Dementia Subtypes Research Articles

Identification of circRNAs linked to Alzheimer's disease and related dementias.

Circular RNAs (circRNAs) exhibit selective expression in the brain and differential regulation in Alzheimer's disease (AD). To explore the role of circRNAs in AD, we investigated how circRNA expression varies between brain regions and with AD-related stress in human neuronal precursor cells (NPCs). Ribosomal RNA-depleted hippocampus RNA-sequencing data were generated. Differentially regulated circRNAs in AD and related dementias were detected using CIRCexplorer3 and limma. circRNA results were validated using quantitative real-time PCRof cDNA from the brain and NPCs. We identified 48 circRNAs that were significantly associated with AD. We observed that circRNA expression differed by dementia subtype. Using NPCs, we demonstrated that exposure to oligomeric tau elicits downregulation of circRNA similar to that observed in the AD brain. Our study shows that differential expression of circRNA can vary by dementia subtype and brain region. We also demonstrated that circRNAs can be regulated by AD-linked neuronal stress independently from their cognate linear messenger RNAs (mRNAs).

The Association of Vitamin D Status with Mild Cognitive Impairment and Dementia Subtypes: A Cross-Sectional Analysis in Dutch Geriatric Outpatients.

Vitamin D deficiency is associated with all-cause dementia and Alzheimer's disease (AD). At the same time, this knowledge is limited specifically for vascular dementia (VaD), while data regarding other subtypes of dementia are even more limited. To investigate the association of 25-hydroxy vitamin D (25(OH)D) status with dementia subtypes in an outpatient geriatric population. In a cross-sectional design, we analyzed data from 1,758 patients of an outpatient memory clinic in The Netherlands. Cognitive disorders were diagnosed by a multidisciplinary team according to international clinical standards. At each first-visit 25(OH)D levels were measured. Data were analyzed using ANCOVA in four models with age, gender, BMI, education, alcohol, smoking, season, polypharmacy, calcium, eGFR, and glucose as co-variates. 25(OH)D was treated as a continuous square rooted (sqr) variable. In the fully adjusted model, reduced 25(OH)D serum levels (sqr) were found in AD (estimated mean 7.77±0.11 CI95% 7.55-7.99): and in VaD (estimated mean 7.60±0.16 CI95% 7.28-7.92) patients compared to no-dementia (ND) patients (estimated mean 8.27±0.09 CI95% 8.10-8.45) (ND-AD: p = 0.006, CI95% 0.08-0.92.; ND-VaD p = 0.004 CI95% 0.13-1.22). We did not find differences in 25(OH)D levels of mild cognitive impairment (MCI) or other dementia patients compared to ND patients, nor differences in comparing dementia subtypes. We observed significantly lower 25(OH)D serum levels in both AD and VaD patients compared to no-dementia patients, but no significant differences between MCI and Lewy body and mixed dementia subtypes in this cross-sectional study of a geriatric outpatient clinic population.

Disaggregating the dementia monolith: An analysis of variation in Medicare costs and use by dementia subtype.

With an aging population, it is necessary to systematically examine variation in costs and use of Medicare services by dementia subtype. We provide the first national estimates for dementia by subtype, and the respective Medicare costs and use. We analyzed Medicare fee-for-service (FFS) claims from 2017 through 2019. The sample included 41 million beneficiaries: 727,700 beneficiaries with a new dementia diagnosis in 2017. We calculated descriptive statistics and conducted generalized linear regression models by subtype of dementia. Annual Medicare costs for beneficiaries with dementia ranged from $22,840 for frontotemporal dementia to $44,896 for vascular dementia compared to $9,034 for beneficiaries without dementia. Comparing beneficiaries across dementia subtypes, the greatest differences were in the use of home health and hospice care. These analyses demonstrate substantial heterogeneity across dementia subtypes, which will be important in developing models of care that improve value for people with dementia.

