Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network.

Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of image contents. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both tissue contrasts and anatomical details.

Paternal gene pool of Malays in Southeast Asia and its applications for the early expansion of Austronesians.

OBJECTIVES: The origin and differentiation of Austronesian populations and their languages have long fascinated linguists, archeologists, and geneticists. However, the founding process of Austronesians and when they separated from their close relatives, such as the Daic and Austro-Asiatic populations in the mainland of Asia, remain unclear. In this study, we explored the paternal origin of Malays in Southeast Asia and the early differentiation of Austronesians. MATERIALS AND METHODS: We generated whole Y-chromosome sequences of 50 Malays and co-analyzed 200 sequences from other Austronesians and related populations. We generated a revised phylogenetic tree with time estimation. RESULTS: We identified six founding paternal lineages among the studied Malays samples. These founding lineages showed a surprisingly coincident expansion age at 5000 to 6000 years ago. We also found numerous mostly close related samples of the founding lineages of Malays among populations from Mainland of Asia. CONCLUSION: Our analyses provided a refined phylogenetic resolution for the dominant paternal lineages of Austronesians found by previous studies. We suggested that the co-expansion of numerous founding paternal lineages corresponds to the initial differentiation of the most recent common ancestor of modern Austronesians. The splitting time and divergence pattern in perspective of paternal Y-chromosome evidence are highly consistent with the previous theories of ethnologists, linguists, and archeologists.

Genetic improvement of nitrogen use efficiency in crops.

Nitrogen (N) is an essential mineral nutrient for plant growth and development. N deficiency is the major factor limiting plant growth and crop production in most natural and agricultural soils. The green revolution of the 1960's boosted crop yields through cultivation of semi-dwarf plant varieties. However, green revolution wheat and rice varieties have relatively poor nitrogen use efficiency (NUE), require a high N fertilizer supply to achieve maximum yield potential, and this leads to an increase in production costs and environmental problem. Therefore, a major challenge for sustainable agriculture is whether improvement of NUE through the reduction of N fertilizer supply can be achieved without yield penalty. In this review, we summarize the recent advances in understanding of molecular mechanisms underlying the regulation of N-responsive plant growth, utilization and possibility for improvements of NUE in crops, and new breeding strategies through modulation of N-responsive growth-metabolism coordination for future sustainable agriculture.

Y-chromosome evidence confirmed the Kerei-Abakh origin of Aksay Kazakhs.

Aksay Kazakhs are the easternmost branch of Kazakhs, residing in Jiuquan city, the forefront of the ancient Silk Road. However, the genetic diversity of Aksay Kazakhs and its relationships with other Kazakhs still lack attention. To clarify this issue, we analyzed the non-recombining portion of the Y-chromosome from 93 Aksay Kazakhs samples, using a high-resolution analysis of 106 biallelic markers and 17 STRs. The lowest haplogroup diversity (0.38) was observed in Aksay Kazakhs among all studied Kazakh populations. The social and cultural traditions of the Kazakhs shaped their current pattern of genetic variation. Aksay Kazakhs tended to migrate with clans and had limited paternal admixture with neighboring populations. Aksay Kazakhs had the highest frequency (80%) of haplogroup C2b1a3a1-F3796 (previous C3*-Star Cluster) among the investigated Eurasian steppe populations, which was now seen as the genetic marker of Kerei clan. Furthermore, NETWORK analysis indicated that Aksay Kazakhs originated from sub-clan Kerei-Abakh in Kazakhstan with DYS448 = 23. TMRCA estimates of three recent descent clusters detected in C2*-M217 (xM48) network, one of which incorporate nearly all of the C2b1a3a1-F3796 Aksay Kazakhs samples, gave the age range of 976-1405 YA for DC1, 1059-1314 YA for DC2, and 1139-1317 YA for DC3, respectively; this is coherent with the 7th to the 11th centuries Altaic-speaking pastoral nomadic population expansion.

Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks.

