scholarly journals Machine learning to detect brain lesions in focal epilepsy

2021 ◽  
Author(s):  
Andrea Perera ◽  
Alireza Sedghi ◽  
Jonah Isen ◽  
Sjoerd Vos ◽  
Parvin Mousavi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Focal Epilepsy ◽  
Brain Lesions

Can we predict anti-seizure medication response in focal epilepsy using machine learning?

2021 ◽  
pp. 107037
Author(s):  
Dong Ah Lee ◽  
Ho-Joon Lee ◽  
Bong Soo Park ◽  
Yoo Jin Lee ◽  
Kang Min Park
Keyword(s):  
Machine Learning ◽  
Focal Epilepsy ◽  
Medication Response

Machine learning volumetry of ischemic brain lesions on CT after thrombectomy—prospective diagnostic accuracy study in ischemic stroke patients

2020 ◽  
Vol 62 (10) ◽  
pp. 1239-1245
Author(s):  
Jiri Kral ◽  
Martin Cabal ◽  
Linda Kasickova ◽  
Jaroslav Havelka ◽  
Tomas Jonszta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ischemic Stroke ◽  
Diagnostic Accuracy ◽  
Brain Lesions ◽  
Diagnostic Accuracy Study ◽  
Stroke Patients ◽  
Ischemic Brain ◽  
Accuracy Study

scholarly journals Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy

2021 ◽  
Vol 15 ◽  
Author(s):  
Giulia Varotto ◽  
Gianluca Susi ◽  
Laura Tassi ◽  
Francesca Gozzo ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Focal Epilepsy ◽  
Geometric Mean ◽  
Ensemble Methods ◽  
Classification Problem ◽  
Brain Regions ◽  
Classification Method ◽  
Machine Learning Techniques ◽  
Epileptogenic Zone ◽  
Original Dataset

Aim: In neuroscience research, data are quite often characterized by an imbalanced distribution between the majority and minority classes, an issue that can limit or even worsen the prediction performance of machine learning methods. Different resampling procedures have been developed to face this problem and a lot of work has been done in comparing their effectiveness in different scenarios. Notably, the robustness of such techniques has been tested among a wide variety of different datasets, without considering the performance of each specific dataset. In this study, we compare the performances of different resampling procedures for the imbalanced domain in stereo-electroencephalography (SEEG) recordings of the patients with focal epilepsies who underwent surgery.Methods: We considered data obtained by network analysis of interictal SEEG recorded from 10 patients with drug-resistant focal epilepsies, for a supervised classification problem aimed at distinguishing between the epileptogenic and non-epileptogenic brain regions in interictal conditions. We investigated the effectiveness of five oversampling and five undersampling procedures, using 10 different machine learning classifiers. Moreover, six specific ensemble methods for the imbalanced domain were also tested. To compare the performances, Area under the ROC curve (AUC), F-measure, Geometric Mean, and Balanced Accuracy were considered.Results: Both the resampling procedures showed improved performances with respect to the original dataset. The oversampling procedure was found to be more sensitive to the type of classification method employed, with Adaptive Synthetic Sampling (ADASYN) exhibiting the best performances. All the undersampling approaches were more robust than the oversampling among the different classifiers, with Random Undersampling (RUS) exhibiting the best performance despite being the simplest and most basic classification method.Conclusions: The application of machine learning techniques that take into consideration the balance of features by resampling is beneficial and leads to more accurate localization of the epileptogenic zone from interictal periods. In addition, our results highlight the importance of the type of classification method that must be used together with the resampling to maximize the benefit to the outcome.

scholarly journals Localizing Epileptogenic Zone from High Density EEG Data Using Machine Learning

2021 ◽  
Vol 17 (06) ◽  
pp. 73
Author(s):  
Sehresh Khan ◽  
Aunsia Khan ◽  
Nazia Hameed ◽  
Muhammad Aleem Taufiq ◽  
Saba Riaz
Keyword(s):  
Machine Learning ◽  
Human Brain ◽  
Antiepileptic Drugs ◽  
Focal Epilepsy ◽  
Brain Regions ◽  
High Density ◽  
Machine Learning Algorithms ◽  
Epileptogenic Zone ◽  
Drug Resistant ◽  
Time Frequency

<span>Drug-resistant focal epilepsy is the failure of antiepileptic drugs scheduled to obtain epileptic free brain activities. In human brain, cerebral hemispheres are the most commonly involved brain regions in epilepsy. In case of antiepileptic drugs failure, surgical treatment is the best cure possible. However, correct localization of epileptogenic region is a challenging task for neurologists, while for computer scientists, automatic localization is. This research work’s aim is to explore the functional activities of all brain regions in drug-resistant focal epileptic patients and achieve high accuracy for the classification of epileptogenic region (ER) with the high-density electroencephalographic (hdEEG) data. The proposed system includes frequency analysis for feature extractions followed by individual subject’s registration of hdEEG signals with anatomical brain images for most precise localization of ER possible. The datasets attained from feature extraction process are then preprocessed for class imbalanced and then evaluated using different machine learning algorithms including the techniques under Bayesian networks, Lazy networks, Meta techniques, Rule based systems and Tree structured algorithms. Considering human brain as stationary object as well as dynamic object, frequency-based and time-frequency based features were considered in 12 subjects respectively. Through this novel approach, 99.70% accuracy is achieved to classify ER from healthy regions using KSTAR and using IBK algorithm, 91.60% accuracy has been achieved to classify generator from propagator.</span>

