fNIRS can robustly measure brain activity during memory encoding and retrieval in healthy subjects

Alteration of prefrontal functional connectivity in preclinical Alzheimer's disease: an fNIRS study

Background

Early detection of Alzheimer's disease (AD) is vital for delaying its progression through timely intervention. The preclinical stage, the longest phase of AD, often goes undetected due to a lack of noticeable symptoms. Developing an accessible and quantitative screening method for AD is essential for enabling appropriate interventions during this stage.

Methods

Functional near-infrared spectroscopy was used to investigate prefrontal functional connectivity in preclinical AD subjects. A total of 99 participants, including healthy controls and preclinical subjects who were amyloid beta (Aβ) positive (n = 45), were recruited. We designed a mixed phonemic and semantic verbal fluency task for the experimental protocol. Functional connectivity was then analyzed as z-values in the left, right, and interhemispheric prefrontal regions during a verbal fluency task. Finally, we assessed the correlation between the participants' z-values and clinical indices.

Results

The preclinical AD group exhibited increased interhemispheric functional connectivity derived from oxygenated and deoxygenated hemoglobin during verbal tasks involving the first phonemic letter. Additionally, significant right and left functional connectivity differences were observed in the healthy control group during verbal tasks with the letter and categories, but not in the preclinical AD group. Lastly, the difference in interhemispheric functional connectivity of oxygenated hemoglobin between the first and second verbal trials was significantly greater in the preclinical AD group. These interhemispheric functional connectivity values were significantly correlated with Aβ results from positron emission tomography.

Conclusion

The initial increase and subsequent reduction of interhemispheric functional connectivity in the preclinical AD group across task repetitions suggests that task-related prefrontal network alterations may occur during the preclinical phase of AD and shows its potential as a biomarker for screening preclinical AD.

Different cortex activation between young and middle-aged people during different type problem-solving: An EEG&fNIRS study

Problem-solving strategies vary depending on the type of problem and aging. This study investigated the hemodynamic response measured by the changes in the oxyhemoglobin concentration (HbO), alpha frequency power, and their interrelation during problem-solving in healthy young and middle-aged individuals, employing combined electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) recordings. The study included 39 young and 30 middle-aged subjects. The brain activation that occurred while answering different questions was recorded using combined EEG and fNIRS. During the EEG & fNIRS recording, four questions (arithmetic, general knowledge, insight, and basic operation) were used for problem-solving. Alpha power (8-13 Hz) and HbO changes were analyzed. The behavioral results indicated significant differences between age groups in various question types. While the middle-aged group performed better on the general knowledge questions, the older group performed better on the insight and four-process questions. The fNIRS results reveal significant differences in brain activation during problem-solving tasks, particularly in regions like DLPFC/TA, STG, pSSC/Wernicke, and STG/angular gyrus Wernicke's area. The young group with the highest HbO was recorded during arithmetic questions, general knowledge questions, and basic operation questions. In contrast, there was no significant highest HbO during insight questions. Similar findings were observed in the middle-aged group, with the highest HbO recorded during general knowledge questions. However, there was no significant HbO in other channels during the solving of other question types in this group. The alpha power varied across different electrodes for various question types in both young and middle-aged groups. The highest alpha frequency band power for different electrodes was recorded while solving general knowledge questions in the young group and insight questions in the middle-aged group. Finally, the EEG and fNIRS correlation results showed positive correlations between HbO and alpha frequency band power in specific brain regions while solving general knowledge questions, particularly in the middle-aged group. The study reveals age-related differences in behavioral performance, brain activation patterns, and neural correlates during various cognitive tasks, showcasing distinct strengths between middle-aged and young individuals in specific question types.

Operational Modal Analysis of Near-Infrared Spectroscopy Measure of 2-Month Exercise Intervention Effects in Sedentary Older Adults with Diabetes and Cognitive Impairment

