[Study based on ICA of "dorsal attention network" in patients with temporal lobe epilepsy]

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Feb;27(1):10-5.
[Article in Chinese]

Abstract

Many functional magnetic resonance imaging (fMRI) studies have revealed the deactivation phenomenon of default mode network in the patients with epilepsy; however, nearly not any of the reports has focused on the dorsal attention network of epilepsy. In this paper, independent component analysis (ICA) was used to isolate the dorsal attention network of 16 patients with temporal lobe epilepsy (TLE) and of 20 healthy normals; and a goodness-of-fit analysis was applied at the individual subject level to choose the interesting component. Intra-group analysis and inter-group analysis were performed. The results indicated that the dorsal attention network included bilateral intraparietal sulcus, middle frontal gyrus, human frontal eye field, posterior lobe of right cerebellum, etc. The TLE group showed decreased functional connectivity in most of the dorsal attention regions with the predominance in the bilateral intraparietal sulcus, middle frontal gyrus, and posterior lobe of right cerebellum. These data suggested that the intrinsic organization of the brain function might be disrupted in TLE. In addition, the decrease of goodness-of-fit scores suggests that activity in the dorsal attention network may ultimately prove a sensitive biomarker for TLE.

Publication types

  • English Abstract

MeSH terms

  • Adolescent
  • Adult
  • Attention / physiology*
  • Attention Deficit and Disruptive Behavior Disorders / etiology
  • Attention Deficit and Disruptive Behavior Disorders / physiopathology
  • Brain Mapping
  • Epilepsy, Temporal Lobe / pathology
  • Epilepsy, Temporal Lobe / physiopathology*
  • Epilepsy, Temporal Lobe / psychology
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted
  • Image Processing, Computer-Assisted / methods
  • Magnetic Resonance Imaging / methods*
  • Male
  • Nerve Net / physiopathology*
  • Principal Component Analysis / methods*
  • Young Adult