Visualizing Hyperactivation in Neurodegeneration Based on Prefrontal Oxygenation: A Comparative Study of Mild Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls

Alterations in brain functional connectivity in patients with mild cognitive impairment: A systematic review and meta-analysis of functional near-infrared spectroscopy studies

Emerging evidences suggest that cognitive deficits in individuals with mild cognitive impairment (MCI) are associated with disruptions in brain functional connectivity (FC). This systematic review and meta-analysis aimed to comprehensively evaluate alterations in FC between MCI individuals and healthy control (HC) using functional near-infrared spectroscopy (fNIRS). Thirteen studies were included in qualitative analysis, with two studies synthesized for quantitative meta-analysis. Overall, MCI patients exhibited reduced resting-state FC, predominantly in the prefrontal, parietal, and occipital cortex. Meta-analysis of two studies revealed a significant reduction in resting-state FC from the right prefrontal to right occipital cortex (standardized mean difference [SMD] = -.56; p < .001), left prefrontal to left occipital cortex (SMD = -.68; p < .001), and right prefrontal to left occipital cortex (SMD = -.53; p < .001) in MCI patients compared to HC. During naming animal-walking task, MCI patients exhibited enhanced FC in the prefrontal, motor, and occipital cortex, whereas a decrease in FC was observed in the right prefrontal to left prefrontal cortex during calculating-walking task. In working memory tasks, MCI predominantly showed increased FC in the medial and left prefrontal cortex. However, a decreased in prefrontal FC and a shifted in distribution from the left to the right prefrontal cortex were noted in MCI patients during a verbal frequency task. In conclusion, fNIRS effectively identified abnormalities in FC between MCI and HC, indicating disrupted FC as potential markers for the early detection of MCI. Future studies should investigate the use of task- and region-specific FC alterations as a sensitive biomarker for MCI.

A comprehensive research setup for monitoring Alzheimer's disease using EEG, fNIRS, and Gait analysis

Alzheimer's disease (AD) has a detrimental impact on brain function, affecting various aspects such as cognition, memory, language, and motor skills. Previous research has dominantly used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to individually measure brain signals or combine the two methods to target specific brain functions. However, comprehending Alzheimer's disease requires monitoring various brain functions rather than focusing on a single function. This paper presents a comprehensive research setup for a monitoring platform for AD. The platform incorporates a 32-channel dry electrode EEG, a custom-built four-channel fNIRS, and gait monitoring using a depth camera and pressure sensor. Various tasks are employed to target multiple brain functions. The paper introduced the detailed instrumentation of the fNIRS system, which measures the prefrontal cortex, outlines the experimental design targeting various brain functioning programmed in BCI2000 for visualizing EEG signals synchronized with experimental stimulation, and describes the gait monitoring hardware and software and protocol design. The ultimate goal of this platform is to develop an easy-to-perform brain and gait monitoring method for elderly individuals and patients with Alzheimer's disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00306-7.

Correlation Between Prefrontal Functional Connectivity and the Degree of Cognitive Impairment in Alzheimer's Disease: A Functional Near-Infrared Spectroscopy Study

Background

The development of Alzheimer's disease (AD) can be divided into subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia. Early recognition of pre-AD stages may slow the progression of dementia.

Objective

This study aimed to explore functional connectivity (FC) changes of the brain prefrontal cortex (PFC) in AD continuum using functional near-infrared spectroscopy (fNIRS), and to analyze its correlation with cognitive function.

Methods

All participants underwent 48-channel fNIRS at resting-state. Based on Brodmann partitioning, the PFC was divided into eight subregions. The NIRSIT Analysis Tool (v3.7.5) was used to analyze mean ΔHbO2 and FC. Spearman correlation analysis was used to examine associations between FC and cognitive function.

Results

Compared with HC group, the mean ΔHbO2 and FC were different between multiple subregions in the AD continuum. Both mean ΔHbO2 in the left dorsolateral PFC and average FC decreased sequentially from SCD to MCI to AD groups. Additionally, seven pairs of subregions differed in FC among the three groups: the differences between the MCI and SCD groups were in heterotopic connectivity; the differences between the AD and SCD groups were in left intrahemispheric and homotopic connectivity; whereas the MCI and AD groups differed only in homotopic connectivity. Spearman correlation results showed that FCs were positively correlated with cognitive function.

Conclusions

These results suggest that the left dorsolateral PFC may be the key cortical impairment in AD. Furthermore, there are different resting-state prefrontal network patterns in AD continuum, and the degree of cognitive impairment is positively correlated with reduced FC strength.

Correlation between cerebral hemodynamic functional near-infrared spectroscopy and positron emission tomography for assessing mild cognitive impairment and Alzheimer's disease: An exploratory study

This study was performed to investigate the usefulness of functional near-infrared spectroscopy (fNIRS) by conducting a comparative analysis of hemodynamic activation detected by fNIRS and positron emission tomography (PET) and magnetic resonance imaging (MRI) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Participants were divided into four groups: the subjective memory impairment (SMI), amnestic MCI (aMCI), non-amnestic MCI (naMCI), and AD groups. We recorded the hemodynamic response during the semantic verbal fluency task (SVFT) using a commercial wireless continuous-wave NIRS system. The correlation between the parameters of the neuroimaging assessments among the groups was analyzed. Region of interest-based comparisons showed that the four groups had significantly different hemodynamic responses during SVFT in the bilateral dorsolateral prefrontal cortex (DLPFC). The linear mixed effect model result indicates that the mean ΔHbO2 from the bilateral DLPFC regions showed a significant positive correlation to the overall FDG-PET after controlling for age and group differences in the fNIRS signals. Amyloid PET signals tended to better differentiate the AD group from other groups, and fNIRS signals tended to better differentiate the SMI group from other groups. In addition, a comparison between the group pairs revealed a mirrored pattern between the hippocampal volume and hemodynamic response in the DLPFC. The hemodynamic response detected by fNIRS showed a significant correlation with metabolic and anatomical changes associated with disease progression. Therefore, fNIRS may be considered as a screening tool to predict the hemodynamic and metabolic statuses of the brain in patients with MCI and AD.

Illuminating neurodegeneration: a future perspective on near-infrared spectroscopy in dementia research

SIGNIFICANCE

Dementia presents a global healthcare crisis, and neuroimaging is the main method for developing effective diagnoses and treatments. Yet currently, there is a lack of sensitive, portable, and low-cost neuroimaging tools. As dementia is associated with vascular and metabolic dysfunction, near-infrared spectroscopy (NIRS) has the potential to fill this gap.

AIM

This future perspective aims to briefly review the use of NIRS in dementia to date and identify the challenges involved in realizing the full impact of NIRS for dementia research, including device development, study design, and data analysis approaches.

APPROACH

We briefly appraised the current literature to assess the challenges, giving a critical analysis of the methods used. To assess the sensitivity of different NIRS device configurations to the brain with atrophy (as is common in most forms of dementia), we performed an optical modeling analysis to compare their cortical sensitivity.

