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How Much Neuronal Information Can We Extract With fMRI?

This article explores the advancements in fMRI technology, methodologies, and interpretation, and discusses the challenges in extracting neuronal information.

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How Much Neuronal Information Can We Extract With fMRI?

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  1. Advancing fMRI Utility How Much Neuronal Information Can We Extract With fMRI? bandettini@nih.gov Peter A. Bandettini, Ph.D Unit on Functional Imaging Methods & Functional MRI Facility Laboratory of Brain and Cognition National Institute of Mental Health

  2. J. Illes, M. P. Kirschen, J. D. E. Gabrielli, Nature Neuroscience, 6 (3)m p.205 Motor (black) Primary Sensory (red) Integrative Sensory (violet) Basic Cognition (green) High-Order Cognition (yellow) Emotion (blue)

  3. Most fMRI studies since 1992: Minimum necessary: • Whole Brain EPI • Field strength of 1.5T or greater • Basic stimulus delivery and feedback • Software for image transfer, analysis, and display Typical advanced features: • Higher resolution whole brain EPI, spiral, or multi-shot • Field strength of 3T to 7T • Quadrature and Surface coils (single, multiple) • Susceptibility correction • ASL (perfusion imaging) • Multiple subject interface devices, including EEG, SCR, eye position. • Multi-subject analysis, more rigorous statistics, more sophisticated display methods, exploratory analysis

  4. 1991-1992 1992-1999

  5. Diff. tensor Technology Mg+ 7T >8 channels 1.5T,3T, 4T EPI on Clin. Syst. Venography Real time fMRI EPI SENSE Nav. pulses “vaso” Local Human Head Gradient Coils Quant. ASL Z-shim Baseline Susceptibility MRI Dynamic IV volume Spiral EPI ASL Current Imaging? BOLD Simultaneous ASL and BOLD Multi-shot fMRI Correlation Analysis CO2 Calibration Methodology Motion Correction Latency and Width Mod Parametric Design Multi-Modal Mapping Surface Mapping Baseline Volume Free-behavior Designs ICA Phase Mapping Mental Chronometry Multi-variate Mapping Linear Regression IVIM Deconvolution Fuzzy Clustering Event-related BOLD models PET correlation Interpretation IV vs EV ASL vs. BOLD Layer spec. latency Bo dep. Pre-undershoot PSF of BOLD TE dep Resolution Dep. Excite and Inhibit Extended Stim. Blood T2 Post-undershoot Metab. Correlation Linearity SE vs. GE CO2 effect Optical Im. Correlation Hemoglobin Fluctuations NIRS Correlation Balloon Model Electrophys. correlation Inflow Veins Complex motor Applications Memory Imagery Emotion Language Epilepsy Children Drug effects Motor learning Tumor vasc. Mirror neurons BOLD -V1, M1, A1 Presurgical Ocular Dominance Attention Volume - Stroke Clinical Populations V1, V2..mapping Priming/Learning D Volume-V1 Performance prediction Plasticity Face recognition 36 82 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03

  6. Engineering Computer Science Physics Statistics Cognitive Science Physiology Medicine Technology Methodology Neuroscience Interpretation Applications

  7. What are the biggest unknowns/challenges? • Technology • Methodology • 3. Interpretation

  8. What are the biggest unknowns/challenges? • Technology • Methodology • 3. Interpretation

  9. Technology • Field strength • Signal to noise • Resolution • Shimming

  10. Field strength Plusses -SNR proportional to Bo -Contrast proportional to Bo Minuses -Susceptibility effects increase -RF penetration problems -SAR problems -Fluctuations increase Bottom Line -SNR buys resolution when technology catches up -Fluctuations may be increasingly interesting

  11. Signal to noise Methods to increase -Increase Bo -Smaller RF coils (arrays) -Reduce noise Issue: -Temporal SNR is most important

  12. More SNR…More “signal” is there… NeuroImage

  13. 2 3 1 4 8 7 6 5 General concept de Zwart et al. MRM 47:1218 (2002).

  14. MRI Reception Hardware – 16 channels Built by Nova Medical Inc. de Zwart et al. MRM 51:22 (2004).

  15. Individual coil images Single combined image

  16. Experimental Data SNR comparison 6 x 1.8 x Both images are in the same scale. Relative intensity corresponds to SNR. 3-fold SNR improvements

  17. TSNR comparison 1 channel (MAI coil) 16 channel a b Figure 1 EPI 128x96 B a TSNR 64x48 128x96 Figure 2 c d Experimental Data TSNR16/TSNR1 : ROI: 64x48 -> 1.98 +/- 0.52 128x96 -> 2.2 +/- 0.53 An average over all slices for both resolutions -> 1.7 +/- 0.3

  18. Bodurka et al.

  19. Signal / Thermal Noise Signal / Physiologic Noise Signal to Noise Ratio Resolution, Speed, Surface Coils, Field Strength, etc..

