51
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Weiss SA, Pastore T, Orosz I, Rubinstein D, Gorniak R, Waldman Z, Fried I, Wu C, Sharan A, Slezak D, Worrell G, Engel J, Sperling MR, Staba RJ. Graph theoretical measures of fast ripples support the epileptic network hypothesis. Brain Commun 2022; 4:fcac101. [PMID: 35620169 PMCID: PMC9128387 DOI: 10.1093/braincomms/fcac101] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 02/10/2022] [Accepted: 04/12/2022] [Indexed: 11/16/2022] Open
Abstract
The epileptic network hypothesis and epileptogenic zone hypothesis are two
theories of ictogenesis. The network hypothesis posits that coordinated activity
among interconnected nodes produces seizures. The epileptogenic zone hypothesis
posits that distinct regions are necessary and sufficient for seizure
generation. High-frequency oscillations, and particularly fast ripples, are
thought to be biomarkers of the epileptogenic zone. We sought to test these
theories by comparing high-frequency oscillation rates and networks in surgical
responders and non-responders, with no appreciable change in seizure frequency
or severity, within a retrospective cohort of 48 patients implanted with
stereo-EEG electrodes. We recorded inter-ictal activity during non-rapid eye
movement sleep and semi-automatically detected and quantified high-frequency
oscillations. Each electrode contact was localized in normalized coordinates. We
found that the accuracy of seizure onset zone electrode contact classification
using high-frequency oscillation rates was not significantly different in
surgical responders and non-responders, suggesting that in non-responders the
epileptogenic zone partially encompassed the seizure onset zone(s)
(P > 0.05). We also found that in the
responders, fast ripple on oscillations exhibited a higher spectral content in
the seizure onset zone compared with the non-seizure onset zone
(P < 1 × 10−5).
By contrast, in the non-responders, fast ripple had a lower spectral content in
the seizure onset zone
(P < 1 × 10−5).
We constructed two different networks of fast ripple with a spectral content
>350 Hz. The first was a rate–distance network that
multiplied the Euclidian distance between fast ripple-generating contacts by the
average rate of fast ripple in the two contacts. The radius of the
rate–distance network, which excluded seizure onset zone nodes,
discriminated non-responders, including patients not offered resection or
responsive neurostimulation due to diffuse multifocal onsets, with an accuracy
of 0.77 [95% confidence interval (CI) 0.56–0.98]. The second fast
ripple network was constructed using the mutual information between the timing
of the events to measure functional connectivity. For most non-responders, this
network had a longer characteristic path length, lower mean local efficiency in
the non-seizure onset zone, and a higher nodal strength among non-seizure onset
zone nodes relative to seizure onset zone nodes. The graphical theoretical
measures from the rate–distance and mutual information networks of 22
non- responsive neurostimulation treated patients was used to train a support
vector machine, which when tested on 13 distinct patients classified
non-responders with an accuracy of 0.92 (95% CI 0.75–1). These
results indicate patients who do not respond to surgery or those not selected
for resection or responsive neurostimulation can be explained by the epileptic
network hypothesis that is a decentralized network consisting of widely
distributed, hyperexcitable fast ripple-generating nodes.
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Affiliation(s)
- Shennan A Weiss
- Dept. of Neurology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Physiology and Pharmacology, State University of New York Downstate, Brooklyn, New York, 11203 USA
- Dept. of Neurology, New York City Health + Hospitals/Kings County, Brooklyn, NY, USA
| | - Tomas Pastore
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Iren Orosz
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Daniel Rubinstein
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard Gorniak
- Dept. of Neuroradiology, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Zachary Waldman
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Itzhak Fried
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Chengyuan Wu
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Ashwini Sharan
- Dept. of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Diego Slezak
- Dept. of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Gregory Worrell
- Dept. of Neurology, Mayo Systems Electrophysiology Laboratory (MSEL), USA
- Dept. of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Jerome Engel
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurosurgery, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Dept. of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Michael R. Sperling
- Depts. of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
| | - Richard J Staba
- Dept. of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
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52
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Khan A, Chen C, Eden CH, Yuan K, Tse CY, Lou W, Tong KY. Impact of Anodal High-Definition Transcranial Direct Current Stimulation of Medial Prefrontal Cortex on Stroop Task performance and its electrophysiological correlates. A pilot study. Neurosci Res 2022; 181:46-54. [DOI: 10.1016/j.neures.2022.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 03/09/2022] [Accepted: 03/17/2022] [Indexed: 11/26/2022]
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53
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Blomsma N, de Rooy B, Gerritse F, van der Spek R, Tewarie P, Hillebrand A, Otte WM, Stam CJ, van Dellen E. Minimum spanning tree analysis of brain networks: A systematic review
of network size effects, sensitivity for neuropsychiatric pathology and disorder
specificity. Netw Neurosci 2022; 6:301-319. [PMID: 35733422 PMCID: PMC9207994 DOI: 10.1162/netn_a_00245] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/10/2022] [Indexed: 11/05/2022] Open
Abstract
Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments. The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.
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Affiliation(s)
- Nicky Blomsma
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Bart de Rooy
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Frank Gerritse
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Rick van der Spek
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Wim M. Otte
- University Medical Center Utrecht, Department of Child Neurology, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Cornelis Jan Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Edwin van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
- University Medical Center Utrecht, Department of Intensive Care Medicine, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
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54
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Networks behind the morphology and structural design of living systems. Phys Life Rev 2022; 41:1-21. [DOI: 10.1016/j.plrev.2022.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/04/2022] [Indexed: 01/06/2023]
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55
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Müller M, Dekkers M, Wiest R, Schindler K, Rummel C. More Than Spikes: On the Added Value of Non-linear Intracranial EEG Analysis for Surgery Planning in Temporal Lobe Epilepsy. Front Neurol 2022; 12:741450. [PMID: 35095712 PMCID: PMC8793863 DOI: 10.3389/fneur.2021.741450] [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: 07/14/2021] [Accepted: 10/18/2021] [Indexed: 11/13/2022] Open
Abstract
Epilepsy surgery can be a very effective therapy in medication refractory patients. During patient evaluation intracranial EEG is analyzed by clinical experts to identify the brain tissue generating epileptiform events. Quantitative EEG analysis increasingly complements this approach in research settings, but not yet in clinical routine. We investigate the correspondence between epileptiform events and a specific quantitative EEG marker. We analyzed 99 preictal epochs of multichannel intracranial EEG of 40 patients with mixed etiologies. Time and channel of occurrence of epileptiform events (spikes, slow waves, sharp waves, fast oscillations) were annotated by a human expert and non-linear excess interrelations were calculated as a quantitative EEG marker. We assessed whether the visually identified preictal events predicted channels that belonged to the seizure onset zone, that were later resected or that showed strong non-linear interrelations. We also investigated whether the seizure onset zone or the resection were predicted by channels with strong non-linear interrelations. In patients with temporal lobe epilepsy (32 of 40), epileptic spikes and the seizure onset zone predicted the resected brain tissue much better in patients with favorable seizure control after surgery than in unfavorable outcomes. Beyond that, our analysis did not reveal any significant associations with epileptiform EEG events. Specifically, none of the epileptiform event types did predict non-linear interrelations. In contrast, channels with strong non-linear excess EEG interrelations predicted the resected channels better in patients with temporal lobe epilepsy and favorable outcome. Also in the small number of patients with seizure onset in the frontal and parietal lobes, no association between epileptiform events and channels with strong non-linear excess EEG interrelations was detectable. In contrast to patients with temporal seizure onset, EEG channels with strong non-linear excess interrelations did neither predict the seizure onset zone nor the resection of these patients or allow separation between patients with favorable and unfavorable seizure control. Our study indicates that non-linear excess EEG interrelations are not strictly associated with epileptiform events, which are one key concept of current clinical EEG assessment. Rather, they may provide information relevant for surgery planning in temporal lobe epilepsy. Our study suggests to incorporate quantitative EEG analysis in the workup of clinical cases. We make the EEG epochs and expert annotations publicly available in anonymized form to foster similar analyses for other quantitative EEG methods.
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Affiliation(s)
- Michael Müller
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Martijn Dekkers
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
| | - Kaspar Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University Bern, Bern, Switzerland
| | - Christian Rummel
- Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland
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56
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Wang Y, Hu X, Li Y. Investigating cognitive flexibility deficit in schizophrenia using task-based whole-brain functional connectivity. Front Psychiatry 2022; 13:1069036. [PMID: 36479558 PMCID: PMC9719952 DOI: 10.3389/fpsyt.2022.1069036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 11/07/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Cognitive flexibility is a core cognitive control function supported by the brain networks of the whole-brain. Schizophrenic patients show deficits in cognitive flexibility in conditions such as task-switching. A large number of neuroimaging studies have revealed abnormalities in local brain activations associated with deficits in cognitive flexibility in schizophrenia, but the relationship between impaired cognitive flexibility and the whole-brain functional connectivity (FC) pattern is unclear. METHOD We investigated the task-based functional connectivity of the whole-brain in patients with schizophrenia and healthy controls during task-switching. Multivariate pattern analysis (MVPA) was utilized to investigate whether the FC pattern can be used as a feature to discriminate schizophrenia patients from healthy controls. Graph theory analysis was further used to quantify the degrees of integration and segregation in the whole-brain networks to interpret the different reconfiguration patterns of brain networks in schizophrenia patients and healthy controls. RESULTS The results showed that the FC pattern classified schizophrenia patients and healthy controls with significant accuracy. Moreover, the altered whole-brain functional connectivity pattern was driven by a lower degree of network integration and segregation in schizophrenia, indicating that both global and local information transfers at the entire-network level were less efficient in schizophrenia patients than in healthy controls during task-switching processing. CONCLUSION These results investigated the group differences in FC profiles during task-switching and not only elucidated that FC patterns are changed in schizophrenic patients, suggesting that task-based FC could be used as a potential neuromarker to discriminate schizophrenia patients from healthy controls in cognitive flexibility but also provide increased insight into the brain network organization that may contribute to impaired cognitive flexibility.
