I have a long-standing interest in brain function and network dynamics in both health and disease. I pursue this interest at the interface between state of the art brain imaging technologies and advanced data modelling. This translates into the investigation of large-scale multimodal datasets that contain information on structural and functional brain properties, genetics, and other biomarker data. More recently, I have developed a strong interest to investigate the genetic underpinnings of neurological disorders and their clinical substrates.

I am currently a post-doc at Stanford university, under the lead of Dr. Michael D Greicius, performing genetics and imaging research into Alzheimer's disease and other complex neurological disorders in humans. My main aims are to identify genetic factors that may be causative to Alzheimer's disease and to determine related endophenotypes from publicly available imaging and biomarker data bases. My current project seeks to unravel differential genetic risk for AD across sub-ethnic groups and by interaction with a patients APOE genotype.

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.

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

  • 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

  • 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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