Enhancing Cognitive Performance Prediction through White Matter Hyperintensity Connectivity Assessment: A Multicenter Lesion Network Mapping Analysis of 3,485 Memory Clinic Patients.

Introduction: White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating cognitive health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. We propose that lesion network mapping (LNM), enables to infer if brain networks are connected to lesions, and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed this approach to test the following hypotheses: (1) LNM-informed markers surpass WMH volumes in predicting cognitive performance, and (2) WMH contributing to cognitive impairment map to specific brain networks. Methods & results: We analyzed cross-sectional data of 3,485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in 4 cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity across 480 atlas-based gray and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. The capacity of total and regional WMH volumes and LNM scores in predicting cognitive function was compared using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention and executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater disruptive effects of WMH on regional connectivity, in gray and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Conclusion: Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network effects, particularly in attentionrelated brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.

A more precise diagnosis by means of amyloid PET contributes to delayed institutionalization, lower mortality, and reduced care costs in a tertiary memory clinic setting.

INTRODUCTION: We aim to study the effect of a more precise diagnosis, by means of amyloid positron emission tomography (PET), on institutionalization, mortality, and health-care costs. METHODS: Between October 27, 2014 and December 31, 2016, we offered amyloid PET to all patients as part of their diagnostic work-up. Patients who accepted to undergo amyloid PET (n = 449) were propensity score matched with patients without amyloid PET (n = 571, i.e., no PET). Matched groups (both n = 444) were compared on rate of institutionalization, mortality, and health-care costs in the years after diagnosis. RESULTS: Amyloid PET patients had a lower risk of institutionalization (10% [n = 45] vs. 21% [n = 92]; hazard ratio [HR] = 0.48 [0.33-0.70]) and mortality rate (11% [n = 49] vs. 18% [n = 81]; HR = 0.51 [0.36-0.73]) and lower health-care costs in the years after diagnosis compared to matched no-PET patients (ß = -4573.49 [-6524.76 to -2523.74], P-value < 0.001). DISCUSSION: A more precise diagnosis in tertiary memory clinic patients positively influenced the endpoints of institutionalization, death, and health-care costs.

Brain Health Services: organization, structure, and challenges for implementation. A user manual for Brain Health Services-part 1 of 6.

Dementia has a devastating impact on the quality of life of patients and families and comes with a huge cost to society. Dementia prevention is considered a public health priority by the World Health Organization. Delaying the onset of dementia by treating associated risk factors will bring huge individual and societal benefit. Empirical evidence suggests that, in higher-income countries, dementia incidence is decreasing as a result of healthier lifestyles. This observation supports the notion that preventing dementia is possible and that a certain degree of prevention is already in action. Further reduction of dementia incidence through deliberate prevention plans is needed to counteract its growing prevalence due to increasing life expectancy.An increasing number of individuals with normal cognitive performance seek help in the current memory clinics asking an evaluation of their dementia risk, preventive interventions, or interventions to ameliorate their cognitive performance. Consistent evidence suggests that some of these individuals are indeed at increased risk of dementia. This new health demand asks for a shift of target population, from patients with cognitive impairment to worried but cognitively unimpaired individuals. However, current memory clinics do not have the programs and protocols in place to deal with this new population.We envision the development of new services, henceforth called Brain Health Services, devoted to respond to demands from cognitively unimpaired individuals concerned about their risk of dementia. The missions of Brain Health Services will be (i) dementia risk profiling, (ii) dementia risk communication, (iii) dementia risk reduction, and (iv) cognitive enhancement. In this paper, we present the organizational and structural challenges associated with the set-up of Brain Health Services.

The Cognitive Online Self-Test Amsterdam (COST-A): Establishing norm scores in a community-dwelling population.

