Reduced spontaneous low frequency oscillations as measured with functional near-infrared spectroscopy in mild cognitive impairment

Low frequency oscillations reflect neurovascular coupling and disappear after cerebral death

Spectrum power analysis in the low frequency oscillations (LFO) region of functional near infrared spectroscopy (fNIRS) is a promising method to deliver information about brain activation and therefore might be used for prognostication in patients with disorders of consciousness in the neurocritical care unit alongside with established methods. In this study, we measure the cortical hemodynamic response measured by fNIRS in the LFO region following auditory and somatosensory stimulation in healthy subjects. The significant hemodynamic reaction in the contralateral hemisphere correlation with the physiologic electric response suggests neurovascular coupling. In addition, we investigate power spectrum changes in steady state measurements of cerebral death patients and healthy subjects in the LFO region, the frequency of the heartbeat and respiration. The spectral power within the LFO region was lower in the patients with cerebral death compared to the healthy subjects, whereas there were no differences in spectral power for physiological activities such as heartbeat and respiration rate. This finding indicates the cerebral origin of our low frequency measurements. Therefore, LFO measurements are a potential method to detect brain activation in patients with disorders of consciousness and cerebral death. However, further studies in patients are needed to investigate its potential clinical use.

A promising tool to explore functional impairment in neurodegeneration: A systematic review of near-infrared spectroscopy in dementia

This systematic review aimed to evaluate previous studies which used near-infrared spectroscopy (NIRS) in dementia given its suitability as a diagnostic and investigative tool in this population. From 800 identified records which used NIRS in dementia and prodromal stages, 88 studies were evaluated which employed a range of tasks testing memory (29), word retrieval (24), motor (8) and visuo-spatial function (4), and which explored the resting state (32). Across these domains, dementia exhibited blunted haemodynamic responses, often localised to frontal regions of interest, and a lack of task-appropriate frontal lateralisation. Prodromal stages, such as mild cognitive impairment, revealed mixed results. Reduced cognitive performance accompanied by either diminished functional responses or hyperactivity was identified, the latter suggesting a compensatory response not present at the dementia stage. Despite clear evidence of alterations in brain oxygenation in dementia and prodromal stages, a consensus as to the nature of these changes is difficult to reach. This is likely partially due to the lack of standardisation in optical techniques and processing methods for the application of NIRS to dementia. Further studies are required exploring more naturalistic settings and a wider range of dementia subtypes.

Stroke analysis and recognition in functional near-infrared spectroscopy signals using machine learning methods

Stroke is a high-incidence disease with high disability and mortality rates. It is a serious public health problem worldwide. Shortened onset-to-image time is very important for the diagnosis and treatment of stroke. Functional near-infrared spectroscopy (fNIRS) is a noninvasive monitoring tool with real-time, noninvasive, and convenient features. In this study, we propose an automatic classification framework based on cerebral oxygen saturation signals to identify patients with hemorrhagic stroke, patients with ischemic stroke, and normal subjects. The reflected fNIRS signals were used to detect the cerebral oxygen saturation and the relative value of oxygen and deoxyhemoglobin concentrations of the left and right frontal lobes. The wavelet time-frequency analysis-based features from these signals were extracted. Such features were used to analyze the differences in cerebral oxygen saturation signals among different types of stroke patients and healthy humans and were selected to train the machine learning models. Furthermore, an important analysis of the features was performed. The accuracy of the models trained was greater than 85%, and the accuracy of the models after data augmentation was greater than 90%, which is of great significance in distinguishing patients with hemorrhagic stroke or ischemic stroke. This framework has the potential to shorten the onset-to-diagnosis time of stroke.

Wavelet Analysis of Cerebral Oxygenation Signal Measured by Near-Infrared Spectroscopy in Moyamoya Disease

BACKGROUND

Spontaneous low-frequency oscillations (LFOs) have been widely studied in cerebrovascular disease, but little is known about their role in moyamoya disease (MMD). The objective of this study was to assess the value of spontaneous LFOs in MMD based on wavelet analysis of near-infrared spectroscopy signals.

