My broad scientific goal is to investigate neurological disorders with the aim of identifying novel mechanisms that improve understanding of disease pathophysiology and that could lead to novel drug development. I pursue this goal by investigating the genetic risk factors of the respective disease under question, studying how they contribute to disruptions of brain function measured by in vivo imaging techniques, and how they correlate with the presentation of disease-sensitive biomarkers. Within this broader scope, my primary interest is to focus specifically on Alzheimer's disease, elucidating the genetic, molecular, and clinical spectrum of the disease, and hopefully, eventually, contributing to the path towards a cure.
I am a highly interdisciplinary scientist with experience in programming (using various scripting languages), advanced data analyses methods, neuroimaging, and studies of preclinical mouse models of Alzheimer’s disease. I also have a long-standing interest in brain function and network dynamics in both health and disease. More recently, I have further gained experience into the clinical aspects, imaging approaches, and genetics of Alzheimer’s disease. Altogether, this translates into my current research strategy in which I investigate large-scale multimodal datasets that contain information on genetics, clinical outcome measures, structural and functional brain properties, and other biomarker data.
I am currently a third-year post-doc at Stanford university, under the lead of Dr. Michael D Greicius. My main aims in this lab are to identify genetic factors that may be causative to Alzheimer's disease. Specifically, I aim to uncover genetic risk factors that interact with the Apolipoprotein E (APOE) gene to alter risk for Alzheimer’s disease. Further, I seek to identify how these genetic interactions with APOE differ by sex, age, and ethnicity. I believe this will allow the identification of novel genes relevant to Alzheimer's disease and contribute to advancing personalized genetic medicine.
During my PhD, supervised by Dr. Marleen Verhoye, Dr. Shella Keilholz and Dr. Georgios A Keliris, I worked on developing dynamic resting state functional (rsf)MRI in mice, which lead to the first observation of mouse Quasi-Periodic patterns, and related applications for Alzheimer's disease research in rodents. I still have an ongoing interest in dynamic rsfMRI research.
Honors & Awards
ADPD21 Junior Faculty Award Winner, International Conference on Alzheimer’s and Parkinson’s Diseases (14 March 2020)
Alzheimer’s Association 2020 Young Investigators Award, Alzheimer's Association (21 October 2020)
Alzheimer's Association Research Fellowship, Alzheimer's Association (April 2020 - April 2023)
Doctor of Science, Universitaire Instelling Antwerpen (2018)
Bachelor of Science, Universitaire Instelling Antwerpen (2011)
Master of Science, Universitaire Instelling Antwerpen (2014)
Michael Greicius, Postdoctoral Faculty Sponsor
A novel age-informed approach for genetic association analysis in Alzheimer's disease.
Alzheimer's research & therapy
2021; 13 (1): 72
BACKGROUND: Many Alzheimer's disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery.METHODS: Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases).RESULTS: Modeling variable AD risk across age results in 5-10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes.CONCLUSION: Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.
View details for DOI 10.1186/s13195-021-00808-5
View details for PubMedID 33794991
Common X-chromosome variants are associated with Parkinson's disease risk.
Annals of neurology
OBJECTIVE: Identify genetic variants on the X-chromosome associated with Parkinson's disease (PD) risk.METHODS: We performed an X-chromosome-wide association study (XWAS) of PD risk by meta-analyzing results from sex-stratified analyses. To avoid spurious associations, we designed a specific harmonization pipeline for the X-chromosome and focused on a European ancestry sample. We included 11,142 cases, 280,164 controls, and 5,379 proxy cases, based on parental history of PD. Additionally, we tested the association of significant variants with: (i) PD risk in an independent replication with 1,561 cases and 2,465 controls, and (ii) putamen volume in 33,360 individuals from the UK Biobank.RESULTS: In the discovery meta-analysis, we identified: rs7066890 (OR=1.10 [1.06-1.14]; P=2.2x10-9 ) intron of GPM6B, and rs28602900 (OR=1.10 [1.07-1.14]; P=1.6x10-8 ) in a high gene density region including RPL10, ATP6A1, FAM50A, PLXNA3. The rs28602900 association with PD was replicated (OR=1.16 [1.03-1.30]; P=0.016) and shown to colocalize with a significant expression quantitative locus (eQTL) regulating RPL10 expression in the putamen and other brain tissues in GTEx. Additionally, the rs28602900 locus was found to be associated with reduced brain putamen volume. No results reached genome-wide significance in the sex-stratified analyses.INTERPRETATION: We report the first XWAS of PD and identify two genome-wide significant loci. The rs28602900 association replicated in an independent PD dataset and showed concordant effects in its association with putamen volume. Critically, rs26802900 is a significant eQTL of RPL10.These results support a role for ribosomal proteins in PD pathogenesis and show that the X-chromosome contributes to PD genetic risk. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/ana.26051
View details for PubMedID 33583074
KLVS heterozygosity reduces brain amyloid in asymptomatic at-risk APOE4 carriers.
