Tracking differential activation of primary and supplementary motor cortex across timing tasks: An fNIRS validation study

The Effect of Social Media Consumption on Emotion and Executive Functioning in College Students: an fNIRS Study in Natural Environment

As of 2023, 69% of adults and 81% of teens in the U.S. use social media. This study explores the immediate effects of social media consumption on executive functioning (EF) and emotion in college students, using a wearable fNIRS system to monitor brain activity in a naturalistic setting. Twenty participants were assessed pre- and post-social media use through EF tasks and emotion questionnaires. Results revealed 55% of participants were classified as addicted, with an average Instagram usage of 5 hours per week. Following social media exposure, significant impairments were observed in tasks like n-back and Go/No-Go, alongside altered brain activity. Specifically, increased medial prefrontal cortex (mPFC) activity indicated heightened cognitive effort and performance monitoring, while decreased dorsolateral prefrontal cortex (dlPFC) and ventrolateral prefrontal cortex (vlPFC) activity were associated with impaired working memory and response inhibition. Inferior frontal gyrus (IFG) activity reductions correlated with difficulties in inhibiting motor responses to No-Go stimuli. Emotional changes were minimal, except for reduced happiness in the control group. These findings highlight the negative impact of social media on EF, emphasizing the need for interventions promoting healthier digital habits.

Cortical neurite microstructural correlates of time perception in healthy older adults

The human experience is significantly impacted by timing as it structures how information is processed. Nevertheless, the neurological foundation of time perception remains largely unresolved. Understanding cortical microstructure related to timing is crucial for gaining insight into healthy aging and recognizing structural alterations that are typical of neurodegenerative diseases associated with age. Given the importance, this study aimed to determine the brain regions that are accountable for predicting time perception in older adults using microstructural measures of the brain. In this study, elderly healthy adults performed the Time-Wall Estimation task to measure time perception through average error time. We used support vector regression (SVR) analyses to predict the average error time using cortical neurite microstructures derived from orientation dispersion and density imaging based on multi-shell diffusion magnetic resonance imaging (dMRI). We found significant correlations between observed and predicted average error times for neurite arborization (ODI) and free water (FISO). Neurite arborization and free water properties in specific regions in the medial and lateral prefrontal, superior parietal, and medial and lateral temporal lobes were among the most significant predictors of timing ability in older adults. Further, our results revealed that greater branching along with lower free water in cortical structures result in shorter average error times. Future studies should assess whether these same networks are contributing to time perception in older adults with mild cognitive impairment (MCI) and whether degeneration of these networks contribute to early diagnosis or detection of dementia.

The influence of sound waves and musical experiences on movement coordination with beats

Synchronizing movement with external stimuli is important in musicians and athletes. This study investigated the effects of sound characteristics, including sound with harmonics (square wave) and without harmonics (sine wave) and levels of expertise in sports and music on rhythmic ability. Thirty-two university students participated in the study. The participants were divided into sixteen music education (ME) and sixteen physical education (PE) majors. They were asked to perform finger tapping tasks with 1,2 and 3 Hz beat rates, tapping in time with the sine and square wave beat produced by a metronome. The relative phase angle of finger tapping and the onset time of metronome sound were calculated using circular statistics. The results showed that type of wave and music experience affected the rhythmic ability of participants. Our study highlights the importance of types of waves on rhythmic ability, especially for participants with no background in music. The square wave is recommended for athletes to learn to synchronize their movement with beats.

Multiple levels of contextual influence on action-based timing behavior and cortical activation

Procedures used to elicit both behavioral and neurophysiological data to address a particular cognitive question can impact the nature of the data collected. We used functional near-infrared spectroscopy (fNIRS) to assess performance of a modified finger tapping task in which participants performed synchronized or syncopated tapping relative to a metronomic tone. Both versions of the tapping task included a pacing phase (tapping with the tone) followed by a continuation phase (tapping without the tone). Both behavioral and brain-based findings revealed two distinct timing mechanisms underlying the two forms of tapping. Here we investigate the impact of an additional-and extremely subtle-manipulation of the study's experimental design. We measured responses in 23 healthy adults as they performed the two versions of the finger-tapping tasks either blocked by tapping type or alternating from one to the other type during the course of the experiment. As in our previous study, behavioral tapping indices and cortical hemodynamics were monitored, allowing us to compare results across the two study designs. Consistent with previous findings, results reflected distinct, context-dependent parameters of the tapping. Moreover, our results demonstrated a significant impact of study design on rhythmic entrainment in the presence/absence of auditory stimuli. Tapping accuracy and hemodynamic responsivity collectively indicate that the block design context is preferable for studying action-based timing behavior.

