Cognitive fluctuations in connection to disgraphia a comparison of Alzheimer's disease with dementia Lewy bodies

Background: The purpose of the present study was to examine the relationship between cognitive impairment and the performance of handwritten scripts presented as “letter-writing” to a close relative by patients with dementia Lewy bodies (DLB), as fluctuations of the symptoms phase, and in a matched group of patients with Alzheimer’s disease (AD). The degree of writing disability and personal, spatial, and temporal orientation was compared in these two groups. Design and methods: Fourteen simple questions, designed in a form that could be utilized by any general practitioner in order to document the level of cognitive functioning of each patient, were presented to 30 AD patients and 26 DLB patients. The initial cognition test was designated PQ1. The patients were examined on tests of letter-writing ability. Directly after the letter-writing, the list of 14 questions presented in PQ1 was presented again in a repeated procedure that was designated PQ2. The difference between these two measures (PQ1 – PQ2) was designated D∆. This test of letter-writing ability and cognitive performance was administered over 19 days. Results: Several markedly strong relationships between dysgraphia and several measures of cognitive performance in AD patients and DLB patients were observed, but the deterioration of performance from PQ1 to PQ2 over all test days were markedly significant in AD patients and not significant in DLB patients. It is possible that in graphic expression even by patients diagnosed with moderate to relatively severe AD and DLB there remains some residual capacity for understanding and intention that may be expressed. Furthermore, the deterioration in performance and the differences noted in AD and DLB patients may be due to the different speed at which the process of the protein degradation occurs for functional modification of synapses. Conclusion: Our method can be used as part of neuropsychological tests to differentiate the diagnosis between AD and DL

Historical Analyses of Disordered Handwriting

Handwritten texts carry significant information, extending beyond the meaning of their words. Modern neurology, for example, benefits from the interpretation of the graphic features of writing and drawing for the diagnosis and monitoring of diseases and disorders. This article examines how handwriting analysis can be used, and has been used historically, as a methodological tool for the assessment of medical conditions and how this enhances our understanding of historical contexts of writing. We analyze handwritten material, writing tests and letters, from patients in an early 20th-century psychiatric hospital in southern Germany (Irsee/Kaufbeuren). In this institution, early psychiatrists assessed handwriting features, providing us novel insights into the earliest practices of psychiatric handwriting analysis, which can be connected to Berkenkotter’s research on medical admission records. We finally consider the degree to which historical handwriting bears semiotic potential to explain the psychological state and personality of a writer, and how future research in written communication should approach these sources

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients

an experimental protocol to support cognitive impairment diagnosis by using handwriting analysis

Abstract Nowadays diseases involving cognitive impairments affect millions of people worldwide, with Alzheimer's and Parkinson's diseases being the most common ones. Because of the worldwide average lifespan increment, it is expected that their incidence will increase in the next few decades. Among the daily activities, handwriting is one of the first affected by cognitive impairments. For this reasons, researchers have also been investigating the analysis of handwriting alterations as diagnostic signs for this kind of diseases. In this paper we present an experimental protocol that we developed for the analysis of the handwriting dynamics of patients affected by cognitive impairments. The aim of this protocol is to build a large database that would allow to effectively train different classifier systems. We also detail the most common and effective features previously used in the literature to represent handwriting dynamics of the subjects affected by cognitive impairments

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page