Neurobiology of aging
2021; 101: 123–29
KLOTHOVS heterozygosity (KLVSHET+) was recently shown to be associated with reduced risk of Alzheimer's disease (AD) in APOE4 carriers. Additional studies suggest that KLVSHET+ protects against amyloid burden in cognitively normal older subjects, but sample sizes were too small to draw definitive conclusions. We performed a well-powered meta-analysis across 5 independent studies, comprising 3581 pre-clinical participants ages 60-80, to investigate whether KLVSHET+ reduces the risk of having an amyloid-positive positron emission tomography scan. Analyses were stratified by APOE4 status. KLVSHET+ reduced the risk of amyloid positivity in APOE4 carriers (odds ratio= 0.67 [0.52-0.88]; p= 3.5*10-3), but not in APOE4 non-carriers (odds ratio= 0.94 [0.73-1.21]; p= 0.63). The combination of APOE4 and KLVS genotypes should help enrich AD clinical trials for pre-symptomatic subjects at increased risk of developing amyloid aggregation and AD. KL-related pathways may help elucidate protective mechanisms against amyloid accumulation and merit exploration for novel AD drug targets. Future investigation of the biological mechanisms by which KL interacts with APOE4 and AD are warranted.
View details for DOI 10.1016/j.neurobiolaging.2021.01.008
View details for PubMedID 33610961
A Likelihood Ratio Test for Gene-Environment Interaction Based on the Trend Effect of Genotype Under an Additive Risk Model Using the Gene-Environment Independence Assumption.
American journal of epidemiology
Several statistical methods have been proposed for testing gene(G)-environment(E) interactions under additive risk models using genome-wide association study data. However, these approaches have strong assumptions on underlying genetic models such as dominant or recessive effects that are known to be less robust when the true genetic model is unknown. We aim to develop a robust trend test employing a likelihood ratio test for detecting G-E interaction under an additive risk model, while incorporating the G-E independence assumption to increase power. We used a constrained likelihood to impose two sets of constraints for (i) the linear trend effect of genotype and (ii) the additive joint effects of G and E. To incorporate the G-E independence assumption, a retrospective likelihood was used versus a standard prospective likelihood. Numerical investigation suggests that the proposed tests are more powerful than tests assuming dominant, recessive, or general models under various parameter settings and under both likelihoods. Incorporation of the independence assumption enhances efficiency by 2.5- fold. We applied the proposed methods to examine gene-smoking interaction for lung cancer and gene-APOE*4 interaction for Alzheimer's disease, which identified two interactions between APOE*4 and loci MS4A and BIN1 at genome-wide significance that were replicated using independent data.
View details for DOI 10.1093/aje/kwaa132
View details for PubMedID 32870973
Association of Klotho-VS Heterozygosity With Risk of Alzheimer Disease in Individuals Who Carry APOE4.
