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Zhou DJ, Gumenyuk V, Taraschenko O, Grobelny BT, Stufflebeam SM, Peled N. Visualization of the Spatiotemporal Propagation of Interictal Spikes in Temporal Lobe Epilepsy: A MEG Pilot Study. Brain Topogr 2024; 37:116-125. [PMID: 37966675 DOI: 10.1007/s10548-023-01017-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 10/25/2023] [Indexed: 11/16/2023]
Abstract
Magnetoencephalography (MEG) is clinically used to localize interictal spikes in discrete brain areas of epilepsy patients through the equivalent current dipole (ECD) method, but does not account for the temporal dynamics of spike activity. Recent studies found that interictal spike propagation beyond the temporal lobe may be associated with worse postsurgical outcomes, but studies using whole-brain data such as in MEG remain limited. In this pilot study, we developed a tool that visualizes the spatiotemporal dynamics of interictal MEG spikes normalized to spike-free sleep activity to assess their onset and propagation patterns in patients with temporal lobe epilepsy (TLE). We extracted interictal source data containing focal epileptiform activity in awake and asleep states from seven patients whose MEG ECD clusters localized to the temporal lobe and normalized the data against spike-free sleep recordings. We calculated the normalized activity over time per cortical label, confirmed maximal activity at onset, and mapped the activity over a 10 ms interval onto each patient's brain using a custom-built Multi-Modal Visualization Tool. The onset of activity in all patients appeared near the clinically determined epileptogenic zone. By 10 ms, four of the patients had propagated source activity restricted to within the temporal lobe, and three had propagated source activity spread to extratemporal regions. Using this tool, we show that noninvasively identifying the onset and propagation of interictal spike activity in MEG can be achieved, which may help provide further insight into epileptic networks and guide surgical planning and interventions in patients with TLE.
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Affiliation(s)
- Daniel J Zhou
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Valentina Gumenyuk
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Olga Taraschenko
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Bartosz T Grobelny
- Department of Neurosurgery, Saint Luke's Health System of Kansas City, Kansas City, MO, USA
| | - Steven M Stufflebeam
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Harvard Medical School, Cambridge, MA, USA
| | - Noam Peled
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
- Harvard Medical School, Cambridge, MA, USA.
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2
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Numan T, Breedt LC, Maciel BDAPC, Kulik SD, Derks J, Schoonheim MM, Klein M, de Witt Hamer PC, Miller JJ, Gerstner ER, Stufflebeam SM, Hillebrand A, Stam CJ, Geurts JJG, Reijneveld JC, Douw L. Regional healthy brain activity, glioma occurrence and symptomatology. Brain 2022; 145:3654-3665. [PMID: 36130310 PMCID: PMC9586543 DOI: 10.1093/brain/awac180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/22/2022] [Accepted: 05/04/2022] [Indexed: 11/24/2022] Open
Abstract
It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients’ tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients’ individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients’ individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients’ individual tumour locations may capture both tumour biology and patients’ performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of ‘cancer neuroscience’.
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Affiliation(s)
- Tianne Numan
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Lucas C Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Bernardo de A P C Maciel
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Jolanda Derks
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Martin Klein
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Philip C de Witt Hamer
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurosurgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Julie J Miller
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Elizabeth R Gerstner
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jaap C Reijneveld
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede 2103 SW, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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Ntolkeras G, Tamilia E, AlHilani M, Bolton J, Ellen Grant P, Prabhu SP, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Presurgical accuracy of dipole clustering in MRI-negative pediatric patients with epilepsy: Validation against intracranial EEG and resection. Clin Neurophysiol 2022; 141:126-138. [PMID: 33875376 PMCID: PMC8803140 DOI: 10.1016/j.clinph.2021.01.036] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/21/2021] [Accepted: 01/27/2021] [Indexed: 01/24/2023]
Abstract
OBJECTIVE To assess the utility of interictal magnetic and electric source imaging (MSI and ESI) using dipole clustering in magnetic resonance imaging (MRI)-negative patients with drug resistant epilepsy (DRE). METHODS We localized spikes in low-density (LD-EEG) and high-density (HD-EEG) electroencephalography as well as magnetoencephalography (MEG) recordings using dipoles from 11 pediatric patients. We computed each dipole's level of clustering and used it to discriminate between clustered and scattered dipoles. For each dipole, we computed the distance from seizure onset zone (SOZ) and irritative zone (IZ) defined by intracranial EEG. Finally, we assessed whether dipoles proximity to resection was predictive of outcome. RESULTS LD-EEG had lower clusterness compared to HD-EEG and MEG (p < 0.05). For all modalities, clustered dipoles showed higher proximity to SOZ and IZ than scattered (p < 0.001). Resection percentage was higher in optimal vs. suboptimal outcome patients (p < 0.001); their proximity to resection was correlated to outcome (p < 0.001). No difference in resection percentage was seen for scattered dipoles between groups. CONCLUSION MSI and ESI dipole clustering helps to localize the SOZ and IZ and facilitate the prognostic assessment of MRI-negative patients with DRE. SIGNIFICANCE Assessing the MSI and ESI clustering allows recognizing epileptogenic areas whose removal is associated with optimal outcome.
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Affiliation(s)
- Georgios Ntolkeras
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michel AlHilani
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; The Hillingdon Hospital NHS Foundation Trust, London, United Kingdom
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, MA, USA
| | - Sanjay P Prabhu
- Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, MA, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Jane and John Justin Neurosciences Center, Cook Children's Health Care System, Fort Worth, TX, USA; School of Medicine, Texas Christian University and University of North Texas Health Science Center, Fort Worth, TX, USA; Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA.
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4
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Nascimento FA, McLaren JR, Westover MB, Zafar SF, Stufflebeam SM. Teaching NeuroImage: Sturge-Weber Syndrome in an Adult. Neurology 2022; 98:814-815. [PMID: 35410908 PMCID: PMC9141624 DOI: 10.1212/wnl.0000000000200512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 02/28/2022] [Indexed: 11/15/2022] Open
Affiliation(s)
- Fábio A Nascimento
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - John R McLaren
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Sahar F Zafar
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Steven M Stufflebeam
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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5
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Gholipour T, You X, Stufflebeam SM, Loew M, Koubeissi MZ, Morgan VL, Gaillard WD. Common functional connectivity alterations in focal epilepsies identified by machine learning. Epilepsia 2022; 63:629-640. [PMID: 34984672 PMCID: PMC9022014 DOI: 10.1111/epi.17160] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 02/04/2023]
Abstract
OBJECTIVE This study was undertaken to identify shared functional network characteristics among focal epilepsies of different etiologies, to distinguish epilepsy patients from controls, and to lateralize seizure focus using functional connectivity (FC) measures derived from resting state functional magnetic resonance imaging (MRI). METHODS Data were taken from 103 adult and 65 pediatric focal epilepsy patients (with or without lesion on MRI) and 109 controls across four epilepsy centers. We used three whole-brain FC measures: parcelwise connectivity matrix, mean FC, and degree of FC. We trained support vector machine models with fivefold cross-validation (1) to distinguish patients from controls and (2) to lateralize the hemisphere of seizure onset in patients. We reported the regions and connections with the highest importance from each model as the common FC differences between the compared groups. RESULTS FC measures related to the default mode and limbic networks had higher importance relative to other networks for distinguishing epilepsy patients from controls. In lateralization models, regions related to somatosensory, visual, default mode, and basal ganglia showed higher importance. The epilepsy versus control classification model trained using a 400-parcel connectivity matrix achieved a median testing accuracy of 75.6% (median area under the curve [AUC] = .83) in repeated independent testing. Lateralization accuracy using the 400-parcel connectivity matrix reached a median accuracy of 64.0% (median AUC = .69). SIGNIFICANCE Machine learning models revealed common FC alterations in a heterogeneous group of patients with focal epilepsies. The distribution of the most altered regions supports the hypothesis that shared functional alteration exists beyond the seizure onset zone and its epileptic network. We showed that FC measures can distinguish patients from controls, and further lateralize focal epilepsies. Future studies are needed to confirm these findings by using larger numbers of epilepsy patients.
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Affiliation(s)
- Taha Gholipour
- Department of Neurology, George Washington University, Washington, District of Columbia, USA.,Center for Neuroscience, Children's National Hospital, Washington, District of Columbia, USA.,Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Xiaozhen You
- Center for Neuroscience, Children's National Hospital, Washington, District of Columbia, USA
| | - Steven M Stufflebeam
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Murray Loew
- Department of Biomedical Engineering, George Washington University, Washington, District of Columbia, USA
| | - Mohamad Z Koubeissi
- Department of Neurology, George Washington University, Washington, District of Columbia, USA
| | | | - William D Gaillard
- Department of Neurology, George Washington University, Washington, District of Columbia, USA.,Center for Neuroscience, Children's National Hospital, Washington, District of Columbia, USA
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6
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Bubrick EJ, Gholipour T, Hibert M, Cosgrove GR, Stufflebeam SM, Young GS. 7T versus 3T MRI in the presurgical evaluation of patients with drug-resistant epilepsy. J Neuroimaging 2021; 32:292-299. [PMID: 34964194 DOI: 10.1111/jon.12958] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 11/03/2021] [Accepted: 11/26/2021] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND AND PURPOSE MRI has a crucial role in presurgical evaluation of drug-resistant focal epilepsy patients. Whether and how much 7T MRI further improves presurgical diagnosis compared to standard of care 3T MRI remains to be established. We investigate the added value 7T MRI offers in surgical candidates with remaining clinical uncertainty after 3T MRI. METHODS 7T brain MRI was obtained on sequential patients with drug-resistant focal epilepsy undergoing presurgical evaluation at a comprehensive epilepsy center, including patients with and without suspected lesions on standard 3T MRI. Clinical information and 3T images informed the interpretation of 7T images. Detection of a new lesion on 7T or better characterization of a suspected lesion was considered to add value to the presurgical workup. RESULTS Interpretable 7T MRI was acquired in 19 patients. 7T MRI identified a lesion relevant to the seizures in three of eight patients (38%) without a lesion on 3T MRI; no lesion in 7/11 patients (64%) with at least one suspected lesion on 3T MRI, contributing to the final classification of all seven as nonlesional; and confirmed and better characterized the lesion suspected at 3T MR in the remaining 4/11 patients. CONCLUSIONS 7T MRI detected new lesions in over a third of 3T MRI nonlesional patients, confirmed and better characterized a 3T suspected lesion in one third of patients, and helped exclude a 3T suspected lesion in the remainder. Our initial experience suggests that 7T MRI adds value to surgical planning by improving detection and characterization of suspected brain lesions in drug-resistant focal epilepsy patients.
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Affiliation(s)
- Ellen J Bubrick
- Edward B. Bromfield Epilepsy Division, Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Taha Gholipour
- Edward B. Bromfield Epilepsy Division, Department of Neurology, Brigham and Women's Hospital & Harvard Medical School, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Neurology, The George Washington University Epilepsy Center, Washington, DC, USA
| | - Matthew Hibert
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, Massachusetts, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital & Harvard Medical School, Boston, Massachusetts, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Charlestown, Massachusetts, USA
| | - Geoffrey S Young
- Department of Radiology, Brigham and Women's Hospital & Harvard Medical School, Boston, Massachusetts, USA
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Corona L, Tamilia E, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Mapping Functional Connectivity of Epileptogenic Networks through Virtual Implantation. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:408-411. [PMID: 34891320 PMCID: PMC8893022 DOI: 10.1109/embc46164.2021.9629686] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Children with medically refractory epilepsy (MRE) require resective neurosurgery to achieve seizure freedom, whose success depends on accurate delineation of the epileptogenic zone (EZ). Functional connectivity (FC) can assess the extent of epileptic brain networks since intracranial EEG (icEEG) studies have shown its link to the EZ and predictive value for surgical outcome in these patients. Here, we propose a new noninvasive method based on magnetoencephalography (MEG) and high-density (HD-EEG) data that estimates FC metrics at the source level through an "implantation" of virtual sensors (VSs). We analyzed MEG, HD-EEG, and icEEG data from eight children with MRE who underwent surgery having good outcome and performed source localization (beamformer) on noninvasive data to build VSs at the icEEG electrode locations. We analyzed data with and without Interictal Epileptiform Discharges (IEDs) in different frequency bands, and computed the following FC matrices: Amplitude Envelope Correlation (AEC), Correlation (CORR), and Phase Locking Value (PLV). Each matrix was used to generate a graph using Minimum Spanning Tree (MST), and for each node (i.e., each sensor) we computed four centrality measures: betweenness, closeness, degree, and eigenvector. We tested the reliability of VSs measures with respect to icEEG (regarded as benchmark) via linear correlation, and compared FC values inside vs. outside resection. We observed higher FC inside than outside resection (p<0.05) for AEC [alpha (8-12 Hz), beta (12-30 Hz), and broadband (1-50 Hz)] on data with IEDs and AEC theta (4-8 Hz) on data without IEDs for icEEG, AEC broadband (1-50 Hz) on data without IEDs for MEG-VSs, as well as for all centrality measures of icEEG and MEG/HD-EEG-VSs. Additionally, icEEG and VSs metrics presented high correlation (0.6-0.9, p<0.05). Our data support the notion that the proposed method can potentially replicate the icEEG ability to map the epileptogenic network in children with MRE.Clinical Relevance - The estimation of FC with noninvasive techniques, such as MEG and HD-EEG, via VSs is a promising tool that would help the presurgical evaluation by delineating the EZ without waiting for a seizure to occur, and potentially improve the surgical outcome of patients with MRE undergoing surgery.
