Biomedical Engineering - Theses

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    Decoding upper-limb kinematics from electrocorticography
    Nurse, Ewan ( 2017)
    Brain-computer interfaces (BCIs) are technologies for assisting individuals with motor impairments. Activity from the brain is recorded and then processed by a computer to control assistive devices. The prominent method for recording neural activity uses microelectrodes that penetrate the cortex to record from localized populations of neurons. This causes a severe inflammatory response, making this method unsuitable after approximately 1-2 years. Electrocorticography (ECoG), a method of recording potentials from the cortical surface, is a prudent alternative that shows promise as the basis of a clinically viable BCI. This thesis investigates aspects of ECoG relevant to the translation of BCI devices: signal longevity, motor information encoding, and decoding intended movement. Data was assessed from a first-in-human ECoG device trial to quantify changes in ECoG over multiple years. The mean power, calculated daily, was steady for all patients. It was demonstrated that the device could consistently record ECoG signal statistically distinct from noise up to approximately 100 Hz for the duration of the study. Therefore, long-term implanted ECoG can be expected to record movement-related high-gamma signals from humans for many years without deterioration of signal. ECoG was recorded from patients undertaking a two-dimensional center-out task. This data was used to generate encoder-decoder directional tuning models to describe and predict arm movement direction from ECoG. All four patients demonstrated channels that were significantly tuned to the direction of motion. Significant tuning was found across the cortex and was not focused on primary motor areas. Decoding significantly above chance with a population-vector approach was achieved in three of the four patients. Decoding accuracy was significantly improved by weighting the population vector by each channel's tuning signal-to-noise ratio. Hence, directional tuning exists in high-frequency ECoG during movement preparation, and movement angle can be decoded using population vector methods. Having confirmed the existence of direction-related information in the recorded data, artificial neural network models were created to decode intended movement direction. A convolutional neural network (CNN) model had significantly higher decoding accuracy than a fully connected model for all four patients for decoding movement direction. Training models on data from all patients and testing on a single patient improved decoding performance for all but the best performing patient with the CNN model. Decoding using data from multiple time-points with a CNN model and averaging the results boosted accuracy when using the mode of the outputs. Overall, it was demonstrated that artificial neural network models can decode intended movement direction from ECoG recordings of a two-dimensional center-out task. This thesis presents results that demonstrate ECoG has the desired signal properties for a clinically-relevant BCI. ECoG is shown to be robust over multiple years, encode direction-related information and can be decoded with high accuracy.
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    An investigation of spatial receptive fields of complex cells in the primary visual cortex
    Almasi, Ali ( 2017)
    One of the main concerns of visual neuroscience is to understand how information is processed by the neural circuits in the visual system. Since the historic experiments of Hubel and Wiesel, many more aspects of visual information processing in the brain have been discovered using experimental approaches. However, a lot of computations underlying such processing remain unclear or even unknown. In the retina and the lateral geniculate nucleus, the basic computations have been identified by measuring the responses of neurons to simple visual stimuli such as gratings and oriented bars. However, in higher areas of the visual pathway, e.g. the cortical visual areas, many neurons (including complex cells) cannot be characterised entirely based on their responses to simple stimuli. The complex cells in the visual cortex do not exhibit linear receptive field properties. Hence, the failure of linear receptive field models to describe the behaviour of such neurons leads neuroscientists to seek more plausible quantitative models. Efficient coding is a computational hypothesis about sensory systems. Recently developed models based on the efficient coding hypothesis were able to capture certain properties of complex cells in the primary visual cortex. The Independent feature Subspace Analysis (ISA) model and the covariance model are such examples of these models. The ISA model employs the notion of the energy model in describing the responses of complex cells, whereas the covariance model is based on a recent speculation that complex cells tend to encode the second-order statistical dependencies of the visual input. In this thesis, the parametric technique of the generalised quadratic model (GQM) in conjunction with white Gaussian noise stimulation is used to identify the spatial receptive fields of complex cells in cat primary visual cortex. The validity of the identified receptive field filters are verified by measuring their performance in predicting the responses to test stimuli using correlation coefficients. The findings suggest that a majority of the complex cells in cat primary visual cortex are best described using a linear and one or more quadratic receptive field filters, which are classified as mixed complex cells. We observed that some complex cells exhibit linear as well as quadratic dependencies on an identified filter of their receptive fields. This often introduces a significant shift in the feature-contrast responses of these cells, which results in violations of the polarity invariance property of complex cells. Lastly, a quantitative comparison is performed between the experiment and theory using statistical analysis of the population of the cells' receptive fields identified by experiment and those predicted by the efficient coding models. For this, motivated by the experimental findings for complex cells, a modification of the ISA model that incorporates a linear term is introduced. The simulated model receptive fields of the modified ISA and the covariance model are then used to draw comparison to the experimental data. While the modified ISA and the covariance models are comparable in predicting the complex cell receptive fields characteristics in the primary visual cortex, the latter shows more capable in explaining the observed intra-receptive field inhomogeneity of complex cells, including differences in orientation preference and ratio spatial frequency for the receptive field filters of the same cell. However, the major discrepancies between theory and experiment lie in the orientation bandwidth and spatial frequency bandwidth of the receptive field filters, where the population of the predicted model receptive field filters demonstrate much narrower bandwidths. These findings, thereby, suggest the sub-optimality of the experimental receptive field filters in terms of the efficiency of the code.
