Imaging the moving brain

Coughing is known as a defensive reflex that protects the airways from harmful substances. Clinically, the cough reflex may be impaired by stroke While brain activity during cough was previously examined by functional magnetic resonance imaging (fMRI) with model analysis, this method does not capture the actual brain activity during coughing. To obtain accurate measurements of brain activity during coughing, we use an unrestrained positron emission tomography (PET) system with head motion correction to correct for head motion while a whole-brain scan was performed during a coughing task. 

While neuroimaging technologies have improved and expanded their capabilities, there has long been a gap in traditional neuroimaging technologies for a noninvasive, high-resolution, portable modality that tolerates participant movement. Established technologies like EEG may be used in real-world settings, but suffer from poor resolution, SNR, and motion susceptibility, while MRI has good spatial resolution, but creates a loud, cramped, unnatural scanning environment. MRI and PET are also stationary, and contraindicated in some populations. Wearable diffuse optical tomography (DOT) systems bridge this gap; our new wearable, high-density (WHD) DOT system offers portability and flexibility like EEG, spatial resolution similar to MRI, and improved robustness against motion artifacts versus fiber-based DOT systems.
DOT is an optical technique that uses multiple, overlapping measurements from high-density imaging arrays to generate 3D tomographic reconstruction of cortical blood oxygenation dynamics (Fig 1a). As with MRI, neuronal activity is inferred from blood oxygenation via neurovascular coupling. While fiber-based DOT systems have been extensively validated against MRI over the last decade, full head fiber DOT systems require that the subject's head remains relatively still. New, wearable DOT systems have recently begun to provide similar imaging performance with the added advantage of permitting subject movement. 

Motor behaviour plays an important role in our ability to interact with the world. Higher levels of physical activity and motor performance are associated with better sleep quality and mental health [1], [2]. Previous studies mainly assessed univariate associations between individual measures rather than exploring the interplay between the latent dimensions of sleep, mental health, and motor behaviour. Furthermore, the neurobiology underlying their interplay remains unclear. In this study, we aimed to assess multivariate links between motor behaviour and the combined factors of sleep and mental health. Additionally, we discerned the interindividual neuroanatomical basis of their interaction through a predictive machine-learning approach in a large-scale sample. 

Spontaneous movement has become a topic of increasing interest, with it being linked to widespread neural activation patterns in rodents. A potential way to study this in humans is to look at spontaneous head motion in the MRI scanner whilst recording brain activity. The fact that motion affects the data being recorded makes this challenging as it may be difficult to disentangle artifactual activity changes from motion-related ones. To attempt to circumvent this problem, we took large motions as spontaneous events and then related event-related maps to distributions of dopamine D2 receptors. This receptor type was chosen as it has been previously linked to spontaneous motion production. 

In traditional human neuroimaging experiments, researchers create experimental paradigms with a psychological/behavioral validity to infer the corresponding neural correlates. Here, we introduce a novel approach called Reinforcement Learning via Brain Feedback (RLBF), that inverts the direction of inference; it seeks for the optimal stimulation or paradigm to maximize (or minimize) response in predefined brain regions or networks (fig.1). The stimulation/paradigm is found via a reinforcement learning algorithm (Kaelbling et al., 1996) that is rewarded based on real-time fMRI (Sulzer et al., 2013) data. Specifically, the reinforcement learning agent manipulates the paradigm space (e.g. via generative AI) to drive neural activity in a specific direction. Then, rewarded by measured brain responses, the agent gradually learns to adjust its choices to converge towards an optimal solution. Here, we present the results of a proof of concept study that aimed to confirm the viability of the proposed approach with simulated and empirical real-time fMRI data. 

Despite recent advances in early diagnosis of cerebral palsy (CP), accurate and timely detection remains elusive. Advances in quantitative MRI and machine learning technology appear promising to enable early, accurate prediction of CP. Our goal was to improve early CP prediction in preterm infants by exploiting advanced quantitative MRI biomarkers acquired at term-equivalent age.