Driving the motor cortex by sensory input
During recent years there has been growing scientific interest in whether sensory stimulation in the absence of movement execution can be used to drive the motor system or memory formation. Many studies have investigated how visual stimulation can be employed in this context (see Alaerts et al. 2009, Alaerts et al. 2010, Zhang et al. 2011 for examples from our group). In comparison, very little is known about the effect of somatosensory training on motor system physiology. In this SNF funded project we are investigating how sensory information activates the motor system and whether motor practice can shape these sensorimotor interactions.
Learning and Memory
Without learning and memory life is merely a series of disconnected fragments. Disorders affecting memory are numerous and often debilitating. We are broadly interested in studying how new memories are encoded, consolidated (Woolley et al. 2014) and subsequently reconsolidated (De Beukelaar et al. 2014), with a focus on applying this knowledge to enhance rehabilitation protocols. Our work includes a range of learning paradigms. Experiments investigating spatial navigation seek to provide convergence between human and animal models of learning and memory (Woolley et al. 2013). Experiments in the motor domain target upper limb fine motor skills with the goal of enhancing learning outcomes. Our research in this field uses several methods including task-related and resting-state fMRI, non-invasive brain stimulation, and a novel muscle-computer interface.
Why do you get up in the morning? Why do you agree to help a friend move apartment? Why do you move at all? Our decisions about the world and how we intend to interact with our environment are largely driven by rewards, i.e. what do I gain by doing this? The study of how reward and motivation influence decision making processes (also called value-based decision making or neuroeconomics) has grown exponentially in the last decade. In the NCM lab there are a number of projects investigating reward processing, including: how the relationship between stimuli and outcomes are encoded in the brain, how we can use rewards to motivate non-preferred actions (i.e. effort from a non-dominant effector), and how a deficit in reward processing could manifest into clinical disorders such as Autism (Apps et al. 2013; Balsters et al. 2015).
Abnormal connectivity in Autism Spectrum Disorder
The most salient and unique feature of Autism Spectrum Disorder (ASD) is the deficit in social interaction. Deficits in social interaction and a lack of motivation to engage in social activities appear to be unique to this disorder, and some have suggested that social interaction deficits are ‘superordinate’, in that they may explain some of the other features of ASD. Previous studies have focused on investigating the neural correlates of social deficits in ASD, however, these studies have repeatedly failed to generate cogent neuropathological models or successful intervention strategies. We believe that by analyzing interactions between brain regions either at rest (Balsters et al. In Prep; Di Martino et al. 2013) or during task execution (Alaerts et al. 2014; Balsters et al. 2015; Delmonte et al. 2013) and by applying computational approaches we can significantly improve our understanding and the future treatment of ASD.
Moreover, progress in understanding the pathophysiology of brain disorders greatly benefits from the use of suitable animal models. In our lab we study animal models for ASD that express molecular and social behavioral deficits similar to humans with ASD. Our primary goal is to provide a crucial methodological link between genetic variations causing molecular/synaptic alterations and dysfunctional connectivity, which is related to ASD-like behavioral traits (ETH fellowship granted to Valerio Zerbi).
Stratification of therapy in cerebral palsy based on connectivity biomarkers
Cerebral palsy (CP) is the leading cause of childhood disability and is caused by a non-progressive lesion of the developing fetal or infant brain. CP causes lifelong impairments that can be highly disabling if untreated. Moreover, the heterogeneous nature of CP requires individually adjusted treatment planning to increase the chance of success. To date, structural MR images of the brain have been used to derive neural biomarkers, such as lesion location and extent, which serve as a general classification scheme of gross motor function and upper limb abilities. However, neural biomarkers that predict upper limb functional outcomes at the level of the individual child are still lacking. In the context of the Marie Curie Project of Dr. E Jaspers, we are focusing on the development of new methods for quantifying the functional connectivity of the sensorimotor system by combining behavioral tests, transcranial magnetic stimulation, EEG and advanced MR imaging (resting state and diffusion weighted imaging).
Stratified medicine refers to matching patients with pharmacological or non-pharmacological therapies based on clinical biomarkers, which can entail any diagnostic test or clinical observation. The strategic and economic significance of such marker-based identification of subgroups or strata of patients has been clearly demonstrated in e.g. cancer research. However, neurological diseases and particularly those occurring during early development like cerebral palsy (CP) can cause highly divergent symptoms, which requires individually adjusted treatment planning to increase the chance of success. With our research we aim to lay the foundation for a stratified therapy approach in CP by developing biomarkers reflecting an individual’s status at the level of the brain.
Neural oscillations in the motor system
The importance of the influence of neural oscillations in human behavior is still a matter of debate. The aim of this project is to investigate the relationship between neural oscillations of different frequency bands and their impact on the motor system. We are doing this firstly by correlating neural oscillations measured with EEG during various motor tasks with behavioural performance, and secondly by influencing those neural oscillations with transcranial magnetic stimulation and transcranial electrical stimulation. With this combination of approaches we are trying to establish a causal link between neural oscillations and function.
Various brain stimulation methods have been developed to improve motor learning and recovery in the healthy population and neurological patients. At first glance, one would assume that in order to influence motor performance, the motor cortex would need to be stimulated. However, this assumption neglects the importance of somatosensory processing in motor control. The role of intact sensory functioning has been demonstrated in several studies, which report that interruption to somatosensory information affects corrective movements or the learning of novel tasks. In our lab we try to enhance sensory perception with different brain stimulation methods. We focus on the tactile, central visual and peripheral nervous systems.
In recent years machine learning, the study and development of data analysis tools capable of extracting patterns from datasets, has become increasingly important in neuroscientific research. With regard to the motor system, new research has shown that machine learning tools can be successfully applied to fMRI data for the classification of fine motor movements, and that the obtained results enable insights into the mechanisms underlying motor control and learning. Our goal is to broaden this research direction by applying suitable machine learning methods not only to the analysis of neuroimaging data from the motor system, but to integrated sensorimotor imaging data.
One of the issues plaguing Cognitive Neuroscience is the use of nebulous terms such as ‘attention’, ‘reward’ and ‘learning and memory’. Without precise definitions it is not possible to accurately investigate these meta-cognitive phenomena. However, by converting these terms into mathematical equations we are able to more precisely define, and as such investigate, these processes. Here, we implement existing computational models such as Rescorla-Wagner and the Hierarchical Gaussian Filter in order to more precisely investigate brain-behavior interactions.
Along with computational models of behavior we also use computational models as tools for advanced data analysis. This includes classical statistical techniques, machine learning models such as support vector machines, nearest neighbor classifiers and deep learning networks to classify neuroimaging data. We are currently implementing these methods in the study of sensorimotor integration, and the classification of psychiatric disorders based on resting state connectivity. We will also develop suitable computational models for the analysis of other types of neuroimaging data.
Multi-modal imaging combines two or more imaging techniques, which allows the integration of the strength of individual modalities, while overcoming their limitations. For example, by combining fMRI with EEG we benefit from both the high spatial resolution of fMRI and high temporal resolution of EEG, resulting in a better estimate of brain connectivity.