Forskningsartikler

Abstract:

Introduction: People with dementia are eligible for rehabilitation for functional difficulties resulting from cognitive symptoms, but no method for this is used in Norwegian municipalities. GREAT cognitive rehabilitation (CR) is an approach which has shown significant positive effects. The study aimed to explore the experiences of dementia case managers using the GREAT CR approach to address the rehabilitation goals of people with dementia.

Method: Six dementia case managers, from four Norwegian municipalities, participated. The pilot study had two phases: phase 1: the participants learnt the approach, and each used it with two clients, to become CR practitioners; phase 2: the participants could use CR in their normal practice. Their experiences were explored in two focus groups. The focus groups were audiotaped, transcribed, and analysed in line with directed content analysis.

Results: Three categories were described: (1) the training and written material, (2) professional development, and (3) proposals for solutions on how to use CR in clinical practice. The case managers found it both engaging and challenging to use CR. They observed that the experience had changed their usual practice: they asked people with dementia more questions about their everyday functioning and resources. The most important barrier to implementing CR was lack of time, although funds were provided to allow municipalities to provide cover for participants’ time, participants still found they lacked the time to use the approach as planned.

Conclusion: This study has demonstrated that it is feasible to implement CR in a Norwegian municipality if enough time is available and sufficient resources are provided. There is an urgent need to identify how healthcare services can be enabled to make rehabilitation methods like CR a regular part of post-diagnostic support.

Marit Mjørud, Mona Michelet, Kariann Krohne, Thea Catherine Bredholt, Suzannah Evans, Linda Clare

Abstract

This article synthesizes findings, from the Autism/Dementia Work Group of the 2nd International Summit on Intellectual Disabilities and Dementia, on the nature of autism/autism spectrum disorder and later-age neuropathologies, particularly dementia. The convened group of experts explored genetic, neurobiological, and environmental risk factors that may affect the lifespan and lived experiences of older adults with autism. A review of current literature indicates a lack of comprehensive information on the demographics and factors associated with aging in autistic adults. However, our understanding of autism is evolving, challenging traditional views of it as a static, inherited neurodevelopmental disorder. The relationship between autism and other neurodevelopmental conditions-such as Down syndrome, fragile X syndrome, and tuberous sclerosis complex-reflects the complex genetic landscape of neurodevelopmental disorders. These genetic and familial factors may contribute to progressive health challenges and cognitive decline in later life. Key findings reveal a complex link between autism and dementia, despite limited research on this relationship, particularly among older adults. The overall prevalence of dementia in this population appears to be influenced by co-occurring intellectual disabilities, particularly Down syndrome. While the association between autism and specific types of dementia is still not well understood, the reviewed evidence suggests a notable connection with frontotemporal dementia, although causality has not been established. Exploration of biomarkers may offer further insights. Currently, the relationship between autism, cognitive health, and cognitive decline in older adults remains a complex and underexplored area of research.

M P Janicki, P McCallion, N Jokinen, F K Larsen, D Mughal, V Palanisamy, F Santos, K Service, A Shih, S Shooshtari, A Thakur, G Tiziano & K Watchman

Abstract:

Background: Studies have shown that quantitative EEG is useful in predicting conversion from mild cognitive impairment (MCI) to Alzheimer’s disease dementia (ADD) and dementia with Lewy bodies (DLB). As subcortical pathology is present and executive impairment is common in DLB, we hypothesized that EEG could predict conversion in patients with impaired executive function and any subcortical pathology.
Methods: We included 113 patients with MCI from five Nordic memory clinics, 80 (71%) with amnestic MCI, 17 (15%) with dysexecutive MCI (deMCI), 3 (3%) with aphasic, 2 (2%) with visuospatial and 11 (10%) with unspecific MCI. Patients were examined with EEG in a resting state applying the statistical pattern recognition (SPR) method and followed up for five years. Eleven drop-outs were assessed after baseline. Receiver operating characteristic (ROC) analyses were used to examine the ability of EEG to predict conversion.

Results: Sixty patients converted to dementia, 47 to ADD, eight to vascular dementia, two to DLB, one to frontotemporal dementia and two to unspecific dementia. Eight (11%) recovered and 45 (40%) remained MCI stable. ROC analyses revealed that EEG predicted conversion from dysexecutive MCI to dementia with area under the curve (AUC) of 0.92 (95% CI 0.76-100), sensitivity of 89% and specificity of 100%. Subcortical pathology was present in 89% of the dysexecutive MCI converters. EEG did not predict conversion from amnestic MCI to dementia.
Conclusion: This study demonstrates that quantitative EEG using the SPR method predicts conversion from deMCI to dementia disorders with subcortical pathology with high sensitivity and specificity.

Knut Engedal, Lars-Olof Wahlund, Christian Sandøe Musaeu,  Peter Hoegh, Maria Lage Barca, Thorkell Eli Gudmundsson, Birgitte Bo Andersen, Daniel Ferreira, Mala Naik, Anne Rita Oeksengaard, Jon Snaedal

Abstract:

Delirium is a common cause and complication of hospitalization in the elderly and is associated with higher risk of future dementia and progression of existing dementia, of which 70% is Alzheimer’s disease (AD). AD and delirium, which are known to be aggravated by one another, represent significant societal challenges, especially in light of the absence of effective treatments capable. The intricate biological mechanisms have led to numerous clinical trial setbacks and likely contribute to the limited efficacy of existing therapeutics. Artificial intelligence (AI) presents a promising avenue for overcoming these hurdles by deploying algorithms to uncover hidden patterns across diverse data types. This review explores the pivotal role of AI in revolutionizing drug discovery for AD and delirium from target identification to the development of small molecule and protein-based therapies. Recent advances in deep learning, particularly in accurate protein structure prediction, are facilitating novel approaches to drug design and expediting the discovery pipeline for biological and small molecule therapeutics. This review concludes with an appraisal of current achievements and limitations, and touches on prospects for the use of AI in advancing drug discovery in AD and delirium, emphasizing its transformative potential in addressing these two and possibly other neurodegenerative conditions.

Ruixue Ai, Xianglu Xiao, Shenglong Deng, Nan Yang, Xiaodan Xing, Leiv Otto Watne, Geir Selbæk, Yehani Wedatilake, Chenglong Xie, David C. Rubinsztein, Jennifer E. Palmer, Bjørn Erik Neerland, Hongming Chen, Zhangming Niu, Guang Yang, Evandro Fei Fang