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    Decoding calcium signalling crosstalk in cardiac hypertrophy
    Bass, Gregory ( 2017)
    The concentration of calcium ions (Ca2+) within a cell is important for governing many different processes across a range of cell types. In heart muscle cells, proper calcium handling is critical for maintaining the rhythmic cycle of contraction and relaxation. Chronic stresses can drive changes in calcium signalling which trigger heart cells to grow in size. This process is called hypertrophy and is a common cause of heart failure. It was unknown how a cell could distinguish calcium signals leading to cell growth from those leading to contraction. The central hypothesis explored in this thesis is that calcium can simultaneously yet specifically effect two distinct responses in a cardiomyocyte by altering the shape of the cellular calcium transient, but how this might be achieved without disrupting contractile function was unknown. New line-scan data shows that IP3R-mediated Ca2+ release widens the cellular transient following RyR-mediated Ca2+-induced Ca2+-release events. The data supports the hypothesis that RyR and IP3R systems interact by inducing a global yet transient elevation in Ca2+. A mathematical model of the whole-cell adult rat ventricular myocyte Ca2+ transient was developed by combining existing models of RyR and IP3R and fitting to the line-scan data. The model includes the two major compartments that interpret the calcium signalling and provide spatial separation -- the cytosol which achieves the calcium-dependent contractile response, and the nucleus which achieves calcium-dependent gene expression. The compartmental model was found to reproduce the observed shape of the Ca2+ transient, but only if IP3Rs exhibit a refractory response. This difference in time-course kinetics could underlie the signal separation between cell contraction and cell growth. Advanced immunofluorescence imaging and statistical methods were used to map the spatial positioning of RyRs and IP3Rs in adult rat heart cells. A statistical tool was developed to simulate physiologically-realistic protein distributions on images of the cell architecture. A spatial model of the Ca2+ transient based on realistic RyR distributions showed that both cellular architecture and the distance between RyR clusters could affect local signalling events. For the first time, super-resolution data was used to establish the relationship between RyRs and IP3Rs. Data analysis indicated that cardiomyocyte-specific IP3R cluster distribution reduces the effective spatial distance between RyR clusters which may promote Ca2+ signal propagation or which may enhance Ca2+ signal strength and longevity. This research combining mathematical modelling and advanced imaging has shown that RyR and IP3R proteins co-exist within the same areas of the heart cell and can modify the normal contractile signal without disrupting it. The modification of the Ca2+ signal may subsequently be interpreted by the cell as a signal to alter calcium-dependent gene expression. This finding is important because it reveals how the cell growth signal might be encoded in the heart cell, and this encoding mechanism may be extensible to many other signaling processes in different cells.
