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

  • 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)

Professional Education

  • Doctor of Science, Universitaire Instelling Antwerpen (2018)
  • Bachelor of Science, Universitaire Instelling Antwerpen (2011)
  • Master of Science, Universitaire Instelling Antwerpen (2014)

Stanford Advisors


All Publications

  • 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 de Rochemonteix, M., Napolioni, V., Sanyal, N., Belloy, M. E., Caporaso, N. E., Landi, M. T., Greicius, M. D., Chatterjee, N., Han, S. S. 2020


    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. JAMA neurology Belloy, M. E., Napolioni, V., Han, S. S., Le Guen, Y., Greicius, M. D. 2020


    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 ?-amyloid (A?) 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 A? CSF and PET, and 3 provided cross-sectional measures of A? 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 A?, 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 A? 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 A? in CSF (?, 0.06 [95% CI, 0.01-0.10]; P?=?.03) and lower A? on PET scans (?, -0.04 [95% CI, -0.07 to -0.00]; P?=?.04).The genotype KL-VSHET+ is associated with reduced AD risk and A? 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

  • Quasi-periodic patterns contribute to functional connectivity in the brain NEUROIMAGE Abbas, A., Belloy, M., Kashyap, A., Billings, J., Nezafati, M., Schumacher, E. H., Keilholz, S. 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 Zinnhardt, B., Belloy, M., Fricke, I. B., Orije, J., Guglielmetti, C., Hermann, S., Wagner, S., Schaefers, M., Van der Linden, A., Jacobs, A. H. 2019; 9 (6): 1523?37

    View details for DOI 10.7150/thno.32461

    View details for Web of Science ID 000460134200001

  • Bottom-up sensory processing can induce negative BOLD responses and reduce functional connectivity in nodes of the default mode-like network in rats. NeuroImage Hinz, R., Peeters, L. M., Shah, D., Missault, S., Belloy, M., Vanreusel, V., Malekzadeh, M., Verhoye, M., Van der Linden, A., Keliris, G. A. 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. Neuron Belloy, M. E., Napolioni, V., Greicius, M. D. 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 NEUROIMAGE Belloy, M. E., Naeyaert, M., Abbas, A., Shah, D., Vanreusel, V., Van Audekerke, J., Keilholz, S. D., Keliris, G. A., Van der Linden, A., Verhoye, M. 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 SCIENTIFIC REPORTS Belloy, M. E., Shah, D., Abbas, A., Kashyap, A., Rossner, S., Van der Linden, A., Keilholz, S. D., Keliris, G. A., Verhoye, M. 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

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