The Global Burden of Disease Study (GBD 2019 Diseases and Injuries Collaborators) found that diabetes significantly increases the overall burden of disease, leading to a 24.4% increase in disability-adjusted life years. Persistently high glucose levels in diabetes can cause structural and functional changes in proteins throughout the body, and the accumulation of protein aggregates in the brain that can be associated with the progression of Alzheimer's Disease (AD). To address this burden in type 2 diabetes mellitus (T2DM), a combined aerobic and resistance exercise program was developed based on the recommendations of the American College of Sports Medicine. The prospectively registered clinical trials (NCT04626453, NCT04812288) involved two groups: an Intervention group of older sedentary adults with T2DM and a Control group of healthy older adults who could be either active or sedentary. The completion rate for the 2-month exercise program was high, with participants completing on an average of 89.14% of the exercise sessions. This indicated that the program was practical, feasible, and well tolerated, even during the COVID-19 pandemic. It was also safe, requiring minimal equipment and no supervision. Our paper presents portable near-infrared spectroscopy (NIRS) based measures that showed muscle oxygen saturation (SmO2), i.e., the balance between oxygen delivery and oxygen consumption in muscle, drop during bilateral heel rise task (BHR) and the 6 min walk task (6MWT) significantly (p < 0.05) changed at the post-intervention follow-up from the pre-intervention baseline in the T2DM Intervention group participants. Moreover, post-intervention changes from pre-intervention baseline for the prefrontal activation (both oxyhemoglobin and deoxyhemoglobin) showed statistically significant (p < 0.05, q < 0.05) effect at the right superior frontal gyrus, dorsolateral, during the Mini-Cog task. Here, operational modal analysis provided further insights into the 2-month exercise intervention effects on the very-low-frequency oscillations (<0.05 Hz) during the Mini-Cog task that improved post-intervention in the sedentary T2DM Intervention group from their pre-intervention baseline when compared to active healthy Control group. Then, the 6MWT distance significantly (p < 0.01) improved in the T2DM Intervention group at post-intervention follow-up from pre-intervention baseline that showed improved aerobic capacity and endurance. Our portable NIRS based measures have practical implications at the point of care for the therapists as they can monitor muscle and brain oxygenation changes during physical and cognitive tests to prescribe personalized physical exercise doses without triggering individual stress response, thereby, enhancing vascular health in T2DM.

Comparing Multi-Dimensional fNIRS Features Using Bayesian Optimization-Based Neural Networks for Mild Cognitive Impairment (MCI) Detection

The diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is essential for initiating timely treatment to delay the onset of AD. Previous studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for diagnosing MCI. However, preprocessing fNIRS measurements requires extensive experience to identify poor-quality segments. Moreover, few studies have explored how proper multi-dimensional fNIRS features influence the classification results of the disease. Thus, this study outlined a streamlined fNIRS preprocessing method to process fNIRS measurements and compared multi-dimensional fNIRS features with neural networks in order to explore how temporal and spatial factors affect the classification of MCI and cognitive normality. More specifically, this study proposed using Bayesian optimization-based auto hyperparameter tuning neural networks to evaluate 1D channel-wise, 2D spatial, and 3D spatiotemporal features of fNIRS measurements for detecting MCI patients. The highest test accuracies of 70.83%, 76.92%, and 80.77% were achieved for 1D, 2D, and 3D features, respectively. Through extensive comparisons, the 3D time-point oxyhemoglobin feature was proven to be a more promising fNIRS feature for detecting MCI by using an fNIRS dataset of 127 participants. Furthermore, this study presented a potential approach for fNIRS data processing, and the designed models required no manual hyperparameter tuning, which promoted the general utilization of fNIRS modality with neural network-based classification to detect MCI.

Functional near-infrared spectroscopy of prefrontal cortex during memory encoding and recall in elderly with type 2 diabetes mellitus

Low-frequency Fahræus-Lindqvist-driven oscillations in the small vessels are crucial because oscillations in small vessels support nutrient supply. Understanding of this is critical in type 2 diabetes mellitus (T2DM) to develop therapeutic measures in order to prevent Alzheimer's Disease Related Dementias. Indeed, vascular factors are known to contribute to cerebrovascular disease as well as mild cognitive impairment and dementia, which are predicted to affect 152 million people by 2050 (Alzheimer's Disease International London, UK, 2019). In this clinical study, we performed functional near-infrared spectroscopy (fNIRS) of the forehead to investigate the effect of the Mini-Cog with three-item recall test on the prefrontal cortex (PFC) activation and the relative oscillatory power in the 0.01-0.02-Hz (FahræΣus-Lindqvist effect) and 0.021-0.052 Hz (smooth muscle autonomic innervation) frequency bands in elderly (60 years and older) T2DM and age-matched controls. We found a significant (p<0.01) difference in the PFC activation between elderly subjects with T2DM and age-matched elderly controls. Moreover, power spectral density (PSD) analysis revealed a significantly lower relative power in 0.021-0.052 Hz (smooth muscle autonomic innervation) frequency band in elderly subjects with T2DM during the Mini-Cog three-item recall test. Furthermore, a drop in the oscillatory power in the 0.01-0.02 Hz frequency band during Mini-Cog three-item recall test was found more pronounced in the elderly subjects with T2DM. Therefore, our study highlighted portable brain imaging to capture cerebrovascular reactivity to cognitive load that may provide a biomarker of cerebrovascular dysfunction in T2DM. Clinical Relevance-Our study establishes forehead portable brain imaging under cognitive load for monitoring cerebrovascular function in T2DM.

