94 results about "Brain cortex" patented technology

The invention provides a full hydrogel neural electrode which is composed of four layers of hydrogel, namely a PSFD hydrogel layer, an LPPDMEAs layer, a PSFD hydrogel layer and an MHD layer. The invention further provides a preparation method and application of the full-hydrogel neural electrode. According to the invention, a PEDOT: PSS hydrogel conductive path with ultrahigh conductivity is provided, so that the recording capability of the PEDOT: PSS hydrogel conductive path is achieved compared with that of a metal electrode; according to the full-hydrogel neural electrode disclosed by the invention, due to tight interlayer combination, excellent interface compliance is ensured by introducing dopamine into each layer of the full-hydrogel neural electrode, so that the full-hydrogel neural electrode can be attached to a complex geometrical shape of a soft nervous tissue, and interface mismatching among a conductive layer, a packaging layer, a shielding layer and a tissue-electrode interface is avoided; the natural polymer packaging layer improves the compatibility, and the electromagnetic shielding layer is used for improving the signal-to-noise ratio. The full-hydrogel neural electrode can provide artificial electrical stimulation for the cerebral cortex of a rat during epileptic seizure, and the prepared full-hydrogel neural electrode has the potential to become a valuable tool for better understanding the development and functions of brain diseases and neurodegenerative diseases and treating the neurodegenerative diseases.

The invention discloses a closed-loop deep brain regulation and control system for epilepsy treatment, and the system comprises an electroencephalogram signal collection module which collects neural electrophysiological signals in real time by implanting a recording electrode in a cerebral cortex or hippocampal brain area; the electroencephalogram signal real-time analysis module is used for outputting the epileptic seizure period prediction probability xi through a seven-layer full-connection neural network based on the 33-dimensional feature vector so as to realize epileptic seizure early warning; when the zeta is greater than or equal to a threshold value, the electrical stimulation regulation and control module is used for triggering electrical stimulation, filtering out a frequency band required to be regulated and controlled according to filtering, calculating and predicting electrical stimulation intensity and then correcting and outputting pulse stimulation, so that personalized electrical stimulation regulation and control are carried out on a specific frequency band, and detection is continuously carried out until the frequency band becomes normal; and the medicine intervention module is used for triggering a micro-injection pump to carry out medicine infusion through a pulse signal. According to the system, epilepsy treatment is improved from empirical parameter adjustment to accurate regulation and control based on real-time nerve physiological signals, and a safer and more effective treatment choice is provided for a drug-refractory epilepsy patient.

The invention relates to the technical field of sleep state monitoring, and particularly discloses an intelligent sleep state monitoring method and device based on brain waves, brain wave signals of a user are collected through EEG equipment, and a signal processing and analysis algorithm based on artificial intelligence and deep learning is introduced at the rear end to analyze the brain wave signals, so that the sleep state monitoring accuracy is improved. And the sleep state of the user is monitored based on full-frequency aggregation of the brain wave signals. In this way, the sleep state of the user can be automatically identified and displayed in real time based on the brain waves of the user, and improvement suggestions are provided when the sleep state is identified to be poor. For example, a brain balance regulator is used for sleep regulation, and a non-invasive and non-invasive weak stimulation technology is adopted for regulation. According to the method, the prior data table between the sleep state and the current mode is constructed by using an experiment, so that after the sleep state is obtained, the current mode can be adaptively adjusted based on prior knowledge to adjust cerebral cortex neural activity, and the sleep improvement of a user can be better assisted.

The present disclosure provides a method and a system for fully automatically segmenting a cerebral cortex surface based on a graph network, belongs to the field of medical image technology and devices, and solves the problem that space mapping noise is easily introduced in the mapping process in the related art, and a lot of time is cost for mapping to a sphere surface space to finish segmentation and then mapping back to an original space. The method includes: registering a cerebral magnetic resonance image to a standard template space; performing cerebral cortex surface reconstruction on the registered cerebral magnetic resonance image based on a deep neural network; calculating adjacency matrices among the vertexes of the reconstructed cerebral cortex; acquiring corresponding distinguishing features of each grid vertex of the cerebral cortex surface as corresponding feature vectors of the point so as to obtain a cerebral cortex surface segmentation result; and mapping the cerebral cortex surface segmentation result back to the original coordinate space from the standard template space. According to the present disclosure, graph structure modeling is performed on the reconstructed cerebral cortex surface, and the global topological structure features are learned based on the graph network, thus realizing the accurate segmentation of the cerebral cortex surface.

