Neuroimaging. BIE601 Advanced Biological Engineering Dr. Boonserm Kaewkamnerdpong Biological Engineering Program, KMUTT. Human Brain Mapping

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1 11/8/2013 Neuroimaging N i i BIE601 Advanced Biological Engineering Dr. Boonserm Kaewkamnerdpong Biological Engineering Program, KMUTT 2 Human Brain Mapping H Human m n brain br in m mapping ppin can nb be ddefined fin d as to visualize is alize brain areas and their interconnection engaged in a certain function by using non-invasive techniques. Source: H. Shibasaki. Human brain mapping: Hemodynamic response and electrophysiology. Clinical Neurophysiology, vol. 119, pp , Apr The main objective is to understand how brain works. works Activation studies depend on imaging changes in brain state within the same scanning session. 1

2 11/8/ Brief History of Human Brain Mapping In 1983, half life 1983 Herscovitch et al. al introduced the use of short short-half-life radiotracers and positron emission tomography (PET) in nuclear medicine. The first activation maps appeared in Lauter et al., 1985 and Fox et al., Up until this time, regional differences among brain scans had been characterized using hand-drawn regions of interest (ROI), reducing h d d off thousands hundreds h d off voxels l to a h handful df l off ROI measurements. The idea of making voxel-specific statistical inferences, through the use of statistical parametric maps, emerged in response to the clear need to make inferences about brain responses without knowing where those responses were going to be expressed. 4 Neuroimaging Modalities The non-invasive techniques currently available for brain mapping are largely divided into two groups based on their principles: Electrophysiological principle Hemodynamic principle Electroencephalography (EEG) Positron Emission Tomography (PET) Magnetoencephalography (MEG) Single-photon Emission Computed Tomography (SPECT) Transcranial Magnetic Stimulation (TMS) Functional MRI (fmri) Near-infrared Spectroscopy (NIRS) 2

3 5 Statistical Parametric Mapping Statistical ti ti parametric mapping is used to identify regionally specific effects in neuroimaging data. Statistical parametric mapping is a prevalent approach to characterizing functional anatomy, specialization and diseaserelated changes. Statistical i parametric mapping is a voxel-based approach, employing topological inference, to make some comment about regionally specific responses to experimental factors. 6 Objective of Study Experimental Design Experiment Inferences in neuroimaging could be about differences expressed when comparing one group of subjects to another or, within subjects, changes over a sequence of observations according to the structural differences and, in turn, neurophysiological measures of brain functions. Image/Signal Data Image Transformation Modeling Inference In order to assign an observed response to a particular brain structure, or cortical area, the data are usually realigned, normalized and mapped into an anatomical space. Output Brain Activity Model 3

4 7 Experimental Design Regional physiology will vary systematically with the degree of cognitive or sensorimotor processing or deficits thereof. The difference between two tasks can be formulated as a separable cognitive or sensorimotor component and that regionally specific differences in haemodynamic responses, evoked by the two tasks, identify the corresponding functionally selective area. Cognitive Subtraction One tests a single hypothesis pertaining to the activation in one task relative to another. Cognitive conjunctions combine a series of subtractions, so several hypotheses are tested, asking whether the activations, in a series of task pairs, are collectively significant. 8 Consider the problem of identifying regionally specific activations due to a particular cognitive component. If one can identify a series of task pairs whose differences have only that component in common, then the region which activates, in all the corresponding subtractions, can be associated with the common component. An fmri study of visual motion processing using radially moving dots To identify areas involved in visual motion, a stationary dots condition was subtracted from the moving dots conditions. 4

5 11/8/ Image Transformation Objectives: j performing the inversion of forward or generative models of how data are caused decomposing the inversion of forward spatiotemporal models into spatial and temporal parts characterizing and removing anatomical differences A series of spatial transformations is performed to reduce unwanted variance components in the voxel time-series induced by movement or shape differences among a series of scans. Processes: realign the data undo the effects of subject movement during the scanning session spatially smooth before inverting the temporal part of the model transform into a standard anatomical space (e.g. Talairach and Tournoux, 1988) using linear or non-linear warps 5

6 11 12 Modeling Neuroscience depends on conceptual, anatomical, statistical ti ti and causal models that link ideas about how the brain works to observed neuronal responses. The functional role of any component (e.g. cortical area, subarea or neuronal population) of the brain is defined largely by its connections. General Linear Model (GLM): expresses an observed response y in terms of a linear combination of explanatory variables in the design matrix X plus a well-behaved error term. 6

7 13 Biophysical Models 14 Dynamic Causal Modeling 7

8 15 Inference The critical issue is whether we want to make an inference about the effect in relation to the within-subject variability or with respect to the between-subject variability. This distinction relates directly to the difference between fixed and random-effect analyses. Ui Using statistics i that compare interesting i effects and the error with classical inference, Bayesian inference and other alternatives 16 8

9 17 Neurovascular Coupling Although functional neuroimaging is now widely used for noninvasively investigating human brain functions in the field of basic and clinical neuroscience, how accurately those images based on the hemodynamic principles reflect neuronal electrical activity is still not clearly understood. Experimentally, in both animal and human, the hemodynamic response has been shown to be a function of electrophysiological activity at least within a certain range, but it is seen over a relatively larger area in space than the electrophysiological activity and lasts longer in time beyond the saturation of local neuronal activity. Detailed mechanisms underlying the coupling of neuronal signals to local vasodilation have not been clarified. 18 Multimodal Neuroimaging Each technique in fntinln functional neuroimaging in based on hemodynamic principle and electrophysiology has unique features in terms of temporal and spatial resolution. The combined use of two or more techniques is expected to complement each other and thus provide more information than the use of a single technique. 1 st Modality 2 nd Modality Neuroimaging Process Information 9

10 19 Multimodal Neuroimaging In practical, the multi-modalmodal approach can be divided into two categories: separate sessions having the underlying disadvantage of uncontrolled background due to uncontrollable experimental conditions between the two sessions simultaneously sessions drawing special attention of many investigators in recent years requiring artifact elimination of interferences from one another 20 EEG-MEG EEG provides electrophysiological data with high temporal resolution but poor spatial resolution. MEG can detect only current flow in the tangential orientation. Nevertheless, MEG is not affected by shunting effect that causes distortion in EEG. Sharon et al. conducted an empirical study of brain activity under focal visual stimuli and found that the combined EEG- MEG yielded more accurate localization or cortical source estimation than either unimodality measurement. 10

11 21 EEG-PET PET scanner detects the emitted radioisotopes at the hemodynamically or metabolically activated region and constructs signal data into images. Combining two modalities from different principles in order to reveal the underlying mechanism in neurovascular coupling between hemodynamic response and electrophysiological activity 22 EEG-fMRI fmri provides images with good spatial resolution but inadequate temporal resolution. fmri is not recommended to operate on infants, toddlers and patients with inserted metallic implants. Th i l f EEG d The simultaneous use of EEG and fmri become attractive and popular in multimodal neuroimaging as the integration of high temporal resolution from EEG and high spatial resolution from fmri. 11

12 11/8/ EEG-NIRS NIRS optically measuring the changes of concentrations of oxyhemoglobin to support h l bi and d deoxyhemoglobin d h l bi in i the h tissue i neuronal activity Both are infant- and toddler-friendly technologies. Compared to fmri, NIRS has better temporal resolution and adequate spatial resolution. NIRS can be recorded concurrentlyy with EEG in a more natural manner for a longer time than fmri. Compared to EEG-fMRI, the integrative analysis of EEG-NIRS for understanding the underlying mechanisms in neurovascular coupling is still in its infancy; it has, however, great potential to improved overall spatiotemporal resolution for the purpose of human brain mapping, cognitive neuroscience studies, and development of neuroimaging diagnostic system

13 25 Research Directions of Human Brain Mapping Choosing the most appropriate pri technique available for solving each specific question Using diffusion fmri which has higher temporal resolution than the conventional blood oxygenation level-dependent (BOLD) analysis of hemodynamic response Ui Using rtms in causing plastic changes or virtual lesions to enable us to more precisely activate a small cortical area in studies Source: H. Shibasaki. Human brain mapping: Hemodynamic response and electrophysiology. Clinical Neurophysiology, vol. 119, pp , Apr References H. Shibasaki. Human brain mapping: Hemodynamic response and electrophysiology. Clinical Neurophysiology, vol. 119, pp , Apr Statistical parametric mapping: The analysis of functional brain images, Eds. Karl J. Friston, John T. Ashburner, Stefan J. Kiebel, Thomas E. Nichols, and William D. Penny, Academic Press, D. Sharon, M.H. Hämäläinen, R.B.H. Tootell, E. Halgren, and J.W. Belliveau. The advantage of combining MEG and EEG: Comparison to fmri in focally stimulated visual cortex. NeuroImage, vol. 36, pp , Jul