Comparison of Model-Based Indices of Cerebral Autoregulation and Vasomotor Reactivity Using Transcranial Doppler versus Near-Infrared Spectroscopy in Patients with Amnestic Mild Cognitive Impairment

Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet)

Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.

CO2 cerebrovascular reactivity measured with CBF-MRI in older individuals: Association with cognition, physical function, amyloid, and tau proteins

Vascular pathology is the second leading cause of cognitive impairment and represents a major contributing factor in mixed dementia. However, biomarkers for vascular cognitive impairment and dementia (VCID) are under-developed. Here we aimed to investigate the potential role of CO2 Cerebrovascular Reactivity (CVR) measured with phase-contrast quantitative flow MRI in cognitive impairment and dementia. Forty-five (69 ± 7 years) impaired (37 mild-cognitive-impairment and 8 mild-dementia by syndromic diagnosis) and 22 cognitively-healthy-control (HC) participants were recruited and scanned on a 3 T MRI. Biomarkers of AD pathology were measured in cerebrospinal fluid. We found that CBF-CVR was lower (p = 0.027) in the impaired (mean±SE, 3.70 ± 0.15%/mmHg) relative to HC (4.28 ± 0.21%/mmHg). After adjusting for AD pathological markers (Aβ42/40, total tau, and Aβ42/p-tau181), higher CBF-CVR was associated with better cognitive performance, including Montreal Cognitive Assessment, MoCA (p = 0.001), composite cognitive score (p = 0.047), and language (p = 0.004). Higher CBF-CVR was also associated with better physical function, including gait-speed (p = 0.006) and time for five chair-stands (p = 0.049). CBF-CVR was additionally related to the Clinical-Dementia-Rating, CDR, including global CDR (p = 0.026) and CDR Sum-of-Boxes (p = 0.015). CBF-CVR was inversely associated with hemoglobin A1C level (p = 0.017). In summary, CBF-CVR measured with phase-contrast MRI shows associations with cognitive performance, physical function, and disease-severity, independent of AD pathological markers.

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.

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.

Dynamic effects of cholinergic blockade upon cerebral blood flow autoregulation in healthy adults

Background: Cerebral flow autoregulation (CFA) is a homeostatic mechanism critical for survival. The autonomic nervous system (ANS) plays a key role in maintaining proper CFA function. More quantitative studies of how the ANS influences CFA are desirable. Objective: To discover and quantify the dynamic effects of cholinergic blockade upon CFA in response to changes of arterial blood pressure and blood CO2 tension in healthy adults. Methods: We analyzed time-series data of spontaneous beat-to-beat mean arterial blood pressure (ABP) and cerebral blood flow velocity in the middle cerebral arteries (CFV), as well as breath-to-breath end-tidal CO2 (CO2), collected in 9 adults before and after cholinergic blockade, in order to obtain subject-specific predictive input-output models of the dynamic effects of changes in ABP and CO2 (inputs) upon CFV (output). These models are defined in convolutional form using "kernel" functions (or, equivalently, Transfer Functions in the frequency domain) that are estimated via the robust method of Laguerre expansions. Results: Cholinergic blockade caused statistically significant changes in the obtained kernel estimates (and the corresponding Transfer Functions) that define the linear dynamics of the ABP-to-CFV and CO2-to-CFV causal relations. The kernel changes due to cholinergic blockade reflect the effects of the cholinergic mechanism and exhibited, in the frequency domain, resonant peaks at 0.22 Hz and 0.06 Hz for the ABP-to-CFV and CO2-to-CFV dynamics, respectively. Conclusion: Quantitative estimates of the dynamics of the cholinergic component in CFA are found as average changes of the ABP-to-CFV and CO2-to-CFV kernels, and corresponding Transfer Functions, before and after cholinergic blockade.

