the therapeutic mind scan real-time functional...
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RealReal--time Functional time Functional NeuroimagingNeuroimaging
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 ailmentsFunctional techniques mentioned: SPECT, fMRI, MRS
NYT, Feb. 20, 2005
Blood Oxygenation Level Dependent Contrast
Vessel diameter ranges from mm to Vessel diameter ranges from mm to mm, blood flow change during mm, blood flow change during activation changes blood oxygenationactivation changes blood oxygenation
Magnetic susceptibility (TMagnetic susceptibility (T22**) )
contrast in blood vessels contrast in blood vessels depends on blood oxygenationdepends on blood oxygenation
Real-time functional MRI
A method to detect and automatically interpret brain activation patterns for online decision making in clinical and neuroscience applicationsExamples:– Pre-surgical mapping– Lie detection
Definitions of Real-Time fMRI
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
Can fMRI capture even neuronal activity?
Real-Time fMRI: Advantages
Monitor data qualityMonitor changes in attention and subject performanceAssess experiment success Rapid results – cost savingNovel paradigm designs with feedbackWatch your own brain activation!
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Real-Time fMRI: Limitations
fMRI sensitivity decreases with increasing temporal resolutionSensitivity to transients (e.g. movement) increases with temporal resolution May be difficult to interpret due to brain noise, dynamic changes in activation patterns and movementMay need to sacrifice spatial resolutionAnalysis not as sophisticated as conventional methodology - post-processing may still be requiredNo group analysis (yet)
Long Term Goals
Improve sensitivity, image quality and acquisition speed of functional MRI
Characterize dynamic cognitive networks and neural correlates of emotions in single trials
Improve interpretation of cognitive activation patterns in real-time using pattern classification methods
Neuro-feedback to control localized brain activation for therapeutic use (motor learning, control of cognition)
! Challenge for measurement sensitivity and control of the experiment
Real-Time Signal Analysis: TurboFIRE
Gembris et al. MRiM 2000, Mathiak & Posse MRiM 2001, Posse et al., HBM 2001
Processing Capabilities
Preprocessing• Multi-echo EPI: T2
*-LM fit or weighted echo average based on localT2
*-value• 3D motion correction• Spatial normalization into Talairach AtlasStatistical Modeling• “Sliding-Window” correlation analysis
•6 simultaneous reference vectors•Reference Vector Optimization
• 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 scanHemodynamic response function: canonical 6-parameter or time-shifted poissonDetrending of up to 5th order cosine-waveforms
Gembris et al. MRiM 1999, Gao and Posse, HBM 2003
RealReal--Time Spatial Normalization in Time Spatial Normalization in Reference to Reference to TalairachTalairach AtlasAtlas
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 normalization parameters generated during normalization of the reference image.
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Automated Cluster Analysis with Anatomical Labels
Single-Shot Water Relaxation Mapping 4 Tesla, TR: 2sec, TEmin: 11 msec, ∆TE: 15 msec, 6 echo times, 64x48 matrix,
16 slices, 2-fold GRAPPA acceleration with 8-channel array coil
TE: 12-213 ms (20 msec/image) Weighted Average
Conventional EPI
Posse et al., MRiM 1999
BOLD Sensitivity Increase by Combining Single-Shot Multi-Echo EPI Data (1.5 T)
Sensitivity Optimized Real-Time Multi-Echo EPI at 4T
6-Echo Times: 9.5-76 msec(~ 2 * T2*)2-fold GRAPPA accelerationEcho-interleaved slice-specific XYZ-shimming in up to 3 regions brain regions to compensate signal lossesPoint-spread-correction using reference scan to reduce geometrical distortionWeighted echo-averaging based on T2* Map and XYZ-shimming scheme to maximize 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
128x128 matrix 32x32 matrix
15 s: eyes open + finger tapping vs. 15 s rest, EPI: 4 mm slice,15 s: eyes open + finger tapping vs. 15 s rest, EPI: 4 mm slice, TR: 3 sTR: 3 s
Single Index Finger Tap(Turbo-PEPSI, 8 echoes: 30 - 158 ms , TR: 1 s)
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Preoperative Language-Lateralization in Patient with High Grade Glioma (1.5 T)
Online data quality control
Short scan time (1 min) -> More procedures per unit time
Significant cost saving
Increased patient comfort and compliance
Pattern Specificity - Can we read the MIND?
Face and object recognition in visual cortex (Haxby)Hand movement direction in motor cortex (Sanes)Syllable vocalization in posterior brainMovement direction in parietal cortex (CMRR)
Increasing evidence Increasing evidence for category for category specific brain specific brain activation patternsactivation patterns
Neuro-Anatomically Constrained Boosting
Data preprocessingZero reductionVectorization
MulticlassClassifierMulticlassClassifier
MulticlassClassifier
Support Vector Machines (SVM) BoostingSplitting
T-Maps
Masks
Masking
Martinez-Ramon et al. Neuroimage 2006
Purpose
Here we intend to classify: – Multimodal activation
patterns – For very brief visual,
motor, cognitive and auditory tasks
– Activation patterns are partly shared across tasks
Across: subjectsfield strengths spatial resolutions fMRI acquisition methods
Visual
Motor Cognitive
Auditory Classification Performance of Boosting vs. Single Classifier
Error rate for different experimental data sets
Boosting (Boost I and Boost II) consistently outperforms single classifierLimitations
– Performance depends on the degree of segmentation of the brain– Information must be sparse
Training
Test
Boost I
Boost II
Single
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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.
