Neuroscience informatics最新文献

Real-time biomedical signal transmission requires IoTs and cloud infrastructure. In this work, we investigate feasible lossy compression approaches that leverage the temporal and spatial dynamics of the signal along with current algorithms based on Compressive Sensing (CS) that use signal correlation in space and time. These techniques are altered so they may be applied efficiently to a distributed WSN. To achieve this, we proposed Convolution Neural Network (CNN) based Optimized Bio-Signals Compression using Auto-Encoder (BCAE), which integrates auto-encoder and feature selection. Instead of using the entire signal as an input, we encode the main part of the signal and send it to the desired location. Reconstruction decrypts without signal loss. Realistic aggregation and data collection procedures can improve data reconstruction accuracy. We compare various techniques' reconstruction error vs. energy requirements. The simulation results reveal that packet loss is 40% and data reconstruction error is 5%. Data forwarding time is lowered by 16.36%, while network energy usage is cut by 23.59%. The proposed method outperforms with existing techniques and the results are validated using MATLAB.

Ocular artifact, namely eye-blink artifact, is an inevitable and one of the most destructive noises of EEG signals. Many solutions of detecting the eye-blink artifact were proposed. Different subsets of EEG features and Machine Learning (ML) classifiers were used for this purpose. But no comprehensive comparison of these features and ML classifiers was presented. This paper presents the comparison of twelve EEG features and five ML classifiers, commonly used in existing studies for the detection of eye-blink artifacts. An EEG dataset, containing 2958 epochs of eye-blink, non-eye-blink, and eye-blink-like (non-eye-blink) EEG activities, is used in this study. The performance of each feature and classifier has been measured using accuracy, precision, recall, and f1-score. Experimental results reveal that scalp topography is the most potential among the selected features in detecting eye-blink artifacts. The best performing classifier is Artificial Neural Network (ANN) among the five classifiers. The combination of scalp topography and ANN classifier performed as the most powerful feature-classifier combination. However, it is expected that the findings of this study will help the future researchers to select appropriate features and classifiers in building eye-blink artifact detection models.

The neuroimaging signs of the clot in acute ischemic stroke are relevant for clot biology and its response to treatment. The diagnostic and predictive value of clot imaging is confirmed by conventional studies and emerges as a topic of interest for artificial intelligence (AI) developments. We performed a systematic review to evaluate the state of the art of AI in clot imaging, how far AI is from becoming clinically beneficial, and what are the perspectives to consider for further developments. In parallel, the review is examining the evidence brought by conventional studies concerning the relevance of clot imaging, from 2019 to August 2022. The automatic detection and segmentation of the clot are the most important advances towards AI implementation in the clinic. Predictive radiomics models require further exploration and methods optimization. Future AI approaches could consider conventional clot imaging characteristics and patient specific vascular features as variables for model development.

Precise detection of Alzheimer's disease (AD), especially at the early stages, i.e., early mild cognitive impairment (EMCI) and MCI, allows the physicians to promptly intervene to prevent the progression to advanced stages. However, identification of such stages using non-invasive brain imaging techniques like DWI, remains one of the most challenging tasks due to the subtle and mild changes in the brain structures of the subjects. Findings from previous studies suggested that topological organization alterations occur in the DTI-derived structural connectomes in MCI patients. Therefore, for improving diagnosis performance, we presented a connectome-based deep learning architecture based on BrainNet Convolutional neural network (CNN) model. The proposed model automatically extracts hidden topological features from structural networks using specially-designed convolutional filters. Experiments on 360 subjects, including 120 subjects with EMCI, 120 subjects with MCI and, 120 normal controls (NCs), with both T1-weighted MRI and DWI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI), provided the highest binary classification accuracies of 0.96, 0.98, and 0.95 for NC/EMCI, NC/MCI and EMCI/MCI respectively.

In addition, we also investigated the effect of different atlas sizes and fiber descriptors as edge weights on the discriminative ability of the classification performance. Experimental results indicate that our approach exhibited superior performance to previous methods and performed effectively without any prior complex feature engineering and regardless the variability of imaging acquisition protocols and medical scanners.

Finally, we observed that DTI-based graph representation of brain regions connections preserve important but hidden connectivity pattern information to discriminate between clinical profiles, and our proposed approach could be easily extended to other neurodegenerative and neuropsychiatric diseases.

Introduction

Pretreatment positron emission tomography (PET) with 2-deoxy-2-[¹⁸F]fluoro-D-glucose (FDG) and magnetic resonance spectroscopy (MRS) may identify biomarkers for predicting remission (absence of depression). Yet, no such image-based biomarkers have achieved clinical validity. The purpose of this study was to identify biomarkers of remission using machine learning (ML) with pretreatment FDG-PET/MRS neuroimaging, to reduce patient suffering and economic burden from ineffective trials.

Methods

This study used simultaneous PET/MRS neuroimaging from a double-blind, placebo-controlled, randomized antidepressant trial on 60 participants with major depressive disorder (MDD) before initiating treatment. After eight weeks of treatment, those with ≤7 on 17-item Hamilton Depression Rating Scale were designated a priori as remitters (free of depression, 37%). Metabolic rate of glucose uptake (metabolism) from 22 brain regions were acquired from PET. Concentrations (mM) of glutamine and glutamate and gamma-aminobutyric acid (GABA) in anterior cingulate cortex were quantified from MRS. The data were randomly split into 67% train and cross-validation ($n=40$), and 33% test ($n=20$) sets. The imaging features, along with age, sex, handedness, and treatment assignment (selective serotonin reuptake inhibitor or SSRI vs. placebo) were entered into the eXtreme Gradient Boosting (XGBoost) classifier for training.

