scholarly journals The etiological classification of the epilepsies: A brain network underpinning?

2019 ◽  
Author(s):  
Geertruida Slinger ◽  
Willem M. Otte ◽  
Lotte Noorlag ◽  
Floor E. Jansen ◽  
Kees P.J. Braun ◽  
...  
Keyword(s):  
Network Topology ◽  
Principal Component ◽  
Brain Network ◽  
Functional Brain ◽  
Eyes Closed ◽  
Functional Brain Network ◽  
Interictal Eeg ◽  
Epilepsy Classification ◽  
Mechanistic Basis ◽  
Patients With Epilepsy

AbstractObjectivethe current epilepsy classification is primarily clinical driven and lacks a mechanistic basis. A mechanistic basis of the classification, and within the classification especially the etiology layer, may help to better understand epilepsy and the associated comorbidities. It may also be helpful in guiding epilepsy treatment. With this study we aimed to investigate if there is a modelled mechanistic underpinning for the etiological epilepsy classification by assessing the association between epilepsy etiology and brain network topology.Methodsto that aim we assessed the association between epilepsy etiology and brain network topology. We included children referred to our outpatient first seizure clinic with suspected epilepsy who had a standard interictal EEG recording. From these EEGs, functional networks were constructed based on eyes-closed resting state time-series. Networks were characterized using measures of segregation, integration, centrality, and network strength. Principal component analyses were used to assess whether patients with epilepsy of similar etiology cluster together based on their functional brain network topology.Resultsin total, 228 children with epilepsy were included. Another 402 children served as control subjects. We were not able to detect a correlation between epilepsy etiology and functional brain network topology. We also did not find a difference in brain network topology between the controls and patients with epilepsy.Conclusionsour results do not support the presence of a brain network underpinning for the etiological epilepsy classification. This may support the hypothesis that brain network abnormalities in epilepsy are a result of ongoing seizure activity rather than the epilepsy etiology itself. Further in-depth analyses of network measures and longitudinal studies are needed to confirm this hypothesis.

scholarly journals Long-Term Effects of Attentional Performance on Functional Brain Network Topology

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e74125 ◽  
Author(s):  
Thomas P. K. Breckel ◽  
Christiane M. Thiel ◽  
Edward T. Bullmore ◽  
Andrew Zalesky ◽  
Ameera X. Patel ◽  
...  
Keyword(s):  
Network Topology ◽  
Brain Network ◽  
Long Term Effects ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Attentional Performance

scholarly journals Functional brain network topology across the menstrual cycle is estradiol dependent and correlates with individual well‐being

2021 ◽  
Author(s):  
Marianna Liparoti ◽  
Emahnuel Troisi Lopez ◽  
Laura Sarno ◽  
Rosaria Rucco ◽  
Roberta Minino ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Network Topology ◽  
Well Being ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals SSVEP Response Is Related to Functional Brain Network Topology Entrained by the Flickering Stimulus

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e72654 ◽  
Author(s):  
Yangsong Zhang ◽  
Peng Xu ◽  
Yingling Huang ◽  
Kaiwen Cheng ◽  
Dezhong Yao
Keyword(s):  
Network Topology ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals State-dependent changes of connectivity patterns and functional brain network topology in autism spectrum disorder

2012 ◽  
Vol 50 (14) ◽  
pp. 3653-3662 ◽  
Author(s):  
Pablo Barttfeld ◽  
Bruno Wicker ◽  
Sebastián Cukier ◽  
Silvana Navarta ◽  
Sergio Lew ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Network Topology ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Functional Brain ◽  
State Dependent ◽  
Functional Brain Network ◽  
Connectivity Patterns

Classification Between Major Depressive Disorder and Healthy Controls Using Functional Brain Network Topology

