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Csukly G, Tombor L, Hidasi Z, Csibri E, Fullajtár M, Huszár Z, Koszovácz V, Lányi O, Vass E, Koleszár B, Kóbor I, Farkas K, Rosenfeld V, Berente DB, Bolla G, Kiss M, Kamondi A, Horvath AA. Low Functional network integrity in cognitively unimpaired and MCI subjects with depressive symptoms: results from a multi-center fMRI study. Transl Psychiatry 2024; 14:179. [PMID: 38580625 PMCID: PMC10997664 DOI: 10.1038/s41398-024-02891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/19/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
Evidence suggests that depressive symptomatology is a consequence of network dysfunction rather than lesion pathology. We studied whole-brain functional connectivity using a Minimum Spanning Tree as a graph-theoretical approach. Furthermore, we examined functional connectivity in the Default Mode Network, the Frontolimbic Network (FLN), the Salience Network, and the Cognitive Control Network. All 183 elderly subjects underwent a comprehensive neuropsychological evaluation and a 3 Tesla brain MRI scan. To assess the potential presence of depressive symptoms, the 13-item version of the Beck Depression Inventory (BDI) or the Geriatric Depression Scale (GDS) was utilized. Participants were assigned into three groups based on their cognitive status: amnestic mild cognitive impairment (MCI), non-amnestic MCI, and healthy controls. Regarding affective symptoms, subjects were categorized into depressed and non-depressed groups. An increased mean eccentricity and network diameter were found in patients with depressive symptoms relative to non-depressed ones, and both measures showed correlations with depressive symptom severity. In patients with depressive symptoms, a functional hypoconnectivity was detected between the Anterior Cingulate Cortex (ACC) and the right amygdala in the FLN, which impairment correlated with depressive symptom severity. While no structural difference was found in subjects with depressive symptoms, the volume of the hippocampus and the thickness of the precuneus and the entorhinal cortex were decreased in subjects with MCI, especially in amnestic MCI. The increase in eccentricity and diameter indicates a more path-like functional network configuration that may lead to an impaired functional integration in depression, a possible cause of depressive symptomatology in the elderly.
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Affiliation(s)
- Gabor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary.
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary.
| | - László Tombor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zoltan Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Eva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Máté Fullajtár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zsolt Huszár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Vanda Koszovácz
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Orsolya Lányi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Edit Vass
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Boróka Koleszár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - István Kóbor
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Katalin Farkas
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
| | - Viktoria Rosenfeld
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
| | - Dalida Borbála Berente
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
| | - Gergo Bolla
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
- Department of Measurement and Information Systems, University of Technology and Economics, Budapest, Hungary
| | - Mate Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Anita Kamondi
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Andras Attila Horvath
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
- Department of Anatomy Histology and Embryology, Semmelweis University, Budapest, Hungary
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Ntolkeras G, Makaram N, Bernabei M, De La Vega AC, Bolton J, Madsen JR, Stone SSD, Pearl PL, Papadelis C, Grant EP, Tamilia E. Interictal EEG source connectivity to localize the epileptogenic zone in patients with drug-resistant epilepsy: A machine learning approach. Epilepsia 2024; 65:944-960. [PMID: 38318986 PMCID: PMC11018464 DOI: 10.1111/epi.17898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/07/2024]
Abstract
OBJECTIVE To deconstruct the epileptogenic networks of patients with drug-resistant epilepsy (DRE) using source functional connectivity (FC) analysis; unveil the FC biomarkers of the epileptogenic zone (EZ); and develop machine learning (ML) models to estimate the EZ using brief interictal electroencephalography (EEG) data. METHODS We analyzed scalp EEG from 50 patients with DRE who had surgery. We reconstructed the activity (electrical source imaging [ESI]) of virtual sensors (VSs) across the whole cortex and computed FC separately for epileptiform and non-epileptiform EEG epochs (with or without spikes). In patients with good outcome (Engel 1a), four cortical regions were defined: EZ (resection) and three non-epileptogenic zones (NEZs) in the same and opposite hemispheres. Region-specific FC features in six frequency bands and three spatial ranges (long, short, inner) were compared between regions (Wilcoxon sign-rank). We developed ML classifiers to identify the VSs in the EZ using VS-specific FC features. Cross-validation was performed using good outcome data. Performance was compared with poor outcomes and interictal spike localization. RESULTS FC differed between EZ and NEZs (p < .05) during non-epileptiform and epileptiform epochs, showing higher FC in the EZ than its homotopic contralateral NEZ. During epileptiform epochs, the NEZ in the epileptogenic hemisphere showed higher FC than its contralateral NEZ. In good outcome patients, the ML classifiers reached 75% accuracy to the resection (91% sensitivity; 74% specificity; distance from EZ: 38 mm) using epileptiform epochs (gamma and beta frequency bands) and 62% accuracy using broadband non-epileptiform epochs, both outperforming spike localization (accuracy = 47%; p < .05; distance from EZ: 57 mm). Lower performance was seen in poor outcomes. SIGNIFICANCE We present an FC approach to extract EZ biomarkers from brief EEG data. Increased FC in various frequencies characterized the EZ during epileptiform and non-epileptiform epochs. FC-based ML models identified the resection better in good than poor outcome patients, demonstrating their potential for presurgical use in pediatric DRE.
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Affiliation(s)
- Georgios Ntolkeras
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Navaneethakrishna Makaram
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Matteo Bernabei
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Aime Cristina De La Vega
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, Texas, USA
| | - Ellen P Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
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Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Bowen Z, Shilling-Scrivo K, Losert W, Kanold PO. Fractured columnar small-world functional network organization in volumes of L2/3 of mouse auditory cortex. PNAS NEXUS 2024; 3:pgae074. [PMID: 38415223 PMCID: PMC10898513 DOI: 10.1093/pnasnexus/pgae074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/06/2024] [Indexed: 02/29/2024]
Abstract
The sensory cortices of the brain exhibit large-scale functional topographic organization, such as the tonotopic organization of the primary auditory cortex (A1) according to sound frequency. However, at the level of individual neurons, layer 2/3 (L2/3) A1 appears functionally heterogeneous. To identify if there exists a higher-order functional organization of meso-scale neuronal networks within L2/3 that bridges order and disorder, we used in vivo two-photon calcium imaging of pyramidal neurons to identify networks in three-dimensional volumes of L2/3 A1 in awake mice. Using tonal stimuli, we found diverse receptive fields with measurable colocalization of similarly tuned neurons across depth but less so across L2/3 sublayers. These results indicate a fractured microcolumnar organization with a column radius of ∼50 µm, with a more random organization of the receptive field over larger radii. We further characterized the functional networks formed within L2/3 by analyzing the spatial distribution of signal correlations (SCs). Networks show evidence of Rentian scaling in physical space, suggesting effective spatial embedding of subnetworks. Indeed, functional networks have characteristics of small-world topology, implying that there are clusters of functionally similar neurons with sparse connections between differently tuned neurons. These results indicate that underlying the regularity of the tonotopic map on large scales in L2/3 is significant tuning diversity arranged in a hybrid organization with microcolumnar structures and efficient network topologies.
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Affiliation(s)
- Zac Bowen
- Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
- Fraunhofer USA Center Mid-Atlantic, Riverdale, MD 20737, USA
| | - Kelson Shilling-Scrivo
- Department of Biology, University of Maryland, College Park, MD 20742, USA
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD 21230, USA
| | - Wolfgang Losert
- Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA
| | - Patrick O Kanold
- Department of Biology, University of Maryland, College Park, MD 20742, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 20215, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 20215, USA
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5
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Matarrese MAG, Loppini A, Fabbri L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Pearl PL, Filippi S, Papadelis C. Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy. Brain 2023; 146:3898-3912. [PMID: 37018068 PMCID: PMC10473571 DOI: 10.1093/brain/awad118] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/06/2023] Open
Abstract
Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity, mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analysed intracranial EEG data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and the direction of information flow among them via Granger causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone and the spike onset on intracranial EEG channels by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (interquartile range: 34-206), a spatial displacement of 14 cm (7.5-22 cm) and a velocity of 0.5 m/s (0.3-0.8 m/s). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 mm versus 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike onset, but not area of spike spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.
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Affiliation(s)
- Margherita A G Matarrese
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Alessandro Loppini
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Lorenzo Fabbri
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s 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
| | - Simonetta Filippi
- Laboratory of Nonlinear Physics and Mathematical Modeling, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, USA
- School of Medicine, Texas Christian University, Fort Worth, TX, USA
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G-Santoyo I, Ramírez-Carrillo E, Sanchez JD, López-Corona O. Potential long consequences from internal and external ecology: loss of gut microbiota antifragility in children from an industrialized population compared with an indigenous rural lifestyle. J Dev Orig Health Dis 2023; 14:469-480. [PMID: 37222148 DOI: 10.1017/s2040174423000144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Human health is strongly mediated by the gut microbiota ecosystem, which, in turn, depends not only on its state but also on its dynamics and how it responds to perturbations. Healthy microbiota ecosystems tend to be in criticality and antifragile dynamics corresponding to a maximum complexity configuration, which may be assessed with information and network theory analysis. Under this complex system perspective, we used a new analysis of published data to show that a children's population with an industrialized urban lifestyle from Mexico City exhibits informational and network characteristics similar to parasitized children from a rural indigenous population in the remote mountainous region of Guerrero, México. We propose then, that in this critical age for gut microbiota maturation, the industrialized urban lifestyle could be thought of as an external perturbation to the gut microbiota ecosystem, and we show that it produces a similar loss in criticality/antifragility as the one observed by internal perturbation due to parasitosis by the helminth A. lumbricoides. Finally, several general complexity-based guidelines to prevent or restore gut ecosystem antifragility are discussed.
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Affiliation(s)
- Isaac G-Santoyo
- Neuroecology Lab, Department of Psychology, UNAM, México, 04510
- Unidad de Investigación en Psicobiología y Neurociencias, Department of Psychology, UNAM, México, 04510
| | | | | | - Oliver López-Corona
- Investigadores por México (IxM)-CONACyT, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas (IIMAS), UNAM, México, 04510
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Corona L, Tamilia E, Perry MS, Madsen JR, Bolton J, Stone SSD, Stufflebeam SM, Pearl PL, Papadelis C. Non-invasive mapping of epileptogenic networks predicts surgical outcome. Brain 2023; 146:1916-1931. [PMID: 36789500 PMCID: PMC10151194 DOI: 10.1093/brain/awac477] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 11/03/2022] [Accepted: 11/30/2022] [Indexed: 02/16/2023] Open
Abstract
Epilepsy is increasingly considered a disorder of brain networks. Studying these networks with functional connectivity can help identify hubs that facilitate the spread of epileptiform activity. Surgical resection of these hubs may lead patients who suffer from drug-resistant epilepsy to seizure freedom. Here, we aim to map non-invasively epileptogenic networks, through the virtual implantation of sensors estimated with electric and magnetic source imaging, in patients with drug-resistant epilepsy. We hypothesize that highly connected hubs identified non-invasively with source imaging can predict the epileptogenic zone and the surgical outcome better than spikes localized with conventional source localization methods (dipoles). We retrospectively analysed simultaneous high-density electroencephalography (EEG) and magnetoencephalography data recorded from 37 children and young adults with drug-resistant epilepsy who underwent neurosurgery. Using source imaging, we estimated virtual sensors at locations where intracranial EEG contacts were placed. On data with and without spikes, we computed undirected functional connectivity between sensors/contacts using amplitude envelope correlation and phase locking value for physiologically relevant frequency bands. From each functional connectivity matrix, we generated an undirected network containing the strongest connections within sensors/contacts using the minimum spanning tree. For each sensor/contact, we computed graph centrality measures. We compared functional connectivity and their derived graph centrality of sensors/contacts inside resection for good (n = 22, ILAE I) and poor (n = 15, ILAE II-VI) outcome patients, tested their ability to predict the epileptogenic zone in good-outcome patients, examined the association between highly connected hubs removal and surgical outcome and performed leave-one-out cross-validation to support their prognostic value. We also compared the predictive values of functional connectivity with those of dipoles. Finally, we tested the reliability of virtual sensor measures via Spearman's correlation with intracranial EEG at population- and patient-level. We observed higher functional connectivity inside than outside resection (P < 0.05, Wilcoxon signed-rank test) for good-outcome patients, on data with and without spikes across different bands for intracranial EEG and electric/magnetic source imaging and few differences for poor-outcome patients. These functional connectivity measures were predictive of both the epileptogenic zone and outcome (positive and negative predictive values ≥55%, validated using leave-one-out cross-validation) outperforming dipoles on spikes. Significant correlations were found between source imaging and intracranial EEG measures (0.4 ≤ rho ≤ 0.9, P < 0.05). Our findings suggest that virtual implantation of sensors through source imaging can non-invasively identify highly connected hubs in patients with drug-resistant epilepsy, even in the absence of frank epileptiform activity. Surgical resection of these hubs predicts outcome better than dipoles.
