Biological motion processing in autism spectrum disorders: a behavioural and fMRI investigation

There has been much controversy as to whether people with Autism Spectrum Disorders (ASDs) have a specific impairment in processing biological motion, with some studies suggesting there is an impairment (Blake, et. al. 2003; Klin et. al. 2003, Klin & Jones, 2008, Klin et. al. 2009) and others finding that people with ASDs show intact abilities to detect biological motion and categorise actions, but are impaired in emotion categorisation (Moore et. al. 1997; Hubert et. al. 2007, Parron et. al. 2008). Recent studies have found that although behavioural measures of biological motion processing show no differences, adults with ASDs show different patterns of brain activation to controls in response to intact point-light displays (PLDs), with the STS, MT+ and ITG regions showing reduced activity in this population (Herrington et. al. 2007; Parron et. al. 2009). The current thesis aimed to clarify the nature of these difficulties and to try to elucidate the brain regions used to process configural information from PLDs using novel techniques and stimuli. The first set of experiments were designed to behaviourally test people with ASDs ability to detect biological motion in noise, to categorise actions and to categorise affect from PLDs. Despite finding differences in the two groups in detection of biological motion and affect categorisation in pilot experiments, there were no significant differences between the groups in the main experiments. However, the ASD group showed slightly poorer performance at detecting biological motion and significantly more variability in the action categorisation tasks, suggesting that there may have been an underlying difference between the two groups. Furthermore, an analysis of the pattern of errors tentatively suggested that the ASD group may be using different strategies to categorise affect than controls, particularly for negative affects. We then devised a novel technique for manipulating the amount of configural information available in a PLD without the need to add different degrees of background noise and used this technique to assess the contribution of configural cues in a direction discrimination task behaviourally and neurally. The results confirmed that in typically developed individuals configural cues significantly improved the participants’ ability to correctly determine the direction of locomotion of a point light walker. Furthermore, the fMRI task found that regions of the inferotemporal, parietal and frontal regions were sensitive to the amount of configural information present in the displays that corresponded to increases in individual participants’ behavioural performance. Lastly, we used the same technique, though with a more powerful fMRI design, to assess the behavioural and neural differences between people with ASDs and controls in response to displays containing different degrees of configural information. We found that both groups were comparable in their ability to discriminate the direction of locomotion from PLDs. However, the brain regions used to process this information were found to be substantially different. In displays in which the configural information enabled participants to accurately judge the direction of locomotion, the control group utilised a similar group of regions as found in the previous experiment. The ASD group showed a pattern of activation suggesting that they predominantly used regions in the temporal and occipital cortex, and more specifically a region in the fusiform gyrus. The results of Granger Causality Mapping analysis, which allows for the mapping of directional to and from seeded regions, confirmed that whereas the control group utilised a network of regions starting from the ITG and connecting to parietal and occipital regions, the ASD group seemed to utilise two separate networks, processing form information in the fusiform gyrus and motion information separately in middle-temporal regions. The results are discussed in terms of a potential dysfunction of the ITG region in early childhood and two different models of biological motion processing that have been proposed in the recent literature. In TD individuals the model of Giese & Poggio (2003) may be more applicable, in that it proposes the integration of static form cues with motion signals in areas such as the STS. However, a dysfunctional ITG or dysfunctional connections from the ITG to more dorsal regions would disrupt the integration of form and motion processing and force the brain to place additional processing demands on form processing regions in the fusiform gyrus. This would be more in line with the model proposed by Lange and Lappe (2006) in which information can be derived from biological motion in noise without recourse to the actual motion information, through a process of temporal analysis of static postures. Both systems though, may be intact in TD individuals and may share processing requirements depending on the task. Furthermore, it is hypothesised that a dysfunctional ITG may force the brain to place additional demands on regions in the fusiform gyrus and this neural rewiring may be the cause of the developmental delay seen in processing biological motion in people with ASDs (Annaz et. al. 2009). Future studies should examine the roles of the ITG and fusiform area in more detail, both in TD people and in people with ASDs, and determine the specific nature of these neural differences and there behavioural implications for both groups

Development of Low-Frequency Repetitive Transcranial Magnetic Stimulation as a Tool to Modulate Visual Disorders: Insights from Neuroimaging