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Mamashli F, Khan S, Hämäläinen M, Jas M, Raij T, Stufflebeam SM, Nummenmaa A, Ahveninen J. Synchronization patterns reveal neuronal coding of working memory content. Cell Rep 2021; 36:109566. [PMID: 34433024 PMCID: PMC8428113 DOI: 10.1016/j.celrep.2021.109566] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 04/26/2021] [Accepted: 07/28/2021] [Indexed: 11/24/2022] Open
Abstract
Neuronal oscillations are suggested to play an important role in auditory working memory (WM), but their contribution to content-specific representations has remained unclear. Here, we measure magnetoencephalography during a retro-cueing task with parametric ripple-sound stimuli, which are spectrotemporally similar to speech but resist non-auditory memory strategies. Using machine learning analyses, with rigorous between-subject cross-validation and non-parametric permutation testing, we show that memorized sound content is strongly represented in phase-synchronization patterns between subregions of auditory and frontoparietal cortices. These phase-synchronization patterns predict the memorized sound content steadily across the studied maintenance period. In addition to connectivity-based representations, there are indices of more local, “activity silent” representations in auditory cortices, where the decoding accuracy of WM content significantly increases after task-irrelevant “impulse stimuli.” Our results demonstrate that synchronization patterns across auditory sensory and association areas orchestrate neuronal coding of auditory WM content. This connectivity-based coding scheme could also extend beyond the auditory domain. Mamashli et al. use machine learning analyses of human magnetoencephalography (MEG) recordings to study “working memory,” maintenance of information in mind over brief periods of time. Their results show that the human brain maintains working memory content in transient functional connectivity patterns across sensory and association areas.
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Affiliation(s)
- Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Departments of Physical Medicine and Rehabilitation and Neurobiology, Northwestern University, 710 North Lake Shore Drive, Chicago, IL 60611, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
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Xu Y, Xu Q, Zhang Q, Stufflebeam SM, Yang F, He Y, Hu Z, Weng Y, Xiao J, Lu G, Zhang Z. Influence of epileptogenic region on brain structural changes in Rolandic epilepsy. Brain Imaging Behav 2021; 16:424-434. [PMID: 34420145 DOI: 10.1007/s11682-021-00517-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/18/2021] [Indexed: 10/20/2022]
Abstract
To investigate the influence of epileptogenic cortex (Rolandic areas) with executive functions in Rolandic epilepsy using structural covariance analysis of structural magnetic resonance imaging (MRI). Structural MRI data of drug-naive patients with Rolandic epilepsy (n = 70) and typically developing children as healthy controls (n = 83) were analyzed using voxel-based morphometry. Gray matter volumes in the patients were compared with those of healthy controls, and were further correlated with epilepsy duration and cognitive score of executive function, respectively. By applying Granger causal analysis to the sequenced morphometric data according to disease progression information, causal network of structural covariance was constructed to assess the causal influence of structural changes from Rolandic cortices to the regions engaging executive function in the patients. Compared with healthy controls, epilepsy patients showed increased gray matter volume in the Rolandic regions, and also the regions engaging in executive function. Covariance network analyses showed that along with disease progression, the Rolandic regions imposed positive causal influence on the regions engaging in executive function. In the patients with Rolandic epilepsy, epileptogenic regions have causal influence on the structural changes in the regions of executive function, implicating damaging effects of Rolandic epilepsy on human brain.
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Affiliation(s)
- Yin Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing Clinical School, Southern Medical University, Nanjing, 210002, China.,Institute of Neurology, Anhui University of Traditional Chinese Medicine, Hefei, China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Qirui Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing Clinical School, Southern Medical University, Nanjing, 210002, China.,Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA, 02129, USA
| | - Fang Yang
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Yan He
- Department of Neurology, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Zheng Hu
- Department of Neurology, Children's Hospital of Nanjing Medical University, Nanjing, China
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Junhao Xiao
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Nanjing Clinical School, Southern Medical University, Nanjing, 210002, China. .,Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China. .,State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, 210093, China.
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Nanjing Clinical School, Southern Medical University, Nanjing, 210002, China. .,Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China. .,State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, 210093, China. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA, 02129, USA.
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10
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Xu Y, Yang F, Hu Z, He Y, Zhang Q, Xu Q, Weng Y, Bernhardt BC, Xie X, Xiao J, Peled N, Stufflebeam SM, Lu G, Zhang Z. Anti-seizure medication correlated changes of cortical morphology in childhood epilepsy with centrotemporal spikes. Epilepsy Res 2021; 173:106621. [PMID: 33873105 DOI: 10.1016/j.eplepsyres.2021.106621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 02/02/2021] [Accepted: 03/20/2021] [Indexed: 12/01/2022]
Abstract
To investigate the morphological changes of cerebral cortex correlating with anti-seizure medication in Childhood Epilepsy with Centrotemporal Spikes (CECTS), and their relationships with seizure control. This study included a total of 188 children, including 62 patients with CECTS taking anti-seizure drugs, 56 patients with drug-naive, and 70 healthy controls. A portion of cases were also followed-up for longitudinal analysis. Cortical morphological parameters were quantitatively measured by applying surface-based morphometry analysis to high-resolution three-dimension T1 weighted images. Among the three groups, the morphological indices were compared to quantify any cortical changes affected by seizures and medication. The relationships among anti-seizure medication, seizure controls and cortical morphometry were investigated using causal mediator analysis. The Rolandic cortex of the drug-naive patients showed abnormal cortical thickness by comparing with that of healthy controls, and thinning by comparing with that of patients with medication. The cortical thickness in the Rolandic regions was negatively correlated with duration of medication and duration of seizure-free. Longitudinal analysis further demonstrated that the thickness of Rolandic cortex thinned in post-medication state relative to the pre-medication state. Mediation analysis revealed that morphological alteration of the Rolandic cortex might act as a mediator in the path of anti-seizure medication on seizure control. Our findings highlighted that anti-seizure medication was associated with regression of abnormal increment of cortical thickness in the Rolandic regions in CECTS. The neuroanatomical alteration might be a mediating factor in the process of seizure control by anti-seizure medication.
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Affiliation(s)
- Yin Xu
- Department of Medical Imaging, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China; Institute of Neurology, Anhui University of Traditional Chinese Medicine, China
| | - Fang Yang
- Department of Neurology, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Zheng Hu
- Department of Neurology, Children's Hospital of Nanjing Medical University, China
| | - Yan He
- Department of Neurology, Children's Hospital of Nanjing Medical University, China
| | - Qirui Zhang
- Department of Medical Imaging, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China; Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Boris C Bernhardt
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Xinyu Xie
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Junhao Xiao
- Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China
| | - Noam Peled
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA, 02129, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA, 02129, USA
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China; Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China; State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, 210093, China.
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China; Department of Medical Imaging, Jinling Hospital, Nanjing University School of Medicine, Nanjing, 210002, China; State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, 210093, China; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA, 02129, USA.
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11
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Xu Q, Zhang Q, Yang F, Weng Y, Xie X, Hao J, Qi R, Gumenyuk V, Stufflebeam SM, Bernhardt BC, Lu G, Zhang Z. Cortico-striato-thalamo-cerebellar networks of structural covariance underlying different epilepsy syndromes associated with generalized tonic-clonic seizures. Hum Brain Mapp 2020; 42:1102-1115. [PMID: 33372704 PMCID: PMC7856655 DOI: 10.1002/hbm.25279] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 09/16/2020] [Accepted: 10/31/2020] [Indexed: 01/05/2023] Open
Abstract
Generalized tonic-clonic seizures (GTCS) are the severest and most remarkable clinical expressions of human epilepsy. Cortical, subcortical, and cerebellar structures, organized with different network patterns, underlying the pathophysiological substrates of genetic associated epilepsy with GTCS (GE-GTCS) and focal epilepsy associated with focal to bilateral tonic-clonic seizure (FE-FBTS). Structural covariance analysis can delineate the features of epilepsy network related with long-term effects from seizure. Morphometric MRI data of 111 patients with GE-GTCS, 111 patients with FE-FBTS and 111 healthy controls were studied. Cortico-striato-thalao-cerebellar networks of structural covariance within the gray matter were constructed using a Winner-take-all strategy with five cortical parcellations. Comparisons of structural covariance networks were conducted using permutation tests, and module effects of disease duration on networks were conducted using GLM model. Both patient groups showed increased connectivity of structural covariance relative to controls, mainly within the striatum and thalamus, and mostly correlated with the frontal, motor, and somatosensory cortices. Connectivity changes increased as a function of epilepsy durations. FE-FBTS showed more intensive and extensive gray matter changes with volumetric loss and connectivity increment than GE-GTCS. Our findings implicated cortico-striato-thalamo-cerebellar network changes at a large temporal scale in GTCS, with FE-FBTS showing more severe network disruption. The study contributed novel imaging evidence for understanding the different epilepsy syndromes associated with generalized seizures.
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Affiliation(s)
- Qiang Xu
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China.,College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Qirui Zhang
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China
| | - Fang Yang
- Department of Neurology, Jinling Hospital, Medical school of Nanjing University, Nanjing, China
| | - Yifei Weng
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China.,Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Xinyu Xie
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China
| | - Jingru Hao
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China
| | - Rongfeng Qi
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China
| | - Valentina Gumenyuk
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Guangming Lu
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China.,College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.,State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
| | - Zhiqiang Zhang
- Department of Medical Imaging, Jinling Hospital, Medical school of Nanjing University, Nanjing, China.,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing, China
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12
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Thorn EL, Ostrowski LM, Chinappen DM, Jing J, Westover MB, Stufflebeam SM, Kramer MA, Chu CJ. Persistent abnormalities in Rolandic thalamocortical white matter circuits in childhood epilepsy with centrotemporal spikes. Epilepsia 2020; 61:2500-2508. [PMID: 32944938 DOI: 10.1111/epi.16681] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 08/01/2020] [Accepted: 08/12/2020] [Indexed: 11/27/2022]
Abstract
OBJECTIVE Childhood epilepsy with centrotemporal spikes (CECTS) is a common, focal, transient, developmental epilepsy syndrome characterized by unilateral or bilateral, independent epileptiform spikes in the Rolandic regions of unknown etiology. Given that CECTS presents during a period of dramatic white matter maturation and thatspikes in CECTS are activated during non-rapid eye movement (REM) sleep, we hypothesized that children with CECTS would have aberrant development of white matter connectivity between the thalamus and the Rolandic cortex. We further tested whether Rolandic thalamocortical structural connectivity correlates with spike rate during non-REM sleep. METHODS Twenty-three children with CECTS (age = 8-15 years) and 19 controls (age = 7-15 years) underwent 3-T structural and diffusion-weighted magnetic resonance imaging and 72-electrode electroencephalographic recordings. Thalamocortical structural connectivity to Rolandic and non-Rolandic cortices was quantified using probabilistic tractography. Developmental changes in connectivity were compared between groups using bootstrap analyses. Longitudinal analysis was performed in four subjects with 1-year follow-up data. Spike rate was quantified during non-REM sleep using manual and automated techniques and compared to Rolandic connectivity using regression analyses. RESULTS Children with CECTS had aberrant development of thalamocortical connectivity to the Rolandic cortex compared to controls (P = .01), where the expected increase in connectivity with age was not observed in CECTS. There was no difference in the development of thalamocortical connectivity to non-Rolandic regions between CECTS subjects and controls (P = .19). Subjects with CECTS observed longitudinally had reductions in thalamocortical connectivity to the Rolandic cortex over time. No definite relationship was found between Rolandic connectivity and non-REM spike rate (P > .05). SIGNIFICANCE These data provide evidence that abnormal maturation of thalamocortical white matter circuits to the Rolandic cortex is a feature of CECTS. Our data further suggest that the abnormalities in these tracts do not recover, but are increasingly dysmature over time, implicating a permanent but potentially compensatory process contributing to disease resolution.
