Advanced Computational Approaches to Biomedical Engineering

There has been rapid growth in biomedical engineering in recent decades, given advancements in medical imaging and physiological modelling and sensing systems, coupled with immense growth in computational and network technology, analytic approaches, visualization and virtual-reality, man-machine interaction and automation. Biomedical engineering involves applying engineering principles to the medical and biological sciences and it comprises several topics including biomedicine, medical imaging, physiological modelling and sensing, instrumentation, real-time systems, automation and control, signal processing, image reconstruction, processing and analysis, pattern recognition, and biomechanics. It holds great promise for the diagnosis and treatment of complex medical conditions, in particular, as we can now target direct clinical applications, research and development in biomedical engineering is helping us to develop innovative implants and prosthetics, create new medical imaging technologies and improve tools and techniques for the detection, prevention and treatment of diseases. The contributing authors in this edited book present representative surveys of advances in their respective fields, focusing in particular on techniques for the analysis of complex biomedical data. The book will be a useful reference for graduate students, researchers and industrial practitioners in computer science, biomedical engineering, and computational and molecular biology.

[1]  Ben H. Jansen,et al.  Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns , 1995, Biological Cybernetics.

[2]  Brian K. Rutt,et al.  Validation of Automatically Classified Magnetic Resonance Images for Carotid Plaque Compositional Analysis , 2006, Stroke.

[3]  KongFatt Wong-Lin,et al.  Computational Study of Hippocampal-Septal Theta Rhythm Changes Due to Beta-Amyloid-Altered Ionic Channels , 2011, PloS one.

[4]  P A Robinson,et al.  Estimation of multiscale neurophysiologic parameters by electroencephalographic means , 2004, Human brain mapping.

[5]  Eugene M. Izhikevich,et al.  Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting , 2006 .

[6]  Aaron Fenster,et al.  Measurement of Carotid Plaque and Effect of Vitamin Therapy for Total Homocysteine , 2003, Clinical chemistry and laboratory medicine.

[7]  B. Amsallem,et al.  Possible role of the nucleus reticularis thalami (nRT) in the control of specific, non-specific thalamic nuclei and cortex activity , 1984, PAIN.

[8]  F. L. D. Silva,et al.  Dynamics of the human alpha rhythm: evidence for non-linearity? , 1999, Clinical Neurophysiology.

[9]  L. Astolfi,et al.  A neural mass model for the simulation of cortical activity estimated from high resolution EEG during cognitive or motor tasks , 2006, Journal of Neuroscience Methods.

[10]  Fei Liu,et al.  MRI of atherosclerosis in clinical trials , 2006, NMR in biomedicine.

[11]  Damien Coyle,et al.  A computational modelling approach to investigate alpha rhythm slowing associated with Alzheimer’s Disease , 2010, BICS 2010.

[12]  Aaron Fenster,et al.  3-D Carotid Multi-Region MRI Segmentation by Globally Optimal Evolution of Coupled Surfaces , 2013, IEEE Transactions on Medical Imaging.

[13]  James Theiler,et al.  On the evidence for how-dimensional chaos in an epileptic electroencephalogram , 1995 .

[14]  J. Kelso,et al.  From the Cover : The phi complex as a neuromarker of human social coordination , 2007 .

[15]  Damien Coyle,et al.  Model-based bifurcation and power spectral analyses of thalamocortical alpha rhythm slowing in Alzheimer's Disease , 2013, Neurocomputing.

[16]  F. L. D. Silva,et al.  Basic mechanisms of cerebral rhythmic activities , 1990 .

[17]  S. Sherman,et al.  Relative distribution of synapses in the A‐laminae of the lateral geniculate nucleus of the cat , 2000, The Journal of comparative neurology.

[18]  K. Kultas‐Ilinsky,et al.  Nucleus reticularis thalami connections with the mediodorsal thalamic nucleus: A light and electron microscopic study in the monkey , 1995, Brain Research Bulletin.

[19]  P. Érdi,et al.  Computational neuropharmacology: dynamical approaches in drug discovery. , 2006, Trends in pharmacological sciences.

[20]  Mauro Ursino,et al.  The generation of rhythms within a cortical region: Analysis of a neural mass model , 2010, NeuroImage.

[21]  H. Braak,et al.  Alzheimer's disease affects limbic nuclei of the thalamus , 2004, Acta Neuropathologica.

