Alzheimer’s disease : writing style changes as dementia progresses


Researchers at the University of Toronto (U of T) specializing in language variation and change have identified a specific relationship between an individual’s use of language, and the transition from healthy to a diagnosis of severe dementia.

In a study of diary entries by Toronto resident Vivian White over a 31-year period, the researchers tracked the omission and then inclusion of the first-person pronoun “I” and found the transition occurred around the time she was diagnosed with Alzheimer’s disease.

The diaries span the period from 1985 to 2016, from age 60 to 90. Throughout the first 24 years, White omitted a subject up to 76 percent of the time (e.g. March 23, 1985: “Made cranberry muffins”).

In contrast, following her diagnosis with Alzheimer’s at age 84, she included the pronoun “I” 100 percent of the time (e.g. January 1, 2016: “I made cranberry muffins.”)

“This suggests that individuals may revert back to a more formal, fundamental writing style when they experience cognitive decline,” said Sali Tagliamonte, a professor in the Department of Linguistics in the Faculty of Arts & Science at U of T, and Canada Research Chair in Language Variation and Change.

“Diary writing is a style, and one that is known to have complex constraints in which the subject ‘I’ is often omitted in specific locations” Tagliamonte said.

“It’s a learned behavior acquired at a later stage than more basic writing or acquisition of the vernacular language.

Research on bilinguals with probable Alzheimer’s Disease has shown that languages learned later in life tend to be lost earlier. Our results suggest that the same might be true for styles acquired later in life.”

“It is rare to find 97 continuous journals across such a lengthy timespan,” said Tagliamonte. “The results show how longitudinal studies can illuminate important aspects of cognitive development and call for further studies of speakers with Alzheimer’s disease and other forms of dementia from a language variation and change perspective.”

PhD Candidate Katharina Pabst, who collaborated with Tagliamonte on the project, notes that most research in language change has focused on the innovations present in younger speakers, and that comparatively little work has been done on language change across the lifespan and among older adults. However, previous work from related disciplines shows that the use of several linguistic features can and does change in later life.

In a study of diary entries by Toronto resident Vivian White over a 31-year period, the researchers tracked the omission and then inclusion of the first-person pronoun “I” and found the transition occurred around the time she was diagnosed with Alzheimer’s disease.

Since White only began keeping a journal in earnest later in life and while still healthy, the collection provided a perfect opportunity for the researchers to investigate the possibility of a relationship between language change and cognitive decline over time.

Tagliamonte and Pabst will present their findings this month at the annual New Ways of Analyzing Variation conference, held October 10-12 at the University of Oregon. Support for the research was provided by the Social Sciences and Humanities Research Council of Canada and the Canada Research Chairs Program.

Alzheimer’s disease (AD) is the most prevalent form of dementia in persons older than 65 years1. Cognitive impairment, mainly related to memory deficits, is the most common manifestation of this disease2.

Available neuroimaging evidence suggests that the neuropathological alterations underlying AD probably begin much earlier than the appearance of clinical symptoms and years before clinical diagnosis3. From these results, it appeared that the pharmacological management was finally implemented in patients with a largely advanced neurodegenerative process, making it difficult to fight against pathological progression.

In this context, the concept of disease-modifying therapies is emerging and the search for early biomarkers of these alterations is currently a hot topic of research4.

Neurodegeneration, assessed by the level of cerebral atrophy, is one of these biomarkers. In recent decades, several MRI studies have investigated neurodegeneration in the prodromal phase of Alzheimer’s disease5,6.

However, very few of them attempted to investigate the preclinical phase of the disease, the very early asymptomatic phase. Indeed, this type of study is a very challenging task since it requires an imaging database starting before the appearance of the disease, and the corresponding long follow-up study.

Therefore, in the past, such studies have been based on subjects with rare autosomal dominant mutations associated with a high risk of developing dementia710 or on longitudinal studies with long follow-up in which brain imaging have been performed before the appearance of clinical symptoms (i.e., memory impairment)1113.

In these previous long follow-up studies, the starting point of the neurodegeneration was not determined since incident cases already present brain morphometric differences at baseline 7 or 10 years before the diagnosis1315 or 5 before the apparition of Mild Cognitive Impairment (MCI)16. Finally, the lifespan evolution of these imaging biomarkers many years before cognitive decline also remains unknown.

Determining the timing of the onset of neurodegeneration would require longitudinal MRI datasets with several decades of follow-up. So far, due to obvious technical reasons, such imaging dataset does not exist yet. Consequently, in this paper, we propose to consider alternative questions such as – When does the AD model diverge from the normal aging model? How do the trajectories of biomarker models differ in AD from normal aging? To answer these questions, we present an innovative approach based on an extrapolated lifespan model of AD brain structures using large-scale databases.

To this end, we propose to take advantage of the new paradigm of BigData sharing in neuroimaging17 by analyzing publically available databases including subjects from a wide age range covering the entire lifespan. Such analysis can be very valuable since epidemiological studies indicate that late dementia is associated with early exposure to risk factors at midlife, highlighting the need to consider brain biomarkers throughout the entire lifespan1820.

Recently, we used BigData approach to propose an analysis of brain trajectory across the entire lifespan using N = 2944 MRI of cognitively normal subjects (CN)21. Herein, we present a study following a similar approach to analyze the lifespan changes of the human brain in AD. To this end, we propose to build an extrapolated model of AD for brain structures. We assume that the neurodegenerative process is slow and progressive.

The slow accumulation of Amyloid-β3,22,23 and the smooth atrophy of brain3,10 seem to indicate that brain alterations occurs progressively in AD. Accordingly, to build our lifespan AD model we combined young CN with aged AD and MCI patients. We used 1385 MRI of AD and MCI patients (from 55 y to 96 y) and 1877 MRI of CN subjects younger than them (from 9 months to 55 y).

In our approach, we used CN subjects as young asymptomatic AD subjects to overcome the absence of datasets including young CN who will develop AD several decades latter. However, MRI studies have showed brain alterations several years before diagnosis or MCI stage1416. Therefore, the proposed approach can be viewed as a conservative lifespan model of AD. In this study, we first propose a comparison of lifespan evolution of global white and gray matter and subcortical structures between AD sample and CN sample. Afterward, we focus on temporal lobe structures such as the hippocampus and amygdala – known to be affected in AD24,25 and the lateral ventricles, also a known AD biomarker26,27 in a supplementary analysis considering three pathological models: an AD model composed of 2303 subjects, an MCI model composed of 2836 samples subjects and an AD/MCI model composed of 3262 subjects.

University of Toronto
Media Contacts:
Sali A. Tagliamonte – University of Toronto
Image Source:
The image is in the public domain.

Original Research: The findings will be presented at New Ways of Analyzing Variation 48 (NWAV48), University of Oregon in Eugene, Oregon, USA.


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