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Affiliation(s)
- Ludovica Corona
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Steve M Stufflebeam
- Athinoula Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health, Cook Children's Health Care System, Fort Worth, TX 76104, USA
- Department of Bioengineering, University of Texas at Arlington, Arlington, TX 76010, USA
- School of Medicine, Texas Christian University, Fort Worth, TX 76129, USA
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8
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van Lutterveld R, Varkevisser T, Kouwer K, van Rooij SJH, Kennis M, Hueting M, van Montfort S, van Dellen E, Geuze E. Spontaneous brain activity, graph metrics, and head motion related to prospective post-traumatic stress disorder trauma-focused therapy response. Front Hum Neurosci 2022; 16:730745. [PMID: 36034114 PMCID: PMC9413840 DOI: 10.3389/fnhum.2022.730745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 07/21/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Trauma-focused psychotherapy for post-traumatic stress disorder (PTSD) is effective in about half of all patients. Investigating biological systems related to prospective treatment response is important to gain insight in mechanisms predisposing patients for successful intervention. We studied if spontaneous brain activity, brain network characteristics and head motion during the resting state are associated with future treatment success. Methods Functional magnetic resonance imaging scans were acquired from 46 veterans with PTSD around the start of treatment. Psychotherapy consisted of trauma-focused cognitive behavioral therapy (tf-CBT), eye movement desensitization and reprocessing (EMDR), or a combination thereof. After intervention, 24 patients were classified as treatment responders and 22 as treatment resistant. Differences between groups in spontaneous brain activity were evaluated using amplitude of low-frequency fluctuations (ALFF), while global and regional brain network characteristics were assessed using a minimum spanning tree (MST) approach. In addition, in-scanner head motion was assessed. Results No differences in spontaneous brain activity and global network characteristics were observed between the responder and non-responder group. The right inferior parietal lobule, right putamen and left superior parietal lobule had a more central position in the network in the responder group compared to the non-responder group, while the right dorsolateral prefrontal cortex (DLPFC), right inferior frontal gyrus and left inferior temporal gyrus had a less central position. In addition, responders showed less head motion. Discussion These results show that areas involved in executive functioning, attentional and action processes, learning, and visual-object processing, are related to prospective PTSD treatment response in veterans. In addition, these findings suggest that involuntary micromovements may be related to future treatment success.
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Affiliation(s)
- Remko van Lutterveld
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
- *Correspondence: Remko van Lutterveld,
| | - Tim Varkevisser
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
| | - Karlijn Kouwer
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
| | - Sanne J. H. van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, United States
| | - Mitzy Kennis
- ARQ National Psychotrauma Centre, ARQ Centre of Expertise for the Impact of Disasters and Crises, Diemen, Netherlands
| | - Martine Hueting
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
| | - Simone van Montfort
- Department of Intensive Care Medicine, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
- Department of Intensive Care Medicine, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Elbert Geuze
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, Netherlands
- Department of Psychiatry, University Medical Centre, Utrecht, Netherlands
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Zhang M, Riecke L, Fraga-González G, Bonte M. Altered brain network topology during speech tracking in developmental dyslexia. Neuroimage 2022; 254:119142. [PMID: 35342007 DOI: 10.1016/j.neuroimage.2022.119142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/15/2022] [Accepted: 03/23/2022] [Indexed: 10/18/2022] Open
Abstract
Developmental dyslexia is often accompanied by altered phonological processing of speech. Underlying neural changes have typically been characterized in terms of stimulus- and/or task-related responses within individual brain regions or their functional connectivity. Less is known about potential changes in the more global functional organization of brain networks. Here we recorded electroencephalography (EEG) in typical and dyslexic readers while they listened to (a) a random sequence of syllables and (b) a series of tri-syllabic real words. The network topology of the phase synchronization of evoked cortical oscillations was investigated in four frequency bands (delta, theta, alpha and beta) using minimum spanning tree graphs. We found that, compared to syllable tracking, word tracking triggered a shift toward a more integrated network topology in the theta band in both groups. Importantly, this change was significantly stronger in the dyslexic readers, who also showed increased reliance on a right frontal cluster of electrodes for word tracking. The current findings point towards an altered effect of word-level processing on the functional brain network organization that may be associated with less efficient phonological and reading skills in dyslexia.
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Affiliation(s)
- Manli Zhang
- Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
| | - Lars Riecke
- Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Gorka Fraga-González
- Department of Child and Adolescent Psychiatry, Faculty of Medicine, University of Zurich, Switzerland
| | - Milene Bonte
- Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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10
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Prediction of seizure outcome following temporal lobectomy: a magnetoencephalography-based graph theory approach". Seizure 2022; 97:73-81. [DOI: 10.1016/j.seizure.2022.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/12/2022] [Accepted: 03/15/2022] [Indexed: 11/22/2022] Open
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11
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Validating EEG, MEG and Combined MEG and EEG Beamforming for an Estimation of the Epileptogenic Zone in Focal Cortical Dysplasia. Brain Sci 2022; 12:brainsci12010114. [PMID: 35053857 PMCID: PMC8796031 DOI: 10.3390/brainsci12010114] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/04/2022] [Accepted: 01/06/2022] [Indexed: 02/04/2023] Open
Abstract
MEG and EEG source analysis is frequently used for the presurgical evaluation of pharmacoresistant epilepsy patients. The source localization of the epileptogenic zone depends, among other aspects, on the selected inverse and forward approaches and their respective parameter choices. In this validation study, we compare the standard dipole scanning method with two beamformer approaches for the inverse problem, and we investigate the influence of the covariance estimation method and the strength of regularization on the localization performance for EEG, MEG, and combined EEG and MEG. For forward modelling, we investigate the difference between calibrated six-compartment and standard three-compartment head modelling. In a retrospective study, two patients with focal epilepsy due to focal cortical dysplasia type IIb and seizure freedom following lesionectomy or radiofrequency-guided thermocoagulation (RFTC) used the distance of the localization of interictal epileptic spikes to the resection cavity resp. RFTC lesion as reference for good localization. We found that beamformer localization can be sensitive to the choice of the regularization parameter, which has to be individually optimized. Estimation of the covariance matrix with averaged spike data yielded more robust results across the modalities. MEG was the dominant modality and provided a good localization in one case, while it was EEG for the other. When combining the modalities, the good results of the dominant modality were mostly not spoiled by the weaker modality. For appropriate regularization parameter choices, the beamformer localized better than the standard dipole scan. Compared to the importance of an appropriate regularization, the sensitivity of the localization to the head modelling was smaller, due to similar skull conductivity modelling and the fixed source space without orientation constraint.
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12
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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. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 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] [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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13
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Fodor Z, Horváth A, Hidasi Z, Gouw AA, Stam CJ, Csukly G. EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance. Front Aging Neurosci 2021; 13:680200. [PMID: 34690735 PMCID: PMC8529331 DOI: 10.3389/fnagi.2021.680200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Accepted: 09/20/2021] [Indexed: 12/18/2022] Open
Abstract
Background: While decreased alpha and beta-band functional connectivity (FC) and changes in network topology have been reported in Alzheimer’s disease, it is not yet entirely known whether these differences can mark cognitive decline in the early stages of the disease. Our study aimed to analyze electroencephalography (EEG) FC and network differences in the alpha and beta frequency band during visuospatial memory maintenance between Mild Cognitive Impairment (MCI) patients and healthy elderly with subjective memory complaints. Methods: Functional connectivity and network structure of 17 MCI patients and 20 control participants were studied with 128-channel EEG during a visuospatial memory task with varying memory load. FC between EEG channels was measured by amplitude envelope correlation with leakage correction (AEC-c), while network analysis was performed by applying the Minimum Spanning Tree (MST) approach, which reconstructs the critical backbone of the original network. Results: Memory load (increasing number of to-be-learned items) enhanced the mean AEC-c in the control group in both frequency bands. In contrast to that, after an initial increase, the MCI group showed significantly (p < 0.05) diminished FC in the alpha band in the highest memory load condition, while in the beta band this modulation was absent. Moreover, mean alpha and beta AEC-c correlated significantly with the size of medial temporal lobe structures in the entire sample. The network analysis revealed increased maximum degree, betweenness centrality, and degree divergence, and decreased diameter and eccentricity in the MCI group compared to the control group in both frequency bands independently of the memory load. This suggests a rerouted network in the MCI group with a more centralized topology and a more unequal traffic load distribution. Conclusion: Alpha- and beta-band FC measured by AEC-c correlates with cognitive load-related modulation, with subtle medial temporal lobe atrophy, and with the disruption of hippocampal fiber integrity in the earliest stages of cognitive decline. The more integrated network topology of the MCI group is in line with the “hub overload and failure” framework and might be part of a compensatory mechanism or a consequence of neural disinhibition.
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Affiliation(s)
- Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - András Horváth
- Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Zoltán Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Alida A Gouw
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands.,Department of Neurology, Alzheimer Center Amsterdam, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Centers, Amsterdam, Netherlands
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
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14
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Resting-State MEG Source Space Network Metrics Associated with the Duration of Temporal Lobe Epilepsy. Brain Topogr 2021; 34:731-744. [PMID: 34652579 DOI: 10.1007/s10548-021-00875-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/28/2021] [Indexed: 10/20/2022]
Abstract
To evaluate the relationship between the network metrics of 68 brain regions and duration of temporal lobe epilepsy (TLE). Magnetoencephalography (MEG) data from 53 patients with TLE (28 left TLE, 25 right TLE) were recorded between seizures at resting state and analyzed in six frequency bands: delta (0.1-4 Hz), theta (4-8 Hz), lower alpha (8-10 Hz), upper alpha (10-13 Hz), beta (13-30 Hz), and lower gamma (30-48 Hz). Three local network metrics, betweenness centrality, nodal degree, and nodal efficiency, were chosen to analyze the functional brain network. In Left, Right, and All (Left + Right) TLE groups, different metrics provide significant positive or negative correlations with the duration of TLE, in different frequency bands, and in different brain regions. In the Left TLE group, significant correlation between TLE duration and metric exists in the delta, beta, or lower gamma band, with network betweenness centrality, nodal degree, or nodal efficiency, in left caudal middle frontal, left middle temporal, or left supramarginal. In the Right TLE group, significant correlation exists in lower gamma or delta band, with nodal degree, or nodal efficiency, in left precuneus or right temporal pole. In the All TLE group, the significant correlation exists in delta, theta, beta, or lower gamma band, with nodal degree, or betweenness centrality, in either left or right hemisphere. Network metrics for some specific brain regions changed in patients with TLE as the duration of their TLE increased. Further researching these changes may be important for studying the pathogenesis, presurgical evaluation, and clinical treatment of long-term TLE.
