KU Leuven, Leuven, Belgium
- Medical Imaging Research Centre, KU Leuven, Leuven, Belgium
- Department of Neurology, UZ Leuven, Leuven, Belgium
| | - Maarten De Vos
- Methods in Neurocognitive Psychology Lab, Department of Psychology, Cluster of Excellence ‘Hearing4all’, European Medical School, Carl von Ossietzky University, Oldenburg, Germany
- Research Center Neurosensory Science, Carl von Ossietzky University, Oldenburg, Germany
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An D, Fahoum F, Hall J, Olivier A, Gotman J, Dubeau F. Electroencephalography/functional magnetic resonance imaging responses help predict surgical outcome in focal epilepsy. Epilepsia 2013; 54:2184-94. [PMID: 24304438 DOI: 10.1111/epi.12434] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2013] [Indexed: 02/05/2023]
Abstract
PURPOSE Simultaneous electroencephalography/functional magnetic resonance imaging (EEG/fMRI) recording can noninvasively map in the whole brain the hemodynamic response following an interictal epileptic discharge. EEG/fMRI is gaining interest as a presurgical evaluation tool. This study aims to determine how hemodynamic responses related to epileptic activity can help predict surgical outcome in patients considered for epilepsy surgery. METHODS Thirty-five consecutive patients with focal epilepsy who had significant hemodynamic responses and eventually surgical resection, were studied. The statistical map of hemodynamic responses were generated and co-registered to postoperative anatomic imaging. Patients were classified into four groups defined by the relative relationship between the location of the maximum hemodynamic response and the resection: group 1, fully concordant; group 2, partially concordant; group 3, partially discordant; and group 4, fully discordant. These findings were correlated with surgical outcome with at least 12-month follow-up. KEY FINDINGS Ten patients in group 1 had the maximum t value (t-max) inside the resection; nine in group 2 had the t-max outside but close to the resection and the cluster with t-max overlapped the resection; five in group 3 had the t-max remote from resection, but with another less significant cluster in the resection; and 11 in group 4 had no response in the resection. The degree of concordance correlated largely with surgical outcome: a good surgical outcome (Engel's class I) was found in 7 of 10 patients of group 1, 4 of 9 of group 2, 3 of 5 of group 3, and only 1 of 11 of group 4. These results indicate that the partially concordant and partially discordant groups are best considered as inconclusive. In contrast, in the fully concordant and fully discordant groups, the sensitivity, specificity, positive predictive value, and negative predictive value were high, 87.5%, 76.9%, 70%, and 90.9%, respectively. SIGNIFICANCE This study demonstrates that hemodynamic responses related to epileptic activity can help delineate the epileptogenic region. Full concordance between maximum response and surgical resection is indicative of seizure freedom, whereas a resection leaving the maximum response intact is likely to lead to a poor outcome. EEG/fMRI is noninvasive but is limited to patients in whom interictal epileptic discharges can be recorded during the 60-90 min scan.
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Affiliation(s)
- Dongmei An
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
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Welvaert M, Rosseel Y. On the definition of signal-to-noise ratio and contrast-to-noise ratio for FMRI data. PLoS One 2013; 8:e77089. [PMID: 24223118 PMCID: PMC3819355 DOI: 10.1371/journal.pone.0077089] [Citation(s) in RCA: 240] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2013] [Accepted: 09/06/2013] [Indexed: 11/20/2022] Open
Abstract
Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.
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Affiliation(s)
- Marijke Welvaert
- Department of Data Analysis, Ghent University, Gent, Belgium
- * E-mail:
| | - Yves Rosseel
- Department of Data Analysis, Ghent University, Gent, Belgium
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Karahanoğlu FI, Caballero-Gaudes C, Lazeyras F, Van de Ville D. Total activation: fMRI deconvolution through spatio-temporal regularization. Neuroimage 2013; 73:121-34. [PMID: 23384519 DOI: 10.1016/j.neuroimage.2013.01.067] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2012] [Revised: 12/31/2012] [Accepted: 01/22/2013] [Indexed: 11/17/2022] Open
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Mapping interictal epileptic discharges using mutual information between concurrent EEG and fMRI. Neuroimage 2012; 68:248-62. [PMID: 23247187 DOI: 10.1016/j.neuroimage.2012.12.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2012] [Revised: 12/04/2012] [Accepted: 12/07/2012] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVE The mapping of haemodynamic changes related to interictal epileptic discharges (IED) in simultaneous electroencephalography (EEG) and functional MRI (fMRI) studies is usually carried out by means of EEG-correlated fMRI analyses where the EEG information specifies the model to test on the fMRI signal. The sensitivity and specificity critically depend on the accuracy of EEG detection and the validity of the haemodynamic model. In this study we investigated whether an information theoretic analysis based on the mutual information (MI) between the presence of epileptic activity on EEG and the fMRI data can provide further insights into the haemodynamic changes related to interictal epileptic activity. The important features of MI are that: 1) both recording modalities are treated symmetrically; 2) no requirement for a-priori models for the haemodynamic response function, or assumption of a linear relationship between the spiking activity and BOLD responses, and 3) no parametric model for the type of noise or its probability distribution is necessary for the computation of MI. METHODS Fourteen patients with pharmaco-resistant focal epilepsy underwent EEG-fMRI and intracranial EEG and/or surgical resection with positive postoperative outcome (seizure freedom or considerable reduction in seizure frequency) was available in 7/14 patients. We used nonparametric statistical assessment of the MI maps based on a four-dimensional wavelet packet resampling method. The results of MI were compared to the statistical parametric maps obtained with two conventional General Linear Model (GLM) analyses based on the informed basis set (canonical HRF and its temporal and dispersion derivatives) and the Finite Impulse Response (FIR) models. RESULTS The MI results were concordant with the electro-clinically or surgically defined epileptogenic area in 8/14 patients and showed the same degree of concordance as the results obtained with the GLM-based methods in 12 patients (7 concordant and 5 discordant). In one patient, the information theoretic analysis improved the delineation of the irritative zone compared with the GLM-based methods. DISCUSSION Our findings suggest that an information theoretic analysis can provide clinically relevant information about the BOLD signal changes associated with the generation and propagation of interictal epileptic discharges. The concordance between the MI, GLM and FIR maps support the validity of the assumptions adopted in GLM-based analyses of interictal epileptic activity with EEG-fMRI in such a manner that they do not significantly constrain the localization of the epileptogenic zone.
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Abstract
Epilepsy is a disease characterized by abnormal spontaneous activity in the brain. Resting-state functional magnetic resonance imaging (RS-fMRI) is a powerful technique for exploring this activity. With good spatial and temporal resolution, RS-fMRI is a promising approach for accurate localization of the focus of seizure activity. Although simultaneous electroencephalogram-fMRI has been performed with patients in the resting state, most studies focused on activation. This mini-review focuses on RS-fMRI alone, including its computational methods and its application to epilepsy.
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