[22]  C. J. Stam,et al.  Global dynamical analysis of the EEG in Alzheimer’s disease: Frequency-specific changes of functional interactions , 2008, Clinical Neurophysiology.

[23]  Chun Yuan,et al.  MRI of carotid atherosclerosis , 2008, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[24]  Roberto Hornero,et al.  Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  Bard Ermentrout,et al.  Simulating, analyzing, and animating dynamical systems - a guide to XPPAUT for researchers and students , 2002, Software, environments, tools.

[26]  S. Murray Sherman,et al.  Interneurons and triadic circuitry of the thalamus , 2004, Trends in Neurosciences.

[27]  Damien Coyle,et al.  Alpha and theta rhythm abnormality in Alzheimer's Disease: a study using a computational model. , 2011, Advances in experimental medicine and biology.

[28]  E. Jonkman,et al.  The role of the electroencephalogram in the diagnosis of dementia of the Alzheimer type: an attempt at technology assessment , 1997, Neurophysiologie Clinique/Clinical Neurophysiology.

[29]  Donald O. Walter,et al.  Mass action in the nervous system , 1975 .

[30]  S. Hughes,et al.  Thalamic Mechanisms of EEG Alpha Rhythms and Their Pathological Implications , 2005, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[31]  J. L. Cantero,et al.  Human alpha oscillations in wakefulness, drowsiness period, and REM sleep: different electroencephalographic phenomena within the alpha band , 2002, Neurophysiologie Clinique/Clinical Neurophysiology.

[32]  J. Bellanger,et al.  Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition , 2002, The European journal of neuroscience.

[33]  A STOLLER,et al.  Slowing of the alpha-rhythm of the electroencephalogram and its association with mental deterioration and epilepsy. , 1949, The Journal of mental science.

[34]  H. Soininen,et al.  Slowing of electroencephalogram and choline acetyltransferase activity in post mortem frontal cortex in definite Alzheimer's disease , 1992, Neuroscience.

[35]  A. Cichocki,et al.  Diagnosis of Alzheimer's disease from EEG signals: where are we standing? , 2010, Current Alzheimer research.

[36]  Mercedes Atienza,et al.  Increased synchronization and decreased neural complexity underlie thalamocortical oscillatory dynamics in mild cognitive impairment , 2009, NeuroImage.

[37]  P. Achermann,et al.  Alpha activity in the human REM sleep EEG: topography and effect of REM sleep deprivation , 1999, Clinical Neurophysiology.

[38]  Katarzyna J. Blinowska,et al.  N eural dynamics underlying brain thalamic oscillations investigated with computational models , 2000 .

[39]  Nelson J. Trujillo-Barreto,et al.  Realistically Coupled Neural Mass Models Can Generate EEG Rhythms , 2007, Neural Computation.

[40]  Damien Coyle,et al.  A thalamo-cortico-thalamic neural mass model to study alpha rhythms in Alzheimer's disease , 2011, Neural Networks.

[41]  F. L. D. Silva,et al.  Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network , 2004, Neuroscience.

[42]  Damien Coyle,et al.  Investigating the Neural Correlates of Pathological Cortical Networks in Alzheimer's Disease Using Heterogeneous Neuronal Models , 2012, IEEE Transactions on Biomedical Engineering.

[43]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[44]  P Erdi,et al.  From systems biology to dynamical neuropharmacology: proposal for a new methodology. , 2006, Systems biology.

[45]  Jaeseung Jeong.  EEG dynamics in patients with Alzheimer's disease , 2004, Clinical Neurophysiology.

[46]  J. Cowan,et al.  A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue , 1973, Kybernetik.

[47]  A. Fenster,et al.  3D ultrasound analysis of carotid plaque volume and surface morphology. , 2006, Ultrasonics.

[48]  Damien Coyle,et al.  Thalamocortical circuitry and alpha rhythm slowing: An empirical study based on a classic computational model , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[49]  Olivier D. Faugeras,et al.  Bifurcation Analysis of Jansen's Neural Mass Model , 2006, Neural Computation.

[50]  E K Perry,et al.  Nerve cell loss in the thalamus in Alzheimer's disease and Parkinson's disease. , 1991, Brain : a journal of neurology.

[51]  Aaron Fenster,et al.  Semi-automated segmentation of carotid artery total plaque volume from three dimensional ultrasound carotid imaging , 2012, Medical Imaging.

[52]  M. Deschenes,et al.  The thalamus as a neuronal oscillator , 1984, Brain Research Reviews.