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Affiliation(s)
- Emily L Thorn
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Elson S. Floyd College of Medicine, Washington State University, Spokane, Washington, USA
| | - Lauren M Ostrowski
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Jin Jing
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Steven M Stufflebeam
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, USA
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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13
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Dirodi M, Tamilia E, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Noninvasive Localization of High-Frequency Oscillations in Children with Epilepsy: Validation against Intracranial Gold-Standard. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:1555-1558. [PMID: 31946191 DOI: 10.1109/embc.2019.8857793] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Patients with medically refractory epilepsy (MRE) need surgical resection of the epileptogenic zone (EZ) to gain seizure-freedom. High-frequency oscillations (HFOs, > 80 Hz) are promising biomarkers of the EZ that are typically localized using intracranial electroencephalography (icEEG). The goal of this study was to localize the cortical generators of HFOs non-invasively using high-density (HD) EEG and magnetoencephalography (MEG) and validate the localization against the gold-standard given by the icEEGdefined HFO-zone. METHODS We analyzed simultaneous HDEEG and MEG data from six children with MRE who underwent icEEG and surgery. We detected interictal HFOs (80-160 Hz) on HD-EEG and MEG separately, using an inhouse automatic detector followed by visual human review, and distinguished between HFOs with and without spikes. We localized the cortical generators of each HFO on HD-EEG or MEG using the wavelet Maximum Entropy on the Mean (wMEM). For the HFOs localized in the brain area covered by icEEG, we estimated the localization error (Eloc) with respect to the gold-standard, and classified them as either concordant (Eloc≤15mm) or not. RESULTS We found that: (i) HD-EEG presented a higher rate of HFOs than MEG (1 vs 0.5 HFOs/min, p=0.031); (ii) HFOs without spikes were more likely to be localized outside the brain regions of interest (i.e. covered by icEEG) than HFOs with spikes; and (iii) both HD-EEG and MEG showed high precision to the gold-standard (92% and 96%). CONCLUSION We reported quantitative evidence that HDEEG and MEG can localize the HFO cortical generators with high precision to the icEEG gold-standard in children with MRE, suggesting that they may possibly limit the need for icEEG prior to surgery. We also showed that HFOs with spikes on HD-EEG/MEG are more likely to be epileptogenic than those independent from spikes, which may represent physiological events from normal brain.
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14
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Tamilia E, Dirodi M, Alhilani M, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Scalp ripples as prognostic biomarkers of epileptogenicity in pediatric surgery. Ann Clin Transl Neurol 2020; 7:329-342. [PMID: 32096612 PMCID: PMC7086004 DOI: 10.1002/acn3.50994] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/29/2020] [Accepted: 01/30/2020] [Indexed: 12/11/2022] Open
Abstract
Objective To assess the ability of high‐density Electroencephalography (HD‐EEG) and magnetoencephalography (MEG) to localize interictal ripples, distinguish between ripples co‐occurring with spikes (ripples‐on‐spike) and independent from spikes (ripples‐alone), and evaluate their localizing value as biomarkers of epileptogenicity in children with medically refractory epilepsy. Methods We retrospectively studied 20 children who underwent epilepsy surgery. We identified ripples on HD‐EEG and MEG data, localized their generators, and compared them with intracranial EEG (icEEG) ripples. When ripples and spikes co‐occurred, we performed source imaging distinctly on the data above 80 Hz (to localize ripples) and below 70 Hz (to localize spikes). We assessed whether missed resection of ripple sources predicted poor outcome, separately for ripples‐on‐spikes and ripples‐alone. Similarly, predictive value of spikes was calculated. Results We observed scalp ripples in 16 patients (10 good outcome). Ripple sources were highly concordant to the icEEG ripples (HD‐EEG concordance: 79%; MEG: 83%). When ripples and spikes co‐occurred, their sources were spatially distinct in 83‐84% of the cases. Removing the sources of ripples‐on‐spikes predicted good outcome with 90% accuracy for HD‐EEG (P = 0.008) and 86% for MEG (P = 0.044). Conversely, removing ripples‐alone did not predict outcome. Resection of spike sources (generated at the same time as ripples) predicted good outcome for HD‐EEG (P = 0.036; accuracy = 87%), while did not reach significance for MEG (P = 0.1; accuracy = 80%). Interpretation HD‐EEG and MEG localize interictal ripples with high precision in children with refractory epilepsy. Scalp ripples‐on‐spikes are prognostic, noninvasive biomarkers of epileptogenicity, since removing their cortical generators predicts good outcome. Conversely, scalp ripples‐alone are most likely generated by non‐epileptogenic areas.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Matilde Dirodi
- G. Tec Medical Engineering GmbHGuger Technologies OGGrazAustria
| | - Michel Alhilani
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - P. Ellen Grant
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterDivision of Newborn MedicineDepartment of MedicineBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Joseph R. Madsen
- Division of Epilepsy SurgeryDepartment of NeurosurgeryBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalHarvard Medical SchoolBostonMassachusetts
| | - Phillip L. Pearl
- Division of Epilepsy and Clinical NeurophysiologyDepartment of NeurologyBoston Children’s HospitalHarvard Medical SchoolBostonMassachusetts
| | - Christos Papadelis
- Laboratory of Children’s Brain DynamicsDivision of Newborn MedicineDepartment of MedicineBoston Children's HospitalHarvard Medical SchoolBostonMassachusetts
- Jane and John Justin Neurosciences CenterCook Children's Health Care SystemFort WorthTexas
- School of MedicineTexas Christian University and University of North Texas Health Science CenterFort WorthTexas
- Department of BioengineeringUniversity of Texas at ArlingtonArlingtonTexas
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15
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DeSalvo MN, Tanaka N, Douw L, Cole AJ, Stufflebeam SM. Contralateral Preoperative Resting-State Functional MRI Network Integration Is Associated with Surgical Outcome in Temporal Lobe Epilepsy. Radiology 2020; 294:622-627. [PMID: 31961245 DOI: 10.1148/radiol.2020191008] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Background Although most patients with medically refractory temporal lobe epilepsy (TLE) experience seizure freedom after anterior temporal lobectomy, approximately 40% may continue to have seizures. Functional network integration, as measured with preoperative resting-state functional MRI, may help stratify patients who are more likely to experience postoperative seizure freedom. Purpose To relate preoperative resting-state functional MRI and surgical outcome in patients with medically refractory TLE. Materials and Methods Data from patients with medically intractable TLE were retrospectively analyzed. Patients underwent preoperative resting-state functional MRI between March 2010 and April 2013 and subsequent unilateral anterior temporal lobectomy. Postoperative seizure-free status was categorized using the Engel Epilepsy Surgery Outcome Scale. Global and regional resting-state functional MRI network properties on preoperative functional MRI scans related to integration were calculated and statistically compared between patients who experienced complete postoperative seizure freedom (Engel class IA) and all others (Engel class IB to class IV) using t tests and multiple logistic regression. Results Forty patients (mean age, 34 years ± 15 [standard deviation]; 21 female) were evaluated. Preoperative global network integration was different (P = .01) between patients who experienced seizure freedom after surgery and all other patients, with 9% lower leaf fraction and 10% lower tree hierarchy in patients with ongoing seizures. Preoperative regional network integration in the contralateral temporoinsular region was different (P = .04) between patients in these two groups. Specifically, the group-level leaf proportion was 59% lower in the entorhinal cortex, 73% lower in the inferior temporal gyrus, 43% lower in the temporal pole, and 69% lower in the insula in patients with ongoing seizures after surgery. When using multivariate regression, contralateral temporoinsular leaf proportion (P = .002) and epilepsy duration (P = .04) were predictive of postoperative seizure freedom, while age (P > .70) and age at seizure onset (P > .50) were not. Conclusion Lower network integration globally and involving the contralateral temporoinsular cortex on preoperative resting-state functional MRI scans is associated with ongoing postoperative seizures in patients with temporal lobe epilepsy. © RSNA, 2020.
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Affiliation(s)
- Matthew N DeSalvo
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., S.M.S.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.N.D., A.J.C., S.M.S.); Sapporo Neuroimaging Research Group, Sapporo, Japan (N.T.); and Department of Anatomy and Neurosciences, Vrije Universiteit Medical Center, Amsterdam, the Netherlands (L.D.)
| | - Naoaki Tanaka
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., S.M.S.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.N.D., A.J.C., S.M.S.); Sapporo Neuroimaging Research Group, Sapporo, Japan (N.T.); and Department of Anatomy and Neurosciences, Vrije Universiteit Medical Center, Amsterdam, the Netherlands (L.D.)
| | - Linda Douw
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., S.M.S.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.N.D., A.J.C., S.M.S.); Sapporo Neuroimaging Research Group, Sapporo, Japan (N.T.); and Department of Anatomy and Neurosciences, Vrije Universiteit Medical Center, Amsterdam, the Netherlands (L.D.)
| | - Andrew J Cole
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., S.M.S.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.N.D., A.J.C., S.M.S.); Sapporo Neuroimaging Research Group, Sapporo, Japan (N.T.); and Department of Anatomy and Neurosciences, Vrije Universiteit Medical Center, Amsterdam, the Netherlands (L.D.)
| | - Steven M Stufflebeam
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 13th St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., S.M.S.); Department of Radiology, Massachusetts General Hospital, Boston, Mass (M.N.D., A.J.C., S.M.S.); Sapporo Neuroimaging Research Group, Sapporo, Japan (N.T.); and Department of Anatomy and Neurosciences, Vrije Universiteit Medical Center, Amsterdam, the Netherlands (L.D.)
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16
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Ostrowski LM, Song DY, Thorn EL, Ross EE, Stoyell SM, Chinappen DM, Eden UT, Kramer MA, Emerton BC, Morgan AK, Stufflebeam SM, Chu CJ. Dysmature superficial white matter microstructure in developmental focal epilepsy. Brain Commun 2019; 1:fcz002. [PMID: 31608323 PMCID: PMC6777514 DOI: 10.1093/braincomms/fcz002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 01/09/2023] Open
Abstract
Benign epilepsy with centrotemporal spikes is a common childhood epilepsy syndrome that predominantly affects boys, characterized by self-limited focal seizures arising from the perirolandic cortex and fine motor abnormalities. Concurrent with the age-specific presentation of this syndrome, the brain undergoes a developmentally choreographed sequence of white matter microstructural changes, including maturation of association u-fibres abutting the cortex. These short fibres mediate local cortico-cortical communication and provide an age-sensitive structural substrate that could support a focal disease process. To test this hypothesis, we evaluated the microstructural properties of superficial white matter in regions corresponding to u-fibres underlying the perirolandic seizure onset zone in children with this epilepsy syndrome compared with healthy controls. To verify the spatial specificity of these features, we characterized global superficial and deep white matter properties. We further evaluated the characteristics of the perirolandic white matter in relation to performance on a fine motor task, gender and abnormalities observed on EEG. Children with benign epilepsy with centrotemporal spikes (n = 20) and healthy controls (n = 14) underwent multimodal testing with high-resolution MRI including diffusion tensor imaging sequences, sleep EEG recordings and fine motor assessment. We compared white matter microstructural characteristics (axial, radial and mean diffusivity, and fractional anisotropy) between groups in each region. We found distinct abnormalities corresponding to the perirolandic u-fibre region, with increased axial, radial and mean diffusivity and fractional anisotropy values in children with epilepsy (P = 0.039, P = 0.035, P = 0.042 and P = 0.017, respectively). Increased fractional anisotropy in this region, consistent with decreased integrity of crossing sensorimotor u-fibres, correlated with inferior fine motor performance (P = 0.029). There were gender-specific differences in white matter microstructure in the perirolandic region; males and females with epilepsy and healthy males had higher diffusion and fractional anisotropy values than healthy females (P ≤ 0.035 for all measures), suggesting that typical patterns of white matter development disproportionately predispose boys to this developmental epilepsy syndrome. Perirolandic white matter microstructure showed no relationship to epilepsy duration, duration seizure free, or epileptiform burden. There were no group differences in diffusivity or fractional anisotropy in superficial white matter outside of the perirolandic region. Children with epilepsy had increased radial diffusivity (P = 0.022) and decreased fractional anisotropy (P = 0.027) in deep white matter, consistent with a global delay in white matter maturation. These data provide evidence that atypical maturation of white matter microstructure is a basic feature in benign epilepsy with centrotemporal spikes and may contribute to the epilepsy, male predisposition and clinical comorbidities observed in this disorder.
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Affiliation(s)
- Lauren M Ostrowski
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Daniel Y Song
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Emily L Thorn
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Erin E Ross
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Sally M Stoyell
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Britt C Emerton
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Amy K Morgan
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Steven M Stufflebeam
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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17
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Kramer MA, Ostrowski LM, Song DY, Thorn EL, Stoyell SM, Parnes M, Chinappen D, Xiao G, Eden UT, Staley KJ, Stufflebeam SM, Chu CJ. Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes. Brain 2019; 142:1296-1309. [PMID: 30907404 PMCID: PMC6487332 DOI: 10.1093/brain/awz059] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 01/09/2019] [Accepted: 01/21/2019] [Indexed: 11/12/2022] Open
Abstract
In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges ('spike ripple events') are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.
