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1. Depression, Vascular Burden, and Dementia Prevalence in Late Middle-Aged and Older Black Adults

   https://doi.org/10.1093/geronb/gbae009  

   Levy, Shellie-Anne; Misiura, Maria_B; Grant, Jeremy_G; Adrien, Tamare_V; Taiwo, Zinat; Armstrong, Rebecca; Dotson, Vonetta_M; Taler, ed., Vanessa (February 2024, The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences)

   Abstract ObjectivesLate-life depression and white matter hyperintensities (WMH) have been linked to increased dementia risk. However, there is a dearth of literature examining these relationships in Black adults. We investigated whether depression or WMH volume are associated with a higher likelihood of dementia diagnosis in a sample of late middle-aged to older Black adults, and whether dementia prevalence is highest in individuals with both depression and higher WMH volume. MethodsSecondary data analysis involved 443 Black participants aged 55+ with brain imaging within 1 year of baseline visit in the National Alzheimer’s Coordinating Center Uniform Data Set. Chi-square analyses and logistic regression models controlling for demographic variables examined whether active depression in the past 2 years, WMH volume, or their combination were associated with higher odds of all-cause dementia. ResultsDepression and higher WMH volume were associated with a higher prevalence of dementia. These associations remained after controlling for demographic factors, as well as vascular disease burden. Dementia risk was highest in the depression/high WMH volume group compared to the depression-only group, high WMH volume-only group, and the no depression/low WMH volume group. Post hoc analyses comparing the Black sample to a demographically matched non-Hispanic White sample showed associations of depression and the combination of depression and higher WMH burden with dementia were greater in Black compared to non-Hispanic White individuals. DiscussionResults suggest late-life depression and WMH have independent and joint relationships with dementia and that Black individuals may be particularly at risk due to these factors. 

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2. How Content Authored by People with Dementia Affects Attitudes towards Dementia

   https://doi.org/10.1145/3479542  

   Lazar, Amanda; Brewer, Robin N.; Kacorri, Hernisa; Hong, Jonggi; Punzalan, Mary Nicole; Mahathir, Maisarah; Vander Hyde, Olivia; Ross III, Warren (October 2021, Proceedings of the ACM on Human-Computer Interaction)

   Negative attitudes shape experiences with stigmatized conditions such as dementia, from affecting social relationships to influencing willingness to adopt technology. Consequently, attitudinal change has been identified as one lever to improve life for people with stigmatized conditions. Though recognized as a scaleable approach, social media has not been studied in terms of how it should best be designed or deployed to target attitudes and understanding of dementia. Through a mixed methods design with 123 undergraduate college students, we study the effect of being exposed to dementia-related media, including content produced by people with dementia. We selected undergraduate college students as the target of our intervention, as they represent the next generation that will work and interact with individuals with dementia. Our analysis describes changes over the period of two weeks in attitudes and understanding of the condition. The shifts in understanding of dementia that we found in our qualitative analysis were not captured by the instrument we selected to assess understanding of dementia. While small improvements in positive and overall attitudes were seen across all interventions and the control, we observe a different pattern with negative attitudes, where transcriptions of content produced by people with dementia significantly reduced negative attitudes. The discussion presents implications for supporting people with dementia as content producers, doing so in ways that best affect attitudes and understanding by drawing on research on cues and interactive media, and supporting students in changing their perspectives towards people with dementia. 

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3. Conceptualizing caregiver workload: an exploratory study guided by NASA TLX

   https://doi.org/10.1177/1071181319631245  

   Rutkowski, R.A.; Ponnala, S.; Gonzalez-Vargas, J.; Gilmore-Bykovskyi, A.; Block, L.; Kind, A.J.; Werner, N.E. (November 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Persons with dementia (PwD) are heavily dependent on the support of informal, dementia caregivers to fulfill their day-to-day care needs. Dementia caregivers, often friends and family members of the PwD, are unpaid, non-professional individuals who take on many of the care responsibilities. Due to the lack of formal training, social support, and information resources, among other factors, dementia caregivers are often at risk for negative outcomes such as stress and burden. There have not been any comprehensive assessment tools to predict these negative outcomes. Therefore, we employ the NASA TLX dimensions to conceptualize caregiver workload. This study operationalizes the NASA TLX dimensions in the context of dementia caregiving and illustrates examples for each of the dimensions. The results indicate that the NASA TLX does not include all of the factors necessary to conceptualize caregiver workload and prescribe a need for developing a robust caregiver workload assessment tool. 

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4. Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

   https://doi.org/10.1073/pnas.2214634120  

   Yin, Chenzhong; Imms, Phoebe; Cheng, Mingxi; Amgalan, Anar; Chowdhury, Nahian F.; Massett, Roy J.; Chaudhari, Nikhil N.; Chen, Xinghe; Thompson, Paul M.; Bogdan, Paul; et al (January 2023, Proceedings of the National Academy of Sciences)

   The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

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5. Different aspects of failing to recover from proactive semantic interference predicts rate of progression from amnestic mild cognitive impairment to dementia

   https://doi.org/10.3389/fnagi.2024.1336008  

   Curiel_Cid, Rosie E; Crocco, Elizabeth A; Duara, Ranjan; Vaillancourt, David; Asken, Breton; Armstrong, Melissa J; Adjouadi, Malek; Georgiou, Mike; Marsiske, Michael; Wang, Wei-in; et al (January 2024, Frontiers in Aging Neuroscience)

   IntroductionThis study investigated the role of proactive semantic interference (frPSI) in predicting the progression of amnestic Mild Cognitive Impairment (aMCI) to dementia, taking into account various cognitive and biological factors. MethodsThe research involved 89 older adults with aMCI who underwent baseline assessments, including amyloid PET and MRI scans, and were followed longitudinally over a period ranging from 12 to 55 months (average 26.05 months). ResultsThe findings revealed that more than 30% of the participants diagnosed with aMCI progressed to dementia during the observation period. Using Cox Proportional Hazards modeling and adjusting for demographic factors, global cognitive function, hippocampal volume, and amyloid positivity, two distinct aspects of frPSI were identified as significant predictors of a faster decline to dementia. These aspects were fewer correct responses on a frPSI trial and a higher number of semantic intrusion errors on the same trial, with 29.5% and 31.6 % increases in the likelihood of more rapid progression to dementia, respectively. DiscussionThese findings after adjustment for demographic and biological markers of Alzheimer’s Disease, suggest that assessing frPSI may offer valuable insights into the risk of dementia progression in individuals with aMCI. 

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