RESULTS

The first NIRS dementia study was published in 1996, and the number of studies has increased over time. In general, these studies identified diminished hemodynamic responses in the frontal lobe and altered functional connectivity in dementia. Our analysis showed that traditional (low-density) NIRS arrays are sensitive to the brain with atrophy (although we see a mean decrease of 22% in the relative brain sensitivity with respect to the healthy brain), but there is a significant improvement (a factor of 50 sensitivity increase) with high-density arrays.

CONCLUSIONS

NIRS has a bright future in dementia research. Advances in technology - high-density devices and intelligent data analysis-will allow new, naturalistic task designs that may have more clinical relevance and increased reproducibility for longitudinal studies. The portable and low-cost nature of NIRS provides the potential for use in clinical and screening tests.

Current opinions on the present and future use of functional near-infrared spectroscopy in psychiatry

Functional near-infrared spectroscopy (fNIRS) is an optical imaging technique for assessing human brain activity by noninvasively measuring the fluctuation of cerebral oxygenated- and deoxygenated-hemoglobin concentrations associated with neuronal activity. Owing to its superior mobility, low cost, and good tolerance for motion, the past few decades have witnessed a rapid increase in the research and clinical use of fNIRS in a variety of psychiatric disorders. In this perspective article, we first briefly summarize the state-of-the-art concerning fNIRS research in psychiatry. In particular, we highlight the diverse applications of fNIRS in psychiatric research, the advanced development of fNIRS instruments, and novel fNIRS study designs for exploring brain activity associated with psychiatric disorders. We then discuss some of the open challenges and share our perspectives on the future of fNIRS in psychiatric research and clinical practice. We conclude that fNIRS holds promise for becoming a useful tool in clinical psychiatric settings with respect to developing closed-loop systems and improving individualized treatments and diagnostics.

Can the fNIRS-derived neural biomarker better discriminate mild cognitive impairment than a neuropsychological screening test?

Introduction

Early detection of mild cognitive impairment (MCI), a pre-clinical stage of Alzheimer's disease (AD), has been highlighted as it could be beneficial to prevent progression to AD. Although prior studies on MCI screening have been conducted, the optimized detection way remain unclear yet. Recently, the potential of biomarker for MCI has gained a lot of attention due to a relatively low discriminant power of clinical screening tools.

Methods

This study evaluated biomarkers for screening MCI by performing a verbal digit span task (VDST) using functional near-infrared spectroscopy (fNIRS) to measure signals from the prefrontal cortex (PFC) from a group of 84 healthy controls and 52 subjects with MCI. The concentration changes of oxy-hemoglobin (HbO) were explored during the task in subject groups.

Results

Findings revealed that significant reductions in HbO concentration were observed in the PFC in the MCI group. Specially, the mean of HbO (mHbO) in the left PFC showed the highest discriminant power for MCI, which was higher than that of the Korean version of montreal cognitive assessment (MoCA-K) widely used as a screening tool for MCI. Furthermore, the mHbO in the PFC during the VDST was identified to be significantly correlated to the MoCA-K scores.

Discussion

These findings shed new light on the feasibility and superiority of fNIRS-derived neural biomarker for screening MCI.

Novel diagnostic tools for identifying cognitive impairment using olfactory-stimulated functional near-infrared spectroscopy: patient-level, single-group, diagnostic trial

Introduction

Basic studies suggest that olfactory dysfunction and functional near-infrared spectroscopy (fNIRS) can be used as tools for the diagnosis of mild cognitive impairment (MCI); however, real-world evidence is lacking. We investigated the potential diagnostic efficacy of olfactory-stimulated fNIRS for early detection of MCI and/or Alzheimer disease (AD).

Methods

We conducted a patient-level, single-group, diagnostic interventional trial involving elderly volunteers (age >60 years) suspected of declining cognitive function. Patients received open-label olfactory-stimulated fNIRS for measurement of oxygenation difference in the orbitofrontal cortex. All participants underwent amyloid PET, MRI, Mini-Mental State Examination (MMSE), and Seoul Neuropsychological Screening Battery (SNSB).

Results

Of 97 subjects, 28 (28.9%) were cognitively normal, 32 (33.0%) had preclinical AD, 21 (21.6%) had MCI, and 16 (16.5%) had AD. Olfactory-stimulated oxygenation differences in the orbitofrontal cortex were associated with cognitive impairment; the association was more pronounced with cognitive severity. Olfactory-stimulated oxygenation difference was associated with MMSE (adjusted β [aβ] 1.001; 95% CI 0.540−1.463), SNSB language and related function (aβ, 1.218; 95% CI, 0.020−2.417), SNSB memory (aβ, 1.963; 95% CI, 0.841−3.084), SNSB frontal/executive function (aβ, 1.715; 95% CI, 0.401−3.029) scores, standard uptake value ratio from amyloid PET (aβ, −10.083; 95% CI, −19.063 to −1.103), and hippocampal volume from MRI (aβ, 0.002; 95% CI, 0.001−0.004). Olfactory-stimulated oxygenation difference in the orbitofrontal cortex was superior in diagnosing MCI and AD (AUC, 0.909; 95% CI, 0.848−0.971), compared to amyloid PET (AUC, 0.793; 95% CI, 0.694−0.893) or MRI (AUC, 0.758; 95% CI, 0.644−0.871).

Discussion

Our trial showed that olfactory-stimulated oxygenation differences in the orbitofrontal cortex detected by fNIRS were associated with cognitive impairment and cognitive-related objectives. This novel approach may be a potential diagnostic tool for patients with MCI and/or AD.

Trial registration

CRIS number, KCT0006197.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13195-022-00978-w.

Screening for Alzheimer's disease using prefrontal resting-state functional near-infrared spectroscopy

INTRODUCTION

Alzheimer's disease (AD) is neurodegenerative dementia that causes neurovascular dysfunction and cognitive impairment. Currently, 50 million people live with dementia worldwide, and there are nearly 10 million new cases every year. There is a need for relatively less costly and more objective methods of screening and early diagnosis.

METHODS

Functional near-infrared spectroscopy (fNIRS) systems are a promising solution for the early Detection of AD. For a practical clinically relevant system, a smaller number of optimally placed channels are clearly preferable. In this study, we investigated the number and locations of the best-performing fNIRS channels measuring prefrontal cortex activations. Twenty-one subjects diagnosed with AD and eighteen healthy controls were recruited for the study.

RESULTS

We have shown that resting-state fNIRS recordings from a small number of prefrontal locations provide a promising methodology for detecting AD and monitoring its progression. A high-density continuous-wave fNIRS system was first used to verify the relatively lower hemodynamic activity in the prefrontal cortical areas observed in patients with AD. By using the episode averaged standard deviation of the oxyhemoglobin concentration changes as features that were fed into a Support Vector Machine; we then showed that the accuracy of subsets of optical channels in predicting the presence and severity of AD was significantly above chance. The results suggest that AD can be detected with a 0.76 sensitivity score and a 0.68 specificity score while the severity of AD could be detected with a 0.75 sensitivity score and a 0.72 specificity score with ≤5 channels.