METHODS

Sixty-four consecutive idiopathic adult patients were prospectively enrolled. The regional tissue oxygenation index (TOI) obtained from continuous near-infrared spectroscopy signals. Five frequency intervals of spontaneous LFOs (I, 0.0095-0.02 Hz; II, 0.02-0.06 Hz; III, 0.06-0.15 Hz; IV, 0.15-0.40 Hz; and V, 0.40-2.00 Hz) were extracted based on wavelet analysis. The data were compared between the patients and healthy control groups. Clinical features, cognitive function, and disease progression of MMD were analyzed using TOI and frequency interval data.

RESULTS

Compared with the healthy control group, patients with MMD had a higher cerebral TOI in both hemispheres. Based on wavelet analysis, the spontaneous LFO of TOI was found to be significantly lower for patients with MMD in frequency intervals II to IV than that for the controls. The spontaneous LFO of TOI is also related to the Suzuki stages in intervals II to IV, stroke in interval III, and cognitive impairment in intervals III to Ⅳ.

CONCLUSIONS

There were significant differences in spontaneous LFO between patients with MMD and healthy controls. The change in spontaneous LFO in MMD is related to Suzuki stage, cerebral infarction, and cognitive impairment. This might be an effective method for evaluating the severity and monitoring the progression of MMD.

Functional NIRS to detect covert consciousness in neurocritical patients

OBJECTIVE

This pilot study assesses the feasibility to detect covert consciousness in clinically unresponsive patients by means of functional near infrared spectroscopy (fNIRS) in a real intensive care unit setting. We aimed to verify if the hemodynamic response to familiar music measured with fNIRS varies according to the level consciousness of the patients.

METHODS

22 neurocritical patients and 6 healthy controls were included. The experiment consisted in 3 subsequent blocks including a first resting state recording, a period of music playback and a second resting state recording. fNIRS measurement were performed on each subject with two optodes on the forehead. Main oscillatory frequencies of oxyhemoglobin signal were analyzed. Spectral changes of low frequency oscillations (LFO) between subsequent experimental blocks were used as a marker of cortical response. Cortical response was compared to the level of consciousness of the patients and their functional outcome, through validated clinical scores.

RESULTS

Cortical hemodynamic response to music on the left prefrontal brain was associated with the level of consciousness of the patients and with their clinical outcome after three months.

CONCLUSIONS

Variations in LFO spectral power measured with fNIRS may be a new marker of cortical responsiveness to detect covert consciousness in neurocritical patients. Left prefrontal cortex may play an important role in the perception of familiar music.

SIGNIFICANCE

We showed the feasibility of a simple fNIRS approach to detect cortical response in the real setting of an intensive care unit.

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.

Factors associated with dropout in the longitudinal Vogel study of cognitive decline

Dementia, including Alzheimer's disease, is a growing problem worldwide. Prevention or early detection of the disease or a prodromal cognitive decline is necessary. By means of our long-term follow-up 'Vogel study', we aim to predict the pathological cognitive decline of a German cohort (mean age was 73.9 ± 1.55 years at first visit) with three measurement time points within 6 years per participant. Especially in samples of the elderly and subjects with chronic or co-morbid diseases, dropouts are one of the biggest problems of long-term studies. In contrast to the large number of research articles conducted on the course of dementia, little research has been done on the completion of treatment. To ensure unbiased and reliable predictors of cognitive decline from study completers, our objective was to determine predictors of dropout. We conducted multivariate analyses of covariance and multinomial logistic regression analyses to compare and predict the subject's dropout behaviour at the second visit 3 years after baseline (full participation, partial participation and no participation/dropout) with neuropsychiatric, cognitive, blood and lifestyle variables. Lower performance in declarative memory, attention and visual-spatial processing predicted dropout rather than full participation. Lower performance in visual-spatial processing predicted partial participation as opposed to full participation. Furthermore, lower performance in mini-mental status examination predicted whether subjects dropped out or participated partially instead of full participation. Baseline cognitive parameters are associated with dropouts at follow-up with a loss of impaired participants. We expect a bias into a healthier sample over time.

Early screening model for mild cognitive impairment based on resting-state functional connectivity: a functional near-infrared spectroscopy study

SIGNIFICANCE

As an early stage of Alzheimer's disease (AD), the diagnosis of amnestic mild cognitive impairment (aMCI) has important clinical value for timely intervention of AD. Functional near-infrared spectroscopy (fNIRS)-based resting-state brain connectivity analysis, which could provide an economic and quick screening strategy for aMCI, remains to be extensively investigated.

