A brief introduction

I am a MASc student under the supervision of Dr. Habib Benali. My research focuses on analyzing resting-state fMRI data to investigate the relationship between brain glutamate levels and motor-related neural activity patterns. By integrating multimodal neuroimaging data, I aim to explore how fluctuations in glutamate concentration are reflected in BOLD signal dynamics, particularly in brain regions involved in motor memory consolidation.

Educational background

I completed my Bachelor of Science in Electrical Engineering at Shahid Beheshti University in Tehran. I was involved in research at the Robotics and Intelligent Automation Lab and SBU EEG Recording Lab, where I gained skills in computer vision, machine learning, and bio-signal processing.

Research

My current research involves preprocessing and analyzing resting-state fMRI data collected during a multimodal study that includes EEG and MRS. I focus on extracting meaningful neural patterns from BOLD signals and investigating their relationship with glutamate concentration in key brain regions such as the supplementary motor area (SMA) and posterior cingulate cortex (PCC). This work is part of a broader effort to understand the neural and metabolic mechanisms that support motor memory formation and consolidation across the sleep-wake cycle.

Journal publication

• B. Moadeli*, Younesi, F., Rabiei, M., Keivanfard, S., Sharifi, M., Najafabadi, M. G. (2025). An Optimized Density-Based Lane Keeping System for A Cost-Efficient Autonomous Vehicle Platform: AurigaBot V1. arXiv preprint arXiv:2504.04217”

Selected publications

• Topology and spectral interconnectivities of higher-order multilayer network. E. M., OBK. Ali, H. Benali, Front. Complex Syst., 2023
• Foundations of topological racks and quandles. M. Elhamdadi, E.M. Journal of Knot Theory and Its Ramifications, 2016.
• Graded Brauer groups of a groupoid with involution. E.M., 2014, Journal of Functional Analysis.

A brief introduction

Giridhar Sunil is a MaSc candidate in the Electrical and Computer Engineering Department at Concordia University. His current research is focused on analysising the patterns in the synaptic and extrasynaptic connections of C. Elegans. He has expertise in Machine Learning and Deep learning, Software engineering, Data analysis and Computer Vision.

Educational background

He received his B.Tech degree in Computer Science Engineering from Vellore Institute of Technology.

Professional background

Research

Understanding the foundational principles of neural communication remains a grand challenge in neuroscience, particularly when it comes to disentangling the multiplexed signaling strategies employed by biological systems. Caenorhabditis elegans, a model organism with a fully mapped synaptic connectome, also exhibits a parallel mode of information transmission through extrasynaptic signaling—facilitated by neuropeptides and monoamines—that is not constrained by direct anatomical connections. Despite its simplicity in neuronal count, the worm leverages this dual signaling architecture to coordinate a broad repertoire of behaviors. In this work, we aim to explore the functional interplay between the synaptic (wired) and extrasynaptic (wireless) connectomes. In the first phase, we segment the extrasynaptic connectome using Kubo-Martin-Schwinger (KMS) states to classify and analyze the dependence of extrasynaptic circuits on synaptic topology. In the second phase, we employ topological data analysis—specifically persistent homology and clustering techniques—to extract higher-order structures and functional motifs embedded within these neural networks. By integrating graph-theoretic and topological tools, our objective is to elucidate how C. elegans exploits extrasynaptic communication to compensate for anatomical limitations, providing insights into the design principles of robust, adaptable neural systems.

Journal publication

• Giridhar, S., Sreeram, K.P., Menon, N., Ravikumar, A. and Sriraman, H., 2024. An Intelligent System for Preventing Accidents Due to Driver Distractions. Procedia Computer Science, 235, pp.2196-2204.

• Sunil, G. and Kuriakose, A., 2023. Emotion Detection with CNN Model and Song Recommendations using Machine Learning Techniques. Emotion, 3(3).

• Alla, S., Giridhar, S., Kunjumon, C.T., Sriraman, H. and Chattu, V.K., 2024. Hiatal Hernia Detection Using Novel End-to-End Deep Learning Model with Explainable AI. In IISE Annual Conference. Proceedings (pp. 1-6). Institute of Industrial and Systems Engineers (IISE).

In a nutshell

Prof. Benali aims to develop educational programs in advanced information acquisition and processing for promoting good health.