DISCUSSION

These scores suggest that fNIRS is a viable technology for conveniently detecting and monitoring AD as well as investigating underlying mechanisms of disease progression.

Decreased Hemodynamic Responses in Left Parietal Lobule and Left Inferior Parietal Lobule in Older Adults with Mild Cognitive Impairment: A Near-Infrared Spectroscopy Study

Background: The brain activation patterns of mild cognitive impairment (MCI) are still unclear and they involve multiple brain regions. Most previous studies have focused on abnormal activation in the frontal and temporal lobes, with few investigating the entire brain. Objective: To identify and compare the changes in cerebral hemodynamics and abnormal activation patterns in the entire brain of MCI patients and healthy older adults. Methods: Patients with MCI (n = 22) and healthy controls (HC, n = 34) matched by age, education levels, sex, and mental state were enrolled. They performed the same letter and category verbal fluency test (VFT) tasks while their behavioral performance and global cerebral hemodynamics were analyzed. Results: The performance during the category VFT task was significantly better than that during the letter VFT task across all participants (HC: correct: p <  0.001; intrusions: p <  0.001; MCI: correct: p <  0.001; intrusions: p <  0.001). The number of correct words during the letter and category VFT tasks was significantly higher in the HC group than in the MCI group (p <  0.001). The deoxygenated-hemoglobin (HbR) concentrations in the left parietal lobule (p = 0.0352) and left inferior parietal lobule (p = 0.0314) were significantly different during the category VFT task. Conclusion: The differences between HC and MCI groups were greater in the category task. The HbR concentration was more sensitive for the category VFT task and concentration changes in the left parietal lobule and left inferior parietal lobule may be useful for clinical screening and application; thus, they deserve more attention.

Magnetic Resonance Imaging and Its Clinical Correlation in Spinocerebellar Ataxia Type 3: A Systematic Review

Background

Spinocerebellar ataxia type 3 (SCA3) is a complex cerebrocerebellar disease primarily characterized by ataxia symptoms alongside motor and cognitive impairments. The heterogeneous clinical presentation of SCA3 necessitates correlations between magnetic resonance imaging (MRI) and clinical findings in reflecting progressive disease changes. At present, an attempt to systematically examine the brain-behavior relationship in SCA3, specifically, the correlation between MRI and clinical findings, is lacking.

Objective

We investigated the association strength between MRI abnormality and each clinical symptom to understand the brain-behavior relationship in SCA3.

Methods

We conducted a systematic review on Medline and Scopus to review studies evaluating the brain MRI profile of SCA3 using structural MRI (volumetric, voxel-based morphometry, surface analysis), magnetic resonance spectroscopy, and diffusion tensor imaging, including their correlations with clinical outcomes.

Results

Of 1,767 articles identified, 29 articles met the eligibility criteria. According to the National Institutes of Health quality assessment tool for case-control studies, all articles were of excellent quality. This systematic review found that SCA3 neuropathology contributes to widespread brain degeneration, affecting the cerebellum and brainstem. The disease gradually impedes the cerebral cortex and basal ganglia in the late stages of SCA3. Most findings reported moderate correlations (r = 0.30–0.49) between MRI features in several regions and clinical findings. Regardless of the MRI techniques, most studies focused on the brainstem and cerebellum.

Conclusions

Clinical findings suggest that rather than individual brain regions, the connectivity between different brain regions in distributed networks (i.e., cerebellar-cerebral network) may be responsible for motor and neurocognitive function in SCA3. This review highlights the importance of evaluating the progressive changes of the cerebellar-cerebral networks in SCA3 patients, specifically the functional connectivity. Given the relative lack of knowledge about functional connectivity on SCA3, future studies should investigate possible functional connectivity abnormalities in SCA3 using fMRI.

Prefrontal Activation During Effortful Processing Differentiates Memory Abilities in Adults with Memory Complaints

Background: Identifying individuals at increased risks for developing Alzheimer’s disease (AD) is crucial for early intervention. Memory complaints are associated with brain abnormalities characteristic of AD in cognitively normal older people. However, the utility of memory complaints for predicting mild cognitive impairment (MCI) or AD onset remains controversial, likely due to the heterogeneous nature of this construct. Objective: We investigated whether prefrontal oxygenation changes measured by functional near-infrared spectroscopy (fNIRS) during an arduous cognitive task, previously shown to be associated with the AD syndrome, could differentiate memory abilities among individuals with memory complaints. Episodic memory performance was adopted as a proxy for MCI/AD risks since it has been shown to predict AD progression across stages. Methods: Thirty-six adults self-reporting memory complaints in the absence of memory impairment completed a verbal list learning test and underwent a digit n-back paradigm with an easy (0-back) and a difficult (2-back) condition. K-means clustering was applied to empirically derive memory complaint subgroups based on fNIRS-based prefrontal oxygenation changes during the effortful 2-back task. Results: Cluster analysis revealed two subgroups characterized by high (n = 12) and low (n = 24) bilateral prefrontal activation during the 2-back but not a 0-back task. The low activation group was significantly less accurate across the n-back task and recalled significantly fewer words on the verbal memory test compared to the high activation group. Conclusion: fNIRS may have the potential to differentiate verbal memory abilities in individuals with self-reported memory complaints.

A Link between Handgrip Strength and Executive Functioning: A Cross-Sectional Study in Older Adults with Mild Cognitive Impairment and Healthy Controls

Older adults with amnestic mild cognitive impairment (aMCI) who in addition to their memory deficits also suffer from frontal-executive dysfunctions have a higher risk of developing dementia later in their lives than older adults with aMCI without executive deficits and older adults with non-amnestic MCI (naMCI). Handgrip strength (HGS) is also correlated with the risk of cognitive decline in the elderly. Hence, the current study aimed to investigate the associations between HGS and executive functioning in individuals with aMCI, naMCI and healthy controls. Older, right-handed adults with amnestic MCI (aMCI), non-amnestic MCI (naMCI), and healthy controls (HC) conducted a handgrip strength measurement via a handheld dynamometer. Executive functions were assessed with the Trail Making Test (TMT A&B). Normalized handgrip strength (nHGS, normalized to Body Mass Index (BMI)) was calculated and its associations with executive functions (operationalized through z-scores of TMT B/A ratio) were investigated through partial correlation analyses (i.e., accounting for age, sex, and severity of depressive symptoms). A positive and low-to-moderate correlation between right nHGS (rp (22) = 0.364; p = 0.063) and left nHGS (rp (22) = 0.420; p = 0.037) and executive functioning in older adults with aMCI but not in naMCI or HC was observed. Our results suggest that higher levels of nHGS are linked to better executive functioning in aMCI but not naMCI and HC. This relationship is perhaps driven by alterations in the integrity of the hippocampal-prefrontal network occurring in older adults with aMCI. Further research is needed to provide empirical evidence for this assumption.