The Dynamic Relationship Between Cortical Oxygenation and End-Tidal CO 2 Transient Changes Is Impaired in Mild Cognitive Impairment Patients

Background: Recent studies have utilized data-based dynamic modeling to establish strong association between dysregulation of cerebral perfusion and Mild Cognitive Impairment (MCI), expressed in terms of impaired CO2 dynamic vasomotor reactivity in the cerebral vasculature. This raises the question of whether this is due to dysregulation of central mechanisms (baroreflex and chemoreflex) or mechanisms of cortical tissue oxygenation (CTO) in MCI patients. We seek to answer this question using data-based input-output predictive dynamic models. Objective: To use subject-specific data-based multivariate input-output dynamic models to quantify the effects of systemic hemodynamic and blood CO2 changes upon CTO and to examine possible differences in CTO regulation in MCI patients versus age-matched controls, after the dynamic effects of central regulatory mechanisms have been accounted for by using cerebral flow measurements as another input. Methods: The employed model-based approach utilized the general dynamic modeling methodology of Laguerre expansions of kernels to analyze spontaneous time-series data in order to quantify the dynamic effects upon CTO (an index of relative capillary hemoglobin saturation distribution measured via near-infrared spectroscopy) of contemporaneous changes in end-tidal CO2 (proxy for arterial CO2), arterial blood pressure and cerebral blood flow velocity in the middle cerebral arteries (measured via transcranial Doppler). Model-based indices (physio-markers) were computed for these distinct dynamic relationships. Results: The obtained model-based indices revealed significant statistical differences of CO2 dynamic vasomotor reactivity in cortical tissue, combined with "perfusivity" that quantifies the dynamic relationship between flow velocity in cerebral arteries and CTO in MCI patients versus age-matched controls (p = 0.006). Significant difference between MCI patients and age-matched controls was also found in the respective model-prediction accuracy (p = 0.0001). Combination of these model-based indices via the Fisher Discriminant achieved even smaller p-value (p = 5 × 10-5) when comparing MCI patients with controls. The differences in dynamics of CTO in MCI patients are in lower frequencies (<0.05 Hz), suggesting impairment in endocrine/metabolic (rather than neural) mechanisms. Conclusion: The presented model-based approach elucidates the multivariate dynamic connectivity in the regulation of cerebral perfusion and yields model-based indices that may serve as physio-markers of possible dysregulation of CTO during transient CO2 changes in MCI patients.

A template for physical resilience research in older adults: Methods of the PRIME-KNEE study

BACKGROUND

Older adults with similar health conditions often experience widely divergent outcomes following health stressors. Variable recovery after a health stressor may be due in part to differences in biological mechanisms at the molecular, cellular, or system level, that are elicited in response to stressors. We describe the PRIME-KNEE study as an example of ongoing research to validate provocative clinical tests and biomarkers that predict resilience to specific health stressors.

METHODS

PRIME-KNEE is an ongoing, prospective cohort study that will enroll 250 adults ≥60 years undergoing total knee arthroplasty. Data are collected at baseline (pre-surgery), during surgery, daily for 7 days after surgery, and at 1, 2, 4, and 6 months post-surgery. Provocative tests include a cognition-motor dual-task walking test, cerebrovascular reactivity assessed by functional near-infrared spectroscopy, peripheral blood mononuclear cell reactivity ex vivo to lipopolysaccharide toxin and influenza vaccine, and heart rate variability during surgery. Cognitive, psychological, and physical performance batteries are collected at baseline to estimate prestressor reserve. Demographics, medications, comorbidities, and stressor characteristics are abstracted from the electronic medical record and via participant interview. Blood-based biomarkers are collected at baseline and postoperative day 1. Repeated measures after surgery include items from a delirium assessment tool and pain scales administered daily by telephone for 7 days and cognitive change index (participant and informant), lower extremity activities of daily living, pain scales, and step counts assessed by Garmin actigraphy at 1, 2, 4, and 6 months after surgery. Statistical models use these measures to characterize resilience phenotypes and evaluate prestressor clinical indicators associated with poststressor resilience.

CONCLUSION

If PRIME-KNEE validates feasible clinical tests and biomarkers that predict recovery trajectories in older surgical patients, these tools may inform surgical decision-making, guide pre-habilitation efforts, and elucidate mechanisms underlying resilience. This study design could motivate future geriatric research on resilience.

