Towards patient-tailored interventions for stroke recovery: graph theoretical approaches and modeling of non-invasive brain stimulation

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.

Characterizing the EEG Correlates of Exploratory Behavior

Nicolas Bourdaud, Ricardo Andres Chavarriaga Lozano

This study aims to characterize the EEG correlates of exploratory behavior. Decision making in an uncertain environment raises a conflict between two opposing needs: gathering information about the environment and exploiting this knowledge in order to optimize the decision. Exploratory behavior has already been studied using fMRI. Based on a usual paradigm in reinforcement learning, this study has shown bilateral activation in the frontal and parietal cortex. To our knowledge, no previous study has been done on it using EEG. The study of the exploratory behavior using EEG signals raises two difficulties. First, the labels of trial as exploitation or exploration cannot be directly derived from the subject action. In order to access this information, a model of how the subject makes his decision must be built. The exploration related information can be then derived from it. Second, because of the complexity of the task, its EEG correlates are not necessarily time locked with the action. So the EEG processing methods used should be designed in order to handle signals that shift in time across trials. Using the same experimental protocol as the fMRI study, results show that the bilateral frontal and parietal areas are also the most discriminant. This strongly suggests that the EEG signal also conveys information about the exploratory behavior.

IDIAP2008

Uncovering the neural encoding of behavior in the adult Drosophila motor system

Chin-Lin Chen

One of the most important goals in neuroscience research has always been to understand how animals control their behavior. However, the long focus on the role of brain neurons in behavioral control might be missing the full story. In fact, brain-wide fluctuations in neural activity are highly correlated to the behavioral state, suggesting a closed-loop system where the behavioral command and the body state interact to interpret sensory inputs and dynamically guide action selection. However, to date, little is known about how and where information about the behavioral state is generated. In principle, information about the behavioral state is a relatively high-level encoding of the body state derived from the integration of signals from primary sensors and the local network. Local motor circuits residing in the spinal cord of mammals or the ventral nerve cord (VNC) of insects may give rise to the behavioral state and pass to the brain via brain-projecting interneurons---termed ascending neurons (ANs). However, what information ANs carry and where this information originates is still a mystery due to the technical challenges in measuring these signals in behaving animals. Therefore, it is critical to develop tools to track neuron identity and record neuronal activity during behavior to crack the functional network. In this work, a method is introduced for imaging the motor circuit in the VNC of Drosophila melanogaster. This preparation demonstrates how to retain fly limb function after dissection and allows simultaneous recording of a tethered fly's behavior and its neural activity in the VNC. Incorporating this methodology with other approaches can verify the conclusions from the experiments such as those manipulating neuronal excitability, and motivate further investigation into neuronal function in specific types of behavior. Since it has long been theorized that ANs are the cellular basis of the behavioral state information sent in the brain, the technique introduced in this work was designed to resolve questions relating to what type of information ANs convey. Here, a large-scale functional screen was performed to relate neural activity, behavior, and neuronal morphology for 247 genetically identified ANs in the adult fly. This is the first large-scale screen of neural activity in a behaving animal, and this work uncovered three fundamental features of AN populations. First, rather than low-level limb movements, ANs encode high-level behaviors like walking, grooming, and resting. Second, ANs project nearly exclusively to an integrative sensory center (AVLP) and an action selection hub (GNG). Remarkably, the encodings projected to these distinct areas reflect their potential roles in contextualizing sensory cues (AVLP) and guiding future actions (GNG). Third, the behavioral encodings of ANs are closely linked to their motor system patterning. In conclusion, this work provides an indispensable tool to study the motor system beyond the brain network by overcoming a major technical barrier in in-vivo recording of the VNC of a behaving fly. This tool was used to help resolve what is encoded by ANs as a higher-level behavioral state, suggesting that the primary cellular origin of the behavioral information that is used to modulate brain function comes from the VNC. Overall, the technique and the findings on AN encoding should inspire follow-up studies to uncover additional neural mechanisms used in adaptive behavior generation.

EPFL2022

Characterizing the EEG Correlates of Exploratory Behavior

Nicolas Bourdaud, Ricardo Andres Chavarriaga Lozano

Institute of Electrical and Electronics Engineers2008

Uncovering the neural encoding of behavior in the adult Drosophila motor system

Chin-Lin Chen

EPFL2022