Acupuncture enhances brain function in patients with mild cognitive impairment: evidence from a functional-near infrared spectroscopy study

Mild cognitive impairment (MCI) is a precursor to Alzheimer’s disease. It is imperative to develop a proper treatment for this neurological disease in the aging society. This observational study investigated the effects of acupuncture therapy on MCI patients. Eleven healthy individuals and eleven MCI patients were recruited for this study. Oxy- and deoxy-hemoglobin signals in the prefrontal cortex during working-memory tasks were monitored using functional near-infrared spectroscopy. Before acupuncture treatment, working-memory experiments were conducted for healthy control (HC) and MCI groups (MCI-0), followed by 24 sessions of acupuncture for the MCI group. The acupuncture sessions were initially carried out for 6 weeks (two sessions per week), after which experiments were performed again on the MCI group (MCI-1). This was followed by another set of acupuncture sessions that also lasted for 6 weeks, after which the experiments were repeated on the MCI group (MCI-2). Statistical analyses of the signals and classifications based on activation maps as well as temporal features were performed. The highest classification accuracies obtained using binary connectivity maps were 85.7% HC vs. MCI-0, 69.5% HC vs. MCI-1, and 61.69% HC vs. MCI-2. The classification accuracies using the temporal features mean from 5 seconds to 28 seconds and maximum (i.e, max(5:28 seconds)) values were 60.6% HC vs. MCI-0, 56.9% HC vs. MCI-1, and 56.4% HC vs. MCI-2. The results reveal that there was a change in the temporal characteristics of the hemodynamic response of MCI patients due to acupuncture. This was reflected by a reduction in the classification accuracy after the therapy, indicating that the patients’ brain responses improved and became comparable to those of healthy subjects. A similar trend was reflected in the classification using the image feature. These results indicate that acupuncture can be used for the treatment of MCI patients.

Refined prefrontal working memory network as a neuromarker for Alzheimer's disease

Detecting Alzheimer's disease (AD) is an important step in preventing pathological brain damage. Working memory (WM)-related network modulation can be a pathological feature of AD, but is usually modulated by untargeted cognitive processes and individual variance, resulting in the concealment of this key information. Therefore, in this study, we comprehensively investigated a new neuromarker, named "refined network," in a prefrontal cortex (PFC) that revealed the pathological features of AD. A refined network was acquired by removing unnecessary variance from the WM-related network. By using a functional near-infrared spectroscopy (fNIRS) device, we evaluated the reliability of the refined network, which was identified from the three groups classified by AD progression: healthy people (N=31), mild cognitive impairment (N=11), and patients with AD (N=18). As a result, we identified edges with significant correlations between cognitive functions and groups in the dorsolateral PFC. Moreover, the refined network achieved a significantly correlating metric with neuropsychological test scores, and a remarkable three-class classification accuracy (95.0%). These results implicate the refined PFC WM-related network as a powerful neuromarker for AD screening.

Running exercise protects spinophilin-immunoreactive puncta and neurons in the medial prefrontal cortex of APP/PS1 transgenic mice

The medial prefrontal cortex (mPFC) is thought to be closely associated with emotional processes, decision making, and memory. Previous studies have identified the prefrontal cortex as one of the most vulnerable brain regions in Alzheimer's disease (AD). Running exercise has widely been recognized as a simple and effective method of physical activity that enhances brain function and slows the progression of AD. However, the effect of exercise on the mPFC of AD is unclear. To address these issues, we investigated the effects of 4 months of exercise on the numbers of spinophilin-immunoreactive puncta and neurons in the mPFC of 12-month-old APPswe/PSEN1dE9 (APP/PS1) transgenic AD model mice using stereological methods. The spatial learning and memory abilities of mice were tested using the Morris water maze. Four months of running exercise delayed declines in spatial learning and memory abilities. The stereological results showed significantly lower numbers of spinophilin-immunoreactive puncta and neurons in the mPFC of APP/PS1 mice than in the wild-type control group. The numbers of spinophilin-immunoreactive puncta and neurons in the mPFC of running APP/PS1 mice were significantly greater than those in the APP/PS1 control mice. In addition, running-induced improvements in spatial learning and memory were significantly associated with running-induced increases in spinophilin-immunoreactive puncta and neurons numbers in the mPFC. Running exercise could delay the loss of spinophilin-immunoreactive puncta and neurons in the mPFC of APP/PS1 mice. This finding might provide an important structural basis for exercise-induced improvements in the spatial learning and memory abilities of individuals with AD.

Cerebral blood flow response rate to task-activation using a novel method can discriminate cognitive impairment from healthy aging

Objective.A new method to classify individuals as 'responders' to task-activated cerebral blood flow velocity (CBFv) has recently been developed. This study investigated whether CBFv response rate to task-activation is affected by Alzheimer's disease (AD) and mild cognitive impairment (MCI).Approach.The 95th thresholds for cross correlation function peak and variance ratio were derived from 270 unstimulated, healthy hemispheres, and were used to classify the presence of a response to task-activation. Thresholds were applied to five cognitive tasks (attention, verbal fluency, language, visuospatial, memory) in CBFv data from 30 healthy older adults (HC), 35 AD and 22 MCI participants. Cumulative response rate (CRR) was calculated from the sum of responses across five tasks, for both hemispheres. Area under the curve (AUC) was derived from receiver operating characteristic (ROC) curve analysis.Main results. The number of responders differed significantly between tasks (p < 0.005) and diagnostic groups (p = 0.011). On post hoc tests there were more responders in the visuospatial (79%-90%) compared to fluency (45%-80%), language (50%-77%), and memory (44%-70%) tasks bilaterally, and responders were greater in the HC (70%) compared to AD (41%) and MCI (23%) groups to at least eight out of ten tasks. At an optimal threshold of 7.5 out of 10 responses, the AUC-ROC distinguished HC from AD and MCI with a; sensitivity 66% and specificity 70% (AUC = 0.72).Significance. Using a novel method to classify responders to cognitive task-activation, HC demonstrated a higher CRR than those with MCI or AD, and a threshold of <8 responses distinguished healthy ageing from dementia.

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.