AIM

This study aimed to verify the feasibility of fNIRS-based resting-state brain connectivity for evaluating brain function in patients with aMCI, and to determine an early screening model for auxiliary diagnosis.

APPROACH

The resting-state fNIRS was utilized for exploring the changes in functional connectivity of 64 patients with aMCI. The region of interest (ROI)-based and channel-based connections with significant inter-group differences have been extracted through the two-sample t -tests and the receiver operating characteristic (ROC). These connections with specificity and sensitivity were then taken as features for classification.

RESULTS

Compared with healthy controls, connections of the MCI group were significantly reduced between the bilateral prefrontal, parietal, occipital, and right temporal lobes. Specifically, the long-range connections from prefrontal to occipital lobe, and from prefrontal to parietal lobe, exhibited stronger identifiability (area under the ROC curve > 0.65 , ** p < 0.01 ). Subsequently, the optimal classification accuracy of ROI-based connections was 71.59%. Furthermore, the most responsive connections were located between the right dorsolateral prefrontal lobe and the left occipital lobe, concomitant with the highest classification accuracy of 73.86%.

CONCLUSION

Our findings indicate that fNIRS-based resting-state functional connectivity analysis could support MCI diagnosis. Notably, long-range connections involving the prefrontal and occipital lobes have the potential to be efficient biomarkers.

Identification of moyamoya disease based on cerebral oxygen saturation signals using machine learning methods

Moyamoya is a cerebrovascular disease with a high mortality rate. Early detection and mechanistic studies are necessary. Near-infrared spectroscopy (NIRS) was used to study the signals of the cerebral tissue oxygen saturation index (TOI) and the changes in oxygenated and deoxygenated hemoglobin concentrations (HbO and Hb) in 64 patients with moyamoya disease and 64 healthy volunteers. The wavelet transforms (WT) of TOI, HbO and Hb signals, as well as the wavelet phase coherence (WPCO) of these signals from the left and right frontal lobes of the same subject, were calculated. Features were extracted from the spontaneous oscillations of TOI, HbO and Hb in five physiological activity-related frequency segments. Machine learning models based on support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) have been built to classify the two groups. For 20-min signals, the 10-fold cross-validation accuracies of SVM, RF and XGBoost were 87%, 85% and 85%, respectively. For 5-min signals, the accuracies of the three methods were 88%, 88% and 84%, respectively. The method proposed in this article has potential for detecting and screening moyamoya with high proficiency. Evaluating the cerebral oxygenation with NIRS shows great potential in screening moyamoya diseases.

Cardio-psycho-metabolic outcomes of bariatric surgery: design and baseline of the WAS trial

Obesity is a rapidly emerging health problem and an established risk factor for cardiovascular diseases. Bariatric surgery profoundly reduces body weight and mitigates sequelae of obesity. The open, randomized controlled Würzburg Adipositas Studie (WAS) trial compares the effects of Roux-en-Y gastric bypass (RYGB) vs psychotherapy-supported lifestyle modification in morbidly obese patients. The co-primary endpoint addresses 1-year changes in cardiovascular function (peak VO2 during cardiopulmonary exercise testing) and the quality of life (QoL) (Short-Form-36 physical functioning scale). Prior to randomization, all included patients underwent a multimodal anti-obesity treatment for 6-12 months. Thereafter, the patients were randomized and followed through month 12 to collect the primary endpoints. Afterwards, patients in the lifestyle group could opt for surgery, and final visit was scheduled for all patients 24 months after randomization. Sample size calculation suggested to enroll 90 patients in order to arrive at minimally 22 patients per group evaluable for the primary endpoint. Secondary objectives were to quantify changes in body weight, left ventricular hypertrophy, systolic and diastolic function (by echocardiography and cardiac MRI), functional brain MRI, psychometric scales, and endothelial and metabolic function. WAS enrolled 93 patients (72 women, median age 38 years, BMI 47.5 kg/m2) exhibiting a relevantly compromised exercise capacity (median peakVO2 18.3 mL/min/kg) and the QoL (median physical functioning scale 50). WAS is the first randomized controlled trial focusing on the effects of RYGB on cardiovascular function beyond hypertension. In addition, it will provide a wealth of high-quality data on the cerebral, psychiatric, hepatic, and metabolic function in obese patients after RYGB.

