351 results about "Minimal cognitive impairment" patented technology

The invention relates to a model for evaluating and predicting mild cognitive impairment risk of old people in a pension institution. The model sequentially comprises a behavior analysis module, a language recognition module, a social modeling module, a toughness calculation module, a feature fusion module, a risk reasoning module and the like. Behavior deviation characteristics and abnormal time periods are extracted by collecting behavior data of daily life, diet, social contact and the like of old people and comparing the behavior data with an institution work and rest template; in combination with nursing records, extracting language anomaly features; analyzing social frequency and structure changes in the abnormal time period, and extracting social variation features; a cognitive toughness index is calculated by integrating the health archive and the recovery ability to the health event; and performing toughness weighting on the multi-dimensional features to construct a time sequence tensor, and inputting the time sequence tensor into a recursive model to predict a cognitive impairment risk value. And if the risk value suddenly changes, the system automatically backtracks the feature trajectory of nearly 7 days, constructs and screens a prediction path with the strongest interpretation force, outputs a dominant prediction result and a key factor sequence, and realizes high-interpretability and high-reliability early recognition and intervention reference.

The invention discloses a rehabilitation assisting method and device suitable for cognitive disorder treatment and a medium, and relates to medical rehabilitation. A tablet personal computer, a millimeter wave radar and the like are included, artificial intelligence technologies such as signal processing and deep learning are utilized, and rehabilitation training of patients with mild cognitive impairment caused by brain injury and cerebral apoplexy is assisted. Basic information, medical history and current cognitive conditions of the patient are recorded and collected through the tablet computer, and a rehabilitation file is generated; based on the cognitive disorder type and severity of the patient, a rehabilitation training plan is intelligently made; a patient performs rehabilitation training through a gamified training interface, and the physiological state in the training process is monitored in real time in a non-contact mode based on millimeter wave radar; a deep learning model is used for analyzing training data of the patient, and rehabilitation progress is evaluated; training and evaluation data are summarized, and a subsequent rehabilitation scheme is optimized. Full-process automatic management from patient information collection, training plan making, training implementation, rehabilitation evaluation to data analysis report generation can be rapidly and accurately achieved, and the cognitive rehabilitation efficiency and effect are improved.

An image processing framework for multi-class classifying a subject into cognitive normal, mild cognitive impairment and Alzheimer's disease (AD) conditions is developed. In one realization of the framework, an AD_Net model, which is an attention-enhanced convolution neural network (CNN) formed by embedding a Convolutional Block Attention Module (CBAM) into a CNN having a Visual Geometry Group 19 (VGG19) architecture, processes an image volume of the subject's brain to generate a plurality of AD_Net feature maps and a first plurality of scores that predict respective likelihoods of the three conditions. To enhance the prediction accuracy, a multilayer perception model formed with a plurality of fully connected layers processes the plurality of AD_Net feature maps and a plurality of influencing factors of AD, such as age, gender, geriatric depression scale score, Mini-Mental State Examination score and clinical dementia rating score, to generate a second plurality of scores that predict the respective likelihoods.

The invention discloses a mild cognitive impairment brain rehabilitation training device and system in the technical field of cognitive nerve rehabilitation, and the device comprises a multi-modal data fusion module, a personalized training task generation module, a multi-sensory stimulation presentation module and a real-time adaptive regulation and control module. And the personalized training task generation module is used for dynamically generating a personalized narrative training task fusing the user interest theme and the cognitive training target by utilizing a reinforcement learning algorithm based on the user cognitive weak domain evaluation result and the user personal interest and life narrative content obtained through natural language processing. Clinical data of a patient and real-time electroencephalogram and behavior data are fused, a narrative training task deeply fused with life experience and interest of the patient is dynamically generated, the difficulty of the training task is automatically adjusted according to real-time physiological indexes, meanwhile, seamless connection with a medical system is achieved through digital biomarkers, and the training efficiency is improved. And the precision and compliance of rehabilitation training are obviously improved.

