Changes in thalamic functional connectivity in post-Covid patients with and without fatigue.

BACKGROUND: Functional brain alterations in post-Covid-19 condition have been minimally explored to date. Here, we investigate differences in resting-state thalamic functional connectivity among post-Covid patients with and without fatigue, alongside structural brain changes and cognition. METHODS: Thirty-nine post-Covid patients (n = 15 fatigued, n = 24 non-fatigued) participated in our study, undergoing comprehensive cognitive assessments, as well as functional and structural neuroimaging. We conducted a seed-based functional connectivity analysis using the thalamus as a seed region, exploring its connectivity with the entire brain. To further elucidate our findings, correlation analyses were performed using the functional coupling between the thalamus and regions showing different connectivity between the two patient groups. RESULTS: Our results reveal that patients experiencing fatigue exhibit anti-correlated functional coupling between the thalamus and motor-associated regions, including the motor cortex (M1), supplementary motor area (SMA), and anterior cingulate cortex (ACC), compared to non-fatigued patients, who are showing positive functional coupling. Furthermore, this observed coupling was found to correlate with both the fatigue scores obtained from a fatigue questionnaire and performance on the Trail Making Test, Part A, which represents a measure of processing speed. CONCLUSIONS: Our study highlights significant differences in resting-state functional connectivity between post-Covid patients with and without fatigue, particularly within motor-associated brain regions. These findings suggest a potential neural mechanism underlying post-Covid fatigue and underscore the importance of considering both functional and structural brain changes in understanding the symptomatic sequelae of post-Covid-19 condition. Further research is warranted to provide insight into the longitudinal trajectories of these neural alterations.

Impaired Kidney Function, Cerebral Small Vessel Disease and Cognitive Disorders: The Framingham Heart Study.

BACKGROUND AND HYPOTHESIS: It remains unclear if the relation of chronic kidney disease (CKD) with cognitive dysfunction is independent of blood pressure (BP). We evaluated kidney function in relation to premorbid BP measurements, cerebral small vessel disease (CSVD) and incident mild cognitive impairment (MCI) and dementia in Framingham Offspring Cohort participants. METHODS: We included Framingham Offspring participants free of dementia, attending an examination during midlife (exam cycle 6, baseline) for ascertainment of kidney function status, with brain MRI late in life (exam cycles 7-9), cognitive outcome data and available interim hypertension and blood pressure assessments. We related CKD (estimated glomerular filtration rate < 60 ml/min/1.73m2) and albuminuria (urine albumin-to-creatinine ratio ≥ 30 mg/g) to CSVD markers and cognitive outcomes using multivariable regression analyses. RESULTS: Among 2604 participants (mean age 67.4 ± 9.2, 64% women, 7% had CKD and 9% albuminuria), albuminuria was independently associated with covert infarcts (adjusted OR, 1.55 [1.00-2.38]; P = 0.049) and incident MCI and dementia (adjusted HR, 1.68 [1.18-2.41]; P = 0.005 and 1.71, [1.11-2.64]; P = 0.015, respectively). CKD was not associated with CSVD markers but was associated with higher risk of incident dementia (HR, 1.53 [1.02-2.29]; P = 0.041), While albuminuria was predictive of the Alzheimer's disease subtype (Adjusted HR = 1.68, [1.03-2.74]; P = 0.04), CKD was predictive of vascular dementia (Adjusted HR, 2.78, [1.16-6.68]; P = 0.023). CONCLUSIONS: Kidney disease was associated with CSVD and cognitive disorders in asymptomatic community dwelling participants. The relation was independent of premorbid BP, suggesting that the link between kidney and brain disease may involve additional mechanisms beyond blood pressure related injury.

Effectiveness of computer-based interventions for community-dwelling people with cognitive decline: a systematic review with meta-analyses.