BACKGROUND: An intracranial aneurysm is a cerebrovascular disorder that can result in various diseases. Clinically, diagnosis of an intracranial aneurysm utilizes digital subtraction angiography (DSA) modality as gold standard. The existing automatic computer-aided diagnosis (CAD) research studies with DSA modality were based on classical digital image processing (DIP) methods. However, the classical feature extraction methods were badly hampered by complex vascular distribution, and the sliding window methods were time-consuming during searching and feature extraction. Therefore, developing an accurate and efficient CAD method to detect intracranial aneurysms on DSA images is a meaningful task. METHODS: In this study, we proposed a two-stage convolutional neural network (CNN) architecture to automatically detect intracranial aneurysms on 2D-DSA images. In region localization stage (RLS), our detection system can locate a specific region to reduce the interference of the other regions. Then, in aneurysm detection stage (ADS), the detector could combine the information of frontal and lateral angiographic view to identify intracranial aneurysms, with a false-positive suppression algorithm. RESULTS: Our study was experimented on posterior communicating artery (PCoA) region of internal carotid artery (ICA). The data set contained 241 subjects for model training, and 40 prospectively collected subjects for testing. Compared with the classical DIP method which had an accuracy of 62.5% and an area under curve (AUC) of 0.69, the proposed architecture could achieve accuracy of 93.5% and the AUC of 0.942. In addition, the detection time cost of our method was about 0.569 s, which was one hundred times faster than the classical DIP method of 62.546 s. CONCLUSION: The results illustrated that our proposed two-stage CNN-based architecture was more accurate and faster compared with the existing research studies of classical DIP methods. Overall, our study is a demonstration that it is feasible to assist physicians to detect intracranial aneurysm on DSA images using CNN.

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images.

BACKGROUND: Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs). METHODS: This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category "3", Category "4A", Category "4B", Category "4C", and Category "5". Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage. RESULTS: We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category "3", 0.940 on Category "4A", 0.734 on Category "4B", 0.922 on Category "4C", and 0.876 on Category "5". CONCLUSION: The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign.

Dispersals of the Siberian Y-chromosome haplogroup Q in Eurasia.

The human Y-chromosome has proven to be a powerful tool for tracing the paternal history of human populations and genealogical ancestors. The human Y-chromosome haplogroup Q is the most frequent haplogroup in the Americas. Previous studies have traced the origin of haplogroup Q to the region around Central Asia and Southern Siberia. Although the diversity of haplogroup Q in the Americas has been studied in detail, investigations on the diffusion of haplogroup Q in Eurasia and Africa are still limited. In this study, we collected 39 samples from China and Russia, investigated 432 samples from previous studies of haplogroup Q, and analyzed the single nucleotide polymorphism (SNP) subclades Q1a1a1-M120, Q1a2a1-L54, Q1a1b-M25, Q1a2-M346, Q1a2a1a2-L804, Q1a2b2-F1161, Q1b1a-M378, and Q1b1a1-L245. Through NETWORK and BATWING analyses, we found that the subclades of haplogroup Q continued to disperse from Central Asia and Southern Siberia during the past 10,000 years. Apart from its migration through the Beringia to the Americas, haplogroup Q also moved from Asia to the south and to the west during the Neolithic period, and subsequently to the whole of Eurasia and part of Africa.

Whole sequence analysis indicates a recent southern origin of Mongolian Y-chromosome C2c1a1a1-M407.

The Y-chromosome haplogroup C2c1a1a1-M407 is a predominant paternal lineage in Mongolic-speaking populations, especially in Buryats and Kalmyks. However, the origin and internal phylogeny of C2c1a1a1-M407 have not been investigated in detail. In this study, we analyzed twenty-three Y-chromosome sequences of haplogroup C2c1a1a1-M407 and its most closely related clades. We generated a high-resolution phylogenetic tree of haplogroup C2c1a1a1-M407 and its upstream clade C2c1a1-CTS2657, including 32 subclades and 144 non-private Y-chromosome polymorphisms. We discover that all available C2c1a1a1-M407 samples from Mongolic-speaking populations belong to its newly defined downstream clade C2c1a1a1b-F8465, whereas all samples of C2c1a1-CTS2657(xF8465) come from northern Han Chinese, Korean, and Japanese. Furthermore, we observe that C2c1a1a1b-F8465 and its subclade C2c1a1a1b1-F8536 expanded at approximately 0.86 and 0.44 thousand years ago, respectively. Therefore, we conclude that C2c1a1a1-M407 in Mongolic-speaking populations has originated from northeastern Asia. C2c1a1a1b1-F8536, the newly defined subclade of C2c1a1a1-M407, probably represents the genetic relationships between ancient Oyrats, modern Kalmyks, Mongolians, and Buryats.

Reconstruction of Y-chromosome phylogeny reveals two neolithic expansions of Tibeto-Burman populations.

