A multimodal investigation of dynamic face perception using functional magnetic resonance imaging and magnetoencephalography

Motion is an important aspect of face perception that has been largely neglected to date. Many of the established findings are based on studies that use static facial images, which do not reflect the unique temporal dynamics available from seeing a moving face. In the present thesis a set of naturalistic dynamic facial emotional expressions was purposely created and used to investigate the neural structures involved in the perception of dynamic facial expressions of emotion, with both functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend the distributed neural system for face perception (Haxby et al.,2000). Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as inferior occipital gyri and superior temporal sulci, along with coupling between superior temporal sulci and amygdalae, as well as with inferior frontal gyri. MEG and Synthetic Aperture Magnetometry (SAM) were used to examine the spatiotemporal profile of neurophysiological activity within this dynamic face perception network. SAM analysis revealed a number of regions showing differential activation to dynamic versus static faces in the distributed face network, characterised by decreases in cortical oscillatory power in the beta band, which were spatially coincident with those regions that were previously identified with fMRI. These findings support the presence of a distributed network of cortical regions that mediate the perception of dynamic facial expressions, with the fMRI data providing information on the spatial co-ordinates paralleled by the MEG data, which indicate the temporal dynamics within this network. This integrated multimodal approach offers both excellent spatial and temporal resolution, thereby providing an opportunity to explore dynamic brain activity and connectivity during face processing

A multimodal investigation of dynamic face perception using functional magnetic resonance imaging and magnetoencephalography

Motion is an important aspect of face perception that has been largely neglected to date. Many of the established findings are based on studies that use static facial images, which do not reflect the unique temporal dynamics available from seeing a moving face. In the present thesis a set of naturalistic dynamic facial emotional expressions was purposely created and used to investigate the neural structures involved in the perception of dynamic facial expressions of emotion, with both functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG). Through fMRI and connectivity analysis, a dynamic face perception network was identified, which is demonstrated to extend the distributed neural system for face perception (Haxby et al.,2000). Measures of effective connectivity between these regions revealed that dynamic facial stimuli were associated with specific increases in connectivity between early visual regions, such as inferior occipital gyri and superior temporal sulci, along with coupling between superior temporal sulci and amygdalae, as well as with inferior frontal gyri. MEG and Synthetic Aperture Magnetometry (SAM) were used to examine the spatiotemporal profile of neurophysiological activity within this dynamic face perception network. SAM analysis revealed a number of regions showing differential activation to dynamic versus static faces in the distributed face network, characterised by decreases in cortical oscillatory power in the beta band, which were spatially coincident with those regions that were previously identified with fMRI. These findings support the presence of a distributed network of cortical regions that mediate the perception of dynamic facial expressions, with the fMRI data providing information on the spatial co-ordinates paralleled by the MEG data, which indicate the temporal dynamics within this network. This integrated multimodal approach offers both excellent spatial and temporal resolution, thereby providing an opportunity to explore dynamic brain activity and connectivity during face processing.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