Identification of genetic factors that interact with the apolipoprotein e4 (APOE4) allele to reduce risk for Alzheimer disease (AD) would accelerate the search for new AD drug targets. Klotho-VS heterozygosity (KL-VSHET+ status) protects against aging-associated phenotypes and cognitive decline, but whether it protects individuals who carry APOE4 from AD remains unclear.To determine if KL-VSHET+ status is associated with reduced AD risk and β-amyloid (Aβ) pathology in individuals who carry APOE4.This study combined 25 independent case-control, family-based, and longitudinal AD cohorts that recruited referred and volunteer participants and made data available through public repositories. Analyses were stratified by APOE4 status. Three cohorts were used to evaluate conversion risk, 1 provided longitudinal measures of Aβ CSF and PET, and 3 provided cross-sectional measures of Aβ CSF. Genetic data were available from high-density single-nucleotide variant microarrays. All data were collected between September 2015 and September 2019 and analyzed between April 2019 and December 2019.The risk of AD was evaluated through logistic regression analyses under a case-control design. The risk of conversion to mild cognitive impairment (MCI) or AD was evaluated through competing risks regression. Associations with Aβ, measured from cerebrospinal fluid (CSF) or brain positron emission tomography (PET), were evaluated using linear regression and mixed-effects modeling.Of 36 530 eligible participants, 13 782 were excluded for analysis exclusion criteria or refusal to participate. Participants were men and women aged 60 years and older who were non-Hispanic and of Northwestern European ancestry and had been diagnosed as being cognitively normal or having MCI or AD. The sample included 20 928 participants in case-control studies, 3008 in conversion studies, 556 in Aβ CSF regression analyses, and 251 in PET regression analyses. The genotype KL-VSHET+ was associated with reduced risk for AD in individuals carrying APOE4 who were 60 years or older (odds ratio, 0.75 [95% CI, 0.67-0.84]; P = 7.4 × 10-7), and this was more prominent at ages 60 to 80 years (odds ratio, 0.69 [95% CI, 0.61-0.79]; P = 3.6 × 10-8). Additionally, control participants carrying APOE4 with KL-VS heterozygosity were at reduced risk of converting to MCI or AD (hazard ratio, 0.64 [95% CI, 0.44-0.94]; P = .02). Finally, in control participants who carried APOE4 and were aged 60 to 80 years, KL-VS heterozygosity was associated with higher Aβ in CSF (β, 0.06 [95% CI, 0.01-0.10]; P = .03) and lower Aβ on PET scans (β, -0.04 [95% CI, -0.07 to -0.00]; P = .04).The genotype KL-VSHET+ is associated with reduced AD risk and Aβ burden in individuals who are aged 60 to 80 years, cognitively normal, and carrying APOE4. Molecular pathways associated with KL merit exploration for novel AD drug targets. The KL-VS genotype should be considered in conjunction with the APOE genotype to refine AD prediction models used in clinical trial enrichment and personalized genetic counseling.
View details for DOI 10.1001/jamaneurol.2020.0414
View details for PubMedID 32282020
Resting Brain Fluctuations Are Intrinsically Coupled to Visual Response Dynamics.
Cerebral cortex (New York, N.Y. : 1991)
How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional magnetic resonance imaging to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) are the most prominent component of mouse resting brain dynamics. These QPPs captured the temporal alignment of anticorrelation between the default mode (DMN)- and task-positive (TPN)-like networks, with global brain fluctuations, and activity in neuromodulatory nuclei of the reticular formation. Specifically, the phase of QPPs prior to stimulation could significantly stratify subsequent visual response magnitude, suggesting QPPs relate to brain state fluctuations. This is the first observation in mice that dynamics of the DMN- and TPN-like networks, and particularly their anticorrelation, capture a brain state dynamic that affects sensory processing. Interestingly, QPPs also displayed transient onset response properties during visual stimulation, which covaried with deactivations in the reticular formation. We conclude that QPPs appear to capture a brain state fluctuation that may be orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.
View details for DOI 10.1093/cercor/bhaa305
View details for PubMedID 33108464
Quasi-periodic patterns contribute to functional connectivity in the brain
2019; 191: 193–204
Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatiotemporal pattern, strength, and frequency of the QPPs between the two groups were compared and the contribution of QPPs to functional connectivity in the brain was measured. In task-performing individuals, the spatiotemporal pattern of the QPP changes, particularly in task-relevant regions, and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the strength of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings contribute to understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.
View details for DOI 10.1016/j.neuroimage.2019.01.076
View details for Web of Science ID 000462145700017
View details for PubMedID 30753928
View details for PubMedCentralID PMC6440826
- Molecular Imaging of Immune Cell Dynamics During De- and Remyelination in the Cuprizone Model of Multiple Sclerosis by [F-18]DPA-714 PET and MRI THERANOSTICS 2019; 9 (6): 1523–37
Bottom-up sensory processing can induce negative BOLD responses and reduce functional connectivity in nodes of the default mode-like network in rats.
2019; 197: 167–76
The default mode network is a large-scale brain network that is active during rest and internally focused states and deactivates as well as desynchronizes during externally oriented (top-down) attention demanding cognitive tasks. However, it is not sufficiently understood if salient stimuli, able to trigger bottom-up attentional processes, could also result in similar reduction of activity and functional connectivity in the DMN. In this study, we investigated whether bottom-up sensory processing could influence the default mode-like network (DMLN) in rats. DMLN activity was examined using block-design visual functional magnetic resonance imaging (fMRI) while its synchronization was investigated by comparing functional connectivity during a resting versus a continuously stimulated brain state by unpredicted light flashes. We demonstrated that the BOLD response in DMLN regions was decreased during visual stimulus blocks and increased during blanks. Furthermore, decreased inter-network functional connectivity between the DMLN and visual networks as well as decreased intra-network functional connectivity within the DMLN was observed during the continuous visual stimulation. These results suggest that triggering of bottom-up attention mechanisms in sedated rats can lead to a cascade similar to top-down orienting of attention in humans and is able to deactivate and desynchronize the DMLN.