Diffusion of Tibeto-Burman populations across the Tibetan Plateau led to the largest human community in a high-altitude environment and has long been a focus of research on high-altitude adaptation, archeology, genetics, and linguistics. However, much uncertainty remains regarding the origin, diversification, and expansion of Tibeto-Burman populations. In this study, we analyzed a 7.0M bp region of 285 Y-chromosome sequences, including 81 newly reported ones, from male samples from Tibeto-Burman populations and other related Eastern Asian populations. We identified several paternal lineages specific to Tibeto-Burman populations, and most of these lineages emerged between 6000 and 2500 years ago. A phylogenetic tree and lineage dating both support the hypothesis that the establishment of Tibeto-Burman ancestral groups was triggered by Neolithic expansions from the middle Yellow River Basin and admixtures with local populations on the Tibetan Plateau who survived the Paleolithic Age. Furthermore, according to the geographical distributions of the haplogroups, we propose that there are two Neolithic expansion origins for all modern Tibeto-Burman populations. Our research provides a clear scenario about the sources, admixture process and later diffusion process of the ancestor population of all Tibeto-Burman populations.

Genetic trail for the early migrations of Aisin Gioro, the imperial house of the Qing dynasty.

The House of Aisin Gioro, the imperial clan of Qing dynasty (1644-1911), affected the history of China and the formation of Manchu ethnicity greatly. However, owing to the lack of historical records and archeological evidences, the origin of the House of Aisin Gioro remains ambiguous. To clarify the origin of Aisin Gioro clan, we conducted whole Y-chromosome sequencing on three samples and Y-single-nucleotide polymorphism (Y-SNP) genotyping on other four samples beside those reported in previous work. We confirmed that the paternal lineage of the Aisin Gioro clan belongs to haplogroup C3b1a3a2-F8951, a brother branch of C3*-Star Cluster (currently named as C3b1a3a1-F3796, once linked to Genghis Khan), which is quite different from the predominant lineage C3c-M48 in other Tungusic-speaking populations. We also determined a series of unique Y-SNP markers for the Aisin Gioro clan. Diversity analyses of haplogroup C3b1a3a2-F8951 revealed the early migration of the ancestors of the Aisin Gioro clan from the middle reaches of Amur River to their later settlement in southeastern Manchuria. Hence, our results suggest that the Aisin Gioro clan may be descendants of ancient populations in Transbaikal region and closely related to origin of current Daur populations. Our research indicated that detailed research of stemma and deep sequencing of Y chromosomes are helpful to explore the prehistoric activities of populations lacking historical records and archeological evidences.

Phylogeny of Y-chromosome haplogroup C3b-F1756, an important paternal lineage in Altaic-speaking populations.

In previous studies, a specific paternal lineage with a null value for the Y-chromosome short tandem repeat (Y-STR) marker DYS448 was identified as common among Mongolic- and Turkic-speaking populations. This paternal lineage (temporarily named C3*-DYS448del) was determined to be M217+, M93-, P39-, M48-, M407-, and P53.1-, and its origin and phylogeny remain ambiguous. Here, we analyzed Y-chromosome sequences of 10 male that are related this paternal lineage and redefined it as C3b1a1a1a-F1756 (C3b-F1756). We generated a highly revised phylogenetic tree of haplogroup C3b-F1756, including 21 sub-clades and 360 non-private Y-chromosome polymorphisms. Additionally, we performed a comprehensive analysis of the C3*-DYS448del lineage in eastern Eurasia, including 18 270 samples from 297 populations. Whole Y-chromosome sequences, Y-STR haplotypes, and frequency data were used to generate a distribution map, a network, and age estimations for lineage C3*-DYS448del and its sub-lineages. Considering the historical records of the studied populations, we propose that two major sub-branches of C3b-F1756 may correspond to early expansions of ancestors of modern Mongolic- and Turkic-speaking populations. The large number of newly defined Y-chromosome polymorphisms and the revised phylogenetic tree for C3b-F1756 will assist in investigation of the early history of Altaic-speaking populations in the future.

[Magnitude image-guided phase unwrapping algorithm of susceptibility weighted images].