BACKGROUND: Cognitive deficits arise with age and can increase the risk for subjective cognitive decline (SCD) and mild cognitive impairment (MCI), which may result in dementia, leading to health problems, care dependency and institutionalization. Computer-based cognitive interventions (CCIs) have the potential to act as important counteraction functions in preserving or improving cognition concomitant to available pharmacological treatment. The aim was to assess the effectiveness of CCIs performed individually with a personal or tablet computer, game console, virtual, augmented, or mixed reality application on cognition in community-dwelling people with SCD, MCI and dementia. METHODS: A systematic review with meta-analyses of randomized controlled trials (RCTs) was performed. The systematic literature search was conducted in MEDLINE, CINAHL, Embase, Cochrane CENTRAL, IEEE Xplore Digital Library, Web of Science, Scopus and PsycINFO. In addition, a search for gray literature and backward citation searching were carried out. To judge on the evidence, two reviewers independently used the Cochrane Risk of Bias Tool. The standardized mean difference (SDM) for pooling comparable studies using the random-effects model was applied. RESULTS: Twenty-four RCTs were identified, of which 1 RCT examined CCIs in individuals with SCD, 18 RCTs with MCI, and 6 RCTs with dementia. Most interventions were conducted with personal computers. Meta-analyses with 12 RCTs showed significant effects of computer-based cognitive interventions for people with MCI in the domains memory, working memory, attention/concentration/processing speed and executive functioning, but no significant improvements in global cognition and language. Regarding dementia a meta-analysis pooled with 4 RCTs demonstrated a tendency towards, but no significant increase of memory functions (SMD 0.33, CI 95% [-0.10, 0.77]). One RCT regarding SCD reported significant improvements in memory functions for participants conducting a cognitive training on a personal computer. CONCLUSIONS: The results demonstrated that CCIs have beneficial effects on domain-specific cognition in people with MCI but no significant effects on people with dementia. In terms of SCD, one study showed significant improvements in memory functions. It seems that the beneficial effect for cognitive preservation or improvement due to CCIs occurs at the earliest intervention state. However, more research on SCD is needed. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CDR42020184069.

The Purdue Pegboard Test: Normative Data From 1,355 Healthy People From Austria.

IMPORTANCE: The Purdue Pegboard Test (PPT) is widely used as a measure of manual dexterity. Declining manual dexterity may predict cognitive decline among elderly people, but normative data for this population are scarce. OBJECTIVE: To identify demographic and clinical predictors of PPT results in normal middle-aged and elderly Austrian people and to provide norms stratified by significant determinants. DESIGN: A prospective, community-based cohort study using baseline data of participants from two study panels (1991-1994 and 1999-2003). SETTING: Monocentric study Participants: 1,355 healthy, randomly selected, community-dwelling people ages 40 to 79 yr. METHOD: Extensive clinical examination, including completion of the PPT. OUTCOMES AND MEASURES: The number of pegs placed within a 30-s time limit on four subtests: using the right hand, left hand, both hands, and assembly (within 60 s), respectively. Demographic outcomes were the highest grade achieved. RESULTS: For all four subtests, increasing age (ßs = -0.400 to -0.118, SEs = 0.006 to 0.019, p < .001) and male sex (ßs = -1.440 to -0.807, SEs = 0.107 to 0.325, p < .001) was related to worse test results. Among vascular risk factors, diabetes (ßs = -1.577 to -0.419, SEs = 0.165 to 0.503, p < .001) was related to worse test results but explained only a small portion (0.7%-1.1%) of the variability in PPT performance. CONCLUSIONS AND RELEVANCE: We provide age- and sex-specific norms of the PPT for a middle-aged and elderly population. The data represent useful reference values when assessing manual dexterity in older age groups. What This Article Adds: Advancing age and male sex relate to worse performance on the PPT in a community-dwelling cohort without signs and symptoms of neurological disease. Vascular risk factors explain only very little of the variance of test results in our population. Our study adds to the limited age- and sex-specific norms of the PPT among middle-aged and older people.

Free water diffusion MRI and executive function with a speed component in healthy aging.