Repetitive transcranial magnetic stimulation (rTMS) has become a popular neuromodulation technique, increasingly employed to manage several neurological and psychological conditions. Despite its popular use, the underlying mechanisms of rTMS remain largely unknown, particularly at the visual cortex. Moreover, the application of rTMS to modulate visual-related disorders is under-investigated. The goal of the present research was to address these issues. I employ a multitude of neuroimaging techniques to gain further insight into neural mechanisms underlying low-frequency (1 Hz) rTMS to the visual cortex. In addition, I begin to develop and refine clinical low-frequency rTMS protocols applicable to visual disorders as an alternative therapy where other treatment options are unsuccessful or where there are simply no existing therapies. One such visual disorder that can benefit from rTMS treatment is the perception of visual hallucinations that can occur following visual pathway damage in otherwise cognitively healthy individuals. In Chapters 23, I investigate the potential of multiday low-frequency rTMS to the visual cortex to alleviate continuous and disruptive visual hallucinations consequent to occipital injury. Combining rTMS with magnetic resonance imaging techniques reveals functional and structural cortical changes that lead to the perception of visual hallucinations; and rTMS successfully attenuates these anomalous visual perceptions. In Chapters 45, I compare the effects of alternative doses of low-frequency rTMS to the visual cortex on neurotransmitter levels and intrinsic functional connectivity to gain insight into rTMS mechanisms and establish the most effective protocol. Differential dose-dependent effects are observed on neurotransmitter levels and functional connectivity that suggest the choice of protocol critically depends on the neurophysiological target. Collectively, this work provides a basic framework for the use of low-frequency rTMS and neuroimaging in clinical application for visual disorders

Human metabolic adaptations and prolonged expensive neurodevelopment: A review

1.	After weaning, human hunter-gatherer juveniles receive substantial (≈3.5-7 MJ day^-1^), extended (≈15 years) and reliable (kin and nonkin food pooling) energy provision.
2.	The childhood (pediatric) and the adult human brain takes a very high share of both basal metabolic rate (BMR) (child: 50-70%; adult: ≈20%) and total energy expenditure (TEE) (child: 30-50%; adult: ≈10%).
3.	The pediatric brain for an extended period (≈4-9 years-of-age) consumes roughly 50% more energy than the adult one, and after this, continues during adolescence, at a high but declining rate. Within the brain, childhood cerebral gray matter has an even higher 1.9 to 2.2-fold increased energy consumption. 
4.	This metabolic expensiveness is due to (i) the high cost of synapse activation (74% of brain energy expenditure in humans), combined with (ii), a prolonged period of exuberance in synapse numbers (up to double the number present in adults). Cognitive development during this period associates with volumetric changes in gray matter (expansion and contraction due to metabolic related size alterations in glial cells and capillary vascularization), and in white matter (expansion due to myelination). 
5.	Amongst mammals, anatomically modern humans show an unique pattern in which very slow musculoskeletal body growth is followed by a marked adolescent size/stature spurt. This pattern of growth contrasts with nonhuman primates that have a sustained fast juvenile growth with only a minor period of puberty acceleration. The existence of slow childhood growth in humans has been shown to date back to 160,000 BP. 
6.	Human children physiologically have a limited capacity to protect the brain from plasma glucose fluctuations and other metabolic disruptions. These can arise in adults, during prolonged strenuous exercise when skeletal muscle depletes plasma glucose, and produces other metabolic disruptions upon the brain (hypoxia, hyperthermia, dehydration and hyperammonemia). These are proportional to muscle mass.
7.	Children show specific adaptations to minimize such metabolic disturbances. (i) Due to slow body growth and resulting small body size, they have limited skeletal muscle mass. (ii) They show other adaptations such as an exercise specific preference for free fatty acid metabolism. (iii) While children are generally more active than adolescents and adults, they avoid physically prolonged intense exertion. 
8.	Childhood has a close relationship to high levels of energy provision and metabolic adaptations that support prolonged synaptic neurodevelopment. 
&#xa

A Single-Cell Level and Connectome-Derived Computational Model of the Drosophila Brain