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15
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Douw L, Nissen IA, Fitzsimmons SMDD, Santos FAN, Hillebrand A, van Straaten ECW, Stam CJ, De Witt Hamer PC, Baayen JC, Klein M, Reijneveld JC, Heyer DB, Verhoog MB, Wilbers R, Hunt S, Mansvelder HD, Geurts JJG, de Kock CPJ, Goriounova NA. Cellular Substrates of Functional Network Integration and Memory in Temporal Lobe Epilepsy. Cereb Cortex 2021; 32:2424-2436. [PMID: 34564728 PMCID: PMC9157285 DOI: 10.1093/cercor/bhab349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 08/19/2021] [Accepted: 08/22/2021] [Indexed: 11/12/2022] Open
Abstract
Temporal lobe epilepsy (TLE) patients are at risk of memory deficits, which have been linked to functional network disturbances, particularly of integration of the default mode network (DMN). However, the cellular substrates of functional network integration are unknown. We leverage a unique cross-scale dataset of drug-resistant TLE patients (n = 31), who underwent pseudo resting-state functional magnetic resonance imaging (fMRI), resting-state magnetoencephalography (MEG) and/or neuropsychological testing before neurosurgery. fMRI and MEG underwent atlas-based connectivity analyses. Functional network centrality of the lateral middle temporal gyrus, part of the DMN, was used as a measure of local network integration. Subsequently, non-pathological cortical tissue from this region was used for single cell morphological and electrophysiological patch-clamp analysis, assessing integration in terms of total dendritic length and action potential rise speed. As could be hypothesized, greater network centrality related to better memory performance. Moreover, greater network centrality correlated with more integrative properties at the cellular level across patients. We conclude that individual differences in cognitively relevant functional network integration of a DMN region are mirrored by differences in cellular integrative properties of this region in TLE patients. These findings connect previously separate scales of investigation, increasing translational insight into focal pathology and large-scale network disturbances in TLE.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 02129 MA, Charlestown, USA
| | - Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Sophie M D D Fitzsimmons
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Fernando A N Santos
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Philip C De Witt Hamer
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Johannes C Baayen
- Department of Neurosurgery, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Martin Klein
- Department of Medical Psychology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VUmc Cancer Center Amsterdam Brain Tumor Center Amsterdam, 1081 HV, Amsterdam, the Netherlands.,Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede 2103 SW, Heemstede, the Netherlands
| | - Djai B Heyer
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Matthijs B Verhoog
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands.,Department of Human Biology, Division of Cell Biology, Neuroscience Institute, University of Cape Town, 7935, Cape Town, South Africa
| | - René Wilbers
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Sarah Hunt
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Huibert D Mansvelder
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Christiaan P J de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
| | - Natalia A Goriounova
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam Neuroscience, 1081 HV, Amsterdam, the Netherlands
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16
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Changes in the Functional Brain Network of Children Undergoing Repeated Epilepsy Surgery: An EEG Source Connectivity Study. Diagnostics (Basel) 2021; 11:diagnostics11071234. [PMID: 34359317 PMCID: PMC8306224 DOI: 10.3390/diagnostics11071234] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 06/29/2021] [Accepted: 07/05/2021] [Indexed: 11/19/2022] Open
Abstract
About 30% of children with drug-resistant epilepsy (DRE) continue to have seizures after epilepsy surgery. Since epilepsy is increasingly conceptualized as a network disorder, understanding how brain regions interact may be critical for planning re-operation in these patients. We aimed to estimate functional brain connectivity using scalp EEG and its evolution over time in patients who had repeated surgery (RS-group, n = 9) and patients who had one successful surgery (seizure-free, SF-group, n = 12). We analyzed EEGs without epileptiform activity at varying time points (before and after each surgery). We estimated functional connectivity between cortical regions and their relative centrality within the network. We compared the pre- and post-surgical centrality of all the non-resected (untouched) regions (far or adjacent to resection) for each group (using the Wilcoxon signed rank test). In alpha, theta, and beta frequency bands, the post-surgical centrality of the untouched cortical regions increased in the SF group (p < 0.001) whereas they decreased (p < 0.05) or did not change (p > 0.05) in the RS group after failed surgeries; when re-operation was successful, the post-surgical centrality of far regions increased (p < 0.05). Our data suggest that removal of the epileptogenic focus in children with DRE leads to a gain in the network centrality of the untouched areas. In contrast, unaltered or decreased connectivity is seen when seizures persist after surgery.
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17
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Li Y, Zhu H, Chen Q, Yang L, Bao X, Chen F, Ma H, Xu H, Luo L, Zhang R. Evaluation of Brain Network Properties in Patients with MRI-Negative Temporal Lobe Epilepsy: An MEG Study. Brain Topogr 2021; 34:618-631. [PMID: 34173926 DOI: 10.1007/s10548-021-00856-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/13/2021] [Indexed: 11/25/2022]
Abstract
Abnormal functional brain networks of temporal lobe epilepsy (TLE) patients with structural abnormalities may partially reflect structural lesions rather than either TLE per se or functional compensatory processes. In this study, we sought to investigate the brain-network properties of intractable TLE patients apart from the effects of structural abnormalities. The brain network properties of 20 left and 23 right MRI-negative TLE patients and 22 healthy controls were evaluated using magnetoencephalographic recordings in six main frequency bands. A slowing of oscillatory brain activity was observed for the left or right TLE group vs. healthy controls. The TLE groups presented significantly increased functional connectivity in the delta, theta, lower alpha and beta bands, and significantly greater values in the normalized clustering coefficient and path length, and significantly smaller values in the weighted small-world measure in the theta band when compared to healthy controls. Alterations in global and regional band powers can be attributed to spectral slowing in TLE patients. The brain networks of TLE patients displayed abnormally high synchronization in multi-frequency bands and shifted toward a more regular architecture with worse network efficiency in the theta band. Without the contamination of structural lesions, these significant findings can be helpful for better understanding of the pathophysiological mechanism of TLE. The theta band can be considered as a preferred frequency band for investigating the brain-network dysfunction of MRI-negative intractable TLE patients.
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Affiliation(s)
- Yuejun Li
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
- Department of Magnetoencephalography, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Haitao Zhu
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Qiqi Chen
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
- Department of Magnetoencephalography, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Lu Yang
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Xincai Bao
- Library of Xinxiang Medical University, Xinxiang, 453003, Henan, China
| | - Fangqing Chen
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Haiyan Ma
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Honghao Xu
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Lei Luo
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China
| | - Rui Zhang
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, Jiangsu, China.
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18
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Derks J, Kulik SD, Numan T, de Witt Hamer PC, Noske DP, Klein M, Geurts JJG, Reijneveld JC, Stam CJ, Schoonheim MM, Hillebrand A, Douw L. Understanding Global Brain Network Alterations in Glioma Patients. Brain Connect 2021; 11:865-874. [PMID: 33947274 DOI: 10.1089/brain.2020.0801] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Introduction: Glioma patients show increased global brain network clustering related to poorer cognition and epilepsy. However, it is unclear whether this increase is spatially widespread, localized in the (peri)tumor region only, or decreases with distance from the tumor. Materials and Methods: Weighted global and local brain network clustering was determined in 71 glioma patients and 53 controls by using magnetoencephalography. Tumor clustering was determined by averaging local clustering of regions overlapping with the tumor, and vice versa for non-tumor regions. Euclidean distance was determined from the tumor centroid to the centroids of other regions. Results: Patients showed higher global clustering compared with controls. Clustering of tumor and non-tumor regions did not differ, and local clustering was not associated with distance from the tumor. Post hoc analyses revealed that in the patient group, tumors were located more often in regions with higher clustering in controls, but it seemed that tumors of patients with high global clustering were located more often in regions with lower clustering in controls. Conclusions: Glioma patients show non-local network disturbances. Tumors of patients with high global clustering may have a preferred localization, namely regions with lower clustering in controls, suggesting that tumor localization relates to the extent of network disruption. Impact statement This work uses the innovative framework of network neuroscience to investigate functional connectivity patterns associated with brain tumors. Glioma (primary brain tumor) patients experience cognitive deficits and epileptic seizures, which have been related to brain network alterations. This study shows that glioma patients have a spatially widespread increase in global network clustering, which cannot be attributed to local effects of the tumor. Moreover, tumors occur more often in brain regions with higher network clustering in controls. This study emphasizes the global character of network alterations in glioma patients and suggests that preferred tumor locations are characterized by particular network profiles.
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Affiliation(s)
- Jolanda Derks
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Tianne Numan
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip C de Witt Hamer
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Neurosurgery, Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - David P Noske
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Neurosurgery, Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Martin Klein
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Medical Psychology, and Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jaap C Reijneveld
- Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Neurology, Overarching Institute, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital, Charlestown, Massachusetts, USA
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19
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Otsubo H, Ogawa H, Pang E, Wong SM, Ibrahim GM, Widjaja E. A review of magnetoencephalography use in pediatric epilepsy: an update on best practice. Expert Rev Neurother 2021; 21:1225-1240. [PMID: 33780318 DOI: 10.1080/14737175.2021.1910024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Introduction: Magnetoencephalography (MEG) is a noninvasive technique that is used for presurgical evaluation of children with drug-resistant epilepsy (DRE).Areas covered: The contributions of MEG for localizing the epileptogenic zone are discussed, in particular in extra-temporal lobe epilepsy and focal cortical dysplasia, which are common in children, as well as in difficult to localize epilepsy such as operculo-insular epilepsy. Further, the authors review current evidence on MEG for mapping eloquent cortex, its performance, application in clinical practice, and potential challenges.Expert opinion: MEG could change the clinical management of children with DRE by directing placement of intracranial electrodes thereby enhancing their yield. With improved identification of a circumscribed epileptogenic zone, MEG could render more patients as suitable candidates for epilepsy surgery and increase utilization of surgery.
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Affiliation(s)
- Hiroshi Otsubo
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Hiroshi Ogawa
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Elizabeth Pang
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada.,Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada
| | - Simeon M Wong
- Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada
| | - George M Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Toronto, Canada.,Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
| | - Elysa Widjaja
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada.,Neuroscience and Mental Health, Hospital for Sick Children, Toronto, Canada.,Diagnostic Imaging, Hospital for Sick Children, Toronto, Canada
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20
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Hao J, Luo W, Xie Y, Feng Y, Sun W, Peng W, Zhao J, Zhang P, Ding J, Wang X. Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy. Front Hum Neurosci 2021; 15:637071. [PMID: 33815082 PMCID: PMC8009991 DOI: 10.3389/fnhum.2021.637071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 02/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background and Purpose Transcranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS (ctDCS) by analyzing the ctDCS-induced functional network alterations. Methods Either the active 5-day, -1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging (fMRI) data. A support vector machine (SVM) prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers. Results Our results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS (at least 20% reduction rate of seizure frequency). Our prediction model achieved the mean prediction accuracy of 68.3% (mean sensitivity: 70.0%; mean specificity: 67.5%) after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance. Conclusion The study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.
