Brain amyloid-ß deposition associated functional connectivity changes of ultra-large structural scale in mild cognitive impairment.

In preclinical Alzheimer's disease, neuro-functional changes due to amyloid-ß (Aß) deposition are not synchronized in different brain lobes and subcortical nuclei. This study aimed to explore the correlation between brain Aß burden, connectivity changes in an ultra-large structural scale, and cognitive function in mild cognitive impairment. Participants with mild cognitive impairment were recruited and underwent florbetapir (F18-AV45) PET, resting-state functional MRI, and multidomain neuropsychological tests. AV-45 standardized uptake value ratio (SUVR) and functional connectivity of all participants were calculated. Of the total 144 participants, 72 were put in the low Aß burden group and 72 in the high Aß burden group. In the low Aß burden group, all connectivities between lobes and nuclei had no correlation with SUVR. In the high Aß burden group, SUVR showed negative correlations with the Subcortical-Occipital connectivity (r=-0.36, P = 0.02) and Subcortical-Parietal connectivity (r=-0.26, P = 0.026). Meanwhile, in the high Aß burden group, SUVR showed positive correlations with the Temporal-Prefrontal connectivity (r = 0.27, P = 0.023), Temporal-Occipital connectivity (r = 0.24, P = 0.038), and Temporal-Parietal connectivity (r = 0.32, P = 0.006). Subcortical to Occipital and Parietal connectivities had positive correlations with general cognition, language, memory, and executive function. Temporal to Prefrontal, Occipital, and Parietal connectivities had negative correlations with memory function, executive function, and visuospatial function, and a positive correlation with language function. In conclusion, Individuals with mild cognitive impairment with high Aß burden have Aß-related bidirectional functional connectivity changes between lobes and subcortical nuclei that are associated with cognitive decline in multiple domains. These connectivity changes reflect neurological impairment and failed compensation.

Dopaminergic Dysfunction and Glucose Metabolism Characteristics in Parkin-Induced Early-Onset Parkinson's Disease Compared to Genetically Undetermined Early-Onset Parkinson's Disease.

While early-onset Parkinson's disease (EOPD) caused by mutations in the parkin gene (PRKN) tends to have a relatively benign course compared to genetically undetermined (GU)-EOPD, the exact underlying mechanisms remain elusive. We aimed to search for the differences between PRKN-EOPD and GU-EOPD by dopamine transporter (DAT) and glucose metabolism positron-emission-tomography (PET) imaging. Twelve patients with PRKN-EOPD and 16 with GU-EOPD who accepted both 11C-2b-carbomethoxy-3b-(4-trimethylstannylphenyl) tropane (11C-CFT) and 18F-fluorodeoxyglucose PET were enrolled. The 11C-CFT uptake was analyzed on both regional and voxel levels, whereas glucose metabolism was assessed in a voxel-wise fashion. Correlations between DAT and glucose metabolism imaging, DAT imaging and clinical severity, as well as glucose metabolism imaging and clinical severity were explored. Both clinical symptoms and DAT-binding patterns in the posterior putamen were highly symmetrical in patients with PRKN-EOPD, and dopaminergic dysfunction in the ipsilateral putamen was severer in patients with PRKN-EOPD than GU-EOPD. Meanwhile, the DAT binding was associated with the severity of motor dysfunction in  patients with GU-EOPD only. Patients with PRKN-EOPD showed increased glucose metabolism in the contralateral medial frontal gyrus (supplementary motor area (SMA)), contralateral substantia nigra, contralateral thalamus, and contralateral cerebellum. Notably, glucose metabolic activity in the contralateral medial frontal gyrus was inversely associated with regional DAT binding in the bilateral putamen. Patients with PRKN-EOPD showed enhanced metabolic connectivity within the bilateral putamen, ipsilateral paracentral and precentral lobules, and the ipsilateral SMA. Collectively, compared to GU-EOPD, PRKN-EOPD is characterized by symmetrical, more severe dopaminergic dysfunction and relative increased glucose metabolism. Meanwhile, SMA with elevated glucose metabolism and enhanced connectivity may act as compensatory mechanisms in PRKN-EOPD. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00077-8.

