Imaging biomarkers in multiple Sclerosis: From image analysis to population imaging

- To illustrate the relevance of the evolution of the medical image analysis domain in multiple sclerosis (MS).
- To show how computational models have been used in the past to provide some relevant, though limited, markers of the disease and its evolution.
- To explain why these existing computational solutions are limited.
- To predict how the medical image analysis domain will evolve in the next decade in order to tackle the remaining challenges and provide imaging biomarkers that become capable of discovering quantitative image descriptors that are not necessarily visible to the human eye and use these descriptors to better represent the dynamics of the pathology and predict accurately the disease course in individual patients.

The production of imaging data in medicine increases more rapidly than the capacity of computing models to extract information from it. The grand challenges of better understanding the brain, offering better care for neurological disorders, and stimulating new drug design will not be achieved without significant advances in computational neuroscience. The road to success is to develop a new, generic, computational methodology and to confront and validate this methodology on relevant diseases with adapted computational infrastructures. This new concept sustains the need to build new research paradigms to better understand the natural history of the pathology at the early phase; to better aggregate data that will provide the most complete representation of the pathology in order to better correlate imaging with other relevant features such as clinical, biological or genetic data. In this context, one of the major challenges of neuroimaging in clinical neurosciences is to detect quantitative signs of pathological evolution as early as possible to prevent disease progression, evaluate therapeutic protocols or even better understand and model the natural history of a given neurological pathology. Many diseases encompass brain alterations often not visible on conventional MRI sequences, especially in normal appearing brain tissues (NABT). MRI has often a low specificity for differentiating between possible pathological changes which could help in discriminating between the different pathological stages or grades. The objective of medical image analysis procedures is to define new quantitative neuroimaging biomarkers to track the evolution of the pathology at different levels. This paper illustrates this issue in one acute neuro-inflammatory pathology: Multiple Sclerosis (MS). It exhibits the current medical image analysis approaches and explains how this field of research will evolve in the next decade to integrate larger scale of information at the temporal, cellular, structural and morphological levels.

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