Cognitive Training with Neurofeedback Using fNIRS Improves Cognitive Function in Older Adults

This study examined the effects of a 4-week cognitive training program with neurofeedback (CT-NF) among 86 healthy adults (M = 66.34 years, range 54-84) randomized to either a treatment (app-based ABC games) or control (Tetris) group. Participants completed seven cognitive assessments, pre- and post-intervention, and measured their cortical brain activity using a XB-01 functional near-infrared spectroscopy (fNIRS) brain sensor, while engaging in CT-NF. The treatment (ABC) group showed significant (pre/post-intervention) improvements in memory (MEM), verbal memory (VBM), and composite cognitive function, while the control group did not. However, both groups showed significant improvements in processing speed (PS) and executive function (EF). In line with other studies, we found that strength of cortical brain activity (measured during CT-NF) was associated with both cognitive (pre and post) and game performance. In sum, our findings suggest that CT-NF and specifically ABC exercises, confer improved cognition in the domains of MEM, VBM, PS, and EF.

Entropy Could Quantify Brain Activation Induced by Mechanical Impedance-Restrained Active Arm Motion: A Functional NIRS Study

Brain activation has been used to understand brain-level events associated with cognitive tasks or physical tasks. As a quantitative measure for brain activation, we propose entropy in place of signal amplitude and beta value, which are widely used, but sometimes criticized for their limitations and shortcomings as such measures. To investigate the relevance of our proposition, we provided 22 subjects with physical stimuli through elbow extension-flexion motions by using our exoskeleton robot, measured brain activation in terms of entropy, signal amplitude, and beta value; and compared entropy with the other two. The results show that entropy is superior, in that its change appeared in limited, well established, motor areas, while signal amplitude and beta value changes appeared in a widespread fashion, contradicting the modularity theory. Entropy can predict increase in brain activation with task duration, while the other two cannot. When stimuli shifted from the rest state to the task state, entropy exhibited a similar increase as the other two did. Although entropy showed only a part of the phenomenon induced by task strength, it showed superiority by showing a decrease in brain activation that the other two did not show. Moreover, entropy was capable of identifying the physiologically important location.

Investigating Language and Domain-General Processing in Neurotypicals and Individuals With Aphasia - A Functional Near-Infrared Spectroscopy Pilot Study

Brain reorganization patterns associated with language recovery after stroke have long been debated. Studying mechanisms of spontaneous and treatment-induced language recovery in post-stroke aphasia requires a network-based approach given the potential for recruitment of perilesional left hemisphere language regions, homologous right hemisphere language regions, and/or spared bilateral domain-general regions. Recent hardware, software, and methodological advances in functional near-infrared spectroscopy (fNIRS) make it well-suited to examine this question. fNIRS is cost-effective with minimal contraindications, making it a robust option to monitor treatment-related brain activation changes over time. Establishing clear activation patterns in neurotypical adults during language and domain-general cognitive processes via fNIRS is an important first step. Some fNIRS studies have investigated key language processes in healthy adults, yet findings are challenging to interpret in the context of methodological limitations. This pilot study used fNIRS to capture brain activation during language and domain-general processing in neurotypicals and individuals with aphasia. These findings will serve as a reference when interpreting treatment-related changes in brain activation patterns in post-stroke aphasia in the future. Twenty-four young healthy controls, seventeen older healthy controls, and six individuals with left hemisphere stroke-induced aphasia completed two language tasks (i.e., semantic feature, picture naming) and one domain-general cognitive task (i.e., arithmetic) twice during fNIRS. The probe covered bilateral frontal, parietal, and temporal lobes and included short-separation detectors for scalp signal nuisance regression. Younger and older healthy controls activated core language regions during semantic feature processing (e.g., left inferior frontal gyrus pars opercularis) and lexical retrieval (e.g., left inferior frontal gyrus pars triangularis) and domain-general regions (e.g., bilateral middle frontal gyri) during hard versus easy arithmetic as expected. Consistent with theories of post-stroke language recovery, individuals with aphasia activated areas outside the traditional networks: left superior frontal gyrus and left supramarginal gyrus during semantic feature judgment; left superior frontal gyrus and right precentral gyrus during picture naming; and left inferior frontal gyrus pars opercularis during arithmetic processing. The preliminary findings in the stroke group highlight the utility of using fNIRS to study language and domain-general processing in aphasia.

Episodic Memory Encoding and Retrieval in Face-Name Paired Paradigm: An fNIRS Study

BACKGROUND

Episodic memory (EM) is particularly sensitive to pathological conditions and aging. In a neurocognitive context, the paired-associate learning (PAL) paradigm, which requires participants to learn and recall associations between stimuli, has been used to measure EM. The present study aimed to explore whether functional near-infrared spectroscopy (fNIRS) can be employed to determine cortical activity underlying encoding and retrieval. Moreover, we examined whether and how different aspects of task (i.e., novelty, difficulty) affects those cortical activities.

METHODS

Twenty-two male college students (age: M = 20.55, SD = 1.62) underwent a face-name PAL paradigm under 40-channel fNIRS covering fronto-parietal and middle occipital regions.

