Amplitude of low-frequency fluctuations on Alzheimer's disease with depression: evidence from resting-state fMRI

The mediating effect of the amygdala-frontal circuit on the association between depressive symptoms and cognitive function in Alzheimer's disease

Depressive symptoms occur commonly in Alzheimer's disease (AD). Although abnormalities in the amygdala-frontal circuit have been linked to emotional dysregulation and cognitive impairment, the neurological basis underlying these associations in AD patients with depressive symptoms (ADD) is unclear. We aimed to investigate the relationship between the amygdala-frontal circuit and depressive symptoms and cognitive function in ADD. We recruited 60 ADD, 60 AD patients without depressive symptoms (ADND), and 60 healthy controls (HC). Functional connectivity (FC) maps of the bilateral amygdala were compared. Fractional anisotropy (FA) of the amygdala-frontal circuit connected by the uncinate fasciculus (UF) was calculated using automated fiber quantification (AFQ). In addition, mediation analysis was performed to explore the effects of the amygdala-frontal circuit on the relationship between depressive symptoms and cognitive function. We found decreased bilateral amygdala FC with the inferior frontal gyrus (IFG) in the ADD group compared to the ADND and HC groups. Moreover, FA in the left frontal UF (nodes 64-97) was significantly lower in the ADD group than ADND group. Notably, amygdala-based FC with IFG and the left frontal UF FA mediated the relationship between depressive symptoms and cognitive function in ADD, with mediating effects ranging between 15 and 18%. Our study is the first to demonstrate the mediating effect of functional and microstructural abnormalities in the amygdala-frontal circuit in ADD. The findings suggest that the amygdala-frontal circuit may underlie emotional dysregulation in ADD, providing potential targets for treatment strategies.

Correlation Between the Fractional Amplitude of Low-Frequency Fluctuation and Cognitive Defects in Alzheimer's Disease

Background

The fractional amplitude of low-frequency fluctuations (fALFFs) can detect spontaneous brain activity. However, the association between abnormal brain activity and cognitive function, amyloid protein (Aβ), and emotion in Alzheimer's disease (AD) patients remains unclear.

Objective

This study aimed to survey alterations in fALFF in different frequency bands and the relationship between abnormal brain activity, depressive mood, and cognitive function to determine the potential mechanism of AD.

Methods

We enrolled 34 AD patients and 32 healthy controls (HC). All the participants underwent resting-state magnetic resonance imaging, and slow-4 and slow-5 fALFF values were measured. Subsequently, the study determined the correlation of abnormal brain activity with mood and cognitive function scores.

Results

AD patients revealed altered mfALFF values in the slow-5 and slow-4 bands. In the slow-4 band, the altered mfALFF regions were the right cerebellar crus I, right inferior frontal orbital gyrus (IFOG), right supramarginal gyrus, right precuneus, angular gyrus, and left middle cingulate gyrus. Elevated mfALFF values in the right IFOG were negatively associated with Montreal Cognitive Assessment scores, Boston Naming Test, and Aβ1-42 levels. The mfALFF value of the AD group was lower than the HC group in the slow-5 band, primarily within the right inferior parietal lobule and right precuneus.

Conclusions

Altered mfALFF values in AD patients are linked with cognitive dysfunction. Compared with HCs, Aβ1-42 levels in AD patients are related to abnormal IFOG activity. Therefore, mfALFF could be a potential biomarker of AD.

Altered static and dynamic intrinsic brain activity in unilateral sudden sensorineural hearing loss

Introduction

Sudden sensorineural hearing loss (SSHL) is a critical otologic emergency characterized by a rapid decline of at least 30 dB across three consecutive frequencies in the pure-tone audiogram within a 72-hour period. This audiological condition has been associated with alterations in brain cortical and subcortical structures, as well as changes in brain functional activities involving multiple networks. However, the extent of cerebral intrinsic brain activity disruption in SSHL remains poorly understood. The aimed of this study is to investigate intrinsic brain activity alterations in SSHL using static and dynamic fractional amplitude of low-frequency fluctuation (fALFF) analysis.

Methods

Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a cohort of SSHL patients (unilateral, n = 102) and healthy controls (n = 73). Static and dynamic fALFF methods were employed to analyze the acquired fMRI data, enabling a comprehensive examination of intrinsic brain activity changes in SSHL.

Results

Our analysis revealed significant differences in static fALFF patterns between SSHL patients and healthy controls. SSHL patients exhibited decreased fALFF in the left fusiform gyrus, left precentral gyrus, and right inferior frontal gyrus, alongside increased fALFF in the left inferior frontal gyrus, left superior frontal gyrus, and right middle temporal gyrus. Additionally, dynamic fALFF analysis demonstrated elevated fALFF in the right superior frontal gyrus and right middle frontal gyrus among SSHL patients. Intriguingly, we observed a positive correlation between static fALFF in the left fusiform gyrus and the duration of hearing loss, shedding light on potential temporal dynamics associated with intrinsic brain activity changes.