Aspirin Therapy, Cognitive Impairment, and Dementia—A Review

Background: Dementia is associated with a greater burden of cardiovascular risk factors. There is a significant vascular contribution to dementia, and aspirin may play a role in targeting this vascular dysregulation via its anti-inflammatory and antiplatelet effects. We provide an overview of the effects of aspirin therapy on the prevention of dementia and cognitive decline in patients with or without dementia and/or cognitive impairment. Methods: We performed a search for studies enrolling adults with or without dementia or MCI and comparing aspirin with placebo, usual care, or active control with respect to cognitive outcomes. Results: We describe aspirin’s effects on the primary prevention of cognitive impairment and various subtypes of dementia, as well as its role in cognitive decline in certain subsets of patients, including those with cerebral small vessel disease (CVSD), coronary heart disease (CHD), and gender differences. Overall, the benefits of aspirin in preventing dementia and cognitive decline remain inconclusive. The majority of cohort studies investigating aspirin’s role in preventing cognitive decline or dementia looked promising, but this was not supported in most randomised controlled trials. However, aspirin may still be beneficial in certain subgroups of patients (such as CHD, VD, and CSVD) and warrants further investigation.

Examining the Lancet Commission risk factors for dementia using Mendelian randomisation

BackgroundDementia incidence is increasing across the globe and currently there are no disease-modifying pharmaceutical treatments. The Lancet Commission on dementia identified 12 modifiable risk factors which explain 40% of dementia...

What Are the Key Diagnostic Cognitive Impairment and Dementia Subtypes and How to Integrate all of the Diagnostic Data to Establish a Diagnosis?

Diagnosis of dementia requires a detailed history, physical examination, imaging, and sometimes neuropsychological testing or ancillary tests. Mild cognitive impairment is defined as an objective impairment in cognitive performance but preserved ability to do activities of daily living. Dementia is diagnosed once impairment in activities of daily living develops. Common types of dementia covered here include mild cognitive impairment, Alzheimer's disease, Lewy body dementia, frontotemporal dementia, the primary progressive aphasias, and vascular dementia.

Prevalence of sleep-related breathing disorders and associated risk factors among people with dementia: A meta-analysis

ObjectiveSleep-related breathing disorders (SRBD) have shown to cause worsened cognitive impairment among people with dementia. Therefore, we conducted the first meta-analysis to estimate the prevalence of SRBD among people with dementia. MethodsComprehensive searches were conducted in Embase, Ovid-MEDLINE, PubMed, PsycINFO, Scopus, Web of Science, and CINAHL. The generalized linear mixed model (GLMM) was used for the pooled prevalence analysis and heterogeneity using I2 test and Cochran's Q-statistic in R-software. Study quality was assessed by Hoy's risk of bias assessment tool. Overall, 20 studies from 1282 studies were included with 1461 participants. ResultsThe pooled prevalence of SRBD among dementia was 59% (95%CI: 44%–73%) with 55% (95%CI: 34%–74%) for obstructive sleep apnea (OSA), 49% (95%CI: 25%–73%) for unspecified SRBD, and 11% (95%CI: 5%–21%) for central sleep apnea (CSA). Regarding dementia subtypes, the prevalence of SRBD was 89% (95%CI: 61%–97%) for Alzheimer's dementia, 56% (95%CI: 48%–63%) for Parkinson's and Idiopathic Parkinson's dementia, and 16% (95%CI: 8%–30%) for Huntington's dementia. Significant moderator variables were male, body mass index, larger waist and hip circumference, waist-hip ratio, and comorbidities including hypertension, dyslipidemia, renal disease, diabetes, heart disease, and stroke. ConclusionsThere is considerable high prevalence of SRBD among dementia people, with OSA and unspecified SRBD being fivefold higher than CSA. Regarding dementia subtypes, Parkinson's and Idiopathic Parkinson's, and Alzheimer's dementia had four to sixfold increased risk of presenting with SRBD than Huntington's dementia. Therefore, assessment and management of SRBD in Alzheimer's, and Parkinson's and Idiopathic Parkinson's dementia deserves more attention in future research.

MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

The relationship between midlife dyslipidemia and lifetime incidence of dementia: A systematic review and meta-analysis of cohort studies.

We conducted a systematic review and meta-analysis to review the relationship between midlife dyslipidemia and lifetime incident dementia. The databases Medline, Embase, Scopus, Web of Science, and Cochrane were searched from inception to February 20, 2022. Longitudinal studies examining the relationship between midlife lipid levels on dementia, dementia subtypes, and/or cognitive impairment were pooled using inverse-variance weighted random-effects meta-analysis. Seventeen studies (1.2 million participants) were included. Midlife hypercholesterolemia was associated with increased incidence of mild cognitive impairment (effect size [ES] = 2.01; 95% confidence interval [CI] 1.19 to 2.84; I2 = 0.0%) and all-cause dementia (ES = 1.14; 95% CI: 1.07 to 1.21; I2 = 0.0%). Each 1 mmol/L increase in low-density lipoprotein was associated with an 8% increase (ES = 1.08, 95% CI: 1.03 to 1.14; I2 = 0.3%) in incidence of all-cause dementia. Midlife dyslipidemia is associated with an increased risk of cognitive impairment in later life.

Prevalence of sleep disturbances in people with dementia living in the community: A systematic review and meta-analysis.

This study aimed to systematically review and meta-analyse the prevalence of sleep disturbances in people with dementia and examine demographic predictors and whether overall prevalence has changed over time. We searched Embase, MEDLINE and PsycINFO for studies reporting the prevalence of sleep disturbances in people with dementia living at home. We meta-analysed the data and calculated the pooled prevalence of sleep disturbances in people with dementia overall and in dementia subtypes. We used meta-regressions to investigate the effects of study characteristics, publication dates and participant demographics. Eleven studies fulfilled the inclusion criteria. The pooled prevalence of any symptoms of sleep disturbance was 26 % (95 % confidence intervals, CI: 23-30 %; n=2719) and of clinically significant sleep disturbance 19 % (13-25 %; n=2753). The pooled prevalence of sleep disturbance symptoms was significantly lower among people with Alzheimer's disease (24 %; 16-33 %, n=310) than Lewy body dementia (49 %; 37-61 %, n=65). Meta-regression analysis did not find that publication year, participant's age, sex and study quality predicted prevalence. Sleep disturbances are common among people with dementia living in the community, especially in Lewy body dementia. There was no change in prevalence according to publication dates, suggesting treatment has not improved over time.

Aggressive behavior of patients with vascular dementia

There is a worldwide trend towards an increase in the number of people aged over 60, which in turn leads to an increase in the prevalence of vascular dementia and neuropsychiatric disorders, including the accompanying aggressive behavior. The authors analyze the relationship of neuropsychiatric symptoms with aggressive behavior and assess the impact of the evolution of dementia, socio-demographic factors on the development of aggressive behavior in some subtypes of vascular dementia based on literature data retrieved from Scopus, PubMed, eLibrary databases as well as national guidelines. Analyzing the publication was made by the method of the system analysis and generalization. The analysis of the literature suggests that aggressive behavior in patients with vascular dementia changes their lifestyle and worsens its quality, in addition, it increases the burden on caregivers. Also, this psychopathological process is one of the reasons for frequent hospitalizations of patients in psychiatric hospitals and social institutions, which becomes an urgent economic and medical problem at the state level. Of all the psychopathological symptoms in the clinical picture of vascular dementia, aggressive behavior periodically occurs together with apathy, depression, anxiety, abnormal motor activity and hallucinations. The question of the highest frequency of occurrence of aggressive tendencies in one or another subtype of vascular dementia remains debatable. Research on the relationship between dementia severity, gender, and aggressive behavior has been mixed. The question of the influence of the level of education on the manifestation of aggression in patients with vascular dementia remains completely unexplored. Thus, the above facts dictate the need for further study of these issues.

