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      Speaking in Alzheimer’s Disease, is That an Early Sign? Importance of Changes in Language Abilities in Alzheimer’s Disease

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          Abstract

          It is known that Alzheimer’s disease (AD) influences the temporal characteristics of spontaneous speech. These phonetical changes are present even in mild AD. Based on this, the question arises whether an examination based on language analysis could help the early diagnosis of AD and if so, which language and speech characteristics can identify AD in its early stage. The purpose of this article is to summarize the relation between prodromal and manifest AD and language functions and language domains. Based on our research, we are inclined to claim that AD can be more sensitively detected with the help of a linguistic analysis than with other cognitive examinations. The temporal characteristics of spontaneous speech, such as speech tempo, number of pauses in speech, and their length are sensitive detectors of the early stage of the disease, which enables an early simple linguistic screening for AD. However, knowledge about the unique features of the language problems associated with different dementia variants still has to be improved and refined.

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          Most cited references 42

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          Semantic memory impairment in Alzheimer's disease: failure of access or degraded knowledge?

          A battery of neuropsychological tests designed to assess semantic knowledge about the same items both within and across different modalities was administered to a group of 22 patients with dementia of the Alzheimer type (DAT) and 26 matched controls. The DAT patients were impaired on tests of category fluency, picture naming, spoken word-picture matching, picture sorting and generation of verbal definitions. A relative preservation of superordinate knowledge on the sorting and definition tests, as well as a disproportionate reduction in the generation of exemplars from lower order categories was noted. Analysis of the errors made by each patient across the different tests, revealed a significant correspondence between the individual items. These findings offer compelling evidence that the semantic breakdown in DAT is caused by storage degradation.
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            Diffusion tensor imaging and tract-based spatial statistics in Alzheimer's disease and mild cognitive impairment.

            We aimed to explore the changes in fractional anisotropy (FA) in subjects with mild cognitive impairment (MCI) and Alzheimer's disease (AD) by analyzing diffusion tensor imaging (DTI) data using the Tract-Based Spatial Statistics (TBSS). DTI data were collected from 17 AD patients, 27 MCI subjects and 19 healthy controls. Voxel-based analysis with TBSS was used to compare FA among the three groups. Additionally, guided by TBSS findings, a region of interest (ROI)-based analysis along the TBSS skeleton was performed on group-level and the accuracy of the method was assessed by the back-projection of ROIs to the native space FA. Neurofiber tracts with decreased FA included: the parahippocampal white matter, cingulum, uncinate fasciculus, inferior and superior longitudinal fasciculus, corpus callosum, fornix, tracts in brain stem, and cerebellar tracts. Quantitative ROI-analysis further demonstrated the significant decrease on FA values in AD patients relative to controls whereas FA values of MCI patients were found in between the controls and AD patients. We conclude that TBSS is a promising method in examining the degeneration of neurofiber tracts in MCI and AD patients. Copyright © 2009 Elsevier Inc. All rights reserved.
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              Language performance in Alzheimer's disease and mild cognitive impairment: a comparative review.

              Mild cognitive impairment (MCI) manifests as memory impairment in the absence of dementia and progresses to Alzheimer's disease (AD) at a rate of around 15% per annum, versus 1-2% in the general population. It thus constitutes a primary target for investigation of early markers of AD. Language deficits occur early in AD, and performance on verbal tasks is an important diagnostic criterion for both AD and MCI. We review language performance in MCI, compare these findings to those seen in AD, and identify the primary issues in understanding language performance in MCI and selecting tasks with diagnostic and prognostic value.
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                Author and article information

                Contributors
                Journal
                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                1663-4365
                20 October 2015
                2015
                : 7
                Affiliations
                1Research Institute for Linguistics, Hungarian Academy of Sciences , Szeged, Hungary
                2Research Institute for Linguistics, Hungarian Academy of Sciences , Budapest, Hungary
                3Department of Linguistics, University of Szeged , Szeged, Hungary
                4MTA-SZTE Research Group on Artificial Intelligence, University of Szeged , Szeged, Hungary
                Author notes

                Edited by: Lia Fernandes, University of Porto, Portugal

                Reviewed by: Valentina Echeverria Moran, Bay Pines VA Medical Center, USA; Johannes Schröder, University of Heidelberg, Germany

                *Correspondence: Greta Szatloczki, szatloczkigreti@ 123456gmail.com
                Article
                10.3389/fnagi.2015.00195
                4611852
                Copyright © 2015 Szatloczki, Hoffmann, Vincze, Kalman and Pakaski.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 0, Tables: 2, Equations: 0, References: 67, Pages: 7, Words: 5875
                Funding
                Funded by: Hungarian National Research
                Award ID: TÁMOP-4.2.2.A-11/1/KONV-2012-0073
                Funded by: János Bolyai Research Scholarship of the Hungarian Academy of Sciences
                Award ID: BO/00254/12
                Funded by: National Excellence Program
                Award ID: A/2-11-1-2012-0001
                Categories
                Neuroscience
                Mini Review

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