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Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele. © 2022. The Author(s).

Cerebral small vessel disease (cSVD) refers to pathological processes that affect the small vessels in the brain. cSVD is a major cause of stroke and dementia. Despite these serious consequences, there is no specific treatment available, which has to do with the fact that underlying disease mechanisms are not entirely clear. The overarching aim of this thesis was to characterize new MRI markers that could help in better understanding disease mechanisms in cSVD. First, we wanted to study a marker that is close to the disease processes that occur in the cerebral small vessels. Because of the small diameter of the affected vessels, we were, for a long time, unable to study cSVD at the level of the small vessels themselves. With the development of stronger MRI scanners, this is now possible. We used such a strong scanner to zoom into the functioning of the small cerebral vessels and found that we could image abnormal small vessel function. These new measures allow to study cSVD at its core, reflecting a paradigm shift in the field. Second, we studied possible causes and consequences of a subtle marker of tissue injury in cSVD; cerebral microinfarcts. We found that microinfarcts are likely caused by cerebral amyloid angiopathy (a specific form of cSVD) and hypoperfusion. More importantly, we found that the presence of an acute microinfarct could predict long-term clinical outcome in patients with cSVD: cognitive decline, stroke, admission to a care facility and death, were more common in patients with an acute microinfarct.

Cerebrovascular disease is a major cause of cognitive impairment and dementia, either by itself or in combination with Alzheimer’s disease. The contribution of vascular brain injury to cognitive impairment and dementia is collectively referred to as Vascular Cognitive Impairment (VCI). In the context of VCI, lesion location is considered a key determinant of cognitive impairment. Lesion-symptom mapping (LSM) is a powerful approach to studying the relationship between clinical symptoms and lesion location. The overarching aim of this thesis was to gain further insight into the cognitive impact of lesion location in cerebrovascular disease using LSM. We focused on infarct location in patients with acute ischemic stroke and white matter hyperintensity (WMH) location in memory clinic patients. Prior to the projects described in this thesis, a comprehensive map of strategic infarct locations was lacking and the relevance of WMH location was unclear. An important limiting factor in previous studies was incomplete brain lesion coverage due to limited sample sizes. Previous studies have shown that even with data from several hundred subjects, only a limited part (approximately 20%) of the brain could be analyzed. We proposed that large-scale multicenter studies might overcome this challenge. In Part 1 we aimed to enable LSM studies with better brain lesion coverage. For this purpose, we established an international collaborative research network: the Meta-VCI-Map consortium. We successfully implemented multicenter data processing, harmonization and analysis procedures to enable large-scale LSM studies. Upscaling of sample sizes resulted in marked improvement of brain lesion coverage. In Part 2 we aimed to create a more comprehensive map of strategic infarct locations, and further pinpoint the neuroanatomical correlates of specific cognitive and neuropsychiatric functions. Through a large-scale LSM study we created the most comprehensive map for risk of post-stroke cognitive impairment (PSCI) to date, in which 87% of the brain was included. We identified the left frontotemporal lobes, left thalamus and right parietal lobe as infarct locations with the highest risk of PSCI. Furthermore, we developed a visual rating scale for assessment of PSCI risk for direct clinical use. Finally, we identified novel neuroanatomical correlates for verbal memory, semantic and phonemic fluency, and depressive symptoms. In Part 3 we examined the relevance of WMH location in memory clinic patients with cerebral small vessel disease. We found that WMH in specific white matter tracts were associated with cognitive impairment in executive functioning, visuomotor speed and memory. Importantly, regional WMH volumes showed a stronger association with cognitive performance than total WMH burden. Furthermore, WMH location in posterior white matter regions were associated with increased cerebral amyloid burden, suggesting a relationship between WMH and amyloid-related processes. In conclusion, the work in this thesis put lesion location better on the map as key determinant of VCI.

