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De Benedictis A, de Palma L, Rossi-Espagnet MC, Marras CE. Connectome-based approaches in pediatric epilepsy surgery: "State-of-the art" and future perspectives. Epilepsy Behav 2023; 149:109523. [PMID: 37944286 DOI: 10.1016/j.yebeh.2023.109523] [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: 06/25/2023] [Revised: 10/29/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023]
Abstract
Modern epilepsy science has overcome the traditional interpretation of a strict region-specific origin of epilepsy, highlighting the involvement of wider patterns of altered neuronal circuits. In selected cases, surgery may constitute a valuable option to achieve both seizure freedom and neurocognitive improvement. Although epilepsy is now considered as a brain network disease, the most relevant literature concerning the "connectome-based" epilepsy surgery mainly refers to adults, with a limited number of studies dedicated to the pediatric population. In this review, the Authors summarized the main current available knowledge on the relevance of WM surgical anatomy in epilepsy surgery, the post-surgical modifications of brain structural connectivity and the related clinical impact of such modifications within the pediatric context. In the last part, possible implications and future perspectives of this approach have been discussed, especially concerning the optimization of surgical strategies and the predictive value of the epilepsy network analysis for planning tailored approaches, with the final aim of improving case selection, presurgical planning, intraoperative management, and postoperative results.
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Affiliation(s)
| | - Luca de Palma
- Epilepsy and Movement Disorders Neurology Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy.
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2
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García-Ramó KB, Sanchez-Catasus CA, Winston GP. Deep learning in neuroimaging of epilepsy. Clin Neurol Neurosurg 2023; 232:107879. [PMID: 37473486 DOI: 10.1016/j.clineuro.2023.107879] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 05/24/2023] [Accepted: 07/04/2023] [Indexed: 07/22/2023]
Abstract
In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional machine learning. It is helpful for the assessment of patients with epilepsy and whilst most published studies have been aimed at the automatic detection and prediction of seizures from electroencephalographic records, there is a growing number of investigations that use neuroimaging modalities (structural and functional magnetic resonance imaging, diffusion-weighted imaging and positron emission tomography) as input data. We review the application of DL to neuroimaging (sMRI, fMRI, DWI and PET) of focal epilepsy, specifically presurgical evaluation of drug-refractory epilepsy. First, a brief theoretical overview of artificial neural networks and deep learning is presented. Next, we review applications of deep learning to neuroimaging of epilepsy: diagnosis and lateralization, automated detection of lesion, presurgical evaluation and prediction of postsurgical outcome. Finally, the limitations, challenges and possible future directions in the application of these methods in the study of epilepsies are discussed. This approach could become an essential tool in clinical practice, particularly in the evaluation of images considered negative by visual inspection, in individualized treatments, and in the approach to epilepsy as a network disorder. However, greater multicenter collaboration is required to achieve the collection of sufficient data with the required quality together with the open access availability of the developed codes and tools.
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Affiliation(s)
- Karla Batista García-Ramó
- Group of Neuroimaging Processing, International Center for Neurological Restoration, Cuba; Department of Clinical Investigations, Center of Isotopes, Cuba.
| | - Carlos A Sanchez-Catasus
- Department of Neurology, Clínica Universidad de Navarra, Spain; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands.
| | - Gavin P Winston
- Division of Neurology, Department of Medicine, Queen's University, Canada; Centre for Neuroscience Studies, Queen's University, Canada.
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3
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Jiang D, Liao J, Zhao C, Zhao X, Lin R, Yang J, Li Z, Zhou Y, Zhu Y, Liang D, Hu Z, Wang H. Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network. Bioengineering (Basel) 2023; 10:870. [PMID: 37508897 PMCID: PMC10375986 DOI: 10.3390/bioengineering10070870] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/24/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Multi-contrast magnetic resonance imaging (MRI) is wildly applied to identify tuberous sclerosis complex (TSC) children in a clinic. In this work, a deep convolutional neural network with multi-contrast MRI is proposed to diagnose pediatric TSC. Firstly, by combining T2W and FLAIR images, a new synthesis modality named FLAIR3 was created to enhance the contrast between TSC lesions and normal brain tissues. After that, a deep weighted fusion network (DWF-net) using a late fusion strategy is proposed to diagnose TSC children. In experiments, a total of 680 children were enrolled, including 331 healthy children and 349 TSC children. The experimental results indicate that FLAIR3 successfully enhances the visibility of TSC lesions and improves the classification performance. Additionally, the proposed DWF-net delivers a superior classification performance compared to previous methods, achieving an AUC of 0.998 and an accuracy of 0.985. The proposed method has the potential to be a reliable computer-aided diagnostic tool for assisting radiologists in diagnosing TSC children.
