Brain imaging and behavior
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Soccer is the most popular sport in the world. Soccer players are at high risk for repetitive subconcussive head impact when heading the ball. Whether this leads to long-term alterations of the brain's structure associated with cognitive decline remains unknown. ⋯ Neurocognitive evaluation revealed decreased memory performance in the soccer players compared to controls. The association of cortical thinning and decreased cognitive performance, as well as exposure to repetitive subconcussive head impact, further supports the hypothesis that repetitive subconcussive head impact may play a role in early cognitive decline in soccer players. Future studies are needed to elucidate the time course of changes in cortical thickness as well as their association with impaired cognitive function and possible underlying neurodegenerative process.
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Brain Imaging Behav · Sep 2016
Abnormal prefrontal cortex resting state functional connectivity and severity of internet gaming disorder.
Internet Gaming Disorder (IGD) among adolescents has become an important public concern and gained more and more attention internationally. Recent studies focused on IGD and revealed brain abnormalities in the IGD group, especially the prefrontal cortex (PFC). However, the role of PFC-striatal circuits in pathology of IGD remains unknown. ⋯ We chose these regions as the seeding areas for the resting-state analysis and found that IGD subjects showed decreased functional connectivity between several cortical regions and our seeds, including the insula, and temporal and occipital cortices. Moreover, significant decreased functional connectivity between some important subcortical regions, i.e., dorsal striatum, pallidum, and thalamus, and our seeds were found in the IGD group and some of those changes were associated with the severity of IGD. Our results revealed the involvement of several PFC regions and related PFC-striatal circuits in the process of IGD and suggested IGD may share similar neural mechanisms with substance dependence at the circuit level.
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Brain Imaging Behav · Sep 2016
Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer's disease.
The study of brain networks by resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method for identifying patients with dementia from healthy controls (HC). Using graph theory, different aspects of the brain network can be efficiently characterized by calculating measures of integration and segregation. In this study, we combined a graph theoretical approach with advanced machine learning methods to study the brain network in 89 patients with mild cognitive impairment (MCI), 34 patients with Alzheimer's disease (AD), and 45 age-matched HC. ⋯ In addition, results based on the parcellation of 264 regions were compared to that of the automated anatomical labeling atlas (AAL), consisted of 90 regions. The accuracy of classification of three groups using AAL was degraded to 83.2 %. Our results show that combining the graph measures with the machine learning approach, on the basis of the rs-fMRI connectivity analysis, may assist in diagnosis of AD and MCI.