Extracellular free water (FW) increases are suggested to better provide pathophysiological information in brain aging than conventional biomarkers such as fractional anisotropy. The aim of the present study was to determine the relationship between conventional biomarkers, FW in white matter hyperintensities (WMH), FW in normal appearing white matter (NAWM) and in white matter tracts and executive functions (EF) with a speed component in elderly persons. We examined 226 healthy elderly participants (median age 69.83 years, IQR: 56.99-74.42) who underwent brain MRI and neuropsychological examination. FW in WMH and in NAWM as well as FW corrected diffusion metrics and measures derived from conventional MRI (white matter hyperintensities, brain volume, lacunes) were used in partial correlation (adjusted for age) to assess their correlation with EF with a speed component. Random forest analysis was used to assess the relative importance of these variables as determinants. Lastly, linear regression analyses of FW in white matter tracts corrected for risk factors of cognitive and white matter deterioration, were used to examine the role of specific tracts on EF with a speed component, which were then ranked with random forest regression. Partial correlation analyses revealed that almost all imaging metrics showed a significant association with EF with a speed component (r = -0.213 - 0.266). Random forest regression highlighted FW in WMH and in NAWM as most important among all diffusion and structural MRI metrics. The fornix (R2=0.421, p = 0.018) and the corpus callosum (genu (R2 = 0.418, p = 0.021), prefrontal (R2 = 0.416, p = 0.026), premotor (R2 = 0.418, p = 0.021)) were associated with EF with a speed component in tract based regression analyses and had highest variables importance. In a normal aging population FW in WMH and NAWM is more closely related to EF with a speed component than standard DTI and brain structural measures. Higher amounts of FW in the fornix and the frontal part of the corpus callosum leads to deteriorating EF with a speed component.

Humanoid socially assistive robots in dementia care: a qualitative study about expectations of caregivers and dementia trainers.

OBJECTIVE: To examine the expectations of informal caregivers, nurses, and dementia trainers regarding the support of (physical and psychosocial) human needs by humanoid social assistive robots (SARs) in dementia care. METHODS: A qualitative study was conducted with 11 homogeneous focus groups of informal caregivers, nurses and dementia trainers providing dementia care at home, in adult daycare centers, or in nursing homes. A qualitative content analysis was performed using a concept- and data-driven coding frame. RESULTS: Focus group discussions with 52 individuals were held. Participants reported mostly positive expectations and stated that SARs could offer potential support in all components of human needs, especially in avoiding danger (e.g. recognise danger, organise help), communication/contact with others (e.g. enable telephone calls, provide company), daily activities (e.g. remind of appointments, household obligations), recreational activities (e.g. provide music), eating/drinking (e.g. help cook), and mobility/body posture (e.g. give reminders/instructions for physical exercise). Participants also mentioned some negative expectations in all human needs, predominantly in communication/contact with others (e.g. loss of interpersonal interaction) and avoiding danger (e.g. scepticism regarding emergencies). CONCLUSION: Participants stated that SARs had great potential to provide assistance in dementia care, especially by reminding, motivating/encouraging and instructing people with dementia. Informal caregivers and nurses also considered them as useful supportive devices for themselves. However, participants also mentioned negative expectations, especially in communication/contact with others and avoiding danger. These findings demonstrate the support caregivers and dementia trainers expect from humanoid SARs and may contribute to their optimisation for dementia care.

Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease.

While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.

Reduced dynamics of functional connectivity and cognitive impairment in multiple sclerosis.

