Advanced Imaging in Neuroimmunological Diseases (ImaginEM) group at IDIBAPS & Hospital Clínic Barcelona. Developing automated MRI analysis methods to identify biomarkers for Multiple Sclerosis.
I am a Postdoctoral Researcher dedicated to computational neuroimaging within the Advanced Imaging in Neuroimmunological Diseases (ImaginEM) group at the Hospital Clínic de Barcelona and the August Pi i Sunyer Biomedical Research Institute (IDIBAPS). My multidisciplinary academic background includes a BSc in Chemical Engineering (2007), an MSc in Biomedical Engineering (2009), and a PhD in Medicine and Translational Research (2017).
For over a decade, my research has been dedicated to developing and applying advanced MRI analysis methods aimed at identifying quantitative biomarkers for Multiple Sclerosis (MS) and other neuroimmunological diseases. My primary contributions focus on using advanced imaging to characterize subtle white and gray matter changes, conducting extensive network analysis to enhance the understanding of how MS disrupts brain connectivity, and applying machine learning tools to develop predictive models for disease progression and severity.
A key technical achievement was leading the implementation of the XNAT platform for automated MRI processing, a development crucial for ensuring reproducible and scalable multi-center studies. I have demonstrated an extensive publication record, which includes more than 60 publications and an h-index of 20 (WoS), with numerous works published in leadership roles (first and corresponding author) in high-impact journals such as the Journal of Neurology, Neurosurgery & Psychiatry, Scientific Reports, NeuroImage: Clinical, and Network Neuroscience. Furthermore, I have been a Collaborating Professor in the Master's in Data Science at the Open University of Catalonia (UOC) since 2019, having most recently carried out a teaching role in the area of Artificial Intelligence during the last semester of 2024/2025.
IDIBAPS - Hospital Clínic de Barcelona | ImaginEM Group
June 2018 – Present
Universitat Oberta de Catalunya (UOC)
2018 – Present
Course Instructor and Tutor for Master programs in Data Science, Bioinformatics, and Computational Engineering.
Hospital Clínic de Barcelona
2010 – 2017
Collaborations as Course Instructor and Tutor for various University Master's Degree programs.
| Semester | Role | Subject / Activity | Official Program |
|---|---|---|---|
| 2024/25 Sem 2 | Course Instructor | Artificial Intelligence | Master in Computational & Math. Engineering |
| 2024/25 Sem 2 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2024/25 Sem 2 | Course Instructor | Statistical Bioinformatics & ML | Master in Bioinformatics & Biostatistics |
| 2024/25 Sem 2 | Tutor | Master's Tutoring | Master in Data Science |
| 2024/25 Sem 1 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2023/24 Sem 2 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2023/24 Sem 2 | Tutor | Master's Tutoring | Master in Data Science |
| 2023/24 Sem 1 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2023/24 Sem 1 | Course Instructor | Statistical Bioinformatics & ML | Master in Bioinformatics & Biostatistics |
| 2023/24 Sem 1 | Tutor | Master's Tutoring | Master in Data Science |
| 2022/23 Sem 2 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2022/23 Sem 2 | Tutor | Master's Tutoring | Master in Data Science |
| 2022/23 Sem 1 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2022/23 Sem 1 | Tutor | Master's Tutoring | Master in Data Science |
| 2021/22 Sem 2 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2021/22 Sem 2 | Tutor | Master's Tutoring | Master in Data Science |
| 2021/22 Sem 1 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2021/22 Sem 1 | Tutor | Master's Tutoring | Master in Data Science |
| 2020/21 Sem 2 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2019/20 Sem 2 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2019/20 Sem 1 | Course Instructor | TFM - Area 3 | Master in Data Science |
| 2018/19 Sem 2 | Course Instructor | FMP - Data Mining & ML | Master in Data Science |
| 2017/18 Sem 2 | Course Instructor | Final Master's Degree Project | Master in Data Science |
Supervision of approved Final Master Projects (TFM) at Universitat Oberta de Catalunya (UOC) for the Data Science and Bioinformatics programs.
