Identification Of Mild Cognitive Impairment Research Articles

Validation of a new cognitive screening tool, the Brain Health Test‐7, for identification of mild cognitive impairment and early dementia

AbstractBackgroundThe Brain Health Test‐7 (BHT‐7) was developed to identify patients with mild cognitive impairment (MCI) and early dementia. Here we report the validity of the BHT‐7 versus the Mini‐Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) in subjects of different psychiatry or neurology clinics.MethodPatients with the chief complaint of memory decline were recruited in this study from the outpatient clinic of psychiatry or neurology in 3 different kinds of hospitals between November 2017 to December 2020. All patients underwent the evaluation of the BHT‐7, MMSE, MoCA, and CDR by trained research assistants or board‐certified psychologists. The final clinical diagnosis (normal, MCI, dementia) was made by consensus meeting, taking into account all available data, including clinical history and symptoms, brain image, cognitive tests, and CDR. Demographic data (age, gender, and education level) and the scores of the MMSE, MoCA, and BHT‐7 between groups were compared. Logistic regression was adopted for analysis of optimal cutoff values, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the area under the ROC curve.ResultWe enrolled a total of 1090 subjects (normal 402, MCI 317, dementia 371); of them, 705 (64.7%) were female. There was a statistically significant difference in age, years of education, and 3 cognitive test scores among the 3 groups of subjects. Compared with the MMSE and MoCA, the BHT‐7 performed slightly better in differentiating MCI and dementia. In sub‐group analysis (divided based on 6 years of education), the BHT‐7 still performed slightly better than MMSE and MoCA. The testing time for the BHT‐7 was about 5‐7 minutes, shorter than that of the MMSE and MoCA. For BHT‐7, the cutoff point was 17 between normal and MCI, and 14 between normal and dementia. These cutoff points of BHT‐7 were consistent through 3 different settings (medical center’s psychiatry and neurology clinics, and outpatient clinic of a psychiatry center). In contrast, the cutoff points were inconsistent for MMSE and MoCA in different settings.ConclusionThe results support that BHT‐7 may be a useful cognitive screening tool for MCI or early dementia in various clinic settings.

Simplify scales for identifying AD and MCI: A machine learning study based on six years of database

AbstractBackgroundAlzheimer’s Disease (AD) is a neurodegenerative disorder characterized by cognitive functional decline accompanied with neuropsychiatric symptoms and impairment of activities of daily living (ADL). Mild Cognitive Impairment (MCI) is considered the prodromal state of AD. Early identification of MCI is a strategy to reduce the burden of caregivers and society. Neuropsychological scales are still the most used method for identification of cognitive decline. The purpose of this study was to simplify the scales by eliminating some insensitive items based on the six years of database and machine learning methods.MethodA total of 458 patients newly diagnosed as AD or MCI were recruited in Memory Clinic, Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University from 2014 to 2019. Neuropsychological battery was used to evaluate the cognitive function, neuropsychiatric symptoms and ADL, including Mini‐mental State Examination (MMSE), Neuropsychiatric Inventory (NPI), Geriatric Depression Scale (GDS), Boston Naming Test (BNT), Digit Span (DS), Auditory Verbal Learning Test (AVLT), Trail Marking Test (TMT) A and B. Alzheimer’s Disease Assessment Scale (ADAS‐cog), Clinical Dementia Rating (CDR), Instrumental Activity of Daily Living Scale (IADL), Physical Self‐Maintenance Scale (PSMS). XGboost and CART regression tree were used to test all items contained in all scales and try to explore the importance of each item. Sensitivity was reflected by the AUC (Area Under Curve).ResultOf which 236 patients were diagnosed as AD and 222 as MCI. The mean age was 72.99±9.04 years and 51.53% were male. Among all the battery items, ADAs‐cog word recognition task showed the best importance in judging AD and MCI, followed correct numbers of AVLT delay recall, ADAs‐cog orientation, MMSE recall, TMT‐B time. The overall sensitivity for predicting AD or MCI for all items was 0.83. However, in our preliminary screening also showed the 31 items, most of which were based on the ADL scale and neuropsychiatry symptoms, contributed little to distinguishing AD from MCI. After removed these items, the sensitivity became to 0.87.ConclusionCognitive assessment such as recall and orientation may be a better strategy than neuropsychiatric symptoms and ADL when identifying AD from MCI.

Identification of late-life depression and mild cognitive impairment via serum surface-enhanced Raman spectroscopy and multivariate statistical analysis.