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Affiliation(s)
- Mark A Kramer
- Boston University, Department of Mathematics and Statistics, Boston, MA, USA
| | - Lauren M Ostrowski
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
| | - Daniel Y Song
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
| | - Emily L Thorn
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
| | - Sally M Stoyell
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
| | - McKenna Parnes
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
| | | | - Grace Xiao
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
| | - Uri T Eden
- Boston University, Department of Mathematics and Statistics, Boston, MA, USA
| | - Kevin J Staley
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Steven M Stufflebeam
- Harvard Medical School, Boston, MA, USA
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA
| | - Catherine J Chu
- Massachusetts General Hospital, Department of Neurology, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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18
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Song DY, Stoyell SM, Ross EE, Ostrowski LM, Thorn EL, Stufflebeam SM, Morgan AK, Emerton BC, Kramer MA, Chu CJ. Beta oscillations in the sensorimotor cortex correlate with disease and remission in benign epilepsy with centrotemporal spikes. Brain Behav 2019; 9:e01237. [PMID: 30790472 PMCID: PMC6422718 DOI: 10.1002/brb3.1237] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Revised: 01/14/2019] [Accepted: 01/16/2019] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Benign epilepsy with centrotemporal spikes (BECTS) is a common form of childhood epilepsy with the majority of those afflicted remitting during their early teenage years. Seizures arise from the lower half of the sensorimotor cortex of the brain (e.g. seizure onset zone) and the abnormal epileptiform discharges observed increase during NREM sleep. To date no clinical factors reliably predict disease course, making determination of ongoing seizure risk a significant challenge. Prior work in BECTS have shown abnormalities in beta band (14.9-30 Hz) oscillations during movement and rest. Oscillations in this frequency band are modulated by state of consciousness and thought to reflect intrinsic inhibitory mechanisms. METHODS We used high density EEG and source localization techniques to examine beta band activity in the seizure onset zone (sensorimotor cortex) in a prospective cohort of children with BECTS and healthy controls during sleep. We hypothesized that beta power in the sensorimotor cortex would be different between patients and healthy controls, and that beta abnormalities would improve with resolution of disease in this self-limited epilepsy syndrome. We further explored the specificity of our findings and correlation with clinical features. Statistical testing was performed using logistic and standard linear regression models. RESULTS We found that beta band power in the seizure onset zone is different between healthy controls and BECTS patients. We also found that a longer duration of time spent seizure-free (corresponding to disease remission) correlates with lower beta power in the seizure onset zone. Exploratory spatial analysis suggests this effect is not restricted to the sensorimotor cortex. Exploratory frequency analysis suggests that this phenomenon is also observed in alpha and gamma range activity. We found no relationship between beta power and the presence or rate of epileptiform discharges in the sensorimotor cortex or a test of sensorimotor performance. CONCLUSION These results provide evidence that cortical beta power in the seizure onset zone may provide a dynamic physiological biomarker of disease in BECTS.
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Affiliation(s)
- Dan Y Song
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Sally M Stoyell
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Erin E Ross
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Lauren M Ostrowski
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Emily L Thorn
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Steven M Stufflebeam
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Amy K Morgan
- Psychological Assessment Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Britt C Emerton
- Psychological Assessment Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, Massachusetts
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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19
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Tamilia E, AlHilani M, Tanaka N, Tsuboyama M, Peters JM, Grant PE, Madsen JR, Stufflebeam SM, Pearl PL, Papadelis C. Assessing the localization accuracy and clinical utility of electric and magnetic source imaging in children with epilepsy. Clin Neurophysiol 2019; 130:491-504. [PMID: 30771726 DOI: 10.1016/j.clinph.2019.01.009] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 12/07/2018] [Accepted: 01/08/2019] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To evaluate the accuracy and clinical utility of conventional 21-channel EEG (conv-EEG), 72-channel high-density EEG (HD-EEG) and 306-channel MEG in localizing interictal epileptiform discharges (IEDs). METHODS Twenty-four children who underwent epilepsy surgery were studied. IEDs on conv-EEG, HD-EEG, MEG and intracranial EEG (iEEG) were localized using equivalent current dipoles and dynamical statistical parametric mapping (dSPM). We compared the localization error (ELoc) with respect to the ground-truth Irritative Zone (IZ), defined by iEEG sources, between non-invasive modalities and the distance from resection (Dres) between good- (Engel 1) and poor-outcomes. For each patient, we estimated the resection percentage of IED sources and tested whether it predicted outcome. RESULTS MEG presented lower ELoc than HD-EEG and conv-EEG. For all modalities, Dres was shorter in good-outcome than poor-outcome patients, but only the resection percentage of the ground-truth IZ and MEG-IZ predicted surgical outcome. CONCLUSIONS MEG localizes the IZ more accurately than conv-EEG and HD-EEG. MSI may help the presurgical evaluation in terms of patient's outcome prediction. The promising clinical value of ESI for both conv-EEG and HD-EEG prompts the use of higher-density EEG-systems to possibly achieve MEG performance. SIGNIFICANCE Localizing the IZ non-invasively with MSI/ESI facilitates presurgical evaluation and surgical prognosis assessment.
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Affiliation(s)
- Eleonora Tamilia
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michel AlHilani
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Naoaki Tanaka
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Sapporo Neuroimaging Research Group, Sapporo, Japan
| | - Melissa Tsuboyama
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christos Papadelis
- Laboratory of Children's Brain Dynamics, Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Fetal-Neonatal Neuroimaging Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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20
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Gilbert TM, Zürcher NR, Wu CJ, Bhanot A, Hightower BG, Kim M, Albrecht DS, Wey HY, Schroeder FA, Rodriguez-Thompson A, Morin TM, Hart KL, Pellegrini AM, Riley MM, Wang C, Stufflebeam SM, Haggarty SJ, Holt DJ, Loggia ML, Perlis RH, Brown HE, Roffman JL, Hooker JM. PET neuroimaging reveals histone deacetylase dysregulation in schizophrenia. J Clin Invest 2018; 129:364-372. [PMID: 30530989 DOI: 10.1172/jci123743] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 11/02/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Patients with schizophrenia (SCZ) experience chronic cognitive deficits. Histone deacetylases (HDACs) are enzymes that regulate cognitive circuitry; however, the role of HDACs in cognitive disorders, including SCZ, remains unknown in humans. We previously determined that HDAC2 mRNA levels were lower in dorsolateral prefrontal cortex (DLPFC) tissue from donors with SCZ compared with controls. Here we investigated the relationship between in vivo HDAC expression and cognitive impairment in patients with SCZ and matched healthy controls using [11C]Martinostat positron emission tomography (PET). METHODS In a case-control study, relative [11C]Martinostat uptake was compared between 14 patients with SCZ or schizoaffective disorder (SCZ/SAD) and 17 controls using hypothesis-driven region-of-interest analysis and unbiased whole brain voxel-wise approaches. Clinical measures, including the MATRICS consensus cognitive battery, were administered. RESULTS Relative HDAC expression was lower in the DLPFC of patients with SCZ/SAD compared with controls, and HDAC expression positively correlated with cognitive performance scores across groups. Patients with SCZ/SAD also showed lower relative HDAC expression in the dorsomedial prefrontal cortex and orbitofrontal gyrus, and higher relative HDAC expression in the cerebral white matter, pons, and cerebellum compared with controls. CONCLUSIONS These findings provide in vivo evidence of HDAC dysregulation in patients with SCZ and suggest that altered HDAC expression may impact cognitive function in humans. FUNDING National Institute of Mental Health (NIMH), Brain and Behavior Foundation, Massachusetts General Hospital (MGH), Athinoula A. Martinos Center for Biomedical Imaging, National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH Shared Instrumentation Grant Program.
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Affiliation(s)
- Tonya M Gilbert
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Nicole R Zürcher
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Christine J Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Anisha Bhanot
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Baileigh G Hightower
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Minhae Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Daniel S Albrecht
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Hsiao-Ying Wey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Frederick A Schroeder
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Anais Rodriguez-Thompson
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Thomas M Morin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | | | | | - Misha M Riley
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Changning Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Stephen J Haggarty
- Center for Genomic Medicine.,Department of Neurology, and.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Daphne J Holt
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Marco L Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Roy H Perlis
- Center for Genomic Medicine.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Hannah E Brown
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua L Roffman
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jacob M Hooker
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
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21
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Numan T, Derks J, de Witt Hamer PC, Gerstner ER, Stufflebeam SM, Alexander B, van Dijk KRA, Cagney DN, Reijneveld JC, Douw L. OS6.1 Glioma anatomic location and clinical phenotype relate to regional healthy brain network signature. Neuro Oncol 2018. [DOI: 10.1093/neuonc/noy139.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- T Numan
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, Netherlands
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, Netherlands
| | - J Derks
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, Netherlands
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, Netherlands
| | - P C de Witt Hamer
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, Netherlands
- Department of Neurosurgery, Neuroscience Campus Amsterdam, Amsterdam, Netherlands
| | - E R Gerstner
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - S M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - B Alexander
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - K R A van Dijk
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - D N Cagney
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - J C Reijneveld
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, Netherlands
- Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, Netherlands
| | - L Douw
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, Netherlands
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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22
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Khan S, Hashmi JA, Mamashli F, Michmizos K, Kitzbichler MG, Bharadwaj H, Bekhti Y, Ganesan S, Garel KLA, Whitfield-Gabrieli S, Gollub RL, Kong J, Vaina LM, Rana KD, Stufflebeam SM, Hämäläinen MS, Kenet T. Maturation trajectories of cortical resting-state networks depend on the mediating frequency band. Neuroimage 2018; 174:57-68. [PMID: 29462724 DOI: 10.1016/j.neuroimage.2018.02.018] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 02/07/2018] [Accepted: 02/10/2018] [Indexed: 12/11/2022] Open
Abstract
The functional significance of resting state networks and their abnormal manifestations in psychiatric disorders are firmly established, as is the importance of the cortical rhythms in mediating these networks. Resting state networks are known to undergo substantial reorganization from childhood to adulthood, but whether distinct cortical rhythms, which are generated by separable neural mechanisms and are often manifested abnormally in psychiatric conditions, mediate maturation differentially, remains unknown. Using magnetoencephalography (MEG) to map frequency band specific maturation of resting state networks from age 7 to 29 in 162 participants (31 independent), we found significant changes with age in networks mediated by the beta (13-30 Hz) and gamma (31-80 Hz) bands. More specifically, gamma band mediated networks followed an expected asymptotic trajectory, but beta band mediated networks followed a linear trajectory. Network integration increased with age in gamma band mediated networks, while local segregation increased with age in beta band mediated networks. Spatially, the hubs that changed in importance with age in the beta band mediated networks had relatively little overlap with those that showed the greatest changes in the gamma band mediated networks. These findings are relevant for our understanding of the neural mechanisms of cortical maturation, in both typical and atypical development.
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Affiliation(s)
- Sheraz Khan
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Department of Radiology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA.
| | - Javeria A Hashmi
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Fahimeh Mamashli
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Konstantinos Michmizos
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Manfred G Kitzbichler
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Hari Bharadwaj
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Yousra Bekhti
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Santosh Ganesan
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Keri-Lee A Garel
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | | | - Randy L Gollub
- Department of Psychiatry MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Jian Kong
- Department of Psychiatry MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Lucia M Vaina
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Department of Biomedical Engineering, Boston University, Boston, USA
| | - Kunjan D Rana
- Department of Biomedical Engineering, Boston University, Boston, USA
| | - Steven M Stufflebeam
- Department of Radiology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Matti S Hämäläinen
- Department of Radiology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
| | - Tal Kenet
- Department of Neurology, MGH, Harvard Medical School, Boston, USA; Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, USA
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23
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Sjoerds Z, Stufflebeam SM, Veltman DJ, Van den Brink W, Penninx BWJH, Douw L. Loss of brain graph network efficiency in alcohol dependence. Addict Biol 2017; 22:523-534. [PMID: 26692359 DOI: 10.1111/adb.12346] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2015] [Revised: 11/06/2015] [Accepted: 11/11/2015] [Indexed: 12/21/2022]
Abstract
Alcohol dependence (AD) is characterized by corticostriatal impairments in individual brain areas such as the striatum. As yet however, complex brain network topology in AD and its association with disease progression are unknown. We applied graph theory to resting-state functional magnetic resonance imaging (RS-fMRI) to examine weighted global efficiency and local (clustering coefficient, degree and eigenvector centrality) network topology and the functional role of the striatum in 24 AD patients compared with 20 matched healthy controls (HCs), and their association with dependence characteristics. Graph analyses were performed based on Pearson's correlations between RS-fMRI time series, while correcting for age, gender and head motion. We found no significant group differences between AD patients and HCs in network topology. Notably, within the patient group, but not in HCs, the whole-brain network showed reduced average cluster coefficient with more severe alcohol use, whereas longer AD duration within the patient group was associated with a global decrease in efficiency, degree and clustering coefficient. Additionally, within four a-priori chosen bilateral striatal nodes, alcohol use severity was associated with lower clustering coefficient in the left caudate. Longer AD duration was associated with reduced clustering coefficient in caudate and putamen, and reduced degree in bilateral caudate, but with increased eigenvector centrality in left posterior putamen. Especially changes in global network topology and clustering coefficient in anterior striatum remained strikingly robust after exploratory variations in network weight. Our results show adverse effects of AD on overall network integration and possibly on striatal efficiency, putatively contributing to the increasing behavioral impairments seen in chronically addicted patients.