The present invention relates to a method for diagnosing and predicting the course of Alzheimer's disease (AD)-related dementia after feature extraction of brain images by Variational Autoencoders (VAE) and using Artificial intelligence and Machine learning (AI / ML) algorithms, as compared with non-demented control cases (CN), mild cognitive impairment (MCI), and other non-Alzheimer's dementia (non-ADD) cases. Method 1 extracts the reduced dimensional latent feature vector from the sMRI scans / brain images using a VAE and does multiple sampling from the latent distribution to reduce the class imbalance followed by classification using different AI / ML models. Method 2, the extracted feature vectors from VAE are used as input to a mixture of class-Restricted Boltzmann Machines (cl-RBM) for AD, MCI, and CN classification. Method-1 diagnoses AD and distinguishes it from MCI, CN, and non-ADD using MRI scans with or without additional aids. Method-2 further extends to diagnosis and prediction of AD.

Early and accurate identification of dementia, such as Alzheimer's disease (AD) is of utmost importance. However, despite several decades of biomarker research, there is still no single diagnostic test or biomarker to definitively diagnose dementia and its underlying neurodegenerative disorder. A combination of structural MRI (fMRI) and functional MRI (fMRI) derived image analysis may provide the unique ability to capture the dynamic state of change in the degenerating brain. Hence, to capture the overall structural and functional anomalies of brain tissues caused by AD, an exhaustive combinatorial system has been developed using structural and / or functional MRI data followed by rigorous image processing and deep learning-based algorithms to diagnose AD and / or mild cognitive impairment (MCI) patients. Whole brain sMRI and fMRI slices are processed, and brain pixel-based intensity features are fed into a deep learning-based convoluted neural network (CNN) algorithm to calculate the AD / healthy probability of each MRI slice.

The invention relates to a device for risk assessment of nervous system diseases, and a processor of the device is configured to receive eye movement data and electroencephalogram data which are associated in a time sequence, and fuse a double-branch deep neural network in a model through multiple modes to assess the risk of the nervous system diseases, performing feature extraction and attention mechanism-based cross-modal feature fusion on the eye movement data and the electroencephalogram data to obtain cross fusion features; and outputting risk assessment results and risk assessment parameters of at least one type of nervous system diseases through an output layer of the multi-modal fusion assessment model. According to the method and the device, the problem that diagnosis sensitivity and universality are insufficient due to the fact that diagnosis only depends on a single information source in related technologies is solved, disease features can be more comprehensively constructed from two levels of neural activity and behavior response by fusing data of two modes of eye movement and electroencephalogram, parallel diagnosis is further performed for multiple types of diseases, and the diagnosis efficiency is improved. The sensitivity and applicability of neuropsychiatric diagnosis are improved, and particularly, the method has more remarkable accuracy in early stages such as mild cognitive impairment and the like.

The invention discloses an Alzheimer's disease early warning method based on white matter lesion omics characteristics, and relates to the field of wisdom medicines.The method comprises the steps that magnetic resonance imaging data of a historical subject in the period from the mild cognitive impairment period to the period before diagnosis of Alzheimer's disease are obtained, and manual labeling of white matter and white matter lesion areas is carried out; a manual annotation data set is obtained; training a deep learning model for white matter lesion recognition based on the manual annotation data set; inputting to-be-identified magnetic resonance imaging data into the deep learning model, and extracting lesion features of the white matter; performing standardization and feature alignment on the extracted lesion features, and inputting the lesion features into a deep clustering model to form clustering results for different white matter lesion feature types; and an early risk assessment model is constructed based on the clustering result and the Alzheimer's disease transformation risk tag corresponding to the clustering result, and the Alzheimer's disease transformation risk level of the subject is output, so that the problems that multiple lesion features are difficult to quantify and details are difficult to identify are solved.

The present invention relates to the field of disease prediction, and particularly to a cognitive impairment prediction system based on synchronously acquired electroencephalogram-electrogastrography signals, comprising: a data acquisition module for acquiring data, where the data includes electroencephalogram data and electrogastrogram data; a first analysis module for obtaining the average electrode inconsistency index of a first channel and a second channel based on a first model according to time series data; and a prediction module for comparing the average electrode inconsistency index of a subject with a preset evaluation threshold so as to predict patients with cognitive impairment. The system provided by the present invention can more accurately and efficiently distinguish patients with mild cognitive impairment from healthy people, and can be used as an auxiliary diagnostic tool for primary screening of mild cognitive impairment in a large population (such as communities, physical examination centers).