Discussion

The observed disruptions in intrinsic brain activity and temporal dynamics among SSHL patients provide valuable insights into the functional reorganization and potential compensatory mechanisms linked to hearing loss. These findings underscore the importance of understanding the underlying neural alterations in SSHL, which could pave the way for the development of targeted interventions and rehabilitation strategies aimed at optimizing SSHL management.

What do we know about pseudodementia?

Depression and dementia can lead to generalised cognitive and memory dysfunction. Thus, differentiating these disorders is important and challenging. Pseudodementia is a term used clinically to describe symptoms that resemble dementia but are caused by other conditions (most frequently depression), rather than being recognised as an official diagnosis. Pseudodementia is characterised by a cognitive impairment that mimics dementia but which does not have its origin in neurological degeneration, deriving instead from functional psychiatric conditions. This condition is more commonly observed in older adults (particularly those over the age of 50 or 60 years), and its risk factors overlap with those for depression. Pseudodementia is essentially characterised by deficits in memory, executive function and speech and, therefore, can easily be confused with dementia, although there are aspects that allow its differentiation. Diagnosing pseudodementia can be difficult, especially as there is significant overlap between its symptoms and those of other conditions. However, it is important to recognise characteristic aspects of this disorder, as its correct identification is essential for proper treatment.

Disrupted topological organization of functional brain networks in Alzheimer's disease patients with depressive symptoms

BACKGROUND

Depression is a common symptom of Alzheimer's disease (AD), but the underlying neural mechanism is unknown. The aim of this study was to explore the topological properties of AD patients with depressive symptoms (D-AD) using graph theoretical analysis.

METHODS

We obtained 3-Tesla rsfMRI data from 24 D-AD patients, 20 non-depressed AD patients (nD-AD), and 20 normal controls (NC). Resting state networks were identified using graph theory analysis. ANOVA with a two-sample t-test post hoc analysis in GRETNA was used to assess the topological measurements.

RESULTS

Our results demonstrate that the three groups show characteristic properties of a small-world network. NCs showed significantly larger global and local efficiency than D-AD and nD-AD patients. Compared with nD-AD patients, D-AD patients showed decreased nodal centrality in the pallidum, putamen, and right superior temporal gyrus. They also showed increased nodal centrality in the right superior parietal gyrus, the medial orbital portion of the right superior frontal gyrus, and the orbital portion of the right superior frontal gyrus. Compared with nD-AD patients, NC showed decreased nodal betweenness in the right superior temporal gyrus, and increased nodal betweenness in medial orbital part of the right superior frontal gyrus.

CONCLUSIONS

These results indicate that D-AD is associated with alterations of topological structure. Our study provides new insights into the brain mechanisms underlying D-AD.

Frequency-specific alterations in intrinsic low-frequency oscillations in newly diagnosed male patients with obstructive sleep apnea

Purpose

Previous studies found abnormal low-frequency spontaneous brain activity related to cognitive impairment in patients with obstructive sleep apnea (OSA). However, it is unclear if low-frequency spontaneous brain activity is related to specific frequency bands in OSA patients. In this study, we used the amplitude of low-frequency fluctuation (ALFF) method in patients with OSA to explore characteristics of spontaneous brain activity in the classical (0.01–0.1 Hz) and five sub-frequency bands (slow-2 to slow-6) and analyzed the relationship between spontaneous brain activity and clinical evaluation was analyzed.

Patients and methods

Resting-state magnetic resonance imaging data and clinical assessments were collected from 52 newly-diagnosed OSA patients and 62 healthy controls (HCs). We calculated the individual group ALFF values in the classical and five different sub-frequency bands. A two-sample t-test compared ALFF differences, and one-way analysis of variance explored interactions in frequency bands between the two groups.

Results

ALFF values in the OSA group were lower than those in the HC group in the bilateral precuneus/posterior cingulate cortex, bilateral angular gyrus, left inferior parietal lobule, brainstem, and right fusiform gyrus. In contrast, ALFF values in the OSA group were higher than those in the HC group in the bilateral cerebellum posterior lobe, bilateral superior frontal gyrus, bilateral middle frontal gyrus, left inferior frontal gyrus, left inferior temporal gyrus, and left fusiform gyrus. Some ALFF values in altered brain regions were associated with body mass index, apnea-hypopnea index, neck circumference, snoring history, minimum SaO₂, average SaO₂, arousal index, oxygen reduction index, deep sleep period naming, abstraction, and delayed recall in specific frequency bands.