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    Continuous wave nuclear magnetic resonance: estimation of spin-system properties from steady-state trajectories
    Korte, James Christopher ( 2017)
    Magnetic resonance imaging (MRI) is a powerful imaging modality, widely used in routine clinical practice and as an investigational tool in basic science. The contrast in MRI is related to both the underlying tissue properties, which undergo disease or injury related changes, and to the MRI method and sequence parameters used. It is the latter with which this thesis is concerned: the design and implementation of novel MRI acquisition paradigms and associated reconstruction methods. The majority of MRI methods excite the object of interest with a series of short RF pulses, varying the weaker spatial magnetic field using the gradients, and ensuring the RF transmitter is inactive while acquiring a series of decaying MR signals. This regime linearises the inherently nonlinear behaviour of a magnetic resonance spin-system, allowing the acquired signals to be considered in a spatial frequency space and an image to be reconstructed using the well known Fourier transform. It is our assertion that nonlinear behaviour of the magnetic spin signal will lead to advantageous attributes in future MR methods, just as moving beyond conventional linear spatial gradients to nonlinear encoding fields led to methods for accelerated imaging and variable spatial resolution. Reconstruction of spin-system properties from nonlinear MR signals requires algorithms beyond the Fourier transform. In this thesis we propose spectroscopy, radial projection imaging and relaxometry methods as optimisation problems which minimise the mismatch between experimental measurements and predictions from Bloch equation based signal models. The use of continuous wave (CW) excitation patterns allows the development of signal models which are computationally efficient as they rely on analytical solutions of the Bloch equations or matrix inversion via harmonic balancing, rather than numerical integration. Ultra-short relaxation methods have been applied to a range of applications and demonstrate that MRI is finding use in areas far beyond traditional soft-tissue imaging. Soft tissues have an easily observable long duration MR signal, whereas the signal decays rapidly for harder tissues such as bone, or in regions that distort the magnetic field due to magnetic susceptibility gradients, such as the lungs. Rabi modulated CW techniques operating in a fully continuous mode have the potential to measure ultra-short relaxation signals in a similar range to `true' zero echo time techniques. Work inspired by quantum optics has shown that exciting a spin-system with a long duration Rabi modulated RF field leads to a significant steady-state MR signal. The steady-state trajectory is highly nonlinear and can be expressed as a series of harmonics of the amplitude modulation frequency of the RF field. This harmonic response provides a natural decoupling of the excitation and measurement bandwidth, and the ability to maintain a steady-state response under low power excitation reduces the isolation requirements between hypothetical transmit and receive chains. Our experimental investigation of steady-state trajectories makes use of two pseudo-simultaneous excitation and measurement protocols. Whilst these methods were adequate to explore the proof-of-concept applications, hardware modifications are suggested to unlock the full potential of continuous wave excitation patterns. This thesis demonstrates that CW excitation patterns allow the construction of efficient prediction models and elicit an information-rich steady-state response from which underlying spin-system properties can be reconstructed. It is anticipated that further development of these concepts and related hardware modifications will lead to new continuous wave imaging paradigms.
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    Tracking the changes of brain states during epileptogenesis by probing
    Cheung, Chi Lik Warwick ( 2017)
    Epilepsy affects around 50 million people worldwide. Brain injuries and lesions, such as traumatic brain injury, central nervous system infection and stroke, are associated with higher risk of developing epilepsy. It is hypothesised that electrically-induced brain responses (probing responses) can reflect physiological changes during epileptogenesis, which may provide insights into epileptogenesis and possible new therapies. A systematic continuous longitudinal study of probing responses in a well-controlled environment with both normal and epileptic brains is presented. The objective of this study is to demonstrate how electrically-induced neural responses (probing) track the physiological changes in the brain during epileptogenesis. Intrahippocampal tetanus toxin rat models were used as the model of epilepsy. Control rats received injections without tetanus toxin. Epidural electrodes were implanted to deliver electrical stimulation and record EEG over periods of 9 to 10 weeks in a room with controlled temperature and automatic dark/light switching. It is demonstrated that probing responses can expose sleep/wake cycles, recovery from surgery and epileptogenesis over the study period. The differences between probing responses in sleep and wake states are quantified by a two-level mixed-effects linear regression model. The changes of probing responses related to the recovery from surgery are modelled by a modified logistic function. The probing responses related to epileptogenesis in tetanus toxin models are uncovered after the effects of surgical recovery and sleep/wake cycle are removed. The changes can be observed in the infradian time scale and several markers are found that are associated with the time of the peak of seizure occurrence and the time of seizure remission. This study is the first step to identify stages of epileptogenesis using probing responses. The potential clinical application of probing can be a biomarker for epileptogenesis, a prognostic tool to assess whether epilepsy will develop after a trauma, a way to predict whether remission will occur after posttraumatic epilepsy has developed or a biomarker to assess the effectiveness of traumatic brain injury therapies.