Identification of age-related neuropsychiatric disorders, i.e., late-life depression (LDD) and mild cognitive impairment (MCI) is of imperative clinical value considering the large probability of misdiagnosis and current lack of sensitive, non-invasive and low-cost diagnostic approaches. Here, the serum surface-enhanced Raman spectroscopy (SERS) technique is proposed to identify healthy controls, LDD and MCI patients. Based on SERS peaks analysis, abnormal levels of ascorbic acid, saccharide, cell-free DNA and amino acids in serum are found to be potential biomarkers for identifying LDD and MCI. These biomarkers might be related to oxidative stress, nutritional status, lipid peroxidation and metabolic abnormalities. Moreover, partial least square analysis-linear discriminant analysis (PLS-LDA) is applied to those collected SERS spectra. Finally, the overall identification accuracy is 83.2%, and accuracies are 91.6% and 85.7% for differentiating healthy versus neuropsychiatric disorders and LDD versus MCI, respectively. Thus, the serum SERS combined with multivariate statistical analysis has proved its successful potential for rapid, sensitive and non-invasive identification of healthy, LDD and MCI, which may open new avenues for early diagnosis and timely intervention for age-related neuropsychiatric disorders.

Identification of Mild Cognitive Impairment by Machine Learning Algorithm

Identification of Mild Cognitive Impairment by Machine Learning Algorithm

Multi-level fusion network for mild cognitive impairment identification using multi-modal neuroimages

Objective. Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease (AD) which is an irreversible progressive neurodegenerative disease and its early diagnosis and intervention are of great significance. Recently, many deep learning methods have demonstrated the advantages of multi-modal neuroimages in MCI identification task. However, previous studies frequently simply concatenate patch-level features for prediction without modeling the dependencies among local features. Also, many methods only focus on modality-sharable information or modality-specific features and ignore their incorporation. This work aims to address above-mentioned issues and construct a model for accurate MCI identification. Approach. In this paper, we propose a multi-level fusion network for MCI identification using multi-modal neuroimages, which consists of local representation learning and dependency-aware global representation learning stages. Specifically, for each patient, we first extract multi-pair of patches from multiple same position in multi-modal neuroimages. After that, in the local representation learning stage, multiple dual-channel sub-networks, each of which consists of two modality-specific feature extraction branches and three sine-cosine fusion modules, are constructed to learn local features that preserve modality-sharable and modality specific representations simultaneously. In the dependency-aware global representation learning stage, we further capture long-range dependencies among local representations and integrate them into global ones for MCI identification. Main results. Experiments on ADNI-1/ADNI-2 datasets demonstrate the superior performance of the proposed method in MCI identification tasks (Accuracy: 0.802, sensitivity: 0.821, specificity: 0.767 in MCI diagnosis task; accuracy: 0.849, sensitivity: 0.841, specificity: 0.856 in MCI conversion task) when compared with state-of-the-art methods. The proposed classification model has demonstrated a promising potential to predict MCI conversion and identify the disease-related regions in the brain. Significance. We propose a multi-level fusion network for MCI identification using multi-modal neuroimage. The results on ADNI datasets have demonstrated its feasibility and superiority.

Based on Tau PET Radiomics Analysis for the Classification of Alzheimer’s Disease and Mild Cognitive Impairment

Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) are closely associated with Tau proteins accumulation. In this study, we aimed to implement radiomics analysis to discover high-order features from pathological biomarker and improve the classification accuracy based on Tau PET images. Two cross-racial independent cohorts from the ADNI database (121 AD patients, 197 MCI patients and 211 normal control (NC) subjects) and Huashan hospital (44 AD patients, 33 MCI patients and 36 NC subjects) were enrolled. The radiomics features of Tau PET imaging of AD related brain regions were computed for classification using a support vector machine (SVM) model. The radiomics model was trained and validated in the ADNI cohort and tested in the Huashan hospital cohort. The standard uptake value ratio (SUVR) and clinical scores model were also performed to compared with radiomics analysis. Additionally, we explored the possibility of using Tau PET radiomics features as a good biomarker to make binary identification of Tau-negative MCI versus Tau-positive MCI or apolipoprotein E (ApoE) ε4 carrier versus ApoE ε4 non-carrier. We found that the radiomics model demonstrated best classification performance in differentiating AD/MCI patients and NC in comparison to SUVR and clinical scores models, with an accuracy of 84.8 ± 4.5%, 73.1 ± 3.6% in the ANDI cohort. Moreover, the radiomics model also demonstrated greater performance in diagnosing AD than other methods in the Huashan hospital cohort, with an accuracy of 81.9 ± 6.1%. In addition, the radiomics model also showed the satisfactory classification performance in the MCI-tau subgroup experiment (72.3 ± 3.5%, 71.9 ± 3.6% and 63.7 ± 5.9%) and in the MCI-ApoE subgroup experiment (73.5 ± 4.3%, 70.1 ± 3.9% and 62.5 ± 5.4%). In conclusion, our study showed that based on Tau PET radiomics analysis has the potential to guide and facilitate clinical diagnosis, further providing evidence for identifying the risk factors in MCI patients.