| Semester | Project Title | Grade |
|---|---|---|
| 2024/25 S2 | Lesion Segmentation in Multiple Sclerosis: A Deep Learning Approach for Accurate Detection | 9.5 |
| 2024/25 S2 | Multimodal brain network integration using graph theoretical analysis in people with multiple sclerosis | 9.4 |
| 2024/25 S2 | Aplicación de modelos de clasificación mediante técnicas avanzadas de ML para el análisis de redes cerebrales en EM | 7.4 |
| 2024/25 S1 | Study of transcriptomics-defined cellular populations in the mouse dentate gyrus and its alteration in epilepsy | 9.6 |
| 2024/25 S1 | Anàlisi de xarxes cerebrals multimodals mitjançant la teoria de grafs en pacients amb Esclerosi Múltiple | 7.4 |
| 2024/25 S1 | Detección de Lesiones Nuevas o Cambiantes en Esclerosis Múltiple | 6.3 |
| 2023/24 S2 | A Classification Model Approach to Brain Imaging: Understanding Fear Conditioning | 9.0 |
| 2023/24 S2 | Synap-Net: Synchronized Neural Analysis of Stroke in FLAIR images through nnU-NET | 8.8 |
| 2023/24 S2 | Detecció automàtica de lesions cròniques actives (o d'expansió lenta) mitjançant ressonància magnètica convencional | 8.1 |
| 2023/24 S1 | From brain disconnection to atrophy: Assessing multi-modal brain network connectivity measures in MS | 9.4 |
| 2023/24 S1 | Longitudinal MRI analysis for Slowly Expanding Lesions (SELs) characterization through nnU-NET | 9.3 |
| 2023/24 S1 | Characterisation of structural connectivity in relation to cognitive profiles in patients with multiple sclerosis | 9.0 |
| 2023/24 S1 | Integració multimodal de connectivitat estructural i funcional cerebral en la detecció d’Esclerosis Múltiple | 8.3 |
| 2022/23 S2 | Automated Identification of Initial and Progressing MS Indicators through multiclass detection of Baseline and New Lesions | 9.7 |
| 2022/23 S2 | Magnetic Resonance Imaging (MRI) image translation with Cycle-consistency GAN | 9.7 |
| 2022/23 S2 | Multilayer approach to diagnose and classify Multiple Sclerosis phenotypes using graph theory measures | 9.4 |
| 2022/23 S2 | Estudio de la conectividad estructural, morfológica y funcional del cerebro en pacientes con esclerosis múltiple | 8.2 |
| 2022/23 S1 | Detección de nuevas lesiones en Esclerosis Múltiple en estudios longitudinales de RM | 9.8 |
| 2022/23 S1 | Detección de lesiones nuevas o cambiantes en EM | 9.8 |
| 2022/23 S1 | Detección de Lesiones de Esclerosis Múltiple (EM) a través de Deep Learning | 7.5 |
| 2021/22 S2 | Detección de lesiones nuevas o cambiantes en EM | 8.7 |
| 2020/21 S2 | Detección de lesiones nuevas o cambiantes en EM | 8.8 |
| 2020/21 S2 | Detección de lesiones nuevas o cambiantes en EM | 8.3 |
| 2019/20 S2 | Segmentación automática de lesiones en EM | 9.3 |
| 2019/20 S2 | Segmentación automática de lesiones en EM | 7.3 |
| 2019/20 S1 | Caracterización del colapso de la red cerebral en pacientes con esclerosis múltiple mediante análisis de grafos | 9.7 |
| 2019/20 S1 | Segmentación de Lesiones del Cerebro en la Esclerosis Múltiple con Redes Neuronales Convolucionales | 7.5 |
| 2019/20 S1 | Optimización de la arquitectura de red neuronal convolucional (FLEXCONN) | 7.2 |
| 2018/19 S2 | ML-7. Segmentación automática de lesiones en EM | 9.7 |
| 2018/19 S2 | Estudio de la red cerebral mediante grafos | 7.9 |
| 2018/19 S2 | Estudio de la red cerebral mediante grafos | 7.1 |
| 2017/18 S2 | Aplicación de algoritmos de aprendizaje automático para predecir la disfunción cognitiva en pacientes de EM | 9.0 |
| Code | Project Name | Agency / Fund | Duration | Role |
|---|---|---|---|---|
| PI24/00567 | MS Disability Accumulation: combined models of damage, repair and reserve | Instituto de Salud Carlos III | 2025 - 2027 | Team Member |
| 2021-SGR-01325 | Grup d'Imatge Avançada en Malalties Neuroimmunològiques (ImaginEM) | AGAUR | 2022 - 2024 | Team Member |
| PI21/01189 | Neuroimaging multi-modal approach (daMoS) | Instituto Carlos III (IDIBAPS) | 2022 - 2024 | Team Member |
| TV3_Ictus_17 | Blood brain barrier disruption after subarachnoid hemorrhage | Fundació La Marató de TV3 | 2018 - 2023 | Team Member |
| PI18/01030 | Rehabilitación cognitiva y plasticidad cerebral en la esclerosis múltiple | Instituto de Salud Carlos III | 2019 - 2021 | Team Member |
| RD16/0015/0002 | Redes temáticas de investigación (REEM) | IDIBAPS | 2017 - 2021 | Team Member |
| PI15/00587 | Biomarcadores de RM avanzada en esclerosis múltiple | Instituto de Salud Carlos III | 2016 - 2020 | Team Member |
| CEIC 7965 | Non-conventional MRI as predictive marker of treatment response | TEVA Spain SLU | 2013 - 2018 | Team Member |