View details for DOI 10.1016/j.neuroimage.2019.04.065
View details for PubMedID 31029872
A Quarter Century of APOE and Alzheimer's Disease: Progress to Date and the Path Forward.
2019; 101 (5): 820–38
Alzheimer's disease (AD) is considered a polygenic disorder. This view is clouded, however, by lingering uncertainty over how to treat the quasi "monogenic" role of apolipoprotein E (APOE). The APOE4 allele is not only the strongest genetic risk factor for AD, it also affects risk for cardiovascular disease, stroke, and other neurodegenerative disorders. This review, based mostly on data from human studies, ranges across a variety of APOE-related pathologies, touching on evolutionary genetics and risk mitigation by ethnicity and sex. The authors also address one of the most fundamental question pertaining to APOE4 and AD: does APOE4 increase AD risk via a loss or gain of function? The answer will be of the utmost importance in guiding future research in AD.
View details for PubMedID 30844401
View details for PubMedCentralID PMC6407643
Dynamic resting state fMRI analysis in mice reveals a set of Quasi-Periodic Patterns and illustrates their relationship with the global signal
2018; 180: 463–84
Time-resolved 'dynamic' over whole-period 'static' analysis of low frequency (LF) blood-oxygen level dependent (BOLD) fluctuations provides many additional insights into the macroscale organization and dynamics of neural activity. Although there has been considerable advancement in the development of mouse resting state fMRI (rsfMRI), very little remains known about its dynamic repertoire. Here, we report for the first time the detection of a set of recurring spatiotemporal Quasi-Periodic Patterns (QPPs) in mice, which show spatial similarity with known resting state networks. Furthermore, we establish a close relationship between several of these patterns and the global signal. We acquired high temporal rsfMRI scans under conditions of low (LA) and high (HA) medetomidine-isoflurane anesthesia. We then employed the algorithm developed by Majeed et al. (2011), previously applied in rats and humans, which detects and averages recurring spatiotemporal patterns in the LF BOLD signal. One type of observed patterns in mice was highly similar to those originally observed in rats, displaying propagation from lateral to medial cortical regions, which suggestively pertain to a mouse Task-Positive like network (TPN) and Default Mode like network (DMN). Other QPPs showed more widespread or striatal involvement and were no longer detected after global signal regression (GSR). This was further supported by diminished detection of subcortical dynamics after GSR, with cortical dynamics predominating. Observed QPPs were both qualitatively and quantitatively determined to be consistent across both anesthesia conditions, with GSR producing the same outcome. Under LA, QPPs were consistently detected at both group and single subject level. Under HA, consistency and pattern occurrence rate decreased, whilst cortical contribution to the patterns diminished. These findings confirm the robustness of QPPs across species and demonstrate a new approach to study mouse LF BOLD spatiotemporal dynamics and mechanisms underlying functional connectivity. The observed impact of GSR on QPPs might help better comprehend its controversial role in conventional resting state studies. Finally, consistent detection of QPPs at single subject level under LA promises a step forward towards more reliable mouse rsfMRI and further confirms the importance of selecting an optimal anesthesia regime.
View details for DOI 10.1016/j.neuroimage.2018.01.075
View details for Web of Science ID 000443271100012
View details for PubMedID 29454935
View details for PubMedCentralID PMC6093802
Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice
2018; 8: 10024
Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regression to determine if QPPs improved disease classification. We present three prominent findings: (1) Compared to controls, TG2576 mice were marked by opposing neural dynamics in which DMN areas were anti-correlated and displayed diminished anti-correlation with the TPN. (2) QPPs reflected lowered DMN functional connectivity in TG2576 mice and revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures significantly improved classification compared to conventional functional connectivity measures. Altogether, our findings provide insight into the neural dynamics of aberrant network connectivity in AD and indicate that QPPs might serve as a translational diagnostic tool.
View details for DOI 10.1038/s41598-018-28237-9
View details for Web of Science ID 000437097000041
View details for PubMedID 29968786
View details for PubMedCentralID PMC6030071