Spatial Information of Somatosensory Stimuli in the Brain: Multivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data

Multivariate pattern analysis of medical imaging-based Alzheimer's disease

Alzheimer's disease (AD) is a devastating brain disorder that steadily worsens over time. It is marked by a relentless decline in memory and cognitive abilities. As the disease progresses, it leads to a significant loss of mental function. Early detection of AD is essential to starting treatments that can mitigate the progression of this disease and enhance patients' quality of life. This study aims to observe AD's brain functional connectivity pattern to extract essential patterns through multivariate pattern analysis (MVPA) and analyze activity patterns across multiple brain voxels. The optimized feature extraction techniques are used to obtain the important features for performing the training on the models using several hybrid machine learning classifiers for performing binary classification and multi-class classification. The proposed approach using hybrid machine learning classification has been applied to two public datasets named the Open Access Series of Imaging Studies (OASIS) and the AD Neuroimaging Initiative (ADNI). The results are evaluated using performance metrics, and comparisons have been made to differentiate between different stages of AD using visualization tools.

The Dynamic Target Motion Perception Mechanism of Tactile-Assisted Vision in MR Environments

In the mixed reality (MR) environment, the task of target motion perception is usually undertaken by vision. This approach suffers from poor discrimination and high cognitive load when the tasks are complex. This cannot meet the needs of the air traffic control field for rapid capture and precise positioning of the dynamic targets in the air. Based on this problem, we conducted a multimodal optimization study on target motion perception judgment by controlling the hand tactile sensor to achieve the use of tactile sensation to assist vision in MR environment. This allows it to adapt to the requirements of future development-led interactive tasks under the mixed reality holographic aviation tower. Motion perception tasks are usually divided into urgency sensing for multiple targets and precise position tracking for single targets according to the number of targets and task division. Therefore, in this paper, we designed experiments to investigate the correlation between tactile intensity-velocity correspondence and target urgency, and the correlation between the PRS (position, rhythm, sequence) tactile indication scheme and position tracking. We also evaluated it through comprehensive experiment. We obtained the following conclusions: (1) high, higher, medium, lower, and low tactile intensities would bias human visual cognitive induction to fast, faster, medium, slower, and slow motion targets. Additionally, this correspondence can significantly improve the efficiency of the participants' judgment of target urgency; (2) under the PRS tactile indication scheme, position-based rhythm and sequence cues can improve the judgment effect of human tracking target dynamic position, and the effect of adding rhythm cues is better. However, when adding rhythm and sequence cues at the same time, it can cause clutter; (3) tactile assisted vision has a good improvement effect on the comprehensive perception of dynamic target movement. The above findings are useful for the study of target motion perception in MR environments and provide a theoretical basis for subsequent research on the cognitive mechanism and quantitative of tactile indication in MR environment.

Relationship between Tactile Sensation, Motor Activity, and Differential Brain Activity in Young Individuals

In this study, we compared the differences in brain activation associated with the different types of objects using functional magnetic resonance imaging (fMRI). Twenty-six participants in their 20s underwent fMRI while grasping four different types of objects. After the experiment, all of the participants completed a questionnaire based on the Likert Scale, which asked them about the sensations they experienced while grasping each object (comfort, hardness, pain, ease in grasping). We investigated the relationship between brain activity and the results of the survey; characteristic brain activity for each object was correlated with the results of the questionnaire, indicating that each object produced a different sensation response in the participants. Additionally, we observed brain activity in the primary somatosensory cortex (postcentral gyrus), the primary motor cortex (precentral gyrus), and the cerebellum exterior during the gripping task. Our study shows that gripping different objects produces activity in specific and distinct brain regions and suggests an “action appraisal” mechanism, which is considered to be the act of integrating multiple different sensory information and connecting it to actual action. To the best of our knowledge, this is the first study to observe brain activity in response to tactile stimuli and motor activity simultaneously.

Exploring Acupuncture Actions in the Body and Brain

Acupuncture's actions have been explained by biomedical research. However, the meridian system used in acupuncture needs further clarification. This review describes how acupuncture affects the body and brain. From the perspective of traditional East Asian medicine, the meridian system is closely connected with acupuncture's treatment effects. In the body, the indications of acupoints, primarily established based on the meridian system, have spatial symptom patterns. Spatial patterns of acupoint indications are distant from the stimulated sites and strongly associated with the corresponding meridian's route. Understanding how acupuncture works based on the original meridian system is important. From a neuroscience perspective, an acupuncture-induced sensation originates from the bottom-up action of simple needling in the peripheral receptor and the reciprocal interaction with top-down brain modulation. In the brain, enhanced bodily attention triggered by acupuncture stimulation can activate the salience network and deactivate the default mode network regardless of the actual stimulation. The application of data science technology to acupuncture research may provide new tools to uncover the principles of acupoint selection and enhance the clinical efficacy of acupuncture treatment in various diseases.

Pain modalities in the body and brain: Current knowledge and future perspectives

Development and validation of pain biomarkers has become a major issue in pain research. Recent advances in multimodal data acquisition have allowed researchers to gather multivariate and multilevel whole-body measurements in patients with pain conditions, and data analysis techniques such as machine learning have led to novel findings in neural biomarkers for pain. Most studies have focused on the development of a biomarker to predict the severity of pain with high precision and high specificity, however, a similar approach to discriminate different modalities of pain is lacking. Identification of more accurate and specific pain biomarkers will require an in-depth understanding of the modality specificity of pain. In this review, we summarize early and recent findings on the modality specificity of pain in the brain, with a focus on distinct neural activity patterns between chronic clinical and acute experimental pain, direct, social, and vicarious pain, and somatic and visceral pain. We also suggest future directions to improve our current strategy of pain management using our knowledge of modality-specific aspects of pain.

Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

Identification of Acupoint Indication from Reverse Inference: Data Mining of Randomized Controlled Clinical Trials

The specificity of acupoint indication (i.e., reverse inference—diseases for which an acupoint could be used) might differ from the specificity of acupoint selection (i.e., forward inference—acupoints used for a disease). In this study, we explore acupoint specificity through reverse inferences from the dataset of prescribed acupoints for a certain disease in clinical trials. We searched acupuncture treatment regimens in randomized controlled trials included in the Cochrane Database of Systematic Reviews. For forward inference, the acupoints prescribed for each disease were quantified. For reverse inference, diseases for each acupoint were quantified. Data were normalized using Z-scores. Bayes factor correction was performed to adjust for the prior probability of diseases. The specificities of acupoint selections in 30 diseases were determined using forward inference. The specificities of acupoint indications regarding 49 acupoints were identified using reverse inference and then subjected to Bayes factor correction. Two types of acupoint indications were identified for 24 acupoints: regional and distal. Our approach suggests that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Acupoint indication will improve our understanding of acupoint specificity and will lead to the establishment of a new model of analysis and educational resources for acupoint characteristics.