[53]  Theo van Walsum,et al.  Region based level set segmentation of the outer wall of the carotid bifurcation in CTA , 2011, Medical Imaging.

[54]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[55]  P N Prinz,et al.  Dominant occipital (alpha) rhythm frequency in early stage Alzheimer's disease and depression. , 1989, Electroencephalography and clinical neurophysiology.

[56]  W. Pritchard,et al.  Measuring chaos in the brain: a tutorial review of nonlinear dynamical EEG analysis. , 1992, The International journal of neuroscience.

[57]  Aaron Fenster,et al.  Manual planimetric measurement of carotid plaque volume using three-dimensional ultrasound imaging. , 2007, Medical physics.

[58]  Damien Coyle,et al.  Assessing retino-geniculo-cortical connectivities in Alzheimer's Disease with a neural mass model , 2011, 2011 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB).

[59]  W J Freeman,et al.  Linear analysis of the dynamics of neural masses. , 1972, Annual review of biophysics and bioengineering.

[60]  Nicholas T. Carnevale,et al.  ModelDB: A Database to Support Computational Neuroscience , 2004, Journal of Computational Neuroscience.

[61]  Aaron Fenster,et al.  3D Ultrasound Measurement of Change in Carotid Plaque Volume: A Tool for Rapid Evaluation of New Therapies , 2005, Stroke.

[62]  Damien Coyle,et al.  Assessing alpha band event-related synchronisation/desynchronisation with a mutually coupled thalamo-cortical circuitry model , 2012 .

[63]  Jaeseung Jeong.  Nonlinear dynamics of EEG in Alzheimer's disease , 2002 .

[64]  A. Babloyantz,et al.  Low-dimensional chaos in an instance of epilepsy. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[65]  David A. McCormick,et al.  Are thalamocortical rhythms the rosetta stone of a subset of neurological disorders? , 1999, Nature Medicine.

[66]  Claudio Babiloni,et al.  Individual analysis of EEG frequency and band power in mild Alzheimer's disease , 2004, Clinical Neurophysiology.

[67]  Hojjat Adeli,et al.  Alzheimer's Disease: Models of Computation and Analysis of EEGs , 2005, Clinical EEG and neuroscience.

[68]  T. Gasser,et al.  Alzheimer disease versus mixed dementias: An EEG perspective , 2008, Clinical Neurophysiology.

[69]  Karl J. Friston,et al.  a K.E. Stephan, a R.B. Reilly, , 2007 .

[70]  John R. Terry,et al.  Derivation and analysis of an ordinary differential equation mean-field model for studying clinically recorded epilepsy dynamics. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[71]  R J Sclabassi,et al.  Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. , 1986, Electroencephalography and clinical neurophysiology.

[72]  Aaron Fenster,et al.  Efficient Global Optimization Based 3D Carotid AB-LIB MRI Segmentation by Simultaneously Evolving Coupled Surfaces , 2012, MICCAI.

[73]  A. Fenster,et al.  Three-dimensional semi-automated segmentation of carotid atherosclerosis from three-dimensional ultrasound images , 2012, Medical Imaging.

[74]  F H da Silva,et al.  Essential differences between alpha rhythms and barbiturate spindles: spectra and thalamo-cortical coherences. , 1973, Electroencephalography and clinical neurophysiology.

[75]  T. To¨mbo¨l.  Short neurons and their synaptic relations in the specific thalamic nuclei. , 1967 .

[76]  H. Wilson.  Spikes, Decisions, and Actions: The Dynamical Foundations of Neuroscience , 1999 .

[77]  D. McCormick,et al.  Sleep and arousal: thalamocortical mechanisms. , 1997, Annual review of neuroscience.

[78]  Jordi García-Ojalvo,et al.  Relating structural and functional anomalous connectivity in the aging brain via neural mass modeling , 2010, NeuroImage.

[79]  Damien Coyle,et al.  Gray matter concentration and effective connectivity changes in Alzheimer’s disease: a longitudinal structural MRI study , 2011, Neuroradiology.

[80]  I. Veer,et al.  Strongly reduced volumes of putamen and thalamus in Alzheimer's disease: an MRI study , 2008, Brain : a journal of neurology.

[81]  R. Guillery,et al.  Exploring the Thalamus , 2000 .

[82]  C. Yuan,et al.  Quantitative Evaluation of Carotid Plaque Composition by In Vivo MRI , 2004, Arteriosclerosis, thrombosis, and vascular biology.