Real-time Functional Neuroimaging

Transcription

1 Real-time Functional Neuroimaging Stefan Posse, PhD Depts. of Psychiatry and Electrical & Computer Engineering, and Physics and Astronomy Univ. of New Mexico, Albuquerque, New Mexico, USA The Therapeutic Mind Scan How looking at a patient s brain might improve the diagnosis of psychiatric ailments Functional techniques mentioned: SPECT, fmri, MRS NYT, Feb. 20, 2005 Blood Oxygenation Level Dependent Contrast Real-time functional MRI Vessel diameter ranges from mm to mm, blood flow change during activation changes blood oxygenation Magnetic susceptibility (T 2* ) contrast in blood vessels depends on blood oxygenation A method to detect and automatically interpret brain activation patterns for online decision making in clinical and neuroscience applications Examples: Pre-surgical mapping Lie detection Definitions of Real-Time fmri Real-Time fmri: Advantages Finish data analysis shortly after the scan is finished (near real-time fmri) See the activation patterns emerge as the scan progresses (initial definition of real-time) Capture changes in activation over short periods of time (single blocks or single trials) Single trial: Non-averaged response to single light flash, movement or thought process Monitor data quality Monitor changes in attention and subject performance Assess experiment success Rapid results cost saving Novel paradigm designs with feedback Watch your own brain activation! Can fmri capture even neuronal activity? 1

2 Real-Time fmri: Limitations Long Term Goals fmri sensitivity decreases with increasing temporal resolution Sensitivity to transients (e.g. movement) increases with temporal resolution May be difficult to interpret due to brain noise, dynamic changes in activation patterns and movement May need to sacrifice spatial resolution Analysis not as sophisticated as conventional methodology - post-processing may still be required No group analysis (yet) Improve sensitivity, image quality and acquisition speed of functional MRI Characterie dynamic cognitive networks and neural correlates of emotions in single trials Improve interpretation of cognitive activation patterns in realtime using pattern classification methods Neuro-feedback to control localied brain activation for therapeutic use (motor learning, control of cognition)! Challenge for measurement sensitivity and control of the experiment Real-Time Signal Analysis: TurboFIRE Processing Capabilities Gembris et al. MRiM 2000, Mathiak & Posse MRiM 2001, Posse et al., HBM 2001 Preprocessing Multi-echo EPI: T 2* -LM fit or weighted echo average based on local T 2* -value 3D motion correction Spatial normaliation into Talairach Atlas Statistical Modeling Sliding-Window correlation analysis 6 simultaneous reference vectors Reference Vector Optimiation Simultaneously: General Linear Model Real-time generation of reference vector Quantification Integrated Talairach Daemon database ROI and cluster analysis Compatibility: UNIX (SUN,SGI,HP,Cray), and LINUX Sliding Window Correlation Analysis with Detrending Correlation only over last N images (sliding window) -> Constant sensitivity to functional signal changes during entire scan Hemodynamic response function: canonical 6-parameter or timeshifted poisson Detrending of up to 5 th order cosine-waveforms Gembris et al. MRiM 1999, Gao and Posse, HBM 2003 Real-Time Spatial Normaliation in Reference to Talairach Atlas 1. Lookup table approach Map Talairach Atlas into individual brain using lookup table Advantages: fast, preserves original images 2. Conventional approach Resample images based on normaliation parameters generated during normaliation of the reference image. 2

3 Automated Cluster Analysis with Anatomical Labels Single-Shot Water Relaxation Mapping 4 Tesla, TR: 2sec, TE min : 11 msec, TE: 15 msec, 6 echo times, 64x48 matrix, 16 slices, 2-fold GRAPPA acceleration with 8-channel array coil BOLD Sensitivity Increase by Combining Single-Shot Multi-Echo EPI Data (1.5 T) Sensitivity Optimied Real-Time Multi-Echo EPI at 4T TE: ms (20 msec/image) Posse et al., MRiM 1999 Weighted Average Conventional EPI 6-Echo Times: msec (~ 2 * T2*) 2-fold GRAPPA acceleration Echo-interleaved slicespecific XYZ-shimming in up to 3 regions brain regions to compensate signal losses Point-spread-correction using reference scan to reduce geometrical distortion Weighted echo-averaging based on T2* Map and XYZshimming scheme to maximie BOLD sensitivity Posse et al. MRiM 1999, Posse et al. Neuroimage 2003 Reducing Spatial Resolution Enhances fmri Sensitivity Match the Dimensions of the Activated Area Single Index Finger Tap (Turbo-PEPSI, 8 echoes: ms, TR: 1 s) 128x128 matrix 32x32 matrix 15 s: eyes open + finger tapping vs. 15 s rest, EPI: 4 mm slice, TR: 3 s 3