Quantitative Comparison of Analytical Solution and Finite Element Method for Investigation of Near-infrared Light Propagation in Brain Tissue Model

Introduction

Functional near-infrared spectroscopy (fNIRS) is an imaging method in which a light source and detector are installed on the head; consequently, the re-emission of light from human skin contains information about cerebral hemodynamic alteration. The spatial probability distribution profile of photons penetrating tissue at a source spot, scattering into the tissue, and being released at an appropriate detector position, represents the spatial sensitivity.

Methods

Modeling light propagation in a human head is essential for quantitative near-infrared spectroscopy and optical imaging. The specific form of the distribution of light is obtained using the theory of perturbation. An analytical solution of the perturbative diffusion equation (DE) and finite element method (FEM) in a Slab media (similar to the human head) makes it possible to study light propagation due to absorption and scattering of brain tissue.

Results

The simulation result indicates that sensitivity is slowly decreasing in the deep area, and the sensitivity below the source and detector is the highest. The depth sensitivity and computation time of both analytical and FEM methods are compared. The simulation time of the analytical approach is four times larger than the FEM.

Conclusion

In this paper, an analytical solution and the performance of FEM methods when applied to the diffusion equation for heterogeneous media with a single spherical defect are compared. The depth sensitivity along with the computation time of simulation has been investigated for both methods. For simple and Slab modes of the human brain, the analytical solution is the right candidate. Whenever the brain model is sophisticated, it is possible to use FEM methods, but it costs a higher computation time.

Highlights

Analytical and finite element method (FEM) depth sensitivity are almost the same.FEM requires more computation time, but can handle complicated head models.The analytical method is proposed for the first step and simple head models.

Plain Language Summary

The functional near-infrared spectroscopy (fNIRS) is a type of neuromonitoring that uses near-infrared light to measure brain activity indirectly and is similar to electroencephalography (EEG). A single-channel fNIRS system contains a near-infrared light source, which emits near-infrared light (NIR), and a detector is placed near the source. A light intensity change received by detectors indicates brain activity when NIR light penetrates into the gray matter. It is necessary to have a prior understanding of light penetration depth in order to measure brain activity more accurately. fNIRS can be better understood, optimized, and investigated through modeling light propagation in brain tissue. In order to study light in tissues, analytical and numerical methods can be used. In this work, we compared these two approaches quantitatively in a simple slab medium. We concluded that the numerical method takes too much time to calculate the results, but it can be applied to complicated head models. The results of these studies provide researchers with new insights into the modeling and simulation of fNIRS and diffuse optical tomography.

Detection of epileptic seizures through EEG signals using entropy features and ensemble learning

Introduction

Epilepsy is a disorder of the central nervous system that is often accompanied by recurrent seizures. World health organization (WHO) estimated that more than 50 million people worldwide suffer from epilepsy. Although electroencephalogram (EEG) signals contain vital physiological and pathological information of brain and they are a prominent medical tool for detecting epileptic seizures, visual interpretation of such tools is time-consuming. Since early diagnosis of epilepsy is essential to control seizures, we present a new method using data mining and machine learning techniques to diagnose epileptic seizures automatically.

Methods

The proposed detection system consists of three main steps: In the first step, the input signals are pre-processed by discrete wavelet transform (DWT) and sub-bands containing useful information are extracted. In the second step, the features of each sub-band are extracted by approximate entropy (ApEn) and sample entropy (SampEn) and then these features are ranked by ANOVA test. Finally, feature selection is done by the FSFS technique. In the third step, three algorithms are used to classify seizures: Least squared support vector machine (LS-SVM), K nearest neighbors (KNN) and Naive Bayes model (NB).

Results and discussion

The average accuracy for both LS-SVM and NB was 98% and it was 94.5% for KNN, while the results show that the proposed method can detect epileptic seizures with an average accuracy of 99.5%, 99.01% of sensitivity and 100% of specificity which show an improvement over most similar methods and can be used as an effective tool in diagnosing this complication.