RESULTS

A decreased activity during encoding in a broad network encompassing the bilateral frontal cortex (Brodmann areas 9, 11, 45, and 46) was observed during the encoding, while an increased activity in the left orbitofrontal cortex (Brodmann area 11) was observed during the retrieval. Increased HbO concentration in the superior parietal cortices and decreased HbO concentration in the inferior parietal cortices were observed during encoding while dominant activation of left PFC was found during retrieval only. Higher task difficulty was associated with greater neural activity in the bilateral prefrontal cortex and higher task novelty was associated with greater activation in occipital regions.

CONCLUSION

Combining the PAL paradigm with fNIRS provided the means to differentiate neural activity characterising encoding and retrieval. Therefore, the fNIRS may have the potential to complete EM assessments in clinical settings.

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees' safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm's performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial

INTRODUCTION

The diagnosis of mild cognitive impairment (MCI), that is, the transitory phase between normal age-related cognitive decline and dementia, remains a challenging task. It was observed that a multimodal approach (simultaneous analysis of several complementary modalities) can improve the classification accuracy. We will combine three noninvasive measurement modalities: functional near-infrared spectroscopy (fNIRS), electroencephalography and heart rate variability via ECG. Our aim is to explore neurophysiological correlates of cognitive performance and whether our multimodal approach can aid in early identification of individuals with MCI.

METHODS AND ANALYSIS

This study will be a cross-sectional with patients with MCI and healthy controls (HC). The neurophysiological signals will be measured during rest and while performing cognitive tasks: (1) Stroop, (2) N-back and (3) verbal fluency test (VFT). Main aims of statistical analysis are to (1) determine the differences in neurophysiological responses of HC and MCI, (2) investigate relationships between measures of cognitive performance and neurophysiological responses and (3) investigate whether the classification accuracy can be improved by using our multimodal approach. To meet these targets, statistical analysis will include machine learning approaches.This is, to the best of our knowledge, the first study that applies simultaneously these three modalities in MCI and HC. We hypothesise that the multimodal approach improves the classification accuracy between HC and MCI as compared with a unimodal approach. If our hypothesis is verified, this study paves the way for additional research on multimodal approaches for dementia research and fosters the exploration of new biomarkers for an early detection of nonphysiological age-related cognitive decline.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the local Ethics Committee (reference: 83/19). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov, NCT04427436, registered on 10 June 2020, https://clinicaltrials.gov/ct2/show/study/NCT04427436.

Cortical Effects of Noisy Galvanic Vestibular Stimulation Using Functional Near-Infrared Spectroscopy

Noisy galvanic vestibular stimulation (nGVS) can improve different motor, sensory, and cognitive behaviors. However, it is unclear how this stimulation affects brain activity to facilitate these improvements. Functional near-infrared spectroscopy (fNIRS) is inexpensive, portable, and less prone to motion artifacts than other neuroimaging technology. Thus, fNIRS has the potential to provide insight into how nGVS affects cortical activity during a variety of natural behaviors. Here we sought to: (1) determine if fNIRS can detect cortical changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin with application of subthreshold nGVS, and (2) determine how subthreshold nGVS affects this fNIRS-derived hemodynamic response. A total of twelve healthy participants received nGVS and sham stimulation during a seated, resting-state paradigm. To determine whether nGVS altered activity in select cortical regions of interest (BA40, BA39), we compared differences between nGVS and sham HbO and HbR concentrations. We found a greater HbR response during nGVS compared to sham stimulation in left BA40, a region previously associated with vestibular processing, and with all left hemisphere channels combined (p < 0.05). We did not detect differences in HbO responses for any region during nGVS (p > 0.05). Our results suggest that fNIRS may be suitable for understanding the cortical effects of nGVS.

Cortical hemodynamics as a function of handgrip strength and cognitive performance: a cross-sectional fNIRS study in younger adults

BACKGROUND

There is growing evidence for a positive correlation between measures of muscular strength and cognitive abilities. However, the neurophysiological correlates of this relationship are not well understood so far. The aim of this study was to investigate cortical hemodynamics [i.e., changes in concentrations of oxygenated (oxyHb) and deoxygenated hemoglobin (deoxyHb)] as a possible link between measures of muscular strength and cognitive performance.

METHODS

In a cohort of younger adults (n = 39, 18-30 years), we assessed (i) handgrip strength by a handhold dynamometer, (ii) short-term working memory performance by using error rates and reaction times in the Sternberg task, and (iii) cortical hemodynamics of the prefrontal cortex (PFC) via functional near-infrared spectroscopy (fNIRS).

