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.

The bvFTD phenocopy syndrome: a case study supported by repeated MRI, [18F]FDG-PET and pathological assessment.

A clinical syndrome with neuropsychiatric features of bvFTD without neuroimaging abnormalities and a lack of decline is a phenocopy of bvFTD (phFTD). Growing evidence suggests that psychological, psychiatric and environmental factors underlie phFTD. We describe a patient diagnosed with bvFTD prior to the revision of the diagnostic guidelines of FTD. Repeated neuroimaging was normal and there was no FTD pathology at autopsy, rejecting the diagnosis. We hypothesize on etiological factors that on hindsight might have played a role. This case report contributes to the understanding of phFTD and adds to the sparse literature of the postmortem assessment of phFTD.

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.

Early recognition and treatment of neuropsychiatric symptoms to improve quality of life in early Alzheimer's disease: protocol of the BEAT-IT study.

BACKGROUND: Neuropsychiatric symptoms (NPS) are very common in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and are associated with various disadvantageous clinical outcomes including a negative impact on quality of life, caregiver burden, and accelerated disease progression. Despite growing evidence of the efficacy of (non)pharmacological interventions to reduce these symptoms, NPS remain underrecognized and undertreated in memory clinics. The BEhavioural symptoms in Alzheimer's disease Towards early Identification and Treatment (BEAT-IT) study is developed to (1) investigate the neurobiological etiology of NPS in AD and (2) study the effectiveness of the Describe, Investigate, Create, Evaluate (DICE) approach to structure and standardize the current care of NPS in AD. By means of the DICE method, we aim to improve the quality of life of AD patients with NPS and their caregivers who visit the memory clinic. This paper describes the protocol for the intervention study that incorporates the latter aim. METHODS: We aim to enroll a total of 150 community-dwelling patients with MCI or AD and their caregivers in two waves. First, we will recruit a control group who will receive care as usual. Next, the second wave of participants will undergo the DICE method. This approach consists of the following steps: (1) describe the context in which NPS occur, (2) investigate the possible causes, (3) create and implement a treatment plan, and (4) evaluate whether these interventions are effective. Primary outcomes are the quality of life of patients and their caregivers. Secondary outcomes include NPS change, caregiver burden, caregivers' confidence managing NPS, psychotropic medication use, the experiences of patients and caregivers who underwent the DICE method, and the cost-effectiveness of the intervention. CONCLUSIONS: This paper describes the protocol of an intervention study that is part of the BEAT-IT study and aims to improve current recognition and treatment of NPS in AD by structuring and standardizing the detection and treatment of NPS in AD using the DICE approach. TRIAL REGISTRATION: The trial was registered on the Netherlands Trial Registry ( NTR7459 ); registered 6 September 2018.

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.

Assessment of instrumental activities of daily living in dementia: diagnostic value of the Amsterdam Instrumental Activities of Daily Living Questionnaire.

BACKGROUND: Measuring impairments in "instrumental activities of daily living" (IADL) is important in dementia, but challenging due to the lack of reliable and valid instruments. We recently developed the Amsterdam Instrumental Activities of Daily Living Questionnaire (A-IADL-Q; note 1). We aim to investigate the diagnostic accuracy of the A-IADL-Q for dementia in a memory clinic setting. METHODS: Patients visiting the Alzheimer Center of the VU University Medical Center with their informants between 2009 and 2011 were included (N = 278). Diagnoses were established in a multidisciplinary consensus meeting, independent of the A-IADL-Q scores. An optimal A-IADL-Q cutoff point was determined, and sensitivity and specificity were calculated. Area under the curves (AUCs) were compared between A-IADL-Q and "disability assessment of dementia" (DAD). The additional diagnostic value of the A-IADL-Q to Mini-Mental State Examination (MMSE) was examined using logistic regression analyses. RESULTS: Dementia prevalence was 50.5%. Overall diagnostic accuracy based on the AUC was 0.75 (95% confidence interval [CI]: 0.70-0.81) for the A-IADL-Q and 0.70 (95% CI: 0.63-0.77) for the DAD, which did not differ significantly. The optimal cutoff score for the A-IADL-Q was 51.4, resulting in sensitivity of 0.74 and specificity of 0.64. Combining the A-IADL-Q with the MMSE improved specificity (0.94), with a decline in sensitivity (0.55). Logistic regression models showed that adding A-IADL-Q improved the diagnostic accuracy (Z = 2.55, P = .011), whereas the DAD did not. CONCLUSIONS: In this study, we showed a fair diagnostic accuracy for A-IADL-Q and an additional value in the diagnosis of dementia. These results support the role of A-IADL-Q as a valuable diagnostic tool.

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.