To better use the phase information to compensate the influence of blood flow, the phase unwrapping problem in susceptibility weighted imaging (SWI) is studied in this paper. In order to improve the accuracy of unwrapping, this paper proposes a magnitude image-guided phase unwrapping algorithm of SWI. The basic idea is as follows: (1) reduce the influence of noise by improving the rotational invariant non-local principal component analysis method (PRI-NL-PCA); (2) extract the corresponding solid region in the phase image to avoid the influence of the background noise on the phase unwrapping method; (3) use the phase compensation method to constrain the phase image reconstructed by the K-space. Finally, the reliability of the unwrapping method is evaluated by using four kinds of statistics as quantification index: the number, mean (M), variance (Var), and positive percentage (Pos) and negative percentage (Neg) of phasic error points. By comparing the simulated data with 226 sets of true head SWI data, the results show that the proposed algorithm has high accuracy compared with the classical branch cut method and the least squares method.

Automatic evaluation of hypernasality based on a cleft palate speech database.

The hypernasality is one of the most typical characteristics of cleft palate (CP) speech. The evaluation outcome of hypernasality grading decides the necessity of follow-up surgery. Currently, the evaluation of CP speech is carried out by experienced speech therapists. However, the result strongly depends on their clinical experience and subjective judgment. This work aims to propose an automatic evaluation system for hypernasality grading in CP speech. The database tested in this work is collected by the Hospital of Stomatology, Sichuan University, which has the largest number of CP patients in China. Based on the production process of hypernasality, source sound pulse and vocal tract filter features are presented. These features include pitch, the first and second energy amplified frequency bands, cepstrum based features, MFCC, short-time energy in the sub-bands features. These features combined with KNN classier are applied to automatically classify four grades of hypernasality: normal, mild, moderate and severe. The experiment results show that the proposed system achieves a good performance. The classification rates for four hypernasality grades reach up to 80.4%. The sensitivity of proposed features to the gender is also discussed.

Function of hormone signaling in regulating nitrogen-use efficiency in plants.

Nitrogen (N) is one of the most important nutrients for crop plant performance, however, the excessive application of nitrogenous fertilizers in agriculture significantly increases production costs and causes severe environmental problems. Therefore, comprehensively understanding the molecular mechanisms of N-use efficiency (NUE) with the aim of developing new crop varieties that combine high yields with improved NUE is an urgent goal for achieving more sustainable agriculture. Plant NUE is a complex trait that is affected by multiple factors, of which hormones are known to play pivotal roles. In this review, we focus on the interaction between the biosynthesis and signaling pathways of plant hormones with N metabolism, and summarize recent studies on the interplay between hormones and N, including how N regulates multiple hormone biosynthesis, transport and signaling and how hormones modulate root system architecture (RSA) in response to external N sources. Finally, we explore potential strategies for promoting crop NUE by modulating hormone synthesis, transport and signaling. This provides insights for future breeding of N-efficient crop varieties and the advancement of sustainable agriculture.

An open relaxation-diffusion MRI dataset in neurosurgical studies.

Diffusion MRI (dMRI) is a safe and noninvasive technique that provides insight into the microarchitecture of brain tissue. Relaxation-diffusion MRI (rdMRI) is an extension of traditional dMRI that captures diffusion imaging data at multiple TEs to detect tissue heterogeneity between relaxation and diffusivity. rdMRI has great potential in neurosurgical research including brain tumor grading and treatment response evaluation. However, the lack of available data has limited the exploration of rdMRI in clinical settings. To address this, we are sharing a high-quality rdMRI dataset from 18 neurosurgical patients with different types of lesions, as well as two healthy individuals as controls. The rdMRI data was acquired using 7 TEs, where at each TE multi-shell dMRI with high spatial and angular resolutions is obtained at each TE. Each rdMRI scan underwent thorough artifact and distortion corrections using a specially designed processing pipeline. The dataset's quality was assessed using standard practices, including quality control and assurance. This resource is a valuable addition to neurosurgical studies, and all data are openly accessible.

Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI.

Multi-sequence MRIs can be necessary for reliable diagnosis in clinical practice due to the complimentary information within sequences. However, redundant information exists across sequences, which interferes with mining efficient representations by learning-based models. To handle various clinical scenarios, we propose a sequence-to-sequence generation framework (Seq2Seq) for imaging-differentiation representation learning. In this study, not only do we propose arbitrary 3D/4D sequence generation within one model to generate any specified target sequence, but also we are able to rank the importance of each sequence based on a new metric estimating the difficulty of a sequence being generated. Furthermore, we also exploit the generation inability of the model to extract regions that contain unique information for each sequence. We conduct extensive experiments using three datasets including a toy dataset of 20,000 simulated subjects, a brain MRI dataset of 1251 subjects, and a breast MRI dataset of 2101 subjects, to demonstrate that (1) top-ranking sequences can be used to replace complete sequences with non-inferior performance; (2) combining MRI with our imaging-differentiation map leads to better performance in clinical tasks such as glioblastoma MGMT promoter methylation status prediction and breast cancer pathological complete response status prediction. Our code is available at https://github.com/fiy2W/mri_seq2seq.