RESULTS

We observed low to moderate negative correlations (rp =  ~ - 0.38 to - 0.51; p < 0.05) between reaction time and levels of oxyHb in specific parts of the PFC. Furthermore, we noticed low to moderate positive correlations (rp =  ~ 0.34 to 0.45; p < 0.05) between reaction times and levels of deoxyHb in distinct parts of the PFC. Additionally, higher levels of oxyHb (rp (35) = 0.401; p = 0.014) and lower levels of deoxyHb (rp (34) = - 0.338; p = 0.043) in specific parts of the PFC were linked to higher percentage of correct answers. We also found low to moderate correlations (p < 0.05) between measures of handgrip strength and levels of oxyHb (rp =  ~ 0.35; p < 0.05) and levels of deoxyHb (rp =  ~ - 0.25 to - 0.49; p < 0.05) in specific parts of the PFC. However, there was neither a correlation between cognitive performance and handgrip strength nor did cortical hemodynamics in the PFC mediate the relationship between handgrip strength and cognitive performance (p > 0.05).

CONCLUSION

The present study provides evidence for a positive neurobehavioral relationship between cortical hemodynamics and cognitive performance. Our findings further imply that in younger adults higher levels of handgrip strength positively influence cortical hemodynamics although the latter did not necessarily culminate in better cognitive performance. Future research should examine whether the present findings can be generalized to other cohorts (e.g., older adults).

Effects of β-Lactolin on Regional Cerebral Blood Flow within the Dorsolateral Prefrontal Cortex during Working Memory Task in Healthy Adults: A Randomized Controlled Trial

Epidemiological studies have reported that consumption of dairy products rich in β-lactolin is beneficial for cognitive decline among elderly individuals. Although previous studies have shown that β-lactolin supplementation improves memory function and attention in healthy adults, the mechanism through which β-lactolin affects human brain function has yet to be elucidated. This placebo-controlled randomized double-blind study therefore examined the effects of β-lactolin on human regional cerebral blood flow (rCBF) using near-infrared spectroscopy (NIRS) according to the Consolidated Standards of Reporting Trials guidelines. A total of 114 healthy participants aged between 50 and 75 years with relatively low cognition were randomly allocated into the β-lactolin or placebo groups (n = 57 for both groups) and received supplementation for 6 weeks. After the 6 weeks of supplementation, total hemoglobin during cognitive tasks (Kraepelin and 2-back tasks) was measured using two-channel NIRS to determine rCBF. Accordingly, the β-lactolin group had significantly higher changes in total hemoglobin at the left dorsolateral prefrontal cortex (DLPFC) area measured using the left-side channel during the 2-back tasks (p = 0.027) compared to the placebo group. The present study suggests that β-lactolin supplementation increases rCBF and DLPFC activity during working memory tasks.

β-lactolin increases cerebral blood flow in dorsolateral prefrontal cortex in healthy adults: a randomized controlled trial

The number of elderly individuals with age-related cognitive decline or dementia is rapidly increasing. Dairy product consumption, including β-lactolin, is beneficial for their cognitive function. The underlying mechanism of β-lactolin's effects on human brain activity is yet to be investigated. We examined the β-lactolin effects on human cerebral blood flow (CBF) using near-infrared spectroscopy (NIRS) in a placebo-controlled randomized double-blind study, which reported according to the CONSORT guidelines. Fifty healthy participants (aged 45-60 years) were randomly allocated into the β-lactolin or the placebo group (n = 25 each) and supplemented for 6 weeks. During the 6^th week, oxy-hemoglobin during the working memory tasks was measured using 34-channels (CHs) NIRS. The changes of oxy-hemoglobin, which represents the CBF, in CH 23 located at the left dorsolateral prefrontal cortex (DLPFC) during the spatial working memory task showed higher statistical significance (false discovery rate (q) = 0.045) in the β-lactolin than in the placebo group. The oxy-Hb changes in CH23 have a co-relationship with the working memory task reaction time. This clinical trial showed an increase in the CBF in the left DLPFC area during the 6-week β-lactolin supplementation. This study contributes to elucidating the underlying mechanisms of β-lactolin on cognitive performance.

Cortical Activation During Shoulder and Finger Movements in Healthy Adults: A Functional Near-Infrared Spectroscopy (fNIRS) Study

Characterization of cortical activation patterns during movement of the upper extremity in healthy adults is helpful in understanding recovery mechanisms following neurological disorders. This study explores cortical activation patterns associated with movements of the shoulder and fingers in healthy adults using functional near-infrared spectroscopy (fNIRS). Twelve healthy right-handed participants were recruited. Two motor tasks (shoulder abduction and finger extension) with two different trial lengths (10 s and 20 s) were performed in a sitting position at a rate of 0.5 Hz. The hemodynamic response, as indicated by oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), over both hemispheres was acquired using a 54-channel fNIRS system. We found a generalized bilateral cortical activation during both motor tasks with greater activation in the contralateral compared to the ipsilateral primary motor cortex. Particularly in the more medial part of the contralateral hemisphere, significant higher activation was found during the shoulder compared to finger movements. Furthermore, cortical activation patterns are affected not only by motor tasks but also by trial lengths. HbO is more sensitive to detect cortical activation during finger movements in longer trials, while HbR is a better surrogate to capture active areas during shoulder movement in shorter trials. Based on these findings, reporting both HbO and HbR is strongly recommended for future fNIRS studies, and trial lengths should be taken into account when designing experiments and explaining results. Our findings demonstrating distinct cortical activation patterns associated with shoulder and finger movements in healthy adults provide a foundation for future research to study recovery mechanisms following neurological disorders.