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Affiliation(s)
- Jiaxin Hao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Wenyi Luo
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuhai Xie
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yu Feng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Sun
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Weifeng Peng
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Ding
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Xin Wang
- Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.,State Key Laboratory of Medical Neurobiology, the Institutes of Brain Science and the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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21
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Godfrey M, Singh KD. Measuring robust functional connectivity from resting-state MEG using amplitude and entropy correlation across frequency bands and temporal scales. Neuroimage 2020; 226:117551. [PMID: 33186722 PMCID: PMC7836237 DOI: 10.1016/j.neuroimage.2020.117551] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 10/08/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
Abstract
MRVE measures the dynamic variability of MEG signals at a range of temporal scales. MRVE correlation and AEC detected robust resting state functional connectivity. The most robust patterns were found for fS=75Hz MRVE correlation and beta band AEC. Connectivity strength negatively correlates with local MRVE at fine time scales. Eye movement affects resting state connectivity measurements across frequencies.
Recent studies have shown how MEG can reveal spatial patterns of functional connectivity using frequency-specific oscillatory coupling measures and that these may be modified in disease. However, there is a need to understand both how repeatable these patterns are across participants and how these measures relate to the moment-to-moment variability (or ‘irregularity) of neural activity seen in healthy brain function. In this study, we used Multi-scale Rank-Vector Entropy (MRVE) to calculate the dynamic timecourses of signal variability over a range of temporal scales. The correlation of MRVE timecourses was then used to detect functional connections in resting state MEG recordings that were robust over 183 participants and varied with temporal scale. We compared these MRVE connectivity patterns to those derived using the more conventional method of oscillatory amplitude envelope correlation (AEC) using methods designed to quantify the consistency of these patterns across participants. Using AEC, the most consistent connectivity patterns, across the cohort, were seen in the alpha and beta frequency bands. At fine temporal scales (corresponding to ‘scale frequencies, fS = 30-150Hz), MRVE correlation detected mostly occipital and parietal connections. These showed high similarity with the networks identified by AEC in the alpha and beta frequency bands. The most consistent connectivity profiles between participants were given by MRVE correlation at fS = 75Hz and AEC in the beta band. The physiological relevance of MRVE was also investigated by examining the relationship between connectivity strength and local variability. It was found that local activity at frequencies fS≳ 10Hz becomes more regular when a region exhibits high levels of resting state connectivity, as measured by fine scale MRVE correlation (fS∼ 30-150Hz) and by alpha and beta band AEC. Analysis of the EOG recordings also revealed that eye movement affected both connectivity measures. Higher levels of eye movement were associated with stronger frontal connectivity, as measured by MRVE correlation. More eye movement was also associated with reduced occipital and parietal connectivity strength for both connectivity measures, although this was not significant after correction for multiple comparisons.
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Affiliation(s)
- Megan Godfrey
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
| | - Krish D Singh
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, Cardiff, UK.
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22
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Zanin M, Belkoura S, Gomez J, Alfaro C, Cano J. Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients. Brain Topogr 2020; 34:6-18. [PMID: 33044705 DOI: 10.1007/s10548-020-00799-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 10/04/2020] [Indexed: 11/30/2022]
Abstract
In spite of the large attention received by brain activity analyses through functional networks, the effects of uncertainty on such representations have mostly been neglected. We here elaborate the hypothesis that such uncertainty is not just a nuisance, but that on the contrary is condition-dependent. We test this hypothesis by analysing a large set of EEG brain recordings corresponding to control subjects and patients suffering from alcoholism, through the reconstruction of the corresponding Maximum Spanning Trees (MSTs), the assessment of their topological differences, and the comparison of two frequentist and Bayesian reconstruction approaches. A machine learning model demonstrates that the Bayesian reconstruction encodes more information than the frequentist one, and that such additional information is related to the uncertainty of the topological structures. We finally show how the Bayesian approach is more effective in the validation of generative models, over and above the frequentist one, by proposing and disproving two models based on additive noise.
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Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122, Palma de Mallorca, Spain.
| | - Seddik Belkoura
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier Gomez
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain
| | - César Alfaro
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain
| | - Javier Cano
- Department of Computer Science and Statistics, Universidad Rey Juan Carlos, Madrid, Spain.,Department of Statistics, University of Auckland, Auckland, New Zealand
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23
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Farashi S, Khosrowabadi R. EEG based emotion recognition using minimum spanning tree. Phys Eng Sci Med 2020; 43:985-996. [PMID: 32632572 DOI: 10.1007/s13246-020-00895-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 06/29/2020] [Indexed: 11/30/2022]
Abstract
Emotion is a fundamental factor that influences human cognition, motivation, decision making and social interactions. This psychological state arises spontaneously and goes with physiological changes that can be recognized by computational methods. In this study, changes in minimum spanning tree (MST) structure of brain functional connectome were used for emotion classification based on EEG data and the obtained results were employed for interpretation about the most informative frequency content of emotional states. For estimation of interaction between different brain regions, several connectivity metrics were applied and interactions were calculated in different frequency bands. Subsequently, the MST graph was extracted from the functional connectivity matrix and its features were used for emotion recognition. The results showed that the accuracy of the proposed method for separating emotions with different arousal levels was 88.28%, while for different valence levels it was 81.25%. Interestingly, the system performance for binary classification of emotions based on quadrants of arousal-valence space was also higher than 80%. The MST approach allowed us to study the change of brain complexity and dynamics in various emotional states. This capability provided us enough knowledge to claim lower-alpha and gamma bands contain the main information for discrimination of emotional states.
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Affiliation(s)
- Sajjad Farashi
- Hamadan University of Medical Sciences, Hamadan, Iran.
- Autism Spectrum Disorder Research Center, Hamadan University of Medical Sciences, Hamadan, Iran.
| | - Reza Khosrowabadi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University GC, Tehran, Iran
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24
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Aydin Ü, Pellegrino G, Ali OBK, Abdallah C, Dubeau F, Lina JM, Kobayashi E, Grova C. Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients. J Neural Eng 2020; 17:035007. [PMID: 32191632 DOI: 10.1088/1741-2552/ab8113] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Focal epilepsy is a disorder affecting several brain networks; however, epilepsy surgery usually targets a restricted region, the so-called epileptic focus. There is a growing interest in embedding resting state (RS) connectivity analysis into pre-surgical workup. APPROACH In this retrospective study, we analyzed Magnetoencephalography (MEG) long-range RS functional connectivity patterns in patients with drug-resistant focal epilepsy. MEG recorded prior to surgery from seven seizure-free (Engel Ia) and five non seizure-free (Engel III or IV) patients were analyzed (minimum 2-years post-surgical follow-up). MEG segments without any detectable epileptic activity were source localized using wavelet-based Maximum Entropy on the Mean method. Amplitude envelope correlation in the theta (4-8 Hz), alpha (8-13 Hz), and beta (13-26 Hz) bands were used for assessing connectivity. MAIN RESULTS For seizure-free patients, we found an isolated epileptic network characterized by weaker connections between the brain region where interictal epileptic discharges (IED) are generated and the rest of the cortex, when compared to connectivity between the corresponding contralateral homologous region and the rest of the cortex. Contrarily, non seizure-free patients exhibited a widespread RS epileptic network characterized by stronger connectivity between the IED generator and the rest of the cortex, in comparison to the contralateral region and the cortex. Differences between the two seizure outcome groups concerned mainly distant long-range connections and were found in the alpha-band. SIGNIFICANCE Importantly, these connectivity patterns suggest specific mechanisms describing the underlying organization of the epileptic network and were detectable at the individual patient level, supporting the prospect use of MEG connectivity patterns in epilepsy to predict post-surgical seizure outcome.
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Affiliation(s)
- Ümit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada. Authors to whom any correspondence should be addressed
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25
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Aerts H, Schirner M, Dhollander T, Jeurissen B, Achten E, Van Roost D, Ritter P, Marinazzo D. Modeling brain dynamics after tumor resection using The Virtual Brain. Neuroimage 2020; 213:116738. [DOI: 10.1016/j.neuroimage.2020.116738] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 02/24/2020] [Accepted: 03/11/2020] [Indexed: 11/28/2022] Open
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26
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Pourmotabbed H, Wheless JW, Babajani-Feremi A. Lateralization of epilepsy using intra-hemispheric brain networks based on resting-state MEG data. Hum Brain Mapp 2020; 41:2964-2979. [PMID: 32400923 PMCID: PMC7336137 DOI: 10.1002/hbm.24990] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 03/07/2020] [Accepted: 03/10/2020] [Indexed: 12/31/2022] Open
Abstract
Focal epilepsy originates within networks in one hemisphere. However, previous studies have investigated network topologies for the entire brain. In this study, magnetoencephalography (MEG) was used to investigate functional intra‐hemispheric networks of healthy controls (HCs) and patients with left‐ or right‐hemispheric temporal lobe or temporal plus extra‐temporal lobe epilepsy. 22 HCs, 25 left patients (LPs), and 16 right patients (RPs) were enrolled. The debiased weighted phase lag index was used to calculate functional connectivity between 246 brain regions in six frequency bands. Global efficiency, characteristic path length, and transitivity were computed for left and right intra‐hemispheric networks. The right global graph measures (GGMs) in the theta band were significantly different (p < .005) between RPs and both LPs and HCs. Right and left GGMs in higher frequency bands were significantly different (p < .05) between HCs and the patients. Right GGMs were used as input features of a Naïve‐Bayes classifier to classify LPs and RPs (78.0% accuracy) and all three groups (75.5% accuracy). The complete theta band brain networks were compared between LPs and RPs with network‐based statistics (NBS) and with the clustering coefficient (CC), nodal efficiency (NE), betweenness centrality (BC), and eigenvector centrality (EVC). NBS identified a subnetwork primarily composed of right intra‐hemispheric connections. Significantly different (p < .05) nodes were primarily in the right hemisphere for the CC and NE and primarily in the left hemisphere for the BC and EVC. These results indicate that intra‐hemispheric MEG networks may be incorporated in the diagnosis and lateralization of focal epilepsy.
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Affiliation(s)
- Haatef Pourmotabbed
- Department of Orthopaedic Surgery and Biomedical Engineering, University of Tennessee Health Science Center, Memphis, Tennessee, USA.,Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA.,Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center, Memphis, Tennessee, USA.,Neuroscience Institute & Le Bonheur Comprehensive Epilepsy Program, Le Bonheur Children's Hospital, Memphis, Tennessee, USA
| | - James W Wheless
- Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center, Memphis, Tennessee, USA.,Neuroscience Institute & Le Bonheur Comprehensive Epilepsy Program, Le Bonheur Children's Hospital, Memphis, Tennessee, USA
| | - Abbas Babajani-Feremi
- Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA.,Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center, Memphis, Tennessee, USA.,Neuroscience Institute & Le Bonheur Comprehensive Epilepsy Program, Le Bonheur Children's Hospital, Memphis, Tennessee, USA.,Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, Tennessee, USA
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27
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Cao R, Hao Y, Wang X, Gao Y, Shi H, Huo S, Wang B, Guo H, Xiang J. EEG Functional Connectivity Underlying Emotional Valance and Arousal Using Minimum Spanning Trees. Front Neurosci 2020; 14:355. [PMID: 32457566 PMCID: PMC7222391 DOI: 10.3389/fnins.2020.00355] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/24/2020] [Indexed: 11/20/2022] Open
Abstract
In recent years, traditional methods such as power spectrum and amplitude analysis have been used to research the emotional electroencephalogram (EEG). The brain network method is also used in emotional EEG research, which can better reflect the activity of brains. A minimum spanning tree (MST) represents the key information flow in the weighted brain network, and it provides a sensitive method to capture subtle information in network organization while effectively avoiding the shortcomings of traditional brain networks. The DEAP dataset provides electroencephalogram (EEG) data for four categories of emotions: high arousal and high valence (HAHV), high arousal and low valence (HALV), low arousal and high valence (LAHV), and low arousal and low valence (LALV). Phase lag index (PLI) weighted matrices were calculated in five frequency bands. On this basis, the minimum spanning trees were constructed. At the same valence level in the gamma (γ) band, HAHV and HALV showed significant higher mean PLI (MPLI), maximum degree (Degreemax) and leaf fraction and significant lower diameter and eccentricity than LAHV and LALV. At the same arousal level in the γ band, HALV showed significant higher MPLI, Degreemax and leaf fraction and significant lower diameter and eccentricity than HAHV. These results indicate that the low-arousal showed more line-shaped configurations than the high-arousal. Additionally, in the high-arousal condition, a shift toward more star-shaped trees from high-valence to low-valence supports the trend toward randomness of the brain network with negative emotions and that the brain is more activated when faced with negative emotions. From a brain network perspective, this phenomenon provides a theoretical basis for negative bias.
