Stroke is a disease that causes the death of precious brain cells that will never grow back and which will affect one in four humans at some point in their life. While therapies and treatments for this debilitating condition are fairly standard, their outcomes are highly heterogeneous, with deficits all too often leading to permanent disability. These unsatisfactory outcomes point to the need for a paradigm shift towards precision medicine, where therapies and treatments are tailored to individual patients. This in turn requires a better understanding of the mechanisms underlying post-stroke impairment and recovery. Recent technological innovations provide an unprecedented opportunity to acquire a detailed picture of the anatomy and electrophysiology of the human brain non-invasively. Therefore, we apply multimodal methodologies to investigate the inner workings of the stroke-affected brain with a view to establishing biomarkers and creating stratifications that maximize the effectiveness of stroke therapy by making it personalized.In the first part of this thesis, network-theoretic models of the brain known as connectomes are created, under the assumption that these provide sufficiently detailed information about the individual patient to inform personalized recommendations. In particular, we leverage existing knowledge about how brain networks are organized into specialized modules connected by a "rich-club" of hubs. In a novel contribution to the field of connectomics, we combine these connectomes with detailed images of the lesion to obtain a network-theoretic lesion profile and a map of the non-lesioned part of the network, which we expect to be a better reflection of true brain connectivity. Using this methodology, we establish that the rich-club, in addition to being a source of network robustness, is also a source of vulnerability in that attacks against it lead to poor behavioral outcomes disproportionate to the size of the attacks.The creation of in-silico simulation of individual patients presents a great opportunity to quickly experiment with treatments, making it a potential game-changer in medical research. However, current technology is not able to faithfully capture the complexity of a whole human brain. The complex brain abnormalities inherent to stroke only add to the difficulty of the challenge. Any models one might wish to create must strike a balance between technological feasibility and sensitivity to inter-patient variability across a large parameter space. In the second part of this thesis, we investigate whether one major source of inter-patient variability, namely the size and location of the lesion, induces substantial variability in the effects of non-invasive brain stimulation to be included as factors in the design of stimulation protocols. Based on our modeling of the stimulation, personalizing stimulation protocols by taking the lesion into account produces little added value, except possibly when one is targeting brain areas close to the lesion and the lesion is significantly large.The proposed studies represent a step of the march towards precision medicine, by examining sources of variability in stroke patients and by associating them with variability in some outcomes of interest. Each step of this march brings stroke therapy farther from the status quo of homogeneous treatments and heterogeneous outcomes and closer to the ideal of heterogeneous treatments and homogeneously positive outcomes.
EPFL2022