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Affiliation(s)
- Zsuzsika Sjoerds
- Max Planck Institute for Human Cognitive and Brain Sciences; Germany
- Department of Psychiatry, Neuroscience Campus Amsterdam; VU University Medical Center; The Netherlands
- Amsterdam Institute for Addiction Research, Department of Psychiatry, Academic Medical Center; University of Amsterdam; The Netherlands
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging; Massachusetts General Hospital; MA USA
| | - Dick J. Veltman
- Department of Psychiatry, Neuroscience Campus Amsterdam; VU University Medical Center; The Netherlands
| | - Wim Van den Brink
- Amsterdam Institute for Addiction Research, Department of Psychiatry, Academic Medical Center; University of Amsterdam; The Netherlands
| | - Brenda W. J. H. Penninx
- Department of Psychiatry, Neuroscience Campus Amsterdam; VU University Medical Center; The Netherlands
| | - Linda Douw
- Athinoula A. Martinos Center for Biomedical Imaging; Massachusetts General Hospital; MA USA
- Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam; VU University Medical Center; The Netherlands
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Chu CJ, Chan A, Song D, Staley KJ, Stufflebeam SM, Kramer MA. A semi-automated method for rapid detection of ripple events on interictal voltage discharges in the scalp electroencephalogram. J Neurosci Methods 2016; 277:46-55. [PMID: 27988323 DOI: 10.1016/j.jneumeth.2016.12.009] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 12/05/2016] [Accepted: 12/13/2016] [Indexed: 10/20/2022]
Abstract
BACKGROUND High frequency oscillations are emerging as a clinically important indicator of epileptic networks. However, manual detection of these high frequency oscillations is difficult, time consuming, and subjective, especially in the scalp EEG, thus hindering further clinical exploration and application. Semi-automated detection methods augment manual detection by reducing inspection to a subset of time intervals. We propose a new method to detect high frequency oscillations that co-occur with interictal epileptiform discharges. NEW METHOD The new method proceeds in two steps. The first step identifies candidate time intervals during which high frequency activity is increased. The second step computes a set of seven features for each candidate interval. These features require that the candidate event contain a high frequency oscillation approximately sinusoidal in shape, with at least three cycles, that co-occurs with a large amplitude discharge. Candidate events that satisfy these features are stored for validation through visual analysis. RESULTS We evaluate the detector performance in simulation and on ten examples of scalp EEG data, and show that the proposed method successfully detects spike-ripple events, with high positive predictive value, low false positive rate, and high intra-rater reliability. COMPARISON WITH EXISTING METHOD The proposed method is less sensitive than the existing method of visual inspection, but much faster and much more reliable. CONCLUSIONS Accurate and rapid detection of high frequency activity increases the clinical viability of this rhythmic biomarker of epilepsy. The proposed spike-ripple detector rapidly identifies candidate spike-ripple events, thus making clinical analysis of prolonged, multielectrode scalp EEG recordings tractable.
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Affiliation(s)
- Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States.
| | - Arthur Chan
- Voci Technologies, Inc. 6301 Forbes Ave. #120, Pittsburg, PA, 15217, United States
| | - Dan Song
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Kevin J Staley
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States; Harvard-MIT Program in Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, United States
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LaPlante RA, Tang W, Peled N, Vallejo DI, Borzello M, Dougherty DD, Eskandar EN, Widge AS, Cash SS, Stufflebeam SM. The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes. Int J Comput Assist Radiol Surg 2016; 12:1829-1837. [PMID: 27915398 DOI: 10.1007/s11548-016-1504-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 11/07/2016] [Indexed: 10/20/2022]
Abstract
PURPOSE Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. METHODS We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. RESULTS We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. CONCLUSIONS The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection.
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Affiliation(s)
- Roan A LaPlante
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th Street, Charlestown, MA, USA.
| | - Wei Tang
- Department of Neurobiology and Anatomy, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Noam Peled
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Deborah I Vallejo
- Cortical Physiology Laboratory, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Mia Borzello
- Cortical Physiology Laboratory, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Emad N Eskandar
- Department of Neurological Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sydney S Cash
- Cortical Physiology Laboratory, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.,Department of Radiology, Harvard Medical School, Boston, MA, USA.,Harvard-MIT Health Sciences and Technology, Cambridge, MA, USA
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26
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DeSalvo MN, Tanaka N, Douw L, Leveroni CL, Buchbinder BR, Greve DN, Stufflebeam SM. Resting-State Functional MR Imaging for Determining Language Laterality in Intractable Epilepsy. Radiology 2016; 281:264-9. [PMID: 27467465 DOI: 10.1148/radiol.2016141010] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Purpose To measure the accuracy of resting-state functional magnetic resonance (MR) imaging in determining hemispheric language dominance in patients with medically intractable focal epilepsies against the results of an intracarotid amobarbital procedure (IAP). Materials and Methods This study was approved by the institutional review board, and all subjects gave signed informed consent. Data in 23 patients with medically intractable focal epilepsy were retrospectively analyzed. All 23 patients were candidates for epilepsy surgery and underwent both IAP and resting-state functional MR imaging as part of presurgical evaluation. Language dominance was determined from functional MR imaging data by calculating a laterality index (LI) after using independent component analysis. The accuracy of this method was assessed against that of IAP by using a variety of thresholds. Sensitivity and specificity were calculated by using leave-one-out cross validation. Spatial maps of language components were qualitatively compared among each hemispheric language dominance group. Results Measurement of hemispheric language dominance with resting-state functional MR imaging was highly concordant with IAP results, with up to 96% (22 of 23) accuracy, 96% (22 of 23) sensitivity, and 96% (22 of 23) specificity. Composite language component maps in patients with typical language laterality consistently included classic language areas such as the inferior frontal gyrus, the posterior superior temporal gyrus, and the inferior parietal lobule, while those of patients with atypical language laterality also included non-classical language areas such as the superior and middle frontal gyri, the insula, and the occipital cortex. Conclusion Resting-state functional MR imaging can be used to measure language laterality in patients with medically intractable focal epilepsy. (©) RSNA, 2016 Online supplemental material is available for this article.
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Affiliation(s)
- Matthew N DeSalvo
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Naoaki Tanaka
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Linda Douw
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Catherine L Leveroni
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Bradley R Buchbinder
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Douglas N Greve
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
| | - Steven M Stufflebeam
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., N.T., L.D., D.N.G., S.M.S.); and Departments of Neurology (C.L.L.) and Radiology (B.R.B., S.M.S.), Massachusetts General Hospital, Boston, Mass
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27
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Takaya S, Liu H, Greve DN, Tanaka N, Leveroni C, Cole AJ, Stufflebeam SM. Altered anterior-posterior connectivity through the arcuate fasciculus in temporal lobe epilepsy. Hum Brain Mapp 2016; 37:4425-4438. [PMID: 27452151 DOI: 10.1002/hbm.23319] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2016] [Revised: 07/04/2016] [Accepted: 07/07/2016] [Indexed: 11/09/2022] Open
Abstract
How the interactions between cortices through a specific white matter pathway change during cognitive processing in patients with epilepsy remains unclear. Here, we used surface-based structural connectivity analysis to examine the change in structural connectivity with Broca's area/the right Broca's homologue in the lateral temporal and inferior parietal cortices through the arcuate fasciculus (AF) in 17 patients with left temporal lobe epilepsy (TLE) compared with 17 healthy controls. Then, we investigated its functional relevance to the changes in task-related responses and task-modulated functional connectivity with Broca's area/the right Broca's homologue during a semantic classification task of a single word. The structural connectivity through the AF pathway and task-modulated functional connectivity with Broca's area decreased in the left midtemporal cortex. Furthermore, task-related response decreased in the left mid temporal cortex that overlapped with the region showing a decrease in the structural connectivity. In contrast, the region showing an increase in the structural connectivity through the AF overlapped with the regions showing an increase in task-modulated functional connectivity in the left inferior parietal cortex. These structural and functional changes in the overlapping regions were correlated. The results suggest that the change in the structural connectivity through the left frontal-temporal AF pathway underlies the altered functional networks between the frontal and temporal cortices during the language-related processing in patients with left TLE. The left frontal-parietal AF pathway might be employed to connect anterior and posterior brain regions during language processing and compensate for the compromised left frontal-temporal AF pathway. Hum Brain Mapp 37:4425-4438, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Shigetoshi Takaya
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Hesheng Liu
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Douglas N Greve
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Naoaki Tanaka
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Catherine Leveroni
- Harvard Medical School, Boston, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew J Cole
- Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Steven M Stufflebeam
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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28
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Meier J, Tewarie P, Hillebrand A, Douw L, van Dijk BW, Stufflebeam SM, Van Mieghem P. A Mapping Between Structural and Functional Brain Networks. Brain Connect 2016; 6:298-311. [PMID: 26860437 DOI: 10.1089/brain.2015.0408] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The relationship between structural and functional brain networks is still highly debated. Most previous studies have used a single functional imaging modality to analyze this relationship. In this work, we use multimodal data, from functional MRI, magnetoencephalography, and diffusion tensor imaging, and assume that there exists a mapping between the connectivity matrices of the resting-state functional and structural networks. We investigate this mapping employing group averaged as well as individual data. We indeed find a significantly high goodness of fit level for this structure-function mapping. Our analysis suggests that a functional connection is shaped by all walks up to the diameter in the structural network in both modality cases. When analyzing the inverse mapping, from function to structure, longer walks in the functional network also seem to possess minor influence on the structural connection strengths. Even though similar overall properties for the structure-function mapping are found for different functional modalities, our results indicate that the structure-function relationship is modality dependent.
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Affiliation(s)
- Jil Meier
- 1 Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology , The Netherlands
| | - Prejaas Tewarie
- 2 Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center , Amsterdam, The Netherlands
| | - Arjan Hillebrand
- 3 Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center , Amsterdam, The Netherlands
| | - Linda Douw
- 4 Department of Anatomy and Neurosciences, VU University Medical Center , Amsterdam, The Netherlands .,5 Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital , Boston, Massachusetts
| | - Bob W van Dijk
- 3 Department of Clinical Neurophysiology and Magnetoencephalography Center, Neuroscience Campus Amsterdam, VU University Medical Center , Amsterdam, The Netherlands
| | - Steven M Stufflebeam
- 5 Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital , Boston, Massachusetts.,6 Department of Radiology, Harvard Medical School , Boston, Massachusetts
| | - Piet Van Mieghem
- 1 Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology , The Netherlands
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29
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Nedic S, Stufflebeam SM, Rondinoni C, Velasco TR, dos Santos AC, Leite JP, Gargaro AC, Mujica-Parodi LR, Ide JS. Using network dynamic fMRI for detection of epileptogenic foci. BMC Neurol 2015; 15:262. [PMID: 26689596 PMCID: PMC4687299 DOI: 10.1186/s12883-015-0514-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 12/04/2015] [Indexed: 01/21/2023] Open
Abstract
Background Epilepsy is one of the most prevalent neurological disorders. It remains medically intractable for about one-third of patients with focal epilepsy, for whom precise localization of the epileptogenic zone responsible for seizure initiation may be critical for successful surgery. Existing fMRI literature points to widespread network disturbances in functional connectivity. Per previous scalp and intracranial EEG studies and consistent with excessive local synchronization during interictal discharges, we hypothesized that, relative to same regions in healthy controls, epileptogenic foci would exhibit less chaotic dynamics, identifiable via entropic analyses of resting state fMRI time series. Methods In order to first validate this hypothesis on a cohort of patients with known ground truth, here we test individuals with well-defined epileptogenic foci (left mesial temporal lobe epilepsy). We analyzed voxel-wise resting-state fMRI time-series using the autocorrelation function (ACF), an entropic measure of regulation and feedback, and performed follow-up seed-to-voxel functional connectivity analysis. Disruptions in connectivity of the region exhibiting abnormal dynamics were examined in relation to duration of epilepsy and patients’ cognitive performance using a delayed verbal memory recall task. Results ACF analysis revealed constrained (less chaotic) functional dynamics in left temporal lobe epilepsy patients, primarily localized to ipsilateral temporal pole, proximal to presumed focal points. Autocorrelation decay rates differentiated, with 100 % accuracy, between patients and healthy controls on a subject-by-subject basis within a leave-one-subject out classification framework. Regions identified via ACF analysis formed a less efficient network in patients, as compared to controls. Constrained dynamics were linked with locally increased and long-range decreased connectivity that, in turn, correlated significantly with impaired memory (local left temporal connectivity) and epilepsy duration (left temporal – posterior cingulate cortex connectivity). Conclusions Our current results suggest that data driven functional MRI methods that target network dynamics hold promise in providing clinically valuable tools for identification of epileptic regions.