Research interest

The research interest of Prof. Benali is motivated by innovative and valid methodological approaches to studying normal aging and pathological processes in the context of a variety of neurological conditions representing critical public health issues. It develops an integrated biomedical approach, ranging from the basic mathematical modelling of the brain's anatomo-functional circuits and their normal and pathological dynamics to the physiological model of brain activity observed by neuroimaging tools allowing us to translate models from normal aging to clinical populations.

Background

After receiving my PhD in Computer Science, Multidimensional data analysis from Rennes I University in 1985, France, I later went on to join the French National Institute of Health and Medical Research (INSERM) in 1989. From 2008 to 2013, I served as Head of the Laboratory of Functional Imaging (INSERM U678 unit with over 65 members) and Deputy Director of the Biomedical Imaging Laboratory, INSERM - The National Center for Scientific Research (CNRS) and UPMC University until 2015. Together with Dr. Julien Doyon, from the Université de Montréal (UdeM), I founded and became co-director of the International Laboratory of Neuroimaging and Modelisation of the INSERM-UPMC and UdeM administrative bodies in 2007. I have been a regular research member of the Centre de Recherche Mathématiques of UdeM since 2002 and a researcher at Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, UdeM, since 2005. Moreover, I was co-PI of the IHU-A-ICM “Institut des Neurosciences Translationnelles de Paris” project (55M euros) which lead to the creation of one of the largest cognitive, clinical neuroscience and technology research center in France. I have established and directed a large research laboratory and have been collaborating with leaders in their fields in clinical and basic research as well as in engineering in Canada and around the world (Harvard Medical School, University of Texas at Dallas, Johns Hopkins University, National Institute of Health, UdeM, McGill, University of Liege, French Alternative Energies and Atomic Energy, CNRS and INSERM).

I am currently Scientific Director of the PERFORM Centre, NSERC CRC Tier 1 in Biomedical Imaging and Healthy Aging and Professor at the Faculty of Engineering and Computer Science at Concordia University. A natural collaborator with an extensive network world-wide, I have been instrumental in solidifying the PERFORM Centre’s role in fostering interdisciplinary research collaborations between numerous departments and faculties across Concordia University as well as with many inter-institutional research networks locally, nationally, and internationally. This served to farther solidify PERFORM’s reputation in advancing health research and fostering innovative and dynamic research collaborations thus positioning the Centre to serve as a vehicle for a School of Health created in 2023.

In the last 30 years, I have gone from starting a new neuroimaging research activity to directing a productive laboratory in France and Canada with activities that span from the development of new computational modeling and multimodal image analysis techniques to cognitive neuroscience and clinical applications. During this productive time in my scientific career, I published more than 300 papers. I supervised more than 80 students and fellows, including some that now became professors and researchers themselves and have scientific careers. In addition to my scientific work, I have also developed technology transfer activities. I have collaborated with several private companies over the past years and helped to found two start-up companies with my former students. In this regard, I have been an author on five patents on image processing devices. I have been awarded several grants from CFI-JELF, NSERC CRC Ter1, Concordia grants, CIHR, Alzheimer Research Foundation, as well as from national (France), European and international agencies, and a variety of companies. In 2019, I have been awarded the Provost‘s Circle of Distinction from Concordia University.

I am Associate Editor for Brain Imaging Methods – Frontiers in Neuroscience. I served as a member of the expert panel of FLAG-ERA, an umbrella organization that unites most funding organisations in Europe, and was an expert member of the French National Research Agency and numerous internal and external committees and boards including, Baycrest Academy for Research and Education Advisory Committee; Executive Committee of the Quebec Biomedical Imaging Network – FRQS; and Chair of the executive committee, to list but a few.

Introduction

Laetitia Jeancolas is currently a postdoctoral fellow at Concordia University. Her main project, done in collaboration with Paris Brain Institute (ICM) and IM2A, focuses on multimodal predictive modeling of Alzheimer’s disease (INSIGHT cohort). Specifically, Laetitia studies the neurovascular coupling in participants with subjective memory loss. She is also specialized in the analysis of voice impairments in neurodegenerative diseases and their use for automatic and early detection and disease monitoring. Her research interests are wide-ranging, centering around the use of signal processing, machine learning and neuroimaging techniques for the early detection of neurodegenerative diseases and the understanding of their mechanisms.