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Affiliation(s)
- Yanqing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xueping Hu
- School of Linguistic Science and Art, Jiangsu Normal University, Xuzhou, China.,Key Laboratory of Language and Cognitive Neuroscience of Jiangsu Province, Collaborative Innovation Center for Language Ability, Xuzhou, China
| | - Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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57
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Santana‐Gomez CE, Engel J, Staba R. Drug-resistant epilepsy and the hypothesis of intrinsic severity: What about the high-frequency oscillations? Epilepsia Open 2021; 7 Suppl 1:S59-S67. [PMID: 34861102 PMCID: PMC9340307 DOI: 10.1002/epi4.12565] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 11/23/2021] [Accepted: 11/30/2021] [Indexed: 11/19/2022] Open
Abstract
Drug‐resistant epilepsy (DRE) affects approximately one‐third of the patients with epilepsy. Based on experimental findings from animal models and brain tissue from patients with DRE, different hypotheses have been proposed to explain the cause(s) of drug resistance. One is the intrinsic severity hypothesis that posits that drug resistance is an inherent property of epilepsy related to disease severity. Seizure frequency is one measure of epilepsy severity, but frequency alone is an incomplete measure of severity and does not fully explain basic research and clinical studies on drug resistance; thus, other measures of epilepsy severity are needed. One such measure could be pathological high‐frequency oscillations (HFOs), which are believed to reflect the neuronal disturbances responsible for the development of epilepsy and the generation of spontaneous seizures. In this manuscript, we will briefly review the intrinsic severity hypothesis, describe basic and clinical research on HFOs in the epileptic brain, and based on this evidence discuss whether HFOs could be a clinical measure of epilepsy severity. Understanding the mechanisms of DRE is critical for producing breakthroughs in the development and testing of novel strategies for treatment.
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Affiliation(s)
| | - Jerome Engel
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
- Brain Research InstituteDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
- Department of NeurobiologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
- Department of Psychiatry and Biobehavioral SciencesDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
| | - Richard Staba
- Department of NeurologyDavid Geffen School of Medicine at UCLALos AngelesCaliforniaUSA
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58
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Slinger G, Otte WM, Braun KPJ, van Diessen E. An updated systematic review and meta-analysis of brain network organization in focal epilepsy: Looking back and forth. Neurosci Biobehav Rev 2021; 132:211-223. [PMID: 34813826 DOI: 10.1016/j.neubiorev.2021.11.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/23/2021] [Accepted: 11/17/2021] [Indexed: 01/10/2023]
Abstract
Abnormalities of the brain network organization in focal epilepsy have been extensively quantified. However, the extent and directionality of abnormalities are highly variable and subtype insensitive. We conducted meta-analyses to obtain a more accurate and epilepsy type-specific quantification of the interictal global brain network organization in focal epilepsy. By using random-effects models, we estimated differences in average clustering coefficient, average path length, and modularity between patients with focal epilepsy and controls, based on 45 studies with a total sample size of 1,468 patients and 1,021 controls. Structural networks had a significant lower level of integration in patients with epilepsy as compared to controls, with a standardized mean difference of -0.334 (95 % confidence interval -0.631 to -0.038; p-value 0.027). Functional networks did not differ between patients and controls, except for the beta band clustering coefficient. Our meta-analyses show that differences in the brain network organization are not as well defined as individual studies often propose. We discuss potential pitfalls and suggestions to enhance the yield and clinical value of network studies.
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Affiliation(s)
- Geertruida Slinger
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands.
| | - Willem M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Kees P J Braun
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Eric van Diessen
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
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59
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A systematic review of resting-state and task-based fmri in juvenile myoclonic epilepsy. Brain Imaging Behav 2021; 16:1465-1494. [PMID: 34786666 DOI: 10.1007/s11682-021-00595-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/20/2021] [Indexed: 10/19/2022]
Abstract
Functional neuroimaging modalities have enhanced our understanding of juvenile myoclonic epilepsy (JME) underlying neural mechanisms. Due to its non-invasive, sensitive and analytical nature, functional magnetic resonance imaging (fMRI) provides valuable insights into relevant functional brain networks and their segregation and integration properties. We systematically reviewed the contribution of resting-state and task-based fMRI to the current understanding of the pathophysiology and the patterns of seizure propagation in JME Altogether, despite some discrepancies, functional findings suggest that corticothalamo-striato-cerebellar network along with default-mode network and salience network are the most affected networks in patients with JME. However, further studies are required to investigate the association between JME's main deficiencies, e.g., motor and cognitive deficiencies and fMRI findings. Moreover, simultaneous electroencephalography-fMRI (EEG-fMRI) studies indicate that alterations of these networks play a role in seizure modulation but fall short of identifying a causal relationship between altered functional properties and seizure propagation. This review highlights the complex pathophysiology of JME, which necessitates the design of more personalized diagnostic and therapeutic strategies in this group.
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60
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Wing S, Gunnarsdottir KM, Gonzalez-Martinez J, Sarma SV. Transfer Entropy between Intracranial EEG Nodes Highlights Network Dynamics that Cause and Stop Epileptic Seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6121-6125. [PMID: 34892513 DOI: 10.1109/embc46164.2021.9629793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Transfer entropy (TE) is used to examine the connectivity between nodes and the roles of nodes in epileptic neural networks during rest, moments before seizure, during seizure, and moments after seizure. There is a set of nodes that dominate information flow to epileptogenic zone (EZ) nodes, regions that trigger seizure, and non-EZ nodes during rest. The TE from the dominant to the EZ nodes decreases shortly before a seizure event and reaches a minimum during seizure. During the seizure, the dominant nodes cease or only weakly interact with the EZ nodes. This supports the hypothesis that seizure occurs when some nodes stop inhibiting the EZ nodes. The TE from the dominant to the EZ nodes peaks immediately after seizure, suggesting that seizure may stop when the brain exerts the highest level of information flow/activation/communication to the EZ nodes. The information flow from the dominant to EZ nodes is different from that to non-EZ nodes. This TE dynamics entering and exiting seizures may identify more accurately the EZ nodes, which may improve surgical planning.
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61
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Dharan AL, Bowden SC, Lai A, Peterson ADH, Cheung MWL, Woldman W, D'Souza WJ. Resting-state functional connectivity in the idiopathic generalized epilepsies: A systematic review and meta-analysis of EEG and MEG studies. Epilepsy Behav 2021; 124:108336. [PMID: 34607215 DOI: 10.1016/j.yebeh.2021.108336] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 09/09/2021] [Accepted: 09/12/2021] [Indexed: 11/20/2022]
Abstract
For idiopathic generalized epilepsies (IGE), brain network analysis is emerging as a biomarker for potential use in clinical care. To determine whether people with IGE show alterations in resting-state brain connectivity compared to healthy controls, and to quantify these differences, we conducted a systematic review and meta-analysis of EEG and magnetoencephalography (MEG) functional connectivity and network studies. The review was conducted according to PRISMA guidelines. Twenty-two studies were eligible for inclusion. Outcomes from individual studies supported hypotheses for interictal, resting-state brain connectivity alterations in IGE patients compared to healthy controls. In contrast, meta-analysis from six studies of common network metrics clustering coefficient, path length, mean degree and nodal strength showed no significant differences between IGE and control groups (effect sizes ranged from -0.151 -1.78). The null findings of the meta-analysis and the heterogeneity of the included studies highlights the importance of developing standardized, validated methodologies for future research. Network neuroscience has significant potential as both a diagnostic and prognostic biomarker in epilepsy, though individual variability in network dynamics needs to be better understood and accounted for.
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Affiliation(s)
- Anita L Dharan
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia.
| | - Stephen C Bowden
- Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia; Department of Clinical Neurosciences, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Alan Lai
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Andre D H Peterson
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Mike W-L Cheung
- Department of Psychology, National University of Singapore, Singapore
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Edgbaston, United Kingdom
| | - Wendyl J D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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62
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Shahabi H, Taylor K, Hirfanoglu T, Koneru S, Bingaman W, Kobayashi K, Kobayashi M, Joshi A, Leahy RM, Mosher JC, Bulacio J, Nair D. Effective connectivity differs between focal cortical dysplasia types I and II. Epilepsia 2021; 62:2753-2765. [PMID: 34541666 DOI: 10.1111/epi.17064] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/26/2021] [Accepted: 08/26/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To determine whether brain connectivity differs between focal cortical dysplasia (FCD) types I and II. METHODS We compared cortico-cortical evoked potentials (CCEPs) as measures of effective brain connectivity in 25 FCD patients with drug-resistant focal epilepsy who underwent intracranial evaluation with stereo-electroencephalography (SEEG). We analyzed the amplitude and latency of CCEP responses following ictal-onset single-pulse electrical stimulation (iSPES). RESULTS In comparison to FCD type II, patients with type I demonstrated significantly larger responses in the electrodes near the ictal-onset zone (<50 mm). These findings persisted when controlling for the location of the epileptogenic zone, as noted in patients with temporal lobe epilepsies, as well as controlling for seizure type, as noted in patients with focal to bilateral tonic-clonic seizures (FBTCS). In type II, the root mean square (RMS) of CCEP responses dropped substantially from the early segment (10-60 ms) to the middle and late segments (60-600 ms). The middle and late CCEP latency segments showed the largest differences between FCD types I and II. SIGNIFICANCE Focal cortical dysplasia type I may have a greater degree of cortical hyperexcitability as compared with FCD type II. In addition, FCD type II displays a more restrictive area of hyperexcitability in both temporal and spatial domains. In patients with FBTCS and type I FCD, the increased amplitudes of RMS in the middle and late CCEP periods appear consistent with the cortico-thalamo-cortical network involvement of FBTCS. The notable differences in degree and extent of hyperexcitability may contribute to the different postsurgical seizure outcomes noted between these two pathological substrates.
