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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!