Educational Background

Laetitia Jeancolas is a former student of Ecole Normale Supérieure where she attended a selective program in fundamental physics and received a Master of Science degree in cognitive science. In 2019, she obtained her PhD in signal processing at Télécom SudParis (Paris-Saclay University), in collaboration with ICM. She received the Best PhD award of the Futur & Ruptures Program by the Fondation Mines Télécom, along with several other awards during her PhD.

Professional background

During her studies, Laetitia Jeancolas worked on several research projects, such as the development of a cognition model in the University of Arizona (USA), or the study of tool use acquisition in infants, at Integrative Neuroscience and Cognition Center (former Laboratoire Psychologie de la Perception, Paris V). She did her M.S research project on brain effective connectivity in early stages of Alzheimer's disease during memory tasks, at ICM and Grenoble Institut des Neurosciences (GIN).

During her PhD, Laetitia Jeancolas worked on the early detection of Parkinson's disease (PD) using voice analysis. She designed the experimental protocol, set up the acquisition tools, collected the data and conducted the analyses. One of the main innovative analyses consisted in adapting a recent deep learning feature extraction technique used in speaker recognition to PD detection. Laetitia also implemented face recordings, to analyze PD hypomimia in the same cohort, as well as a task based functional MRI protocol that enables the investigation of the neural disruptions causing the early PD speech impairments.

She expanded her PhD work during a postdoc at ICM where she focused more precisely on the neural correlates of the Parkinson-related voice impairments. Since, she has continued to work in this field through several collaborations with ICM, Télécom SudParis, Lille university, McGill university and Concordia university, supervising several PhD students, and M.S. research projects, on Parkinson and Alzheimer’s speech impairments and PD hypomimia.

Selected Publications

• Jeancolas, L., et al., (2022) « Voice Characteristics from Isolated Rapid Eye Movement Sleep Behavior Disorder to Early Parkinson’s Disease ». Parkinsonism & Related Disorders 95 : 86‑91. https://doi.org/10.1016/j.parkreldis.2022.01.003.

• Jeancolas L., et al., (2021) « X-Vectors: New Quantitative Biomarkers for Early Parkinson’s Disease Detection From Speech ». Frontiers in Neuroinformatics 15. https://doi.org/10.3389/fninf.2021.578369.

• Jeancolas L., et al., (2019) « Comparison of Telephone Recordings and Professional Microphone Recordings for Early Detection of Parkinson’s Disease, Using Mel-Frequency Cepstral Coefficients with Gaussian Mixture Models ». In Interspeech 2019, 3033‑37. Graz, Austria: ISCA. https://doi.org/10.21437/Interspeech.2019-2825.

• Jeancolas L., et al., (2022) « Can Infants Generalize Tool Use From Spoon to Rake at 18 Months? » Journal of Motor Learning and Development, 2022, 1‑17. https://doi.org/10.1123/jmld.2022-0006.

Videos

• Parkinson's disease detection using voice analysis: https://www.youtube.com/watch?v=uJuBqR0duhw

• 5-min PhD summary: https://www.youtube.com/watch?v=mtt8W1wU8Ig

• Fondation Recherche Alzheimer's video on my postdoc project: https://www.youtube.com/watch?v=LXPOjGPjCoQ

Contact

Email: laetitia.jeancolas@concordia.ca

A brief introduction

I am a MASc candidate in the Department of Electrical and Computer Engineering at Concordia University. My current research focuses on predicting multimodal brain connectomes using graph neural networks: a unified approach for neuroimaging and Alzheimer’s disease.

Educational background

I completed my bachelor’s degree in Computer Engineering at the Islamic Azad University of Tehran. During my studies, I explored applications of machine learning in healthcare, which sparked my interest in computational neuroscience. I gained hands-on experience in computer science fundamentals, data science methods, and software engineering, building a solid foundation for my current research on brain connectomes.

Research

My research focuses on developing advanced graph neural network (GNN) models for multimodal brain connectome prediction. In the first phase, I aim to design a model that takes as input a brain network derived from one neuroimaging modality—such as EEG or MEG—and accurately predicts the corresponding functional or structural brain graph from another modality, such as fMRI. This approach enables the inference of hard-to-acquire or expensive brain connectivity data from more accessible measurements. In the second phase, I will extend this framework to handle dynamic brain networks, predicting time-varying graphs from input graphs that themselves evolve over time. By modeling these spatiotemporal dynamics, the project seeks to capture the progression of brain connectivity patterns, which is critical for advancing our understanding of healthy aging and enabling the early detection of neurodegenerative diseases such as Alzheimer’s. Ultimately, this work aims to provide novel tools for brain research and potential biomarkers for clinical diagnosis.

