Differences in Cerebral Structure Associated With Depressive Symptoms in the Elderly With Alzheimer's Disease

The Relationship between Diet, Depression, and Alzheimer's Disease: A Narrative Review

PURPOSE OF REVIEW

This narrative review evaluates the role of diet in the relationship between depression and Alzheimer's disease (AD).

RECENT FINDINGS

AD and depression are often comorbid, and depression appears to independently increase the future risk of AD. Evidence suggests diet influences the risk of both conditions directly and indirectly. Diet impacts neurochemical and biological processes that may affect the development and progression of depression and cognitive dysfunction. The dietary components offering the greatest protection against depression and AD are yet to be determined. Current evidence highlights the importance of polyphenolic compounds, folate, B vitamins, and polyunsaturated fatty acids, along with adherence to dietary patterns like the Mediterranean diet, which includes multiple beneficial dietary factors.

SUMMARY

The investigation of dietary factors in the prevention of depression and AD is a comparatively young field of research. Comprehensive highly characterised longitudinal datasets and advanced analytical approaches are required to further examine the complex relationship between diet, depression, and AD. There is a critical need for more research in this area to develop effective preventive strategies aimed at maintaining mental and physical health with advancing age.

Multivariate word properties in fluency tasks reveal markers of Alzheimer's dementia

INTRODUCTION

Verbal fluency tasks are common in Alzheimer's disease (AD) assessments. Yet, standard valid response counts fail to reveal disease-specific semantic memory patterns. Here, we leveraged automated word-property analysis to capture neurocognitive markers of AD vis-à-vis behavioral variant frontotemporal dementia (bvFTD).

METHODS

Patients and healthy controls completed two fluency tasks. We counted valid responses and computed each word's frequency, granularity, neighborhood, length, familiarity, and imageability. These features were used for group-level discrimination, patient-level identification, and correlations with executive and neural (magnetic resonanance imaging [MRI], functional MRI [fMRI], electroencephalography [EEG]) patterns.

RESULTS

Valid responses revealed deficits in both disorders. Conversely, frequency, granularity, and neighborhood yielded robust group- and subject-level discrimination only in AD, also predicting executive outcomes. Disease-specific cortical thickness patterns were predicted by frequency in both disorders. Default-mode and salience network hypoconnectivity, and EEG beta hypoconnectivity, were predicted by frequency and granularity only in AD.

DISCUSSION

Word-property analysis of fluency can boost AD characterization and diagnosis.

HIGHLIGHTS

We report novel word-property analyses of verbal fluency in AD and bvFTD. Standard valid response counts captured deficits and brain patterns in both groups. Specific word properties (e.g., frequency, granularity) were altered only in AD. Such properties predicted cognitive and neural (MRI, fMRI, EEG) patterns in AD. Word-property analysis of fluency can boost AD characterization and diagnosis.

Multimodal data fusion based on IGERNNC algorithm for detecting pathogenic brain regions and genes in Alzheimer's disease

At present, the study on the pathogenesis of Alzheimer's disease (AD) by multimodal data fusion analysis has been attracted wide attention. It often has the problems of small sample size and high dimension with the multimodal medical data. In view of the characteristics of multimodal medical data, the existing genetic evolution random neural network cluster (GERNNC) model combine genetic evolution algorithm and neural network for the classification of AD patients and the extraction of pathogenic factors. However, the model does not take into account the non-linear relationship between brain regions and genes and the problem that the genetic evolution algorithm can fall into local optimal solutions, which leads to the overall performance of the model is not satisfactory. In order to solve the above two problems, this paper made some improvements on the construction of fusion features and genetic evolution algorithm in GERNNC model, and proposed an improved genetic evolution random neural network cluster (IGERNNC) model. The IGERNNC model uses mutual information correlation analysis method to combine resting-state functional magnetic resonance imaging data with single nucleotide polymorphism data for the construction of fusion features. Based on the traditional genetic evolution algorithm, elite retention strategy and large variation genetic algorithm are added to avoid the model falling into the local optimal solution. Through multiple independent experimental comparisons, the IGERNNC model can more effectively identify AD patients and extract relevant pathogenic factors, which is expected to become an effective tool in the field of AD research.

