S-cone signals invisible to the motion system can improve motion extraction via grouping by color

Peer reviewedPublisher PD

Predicting object-mediated gestures from brain activity: an EEG study on gender differences

Recent functional magnetic resonance imaging (fMRI) studies have identified specific neural patterns related to three different categories of movements: intransitive (i.e., meaningful gestures that do not include the use of objects), transitive (i.e., actions involving an object), and tool-mediated (i.e., actions involving a tool to interact with an object). However, fMRI intrinsically limits the exploitation of these results in a real scenario, such as a brain-machine interface (BMI). In this study, we propose a new approach to automatically predict intransitive, transitive, or tool-mediated movements of the upper limb using electroencephalography (EEG) spectra estimated during a motor planning phase. To this end, high-resolution EEG data gathered from 33 healthy subjects were used as input of a three-class k-Nearest Neighbours classifier. Different combinations of EEGderived spatial and frequency information were investigated to find the most accurate feature vector. In addition, we studied gender differences further splitting the dataset into only-male data, and only-female data. A remarkable difference was found between accuracies achieved with male and female data, the latter yielding the best performance (78.55% of accuracy for the prediction of intransitive, transitive and tool-mediated actions). These results potentially suggest that different gender-based models should be employed for future BMI applications

Motor learning induced neuroplasticity in minimally invasive surgery

Technical skills in surgery have become more complex and challenging to acquire since the introduction of technological aids, particularly in the arena of Minimally Invasive Surgery. Additional challenges posed by reforms to surgical careers and increased public scrutiny, have propelled identification of methods to assess and acquire MIS technical skills. Although validated objective assessments have been developed to assess motor skills requisite for MIS, they poorly understand the development of expertise. Motor skills learning, is indirectly observable, an internal process leading to relative permanent changes in the central nervous system. Advances in functional neuroimaging permit direct interrogation of evolving patterns of brain function associated with motor learning due to the property of neuroplasticity and has been used on surgeons to identify the neural correlates for technical skills acquisition and the impact of new technology. However significant gaps exist in understanding neuroplasticity underlying learning complex bimanual MIS skills. In this thesis the available evidence on applying functional neuroimaging towards assessment and enhancing operative performance in the field of surgery has been synthesized. The purpose of this thesis was to evaluate frontal lobe neuroplasticity associated with learning a complex bimanual MIS skill using functional near-infrared spectroscopy an indirect neuroimaging technique. Laparoscopic suturing and knot-tying a technically challenging bimanual skill is selected to demonstrate learning related reorganisation of cortical behaviour within the frontal lobe by shifts in activation from the prefrontal cortex (PFC) subserving attention to primary and secondary motor centres (premotor cortex, supplementary motor area and primary motor cortex) in which motor sequences are encoded and executed. In the cross-sectional study, participants of varying expertise demonstrate frontal lobe neuroplasticity commensurate with motor learning. The longitudinal study involves tracking evolution in cortical behaviour of novices in response to receipt of eight hours distributed training over a fortnight. Despite novices achieving expert like performance and stabilisation on the technical task, this study demonstrates that novices displayed persistent PFC activity. This study establishes for complex bimanual tasks, that improvements in technical performance do not accompany a reduced reliance in attention to support performance. Finally, least-squares support vector machine is used to classify expertise based on frontal lobe functional connectivity. Findings of this thesis demonstrate the value of interrogating cortical behaviour towards assessing MIS skills development and credentialing.Open Acces