NEUROINFLAMMATION AND DEFECTIVE MYELINATION IN POLYMICROGYRIA

Polymicrogyria (PMG) is a condition characterized by abnormal prenatal brain development and excessive number of ectopic small gyri in the cerebral cortex. PMG patients present an excessive number of abnormally small gyri separated by shallow sulci, associated with fusion of the overlying molecular layer of the cerebral cortex. The topographic distribution of PMG may be focal, multifocal or diffuse; unilateral or bilateral; symmetric or asymmetric. Clinical manifestations have a large spectrum, ranging from isolated selective impairment of cognitive functions to severe encephalopathy and intractable epilepsy. The severity of neurological manifestations and the age at presentation are in part influenced by the extent and localization of the cortical malformations but may also depend on its specific aetiology. The pathogenesis is still poorly understood, several causative gene mutations have been recently found, but also other causes has been identified (prenatal infections, ipoxia). Experimentally, the mouse model of polymicrogyria (PMG) displays the formation of ectopic microgyri in the mouse cortex, enhanced excitatory and inhibitory synaptic transmission accompanied by increased connectivity in the paramicrogyral cortex and higher susceptibility to epilepsy in vitro. Besides the alteration in the cortical layering, the molecular, morphological and behavioural analysis of PMG mice reveal a significant astrogliosis and microglial activation, indicating the occurrence of an inflammatory process. In addition, a diffuse cortical hypomyelination is evident in brain slices stained for myelin basic protein (MBP). Furthermore, PMG mice displayed altered EEG profile and defective motor skills such as reduced brawn. All these features make PMG model suitable for the study of the pathology and to investigate possible therapeutic approaches. Here we found that transplantation of human neural stem cells (hNSCs), which has been demonstrated to exert positive effects on inherited or acquired myelination disorders and to dampen brain inflammation, plays a beneficial effect on the pathological condition of PMG ameliorating the myelination defect by promoting oligodendrocyte precursors proliferation and remodelling of myelin fibres. Our data also show that hNSC transplantation restores normal EEG brain activity and improves motor performances. Moreover, we tried a pharmacological blockade of IL-1R activation by the IL-1R antagonist: anakinra. We found that this treatment leads to a significant improvement of EEG and motor skills in adult PMG mice thus suggesting a possible role of inflammation at the root of the pathology and identifying a therapeutic time window for the treatment

Malformations of the Human Cerebral Cortex: patterns and causes

Malformations of cortical development (MCD) are a group of disorders characterized by a congenital abnormal structure of the cerebral cortex. In general, malformations are defined as structural abnormalities caused by a disturbance in cell organization or function within a tissue type. When a disturbance results in an abnormal structure of the cerebral cortex we call this: malformations of cortical development. MCD are heterogeneous as a group, as they include several different structural abnormalities, and they have a diverse array of causes, both genetic and environmental

The signer and the sign: Cortical correlates of person identity and language processing from point-light displays

In this study, the first to explore the cortical correlates of signed language (SL) processing under point-light display conditions, the observer identified either a signer or a lexical sign from a display in which different signers were seen producing a number of different individual signs. many of the regions activated by point-light under these conditions replicated those previously reported for full-image displays, including regions within the inferior temporal cortex that are specialised for face and body-part identification, although such body parts were invisible in the display. Right frontal regions were also recruited - a pattern not usually seen in full-image SL processing. This activation may reflect the recruitment of information about person identity from the reduced display. A direct comparison of identify-signer and identify-sign conditions showed these tasks relied to a different extent on the posterior inferior regions. Signer identification elicited greater activation than sign identification in (bilateral) inferior temporal gyri (BA 37/19), fusiform gyri (BA 37), middle and posterior portions of the middle temporal gyri (BAs 37 and 19), and superior temporal gyri (BA 22 and 42). Right inferior frontal cortex was a further focus of differential activation (signer > sign).These findings suggest that the neural systems supporting point-light displays for the processing of SL rely on a cortical network including areas of the inferior temporal cortex specialized for face and body identification. While this might be predicted from other studies of whole body point-light actions (Vaina, Solomon, Chowdhury, Sinha, & Belliveau, 2001) it is not predicted from the perspective of spoken language processing, where voice characteristics and speech content recruit distinct cortical regions (Stevens, 2004) in addition to a common network. In this respect, our findings contrast with studies of voice/speech recognition (Von Kriegstein, Kleinschmidt, Sterzer, & Giraud, 2005). Inferior temporal regions associated with the visual recognition of a person appear to be required during SL processing, for both carrier and content information. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved