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    Musculoskeletal modelling in the evaluation of shoulder muscle and joint function
    Wu, Wen ( 2017)
    Evaluation of the muscle forces generated during shoulder motion is critical in our understanding of shoulder disease and injury prevention, prosthesis design and rehabilitation. Since non-invasive muscle force measurement strategies are not readily available, computational simulations are widely adopted. This study presented a novel subject-specific experimental testing and modelling framework to evaluate shoulder muscle and joint function. Currently, upper limb muscle force estimations using Hill-type muscle models depend on musculotendon parameter values, which cannot be easily measured non-invasively. Generic and scaled-generic parameters may be quickly and easily employed, but these approaches do not account for an individual subject's joint torque capacity. The first aim of this thesis was therefore to assess the capacity of generic and scaled-generic musculotendon parameters to predict muscle and joint function using the subject-specific modelling framework. Models employing subject-specific, scaled-generic and generic musculotendon parameters were used to calculate shoulder muscle and joint loading in healthy subjects during activities of daily living. Muscle and joint forces calculated using generic and scaled-generic models were significantly different to those of subject-specific models (p < 0.05), and task dependent; however, scaled-generic model calculations of shoulder glenohumeral joint force demonstrated better agreement with those of subject-specific models during abduction and flexion. The findings suggest that generic and scaled-generic musculotendon parameters may not provide sufficient accuracy in prediction of shoulder muscle and joint loading when compared to models that employ subject-specific parameter-estimation approaches. Kinematics of the shoulder girdle obtained from non-invasive measurement systems such as video motion analysis, accelerometers and magnetic tracking sensors has been shown to be adversely affected by instrumentation measurement errors and skin motion artefact, yet the degree to which musculoskeletal model calculations of shoulder muscle and joint loading are influenced by variations in joint kinematics is not well understood. The second aim of this thesis was to use a subject-specific modelling framework to evaluate the sensitivity of shoulder muscle and joint force calculations to changes in bone kinematics. Monte-Carlo analyses were performed by randomly perturbing scapular and humeral joint coordinates during abduction and flexion, and the effect on muscle and joint force calculations quantified. The findings suggest that musculoskeletal model sensitivity to changes in kinematics is task-specific, and varies depending on the plane of motion. Calculations of shoulder muscle and joint function depend on accurate humeral and scapular motion data, particularly that of humeral elevation and scapula medial-lateral rotation. Robotic exoskeletons enable frequent repetitive movements without the presence of a full-time therapist; however, the way in which exoskeletons interact with the upper limb to modulate muscle and joint function is not well understood. The third and final aim of this thesis was to quantify the use of a commercially available robotic assistive exoskeleton in modulating shoulder muscle and joint force using the musculoskeletal models. Healthy subjects were asked to perform activities of daily living under three conditions: free motion (no exoskeleton), motion using an exoskeleton with upper limb weight compensation (weightlessness), and motion using the exoskeleton with negligible upper limb weight compensation. Muscle EMG, joint kinematics, and joint torques were simultaneously recorded, and shoulder muscle and joint forces calculated using subject-specific musculoskeletal modelling. The robotic exoskeleton reduced the peak joint torque, muscle forces and joint loading, with the degree of load attenuation strongly task dependent. Exoskeleton assistance significantly reduced the muscle force and EMG of the prime movers during upper limb motion, particularly those of the deltoid sub-regions, while the axiohumeral muscles were less affected. This thesis provides a subject-specific experimental testing and modelling framework for evaluating shoulder muscle and joint function, and uses this framework to provide insights into how an assistive exoskeleton influences upper limb muscle and joint behaviour, as well as the sensitivity of the model calculations to changes in joint kinematics. The outcomes of this thesis will be useful for future development of upper limb musculoskeletal models and their applications in rehabilitation, physiotherapy, surgical planning and sports biomechanics.
