Idioma:

Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity

Rashid, Barnaly ; Arbabshirani, Mohammad R. ; Damaraju, Eswar ; Cetin, Mustafa S. ; Miller, Robyn ; Pearlson, Godfrey D. ; Calhoun, Vince D.

NeuroImage (Orlando, Fla.), 2016-07, Vol.134, p.645-657 [Periódico revisado por pares]

United States: Elsevier Inc

Texto completo disponível

Citações Citado por

Enviar para

Título:
Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity
Autor: Rashid, Barnaly ; Arbabshirani, Mohammad R. ; Damaraju, Eswar ; Cetin, Mustafa S. ; Miller, Robyn ; Pearlson, Godfrey D. ; Calhoun, Vince D.
Assuntos: Accuracy ; Adult ; Algorithms ; Bipolar ; Bipolar disorder ; Bipolar Disorder - classification ; Bipolar Disorder - diagnostic imaging ; Bipolar Disorder - physiopathology ; Brain ; Brain - diagnostic imaging ; Brain - physiopathology ; Brain architecture ; Brain mapping ; Brain Mapping - methods ; Classification ; Dynamic functional network connectivity ; Female ; fMRI ; Functional magnetic resonance imaging ; Functional morphology ; Humans ; Magnetic Resonance Imaging ; Male ; Mental disorders ; Neural networks ; Neural Pathways - diagnostic imaging ; Neural Pathways - physiopathology ; NMR ; Nuclear magnetic resonance ; Pattern Recognition, Automated - methods ; Resting-state ; Schizophrenia ; Schizophrenia - classification ; Schizophrenia - diagnostic imaging ; Schizophrenia - physiopathology ; Sensitivity and Specificity ; Studies
É parte de: NeuroImage (Orlando, Fla.), 2016-07, Vol.134, p.645-657
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: Recently, functional network connectivity (FNC, defined as the temporal correlation among spatially distant brain networks) has been used to examine the functional organization of brain networks in various psychiatric illnesses. Dynamic FNC is a recent extension of the conventional FNC analysis that takes into account FNC changes over short periods of time. While such dynamic FNC measures may be more informative about various aspects of connectivity, there has been no detailed head-to-head comparison of the ability of static and dynamic FNC to perform classification in complex mental illnesses. This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features. Also, we compare cross-validated classification performance between static and dynamic FNC. Results show that the dynamic FNC significantly outperforms the static FNC in terms of predictive accuracy, indicating that features from dynamic FNC have distinct advantages over static FNC for classification purposes. Moreover, combining static and dynamic FNC features does not significantly improve the classification performance over the dynamic FNC features alone, suggesting that static FNC does not add any significant information when combined with dynamic FNC for classification purposes. A three-way classification methodology based on static and dynamic FNC features discriminates individual subjects into appropriate diagnostic groups with high accuracy. Our proposed classification framework is potentially applicable to additional mental disorders. •Performed classification using static and dynamic connectivity features in schizophrenia and bipolar disorder during rest.•Classification using connectivity features discriminates subjects into appropriate diagnostic groups with high accuracy.•Classification using dynamic connectivity features has significantly higher predictive accuracy than static FNC.•Combining both connectivity features does not add significant information for classification purposes.
Editor: United States: Elsevier Inc
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Realização: Logos de Redes Sociais:

Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity

Rashid, Barnaly ; Arbabshirani, Mohammad R. ; Damaraju, Eswar ; Cetin, Mustafa S. ; Miller, Robyn ; Pearlson, Godfrey D. ; Calhoun, Vince D.

United States: Elsevier Inc

Buscando em bases de dados remotas. Favor aguardar.