Cortical Thickness of Resting State Networks in the Brain of Male Patients with Alcohol Dependence

남성 알코올 의존 환자 대뇌의 휴지기 네트워크별 피질 두께

  • Lee, Jun-Ki (Department of Psychiatry, Chungbuk National University College of Medicine) ;
  • Kim, Siekyeong (Department of Psychiatry, Chungbuk National University College of Medicine)
  • 이준기 (충북대학교 의과대학 정신건강의학교실) ;
  • 김시경 (충북대학교 의과대학 정신건강의학교실)
  • Received : 2016.12.15
  • Accepted : 2017.01.31
  • Published : 2017.05.31

Abstract

Objectives It is well known that problem drinking is associated with alterations of brain structures and functions. Brain functions related to alcohol consumption can be determined by the resting state functional connectivity in various resting state networks (RSNs). This study aims to ascertain the alcohol effect on the structures forming predetermined RSNs by assessing their cortical thickness. Methods Twenty-six abstinent male patients with alcohol dependence and the same number of age-matched healthy control were recruited from an inpatient mental hospital and community. All participants underwent a 3T MRI scan. Averaged cortical thickness of areas constituting 7 RSNs were determined by using FreeSurfer with Yeo atlas derived from cortical parcellation estimated by intrinsic functional connectivity. Results There were significant group differences of mean cortical thicknesses (Cohen's d, corrected p) in ventral attention (1.01, < 0.01), dorsal attention (0.93, 0.01), somatomotor (0.90, 0.01), and visual (0.88, 0.02) networks. We could not find significant group differences in the default mode network. There were also significant group differences of gray matter volumes corrected by head size across the all networks. However, there were no group differences of surface area in each network. Conclusions There are differences in degree and pattern of structural recovery after abstinence across areas forming RSNs. Considering the previous observation that group differences of functional connectivity were significant only in networks related to task-positive networks such as dorsal attention and cognitive control networks, we can explain recovery pattern of cognition and emotion related to the default mode network and the mechanisms for craving and relapse associated with task-positive networks.

Keywords

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