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Affiliation(s)
- Hossein Shahabi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - Kenneth Taylor
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Tugba Hirfanoglu
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA.,Department of Pediatric Neurology, School of Medicine, Gazi University, Ankara, Turkey
| | - Shreekanth Koneru
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - William Bingaman
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Katsuya Kobayashi
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Masako Kobayashi
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anand Joshi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - John C Mosher
- University of Texas Health Sciences Center, Houston, TX, USA
| | - Juan Bulacio
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Dileep Nair
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
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Nonperiodic stimulation for the treatment of refractory epilepsy: Applications, mechanisms, and novel insights. Epilepsy Behav 2021; 121:106609. [PMID: 31704250 DOI: 10.1016/j.yebeh.2019.106609] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 10/14/2019] [Accepted: 10/14/2019] [Indexed: 11/21/2022]
Abstract
Electrical stimulation of the central nervous system is a promising alternative for the treatment of pharmacoresistant epilepsy. Successful clinical and experimental stimulation is most usually carried out as continuous trains of current or voltage pulses fired at rates of 100 Hz or above, since lower frequencies yield controversial results. On the other hand, stimulation frequency should be as low as possible, in order to maximize implant safety and battery efficiency. Moreover, the development of stimulation approaches has been largely empirical in general, while they should be engineered with the neurobiology of epilepsy in mind if a more robust, efficient, efficacious, and safe application is intended. In an attempt to reconcile evidence of therapeutic effect with the understanding of the underpinnings of epilepsy, our group has developed a nonstandard form of low-frequency stimulation with randomized interpulse intervals termed nonperiodic stimulation (NPS). The rationale was that an irregular temporal pattern would impair neural hypersynchronization, which is a hallmark of epilepsy. In this review, we start by briefly revisiting the literature on the molecular, cellular, and network level mechanisms of epileptic phenomena in order to highlight this often-overlooked emergent property of cardinal importance in the pathophysiology of the disease. We then review our own studies on the efficacy of NPS against acute and chronic experimental seizures and also on the anatomical and physiological mechanism of the method, paying special attention to the hypothesis that the lack of temporal regularity induces desynchronization. We also put forward a novel insight regarding the temporal structure of NPS that may better encompass the set of findings published by the group: the fact that intervals between stimulation pulses have a distribution that follows a power law and thus may induce natural-like activity that would compete with epileptiform discharge for the recruitment of networks. We end our discussion by mentioning ongoing research and future projects of our lab.
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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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Xu N, Shan W, Qi J, Wu J, Wang Q. Presurgical Evaluation of Epilepsy Using Resting-State MEG Functional Connectivity. Front Hum Neurosci 2021; 15:649074. [PMID: 34276321 PMCID: PMC8283278 DOI: 10.3389/fnhum.2021.649074] [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: 01/03/2021] [Accepted: 06/07/2021] [Indexed: 11/21/2022] Open
Abstract
Epilepsy is caused by abnormal electrical discharges (clinically identified by electrophysiological recording) in a specific part of the brain [originating in only one part of the brain, namely, the epileptogenic zone (EZ)]. Epilepsy is now defined as an archetypical hyperexcited neural network disorder. It can be investigated through the network analysis of interictal discharges, ictal discharges, and resting-state functional connectivity. Currently, there is an increasing interest in embedding resting-state connectivity analysis into the preoperative evaluation of epilepsy. Among the various neuroimaging technologies employed to achieve brain functional networks, magnetoencephalography (MEG) with the excellent temporal resolution is an ideal tool for estimating the resting-state connectivity between brain regions, which can reveal network abnormalities in epilepsy. What value does MEG resting-state functional connectivity offer for epileptic presurgical evaluation? Regarding this topic, this paper introduced the origin of MEG and the workflow of constructing source-space functional connectivity based on MEG signals. Resting-state functional connectivity abnormalities correlate with epileptogenic networks, which are defined by the brain regions involved in the production and propagation of epileptic activities. This paper reviewed the evidence of altered epileptic connectivity based on low- or high-frequency oscillations (HFOs) and the evidence of the advantage of using simultaneous MEG and intracranial electroencephalography (iEEG) recordings. More importantly, this review highlighted that MEG-based resting-state functional connectivity has the potential to predict postsurgical outcomes. In conclusion, resting-state MEG functional connectivity has made a substantial progress toward serving as a candidate biomarker included in epileptic presurgical evaluations.
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Affiliation(s)
- Na Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei Shan
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jing Qi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianping Wu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- National Clinical Research Center for Neurological Diseases, Beijing, China
- Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders, Capital Medical University, Beijing, China
- Beijing Key Laboratory of Neuromodulation, Beijing, China
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66
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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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67
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Sun K, Wang H, Bai Y, Zhou W, Wang L. MRIES: A Matlab Toolbox for Mapping the Responses to Intracranial Electrical Stimulation. Front Neurosci 2021; 15:652841. [PMID: 34194294 PMCID: PMC8236813 DOI: 10.3389/fnins.2021.652841] [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: 01/13/2021] [Accepted: 04/26/2021] [Indexed: 11/26/2022] Open
Abstract
Propose Directed cortical responses to intracranial electrical stimulation are a good standard for mapping inter-regional direct connectivity. Cortico-cortical evoked potential (CCEP), elicited by single pulse electrical stimulation (SPES), has been widely used to map the normal and abnormal brain effective network. However, automated processing of CCEP datasets and visualization of connectivity results remain challenging for researchers and clinicians. In this study, we develop a Matlab toolbox named MRIES (Mapping the Responses to Intracranial Electrical Stimulation) to automatically process CCEP data and visualize the connectivity results. Method The MRIES integrates the processing pipeline of the CCEP datasets and various methods for connectivity calculation based on low- and high-frequency signals with stimulation artifacts removed. The connectivity matrices are saved in different folders for visualization. Different visualization patterns (connectivity matrix, circle map, surface map, and volume map) are also integrated to the graphical user interface (GUI), which makes it easy to intuitively display and compare different connectivity measurements. Furthermore, one sample CCEP data set collected from eight epilepsy patients is used to validate the MRIES toolbox. Result We show the GUI and visualization functions of MRIES using one example CCEP data that has been described in a complete tutorial. We applied this toolbox to the sample CCEP data set to investigate the direct connectivity between the medial temporal lobe and the insular cortex. We find bidirectional connectivity between MTL and insular that are consistent with the findings of previous studies. Conclusion MRIES has a friendly GUI and integrates the full processing pipeline of CCEP data and various visualization methods. The MRIES toolbox, tutorial, and example data can be freely downloaded. As an open-source package, MRIES is expected to improve the reproducibility of CCEP findings and facilitate clinical translation.
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Affiliation(s)
- Kaijia Sun
- School of Systems Science, Beijing Normal University, Beijing, China.,CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Haixiang Wang
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Yunxian Bai
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China
| | - Wenjing Zhou
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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Ictal gamma-band interactions localize ictogenic nodes of the epileptic network in focal cortical dysplasia. Clin Neurophysiol 2021; 132:1927-1936. [PMID: 34157635 DOI: 10.1016/j.clinph.2021.04.016] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 03/18/2021] [Accepted: 04/05/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Epilepsy surgery fails in > 30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis. METHODS The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation. RESULTS Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p < 0.005) - ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p < 0.012). CONCLUSION The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone. SIGNIFICANCE Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.
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Davis KA, Jirsa VK, Schevon CA. Wheels Within Wheels: Theory and Practice of Epileptic Networks. Epilepsy Curr 2021; 21:15357597211015663. [PMID: 33988042 PMCID: PMC8512917 DOI: 10.1177/15357597211015663] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Affiliation(s)
- Kathryn A. Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Viktor K. Jirsa
- Aix-Marseille Universite, Marseille, Provence-Alpes-Cote d’Azu, France
- INSERM, Paris, Ile-de-France, France
- Institute de Neurosciences des Systemes,
Marseille, Provence-Alpes-Cote d’Azu, France
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Hatlestad-Hall C, Bruña R, Syvertsen MR, Erichsen A, Andersson V, Vecchio F, Miraglia F, Rossini PM, Renvall H, Taubøll E, Maestú F, Haraldsen IH. Source-level EEG and graph theory reveal widespread functional network alterations in focal epilepsy. Clin Neurophysiol 2021; 132:1663-1676. [PMID: 34044189 DOI: 10.1016/j.clinph.2021.04.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 03/19/2021] [Accepted: 04/20/2021] [Indexed: 01/15/2023]
Abstract
OBJECTIVE The hypersynchronous neuronal activity associated with epilepsy causes widespread functional network disruptions extending beyond the epileptogenic zone. This altered network topology is considered a mediator for non-seizure symptoms, such as cognitive impairment. The aim of this study was to investigate functional network alterations in focal epilepsy patients with good seizure control and high quality of life. METHODS We compared twenty-two focal epilepsy patients and sixteen healthy controls on graph metrics derived from functional connectivity of source-level resting-state EEG. Graph metrics were calculated over a range of network densities in five frequency bands. RESULTS We observed a significantly increased small world index in patients relative to controls. On the local level, two left-hemisphere regions displayed a shift towards greater alpha band "hubness". The findings were not mediated by age, sex or education, nor by age of epilepsy onset, duration or focus lateralisation. CONCLUSIONS Widespread functional network alterations are evident in focal epilepsy, even in a cohort characterised by successful anti-seizure medication therapy and high quality of life. These findings might support the position that functional network analysis could hold clinical relevance for epilepsy. SIGNIFICANCE Focal epilepsy is accompanied by global and local functional network aberrancies which might be implied in the sustenance of non-seizure symptoms.
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Affiliation(s)
| | - Ricardo Bruña
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Marte Roa Syvertsen
- Department of Neurology, Drammen Hospital, Vestre Viken Health Care Trust, Drammen, Norway.
| | - Aksel Erichsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Department of Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
| | | | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Paolo M Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Diagnostic Center, Helsinki University Hospital, University of Helsinki and Aalto University School of Science, Helsinki, Finland.
| | - Erik Taubøll
- Department of Neurology, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
| | - Fernando Maestú
- Center for Biomedical Technology, Technical University of Madrid, Pozuelo de Alarcón, Spain; Department of Experimental Psychology, Complutense University of Madrid, Pozuelo de Alarcón, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.
| | - Ira H Haraldsen
- Department of Neurology, Oslo University Hospital, Oslo, Norway.
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71
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Alterations in coordinated EEG activity precede the development of seizures in comatose children. Clin Neurophysiol 2021; 132:1505-1514. [PMID: 34023630 DOI: 10.1016/j.clinph.2021.03.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 02/21/2021] [Accepted: 03/12/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE We aimed to test the hypothesis that computational features of the first several minutes of EEG recording can be used to estimate the risk for development of acute seizures in comatose critically-ill children. METHODS In a prospective cohort of 118 comatose children, we computed features of the first five minutes of artifact-free EEG recording (spectral power, inter-regional synchronization and cross-frequency coupling) and tested if these features could help identify the 25 children who went on to develop acute symptomatic seizures during the subsequent 48 hours of cEEG monitoring. RESULTS Children who developed acute seizures demonstrated higher average spectral power, particularly in the theta frequency range, and distinct patterns of inter-regional connectivity, characterized by greater connectivity at delta and theta frequencies, but weaker connectivity at beta and low gamma frequencies. Subgroup analyses among the 97 children with the same baseline EEG background pattern (generalized slowing) yielded qualitatively and quantitatively similar results. CONCLUSIONS These computational features could be applied to baseline EEG recordings to identify critically-ill children at high risk for acute symptomatic seizures. SIGNIFICANCE If confirmed in independent prospective cohorts, these features would merit incorporation into a decision support system in order to optimize diagnostic and therapeutic management of seizures among comatose children.
