Alzheimer's Disease (AD) is a chronic neurodegenerative disease. Early diagnosis will considerably decrease the risk of further deterioration. Unfortunately, current studies mainly focus on classifying the states of disease in its current stage, instead of predicting the possible development of the disease. Long short-term memory (LSTM) is a special kind of recurrent neural network, which might be able to connect previous information to the present task. Noticing that the temporal data for a patient are potentially meaningful for predicting the development of the disease, we propose a predicting model based on LSTM. Therefore an LSTM network, with fully connected layer and activation layers, is built to encode the temporal relation between features and the next stage of Alzheimer's Disease. The Experiments show that our model outperforms most of the existing models.

The subjective cognitive decline (SCD) may last for decades prior to the onset of dementia and has been proposed as a risk population for development to amnestic mild cognitive impairment (aMCI) and Alzheimer disease (AD). Disruptions of functional connectivity and causal connectivity (CC) in the salience network (SN) are generally perceived as prominent hallmarks of the preclinical AD. Nevertheless, the alterations in anterior SN (aSN), and posterior SN (pSN) remain unclear. Here, we hypothesized that both the functional connectivity (FC) and CC of the SN subnetworks, comprising aSN and pSN, were distinct disruptive in the SCD and aMCI. We utilized resting-state functional magnetic resonance imaging to investigate the altered FC and CC of the SN subnetworks in 28 healthy controls, 23 SCD subjects, and 29 aMCI subjects. In terms of altered patterns of FC in SN subnetworks, aSN connected to the whole brain was significantly increased in the left orbital superior frontal gyrus, left insula lobule, right caudate lobule, and left rolandic operculum gyrus (ROG), whereas decreased FC was found in the left cerebellum superior lobule and left middle temporal gyrus when compared with the HC group. Notably, no prominent statistical differences were obtained in pSN. For altered patterns of CC in SN subnetworks, compared to the HC group, the aberrant connections in aMCI group were separately involved in the right cerebellum inferior lobule (CIL), right supplementary motor area (SMA), and left ROG, whereas the SCD group exhibited more regions of aberrant connection, comprising the right superior parietal lobule, right CIL, left inferior parietal lobule, left post-central gyrus (PG), and right angular gyrus. Especially, SCD group showed increased CC in the right CIL and left PG, whereas the aMCI group showed decreased CC in the left pre-cuneus, corpus callosum, and right SMA when compared to the SCD group. Collectively, our results suggest that analyzing the altered FC and CC observed in SN subnetworks, served as impressible neuroimaging biomarkers, may supply novel insights for designing preclinical interventions in the preclinical stages of AD.

Convolutional neural networks (CNNs)-based classifiers improve the accuracy of diagnosis and prediction for Alzheimer's disease (AD). However, exploiting specific brain regions with the AD is essential to understand pathological alteration in the AD and monitor its progression. This paper aims to construct novel AD classification models which have a good performance and interpretation on AD diagnosis. We propose the three classifiers including a simple broaden plain CNNs (SBPCNNs), a major slice-assemble CNNs (SACNNs) and a multi-slice CNNs (MSCNNs), which record the slice positions but have fewer parameters. Specifically, we integrate the ranking and the random forest methods to find the discriminative region that is consistent with domain knowledge about the AD. The results of the visualization explanation of pixel and slice level deliver a clearer understanding of the AD to specialists. The experimental results indicate that the proposed models are meaningful for AD classification.INDEX TERMS Alzheimer's disease, CNNs-based classification, structural magnetic resonance imaging (sMRI), visual explanation. I. INTRODUCTIONWhen Alzheimer's disease (AD) presents a stern strategic challenge around the world [1], computer-aided diagnosis (CAD) has improved AD diagnosis and prediction performance. AD, being incurable, fatal dementia, bring significant pain and cost to patients and their families [2]. Thus, the accurate diagnosis of AD is important to postpone the disease progression and improve the quality of life of people with AD [3]. With the development of computer vision, machine learning, and deep learning techniques, novel models using medical images promote AD diagnostic accuracy [4], [5]. However, several difficulties constrain the models' performance.Main challenges of data-driven models of AD-related tasks are limited data, multiple dimensions, and sophisticatedThe associate editor coordinating the review of this manuscript and approving it for publication was Linbo Qing.

Alzheimer's disease (AD), which most commonly occurs in the elder, is a chronic neurodegenerative disease with no agreed drugs or treatment protocols at present. Amnestic mild cognitive impairment (aMCI), earlier than AD onset and later than subjective cognitive decline (SCD) onset, has a serious probability of converting into AD. The SCD, which can last for decades, subjectively complains of decline impairment in memory. Distinct altered patterns of default mode network (DMN) subnetworks connected to the whole brain are perceived as prominent hallmarks of the early stages of AD. Nevertheless, the aberrant phase position connectivity (PPC) connected to the whole brain in DMN subnetworks remains unknown. Here, we hypothesized that there exist distinct variations of PPC in DMN subnetworks connected to the whole brain for patients with SCD and aMCI, which might be acted as discriminatory neuroimaging biomarkers. We recruited 27 healthy controls (HC), 20 SCD and 28 aMCI subjects, respectively, to explore aberrant patterns of PPC in DMN subnetworks connected to the whole brain. In anterior DMN (aDMN), SCD group exhibited aberrant PPC in the regions of right superior cerebellum lobule (SCL), right superior frontal gyrus of medial part (SFGMP), and left fusiform gyrus (FG) in comparison of HC group, by contrast, no prominent difference was found in aMCI group. It is important to note that aMCI group showed increased PPC in the right SFGMP in comparison with SCD group. For posterior DMN (pDMN), SCD group showed decreased PPC in the left superior parietal lobule (SPL) and right superior frontal gyrus (SFG) compared to HC group. It is noteworthy that aMCI group showed decreased PPC in the left middle frontal gyrus of orbital part (MFGOP) and right SFG compared to HC group, yet increased PPC was found in the left superior temporal gyrus of temporal pole (STGTP). Additionally, aMCI group exhibited

Taking the Guangxi Beibu Gulf Economic Zone as the study area, this paper utilizes the geographical detector model to quantify the feedback effects from the terrestrial environment on precipitation variation from 1985 to 2010 with a comprehensive consideration of natural factors (forest coverage rate, vegetation type, terrain, terrestrial ecosystem types, land use and land cover change) and social factors (population density, farmland rate, GDP and urbanization rate). First, we found that the precipitation trend rate in the Beibu Gulf Economic Zone is between −47 and 96 mm/10a. Second, forest coverage rate change (FCRC), urbanization rate change (URC), GDP change (GDPC) and population density change (PDC) have a larger contribution to precipitation change through land-surface feedback, which makes them the leading factors. Third, the human element is found to primarily account for the precipitation changes in this region, as humans are the active media linking and enhancing these impact factors. Finally, it can be concluded that the interaction of impact factor pairs has a significant effect compared to the corresponding single factor on precipitation changes. The geographical detector model offers an analytical framework to reveal the terrestrial factors affecting the precipitation change, which gives direction for future work on regional climate modeling and analyses.