Reduced parietal activation in participants with mild cognitive impairments during visual-spatial processing measured with functional near-infrared spectroscopy

Functional Near Infrared Spectroscopy (fNIRS) may be a suitable, simple, and cost-effective brain imaging technique for detecting divergent neuronal patterns at an early stage of neurodegeneration. In course of Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD), a deficit in visual-spatial processing, located in the parietal cortex, is a reliable risk factor. Earlier, we established the application of the clock-hand-angle-discrimination task (ADT) during fNIRS to identify neuronal correlates of the visual-spatial processing in a healthy elderly sample. In this analysis, we aimed to measure and find out differences in the hemodynamic response in MCI participants compared to matched healthy controls. As expected, MCI participants showed more errors over all conditions of pointer length and a higher reaction time in the long and middle pointer length condition. Moreover, results revealed a significant reduction of cortical activation in MCI patients. There was a generally increased activity in both the right as compared to the left hemisphere and the superior parietal brain region as compared to the inferior parietal brain region in both groups. In summary, fNIRS can be implemented in the measurement of visual-spatial processing in MCI patients and healthy elderly based on ADT. MCI participants had difficulties to cope with the ADT. Since neuronal hypoactivity occurs with concomitant behavioral deficits, an additional analysis was performed on a subgroup of MCI patients who performed as well as the control group in behavior. This subgroup analysis also showed a hypoactivation of the parietal cortex, without evidence of a compensatory activation. Therefore, we assume that MCI patients are characterized by a deficit in the parietal cortex. Overall, these findings confirm our hypothesis that hemodynamic deficits in visual-spatial processing, localized in the parietal cortex, are reliable and early diagnostic markers for cognitive decline in risk groups for the development of AD.

Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research

OBJECTIVE

Alzheimer's disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline.

METHODS

An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart).

RESULTS

EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual-spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach.

CONCLUSION

The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.

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.

Neuronal correlates of the visual-spatial processing measured with functional near-infrared spectroscopy in healthy elderly individuals

Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) are a globally rising issue. It is necessary to detect such diseases early to find strategies for prevention. Typically, patients with MCI or AD show deviant neuronal patterns, which could be detected early through brain imaging techniques enabling assumptions about pre-existing diseases. Functional Near-Infrared Spectroscopy (fNIRS) is an appropriate imaging method because of its easy and economical nature with hardly any drawbacks. An early measurable risk factor indicating neurodegenerative processes could be a deficit in visual-spatial processing, which is localized in the parietal cortex. In this study, we aimed to measure the hemodynamic response of the visual-spatial processing in the healthy elderly participants of our long-term Vogel Study with fNIRS during the clock-hand-angle-discrimination task (ADT) to deepen our understanding of healthy brain mechanisms. Our results revealed for our healthy sample a significantly increased neuronal brain activity with increasing task difficulties, namely from the long to the middle to the short clock hand during ADT and significantly higher activation in the right hemisphere compared to the left hemisphere as well as in the superior parietal cortex compared to the inferior parietal cortex. Additionally, our behavioral data demonstrated longer reaction times and more errors with an increasing task requirement. We, therefore, assume that visual-spatial processing can successfully be operationalized with fNIRS for healthy elderly people based on ADT. Further fNIRS analyses are planned to investigate pathological neuronal correlates of visual-spatial function in MCI or AD study participants.

A Systematic Review of the Application of Functional Near-Infrared Spectroscopy to the Study of Cerebral Hemodynamics in Healthy Aging

Positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) studies have shown that healthy aging is associated with functional brain deterioration that preferentially affects the prefrontal cortex. This article reviews the application of an alternative method, functional near-infrared spectroscopy (fNIRS), to the study of age-related changes in cerebral hemodynamics and factors that influence cerebral hemodynamics in the elderly population. We conducted literature searches in PudMed and PsycINFO, and selected only English original research articles that used fNIRS to study healthy individuals with a mean age of ≥ 55 years. All articles were published in peer-reviewed journals between 1977 and May 2019. We synthesized 114 fNIRS studies examining hemodynamic changes that occurred in the resting state and during the tasks of sensation and perception, motor control, semantic processing, word retrieval, attentional shifting, inhibitory control, memory, and emotion and motivation in healthy older adults. This review, which was not registered in a registry, reveals an age-related reduction in resting-state cerebral oxygenation and connectivity in the prefrontal cortex. It also shows that aging is associated with a reduction in functional hemispheric asymmetry and increased compensatory activity in the frontal lobe across multiple task domains. In addition, this article describes the beneficial effects of healthy lifestyles and the detrimental effects of cardiovascular disease risk factors on brain functioning among nondemented older adults. Limitations of this review include exclusion of gray and non-English literature and lack of meta-analysis. Altogether, the fNIRS literature provides some support for various neurocognitive aging theories derived from task-based PET and fMRI studies. Because fNIRS is relatively motion-tolerant and environmentally unconstrained, it is a promising tool for fostering the development of aging biomarkers and antiaging interventions.

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.

Intracranial vascular flow oscillations in Alzheimer's disease from 4D flow MRI

Recent modeling and experimental evidence suggests clearance of soluble metabolites from the brain can be driven by low frequency flow oscillations (LFOs) through the intramural periarterial drainage (IPAD) pathway. This study investigates the use of 4D flow MRI to derive LFOs from arterial and venous measures of blood flow. 3D radial 4D flow MRI data were acquired on a 3.0 T scanner and reconstructed using a low-rank constraint to produce time resolved measurements of blood flow. Physical phantom experiments were performed to validate the time resolved 4D flow against a standard 2D phase contrast (PC) approach. To evaluate the ability of 4D flow to distinguish physiologic flow changes from noise, healthy volunteers were scanned during a breath-hold (BH) maneuver and compared against 2D PC measures. Finally, flow measures were performed in intracranial arteries and veins of 112 participants including subjects diagnosed with Alzheimer's disease (AD) clinical syndrome (n = 23), and healthy controls (n = 89) on whom apolipoprotein ɛ4 positivity (APOE4+) and parental history of AD dementia (FH+) was known. To assess LFOs, flow range, standard deviation, demeaned temporal flow changes, and power spectral density were quantified from the time series. Group differences were assessed using ANOVA followed by Tukey-Kramer method for pairwise comparison for adjusted means (P < 0.05). Significantly lower LFOs as measured from flow variation range and standard deviations were observed in the arteries of AD subjects when compared to age-matched controls (P = 0.005, P = 0.011). Results suggest altered vascular function in AD subjects. 4D flow based spontaneous LFO measures might hold potential for longitudinal studies aimed at predicting cognitive trajectories in AD and study disease mechanisms.

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.

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.

Assessing low-frequency oscillations in cerebrovascular diseases and related conditions with near-infrared spectroscopy: a plausible method for evaluating cerebral autoregulation?

BACKGROUND

Cerebral autoregulation (CA) is the brain's ability to always maintain an adequate and relatively constant blood supply, which is often impaired in cerebrovascular diseases. Near-infrared spectroscopy (NIRS) examines oxygenated hemoglobin (OxyHb) in the cerebral cortex. Low- and very low-frequency oscillations ( LFOs ≈ 0.1    Hz and VLFOs ≈ 0.05 to 0.01 Hz) in OxyHb have been proposed to reflect CA.

AIM

To systematically review published results on OxyHb LFOs and VLFOs in cerebrovascular diseases and related conditions measured with NIRS.

APPROACH

A systematic search was performed in the MEDLINE database, which generated 36 studies relevant for inclusion.

RESULTS

Healthy people have relatively stable LFOs. LFO amplitude seems to reflect myogenic CA being decreased by vasomotor paralysis in stroke, by smooth muscle damage or as compensatory action in other conditions but can also be influenced by the sympathetic tone. VLFO amplitude is believed to reflect neurogenic and metabolic CA and is lower in stroke, atherosclerosis, and with aging. Both LFO and VLFO synchronizations appear disturbed in stroke, while the former is also altered in internal carotid stenosis and hypertension.

CONCLUSION

We conclude that amplitudes of LFOs and VLFOs are relatively robust measures for evaluating mechanisms of CA and synchronization analyses can show temporal disruption of CA. Further research and more coherent methodologies are needed.