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Affiliation(s)
- Sanja Nedic
- Department of Biomedical Engineering, Stony Brook University School of Medicine, Stony Brook, NY, 11794, USA. .,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Steven M Stufflebeam
- Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Carlo Rondinoni
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Tonicarlo R Velasco
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Antonio C dos Santos
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Joao P Leite
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Ana C Gargaro
- Department of Neurosciences and Behavior, University of Sao Paulo (USP), Ribeirao Preto, SP, 14049, Brazil.
| | - Lilianne R Mujica-Parodi
- Department of Biomedical Engineering, Stony Brook University School of Medicine, Stony Brook, NY, 11794, USA. .,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Jaime S Ide
- Department of Biomedical Engineering, Stony Brook University School of Medicine, Stony Brook, NY, 11794, USA. .,Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA. .,Department of Science and Technology, Federal University of Sao Paulo, Sao Jose dos Campos, SP, 12231, Brazil.
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30
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Takaya S, Kuperberg GR, Liu H, Greve DN, Makris N, Stufflebeam SM. Asymmetric projections of the arcuate fasciculus to the temporal cortex underlie lateralized language function in the human brain. Front Neuroanat 2015; 9:119. [PMID: 26441551 PMCID: PMC4569731 DOI: 10.3389/fnana.2015.00119] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Accepted: 08/20/2015] [Indexed: 01/06/2023] Open
Abstract
The arcuate fasciculus (AF) in the human brain has asymmetric structural properties. However, the topographic organization of the asymmetric AF projections to the cortex and its relevance to cortical function remain unclear. Here we mapped the posterior projections of the human AF in the inferior parietal and lateral temporal cortices using surface-based structural connectivity analysis based on diffusion MRI and investigated their hemispheric differences. We then performed the cross-modal comparison with functional connectivity based on resting-state functional MRI (fMRI) and task-related cortical activation based on fMRI using a semantic classification task of single words. Structural connectivity analysis showed that the left AF connecting to Broca's area predominantly projected in the lateral temporal cortex extending from the posterior superior temporal gyrus to the mid part of the superior temporal sulcus and the middle temporal gyrus, whereas the right AF connecting to the right homolog of Broca's area predominantly projected to the inferior parietal cortex extending from the mid part of the supramarginal gyrus to the anterior part of the angular gyrus. The left-lateralized projection regions of the AF in the left temporal cortex had asymmetric functional connectivity with Broca's area, indicating structure-function concordance through the AF. During the language task, left-lateralized cortical activation was observed. Among them, the brain responses in the temporal cortex and Broca's area that were connected through the left-lateralized AF pathway were specifically correlated across subjects. These results suggest that the human left AF, which structurally and functionally connects the mid temporal cortex and Broca's area in asymmetrical fashion, coordinates the cortical activity in these remote cortices during a semantic decision task. The unique feature of the left AF is discussed in the context of the human capacity for language.
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Affiliation(s)
- Shigetoshi Takaya
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School Charlestown, MA, USA
| | - Gina R Kuperberg
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School Charlestown, MA, USA ; Department of Psychology, Tufts University Medford, MA, USA
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School Charlestown, MA, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School Charlestown, MA, USA
| | - Nikos Makris
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School Charlestown, MA, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School Charlestown, MA, USA ; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology Cambridge, MA, USA
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31
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Affiliation(s)
- Bruce R Rosen
- Massachusetts General Hospital, Boston2Massachusetts General Hospital-East, Charlestown
| | - Susie Y Huang
- Massachusetts General Hospital, Boston2Massachusetts General Hospital-East, Charlestown
| | - Steven M Stufflebeam
- Massachusetts General Hospital, Boston2Massachusetts General Hospital-East, Charlestown
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32
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Prah MA, Stufflebeam SM, Paulson ES, Kalpathy-Cramer J, Gerstner ER, Batchelor TT, Barboriak DP, Rosen BR, Schmainda KM. Repeatability of Standardized and Normalized Relative CBV in Patients with Newly Diagnosed Glioblastoma. AJNR Am J Neuroradiol 2015; 36:1654-61. [PMID: 26066626 DOI: 10.3174/ajnr.a4374] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2014] [Accepted: 01/23/2015] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE For more widespread clinical use advanced imaging methods such as relative cerebral blood volume must be both accurate and repeatable. The aim of this study was to determine the repeatability of relative CBV measurements in newly diagnosed glioblastoma multiforme by using several of the most commonly published estimation techniques. MATERIALS AND METHODS The relative CBV estimates were calculated from dynamic susceptibility contrast MR imaging in double-baseline examinations for 33 patients with treatment-naïve and pathologically proved glioblastoma multiforme (men = 20; mean age = 55 years). Normalized and standardized relative CBV were calculated by using 6 common postprocessing methods. The repeatability of both normalized and standardized relative CBV, in both tumor and contralateral brain, was examined for each method with metrics of repeatability, including the repeatability coefficient and within-subject coefficient of variation. The minimum sample size required to detect a parameter change of 10% or 20% was also determined for both normalized relative CBV and standardized relative CBV for each estimation method. RESULTS When ordered by the repeatability coefficient, methods using postprocessing leakage correction and ΔR2*(t) techniques offered superior repeatability. Across processing techniques, the standardized relative CBV repeatability in normal-appearing brain was comparable with that in tumor (P = .31), yet inferior in tumor for normalized relative CBV (P = .03). On the basis of the within-subject coefficient of variation, tumor standardized relative CBV estimates were less variable (13%-20%) than normalized relative CBV estimates (24%-67%). The minimum number of participants needed to detect a change of 10% or 20% is 118-643 or 30-161 for normalized relative CBV and 109-215 or 28-54 for standardized relative CBV. CONCLUSIONS The ΔR2* estimation methods that incorporate leakage correction offer the best repeatability for relative CBV, with standardized relative CBV being less variable and requiring fewer participants to detect a change compared with normalized relative CBV.
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Affiliation(s)
- M A Prah
- From the Departments of Radiology (M.A.P., K.M.S., E.S.P.)
| | - S M Stufflebeam
- Department of Radiology (S.M.S., J.K.-C., E.R.G., T.T.B., B.R.R.), Massachusetts General Hospital, Boston, Massachusetts
| | - E S Paulson
- From the Departments of Radiology (M.A.P., K.M.S., E.S.P.) Radiation Oncology (E.S.P.), Medical College of Wisconsin, Milwaukee, Wisconsin
| | - J Kalpathy-Cramer
- Department of Radiology (S.M.S., J.K.-C., E.R.G., T.T.B., B.R.R.), Massachusetts General Hospital, Boston, Massachusetts
| | - E R Gerstner
- Department of Radiology (S.M.S., J.K.-C., E.R.G., T.T.B., B.R.R.), Massachusetts General Hospital, Boston, Massachusetts
| | - T T Batchelor
- Department of Radiology (S.M.S., J.K.-C., E.R.G., T.T.B., B.R.R.), Massachusetts General Hospital, Boston, Massachusetts
| | - D P Barboriak
- Department of Radiology (D.P.B.), Duke University Medical Center, Durham, North Carolina
| | - B R Rosen
- Department of Radiology (S.M.S., J.K.-C., E.R.G., T.T.B., B.R.R.), Massachusetts General Hospital, Boston, Massachusetts
| | - K M Schmainda
- From the Departments of Radiology (M.A.P., K.M.S., E.S.P.)
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Douw L, Leveroni CL, Tanaka N, Emerton BC, Cole AC, Reinsberger C, Stufflebeam SM. Loss of resting-state posterior cingulate flexibility is associated with memory disturbance in left temporal lobe epilepsy. PLoS One 2015; 10:e0131209. [PMID: 26110431 PMCID: PMC4481466 DOI: 10.1371/journal.pone.0131209] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2015] [Accepted: 05/30/2015] [Indexed: 01/08/2023] Open
Abstract
The association between cognition and resting-state fMRI (rs-fMRI) has been the focus of many recent studies, most of which use stationary connectivity. The dynamics or flexibility of connectivity, however, may be seminal for understanding cognitive functioning. In temporal lobe epilepsy (TLE), stationary connectomic correlates of impaired memory have been reported mainly for the hippocampus and posterior cingulate cortex (PCC). We therefore investigate resting-state and task-based hippocampal and PCC flexibility in addition to stationary connectivity in left TLE (LTLE) patients. Sixteen LTLE patients were analyzed with respect to rs-fMRI and task-based fMRI (t-fMRI), and underwent clinical neuropsychological testing. Flexibility of connectivity was calculated using a sliding-window approach by determining the standard deviation of Fisher-transformed Pearson correlation coefficients over all windows. Stationary connectivity was also calculated. Disturbed memory was operationalized as having at least one memory subtest score equal to or below the 5th percentile compared to normative data. Lower PCC flexibility, particularly in the contralateral (i.e. right) hemisphere, was found in memory-disturbed LTLE patients, who had up to 22% less flexible connectivity. No significant group differences were found with respect to hippocampal flexibility, stationary connectivity during both rs-fMRI and t-fMRI, or flexibility during t-fMRI. Contralateral resting-state PCC flexibility was able to classify all but one patient with respect to their memory status (94% accuracy). Flexibility of the PCC during rest relates to memory functioning in LTLE patients. Loss of flexible connectivity to the rest of the brain originating from the PCC, particularly contralateral to the seizure focus, is able to discern memory disturbed patients from their preserved counterparts. This study indicates that the dynamics of resting-state connectivity are associated with cognitive status of LTLE patients, rather than stationary connectivity.
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Affiliation(s)
- Linda Douw
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America
- Department of Radiology, Harvard Medical School, Boston, MA, United States of America
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
- * E-mail:
| | - Catherine L. Leveroni
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States of America
| | - Naoaki Tanaka
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America
- Department of Radiology, Harvard Medical School, Boston, MA, United States of America
| | - Britt C. Emerton
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States of America
| | - Andrew C. Cole
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States of America
| | - Claus Reinsberger
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America
- Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States of America
- Institute of Sports Medicine, Faculty of Science, University of Paderborn, Paderborn, Germany
| | - Steven M. Stufflebeam
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America
- Department of Radiology, Harvard Medical School, Boston, MA, United States of America
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Jafari-Khouzani K, Emblem KE, Kalpathy-Cramer J, Bjørnerud A, Vangel MG, Gerstner ER, Schmainda KM, Paynabar K, Wu O, Wen PY, Batchelor T, Rosen B, Stufflebeam SM. Repeatability of Cerebral Perfusion Using Dynamic Susceptibility Contrast MRI in Glioblastoma Patients. Transl Oncol 2015; 8:137-46. [PMID: 26055170 PMCID: PMC4486737 DOI: 10.1016/j.tranon.2015.03.002] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 03/10/2015] [Accepted: 03/17/2015] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVES This study evaluates the repeatability of brain perfusion using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) with a variety of post-processing methods. METHODS Thirty-two patients with newly diagnosed glioblastoma were recruited. On a 3-T MRI using a dual-echo, gradient-echo spin-echo DSC-MRI protocol, the patients were scanned twice 1 to 5 days apart. Perfusion maps including cerebral blood volume (CBV) and cerebral blood flow (CBF) were generated using two contrast agent leakage correction methods, along with testing normalization to reference tissue, and application of arterial input function (AIF). Repeatability of CBV and CBF within tumor regions and healthy tissues, identified by structural images, was assessed with intra-class correlation coefficients (ICCs) and repeatability coefficients (RCs). Coefficients of variation (CVs) were reported for selected methods. RESULTS CBV and CBF were highly repeatable within tumor with ICC values up to 0.97. However, both CBV and CBF showed lower ICCs for healthy cortical tissues (up to 0.83), healthy gray matter (up to 0.95), and healthy white matter (WM; up to 0.93). The values of CV ranged from 6% to 10% in tumor and 3% to 11% in healthy tissues. The values of RC relative to the mean value of measurement within healthy WM ranged from 22% to 42% in tumor and 7% to 43% in healthy tissues. These percentages show how much variation in perfusion parameter, relative to that in healthy WM, we expect to observe to consider it statistically significant. We also found that normalization improved repeatability, but AIF deconvolution did not. CONCLUSIONS DSC-MRI is highly repeatable in high-grade glioma patients.
