scholarly journals Recurring functional interactions predict network architecture of interictal and ictal states in neocortical epilepsy

2016 ◽  
Author(s):  
Ankit N. Khambhati ◽  
Danielle S. Bassett ◽  
Brian S. Oommen ◽  
Stephanie H. Chen ◽  
Timothy H. Lucas ◽  
...  
Keyword(s):  
Network Architecture ◽  
Brain Regions ◽  
Normal Function ◽  
Machine Learning Technique ◽  
Functional Interactions ◽  
Human Epilepsy ◽  
Spontaneous Seizures ◽  
Learning Technique ◽  
Neocortical Epilepsy ◽  
Intracranial Recordings

AbstractHuman epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.

scholarly journals A New, Progressive Myoclonic Epilepsy: Is it a Chronicle of the Noncanonical or a Failure to REST?

2009 ◽  
Vol 9 (3) ◽  
pp. 82-84
Author(s):  
Martin Gallagher
Keyword(s):  
Wnt Signaling Pathway ◽  
Brain Regions ◽  
Normal Function ◽  
Homozygous Mutation ◽  
Human Epilepsy ◽  
Progressive Myoclonus Epilepsy ◽  
Myoclonus Epilepsy ◽  
Overexpression System

A Homozygous Mutation in Human PRICKLE1 Causes an Autosomal-Recessive Progressive Myoclonus Epilepsy-Ataxia Syndrome. Bassuk AG, Wallace RH, Buhr A, Buller AR, Afawi Z, Shimojo M, Miyata S, Chen S, Gonzalez-Alegre P, Griesbach HL, Wu S, Nashelsky M, Vladar EK, Antic D, Ferguson PJ, Cirak S, Voit T, Scott MP, Axelrod JD, Gurnett C, Daoud AS, Kivity S, Neufeld MY, Mazarib A, Straussberg R, Walid S, Korczyn AD, Slusarski DC, Berkovic SF, El-Shanti HI. Am J Hum Genet 2008;83(5):572–581. Progressive myoclonus epilepsy (PME) is a syndrome characterized by myoclonic seizures (lightning-like jerks), generalized convulsive seizures, and varying degrees of neurological decline, especially ataxia and dementia. Previously, we characterized three pedigrees of individuals with PME and ataxia, where either clinical features or linkage mapping excluded known PME loci. This report identifies a mutation in PRICKLE1 (also known as RILP for REST/NRSF interacting LIM domain protein) in all three of these pedigrees. The identified PRICKLE1 mutation blocks the PRICKLE1 and REST interaction in vitro and disrupts the normal function of PRICKLE1 in an in vivo zebrafish overexpression system. PRICKLE1 is expressed in brain regions implicated in epilepsy and ataxia in mice and humans, and, to our knowledge, is the first molecule in the noncanonical WNT signaling pathway to be directly implicated in human epilepsy.

scholarly journals Recurring Functional Interactions Predict Network Architecture of Interictal and Ictal States in Neocortical Epilepsy

eNeuro ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. ENEURO.0091-16.2017 ◽  
Author(s):  
Ankit N. Khambhati ◽  
Danielle S. Bassett ◽  
Brian S. Oommen ◽  
Stephanie H. Chen ◽  
Timothy H. Lucas ◽  
...  
Keyword(s):  
Network Architecture ◽  
Functional Interactions ◽  
Neocortical Epilepsy

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique
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