Shedding light on neuroscience: Two decades of functional near-infrared spectroscopy applications and advances from a bibliometric perspective

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical brain-imaging technique that detects changes in hemoglobin concentration in the cerebral cortex. fNIRS devices are safe, silent, portable, robust against motion artifacts, and have good temporal resolution. fNIRS is reliable and trustworthy, as well as an alternative and a complement to other brain-imaging modalities, such as electroencephalography or functional magnetic resonance imaging. Given these advantages, fNIRS has become a well-established tool for neuroscience research, used not only for healthy cortical activity but also as a biomarker during clinical assessment in individuals with schizophrenia, major depressive disorder, bipolar disease, epilepsy, Alzheimer's disease, vascular dementia, and cancer screening. Owing to its wide applicability, studies on fNIRS have increased exponentially over the last two decades. In this study, scientific publications indexed in the Web of Science databases were collected and a bibliometric-type methodology was developed. For this purpose, a comprehensive science mapping analysis, including top-ranked authors, journals, institutions, countries, and co-occurring keywords network, was conducted. From a total of 2310 eligible documents, 6028 authors and 531 journals published fNIRS-related papers, Fallgatter published the highest number of articles and was the most cited author. University of Tübingen in Germany has produced the most trending papers since 2000. USA was the most prolific country with the most active institutions, followed by China, Japan, Germany, and South Korea. The results also revealed global trends in emerging areas of research, such as neurodevelopment, aging, and cognitive and emotional assessment.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

BACKGROUND

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors.

OBJECTIVE

This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals.

METHODS

In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration.

RESULTS

There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input.

CONCLUSION

The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex

Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

A Systematic Review of Cerebral Functional Near-Infrared Spectroscopy in Chronic Neurological Diseases-Actual Applications and Future Perspectives

BACKGROUND

The management of people affected by age-related neurological disorders requires the adoption of targeted and cost-effective interventions to cope with chronicity. Therapy adaptation and rehabilitation represent major targets requiring long-term follow-up of neurodegeneration or, conversely, the promotion of neuroplasticity mechanisms. However, affordable and reliable neurophysiological correlates of cerebral activity to be used throughout treatment stages are often lacking. The aim of this systematic review is to highlight actual applications of functional Near-Infrared Spectroscopy (fNIRS) as a versatile optical neuroimaging technology for investigating cortical hemodynamic activity in the most common chronic neurological conditions.

METHODS

We reviewed studies investigating fNIRS applications in Parkinson's Disease (PD), Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) as those focusing on motor and cognitive impairment in ageing and Multiple Sclerosis (MS) as the most common chronic neurological disease in young adults. The literature search was conducted on NCBI PubMed and Web of Science databases by PRISMA guidelines.

RESULTS

We identified a total of 63 peer-reviewed articles. The AD spectrum is the most investigated pathology with 40 articles ranging from the traditional monitoring of tissue oxygenation to the analysis of functional resting-state conditions or cognitive functions by means of memory and verbal fluency tasks. Conversely, applications in PD (12 articles) and MS (11 articles) are mainly focused on the characterization of motor functions and their association with dual-task conditions. The most investigated cortical area is the prefrontal cortex, since reported to play an important role in age-related compensatory mechanism and neurofunctional changes associated to these chronic neurological conditions. Interestingly, only 9 articles applied a longitudinal approach.

CONCLUSION

The results indicate that fNIRS is mainly employed for the cross-sectional characterization of the clinical phenotypes of these pathologies, whereas data on its utility for longitudinal monitoring as surrogate biomarkers of disease progression and rehabilitation effects are promising but still lacking.

Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease

While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.

Validating a functional near-infrared spectroscopy diagnostic paradigm for Major Depressive Disorder

Reduced haemodynamic response in the frontotemporal cortices of patients with major depressive disorder (MDD) has been demonstrated using functional near-infrared spectroscopy (fNIRS). Most notably, changes in cortical oxy-haemoglobin during a Japanese phonetic fluency task can differentiate psychiatric patients from healthy controls (HC). However, this paradigm has not been validated in the English language. Therefore, the present work aimed to distinguish patients with MDD from HCs, using haemodynamic response measured during an English letter fluency task. One hundred and five HCs and 105 patients with MDD took part in this study. NIRS signals during the verbal fluency task (VFT) was acquired using a 52-channel system, and changes in oxy-haemoglobin in the frontal and temporal regions were quantified. Depression severity, psychosocial functioning, pharmacotherapy and psychiatric history were noted. Patients with MDD had smaller changes in oxy-haemoglobin in the frontal and temporal cortices than HCs. In both regions of interest, oxy-haemoglobin was not associated with any of the clinical variables studied. 75.2% and 76.5% of patients with MDD were correctly classified using frontal and temporal region oxy-haemoglobin, respectively. Haemodynamic response measured by fNIRS during an English letter fluency task is a promising biomarker for MDD.

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.

Functional near-infrared spectroscopy reveals decreased resting oxygenation levels and task-related oxygenation changes in mild cognitive impairment and dementia: A systematic review

Nuclear medicine and functional magnetic resonance imaging studies have shown that mild cognitive impairment (MCI) and dementia, including Alzheimer's disease (AD), are characterized by changes in cerebral blood flow. This article reviews the application of an alternative method, functional near-infrared spectroscopy (fNIRS), to the study of cerebral oxygenation changes in MCI and dementia. We synthesized 36 fNIRS studies that examined hemodynamic changes during both the resting state and the execution of tasks of word retrieval, memory, motor control, and visuospatial perception in MCI and dementia. This qualitative review reveals that (amnestic) MCI and AD patients have disrupted frontal and long-range connectivity in the resting state compared to individuals with normal cognition (NC). These patients also exhibit reduced frontal oxygenation changes in various cognitive domains. The review also shows that disrupted connectivity and decreased frontal oxygenation levels/changes are more severe in AD than in (amnestic) MCI, confirming that MCI is an intermediate stage between NC and dementia. Thus, there is reduced resting frontal perfusion, which is greater than expected for age, and a lack of frontal compensatory responses to functional decline across cognitive operations (i.e., word retrieval and memory functioning) in MCI and AD. These indices might potentially serve as perfusion- or oxygenation-based biomarkers for MCI/dementia. To expand the utility of fNIRS for MCI and dementia, further studies that measure tissue oxygenation in a wider range of brain regions and cognitive domains, compare different MCI and dementia types, and correlate changes in cerebral oxygenation over time with disease progression are needed.

An EEG-fNIRS hybridization technique in the four-class classification of alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is projected to become one of the most expensive diseases in modern history, and yet diagnostic uncertainties exist that can only be confirmed by postmortem brain examination. Machine Learning (ML) algorithms have been proposed as a feasible alternative to the diagnosis of several neurological diseases and disorders, such as AD. An ideal ML-derived diagnosis should be inexpensive and noninvasive while retaining the accuracy and versatility that make ML techniques desirable for medical applications.

NEW METHODS

Two portable modalities, Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) have been widely employed in constructing hybrid classification models to compensate for each other's weaknesses. In this study, we present a hybrid EEG-fNIRS model for classifying four classes of subjects including one healthy control (HC) group, one mild cognitive impairment (MCI) group, and, two AD patient groups. A concurrent EEG-fNIRS setup was used to record data from 29 subjects during a random digit encoding-retrieval task. EEG-derived and fNIRS-derived features were sorted using a Pearson correlation coefficient-based feature selection (PCCFS) strategy and then fed into a linear discriminant analysis (LDA) classifier to evaluate their performance.