Association of shift work with incident dementia: a community-based cohort study

BackgroundSome observational studies had found that shift work would increase risks of metabolic disorders, cancers, and cardiovascular diseases, but there was no homogeneous evidence of such an association between shift work and incident dementia. This study aimed to investigate whether shift work would increase the risk of dementia in a general population.MethodsOne hundred seventy thousand seven hundred twenty-two employed participants without cognitive impairment or dementia at baseline recruited between 2006 and 2010 were selected from the UK Biobank cohort study. Follow-up occurred through June 2021. Shift work status at baseline was self-reported by participants and they were categorized as non-shift workers or shift workers. Among shift workers, participants were further categorized as night shift workers or shift but non-night shift workers. The primary outcome was all-cause dementia in a time-to-event analysis, and the secondary outcomes were subtypes of dementia, including Alzheimer’s disease, vascular dementia, and other types of dementia.ResultsIn total, 716 dementia cases were observed among 170,722 participants over a median follow-up period of 12.4 years. Shift workers had an increased risk of all-cause dementia as compared with non-shift workers after multivariable adjustment (hazard ratio [HR], 1.30, 95% confidence interval [CI], 1.08–1.58); however, among shift workers, night shift work was not associated with the risk of dementia (HR, 1.04, 95% CI, 0.73–1.47). We found no significant interaction between shift work and genetic predisposition to dementia on the primary outcome (P for interaction = 0.77).ConclusionsShift work at baseline was associated with an increased risk of all-cause dementia. Among shift workers, there was no significant association between night shift work and the risk of dementia. The increased incidence of dementia in shift workers did not differ between participants in different genetic risk strata for dementia.

Prescreening MCI and Dementia Using Shank-Mounted IMU During TUG Task

Detection of mild cognitive impairment (MCI) and dementia (DEM) is an important topic because, unless it is treated early, MCI can progress to DEM, which is an untreatable disease. This article proposes a timed-up-and-go (TUG) task features analysis and classification of MCI and DEM using inertial measurement units (IMUs) in wearable devices. Our goal is to create a generalized model that can be used for preclinical screening. As a result, rather than classifying only one subtype of DEM, such as Alzheimer’s disease (AD) or Parkinson’s disease (PD), we classify all subtypes as DEM. We also utilize feature selection methods on features from TUG tasks to optimize the MCI and DEM classification performance. From the results, our generalized model can outperform other works in normal control (NC)-MCI&DEM classification with an accuracy of 86.94% and sensitivity of 97.40%. For NC-DEM classification, the performance of our generalized model is slightly lower than that of specific-subtype models (e.g., NC versus AD). However, our generalized model can outperform the specific-subtype models when using a diverse variety of subtypes. It is a reasonable tradeoff, and it can be a good first step toward a future where the patient can preclinically self-screening the cognitive impairments using wearable devices in free-living environments. This could allow patients to notice the cognitive impairment early on and seek a comprehensive diagnosis from a doctor.

Prediction of Dementia Subtypes by Machine Learning: Feasibility of Error Control

AbstractBackgroundAccurate prediction of dementia subtypes is important in geriatric decision‐making and in designing clinical trials. We used machine learning and a novel error control algorithm to analyze longitudinal data collected by the National Alzheimer Coordinating Center (NACC) from June 2005 to November 2021. Our goals were (1) Predicting the conversion to several dementia subtypes in patients who were dementia‐free at baseline; (2) Finding important clinical metrics and risk factors that contribute to the prediction; (3) Controlling the prediction error rate on specific dementia subtypes to help design clinical trials.MethodWe studied the 13,243 NACC participants who had normal clinical etiologic diagnosis results at their first visit. Then, we fit different machine learning models to predict the diagnosis result of their last visit. Among 13,243 patients, 10,679 remained dementia‐free, 1,793 had Alzheimer’s disease (AD), 153 had Lewy body disease (LBD), and 290 had Vascular dementia (VD) as the primary diagnosis at their last exam. After removing predictors with over 10% missing values and imputing missing values on the remaining predictors, our models tested 249 predictors, which included clinical, family history, and behavioral features. Cases were randomly split the data into training (90%) and testing (10%) subsets for 500 rounds. Multinomial regression, penalized multinomial regression, and random forest models were analyzed. We further manipulated the penalized multinomial regression model by controlling the prediction error rate on a given dementia subtype.ResultMultinomial regression model, penalized multinomial regression model and random forest led to the overall prediction error rate of 10.73%, 9.26%, and 9.38%, respectively. Significant predictors included the length of time from the initial visit to the most recent visit, the cognitive status of the patient in the visit, and metrics of memory deficits. Error control results (Figure 1) showed that relaxing the strict control on some dementia subtypes serves to improve the prediction performance of the other dementia subtypes.ConclusionMachine learning methods can help predict the progression to dementia using commonly available variables available in memory clinics even in the absence of imaging and fluid biomarkers. ML is helpful in identifying most important clinical features and key risk factors.

