Jahng GH, et al. Korean J Radiol. 2014 Sep-Oct;15(5):554-577. https://doi.org/10.3348/kjr.2014.15.5.554

The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD.

Acupuncture is increasingly used as an additional treatment for patients with Parkinson’s disease (PD). In this functional magnetic resonance imaging study, brain activation in response to acupuncture in a group of 12 patients with PD was compared with a group of 12 healthy participants. Acupuncture was conducted on a specific acupoint, the right GB 34 (Yanglingquan), which is a frequently used acupoint for motor function treatment in the oriental medical field. Acupuncture stimulation on this acupoint activates the prefrontal cortex, precentral gyrus, and putamen in patients with PD; areas that are known to be impaired in patients with PD. Compared with healthy participants, patients with PD showed significantly higher brain activity in the prefrontal cortex and precentral gyrus, especially visible in the left hemisphere. The neuroimaging results of our study suggest that in future acupuncture research; the prefrontal cortex as well as the precentral gyrus should be treated for symptoms of Parkinson’s disease and that GB 34 seems to be a suitable acupoint. Moreover, acupuncture evoked different brain activations in patients with Parkinson’s disease than in healthy participants in our study, stressing the importance of conducting acupuncture studies on both healthy participants as well as patients within the same study, in order to detect acupuncture efficacy. KCT0001122 at (registration date: 20140530)

BACKGROUND AND PURPOSE: Limited evidence has suggested that a deep learning automatic brain segmentation and classification method, based on T1-weighted brain MR images, can predict Alzheimer disease. Our aim was to develop and validate a deep learning–based automatic brain segmentation and classification algorithm for the diagnosis of Alzheimer disease using 3D T1-weighted brain MR images. MATERIALS AND METHODS: A deep learning–based algorithm was developed using a dataset of T1-weighted brain MR images in consecutive patients with Alzheimer disease and mild cognitive impairment. We developed a 2-step algorithm using a convolutional neural network to perform brain parcellation followed by 3 classifier techniques including XGBoost for disease prediction. All classification experiments were performed using 5-fold cross-validation. The diagnostic performance of the XGBoost method was compared with logistic regression and a linear Support Vector Machine by calculating their areas under the curve for differentiating Alzheimer disease from mild cognitive impairment and mild cognitive impairment from healthy controls. RESULTS: In a total of 4 datasets, 1099, 212, 711, and 705 eligible patients were included. Compared with the linear Support Vector Machine and logistic regression, XGBoost significantly improved the prediction of Alzheimer disease (P < .001). In terms of differentiating Alzheimer disease from mild cognitive impairment, the 3 algorithms resulted in areas under the curve of 0.758–0.825. XGBoost had a sensitivity of 68% and a specificity of 70%. In terms of differentiating mild cognitive impairment from the healthy control group, the 3 algorithms resulted in areas under the curve of 0.668–0.870. XGBoost had a sensitivity of 79% and a specificity of 80%. CONCLUSIONS: The deep learning–based automatic brain segmentation and classification algorithm allowed an accurate diagnosis of Alzheimer disease using T1-weighted brain MR images. The widespread availability of T1-weighted brain MR imaging suggests that this algorithm is a promising and widely applicable method for predicting Alzheimer disease.

Alzheimer’s disease (AD) is the most common type of dementia. AD involves major pathologies such as amyloid-β (Aβ) plaques and neurofibrillary tangles in the brain. During the progression of AD, microglia can be polarized from anti-inflammatory M2 to pro-inflammatory M1 phenotype. The activation of triggering receptor expressed on myeloid cells 2 (TREM2) may result in microglia phenotype switching from M1 to M2, which finally attenuated Aβ deposition and memory loss in AD. Low-dose ionizing radiation (LDIR) is known to ameliorate Aβ pathology and cognitive deficits in AD; however, the therapeutic mechanisms of LDIR against AD-related pathology have been little studied. First, we reconfirm that LDIR (two Gy per fraction for five times)-treated six-month 5XFAD mice exhibited (1) the reduction of Aβ deposition, as reflected by thioflavins S staining, and (2) the improvement of cognitive deficits, as revealed by Morris water maze test, compared to sham-exposed 5XFAD mice. To elucidate the mechanisms of LDIR-induced inhibition of Aβ accumulation and memory loss in AD, we examined whether LDIR regulates the microglial phenotype through the examination of levels of M1 and M2 cytokines in 5XFAD mice. In addition, we investigated the direct effects of LDIR on lipopolysaccharide (LPS)-induced production and secretion of M1/M2 cytokines in the BV-2 microglial cells. In the LPS- and LDIR-treated BV-2 cells, the M2 phenotypic marker CD206 was significantly increased, compared with LPS- and sham-treated BV-2 cells. Finally, the effect of LDIR on M2 polarization was confirmed by detection of increased expression of TREM2 in LPS-induced BV2 cells. These results suggest that LDIR directly induced phenotype switching from M1 to M2 in the brain with AD. Taken together, our results indicated that LDIR modulates LPS- and Aβ-induced neuroinflammation by promoting M2 polarization via TREM2 expression, and has beneficial effects in the AD-related pathology such as Aβ deposition and memory loss.