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI.

Functional brain networks (FBNs) estimated from functional magnetic resonance imaging (fMRI) data has become a potentially useful way for computer-aided diagnosis of neurological disorders, such as mild cognitive impairment (MCI), a prodromal stage of Alzheimer's Disease (AD). Currently, Pearson's correlation (PC) is the most widely-used method for constructing FBNs. Despite its popularity and simplicity, the conventional PC-based method usually results in dense networks where regions-of-interest (ROIs) are densely connected. This is not accordance with the biological prior that ROIs may be sparsely connected in the brain. To address this issue, previous studies proposed to employ a threshold or l_1-regularizer to construct sparse FBNs. However, these methods usually ignore rich topology structures, such as modularity that has been proven to be an important property for improving the information processing ability of the brain. To this end, in this paper, we propose an accurate module induced PC (AM-PC) model to estimate FBNs with a clear modular structure, by including sparse and low-rank constraints on the Laplacian matrix of the network. Based on the property that zero eigenvalues of graph Laplacian matrix indicate the connected components, the proposed method can reduce the rank of the Laplacian matrix to a pre-defined number and obtain FBNs with an accurate number of modules. To validate the effectiveness of the proposed method, we use the estimated FBNs to classify subjects with MCI from healthy controls. Experimental results on 143 subjects from Alzheimer's Disease Neuroimaging Initiative (ADNI) with resting-state functional MRIs show that the proposed method achieves better classification performance than previous methods.

A New Smart 2-Min Mobile Alerting Method for Mild Cognitive Impairment Due to Alzheimer’s Disease in the Community

The early identification of mild cognitive impairment (MCI) due to Alzheimer's disease (AD), in an early stage of AD can expand the AD warning window. We propose a new capability index evaluating the spatial execution process (SEP), which can dynamically evaluate the execution process in the space navigation task. The hypothesis is proposed that there are neurobehavioral differences between normal cognitive (NC) elderly and AD patients with MCI reflected in digital biomarkers captured during SEP. According to this, we designed a new smart 2-min mobile alerting method for MCI due to AD, for community screening. Two digital biomarkers, total mission execution distance (METRtotal) and execution distance above the transverse obstacle (EDabove), were selected by step-up regression analysis. For the participants with more than 9 years of education, the alerting efficiency of the combination of the two digital biomarkers for MCI due to AD could reach 0.83. This method has the advantages of fast speed, high alerting efficiency, low cost and high intelligence and thus has a high application value for community screening in developing countries. It also provides a new intelligent alerting approach based on the human-computer interaction (HCI) paradigm for MCI due to AD in community screening.

Personalized Functional Connectivity Based Spatio-Temporal Aggregated Attention Network for MCI Identification.

Functional connectivity (FC) networks deri- ved from resting-state magnetic resonance image (rs-fMRI) are effective biomarkers for identifying mild cognitive impairment (MCI) patients. However, most FC identification methods simply extract features from group-averaged brain templates, and neglect inter-subject functional variations. Furthermore, the existing methods generally concentrate on spatial correlation among brain regions, resulting in the inefficient capture of the fMRI temporal features. To address these limitations, we propose a novel personalized functional connectivity based dual-branch graph neural network with spatio-temporal aggregated attention (PFC-DBGNN-STAA) for MCI identification. Specifically, a personalized functional connectivity (PFC) template is firstly constructed to align 213 functional regions across samples and generate discriminative individualized FC features. Secondly, a dual-branch graph neural network (DBGNN) is conducted by aggregating features from the individual- and group-level templates with the cross-template FC, which is beneficial to improve the feature discrimination by considering dependency between templates. Finally, a spatio-temporal aggregated attention (STAA) module is investigated to capture the spatial and dynamic relationships between functional regions, which solves the limitation of insufficient temporal information utilization. We evaluate our proposed method on 442 samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieve the accuracies of 90.1%, 90.3%, 83.3% for normal control (NC) vs. early MCI (EMCI), EMCI vs. late MCI (LMCI), and NC vs. EMCI vs. LMCI classification tasks, respectively, indicating that our method boosts MCI identification performance and outperforms state-of-the-art methods.

