Forskningsartikler

Abstract:

Background: Mental and neurological conditions have been linked to structural brain variations. However, aside from dementia, the value of brain structural characteristics derived from brain scans for prediction is relatively low. One reason for this limitation is the clinical and biological heterogeneity inherent to such conditions. Recent studies have implicated aberrations in the cerebellum, a relatively understudied brain region, in these clinical conditions.

Methods: Here, we used machine learning to test the value of individual deviations from normative cerebellar development across the lifespan (based on trained data from >27,000 participants) for prediction of autism spectrum disorder (ASD) (n = 317), bipolar disorder (n = 238), schizophrenia (SZ) (n = 195), mild cognitive impairment (n = 122), and Alzheimer’s disease (n = 116); individuals without diagnoses were matched to the clinical cohorts. We applied several atlases and derived median, variance, and percentages of extreme deviations within each region of interest.

Results: The results show that lobular and voxelwise cerebellar data can be used to discriminate reference samples from individuals with ASD and SZ with moderate accuracy (the area under the receiver operating characteristic curves ranged from 0.56 to 0.65). Contributions to these predictive models originated from both anterior and posterior regions of the cerebellum.

Conclusions: Our study highlights the utility of cerebellar normative modeling in predicting ASD and SZ, aided by 4 cerebellar atlases that enhanced the interpretability of the findings.

Milin Kim, Nitin Sharma, Esten H Leonardsen, Saige Rutherford, Geir Selbæk, Karin Persson, Nils Eiel Steen, Olav B Smeland, Torill Ueland, Geneviève Richard, Aikaterina Manoli, Sofie L Valk, Dag Alnæs, Christian F Beckman, Andre F Marquand, Ole A Andreassen, Lars T Westlye, Thomas Wolfers, Torgeir Moberget

Abstract:

Introduction: Development of accessible group exercise sessions is warranted for home-dwelling older people with cognitive impairment or dementia. This study aims to explore the experiences of participants in a group exercise session organized with volunteers both as instructors and as those providing support for the participants.

Methods: This qualitative descriptive study reports on a primary analysis of qualitative data collected through semistructured focus group interviews with 12 people with cognitive impairment or dementia at three group exercise sessions in three municipalities. Interviews were audio-recorded, transcribed, and analyzed using thematic analysis.

Results: Participants expressed an overall satisfaction with the new group exercise sessions. We found four meaning units forming a chain of support. The participants highlighted support to be motivated enough to engage in the sessions, support by offering transportation, the exercise content, and the role of instructors. Overall, the participants expressed that these exercise sessions had become a social arena for them, and all wanted to continue.

Discussion and conclusions: Exercise groups can be a meaningful and social arena for people with cognitive impairment or dementia, through careful organization with volunteers and minimal involvement from informal caregivers. This study underlines the need for properly organized activities, outside the home, to overcome the challenges associated with participation for this population.

Kristin Taraldsen, Arnhild J. Nygård, Elisabeth Boulton, Guro Grønningsæter, Marit H. Erland, Nina Waldenstrøm, Linda Johnsen, Gro G. Tangen and Randi Granbo

Abstract

Objectives: The second International Summit on Intellectual Disability and Dementia, held in 2023, highlighted the unique challenges of diagnosing dementia in older autistic adults, particularly those with intellectual disabilities, due to the complex interplay of cognitive, communicative, and behavioral factors. This article addresses key diagnostic issues and post-diagnostic considerations for this population.

Method:
A consensus report was developed by the Summit’s Autism/Dementia Working Group through background reviews, expert discussions at the Summit, and iterative draft revisions, incorporating feedback from internal and external stakeholders. Key issues were extracted from the report and abridged for this manuscript.