RESULTS

The hybrid EEG-fNIRS feature set was able to achieve a higher accuracy (79.31 %) by integrating their complementary properties, compared to using EEG (65.52 %) or fNIRS alone (58.62 %). Moreover, our results indicate that the right prefrontal and left parietal regions are associated with the progression of AD.

COMPARISON WITH EXISTING METHODS

Our hybrid and portable system provided enhanced classification performance in multi-class classification of AD population.

CONCLUSIONS

These findings suggest that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process.

Functional Near-Infrared Spectroscopy to Study Cerebral Hemodynamics in Older Adults During Cognitive and Motor Tasks: A Review

The integrity of the frontal areas of the brain, specifically the prefrontal cortex, are critical to preserve cognition and mobility in late life. Prefrontal cortex regions are involved in executive functions and gait control and have been related to the performance of dual-tasks. Dual-task performance assessment may help identify older adults at risk of negative health outcomes. As an alternative to neuroimaging techniques that do not allow assessment during actual motion, functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive technique that can assess neural activation through the measurement of cortical oxygenated and deoxygenated hemoglobin levels, while the person is performing a motor task in a natural environment as well as during cognitive tasks. The aim of this review was to describe the use of fNIRS to study frontal lobe hemodynamics during cognitive, motor and dual-tasks in older adults. From the 46 included publications, 20 studies used only cognitive tasks, three studies used motor tasks and 23 used dual-tasks. Our findings suggest that fNIRS detects changes in frontal activation in older adults (cognitively healthy and mild cognitive impairment), especially while performing cognitive and dual-tasks. In both the comparison between older and younger adults, and in people with different neurological conditions, compared to healthier controls, the prefrontal cortex seems to experience a higher activation, which could be interpreted in the context of proposed neural inefficiency and limited capacity models. Further research is needed to establish standardized fNIRS protocols, study the cerebral hemodynamic in different neurological and systemic conditions that might influence cortical activation and explore its role in predicting incident health outcomes such as dementia.

Beta wave enhancement neurofeedback improves cognitive functions in patients with mild cognitive impairment: A preliminary pilot study

BACKGROUND

Mild cognitive impairment (MCI) is a symptom characterizing cognitive decline and a transitional state between normal aging and dementia; however, there is no definitive diagnosis and treatment for MCI. Neurofeedback (NF), which is a training mechanism that employs operant conditioning to regulate brain activity, has been increasingly investigated concerning its beneficial effects for dementia and MCI.

METHODS

This study investigated cognitive improvement and hemodynamic changes in the prefrontal cortex (PFC) following NF training in patients with MCI. Five patients with MCI received NF training for enhanced beta band activity in the dorsolateral PFC-16 sessions for 8 weeks-with each session divided into 9 5-minute trials. The primary outcome measure was a cognitive assessment tool: the Korean version of the Montreal Cognitive Assessment. The secondary outcome measures were the Central Nervous System Vital Signs for neurocognitive testing, hemodynamic changes using functional near-infrared spectroscopy in the PFC during a working-memory task, and Beck Depression Inventory scores.

RESULTS

After completing the training, patients' cognitive function significantly improved in domains such as composite memory, cognitive flexibility, complex attention, reaction time, and executive function. Increased electroencephalogram beta power was observed over NF training sessions (Spearman rank correlation test: r = 0.746, P = .001). The threshold value for gaining positive feedback from pre-NF baseline on beta power significantly increased (Spearman rank correlation test: r = 0.805, P = .001). Hemodynamic response in PFC changed after NF training, and individual differences were identified. Specifically, hypoactivation of the hemodynamic response by emotional distraction recovered following NF training.

CONCLUSION

We suggest that patients' cognitive processing efficiency was improved by the NF training. These beneficial results suggest that NF training may have potential therapeutic applications to prevent the progression from MCI to dementia.

TRIAL REGISTRATION NUMBER

Clinical Research Information Service (KCT0003433).

Assessing Neural Compensation With Visuospatial Working Memory Load Using Near-Infrared Imaging

Alzheimer's disease is characterized by the progressive deterioration of cognitive abilities particularly working memory while mild cognitive impairment (MCI) represents its prodrome. It is generally believed that neural compensation is intact in MCI but absent in Alzheimer's disease. This study investigated the effects of increasing task load as a means to induce neural compensation through a novel visual working memory (VSWM) task using functional near-infrared spectroscopy (fNIRS). The bilateral prefrontal cortex (PFC) was explored due to its relevance in VSWM and neural compensation. A total of 31 healthy controls (HC), 12 patients with MCI and 18 patients with mild Alzheimer's disease (mAD) were recruited. Although all groups showed sensitivity in terms of behavioral performance (i.e. score) towards increasing task load (level 1 to 3), only in MCI load effect on cortical response (as measured by fNIRS) was significant. At lower task load, bilateral PFC activation did not differ between MCI and HC. Neural compensation in the form of hyperactivation was only noticeable in MCI with a moderate task load. Lack of hyperactivation in mAD, coupled with significantly poorer task performance across task loads, suggested the inability to compensate due to a greater degree of neurodegeneration. Our findings provided an insight into the interaction of cognitive load theory and neural compensatory mechanisms. The experiment results demonstrated the feasibility of inducing neural compensation with the proposed VSWM task at the right amount of cognitive load. This may provide a promising avenue to develop an effective cognitive training and rehabilitation for dementia population.

Evaluation of Neural Degeneration Biomarkers in the Prefrontal Cortex for Early Identification of Patients With Mild Cognitive Impairment: An fNIRS Study

Mild cognitive impairment (MCI), a condition characterizing poor cognition, is associated with aging and depicts early symptoms of severe cognitive impairment, known as Alzheimer's disease (AD). Meanwhile, early detection of MCI can prevent progression to AD. A great deal of research has been performed in the past decade on MCI detection. However, availability of biomarkers for MCI detection requires greater attention. In our study, we evaluated putative and reliable biomarkers for diagnosing MCI by performing different mental tasks (i.e., N-back task, Stroop task, and verbal fluency task) using functional near-infrared spectroscopy (fNIRS) signals on a group of 15 MCI patients and 9 healthy control (HC). The 15 digital biomarkers (i.e., five means, seven slopes, peak, skewness, and kurtosis) and two image biomarkers (t-map, correlation map) in the prefrontal cortex (PFC) (i.e., left PFC, middle PFC, and right PFC) between the MCI and HC groups were investigated by the statistical analysis, linear discriminant analysis (LDA), and convolutional neural network (CNN) individually. The results reveal that the statistical analysis using digital biomarkers (with a p-value < 0.05) could not distinguish the MCI patients from the HC over 60% accuracy. Therefore, the current statistical analysis needs to be improved to be used for diagnosing the MCI patients. The best accuracy with LDA was 76.67% with the N-back and Stroop tasks. However, the CNN classification results trained by image biomarkers showed a high accuracy. In particular, the CNN results trained via t-maps revealed the best accuracy (90.62%) with the N-back task, whereas the CNN result trained by the correlation maps was 85.58% with the N-back task. Also, the results illustrated that investigating the sub-regions (i.e., right, middle, left) of the PFC for detecting MCI would be better than examining the whole PFC. The t-map (or/and the correlation map) is conclusively recommended as an image biomarker for early detection of AD. The combination of CNN and image biomarkers can provide a reliable clinical tool for diagnosing MCI patients.