Pinus halepensis Essential Oil Ameliorates Aβ1-42-Induced Brain Injury by Diminishing Anxiety, Oxidative Stress, and Neuroinflammation in Rats

The Pinus L. genus comprises around 250 species, being popular worldwide for their medicinal and aromatic properties. The present study aimed to evaluate the P. halepensis Mill. essential oil (PNO) in an Alzheimer’s disease (AD) environment as an anxiolytic and antidepressant agent. The AD-like symptoms were induced in Wistar male rats by intracerebroventricular administration of amyloid beta1-42 (Aβ1-42), and PNO (1% and 3%) was delivered to Aβ1-42 pre-treated rats via inhalation route for 21 consecutive days, 30 min before behavioral assessments. The obtained results indicate PNO’s potential to relieve anxious–depressive features and to restore redox imbalance in the rats exhibiting AD-like neuropsychiatric impairments. Moreover, PNO presented beneficial effects against neuroinflammation and neuroapoptosis in the Aβ1-42 rat AD model.

Anxiety and depression in Alzheimer's disease: a systematic review of pathogenetic mechanisms and relation to cognitive decline

Objectives

To explore the pathogenetic hypothesis provided to explain the comorbidity of anxious and depressive symptomatology and AD and to assess the association between anxious and depressive symptoms and the AD-related cognitive impairment.

Methods

In October 2020 and March 2021, PsycINFO, Embase, Ovid, and CINAHL were searched for peer-reviewed original articles investigating anxiety and/or depression in AD.

Results

A total of 14,760 studies were identified and 34 papers on AD patients were included in the review. Suggested biological causes of depression and anxiety in AD include higher strychnine-sensitive glycine receptor (GlyRS) functioning and selective reduction of N-methyl-d-aspartate (NMDA) receptor NR2A density, cortical and limbic atrophy, lower resting cortical metabolism, lower CSF Aβ42 and higher t-tau and p-tau levels, and neuritic plaques. At the same time, dysthymia arises in the early stages of AD as an emotional reaction to the progressive cognitive decline and can cause it; anxiety can appear as an initial compensating behaviour; and depression might be related to AD awareness and loss of functional abilities. Affective symptoms and the expression of the depressive symptoms tend to reduce as AD progresses.

Conclusion

The neurodegeneration of areas and circuits dealing with emotions can elicit anxiety and depression in AD. In the early stages of the disease, anxiety and depression could arise as a psychological reaction to AD and due to coping difficulties. In late AD stages, the cognitive impairment reduces the emotional responses and their expression. Anxiety and depression are more intense in early-onset AD, due to the major impact of AD on the individual.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10072-022-06068-x.

Depressive Symptoms Have Distinct Relationships With Neuroimaging Biomarkers Across the Alzheimer’s Clinical Continuum

Background

Depressive and anxiety symptoms are frequent in Alzheimer’s disease and associated with increased risk of developing Alzheimer’s disease in older adults. We sought to examine their relationships to Alzheimer’s disease biomarkers across the preclinical and clinical stages of the disease.

Method

Fifty-six healthy controls, 35 patients with subjective cognitive decline and 56 amyloid-positive cognitively impaired patients on the Alzheimer’s continuum completed depression and anxiety questionnaires, neuropsychological tests and neuroimaging assessments. We performed multiple regressions in each group separately to assess within group associations of depressive and anxiety symptoms with either cognition (global cognition and episodic memory) or neuroimaging data (gray matter volume, glucose metabolism and amyloid load).

Results

Depressive symptoms, but not anxiety, were higher in patients with subjective cognitive decline and cognitively impaired patients on the Alzheimer’s continuum compared to healthy controls. Greater depressive symptoms were associated with higher amyloid load in subjective cognitive decline patients, while they were related to higher cognition and glucose metabolism, and to better awareness of cognitive difficulties, in cognitively impaired patients on the Alzheimer’s continuum. In contrast, anxiety symptoms were not associated with brain integrity in any group.

Conclusion

These data show that more depressive symptoms are associated with greater Alzheimer’s disease biomarkers in subjective cognitive decline patients, while they reflect better cognitive deficit awareness in cognitively impaired patients on the Alzheimer’s continuum. Our findings highlight the relevance of assessing and treating depressive symptoms in the preclinical stages of Alzheimer’s disease.