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Affiliation(s)
- Kourosh Jafari-Khouzani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Kyrre E Emblem
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA; The Intervention Centre, Rikshospitalet, Oslo University Hospital, Oslo, Norway
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Atle Bjørnerud
- The Intervention Centre, Rikshospitalet, Oslo University Hospital, Oslo, Norway; Department of Physics, University of Oslo, Oslo, Norway
| | - Mark G Vangel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elizabeth R Gerstner
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Kathleen M Schmainda
- Department of Radiology & Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Kamran Paynabar
- H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tracy Batchelor
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Bruce Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
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Douw L, DeSalvo MN, Tanaka N, Cole AJ, Liu H, Reinsberger C, Stufflebeam SM. Dissociated multimodal hubs and seizures in temporal lobe epilepsy. Ann Clin Transl Neurol 2015; 2:338-52. [PMID: 25909080 PMCID: PMC4402080 DOI: 10.1002/acn3.173] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 12/22/2014] [Accepted: 12/22/2014] [Indexed: 11/16/2022] Open
Abstract
Objective Brain connectivity at rest is altered in temporal lobe epilepsy (TLE), particularly in “hub” areas such as the posterior default mode network (DMN). Although both functional and anatomical connectivity are disturbed in TLE, the relationships between measures as well as to seizure frequency remain unclear. We aim to clarify these associations using connectivity measures specifically sensitive to hubs. Methods Connectivity between 1000 cortical surface parcels was determined in 49 TLE patients and 23 controls with diffusion and resting-state functional magnetic resonance imaging. Two types of hub connectivity were investigated across multiple brain modules (the DMN, motor system, etcetera): (1) within-module connectivity (a measure of local importance that assesses a parcel's communication level within its own subnetwork) and (2) between-module connectivity (a measure that assesses connections across multiple modules). Results In TLE patients, there was lower overall functional integrity of the DMN as well as an increase in posterior hub connections with other modules. Anatomical between-module connectivity was globally decreased. Higher DMN disintegration (DD) coincided with higher anatomical between-module connectivity, whereas both were associated with increased seizure frequency. DD related to seizure frequency through mediating effects of anatomical connectivity, but seizure frequency also correlated with anatomical connectivity through DD, indicating a complex interaction between multimodal networks and symptoms. Interpretation We provide evidence for dissociated anatomical and functional hub connectivity in TLE. Moreover, shifts in functional hub connections from within to outside the DMN, an overall loss of integrative anatomical communication, and the interaction between the two increase seizure frequency.
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Affiliation(s)
- Linda Douw
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, Massachusetts ; Department of Radiology, Harvard Medical School Boston, Massachusetts ; Department of Anatomy and Neurosciences, VU University Medical Center Amsterdam, The Netherlands
| | - Matthew N DeSalvo
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, Massachusetts ; Department of Radiology, Harvard Medical School Boston, Massachusetts
| | - Naoaki Tanaka
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, Massachusetts ; Department of Radiology, Harvard Medical School Boston, Massachusetts
| | - Andrew J Cole
- Department of Neurology, Massachusetts General Hospital Boston, Massachusetts
| | - Hesheng Liu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, Massachusetts ; Department of Radiology, Harvard Medical School Boston, Massachusetts
| | - Claus Reinsberger
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, Massachusetts ; Department of Neurology, Brigham and Women's Hospital Boston, Massachusetts
| | - Steven M Stufflebeam
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, Massachusetts ; Department of Radiology, Harvard Medical School Boston, Massachusetts
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LaPlante RA, Douw L, Tang W, Stufflebeam SM. The Connectome Visualization Utility: software for visualization of human brain networks. PLoS One 2014; 9:e113838. [PMID: 25437873 PMCID: PMC4250035 DOI: 10.1371/journal.pone.0113838] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2014] [Accepted: 10/31/2014] [Indexed: 11/19/2022] Open
Abstract
In analysis of the human connectome, the connectivity of the human brain is collected from multiple imaging modalities and analyzed using graph theoretical techniques. The dimensionality of human connectivity data is high, and making sense of the complex networks in connectomics requires sophisticated visualization and analysis software. The current availability of software packages to analyze the human connectome is limited. The Connectome Visualization Utility (CVU) is a new software package designed for the visualization and network analysis of human brain networks. CVU complements existing software packages by offering expanded interactive analysis and advanced visualization features, including the automated visualization of networks in three different complementary styles and features the special visualization of scalar graph theoretical properties and modular structure. By decoupling the process of network creation from network visualization and analysis, we ensure that CVU can visualize networks from any imaging modality. CVU offers a graphical user interface, interactive scripting, and represents data uses transparent neuroimaging and matrix-based file types rather than opaque application-specific file formats.
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Affiliation(s)
- Roan A. LaPlante
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
| | - Linda Douw
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
- VU University Medical Center, Department of Anatomy & Clinical Neurosciences, Amsterdam, The Netherlands
| | - Wei Tang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, United States of America
- Harvard-MIT Health Sciences and Technology, Cambridge, Massachusetts, United States of America
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Tanaka N, Stufflebeam SM. Clinical application of spatiotemporal distributed source analysis in presurgical evaluation of epilepsy. Front Hum Neurosci 2014; 8:62. [PMID: 24574999 PMCID: PMC3919017 DOI: 10.3389/fnhum.2014.00062] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2013] [Accepted: 01/25/2014] [Indexed: 11/17/2022] Open
Abstract
Magnetoencephalography (MEG), which acquires neuromagnetic fields in the brain, is a useful diagnostic tool in presurgical evaluation of epilepsy. Previous studies have shown that MEG affects the planning intracranial electroencephalography placement and correlates with surgical outcomes by using a single dipole model. Spatiotemporal source analysis using distributed source models is an advanced method for analyzing MEG, and has been recently introduced for analyzing epileptic spikes. It has advantages over the conventional single dipole analysis for obtaining accurate sources and understanding the propagation of epileptic spikes. In this article, we review the source analysis methods, describe the techniques of the distributed source analysis, interpretation of source distribution maps, and discuss the benefits and feasibility of this method in evaluation of epilepsy.
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Affiliation(s)
- Naoaki Tanaka
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Charlestown, MA , USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Charlestown, MA , USA
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DeSalvo MN, Douw L, Takaya S, Liu H, Stufflebeam SM. Task-dependent reorganization of functional connectivity networks during visual semantic decision making. Brain Behav 2014; 4:877-85. [PMID: 25365802 PMCID: PMC4178300 DOI: 10.1002/brb3.286] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2014] [Revised: 08/23/2014] [Accepted: 09/01/2014] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Functional MRI is widely used to study task-related changes in neuronal activity as well as resting-state functional connectivity. In this study, we explore task-related changes in functional connectivity networks using fMRI. Dynamic connectivity may represent a new measure of neural network robustness that would impact both clinical and research efforts. However, prior studies of task-related changes in functional connectivity have shown apparently conflicting results, leading to several competing hypotheses regarding the relationship between task-related and resting-state brain networks. METHODS We used a graph theory-based network approach to compare functional connectivity in healthy subjects between the resting state and when performing a clinically used semantic decision task. We analyzed fMRI data from 21 healthy, right-handed subjects. RESULTS While three nonoverlapping, highly intraconnected functional modules were observed in the resting state, an additional language-related module emerged during the semantic decision task. Both overall and within-module connectivity were greater in default mode network (DMN) and classical language areas during semantic decision making compared to rest, while between-module connectivity was diffusely greater at rest, revealing a more widely distributed pattern of functional connectivity at rest. CONCLUSIONS The results of this study suggest that there are differences in network topology between resting and task states. Specifically, semantic decision making is associated with a reduction in distributed connectivity through hub areas of the DMN as well as an increase in connectivity within both default and language networks.
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Affiliation(s)
- Matthew N DeSalvo
- Athinoula A. Martinos Center for Biomedical Imaging Charlestown, Massachusetts ; Massachusetts General Hospital Boston, Massachusetts
| | - Linda Douw
- Athinoula A. Martinos Center for Biomedical Imaging Charlestown, Massachusetts ; Massachusetts General Hospital Boston, Massachusetts
| | - Shigetoshi Takaya
- Athinoula A. Martinos Center for Biomedical Imaging Charlestown, Massachusetts ; Massachusetts General Hospital Boston, Massachusetts
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging Charlestown, Massachusetts ; Massachusetts General Hospital Boston, Massachusetts
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging Charlestown, Massachusetts ; Massachusetts General Hospital Boston, Massachusetts
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Tanaka N, Peters JM, Prohl AK, Takaya S, Madsen JR, Bourgeois BF, Dworetzky BA, Hämäläinen MS, Stufflebeam SM. Clinical value of magnetoencephalographic spike propagation represented by spatiotemporal source analysis: correlation with surgical outcome. Epilepsy Res 2013; 108:280-8. [PMID: 24315019 DOI: 10.1016/j.eplepsyres.2013.11.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2013] [Revised: 10/05/2013] [Accepted: 11/03/2013] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To investigate the correlation between spike propagation represented by spatiotemporal source analysis of magnetoencephalographic (MEG) spikes and surgical outcome in patients with temporal lobe epilepsy. METHODS Thirty-seven patients were divided into mesial (n=27) and non-mesial (n=10) groups based on the presurgical evaluation. In each patient, ten ipsilateral spikes were averaged, and spatiotemporal source maps of the averaged spike were obtained by using minimum norm estimate. Regions of interest (ROIs) were created including temporoparietal, inferior frontal, mesial temporal, anterior and posterior part of the lateral temporal cortex. We extracted activation values from the source maps and the threshold was set at half of the maximum activation at the peak latency. The leading and propagated areas of the spike were defined as those ROIs with activation reaching the threshold at the earliest and at the peak latencies, respectively. Surgical outcome was assessed based on Engel's classification. Binary variables were created from leading areas (restricted to the anterior and mesial temporal ROIs or not) and from propagation areas (involving the temporoparietal ROI or not), and for surgical outcome (Class I or not). Fisher's exact test was used for significance testing. RESULTS In total and mesial group, restricted anterior/mesial temporal leading areas were correlated with Class I (p<0.05). Temporoparietal propagation was correlated with Class II-IV (p<0.05). For the non-mesial group, no significant relation was found. CONCLUSIONS Spike propagation patterns represented by spatiotemporal source analysis of MEG spikes may provide useful information for prognostic implication in presurgical evaluation of epilepsy.
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Affiliation(s)
- Naoaki Tanaka
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA.
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Anna K Prohl
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Shigetoshi Takaya
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA
| | - Joseph R Madsen
- Department of Neurosurgery, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Blaise F Bourgeois
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA
| | - Barbara A Dworetzky
- Department of Neurology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 Thirteenth Street, Suite 2301, Charlestown, MA 02129, USA
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Abstract
PURPOSE To study differences in the whole-brain structural connectomes of patients with left temporal lobe epilepsy (TLE) and healthy control subjects. MATERIALS AND METHODS This study was approved by the institutional review board, and all individuals gave signed informed consent. Sixty-direction diffusion-tensor imaging and magnetization-prepared rapid acquisition gradient-echo (MP-RAGE) magnetic resonance imaging volumes were analyzed in 24 patients with left TLE and in 24 healthy control subjects. MP-RAGE volumes were segmented into 1015 regions of interest (ROIs) spanning the entire brain. Deterministic white matter tractography was performed after voxelwise tensor calculation. Weighted structural connectivity matrices were generated by using the pairwise density of connecting fibers between ROIs. Graph theoretical measures of connectivity networks were compared between groups by using linear models with permutation testing. RESULTS Patients with TLE had 22%-45% reduced (P < .01) distant connectivity in the medial orbitofrontal cortex, temporal cortex, posterior cingulate cortex, and precuneus, compared with that in healthy subjects. However, local connectivity, as measured by means of network efficiency, was increased by 85%-270% (P < .01) in the medial and lateral frontal cortices, insular cortex, posterior cingulate cortex, precuneus, and occipital cortex in patients with TLE as compared with healthy subjects. CONCLUSION This study suggests that TLE involves altered structural connectivity in a network that reaches beyond the temporal lobe, especially in the default mode network.
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Affiliation(s)
- Matthew N DeSalvo
- From the Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth St, Suite 2301, Charlestown, MA 02129 (M.N.D., L.D., N.T., C.R., S.M.S.); Department of Radiology, Massachusetts General Hospital, Charlestown, Mass (M.N.D., L.D., N.T., S.M.S.); and Department of Neurology, Brigham and Women's Hospital, Boston, Mass (C.R.)