The invention relates to the technical field of cranial nerve regulation and control, and discloses a dynamic closed-loop nerve regulation and control method integrating an ultrasonic array element and an fNIRS probe, which adopts a mode of combining a near-infrared light detector and the ultrasonic array element, can utilize the characteristics of transcranial focused ultrasound to stimulate deep nuclei of the brain, and utilizes near-infrared to observe the change of cerebral cortex in real time. Real-time observation of brain blood oxygen level change caused by cortical excitability of each brain region of a brain function network is realized, closed-loop feedback is formed, then dynamic parameter adjustment is performed on the phased array ultrasonic array, dynamic closed-loop nerve regulation and control of the integrated ultrasonic array element and the near-infrared brain function imaging probe are realized, and the brain blood oxygen level change is observed in real time. And the maximum change of the cortex excitability of the expected brain region or the brain network node is obtained through the focusing process, so that the precision of the stimulation target region is realized.

The invention relates to the technical field of medical monitoring, and provides a full cortical nerve-immune monitoring and closed-loop regulation and control system based on nerve electrical stimulation, and the system comprises a regulation and control electrode which is used for detecting the cerebral cortex of a target brain region and the neuroelectric signals of a deep brain region, and is used for the electrical stimulation regulation and control of neurons of the deep brain region; the brain temperature detection assembly is used for detecting temperature information of a target brain region in real time; the cortex imaging device is used for collecting nerve and immune activity images of the whole cortex; the external control device is electrically connected with the regulation and control electrode, the brain temperature detection assembly and the cortex imaging device, and the external control device is used for receiving temperature information detected by the brain temperature detection assembly and image information detected by the cortex imaging device and controlling the regulation and control electrode to work according to the temperature information. According to the invention, high-precision synchronous monitoring and closed-loop regulation and control of deep brain area nerve electrical stimulation and whole cortical nerve-immune activity are realized, an innovative technical means is provided for neuroscience, immunology and clinical medicine research, and the method has important scientific research and clinical application values.

A Bifidobacterium animalis subsp. lactis for reducing Abeta42 deposition and an application, a name of the Bifidobacterium animalis subsp. lactis is Bifidobacterium animalis subsp. lactis IOB-LO7, and classified as Bifidobacterium animalis subsp. lactis, the Bifidobacterium animalis subsp. lactis IOB-LO7 is preserved in the General Microbiology Center of the China General Microbiological Culture Collection Center (CGMCC) on Dec. 23, 2021, with the collection number of CGMCC No. 24185. By reducing the levels of Aβ42 in the cerebral cortex and hippocampus, clearing Aβ amyloid plaques, improving communication between neurons, reducing brain neuroinflammation, and protecting nerve cells, it helps to restore cognitive function and improve Alzheimer's disease. Additionally, it can also improve gut microbiota imbalance caused by Alzheimer's disease.

The invention discloses a brain age prediction method, system and device and a medium, and relates to the technical field of biomedical image analys.The method comprises the steps that firstly, precise local detail features representing the cerebral cortex and brain tissue are precisely captured, then the dependency relationship between local areas of different images is obtained based on a windowed multi-head self-attention mechanism, and the brain age prediction result is obtained; a channel attention mechanism is introduced, importance weights of different feature channels are learned, and the importance weights are applied to feature fusion, so that long-distance dependency relationships and fine structure features are accurately captured; and then based on a cross attention mechanism, guiding global detail features to focus on a region with rich local features and guiding the local detail features to focus on a most relevant local region so as to mine deeper detail features, and finally dynamically adjusting the contribution proportion of the local detail features and global context detail features in a final decision. And the features are fused into final features so as to perceive local detail features and global detail features in a deeper level.