Video

3 Minute Thesis summary: https://www.linkedin.com/posts/leila-mousavi-8a3b9a2aa_3mt-mt180-concordiauniversity-activity-7311159662794412033-v5I9/?utm_source=share&utm_medium=member_desktop&rcm=ACoAAEqSBXsBsoYUEUfUtm4KP7ijh9fXRzJjiBM

Introduction

He is currently pursuing his Ph.D. degree in Electrical and Computer Engineering with Concordia University, Montreal, QC, Canada, co-supervised by Dr. Habib Benali and Dr. Hassan Rivaz. He is a member of IMage Processing And Characterization of Tissue (IMPACT), as well as Biomedical Imaging for Healthy Aging laboratories, and his current research is mainly focused on improving the performance of the ultrasound modality for biomedical applications by employing deep learning approaches.

Educational Background

He received his B.S. and M.S. (as the first-ranked student) degrees in Electrical Engineering - Communication Systems from Iran University of Science and Technology, Tehran, Iran, in 2013 and 2016, respectively, and holds the Concordia International Tuition Award of Excellence during Ph.D. M.S. Thesis: Multimodal Image Registration with Incorporating Intensity and Geometric Information of Phase. B.S. Thesis: Design and implementation of a GSM/GPRS/GPS-based vehicle tracker. Professional background – He worked for two years as a full-time Software Developer (.NET and C#)

Publications:

1. Sharifzadeh, M., Benali, H. and Rivaz, H., 2022. Investigating Shift Variance of Convolutional Neural Networks in Ultrasound Image Segmentation. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(5), pp.1703-1713. IEEE Xplore
2. Sharifzadeh, M., Benali, H. and Rivaz, H., 2020. Phase Aberration Correction: A Convolutional Neural Network Approach. IEEE Access, 8, pp.162252-162260. IEEE Xplore
3. Tehrani, A.K., Sharifzadeh, M., Boctor, E. and Rivaz, H., 2022. Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 69(4), pp.1181-1190. IEEE Xplore, arXiv
4. Sharifzadeh, M., Benali, H. and Rivaz, H., 2021, September. An Ultra-Fast Method for Simulation of Realistic Ultrasound Images. In 2021 IEEE International Ultrasonics Symposium (IUS) (pp. 1-4). IEEE. IEEE Xplore, arXiv
5. Sharifzadeh, M., Tehrani, A.K., Benali, H. and Rivaz, H., 2021, September. Ultrasound Domain Adaptation Using Frequency Domain Analysis. In 2021 IEEE International Ultrasonics Symposium (IUS) (pp. 1-4). IEEE. IEEE Xplore, arXiv
6. Sharifzadeh, M., Benali, H. and Rivaz, H., 2021, September. Shift-invariant segmentation in breast ultrasound images. In 2021 IEEE International Ultrasonics Symposium (IUS) (pp. 1-4). IEEE. IEEE Xplore
7. Sharifzadeh, M., Benali, H. and Rivaz, H., 2021. Abnormality Segmentation in Brain MRI: U-Net Is Not Shift-Invariant. The Organization for Human Brain Mapping (OHBM).
8. Sharifzadeh, M., Benali, H. and Rivaz, H., 2020. Skull aberration correction in ultrasound brain imaging. The Organization for Human Brain Mapping (OHBM).

Introduction

Astrocytes, a major type of glia, possess assorted structural and functional properties making them inseparable from their neighbouring neurons. However, most published computational models of whole-brain activity, if not all, remain focused on neurons while ignoring astrocytes. We herewith introduce a biophysical model built upon neural network mass and compartmental modelling techniques, where large-scale astrocytic and neuronal networks couple their activity through glutamatergic and GABAergic transmission systems. We formulate a network scheme where neural dynamics are constrained by a two-layered structural network interconnecting either astrocytic or neuronal populations, and we ask how astrocytic networks contribute to whole-brain activity and emerging functional connectivity patterns. By developing a biologically plausible simulation approach based on bifurcation and multilayer network theories, we demonstrate that astrocytic and neuronal networks engage in a dialogue over fast and slow fluctuations or over phase-based and amplitude-based network connectivity. Our study is a step forward for more thoroughly investigating the role of glia alongside neurons in health or disease conditions.