What subtypes are driving the association between hypertensive disorders of pregnancy and dementia? Findings from an 80‐year retrospective cohort study

AbstractBackgroundWe recently found that women with, versus without, a history of HDP had a higher hazard of all‐cause dementia, vascular dementia (VaD), and other/unspecified dementia, but not Alzheimer’s disease (AD). Here, we assess associations of HDP subtypes with later life dementias.MethodsWe performed a retrospective cohort study among women with at least 1 singleton pregnancy (1939–2019) in Utah. Classification of HDP was done via birth certificates (text string, 1939–1977; ICD9 codes, 1978–1988; and check boxes with additional text, 1989–2013) with death certificates and inpatient records used for validation. Classification of dementia was assessed using ICD 9/10 codes via death, inpatient, and Medicare records. HDP exposed women (n=19,989) were one‐to‐two matched with unexposed women (n=39,679) by 5‐year age groups, year of childbirth, and parity at the time of pregnancy (Figure 1). Cox regression models were used to estimate adjusted Hazard Ratios (aHR) and 95% CI for HDP subtypes with all‐cause and specific dementias.ResultsHDP pregnancies were comprised of preeclampsia/eclampsia (65.9%) and gestational hypertension (33.5%). The remaining HDP cases were due to HELLP syndrome (0.6%), which we did not evaluate here due to small case count. Incidence of dementia over follow‐up (1979–2019) was 4.1%; of these, 70% were other/unspecified, 24% were AD, and 6% were VaD. Women with a history of preeclampsia/eclampsia, compared to unexposed, had a 1.38 higher hazard of all‐cause dementia, while women with gestational hypertension had a 1.36 higher hazard (Table 1). Breaking down by dementia subtypes, women with a history of preeclampsia/eclampsia had a 1.51 higher hazard of other/unspecified dementia, while women with gestational hypertension had a 1.31 higher hazard. The strength of association of gestational hypertension with VaD was 2.75, nearly double that for preeclampsia/eclampsia, which was 1.58. HDP subtypes were not associated with AD.ConclusionOur results are in line with the largest study to date conducted in Denmark that found preeclampsia to be most strongly associated with VaD compared to other dementia subtypes. Our results further suggest that risk of VaD may be just as high for women with a history of gestational hypertension as for preeclampsia.

