Flora Jung1, Samaneh Kazemifar2, Robert Bartha2, Nagalingam Rajakumar3. 1. Department of Physiology, Western University, London, ON, Canada. 2. Department of Medical Biophysics, Western University, London, ON, Canada. 3. Department of Anatomy & Cell Biology, Western University, London, ON, Canada.
Abstract
BACKGROUND AND PURPOSE: The anterior cingulate cortex (ACC) is involved in several cognitive processes including executive function. Degenerative changes of ACC are consistently seen in Alzheimer's disease (AD). However, volumetric changes specific to the ACC in AD are not clear because of the difficulty in segmenting this region. The objectives of the current study were to develop a precise and high-throughput approach for measuring ACC volumes and to correlate the relationship between ACC volume and cognitive function in AD. METHODS: Structural T1 -weighted magnetic resonance images of AD patients (n = 47) and age-matched controls (n = 47) at baseline and at 24 months were obtained from the Alzheimer's disease neuroimaging initiative (ADNI) database and studied using a custom-designed semiautomated segmentation protocol. RESULTS: ACC volumes obtained using the semiautomated protocol were highly correlated to values obtained from manual segmentation (r = .98) and the semiautomated protocol was considerably faster. When comparing AD and control subjects, no significant differences were observed in baseline ACC volumes or in change in ACC volumes over 24 months using the two segmentation methods. However, a change in ACC volume over 24 months did not correlate with a change in mini-mental state examination scores. CONCLUSIONS: Our results indicate that the proposed semiautomated segmentation protocol is reliable for determining ACC volume in neurodegenerative conditions including AD.
BACKGROUND AND PURPOSE: The anterior cingulate cortex (ACC) is involved in several cognitive processes including executive function. Degenerative changes of ACC are consistently seen in Alzheimer's disease (AD). However, volumetric changes specific to the ACC in AD are not clear because of the difficulty in segmenting this region. The objectives of the current study were to develop a precise and high-throughput approach for measuring ACC volumes and to correlate the relationship between ACC volume and cognitive function in AD. METHODS: Structural T1 -weighted magnetic resonance images of ADpatients (n = 47) and age-matched controls (n = 47) at baseline and at 24 months were obtained from the Alzheimer's disease neuroimaging initiative (ADNI) database and studied using a custom-designed semiautomated segmentation protocol. RESULTS: ACC volumes obtained using the semiautomated protocol were highly correlated to values obtained from manual segmentation (r = .98) and the semiautomated protocol was considerably faster. When comparing AD and control subjects, no significant differences were observed in baseline ACC volumes or in change in ACC volumes over 24 months using the two segmentation methods. However, a change in ACC volume over 24 months did not correlate with a change in mini-mental state examination scores. CONCLUSIONS: Our results indicate that the proposed semiautomated segmentation protocol is reliable for determining ACC volume in neurodegenerative conditions including AD.