Superior fitting of arterial resistance and compliance parameters with genetic algorithms in models of dynamic cerebral autoregulation

OBJECTIVE

The capacity of discriminating between normal and impaired dynamic cerebral autoregulation (dCA), based on spontaneous fluctuations in arterial blood pressure (ABP) and cerebral blood flow (CBF), has considerable clinical relevance. This study aimed to quantify the separate contributions of vascular resistance and compliance as parameters that could reflect myogenic and metabolic mechanisms to dCA.

METHODS

Forty-five subjects were studied under normo and hypercapnic conditions induced by breathing a mixture of 5% carbon dioxide in air. Dynamic cerebrovascular resistance and compliance models with ABP as input and CBFV as output were fitted using Genetic Algorithms to identify parameter values for each subject, and respiratory condition.

RESULTS

The efficiency of dCA was assessed from the models generated CBFV response to an ABP step change, corresponding to an autoregulation index of 5.561.57 in normocapnia and 2.381.73 in hypercapnia, with an area under the ROC curve (AUC) of 0.9 between both conditions. Vascular compliance increased from 0.750.7 ml/mmHg in normocapnia to 5.8212.0 ml/mmHg during hypercapnia, with an AUC of 0.88.

CONCLUSION

we demonstrated that Genetic Algorithms are a powerful tool to provide accurate identification of model parameters expressing the performance of human CA Significance: Further work is needed to validate this approach in clinical applications where individualised model parameters could provide relevant diagnostic and prognostic information about dCA impairment Index Terms arterial compliance, autoregulation impairment, cerebral blood flow, Genetic Algorithms, hypercapnia.

Closed-Loop Dynamic Modeling of the Heart-Rate Reflex to Concurrent Spontaneous Changes of Arterial Blood Pressure and CO2 Tension: Quantification of the Effects of Mild Cognitive Impairment

OBJECTIVE

To extend closed-loop modeling of the heart-rate reflex (HRR) by including the dynamic effects of concurrent changes in blood CO2 tension. This extended dynamic model can be used to generate physio-markers of "baroreflex gain" (BRG) and "chemoreflex gain" (CRG) that allow quantitative assessment of the possible impact of pathologies upon HRR. Mild Cognitive Impairment (MCI) is used as an example.

METHODS

The proposed data-based closed-loop modeling methodology estimates the forward and reverse dynamic components of the model via Laguerre kernel expansions of two open-loop models using spontaneous time-series data collected in 45 MCI patients and 15 controls. The BRG and CRG physio-markers are subsequently computed for each subject via simulation of the obtained closed-loop model for unit-step change of arterial pressure or blood CO2 tension, respectively.

RESULTS

Both open-loop and closed-loop HRR modeling revealed that MCI patients exhibit significantly smaller CRG relative to controls (p<0.001), but not significantly different BRG. Furthermore, the closed-loop model captured the dynamic effect of sympathetic activity as resonant peak around 0.1 Hz (Mayer wave) in the chemoreflex and baroreflex transfer functions (not captured via open-loop modeling). This may prove valuable in advancing our understanding of how sympathetic activity impacts HRR in various pathologies.

CONCLUSION

The extended HRR model, incorporating the dynamic effects of concurrent changes of blood CO2 tension, revealed significantly reduced chemoreflex gain (but not baroreflex gain) in MCI patients. Furthermore, the closed-loop model captured the sympathetic influence around 0.1 Hz.

SIGNIFICANCE

Multivariate closed-loop dynamic modeling is valuable for understanding physiological autoregulation.