BACKGROUND: In multiple sclerosis (MS), abnormalities of brain network dynamics and their relevance for cognitive impairment have never been investigated. OBJECTIVES: The aim of this study was to assess the dynamic resting state (RS) functional connectivity (FC) on 62 relapsing-remitting MS patients and 65 sex-matched healthy controls enrolled at 7 European sites. METHODS: MS patients underwent clinical and cognitive evaluation. Between-group network FC differences were evaluated using a dynamic approach (based on sliding-window correlation analysis) and grouping correlation matrices into recurrent FC states. RESULTS: Dynamic FC analysis revealed, in healthy controls and MS patients, three recurrent FC states: two characterized by strong intra- and inter-network connectivity and one characterized by weak inter-network connectivity (State 3). A total of 23 MS patients were cognitively impaired (CI). Compared to cognitively preserved (CP), CI-MS patients had reduced RS-FC between subcortical and default-mode networks in the low-connectivity State 3 and lower dwell time (i.e. time spent in a given state) in the high-connectivity State 2. CI-MS patients also exhibited a lower number and a less frequent switching between meta-states, as well as a smaller distance traveled through connectivity states. CONCLUSION: Time-varying RS-FC was markedly less dynamic in CI- versus CP-MS patients, suggesting that slow inter-network connectivity contributes to cognitive dysfunction in MS.

Detection of mild cognitive impairment in a community-dwelling population using quantitative, multiparametric MRI-based classification.

Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population.

A comprehensive analysis of resting state fMRI measures to classify individual patients with Alzheimer's disease.

Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly.

Individual classification of Alzheimer's disease with diffusion magnetic resonance imaging.

Diffusion magnetic resonance imaging (MRI) is a powerful non-invasive method to study white matter integrity, and is sensitive to detect differences in Alzheimer's disease (AD) patients. Diffusion MRI may be able to contribute towards reliable diagnosis of AD. We used diffusion MRI to classify AD patients (N=77), and controls (N=173). We use different methods to extract information from the diffusion MRI data. First, we use the voxel-wise diffusion tensor measures that have been skeletonised using tract based spatial statistics. Second, we clustered the voxel-wise diffusion measures with independent component analysis (ICA), and extracted the mixing weights. Third, we determined structural connectivity between Harvard Oxford atlas regions with probabilistic tractography, as well as graph measures based on these structural connectivity graphs. Classification performance for voxel-wise measures ranged between an AUC of 0.888, and 0.902. The ICA-clustered measures ranged between an AUC of 0.893, and 0.920. The AUC for the structural connectivity graph was 0.900, while graph measures based upon this graph ranged between an AUC of 0.531, and 0.840. All measures combined with a sparse group lasso resulted in an AUC of 0.896. Overall, fractional anisotropy clustered into ICA components was the best performing measure. These findings may be useful for future incorporation of diffusion MRI into protocols for AD classification, or as a starting point for early detection of AD using diffusion MRI.

Correlates of Executive Functions in Multiple Sclerosis Based on Structural and Functional MR Imaging: Insights from a Multicenter Study.

Purpose To study the concomitant use of structural and functional magnetic resonance (MR) imaging correlates to explain information processing speed (IPS) and executive function (EF) in multiple sclerosis (MS). Materials and Methods Local ethics committee approval was obtained at all sites for this prospective, multicenter study. All subjects provided written informed consent. Twenty-six patients with relapsing-remitting MS and 32 healthy control subjects from four centers underwent structural and functional MR imaging, including a go/no-go task and neuropsychological assessment. Subtests of the Brief Repeatable Battery of Neuropsychological Tests, the Wisconsin Card Sorting Test, and the performance with the functional MR imaging paradigm were used as estimates of IPS and EF. Activation of the thalamus and the inferior frontal gyrus (pars triangularis), thalamic volume, T2 lesion load, and age were used to explain IPS and EF in regression models. Results Compared with control subjects, patients showed increased activation in a frontoparietal network, including both thalami, during the execution of the go/no-go task. Patients had decreased thalamic volume (P < .001). Among tested variables, thalamic volume (ß = 0.606, P = .001), together with thalamic activation (ß = -0.410, P = .022), were the best predictors of IPS and EF and helped explain 52.7% of the variance in IPS and EF. Conclusion This study highlights the potential of the combined use of functional and morphologic parameters to explain IPS and EF in patients with relapsing-remitting MS and confirms the central role of the thalamus as a relay station in executive functioning. (©) RSNA, 2016.