The invention discloses an acoustic Fresnel metamaterial with non-uniform thickness matching and transmission enhancing capabilities, which comprises a Fresnel layer and a coupling layer, the Fresnel layer is composed of a plurality of Fresnel unit cells with different heights and inclination angles, and the coupling layer is used for tightly coupling the Fresnel layer and a skull; the Fresnel unit cell is designed based on the thickness and curvature of different areas of the skull, and phase modulation and path control of ultrasonic waves are achieved by adjusting the height and the inclination angle of the unit cell. According to the invention, the ultrasonic wave can form an efficient and accurate focus area after penetrating through the skull, so that non-invasive nerve stimulation or accurate thermal injury treatment can be carried out on the brain cortex. Acoustic phase distortion and energy attenuation caused by nonuniformity of the skull can be effectively reduced, the treatment precision and efficiency are improved, and the method has the advantages of being easy and convenient to operate, high in adaptability and high in clinical application potential and is suitable for popularization and application of transcranial ultrasonic nerve regulation and related brain disease treatment.

The invention discloses a brain tumor real-time identification system and method based on cortical electroencephalogram signals, and relates to the technical field of medical artificial intelligence, and the method comprises the following steps: obtaining multi-channel ECoG signals in real time; the multi-channel ECoG signals are updated in real time, the multi-channel ECoG signals updated in real time are preprocessed, and the preprocessed multi-channel ECoG signals are obtained; performing channel feature extraction on the ECoG signal of each channel to obtain various ECoG signal features under each channel as channel features; calculating segmentation thresholds of different channel characteristics under each channel based on an automatic threshold segmentation algorithm; carrying out confidence evaluation on the segmentation threshold values of different channel characteristics under each channel; and marking the channel of which the confidence coefficient is smaller than a set tumor marking threshold value as a tumor area. Accurate positioning of a tumor area can be achieved, functional state changes of the cerebral cortex can be reflected in real time, and doctors can conveniently master real-time changes of cortex functions in time.

A cerebral cortex model-based simplicial network for diagnosing mental disorder is disclosed. A cerebral cortex model-based method for diagnosing mental disorder performed by a mental disorder diagnosis system, according to one embodiment, may comprise the steps of: modeling a cerebral cortex mesh as a cerebral cortex simplicial complex; and performing message passing for diagnosing a mental disorder using relationship information of the modeled cerebral cortex simplicial complex through a message passing neural network.

The application relates to the technical field of brain image analysis, and discloses a fine-grained brain age prediction method, a system, a terminal and a storage medium.The method comprises the following steps: a plurality of brain tissue structures are segmented from a brain structure nuclear magnetic resonance image of a target object after preprocessing; a plurality of feature maps are generated according to the brain tissue structures and are mapped to a plurality of positive faces; a four-stage network model is used to process the mapped samples; and dynamic adaptive lateral attention is added after each stage to predict the brain age of each brain region.The application predicts the brain age by using the fine-grained brain cortex region level, and introduces a dynamic adaptive lateral attention mechanism to simulate the real brain structure lateral relationship, so that the topological structure and local features of the cortex can be effectively expressed, and the error caused by the brain morphology difference is reduced.

The present invention belongs to the technical field of medical image segmentation methods and relates to an automatic segmentation method for the cerebral cortex surface based on a geometric neural network. The method comprises acquiring a brain sMRI image, reconstructing the cerebral cortex surface, and extracting multidimensional morphological features of the cerebral cortex surface; decomposing the multidimensional morphological features of the cerebral cortex surface using a graph Laplacian to obtain a spectral representation of the cerebral cortex surface manifold; extracting contextual information of the cerebral cortex surface of a target image using a segmentation module with a U-shaped hierarchical structure, extracting anatomical prior information of the cerebral cortex surface of an atlas image using an auxiliary module with the same structure, and fusing information from the segmentation module and the auxiliary module using a non-local attention feature fusion module to output a predicted probability map of the segmentation label of the cerebral cortex surface of the target image. The present invention has better real-time performance and makes full use of the intrinsic structural information of the cortical surface and the global information of brain regions.

The invention relates to a cerebral cortex metabolism visualization method and system. The method comprises the steps of obtaining a first functional image and a structure image of the brain of a target patient; based on the target neural network model, performing denoising processing on the first functional image to obtain a second functional image; wherein the target neural network model is obtained by training an initial neural network model based on a sample set, the sample set comprises multiple groups of samples, and each group of samples comprises a first functional image sample and a structure image sample of the brain of a sample patient; generating a three-dimensional grid model of the cerebral cortex according to the structure image; and mapping the metabolic information in the second functional image into the three-dimensional grid model, and visualizing the metabolic information in the three-dimensional grid model. By adopting the method, accurate correspondence and visual fusion expression of the brain anatomical structure information and metabolism information of the target patient can be realized, and the observation efficiency of a doctor is improved.