The Utility of Cerebrovascular Reactivity MRI in Brain Rehabilitation: A Mechanistic Perspective

Cerebrovascular control and its integration with other physiological systems play a key role in the effective maintenance of homeostasis in brain functioning. Maintenance, restoration, and promotion of such a balance are one of the paramount goals of brain rehabilitation and intervention programs. Cerebrovascular reactivity (CVR), an index of cerebrovascular reserve, plays an important role in chemo-regulation of cerebral blood flow. Improved vascular reactivity and cerebral blood flow are important factors in brain rehabilitation to facilitate desired cognitive and functional outcomes. It is widely accepted that CVR is impaired in aging, hypertension, and cerebrovascular diseases and possibly in neurodegenerative syndromes. However, a multitude of physiological factors influence CVR, and thus a comprehensive understanding of underlying mechanisms are needed. We are currently underinformed on which rehabilitation method will improve CVR, and how this information can inform on a patient's prognosis and diagnosis. Implementation of targeted rehabilitation regimes would be the first step to elucidate whether such regimes can modulate CVR and in the process may assist in improving our understanding for the underlying vascular pathophysiology. As such, the high spatial resolution along with whole brain coverage offered by MRI has opened the door to exciting recent developments in CVR MRI. Yet, several challenges currently preclude its potential as an effective diagnostic and prognostic tool in treatment planning and guidance. Understanding these knowledge gaps will ultimately facilitate a deeper understanding for cerebrovascular physiology and its role in brain function and rehabilitation. Based on the lessons learned from our group's past and ongoing neurorehabilitation studies, we present a systematic review of physiological mechanisms that lead to impaired CVR in aging and disease, and how CVR imaging and its further development in the context of brain rehabilitation can add value to the clinical settings.

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 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.

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.

Prognosis and risk factors for reocclusion after mechanical thrombectomy

Objective

This study evaluates reocclusion prognostic outcomes and explores reocclusion risk factors after mechanical thrombectomy (MT) in Chinese stroke patients.

Methods

Altogether, 614 patients with AIS with successful recanalization after MT were recruited in this study and divided into the reocclusion and the non‐reocclusion group depending on the 24‐h imaging results after MT. Differences between the two groups were compared including 24‐h and 7‐day National Institutes of Health Stroke Scale (NIHSS) scores, 90‐day modified Rankin scale(mRS) scores, good prognosis (mRS:0–2) rates, incidence of intracranial hemorrhage, and 90‐day mortality.

Results

Forty‐four (7.2%) patients experienced reocclusion within 24 h. Compared with the non‐reocclusion group, patients in the reocclusion group had higher 24‐h (15 vs. 13) and 7‐day (15 vs. 9) NIHSS scores, 90‐day mRS scores (4 vs. 3), and 90‐day mortality rates (34.1% vs. 18.6%); lower rates of good prognosis (13.6% vs. 9.3%); and a higher incidence of early neurological deterioration (36.4% vs. 14.7%). Age, internal carotid artery occlusion (ICA), intravenous thrombolysis (IVT), number of thrombectomy passes, stent implantation, and levels of D‐dimer (adjusted odds ratio and 95% confidence interval: 0.97, 0.94–0.99; 2.40, 1.10–5.23; 2.21, 1.05–4.66; 2.60, 1.04–6.47; 0.25, 0.09–0.67; and 1.06, 1.01–1.12, respectively) were independently associated with 24‐h reocclusion.

Interpretation

The prognosis of reocclusion after MT was poor. Timely evaluation of these factors including age, D‐dimer, ICA occlusion, IVT, number of passes, and stent implantation and appropriate intervention could reduce the incidence of reocclusion for Chinese stroke patients.

Closed-loop modeling of the heart-rate reflex for improved diagnosis and monitoring of Mild Cognitive Impairment

Analysis of beat-to-beat spontaneous cerebral hemodynamic data has yielded predictive dynamic models of cerebral hemodynamics and has shown previously that patients with Mild Cognitive Impairment (MCI) exhibit significantly reduced cerebral vasomotor reactivity to CO₂ relative to cognitively normal control subjects [1]. The present work examines the heart-rate reflex (HRR) dynamics of 46 MCI patients compared to 20 control subjects, using closed-loop modeling of HRR under resting conditions of spontaneous variations of arterial blood pressure (baroreflex) and end-tidal CO₂ (chemoreflex). These subject-specific predictive dynamic models are obtained via the methodology of Principal Dynamic Modes [2] and allow the computation of model-based markers of baroreflex and chemoreflex function. We found that the chemoreflex gain is significantly weakened in MCI patients relative to controls (p=0.0086), while the baroreflex is not significantly affected. These findings offer another tool for diagnosis and monitoring of MCI (via model-based markers), when used in conjunction with current methods.