Identification of focal epilepsy by diffusion tensor imaging using machine learning

2021 ◽  
Author(s):  
Dong Ah Lee ◽  
Ho‐Joon Lee ◽  
Byung Joon Kim ◽  
Bong Soo Park ◽  
Sung Eun Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Focal Epilepsy

scholarly journals Mosaic trisomy of chromosome 1q in human brain tissue associates with unilateral polymicrogyria, very early-onset focal epilepsy, and severe developmental delay

2020 ◽  
Vol 140 (6) ◽  
pp. 881-891
Author(s):  
Katja Kobow ◽  
Samir Jabari ◽  
Tom Pieper ◽  
Manfred Kudernatsch ◽  
Tilman Polster ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Developmental Delay ◽  
Copy Number ◽  
Focal Epilepsy ◽  
Focal Cortical Dysplasia ◽  
Intractable Epilepsy ◽  
Copy Number Variations ◽  
Brain Lesions ◽  
Chromosome 1 ◽  
Structural Brain

AbstractPolymicrogyria (PMG) is a developmental cortical malformation characterized by an excess of small and frustrane gyration and abnormal cortical lamination. PMG frequently associates with seizures. The molecular pathomechanisms underlying PMG development are not yet understood. About 40 genes have been associated with PMG, and small copy number variations have also been described in selected patients. We recently provided evidence that epilepsy-associated structural brain lesions can be classified based on genomic DNA methylation patterns. Here, we analyzed 26 PMG patients employing array-based DNA methylation profiling on formalin-fixed paraffin-embedded material. A series of 62 well-characterized non-PMG cortical malformations (focal cortical dysplasia type 2a/b and hemimegalencephaly), temporal lobe epilepsy, and non-epilepsy autopsy controls was used as reference cohort. Unsupervised dimensionality reduction and hierarchical cluster analysis of DNA methylation profiles showed that PMG formed a distinct DNA methylation class. Copy number profiling from DNA methylation data identified a uniform duplication spanning the entire long arm of chromosome 1 in 7 out of 26 PMG patients, which was verified by additional fluorescence in situ hybridization analysis. In respective cases, about 50% of nuclei in the center of the PMG lesion were 1q triploid. No chromosomal imbalance was seen in adjacent, architecturally normal-appearing tissue indicating mosaicism. Clinically, PMG 1q patients presented with a unilateral frontal or hemispheric PMG without hemimegalencephaly, a severe form of intractable epilepsy with seizure onset in the first months of life, and severe developmental delay. Our results show that PMG can be classified among other structural brain lesions according to their DNA methylation profile. One subset of PMG with distinct clinical features exhibits a duplication of chromosomal arm 1q.

scholarly journals Mosaic trisomy of chromosome 1q in human brain tissue associates with unilateral polymicrogyria, very early-onset focal epilepsy, and severe developmental delay

2020 ◽  
Author(s):  
Katja Kobow ◽  
Samir Jabari ◽  
Tom Pieper ◽  
Manfred Kudernatsch ◽  
Tilman Polster ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Developmental Delay ◽  
Copy Number ◽  
Focal Epilepsy ◽  
Focal Cortical Dysplasia ◽  
Intractable Epilepsy ◽  
Copy Number Variations ◽  
Brain Lesions ◽  
Chromosome 1 ◽  
Structural Brain

AbstractPolymicrogyria (PMG) is a developmental cortical malformation characterized by an excess of small and frustrane gyration and abnormal cortical lamination. PMG frequently associates with seizures. The molecular pathomechanisms underlying PMG development are not yet understood. About 40 genes have been associated with PMG, and small copy number variations have also been described in selected patients. We recently provided evidence that epilepsy-associated structural brain lesions can be classified based on genomic DNA methylation patterns. Here we analyzed 26 PMG patients employing array-based DNA-methylation profiling on formalin-fixed paraffin-embedded material. A series of 62 well-characterized non-PMG cortical malformations (focal cortical dysplasia type 2a/b and hemimegalencephaly), temporal lobe epilepsy, and non-epilepsy autopsy controls was used as reference cohort. Unsupervised dimensionality reduction and hierarchical cluster analysis of DNA methylation profiles showed that PMG formed a distinct DNA methylation class. Copy number profiling from DNA methylation data identified a uniform duplication spanning the entire long arm of chromosome 1 in 7 out of 26 PMG patients, which was verified by additional fluorescence in situ hybridization analysis. In respective cases about 50% of nuclei in the center of the PMG lesion were 1q triploid. No chromosomal imbalance was seen in adjacent, architecturally normal-appearing tissue indicating mosaicism. Clinically, PMG 1q patients presented with a unilateral frontal or hemispheric PMG without hemimegalencephaly, a severe form of intractable epilepsy with seizure onset in the first months of life, and severe developmental delay. Our results show that PMG can be classified among other structural brain lesions according to their DNA methylation profile. One subset of PMG with distinct clinical features exhibits a duplication of chromosomal arm 1q.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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