Pharmacokinetics of melatonin as a neuroprotectant In preterm infants

Background and purpose: Advances in perinatal care have increased survival rates of infants but long-term neurodisability and social consequences have remained unchanged over the last decade. Preterm infants are deprived of the normal intrauterine exposure to maternal melatonin and experimental studies suggest that melatonin has a neuroprotective effect on cerebral white matter injury. However, pharmacokinetic data on melatonin in preterm infants are lacking, which hinders potential therapeutic trials. The aims of this study were to determine the pharmacokinetics of melatonin in the relevant preterm population, assess the tolerability of melatonin and determine a dose regime that would allow replication of adult melatonin levels. Methods: In a multi-centre, single dose escalation/de-escalation, open label study in preterm infants less than 31 weeks gestation, melatonin was administered to eighteen infants in doses ranging from 0.04-0.6 micrograms/kilograms, over 0.5-6 hours. Pharmacokinetic profiles were analysed individually and by population methods. Results: Baseline melatonin was largely undetectable. At the highest and lowest doses half-life could not be calculated due to blood concentrations not reaching a consistent steady state, but infants receiving melatonin at 0.1 micrograms/kilogram/hour for 2 hours showed a median half-life of 15.82 hours and median maximum plasma concentration of 203.3 picograms/millilitre. Population pharmacokinetic analysis showed that clearance was 0.045 litre/hour, volume of distribution 1.098 litres and elimination half-life 16.91 hours with gender (p=0.047) and race (p<0.0001) as significant covariates. Melatonin infusion appeared to be well tolerated in preterm infants. Conclusions: The pharmacokinetic profile of melatonin in preterm infants differs from that of adults. Slow clearance makes replication of adult and thus fetal concentrations of melatonin problematic. Further studies are needed to confirm these findings.Open acces

A Probabilistic Adaptive Cerebral Cortex Segmentation Algorithm for Magnetic Resonance Human Head Scan Images

The total efficiency of Magnetic Resonance Imaging (MRI) results in the need for human involvement in order to appropriately detect information contained in the image. Currently, there has been a surge in interest in automated algorithms that can more precisely divide medical image structures into substructures than prior attempts. Instant segregation of cerebral cortex width from MRI scanned images is difficult due to noise, Intensity Non-Uniformity (INU), Partial Volume Effects (PVE), MRI's low resolution, and the very complicated architecture of the cortical folds. In this paper, a Probabilistic Adaptive Cerebral Cortex Segmentation (PACCS) approach is proposed for segmenting brain areas of T1 weighted MRI of human head images. Skull Stripping (SS), Brain Hemisphere Segmentation (BHS) and CCS are the three primary processes in the suggested technique. In step 1, Non-Brain Cells (NBC) is eliminated by a Contour-Based Two-Stage Brain Extraction Method (CTS-BEM). Step 2 details a basic BHS technique for Curve Fitting (CF) detection in MRI human head images. The left and right hemispheres are divided using the discovered Mid-Sagittal Plane (MSP). At last, to enhance a probabilistic CCS structure with adjustments such as prior facts change to remove segmentation bias; the creation of express direct extent training; and a segmentation version based on a regionally various Gaussian Mixture Model- Hidden Markov Random Field – Expectation Maximization (GMM-HMRF-EM). The underlying partial extent categorization and its interplay with found image intensities are represented as a spatially correlated HMRF within the GMM-HMRF-EM method. The proposed GMM-HMRF method estimates HMRF parameters using the EM technique. Finally, the outcomes of segmentation are evaluated in terms of precision, recall, specificity, Jaccard Similarity (JS), and Dice Similarity (DS). The proposed method works better and more consistently than the present locally Varying MRF (LV-MRF), according to the experimental findings obtained by using the suggested GMM-HMRF-EM methodology to 18 individuals' brain images

Overlapping phenotypes - a clinical and magnetic resonance imaging investigation of schizotypy and pervasive developmental disorders in adolescents with cognitive impairment

