Alzheimer's Disease Genetic Influences Impact the Associations between Diet and Resting-State Functional Connectivity: A Study from the UK Biobank

BACKGROUND

Red wine and dairy products have been staples in human diets for a long period. However, the impact of red wine and dairy intake on brain network activity remains ambiguous and requires further investigation.

METHODS

This study investigated the associations between dairy and red wine consumption and seven neural networks' connectivity with functional magnetic resonance imaging (fMRI) data from a sub-cohort of the UK Biobank database. Linear mixed models were employed to regress dairy and red wine consumption against the intrinsic functional connectivity for each neural network. Interactions with Alzheimer's disease (AD) risk factors, including apolipoprotein E4 (APOE4) genotype, TOMM40 genotype, and family history of AD, were also assessed.

RESULT

More red wine consumption was associated with enhanced connectivity in the central executive function network and posterior default mode network. Greater milk intake was correlated with more left executive function network connectivity, while higher cheese consumption was linked to reduced posterior default mode network connectivity. For participants without a family history of Alzheimer's disease (AD), increased red wine consumption was positively correlated with enhanced left executive function network connectivity. In contrast, participants with a family history of AD displayed diminished network connectivity in relation to their red wine consumption. The association between cheese consumption and neural network connectivity was influenced by APOE4 status, TOMM40 status, and family history, exhibiting contrasting patterns across different subgroups.

CONCLUSION

The findings of this study indicate that family history modifies the relationship between red wine consumption and network strength. The interaction effects between cheese intake and network connectivity may vary depending on the presence of different genetic factors.

Regional Hurst Exponent Reflects Impulsivity-Related Alterations in Fronto-Hippocampal Pathways Within the Waiting Impulsivity Network

In general, the Hurst exponent. is used as a measure of long-term memory of time series. In previous neuroimaging studies, H has been introduced as one important parameter to define resting-state networks, reflecting upon global scale-free properties emerging from a network. H has been examined in the waiting impulsivity (WI) network in an earlier study. We found that alterations of H in the anterior cingulate cortex (H^ACC) and the nucleus accumbens (H^NAcc) were lower in high impulsive (highIMP) compared to low impulsive (lowIMP) participants. Following up on those findings, we addressed the relation between altered fractality in H^ACC and H^NAcc and brain activation and neural network connectivity. To do so, brain activation maps were calculated, and network connectivity was determined using the Dynamic Causal Modeling (DCM) approach. Finally, 1–H scores were determined to quantify the alterations of H. This way, the focus of the analyses was placed on the potential effects of alterations of H on neural network activation and connectivity. Correlation analyses between the alterations of H^ACC/H^NAcc and activation maps and DCM estimates were performed. We found that the alterations of H predominantly correlated with fronto-hippocampal pathways and correlations were significant only in highIMP subjects. For example, alterations of H^ACC was associated with a decrease in neural activation in the right HC in combination with increased ACC-hippocampal connectivity. Alteration inH^NAcc, in return, was related to an increase in bilateral prefrontal activation in combination with increased fronto-hippocampal connectivity. The findings, that the WI network was related to H alteration in highIMP subjects indicated that impulse control was not reduced per se but lacked consistency. Additionally, H has been used to describe long-term memory processes before, e.g., in capital markets, energy future prices, and human memory. Thus, current findings supported the relation of H toward memory processing even when further prominent cognitive functions were involved.

Comparison Analysis between the Medication Efficacy of the Milnacipran and Functional Connectivity of Neural Networks in Fibromyalgia Patients

Milnacipran is a reuptake inhibitor of both serotonin and noradrenaline, used in the treatment of fibromyalgia with severe depression. However, few studies have been conducted on the efficacies of milnacipran drug on the functional connectivity of the neural network. The authors aimed to find the correlation between the drug efficacy and the changes in neural network in fibromyalgia patients. Resting-state-functional magnetic resonance imaging (rs-fMRI) were obtained before and after milnacipran drug administration. Graph theory indexes and small-worldness were calculated using preprocessed blood-oxygen-level-dependent signals from the rs-fMRI scans of 14 brain regions-of-interest. Statistical analyses were conducted to compare the topological network parameters. Significant changes in the neural network indexes appeared in three of the 14 brain regions-of-interest. In the pain network, the average path length on the left side of Brodmann area 32 was shortened. In the default mode network, functional connectivity changes were observed in the left lateral parietal cortex and medial prefrontal cortex. In the left lateral parietal cortex, the degree and betweenness centrality increased, whereas the clustering coefficient decreased. In the medial prefrontal cortex, local efficiency decreased. The small-worldness declined after milnacipran medication. The present results demonstrate that functional connectivity indexes in the brains of female fibromyalgia patients obtained from rs-fMRI data can be used as potential prognosis markers of milnacipran drug treatment.