Classifying Chinese Medicine Constitution Using Multimodal Deep-Learning Model.

OBJECTIVE: To develop a multimodal deep-learning model for classifying Chinese medicine constitution, i.e., the balanced and unbalanced constitutions, based on inspection of tongue and face images, pulse waves from palpation, and health information from a total of 540 subjects. METHODS: This study data consisted of tongue and face images, pulse waves obtained by palpation, and health information, including personal information, life habits, medical history, and current symptoms, from 540 subjects (202 males and 338 females). Convolutional neural networks, recurrent neural networks, and fully connected neural networks were used to extract deep features from the data. Feature fusion and decision fusion models were constructed for the multimodal data. RESULTS: The optimal models for tongue and face images, pulse waves and health information were ResNet18, Gate Recurrent Unit, and entity embedding, respectively. Feature fusion was superior to decision fusion. The multimodal analysis revealed that multimodal data compensated for the loss of information from a single mode, resulting in improved classification performance. CONCLUSIONS: Multimodal data fusion can supplement single model information and improve classification performance. Our research underscores the effectiveness of multimodal deep learning technology to identify body constitution for modernizing and improving the intelligent application of Chinese medicine.

Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer's disease: A cross-validation study with Chinese and ADNI cohorts.

OBJECTIVE: Disease-related metabolic brain patterns have been verified for a variety of neurodegenerative diseases including Alzheimer's disease (AD). This study aimed to explore and validate the pattern derived from cognitively normal controls (NCs) in the Alzheimer's continuum. METHODS: This study was based on two cohorts; one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) and [18F]florbetapir-PET imaging. Participants were binary-grouped based on ß-amyloid (Aß) status, and the positivity was defined as Aß+. Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate the "at-risk AD-related metabolic pattern (ARADRP)" for NCs. The pattern expression score was obtained and compared between the groups, and receiver operating characteristic curves were drawn. Notably, we conducted cross-validation to verify the robustness and correlation analyses to explore the relationships between the score and AD-related pathological biomarkers. RESULTS: Forty-eight Aß+ NCs and 48 Aß- NCs were included in the ADNI cohort, and 25 Aß+ NCs and 30 Aß- NCs were included in the SILCODE cohort. The ARADRPs were identified from the combined cohorts and the two separate cohorts, characterized by relatively lower regional loadings in the posterior parts of the precuneus, posterior cingulate, and regions of the temporal gyrus, as well as relatively higher values in the superior/middle frontal gyrus and other areas. Patterns identified from the two separate cohorts showed some regional differences, including the temporal gyrus, basal ganglia regions, anterior parts of the precuneus, and middle cingulate. Cross-validation suggested that the pattern expression score was significantly higher in the Aß+ group of both cohorts (p < 0.01), and contributed to the diagnosis of Aß+ NCs (with area under the curve values of 0.696-0.815). The correlation analysis revealed that the score was related to tau pathology measured in cerebrospinal fluid (p-tau: p < 0.02; t-tau: p < 0.03), but not Aß pathology assessed with [18F]florbetapir-PET (p > 0.23). CONCLUSIONS: ARADRP exists for NCs, and the acquired pattern expression score shows a certain ability to discriminate Aß+ NCs from Aß- NCs. The SSM/PCA method is expected to be helpful in the ultra-early diagnosis of AD in clinical practice.

Functional Connectivity Alterations Based on Hypometabolic Region May Predict Clinical Prognosis of Temporal Lobe Epilepsy: A Simultaneous 18F-FDG PET/fMRI Study.