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72
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Wachsmuth L, Datunashvili M, Kemper K, Albers F, Lambers H, Lüttjohann A, Kreitz S, Budde T, Faber C. Retrosplenial Cortex Contributes to Network Changes during Seizures in the GAERS Absence Epilepsy Rat Model. Cereb Cortex Commun 2021; 2:tgab023. [PMID: 34296168 PMCID: PMC8263073 DOI: 10.1093/texcom/tgab023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 11/14/2022] Open
Abstract
Resting state-fMRI was performed to explore brain networks in Genetic Absence Epilepsy Rats from Strasbourg and in nonepileptic controls (NEC) during monitoring of the brain state by simultaneous optical Ca2+-recordings. Graph theoretical analysis allowed for the identification of acute and chronic network changes and revealed preserved small world topology before and after seizure onset. The most prominent acute change in network organization during seizures was the segregation of cortical regions from the remaining brain. Stronger connections between thalamic with limbic regions compared with preseizure state indicated network regularization during seizures. When comparing between strains, intrathalamic connections were prominent in NEC, on local level represented by higher thalamic strengths and hub scores. Subtle differences were observed for retrosplenial cortex (RS), forming more connections beyond cortex in epileptic rats, and showing a tendency to lateralization during seizures. A potential role of RS as hub between subcortical and cortical regions in epilepsy was supported by increased numbers of parvalbumin-positive (PV+) interneurons together with enhanced inhibitory synaptic activity and neuronal excitability in pyramidal neurons. By combining multimodal fMRI data, graph theoretical methods, and electrophysiological recordings, we identified the RS as promising target for modulation of seizure activity and/or comorbidities.
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Affiliation(s)
- Lydia Wachsmuth
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Maia Datunashvili
- Institute of Physiology I, University of Münster, 48149 Münster, Germany
| | - Katharina Kemper
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Franziska Albers
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Henriette Lambers
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
| | - Annika Lüttjohann
- Institute of Physiology I, University of Münster, 48149 Münster, Germany
| | - Silke Kreitz
- Experimental and Clinical Pharmacology and Toxicology, University of Erlangen, 91054 Erlangen, Germany
| | - Thomas Budde
- Institute of Physiology I, University of Münster, 48149 Münster, Germany
| | - Cornelius Faber
- Translational Research Imaging Center, Clinic for Radiology, University Hospital Münster, 48149 Münster, Germany
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Abstract
Epilepsy is characterized by specific alterations in network organization. The main parameters at the basis of epileptogenic network formation are alterations of cortical thickness, development of pathologic hubs, modification of hub distribution, and white matter alterations. The effect is a reinforcement of brain connectivity in both the epileptogenic zone and the propagation zone. Moreover, the epileptogenic network is characterized by some specific neurophysiologic biomarkers that evidence the tendency of the network itself to shift from an interictal state to an ictal one. The recognition of these features is crucial in planning epilepsy surgery.
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74
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Harris WB, Phillips HW, Fallah A, Mathern GW. Pediatric Epilepsy Surgery in Focal and Generalized Epilepsy: Current Trends and Recent Advancements. JOURNAL OF PEDIATRIC EPILEPSY 2021. [DOI: 10.1055/s-0040-1722298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
AbstractFor a subset of children with medically intractable epilepsy, surgery may provide the best chances of seizure freedom. Whereas the indications for epilepsy surgery are commonly thought to be limited to patients with focal epileptogenic foci, modern imaging and surgical interventions frequently permit successful surgical treatment of generalized epilepsy. Resection continues to be the only potentially curative intervention; however, the advent of various neuromodulation interventions provides an effective palliative strategy for generalized or persistent seizures. Although the risks and benefits vary greatly by type and extent of intervention, the seizure outcomes appear to be uniformly favorable. Advances in both resective and nonresective surgical interventions provide promise for improved seizure freedom, function, and quality of life. This review summarizes the current trends and recent advancements in pediatric epilepsy surgery from diagnostic workup and indications through surgical interventions and postoperative outcomes.
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Affiliation(s)
- William B. Harris
- John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawai'i
| | - H. Westley Phillips
- Department of Neurosurgery, University of California Los Angeles, California, United States
| | - Aria Fallah
- Department of Neurosurgery, University of California Los Angeles, California, United States
| | - Gary W. Mathern
- Department of Neurosurgery, University of California Los Angeles, California, United States
- Department of Psychiatry and Biobehavioral Medicine, David Geffen School of Medicine at UCLA, California, United States
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Carr SJA, Gershon A, Shafiabadi N, Lhatoo SD, Tatsuoka C, Sahoo SS. An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data. Front Integr Neurosci 2021; 14:491403. [PMID: 33510622 PMCID: PMC7835283 DOI: 10.3389/fnint.2020.491403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/30/2020] [Indexed: 12/22/2022] Open
Abstract
A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.
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Affiliation(s)
- Sarah J. A. Carr
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Arthur Gershon
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Nassim Shafiabadi
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Samden D. Lhatoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
| | - Curtis Tatsuoka
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Satya S. Sahoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
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76
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Zhang Q, Liao Y, Wang X, Zhang T, Feng J, Deng J, Shi K, Chen L, Feng L, Ma M, Xue L, Hou H, Dou X, Yu C, Ren L, Ding Y, Chen Y, Wu S, Chen Z, Zhang H, Zhuo C, Tian M. A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy. Eur J Nucl Med Mol Imaging 2021; 48:2476-2485. [PMID: 33420912 PMCID: PMC8241642 DOI: 10.1007/s00259-020-05108-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 11/08/2020] [Indexed: 01/10/2023]
Abstract
PURPOSE Epilepsy is one of the most disabling neurological disorders, which affects all age groups and often results in severe consequences. Since misdiagnoses are common, many pediatric patients fail to receive the correct treatment. Recently, 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) imaging has been used for the evaluation of pediatric epilepsy. However, the epileptic focus is very difficult to be identified by visual assessment since it may present either hypo- or hyper-metabolic abnormality with unclear boundary. This study aimed to develop a novel symmetricity-driven deep learning framework of PET imaging for the identification of epileptic foci in pediatric patients with temporal lobe epilepsy (TLE). METHODS We retrospectively included 201 pediatric patients with TLE and 24 age-matched controls who underwent 18F-FDG PET-CT studies. 18F-FDG PET images were quantitatively investigated using 386 symmetricity features, and a pair-of-cube (PoC)-based Siamese convolutional neural network (CNN) was proposed for precise localization of epileptic focus, and then metabolic abnormality level of the predicted focus was calculated automatically by asymmetric index (AI). Performances of the proposed framework were compared with visual assessment, statistical parametric mapping (SPM) software, and Jensen-Shannon divergence-based logistic regression (JS-LR) analysis. RESULTS The proposed deep learning framework could detect the epileptic foci accurately with the dice coefficient of 0.51, which was significantly higher than that of SPM (0.24, P < 0.01) and significantly (or marginally) higher than that of visual assessment (0.31-0.44, P = 0.005-0.27). The area under the curve (AUC) of the PoC classification was higher than that of the JS-LR (0.93 vs. 0.72). The metabolic level detection accuracy of the proposed method was significantly higher than that of visual assessment blinded or unblinded to clinical information (90% vs. 56% or 68%, P < 0.01). CONCLUSION The proposed deep learning framework for 18F-FDG PET imaging could identify epileptic foci accurately and efficiently, which might be applied as a computer-assisted approach for the future diagnosis of epilepsy patients. TRIAL REGISTRATION NCT04169581. Registered November 13, 2019 Public site: https://clinicaltrials.gov/ct2/show/NCT04169581.
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Affiliation(s)
- Qinming Zhang
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yi Liao
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiawan Wang
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Teng Zhang
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jianhua Feng
- Department of Pediatrics, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jianing Deng
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kexin Shi
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lin Chen
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Liu Feng
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Mindi Ma
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Le Xue
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haifeng Hou
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiaofeng Dou
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Congcong Yu
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Ren
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yao Ding
- Department of Neurology, Epilepsy Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yufei Chen
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuang Wu
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Zexin Chen
- Center of Clinical Epidemiology & Biostatistics, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Hong Zhang
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. .,Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China. .,College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Cheng Zhuo
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Mei Tian
- Department of Nuclear Medicine and PET-CT Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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77
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Patil AU, Ghate S, Madathil D, Tzeng OJL, Huang HW, Huang CM. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep 2021; 11:165. [PMID: 33420212 PMCID: PMC7794287 DOI: 10.1038/s41598-020-80293-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 12/08/2020] [Indexed: 01/29/2023] Open
Abstract
Creative cognition is recognized to involve the integration of multiple spontaneous cognitive processes and is manifested as complex networks within and between the distributed brain regions. We propose that the processing of creative cognition involves the static and dynamic re-configuration of brain networks associated with complex cognitive processes. We applied the sliding-window approach followed by a community detection algorithm and novel measures of network flexibility on the blood-oxygen level dependent (BOLD) signal of 8 major functional brain networks to reveal static and dynamic alterations in the network reconfiguration during creative cognition using functional magnetic resonance imaging (fMRI). Our results demonstrate the temporal connectivity of the dynamic large-scale creative networks between default mode network (DMN), salience network, and cerebellar network during creative cognition, and advance our understanding of the network neuroscience of creative cognition.
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Affiliation(s)
- Abhishek Uday Patil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan
| | - Sejal Ghate
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Deepa Madathil
- Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Ovid J L Tzeng
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan
- Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan
- College of Humanities and Social Sciences, Taipei Medical University, Taipei, Taiwan
- Department of Educational Psychology and Counseling, National Taiwan Normal University, Taipei, Taiwan
- Hong Kong Institute for Advanced Study, City University of Hong Kong, Kowloon, Hong Kong
| | - Hsu-Wen Huang
- Department of Linguistics and Translation, City University of Hong Kong, Kowloon, Hong Kong
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan.
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), National Chiao Tung University, Hsinchu, Taiwan.