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Affiliation(s)
- Dian Jiang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Jianxiang Liao
- Department of Neurology, Shenzhen Children’s Hospital, Shenzhen 518000, China; (J.L.); (X.Z.)
| | - Cailei Zhao
- Department of Radiology, Shenzhen Children’s Hospital, Shenzhen 518000, China;
| | - Xia Zhao
- Department of Neurology, Shenzhen Children’s Hospital, Shenzhen 518000, China; (J.L.); (X.Z.)
| | - Rongbo Lin
- Department of Emergency, Shenzhen Children’s Hospital, Shenzhen 518000, China;
| | - Jun Yang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Zhichen Li
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
| | - Yihang Zhou
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong 999077, China
| | - Yanjie Zhu
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Dong Liang
- Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China; (D.J.); (J.Y.); (Z.L.); (Y.Z.); (D.L.)
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
| | - Zhanqi Hu
- Department of Neurology, Shenzhen Children’s Hospital, Shenzhen 518000, China; (J.L.); (X.Z.)
| | - Haifeng Wang
- University of Chinese Academy of Sciences, Beijing 100049, China;
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China
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Banerjee S, Dong M, Shi W. Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecasting. SMART HEALTH (AMSTERDAM, NETHERLANDS) 2022; 26:100348. [PMID: 36277841 PMCID: PMC9577246 DOI: 10.1016/j.smhl.2022.100348] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 11/17/2022]
Abstract
COVID-19 has become a matter of serious concern over the last few years. It has adversely affected numerous people around the globe and has led to the loss of billions of dollars of business capital. In this paper, we propose a novel Spatial-Temporal Synchronous Graph Transformer network (STSGT) to capture the complex spatial and temporal dependency of the COVID-19 time series data and forecast the future status of an evolving pandemic. The layers of STSGT combine the graph convolution network (GCN) with the self-attention mechanism of transformers on a synchronous spatial-temporal graph to capture the dynamically changing pattern of the COVID time series. The spatial-temporal synchronous graph simultaneously captures the spatial and temporal dependencies between the vertices of the graph at a given and subsequent time-steps, which helps capture the heterogeneity in the time series and improve the forecasting accuracy. Our extensive experiments on two publicly available real-world COVID-19 time series datasets demonstrate that STSGT significantly outperforms state-of-the-art algorithms that were designed for spatial-temporal forecasting tasks. Specifically, on average over a 12-day horizon, we observe a potential improvement of 12.19% and 3.42% in Mean Absolute Error (MAE) over the next best algorithm while forecasting the daily infected and death cases respectively for the 50 states of US and Washington, D.C. Additionally, STSGT also outperformed others when forecasting the daily infected cases at the state level, e.g., for all the counties in the State of Michigan. The code and models are publicly available at https://github.com/soumbane/STSGT.