Socioeconomic Deprivation, Genetics and Risk of Dementia

AbstractBackgroundThe risk of Alzheimer’s disease and other subtypes of dementia is determined by multiple pathways including genetic, environmental and lifestyle factors. With higher risk in socioeconomically deprived individuals, it remains unclear whether associations of genetic risk with dementia incidence may be modified by individual‐ or area‐level socioeconomic deprivation.MethodParticipants of the UK Biobank study attended baseline assessments at 22 assessment centers between 2006 and 2010 and were followed‐up until 2016 or 2017. Cox proportional‐hazards regressions with area‐level socioeconomic deprivation based on the Townsend Deprivation Index and individual‐level socioeconomic deprivation based on car and home ownership, housing type, and income were run. A comprehensive polygenic risk score was employed and potential mediation through lifestyle and depressive symptoms was assessed. In an imaging substudy, associations of socioeconomic deprivation with six imaging‐derived phenotypes including white matter hyperintensities were examined.Result196,368 participants (mean [standard deviation] age, 64.1 [2.9] years, 52.7% female) aged 60 years and older, of European ancestry, without dementia diagnosis or cognitive impairment at baseline were followed‐up for 1,545,316 person‐years (median [interquartile range] follow‐up, 8.0 [7.4‐8.6] years). In high genetic risk and high area‐level deprivation 1.71% (95% confidence interval, 1.44% to 2.01%) developed dementia compared to 0.56% (95% confidence interval, 0.48% to 0.65%) in low genetic risk and low‐to‐moderate area‐level deprivation (hazard ratio, 2.31; 95% confidence interval, 1.84 to 2.91). In high genetic risk and high individual‐level deprivation 1.78% (95% confidence interval, 1.50% to 2.09%) developed dementia compared to 0.31% (95% confidence interval, 0.20% to 0.45%) in low genetic risk and low individual‐level deprivation (hazard ratio, 4.06; 95% confidence interval, 2.63 to 6.26). There was no significant interaction between genetic risk and area‐level (P =.77) or individual‐level (P =.07) deprivation. An imaging substudy including 11,083 participants found greater burden of white matter hyperintensities associated with higher socioeconomic deprivation.ConclusionIn older adults without dementia area‐level and individual‐level socioeconomic deprivation and genetic risk were significantly and independently associated with a higher risk of dementia. Dementia prevention interventions may be particularly effective if targeted to people living in deprived households and areas, regardless of genetic vulnerability.

Dysphagia Evaluation in a Multidisciplinary VA Cognitive Care Clinic.

The VA Cognitive Care Clinic (CCC) provides comprehensive evaluation and longitudinal treatment for older Veterans with neurocognitive disorders and their caregivers through multidisciplinary care including geriatrics, palliative medicine, neuropsychology, and social work. Speech-language pathology (SLP) was recently incorporated into the CCC team in response to calls for more proactive approaches to diagnosis and management of dysphagia (swallowing dysfunction) in persons with dementia. While dysphagia is estimated to be highly prevalent in this population, prior work has been limited by inadequate dementia characterization. The purpose of this novel quality improvement project was to provide descriptive information regarding the occurrence of dysphagia in persons with dementia seen in CCC. CCC patients underwent comprehensive neuropsychological and geriatrics assessments that led to a multidisciplinary determination of dementia diagnosis and subtype. Validated dysphagia-specific clinical evaluation tools included the Eating Assessment Tool (EAT-10; dysphagia-specific patient-reported outcome measure), the 3-ounce water swallow test to assess swallowing safety (aspiration risk), and the Test of Masticating and Swallowing Solids (TOMASS) to assess swallowing efficiency. A REDCap database was constructed to track CCC evaluation results and patient-specific variables derived from the electronic health record. Descriptive statistics were calculated for dementia diagnosis, subtype, and dysphagia measures. 124 patients were seen in CCC between 2017 and 2021. Eighty-eight patients (73%) received a dementia diagnosis with the majority meeting criteria for Alzheimer's disease (53%) or mixed etiology (24%). Sixty-eight (55%) of these patients were evaluated by an SLP during their initial intake appointment. Twenty of 59 patients (39%) scored 3 or higher on the EAT-10, indicating concern for dysphagia. Twenty-six of 64 patients (41%) failed the 3-ounce water swallow test. Thirty-one of 62 patients (50%) demonstrated inefficient mastication based on length of total masticatory time on the TOMASS. Findings from this initial cohort indicate that approximately one-half of patients with dementia assessed by a SLP demonstrated clinical signs of swallowing safety or efficiency impairment. Patient report of dysphagia-related symptoms occurred at a significantly lower rate, supporting the need for SLP services in multidisciplinary memory and geriatrics clinics to facilitate early identification of dysphagia and avoid serious dysphagia-related consequences, including pneumonia and malnutrition.