Detection of Mild Cognitive Impairment Using Convolutional Neural Network: Temporal-Feature Maps of Functional Near-Infrared Spectroscopy

Mild cognitive impairment (MCI) is the clinical precursor of Alzheimer's disease (AD), which is considered the most common neurodegenerative disease in the elderly. Some MCI patients tend to remain stable over time and do not evolve to AD. It is essential to diagnose MCI in its early stages and provide timely treatment to the patient. In this study, we propose a neuroimaging approach to identify MCI using a deep learning method and functional near-infrared spectroscopy (fNIRS). For this purpose, fifteen MCI subjects and nine healthy controls (HCs) were asked to perform three mental tasks: N-back, Stroop, and verbal fluency (VF) tasks. Besides examining the oxygenated hemoglobin changes (ΔHbO) in the region of interest, ΔHbO maps at 13 specific time points (i.e., 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, and 65 s) during the tasks and seven temporal feature maps (i.e., two types of mean, three types of slope, kurtosis, and skewness) in the prefrontal cortex were investigated. A four-layer convolutional neural network (CNN) was applied to identify the subjects into either MCI or HC, individually, after training the CNN model with ΔHbO maps and temporal feature maps above. Finally, we used the 5-fold cross-validation approach to evaluate the performance of the CNN. The results of temporal feature maps exhibited high classification accuracies: The average accuracies for the N-back task, Stroop task, and VFT, respectively, were 89.46, 87.80, and 90.37%. Notably, the highest accuracy of 98.61% was achieved from the ΔHbO slope map during 20-60 s interval of N-back tasks. Our results indicate that the fNIRS imaging approach based on temporal feature maps is a promising diagnostic method for early detection of MCI and can be used as a tool for clinical doctors to identify MCI from their patients.

Hemodynamics Analysis of Patients With Mild Cognitive Impairment During Working Memory Tasks

Diagnosis of dementia in early stage is important to prevent progression of dementia in the aging society. Mild cognitive impairment (MCI) denotes an early stage of Alzheimer disease (AD). In this paper, we aim to classify MCI patients from healthy controls (HC) during working memory tasks using functional near-infrared spectroscopy (fNIRS). To achieve this objective, t-values and correlation coefficients are calculated to find the region of interest (ROI) channels and brain connectivity. From the ROI channels averaged over subjects, features (mean and slope) of hemodynamic responses were extracted for classification. Extracted features were labelled as two classes and classified via two classifiers, linear discriminant analysis (LDA) and support vector machine (SVM). The classification accuracies were 73.08 % with LDA and 71.15 % with SVM. The results show that there are significant differences in the hemodynamic responses (HR) between MCI patients and healthy controls. Therefore, these results suggest a possibility of using fNIRS as a diagnostic tool for MCI patients.

Fixed and flexible: Dynamic prefrontal activations and working memory capacity relationships vary with memory demand

Prefrontal cortex (PFC) activation during encoding of memoranda (proactive responses) is associated with better working memory (WM) compared to reactive/retrieval-based activation. This suggests that dynamic PFC activation patterns may be fixed, based upon one's WM ability, with individuals who have greater WM ability relying more on proactive processes and individuals with lesser WM ability relying more on reactive processes. We newly tested whether this heuristic applied when challenging an individual's WM capacity. Twenty-two participants (N = 22) underwent functional near-infrared spectroscopy (fNIRS) during a modified Sternberg WM paradigm. We tested whether the relationship between dynamic PFC activation patterns and WM capacity changed, as a function of WM demands (N = 14 after quality control). Here, higher-WM capacity was associated with more proactive PFC patterns, but only when WM capacity was overloaded. Lower-WM capacity was associated with these same patterns, but only when WM demand was low. Findings are inconsistent with a purely fixed view of dynamic PFC activation patterns and suggest higher- and lower-WM-capacity individuals flexibly engage PFC processes in a fundamentally different manner, dependent upon current WM demands.