Investigation of brain functional connectivity in patients with mild cognitive impairment: A functional near-infrared spectroscopy (fNIRS) study

This study examines brain functional connectivity in both cognitively normal seniors and patients with mild cognitive impairment (MCI) to elucidate prospective markers of MCI. A homemade four-channel functional near-infrared spectroscopy (fNIRS) system was employed to measure hemodynamic responses in the subjects' prefrontal cortex during a resting state, an oddball task, a 1-back task, and a verbal fluency task. Brain functional connectivity was calculated as the Pearson correlation coefficients between fNIRS channels. The results show that during the verbal fluency task, while the healthy control (HC) group presents a significantly stronger inter-hemispheric connectivity compared to intra-hemispheric connectivity, there is no difference between the inter- and intra-hemispheric connectivity in the MCI group. In addition, a comparison between the MCI and HC connectivity reveals that the MCI group has a statistically higher right and inter-hemispheric connectivity during the resting state, but a significantly lower left and inter-hemispheric connectivity during the verbal fluency test. These findings demonstrate the potential of fNIRS to study brain functional connectivity in neurodegenerative diseases.

Effects of Acupuncture Therapy on MCI Patients Using Functional Near-Infrared Spectroscopy

Acupuncture therapy (AT) is a non-pharmacological method of treatment that has been applied to various neurological diseases. However, studies on its longitudinal effect on the neural mechanisms of patients with mild cognitive impairment (MCI) for treatment purposes are still lacking in the literature. In this clinical study, we assess the longitudinal effects of ATs on MCI patients using two methods: (i) Montreal Cognitive Assessment test (MoCA-K, Korean version), and (ii) the hemodynamic response (HR) analyses using functional near-infrared spectroscopy (fNIRS). fNIRS signals of a working memory (WM) task were acquired from the prefrontal cortex. Twelve elderly MCI patients and 12 healthy people were recruited as target and healthy control (HC) groups, respectively. Each group went through an fNIRS scanning procedure three times: The initial data were obtained without any ATs, and subsequently a total of 24 AT sessions were conducted for MCI patients (i.e., MCI-0: the data prior to ATs, MCI-1: after 12 sessions of ATs for 6 weeks, MCI-2: another 12 sessions of ATs for 6 weeks). The mean HR responses of all MCI-0–2 cases were lower than those of HCs. To compare the effects of AT on MCI patients, MoCA-K results, temporal HR data, and spatial activation patterns (i.e., t-maps) were examined. In addition, analyses of functional connectivity (FC) and graph theory upon WM tasks were conducted. With ATs, (i) the averaged MoCA-K test scores were improved (MCI-1, p = 0.002; MCI-2, p = 2.9e^–4); (ii) the mean HR response of WM tasks was increased (p < 0.001); and (iii) the t-maps of MCI-1 and MCI-2 were enhanced. Furthermore, an increased FC in the prefrontal cortex in both MCI-1/MCI-2 cases in comparison to MCI-0 was obtained (p < 0.01), and an increasing trend in the graph theory parameters was observed. All these findings reveal that ATs have a positive impact on improving the cognitive function of MCI patients. In conclusion, ATs can be used as a therapeutic tool for MCI patients as a non-pharmacological method (Clinical trial registration number: KCT 0002451 https://cris.nih.go.kr/cris/en/).

Neural Compensatory Response During Complex Cognitive Function Tasks in Mild Cognitive Impairment: A Near-Infrared Spectroscopy Study

The present pilot study was aimed at conducting a comparative analysis of the level of activation in the prefrontal cortex among a normal elderly group and amnestic and nonamnestic mild cognitive impairment (MCI) groups and investigating the presence of neural compensatory mechanisms according to types of MCI and different cognitive tasks. We performed functional near-infrared spectroscopy (fNIRS) along with cognitive tasks, including two-back test, Korean color word Stroop test, and semantic verbal fluency task (SVFT), to investigate hemodynamic response and the presence of neural compensation and neuroplasticity in the prefrontal cortex of patients with amnestic and nonamnestic MCI compared with a healthy elderly group. During the two-back test, there was no significant difference in the bilateral region-of-interest (ROI) analysis in the three groups. During the Stroop test, right-sided hyperactivation compared to the left side during the task was shown in the nonamnestic MCI and normal groups with statistical significance. Mean acc∆HbO₂  on the right side was highest in the nonamnestic MCI group (0.30 μM) followed by the normal group (0.07 μM) and the amnestic MCI group (-0.10 μM). Otherwise, intergroup ROI analysis of acc∆HbO₂ in these activated right sides showed no significant difference. During the VFT test, there was no significant difference in the bilateral region-of-interest analysis in the three groups. The highest mean acc∆HbO₂ was shown in the normal group (0.79 μM) followed by the nonamnestic MCI group (0.52 μM) and the amnestic MCI group (0.21 μM). Otherwise, there was no significant difference between groups. The hemodynamic response during fNIRS showed different findings according to MCI types and cognitive tasks. Among the three tasks, the Stroop test showed results that were suggestive of neural compensatory mechanisms in the prefrontal cortex in nonamnestic MCI.

Functional Connectivity Analysis on Mild Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging using fNIRS

This paper reports a functional connectivity analysis at prefrontal cortex (PFC) during semantic verbal fluency task (SVFT) for three groups of elderly people, i.e., normal aging (NA), mild cognitive impairment (MCI) and mild Alzheimer's disease (AD). Functional Near Infrared Spectroscopy (fNIRS) was used to measure neuronal activities. A new software algorithm was developed to process fNIRS signals and to derive the parameters of functional connectivity. The synchronization of oxygenated hemoglobin signals from paired channels was evaluated using their temporal correlation. Results from 61 subjects of experiment show that a general decline in functional connectivity from NA (edge count $=$ 307) to AD (edge count $=$170), and the laterality between left and right PFC became insignificant $( \mathrm {p}>0.01)$ at AD stage. Moreover, the NA group demonstrated a significantly higher clustering coefficient than the AD group $( \mathrm {p}< 0.01)$, indicating the NA has higher regularity in brain network. Using semantic verbal fluency task, this work demonstrated fNIRS as a feasible measuring instrument to differentiate AD from NA based on functional connectivity, with clustering coefficient and laterality as suitable biomarkers.