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Affiliation(s)
- Rui Cao
- College of Software Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Yan Hao
- College of Software Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Xin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yuan Gao
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Huiyu Shi
- College of Software Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Shoujun Huo
- College of Software Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
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28
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The small scale functional topology of movement control: Hierarchical organization of local activity anticipates movement generation in the premotor cortex of primates. Neuroimage 2020; 207:116354. [DOI: 10.1016/j.neuroimage.2019.116354] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 10/24/2019] [Accepted: 11/11/2019] [Indexed: 11/23/2022] Open
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29
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Liu L, Zhang H, Wu J, Yu Z, Chen X, Rekik I, Wang Q, Lu J, Shen D. Overall survival time prediction for high-grade glioma patients based on large-scale brain functional networks. Brain Imaging Behav 2020; 13:1333-1351. [PMID: 30155788 DOI: 10.1007/s11682-018-9949-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning.
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Affiliation(s)
- Luyan Liu
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jinsong Wu
- Glioma Surgery Division, Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, 200040, China.,Shanghai Key Lab of Medical Image Computing and Computer-Assisted Intervention, Shanghai, 200040, China.,Neurosurgery Department of Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai, 200040, China
| | - Zhengda Yu
- Glioma Surgery Division, Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Xiaobo Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,BASIRA Lab, CVIP Group, School of Science and Engineering, Computing, University of Dundee, Dundee, UK
| | - Qian Wang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
| | - Junfeng Lu
- Glioma Surgery Division, Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, 200040, China. .,Shanghai Key Lab of Medical Image Computing and Computer-Assisted Intervention, Shanghai, 200040, China. .,Neurosurgery Department of Huashan Hospital, 12 Wulumuqi Zhong Road, Shanghai, 200040, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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30
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Wang B, Miao L, Niu Y, Cao R, Li D, Yan P, Guo H, Yan T, Wu J, Xiang J. Abnormal Functional Brain Networks in Mild Cognitive Impairment and Alzheimer's Disease: A Minimum Spanning Tree Analysis. J Alzheimers Dis 2019; 65:1093-1107. [PMID: 30149457 DOI: 10.3233/jad-180603] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Alzheimer's disease (AD) disrupts the topological architecture of whole-brain connectivity. Minimum spanning tree (MST), which captures the most important connections in a network, has been considered an unbiased method for brain network analysis. However, the alterations in the MST of functional brain networks during the progression of AD remain unclear. Here, we performed an MST analysis to examine the alterations in functional networks among normal controls (NCs), mild cognitive impairment (MCI) patients, and AD patients. We identified substantial differences in the connections among the three groups. The maximum betweenness centrality, leaf number, and tree hierarchy of the MSTs showed significant group differences, indicating a more star-like topology in the MCI patients and a more line-like topology in the NCs and AD patients. These findings may correspond to changes in the core of the functional brain networks. For nodal properties (degree and betweenness centrality), we determined that brain regions around the cingulate gyrus, occipital lobes, subcortex, and inferior temporal gyrus showed significant differences among the three groups and contributed to the global topological alterations. The leaf number and tree hierarchy, as well as the nodal properties, were significantly correlated with clinical features in the MCI and AD patients, which demonstrated that more star-to-line topology changes were associated with worse cognitive performance in these patients. These findings indicated that MST properties could capture slight alterations in network topology, particularly for the differences between NCs and MCI patients, and may be applicable as neuroimaging markers of the early stage of AD.
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31
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Background EEG Connectivity Captures the Time-Course of Epileptogenesis in a Mouse Model of Epilepsy. eNeuro 2019; 6:ENEURO.0059-19.2019. [PMID: 31346002 PMCID: PMC6709215 DOI: 10.1523/eneuro.0059-19.2019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/12/2019] [Accepted: 05/30/2019] [Indexed: 11/21/2022] Open
Abstract
Large-scale brain networks are increasingly recognized as important for the generation of seizures in epilepsy. However, how a network evolves from a healthy state through the process of epileptogenesis remains unclear. To address this question, here, we study longitudinal epicranial background EEG recordings (30 electrodes, EEG free from epileptiform activity) of a mouse model of mesial temporal lobe epilepsy. We analyze functional connectivity networks and observe that over the time course of epileptogenesis the networks become increasingly asymmetric. Furthermore, computational modelling reveals that a set of nodes, located outside of the region of initial insult, emerges as particularly important for the network dynamics. These findings are consistent with experimental observations, thus demonstrating that ictogenic mechanisms can be revealed on the EEG, that computational models can be used to monitor unfolding epileptogenesis and that both the primary focus and epileptic network play a role in epileptogenesis.
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32
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Krukow P, Jonak K, Karpiński R, Karakuła-Juchnowicz H. Abnormalities in hubs location and nodes centrality predict cognitive slowing and increased performance variability in first-episode schizophrenia patients. Sci Rep 2019; 9:9594. [PMID: 31270391 PMCID: PMC6610093 DOI: 10.1038/s41598-019-46111-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 06/21/2019] [Indexed: 01/10/2023] Open
Abstract
Introducing the Minimum Spanning Tree (MST) algorithms to neural networks science eliminated the problem of arbitrary setting of the threshold for connectivity strength. Despite these advantages, MST has been rarely used to study network abnormalities in schizophrenia. An MST graph mapping a network structure is its simplification, therefore, it is important to verify whether the reconfigured network is significantly related to the behavioural dimensions of the clinical picture of schizophrenia. 35 first-episode schizophrenia patients and 35 matched healthy controls underwent an assessment of information processing speed, cognitive inter-trial variability modelled with ex-Gaussian distributional analysis of reaction times and resting-state EEG recordings to obtain frequency-specific functional connectivity matrices from which MST graphs were computed. The patients’ network had a more random structure and star-like arrangement with overloaded hubs positioned more posteriorly than it was in the case of the control group. Deficient processing speed in the group of patients was predicted by increased maximal betweenness centrality in beta and gamma bands, while decreased consistency in cognitive processing was predicted by the betweenness centrality of posterior nodes in the gamma band, together with duration of illness. The betweenness centrality of posterior nodes in the gamma band was also significantly correlated with positive psychotic symptoms in the clinical group.
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Affiliation(s)
- Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland.
| | - Kamil Jonak
- Department of Biomedical Engineering, Lublin University of Technology, Lublin, Poland.,Chair and I Clinic of Psychiatry, Psychotherapy and Early Intervention, Medical University of Lublin, Lublin, Poland
| | - Robert Karpiński
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Lublin, Poland
| | - Hanna Karakuła-Juchnowicz
- Chair and I Clinic of Psychiatry, Psychotherapy and Early Intervention, Medical University of Lublin, Lublin, Poland
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Liu W, Tian S, Zhang J, Huang P, Wang T, Deng Y, Liu X, Miao F, Sun B, Zhan S. Utility of stereo-electroencephalography recording guided by magnetoencephalography in the surgical treatment of epilepsy patients with negative magnetic resonance imaging results. Int J Neurosci 2019; 129:1045-1052. [PMID: 31215295 DOI: 10.1080/00207454.2019.1634066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Objective: It is challenging for neurosurgeons to perform surgeries on patients without detectable structural lesions. Therefore, this retrospective study aimed to explore the outcome of stereo-electroencephalography (SEEG) in suspicious areas guided by magnetoencephalography (MEG)-magnetic resonance imaging (MRI) reconstruction in MRI-negative epilepsy patients. Methods: This study included 47 patients with negative-MRI epilepsy. Seizure outcome at 24 months was assessed using a modified Engel's classification. Accordingly, class I and II were considered favorable outcomes, whereas classes III and IV were unfavorable. Furthermore, patients were classified into a consistent group if the results of MEG and SEEG indicated the same area of the brain. The relationship between surgical outcome and the concordance of MEG and SEEG was analyzed. Results: A complete seizure-free condition was achieved in 22 (47%) patients. Sex, handedness, age and duration of illness were not significantly associated with seizure-free outcome (p = .187 [Pearson chi-squared test]). The number of patients with favorable outcome (Engle I and II) was as high as 68% at the time of follow-up. Furthermore, more seizure-free patients were found in the SEEG and MEG consistent group. Conclusions: SEEG is a valuable tool in the pre-evaluation for resective epilepsy surgery, particularly in negative-MRI epilepsy patients; MEG greatly facilitates localization for SEEG electrode implantation. However, none of these tools are absolutely sensitive and reliable; therefore, collecting as much information as possible is necessary to achieve satisfactory results in epilepsy surgery.
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Affiliation(s)
- Wei Liu
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Shuaiwei Tian
- Department of Neurosurgery, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Jing Zhang
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Peng Huang
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Tao Wang
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Yulei Deng
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Xiaoying Liu
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Fei Miao
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Bomin Sun
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
| | - Shikun Zhan
- Department of Stereotactic and Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai , China
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Chen CJ, Wang JL. A New Approach for Functional Connectivity via Alignment of Blood Oxygen Level-Dependent Signals. Brain Connect 2019; 9:464-474. [PMID: 31219308 PMCID: PMC6909746 DOI: 10.1089/brain.2018.0636] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Due to technological advances, spatially indexed objects, such as blood oxygen level-dependent time series or electroencephalography data, are commonly observed across different scientific disciplines. Such object data are typically high dimensional and therefore challenging to handle. We propose a new approach for spatially indexed object data by mapping their spatial locations to a targeted one-dimensional interval so objects that are similar are placed near each other on the new target space. The proposed alignment not only provides a visualization tool for such complex object data but also facilitates a new way to study brain functional connectivity. Specifically, we introduce a new concept of path length to quantify the functional connectivity and a new community detection method. The advantages of the proposed methods are illustrated by simulations and in a study of functional connectivity for Alzheimer's disease.