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    Prediction and shaping of visual cortex activity for retinal prostheses
    Halupka, Kerry ( 2017)
    Retinal prostheses are a promising treatment for blindness caused by photoreceptor degeneration. Electrodes implanted in the retina deliver electrical stimuli in the form of current pulses that activate surviving neurons to restore a sense of vision. Clinical trials for such devices have shown that the visual percepts evoked are informative, and can improve the day-to-day life of recipients. However, the spatial resolution of retinal prostheses is a limiting factor, with those who have the highest reported acuity measures still classified as legally blind. Simultaneous stimulation of multiple electrodes is a possible strategy to improve device resolution without increasing the number of physical electrodes. However, electrode interactions that occur during simultaneous stimulation are not well understood. This thesis investigates the characteristics of cortical responses to simultaneous stimulation of multiple electrodes. We formulated a quantitative model to characterise the responses of visual cortex neurons to multi-electrode stimulation of the retina to understand how simultaneous stimulation can improve resolution. Activity was recorded in the visual cortex of normally sighted, anaesthetised cats in response to temporally sparse, spatially white stimulation with 21 or 42 electrodes in the suprachoroidal space of the retina. These data were used to constrain the parameters of a linear-nonlinear model using a spike-triggered covariance technique. The recovered model accurately predicted cortical responses to arbitrary patterns of stimulation, and demonstrated that interactions between electrodes are predominantly linear. The linear filters of the model, which can be considered as weighting matrices for the effect of the stimulating electrodes on each cortical site, showed that cortical responses were topographically organised. Photoreceptor degeneration results in a number of changes in the surviving cells of the retina that can negatively impact stimulation strategies. Therefore, in the second study, we investigated the effect of multi-electrode stimulation on the degenerate retina. Characteristics of cortical responses to simultaneous stimulation of multiple electrodes were evaluated in unilaterally, chronically blind anaesthetised cats, bilaterally implanted with suprachoroidal retinal prostheses. Significant differences were found between responses to stimulation of the normally sighted and blind eyes, which may help to explain the varied perceptual observations in clinical trials with simultaneous stimulation. The success of the linear-nonlinear model in predicting responses to arbitrary patterns of stimulation indicated that it may provide a basis for optimising stimulation strategies to shape cortical activity. Therefore, we investigated the possibility of inverting the model to generate stimuli aimed at reliably altering the spatial characteristics of cortical responses. An in vivo preparation with a normally sighted, anaesthetised cat showed that the response characteristics derived by the model could be exploited to steer current and evoke predictable cortical activity. Overall, these results demonstrate that cortical responses to simultaneous stimulation of both the normal and degenerate retina are repeatable, and can be predicted by a simple linear-nonlinear model. Furthermore, the interactions between electrodes are predominantly linear, and can be harnessed to shape cortical activity through inversion of the model. The method shows promise for improving the efficacy of retinal prostheses and patient outcomes.
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    Modeling electrical properties of neural tissue using a cellular composite approach
    Monfared, Omid ( 2017)
    This research develops a framework for modeling the electrical properties of neu- ral tissue based on its cellular constituents. This has application to modeling of electrical stimulation of neural tissue, including for therapeutic purposes. It also has application to the modeling and interpretation of intrinsic electrical signals in the brain such as spiking, multi-unit activity, local field potential and electroencephalogram (EEG). Standard volume conductor models of neural tissue approximate the electrical properties of tissue with a locally homogeneous conductivity. This is despite the fact that realistic neural tissue is composed of cells with different geometries, orientations and electrical properties. The framework presented here suggests that these cellular level properties have a profound effect on the bulk electrical properties of tissue that cannot be captured by a simple conductivity. The membrane lipid bilayer structure behaves as a capacitance that relates the applied current density to the extracellular potential at previous times. Also, the cells within tissue are tightly packed causing higher resistance in the extracellular space compared with the wider intracellular space, which creates different current paths for the passage of electrical current flow in these spaces. In this mathematical and computational study, we replace the conductivity of tissue in the standard volume conductor approach with an admittivity which depends on spatial and temporal frequencies. Our expressions for bulk tissue admittivity, are derived from single cell properties by using a mean-field approach. The temporal frequency dependence arises through the capacitance of the membrane lipid bilayer and is related to the membrane time constant. The spatial frequency arises due to the passage of current from the highly confined extracellular space into the less confined intracellular space of a neuron and is related to the electrotonic length constant of neurites. Expressions for the admittivity are calculated for tissue consisting of a variety of morphological cell types. These include fiber bundles, layered structures in which the dendrites are confined to a plane and tissue composed of cells with a stellate morphology. Finally, these morphological types are combined in model of cortical grey matter that include the effect of glia as well as neurons on the tissue admittivity. The results show that the effective admittivity changes depending on whether tissue is in the near-, intermediate- or far-field regions relative to a stimulating electrode. The definition of the limits of these regions depends on both spatial and temporal frequencies being applied. The magnitude of the admittivity is smaller in the near-field than the far-field. It is also shown how anisotropic tissue responds to the electrical stimulation depending on the distance of fibers from an electrode. Anisotropic behavior is more prevalent in the far-field region compared to the near-field range in cases where the distribution of fiber orientations shows a bias. The effect of pulse width on tissue response is also investigated and our results demonstrate that for longer pulse widths the transition between near-field to far-field is displaced away from the electrode compared to shorter pulse widths. These results are all explained in details in Chapters 3 and 4 of the thesis. The glia influence on shaping the admittivity response of tissue based on their population is considered in Chapter 5. The new, more realistic model will facilitate a more accurate application of electrical stimulation to any neural tissue and specifically the brain. More accurate stimulation will improve emerging neurological therapies, such as deep brain stimulation for epilepsy and Parkinson’s disease.