Connectivity and variability of related cognitive subregions lead to different stages of progression toward Alzheimer's disease

Single modality MRI data is not enough to depict and discern the cause of the underlying brain pathology of Alzheimer's disease (AD). Most existing studies do not perform well with multi-group classification. To reveal the structural, functional connectivity and functional topological relationships among different stages of mild cognitive impairment (MCI) and AD, a novel method was proposed in this paper for the analysis of regional importance with an improved deep learning model. Obvious drift of related cognitive regions can be observed in the prefrontal lobe and surrounding the cingulate area in the right hemisphere when comparing AD and healthy controls (HC) based on absolute weights in the classification mode. Alterations of these regions being responsible for cognitive impairment have been previously reported. Different parcellation atlases of the human cerebral cortex were compared, and the fine-grained multimodal parcellation HCPMMP performed the best with 180 cortical areas per hemisphere. In multi-group classification, the highest accuracy achieved was 96.86% with the utilization of structural and functional topological modalities as input to the training model. Weights in the trained model with perfect discriminating ability quantify the importance of each cortical region. This is the first time such a phenomenon is discovered and weights in cortical areas are precisely described in AD and its prodromal stages to the best of our knowledge. Our findings can establish other study models to differentiate the patterns in various diseases with cognitive impairments and help to identify the underlying pathology.

The Cerebellum Is Related to Cognitive Dysfunction in White Matter Hyperintensities

Objective

White matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is frequently presumed to be secondary to cerebral small vessel disease (CSVD) and associated with cognitive decline. The cerebellum plays a key role in cognition and has dense connections with other brain regions. Thus, the aim of this study was to investigate if cerebellar abnormalities could occur in CSVD patients with WMHs and the possible association with cognitive performances.

Methods

A total of 104 right-handed patients with WMHs were divided into the mild WMHs group (n = 39), moderate WMHs group (n = 37), and severe WMHs group (n = 28) according to the Fazekas scale, and 36 healthy controls were matched for sex ratio, age, education years, and acquired resting-state functional MRI. Analysis of voxel-based morphometry of gray matter volume (GMV) and seed-to-whole-brain functional connectivity (FC) was performed from the perspective of the cerebellum, and their correlations with neuropsychological variables were explored.

Results

The analysis revealed a lower GMV in the bilateral cerebellum lobule VI and decreased FC between the left- and right-sided cerebellar lobule VI with the left anterior cingulate gyri in CSVD patients with WMHs. Both changes in structure and function were correlated with cognitive impairment in patients with WMHs.

Conclusion

Our study revealed damaged GMV and FC in the cerebellum associated with cognitive impairment. This indicates that the cerebellum may play a key role in the modulation of cognitive function in CSVD patients with WMHs.

Cortical thickness in chronic pain: A protocol for systematic review and meta-analysis

BACKGROUND

Numerous studies using a variety of non-invasive neuroimaging techniques in vivo have demonstrated that chronic pain (CP) is associated with brain alterations. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis of magnetic resonance imaging data is a valid and sensitive method to investigate the structure of brain gray matter. Many studies have employed SBM to measure CTh difference between patients with CP and pain-free controls and provided important insights into the brain basis of CP. However, the findings from these studies were inconsistent and have not been quantitatively reviewed.

METHODS

Three major electronic medical databases: PubMed, Web of Science, and Embase were searched for eligible studies published in English on April 3, 2020. This protocol was prepared based on the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. The Seed-based d Mapping with Permutation of Subject Images software package will be employed to conducted a coordinate-based meta-analysis (CBMA) to identify consistent CTh differences between patients with CP and pain-free controls. Several complementary analyses, including sensitivity analysis, heterogeneity analysis, publication bias, subgroup analysis, and meta-regression analysis, will be further conducted to test the robustness of the results.

RESULTS

This CBMA will tell us whether CP with different subtypes shares common CTh alterations and what the pattern of its characterized alterations is.

CONCLUSIONS

To the best of our knowledge, this will be the first CBMA of SBM studies that characterizes brain CTh alterations in CP. The CBMA will provide the quantitative evidence of common brain cortical morphometry of CP. The findings will help us to understand the neural basis underlying CP.

TRIAL REGISTRATION NUMBER

INPLASY202050069.