Electrophysiological resting state brain network and episodic memory in healthy aging adults

Lateralization of the Aberrant Amplitude of Low-Frequency Fluctuation within the Default Mode Network in Patients with Mild Cognitive Impairment

RATIONALE AND OBJECTIVES

Alzheimer's disease (AD) is the most common pathogenesis of dementia, and mild cognitive impairment (MCI) is considered as the intermediate stage from normal elderly to AD. Early detection of MCI is an essential step for the timely intervention of AD to slow the progression of this disease. Different form previous studies in the whole-brain spontaneous activities, this research aimed to explore the low-frequency amplitude spectrum activities of patients with MCI within the default mode network (DMN), which has been involved in the process of maintaining normal cognitive function.

MATERIALS AND METHODS

Based on resting-state functional magnetic resonance imaging, the amplitude of low-frequency fluctuation (ALFF) was used to analyze alterations in brain regions. The Mini-Mental State Examination and Montreal Cognitive Assessment were used for cognitive assessments. The correlation between imaging and behavioral results was analyzed among patients with MCI (n=36) and normal controls (n=26).

RESULTS

The DMN is the highest coverage of brain network regarding changes in local brain activity in patients with MCI. And the MCI group showed significant aberrant lateralization of the ALFF value.

CONCLUSION

The current results of our study has confirmed the hypothesis of cerebral functional impairment and compensation, and suggests that functional changes in the brain regions with reduced values of the ALFF occurred earlier than those with increased values. In a word, it suggested that the aberrant spontaneous brain activity in the DMN might be a specific imaging marker for improving MCI diagnoses.

Functional connectivity of default mode network in non-hospitalized patients with post-COVID cognitive complaints

Introduction

Neurologic impairment is common in patients with acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. While patients with severe COVID have a higher prevalence of neurologic symptoms, as many as one in five patients with mild COVID may also be affected, exhibiting impaired memory as well as other cognitive dysfunctions.

Methods

To characterize the effect of COVID on the brain, the current study recruited a group of adults with post-COVID cognitive complaints but with mild, non-hospitalized cases. They were then evaluated through formal neuropsychological testing and underwent functional MRI of the brain. The participants in our study performed nearly as expected for cognitively intact individuals. Additionally, we characterized the functional connectivity of the default mode network (DMN), which is known for cognitive functions including memory as well as the attention functions involved in normal aging and degenerative diseases.

Results

Along with the retention of functional connectivity in the DMN, our results found the DMN to be associated with neurocognitive performance through region-of-interest and whole-brain analyses. The connectivity between key nodes of the DMN was positively correlated with cognitive scores (r = 0.51, p = 0.02), with higher performers exhibiting higher DMN connectivity.

Discussion

Our findings provide neuroimaging evidence of the functional connectivity of brain networks among individuals experiencing cognitive deficits beyond the recovery of mild COVID. These imaging outcomes indicate expected functional trends in the brain, furthering understanding and guidance of the DMN and neurocognitive deficits in patients recovering from COVID.

Age-related p53 SUMOylation accelerates senescence and tau pathology in Alzheimer's disease

Aging is a major risk factor for Alzheimer's disease (AD). With the prevalence of AD increased, a mechanistic linkage between aging and the pathogenesis of AD needs to be further addressed. Here, we report that a small ubiquitin-related modifier (SUMO) modification of p53 is implicated in the process which remarkably increased in AD patient's brain. Mechanistically, SUMOylation of p53 at K386 residue causes the dissociation of SET/p53 complex, thus releasing SET into the cytoplasm, SET further interacts with cytoplasmic PP2A and inhibits its activity, resulting in tau hyperphosphorylation in neurons. In addition, SUMOylation of p53 promotes the p53 Ser15 phosphorylation that mediates neuronal senescence. Notably, p53 SUMOylation contributes to synaptic damage and cognitive defects in AD model mice. We also demonstrate that the SUMOylation inhibiter, Ginkgolic acid, recovering several senescent phenotypes drove by p53 SUMOylation in primary neurons. These findings suggest a previously undiscovered etiopathogenic relationship between aging and AD that is linked to p53 SUMOylation and the potential of SUMOylated p53-based therapeutics for neurodegeneration such as Alzheimer's disease.

SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks

Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.