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Affiliation(s)
- Soumyanil Banerjee
- Department of Computer Science, Wayne State University, 5057 Woodward Ave, Detroit, MI 48202, USA
| | - Ming Dong
- Department of Computer Science, Wayne State University, 5057 Woodward Ave, Detroit, MI 48202, USA
| | - Weisong Shi
- Department of Computer Science, Wayne State University, 5057 Woodward Ave, Detroit, MI 48202, USA
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Huang J, Shlobin NA, DeCuypere M, Lam SK. Deep Learning for Outcome Prediction in Neurosurgery: A Systematic Review of Design, Reporting, and Reproducibility. Neurosurgery 2022; 90:16-38. [PMID: 34982868 DOI: 10.1227/neu.0000000000001736] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Deep learning (DL) is a powerful machine learning technique that has increasingly been used to predict surgical outcomes. However, the large quantity of data required and lack of model interpretability represent substantial barriers to the validity and reproducibility of DL models. The objective of this study was to systematically review the characteristics of DL studies involving neurosurgical outcome prediction and to assess their bias and reporting quality. Literature search using the PubMed, Scopus, and Embase databases identified 1949 records of which 35 studies were included. Of these, 32 (91%) developed and validated a DL model while 3 (9%) validated a pre-existing model. The most commonly represented subspecialty areas were oncology (16 of 35, 46%), spine (8 of 35, 23%), and vascular (6 of 35, 17%). Risk of bias was low in 18 studies (51%), unclear in 5 (14%), and high in 12 (34%), most commonly because of data quality deficiencies. Adherence to transparent reporting of a multivariable prediction model for individual prognosis or diagnosis reporting standards was low, with a median of 12 transparent reporting of a multivariable prediction model for individual prognosis or diagnosis items (39%) per study not reported. Model transparency was severely limited because code was provided in only 3 studies (9%) and final models in 2 (6%). With the exception of public databases, no study data sets were readily available. No studies described DL models as ready for clinical use. The use of DL for neurosurgical outcome prediction remains nascent. Lack of appropriate data sets poses a major concern for bias. Although studies have demonstrated promising results, greater transparency in model development and reporting is needed to facilitate reproducibility and validation.
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Affiliation(s)
- Jonathan Huang
- Ann and Robert H. Lurie Children's Hospital, Division of Pediatric Neurosurgery, Department of Neurological Surgery, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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Yan R, Zhang H, Wang J, Zheng Y, Luo Z, Zhang X, Xu Z. Application value of molecular imaging technology in epilepsy. IBRAIN 2021; 7:200-210. [PMID: 37786793 PMCID: PMC10528966 DOI: 10.1002/j.2769-2795.2021.tb00084.x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/16/2021] [Accepted: 09/02/2021] [Indexed: 10/04/2023]
Abstract
Epilepsy is a common neurological disease with various seizure types, complicated etiologies, and unclear mechanisms. Its diagnosis mainly relies on clinical history, but an electroencephalogram is also a crucial auxiliary examination. Recently, brain imaging technology has gained increasing attention in the diagnosis of epilepsy, and conventional magnetic resonance imaging can detect epileptic foci in some patients with epilepsy. However, the results of brain magnetic resonance imaging are normal in some patients. New molecular imaging has gradually developed in recent years and has been applied in the diagnosis of epilepsy, leading to enhanced lesion detection rates. However, the application of these technologies in epilepsy patients with negative brain magnetic resonance must be clarified. Thus, we reviewed the relevant literature and summarized the information to improve the understanding of the molecular imaging application value of epilepsy.
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Affiliation(s)
- Rong Yan
- Department of NeurologyThe Affiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Hai‐Qing Zhang
- Department of NeurologyThe Affiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Jing Wang
- Prevention and Health Care, The Affiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Yong‐Su Zheng
- Department of NeurologyThe Affiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Zhong Luo
- Department of NeurologyThe Affiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Xia Zhang
- Department of NeurologyThe Affiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
| | - Zu‐Cai Xu
- Department of NeurologyThe Affiliated Hospital of Zunyi Medical UniversityZunyiGuizhouChina
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Jeong J, Banerjee S, Lee M, O'Hara N, Behen M, Juhász C, Dong M. Deep reasoning neural network analysis to predict language deficits from psychometry-driven DWI connectome of young children with persistent language concerns. Hum Brain Mapp 2021; 42:3326-3338. [PMID: 33949048 PMCID: PMC8193535 DOI: 10.1002/hbm.25437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 03/06/2021] [Accepted: 03/26/2021] [Indexed: 12/17/2022] Open
Abstract
This study investigated whether current state-of-the-art deep reasoning network analysis on psychometry-driven diffusion tractography connectome can accurately predict expressive and receptive language scores in a cohort of young children with persistent language concerns (n = 31, age: 4.25 ± 2.38 years). A dilated convolutional neural network combined with a relational network (dilated CNN + RN) was trained to reason the nonlinear relationship between "dilated CNN features of language network" and "clinically acquired language score". Three-fold cross-validation was then used to compare the Pearson correlation and mean absolute error (MAE) between dilated CNN + RN-predicted and actual language scores. The dilated CNN + RN outperformed other methods providing the most significant correlation between predicted and actual scores (i.e., Pearson's R/p-value: 1.00/<.001 and .99/<.001 for expressive and receptive language scores, respectively) and yielding MAE: 0.28 and 0.28 for the same scores. The strength of the relationship suggests elevated probability in the prediction of both expressive and receptive language scores (i.e., 1.00 and 1.00, respectively). Specifically, sparse connectivity not only within the right precentral gyrus but also involving the right caudate had the strongest relationship between deficit in both the expressive and receptive language domains. Subsequent subgroup analyses inferred that the effectiveness of the dilated CNN + RN-based prediction of language score(s) was independent of time interval (between MRI and language assessment) and age of MRI, suggesting that the dilated CNN + RN using psychometry-driven diffusion tractography connectome may be useful for prediction of the presence of language disorder, and possibly provide a better understanding of the neurological mechanisms of language deficits in young children.