4 Preoperative Language-Lateraliation in Patient with High Grade Glioma (1.5 T) Pattern Specificity - Can we read the MIND? Online data quality control Short scan time (1 min) -> More procedures per unit time Significant cost saving Increased patient comfort and compliance Neuro-Anatomically Constrained Boosting T-Maps Splitting Increasing evidence for category specific brain activation patterns Support Vector Data preprocessing Machines Zero reduction (SVM) Vectoriation Multiclass Classifier Multiclass Classifier Boosting Masking Face and object recognition in visual cortex (Haxby) Hand movement direction in motor cortex (Sanes) Syllable vocaliation in posterior brain Movement direction in parietal cortex (CMRR) Purpose Masks Visual Auditory Here we intend to classify: Multiclass Classifier Classification Performance of Boosting vs. Single Classifier Multimodal activation patterns For very brief visual, motor, cognitive and auditory tasks Activation patterns are partly shared across tasks Error rate for different experimental data sets Training Test Boost I Boost II Single Across: Martine-Ramon et al. Neuroimage 2006 subjects field strengths spatial resolutions fmri acquisition methods Boosting (Boost I and Boost II) consistently outperforms single classifier Limitations Motor Cognitive Performance depends on the degree of segmentation of the brain Information must be sparse 4

5 Neurofeedback Differs from earlier attempts to provide bio-feedback using measurements of respiration, pulse, skin temperature or less spatially precise brain measurements from EEG. Real-time fmri allows to isolate and provide feedback associated with single cognitive or sensory events from a specific brain region. Single Trial Amygdala Activation during Sad Mood Induction 1.5 Tesla, multi-echo EPI with echo-specific gradient compensation in amygdala and real-time weighted echo averaging Single Trial: Self-rating Feedback of Amygdala Activation Button press 20 s 30 s 10 s 2 male, 4 female, years, 10 randomied trials (5 neutral, 5 sad) Several papers have recently reported success in real-time fmri guided self-regulation through neuro-feedback (DeCharms et al., 2004 and 2006; Posse et al., 2003; Weiskopf et al., 2004; Yoo et al., 2004). Posse et al. Neuroimage 2003 Self-Regulation of Visual Cortex with Real Time Neurofeedback Loop Feedback Modulation in Visual Cortex Aim: Graded control of activity in visual cortex through conscious modulation of visual attention to feedback signal Subject instruction: TARGET SCAN: Up and down regulate the bar with visual feedback CONTROL SCANS: (1) false feedback, (2) focus and blur vision Posse et al, HBM-Abstract Tesla, multi-echo EPI with weighted echo averaging 3 healthy subjects aged 23, 34, 43 Visual cortex identified via functional localier and pilot attention modulation scan Inferior parts of visual cortex appear to be under attentional control Outlook Pre-, intra- and post-operative real-time fmri Real-time time-domain pattern classification Neurofeedback assisted training (PTSD, Pain control) and learning Neuro-Economics Neuro-Law Brain-controlled Games Functional Metabolic Imaging A method to non-invasively map dynamic bio-chemical changes for clinical and neuroscience research Focus on 1 H spectroscopy due to its high sensitivity as compared to other nuclei Examples: Physiological and metabolic challenges Neuronal activation 5

6 The Challenges of 1 H Metabolic MRI Proton-Echo-Planar-Spectroscopic- Imaging (PEPSI) The concentration gap limits spatial and temporal resolution MRI (water protons): 110 M Cho, Cr, NAA, Ino, Glu, Gln, Lac, GABA, Ala, Glc: ~ 1-10 mm) Magnetic field inhomogeneity broadens the spectral lines Current state spectral lines merge, silent brain of the art areas Metabolic MRI is very slow Proton-Echo-Planar-Spectroscopic- Imaging (PEPSI) t PEPSI Spectroscopic FID or Half- Echo S G 1/SW t Echo: E O E O E Spectral Quantification Spectral fit based on analytically modeled spectral basis sets (18 metabolites) Absolute quantification based on tissue water from reference scan Partial volume and relaxation correction Fully automated Cho Ino Cr Glu/Gln NAA 3 Tesla 8.5 min scan Large-N Phased Array Coil TE: 14 ms, 8 mm in plane resolution, 32x32 matrix, 1 cc, 8 averages 3T Trio: 32-channel array 1.5T Avanto: 90-channel array MRI Ins tcho Cr+PCr NAA NAA/G Glu Gln PE Asp GABA MM09 MM12 MM20 Developed at MGH G. Wiggins, L. Wald 6

7 Sub-minute (12 s) 2D SENSE-PEPSI with 32- Channel Array at 3 T Regional Brain Metabolic Response to Hyperventilation (1.5 T) TE: 15 ms, TR: 2 s, 32x32 matrix, 1.1 cc, single average Baseline Hyperventilation MRI Lactate increase Posse et al., MRiM,, 1997 Functional Biochemical Imaging in Anxiety Disorders (1.5 T) Acknowledgments Lacate infusion measured by high speed MRSI baseline lactate infusion post infusion Dager et al., Arch. Gen. Psych., 1999 Control Panic Responder University of New Mexico Juan Bustillo, Hongji Chen, Arvind Caprihan, Kunxiu Gao, Chuck Gasparovic, Greg Heileman, Donner Holten, Vladimir Koltchinskii, Ting Li, Manel Martíne-Ramón, Andrew Mayer, Ricardo Otao, Gerardo Villarreal, Jing Xu A.A. Martinos Center for Biomedical Imaging - Massachusetts General Hospital, Harvard Medical School Fa-Hsuan Lin, Lawrence Wald Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN Pierre-Gilles Henry, Malgorata Marjanska, Bryon Mueller, Kamil Ugurbil, Kelvin O. Lim, McLean Hospital Brain Imaging Center, McLean Hospital, Harvard University, MA Chun Zuo, Perry Renshaw Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, CA Jeffry R. Alger University of Washington Stephen Dager Thank you for your attention! 7