As brainstem nuclei are interconnected with several cortical structures and regulate several autonomic, cognitive, and behavioral functions, it might be important to place the brainstem within an important pathologic core in the progression of Alzheimer’s disease (AD). Although there have been several postmortem studies reporting neuropathological alterations of the brainstem in AD, there has been no in-vivo structural neuroimaging study of the brainstem in the patients with AD. The aim of this study was to investigate differences in the brainstem volume and shape between patients with AD and elderly normal controls. Fifty AD patients (the Clinical Dementia Rating Scale≥1) and 50 normal controls were recruited, and the brainstem volumes and deformations were compared between the AD and the controls. Patients with AD showed significant total volume [(mean±SD) 21007±1640 mm³] reduction in the brainstem compared with the controls [(mean±SD) 22530±1750 mm³] (P<0.001). In addition, AD patients showed significant brainstem deformations in the upper posterior brainstem corresponding to the midbrain compared with the healthy individuals (false discovery rate corrected P<0.05). This study is the first to explore brainstem volume change and deformations in AD. These structural changes in the midbrain areas might be at the core of the underlying neurobiological mechanisms of brainstem dysfunction with relevance to their various cognitive and behavioral symptoms such as memory impairment, sleep, and emotional disturbance in AD. However, further longitudinal studies might be needed to confirm these findings.

Dong Woo Kang, Chang Uk Lee, and Hyun Kook Lim. Clin Psychopharmacol Neurosci 2017;15:89-99. https://doi.org/10.9758/cpn.2017.15.2.89

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging findings. Therefore, we introduce Split-Attention U-Net (SAU-Net), a convolutional neural network with skip pathways and a split-attention module that segments brain MRI scans. The proposed architecture employs split-attention blocks, skip pathways with pyramid levels, and evolving normalization layers. For efficient training, we performed pre-training and fine-tuning with the original and manually modified FreeSurfer labels, respectively. This learning strategy enables involvement of heterogeneous neuroimaging data in the training without the need for many manual annotations. Using nine evaluation datasets, we demonstrated that SAU-Net achieved better segmentation accuracy with better reliability that surpasses those of state-of-the-art methods. We believe that SAU-Net has excellent potential due to its robustness to neuroanatomical variability that would enable almost instantaneous access to accurate neuroimaging biomarkers and its swift processing runtime compared to other methods investigated.

Alzheimer’s disease (AD) accounts for 60–70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10–15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer’s Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician’s early diagnosis and treatment plan design.

Sheng-Min Wang, Nak-Young Kim, Hae-Ran Na, Hyun Kook Lim, Young Sup Woo, Chi-Un Pae, Won-Myong Bahk. Clin Psychopharmacol Neurosci 2021;19:341-54. https://doi.org/10.9758/cpn.2021.19.2.341

Objective Alzheimer’s disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient’s severity of neurodegeneration independent from the patient’s clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).Methods We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer’s which is a commonly used segmentation software.Results Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer’s (6–8 hours).Conclusion Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.