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Matsushima T, Yoshinaga K, Wakasugi N, Togo H, Hanakawa T. Functional connectivity-based classification of rapid eye movement sleep behavior disorder. Sleep Med 2024; 115:5-13. [PMID: 38295625 DOI: 10.1016/j.sleep.2024.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose. METHODS We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs. RESULTS Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients. CONCLUSIONS Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD.
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Affiliation(s)
- Toma Matsushima
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan; Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, Koganei, Tokyo, 184-8588, Japan
| | - Kenji Yoshinaga
- Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Noritaka Wakasugi
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan
| | - Hiroki Togo
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan; Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan
| | - Takashi Hanakawa
- Department of Advanced Neuroimaging, Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira, Tokyo, 187-8501, Japan; Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto, 606-8501, Japan.
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2
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Carmichael O. The Role of fMRI in Drug Development: An Update. ADVANCES IN NEUROBIOLOGY 2023; 30:299-333. [PMID: 36928856 DOI: 10.1007/978-3-031-21054-9_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Functional magnetic resonance imaging (fMRI) of the brain is a technology that holds great potential for increasing the efficiency of drug development for the central nervous system (CNS). In preclinical studies and both early- and late-phase human trials, fMRI has the potential to improve cross-species translation of drug effects, help to de-risk compounds early in development, and contribute to the portfolio of evidence for a compound's efficacy and mechanism of action. However, to date, the utilization of fMRI in the CNS drug development process has been limited. The purpose of this chapter is to explore this mismatch between potential and utilization. This chapter provides introductory material related to fMRI and drug development, describes what is required of fMRI measurements for them to be useful in a drug development setting, lists current capabilities of fMRI in this setting and challenges faced in its utilization, and ends with directions for future development of capabilities in this arena. This chapter is the 5-year update of material from a previously published workshop summary (Carmichael et al., Drug DiscovToday 23(2):333-348, 2018).
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Affiliation(s)
- Owen Carmichael
- Pennington Biomedical Research Center, Baton Rouge, LA, USA.
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3
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Piper RJ, Richardson RM, Worrell G, Carmichael DW, Baldeweg T, Litt B, Denison T, Tisdall MM. Towards network-guided neuromodulation for epilepsy. Brain 2022; 145:3347-3362. [PMID: 35771657 PMCID: PMC9586548 DOI: 10.1093/brain/awac234] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 05/30/2022] [Accepted: 06/16/2022] [Indexed: 11/30/2022] Open
Abstract
Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.
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Affiliation(s)
- Rory J Piper
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | | | | | - Torsten Baldeweg
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Brian Litt
- Department of Neurology and Bioengineering, University of Pennsylvania, Philadelphia, USA
| | | | - Martin M Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK
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4
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Hakeem H, Feng W, Chen Z, Choong J, Brodie MJ, Fong SL, Lim KS, Wu J, Wang X, Lawn N, Ni G, Gao X, Luo M, Chen Z, Ge Z, Kwan P. Development and Validation of a Deep Learning Model for Predicting Treatment Response in Patients With Newly Diagnosed Epilepsy. JAMA Neurol 2022; 79:986-996. [PMID: 36036923 PMCID: PMC9425285 DOI: 10.1001/jamaneurol.2022.2514] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/17/2022] [Indexed: 11/14/2022]
Abstract
Importance Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed. Objective To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients. Design, Setting, and Participants This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables. Exposures One of 7 antiseizure medications. Main Outcomes and Measures With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models. Results The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC. Conclusions and Relevance In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.
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Affiliation(s)
- Haris Hakeem
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Wei Feng
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
- Monash-Airdoc Research, Monash University, Melbourne, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Jiun Choong
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
| | - Martin J. Brodie
- Department of Medicine and Clinical Pharmacology, University of Glasgow, Glasgow, Scotland
| | - Si-Lei Fong
- Neurology Division, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Kheng-Seang Lim
- Neurology Division, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Junhong Wu
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
| | - Xuefeng Wang
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
| | - Nicholas Lawn
- WA Adult Epilepsy Service, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
| | - Guanzhong Ni
- Department of Neurology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiang Gao
- Department of Pharmacy, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Mijuan Luo
- Department of Pharmacy, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ziyi Chen
- Department of Neurology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zongyuan Ge
- Department of Electrical and Computer Systems Engineering, Monash University, Clayton, Victoria, Australia
- Monash-Airdoc Research, Monash University, Melbourne, Victoria, Australia
- Monash eResearch Centre, Monash University, Melbourne, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Neurology, Alfred Health, Melbourne, Victoria, Australia
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing Key Laboratory of Neurology, Chongqing, China
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5
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Wheless JW, Friedman D, Krauss GL, Rao VR, Sperling MR, Carrazana E, Rabinowicz AL. Future Opportunities for Research in Rescue Treatments. Epilepsia 2022; 63 Suppl 1:S55-S68. [PMID: 35822912 PMCID: PMC9541657 DOI: 10.1111/epi.17363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/16/2022] [Accepted: 07/11/2022] [Indexed: 11/30/2022]
Abstract
Clinical studies of rescue medications for seizure clusters are limited and are designed to satisfy regulatory requirements, which may not fully consider the needs of the diverse patient population that experiences seizure clusters or utilize rescue medication. The purpose of this narrative review is to examine the factors that contribute to, or may influence the quality of, seizure cluster research with a goal of improving clinical practice. We address five areas of unmet needs and provide advice for how they could enhance future trials of seizure cluster treatments. The topics addressed in this article are: (1) unaddressed end points to pursue in future studies, (2) roles for devices to enhance rescue medication clinical development programs, (3) tools to study seizure cluster prediction and prevention, (4) the value of other designs for seizure cluster studies, and (5) unique challenges of future trial paradigms for seizure clusters. By focusing on novel end points and technologies with value to patients, caregivers, and clinicians, data obtained from future studies can benefit the diverse patient population that experiences seizure clusters, providing more effective, appropriate care as well as alleviating demands on health care resources.