| RD12/0032/0002 | Red Española de Esclerosis Múltiple (REEM) | Ministerio de Sanidad | 2013 - 2016 | Team Member |
| - | Rehabilitación cognitiva y plasticidad cerebral en la Esclerosis Múltiple | Fundación Merck Salud | 2017 - 2021 | Team Member |
| Year | Presentation Title | Event / Location |
|---|---|---|
| 2025 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2025 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
| 2025 | Therapeutic images: healing, visuality and trust | VisualTrust Congress, Barcelona |
| 2025 | Workflow in neuroimaging research (RICORS) | Internal/Hospital Clínic |
| 2024 | Advanced Imaging in neuroimmunological diseases (KISTEP) | Internal/Hospital Clínic |
| 2024 | Deep Learning: Introducción y aplicación en la neuroimagen | Internal/Hospital Clínic |
| 2024 | Practical use of synthetic imaging (Uso práctico de la imagen sintética) | Internal/Hospital Clínic |
| 2024 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2024 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
| 2023 | Adaptability of advanced MR imaging techniques to clinical research | Internal/Hospital Clínic |
| 2023 | Integration of automated MRI Image processing into XNAT platform | Internal/Hospital Clínic |
| 2023 | How quantitative images are processed: obtaining values of cerebral and spinal atrophy | Internal/Hospital Clínic |
| 2023 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2023 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
| 2022 | medical image storage and processing infrastructure (XNAT) | Internal/Hospital Clínic |
| 2022 | Deep Learning: Introducción y aplicación en la neuroimagen | Internal/Hospital Clínic |
| 2022 | How quantitative images are processed: obtaining values of cerebral and spinal atrophy | Internal/Hospital Clínic |
| 2022 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2022 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
| 2021 | Advanced diffusion-weighted imaging: Quantitative microstructure properties | Internal/Hospital Clínic |
| 2021 | How quantitative images are processed: obtaining values of cerebral and spinal atrophy | Internal/Hospital Clínic |
| 2021 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2021 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
| 2020 | Open Science Resources: New insights for researchers | Internal/Hospital Clínic |
| 2020 | medical image storage and processing infrastructure (XNAT) | Internal/Hospital Clínic |
| 2020 | How quantitative images are processed: obtaining values of cerebral and spinal atrophy | Internal/Hospital Clínic |
| 2020 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2020 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
| 2019 | Microscopic diffusion anisotropy imaging as potential biomarker for MS | Internal/Hospital Clínic |
| 2019 | How quantitative images are processed: obtaining values of cerebral and spinal atrophy | Internal/Hospital Clínic |
| 2019 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2019 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
| 2018 | Structural connectivity in patients with anti-NMDA receptor encephalitis | Internal/Hospital Clínic |
| 2018 | High order tractography models to obtain a more reliable structural connectivity | Internal/Hospital Clínic |
| 2018 | Quantitative MRI of the spinal cord | Internal/Hospital Clínic |
| 2018 | How quantitative images are processed: obtaining values of cerebral and spinal atrophy | Internal/Hospital Clínic |
| 2018 | What advanced magnetic resonance imaging techniques are | Internal/Hospital Clínic |
| 2018 | Deep learning: introduction and application in neuroimaging | Internal/Hospital Clínic |
Showing highlights as First, Co-First, or Senior Author. See full list on PubMed.
Calvi A, Vivó Pascual F, ..., Martinez-Heras E, Llufriu S.
J Neurol Neurosurg Psychiatry (2025) | DOI: 10.1136/jnnp-2025-335884
Vivó F, Solana E, ..., Martinez-Heras E.
Hum Brain Mapp (2024) | DOI: 10.1002/hbm.26706
Martinez-Heras E, Solana E, Vivó F, ..., Llufriu S.
J Neurol Neurosurg Psychiatry (2023) | DOI: 10.1136/jnnp-2023-331531
Casas-Roma J, Martinez-Heras E, Solé-Ribalta A, ..., Prados F.
Netw Neurosci (2022) | DOI: 10.1162/netn_a_00258
Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S.
Semin Ultrasound CT MR (2021) | DOI: 10.1053/j.sult.2021.07.006
Solana E, Martinez-Heras E, Montal V, ..., Llufriu S.
Sci Rep (2021) | DOI: 10.1038/s41598-021-96132-x
Martínez-Heras E, Solana E, Prados F, ..., Llufriu S.
Neuroimage Clin (2020) | DOI: 10.1016/j.nicl.2020.102411