Educational Background

Publications

• Ü Aydin, G Pellegrino, OBK Ali, C Abdallah, F Dubeau, JM Lina, E Kobayashi, C Grova (2020). Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients. J. Neural Eng. 17 035007

• Cross NE, Pomares FB, Nguyen A, Perrault AA, Jegou A, Uji M, K Lee, F Razavipour, OBK Ali, U Aydin, H Benali, C Grova, TT Dang-Vu (2021) An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation. PLoS Biol 19(11): e3001232. https://doi.org/10.1371/journal.pbio.3001232

• OBK Ali, A Vidal, C Grova, H Benali. Glial glutamate regulation, critical determinant of whole brain physiology: a computational study. Universite d’evry, France

• F Mohammadi, JP Soucy, P Rosa-Neto, TA Pascoal, OBK Ali, A Rahimabadi, M Savard, FZ Lussier, MS Kang, J Therriault, H Benali. Relationship between brain distributions of tau and atrophy to brain regional connectivity. Alzheimer's Association International Conference, 2020

• Soucy, J., Razavipour, F., Ali, O., Bin, K. B., Lee, K., Blinder, S., ... & Grova, C. (2022). Multiresolution metabolic profile of functional hubness in the resting human brain. In journal of cerebral blood flow and metabolism, vol. 42, no. 1_ suppl, pp. 157-158

Introduction

Stroke can induce irreversible lesions, disrupting cerebral blood flow (CBF) even beyond the necrotic zone: in the perilesional tissues (PLT). Neurovascular coupling is largely based on blood flow physiological mechanisms, whose estimation is crucial in the context of brain activity detection model used in functional MRI (fMRI). The presence of a chronic lesion therefore questions the detection of brain activity in PLT. In addition, brain plasticity mechanisms are playing a crucial role in post-stroke functional recovery and can occur in PLT. Former studies showed a decreased CBF in PLT, but there is no consensus about the best way to define PLT, any criteria to determine inhomogeneous properties within the PLT or either solution to include them in classical fMRI analysis. To explore these issues, the first research study, presented in this thesis, was conducted on thirteen post stroke participants in chronic phase to investigate the PLT properties through a multimodal MRI protocol. Arterial spin labeling (pcASL) was used to map CBF; fMRI was acquired at resting state, to get the spontaneous hemodynamic time courses and thereby compute brain homotopic functional connectivity. The data were processed in each patient native space. Lesions were manually demarcated, and brain parts were segmented to extract the information from regions of interest. When normalized with homologous control brain regions, results can be related to the distance from the lesion and fitted with linear model. In other words, the further the PLTs from the lesion, the better the CBF and the brain functional connectivity recovers. Moreover, we demonstrated that gradient of perfusion and gradient of homotopic connectivity are highly correlated together in approximately 80% of the remaining tissue of the ipsilateral hemisphere. Those highlighted patterns suggest that, instead of rejecting the PLT tissue, an adjustment of neurovascular model, used in fMRI, could be applied to stroke population

Educational Background

• Since 2015: PhD in BioSciences: Image processing & Computer Sciences.

• 2013-2015:Research Engineer, ICM, Paris, France

• 2013 Master degree, UPEC, honors, Signal & Image for Medicine, University Paris Est Creteil.

• 2008-2012 Engineer school, ISBS Paris. Majors: Image processing - Bio-informatics - Drug Discovery

Publications

• Berroir, P., Ghazi-Saidi, L., Dash, T., Adrover-Roig, D., Benali, H., and Ansaldo, A. I. (2016). In- terference control at the response level : Functional networks reveal higher efficiency in the bilingual brain. Journal of Neurolinguistics.

• Philippe, A.-C., Berroir, P., Bardinet, E., Vidailhet, M., and Lehericy, S. (2015). Basal ganglia func- tional parcellation into specific and overlapping territories with resting state f-mri. In Biomedical Imaging(ISBI), 2015 IEEE 12th International Symposium on, pages 1352–1355. IEEE.

• Durand, E., Berroir, P., & Ansaldo, A. I. (2018). The Neural and Behavioral Correlates of Anomia Recovery following Personalized Observation, Execution, and Mental Imagery Therapy: A Proof of Concept. Neural Plasticity, 2018, e5943759. https://doi.org/10.1155/2018/5943759