- Cognitive Neuroscience Laboratory, Institute of Linguistics, Academia Sinica, Taipei, Taiwan.
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78
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Yaksi E, Jamali A, Diaz Verdugo C, Jurisch-Yaksi N. Past, present and future of zebrafish in epilepsy research. FEBS J 2021; 288:7243-7255. [PMID: 33394550 DOI: 10.1111/febs.15694] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 12/17/2020] [Accepted: 12/31/2020] [Indexed: 12/17/2022]
Abstract
Animal models contribute greatly to our understanding of brain development and function as well as its dysfunction in neurological diseases. Epilepsy research is a very good example of how animal models can provide us with a mechanistic understanding of the genes, molecules, and pathophysiological processes involved in disease. Over the course of the last two decades, zebrafish came in as a new player in epilepsy research, with an expanding number of laboratories using this animal to understand epilepsy and to discover new strategies for preventing seizures. Yet, zebrafish as a model offers a lot more for epilepsy research. In this viewpoint, we aim to highlight some key contributions of zebrafish to epilepsy research, and we want to emphasize the great untapped potential of this animal model for expanding these contributions. We hope that our suggestions will trigger further discussions between clinicians and researchers with a common goal to understand and cure epilepsy.
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Affiliation(s)
- Emre Yaksi
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ahmed Jamali
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, St Olav University Hospital, Trondheim, Norway
| | - Carmen Diaz Verdugo
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Nathalie Jurisch-Yaksi
- Kavli Institute for Systems Neuroscience and Centre for Neural Computation, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway.,Department of Neurology and Clinical Neurophysiology, St Olav University Hospital, Trondheim, Norway.,Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway
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79
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Dynamic functional connectivity of the EEG in relation to outcome of postanoxic coma. Clin Neurophysiol 2021; 132:157-164. [DOI: 10.1016/j.clinph.2020.10.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/18/2020] [Accepted: 10/11/2020] [Indexed: 02/07/2023]
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80
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Li Z, Zhou J, Lan L, Cheng S, Sun R, Gong Q, Wintermark M, Zeng F, Liang F. Concurrent brain structural and functional alterations in patients with migraine without aura: an fMRI study. J Headache Pain 2020; 21:141. [PMID: 33287704 PMCID: PMC7720576 DOI: 10.1186/s10194-020-01203-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 11/18/2020] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES To explore the possible concurrent brain functional and structural alterations in patients with migraine without aura (MwoA) patients compared to healthy subjects (HS). METHODS Seventy-two MwoA patients and forty-six HS were recruited. 3D-T1 and resting state fMRI data were collected during the interictal period for MwoA and HS. Voxel-based morphometry (VBM) for structure analysis and regional homogeneity (Reho) for fMRI analysis were applied. The VBM and Reho maps were overlapped to determine a possible brain region with concurrent functional and structural alteration in MwoA patients. Further analysis of resting state functional connectivity (FC) alteration was applied with this brain region as the seed. RESULTS Compared with HS, MwoA patients showed decreased volume in the bilateral superior and inferior colliculus, periaqueductal gray matter (PAG), locus ceruleus, median raphe nuclei (MRN) and dorsal pons medulla junction. MwoA patients showed decreased Reho values in the middle occipital gyrus and inferior occipital gyrus, and increased Reho values in the MRN. Only a region in the MRN showed both structural and functional alteration in MwoA patients. Pearson correlation analysis showed that there was no association between volume or Reho values of the MRN and headache frequency, headache intensity, disease duration, self-rating anxiety scale or self-rating depression scale in MwoA patients. Resting state functional connectivity (FC) with the MRN as the seed showed that MwoA patients had increased FC between the MRN and PAG. CONCLUSIONS MRN are involved in the pathophysiology of migraine during the interictal period. This study may help to better understand the migraine symptoms. TRIAL REGISTRATION NCT01152632 . Registered 27 June 2010.
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Affiliation(s)
- Zhengjie Li
- Acupuncture & Tuina School / The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610036, Sichuan, China
- Radiology Department, Stanford University, Stanford, California, 94305, USA
| | - Jun Zhou
- Acupuncture & Tuina School / The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610036, Sichuan, China
| | - Lei Lan
- Acupuncture & Tuina School / The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610036, Sichuan, China
| | - Shirui Cheng
- Acupuncture & Tuina School / The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610036, Sichuan, China
| | - Ruirui Sun
- Acupuncture & Tuina School / The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610036, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Max Wintermark
- Radiology Department, Stanford University, Stanford, California, 94305, USA
| | - Fang Zeng
- Acupuncture & Tuina School / The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610036, Sichuan, China.
| | - Fanrong Liang
- Acupuncture & Tuina School / The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, 610036, Sichuan, China.
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81
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Frassineti L, Parente A, Manfredi C. Multiparametric EEG analysis of brain network dynamics during neonatal seizures. J Neurosci Methods 2020; 348:109003. [PMID: 33249182 DOI: 10.1016/j.jneumeth.2020.109003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 11/06/2020] [Accepted: 11/15/2020] [Indexed: 11/30/2022]
Abstract
BACKGROUND One of the most challenging issues in paediatric neurology is the diagnosis of neonatal seizures, whose delayed treatment may affect the neurodevelopment of the newborn. Formulation of the correct diagnosis is conditioned by the high number of perceptually or automatically detected false positives. NEW METHOD New methodologies are proposed to assess neonatal seizures trend over time. Our approach is based on the analysis of standardized trends of two properties of the brain network: the Synchronizabilty (S) and the degree of phase synchronicity given by the Circular Omega Complexity (COC). Qualitative and quantitative methods based on network dynamics allow differentiating seizure events from interictal periods and seizure-free patients. RESULTS The methods were tested on a public dataset of labelled neonatal seizures. COC shows significant differences among seizure and non-seizure events (p-value <0.001, Cohen's d 0.86). Combining S and COC in standardized temporal instants provided a reliable description of the physiological behaviour of the brain's network during neonatal seizures. COMPARISON WITH EXISTING METHOD(S) Few of the existing network methods propose an operative way for carrying their analytical approach into the diagnostic process of neonatal seizures. Our methods offer a simple representation of brain network dynamics easily implementable and understandable also by less experienced staff. CONCLUSIONS Our findings confirm the usefulness of the evaluation of brain network dynamics over time for a better understanding and interpretation of the complex mechanisms behind neonatal seizures. The proposed methods could also reliably support existing seizure detectors as a post-processing step in doubtful cases.
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Affiliation(s)
- Lorenzo Frassineti
- Department of Information Engineering, Universita' degli Studi di Firenze, Firenze, Italy; Department of Medical Biotechnologies, Universita' degli Studi di Siena, Siena, Italy.
| | - Angela Parente
- School of Engineering, Universita' degli Studi di Firenze, Firenze, Italy.
| | - Claudia Manfredi
- Department of Information Engineering, Universita' degli Studi di Firenze, Firenze, Italy.
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82
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Gupta K, Grover P, Abel TJ. Current Conceptual Understanding of the Epileptogenic Network From Stereoelectroencephalography-Based Connectivity Inferences. Front Neurol 2020; 11:569699. [PMID: 33324320 PMCID: PMC7724044 DOI: 10.3389/fneur.2020.569699] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 10/13/2020] [Indexed: 11/13/2022] Open
Abstract
Localization of the epileptogenic zone (EZ) is crucial in the surgical treatment of focal epilepsy. Recently, EEG studies have revealed that the EZ exhibits abnormal connectivity, which has led investigators to now consider connectivity as a biomarker to localize the EZ. Further, abnormal connectivity of the EZ may provide an explanation for the impact of focal epilepsy on more widespread brain networks involved in typical cognition and development. Stereo-electroencephalography (sEEG) is a well-established method for localizing the EZ that has recently been applied to examine altered brain connectivity in epilepsy. In this manuscript, we review recent computational methods for identifying the EZ using sEEG connectivity. Findings from previous sEEG studies indicate that during interictal periods, the EZ is prone to seizure generation but concurrently receives inward connectivity preventing seizures. At seizure onset, this control is lost, allowing seizure activity to spread from the EZ. Regulatory areas within the EZ may be important for subsequently ending the seizure. After the seizure, the EZ appears to regain its influence on the network, which may be how it is able to regenerate epileptiform activity. However, more research is needed on the dynamic connectivity of the EZ in order to build a biomarker for EZ localization. Such a biomarker would allow for patients undergoing sEEG to have electrode implantation, localization of the EZ, and resection in a fraction of the time currently needed, preventing patients from having to endure long hospital stays and induced seizures.
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Affiliation(s)
- Kanupriya Gupta
- University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States
| | - Pulkit Grover
- Center for the Neural Basis of Cognition, Carnegie Mellon University/University of Pittsburgh, Pittsburgh, PA, United States.,Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Taylor J Abel
- University of Pittsburgh School of Medicine, Pittsburgh, PA, United States.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States.,Center for the Neural Basis of Cognition, Carnegie Mellon University/University of Pittsburgh, Pittsburgh, PA, United States.,Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, United States
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83
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Reh R, Williams LJ, Todd RM, Ward LM. Warped rhythms: Epileptic activity during critical periods disrupts the development of neural networks for human communication. Behav Brain Res 2020; 399:113016. [PMID: 33212087 DOI: 10.1016/j.bbr.2020.113016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 12/27/2022]
Abstract
It is well established that temporal lobe epilepsy-the most common and well-studied form of epilepsy-can impair communication by disrupting social-emotional and language functions. In pediatric epilepsy, where seizures co-occur with the development of critical brain networks, age of onset matters: The earlier in life seizures begin, the worse the disruption in network establishment, resulting in academic hardship and social isolation. Yet, little is known about the processes by which epileptic activity disrupts developing human brain networks. Here we take a synthetic perspective-reviewing a range of research spanning studies on molecular and oscillatory processes to those on the development of large-scale functional networks-in support of a novel model of how such networks can be disrupted by epilepsy. We seek to bridge the gap between research on molecular processes, on the development of human brain circuitry, and on clinical outcomes to propose a model of how epileptic activity disrupts brain development.