Quantification of dynamic cerebral autoregulation and CO2 dynamic vasomotor reactivity impairment in essential hypertension

The study of dynamic cerebral autoregulation (DCA) in essential hypertension has received considerable attention because of its clinical importance. Several studies have examined the dynamic relationship between spontaneous beat-to-beat arterial blood pressure data and contemporaneous cerebral blood flow velocity measurements (obtained via transcranial Doppler at the middle cerebral arteries) in the form of a linear input-output model using transfer function analysis. This analysis is more reliable when the contemporaneous effects of changes in blood CO₂ tension are also taken into account, because of the significant effects of CO₂ dynamic vasomotor reactivity (DVR) upon cerebral flow. In this article, we extract such input-output predictive models from spontaneous time series hemodynamic data of 24 patients with essential hypertension and 20 normotensive control subjects under resting conditions, using the novel methodology of principal dynamic modes (PDMs) that achieves improved estimation accuracy over previous methods for relatively short and noisy data. The obtained data-based models are subsequently used to compute indexes and markers that quantify DCA and DVR in each subject or patient and therefore can be used to assess the effects of essential hypertension. These model-based DCA and DVR indexes were properly defined to capture the observed effects of DCA and VR and found to be significantly different (P < 0.05) in the hypertensive patients. We also found significant differences between patients and control subjects in the relative contribution of three PDMs to the model output prediction, a finding that offers the prospect of identifying the physiological mechanisms affected by essential hypertension when the PDMs are interpreted in terms of specific physiological mechanisms.NEW & NOTEWORTHY This article presents novel model-based methodology for obtaining diagnostic indexes of dynamic cerebral autoregulation and dynamic vasomotor reactivity in hypertension.

Dysregulation of CO2-Driven Heart-Rate Chemoreflex Is Related Closely to Impaired CO2 Dynamic Vasomotor Reactivity in Mild Cognitive Impairment Patients

BACKGROUND

Significant reduction of dynamic vasomotor reactivity (DVR) was recently reported in patients with amnestic mild cognitive impairment (MCI) relative to age-matched controls. These results were obtained via a novel approach that utilizes data-based predictive dynamic models to quantify DVR.

OBJECTIVE

Using the same methodological approach, we seek to quantify the dynamic effects of the CO2-driven chemoreflex and baroreflex upon heart-rate in order to examine their possible correlation with the observed DVR impairment in each MCI patient.

METHODS

The employed approach utilizes time-series data to obtain subject-specific predictive input-output models of the dynamic effects of changes in arterial blood pressure and end-tidal CO2 (putative "inputs") upon cerebral blood flow velocity in large cerebral arteries, cortical tissue oxygenation, and heart-rate (putative "outputs").

RESULTS

There was significant dysregulation of CO2-driven heart-rate chemoreflex (p = 0.0031), but not of baroreflex (p = 0.5061), in MCI patients relative to age-matched controls. The model-based index of CO2-driven heart-rate chemoreflex gain (CRG) correlated significantly with the DVR index in large cerebral arteries (p = 0.0146), but not with the DVR index in small/micro-cortical vessels (p = 0.1066). This suggests that DVR impairment in small/micro-cortical vessels is not mainly due to CO2-driven heart-rate chemoreflex dysregulation, but to other factors (possibly dysfunction of neurovascular coupling).

CONCLUSION

Improved delineation between MCI patients and controls is achieved by combining the DVR index for small/micro-cortical vessels with the CRG index (p = 2×10-5). There is significant correlation (p < 0.01) between neuropsychological test scores and model-based DVR indices. Combining neuropsychological scores with DVR indices reduces the composite diagnostic index p-value (p∼10-10).

Cerebral Microcirculatory Blood Flow Dynamics During Rest and a Continuous Motor Task

Objectives: To examine the brain’s microcirculatory response over the course of a continuous 5-min elbow movement task in order to estimate its potential role in grading vaso-neural coupling compared to the macrocirculatory response.