Several papers have recently reported success in real-time fMRI guided self-regulation through neuro-feedback (DeCharmset al., 2004 and 2006; Posse et al., 2003; Weiskopf et al., 2004; Yoo et al., 2004).
Single Trial Amygdala Activation during Sad Mood Induction1.5 Tesla, multi-echo EPI with echo-specific gradient compensation in amygdala and real-time weighted echo averagingSingle Trial:
20 s 30 s 10 s
2 male, 4 female, 22-42 years, 10 randomized trials (5 neutral, 5 sad)
Feedback of AmygdalaActivation
Self-rating
Button press
Posse et al. Neuroimage 2003
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
Self-Regulation of Visual Cortex with Real Time Neurofeedback Loop
Posse et al, HBM-Abstract 2005
4 Tesla, multi-echo EPI with weighted echo averaging3 healthy subjects aged 23, 34, 43Visual cortex identified via functional localizer and pilot attention modulation scanInferior parts of visual cortex appear to be under attentional control
Feedback Modulation in Visual Cortex
Outlook
Pre-, intra- and post-operative real-time fMRIReal-time time-domain pattern classificationNeurofeedback assisted training (PTSD, Pain control) and learningNeuro-EconomicsNeuro-LawBrain-controlled Games…
Functional Metabolic Imaging
A method to non-invasively map dynamic bio-chemical changes for clinical and neuroscience researchFocus on 1H spectroscopy due to its high sensitivity as compared to other nucleiExamples:– Physiological and metabolic challenges– Neuronal activation
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The Challenges of 1H Metabolic MRI
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 spectral lines merge, silent brain areasMetabolic MRI is very slow Proton-Echo-Planar-Spectroscopic-Imaging (PEPSI)
t
Current state PEPSIof the art
Proton-Echo-Planar-Spectroscopic-Imaging (PEPSI)
t
S
1/SW
Spectroscopic FID or Half-Echo
G
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 scanPartial volume and relaxation correctionFully automated
NAA
Glu/Gln
CrCho
Ino
3 Tesla – 8.5 min scan
MRI Ins MRI Ins tChotCho Cr+PCrCr+PCr NAA NAA/G NAA NAA/G GluGlu
GlnGln PE Asp GABA MM09 MM12 MM20PE Asp GABA MM09 MM12 MM20
TE: 14 ms, 8 mm in plane resolution, 32x32 matrix, 1 cc, 8 averaTE: 14 ms, 8 mm in plane resolution, 32x32 matrix, 1 cc, 8 averagesges 3T Trio: 32-channel array 1.5T Avanto: 90-channel array
Large-N Phased Array Coil
Developed at MGH
G. Wiggins, L. Wald
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Sub-minute (12 s) 2D SENSE-PEPSI with 32-Channel Array at 3 T
TE: 15 ms, TR: 2 s, 32x32 matrix, 1.1 cc, single average
Regional Brain Metabolic Response to Hyperventilation (1.5 T)
Baseline Hyperventilation
MRI Lactateincrease
Posse et al., Posse et al., MRiMMRiM, 1997, 1997
Functional Biochemical Imaging in Anxiety Disorders (1.5 T)
Lacate infusion measured by high speed MRSI
Control Panic Responder
baseline
lactate infusion
post infusion
DagerDager et al., Arch. et al., Arch. Gen. Psych., 1999Gen. Psych., 1999
Acknowledgments
University of New Mexico– Juan Bustillo, Hongji Chen, Arvind Caprihan, Kunxiu Gao, Chuck Gasparovic, Greg
Heileman, Donner Holten, Vladimir Koltchinskii, Ting Li, Manel Martínez-Ramón, Andrew Mayer, Ricardo Otazo, Gerardo Villarreal, Jing Xu
A.A. Martinos Center for Biomedical Imaging - Massachusetts General Hospital, Harvard Medical School
– Fa-Hsuan Lin, Lawrence WaldCenter for Magnetic Resonance Research, University of Minnesota,Minneapolis, MN
– Pierre-Gilles Henry, Malgorzata Marjanska, Bryon Mueller, Kamil Ugurbil, Kelvin O. Lim,
McLean Hospital Brain Imaging Center, McLean Hospital, Harvard University, MA
– Chun Zuo, Perry RenshawAhmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, CA
– Jeffry R. AlgerUniversity of Washington
– Stephen Dager
Thank you for your attention!