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Affiliation(s)
- Rebecca Reh
- University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver BC V6T 1Z4, Canada
| | - Lynne J Williams
- BC Children's Hospital MRI Research Facility, 4480 Oak Street, Vancouver, BC V6H 0B3, Canada
| | - Rebecca M Todd
- University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver BC V6T 1Z4, Canada; University of British Columbia, Djavad Mowafaghian Centre for Brain Health, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada.
| | - Lawrence M Ward
- University of British Columbia, Department of Psychology, 2136 West Mall, Vancouver BC V6T 1Z4, Canada; University of British Columbia, Djavad Mowafaghian Centre for Brain Health, 2215 Wesbrook Mall, Vancouver, BC V6T 1Z3, Canada
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84
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Junges L, Woldman W, Benjamin OJ, Terry JR. Epilepsy surgery: Evaluating robustness using dynamic network models. CHAOS (WOODBURY, N.Y.) 2020; 30:113106. [PMID: 33261362 DOI: 10.1063/5.0022171] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 10/08/2020] [Indexed: 06/12/2023]
Abstract
Epilepsy is one of the most common neurological conditions affecting over 65 million people worldwide. Over one third of people with epilepsy are considered refractory: they do not respond to drug treatments. For this significant cohort of people, surgery is a potentially transformative treatment. However, only a small minority of people with refractory epilepsy are considered suitable for surgery, and long-term seizure freedom is only achieved in half the cases. Recently, several computational approaches have been proposed to support presurgical planning. Typically, these approaches use a dynamic network model to explore the potential impact of surgical resection in silico. The network component of the model is informed by clinical imaging data and is considered static thereafter. This assumption critically overlooks the plasticity of the brain and, therefore, how continued evolution of the brain network post-surgery may impact upon the success of a resection in the longer term. In this work, we use a simplified dynamic network model, which describes transitions to seizures, to systematically explore how the network structure influences seizure propensity, both before and after virtual resections. We illustrate key results in small networks, before extending our findings to larger networks. We demonstrate how the evolution of brain networks post resection can result in a return to increased seizure propensity. Our results effectively determine the robustness of a given resection to possible network reconfigurations and so provide a potential strategy for optimizing long-term seizure freedom.
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Affiliation(s)
- Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
| | - Oscar J Benjamin
- Department of Engineering Mathematics, University of Bristol, Bristol BS8 1UB, United Kingdom
| | - John R Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham B15 2TT, United Kingdom
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85
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Robust dynamic community detection with applications to human brain functional networks. Nat Commun 2020; 11:2785. [PMID: 32503997 PMCID: PMC7275079 DOI: 10.1038/s41467-020-16285-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 04/21/2020] [Indexed: 02/07/2023] Open
Abstract
While current technology permits inference of dynamic brain networks over long time periods at high temporal resolution, the detailed structure of dynamic network communities during human seizures remains poorly understood. We introduce a new methodology that addresses critical aspects unique to the analysis of dynamic functional networks inferred from noisy data. We propose a dynamic plex percolation method (DPPM) that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time. We show in simulation that DPPM outperforms existing methods in accurately capturing certain stereotypical dynamic community behaviors in noisy situations. We then illustrate the ability of this method to track dynamic community organization during human seizures, using invasive brain voltage recordings at seizure onset. We conjecture that application of this method will yield new targets for surgical treatment of epilepsy, and more generally could provide new insights in other network neuroscience applications. Understanding how brain networks evolve in time remains a challenge, with the potential for significant impact to human health and disease. Here, the authors introduce a new methodology to track dynamic functional networks that is robust to edge noise, and yields well-defined spatiotemporal communities that span forward and backwards in time.
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86
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Nieboer D, Sorrentino P, Hillebrand A, Heymans MW, Twisk JWR, Stam CJ, Douw L. Brain Network Integration in Patients with Migraine: A Magnetoencephalography Study. Brain Connect 2020; 10:224-235. [PMID: 32397732 DOI: 10.1089/brain.2019.0705] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Migraine is a common disorder with high social and medical impact. Patients with migraine have a much higher chance of experiencing headache attacks compared with the general population. Recent neuroimaging studies have confirmed that pathophysiology in the brain is not only limited to the moment of the attack but is also present in between attacks, the interictal phase. In this study, we hypothesized that the topology of functional brain networks is also different in the interictal state, compared with people who are not affected by migraine. We also expected that the level of network disturbances scales with the number of years people have suffered from migraine. Functional connectivity between 78 cortical brain regions was estimated for source-level magnetoencephalography data by calculating the phase lag index, in five frequency bands (delta-beta), and compared between healthy controls (n = 24) and patients who had been suffering from migraine for longer than 6 years (n = 12) or shorter than 6 years (n = 12). Moreover, the topology of the functional networks was characterized using the minimum spanning tree. The migraine groups did not differ from each other in functional connectivity. However, the network topology was different compared with healthy controls. The results were frequency specific, and higher average nodal betweenness centrality was specifically evident in higher frequency bands in patients with longer disease duration, while an opposite trend was present for lower frequencies. This study shows that patients with migraine have a different network topology in the resting state compared with healthy controls, whereby specific brain areas have altered topological roles in a frequency-specific manner. Some alterations appear specifically in patients with long-term migraine, which might show the long-term effects of the disease.
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Affiliation(s)
- Dagmar Nieboer
- Department of Methodology and Applied Biostatistics, Faculty of Beta Science, VU University Amsterdam, Amsterdam, The Netherlands
| | - Pierpaolo Sorrentino
- Department of Clinical Neurophysiology and MEG Centre, Amsterdam Neuroscience, Amsterdam UMC, VU University Medical Center, Amsterdam, The Netherlands.,Istituto di Diagnosi e Cura Hermitage-Capodimonte, Naples, Italy.,Institute of Applied Sciences and Intelligent Systems, National Research Council, Pozzuoli, Italy.,Deparment of Engineering, University of Parthenope, Naples, Italy
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Centre, Amsterdam Neuroscience, Amsterdam UMC, VU University Medical Center, Amsterdam, The Netherlands
| | - Martijn W Heymans
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, Netherlands
| | - Jos W R Twisk
- Department of Epidemiology and Biostatistics, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Centre, Amsterdam Neuroscience, Amsterdam UMC, VU University Medical Center, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC, VU University Medical Center, Amsterdam, The Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging/Massachusetts General Hospital, Boston, Massachusetts, USA
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87
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Sundaram P, Luessi M, Bianciardi M, Stufflebeam S, Hamalainen M, Solo V. Individual Resting-State Brain Networks Enabled by Massive Multivariate Conditional Mutual Information. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1957-1966. [PMID: 31880547 PMCID: PMC7593831 DOI: 10.1109/tmi.2019.2962517] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Individual-level resting-state networks (RSNs) based on resting-state fMRI (rs-fMRI) are of great interest due to evidence that network dysfunction may underlie some diseases. Most current rs-fMRI analyses use linear correlation. Since correlation is a bivariate measure of association, it discards most of the information contained in the spatial variation of the thousands of hemodynamic signals within the voxels in a given brain region. Subject-specific functional RSNs using typical rs-fMRI data, are therefore dominated by indirect connections and loss of spatial information and can only deliver reliable connectivity after group averaging. While bivariate partial correlation can rule out indirect connections, it results in connectivity that is too sparse due to lack of sensitivity. We have developed a method that uses all the spatial variation information in a given parcel by employing a multivariate information-theoretic association measure based on canonical correlations. Our method, multivariate conditional mutual information (mvCMI) reliably constructs single-subject connectivity estimates showing mostly direct connections. Averaging across subjects is not needed. The method is applied to Human Connectome Project data and compared to diffusion MRI. The results are far superior to those obtained by correlation and partial correlation.
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88
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Wang H, Sun Y, Lan F, Liu Y. Altered brain network topology related to working memory in internet addiction. J Behav Addict 2020; 9:325-338. [PMID: 32644933 PMCID: PMC8939409 DOI: 10.1556/2006.2020.00020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 03/28/2020] [Accepted: 04/15/2020] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND AND AIMS The working memory (WM) ability of internet addicts and the topology underlying the WM processing in internet addiction (IA) are poorly understood. In this study, we employed a graph theoretical framework to characterize the topological properties of the IA brain network in the source cortical space during WM task. METHODS A sample of 24 subjects with IA and 23 matched healthy controls (HCs) performed visual 2-back task. Exact Low Resolution Electromagnetic Tomography was adopted to project the pre-processed EEG signals into source space. Subsequently, Lagged phase synchronization was calculated between all pairs of Brodmann areas, the graph theoretical approaches were then employed to estimate the brain topological properties of all participants during the WM task. RESULTS We found better WM behavioral performance in IA subjects compared with the HCs. Moreover, compared to the HC group, more integrated and hierarchical brain network was revealed in the IA subjects in alpha band. And altered regional centrality was mainly resided in frontal and limbic lobes. In addition, significant relationships between the IA severity and the significant altered graph indices were found. CONCLUSIONS In conclusion, these findings provide evidence to support the notion that altered topological configuration may underline changed WM function observed in IA.
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Affiliation(s)
- Hongxia Wang
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China,Department of Psychology, Renmin University of China, Beijing, 100872, China
| | - Yan Sun
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China,Corresponding author’s e-mail:
| | - Fan Lan
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China
| | - Yan Liu
- School of Psychology, Liaoning Normal University, Da Lian, 116029, China
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89
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Hashemi M, Vattikonda AN, Sip V, Guye M, Bartolomei F, Woodman MM, Jirsa VK. The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. Neuroimage 2020; 217:116839. [PMID: 32387625 DOI: 10.1016/j.neuroimage.2020.116839] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 04/02/2020] [Accepted: 04/07/2020] [Indexed: 12/28/2022] Open
Abstract
Despite the importance and frequent use of Bayesian frameworks in brain network modeling for parameter inference and model prediction, the advanced sampling algorithms implemented in probabilistic programming languages to overcome the inference difficulties have received relatively little attention in this context. In this technical note, we propose a probabilistic framework, namely the Bayesian Virtual Epileptic Patient (BVEP), which relies on the fusion of structural data of individuals to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. To invert the individualized whole-brain model employed in this study, we use the recently developed algorithms known as No-U-Turn Sampler (NUTS) as well as Automatic Differentiation Variational Inference (ADVI). Our results indicate that NUTS and ADVI accurately estimate the degree of epileptogenicity of brain regions, therefore, the hypothetical brain areas responsible for the seizure initiation and propagation, while the convergence diagnostics and posterior behavior analysis validate the reliability of the estimations. Moreover, we illustrate the efficiency of the transformed non-centered parameters in comparison to centered form of parameterization. The Bayesian framework used in this work proposes an appropriate patient-specific strategy for estimating the epileptogenicity of the brain regions to improve outcome after epilepsy surgery.