Results:
Diagnostic challenges stem from overlapping symptoms of co-occurring neurodevelopmental and psychiatric conditions, rendering standard dementia tools insufficient. Comprehensive evaluations tailored to autism-related traits, sensory sensitivities, and alternative communication methods are essential. Building diagnostic capacity among clinicians and fostering multidisciplinary collaboration are critical. Longitudinal assessments, initiated before dementia symptoms appear, facilitate early detection of subtle changes. Emerging biomarkers and neuroimaging techniques show promise and should be incorporated where feasible. Accommodations, such as virtual assessments in familiar settings, can enhance diagnostic accuracy by reducing anxiety. Creating transition processes from diagnostics to post-diagnostic supports will aid in mitigating challenges and enhance life quality when dementia is a factor.

Conclusions:
Research and clinician education are urgently needed to improve diagnostic approaches and streamline the transition from diagnosis to tailored post-diagnostic support. An integrated framework of comprehensive efforts is vital for our better understanding of age-associated neuropathological diagnostics and enabling long-term well-being of older autistic adults with dementia.

Matthew P Janicki, Philip McCallion, Nancy Jokinen, Frode Kibsgaard Larsen, Kathyrn P Service, Dawna T Mughal, Karen Watchman, Tiziano Gomiero, Seth M Keller

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

Abstarct: 

We examined associations between body mass index (BMI), waist circumference (WC), and dementia risk, and differences in BMI and WC trajectories before dementia diagnosis. We included 9,739 participants (54% women) aged 70+ from the Trøndelag Health Study (HUNT4 70+). BMI was measured four times (1984-2019) and WC three times (1995-2019). Dementia diagnoses were clinically assessed at HUNT4 70+ . Women and men with dementia had higher midlife BMI and WC than those without dementia. These differences diminished closer to diagnosis, especially in women. Midlife obesity in both sexes and midlife overweight, high WC, and overweight/obesity with high WC in men were linked to higher dementia risk. Lower dementia risk was observed with late-life overweight for both sexes, late-life high WC in women, late-life overweight/obesity with normal WC in men or high WC in women. Adiposity measures and their changes influence dementia risk differently in women and men.

Ekaterina Zotcheva, Bjørn Heine Strand, Vegard Skirbekk, Kay Deckers, Steinar Krokstad, Gill Livingston, Archana Singh-Manoux, Geir Selbæk

Abstarct:

Despite a well-known inverse association between education and dementia risk, the mediating mechanisms are not well understood. We explored how lifestyle and health risk factors across the life-course mediate the relationship between education and dementia among adults aged 70 + years. We included 7,655 participants with dementia diagnoses and education information, using a historical cohort design linking prospective exposure data across the life course from the HUNT4 70 + Study with registry data from Statistics Norway and earlier HUNT surveys. We conducted causal mediation analysis to assess the mediating roles of occupational characteristics, lifestyle factors (smoking, physical inactivity), and health risk factors (obesity, hypertension, diabetes, hearing impairment, cardiovascular diseases, LDL cholesterol, depression, anxiety) assessed during early, middle, and late adulthood in the relationship between education and dementia in later life. Participants with lower education were more likely to have dementia with odds ratios of 1.99, 1.88, 1.83 for the model’s accounting exposure to mediators during early, middle, and late adulthood, respectively. These associations were partially mediated by the joint effect of health and lifestyle risk factors from early through late adulthood (mediated 11.55-19.50%). Health risk factors from early to late adulthood jointly mediated 6.85-13.06% of the effect of low education on dementia risk later in life. Additionally, lifestyle factors during middle and late adulthood jointly mediated 4.11-4.96% of the total effect of low education on dementia risk later in life. Educational differences in dementia risk can partly mediated by lifestyle and health factors across the life course. These findings suggest potential targets to address varying dementia risks linked to education levels.

Teferi Mekonnen, Vegard Skirbekk, Asta Kristine Håberg, Bo Engdahl, Ekaterina Zotcheva, Astanand Jugessur, Catherine Bowen, Geir Selbaek, Hans-Peter Kohler, Jennifer R Harris, Sarah E Tom, Steinar Krokstad, Trine Holt Edwin, Dana Kristjansson, Merete Ellingjord-Dale, Yaakov Stern, Bernt Bratsberg, Bjørn Heine Strand