The invention discloses a mild cognitive impairment screening and diagnosis system based on deep learning, and relates to the technical field of cognitive impairment screening and diagnosis, and the system comprises a data collection and extraction module which is used for collecting voice data and a digital drawing track of a subject; the low-frequency window determination module is used for determining a low-frequency window according to the voice envelope and the handwriting speed; the lag index calculation module is used for obtaining a speech writing low-frequency lag index based on the phase relation and the correlation intensity; the disturbance quantity extraction module is used for extracting a listening disturbance quantity and a motion disturbance quantity; the depolarization residual calculation module is used for calculating the depolarization residual of the speech writing low-frequency lag index; and the risk calculation and grading module is used for calculating the risk probability and determining a grading result. According to the method, the interference of non-cognitive factors on mild cognitive impairment screening can be eliminated, and the screening accuracy is improved.

The invention discloses a machine learning-based MCI (Mild Cognitive Impairment) and a system for predicting the progress of MCI into dementia, and the machine learning-based mild cognitive impairment prediction system comprises the following steps: screening candidate factors to obtain prediction factors in people with known mild cognitive impairment states; the method comprises the following steps: taking people with known mild cognitive impairment states as a data set, and dividing the data set into a training set and a test set; inputting the training set into all machine learning models, and training the models to obtain the machine learning models after preliminary training; inputting the test set into the preliminarily trained machine learning model, testing the model, and taking the machine learning model of which the test index value exceeds a set threshold value as a final trained mild cognitive impairment machine learning model; and inputting a plurality of prediction factors of a to-be-detected object into the finally trained mild cognitive impairment machine learning model to obtain a prediction result of whether the to-be-detected object suffers from the mild cognitive impairment or not.

The invention discloses a mild cognitive impairment auxiliary recognition system and method based on eye movement characteristics, and relates to the technical field of computer-aided diagnosis, and the system comprises a data obtaining module which is used for obtaining eye movement data of a patient in the emotion classification task process; the data preprocessing module is used for performing data preprocessing on the acquired eye movement data; the auxiliary recognition module is used for inputting the preprocessed eye movement data into an auxiliary recognition model based on a double-flow fusion network, the auxiliary recognition model comprises a spatial feature extraction module, a time sequence feature extraction module and a multi-modal fusion classification module, key features of the eye movement hotspot map are extracted through the spatial feature extraction module, and the key features of the eye movement hotspot map are extracted through the time sequence feature extraction module; the time sequence features of the dynamic eye movement sequence are extracted through the time sequence feature extraction module, the features extracted by the two feature extraction modules are spliced and fused through the multi-modal fusion and classification module, and final accurate classification and recognition results of the Alzheimer's disease, the mild cognitive impairment and the health are output.

This invention relates to benzodiazepine derivatives, compositions comprising therapeutically effective amounts of those derivatives and methods of using those derivatives or compositions in treating cognitive impairment associated with CNS disorders. It also relates to the use of an α5-containing GABAA receptor agonist (e.g., an α5-containing GABAA receptor positive allosteric modulator) in treating cognitive impairment associated with CNS disorders in a subject in need or at risk thereof, including age-related cognitive impairment, Mild Cognitive Impairment (MCI), amnestic MCI, Age-Associated Memory Impairment, Age Related Cognitive Decline, dementia, Alzheimer's Disease (AD), prodromal AD, PTSD, schizophrenia, bipolar disorder, ALS, cancer-therapy-related cognitive impairment, mental retardation, Parkinson's disease, autism spectrum disorders, fragile X disorder, Rett syndrome, compulsive behavior, and substance addiction. It also relates to the use of an α5-containing GABAA receptor agonist (e.g., an α5-containing GABAA receptor positive allosteric modulator) in treating brain cancers (including brain tumors, e.g., medulloblastomas), and cognitive impairment associated therewith.

The invention relates to the technical field of early screening of mild cognitive impairment, and discloses a bimodal signal processing method for intelligent screening of mild cognitive impairment, comprising: acquiring an electroencephalogram signal and an eye movement signal of a target object driven by a cognitive task; registering the electroencephalogram signal and the eye movement signal according to the cognitive task to obtain the electroencephalogram signal and the eye movement signal with the same time sequence length; a target electroencephalogram signal and a target eye movement signal which are consistent in distribution attribute are obtained by minimizing the statistical difference of the registered electroencephalogram signal and eye movement signal in each corresponding time window and maximizing the time sequence correlation of the registered electroencephalogram signal and eye movement signal in each corresponding time window; wherein the statistical difference comprises a mean value difference, a variance / standard deviation difference and a distribution shape difference. According to the bimodal signal processing method for intelligent screening of mild cognitive impairment, the data acquisition cost is relatively low, and the representation accuracy of the finally obtained signal on cognitive features is relatively high.

