Different Cortex Activation and Functional Connectivity in Executive Function Between Young and Elder People During Stroop Test: An fNIRS Study

Differences in working memory function are associated with motor imagery-induced changes in spinal motor nerve excitability and subsequent motor skill changes

Verification of the effectiveness of motor imagery (MI) has mainly focused on the method of implementing MI, and few studies have assessed individual factors. This study examined the individual differences in MI effects from the viewpoint of the multiple components of working memory. Forty-six healthy subjects (mean age 20.8 years) performed the Stroop Test (central executive within working memory) and reverse chanting (phonological loop within working memory). Then, F-waves were measured at rest for 30 s, the Purdue Pegboard was performed with the non-dominant hand to evaluate finger dexterity (Peg score) before MI, F-waves were measured during 30 s of kinesthetic MI, and the Peg score was evaluated after MI. For statistical analysis, the amplitude F/M ratio and Peg score were used as dependent variables, and the subjects were divided into Good and Poor groups according to cognitive function. The results showed an interaction for the amplitude F/M ratio and Peg score when grouped by reverse inverse chanting. In the subsequent simple main effect analysis, the Peg score was significantly improved after MI in both groups. The amplitude F/M ratio was significantly increased during MI compared to the resting state only in the Poor phonological loop group. Conversely, there was no interaction when the groups were divided by Stroop interference. No relationship was found between individual differences in central executive and changes in hand finger dexterity and spinal motor nerve excitability induced by MI. However, there may be a relationship between individual differences in phonological loops and changes in MI-induced finger dexterity and spinal motor nerve excitability.

Impaired Neurovascular Coupling and Increased Functional Connectivity in the Frontal Cortex Predict Age-Related Cognitive Dysfunction

Impaired cerebrovascular function contributes to the genesis of age-related cognitive decline. In this study, the hypothesis is tested that impairments in neurovascular coupling (NVC) responses and brain network function predict cognitive dysfunction in older adults. Cerebromicrovascular and working memory function of healthy young (n = 21, 33.2±7.0 years) and aged (n = 30, 75.9±6.9 years) participants are assessed. To determine NVC responses and functional connectivity (FC) during a working memory (n-back) paradigm, oxy- and deoxyhemoglobin concentration changes from the frontal cortex using functional near-infrared spectroscopy are recorded. NVC responses are significantly impaired during the 2-back task in aged participants, while the frontal networks are characterized by higher local and global connection strength, and dynamic FC (p < 0.05). Both impaired NVC and increased FC correlate with age-related decline in accuracy during the 2-back task. These findings suggest that task-related brain states in older adults require stronger functional connections to compensate for the attenuated NVC responses associated with working memory load.

How can graph theory inform the dual-stream model of speech processing? a resting-state fMRI study of post-stroke aphasia

The dual-stream model of speech processing has been proposed to represent the cortical networks involved in speech comprehension and production. Although it is arguably the prominent neuroanatomical model of speech processing, it is not yet known if the dual-stream model represents actual intrinsic functional brain networks. Furthermore, it is unclear how disruptions after a stroke to the functional connectivity of the dual-stream model's regions are related to specific types of speech production and comprehension impairments seen in aphasia. To address these questions, in the present study, we examined two independent resting-state fMRI datasets: (1) 28 neurotypical matched controls and (2) 28 chronic left-hemisphere stroke survivors with aphasia collected at another site. Structural MRI, as well as language and cognitive behavioral assessments, were collected. Using standard functional connectivity measures, we successfully identified an intrinsic resting-state network amongst the dual-stream model's regions in the control group. We then used both standard functional connectivity analyses and graph theory approaches to determine how the functional connectivity of the dual-stream network differs in individuals with post-stroke aphasia, and how this connectivity may predict performance on clinical aphasia assessments. Our findings provide strong evidence that the dual-stream model is an intrinsic network as measured via resting-state MRI, and that weaker functional connectivity of the hub nodes of the dual-stream network defined by graph theory methods, but not overall average network connectivity, is weaker in the stroke group than in the control participants. Also, the functional connectivity of the hub nodes predicted specific types of impairments on clinical assessments. In particular, the relative strength of connectivity of the right hemisphere's homologues of the left dorsal stream hubs to the left dorsal hubs versus right ventral stream hubs is a particularly strong predictor of post-stroke aphasia severity and symptomology.

Study of Motion Sickness Model Based on fNIRS Multiband Features during Car Rides

Motion sickness is a common physiological discomfort phenomenon during car rides. In this paper, the functional near-infrared spectroscopy (fNIRS) technique was used in real-world vehicle testing. The fNIRS technique was utilized to model the relationship between changes in blood oxygenation levels in the prefrontal cortex of passengers and motion sickness symptoms under different motion conditions. To enhance the accuracy of motion sickness classification, the study utilized principal component analysis (PCA) to extract the most significant features from the test data. Wavelet decomposition was used to extract the power spectrum entropy (PSE) features of five frequency bands highly related to motion sickness. The correlation between motion sickness and cerebral blood oxygen levels was modeled by a 6-point scale calibration for the subjective evaluation of the degree of passenger motion sickness. A support vector machine (SVM) was used to build a motion sickness classification model, achieving an accuracy of 87.3% with the 78 sets of data. However, individual analysis of the 13 subjects showed a varying range of accuracy from 50% to 100%, suggesting the presence of individual differences in the relationship between cerebral blood oxygen levels and motion sickness symptoms. Thus, the results demonstrated that the magnitude of motion sickness during the ride was closely related to the change in the PSE of the five frequency bands of cerebral prefrontal blood oxygen, but further studies are needed to investigate individual variability.