Postganglionic Sudomotor Dysfunction and Brain Glucose Hypometabolism in Patients with Multiple System Atrophy.

BACKGROUND: Sudomotor dysfunction is common in patients with multiple system atrophy (MSA). Postganglionic sudomotor dysfunction in MSA, which can be assessed using quantitative sudomotor axon reflex testing (QSART), results from the degeneration of preganglionic sympathetic neurons and direct loss of postganglionic fibers. OBJECTIVE: We investigate whether abnormal QSART responses in patients with MSA are associated with disease severity. METHODS: In this retrospective study, patients with probable MSA who underwent both 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) and autonomic function tests were included. Autonomic function test results were integrated divided into three sub-scores, including sudomotor, cardiovagal, and adrenergic sub-scores. The sudomotor sub-score represented postganglionic sudomotor function. Unified Multiple System Atrophy Rating Scale (UMSARS) Part I, Part II, and sum of Part I and II scores (Part I + II) to reflect disease severity and 18F-FDG-PET/CT results were collected. RESULTS: Of 74 patients with MSA, 62.2%demonstrated abnormal QSART results. The UMSARS Part I + II score was significantly higher in the abnormal QSART group than in the normal QSART group (p = 0.037). In the regression analysis, both UMSARS Part I (ß= 1.185, p = 0.013) and Part II (ß= 1.266, p = 0.021) scores were significantly associated with the sudomotor sub-score. On 18F-FDG-PET/CT, the abnormal QSART group exhibited more severely decreased metabolic activity in the cerebellum and basal ganglia in patients with MSA-P and MSA-C, respectively. The sudomotor sub-score was significantly associated with regional metabolism in these areas. CONCLUSION: Patients with MSA and postganglionic sudomotor dysfunction may have worse disease severity and greater neuropathological burden than those without.

Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography.

BACKGROUND: Alzheimer's Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to manage, accurate diagnosis of the disorder is critical. Previous studies have revealed effective deep learning methods of classification. However, deep learning methods require a large number of image datasets. Moreover, medical images are affected by various environmental factors. In the current study, we propose a deep learning-based method for diagnosis of Alzheimer's disease (AD) that is less sensitive to different datasets for external validation, based upon F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT). RESULTS: The accuracy, sensitivity, and specificity of our proposed network were 86.09%, 80.00%, and 92.96% (respectively) using our dataset, and 91.02%, 87.93%, and 93.57% (respectively) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We observed that our model classified AD and normal cognitive (NC) cases based on the posterior cingulate cortex (PCC), where pathological changes occur in AD. The performance of the GAP layer was considered statistically significant compared to the fully connected layer in both datasets for accuracy, sensitivity, and specificity (p < 0.01). In addition, performance comparison between the ADNI dataset and our dataset showed no statistically significant differences in accuracy, sensitivity, and specificity (p > 0.05). CONCLUSIONS: The proposed model demonstrated the effectiveness of AD classification using the GAP layer. Our model learned the AD features from PCC in both the ADNI and Severance datasets, which can be seen in the heatmap. Furthermore, we showed that there were no significant differences in performance using statistical analysis.

Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial Network.

Recently, portable electrocardiogram (ECG) hardware devices have been developed using limb-lead measurements. However, portable ECGs provide insufficient ECG information because of limitations in the number of leads and measurement positions. Therefore, in this study, V-lead ECG signals were synthesized from limb leads using an R-peak aligned generative adversarial network (GAN). The data used the Physikalisch-Technische Bundesanstalt (PTB) dataset provided by PhysioNet. First, R-peak alignment was performed to maintain the physiological information of the ECG. Second, time domain ECG was converted to bi-dimensional space by ordered time-sequence embedding. Finally, the GAN was learned through the pairs between the modified limb II (MLII) lead and each chest (V) lead. The result showed that the mean structural similarity index (SSIM) was 0.92, and the mean error rate of the percent mean square difference (PRD) of the chest leads was 7.21%.

Level-Set Segmentation-Based Respiratory Volume Estimation Using a Depth Camera.

In this paper, a method is proposed to measure human respiratory volume using a depth camera. The level-set segmentation method, combined with spatial and temporal information, was used to measure respiratory volume accurately. The shape of the human chest wall was used as spatial information. As temporal information, the segmentation result from the previous frame in the time-aligned depth image was used. The results of the proposed method were verified using a ventilator. The proposed method was also compared with other level-set methods. The result showed that the mean tidal volume error of the proposed method was 8.41% compared to the actual tidal volume. This was calculated to have less error than with two other methods: the level-set method with spatial information (14.34%) and the level-set method with temporal information (10.93%). The difference between these methods of tidal volume error was statistically significant [Formula: see text]. The intra-class correlation coefficient (ICC) of the respiratory volume waveform measured by a ventilator and by the proposed method was 0.893 on an average, while the ICC between the ventilator and the other methods were 0.837 and 0.879 on an average.