BACKGROUND: Heightened public awareness about Alzheimer's disease and dementia increases the need for at-home cognitive self-testing. We offered Cognitive Online Self-Test Amsterdam (COST-A) to independent groups of cognitively normal adults and investigated the robustness of a norm-score formula and cutoff. METHODS: Three thousand eighty-eight participants (mean age ± standard deviation = 61 ± 12 years, 70% female) completed COST-A and evaluated it. Demographically adjusted norm scores were the difference between expected COST-A scores, based on age, gender, and education, and actual scores. We applied the resulting norm-score formula to two independent cohorts. RESULTS: Participants evaluated COST-A to be of adequate difficulty and duration. Our norm-score formula was shown to be robust: ≈8% of participants in two cognitively normal cohorts had abnormal scores. A cutoff of -1.5 standard deviations proved optimal for distinguishing normal from impaired cognition. CONCLUSION: With robust norm scores, COST-A is a promising new tool for research and clinical practice, providing low cost and minimally invasive remote assessment of cognitive functioning.

Energy intake and expenditure in patients with Alzheimer's disease and mild cognitive impairment: the NUDAD project.

BACKGROUND: Malnutrition is common in patients with Alzheimer's disease (AD) dementia and mild cognitive impairment (MCI) and is associated with institutionalization and increased mortality. Malnutrition is the result of a negative energy balance, which could be due to reduced dietary intake and/or higher energy expenditure. To study underlying mechanisms for malnutrition, we investigated dietary intake and resting energy expenditure (REE) of patients with AD dementia, MCI, and controls. In addition, we studied associations of global cognition (Mini-Mental State Examination (MMSE)) and AD biomarkers with dietary intake and REE. METHODS: We included 219 participants from the NUDAD project, 71 patients with AD dementia (age 68 ± 8 years, 58% female, MMSE 24 ± 3), 52 with MCI (67 ± 8 years, 42% female, MMSE 26 ± 2), and 96 controls (62 ± 7 years, 52% female, MMSE 28 ± 2). We used a 238-item food frequency questionnaire to assess dietary intake (energy, protein, carbohydrate, and fat). In a subgroup of 92 participants (30 patients with AD dementia, 22 with MCI, and 40 controls) we measured REE with indirect calorimetry. Between-group differences in dietary intake and REE were tested with ANOVAs. In the total sample, linear regression analyses were used to explore potential associations of MMSE score and AD biomarkers with dietary intake and REE. All analyses were adjusted for age, sex, education, and body mass index or fat-free mass. RESULTS: Patients with AD dementia and MCI did not differ from controls in total energy intake (1991 ± 71 and 2172 ± 80 vs 2022 ± 61 kcal/day, p > 0.05) nor in protein, carbohydrate, or fat intake. Patients with AD dementia and MCI had a higher REE than controls (1704 ± 41 and 1754 ± 47 vs 1569 ± 34 kcal/day, p < 0.05). We did not find any association of MMSE score or AD biomarkers with dietary intake or REE. CONCLUSIONS: We found a higher REE, despite similar energy intake in patients with AD and MCI compared to controls. These findings suggest that elevated metabolism rather than reduced energy intake explains malnutrition in AD. These results could be useful to optimize dietary advice for patients with AD dementia and MCI.

Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy.

INTRODUCTION: An accurate and timely diagnosis for Alzheimer's disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. METHODS: We included 535 subjects (139 controls, 286 Alzheimer's disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. RESULTS: The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). CONCLUSIONS: We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.

Assessment of the appropriate use criteria for amyloid PET in an unselected memory clinic cohort: The ABIDE project.

INTRODUCTION: The objective of this study was to assess the usefulness of the appropriate use criteria (AUC) for amyloid imaging in an unselected cohort. METHODS: We calculated sensitivity and specificity of appropriate use (increased confidence and management change), as defined by Amyloid Imaging Taskforce in the AUC, and other clinical utility outcomes. Furthermore, we compared differences in post-positron emission tomography diagnosis and management change between "AUC-consistent" and "AUC-inconsistent" patients. RESULTS: Almost half (250/507) of patients were AUC-consistent. In both AUC-consistent and AUC-inconsistent patients, post-positron emission tomography diagnosis (28%-21%) and management (32%-17%) change was substantial. The Amyloid Imaging Taskforce's definition of appropriate use occurred in 55/507 (13%) patients, detected by the AUC with a sensitivity of 93%, and a specificity of 56%. Diagnostic changes occurred independently of AUC status (sensitivity: 57%, specificity: 53%). DISCUSSION: The current AUC are not sufficiently able to discriminate between patients who will benefit from amyloid positron emission tomography and those who will not.