The invention relates to the technical field of medical instruments, in particular to a rehabilitation assistive device for training mild cognitive impairment of old people. The existing rehabilitation training assistive device is difficult to switch between high-intensity intermittent training and aerobic training, and the training strength is difficult to change when the high-intensity intermittent training is carried out, so that the rehabilitation training effect of a patient is not good enough. A rehabilitation assistive device for training mild cognitive impairment of old people comprises a bottom plate, a seat, a shell, a supporting seat and the like. A seat is fixedly connected to the bottom plate, a shell is fixedly connected to the bottom plate, and a supporting seat is fixedly connected to the bottom plate. The moving plate continues to move upwards to compress a spring seat, the spring seat is compressed to enable a friction frame to be in closer contact with a rotating wheel, the rotating resistance of the rotating wheel is gradually increased, then the force of treading a pedal assembly by the patient is gradually increased, and therefore the patient can conduct high-strength training.

The invention provides an audio-based mild cognitive impairment auxiliary diagnosis method and device, and relates to the technical field of computer-aided diagnosis. The method comprises the following steps: acquiring original dialogue audio; performing audio and acoustic parameter feature extraction according to the original dialogue audio to obtain a multi-resolution spectrogram feature and a side language feature; based on a multi-scale double attention mechanism of a hierarchical attention weight generation strategy, performing dynamic feature fusion according to the multi-resolution spectrogram features to obtain optimized spectrogram features; performing optimization fine tuning on a pre-trained auxiliary diagnosis model by using the optimized spectrogram features and the secondary language features to obtain an optimized auxiliary diagnosis model; and obtaining an actual dialogue audio, and performing cognitive disorder detection by using the optimized auxiliary diagnosis model to obtain an auxiliary diagnosis result. The invention provides an efficient and accurate mild cognitive impairment auxiliary diagnosis method based on audio.

The invention belongs to the technical field of auxiliary medical treatment, and particularly relates to a rapid auxiliary screening system for mild cognitive impairment of middle-aged and elderly people based on machine learning. The system includes: an input module configured to input examination data of a subject; the prediction module is configured to input the examination data into a machine learning model to obtain a prediction result whether the middle-aged and elderly mild cognitive impairment risk exists or not; and the output module is configured to output the prediction result. The examination data is selected from at least one of the following indexes: calf circumference / neck circumference, height / calf circumference, height / neck circumference, calf circumference, upper arm middle circumference and delayed recall. The MCI screening model for accurately predicting mild cognitive impairment of middle-aged and elderly people based on indexes easy to collect is successfully constructed. The model ensures the prediction performance, simplifies the collection of indexes, has strong generalization ability, promotes the popularization of the technology, and shows huge practical potential.

The invention relates to the field of computer-aided diagnosis, cross-modal fusion, image morphological operation and feature extraction related to computer vision and artificial intelligence, in particular to a method for predicting mild cognitive impairment in the progress stage of Alzheimer's disease, which comprises the following steps: S1, respectively inputting MRI (Magnetic Resonance Imaging), Clinical Table and PET (Positron Emission Tomography) data into respective encoders, and extracting initial image features and table features; s2, for the MRI branch and the PET branch, inputting respective image features and table features into a mixed channel attention module together, and calculating a mixed channel attention feature; s3, performing element-by-element multiplication on the mixed channel attention features obtained in the step S2 and original image features of corresponding branches to realize feature enhancement of channel dimensions; and S4, inputting the feature enhanced in the step S3 into a space attention module, and calculating a space attention feature. Structural MRI, functional PET and clinical table data are integrated for predicting the conversion from MCI to AD.