Characteristics and burden of acute COVID-19 and long-COVID: Demographic, physical, mental health, and economic perspectives.

BACKGROUND: COVID-19 infection and its associated consequence, known as long-COVID, lead to a significant burden on the global healthcare system and limitations in people's personal and work lives. This study aims to provide further insight into the impact of acute and ongoing COVID-19 symptoms and investigates the role of patients' gender and vaccination status. METHODS: 416 individuals (73.9% female) between the ages of 16 and 80 years (M = 44.18, SD = 12.90) with self-reported symptoms of long-COVID participated in an online survey conducted between March and May 2022. RESULTS: 6.0%, 74.3%, and 19.7% of all respondents reported having had an asymptomatic, mild, or severe acute illness, respectively. Out of all participants, 7.8% required hospitalization. The most prevalent symptoms during the acute infection (Mdn = 23.50 symptoms, IQR = 13-39) included fatigue, exhaustion, cough, brain fog, and memory problems. The median long-COVID disease duration was 12.10 months (IQR = 2.8-17.4). Among 64 inquired long-COVID symptoms (Mdn = 17.00 symptoms, IQR = 9-27), participants reported fatigue, exhaustion, memory problems, brain fog, and dyspnea as the most common ongoing symptoms, which were generally experienced as fluctuating and deteriorating after physical or cognitive activity. Common consequences of long-COVID included financial losses (40.5%), changes in the participants' profession (41.0%), stress resistance (87.5%), sexual life (38.1%), and mood (72.1%), as well as breathing difficulties (41.3%), or an increased drug intake (e.g., medicine, alcohol; 44.6%). In addition, vaccinated individuals exhibited a shorter acute illness duration and an earlier onset of long-COVID symptoms. In general, women reported more long-COVID symptoms than men. CONCLUSION: Long-COVID represents a heterogeneous disease and impacts multiple life aspects of those affected. Tailored rehabilitation programs targeting the plurality of physical and mental symptoms are needed.

Clusterwise Independent Component Analysis (C-ICA): Using fMRI resting state networks to cluster subjects and find neurofunctional subtypes.

BACKGROUND: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously. NEW METHOD: We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs. RESULTS: In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. COMPARISON WITH OTHER METHODS: Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods. CONCLUSIONS: The successful performance of C-ICA indicates that it is a promising method to extract neurofunctional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity.

Analyzing Hierarchical Multi-View MRI Data With StaPLR: An Application to Alzheimer's Disease Classification.

Multi-view data refers to a setting where features are divided into feature sets, for example because they correspond to different sources. Stacked penalized logistic regression (StaPLR) is a recently introduced method that can be used for classification and automatically selecting the views that are most important for prediction. We introduce an extension of this method to a setting where the data has a hierarchical multi-view structure. We also introduce a new view importance measure for StaPLR, which allows us to compare the importance of views at any level of the hierarchy. We apply our extended StaPLR algorithm to Alzheimer's disease classification where different MRI measures have been calculated from three scan types: structural MRI, diffusion-weighted MRI, and resting-state fMRI. StaPLR can identify which scan types and which derived MRI measures are most important for classification, and it outperforms elastic net regression in classification performance.

Factors influencing serum neurofilament light chain levels in normal aging.