Amyloid deposition is associated with different patterns of hippocampal connectivity in men versus women

Compared to men, women are disproportionally affected by Alzheimer's disease (AD) and have an accelerated trajectory of cognitive decline and disease progression. Neurobiological factors underlying gender differences in AD remain unclear. This study investigated brain beta-amyloid (Aβ)-related neural system differences in cognitively normal older men and women (N = 61; 41 females, 65-93 years old). We found that men and women showed different associations between Aβ load and hippocampal functional connectivity. During associative memory encoding, in men greater Aβ burden was accompanied by greater hippocampus-prefrontal connectivity (i.e., more synchronized activities), whereas in women hippocampal connectivity did not vary by Aβ burden. For resting-state data, the interaction of gender × Aβ on hippocampal connectivity did not survive multiple comparison in the whole-brain analyses. In the region of interest-based analyses, resting-state hippocampal-prefrontal connectivity was positively correlated with Aβ load in men and was negatively correlated with Aβ load in women. The observed Aβ-related neural differences may explain the accelerated trajectory of cognitive decline and AD progression in women.

An optimal strategy for prediction of sudden cardiac death through a pioneering feature-selection approach from HRV signal

BACKGROUND AND OBJECTIVE

Taking into consideration the critical importance of Sudden cardiac death (SCD), as it could be the first and the last heart condition to be diagnosed in a person while continuing to claim millions of lives around the world, prediction of sudden cardiac death has increasingly been regarded as a matter of substantive significance. This study does not seek to once again define features to predict and detect SCD, as there already has been adequate discussion addressing feature extraction in our previous works and other recent studies. What seems to be lacking attention is the need for an appropriate strategy to manage the extracted features to such an extent that the best classification is presented. To this end, deploying a suitable tactic to select extracted features could bring about outstanding results compared to other works in the literature.

METHODS

This research has accordingly applied a novel and automated approach to Local Feature Subset Selection through the most rigorous methodologies, which have formerly been developed in previous works of this team, for extracting features from nonlinear, time-frequency and classical processes. We are therefore enabled to select features that differ from one another in each minute before the incident through the agency of optimal feature selection in each one-minute interval of the signal.

RESULTS

Using the proposed algorithm, SCD can be predicted 13 min before the onset, thus, better results are achieved compared to other techniques proposed in this field. Additionally, through defining a Utility Function and employing statistical analysis the alarm threshold has been effectively determined as 84% for the prediction accuracy. Having selected the best combination of features, based on their ability to generate the highest classification accuracy, the two classes are classified by means of the Multilayer Perceptron (MLP), K- Nearest Neighbor (KNN) the Support Vector Machine (SVM), and the Mixture of Expert (ME) classifier. The Mixture of Experts classification yielded more precise class discrimination than other well-known classifiers. The performance of the proposed method was evaluated using the MIT-BIH database which led to sensitivity, specificity, and accuracy of 84.24%, 85.71%, and 82.85%, respectively for thirteen one-minute.

CONCLUSIONS

The outcome of the obtained prediction would be analyzed and compared to other results. The most applicable and effective features would subsequently be presented according to the number of times they have been chosen. Finally, principal features in each time interval are discussed and the importance of each type of processing will be drawn into focus. The results indicate the significant capacity of the proposed method for predicting SCD from Electrocardoigram (ECG) signals as well as selecting the appropriate processing method at any time before the incident.

Altered near-infrared spectroscopy response to breath-holding in patients with fibromyalgia

Fibromyalgia (FM) is a complex syndrome characterized by chronic widespread pain and a heightened response to pressure. Most medical researches pointed out that FM patients with endothelial dysfunction and arterial stiffness. A continuous-wave near-infrared spectroscopy (NIRS) system is used in present study to measure the hemodynamic changes elicited by breath-holding task in patients with FM. Each patient completed a questionnaire survey including demographics, characteristics of body pain, associated symptoms, headache profiles and Hospital Anxiety and Depression Scale. A total of 27 FM patients and 26 health controls were enrolled. In comparison with healthy controls, patients with FM showed lower maximal and averaged change of oxyhemoglobin concentration in both the left (1.634 ±0.890 and 0.810 ±0.525 μM) and the right (1.576 ±0.897 and 0.811 ±0.601 μM) prefrontal cortex than healthy controls (P < .05 for both sides) during the breath-holding task. In conclusion, FM is associated with altered cerebrovascular reactivity measured by NIRS and breath-holding task, which may reflect endothelial dysfunction or arterial stiffness. Oxygenated hemoglobin concentration changes of healthy controls and FM patients.