The Value of Handgrip Strength and Self-Rated Squat Ability in Predicting Mild Cognitive Impairment: Development and Validation of a Prediction Model.

Early identification of individuals with mild cognitive impairment (MCI) is essential to combat worldwide dementia threats. Physical function indicators might be low-cost early markers for cognitive decline. To establish an early identification tool for MCI by combining physical function indicators (upper and lower limb function) via a clinical prediction modeling strategy. A total of 5393 participants aged 60 or older were included in the model. The variables selected for the model included sociodemographic characteristics, behavioral factors, mental status and chronic conditions, upper limb function (handgrip strength), and lower limb function (self-rated squat ability). Two models were developed to test the predictive value of handgrip strength (Model 1) or self-rated squat ability (Model 2) separately, and Model 3 was developed by combining handgrip strength and self-rated squat ability. The 3 models all yielded good discrimination performance (area under the curve values ranged from 0.719 to 0.732). The estimated net reclassification improvement values were 0.3279 and 0.1862 in Model 3 when comparing Model 3 to Model 1 and Model 2, respectively. The integrated discrimination improvement values were estimated as 0.0139 and 0.0128 when comparing Model 3 with Model 1 and Model 2, respectively. The model that contains both upper and lower limb function has better performance in predicting MCI. The final prediction model is expected to assist health workers in early identification of MCI, thus supporting early interventions to reduce future risk of AD, particularly in socioeconomically deprived communities.

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI

Accurate module induced brain network construction for mild cognitive impairment identification with functional MRI

Reliability and sensitivity of two clock drawing test rubrics as screening measures for mild cognitive impairment

AbstractBackgroundEarly identification of mild cognitive impairment (MCI) is critical, as MCI precipitates disability and may be a precursor to major neurocognitive disorder. The Clock Drawing Test (CDT) is a widely adopted screening measure for dementia however it is not known whether paper‐and‐pencil CDT scoring rubrics are sensitive to subtle losses consistent with MCI. We examined the reliability and sensitivity to MCI of two free‐drawn Clock Drawing Test rubrics ‐ the 10‐point Rouleau, and the 20‐point Clock Drawing Interpretation Scale (CDIS) in community‐dwelling older adults.Method345 adults enrolled in a study of functional cognition completed the Montreal Cognitive Assessment (MoCA), the Alzheimer’s Disease Cooperative Study Scale (ADCS‐ADL), and freely‐drawn clocks scored with CDIS and Roleau rubrics. We computed Cronbach’s alpha, receiver operating characteristic (ROC) analyses using the MoCA as the index measure, and one‐way ANOVAs with the ADCS‐ADL for each rubric.ResultOur sample was 74% female, 83.2% white. Mean age was 70.01 ±8.89, and mean education was 15.5 ±3.735 years. Average scores were 24.28 ±3.44 (MoCA), 74.85 ±4.16 (ADCS), 8.8 ±1.68 (Rouleau) and 18.17 ±2.09 (CDIS). alphas were .65 for the CDIS and .479 for the Rouleau. ROC analyses using MoCA scores consistent with any impairment (<26) resulted in a CDIS AOC of .677, with sensitivity of .681 and specificity of .58 at a cut score of 19. The Rouleau AOC was .684, with sensitivity of .542 and specificity of .76 at a cut score of 9. ANOVAs indicated that individuals classified as impaired by the CDIS ((F = 13.095, p<.001) or the Rouleau (F = 22.2, p<.001) rubrics reported significantly more ADL/IADL difficulties than those classified as unimpaired with either rubric. Differences between impaired and unimpaired groups were larger for the CDIS, suggesting this rubric is more sensitive to ADL/IADL deficits.ConclusionBoth rubrics produced generally similar results when compared to the MoCA and adequately identified individuals needing further evaluation given reported functional problems. The CDIS produced more robust differences suggesting that it may be more appropriate for community screening to identify individuals in need of comprehensive cognitive and functional evaluation.