Prediction of preoperative microvascular invasion by dynamic radiomic analysis based on contrast-enhanced computed tomography.

PURPOSE: Microvascular invasion (MVI) is a common complication of hepatocellular carcinoma (HCC) surgery, which is an important predictor of reduced surgical prognosis. This study aimed to develop a fully automated diagnostic model to predict pre-surgical MVI based on four-phase dynamic CT images. METHODS: A total of 140 patients with HCC from two centers were retrospectively included (training set, n = 98; testing set, n = 42). All CT phases were aligned to the portal venous phase, and were then used to train a deep-learning model for liver tumor segmentation. Radiomics features were extracted from the tumor areas of original CT phases and pairwise subtraction images, as well as peritumoral features. Lastly, linear discriminant analysis (LDA) models were trained based on clinical features, radiomics features, and hybrid features, respectively. Models were evaluated by area under curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values (PPV and NPV). RESULTS: Overall, 86 and 54 patients with MVI- (age, 55.92 ± 9.62 years; 68 men) and MVI+ (age, 53.59 ± 11.47 years; 43 men) were included. Average dice coefficients of liver tumor segmentation were 0.89 and 0.82 in training and testing sets, respectively. The model based on radiomics (AUC = 0.865, 95% CI: 0.725-0.951) showed slightly better performance than that based on clinical features (AUC = 0.841, 95% CI: 0.696-0.936). The classification model based on hybrid features achieved better performance in both training (AUC = 0.955, 95% CI: 0.893-0.987) and testing sets (AUC = 0.913, 95% CI: 0.785-0.978), compared with models based on clinical and radiomics features (p-value < 0.05). Moreover, the hybrid model also provided the best accuracy (0.857), sensitivity (0.875), and NPV (0.917). CONCLUSION: The classification model based on multimodal intra- and peri-tumoral radiomics features can well predict HCC patients with MVI.

Self-poled piezoelectric polymer composites via melt-state energy implantation.

Lightweight flexible piezoelectric polymers are demanded for various applications. However, the low instinctively piezoelectric coefficient (i.e. d33) and complex poling process greatly resist their applications. Herein, we show that introducing dynamic pressure during fabrication is capable for poling polyvinylidene difluoride/barium titanate (PVDF/BTO) composites with d33 of ~51.20 pC/N at low density of ~0.64 g/cm3. The melt-state dynamic pressure driven energy implantation induces structure evolutions of both PVDF and BTO are demonstrated as reasons for self-poling. Then, the porous material is employed as pressure sensor with a high output of ~20.0 V and sensitivity of ~132.87 mV/kPa. Besides, the energy harvesting experiment suggests power density of ~58.7 mW/m2 can be achieved for 10 N pressure with a long-term durability. In summary, we not only provide a high performance lightweight, flexible piezoelectric polymer composite towards sustainable self-powered sensing and energy harvesting, but also pave an avenue for electrical-free fabrication of piezoelectric polymers.

Modified Dynamic Movement Primitives: Robot Trajectory Planning and Force Control Under Curved Surface Constraints.

Dynamic movement primitives (DMPs) have been widely applied in robot motion planning and control. However, in some special cases, original discrete DMP fails to generalize proper trajectories. Moreover, it is difficult to produce trajectories on the curved surface. To solve the above problems, a modified DMP method is proposed for robot control by adding the scaling factor and force coupling term. First, the adjusted cosine similarity is defined to assess the similarity of the generalized trajectory with respect to the demonstrated trajectory. By optimizing the similarity, the trajectories can be generated in all situations. Next, by adding the force coupling term derived from adaptive admittance control to the transformation system of the original DMP, the controller achieves the force control ability. Then, the modified DMP-based robot control system is developed. The stability and convergence of the system are proved. Finally, the high precisions of the proposed method are verified by simulations and experiments. The method is significant for trajectory learning and generalization on the curved surface.

Alterations via inter-regional connective relationships in Alzheimer's disease.

Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.