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Affiliation(s)
- Chun-Jui Chen
- Department of Statistics, University of California Davis, Davis, California
| | - Jane-Ling Wang
- Department of Statistics, University of California Davis, Davis, California
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35
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Derks J, Kulik S, Wesseling P, Numan T, Hillebrand A, van Dellen E, de Witt Hamer PC, Geurts JJG, Reijneveld JC, Stam CJ, Klein M, Douw L. Understanding cognitive functioning in glioma patients: The relevance of IDH-mutation status and functional connectivity. Brain Behav 2019; 9:e01204. [PMID: 30809977 PMCID: PMC6456787 DOI: 10.1002/brb3.1204] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 11/15/2018] [Accepted: 12/11/2018] [Indexed: 12/20/2022] Open
Abstract
INTRODUCTION Cognitive deficits occur frequently in diffuse glioma patients, but are limitedly understood. An important marker for survival in these patients is isocitrate dehydrogenase (IDH) mutation (IDH-mut). Patients with IDH-mut glioma have a better prognosis but more often suffer from epilepsy than patients with IDH-wildtype (IDH-wt) glioma, who are generally older and more often have cognitive deficits. We investigated whether global brain functional connectivity differs between patients with IDH-mut and IDH-wt glioma, and whether this measure reflects variations in cognitive functioning in these subpopulations beyond the associated differences in age and presence of epilepsy. METHODS We recorded magnetoencephalography and tested cognitive functioning in 54 diffuse glioma patients (31 IDH-mut, 23 IDH-wt). Global functional connectivity between 78 atlas regions spanning the entire cortex was calculated in two frequency bands (theta and alpha). Group differences in global functional connectivity were tested, as was their association with cognitive functioning, controlling for age, education, and presence of epilepsy. RESULTS Patients with IDH-wt glioma had lower functional connectivity in the alpha band than patients with IDH-mut glioma (p = 0.040, corrected for age and presence of epilepsy). Lower alpha band functional connectivity was associated with poorer cognitive performance (p < 0.034), corrected for age, education, and presence of epilepsy. CONCLUSION Global functional connectivity is lower in patients with IDH-wt diffuse glioma compared to patients with IDH-mut diffuse glioma. Moreover, having lower functional alpha connectivity relates to poorer cognitive performance in patients with diffuse glioma, regardless of age, education, and presence of epilepsy.
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Affiliation(s)
- Jolanda Derks
- Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, The Netherlands.,VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
| | - Shanna Kulik
- Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, The Netherlands.,VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
| | - Pieter Wesseling
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands.,Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands.,Department of Pathology, Princess Máxima Center for Pediatric Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Tianne Numan
- Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, The Netherlands.,VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, University Medical Center Utrecht, Utrecht, The Netherlands.,Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Philip C de Witt Hamer
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands.,Department of Neurosurgery, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Jaap C Reijneveld
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands.,Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Martin Klein
- VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands.,Department of Medical Psychology, VU University Medical Center, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy & Neurosciences, VU University Medical Center, Amsterdam, The Netherlands.,VUmc CCA Brain Tumor Center Amsterdam, Amsterdam, The Netherlands.,Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital, Charlestown, Massachusetts
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36
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Saba V, Premi E, Cristillo V, Gazzina S, Palluzzi F, Zanetti O, Gasparotti R, Padovani A, Borroni B, Grassi M. Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia. Front Neurosci 2019; 13:211. [PMID: 30930736 PMCID: PMC6427927 DOI: 10.3389/fnins.2019.00211] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/25/2019] [Indexed: 12/12/2022] Open
Abstract
Brain functional disruption and cognitive shortfalls as consequences of neurodegeneration are among the most investigated aspects in current clinical research. Traditionally, specific anatomical and behavioral traits have been associated with neurodegeneration, thus directly translatable in clinical terms. However, these qualitative traits, do not account for the extensive information flow breakdown within the functional brain network that deeply affect cognitive skills. Behavioural variant Frontotemporal Dementia (bvFTD) is a neurodegenerative disorder characterized by behavioral and executive functions disturbances. Deviations from the physiological cognitive functioning can be accurately inferred and modeled from functional connectivity alterations. Although the need for unbiased metrics is still an open issue in imaging studies, the graph-theory approach applied to neuroimaging techniques is becoming popular in the study of brain dysfunction. In this work, we assessed the global connectivity and topological alterations among brain regions in bvFTD patients using a minimum spanning tree (MST) based analysis of resting state functional MRI (rs-fMRI) data. Whilst several graph theoretical methods require arbitrary criteria (including the choice of network construction thresholds and weight normalization methods), MST is an unambiguous modeling solution, ensuring accuracy, robustness, and reproducibility. MST networks of 116 regions of interest (ROIs) were built on wavelet correlation matrices, extracted from 41 bvFTD patients and 39 healthy controls (HC). We observed a global fragmentation of the functional network backbone with severe disruption of information-flow highways. Frontotemporal areas were less compact, more isolated, and concentrated in less integrated structures, respect to healthy subjects. Our results reflected such complex breakdown of the frontal and temporal areas at both intra-regional and long-range connections. Our findings highlighted that MST, in conjunction with rs-fMRI data, was an effective method for quantifying and detecting functional brain network impairments, leading to characteristic bvFTD cognitive, social, and executive functions disorders.
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Affiliation(s)
- Valentina Saba
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Enrico Premi
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Viviana Cristillo
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Stefano Gazzina
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Fernando Palluzzi
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Orazio Zanetti
- Alzheimer's Research Unit, IRCCS Fatebenefratelli, Brescia, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Mario Grassi
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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Wang H, Sun Y, Lv J, Bo S. Random topology organization and decreased visual processing of internet addiction: Evidence from a minimum spanning tree analysis. Brain Behav 2019; 9:e01218. [PMID: 30706671 PMCID: PMC6422800 DOI: 10.1002/brb3.1218] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Revised: 10/31/2018] [Accepted: 12/10/2018] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVES Internet addiction (IA) has been associated with widespread brain alterations. Functional connectivity (FC) and network analysis results related to IA are inconsistent between studies, and how network hubs change is not known. The aim of this study was to evaluate functional and topological networks using an unbiased minimum spanning tree (MST) analysis on electroencephalography (EEG) data in IA and healthy control (HC) college students. METHODS In this study, Young's internet addiction test was used as an IA severity measure. EEG recordings were obtained in IA (n = 30) and HC participants (n = 30), matched for age and sex, during rest. The phase lag index (PLI) and MST were applied to analyze FC and network topology. We expected to obtain evidence of underlying alterations in functional and topological networks related to IA. RESULTS IA participants showed higher delta FC between left-side frontal and parieto-occipital areas compared to the HC group (p < 0.001), global MST measures revealed a more star-like network in IA participants in the upper alpha and beta bands, and the occipital brain region was relatively less important in the IA relative to the HC group in the lower band. The correlation results were consistent with the MST results: higher IA severity correlated with higher Max degree and kappa, and lower eccentricity and diameter. CONCLUSIONS Functional networks of the IA group were characterized by increased FC, a more random organization, and a decrease of relative functional importance of the visual processing area. Taken together, these alterations can help us understand the influence of IA to brain mechanism.
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Affiliation(s)
- Hongxia Wang
- School of Psychology, Liaoning Normal University, Da Lian, China
| | - Yan Sun
- School of Psychology, Liaoning Normal University, Da Lian, China
| | - Jiaojiao Lv
- School of Psychology, Liaoning Normal University, Da Lian, China
| | - Siyu Bo
- School of Psychology, Liaoning Normal University, Da Lian, China
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38
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Kuang L, Han X, Chen K, Caselli RJ, Reiman EM, Wang Y. A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative. Hum Brain Mapp 2019; 40:1062-1081. [PMID: 30569583 PMCID: PMC6570412 DOI: 10.1002/hbm.24383] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Revised: 07/25/2018] [Accepted: 08/26/2018] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.
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Affiliation(s)
- Liqun Kuang
- School of Computer Science and TechnologyNorth University of ChinaTaiyuanShanxiChina
- School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeArizona
| | - Xie Han
- School of Computer Science and TechnologyNorth University of ChinaTaiyuanShanxiChina
| | - Kewei Chen
- Banner Alzheimer's InstitutePhoenixArizona
| | | | | | - Yalin Wang
- School of Computing, Informatics, and Decision Systems EngineeringArizona State UniversityTempeArizona
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Mouthaan BE, Rados M, Boon P, Carrette E, Diehl B, Jung J, Kimiskidis V, Kobulashvili T, Kuchukhidze G, Larsson PG, Leitinger M, Ryvlin P, Rugg-Gunn F, Seeck M, Vulliémoz S, Huiskamp G, Leijten FSS, Van Eijsden P, Trinka E, Braun KPJ. Diagnostic accuracy of interictal source imaging in presurgical epilepsy evaluation: A systematic review from the E-PILEPSY consortium. Clin Neurophysiol 2019; 130:845-855. [PMID: 30824202 DOI: 10.1016/j.clinph.2018.12.016] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2018] [Revised: 11/16/2018] [Accepted: 12/20/2018] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Interictal high resolution (HR-) electric source imaging (ESI) and magnetic source imaging (MSI) are non-invasive tools to aid epileptogenic zone localization in epilepsy surgery candidates. We carried out a systematic review on the diagnostic accuracy and quality of evidence of these modalities. METHODS Embase, Pubmed and the Cochrane database were searched on 13 February 2017. Diagnostic accuracy studies taking post-surgical seizure outcome as reference standard were selected. Quality appraisal was based on the QUADAS-2 framework. RESULTS Eleven studies were included: eight MSI (n = 267), three HR-ESI (n = 127) studies. None was free from bias. This mostly involved: selection of operated patients only, interference of source imaging with surgical decision, and exclusion of indeterminate results. Summary sensitivity and specificity estimates were 82% (95% CI: 75-88%) and 53% (95% CI: 37-68%) for overall source imaging, with no statistical difference between MSI and HR-ESI. Specificity is higher when partially concordant results were included as non-concordant (p < 0.05). Inclusion of indeterminate test results as non-concordant lowered sensitivity (p < 0.05). CONCLUSIONS Source imaging has a relatively high sensitivity but low specificity for identification of the epileptogenic zone. SIGNIFICANCE We need higher quality studies allowing unbiased test evaluation to determine the added value and diagnostic accuracy of source imaging in the presurgical workup of refractory focal epilepsy.
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Affiliation(s)
- Brian E Mouthaan
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Matea Rados
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Paul Boon
- Reference Center for Refractory Epilepsy, Department of Neurology, Ghent University Hospital, Belgium
| | - Evelien Carrette
- Reference Center for Refractory Epilepsy, Department of Neurology, Ghent University Hospital, Belgium
| | - Beate Diehl
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, United Kingdom; Department of Clinical and Experimental Epilepsy, University College, London, UK
| | - Julien Jung
- Department of Functional Neurology and Epileptology, Institute of Epilepsies (IDEE), Hospices Civils de Lyon, Lyon, France
| | - Vasilios Kimiskidis
- Laboratory of Clinical Neurophysiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
| | - Teia Kobulashvili
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Giorgi Kuchukhidze
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Pål G Larsson
- Department of Neurosurgery, Clinic of Surgery and Neuroscience, Oslo University Hospital, Norway
| | - Markus Leitinger
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - Fergus Rugg-Gunn
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, United Kingdom; Department of Clinical and Experimental Epilepsy, University College, London, UK
| | - Margitta Seeck
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Switzerland
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, University Hospital of Geneva, Switzerland
| | - Geertjan Huiskamp
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Frans S S Leijten
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Pieter Van Eijsden
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands
| | - Eugen Trinka
- Department of Neurology, Christian-Doppler University Hospital, Paracelsus Medical University, and Centre for Cognitive Neuroscience, Salzburg, Austria; Institute of Public Health, Medical Decision Making and HTA, UMIT, Private University for Health Sciences, Medical Informatics and Technology, Hall in Tyrol, Austria
| | - Kees P J Braun
- Department of (Child) Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, PO Box 85090, 3508 AB Utrecht, The Netherlands.