Identification of Mild Cognitive Impairment Among Black and White Community Dwelling Older Adults Using NIH‐Toolbox Cognition Tablet Battery

AbstractBackgroundNIH Toolbox‐Cognition Battery (NIHTB‐CB) is a computerized cognitive assessment battery frequently used in clinical research. However, the utility of the tablet adaptation has not been well established for clinical characterization, nor have possible racial differences been examined. We aim to identify which of the NIHTB‐CB subtests best differentiate healthy controls (HC) from those with mild cognitive impairment (MCI), as well as examine possible differences in diagnostic identification between Black/African‐American (B/AA) and white older adults.MethodParticipants were community‐dwelling adults 65 years and up (HC = 96; MCI = 63), recruited through affiliated studies within the Michigan Alzheimer’s Disease Research Center (NIA/NIH‐P30AG053760) and diagnosed by consensus conference through National Alzheimer’s Coordinating Center criteria. They then completed the NIHTB‐CB tablet version for iPad. Discriminant function analysis was used to determine which cognitive tests best differentiated clinical diagnoses in the total sample and separately for B/AA (n = 81) and white participants (n = 78).ResultIn the total sample, 78% of cases were correctly identified with Picture Sequence Memory (PSM; Standardized Coefficient (SC) = ‐.73) being the strongest predictor. The cross‐validated model correctly identified 79.2% of HCs and 60.3% of MCIs. Analyses were then stratified by race. In the B/AA sample, the model was able to correctly identify 60% of HCs and 68.4% of MCIs, with PSM (SC = .68) being the strongest predictor followed by List Sorting Working Memory (LSWM; SC = .42). In the white sample, 83% of HCs and 44% of MCIs were correctly identified and PSM (SC = .84) was the strongest predictor.ConclusionAs more treatments become available for Alzheimer’s disease, it is increasingly important to identify preclinical stages, such as MCI, so that we may intervene as early as possible in the disease process. NIHTB‐CB is one tool that may aid in identification. The memory subtest PSM was the best predictor of diagnosis in each model. While the overall model was fairly accurate at predicting clinical diagnosis, the models proved less accurate when cross validated and stratified by race. Thus, our results suggest that it may be important to consider race and other aspects in identifying specific diagnostic groups.

An AI‐based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

AbstractBackgroundReliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide‐spread screening for dementia and accelerate preventative efforts.MethodWe used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub‐tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI.ResultWe evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held‐out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub‐tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task.ConclusionThe proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develop a remote screening tool that could be easily adapted to any language.

Tensor-Based Multi-Modality Feature Selection and Regression for Alzheimer's Disease Diagnosis.

The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation information inherent in the multi-modality data, and investigate tensor-level sparsity in the multilinear regression model. We present the practical advantages of our method for the analysis of ADNI data using three imaging modalities (VBM-MRI, FDG-PET and AV45-PET) with clinical parameters of disease severity and cognitive scores. The experimental results demonstrate the superior performance of our proposed method against the state-of-the-art for the disease diagnosis and the identification of disease-specific regions and modality-related differences. The code for this work is publicly available at https://github.com/junfish/BIOS22.

Validation of the Informant Quick Dementia Rating System (QDRS) among Older Adults in Singapore.

The Quick Dementia Rating System (QDRS) is a brief and rapid tool that can be administered by an informant without the need for a trained assessor. Our objective was to examine the validity, reliability, and cost-effectiveness of the informant QDRS in a Singapore memory clinic sample. We assessed a total of 177 older adults, among whom, 32 had no cognitive impairment (NCI), 61 had mild cognitive impairment (MCI), and 84 had dementia. Elderly underwent 1) the informant QDRS, 2) the Clinical Dementia Rating (CDR) as the gold standard diagnosis, 3) the Mini-Mental State Examination (MMSE), and 4) the Ascertain Dementia 8 (AD8) as comparisons to the QDRS. The extent to which the QDRS may reduce the recruitment cost (time) of clinical trials was also calculated. The QDRS had excellent internal consistency (Cronbach alpha = 0.939). It correlated highly with the CDR-global (R = 0.897), CDR Sum-of-Boxes (R = 0.915), MMSE (R = -0.848), and the AD8 (R = 0.747), showing good concurrent validity. With an optimal cut-off of 1.5 for MCI (sensitivity 85.2%, specificity 96.3%) and 6 for dementia (sensitivity 90.1%, specificity 89.2%), the QDRS achieved a higher overall accuracy of 85.0%, as compared to MMSE (71.2%) and AD8 (73.4%). A simulated clinical trial recruitment scenario demonstrated that pre-screening with the QDRS followed by a confirmatory CDR would reduce the time needed to identify NCI subjects by 23.3% and MCI subjects by 75.3%. The QDRS is a reliable cognitive impairment screening tool which is suitable for informant-administration, especially for identification of MCI.