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Affiliation(s)
- James W Wheless
- Le Bonheur Children's Hospital, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Daniel Friedman
- New York University Grossman School of Medicine, New York, New York, USA
| | - Gregory L Krauss
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Vikram R Rao
- University of California, San Francisco, California, USA
| | | | - Enrique Carrazana
- Neurelis, San Diego, California, USA.,John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii, USA
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Vetkas A, Germann J, Elias G, Loh A, Boutet A, Yamamoto K, Sarica C, Samuel N, Milano V, Fomenko A, Santyr B, Tasserie J, Gwun D, Jung HH, Valiante T, Ibrahim GM, Wennberg R, Kalia SK, Lozano AM. Identifying the neural network for neuromodulation in epilepsy through connectomics and graphs. Brain Commun 2022; 4:fcac092. [PMID: 35611305 PMCID: PMC9123846 DOI: 10.1093/braincomms/fcac092] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 12/13/2021] [Accepted: 03/31/2022] [Indexed: 02/01/2023] Open
Abstract
Deep brain stimulation is a treatment option for patients with drug-resistant epilepsy. The precise mechanism of neuromodulation in epilepsy is unknown, and biomarkers are needed for optimizing treatment. The aim of this study was to describe the neural network associated with deep brain stimulation targets for epilepsy and to explore its potential application as a novel biomarker for neuromodulation. Using seed-to-voxel functional connectivity maps, weighted by seizure outcomes, brain areas associated with stimulation were identified in normative resting state functional scans of 1000 individuals. To pinpoint specific regions in the normative epilepsy deep brain stimulation network, we examined overlapping areas of functional connectivity between the anterior thalamic nucleus, centromedian thalamic nucleus, hippocampus and less studied epilepsy deep brain stimulation targets. Graph network analysis was used to describe the relationship between regions in the identified network. Furthermore, we examined the associations of the epilepsy deep brain stimulation network with disease pathophysiology, canonical resting state networks and findings from a systematic review of resting state functional MRI studies in epilepsy deep brain stimulation patients. Cortical nodes identified in the normative epilepsy deep brain stimulation network were in the anterior and posterior cingulate, medial frontal and sensorimotor cortices, frontal operculum and bilateral insulae. Subcortical nodes of the network were in the basal ganglia, mesencephalon, basal forebrain and cerebellum. Anterior thalamic nucleus was identified as a central hub in the network with the highest betweenness and closeness values, while centromedian thalamic nucleus and hippocampus showed average centrality values. The caudate nucleus and mammillothalamic tract also displayed high centrality values. The anterior cingulate cortex was identified as an important cortical hub associated with the effect of deep brain stimulation in epilepsy. The neural network of deep brain stimulation targets shared hubs with known epileptic networks and brain regions involved in seizure propagation and generalization. Two cortical clusters identified in the epilepsy deep brain stimulation network included regions corresponding to resting state networks, mainly the default mode and salience networks. Our results were concordant with findings from a systematic review of resting state functional MRI studies in patients with deep brain stimulation for epilepsy. Our findings suggest that the various epilepsy deep brain stimulation targets share a common cortico-subcortical network, which might in part underpin the antiseizure effects of stimulation. Interindividual differences in this network functional connectivity could potentially be used as biomarkers in selection of patients, stimulation parameters and neuromodulation targets.