Network organization of resting-state cerebral hemodynamics and their aliasing contributions measured by functional near-infrared spectroscopy

Objective. Spontaneous fluctuations of cerebral hemodynamics measured by functional magnetic resonance imaging (fMRI) are widely used to study the network organization of the brain. The temporal correlations among the ultra-slow, <0.1 Hz fluctuations across the brain regions are interpreted as functional connectivity maps and used for diagnostics of neurological disorders. However, despite the interest narrowed in the ultra-slow fluctuations, hemodynamic activity that exists beyond the ultra-slow frequency range could contribute to the functional connectivity, which remains unclear.Approach. In the present study, we have measured the brain-wide hemodynamics in the human participants with functional near-infrared spectroscopy (fNIRS) in a whole-head, cap-based and high-density montage at a sampling rate of 6.25 Hz. In addition, we have acquired resting state fMRI scans in the same group of participants for cross-modal evaluation of the connectivity maps. Then fNIRS data were deliberately down-sampled to a typical fMRI sampling rate of ∼0.5 Hz and the resulted differential connectivity maps were subject to a k-means clustering.Main results. Our diffuse optical topographical analysis of fNIRS data have revealed a default mode network (DMN) in the spontaneous deoxygenated and oxygenated hemoglobin changes, which remarkably resemble the same fMRI network derived from participants. Moreover, we have shown that the aliased activities in the down-sampled optical signals have altered the connectivity patterns, resulting in a network organization of aliased functional connectivity in the cerebral hemodynamics.Significance.The results have for the first time demonstrated that fNIRS as a broadly accessible modality can image the resting-state functional connectivity in the posterior midline, prefrontal and parietal structures of the DMN in the human brain, in a consistent pattern with fMRI. Further empowered by the fast sampling rate of fNIRS, our findings suggest the presence of aliased connectivity in the current understanding of the human brain organization.

Age-related changes of whole-brain dynamics in spontaneous neuronal coactivations

Human brains experience whole-brain anatomic and functional changes throughout the lifespan. Age-related whole-brain network changes have been studied with functional magnetic resonance imaging (fMRI) to determine their low-frequency spatial and temporal characteristics. However, little is known about age-related changes in whole-brain fast dynamics at the scale of neuronal events. The present study investigated age-related whole-brain dynamics in resting-state electroencephalography (EEG) signals from 73 healthy participants from 6 to 65 years old via characterizing transient neuronal coactivations at a resolution of tens of milliseconds. These uncovered transient patterns suggest fluctuating brain states at different energy levels of global activations. Our results indicate that with increasing age, shorter lifetimes and more occurrences were observed in the brain states that show the global high activations and more consecutive visits to the global highest-activation brain state. There were also reduced transitional steps during consecutive visits to the global lowest-activation brain state. These age-related effects suggest reduced stability and increased fluctuations when visiting high-energy brain states and with a bias toward staying low-energy brain states. These age-related whole-brain dynamics changes are further supported by changes observed in classic alpha and beta power, suggesting its promising applications in examining the effect of normal healthy brain aging, brain development, and brain disease.

Exploring Network Properties Across Preclinical Stages of Alzheimer’s Disease Using a Visual Short-Term Memory and Attention Task with High-Density Electroencephalography: A Brain-Connectome Neurophysiological Study

Background: Visual short-term memory (VSTMT) and visual attention (VAT) exhibit decline in the Alzheimer’s disease (AD) continuum; however, network disruption in preclinical stages is scarcely explored. Objective: To advance our knowledge about brain networks in AD and discover connectivity alterations during VSTMT and VAT. Methods: Twelve participants with AD, 23 with mild cognitive impairment (MCI), 17 with subjective cognitive decline (SCD), and 21 healthy controls (HC) were examined using a neuropsychological battery at baseline and follow-up (three years). At baseline, the subjects were examined using high density electroencephalography while performing a VSTMT and VAT. For exploring network organization, we constructed weighted undirected networks and examined clustering coefficient, strength, and betweenness centrality from occipito-parietal regions. Results: One-way ANOVA and pair-wise t-test comparisons showed statistically significant differences in HC compared to SCD (t (36) = 2.43, p = 0.026), MCI (t (42) = 2.34, p = 0.024), and AD group (t (31) = 3.58, p = 0.001) in Clustering Coefficient. Also with regards to Strength, higher values for HC compared to SCD (t (36) = 2.45, p = 0.019), MCI (t (42) = 2.41, p = 0.020), and AD group (t (31) = 3.58, p = 0.001) were found. Follow-up neuropsychological assessment revealed converge of 65% of the SCD group to MCI. Moreover, SCD who were converted to MCI showed significant lower values in all network metrics compared to the SCD that remained stable. Conclusion: The present findings reveal that SCD exhibits network disorganization during visual encoding and retrieval with intermediate values between MCI and HC.