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Affiliation(s)
- M Hashemi
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
| | - A N Vattikonda
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - V Sip
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - M Guye
- Aix Marseille Univ, CNRS, CRMBM, Marseille, France
| | - F Bartolomei
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France; Epileptology Department, and Clinical Neurophysiology Department, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - M M Woodman
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
| | - V K Jirsa
- Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
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90
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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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91
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Abstract
Temporal lobe epilepsy (TLE) is the most common type of drug-resistant focal epilepsy. Epilepsy can be conceptualized as a network disorder with the epileptogenic zone a critical node of the network. Temporal lobe networks can be identified on the microscale and macroscale, both during the interictal and ictal periods. This review summarizes the current understanding of TLE networks as studied by neurophysiological and imaging techniques discussing both functional and structural connectivity.
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92
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Ren Y, Pan L, Du X, Hou Y, Li X, Song Y. Functional brain network mechanism of executive control dysfunction in temporal lobe epilepsy. BMC Neurol 2020; 20:137. [PMID: 32295523 PMCID: PMC7161158 DOI: 10.1186/s12883-020-01711-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 03/30/2020] [Indexed: 11/10/2022] Open
Abstract
Background Executive control dysfunction is observed in a sizable number of patients with temporal lobe epilepsy (TLE). Neural oscillations in the theta band are increasingly recognized as having a crucial role in executive control network. The purpose of this study was to investigate the alterations in the theta band in executive control network and explore the functional brain network mechanisms of executive control dysfunction in TLE patients. Methods A total of 20 TLE patients and 20 matched healthy controls (HCs) were recruited in the present study. All participants were trained to perform the executive control task by attention network test while the scalp electroencephalogram (EEG) data were recorded. The resting state signals were collected from the EEG in the subjects with quiet and closed eyes conditions. Functional connectivity among EEGs in the executive control network and resting state network were respectively calculated. Results We found the significant executive control impairment in the TLE group. Compared to the HCs, the TLE group showed significantly weaker functional connectivity among EEGs in the executive control network. Moreover, in the TLE group, we found that the functional connectivity was significantly positively correlated with accuracy and negatively correlated with EC_effect. In addition, the functional connectivity of the executive control network was significantly higher than that of the resting state network in the HCs. In the TLE group, however, there was no significant change in functional connectivity strengths between the executive control network and resting state network. Conclusion Our results indicate that the decreased functional connectivity in theta band may provide a potential mechanism for executive control deficits in TLE patients.
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Affiliation(s)
- Yanping Ren
- Department of Neurology, Tianjin Medical University General Hospital, Key Laboratory of Neurotrauma, Variation and Regeneration, Ministry of Education and 4Tianjin Municipal Government, Tianjin Neurological Institute, Tianjin, 300052, China
| | - Liping Pan
- Department of Neurology, Tianjin Medical University General Hospital, Key Laboratory of Neurotrauma, Variation and Regeneration, Ministry of Education and 4Tianjin Municipal Government, Tianjin Neurological Institute, Tianjin, 300052, China
| | - Xueyun Du
- Department of Neurology, Tianjin Medical University General Hospital, Key Laboratory of Neurotrauma, Variation and Regeneration, Ministry of Education and 4Tianjin Municipal Government, Tianjin Neurological Institute, Tianjin, 300052, China
| | - Yuying Hou
- Department of Neurology, Tianjin Medical University General Hospital, Key Laboratory of Neurotrauma, Variation and Regeneration, Ministry of Education and 4Tianjin Municipal Government, Tianjin Neurological Institute, Tianjin, 300052, China
| | - Xun Li
- Department of Neurology, Tianjin Medical University General Hospital, Key Laboratory of Neurotrauma, Variation and Regeneration, Ministry of Education and 4Tianjin Municipal Government, Tianjin Neurological Institute, Tianjin, 300052, China
| | - Yijun Song
- Department of Neurology, Tianjin Medical University General Hospital, Key Laboratory of Neurotrauma, Variation and Regeneration, Ministry of Education and 4Tianjin Municipal Government, Tianjin Neurological Institute, Tianjin, 300052, China.
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93
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Hu DK, Mower A, Shrey DW, Lopour BA. Effect of interictal epileptiform discharges on EEG-based functional connectivity networks. Clin Neurophysiol 2020; 131:1087-1098. [PMID: 32199397 DOI: 10.1016/j.clinph.2020.02.014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/22/2020] [Accepted: 02/04/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis. METHODS We introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed. RESULTS IEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure. CONCLUSIONS Increases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS. SIGNIFICANCE Dynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.
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Affiliation(s)
- Derek K Hu
- Department of Biomedical Engineering, University of California, Irvine, CA, USA
| | - Andrew Mower
- Division of Neurology, Children's Hospital Orange County, Orange, CA, USA; Department of Pediatrics, University of California, Irvine, CA, USA
| | - Daniel W Shrey
- Division of Neurology, Children's Hospital Orange County, Orange, CA, USA; Department of Pediatrics, University of California, Irvine, CA, USA
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, CA, USA.
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94
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Zaveri HP, Schelter B, Schevon CA, Jiruska P, Jefferys JGR, Worrell G, Schulze-Bonhage A, Joshi RB, Jirsa V, Goodfellow M, Meisel C, Lehnertz K. Controversies on the network theory of epilepsy: Debates held during the ICTALS 2019 conference. Seizure 2020; 78:78-85. [PMID: 32272333 DOI: 10.1016/j.seizure.2020.03.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/13/2020] [Accepted: 03/15/2020] [Indexed: 12/21/2022] Open
Abstract
Debates on six controversial topics on the network theory of epilepsy were held during two debate sessions, as part of the International Conference for Technology and Analysis of Seizures, 2019 (ICTALS 2019) convened at the University of Exeter, UK, September 2-5 2019. The debate topics were (1) From pathologic to physiologic: is the epileptic network part of an existing large-scale brain network? (2) Are micro scale recordings pertinent for defining the epileptic network? (3) From seconds to years: do we need all temporal scales to define an epileptic network? (4) Is it necessary to fully define the epileptic network to control it? (5) Is controlling seizures sufficient to control the epileptic network? (6) Does the epileptic network want to be controlled? This article, written by the organizing committee for the debate sessions and the debaters, summarizes the arguments presented during the debates on these six topics.
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Affiliation(s)
- Hitten P Zaveri
- Department of Neurology, Yale University, New Haven, CT 06520, USA
| | - Björn Schelter
- Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK
| | | | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
| | - John G R Jefferys
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic; Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
| | - Gregory Worrell
- Mayo Systems Electrophysiology Laboratory, Departments of Neurology and Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Rasesh B Joshi
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix Marseille University, Marseille, France
| | - Marc Goodfellow
- Living Systems Institute, University of Exeter, Exeter, UK; Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Christian Meisel
- Department of Neurology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA; Department of Neurology, University Clinic Carl Gustav Carus, Fetscherstrasse 74, 01307 Dresden, Germany
| | - Klaus Lehnertz
- Department of Epileptology, University of Bonn, Venusberg Campus 1, 53127 Bonn, Germany; Interdisciplinary Center for Complex Systems, University of Bonn, Brühler Str. 7, 53175 Bonn, Germany.
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95
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Rodríguez-Cruces R, Bernhardt BC, Concha L. Multidimensional associations between cognition and connectome organization in temporal lobe epilepsy. Neuroimage 2020; 213:116706. [PMID: 32151761 DOI: 10.1016/j.neuroimage.2020.116706] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 01/14/2020] [Accepted: 03/03/2020] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. Here, we sought to identify multidimensional associations between cognitive performance and structural network topology in TLE. METHODS We studied 34 drug-resistant adult TLE patients and 24 age- and sex-matched healthy controls. Participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based hierarchical clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies. RESULTS Both methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized cognitive performance better than morphometric measures did. CONCLUSIONS Our multivariate approach emphasized a close coupling of cognitive dysfunction and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy.
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Affiliation(s)
- Raúl Rodríguez-Cruces
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico; MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Boris C Bernhardt
- MICA Laboratory, Montreal Neurological Institute and Hospital, Montreal, Canada.
| | - Luis Concha
- Universidad Nacional Autónoma de México, Instituto de Neurobiología, Querétaro, Querétaro, Mexico.
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96
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Hong SJ, Lee HM, Gill R, Crane J, Sziklas V, Bernhardt BC, Bernasconi N, Bernasconi A. A connectome-based mechanistic model of focal cortical dysplasia. Brain 2020; 142:688-699. [PMID: 30726864 DOI: 10.1093/brain/awz009] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 09/07/2018] [Accepted: 11/19/2018] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies have consistently shown distributed brain anomalies in epilepsy syndromes associated with a focal structural lesion, particularly mesiotemporal sclerosis. Conversely, a system-level approach to focal cortical dysplasia has been rarely considered, likely due to methodological difficulties in addressing variable location and topography. Given the known heterogeneity in focal cortical dysplasia histopathology, we hypothesized that lesional connectivity consists of subtypes with distinct structural signatures. Furthermore, in light of mounting evidence for focal anomalies impacting whole-brain systems, we postulated that patterns of focal cortical dysplasia connectivity may exert differential downstream effects on global network topology. We studied a cohort of patients with histologically verified focal cortical dysplasia type II (n = 27), and age- and sex-matched healthy controls (n = 34). We subdivided each lesion into similarly sized parcels and computed their connectivity to large-scale canonical functional networks (or communities). We then dichotomized connectivity profiles of lesional parcels into those belonging to the same functional community as the focal cortical dysplasia (intra-community) and those adhering to other communities (inter-community). Applying hierarchical clustering to community-reconfigured connectome profiles identified three lesional classes with distinct patterns of functional connectivity: decreased intra- and inter-community connectivity, a selective decrease in intra-community connectivity, and increased intra- as well as inter-community connectivity. Hypo-connectivity classes were mainly composed of focal cortical dysplasia type IIB, while the hyperconnected lesions were type IIA. With respect to whole-brain networks, patients with hypoconnected focal cortical dysplasia and marked structural damage showed only mild imbalances, while those with hyperconnected subtle lesions had more pronounced topological alterations. Correcting for interictal epileptic discharges did not impact connectivity patterns. Multivariate structural equation analysis provided a mechanistic model of such complex, diverging interactions, whereby the focal cortical dysplasia structural makeup shapes its functional connectivity, which in turn modulates whole-brain network topology.