Computer simulations play an important role in testing hypotheses, integrating knowledge, and providing predictions of neural circuit functions. While considerable effort has been dedicated into simulating primate or rodent brains, the fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior, and abundancy of data available from genes to circuits. Moreover, several Drosophila connectome projects have generated a large number of neuronal images that account for a significant portion of the brain, making a systematic investigation of the whole brain circuit possible. Supported by FlyCircuit (http://www.flycircuit.tw), one of the largest Drosophila neuron image databases, we began a long-term project with the goal to construct a whole-brain spiking network model of the Drosophila brain. In this paper, we report the outcome of the first phase of the project. We developed the Flysim platform, which (1) identifies the polarity of each neuron arbor, (2) predicts connections between neurons, (3) translates morphology data from the database into physiology parameters for computational modeling, (4) reconstructs a brain-wide network model, which consists of 20,089 neurons and 1,044,020 synapses, and (5) performs computer simulations of the resting state. We compared the reconstructed brain network with a randomized brain network by shuffling the connections of each neuron. We found that the reconstructed brain can be easily stabilized by implementing synaptic short-term depression, while the randomized one exhibited seizure-like firing activity under the same treatment. Furthermore, the reconstructed Drosophila brain was structurally and dynamically more diverse than the randomized one and exhibited both Poisson-like and patterned firing activities. Despite being at its early stage of development, this single-cell level brain model allows us to study some of the fundamental properties of neural networks including network balance, critical behavior, long-term stability, and plasticity

Characterising population variability in brain structure through models of whole-brain structural connectivity

Models of whole-brain connectivity are valuable for understanding neurological function. This thesis seeks to develop an optimal framework for extracting models of whole-brain connectivity from clinically acquired diffusion data. We propose new approaches for studying these models. The aim is to develop techniques which can take models of brain connectivity and use them to identify biomarkers or phenotypes of disease. The models of connectivity are extracted using a standard probabilistic tractography algorithm, modified to assess the structural integrity of tracts, through estimates of white matter anisotropy. Connections are traced between 77 regions of interest, automatically extracted by label propagation from multiple brain atlases followed by classifier fusion. The estimates of tissue integrity for each tract are input as indices in 77x77 ”connectivity” matrices, extracted for large populations of clinical data. These are compared in subsequent studies. To date, most whole-brain connectivity studies have characterised population differences using graph theory techniques. However these can be limited in their ability to pinpoint the locations of differences in the underlying neural anatomy. Therefore, this thesis proposes new techniques. These include a spectral clustering approach for comparing population differences in the clustering properties of weighted brain networks. In addition, machine learning approaches are suggested for the first time. These are particularly advantageous as they allow classification of subjects and extraction of features which best represent the differences between groups. One limitation of the proposed approach is that errors propagate from segmentation and registration steps prior to tractography. This can cumulate in the assignment of false positive connections, where the contribution of these factors may vary across populations, causing the appearance of population differences where there are none. The final contribution of this thesis is therefore to develop a common co-ordinate space approach. This combines probabilistic models of voxel-wise diffusion for each subject into a single probabilistic model of diffusion for the population. This allows tractography to be performed only once, ensuring that there is one model of connectivity. Cross-subject differences can then be identified by mapping individual subjects’ anisotropy data to this model. The approach is used to compare populations separated by age and gender