A Deep Generative-Discriminative Learning for Multimodal Representation in Imaging Genetics.

Imaging genetics, one of the foremost emerging topics in the medical imaging field, analyzes the inherent relations between neuroimaging and genetic data. As deep learning has gained widespread acceptance in many applications, pioneering studies employed deep learning frameworks for imaging genetics. However, existing approaches suffer from some limitations. First, they often adopt a simple strategy for joint learning of phenotypic and genotypic features. Second, their findings have not been extended to biomedical applications, e.g., degenerative brain disease diagnosis and cognitive score prediction. Finally, existing studies perform insufficient and inappropriate analyses from the perspective of data science and neuroscience. In this work, we propose a novel deep learning framework to simultaneously tackle the aforementioned issues. Our proposed framework learns to effectively represent the neuroimaging and the genetic data jointly, and achieves state-of-the-art performance when used for Alzheimer's disease and mild cognitive impairment identification. Furthermore, unlike the existing methods, the framework enables learning the relation between imaging phenotypes and genotypes in a nonlinear way without any prior neuroscientific knowledge. To demonstrate the validity of our proposed framework, we conducted experiments on a publicly available dataset and analyzed the results from diverse perspectives. Based on our experimental results, we believe that the proposed framework has immense potential to provide new insights and perspectives in deep learning-based imaging genetics studies.

Development and validation of a nomogram for predicting mild cognitive impairment in middle-aged and elderly people.

Mild cognitive impairment (MCI) is a clinical cognitive impairment state between dementia and normal aging. Early identification of MCI is beneficial, and it can delay the development of dementia. We aimed to develop and validate a prediction model to predict MCI of middle-aged and elderly people (aged 45 years and over). According to 478 middle-aged and elderly people (48-85 years old) from a cross-sectional study, we developed and validated a predictive nomogram. The least absolute shrinkage and selection operator (LASSO) regression model and multivariate logistic regression analysis were used to select variables and develop a prediction model. The performance of the nomogram was evaluated in terms of its discriminative power, calibration, and decision curve analysis (DCA). The predictive nomogram was composed of the following: age, gender, education level, residence, and reading. The model showed good discrimination power (area under receiver-operating characteristic (ROC) curve was 0.8704) and good calibration. Similar results were seen in 10-fold cross-validation. The nomogram showed clinically useful in DCA analysis. This predictive nomogram provides researchers with a practical tool for predicting MCI. The variables included in this nomogram were readily available. The population used for this nomogram was middle-aged and elderly people.

Joint selection of brain network nodes and edges for MCI identification

Background and ObjectiveFunctional brain graph (FBG), by describing the interactions between different brain regions, provides an effective representation of fMRI data for identifying mild cognitive impairment (MCI), an early stage of Alzheimer's Disease (AD). Prior to the identification task, selecting features from the estimated FBG is a necessary step for reducing computational cost, alleviating the risk of overfitting, and finding potential biomarkers of brain diseases. In practice, either node-based features (e.g., local clustering coefficients) or edge-based features (e.g., adjacency weights) are generally considered in current studies. Despite their popularity, these schemes can only capture one granularity (node or edge) of information in the FBG, which might be insufficient for the classification task and the interpretation of the classification result. MethodsTo address this issue, in this paper, we propose to jointly select nodes and edges from the estimated FBGs. Specifically, we first assign the edges to different node groups. Then, sparse group least absolute shrinkage and selection operator (sgLASSO) is used to select groups (nodes) and edges in the groups towards a better classification performance. Such a technique enables us to simultaneously locate discriminative brain regions, as well as connections between these brain regions, making the classification results more interpretable. ResultsExperimental results show that the proposed method achieves better classification performance than state-of-the-art methods. Moreover, by exploring brain network “features” that contributed most to MCI identification, we discover potential biomarkers for MCI diagnosis. ConclusionA novel method for jointly selecting nodes and edges from the estimated functional brain graphs (FBGs) is proposed.

A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification.

Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%.