The invention belongs to the technical field of auxiliary medical treatment, and particularly relates to a simple multi-modal index rapid auxiliary screening system for middle-aged and elderly mild cognitive impairment. The system includes: an input module configured to input blood indicators, physical examination data, and inquiry variables of a subject; the prediction module is configured to input the blood indexes, the physical examination data and the inquiry variables into a machine learning model to obtain a prediction result whether the middle-aged and elderly mild cognitive impairment risk exists or not; and the output module is configured to output the prediction result. The MCI screening model which can accurately predict the mild cognitive impairment of the middle-aged and elderly people by using simply acquired indexes is successfully constructed. On the basis of ensuring the prediction performance, the model is simple in index acquisition and strong in generalization ability, is beneficial to large-scale popularization of the technology, and has a very good application prospect.

The invention relates to the field of disease prediction, in particular to a cognitive impairment prediction system based on synchronously acquired electroencephalogram-stomach electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and stomach and intestine electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict a cognitive impairment patient. The system provided by the invention can more accurately and efficiently distinguish patients with mild cognitive impairment from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of mild cognitive impairment on relatively large people (such as communities and physical examination centers).

The invention relates to the field of disease prediction, in particular to a cognitive impairment prediction system based on synchronously acquired electroencephalogram-stomach electrical signals, comprising: a data acquisition module for acquiring data including electroencephalogram data and stomach and intestine electrical data; the first analysis module is used for obtaining an average electrode inconsistency index of the first channel and the second channel based on a first model according to the time sequence data; and the prediction module is used for comparing the average electrode inconsistency index of the subject with a preset evaluation threshold value so as to predict a cognitive impairment patient. The system provided by the invention can more accurately and efficiently distinguish patients with mild cognitive impairment from healthy people, and can be used as an auxiliary diagnosis tool to be applied to primary screening of mild cognitive impairment on relatively large people (such as communities and physical examination centers).

The invention discloses a mild cognitive impairment recognition method and system based on fNIRS data under multi-task normal form fusion. The method comprises the steps that fNIRS data, obtained based on a resting state task and an n-back work memory task, of a testee are obtained; based on the obtained fNIRS data, extracting fNIRS related features; performing feature selection on the extracted fNIRS related features based on a feature selection algorithm to form a feature set; and inputting the formed feature set into the trained deep learning model, and outputting a mild cognitive impairment recognition result. According to the method, the data of the fNIRS under the resting state and the n-back work memory cognition task are obtained, the multi-dimensional fNIRS related features are calculated, feature selection is carried out based on a Gradient Boosting algorithm, and finally accurate MCI recognition is carried out in combination with a deep learning model. The method not only can effectively extract high-dimensional features, but also can realize automatic MCI diagnosis through model training and reasoning, and has relatively high accuracy and relatively strong popularization and application potential.

A method of pre-screening a human subject for a clinical trial for treatment of tauopathy or an amyloidogenic disease. The method comprises obtaining a plasma sample from the subject and determining a concentration of p217+tau present in the plasma sample. The method further comprises indicating the subject for further screening for the clinical trial when the concentration of p217+tau present in the plasma sample is greater than or equal to a minimum threshold and less than or equal to a maximum threshold. The minimum threshold corresponds to an amount of p217+tau in plasma over which subjects present with mild cognitive impairment (MCI) and an increased accumulation of tau tangles in the brain as compared to a cognitively normal patient. The maximum threshold corresponds to an amount of p217+tau present in plasma over which subjects present with pathology of widespread accumulation of tau tangles in multiple regions of the brain.

The invention discloses a closed-loop feedback target extraction method based on a frontal top electroencephalogram network activity reference model, and relates to the fields of neural information decoding, neuroscience application and brain-computer interfaces. According to the method, the problem of insufficient target selection and feedback regulation and control precision in traditional electroencephalogram closed-loop feedback is solved, and a new electroencephalogram network target extraction mode which is good in ecological property and high in robustness is provided. The method mainly comprises the steps that firstly, a brain network path closely related to memory cognitive function abnormity of mild cognitive impairment (MCI) is extracted in a natural film watching state, network characteristics are refined in combination with metabolic map information, a new brain network target fed back by a real-time electroencephalogram closed loop is formed, and the ecological property of the target and the pertinence of feedback are remarkably improved. Besides, based on a large amount of normal elderly group electroencephalogram data, a frontal top electroencephalogram network activity reference model of the normal elderly group in a movie recalling state is constructed, key feedback indexes are extracted by using a machine learning algorithm, real-time evaluation of the relative performance level of MCI individuals is realized, and the accuracy of evaluation is improved. And a technical support is provided for improving the cognitive function of the MCI patient.

