Language Network Connectivity Increases in Early Alzheimer’s Disease

Abstract

Background: Language production deficits occur early in the course of Alzheimer’s disease (AD); however, only a few studies have focused on language network’s functional connectivity in mild cognitive impairment (MCI) due to AD. Objective: The current study aims to uncover the extent of language alteration at the MCI stage, at a behavioral and neural level, using univariate and multivariate analyses of structural MRI and resting-state fMRI. Methods: Twenty-four MCI due to AD participants and 24 matched healthy controls underwent a comprehensive language evaluation, a structural T1-3D MRI, and resting-state fMRI. We performed seed-based analyses, using the left inferior frontal gyrus and left posterior temporal gyrus as seeds. Then, we analyzed connectivity between executive control networks and language network in each group. Finally, we used multivariate pattern analyses to test whether the two groups could be distinguished based on the pattern of atrophy within the language network; within the executive control networks, as well as the pattern of functional connectivity within the language network and within the executive control networks. Results: MCI due to AD participants had language impairment during standardized language tasks and connected-speech production. Regarding functional connectivity, univariate analyses were not able to discriminate participants, while multivariate pattern analyses could significantly predict participants’ group. Language network’s functional connectivity could discriminate MCI due to AD participants better than executive control networks. Most notably, they revealed an increased connectivity at the MCI stage, positively correlated with language performance. Conclusion: Multivariate analyses represent a useful tool for investigating the functional and structural (re-)organization of the neural bases of language.

Differences in EEG oscillations between normal aging and mild cognitive impairment during semantic memory retrieval

“…It is unclear what this reverse pattern indicates, but these findings suggest that HA and MCI groups may use different strategies to retrieve or process semantic information during the task. Nevertheless, the MCI group shows patterns of neural oscillations that can be differentiated from the HA group on the SORT task and these alterations may be indicative of early neural decompensation, as suggested in prior studies (Woodard et al, 2009;Barbeau et al, 2012;Pistono et al, 2021).…”

Quantitative Assessment of Resting‐State Functional Connectivity MRI to Differentiate Amnestic Mild Cognitive Impairment, Late‐Onset Alzheimer's Disease From Normal Subjects

“…Compared to the normal group, a reduction in FC strength was found throughout the LN in the AD group. Neuroimaging studies in AD patients have shown that language disorders are associated with atrophy or hypometabolism in the left IFG and temporal regions 32,33 . Also, impairments in phonological processing are associated with damage to the left pSTG 34 .…”

“…Neuroimaging studies in AD patients have shown that language disorders are associated with atrophy or hypometabolism in the left IFG and temporal regions. 32,33 Also, impairments in phonological processing are associated with damage to the left pSTG. 34 Possibly due to dysfunction and a lower FC between nodes of the network, a decrease in semantic and pragmatic processing may occur as the disease progresses.…”

Development of digital voice biomarkers and associations with cognition, cerebrospinal biomarkers, and neural representation in early Alzheimer's disease

“…These changes also map onto altered network connectivity 19,20 including the semantic control network (SCN) 21 . Resting state functional MRI (rs‐fMRI) investigations have revealed alterations of functional connectivity (FC) in the cognitive control network (CCN) linked to linguistic abilities 22–24 . Since digital voice biomarkers may reflect both linguistic and speech changes, it is unclear if these novel biomarkers will map to previously known linguistic or speech brain networks.…”