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    Computational models of V1 and MT neurons for estimation of visual motion direction
    ZAREI ESKIKAND, PARVIN ( 2017)
    The processing of motion information in the brain initiates in the primary area of visual cortex (V1). V1 neurons transmit initial estimates of the motion of stimuli to neurons in higher areas of the cortex. However, V1 neurons measure only the component of the motion that is perpendicular to the edge of the stimulus because of their small receptive fields. A computational neural network model based on area MT is developed to estimate the correct direction of motion from the ambiguous information supplied by V1 neurons. The neural model processes the motion information of the stimulus through two stages. Complex V1 neurons at the first stage are spatiotemporal filters that represent ambiguous motion information along the edge of the stimulus but correct motion information at the end points (terminators) of the stimulus. The neurons responding to the terminators of the stimulus provide an unambiguous estimation of the direction of motion because of the two-dimensional structure of the corners. End-stopped neurons in V1 exclusively respond to the motion of terminators, which are modeled by inhibitory interconnections between the neighboring neurons. The incoming motion signals provided by complex V1 neurons and end-stopped V1 neurons proceed to MT neurons at the second stage. The excitatory and inhibitory interconnections between MT neurons result in the propagation of unambiguous motion information from the terminators to the other regions of the stimulus to achieve responses to the correct direction of motion. The information provided by end-stopped neurons is essential for the MT neurons to distinguish ambiguous motion information of the edges from the unambiguous information of the terminators. Although, end-stopped neurons provide a correct estimation of the direction of motion at the end-points (intrinsic terminators) of the stimulus, their represented local motion signals at extrinsic terminators (formed at the intersection of two overlapping stimuli moving in different directions) conflicts with the global motion direction of the stimuli. The neural model explains how interactions between form and motion information may assist neurons in the motion-specific regions of primate cortex to differentiate intrinsic from extrinsic terminators. In the proposed model, MT neurons additionally receive form information from neurons in the V1 area sensitive to the luminance of the stimulus with suppressive surrounds. As these neurons receive stronger inhibition from their surrounds at the extrinsic terminators, the excitatory inputs from these V1 neurons assist unambiguous motion signals at the intrinsic terminators to dominate over the local motion signals generated at the extrinsic terminators. The results show that, despite the inability of end-stopped neurons to distinguish two different types of terminators, center-surround V1 neurons have higher activity at the intrinsic terminators resulting in an accurate representation of motion by MT neurons. The proposed model also shows that the strength of the excitatory connections from center-surround V1 neurons, which supply initial form information to MT neurons, determines the pattern or component selectivity of MT neurons. A strong input from the center-surround V1 neurons inhibits the local motion information of the extrinsic terminators. Therefore, MT neurons reflect the component motion information of the stimuli received from V1 neurons in the absence of the intrinsic terminators. However, in the case of weak excitatory connections from center-surround V1 neurons, the pattern motion information propagates from extrinsic terminators to other regions over time and, after a temporal delay, MT neurons represent the pattern motion of the input stimulus. The proposed model of the V1 and MT neurons suggest the key role of terminators and the neurons enhancing or suppressing the motion information of these regions in the perceived motion of the stimulus. The findings are summarized in three main components: The results show that resolving ambiguity of the motion information along the edges is a two-stage process that is initiated by end-stopped V1 neurons and finalized by interconnections between MT neurons. - The model also suggests the necessity of form information for suppressing the effects of extrinsic terminators in the case of overlapping stimuli. - The results of the model question the generally believed hypothesis that the hierarchical process of pattern motion computation of MT neurons is based on the integration of component motions. The model suggests that the pattern or component motion preference of MT neurons is highly dependent on the strength of the input from center-surround V1 neurons.