A method for achieving super-resolution in localizing electrical fields measured at the head surface with electroencephalography through a generative model of the cerebral cortex that has a very high resolution of cortical surface dipoles constructed from the known properties of human cerebral cortex and adapted to optimize the Bayesian explanation the individual's cortical surface electrical fields. The iterative optimization of the prior (generative) with the posterior (observed) fields with extensive data from extended recordings provides a probabilistic estimation of the individual's functional brain activity that can be used to train artificial neural network approximations of the individual's mental activity.

The invention provides a cerebral cortex thickness measurement method and system based on a level set image segmentation algorithm, and belongs to the technical field of neural image analysis, and the method comprises the steps: obtaining a brain magnetic resonance imaging image, and carrying out the skull stripping processing; performing three-dimensional visualization on the stripped image through volume rendering, and acquiring three-dimensional coordinates of points of interest and a region of interest on the surface of the cortex by using a preset interaction means; using a level set method to carry out brain grey matter and brain white matter segmentation on the region of interest to obtain a brain grey matter segmentation layer and a brain white matter segmentation layer, and storing three-dimensional coordinate data of points in the segmentation layers; calculating the minimum Euclidean distance according to the three-dimensional coordinate data of the points in the segmentation layer and the three-dimensional coordinates of the points of interest to obtain the cortex thickness of the points of interest; the method solves the problems that the existing cerebral cortex thickness measurement lacks a visual means, is not supported by a mathematical model, cannot be accurate to the thickness of an interest point, and cannot display and retain comprehensive information.

The present invention belongs to the field of brain-computer interface, and specifically relates to an emotion recognition method and system based on a small number of EEG channels, including obtaining EEG signals from four channels of a user's scalp, and preprocessing the EEG signals; converting the preprocessed EEG signals into basic signals, decomposing and reconstructing the basic signals to obtain a series of component signals; inputting the smoothed features into a trained classification model to detect user emotions; the present invention uses EEG signals from fewer channels to detect user emotions, and the user only needs to mobilize emotions according to actual scenarios. The PC will analyze the collected EEG signals from the user's cerebral cortex, and through preprocessing and feature extraction, the final features will be input into a classifier for classification, which can reflect the user's emotion category at that time.

The present invention discloses an emotional state and neuroelectrophysiological state regulating device, verification system and method, which belongs to the field of physical state regulation, and includes: a voltage generating module, which is used to output an extremely low frequency high voltage; a radiation module, which is used to convert the high voltage into an electric field, so that the subject is in the electric field, and then regulates the subject's subjective emotions, physiological reactions, neural electrical activities, etc. After the electric field intervention, the subject showed a lower level of cerebral cortex arousal, which was manifested as an increase in the power spectrum density of α and θ frequencies, and a significant decrease in subjective emotions related to exploration motivation and alertness, indicating that the subject was in a more relaxed state after acute stress; the extremely low frequency high voltage electric field can promote emotional regulation, and the overall negative emotion score of the subjects after the intervention is lower, and the anxiety level is also significantly reduced. The heart rate and blood pressure levels of the subjects after the electric field intervention are reduced, and both subjective and physiological indicators reflect that the physical and mental state of the subjects after the electric field intervention is relatively more stable.

The invention provides traditional Chinese medicine compound essential oil and application thereof in preparation of anti-depression products, and relates to the technical field of traditional Chinese medicine essential oil pharmacy. The traditional Chinese medicine compound essential oil is prepared from 3-15 parts of benzoin essential oil, 2-10 parts of China rose essential oil, 2-10 parts of immature bitter orange essential oil, 1-8 parts of amomum cardamomum essential oil and 1-8 parts of fennel essential oil. The traditional Chinese medicine compound essential oil disclosed by the invention is administered in a sniffing manner, and improves cerebral depression symptoms by stimulating olfactory nerves and influencing cerebral cortex edges and neurotransmitters. After a CUMS depression animal model is treated by the traditional Chinese medicine compound essential oil disclosed by the invention, the symptom of pleasant sensation disappearance is well reversed and improved, and the state of animal movement reduction and the behavior state of despair and struggling abandoning of the depression animal model are obviously improved; meanwhile, the levels of DA, 5-HT and NE in brain tissues of the depression animal model are remarkably improved, and the pathological change of the brain-sea horse area of the depression animal model is remarkably improved. The traditional Chinese medicine compound essential oil disclosed by the invention can be used for preparing medicines for aromatherapy, has no adverse reaction and high acceptability, and provides a new way for treating depression.