Human metabolic adaptations and prolonged expensive neurodevelopment: A review

1.	After weaning, human hunter-gatherer juveniles receive substantial (≈3.5-7 MJ day^-1^), extended (≈15 years) and reliable (kin and nonkin food pooling) energy provision.
2.	The childhood (pediatric) and the adult human brain takes a very high share of both basal metabolic rate (BMR) (child: 50-70%; adult: ≈20%) and total energy expenditure (TEE) (child: 30-50%; adult: ≈10%).
3.	The pediatric brain for an extended period (≈4-9 years-of-age) consumes roughly 50% more energy than the adult one, and after this, continues during adolescence, at a high but declining rate. Within the brain, childhood cerebral gray matter has an even higher 1.9 to 2.2-fold increased energy consumption. 
4.	This metabolic expensiveness is due to (i) the high cost of synapse activation (74% of brain energy expenditure in humans), combined with (ii), a prolonged period of exuberance in synapse numbers (up to double the number present in adults). Cognitive development during this period associates with volumetric changes in gray matter (expansion and contraction due to metabolic related size alterations in glial cells and capillary vascularization), and in white matter (expansion due to myelination). 
5.	Amongst mammals, anatomically modern humans show an unique pattern in which very slow musculoskeletal body growth is followed by a marked adolescent size/stature spurt. This pattern of growth contrasts with nonhuman primates that have a sustained fast juvenile growth with only a minor period of puberty acceleration. The existence of slow childhood growth in humans has been shown to date back to 160,000 BP. 
6.	Human children physiologically have a limited capacity to protect the brain from plasma glucose fluctuations and other metabolic disruptions. These can arise in adults, during prolonged strenuous exercise when skeletal muscle depletes plasma glucose, and produces other metabolic disruptions upon the brain (hypoxia, hyperthermia, dehydration and hyperammonemia). These are proportional to muscle mass.
7.	Children show specific adaptations to minimize such metabolic disturbances. (i) Due to slow body growth and resulting small body size, they have limited skeletal muscle mass. (ii) They show other adaptations such as an exercise specific preference for free fatty acid metabolism. (iii) While children are generally more active than adolescents and adults, they avoid physically prolonged intense exertion. 
8.	Childhood has a close relationship to high levels of energy provision and metabolic adaptations that support prolonged synaptic neurodevelopment. 
&#xa

Nutrient supply impacts osteocytic specification by regulating a nuclear transcription program

[spa] El hueso es un órgano con múltiples funciones. No sólo actúa como elemento de soporte, protección y locomoción, sino que también resulta indispensable en el mantenimiento del equilibrio mineral y ácido/base, conforma un nicho adecuado para el desarrollo de la hematopoyesis, y mantiene la homeostasis energética del organismo. En estos procesos, los osteocitos tienen un papel especialmente relevante, ya que actúan transduciendo estímulos mecánicos en señales bioquímicas. Los osteocitos constituyen el principal componente celular óseo. Derivan de osteoblastos, los cuales a su vez proceden de células madre mensenquimales (MSC). Los osteoblastos pueden seguir tres destinos alternativos: entrar en apoptosis, originar células de revestimiento óseo o progresar en la diferenciación hacia osteocitos. Actualmente, los estímulos y vías de señalización que regulan cada uno de estos procesos son desconocidos. Por ello, y teniendo en cuenta la importancia de los osteocitos en la homeostasis del organismo, consideramos necesario profundizar en su investigación. Durante el proceso de diferenciación ósea, los osteoblastos quedan embebidos en una matriz mineralizada que limita su disponibilidad de nutrientes, estando expuestos a un ambiente hipoglucémico al cual deben adaptarse. En este contexto Wei et al. demostraron que la glucosa juega un papel importante en la regulación de la diferenciación osteoblástica. Por otro lado, se ha observado que, en ambientes hiperglucémicos, típicos de pacientes diabéticos, se produce una reducción del número y función osteoblástica, así como una disminución de la densidad mineral ósea y alteración de la microarquitectura ósea. Teniendo en cuenta todo lo expuesto, estudiamos la capacidad de diferenciación de las IDG-SW3 en condiciones de hipoglucemia (1mM glucosa), normoglucemia (5mM glucosa) o hiperglucemia (25mM glucosa). Las condiciones de hipoglucemia promueven la diferenciación osteocitica, mientras que altas concentraciones de glucosa dificultan este proceso. A nivel metabólico, las condiciones de baja glucosa aumentan la cantidad de mitocondrias y su agrupación en forma de redes. Por otro lado, los ambientes hipoglucémicos, promueven los eventos de fisión mitocondrial. En este contexto PGC1α podría desempeñar un papel crucial como nexo entre el estrés metabólico y la reprogramación génica de los osteoblastos. PGC1α es un coactivador transcripcional que responde a diferentes tipos de estrés. Aunque sus dianas son múltiples, afectan principalmente a la expresión de genes implicados en el metabolismo, así como la biogénesis y función mitocondrial. PGC1 se activa a través de fosforilación y acetilación mediadas por AMPK y SIRT1. La activación de PGC1α, podría iniciar una reprogramación metabólica y génica que culminaría en una inducción de la diferenciación osteocítica