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Duhaime AC, Stufflebeam SM. Editorial: Connectivity via magnetoencephalography. J Neurosurg 2013; 118:1304-5; discussion 1305. [PMID: 23600936 DOI: 10.3171/2012.10.jns121408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Gallagher A, Tanaka N, Suzuki N, Liu H, Thiele EA, Stufflebeam SM. Diffuse cerebral language representation in tuberous sclerosis complex. Epilepsy Res 2012; 104:125-33. [PMID: 23092910 DOI: 10.1016/j.eplepsyres.2012.09.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2012] [Revised: 09/26/2012] [Accepted: 09/30/2012] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Tuberous sclerosis complex (TSC) is a multisystem genetic disorder affecting multiple organs, including the brain, and very often associated with epileptic activity. Language acquisition and development seems to be altered in a significant proportion of patients with TSC. In the present study, we used magnetoencephalography (MEG) to investigate spatiotemporal cerebral language processing in subjects with TSC and epilepsy during a reading semantic decision task, compared to healthy control participants. METHODS Fifteen patients with TSC and 31 healthy subjects performed a lexico-semantic decision task during MEG recording. Minimum-norm estimates (MNE) were computed allowing identification of cerebral generators of language evoked fields (EF) in each subject. RESULTS Source analysis of the language EF demonstrated early bilateral medial occipital activation (125ms) followed by a fusiform gyrus activation around 135ms. At 270ms post stimuli presentation, a strong cerebral activation was recorded in the left basal temporal language area. Finally, cerebral activations were measured in Wernicke's area followed by Broca's area. The healthy control group showed larger and earlier language activations in Broca and Wernicke's areas compared to TSC patients. Moreover, cerebral activation from Broca's area was greater than activation from Wernicke's area in both groups, but this difference between anterior and posterior regions was smaller in the TSC group. Finally, the activation latency difference between Broca and Wernicke's areas was greater in healthy controls than in TSC patients, which shows that activations in these areas are more serial in control subjects compared to TSC patients in whom activations occur more simultaneously. CONCLUSIONS This is the first study to investigate cerebral language pattern in patients with TSC. Compared to healthy controls, atypical neuromagnetic language responses may reflect cerebral reorganization in these patients in response to early epileptogenic activity or presence at birth of multiple brain lesions.
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Affiliation(s)
- Anne Gallagher
- Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Charlestown, MA 02129, USA.
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Gallagher A, Tanaka N, Suzuki N, Liu H, Thiele EA, Stufflebeam SM. Decreased language laterality in tuberous sclerosis complex: a relationship between language dominance and tuber location as well as history of epilepsy. Epilepsy Behav 2012; 25:36-41. [PMID: 22980079 PMCID: PMC3708307 DOI: 10.1016/j.yebeh.2012.06.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2012] [Revised: 06/13/2012] [Accepted: 06/15/2012] [Indexed: 11/16/2022]
Abstract
Nearly 90% of patients with tuberous sclerosis complex (TSC) have epilepsy. Epilepsy surgery can be considered, which often requires a presurgical assessment of language lateralization. This is the first study to investigate language lateralization in TSC patients using magnetoencephalography. Fifteen patients performed a language task during magnetoencephalography recording. Cerebral generators of language-evoked fields (EF) were identified in each patient. Laterality indices (LI) were computed using magnetoencephalography data extracted from the inferior frontal as well as middle and superior temporal gyri from both hemispheres between 250 and 550 ms. Source analysis demonstrated a fusiform gyrus activation, followed by an activation located in the basal temporal language area and middle and superior temporal gyri responses, ending with an inferior frontal activation. Eleven patients (73.3%) had left-hemisphere language dominance, whereas four patients (26.7%) showed a bilateral language pattern, which was associated with a history of epilepsy and presence of tubers in language-related areas.
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Affiliation(s)
- Anne Gallagher
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, USA.
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Tanaka N, Liu H, Reinsberger C, Madsen JR, Bourgeois BF, Dworetzky BA, Hämäläinen MS, Stufflebeam SM. Language lateralization represented by spatiotemporal mapping of magnetoencephalography. AJNR Am J Neuroradiol 2012; 34:558-63. [PMID: 22878013 DOI: 10.3174/ajnr.a3233] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Determination of hemispheric language dominance is critical for planning epilepsy surgery. We assess the usefulness of spatiotemporal source analysis of magnetoencephalography for determining language laterality. MATERIALS AND METHODS Thirty-five patients with epilepsy were studied. The patients performed a semantic word-processing task during MEG recording. Epochs containing language-related neuromagnetic activity were averaged after preprocessing. The averaged data between 250 and 550 ms after stimulus were analyzed by using dynamic statistical parametric mapping. ROIs were obtained in the opercular and triangular parts of the inferior frontal gyrus, superior temporal gyrus, and supramarginal gyrus in both hemispheres. We calculated laterality indices according to 1) dSPM-amplitude method, based on the amplitude of activation in the ROIs, and 2) dSPM-counting method, based on the number of unit dipoles with activation over a threshold in the ROIs. The threshold was determined as half of the maximum value in all ROIs for each patient. A LI ≥0.10 or ≤-0.10 was considered left- or right-hemisphere dominance, respectively; a LI between -0.10 and 0.10 was considered bilateral. All patients underwent an intracarotid amobarbital procedure as part of presurgical evaluation. RESULTS The dSPM-counting method demonstrated laterality consistent with the IAP in 32 of 35 patients (91.4%), the remaining 3 (8.6%) demonstrated bilateral language representation, whereas the dSPM-amplitude method showed 18 (51.4%) concordant and 17 (48.6%) bilateral. No laterality opposite to the IAP was found. CONCLUSIONS Spatiotemporal mapping of language lateralization with the dSPM-counting method may reduce the necessity for an IAP in as many as 90% of patients.
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Affiliation(s)
- N Tanaka
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA.
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Shiraishi H, Ahlfors SP, Stufflebeam SM, Knake S, Larsson PG, Hämäläinen MS, Takano K, Okajima M, Hatanaka K, Saitoh S, Dale AM, Halgren E. Comparison of three methods for localizing interictal epileptiform discharges with magnetoencephalography. J Clin Neurophysiol 2011; 28:431-40. [PMID: 21946369 PMCID: PMC3190234 DOI: 10.1097/wnp.0b013e318231c86f] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE To compare three methods of localizing the source of epileptiform activity recorded with magnetoencephalography: equivalent current dipole, minimum current estimate, and dynamic statistical parametric mapping (dSPM), and to evaluate the solutions by comparison with clinical symptoms and other electrophysiological and neuroradiological findings. METHODS Fourteen children of 3 to 15 years were studied. Magnetoencephalography was collected with a whole-head 204-channel helmet-shaped sensor array. We calculated equivalent current dipoles and made minimum current estimate and dSPM movies to estimate the cortical distribution of interictal epileptiform discharges in these patients. RESULTS The results for four patients with localization-related epilepsy and one patient with Landau-Kleffner Syndrome were consistent among all the three analysis methods. In the rest of the patients, minimum current estimate and dSPM suggested multifocal or widespread activity; in these patients, the equivalent current dipole results were so scattered that interpretation of the results was not possible. For 9 patients with localization-related epilepsy and generalized epilepsy, the epileptiform discharges were wide spread or only slow waves, but dSPM suggested a possible propagation path of the interictal epileptiform discharges. CONCLUSION Minimum current estimate and dSPM could identify the propagation of epileptiform activity with high temporal resolution. The results of dSPM were more stable because the solutions were less sensitive to background brain activity.
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Affiliation(s)
- Hideaki Shiraishi
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.
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Tanaka N, Grant PE, Suzuki N, Madsen JR, Bergin AM, Hämäläinen MS, Stufflebeam SM. Multimodal imaging of spike propagation: a technical case report. AJNR Am J Neuroradiol 2011; 33:E82-4. [PMID: 21960488 DOI: 10.3174/ajnr.a2701] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
We report an 11-year-old boy with intractable epilepsy, who had cortical dysplasia in the right superior frontal gyrus. Spatiotemporal source analysis of MEG and EEG spikes demonstrated a similar time course of spike propagation from the superior to inferior frontal gyri, as observed on intracranial EEG. The tractography reconstructed from DTI showed a fiber connection between these areas. Our multimodal approach demonstrates spike propagation and a white matter tract guiding the propagation.
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Affiliation(s)
- N Tanaka
- Athinoula A. Martinos Center for Biomedical Imaging,, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA.
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Abstract
Noninvasive neuroimaging aids in surgical planning and in counseling patients about possible risks of surgery. Magnetoencephalography (MEG) performs the most common types of surgical planning that the neurosurgeon faces, including localization of epileptic discharges, determination of the hemispheric dominance of verbal processing, and the ability to locate eloquent cortex. MEG is most useful when it is combined with structural imaging, most commonly with structural magnetic resonance (MR) imaging and MR diffusion imaging. This article reviews the history of clinical MEG, introduces the basic concepts about the biophysics of MEG, and outlines the basic neurosurgical applications of MEG.
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Affiliation(s)
- Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA.
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Stufflebeam SM, Liu H, Sepulcre J, Tanaka N, Buckner RL, Madsen JR. Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging. J Neurosurg 2011; 114:1693-7. [PMID: 21351832 DOI: 10.3171/2011.1.jns10482] [Citation(s) in RCA: 64] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECT In patients with medically refractory epilepsy the accurate localization of the seizure onset zone is critical for successful surgical treatment. The object of this study was to investigate whether the degree of coupling of spontaneous brain activity as measured with functional connectivity MR imaging (fcMR imaging) can accurately identify and localize epileptic discharges. METHODS The authors studied 6 patients who underwent fcMR imaging presurgical mapping and subsequently underwent invasive electroencephalography. RESULTS Focal regions of statistically significant increases in connectivity were identified in 5 patients when compared with an ad hoc normative sample of 300 controls. The foci identified by fcMR imaging overlapped the epileptogenic areas identified by invasive encephalography in all 5 patients. CONCLUSIONS These results suggest that fcMR imaging may provide an effective high-spatial resolution and noninvasive method of localizing epileptic discharges in patients with refractory epilepsy.
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Affiliation(s)
- Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
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Abstract
Magnetoencephalography noninvasively measures the magnetic fields produced by the brain. Pertinent research articles from 1993 to 2009 that measured spontaneous, whole-head magnetoencephalography activity in patients with schizophrenia were reviewed. Data on localization of oscillatory activity and correlation of these findings with psychotic symptoms are summarized. Although the variety of measures used by different research groups makes a quantitative meta-analysis difficult, it appears that magnetoencephalography activity in patients may exhibit identifiable patterns, defined by topographic organization and frequency band. Specifically, 11 of the 12 studies showed increased theta (4-8 Hz) and delta (1-4 Hz) band oscillations in the temporal lobes of patients; of the 10 studies that examined the relationship between oscillatory activity and symptomatology, 8 found a positive correlation between temporal lobe theta activity and positive schizophrenic symptoms. Abnormally high frontal delta activity was not seen. These findings are analyzed in comparison with the electroencephalogram literature on schizophrenics, and possible confounds (e.g., medication effects) are discussed. In the future, magnetoencephalography might be used to assist in diagnosis or might be fruitfully used in conjunction with new neuroscience research approaches such as computational modeling, which may be able to link oscillatory activity and cellular-level pathology.
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Affiliation(s)
- Peter J Siekmeier
- Harvard Medical School and McLean Hospital, Belmont, Massachusetts 02478, USA.
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Reinsberger C, Tanaka N, Cole AJ, Lee JW, Dworetzky BA, Bromfield EB, Hamiwka L, Bourgeois BF, Golby AJ, Madsen JR, Stufflebeam SM. Current dipole orientation and distribution of epileptiform activity correlates with cortical thinning in left mesiotemporal epilepsy. Neuroimage 2010; 52:1238-42. [PMID: 20472073 DOI: 10.1016/j.neuroimage.2010.04.264] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2010] [Revised: 04/16/2010] [Accepted: 04/28/2010] [Indexed: 11/28/2022] Open
Abstract
To evaluate cortical architecture in mesial temporal lobe epilepsy (MTLE) with respect to electrophysiology, we analyze both magnetic resonance imaging (MRI) and magnetoencephalography (MEG) in 19 patients with left MTLE. We divide the patients into two groups: 9 patients (Group A) have vertically oriented antero-medial equivalent current dipoles (ECDs). 10 patients (Group B) have ECDs that are diversely oriented and widely distributed. Group analysis of MRI data shows widespread cortical thinning in Group B compared with Group A, in the left hemisphere involving the cingulate, supramarginal, occipitotemporal and parahippocampal gyri, precuneus and parietal lobule, and in the right hemisphere involving the fronto-medial, -central and -basal gyri and the precuneus. These results suggest that regardless of the presence of hippocampal sclerosis, in a subgroup of patients with MTLE a large cortical network is affected. This finding may, in part, explain the unfavorable outcome in some MTLE patients after epilepsy surgery.
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Affiliation(s)
- Claus Reinsberger
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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