The Brain Dynamics Centre Research Directions The Brain Dynamics Centre will be focused on bringing together information from selected genetic polymorphisms, brain imaging, the electrochemical physiology of brain function, and psychological and cognitive measures, to elucidate human brain structure and function. This integrative approach will be applied to identifying diagnostic profiles for brain related illnesses, across the life span. The research relies heavily on the development of frontier technologies and infrastructure for data management, fusion and modeling. The Network cohesion will be ensured by the shared access to data, methodologies and techniques with a standardized framework across the Brain Dynamics Centre nodes. The standardized procedures and methods overcome previous weaknesses of small or ad hoc datasets, and individual research groups. The Brain Dynamics Centre funding will support the standardized 'hub and spoke' structure of the network, and the consolidation of integrative technologies to support a scientific brain database. By integrating structural and brain-physiological measures with the genetic information for the first time, the Brain Dynamics Centre will advance methodological techniques for dealing with the genetics of brain structure and function, and spawn testable hypotheses concerning their inter-relationships. In the short term, selected single nucleotide polymorphisms of reported functional significance will be assessed. In the longer term, the objective is to have a comprehensive genome wide set of genetic polymorphism data collected on large cohorts, with concurrent brain imaging, electrophysiological and psychological data. Aims and Overview: Integrative brain research A number of complementary methodological innovations enable the impact of genetic variation on brain structure and function to begin to be unravelled. The confluence of innovations include: the advent of high resolution structural Magnetic Resonance Imaging (sMRI) and new software techniques to analyze brain structure; the human genome project and the development of low cost, high-throughput genetic analyses; the maturing of realistic numerical models of global brain function; the human brain project and the first large-scale brain databases integrating functional and structural information. This convergence allows specific hypotheses regarding the genetic bases of whole-brain structure and function to be tested. Bringing together these sources of information requires support from a cooperative and integrative network, with a standardized approach to neuroinformatics, incorporating phenotypic, genetic, structural and functional measures - of the kind proposed in the Brain Dynamics Centre. Whilst there are numerous studies showing possible distinctive patterns of brain structure and function in previous research, they have been undertaken using selective aspects of brain physiology, performance or behaviour and usually within relatively small data-set. It may be myopic to continue to generate large numbers of such outcomes, without some evaluation of the relative amount of variance explained by the factors such as age and gender. Most studies to date either look at the genetic basis of different phenotypes, or differences in brain structure for different phenotypes, or functional measures for different clinical groups. the Brain Dynamics Centre participants will bring together four different modalities of data (phenotypic, genetic, structural and functional MRI) within the same standardized framework. The advantage of this integrative approach to data collection is the illumination of patterns in the data that are otherwise obscured when the elements are looked at in isolation. Importance of frontier technology Fundamental progress in understanding the biology of the brain awaits better models that reflect a clearer understanding of what imaging findings in neuroscience actually mean. A significant element of the Brain Dynamics Centre's integrative approach is to apply new frontier technology developments to the analysis of the various phenotypic and genetic groups. For instance, biophysical modelling will allow for integration of brain information across microscopic to whole-brain scales. The Brain Dynamics Centre will also further develop integrative neuroinformatics techniques for phenotypic, genetic, brain-structural and -functional measures and to share these techniques and further their availability to other researchers. For instance, we will make use of a data integration method called Projection onto Centroid Difference Vectors (PCDV) first developed by the Network's Sydney participants. The PCDV procedure is designed to overcome problems of low power when multiple measures have been taken-such as a spatial array of EEG electrode sites. Recent international work has improved the robustness of the measure using permutation methods, and we intend to include these as automatic features in a package that also allows graphical display of the brain imaging results. Extension of the technique to encompass simultaneous analysis of multiple data types is also planned. The Brain Dynamics Centre will make use of the experience of its key participants in collecting large, quality-controlled datasets. Brain databases are rapidly being developed, but total quality control and consistency of paradigms across laboratories have previously limited the control of confounding variables (such as technical and methodological differences between laboratories). the Brain Dynamics Centre will provide a key scientific framework for capitalising on existing experience and infrastructure, allowing for rapid outcomes, and expansion of new Network linkages. Significance and Key Directions The overarching significance of the Brain Dynamics Centre is in providing a cohesive scientific framework for bringing together normally disparate brain information. the Brain Dynamics Centre also provides access to frontier techniques for optimizing outcomes from these data. Currently, neuroscientists have produced a large amount of data worldwide, but its integration is constrained by the lack of standardisation (see pp.822-825, Nature, 406, 24th August, 2000). The the Brain Dynamics Centre project draws on a standardized approach using integrative neuroscience measures, which will offer an unprecedented source of information for both scientific purposes, and clinical application. This approach is important for providing the normative base for both diagnostic screens and treatment evaluation. A major limitation of current diagnosis in psychopathology resides in the fact that we lack of a robust understanding of what constitutes normal brain function. Neuropsychiatric disorders are defined by breakdowns in complex brain functions, involving memory, attention, emotion and executive planning - as seen in depression, attention deficit disorder, schizophrenia, borderline personality disorder, split personality, neurotoxic disorders, anxiety disorder, panic disorders and post-traumatic stress disorder. Many such disorders present without observable structural damage to the brain. For instance, post-traumatic stress disorder (PTSD) is characterized by excessive responding to fear-related events (referred to as defensive responding). Attention deficit hyperactivity disorder (ADHD) is defined by a failure to shift attention to relevant events, referred to as orienting. Similarly, Alzheimer's disease (AD) is characterized by impairments in memory, particularly recent memories. While we have a reasonably good understanding of more fundamental functions involving our motor actions and our senses, there are virtually no robust biological markers of normal variation in complex brain functions. A second limitation is the poor specificity and sensitivity of current diagnostic methods of these so-called "functional" disorders - which derives from the lack of reliable measures to reveal the functional breakdown in the systems of the brain. The Brain Dynamics Centre will provide significant insights into the development of biological markers of complex brain functions, and their application in diagnosis and treatment evaluation. The key research directions include: Neuroimaging and brain markers The Brain Dynamics Centre participants have a strong track record in neuroimaging research, focused in the past five years on the most recent, high resolution and non-invasive technique of functional magnetic resonance imaging (fMRI). fMRI research has demonstrated the value of this technique in elucidating the brain regions activated during complex functions, such as memory, attention, executive processing and emotion. Previously, determination of the neuroanatomical basis of these functions relied on lesion-based approaches, which may not necessarily apply to the intact healthy brain. Electrochemical physiology and brain markers Electrophysiological techniques (such as electroencephalographic, EEG and event-related potential, ERP, recordings) have frequently been used as indices of cognitive brain function. Quantified EEG in particular has shown substantial promise as a marker of both disordered brain states, and response to treatment. To our knowledge, these techniques have not been examined using an integrated approach, with neurophysiology, neuroimaging and genetics. Genetics and brain markers With the completion of the sequencing of the human genome (within the Human Genome Project), it has been possible to elucidate the function of genes related to brain function. The focus of the Brain Dynamics Centre will be on a number of selected candidate genes, with known Single Nucleotide Polymorphisms (SNPs) or other allelic variants that have been shown irrefutably to have an allelic effect on brain function or on candidate genes that likely have an effect on brain function. These include apolipoprotein E (APOE), brain-derived neurotrophic factor (BDNF), and genes associated with neurotransmitter metabolism, catechol-O-methyltransferase (COMT) and dopamine receptor D4 (DRD4) on chromosome 11p15.5. BDNF has also been shown to mediate hippocampal plasticity and hippocampal-dependent memory in cell models and in animals. Integration of neuroimaging, electrochemical and genetic information with psychological information The Brain Dynamics Centre will focus on electrophysiological and neuroimaging measures acquired during cognitive tasks that are relevant to both healthy brain function, and to specific illnesses (eg., memory tasks for Alzheimer's disease). For the study of specific brain related illnesses, a battery of targeted psychological and clinical assessments will also be included, for integration with the brain and genetic measures. Integration using neuroinformatics, data fusion and modeling techniques Several recent findings highlight the potential for combining neuroimaging and neurophysiology data with genetic data (BDNF, APOE, COMT) to define evidence-based brain markers that could be used to screen for deficits associated with neuropsychiatric disease. For instance, while genotypes alone are unable to predict Alzheimer's disease, the addition of cognitive and brain imaging evidence makes this possible. Evidence that these gene variants are associated with a range of normative performance of psychological tests of memory and attention provides a framework for validation of biological markers. The Brain Dynamics Centre will make use of cutting edge new analytic developments to integrate and fuse different sources of information . As one key example, the Brain Dynamics Centre will develop a biophysical model of large-scale electrical activity in the brain, that will allow scientists to integrate data across microscopic and whole-brain scales. This approach differs from the standard neuroscience account of the brain because of its emphasis on global dynamics, rather than localized areas of activation or local anatomical features. Yet, the model does not abandon local neural properties: its strength lies in its ability to relate microscopic variables to the global activity of the brain. In addition, the Brain Dynamics Centre participants will draw on the frontier data fusion techniques currently used in field such as robotics to optimize data integration.

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