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Affiliation(s)
- Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Hyo-Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ravnoor Gill
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Joelle Crane
- Department of Psychology, Neuropsychology Unit, McGill University, Montreal, Quebec, Canada
| | - Viviane Sziklas
- Department of Psychology, Neuropsychology Unit, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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97
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Stacey W, Kramer M, Gunnarsdottir K, Gonzalez-Martinez J, Zaghloul K, Inati S, Sarma S, Stiso J, Khambhati AN, Bassett DS, Smith RJ, Liu VB, Lopour BA, Staba R. Emerging roles of network analysis for epilepsy. Epilepsy Res 2020; 159:106255. [PMID: 31855828 PMCID: PMC6990460 DOI: 10.1016/j.eplepsyres.2019.106255] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 12/08/2019] [Indexed: 11/29/2022]
Abstract
In recent years there has been increasing interest in applying network science tools to EEG data. At the 2018 American Epilepsy Society conference in New Orleans, LA, the yearly session of the Engineering and Neurostimulation Special Interest Group focused on emerging, translational technologies to analyze seizure networks. Each speaker demonstrated practical examples of how network tools can be utilized in clinical care and provide additional data to help care for patients with intractable epilepsy. The groups presented advances using tools from functional connectivity, control theory, and graph theory to analyze human EEG data. These tools have great potential to augment clinical interpretation of EEG signals.
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Affiliation(s)
- William Stacey
- Department of Neurology, Department of Biomedical Engineering, University of Michigan, United States.
| | - Mark Kramer
- Department of Mathematics and Statistics, Center of Systems Neuroscience, Boston University, United States
| | | | | | - Kareem Zaghloul
- Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, NIH, United States
| | - Sara Inati
- Office of the Clinical Director, National Institute of Neurological Disorders and Stroke, NIH, United States
| | - Sridevi Sarma
- Department of Neurology, Department of Biomedical Engineering, University of Michigan, United States
| | - Jennifer Stiso
- Department of Bioengineering, University of Pennsylvania, United States
| | - Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, United States
| | | | - Rachel J Smith
- Department of Biomedical Engineering, University of California, Irvine, United States
| | - Virginia B Liu
- Department of Pediatrics, University of California, Irvine, United States; Department of Child Neurology, Children's Hospital of Orange County, CA, United States
| | - Beth A Lopour
- Department of Biomedical Engineering, University of California, Irvine, United States
| | - Richard Staba
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States
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98
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Abstract
Psychiatric illnesses, including depression and anxiety, are highly comorbid with epilepsy (for review see Josephson and Jetté (Int Rev Psychiatry 29:409-424, 2017), Salpekar and Mula (Epilepsy Behav 98:293-297, 2019)). Psychiatric comorbidities negatively impact the quality of life of patients (Johnson et al., Epilepsia 45:544-550, 2004; Cramer et al., Epilepsy Behav 4:515-521, 2003) and present a significant challenge to treating patients with epilepsy (Hitiris et al., Epilepsy Res 75:192-196, 2007; Petrovski et al., Neurology 75:1015-1021, 2010; Fazel et al., Lancet 382:1646-1654, 2013) (for review see Kanner (Seizure 49:79-82, 2017)). It has long been acknowledged that there is an association between psychiatric illnesses and epilepsy. Hippocrates, in the fourth-fifth century B.C., considered epilepsy and melancholia to be closely related in which he writes that "melancholics ordinarily become epileptics, and epileptics, melancholics" (Lewis, J Ment Sci 80:1-42, 1934). The Babylonians also recognized the frequency of psychosis in patients with epilepsy (Reynolds and Kinnier Wilson, Epilepsia 49:1488-1490, 2008). Despite the fact that the relationship between psychiatric comorbidities and epilepsy has been recognized for thousands of years, psychiatric illnesses in people with epilepsy still commonly go undiagnosed and untreated (Hermann et al., Epilepsia 41(Suppl 2):S31-S41, 2000) and systematic research in this area is still lacking (Devinsky, Epilepsy Behav 4(Suppl 4):S2-S10, 2003). Thus, although it is clear that these are not new issues, there is a need for improvements in the screening and management of patients with psychiatric comorbidities in epilepsy (Lopez et al., Epilepsy Behav 98:302-305, 2019) and progress is needed to understand the underlying neurobiology contributing to these comorbid conditions. To that end, this chapter will raise awareness regarding the scope of the problem as it relates to comorbid psychiatric illnesses and epilepsy and review our current understanding of the potential mechanisms contributing to these comorbidities, focusing on both basic science and clinical research findings.
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Chen S, Bowman FD, Xing Y. Detecting and Testing Altered Brain Connectivity Networks with K-partite Network Topology. Comput Stat Data Anal 2020; 141:109-122. [PMID: 32831438 PMCID: PMC7442212 DOI: 10.1016/j.csda.2019.06.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Emerging brain connectivity network studies suggest that interactions between various distributed neuronal populations may be characterized by an organized complex topological structure. Many neuropsychiatric disorders are associated with altered topological patterns of brain connectivity. Therefore, a key inquiry of connectivity analysis is to detect group-level differentially expressed connectome patterns from the massive neuroimaging data. Recently, statistical methods have been developed to detect differentially expressed connectivity features at a subnetwork level, extending more commonly applied edge level analysis. However, the graph topological structures in these methods are limited to community/cliques which may not effectively uncover the underlying complex and disease-related brain circuits/subnetworks. Building on these previous subnetwork detection methods, a new statistical approach is developed to automatically identify the latent differentially expressed brain connectivity subnetworks with k-partite graph topological structures from large brain connectivity matrices. In addition, statistical inferential techniques are provided to test the detected topological structure. The new methods are evaluated via extensive simulation studies and then applied to resting state fMRI data (24 cases and 18 controls) for Parkinson's disease research. A differentially expressed connectivity network with the k-partite graph topological structure is detected which reveals underlying neural features distinguishing Parkinson's disease patients from healthy control subjects.
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Affiliation(s)
- Shuo Chen
- Division of Biostatistics and Bioinformatics, School of
Medicine, University of Maryland, Baltimore, MD, USA
- Maryland Psychiatric Research Center, School of Medicine,
University of Maryland, Baltimore, MD, USA
| | - F. DuBois Bowman
- Department of Biostatistics, School of Public Health,
University of Michigan, Ann Arbor, MI, USA
| | - Yishi Xing
- Department of Electrical and Computer Engineering,
University of Maryland, College Park, MD, USA
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100
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Gil F, Padilla N, Soria-Pastor S, Setoain X, Boget T, Rumiá J, Roldán P, Reyes D, Bargalló N, Conde E, Pintor L, Vernet O, Manzanares I, Ådén U, Carreño M, Donaire A. Beyond the Epileptic Focus: Functional Epileptic Networks in Focal Epilepsy. Cereb Cortex 2019; 30:2338-2357. [DOI: 10.1093/cercor/bhz243] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Abstract
Focal epilepsy can be conceptualized as a network disorder, and the functional epileptic network can be described as a complex system of multiple brain areas that interact dynamically to generate epileptic activity. However, we still do not fully understand the functional architecture of epileptic networks. We studied a cohort of 21 patients with extratemporal focal epilepsy. We used independent component analysis of functional magnetic resonance imaging (fMRI) data. In order to identify the epilepsy-related components, we examined the general linear model-derived electroencephalography-fMRI (EEG–fMRI) time courses associated with interictal epileptic activity as intrinsic hemodynamic epileptic biomarkers. Independent component analysis revealed components related to the epileptic time courses in all 21 patients. Each epilepsy-related component described a network of spatially distributed brain areas that corresponded to the specific epileptic network in each patient. We also provided evidence for the interaction between the epileptic activity generated at the epileptic network and the physiological resting state networks. Our findings suggest that independent component analysis, guided by EEG–fMRI epileptic time courses, have the potential to define the functional architecture of the epileptic network in a noninvasive way. These data could be useful in planning invasive EEG electrode placement, guiding surgical resections, and more effective therapeutic interventions.
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Affiliation(s)
- Francisco Gil
- Epilepsy Program, Department of Neurology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - Nelly Padilla
- Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Sara Soria-Pastor
- Department of Psychiatry, Consorci Sanitari del Maresme, Hospital of Mataro, CP 08304, Mataro, Spain
| | - Xavier Setoain
- Epilepsy Program, Department of Nuclear Medicine, Hospital Clínic, CDIC, CP 08036, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Clinical and Experimental Neuroscience, Clinical Neurophysiology, CP 08036, Barcelona, Spain
- Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), University of Barcelona, CP 08036, Barcelona, Spain
| | - Teresa Boget
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Clinical and Experimental Neuroscience, Clinical Neurophysiology, CP 08036, Barcelona, Spain
- Epilepsy Program, Department of Neuropsychology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - Jordi Rumiá
- Epilepsy Program, Department of Neurosurgery, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - Pedro Roldán
- Epilepsy Program, Department of Neurosurgery, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - David Reyes
- Epilepsy Program, Department of Neurology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - Núria Bargalló
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Clinical and Experimental Neuroscience, Clinical Neurophysiology, CP 08036, Barcelona, Spain
- Epilepsy Program, Department of Radiology, Hospital Clínic, CDIC, CP 08036, Barcelona, Spain
| | - Estefanía Conde
- Epilepsy Program, Department of Neurology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - Luis Pintor
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Clinical and Experimental Neuroscience, Clinical Neurophysiology, CP 08036, Barcelona, Spain
- Epilepsy Program, Department of Psychiatry, Hospital Clínic, CDIC, CP 08036, Barcelona, Spain
| | - Oriol Vernet
- Epilepsy Program, Department of Neurology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - Isabel Manzanares
- Epilepsy Program, Department of Neurology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
| | - Ulrika Ådén
- Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden
| | - Mar Carreño
- Epilepsy Program, Department of Neurology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Clinical and Experimental Neuroscience, Clinical Neurophysiology, CP 08036, Barcelona, Spain
| | - Antonio Donaire
- Epilepsy Program, Department of Neurology, Hospital Clínic, Neuroscience Institute, CP 08036, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Clinical and Experimental Neuroscience, Clinical Neurophysiology, CP 08036, Barcelona, Spain
- Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), University of Barcelona, CP 08036, Barcelona, Spain
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