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Affiliation(s)
- Artur Vetkas
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Neurology clinic, Department of Neurosurgery, Tartu University Hospital, University of Tartu, Tartu, Estonia
| | - Jürgen Germann
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Gavin Elias
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Aaron Loh
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Alexandre Boutet
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
| | - Kazuaki Yamamoto
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Can Sarica
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Nardin Samuel
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Vanessa Milano
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Anton Fomenko
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Section of Neurosurgery, Health Sciences Centre, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Brendan Santyr
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Jordy Tasserie
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Dave Gwun
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Hyun Ho Jung
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Department of Neurosurgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Taufik Valiante
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, ON, M5G 2A2, Canada
- The KITE Research Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
| | - George M Ibrahim
- Division of Pediatric Neurosurgery, Sick Kids Toronto, University of Toronto, Toronto, ON, Canada
| | - Richard Wennberg
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
| | - Suneil K Kalia
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, ON, M5G 2A2, Canada
- The KITE Research Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
| | - Andres M Lozano
- Division of Neurosurgery, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada
- Krembil Research Institute, Toronto, Ontario, Canada
- CRANIA, University Health Network and University of Toronto, Toronto, ON, M5G 2A2, Canada
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7
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Choo BKM, Kundap UP, Faudzi SMM, Abas F, Shaikh MF, Samarut É. Identification of curcumin analogues with anti-seizure potential in vivo using chemical and genetic zebrafish larva seizure models. Biomed Pharmacother 2021; 142:112035. [PMID: 34411917 DOI: 10.1016/j.biopha.2021.112035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/19/2021] [Accepted: 08/07/2021] [Indexed: 11/28/2022] Open
Abstract
Seizures are the outward manifestation of abnormally excessive or synchronous brain activity. While seizures can be somewhat symptomatically managed with anti-epileptic drugs (AEDs), many patients are still refractory to the currently available AEDs. As a result, there is a need to identify new molecules with anti-seizure properties. Curcumin is the principle curcuminoid of Curcuma longa, or colloquially turmeric, and has been experimentally proven to have anti-convulsive properties, but its poor bioavailability has dampened further therapeutic interest. Hence, this study aimed to ask if structural analogues of curcumin with an adequate bioavailability could have an anti-seizure effect in vivo. To do so, we tested these analogues following a multipronged approach combining the use of several zebrafish seizure models (chemically-induced and genetic) and complementary assays (behavioural and brain activity). Overall, from the 68 analogues tested, we found 15 different derivatives that were able to significantly decrease the behavioural hyperactivity induced by pentylenetetrazol. Of those, only a few showed an effect on the hyperactivity phenotype of two genetic models of brain seizures that are the gabra1 and gabrg2 knockouts. Two analogues, CA 80(1) and CA 74(1), were able to significantly alleviate brain seizures of gabrg2-mutant larvae. As a result, these analogues are good candidates as novel anti-seizure agents.
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Affiliation(s)
- Brandon Kar Meng Choo
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Selangor, Malaysia
| | - Uday Praful Kundap
- Department of Neurosciences, Research Center of the University of Montreal Hospital Center (CRCHUM), Université de Montréal, Montréal, QC, Canada
| | - Siti Munirah Mohd Faudzi
- Department of Chemistry, Faculty of Science, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia; Institute Bioscience, Universiti Putra Malaysia (UPM), Serdang 43400, Selangor, Malaysia
| | - Faridah Abas
- Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
| | - Mohd Farooq Shaikh
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway 47500, Selangor, Malaysia
| | - Éric Samarut
- Department of Neurosciences, Research Center of the University of Montreal Hospital Center (CRCHUM), Université de Montréal, Montréal, QC, Canada; Modelis inc., Montreal, QC, Canada.
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8
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Josephson CB, Wiebe S. Precision Medicine: Academic dreaming or clinical reality? Epilepsia 2020; 62 Suppl 2:S78-S89. [PMID: 33205406 DOI: 10.1111/epi.16739] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 12/26/2022]
Abstract
Precision medicine can be distilled into a concept of accounting for an individual's unique collection of clinical, physiologic, genetic, and sociodemographic characteristics to provide patient-level predictions of disease course and response to therapy. Abundant evidence now allows us to determine how an average person with epilepsy will respond to specific medical and surgical treatments. This is useful, but not readily applicable to an individual patient. This has brought into sharp focus the desire for a more individualized approach through which we counsel people based on individual characteristics, as opposed to population-level data. We are now accruing data at unprecedented rates, allowing us to convert this ideal into reality. In addition, we have access to growing volumes of administrative and electronic health records data, biometric, imaging, genetics data, microbiome, and other "omics" data, thus paving the way toward phenome-wide association studies and "the epidemiology of one." Despite this, there are many challenges ahead. The collating, integrating, and storing sensitive multimodal data for advanced analytics remains difficult as patient consent and data security issues increase in complexity. Agreement on many aspects of epilepsy remains imperfect, rendering models sensitive to misclassification due to a lack of "ground truth." Even with existing data, advanced analytics models are prone to overfitting and often failure to generalize externally. Finally, uptake by clinicians is often hindered by opaque, "black box" algorithms. Systematic approaches to data collection and model generation, and an emphasis on education to promote uptake and knowledge translation, are required to propel epilepsy-based precision medicine from the realm of the theoretical into routine clinical practice.
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Affiliation(s)
- Colin B Josephson
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.,Centre for Health Informatics, University of Calgary, Calgary, AB, Canada
| | - Samuel Wiebe
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.,Clinical Research Unit, University of Calgary, Calgary, AB, Canada
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