Higher Functional Connectivity of Ventral Attention and Visual Network to Maintain Cognitive Performance in White Matter Hyperintensity

Resting-State Brain Network Characteristics Related to Mild Cognitive Impairment: A Preliminary fNIRS Proof-of-Concept Study

BACKGROUND

This study investigates the reliability of functional near-infrared spectroscopy (fNIRS) in detecting resting-state brain network characteristics in patients with mild cognitive impairment (MCI), focusing on static resting-state functional connectivity (sRSFC) and dynamic resting-state functional connectivity (dRSFC) patterns in MCI patients and healthy controls (HCs) without cognitive impairment.

METHODS

A total of 89 MCI patients and 83 HCs were characterized using neuropsychological scales. Subject sRSFC strength and dRSFC variability coefficients were evaluated via fNIRS. The study evaluated the feasibility of using fNIRS to measure these connectivity metrics and compared resting-state brain network characteristics between the two groups. Correlations with Montreal Cognitive Assessment (MoCA) scores were also explored.

RESULTS

sRSFC strength in homologous brain networks was significantly lower than in heterologous networks (p < 0.05). A significant negative correlation was also observed between sRSFC strength and dRSFC variability at both the group and individual levels (p < 0.001). While sRSFC strength did not differentiate between MCI patients and HCs, the dRSFC variability between the dorsal attention network (DAN) and default mode network (DMN), and between the ventral attention network (VAN) and visual network (VIS), emerged as sensitive biomarkers after false discovery rate correction (p < 0.05). No significant correlation was found between MoCA scores and connectivity measures.

CONCLUSIONS

fNIRS can be used to study resting-state brain networks, with dRSFC variability being more sensitive than sRSFC strength for discriminating between MCI patients and HCs. The DAN-DMN and VAN-VIS regions were found to be particularly useful for the identification of dRSFC differences between the two groups.

CLINICAL TRIAL REGISTRATION

ChiCTR2200057281, registered on 6 March, 2022; https://www.chictr.org.cn/showproj.html?proj=133808.

Association of white matter hyperintensities with cognitive decline and neurodegeneration

Background

The relationship between white matter hyperintensities (WMH) and the core features of Alzheimer's disease (AD) remains controversial. Further, due to the prevalence of co-pathologies, the precise role of WMH in cognition and neurodegeneration also remains uncertain.

Methods

Herein, we analyzed 1803 participants with available WMH volume data, extracted from the ADNI database, including 756 cognitively normal controls, 783 patients with mild cognitive impairment (MCI), and 264 patients with dementia. Participants were grouped according to cerebrospinal fluid (CSF) pathology (A/T profile) severity. Linear regression analysis was applied to evaluate the factors associated with WMH volume. Modeled by linear mixed-effects, the increase rates (Δ) of the WMH volume, cognition, and typical neurodegenerative markers were assessed. The predictive effectiveness of WMH volume was subsequently tested using Cox regression analysis, and the relationship between WMH/ΔWMH and other indicators such as cognition was explored through linear regression analyses. Furthermore, we explored the interrelationship among amyloid-β deposition, cognition, and WMH using mediation analysis.

Results

Higher WMH volume was associated with older age, lower CSF amyloid-β levels, hypertension, and smoking history (all p ≤ 0.001), as well as cognitive status (MCI, p < 0.001; dementia, p = 0.008), but not with CSF tau levels. These results were further verified in any clinical stage, except hypertension and smoking history in the dementia stage. Although WMH could not predict dementia conversion, its increased levels at baseline were associated with a worse cognitive performance and a more rapid memory decline. Longitudinal analyses showed that baseline dementia and positive amyloid-β status were associated with a greater accrual of WMH volume, and a higher ΔWMH was also correlated with a faster cognitive decline. In contrast, except entorhinal cortex thickness, the WMH volume was not found to be associated with any other neurodegenerative markers. To a lesser extent, WMH mediates the relationship between amyloid-β and cognition.

Conclusion

WMH are non-specific lesions that are associated with amyloid-β deposition, cognitive status, and a variety of vascular risk factors. Despite evidence indicating only a weak relationship with neurodegeneration, early intervention to reduce WMH lesions remains a high priority for preserving cognitive function in the elderly.

Gradients of O-information highlight synergy and redundancy in physiological applications

The study of high order dependencies in complex systems has recently led to the introduction of statistical synergy, a novel quantity corresponding to a form of emergence in which patterns at large scales are not traceable from lower scales. As a consequence, several works in the last years dealt with the synergy and its counterpart, the redundancy. In particular, the O-information is a signed metric that measures the balance between redundant and synergistic statistical dependencies. In spite of its growing use, this metric does not provide insight about the role played by low-order scales in the formation of high order effects. To fill this gap, the framework for the computation of the O-information has been recently expanded introducing the so-called gradients of this metric, which measure the irreducible contribution of a variable (or a group of variables) to the high order informational circuits of a system. Here, we review the theory behind the O-information and its gradients and present the potential of these concepts in the field of network physiology, showing two new applications relevant to brain functional connectivity probed via functional resonance imaging and physiological interactions among the variability of heart rate, arterial pressure, respiration and cerebral blood flow.