Decision tree supports the interpretation of CSF biomarkers in Alzheimer's disease.

INTRODUCTION: We developed and validated a clinically applicable decision tree for the use of cerebrospinal fluid biomarkers in the diagnosis of Alzheimer's disease (AD). METHODS: Subjects with probable AD (n = 1004) and controls (n = 442) were included. A decision tree was modeled using Classification And Regression Tree analysis in a training cohort (AD n = 221; controls n = 221) and validated in an independent cohort (AD n = 783; controls n = 221). Diagnostic performance was compared to previously defined cutoffs (amyloid ß 1-42 < 813 pg/ml; tau>375 pg/ml). RESULTS: Two cerebrospinal fluid AD biomarker profiles were revealed: the "classical" AD biomarker profile (amyloid ß 1-42: 647-803 pg/ml; tau >374 pg/ml) and an "atypical" AD biomarker profile with strongly decreased amyloid ß 1-42 (<647 pg/ml) and normal tau concentrations (<374 pg/ml). Compared to previous cutoffs, the decision tree performed better on diagnostic accuracy (86% [84-88] vs 80% [78-83]). DISCUSSION: Two cerebrospinal fluid AD biomarker profiles were identified and incorporated in a readily applicable decision tree, which improved diagnostic accuracy.

The need for harmonisation and innovation of neuropsychological assessment in neurodegenerative dementias in Europe: consensus document of the Joint Program for Neurodegenerative Diseases Working Group.

Cognitive, behavioural, and functional assessment is crucial in longitudinal studies of neurodegenerative dementias (NDD). Central issues, such as the definition of the study population (asymptomatic, at risk, or individuals with dementia), the detection of change/decline, and the assessment of relevant outcomes depend on quantitative measures of cognitive, behavioural, and functional status.Currently, we are far from having available reliable protocols and tools for the assessment of dementias in Europe. The main problems are the heterogeneity of the tools used across different European countries, the lack of standardisation of administration and scoring methods across centres, and the limited information available about the psychometric properties of many tests currently in widespread use. This situation makes it hard to compare results across studies carried out in different centres, thus hampering research progress, in particular towards the contribution to a "big data" common data set.We present here the results of a project funded by the Joint Program for Neurodegenerative Diseases (JPND) and by the Italian Ministry of Health. The project aimed at providing a consensus framework for the harmonisation of assessment tools to be applied to research in neurodegenerative disorders affecting cognition across Europe. A panel of European experts reviewed the current methods of neuropsychological assessment, identified pending issues, and made recommendations for the harmonisation of neuropsychological assessment of neurodegenerative dementias in Europe.A consensus was achieved on the general recommendations to be followed in developing procedures and tools for neuropsychological assessment, with the aim of harmonising tools and procedures to achieve more reliable data on the cognitive-behavioural examination. The results of this study should be considered as a first step to enhancing a common view and practise on NDD assessment across European countries.

Joint assessment of white matter integrity, cortical and subcortical atrophy to distinguish AD from behavioral variant FTD: A two-center study.

We investigated the ability of cortical and subcortical gray matter (GM) atrophy in combination with white matter (WM) integrity to distinguish behavioral variant frontotemporal dementia (bvFTD) from Alzheimer's disease (AD) and from controls using voxel-based morphometry, subcortical structure segmentation, and tract-based spatial statistics. To determine which combination of MR markers differentiated the three groups with the highest accuracy, we conducted discriminant function analyses. Adjusted for age, sex and center, both types of dementia had more GM atrophy, lower fractional anisotropy (FA) and higher mean (MD), axial (L1) and radial diffusivity (L23) values than controls. BvFTD patients had more GM atrophy in orbitofrontal and inferior frontal areas than AD patients. In addition, caudate nucleus and nucleus accumbens were smaller in bvFTD than in AD. FA values were lower; MD, L1 and L23 values were higher, especially in frontal areas of the brain for bvFTD compared to AD patients. The combination of cortical GM, hippocampal volume and WM integrity measurements, classified 97-100% of controls, 81-100% of AD and 67-75% of bvFTD patients correctly. Our results suggest that WM integrity measures add complementary information to measures of GM atrophy, thereby improving the classification between AD and bvFTD.