Early Detection of Alzheimer's Disease Using Non-invasive Near-Infrared Spectroscopy

Mild cognitive impairment (MCI) is a cognitive disorder characterized by memory impairment, wherein patients have an increased likelihood of developing Alzheimer’s disease (AD). The classification of MCI and different AD stages is therefore fundamental for understanding and treating the disease. This study aimed to comprehensively investigate the hemodynamic response patterns among various subject groups. Functional near-infrared spectroscopy (fNIRS) was employed to measure signals from the frontal and bilateral parietal cortices of healthy controls (n = 8), patients with MCI (n = 9), mild (n = 6), and moderate/severe AD (n = 7) during a digit verbal span task (DVST). The concentration changes of oxygenated hemoglobin (HbO) in various subject groups were thoroughly explored and tested. Result revealed that abnormal patterns of hemodynamic response were observed across all subject groups. Greater and steeper reductions in HbO concentration were consistently observed across all regions of interest (ROIs) as disease severity developed from MCI to moderate/severe AD. Furthermore, all the fNIRS-derived indexes were found to be significantly and positively correlated to the clinical scores in all ROIs (R ≥ 0.4, P < 0.05). These findings demonstrate the feasibility of utilizing fNIRS for the early detection of AD, suggesting that fNIRS-based approaches hold great promise for exploring the mechanisms underlying the progression of AD.

Manual Dexterity and Aging: A Pilot Study Disentangling Sensorimotor From Cognitive Decline

Manual dexterity measures can be useful for early detection of age-related functional decline and for prediction of cognitive decline. However, what aspects of sensorimotor function to assess remains unclear. Manual dexterity markers should be able to separate impairments related to cognitive decline from those related to healthy aging. In this pilot study, we aimed to compare manual dexterity components in patients diagnosed with cognitive decline (mean age: 84 years, N = 11) and in age comparable cognitively intact elderly subjects (mean age: 78 years, N = 11). In order to separate impairments due to healthy aging from deficits due to cognitive decline we also included two groups of healthy young adults (mean age: 26 years, N = 10) and middle-aged adults (mean age: 41 years, N = 8). A comprehensive quantitative evaluation of manual dexterity was performed using three tasks: (i) visuomotor force tracking, (ii) isochronous single finger tapping with auditory cues, and (iii) visuomotor multi-finger tapping. Results showed a highly significant increase in force tracking error with increasing age. Subjects with cognitive decline had increased finger tapping variability and reduced ability to select the correct tapping fingers in the multi-finger tapping task compared to cognitively intact elderly subjects. Cognitively intact elderly subjects and those with cognitive decline had prolonged force release and reduced independence of finger movements compared to young adults and middle-aged adults. The findings suggest two different patterns of impaired manual dexterity: one related to cognitive decline and another related to healthy aging. Manual dexterity tasks requiring updating of performance, in accordance with (temporal or spatial) task rules maintained in short-term memory, are particularly affected in cognitive decline. Conversely, tasks requiring online matching of motor output to sensory cues were affected by age, not by cognitive status. Remarkably, no motor impairments were detected in patients with cognitive decline using clinical scales of hand function. The findings may have consequences for the development of manual dexterity markers of cognitive decline.

Early compensatory responses against neuronal injury: A new therapeutic window of opportunity for Alzheimer's Disease?

Alzheimer's disease (AD) is characterized by extensive neurodegeneration and inflammation in selective brain areas, linked to severely disabling cognitive deficits. Before full manifestation, different stages appear with progressively increased brain pathology and cognitive impairment. This significantly extends the time lag between initial molecular triggers and appearance of detectable symptoms. Notably, a number of studies in the last decade have revealed that in the early stage of mild cognitive impairment, events that appear in contrast with neuronal distress may occur. These have been reproduced in vitro and in animal models and include increase in synaptic elements, increase in synaptic and metabolic activity, enhancement of neurotrophic milieu and changes in glial cell reactivity and inflammation. They have been interpreted as compensatory responses that could either delay disease progression or, in the long run, result detrimental. For this reason, these mechanisms define a new and previously undervalued window of opportunity for intervention. Their importance resides especially in their early appearance. Directing efforts to better characterize this stage, in order to identify new pharmacological targets, is an exciting new avenue to future advances in AD research.

Simultaneous resting-state FDG-PET/fMRI in Alzheimer Disease: Relationship between glucose metabolism and intrinsic activity

Simultaneously evaluating resting-state brain glucose metabolism and intrinsic functional activity has potential to impact the clinical neurosciences of Alzheimer Disease (AD). Indeed, integrating such combined information obtained in the same physiological setting may clarify how impairments in neuroenergetic and neuronal function interact and contribute to the mechanisms underlying AD. The present study used this multimodality approach to investigate, by means of a hybrid PET/MR scanner, the coupling between glucose consumption and intrinsic functional activity in 23 patients with AD-related cognitive impairment ranging from amnestic mild cognitive impairment (MCI) to mild-moderate AD (aMCI/AD), in comparison with a group of 23 healthy elderly controls. Between-group (Controls > Patients) comparisons were conducted on data from both imaging modalities using voxelwise 2-sample t-tests, corrected for partial-volume effects, head motion, age, gender and multiple tests. FDG-PET/fMRI relationships were assessed within and across subjects using Spearman partial correlations for three different resting-state fMRI (rs-fMRI) metrics sensitive to AD: fractional amplitude of low frequency fluctuations (fALFF), regional homogeneity (ReHo) and group independent component analysis with dual regression (gICA-DR). FDG and rs-fMRI metrics distinguished aMCI/AD from controls according to spatial patterns analogous to those found in stand-alone studies. Within-subject correlations were comparable across the three rs-fMRI metrics. Correlations were overall high in healthy controls (ρ = 0.80 ± 0.04), but showed a significant 17% reduction (p < 0.05) in aMCI/AD patients (ρ = 0.67 ± 0.05). Positive across-subject correlations were overall moderate (ρ = 0.33 ± 0.07) and consistent across rs-fMRI metrics. These were confined around AD-target posterior regions for metrics of functional connectivity (ReHo and gICA-DR). In contrast, FDG/fALFF correlations were distributed in the frontal gyrus, thalami and caudate nuclei. Taken together, these results support the presence of bioenergetic coupling between glucose utilization and rapid transmission of neural information in healthy ageing, which is substantially reduced in aMCI/AD, suggesting that abnormal glucose utilization is in some way linked to communication breakdown among brain regions impacted by the underlying pathological process.

Non-invasive imaging modalities to study neurodegenerative diseases of aging brain

The aim of this article is to highlight current approaches for imaging elderly brain, indispensable for cognitive neuroscience research with emphasis on the basic physical principles of various non-invasive neuroimaging techniques. The first part of this article presents a quick overview of the primary non-invasive neuroimaging modalities used by cognitive neuroscientists such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), electroencephalography (EEG), magnetoencephalography (MEG), single photon emission computed tomography (SPECT), positron emission tomography (PET), magnetic resonance spectroscopic imaging (MRSI), Profusion imaging, functional magnetic resonance imaging (fMRI), near infrared spectroscopy (NIRS) and diffusion tensor imaging (DTI) along with tractography and connectomics. The second part provides a comprehensive overview of different multimodality imaging techniques for various cognitive neuroscience studies of aging brain.