Idioma:

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach

Sartori, Juliana M. ; Reckziegel, Ramiro ; Passos, Ives Cavalcante ; Czepielewski, Leticia S. ; Fijtman, Adam ; Sodré, Leonardo A. ; Massuda, Raffael ; Goi, Pedro D. ; Vianna-Sulzbach, Miréia ; Cardoso, Taiane de Azevedo ; Kapczinski, Flávio ; Mwangi, Benson ; Gama, Clarissa S.

Journal of psychiatric research, 2018-08, Vol.103, p.237-243 [Periódico revisado por pares]

England: Elsevier Ltd

Texto completo disponível

Citações Citado por

Enviar para

Título:
Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach
Autor: Sartori, Juliana M. ; Reckziegel, Ramiro ; Passos, Ives Cavalcante ; Czepielewski, Leticia S. ; Fijtman, Adam ; Sodré, Leonardo A. ; Massuda, Raffael ; Goi, Pedro D. ; Vianna-Sulzbach, Miréia ; Cardoso, Taiane de Azevedo ; Kapczinski, Flávio ; Mwangi, Benson ; Gama, Clarissa S.
Assuntos: Adult ; Biomarkers ; Bipolar disorder ; Bipolar Disorder - diagnostic imaging ; Brain - diagnostic imaging ; Brain imaging ; Brain Mapping ; Correlation of Data ; Female ; Functioning ; Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Magnetic Resonance Imaging - methods ; Male ; Middle Aged ; Psychiatric Status Rating Scales ; Staging ; Statistics, Nonparametric
É parte de: Journal of psychiatric research, 2018-08, Vol.103, p.237-243
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well established, daily-life functioning impairments bulge among the consequences of the proposed progression. The objective of this study was to analyze MRI volumetric modifications in BD and healthy controls (HC) as possible predictors of daily-life functioning through a machine learning approach. Ninety-four participants (35 DSM-IV BD type I and 59 HC) underwent clinical and functioning assessments, and structural MRI. Functioning was assessed using the Functioning Assessment Short Test (FAST). The machine learning analysis was used to identify possible candidates of regional brain volumes that could predict functioning status, through a support vector regression algorithm. Patients with BD and HC did not differ in age, education and marital status. There were significant differences between groups in gender, BMI, FAST score, and employment status. There was significant correlation between observed and predicted FAST score for patients with BD, but not for controls. According to the model, the brain structures volumes that could predict FAST scores were: left superior frontal cortex, left rostral medial frontal cortex, right white matter total volume and right lateral ventricle volume. The machine learning approach demonstrated that brain volume changes in MRI were predictors of FAST score in patients with BD and could identify specific brain areas related to functioning impairment.
Editor: England: Elsevier Ltd
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Anterior Resultado 2 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach

Sartori, Juliana M. ; Reckziegel, Ramiro ; Passos, Ives Cavalcante ; Czepielewski, Leticia S. ; Fijtman, Adam ; Sodré, Leonardo A. ; Massuda, Raffael ; Goi, Pedro D. ; Vianna-Sulzbach, Miréia ; Cardoso, Taiane de Azevedo ; Kapczinski, Flávio ; Mwangi, Benson ; Gama, Clarissa S.

England: Elsevier Ltd

Buscando em bases de dados remotas. Favor aguardar.