OBJECTIVE: Serum neurofilament light (sNfL) is a promising marker for neuro-axonal damage and it is now well known that its levels also increase with higher age. However, the effect of other determinants besides age is still poorly investigated. We therefore aimed to identify factors influencing the sNfL concentration by analysing a large set of demographical, life-style and clinical variables in a normal aging cohort. METHODS: sNfL was quantified by single molecule array (Simoa) assay in 327 neurologically inconspicuous individuals (median age 67.8±10.7 years, 192 female) who participated in the Austrian Stroke Prevention Family Study (ASPS-Fam). Random forest regression analysis was used to rank the association of included variables with sNfL in the entire cohort and in age-stratified subgroups. Linear regression then served to identify factors independently influencing sNfL concentration. RESULTS: Age (ß=0.513, p<0.001) was by far the most important factor influencing sNfL, which was mainly driven by individuals ≥60 years. In age stratified sub-groups, body mass index (BMI) (ß=-0.298, p<0.001) independently predicted sNfL in individuals aged 38-60 years. In individuals ≥60 years, age (ß=0.394, p<0.001), renal function (ß=0.376, p<0.001), blood volume (ß=-0.198, p=0.008) and high density lipoprotein (HDL) (ß=0.149, p=0.013) were associated with sNfL levels. CONCLUSIONS: Age is the most important factor influencing sNfL concentrations, getting increasingly relevant in elderly people. BMI further influences sNfL levels, especially at younger age, whereas renal function gets increasingly relevant in the elderly.

Association of low-frequency and rare coding variants with information processing speed.

Measures of information processing speed vary between individuals and decline with age. Studies of aging twins suggest heritability may be as high as 67%. The Illumina HumanExome Bead Chip genotyping array was used to examine the association of rare coding variants with performance on the Digit-Symbol Substitution Test (DSST) in community-dwelling adults participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. DSST scores were available for 30,576 individuals of European ancestry from nine cohorts and for 5758 individuals of African ancestry from four cohorts who were older than 45 years and free of dementia and clinical stroke. Linear regression models adjusted for age and gender were used for analysis of single genetic variants, and the T5, T1, and T01 burden tests that aggregate the number of rare alleles by gene were also applied. Secondary analyses included further adjustment for education. Meta-analyses to combine cohort-specific results were carried out separately for each ancestry group. Variants in RNF19A reached the threshold for statistical significance (p = 2.01 × 10-6) using the T01 test in individuals of European descent. RNF19A belongs to the class of E3 ubiquitin ligases that confer substrate specificity when proteins are ubiquitinated and targeted for degradation through the 26S proteasome. Variants in SLC22A7 and OR51A7 were suggestively associated with DSST scores after adjustment for education for African-American participants and in the European cohorts, respectively. Further functional characterization of its substrates will be required to confirm the role of RNF19A in cognitive function.

Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer's Disease.

The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer's disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.

Simple MRI score aids prediction of dementia in cerebral small vessel disease.

OBJECTIVE: To determine whether a simple small vessel disease (SVD) score, which uses information available on rapid visual assessment of clinical MRI scans, predicts risk of cognitive decline and dementia, above that provided by simple clinical measures. METHODS: Three prospective longitudinal cohort studies (SCANS [St George's Cognition and Neuroimaging in Stroke], RUN DMC [Radboud University Nijmegen Diffusion Imaging and Magnetic Resonance Imaging Cohort], and the ASPS [Austrian Stroke Prevention Study]), which covered a range of SVD severity from mild and asymptomatic to severe and symptomatic, were included. In all studies, MRI was performed at baseline, cognitive tests repeated during follow-up, and progression to dementia recorded prospectively. Outcome measures were cognitive decline and onset of dementia during follow-up. We determined whether the SVD score predicted risk of cognitive decline and future dementia. We also determined whether using the score to select a group of patients with more severe disease would reduce sample sizes for clinical intervention trials. RESULTS: In a pooled analysis of all 3 cohorts, the score improved prediction of dementia (area under the curve [AUC], 0.85; 95% confidence interval [CI], 0.81-0.89) compared with that from clinical risk factors alone (AUC, 0.76; 95% CI, 0.71-0.81). Predictive performance was higher in patients with more severe SVD. Power calculations showed selecting patients with a higher score reduced sample sizes required for hypothetical clinical trials by 40%-66% depending on the outcome measure used. CONCLUSIONS: A simple SVD score, easily obtainable from clinical MRI scans and therefore applicable in routine clinical practice, aided prediction of future dementia risk.

Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients.

Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer's disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics. To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models. For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model. In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice.