Analysis Of Functional Brain Images Research Articles

ID:16413 Case Study: Dynamic Network Connectivity of Chronic Back Pain

Failed back surgery syndrome (FBSS) is a condition which can result in neuropathic pain and affect up to 20%-40% of patients following lumbar spine surgery. Spinal cord stimulation (SCS) has been well established as a pain-relieving treatment [1]. Chronic pain conditions have been shown with abnormalities in structural and functional brain imaging analysis, such as decreased gray-matter in certain brain regions [2] and brain activity shifting from nociceptive to emotional networks with the transition from acute to chronic pain [3].

8 Brain reading

David Linden read medicine, classics and philosophy in Germany. He obtained a DPhil from the University of Oxford for his work on medical ethics in antiquity in 2000 and a Dr. med. in neuroscience at the Max Planck Institute for Brain Research in 1999. He trained in psychiatry at Frankfurt University and has a special interest in clinical neuropsychiatry and neurodevelopmental genetic syndromes. Since 2011 he has been Professor of Translational Neuroscience at Cardiff University and Head of the Neuroimaging theme group of the MRC Centre for Neuropsychiatric Genetics and Genomics. He is also the director for clinical research at the Cardiff University Brain Research Imaging Centre. In his research, he applies structural and functional magnetic resonance imaging (MRI), neurophysiological techniques and genetics in order to understand the function of the brain in health and disease. Current research interests include functional imaging of psychopathology, neural substrates of social cognition and decision making, genetic imaging, treatment and training effects on the brain, and neurofeedback. David is the author of “The Biology of Psychological Disorders” (2012), “Brain Control” (2014) and “Neuroimaging and Neurophysiology in Psychiatry” (2016) and over 180 scientific papers in the fields of biological psychiatry, functional brain imaging, visual and social cognitive neuroscience, and neurofeedback. He has coordinated trials of functional MRI-based neurofeedback in depression and Parkinson’s disease. He is also the coordinator of the European Consortium “BRAINTRAIN” (www.braintrainproject.eu), funded by the European Commission under the 7th Framework Programme, which develops and evaluates imaging-based neurofeedback methods for a range of mental disorders. He is a consultant psychiatrist with the Cardiff and Vale University Health Board and provides clinics at the Welsh Neuropsychiatric Service, the Cardiff University Psychiatric Service (specialising in neurodevelopmental genetic syndromes) and the Welsh Huntington’s Disease Service. He has been a member of the board of directors of the BNPA since 2014. The techniques of brain imaging and neurophysiology (both invasive and non-invasive) hold considerable promise for the understanding of human mental processes. One branch of neurophysiology and cognitive neuroscience research is concerned with the inference of particular mental states from neural activation patterns. One of the aims of this research is to identify intentions in patients with severe communication disorders (for example the “locked-in” state). Another proposed application is in the distinction between disorders of consciousness and disorders of communication. Recent advances in the analysis of functional brain images that allow for the classification of mental states also raise the question whether these techniques can be used to underpin psychiatric diagnoses, possibly even support or refute patients’ claim about their own mental states. This talk discusses the clinical promise and also the technical and ethical limitations of these approaches and puts them in the context of the history and future of neuropsychiatry. References . David Linden. Brain Control – Developments in Therapy and Implications for Society. Palgrave Macmillan 2014. . Sarah Richmond, Geraint Rees, Sarah JL, Edwards (eds). I know what you’re thinking – Brain imaging and mental privacy. Oxford University Press 2012.

Alzheimer's disease detection in functional images using 2D Gabor wavelet analysis

Presented is a Gabor wavelet (GW) based analysis of functional brain images by integrating the 2D GW representation of the images for image classification applied to early diagnosis of Alzheimer's disease. The 2D GW representation of the brain images is processed by means of a principal component analysis (PCA) for feature extraction and support vector machines (SVMs) for image classification. The proposed method yields up to 96% classification accuracy with 100% sensitivity, thus becoming an accurate method for image classification. Comparison between the conventional PCA plus SVM method and the proposed method is also provided. In addition, the proposed method with Gabor wavelets increases the outcomes of other methods based on voxel as features (VAF), PCA, and so on.

Functional Data Analysis in brain imaging studies

Functional data analysis (FDA) considers the continuity of the curves or functions, and is a topic of increasing interest in the statistics community. FDA is commonly applied to time-series and spatial-series studies. The development of functional brain imaging techniques in recent years made it possible to study the relationship between brain and mind over time. Consequently, an enormous amount of functional data is collected and needs to be analyzed. Functional techniques designed for these data are in strong demand. This paper discusses three statistically challenging problems utilizing FDA techniques in functional brain imaging analysis. These problems are dimension reduction (or feature extraction), spatial classification in functional magnetic resonance imaging studies, and the inverse problem in magneto-encephalography studies. The application of FDA to these issues is relatively new but has been shown to be considerably effective. Future efforts can further explore the potential of FDA in functional brain imaging studies.

Tips on the functional brain imaging analysis

Tips on the functional brain imaging analysis

Endogenous Control and Task Representation: An fMRI Study in Algebraic Problem-solving

The roles of prefrontal and anterior cingulate cortices have been widely studied, yet little is known on how they interact to enable complex cognitive abilities. We investigated this issue in a complex yet well-defined symbolic paradigm: algebraic problem solving. In our experimental problems, the demands for retrieving arithmetic facts and maintaining intermediate problem representations were manipulated separately. An analysis of functional brain images acquired while participants were solving the problems confirmed that prefrontal regions were affected by the retrieval of arithmetic facts, but only scarcely by the need to manipulate intermediate forms of the equations, hinting at a specific role in memory retrieval. Hemodynamic activity in the dorsal cingulate, on the contrary, increased monotonically as more information processing steps had to be taken, independent of their nature. This pattern was essentially mimicked in the caudate nucleus, suggesting a related functional role in the control of cognitive actions. We also implemented a computational model within the Adaptive Control of Thought-Rational (ACT-R) cognitive architecture, which was able to reproduce both the behavioral data and the time course of the hemodynamic activity in a number of relevant regions of interest. Therefore, imaging results and computer simulation provide evidence that symbolic cognition can be explained by the functional interaction of medial structures supporting control and serial execution, and prefrontal cortices engaged in the on-line retrieval of specific relevant information.

A 4D approach to the analysis of functional brain images: Application to FMRI data

A 4D approach to the analysis of functional brain images: Application to FMRI data

A 4D approach to the analysis of functional brain images: Application to FMRI data

This paper presents a new approach to functional magnetic resonance imaging (FMRI) data analysis. The main difference lies in the view of what comprises an observation. Here we treat the data from one scanning session (comprising t volumes, say) as one observation. This is contrary to the conventional way of looking at the data where each session is treated as t different observations. Thus instead of viewing the v voxels comprising the 3D volume of the brain as the variables, we suggest the usage of the vt hypervoxels comprising the 4D volume of the brain-over-session as the variables. A linear model is fitted to the 4D volumes originating from different sessions. Parameter estimation and hypothesis testing in this model can be performed with standard techniques. The hypothesis testing generates 4D statistical images (SIs) to which any relevant test statistic can be applied. In this paper we describe two test statistics, one voxel based and one cluster based, that can be used to test a range of hypotheses. There are several benefits in treating the data from each session as one observation, two of which are: (i) the temporal characteristics of the signal can be investigated without an explicit model for the blood oxygenation level dependent (BOLD) contrast response function, and (ii) the observations (sessions) can be assumed to be independent and hence inference on the 4D SI can be made by nonparametric or Monte Carlo methods. The suggested 4D approach is applied to FMRI data and is shown to accurately detect the expected signal.

Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares

This paper introduces a new tool for functional neuroimage analysis: partial least squares (PLS). It is unique as a multivariate method in its choice of emphasis for analysis, that being the covariance between brain images and exogenous blocks representing either the experiment design or some behavioral measure. What emerges are spatial patterns of brain activity that represent the optimal association between the images and either of the blocks. This process differs substantially from other multivariate methods in that rather than attempting to predict the individual values of the image pixels, PLS attempts to explain the relation between image pixels and task or behavior. Data from a face encoding and recognition PET rCBF study are used to illustrate two types of PLS analysis: an activation analysis of task with images and a brain–behavior analysis. The commonalities across the two analyses are suggestive of a general face memory network differentially engaged during encoding and recognition. PLS thus serves as an important extension by extracting new information from imaging data that is not accessible through other currently used univariate and multivariate image analysis tools.

Visual inspection versus quantified analysis of functional brain images.

Acta Neurologica ScandinavicaVolume 89, Issue S152 p. 169-174 Visual inspection versus quantified analysis of functional brain images L. Friberg, Corresponding Author L. Friberg Clinical Brain Physiology Unit, Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, USA, Department of Clinical Physiology and Nuclear Medicine, Glostrup Hospital, Denmark.aClinical Brain Physiology Unit, Department of Neurological Surgery, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75235–8855. USASearch for more papers by this authorH. Lenborg-Jensen, H. Lenborg-Jensen Clinical Brain Physiology Unit, Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, USA, Department of Clinical Physiology and Nuclear Medicine, Glostrup Hospital, Denmark.Search for more papers by this author L. Friberg, Corresponding Author L. Friberg Clinical Brain Physiology Unit, Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, USA, Department of Clinical Physiology and Nuclear Medicine, Glostrup Hospital, Denmark.aClinical Brain Physiology Unit, Department of Neurological Surgery, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75235–8855. USASearch for more papers by this authorH. Lenborg-Jensen, H. Lenborg-Jensen Clinical Brain Physiology Unit, Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, USA, Department of Clinical Physiology and Nuclear Medicine, Glostrup Hospital, Denmark.Search for more papers by this author First published: March 1994 https://doi.org/10.1111/j.1600-0404.1994.tb05215.xCitations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume89, IssueS152March 1994Pages 169-174 RelatedInformation

Neuroanatomical analysis of functional brain images: Validation with retinotopic mapping

AbstractThe analysis of functional brain images generated by positron emission tomography (PET) has depended on averaging individual images in a common stereotactic space to improve the signal‐to‐noise ratio in activation maps. Although the precise relationship of stereotactic coordinates to sulcul and gyral anatomy is obscured, these techniques have productively enabled exploratory, pixel‐based analysis of PET images. Coregistered three‐dimensional magnetic resonance (MR) datasets, not widely available at the time PET techniques were pioneered, now make it possible to develop complementary hypothesis‐led analysis techniques that respect individual anatomy and, therefore, allow conclusions in terms of sulcal and gyral anatomy. We report the development of such a technique, involving construction of a local standard space within anatomically‐defined boundaries. We validate the technique with a retinotopic mapping paradigm. In the analysis based on this standard space, regions of activation from the contributing subjects superimpose better than they do when Talairach's transformation is used. This approach also allows conclusions to be drawn specifically about striate cortex, whereas analysis of stereotactically transformed data does not distinguish V1 and V2. This approach is likely to be particularly useful for studying brain‐damaged subjects, to whom a universal stereotactic space cannot be applied. © 1995 Wiley‐Liss, Inc.