The aim of the work described in this thesis was to advance the implementation of diffusion MRI in SVD research by exploring methods that improve its technical validity. The two key objectives were 1) to improve the validity of multicentre dMRI, by removing variability related to acquisition hardware in dMRI across sites with harmonization of the raw diffusion signal and 2) to improve the validity of dMRI-based network analyses, in the presence of pathology, by removing false positive connections with thresholding. We addressed our first key objective in Chapters 2 and 5, where we showed proof of concept of effective harmonization with data from five cohorts of patients with SVD and controls, using rotation invariant spherical harmonic (RISH) features. In chapter 2 we showed that the RISH method removes acquisition-related differences in scalar dMRI metrics between matched controls of different sites, while preserving disease-related effect sizes (i.e., differences between patients and controls and associations between dMRI metrics and markers of SVD). Consequently, the harmonized data could be pooled to increase sample size and infer associations between dMRI metrics and markers of SVD with improved power. In chapter 5 we applied RISH harmonization beyond the scalar metrics explored in Chapter 2, by investigating if it also improves cross-site consistency of brain networks. We demonstrated that harmonization helps to achieve more similar network architectures across sites. Furthermore, harmonization facilitated data pooling to infer patterns of network injury with improved sensitivity as compared to single centre datasets. We addressed our second key objective in chapters 3, 4 and 5 where we examined whether network thresholding increases consistency of brain networks in studies with cross-sectional design, longitudinal design and in multicentre analysis. In chapter 3 we proposed fixed-density thresholding as method to control for differences in network density (i.e., number of detected connections) when comparing networks of patients and controls. We showed that thresholding networks to a fixed density across all subjects preserves the original network architecture over a large range of thresholds and maintains the sensitivity to detected group-differences between patients with SVD and controls in global and local metrics. In this manner, networks can be compared across groups without the risk of measures being affected by density bias. In chapter 4, we tackled the effect of false positive connections causing low reproducibility of brain networks in longitudinal studies. We showed that weight-based thresholding improves scan-rescan network consistency in healthy young subjects (with relatively short-rescan inter¬vals) but also in patients with SVD scanned over long time periods. Importantly, we proved that thresholding preserves sensitivity to detect statistical group-differences between patients with low and high SVD burden. Finally, in chapter 5 we demonstrated that thresholding, in combination with RISH harmonization, helps to generate more consistent networks in multicentre data. In this analysis we showed that thresholding complements harmonization by reducing the number of false positives in the network, ultimately improving precision to detect patterns of network injury in multicentre data of patients SVD. Altogether, the work presented in this thesis shows the benefits of dMRI harmoni¬zation by making data more comparable across centres and enabling data pooling to increase sample size. Our work also shows that advantages of network thresholding by improving precision of network analysis, paving the way for robust large scale dMRI network analysis in SVD.

Stroke is one of the leading causes of death and long-term disability worldwide. The incidence of ischemic stroke in The Netherlands alone is 35.000 per year, translating to roughly 100 new patients with ischemic stroke every day. Apart from the physical consequences, up to 75% of the patients have cognitive deficits after ischemic stroke. Cognitive deficits after ischemic stroke are independently associated with lower quality of life, poorer functional outcome, and worse community reintegration. Fortunately, roughly half of the patients with cognitive deficits after ischemic stroke show recovery over time. The other half, however, remains cognitively impaired or even deteriorates. Predicting cognitive recovery in patients with a cognitive disorder after ischemic stroke could have important consequences for daily practice: it could help in providing realistic psycho-education for patients and their families, and it could ultimately help in matching the goals in rehabilitation programs to the patient’s potential for recovery. However, prediction of cognitive recovery after ischemic stroke on clinical features alone is still inaccurate. Measures of brain connectivity have emerged in the literature as important markers of various types of brain injury that are relevant for cognition, and may add value to conventional predictors in predicting cognitive recovery after stroke. When looking more specifically at the cognitive deficits that are often seen after ischemic stroke, these can involve the ‘traditional’ cognitive domains memory, executive functioning, visuospatial functioning, language, and attention and processing speed. Another cognitive domain, social cognition, has been studied less frequently following stroke. Social cognition involves the psychological processes by which one perceives, processes and interprets social information, and it involves adequately responding to this social information. There is emerging evidence that ischemic stroke is also associated with deficits in social cognition. In the work described in this thesis we assessed 1) whether measures derived from the structural brain network predict cognitive recovery after ischemic stroke and 2) how often impairments in social cognition occur after ischemic stroke, and more precisely, emotion recognition. This thesis described the development of the lesion impact score, a score that combines information on lesion size with network topology, which was an independent predictor of cognitive recovery after ischemic stroke. By contrast, we could not show there was additional value of more global brain connectivity measures in predicting cognitive recovery over other conventional predictors. We conclude that measures of brain connectivity do not appear to be ready for implementation in daily practice yet, although our findings do show promise. However, further research is needed to assess how these measures can add value in daily practice. In this thesis we also showed that emotion recognition is impaired in one out of three patients, while it is often not recognized by the clinician or the patient. Clinicians should be aware that these impairments occur often and should routinely inform patients about changes in social behavior. If there are signs of changes in a patient’s social behavior or empathy, patients or their caregivers should be provided with psychoeducation and referral to a neuropsychologist should be considered.