Creating a new tool for Post-Traumatic Disorder treatment

The first article on real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback was published in 2003 (Weiskopf et al., 2003) with the aim to enable the subject to learn to control activation in rostral-ventral and dorsal anterior cingulate cortex (ACC). Rt-fMRI neurofeedback involves data collection of neural activity, real-time data preprocessing, online statistical analysis, providing the results back to the participant, and active effort of participant in order to either up- and/or down-regulate the target region’s activation. In the last 16 years the topic attracted great attention from different labs around the world and many different brain regions were regulated with the help of rt-fMRI neurofeedback. Nevertheless it had the most distinct impact in the clinical research as it could be used with clinical population in order to normalize their abnormal neural activity. The dissertation focused on the implementation of the rt-fMRI neurofeedback to the Post-Traumatic Stress Disorder (PTSD) patients. PTSD is developed as a result of experiencing a traumatic event in first hand or hearing that a close one experienced it. PTSD has a high prevalence (Kessler et al., 2005) and also high impact on the patient’s life quality (Warshaw et al., 1993). Unfortunately the response rate to the therapy is around 50% (Bradley et al., 2005; Stein et al., 2006). Hence, there is a need for a new treatment tool for PTSD. The neurocircuitry model of PTSD indicate that there is increased activity in amygdala, decreased activity in ventromedial prefrontral cortex (vmPFC)/rostral ACC (rACC) and hippocampus (Rauch et al., 2006). Animal model of PTSD revealed that stimulating rACC led to increase in extinction learning and rats exhibited less PTSD symptoms (Milad & Quirk, 2002). Following these findings, we decided to implement rACC rt-fMRI neurofeedback to PTSD patients. The first study focused to develop a new paradigm to target rACC and tested it with healthy population. We used Ekman faces as functional localizer in order to locate the rACC. Experimental design constituted of four functional runs in one session. The main aim was to assess the methods effectiveness in one session. Surprisingly eight out of sixteen female participants learned to regulate their rACC, whereas only four out of sixteen male participants were able to regulate their rACC at will. Interestingly the learner/non-learners are not widely reported in the rt-fMRI literature and no gender difference has been reported so far. As a result we decided to implement it with only one sex in PTSD group. In the second study we tested the paradigm with the female PTSD patients. Eight out of sixteen PTSD patients gained control over their rACC. We also found that PTSD patients recruited more brain regions, especially multi-sensory brain regions for the upregulation of rACC in comparison to healthy subjects. We failed to find a single factor to predict rACC control success across groups. There is a need for further study to identify the predictor factors. As a result we concluded that the best practice of rt-fMRI with PTSD patients would be to use it as a supportive tool to psychotherapy in order to identify the best working strategy for their treatment. Further research recommendations are discussed below

DICOM for EIT

With EIT starting to be used in routine clinical practice [1], it important that the clinically relevant information is portable between hospital data management systems. DICOM formats are widely used clinically and cover many imaging modalities, though not specifically EIT. We describe how existing DICOM specifications, can be repurposed as an interim solution, and basis from which a consensus EIT DICOM ‘Supplement’ (an extension to the standard) can be writte

Characterization of Neuroimage Coupling Between EEG and FMRI Using Within-Subject Joint Independent Component Analysis

The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing the relationship between EEG and fMRI within healthy subjects, it allows for comparison with a diseased population, and may offer ways to detect some of these conditions earlier. The correspondence between fMRI and EEG was first examined, and a methodological approach which was capable of informing to what degree the fMRI and EEG sources corresponded to each other was developed. Once it was certain that the EEG activity observed corresponded to the fMRI activity collected a methodological approach was developed to characterize the coupling between fMRI and EEG. Finally, this dissertation addresses the question of whether the use of jICA to perform this analysis increases the sensitivity to subcortical sources to determine to what degree subcortical sources should be taken into consideration for future studies. This dissertation was the first to propose a way to characterize the relationship between fMRI and EEG signals using blind source separation. Additionally, it was the first to show that jICA significantly improves the detection of subcortical activity, particularly in the case when both physiological noise and a cortical source are present. This new knowledge can be used to design studies to investigate subcortical signals, as well as to begin characterizing the relationship between fMRI and EEG across various task conditions

Estimation of thorax shape for forward modelling in lungs EIT

The thorax models for pre-term babies are developed based on the CT scans from new-borns and their effect on image reconstruction is evaluated in comparison with other available models

Rapid generation of subject-specific thorax forward models

For real-time monitoring of lung function using accurate patient geometry, shape information needs to be acquired and a forward model generated rapidly. This paper shows that warping a cylindrical model to an acquired shape results in meshes of acceptable mesh quality, in terms of stretch and aspect ratio

Torso shape detection to improve lung monitoring

Two methodologies are proposed to detect the patient-specific boundary of the chest, aiming to produce a more accurate forward model for EIT analysis. Thus, a passive resistive and an inertial prototypes were prepared to characterize and reconstruct the shape of multiple phantoms. Preliminary results show how the passive device generates a minimum scatter between the reconstructed image and the actual shap