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Pellegrino G, Mecarelli O, Pulitano P, Tombini M, Ricci L, Lanzone J, Brienza M, Davassi C, Di Lazzaro V, Assenza G. Eslicarbazepine Acetate Modulates EEG Activity and Connectivity in Focal Epilepsy. Front Neurol 2018; 9:1054. [PMID: 30619030 PMCID: PMC6297144 DOI: 10.3389/fneur.2018.01054] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 11/20/2018] [Indexed: 12/31/2022] Open
Abstract
Introduction: Eslicarbazepine acetate (ESL) is an antiepileptic drug approved as monotherapy or add-on for the treatment of epilepsy with seizures of focal onset. ESL owns a good profile in terms of efficacy and tolerability, but its effects on EEG activity and connectivity are unknown. The purpose of this study was to investigate EEG activity and connectivity changes after ESL treatment in persons with focal epilepsy (PFE). Material and Methods: We performed a multicentre, longitudinal, retrospective, quantitative EEG study on a population of 22 PFE, and a group of 40 controls. We investigated the ESL-related changes of EEG power spectral activity and global connectivity [phase locking value (PLV), amplitude envelope correlation (AEC) and amplitude envelope correlation of orthogonalized signals (Ortho-AEC)] for standard frequency bands (delta to gamma). Seizure frequency was evaluated to assess ESL efficacy in our cohort. Results: ESL significantly enhanced both global power spectral density and connectivity for all frequency bands, similarly for all connectivity measures. When compared to the control group, Post-ESL power was significantly higher in theta and gamma band. Pre-ESL connectivity values were significantly lower than control for all frequency bands. Post-ESL connectivity increased and the gap between the two groups was no longer significant. ESL induced a 52.7 ± 41.1% reduction of seizure frequency, with 55% of clinical responders (reduction of seizures ≥50%). Discussion: ESL therapy induces significant enhancement of brain activity and connectivity. Post-ESL connectivity profile of epilepsy patients was similar to the one of healthy controls.
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Affiliation(s)
| | - Oriano Mecarelli
- Department of Human Neurosciences, Sapienza University, Policlinico Umberto I Hospital, Rome, Italy
| | - Patrizia Pulitano
- Department of Human Neurosciences, Sapienza University, Policlinico Umberto I Hospital, Rome, Italy
| | - Mario Tombini
- Neurology Department, Campus Biomedico University of Rome, Rome, Italy
| | - Lorenzo Ricci
- Neurology Department, Campus Biomedico University of Rome, Rome, Italy
| | - Jacopo Lanzone
- Neurology Department, Campus Biomedico University of Rome, Rome, Italy
| | - Marianna Brienza
- Department of Human Neurosciences, Sapienza University, Policlinico Umberto I Hospital, Rome, Italy
| | - Chiara Davassi
- Department of Human Neurosciences, Sapienza University, Policlinico Umberto I Hospital, Rome, Italy
| | | | - Giovanni Assenza
- Neurology Department, Campus Biomedico University of Rome, Rome, Italy
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Sinke MR, Otte WM, van Meer MP, van der Toorn A, Dijkhuizen RM. Modified structural network backbone in the contralesional hemisphere chronically after stroke in rat brain. J Cereb Blood Flow Metab 2018; 38:1642-1653. [PMID: 28604153 PMCID: PMC6120129 DOI: 10.1177/0271678x17713901] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Functional outcome after stroke depends on the local site of ischemic injury and on remote effects within connected networks, frequently extending into the contralesional hemisphere. However, the pattern of large-scale contralesional network remodeling remains largely unresolved. In this study, we applied diffusion-based tractography and graph-based network analysis to measure structural connectivity in the contralesional hemisphere chronically after experimental stroke in rats. We used the minimum spanning tree method, which accounts for variations in network density, for unbiased characterization of network backbones that form the strongest connections in a network. Ultrahigh-resolution diffusion MRI scans of eight post-mortem rat brains collected 70 days after right-sided stroke were compared against scans from 10 control brains. Structural network backbones of the left (contralesional) hemisphere, derived from 42 atlas-based anatomical regions, were found to be relatively stable across stroke and control animals. However, several sensorimotor regions showed increased connection strength after stroke. Sensorimotor function correlated with specific contralesional sensorimotor network backbone measures of global integration and efficiency. Our findings point toward post-stroke adaptive reorganization of the contralesional sensorimotor network with recruitment of distinct sensorimotor regions, possibly through strengthening of connections, which may contribute to functional recovery.
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Affiliation(s)
- Michel Rt Sinke
- 1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willem M Otte
- 1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,2 Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maurits Pa van Meer
- 1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Annette van der Toorn
- 1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Rick M Dijkhuizen
- 1 Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
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Lai M, Demuru M, Hillebrand A, Fraschini M. A comparison between scalp- and source-reconstructed EEG networks. Sci Rep 2018; 8:12269. [PMID: 30115955 PMCID: PMC6095906 DOI: 10.1038/s41598-018-30869-w] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 08/07/2018] [Indexed: 12/14/2022] Open
Abstract
EEG can be used to characterise functional networks using a variety of connectivity (FC) metrics. Unlike EEG source reconstruction, scalp analysis does not allow to make inferences about interacting regions, yet this latter approach has not been abandoned. Although the two approaches use different assumptions, conclusions drawn regarding the topology of the underlying networks should, ideally, not depend on the approach. The aim of the present work was to find an answer to the following questions: does scalp analysis provide a correct estimate of the network topology? how big are the distortions when using various pipelines in different experimental conditions? EEG recordings were analysed with amplitude- and phase-based metrics, founding a strong correlation for the global connectivity between scalp- and source-level. In contrast, network topology was only weakly correlated. The strongest correlations were obtained for MST leaf fraction, but only for FC metrics that limit the effects of volume conduction/signal leakage. These findings suggest that these effects alter the estimated EEG network organization, limiting the interpretation of results of scalp analysis. Finally, this study also suggests that the use of metrics that address the problem of zero lag correlations may give more reliable estimates of the underlying network topology.
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Affiliation(s)
- Margherita Lai
- Department of Electrical and Electronic Engineering, University of Cagliari, Piazza D'armi, Cagliari, I-09123, Italy
| | - Matteo Demuru
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Centre, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Centre, Amsterdam, The Netherlands
| | - Matteo Fraschini
- Department of Electrical and Electronic Engineering, University of Cagliari, Piazza D'armi, Cagliari, I-09123, Italy.
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43
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Derks J, Wesseling P, Carbo EWS, Hillebrand A, van Dellen E, de Witt Hamer PC, Klein M, Schenk GJ, Geurts JJG, Reijneveld JC, Douw L. Oscillatory brain activity associates with neuroligin-3 expression and predicts progression free survival in patients with diffuse glioma. J Neurooncol 2018; 140:403-412. [PMID: 30094719 PMCID: PMC6244774 DOI: 10.1007/s11060-018-2967-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 08/01/2018] [Indexed: 01/17/2023]
Abstract
Introduction Diffuse gliomas have local and global effects on neurophysiological brain functioning, which are often seen as ‘passive’ consequences of the tumor. However, seminal preclinical work has shown a prominent role for neuronal activity in glioma growth: mediated by neuroligin-3 (NLGN3), increased neuronal activity causes faster glioma growth. It is unclear whether the same holds true in patients. Here, we investigate whether lower levels of oscillatory brain activity relate to lower NLGN3 expression and predict longer progression free survival (PFS) in diffuse glioma patients. Methods Twenty-four newly diagnosed patients with diffuse glioma underwent magnetoencephalography and subsequent tumor resection. Oscillatory brain activity was approximated by calculating broadband power (0.5–48 Hz) of the magnetoencephalography. NLGN3 expression in glioma tissue was semi-quantitatively assessed by immunohistochemistry. Peritumor and global oscillatory brain activity was then compared between different levels of NLGN3 expression with Kruskal–Wallis tests. Cox proportional hazards analyses were performed to estimate the predictive value of oscillatory brain activity for PFS. Results Patients with low expression of NLGN3 had lower levels of global oscillatory brain activity than patients with higher NLGN3 expression (P < 0.001). Moreover, lower peritumor (hazard ratio 2.17, P = 0.008) and global oscillatory brain activity (hazard ratio 2.10, P = 0.008) predicted longer PFS. Conclusions Lower levels of peritumor and global oscillatory brain activity are related to lower NLGN3 expression and longer PFS, corroborating preclinical research. This study highlights the important interplay between macroscopically measured brain activity and glioma progression, and may lead to new therapeutic interventions in diffuse glioma patients. Electronic supplementary material The online version of this article (10.1007/s11060-018-2967-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jolanda Derks
- Department of Anatomy & Neurosciences, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,VUmc CCA Brain Tumor Center Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Pieter Wesseling
- VUmc CCA Brain Tumor Center Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Department of Pathology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands.,Department of Pathology, Princess Máxima Center for Pediatric Oncology and University Medical Center Utrecht, Lundlaan 6, 3584 EA, Utrecht, The Netherlands
| | - Ellen W S Carbo
- Department of Anatomy & Neurosciences, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.,Brain Center Rudolf Magnus, Universiteitsweg 100, 3584 CG, Utrecht, The Netherlands
| | - Philip C de Witt Hamer
- Department of Neurosurgery, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Martin Klein
- Department of Medical Psychology, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Geert J Schenk
- Department of Anatomy & Neurosciences, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy & Neurosciences, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Jaap C Reijneveld
- Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy & Neurosciences, VU University Medical Center, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. .,VUmc CCA Brain Tumor Center Amsterdam, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. .,Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital, 149 13th St, Charlestown, MA, 02129, USA.
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44
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Guo H, Yan P, Cheng C, Li Y, Chen J, Xu Y, Xiang J. fMRI classification method with multiple feature fusion based on minimum spanning tree analysis. Psychiatry Res Neuroimaging 2018; 277:14-27. [PMID: 29793077 DOI: 10.1016/j.pscychresns.2018.05.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2017] [Revised: 05/08/2018] [Accepted: 05/09/2018] [Indexed: 01/07/2023]
Abstract
Resting state functional brain networks have been widely studied in brain disease research. Conventional network analysis methods are hampered by differences in network size, density and normalization. Minimum spanning tree (MST) analysis has been recently suggested to ameliorate these limitations. Moreover, common MST analysis methods involve calculating quantifiable attributes and selecting these attributes as features in the classification. However, a disadvantage of these methods is that information about the topology of the network is not fully considered, limiting further improvement of classification performance. To address this issue, we propose a novel method combining brain region and subgraph features for classification, utilizing two feature types to quantify two properties of the network. We experimentally validated our proposed method using a major depressive disorder (MDD) patient dataset. The results indicated that MSTs of MDD patients were more similar to random networks and exhibited significant differences in certain regions involved in the limbic-cortical-striatal-pallidal-thalamic (LCSPT) circuit, which is considered to be a major pathological circuit of depression. Moreover, we demonstrated that this novel classification method could effectively improve classification accuracy and provide better interpretability. Overall, the current study demonstrated that different forms of feature representation provide complementary information.