The invention belongs to the field of intelligent interaction equipment, and particularly relates to cognitive decline old people intelligent interaction equipment based on AR glasses, which comprises an AR glasses module, an intelligent interaction equipment module and a cloud server module, and the AR glasses module, the intelligent interaction equipment module and the cloud server module carry out real-time data exchange and synchronization through a wireless communication technology. The cloud server module comprises a user interface, a core processing unit, a family member end collaborative management applet and a nursing management module, the nursing management module comprises a nursing closed-loop management module, and eight modules of health management, cognition activation, emotion activation, diet activation, exercise activation, social activation, barrier-free interaction and nursing closed-loop management are combined. All-around intervention is provided for patients with mild cognitive impairment and Alzheimer's disease, cognitive decline is delayed, and the life quality is improved.

One aspect relates to a method for providing information for predicting a risk group for developing Alzheimer's disease dementia or a risk group for early onset of Alzheimer's symptoms, or a risk group for developing amnestic mild cognitive impairment and / or a positron emission tomography (PET)-positive risk group for amyloid β deposition, based on a European population-East Asian data. According to one aspect, the method makes it possible to accurately predict a risk group for developing Alzheimer's disease dementia or a risk group for early onset of Alzheimer's symptoms, or a risk group for developing amnestic mild cognitive impairment and / or a positron emission tomography (PET)-positive risk group for amyloid β deposition by using only at least 12 single-nucleotide polymorphisms, and the ability to predict the risk groups is further enhanced when up to and at most 80 additional single-nucleotide polymorphisms are used. In addition, the method further includes identifying age, sex, years of education, and APOE genotype as indicators, and thus the ability to predict the risk groups is further enhanced, and the risk groups can be predicted at an early stage with high accuracy.

The invention provides a biomarker for early Alzheimer's disease detection, a kit, a preparation method and application. The kit mainly contains seven capture antibodies and detection antibodies which can be specifically combined with human blood metabolites, has the characteristics of high detection sensitivity, high specificity and high accuracy, not only can distinguish blood samples of forgetting mild cognitive impairment patients, but also can identify early-stage Alzheimer's disease patients.

The invention discloses a mild cognitive impairment detection method based on combination of local and global spatial-temporal characteristics. In recent years, detection of mild cognitive impairment by using the brain function connection state has achieved a certain effect, however, an existing detection method is relatively simple in mode of extracting and fusing spatio-temporal characteristics. According to the adaptive brain region signal segmentation method provided by the invention, brain region signal segmentation is regarded as a maximum likelihood estimation problem of Gaussian distribution, different segments are adaptively divided according to different brain connection states, and the problem that accurate segmentation cannot be realized by a sliding window segmentation method is overcome. The invention provides a graph Transform framework fusing local and global spatial-temporal features, and the designed framework can effectively perform modeling on the local and global spatial-temporal features so as to realize accurate mild cognitive impairment detection. Experimental results show that the method can fully utilize local and global spatio-temporal information to accurately detect the mild cognitive impairment.

The invention relates to neurodegenerative disorders, and particularly to diagnostic and prognostic methods, apparatus and kits for neurodegenerative disorders, such as mild cognitive impairment (MCI), Alzheimer's disease, Parkinson's disease, Huntington's disease or Motor Neurone disease. The invention also extends to methods of detecting novel biomarkers, and methods for treating patients diagnosed or prognosed with a neurodegenerative disorder. A mathematical correlation between the concentrations of Tau protein and T14 peptide is found in samples from healthy subjects without a mood disorder, but not in samples from subjects having neurodegenerative disorders or a predisposition thereto.

A use of a new highly toxic amyloid oligomer Apo*3F as a target for diagnosing Alzheimer's disease (AD) in the early stage and the middle-late stage and mild cognitive impairment (MCI) caused by AD. The Aβo*3F specifically binds to an antibody 3F and is present in cerebrospinal fluid (CSF), blood and / or brain tissue of AD patients and patients with MCI caused by AD, and the levels show highly significant differences in CSF, blood and / or brain tissue of AD patients, MCI patients and healthy elderly persons. In addition, the Apo*3F is an ultra-highly toxic oligomer, is the most important toxic component in an Aβ oligomer mixture, has a strong pathogenic effect, and plays a key role in the occurrence and development of AD.