Methods: We simultaneously recorded cerebral blood flow velocity (CBFV), changes in oxygenated/deoxygenated hemoglobin concentrations ([oxHb], [deoxHb]), blood pressure (BP), and end-tidal CO₂ over 5-min periods of rest and left elbow movements in 24 healthy persons (13 women and 11 men of mean age ± SD, 38 ± 11 years). A low frequency range (0.07–0.15 Hz) was used for analysis by transfer function estimates of phase and gain.

Results: Elbow movement led to a small BP increase (mean BP at rest 83 mm Hg, at movement 87; p < 0.01) and a small ETCO₂ decrease (at rest 44.6 mm Hg, at movement 41.7 mm Hg; p < 0.01). Further, it increased BP-[oxHb] phase from 55° (both sides) to 74° (right; p < 0.05)/69° (left; p < 0.05), and BP-[deoxHb] phase from 264° (right)/270° (left) to 288° (right; p < 0.05)/297° (left; p = 0.09). The cerebral mean transit time at 0.1 Hz of 5.6 s of rest remained unchanged by movement. Elbow movement significantly decreased BP-CBFV gain on both sides, and BP-CBFV phase only on the right side (p = 0.05).

Conclusion: Elbow movement leads to an increased time delay between BP and [oxHb]/[deoxHb] while leaving the cerebral mean transit time unchanged. Phase shifting is usually the more robust parameter when using a transfer function to estimate dynamic cerebral autoregulation; phase shifting at the microcirculatory level seems to be a better marker of VNC-induced changes than phase shifting between BP and CBFV.

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.

Comparing model-based cerebrovascular physiomarkers with DTI biomarkers in MCI patients

OBJECTIVE

To compare the novel model-based hemodynamic physiomarker of Dynamic Vasomotor Reactivity (DVR) with biomarkers based on Diffusion Tensor Imaging (DTI) and some widely used neurocognitive scores in terms of their ability to delineate patients with amnestic Mild Cognitive Impairment (MCI) from age-matched cognitively normal controls.

MATERIALS & METHODS

The model-based DVR and MRI-based DTI markers were obtained from 36 patients with amnestic MCI and 16 age-matched controls without cognitive impairment, for whom widely used neurocognitive scores were available. These markers and scores were subsequently compared in terms of statistical delineation between patients and controls.

RESULTS

It was found that statistically significant delineation between MCI patients and controls was comparable for DVR or DTI markers (p < 0.01). The performance of both types of markers was consistent with the scores of some (but not all) widely used neurocognitive tests.

CONCLUSION

Since DTI offers a measure of cerebral white matter integrity, the results suggest that the model-based hemodynamic marker of DVR may correlate with cognitive impairment due to white matter lesions. This finding is consistent with the hypothesis that dysregulation of cerebral microcirculation may be an early cause of cognitive impairment, which has been recently corroborated by several studies.

Modeling of dynamic cerebrovascular reactivity to spontaneous and externally induced CO2 fluctuations in the human brain using BOLD-fMRI

In this work, we investigate the regional characteristics of the dynamic interactions between arterial CO₂ and BOLD (dynamic cerebrovascular reactivity - dCVR) during normal breathing and hypercapnic, externally induced step CO₂ challenges. To obtain dCVR curves at each voxel, we use a custom set of basis functions based on the Laguerre and gamma basis sets. This allows us to obtain robust dCVR estimates both in larger regions of interest (ROIs), as well as in individual voxels. We also implement classification schemes to identify brain regions with similar dCVR characteristics. Our results reveal considerable variability of dCVR across different brain regions, as well as during different experimental conditions (normal breathing and hypercapnic challenges), suggesting a differential response of cerebral vasculature to spontaneous CO₂ fluctuations and larger, externally induced CO₂ changes that are possibly associated with the underlying differences in mean arterial CO₂ levels. The clustering results suggest that anatomically distinct brain regions are characterized by different dCVR curves that in some cases do not exhibit the standard, positive valued curves that have been previously reported. They also reveal a consistent set of dCVR cluster shapes for resting and forcing conditions, which exhibit different distribution patterns across brain voxels.