Visual assessment of posterior atrophy development of a MRI rating scale.

OBJECTIVE: To develop a visual rating scale for posterior atrophy (PA) assessment and to analyse whether this scale aids in the discrimination between Alzheimer's disease (AD) and other dementias. METHODS: Magnetic resonance imaging of 118 memory clinic patients were analysed for PA (range 0-3), medial temporal lobe atrophy (MTA) (range 0-4) and global cortical atrophy (range 0-3) by different raters. Weighted-kappas were calculated for inter- and intra-rater agreement. Relationships between PA and MTA with the MMSE and age were estimated with linear-regression analysis. RESULTS: Intra-rater agreement ranged between 0.93 and 0.95 and inter-rater agreement between 0.65 and 0.84. Mean PA scores were higher in AD compared to controls (1.6 ± 0.9 and 0.6 ± 0.7, p < 0.01), and other dementias (0.8 ± 0.8, p < 0.01). PA was not associated with age compared to MTA (B = 1.1 (0.8) versus B = 3.1 (0.7), p < 0.01)). PA and MTA were independently negatively associated with the MMSE (B = -1.6 (0.5), p < 0.01 versus B = -1.4 (0.5), p < 0.01). CONCLUSION: This robust and reproducible scale for PA assessment conveys independent information in a clinical setting and may be useful in the discrimination of AD from other dementias.

Molecular imaging in the diagnosis of Alzheimer's disease: visual assessment of [11C]PIB and [18F]FDDNP PET images.

OBJECTIVE: To evaluate visual assessment of [(11)C]PIB and [(18)F]FDDNP PET images as potential supportive diagnostic markers for Alzheimer's disease (AD). METHODS: Twenty-one AD patients and 20 controls were included. Parametric [(11)C]PIB and [(18)F]FDDNP global binding potential (BP(ND)) images were visually rated as 'AD' or 'normal.' Data were compared with ratings of [(18)F]FDG PET images and MRI-derived medial temporal lobe atrophy (MTA) scores. Inter-rater agreement and agreement with clinical diagnosis were assessed for all imaging modalities. In addition, cut-off values for quantitative global [(11)C]PIB and [(18)F]FDDNP BP(ND) were determined. Visual ratings were compared with dichotomised quantitative values. RESULTS: Agreement between readers was excellent for [(11)C]PIB, [(18)F]FDDNP and MTA (Cohen kappa kappa> or =0.85) and moderate for [(18)F]FDG (kappa=0.56). The highest sensitivity was found for [(11)C]PIB and [(18)F]FDG (both 1.0). The highest specificity was found for MTA (0.90) and [(11)C]PIB (0.85). [(18)F]FDDNP had the lowest sensitivity and specificity (0.67 and 0.53, respectively). The cut-off for quantitative [(11)C]PIB BP(ND) was 0.54 (sensitivity and specificity both 0.95) and for [(18)F]FDDNP BP(ND) 0.07 (sensitivity 0.80, specificity 0.73). Agreement between quantitative analyses and visual ratings was excellent for [(11)C]PIB (kappa=0.85) and fair for [(18)F]FDDNP (kappa=0.40). CONCLUSION: Visual assessment of [(11)C]PIB images was straightforward and accurate, showing promise as a supportive diagnostic marker for AD. Moreover, [(11)C]PIB showed the best combination of sensitivity and specificity. Visual assessment of [(18)F]FDDNP images was insufficient. The quantitative analysis of [(18)F]FDDNP data showed a considerably higher diagnostic value than the visual analysis.

Transcranial Doppler blood flow assessment in patients with mild heart failure: correlates with neuroimaging and cognitive performance.

Cardiac output and cerebral perfusion are reduced in patients with advanced stages of heart failure. Our aim was to determine whether cerebral blood flow velocity measured by transcranial Doppler ultrasonography was reduced in outpatients with mild heart failure in comparison to controls and, if so, whether this reduction was related to cognitive performance and abnormalities of the brain diagnosed by magnetic resonance imaging.