(1) Background: Accurate localization of the epileptogenic zone and understanding the related functional connectivity (FC) alterations are critical for the prediction of clinical prognosis in patients with temporal lobe epilepsy (TLE). We aim to localize the hypometabolic region in TLE patients, compare the differences in FC alterations based on hypometabolic region and structural lesion, respectively, and explore their relationships with clinical prognosis. (2) Methods: Thirty-two TLE patients and 26 controls were recruited. Patients underwent 18F-FDG PET/MR scan, surgical treatment, and a 2−3-year follow-up. Visual assessment and voxel-wise analyses were performed to identify hypometabolic regions. ROI-based FC analyses were performed. Relationships between clinical prognosis and FC values were performed by using Pearson correlation analyses and receiver operating characteristic (ROC) analysis. (3) Results: Hypometabolic regions in TLE patients were found in the ipsilateral hippocampus, parahippocampal gyrus, and temporal lobe (p < 0.001). Functional alterations based on hypometabolic regions showed a more extensive whole-brain FC reduction. FC values of these regions negatively correlated with epilepsy duration (p < 0.05), and the ROC curve of them showed significant accuracy in predicting postsurgical outcome. (4) Conclusions: In TLE patients, FC related with hypometabolic region obtained by PET/fMRI may provide value in the prediction of disease progression and seizure-free outcome.

Glucose metabolism in the right middle temporal gyrus could be a potential biomarker for subjective cognitive decline: a study of a Han population.

INTRODUCTION: Subjective cognitive decline (SCD) represents a cognitively normal state but at an increased risk for developing Alzheimer's disease (AD). Recognizing the glucose metabolic biomarkers of SCD could facilitate the location of areas with metabolic changes at an ultra-early stage. The objective of this study was to explore glucose metabolic biomarkers of SCD at the region of interest (ROI) level. METHODS: This study was based on cohorts from two tertiary medical centers, and it was part of the SILCODE project (NCT03370744). Twenty-six normal control (NC) cases and 32 SCD cases were in cohort 1; 36 NCs, 23 cases of SCD, 32 cases of amnestic mild cognitive impairment (aMCIs), 32 cases of AD dementia (ADDs), and 22 cases of dementia with Lewy bodies (DLBs) were in cohort 2. Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) imaging and magnetic resonance imaging (MRI), and subjects from cohort 1 additionally underwent amyloid-PET scanning. The ROI analysis was based on the Anatomical Automatic Labeling (AAL) template; multiple permutation tests and repeated cross-validations were conducted to determine the metabolic differences between NC and SCD cases. In addition, receiver operating characteristic curves were used to evaluate the capabilities of potential glucose metabolic biomarkers in distinguishing different groups. Pearson correlation analysis was also performed to explore the correlation between glucose metabolic biomarkers and neuropsychological scales or amyloid deposition. RESULTS: Only the right middle temporal gyrus (RMTG) passed the methodological verification, and its metabolic levels were correlated with the degrees of complaints (R = - 0.239, p = 0.009), depression (R = - 0.200, p = 0.030), and abilities of delayed memory (R = 0.207, p = 0.025), and were weakly correlated with cortical amyloid deposition (R = - 0.246, p = 0.066). Furthermore, RMTG metabolism gradually decreased across the cognitive continuum, and its diagnostic efficiency was comparable (NC vs. ADD, aMCI, or DLB) or even superior (NC vs. SCD) to that of the metabolism of the posterior cingulate cortex or precuneus. CONCLUSIONS: These findings suggest that the hypometabolism of RMTG could be a typical feature of SCD, and the large-scale hypometabolism in patients with symptomatic stages of AD may start from the RMTG, which gradually progresses starting in the preclinical stage. The specificity of identifying SCD from the perspective of self-perceived symptoms is likely to be increased by the detection of RMTG metabolism.

Staging tau pathology with tau PET in Alzheimer's disease: a longitudinal study.

A biological research framework to define Alzheimer' disease with dichotomized biomarker measurement was proposed by National Institute on Aging-Alzheimer's Association (NIA-AA). However, it cannot characterize the hierarchy spreading pattern of tau pathology. To reflect in vivo tau progression using biomarker, we constructed a refined topographic 18F-AV-1451 tau PET staging scheme with longitudinal clinical validation. Seven hundred and thirty-four participants with baseline 18F-AV-1451 tau PET (baseline age 73.9 ± 7.7 years, 375 female) were stratified into five stages by a topographic PET staging scheme. Cognitive trajectories and clinical progression were compared across stages with or without further dichotomy of amyloid status, using linear mixed-effect models and Cox proportional hazard models. Significant cognitive decline was first observed in stage 1 when tau levels only increased in transentorhinal regions. Rates of cognitive decline and clinical progression accelerated from stage 2 to stage 3 and stage 4. Higher stages were also associated with greater CSF phosphorylated tau and total tau concentrations from stage 1. Abnormal tau accumulation did not appear with normal ß-amyloid in neocortical regions but prompt cognitive decline by interacting with ß-amyloid in temporal regions. Highly accumulated tau in temporal regions independently led to cognitive deterioration. Topographic PET staging scheme have potentials in early diagnosis, predicting disease progression, and studying disease mechanism. Characteristic tau spreading pattern in Alzheimer's disease could be illustrated with biomarker measurement under NIA-AA framework. Clinical-neuroimaging-neuropathological studies in other cohorts are needed to validate these findings.

Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study.

Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into "converters" and "nonconverters" according to individuals' future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer's Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7-95.9% and 87.1-90.8% in the validation set and 81.9-89.1% and 83.2-83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-ß deposition and predicted future cognitive decline (areas under the curve 0.649-0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.

Forniceal deep brain stimulation in severe Alzheimer's disease: A case report.

BACKGROUND: Forniceal deep brain stimulation (DBS) has been proposed as an alternative treatment for Alzheimer's disease (AD). Previous studies on mild to moderate AD patients demonstrated improvements in cognitive functions brought about by forniceal DBS. Here, we report our longitudinal findings in one severe AD patient for whom the activities of daily living (ADL) rather than cognitive function significantly improved after 3 mo of continuous stimulation. CASE SUMMARY: In 2011, a 62-year-old Chinese male with no previous history of brain injury or other neuropsychological diseases and no family history of dementia developed early symptoms of memory decline and cognitive impairment. Five years later, the symptoms had increased to the extent that they affected his daily living. He lost the ability to work as a businessman and to take care of himself. The patient was given a clinical diagnosis of probable AD and was prescribed donepezil and subsequently memantine, but no improvement in symptoms was observed. The patient then received DBS surgery. After 3 mo of continuous stimulation, the patient's ADL score decreased from 65 points to 47 points, indicating the quality of the patient's daily living improved distinctly. Other scores remained unchanged, suggesting no significant improvement in cognitive function. A follow-up positron emission tomography scan demonstrated perceivable increased glucose metabolism in the classical AD-related brain regions. CONCLUSION: Based on this case we hypothesize that forniceal DBS may improve ADL through elevating regional glucose metabolism in the brain.

Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls.

BACKGROUND: Parkinson's disease (PD) is an irreversible neurodegenerative disease. The diagnosis of PD based on neuroimaging is usually with low-level or deep learning features, which results in difficulties in achieving precision classification or interpreting the clinical significance. Herein, we aimed to extract high-order features by using radiomics approach and achieve acceptable diagnosis accuracy in PD. METHODS: In this retrospective multicohort study, we collected 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and clinical scale [the Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn & Yahr scale (H&Y)] from two cohorts. One cohort from Huashan Hospital had 91 normal controls (NC) and 91 PD patients (UPDRS: 22.7±11.7, H&Y: 1.8±0.8), and the other cohort from Wuxi 904 Hospital had 26 NC and 22 PD patients (UPDRS: 20.9±11.6, H&Y: 1.7±0.9). The Huashan cohort was used as the training and test sets by 5-fold cross-validation and the Wuxi cohort was used as another separate test set. After identifying regions of interests (ROIs) based on the atlas-based method, radiomic features were extracted and selected by using autocorrelation and fisher score algorithm. A support vector machine (SVM) was trained to classify PD and NC based on selected radiomic features. In the comparative experiment, we compared our method with the traditional voxel values method. To guarantee the robustness, above processes were repeated in 500 times. RESULTS: Twenty-six brain ROIs were identified. Six thousand one hundred and ten radiomic features were extracted in total. Among them 30 features were remained after feature selection. The accuracies of the proposed method achieved 90.97%±4.66% and 88.08%±5.27% in Huashan and Wuxi test sets, respectively. CONCLUSIONS: This study showed that radiomic features and SVM could be used to distinguish between PD and NC based on 18F-FDG PET images.