2020 ◽  
Vol 87 (9) ◽  
pp. S260
Author(s):  
Yael Jacob ◽  
Laurel Morris ◽  
Kuang-Han Huang ◽  
Molly Schneider ◽  
Sarah Rutter ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Network Topology ◽  
Brain Network ◽  
Healthy Controls ◽  
Major Depressive ◽  
Functional Brain ◽  
Functional Brain Network

scholarly journals The power of language: functional brain network topology of deaf and hearing in relation to sign language experience

2018 ◽  
Author(s):  
Michel R.T. Sinke ◽  
Jan W. Buitenhuis ◽  
Frank van der Maas ◽  
Job Nwiboko ◽  
Rick M. Dijkhuizen ◽  
...  
Keyword(s):  
Sign Language ◽  
Sensory Deprivation ◽  
Brain Network ◽  
Functional Network ◽  
Deaf People ◽  
Language Experience ◽  
Functional Brain ◽  
Eyes Closed ◽  
Functional Brain Network ◽  
Eyes Open

AbstractProlonged auditory sensory deprivation leads to brain reorganization, indicated by functional enhancement in remaining sensory systems, a phenomenon known as cross-modal plasticity. In this study we investigated differences in functional brain network shifts from eyes-closed to eyes-open conditions between deaf and hearing people. Electroencephalography activity was recorded in deaf (N = 71) and hearing people (N = 122) living in rural Africa, which yielded a unique data-set of congenital, pre-lingual and post-lingual deaf people, with a divergent experience in American Sign Language. Functional networks were determined from the synchronization of electroencephalography signals between fourteen electrodes distributed over the scalp. We studied the synchronization between the auditory and visual cortex and performed whole-brain minimum spanning tree analysis based on the phase lag index of functional connectivity. This tree analysis accounts for variations in global network density and allows unbiased characterization of functional network backbones. We found increased functional connectivity between the auditory and visual cortex in deaf people during the eyes-closed condition in both the alpha and beta bands. Furthermore, we found functional network backbone shifts both in deaf and healthy people as they went from eyes-closed to eyes-open conditions. In both the alpha and beta band the deafs’ brain showed larger functional backbone-shifts in node strength compared to controls. In the alpha band this shift in network strength differed among deaf participants and depended on type of deafness: congenital, pre-lingual or post-lingual deafness. In addition, a correlation was found between functional backbone characteristics and experience of sign language. Our study revealed more insights in functional network reorganization specifically due to prolonged lack of auditory input, but might also be helpful for sensory deprivation and cross-modal plasticity in general. Global cortical network reorganization in deaf people supports the plastic capacities of the young brain. The differences between type of deafness stresses that etiology affects functional reorganization, whereas the association between network organization and acquired sign language experience reflects ongoing brain adaptation in people with hearing disabilities.

Research on Functional Brain Network Metrics for Depression Patients Automatic Identification

2013 ◽  
Vol 427-429 ◽  
pp. 1440-1446
Author(s):  
Chen Cheng ◽  
Wen Zhao Liu ◽  
Jun Jie Chen
Keyword(s):  
Network Topology ◽  
Neurological Diseases ◽  
Brain Network ◽  
Classification Model ◽  
Support Vector ◽  
Automatic Identification ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Network Metrics ◽  
Abnormal Brain

Nowadays, Brain network as a means of emerging brain disease research has been fully recognized which is applied to the neurological diseases, such as major depressive disorder (MDD). It also can detect the exception of the whole brain network topological. But there is no evidence to prove that abnormal brain network topology metrics can be an effective feature in the classification model to distinguish the healthy control and MDD. So, we hypothesize the abnormal brain network topology metrics can be used as an valid classification feature. Resting state functional brain networks were constructed for 26 healthy controls and 34 MDD patients by thresholding partial correlation matrices of 90 regions. According to the theory-based approaches, the global and local metrics were calculated. Non-parametric permutation tests were then used for group comparisons of topological metrics, which were used as classified features in support vector machine algorithm. The current study demonstrate that MDD is associated with abnormal function brain network topological metrics and statistically significance network metrics can be successfully used for feature selection in classification algorithms.

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