The invention relates to a method for constructing a growth model of a brain in a life cycle, and the method comprises the steps: obtaining brain MRI images of individuals of different age groups, and dividing the cerebral cortex into n regions; constructing a morphological feature network for sMRI of the brain MRI image of the individual, and performing morphological feature similarity degree analysis to obtain a first phenotype; constructing a functional network for the fMRI of the brain MRI image of the individual; constructing a structure-function coupling matrix for the morphological feature network and the function network, and calculating to obtain a second phenotype; selecting the first phenotype and the second phenotype of each individual as dependent variables and the age of each individual as a smooth item to carry out GAMLSS modeling, and selecting JSU distribution to respectively fit the change of the first phenotype and the second phenotype along with the age to generate a reference growth curve. The method can be used for studying the growth mode of the brain network in the whole life cycle, studying life cycle association between morphological characteristics and functional characteristics of different areas and the like.

The invention provides a brain cortex stimulation device, and relates to the technical field of brain electrical stimulation devices.The stimulation device comprises a generation module and a stimulation module, the generation module generates stimulation signals, the stimulation module makes contact with the brain cortex and transmits the stimulation signals to the brain cortex, and electrical stimulation on the brain cortex is achieved. The stimulation module comprises at least one column arranged in the first direction, each column of the stimulation module comprises a wire and a plurality of stimulation contacts electrically connected to the wire, the wire extends in the second direction, and the stimulation contacts located on the same wire are arranged in the second direction. The wire is electrically connected with the generation module, the target area of the stimulation contact is in contact with the cortex of the brain, and the stimulation signal is transmitted to the cortex of the brain through the wire and the target area of the stimulation contact in sequence. Wherein in the plurality of stimulation contacts located on the same wire, a gap is formed between every two adjacent stimulation contacts.

The application discloses a kind of single-photon counting circuit and detection device based on time-domain near infrared spectrum, circuit includes: pulse light module, for generating near infrared light to cerebral cortex tissue is irradiated;Light signal receiving amplification module, for receiving after the light signal of diffusion absorption of the cerebral cortex tissue, after amplification conversion into electrical signal;Single-photon counting module, including counting window generation unit and D flip-flop one, for in the counting window of generation, through D flip-flop one output electrical signal's counting waveform;Control module, including counter, for receiving electrical signal's counting waveform, and according to counting waveform carries out counting, generates histogram.The single-photon counting circuit of time-domain near infrared brain function imaging disclosed in the application is low in cost, simple in structure, high in integration, and can effectively solve the shortcomings of existing timing instrument, such as complexity and high cost, and the limitation brought by conversion time.

The embodiment of the invention provides an autism auxiliary diagnosis method. The autism auxiliary diagnosis method comprises the following steps: constructing an individual brain function connection diagram; based on a community detection algorithm of a graph theory, performing functional region division on the individual brain function link graph to generate an individual brain map; taking the surface area percentage of the functional network of the healthy control group in the cerebral cortex as a response variable, taking the individual age as a smooth nonlinear independent variable, introducing an average head movement parameter as a linear covariable, and constructing a normal population development reference model; and comparing the surface area percentage of the functional network of the individual brain map with the normal population development reference model of the same age, and quantifying the deviation degree to realize auxiliary diagnosis of the autism. According to the autism auxiliary diagnosis method provided by the embodiment of the invention, individualized autism auxiliary diagnosis considering individual differences can be realized. The embodiment of the invention further provides an autism auxiliary diagnosis device and electronic equipment.

A method of performing personalized neuromodulation on a subject is provided. The method includes acquiring functional magnetic resonance imaging (fMRI) data of a brain of the subject. The method also includes calculating functional connectivity of the brain between a voxel in a subcortical region of the brain and a voxel in a cortical region of the brain, based on the fMRI data. The method also includes identifying a target location in the brain to be targeted by neuromodulation based on the calculated functional connectivity.