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Affiliation(s)
- Hao Guo
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, PR China.
| | - Pengpeng Yan
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
| | - Chen Cheng
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China; National Laboratory of Pattern Recognition, Institute of Automation, The Chinese Academy of Sciences, Beijing, PR China
| | - Yao Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
| | - Junjie Chen
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, PR China
| | - Jie Xiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, PR China
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45
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Lee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C. Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2018; 20:71-84. [PMID: 30094158 PMCID: PMC6070692 DOI: 10.1016/j.nicl.2018.06.029] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 05/26/2018] [Accepted: 06/27/2018] [Indexed: 01/05/2023]
Abstract
Hubs of brain networks are brain regions exhibiting denser connections than others, promoting long-range communication. Studies suggested the reorganization of hubs in epilepsy. The patterns of connector hub abnormalities specific to mesial temporal lobe epilepsy (mTLE) are unclear. We wish to quantify connector hub abnormalities in mTLE and identify epilepsy-related resting state networks involving abnormal connector hubs. A recently developed sparsity-based analysis of reliable k-hubness (SPARK) allowed us to address this question by using resting state functional MRI in 20 mTLE patients and 17 healthy controls. Handling the multicollinearity of functional networks, SPARK measures a new metric of hubness by counting the number (k) of networks involved in each voxel, and identifies which networks are actually associated to each connector hub. This measure provides new information about the network architecture involving connector hubs and a realistic range of k-hubness. We quantified the disruption and emergence of connector hubs in individual epileptic subjects and assessed the lateralization of networks involving connector hubs. In mTLE, we found pathological disruptions of normal connector hubs in the mTL and within the default mode network. Right mTLE had remarkably higher emergence of new connector hubs in the mTL than left mTLE. Different patterns of lateralization of the salience network involving the abnormal hippocampus were found in right versus left mTLE. The temporal, cerebellar, default mode, subcortical and motor networks also contributed to the lateralization of hippocampal networks. We finally observed an asymmetrical connector hub reorganization and overall regularization of epilepsy-related resting state networks in mTLE, characterized by the disruption of distant connections and the emergence of local connections. Individually reproducible brain network hubs in mesial Temporal Lobe Epilepsy (mTLE). We observed asymmetrical connector hub reorganization and network regularization in mTLE. We found connector hub disruptions within the mTL and default mode network. Emergence of new connector hubs in the mTL was prominent in right but not in left mTLE. Lateralization of hippocampal connectivity was associated with the salience network.
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Affiliation(s)
- Kangjoo Lee
- Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Duff Medical Building, 3775 Rue University, Montreal, QC H3A 2B4, Canada; Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada.
| | - Hui Ming Khoo
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada; Department of Neurosurgery, Osaka University, 2-2 Yamadaoka, Suita, Osaka Prefecture, 565-0871, Japan
| | - Jean-Marc Lina
- École de Technologie Supérieure, 1100 Rue Notre-Dame O, Montreal, QC H3C 1K3, Canada; Centre de Recherches Mathématiques, Université de Montréal, Pavillon André-Aisenstadt 2920 Chemin de la tour, Montreal, QC H3T 1J4, Canada
| | - François Dubeau
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada
| | - Jean Gotman
- Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of Biomedical Engineering, McGill University, Duff Medical Building, 3775 Rue University, Montreal, QC H3A 2B4, Canada; Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Canada; Centre de Recherches Mathématiques, Université de Montréal, Pavillon André-Aisenstadt 2920 Chemin de la tour, Montreal, QC H3T 1J4, Canada; Department of Physics and PERFORM Centre, Concordia University, 7200 Rue Sherbrooke St. W, Montreal, QC H4B 1R6, Canada
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Tecchio F, Cottone C, Porcaro C, Cancelli A, Di Lazzaro V, Assenza G. Brain Functional Connectivity Changes After Transcranial Direct Current Stimulation in Epileptic Patients. Front Neural Circuits 2018; 12:44. [PMID: 29899691 PMCID: PMC5988884 DOI: 10.3389/fncir.2018.00044] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 05/08/2018] [Indexed: 01/03/2023] Open
Abstract
Focal epilepsy is a network pathology, where the brain connectivity of the epileptic focus (EF) influences seizure frequency and cortical dysfunction. Growing evidence supports a clinical efficacy of cathodal transcranial direct current stimulation (ctDCS) in drug-resistant epilepsy (DRE). ctDCS effects can be merely attributed to the inhibition of cortical excitability, which is abnormally increased in epilepsy, but its effect on brain network of DRE patients has never been reported. We aimed at exploring the hypothesis that functional connectivity (FC) changes may explain part of ctDCS clinical effects in DRE patients. We assessed the ctDCS-induced changes of electroencephalography-derived brain FC of a group of six temporal lobe DRE patients receiving a seizure reduction after ctDCS. By a single-subject eLORETA analysis, we compared the FC among the EF region and other nine bilateral macro-regions, before and after Real and Sham ctDCS in a double-blind Sham-controlled crossover design. FC changed after Real ctDCS in all patients despite no appreciable changes occurred after Sham. Most of FC changes (73%) involved the EF region. The epileptic seizure reduction correlated with the increase of the EF FC, in the whole frequency band and in the theta band. This small-sample analysis clearly revealed that ctDCS induced FC changes in the brain network of temporal lobe DRE patients. Our data support the hypothesis that FC changes may contribute to explain the effects of ctDCS in epilepsy, offering a new scenario in the personalization of neuromodulation interventions in epileptic people.
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Affiliation(s)
- Franca Tecchio
- Laboratory of Electrophysiology for Translational neuroScience, Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Carlo Cottone
- Laboratory of Electrophysiology for Translational neuroScience, Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Camillo Porcaro
- Laboratory of Electrophysiology for Translational neuroScience, Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.,Movement Control and Neuroplasticity Research Group, Department of Kinesiology, KU Leuven, Leuven, Belgium.,Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Andrea Cancelli
- Laboratory of Electrophysiology for Translational neuroScience, Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Giovanni Assenza
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
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47
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Li Hegner Y, Marquetand J, Elshahabi A, Klamer S, Lerche H, Braun C, Focke NK. Increased Functional MEG Connectivity as a Hallmark of MRI-Negative Focal and Generalized Epilepsy. Brain Topogr 2018; 31:863-874. [PMID: 29766384 DOI: 10.1007/s10548-018-0649-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Accepted: 05/08/2018] [Indexed: 01/13/2023]
Abstract
Epilepsy is one of the most prevalent neurological diseases with a high morbidity. Accumulating evidence has shown that epilepsy is an archetypical neural network disorder. Here we developed a non-invasive cortical functional connectivity analysis based on magnetoencephalography (MEG) to assess commonalities and differences in the network phenotype in different epilepsy syndromes (non-lesional/cryptogenic focal and idiopathic/genetic generalized epilepsy). Thirty-seven epilepsy patients with normal structural brain anatomy underwent a 30-min resting state MEG measurement with eyes closed. We only analyzed interictal epochs without epileptiform discharges. The imaginary part of coherency was calculated as an indicator of cortical functional connectivity in five classical frequency bands. This connectivity measure was computed between all sources on individually reconstructed cortical surfaces that were surface-aligned to a common template. In comparison to healthy controls, both focal and generalized epilepsy patients showed widespread increased functional connectivity in several frequency bands, demonstrating the potential of elevated functional connectivity as a common pathophysiological hallmark in different epilepsy types. Furthermore, the comparison between focal and generalized epilepsies revealed increased network connectivity in bilateral mesio-frontal and motor regions specifically for the generalized epilepsy patients. Our study indicated that the surface-based normalization of MEG sources of individual brains enables the comparison of imaging findings across subjects and groups on a united platform, which leads to a straightforward and effective disclosure of pathological network characteristics in epilepsy. This approach may allow for the definition of more specific markers of different epilepsy syndromes, and increased MEG-based resting-state functional connectivity seems to be a common feature in MRI-negative epilepsy syndromes.
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Affiliation(s)
- Yiwen Li Hegner
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.
| | - Justus Marquetand
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Adham Elshahabi
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.,Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
| | - Silke Klamer
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany
| | - Holger Lerche
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.,Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany
| | - Christoph Braun
- Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.,MEG Center, University of Tübingen, Tübingen, Germany.,CIMeC, Center for Mind/Brain Sciences, University of Trento, Trento, Italy
| | - Niels K Focke
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tübingen, Hope-Seyler-Straße 3, 72076, Tübingen, Germany.,Werner Reichardt Centre for Integrative Neuroscience, Tübingen, Germany.,Clinical Neurophysiology, University of Göttingen, Göttingen, Germany
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48
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van Dellen E, Sommer IE, Bohlken MM, Tewarie P, Draaisma L, Zalesky A, Di Biase M, Brown JA, Douw L, Otte WM, Mandl RCW, Stam CJ. Minimum spanning tree analysis of the human connectome. Hum Brain Mapp 2018; 39:2455-2471. [PMID: 29468769 PMCID: PMC5969238 DOI: 10.1002/hbm.24014] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 01/15/2018] [Accepted: 02/10/2018] [Indexed: 12/18/2022] Open
Abstract
One of the challenges of brain network analysis is to directly compare network organization between subjects, irrespective of the number or strength of connections. In this study, we used minimum spanning tree (MST; a unique, acyclic subnetwork with a fixed number of connections) analysis to characterize the human brain network to create an empirical reference network. Such a reference network could be used as a null model of connections that form the backbone structure of the human brain. We analyzed the MST in three diffusion‐weighted imaging datasets of healthy adults. The MST of the group mean connectivity matrix was used as the empirical null‐model. The MST of individual subjects matched this reference MST for a mean 58%–88% of connections, depending on the analysis pipeline. Hub nodes in the MST matched with previously reported locations of hub regions, including the so‐called rich club nodes (a subset of high‐degree, highly interconnected nodes). Although most brain network studies have focused primarily on cortical connections, cortical–subcortical connections were consistently present in the MST across subjects. Brain network efficiency was higher when these connections were included in the analysis, suggesting that these tracts may be utilized as the major neural communication routes. Finally, we confirmed that MST characteristics index the effects of brain aging. We conclude that the MST provides an elegant and straightforward approach to analyze structural brain networks, and to test network topological features of individual subjects in comparison to empirical null models.
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Affiliation(s)
- Edwin van Dellen
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia
| | - Iris E Sommer
- Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands
| | - Marc M Bohlken
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Laurijn Draaisma
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia.,Melbourne School of Engineering, The University of Melbourne, Melbourne, Australia
| | - Maria Di Biase
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Australia
| | - Jesse A Brown
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, San Francisco, California
| | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Willem M Otte
- Biomedical MR Imaging and Spectroscopy, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Pediatric Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - René C W Mandl
- Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
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49
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Machine-Learning Classifier for Patients with Major Depressive Disorder: Multifeature Approach Based on a High-Order Minimum Spanning Tree Functional Brain Network. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2017; 2017:4820935. [PMID: 29387141 PMCID: PMC5745775 DOI: 10.1155/2017/4820935] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 10/10/2017] [Accepted: 11/09/2017] [Indexed: 01/12/2023]
Abstract
High-order functional connectivity networks are rich in time information that can reflect dynamic changes in functional connectivity between brain regions. Accordingly, such networks are widely used to classify brain diseases. However, traditional methods for processing high-order functional connectivity networks generally include the clustering method, which reduces data dimensionality. As a result, such networks cannot be effectively interpreted in the context of neurology. Additionally, due to the large scale of high-order functional connectivity networks, it can be computationally very expensive to use complex network or graph theory to calculate certain topological properties. Here, we propose a novel method of generating a high-order minimum spanning tree functional connectivity network. This method increases the neurological significance of the high-order functional connectivity network, reduces network computing consumption, and produces a network scale that is conducive to subsequent network analysis. To ensure the quality of the topological information in the network structure, we used frequent subgraph mining technology to capture the discriminative subnetworks as features and combined this with quantifiable local network features. Then we applied a multikernel learning technique to the corresponding selected features to obtain the final classification results. We evaluated our proposed method using a data set containing 38 patients with major depressive disorder and 28 healthy controls. The experimental results showed a classification accuracy of up to 97.54%.
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50
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Guo H, Liu L, Chen J, Xu Y, Jie X. Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset. Front Neurosci 2017; 11:639. [PMID: 29249926 PMCID: PMC5717514 DOI: 10.3389/fnins.2017.00639] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 11/03/2017] [Indexed: 12/22/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease.
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Affiliation(s)
- Hao Guo
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lei Liu
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Xiang Jie
- Department of Software Engineering, College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
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