Gait Correlates of Cognitive Function and Future Decline

AbstractBackgroundSlow walking speed is associated with numerous negative health outcomes including global cognitive decline.1 More detailed gait analysis can differentiate among different dementia subtypes,10 but the predictive power of baseline gait metrics for domain‐specific cognitive decline needs further characterization.MethodWe conducted a follow up study on 534 participants of the Mayo Clinic Study of Aging who underwent evaluations every 15 months (median follow‐up = 5.4 years). We analyzed quantitative gait metrics, acquired with a pressure‐sensing walkway (GAITRite® instrument), and cognitive scores using a linear mixed effects model to identify correlations between z‐scored baseline gait metrics (cadence, swing time, stance time, velocity, double support time (DST), stride length, stride length standard deviation (SD), stride time SD, swing time SD, stance time SD, DST SD) and cognitive domain z‐scores (memory, attention, language, visuospatial, global) at baseline and over time. Models controlled for age, sex, and probable REM sleep behavior disorder at baseline.ResultThe table includes both baseline associations and difference in slopes (Δ), relative to the average person, in cognitive domains. These slopes ranged from about ‐0.05 for visuospatial to ‐0.12 for attention.ConclusionIncreased gait velocity and stride length are associated with less severe domain‐specific and global cognitive decline while increased swing time SD and DST are associated with more severe rates of decline in some domains. These findings suggest that baseline gait characteristics could be a helpful predictor of future cognitive changes.

Neuroimaging in dementia syndromes

Neuroimaging is essential for early diagnosis of different types of dementia. Structural imaging is recommended for patients with new-onset cognitive impairment and magnetic resonance imaging is the modality of choice. When atypical features are present, functional and molecular imaging is helpful for further characterisation of neurodegenerative changes. Accurate identification of dementia subtypes enables early initiation of specific molecular targeted therapies. This article provides an overview of the structural, biochemical and functional changes in common dementia subtypes that may be diagnosed through neuroimaging, emphasising new techniques like perfusion and functional imaging.

Novel advances in the characterization of dementia subtypes: from multi‐feature classification to whole brain computational models

AbstractBackgroundBrain functional connectivity analyses derived from electroencephalography (EEG) provides relevant information for classification of dementia subtypes. The predictive strength of classification tools can be benefit from integrative, multi‐feature analysis of EEG which result in composite metric of functional connectivity. Additionally, significant improvement can be obtained when connectivity analyses consider the dependence between groups of regions as a whole system (high order interactions), instead of conceiving the network as a collection of pairwise interactions.MethodFive minutes, resting‐state EEG (rsEEG) was recorded from healthy controls (HC, n=42), and participants diagnosed with either behavioral variant frontotemporal dementia (bvFTD, n=19) or Alzheimer Disease (AD, n=33). EEG source localization analyses were conducted. Whole brain functional connectivity (82 anatomic compartments, AAL atlas) was analyzed using 17 metrics to capture different statistical dependence between source space rsEEG time series, in both time‐, and frequency‐domains. Furthermore, high‐order, non‐lineal statistical interdependencies between group of brain regions were assessed using multivariate information theory. Gradient boosting classifiers with Bayesian optimization, harmonization, and feature elimination were implemented.ResultClassification of dementia subtypes, using a particular frequency‐domain connectivity metric, displayed better performances as features for classification comprised information from multiple EEG frequency bands. Likewise, classification systems including information from several connectivity metrics outperformed those achieved with individual metrics. Dementia classification conducted with connectivity metrics derived from mutual information significantly improved as order of interaction (number of brain regions consider as a network) increased. This latter approach boosted multimodal (fMRI‐EEG) classification of dementias based on brain functional connectivity, and served as input for whole‐brain computational models describing the pathophysiology of neurodegenerative diseases.ConclusionIntegrative, composite metrics of connectivity, and connectivity analyses of source localized rsEEG based in high order interactions capture relevant information to discriminate dementia subtypes, and provide a reliable and interpretable description of brain functional connectivity and neurodegeneration.