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Affiliation(s)
- Jeong‐Won Jeong
- Departments of PediatricsWayne State UniversityDetroitMichiganUSA
- NeurologyWayne State UniversityDetroitMichiganUSA
- Translational Neuroscience ProgramWayne State UniversityDetroitMichiganUSA
- Translational Imaging LaboratoryChildren's Hospital of MichiganDetroitMichiganUSA
| | | | - Min‐Hee Lee
- Departments of PediatricsWayne State UniversityDetroitMichiganUSA
- Translational Imaging LaboratoryChildren's Hospital of MichiganDetroitMichiganUSA
| | - Nolan O'Hara
- Translational Neuroscience ProgramWayne State UniversityDetroitMichiganUSA
- Translational Imaging LaboratoryChildren's Hospital of MichiganDetroitMichiganUSA
| | - Michael Behen
- Departments of PediatricsWayne State UniversityDetroitMichiganUSA
- NeurologyWayne State UniversityDetroitMichiganUSA
- Translational Imaging LaboratoryChildren's Hospital of MichiganDetroitMichiganUSA
| | - Csaba Juhász
- Departments of PediatricsWayne State UniversityDetroitMichiganUSA
- NeurologyWayne State UniversityDetroitMichiganUSA
- Translational Neuroscience ProgramWayne State UniversityDetroitMichiganUSA
- Translational Imaging LaboratoryChildren's Hospital of MichiganDetroitMichiganUSA
| | - Ming Dong
- Computer ScienceWayne State UniversityDetroitMichiganUSA
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Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network. Epilepsy Behav 2021; 117:107909. [PMID: 33740493 PMCID: PMC8035310 DOI: 10.1016/j.yebeh.2021.107909] [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: 12/21/2020] [Revised: 02/23/2021] [Accepted: 02/25/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE Focal epilepsy is a risk factor for language impairment in children. We investigated whether the current state-of-the-art deep learning network on diffusion tractography connectome can accurately predict expressive and receptive language scores of children with epilepsy. METHODS We studied 37 children with a diagnosis of drug-resistant focal epilepsy (age: 11.8 ± 3.1 years) using 3 T MRI and diffusion tractography connectome: G = (S, Ω), where S is an adjacency matrix of edges representing the connectivity strength (number of white-matter tract streamlines) between each pair of brain regions, and Ω reflects a set of brain regions. A convolutional neural network (CNN) was trained to learn the nonlinear relationship between 'S (input)' and 'language score (output)'. Repeated hold-out validation was then employed to measure the Pearson correlation and mean absolute error (MAE) between CNN-predicted and actual language scores. RESULTS We found that CNN-predicted and actual scores were significantly correlated (i.e., Pearson's R/p-value: 0.82/<0.001 and 0.75/<0.001), yielding MAE: 7.77 and 7.40 for expressive and receptive scores, respectively. Specifically, sparse connectivity not only within the left cortico-cortical network but also involving the right subcortical structures was predictive of language impairment of expressive or receptive domain. Subsequent subgroup analyses inferred that the effectiveness of diffusion tractography-based prediction of language outcome was independent of clinical variables. Intrinsic diffusion tractography connectome properties may be useful for predicting the severity of baseline language dysfunction and possibly provide a better understanding of the biological mechanisms of epilepsy-related language impairment in children.
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