Conclusion

Our results indicated the existence of frequency-specific differences in spontaneous brain activity in OSA patients, which were related to cognitive and other clinical symptoms. This study identified frequency-band characteristics related to brain damage, expanded the cognitive neuroimaging mechanism, and provided additional OSA neuroimaging markers.

Intrinsic brain activity reorganization contributes to long-term compensation of higher-order hearing abilities in single-sided deafness

Single-sided deafness (SSD) is an extreme case of partial hearing deprivation and results in a significant decline in higher-order hearing abilities, including sound localization and speech-in-noise recognition. Clinical studies have reported that patients with SSD recover from these higher-order hearing abilities to some extent over time. Neuroimaging studies have observed extensive brain functional plasticity in patients with SSD. However, studies investigating the role of plasticity in functional compensation, particularly those investigating the relationship between intrinsic brain activity alterations and higher-order hearing abilities, are still limited. In this study, we used resting-state functional MRI to investigate intrinsic brain activity, measured by the amplitude of low-frequency fluctuation (ALFF), in 19 patients with left SSD, 17 patients with right SSD, and 21 normal hearing controls (NHs). All patients with SSD had durations of deafness longer than 2 years. Decreased ALFF values in the bilateral precuneus (PCUN), lingual gyrus, and left middle frontal gyrus were observed in patients with SSD compared with the values of NHs. Longer durations of deafness were correlated with better hearing abilities, as well as higher ALFF values in the left inferior parietal lobule, the angular gyrus, the middle occipital gyrus, the bilateral PCUN, and the posterior cingulate gyrus. Moreover, we observed a generally consistent trend of correlation between ALFF values and higher-order hearing abilities in specific brain areas in patients with SSD. That is, better abilities were correlated with lower ALFF values in the frontal regions and higher ALFF values in the PCUN and surrounding parietal-occipital areas. Furthermore, mediation analysis revealed that the ALFF values in the PCUN were a significant mediator of the relationship between the duration of deafness and higher-order hearing abilities. Our study reveals significant plasticity of intrinsic brain activity in patients with SSD and suggests that reorganization of intrinsic brain activity may be one of the compensatory mechanisms that facilitate improvement in higher-order hearing abilities in these patients over time.

Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view Canonical Correlation Analysis

Image genetics mainly explores the pathogenesis of Alzheimer's disease (AD) by studying the relationship between genetic data (such as SNP, gene expression data, and DNA methylation) and imaging data (such as structural MRI (sMRI), fMRI, and PET). Most of the existing research on brain imaging genomics uses two-way or three-way bi-multivariate methods to explore the correlation analysis between genes and brain imaging. However, many of these methods are still affected by the gradient domination or cannot take into account the effect of feature redundancy on the results, so that the typical correlation coefficient and program running speed are not significantly improved. In order to solve the above problems, this paper proposes a multi-constrained uncertainty-aware adaptive sparse multi-view canonical correlation analysis method (MC-unAdaSMCCA) to explore associations among SNPs, gene expression data, and sMRI; that is, based on traditional unAdaSMCCA, orthogonal constraints are imposed on the weights of the three data features through linear programming, which can reduce the redundancy of feature weights to improve the correlation between the data and reduce the complexity of the algorithm to significantly speed up the running speed of the program. Three adaptive sparse multi-view canonical correlation analysis methods are used as benchmarks to evaluate the difference between real neuroimaging data and synthetic data. Compared with the other three methods, our proposed method has obtained better or comparable typical correlation coefficients and typical weights. Moreover, the following experimental results show that the MC-unAdaSMCCA method cannot only identify biomarkers related to AD and mild cognitive impairment (MCI), but also has a strong ability to resist noise and process high-dimensional data. Therefore, our proposed method provides a reliable approach to multi-modal imaging genetic researches.

INCloud: integrated neuroimaging cloud for data collection, management, analysis and clinical translations

Background

Neuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses.

Aims

To tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications.

Methods

INCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders.

Results

Users can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown.

Conclusions

Compared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields.

Neuroinflammation in perioperative neurocognitive disorders: From bench to the bedside

The perioperative neurocognitive disorders (PNDs) are one of the most common complications in elderly patients characterized by various forms of cognitive decline after anesthesia and surgery. Although the etiology for PNDs remained unclear, neuroinflammation has been characterized as one of the major causes, especially in the elderly patients. The activation of glial cells including microglia and astrocytes plays a significant role in the inflammatory responses in central nerve system (CNS). Although carefully designed, clinical studies on PNDs showed controversial results. Meanwhile, preclinical studies provided evidence from various levels, including behavior performance, protein levels, and gene expression. In this review, we summarize high‐quality studies and recent advances from both clinical and preclinical studies and provide a broad view from the onset of PNDs to its potential therapeutic targets. Future studies are needed to investigate the signaling pathways in PNDs for prevention and treatment, as well as the relationship of PNDs and future neurocognitive dysfunction.