  20. Resolution Methods to increase: -Faster sampling rate per image -Faster gradient switching -Longer readout window -Partial k-space -Multi-shot techniques -Parallel Imaging Bottom Line: -Up against limits in most methods -Multi-shot still problematic (time, stability) -Parallel imaging is most promising

  21. SENSE Imaging ≈ 5 to 30 ms Pruessmann, et al.

  22. Axial-oblique single shot SENSE EPI using 16-channel reception. 192x144 : 1.25x1.25x2mm

  23. Average Temporal Signal-to-Noise ratio Comparison Between Coils Anterior Medial 150% 100% 50% 16 Element Array> GE coil Posterior

  24. Shimming A solvable problem: -more shim coils and/or coil designs -increased shim currents -higher resolution (fixes dropout) -shorter readout window (fixes distortion) -shim inserts -z-shim methods

  25. Methodology • Temporal resolution • Spatial specificity • Magnitude Calibration • Multi-subject averaging/normalization at very high resolution • Paradigm design • Motion (very slow and motion correlated) • Scanner acoustic noise effect removal • Individual Map “Classification” • Local pattern effect mapping and classification • Exploratory analysis techniques (ICA, PCA..) • Temporal fluctuations (removal and use) • Simultaneous measures with fMRI • Baseline susceptibility mapping • Non-invasive blood volume imaging • Multimodal integration • Functional Connectivity mapping • Real time fMRI • Neuronal Current MRI

  26. Methodology • Temporal resolution • Spatial specificity • Magnitude Calibration • Multi-subject averaging/normalization at very high resolution • Paradigm design • Motion (very slow and motion correlated) • Scanner acoustic noise effect removal • Individual Map “Classification” • Local pattern effect mapping and classification • Exploratory analysis techniques (ICA, PCA..) • Temporal fluctuations (removal and use) • Simultaneous measures with fMRI • Baseline susceptibility mapping • Non-invasive blood volume imaging • Multimodal integration • Functional Connectivity mapping • Real time fMRI • Neuronal Current MRI

  27. task task • Temporal resolution

  28. The major obstacle in BOLD contrast temporal resolution: + 2 sec Latency - 2 sec Magnitude Venogram P. A. Bandettini, The temporal resolution of Functional MRI in "Functional MRI" (C. Moonen, and P. Bandettini., Eds.), p. 205-220, Springer - Verlag,. 1999.

  29. Hemi-Field Experiment 9.0 seconds 15 seconds 500 msec 500 msec 20 30 10 Time (seconds) Right Hemisphere Left Hemisphere

  30. 0 10 20 30 500 ms 500 ms RightHemifield Left Hemifield + 2.5 s - = 0 s - 2.5 s

  31. Cognitive Neuroscience Application: PNAS

  32. A A A B B B Word vs. Non-word 0o, 60o, 120o Rotation Regions of Interest Inferior Frontal Gyrus Precentral Gyrus Middle Temporal Gyrus

  33. sdelay = 107ms 1 run: 1% Noise 4% BOLD 256 time pts /run 1 second TR Number t -1000 -500 0 500 1000 delay estimate (ms) 500 400 16 sec on/off Smallest latency Variation Detectable (ms) (p < 0.001) 300 8 sec on/off 200 100 0 0 5 10 15 20 25 30 11 Number of runs

  34. No calibration 11.7 T

  35. Paradigm Design • Block Design • 2. Parametric Design • 3. Frequency Encoding • 4. Phase Encoding • 5. Event Related • 6. Orthogonal Design • 7. Free Behavior Design

  36. The Skin Conductance Response (SCR) Ventromedial PFC Orbitofrontal Cortex Amygdala Hypothalamus Sympathetic Nervous System Resistance change across two electrodes induced by changes in sweating. Sweat Gland

  37. Brain activity correlated with SCR during “Rest” J. C. Patterson II, L. G. Ungerleider, and P. A Bandettini, Task - independent functional brain activity correlation with skin conductance changes: an fMRI study. NeuroImage 17: 1787-1806, (2002).

  38. Motion (very slow and activation correlated) Very slow: -a problem when looking at slow state changes -one solution: ASL techniques Activation correlated: -separable from hemodynamic response

  39. ASL Techniques show more temporal stability Experimental design and the relative sensitivity of BOLD and perfusion fMRI Aguirre GK, Detre JA, Zarahn E, Alsop DC, NEUROIMAGE 15 (3): 488-500 MAR 2002

  40. BOLD response motion task t fMRI during tasks that involve brief motion Blocked Design motion BOLD response task Event-Related Design R. M. Birn, P. A. Bandettini, R. W. Cox, R. Shaker, Event - related fMRI of tasks involving brief motion. Human Brain Mapping 7: 106-114 (1999).

  41. Overt Word Production 2 3 4 5 6 7 8 9 10 11 12 13 R. M. Birn, P. A. Bandettini, R. W. Cox, R. Shaker, Event - related fMRI of tasks involving brief motion. Human Brain Mapping 7: 106-114 (1999).

  42. motion t t Expected Response BOLD response Speaking – Blocked design R.M. Birn, et al. Human Brain Mapping 7(2), 106-114, 1999

  43. avg Expected Response avg Speaking – Event related design Constant ISI R.M. Birn, et al. Human Brain Mapping 7(2), 106-114, 1999

  44. deconv Expected Response deconv Variable ISI Speaking - ER-fMRI

  45. 0 50 100 150 200 250 300 0 50 100 150 200 250 300 0 50 100 150 200 250 300 0 0 50 100 150 200 250 300 300 0 50 100 150 200 250 Optimizing the stimulus paradigm Blocked (motion highly correlated) Blocked / Event-Related (low correlation w/ motion)

  46. Swallowing - Event-Related M.K. Kern, R.M. Birn, S. Jaradeh, et al., Am J Physiol Gastrointest Liver Physiol, 280(4), G531-538, 2001.

  47. Facial muscle movement R.M. Birn, et al. Human Brain Mapping 7(2), 106-114, 1999 Blocked design Event - Related design

  48. Individual Map “Classification” The issue: We can make inferences about groups when averaging individual maps, but can we make inferences which group an individual belongs to? Not yet. Requires extensive classification techniques.

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