Area and volume ratios for prediction of visual outcome in idiopathic macular hole.

AIM: To predict the visual outcome in patients undergoing macular hole surgery by two novel three-dimensional morphological parameters on optical coherence tomography (OCT): area ratio factor (ARF) and volume ratio factor (VRF). METHODS: A clinical case series was conducted, including 54 eyes of 54 patients with an idiopathic macular hole (IMH). Each patient had an OCT examination before and after surgery. Morphological parameters of the macular hole, such as minimum diameter, base diameter, and height were measured. Then, the macular hole index (MHI), tractional hole index (THI), and hole form factor (HFF) were calculated. Meanwhile, novel postoperative macular hole (MH) factors, ARF and VRF were calculated by three-dimensional morphology. Bivariate correlations were performed to acquire asymptotic significance values between the steady best corrected visual acuity (BCVA) after surgery and 2D/3D arguments of MH by the Pearson method with two-tailed test. All significant factors were analyzed by the receiver operating characteristic (ROC) curve analysis of SPSS software which were responsible for vision recovery. ROC curves analyses were performed to further discuss the different parameters on the prediction of visual outcome. RESULTS: The mean and standard deviation values of patients' age, symptoms duration, and follow-up time were 64.8±8.9y (range: 28-81), 18.6±11.5d (range: 2-60), and 11.4±0.4mo (range: 6-24), respectively. Steady-post-BCVA analyzed with bivariate correlations was found to be significantly correlated with base diameter (r=0.521, P<0.001), minimum diameter (r=0.514, P<0.001), MHI (r=-0.531, P<0.001), THI (r=-0.386, P=0.004), HFF (r=-0.508, P<0.001), and ARF (r=-0.532, P<0.001). Other characteristic parameters such as age, duration of surgery, height, diameter hole index, and VRF were not statistically significant with steady-post-BCVA. According to area under the curve (AUC) values, values of ARF, MHI, HFF, minimum diameter, THI, and base diameter are 0.806, 0.772, 0.750, 0.705, 0.690, and 0.686, respectively. However, Steady-post-BCVA analysis with bivariate correlations for VRF was no statistical significance. Results of ROC curve analysis indicated that the MHI value, HFF, and ARF was greater than 0.427, 1.027 and 1.558 respectively which could correlate with better visual acuity. CONCLUSION: Compared with MHI and HFF, ARF could effectively express three-dimensional characteristics of macular hole and achieve better sensitivity and specificity. Thus, ARF could be the most effective parameter to predict the visual outcome in macular hole surgery.

[Design of the Medical Digital Assistant for "bottom of the pyramid"].

To meet the challenges such as raising the efficiency of medical resources, slowing the fastly-increasing medical costs and keeping the sustainability of the aging society and chronic diseases, this paper introduces a Medical Digital Assistant system for "BOP", which includes a mobile monitoring unit,a data transferring unit, and a managing unit, and presents the system's evaluating method. In addition, a new application model and the service workflow are discussed.

[Wireless medical monitoring--current status and developments].

Wireless medical monitoring overcomes the limitations of conventional wired-monitoring, enabling people to get their physiological and pathological informations anytime and anywhere. This review summarizes the basic components and principles of wireless medical monitoring, including the techniques for measuring and transmitting vital signs. Its status,developments in R&D and applications are presented with a series of examples. Limitations and potential uses of wireless medical monitoring are also discussed.