Introduction: The neurobiological bases of pervasive developmental disorders (PDD) and schizotypy are not well established. In addition there are clinical overlaps between the two which can make diagnostic determination difficult. The primary aim of this thesis was to explore the relationship between PDD and schizotypy by examining their associated clinical and brain structural features in a group of cognitively impaired adolescents. Methods: 138 adolescents receiving special educational assistance and 62 typically developing controls were recruited. Schizotypal features were measured using the Structured Interview for Schizotypy (SIS) and PDD features were measured using the Social Communication Questionnaire (SCQ). Each participant also received a standardised clinical interview and a magnetic resonance imaging (MRI) scan. Whole brain volume, midsagittal corpus callosum area and prefrontal lobe volume and gyrification index (GI) were measured using automated, semi-automated and manual region of interest techniques. The subjects in special education were considered in different groupings in three main analyses. In the first, the SIS was used to divide the subjects into those with and without schizotypal features. In the second, the standard SCQ cut-offs were used to divide the subjects into those with autism, those with non-specific pervasive developmental disorder (PDD-NOS) and those with neither. Finally, both the SIS and the SCQ were used contemporaneously to divide the subjects into 6 groups: schizotypal; autistic; PDD-NOS; comorbid schizotypy and autism; comorbid schizotypy and PDD-NOS; and neither schizotypal nor autistic. In each analysis the groups were compared to each other and to the controls with respect to the clinical features and brain structural measures. Results: The schizotypal subjects showed an increase in right prefrontal volume and changes in the anterior and posterior corpus callosum relative to those without schizotypy and the controls. The autism group had reduced right prefrontal GI relative to the other groups as well as anterior callosal changes. The PDD-NOS group had the highest level of psychiatric symptomatology on the CIS, in particular those who were comorbid for PDD-NOS and schizotypy. This comorbid group, both clinically and structurally resembled the schizotypy group rather than the PDD-NOS group. Conclusions: Distinct neuroanatomical differences can be seen in educationally impaired adolescents with schizotypal features and in those with autistic features. These can be related to the observed clinical impairment and may help to distinguish these disorders in the future. It is possible that adolescents with features of both schizotypy and PDD-NOS suffer from an underlying schizophrenia spectrum disorder rather than an autistic spectrum disorder

Adaptive processing of thin structures to augment segmentation of dual-channel structural MRI of the human brain

This thesis presents a method for the segmentation of dual-channel structural magnetic resonance imaging (MRI) volumes of the human brain into four tissue classes. The state-of-the-art FSL FAST segmentation software (Zhang et al., 2001) is in widespread clinical use, and so it is considered a benchmark. A significant proportion of FAST’s errors has been shown to be localised to cortical sulci and blood vessels; this issue has driven the developments in this thesis, rather than any particular clinical demand. The original theme lies in preserving and even restoring these thin structures, poorly resolved in typical clinical MRI. Bright plate-shaped sulci and dark tubular vessels are best contrasted from the other tissues using the T2- and PD-weighted data, respectively. A contrasting tube detector algorithm (based on Frangi et al., 1998) was adapted to detect both structures, with smoothing (based on Westin and Knutsson, 2006) of an intermediate tensor representation to ensure smoothness and fuller coverage of the maps. The segmentation strategy required the MRI volumes to be upscaled to an artificial high resolution where a small partial volume label set would be valid and the segmentation process would be simplified. A resolution enhancement process (based on Salvado et al., 2006) was significantly modified to smooth homogeneous regions and sharpen their boundaries in dual-channel data. In addition, it was able to preserve the mapped thin structures’ intensities or restore them to pure tissue values. Finally, the segmentation phase employed a relaxation-based labelling optimisation process (based on Li et al., 1997) to improve accuracy, rather than more efficient greedy methods which are typically used. The thin structure location prior maps and the resolution-enhanced data also helped improve the labelling accuracy, particularly around sulci and vessels. Testing was performed on the aged LBC1936 clinical dataset and on younger brain volumes acquired at the SHEFC Brain Imaging Centre (Western General Hospital, Edinburgh, UK), as well as the BrainWeb phantom. Overall, the proposed methods rivalled and often improved segmentation accuracy compared to FAST, where the ground truth was produced by a radiologist using software designed for this project. The performance in pathological and atrophied brain volumes, and the differences with the original segmentation algorithm on which it was based (van Leemput et al., 2003), were also examined. Among the suggestions for future development include a soft labelling consensus formation framework to mitigate rater bias in the ground truth, and contour-based models of the brain parenchyma to provide additional structural constraints