The invention discloses a training method and a recognition method of a brain morphological network analysis model, and the training method comprises the steps: carrying out the brain network construction of a normal cerebral cortex image set and an abnormal cerebral cortex image set, and obtaining a first brain morphological network group and a second brain morphological network group; performing inter-group network redundancy analysis on the first brain morphological network group and the second brain morphological network group to obtain a first node feature set; performing inter-group long and short distance information analysis on the first brain morphological network group and the second brain morphological network group to obtain a second node feature set; and according to the first node feature set and / or the second node feature set, performing parameter updating on the initialized brain morphological network analysis model to obtain a trained brain morphological network analysis model. According to the training method, a brain morphological network analysis model can be provided, and the accuracy and interpretability of brain morphological network analysis can be improved. The invention relates to the technical field of image processing.

Magnetic field generating means (2) is disposed on a scalp (1) of a subject (P) and generates a magnetic field that is radiated from a north pole, passes through a cerebral cortex (3) of the subject (P) and returns to a south pole in a loop path. Magnetic field detecting means (5) is disposed on the scalp (1) and detects a change in the magnetic field (4) as a signal reflecting an activity status of the cerebral cortex (3).

The application relates to a magnetoencephalography device and a method for registering the magnetoencephalography device and MRI, and belongs to the field of magnetoencephalography research. The method comprises the following steps: acquiring a head image of a subject wearing a magnetoencephalography device; point cloudizing the head image of the subject to obtain a scene point cloud; performing corresponding point clustering processing on the scene point cloud and a marker module model point cloud, identifying the marker module, and determining the position coordinates of the marker module; determining the sensor probe position according to the position coordinates and the relative position relationship between the marker module and the sensor array; acquiring a three-dimensional MRI image of the head of the subject, and separating the three-dimensional MRI image into a scalp three-dimensional image and a cerebral cortex anatomical structure; extracting an MRI facial image according to the scalp three-dimensional image; and registering the MRI facial image and the head image of the subject based on the sensor probe position, so that the sensor array and the cerebral cortex anatomical structure are in a unified coordinate system. The method improves the accuracy of registration.

The invention relates to a target spot optimization method, system and device and a storage medium. The method comprises the steps of obtaining a target device model; acquiring a head model of the detected object, and determining a target intervention target area on the head model; calculating cerebral cortex induced electric field intensity on the target intervention target spot area when the target equipment is in different poses; and taking the pose of the target equipment when the cerebral cortex induced electric field is optimal as the target pose of the target equipment. A head model of a detected object is acquired, a target intervention target area of the detected object on the head model is determined, so that target equipment can accurately determine a brain area, needing to be intervened, of the detected object, and cerebral cortex induced electric field intensities under different poses on the target intervention target area are calculated through simulation; and the treatment effect of target intervention is improved according to the obtained optimal placement pose of the target equipment.

The method of simulating a human brain neural field in a computerized platform modelling various zones of a human brain and connectivity between the zones includes: providing the computerized platform modelling the various zones of the human brain and connectivity between the zones; acquiring three-dimensional anatomical structural imaging data of a folded surface of a cortex of a brain of a human patient; personalizing the computerized platform according to the structural data; providing an equation describing a spatiotemporal evolution of the neural field and loading the equation in the computerized platform; performing a projection of the surface of the cortex of the brain of the patient on a spherical surface domain; simulating the neural field in the spherical domain; and translating the simulated neural field in the spherical domain in the cortical domain.

The invention belongs to the technical field of coffee products, and particularly relates to a coffee composition for relieving depressive symptoms and a preparation method thereof. 25-30 parts of coffee beans, 6-10 parts of fructus rosae laevigatae, 6-10 parts of semen boitae, 8-12 parts of cortex albiziae and 1-3 parts of seaweed are limited, and different raw materials are extracted, decocted or subjected to enzymolysis, so that separation and retention of active ingredients of the raw materials are facilitated, and the active ingredients in fructus rosae laevigatae, semen boitae, cortex albiziae and seaweed have a protection effect on brain cells; the traditional Chinese medicine composition is beneficial to recovering the balance of a nervous system, relieving the inflammatory response in the brain, reducing the generation of oxygen free radicals, reducing the activity of xanthine oxidase in the cerebral cortex and improving the symptom of depression; and chlorogenic acid, nicotinic acid and the like in coffee can generate positive influence on depression symptoms. Therefore, the coffee composition provided by the invention can be used for relieving depression symptoms and provides a basis for adjuvant therapy of depression.