Current and future functional imaging techniques for post-traumatic stress disorder

Posttraumatic stress disorder (PTSD) is a trauma and stressor related psychiatric disorder associated with structural, metabolic, and molecular alternations in several brain regions including diverse cortical areas, neuroendocrine regions, the striatum, dopaminergic, adrenergic and serotonergic pathways, and the limbic system. We are in critical need of novel therapeutics and biomarkers for PTSD and a deep understanding of cutting edge imaging and spectroscopy methods is necessary for the development of promising new approaches to better diagnose and treat the disorder. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) criterion, all forms of traumatic stress-induced disorder are considered acute stress disorder for the first month following the stressor. Only after symptoms do not remit for one month can the disorder be deemed PTSD. It would be particularly useful to differentiate between acute stress disorder and PTSD during the one month waiting period so that more intensive treatments can be applied early on to patients with a high likelihood of developing PTSD. This would potentially enhance treatment outcomes and/or prevent the development of PTSD. Comprehension of the qualities and limitations of currently applied methods as well as the novel emerging techniques provide invaluable knowledge for fast paced development. Conventional methods of studying PTSD have proven to be insufficient for diagnosis, measurement of treatment efficacy, and monitoring disease progression. As the field currently stands, there is no diagnostic biomarker available for any psychiatric disease, PTSD included. Currently, emerging and available technologies are not utilized to their full capacity and in appropriate experimental designs for the most fruitful possible studies in this area. Therefore, there is an apparent need for improved methods in PTSD research. This review demonstrates the current state of the literature in PTSD, including molecular, cellular, and behavioral indicators, possible biomarkers and clinical and pre-clinical imaging techniques relevant to PTSD, and through this, elucidate the void of current practical imaging and spectroscopy methods that provide true biomarkers for the disorder and the significance of devising new techniques for future investigations. We are unlikely to develop a single biomarker for any psychiatric disorder however. As psychiatric disorders are incomparably complex compared to other medical diagnoses, its most likely that transcriptomic, metabolomic and structural and connectomic imaging data will have to be analyzed in concert in order to produce a dependable non-behavioral marker of PTSD. This can explain the necessity of bridging conventional approaches to novel technologies in order to create a framework for further discoveries in the treatment of PTSD.

Conventional methods of studying posttraumatic stress disorder (PTSD) have proven to be insufficient for diagnosis. We have reviewed clinical and preclinical imaging techniques as well as molecular, cellular, and behavioral indicators for PTSD.

TOWARDS AN AUTOMATIC DIAGNOSIS SYSTEM FOR LUMBAR DISC HERNIATION: THE SIGNIFICANCE OF LOCAL SUBSET FEATURE SELECTION

Herniation in the lumbar area is one of the most common diseases which results in lower back pain (LBP) causing discomfort and inconvenience in the patients’ daily lives. A computer aided diagnosis (CAD) system can be of immense benefit as it generates diagnostic results within a short time while increasing precision of diagnosis and eliminating human errors. We have proposed a new method for automatic diagnosis of lumbar disc herniation based on clinical MRI data. We use T2-W sagittal and myelograph images. The presented method has been applied on 30 clinical cases, each containing 7 discs (210 lumbar discs) for the herniation diagnosis. We employ Otsu thresholding method to extract the spinal cord from MR images of lumbar disc. A third order polynomial is then aligned on the extracted spinal cords, and by the end of preprocessing stage, all the T2-W sagittal images will have been prepared for specifying disc boundary and labeling. Having extracted an ROI for each disc, we proceed to use intensity and shape features for classification. The extracted features have been selected by Local Subset Feature Selection. The results demonstrated 91.90%, 92.38% and 95.23% accuracy for artificial neural network, K-nearest neighbor and support vector machine (SVM) classifiers respectively, indicating the superiority of the proposed method to those mentioned in similar studies.

Reduced motor cortex inhibition and a 'cognitive-first' prioritisation strategy for older adults during dual-tasking

It is well established that older adults are less able to perform attentionally demanding motor tasks, placing them at greater risk of accident-related injury. The primary purpose of this study was to investigate whether the interplay between prefrontal and motor cortex activity could predict such age-related performance deficits. Using a dual-task (DT) paradigm, 15 younger and 15 older adults participated in experiment 1, where brain activity was simultaneously measured using functional near infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS). Experiment 1 demonstrated poorer performance for the older group across a range of DTs combining visuomotor arm tracking with a secondary cognitive or motor task. Interestingly however, older adults' DT performance error was isolated to the motor component of DTs. TMS data revealed reduced motor cortex (M1) inhibition during DTs for older adults, and a trend for this correlating with poorer performance. In contrast, poorer performing younger adults showed significantly higher M1 inhibition. Experiment 2 was conducted given a high amount of movement artifact in experiment 1 fNIRS data. Using fNIRS to measure prefrontal, premotor, and motor cortex activity in an additional 15 older adults, we found no evidence of an interplay between these regions predicting DT performance. Nevertheless, performance data replicated experiment 1 in showing that DT error was isolated to motor tasks in older adults, with no significant cognitive task error. Overall, this study shows that older adults seemed to adopt a 'cognitive-first' prioritisation strategy during the DTs involved in our study, and that deficits in DT performance may be related to the modulation of M1 inhibitory mechanisms. We propose that clinicians advise older adults to allocate greater attention to motor tasks during activities where they may be at risk of accident-related injury.