Results

In test data, the model showed 62% sensitivity, 92% specificity, and 77% weighted accuracy. Pretreatment metabolism of left hippocampus from PET was the most predictive of remission.

Conclusions

The pretreatment neuroimaging takes around 60 minutes but has potential to prevent weeks of failed treatment trials. This study effectively addresses common issues for neuroimaging analysis, such as small sample size, high dimensionality, and class imbalance.

Skin cancer is one of the most prevalent and deadly types of cancer. Dermatologists diagnose this disease primarily visually. Multiclass skin cancer classification is challenging due to the fine-grained variability in the appearance of its various diagnostic categories. On the other hand, recent studies have demonstrated that convolutional neural networks outperform dermatologists in multiclass skin cancer classification. We developed a preprocessing image pipeline for this work. We removed hairs from the images, augmented the dataset, and resized the imageries to meet the requirements of each model. By performing transfer learning on pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks, we trained the EfficientNets B0-B7 on the HAM10000 dataset. We evaluated the performance of all EfficientNet variants on this imbalanced multiclass classification task using metrics such as Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to determine the effect of transfer learning with fine-tuning. This article presents the classification scores for each class as Confusion Matrices for all eight models. Our best model, the EfficientNet B4, achieved an F1 Score of 87 percent and a Top-1 Accuracy of 87.91 percent. We evaluated EfficientNet classifiers using metrics that take the high-class imbalance into account. Our findings indicate that increased model complexity does not always imply improved classification performance. The best performance arose with intermediate complexity models, such as EfficientNet B4 and B5. The high classification scores resulted from many factors such as resolution scaling, data enhancement, noise removal, successful transfer learning of ImageNet weights, and fine-tuning [70], [71], [72]. Another discovery was that certain classes of skin cancer worked better at generalization than others using Confusion Matrices.

Background

Modern day's society is engaged in commitment-based and time-bound jobs. This invites tension and mental depression among many people who are not able to cope up with this type of working environment. Cases of mental depression are increasing day by day all over the world. Recently, the onset of the COVID-19 pandemic has added further fuel to the fire. In many countries, the ratio between patients with mental depression and psychiatrists or psychologists is remarkably poor. Under such a situation, the design, and development of an expert system by exploiting the hidden power of various deep learning (DL) and machine learning (ML) techniques can solve the problem up to a greater extent.

Methodology

Each deep learning and machine learning technique has got its advantages and disadvantages to handle different classification problems. In this article four neural network-based deep learning architectures namely MLP, CNN, RNN, RNN with LSTM, and two Supervised Machine Learning Techniques such as SVM and LR are implemented to investigate and compare their suitability to track the mental depression from EEG Data.

Result

Among Neural Network-Based Deep Learning techniques RNN model has achieved the highest accuracy with 97.50% in Training Set and 96.50% in the Testing set respectively. It has been followed with RNN with LSTM model when there were 40% data in the Testing Set. Whereas both the Supervised Machine Learning Models namely SVM and LR have outperformed with 100.00% accuracies in Training Phase and approximately 97.25% accuracies in Testing Phase respectively.

Conclusion

This investigation and comparison-oriented study establish the suitability of RNN, RNN with LSTM, SVM and LR model to track mental depression from EEG data. This type of comparative research using Machine Learning and Deep learning architectures must be framed out on this topic to finalize the design and development of an expert system for the automatic detection of depression from EEG data.

Parkinson's disease (PD) is a heterogeneous neurodegenerative disorder associated with several motor and non-motor dysfunctions. The wide variety of clinical features often leads to divergent symptom progressions. Most PD studies have attempted subgrouping based on clinical features to help understand the disease etiology and thereby contribute toward specific treatment. However, clinical symptoms have proven to be overlapping, arbitrary, and non-reliable in several cases, often biasing the deciphered subgroups. Moreover, the prodromal phase complicates diagnosis and subgrouping as it is characterized by limited clinical symptom expression. Hence, recent studies have used data-driven machine learning and deep learning methods to data-mine the heterogeneity and obtain subgroups. Structural Magnetic Resonance Imaging (sMRI) is a non-invasive approach for visualization and analysis of anatomical tissue properties of brain. It has enabled the detection of brain abnormalities and is a potential modality for subgrouping.

This review article starts with a comprehensive discussion of clinical symptoms-based and data-driven structural neuroimaging-based subgrouping approaches in PD. Secondly, we summarize the work done in brain connectivity studies using structural MRI for PD. We give an overview of mathematical definitions, connectivity metrics, brain connectivity software, and widespread network atlases. Finally, we discuss the inherent challenges and give practical suggestions on selecting methods that could be attempted for subgrouping and connectivity analysis using structural MRI data for future Parkinson's research.

Cerebral temperature is one of the key indicators of fever, trauma, and physical activity. It has been reported that the temperature of the healthy brain is up to 2 °C higher than the core body temperature. The main methods to monitor brain temperature include infrared spectroscopy, radiometry, and acoustic thermometry. While these methods are useful, they are not very effective when portability is desired, the temperature needs to be monitored for a longer period, or localized monitoring is required. This paper presents a short review of invasive and non-invasive brain temperature monitoring sensors and tools. We discuss the type of temperature sensors that can be integrated with probes. Furthermore, implantable and bioresorbable sensors are briefly mentioned. Biocompatibility and invasiveness of the sensors in terms of their functional materials, encapsulation, and size are highlighted.