Effects of motor pacing on frontal-hemodynamic responses during continuous upper-limb and whole-body movements

Advances in timing research advocate for the existence of two timing mechanisms (automatic vs. controlled) that are related to the level of cognitive control intervening for motor behavior regulation. In the present study, we used the functional near-infrared spectroscopy (fNIRS) cutting-edge technique to examine the hypothesis that prefrontal inhibitory control is needed to perform slow motor activities. Participants were asked to perform a sensorimotor-synchronization task at various paces (i.e., slow, close-to-spontaneous, fast). We contrasted upper-limb circle drawing to a more naturalistic behavior that required whole-body movements (i.e., steady-state walking). Results indicated that whole-body movements led to greater brain oxygenation over the motor regions when compared with upper-limb activities. The effect of motor pace was found in the walking task only, with more bilateral orbitofrontal and left dorsolateral activation at slow versus fast pace. Exploratory analyses revealed a positive correlation between the activation of the orbitofrontal and motor areas for the close-to-spontaneous pace in both tasks. Overall, results support the key role of prefrontal cognitive control in the production of slow whole-body movements. In addition, our findings confirm that upper-limb (laboratory-based) tasks might not be representative of those engaged during everyday-life motor behaviors. The fNIRS technique may be a valuable tool to decipher the neurocognitive mechanisms underlying naturalistic, adaptive motor behaviors.

Observation and motor imagery balance tasks evaluation: An fNIRS feasibility study

In this study, we aimed at exploring the feasibility of functional near-infrared spectroscopy (fNIRS) for studying the observation and/or motor imagination of various postural tasks. Thirteen healthy adult subjects followed five trials of static and dynamic standing balance tasks, throughout three different experimental setups of action observation (AO), a combination of action observation and motor imagery (AO+MI), and motor imagery (MI). During static and dynamic standing tasks, both the AO+MI and MI experiments revealed that many channels in prefrontal or motor regions are significantly activated while the AO experiment showed almost no significant increase in activations in most of the channels. The contrast between static and dynamic standing tasks showed that with more demanding balance tasks, relative higher activation patterns were observed, particularly during AO and in AO+MI experiments in the frontopolar area. Moreover, the AO+MI experiment revealed a significant difference in premotor and supplementary motor cortices that are related to balance control. Furthermore, it has been observed that the AO+MI experiment induced relatively higher activation patterns in comparison to AO or MI alone. Remarkably, the results of this work match its counterpart from previous functional magnetic resonance imaging studies. Therefore, they may pave the way for using the fNIRS as a diagnostic tool for evaluating the performance of the non-physical balance training during the rehabilitation period of temporally immobilized patients.

An fNIRS Study of Brain Lateralization During Observation and Execution of a Fine Motor Task

Brain activity in the action observation network (AON) is lateralized during action execution, with greater activation in the contralateral hemisphere to the side of the body used to perform the task. However, it is unknown whether the AON is also lateralized when watching another person perform an action. In this study, we use fNIRS to measure brain activity over the left and right cortex while participants completed actions with their left and right hands and watched an actor complete action with their left and right hands. We show that while activation is lateralized when the participants themselves are moving, brain lateralization is not affected by the side of the body when the participant is observing another person's action. In addition, we demonstrate that individual differences in hand preference and dexterity between the right and left hands are related to brain lateralization patterns.

Cerebral hemodynamic response during a live action-observation and action-execution task: A fNIRS study

Although many studies have examined the location of the action observation network (AON) in human adults, the shared neural correlates of action-observation and action-execution are still unclear partially due to lack of ecologically valid neuroimaging measures. In this study, we aim to demonstrate the feasibility of using functional near infrared spectroscopy (fNIRS) to measure the neural correlates of action-observation and action execution regions during a live task. Thirty adults reached for objects or observed an experimenter reaching for objects while their cerebral hemodynamic responses including oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) were recorded in the sensorimotor and parietal regions. Our results indicated that the parietal regions, including bilateral superior parietal lobule (SPL), bilateral inferior parietal lobule (IPL), right supra-marginal region (SMG) and right angular gyrus (AG) share neural activity during action-observation and action-execution. Our findings confirm the applicability of fNIRS for the study of the AON and lay the foundation for future work with developmental and clinical populations.

Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research

FNIRS pre-processing and processing methodologies are very important-how a researcher chooses to process their data can change the outcome of an experiment. The purpose of this review is to provide a guide on fNIRS pre-processing and processing techniques pertinent to the field of human motor control research. One hundred and twenty-three articles were selected from the motor control field and were examined on the basis of their fNIRS pre-processing and processing methodologies. Information was gathered about the most frequently used techniques in the field, which included frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM). We discuss the methodologies of and considerations for these frequently used techniques, as well as those for some alternative techniques. Additionally, general considerations for processing are discussed.