The functional connectivity of language network across the life span: Disentangling the effects of typical aging from Alzheimer's disease

Neural basis of writing in prodromal to mild dementia with lewy bodies

OBJECTIVES

We have previously demonstrated difficulties in written production in dementia with Lewy bodies (DLB) patients. We now aim to determine the neural correlates of writing production in DLB, combining clinical data and structural MRI measures.

METHOD

Sixteen prodromal to mild DLB patients were selected to participate in the study. The GREMOTS test was used to assess writing production. Using three-dimensional T1 brain MRI images, correlations between the GREMOTS test and grey matter (GM) volume were performed using voxel-based morphometry (VBM; SPM12, XjView and Matlab R2021b softwares).

RESULTS

VBM analysis (p < 0.001, uncorrected) revealed a positive and significant correlation between both left anterior insula and left supramarginal gyrus GM volumes and DLB patients' ability to write logatoms using the phonological route. The handwriting deficit was negatively and significantly correlated to the supplementary motor area. The parkinsonism-like characteristics of agraphia were negatively and significantly correlated with both right anterior and right posterior cerebellum GM volumes. Our study also revealed a negative and significant correlation between grammatical spelling impairments and an area of the orbitofrontal gyrus, and a negative and significant correlation between supramarginal gyrus and general slowness in dictation tasks.

CONCLUSION

Writing disorders in early DLB patients appears to be GM decreases in several brain regions, such as the left anterior insula, the left supramaginal gyrus, as well as two areas of the right cerebellum.

Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment

Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of Aβ42 peptide in the cerebrospinal fluid (CSF). Consequently, the development of non-invasive, low-cost, and easy-to-administer proxies for detecting Aβ42 positivity in CSF becomes particularly valuable. A promising approach to achieve this is spontaneous speech analysis, which combined with machine learning (ML) techniques, has proven highly useful in AD. In this study, we examined the relationship between amyloid status in CSF and acoustic features derived from the description of the Cookie Theft picture in MCI patients from a memory clinic. The cohort consisted of fifty-two patients with MCI (mean age 73 years, 65% female, and 57% positive amyloid status). Eighty-eight acoustic parameters were extracted from voice recordings using the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and several ML models were used to classify the amyloid status. Furthermore, interpretability techniques were employed to examine the influence of input variables on the determination of amyloid-positive status. The best model, based on acoustic variables, achieved an accuracy of 75% with an area under the curve (AUC) of 0.79 in the prediction of amyloid status evaluated by bootstrapping and Leave-One-Out Cross Validation (LOOCV), outperforming conventional neuropsychological tests (AUC = 0.66). Our results showed that the automated analysis of voice recordings derived from spontaneous speech tests offers valuable insights into AD biomarkers during the preclinical stages. These findings introduce novel possibilities for the use of digital biomarkers to identify subjects at high risk of developing AD.

Turning the Spotlight to Cholinergic Pharmacotherapy of the Human Language System

Even though language is essential in human communication, research on pharmacological therapies for language deficits in highly prevalent neurodegenerative and vascular brain diseases has received little attention. Emerging scientific evidence suggests that disruption of the cholinergic system may play an essential role in language deficits associated with Alzheimer's disease and vascular cognitive impairment, including post-stroke aphasia. Therefore, current models of cognitive processing are beginning to appraise the implications of the brain modulator acetylcholine in human language functions. Future work should be directed further to analyze the interplay between the cholinergic system and language, focusing on identifying brain regions receiving cholinergic innervation susceptible to modulation with pharmacotherapy to improve affected language domains. The evaluation of language deficits in pharmacological cholinergic trials for Alzheimer's disease and vascular cognitive impairment has thus far been limited to coarse-grained methods. More precise, fine-grained language testing is needed to refine patient selection for pharmacotherapy to detect subtle deficits in the initial phases of cognitive decline. Additionally, noninvasive biomarkers can help identify cholinergic depletion. However, despite the investigation of cholinergic treatment for language deficits in Alzheimer's disease and vascular cognitive impairment, data on its effectiveness are insufficient and controversial. In the case of post-stroke aphasia, cholinergic agents are showing promise, particularly when combined with speech-language therapy to promote trained-dependent neural plasticity. Future research should explore the potential benefits of cholinergic pharmacotherapy in language deficits and investigate optimal strategies for combining these agents with other therapeutic approaches.