Peter A. Bandettini, Ph.D. Section on Functional Imaging Methods Laboratory of Brain and Cognition & Functional MRI Facility Contentious Issues in fMRI

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Yacoub E, Le TH, Ugurbil K, Hu X (1999) Magn Res Med 41(3): Courtesy of Arno Villringer The Undershoots

CBF BOLD CBF (% change) BOLD (% change) 20 sec Motor Stimulation Time (sec) A BOLD undershoot without a CBF undershoot could be due to a slow return to baseline of either CBV or CMRO 2 BOLD post-stimulus undershoot

BOLD Signal Dynamics Courtesy Rick Buxton

“During the poststimulus period, the amplitude and directionality (positive/negative) of the BOLD signal in both contralateral and ipsilateral sensorimotor cortex depends on the poststimulus synchrony of 8–13 Hz EEG neuronal activity, which is often considered to reflect cortical inhibition, along with concordant changes in CBF and metabolism.” “We suggest that the poststimulus EEG and fMRI responses may be required for the resetting of the entire sensory network to enable a return to resting-state activity levels.”

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

McKiernan, et al (2003), Journ. of Cog. Neurosci. 15 (3), Regions showing negative signal changes during cognitive tasks Negative Signal Change

Schmuel et al. (2002) Neuron, Vol. 36, 1195–1210

B: BOLD C: Volume D: -Volume Goense, J., Merkle, H., and Logothetis, N.K.(2012). Neuron 76, 629–639.

Bandettini, PA (2012). Neuron 76,

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Parametric Manipulation Motor Cortex Auditory Cortex S. M. Rao et al, (1996) “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex.” J. Cereb. Blood Flow and Met. 16, J. R. Binder, et al, (1994). “Effects of stimulus rate on signal response during functional magnetic resonance imaging of auditory cortex.” Cogn. Brain Res. 2, 31-38

fMRI responses in human V1 are proportional to average firing rates in monkey V1 0.4 spikes/sec -> 1% BOLD Rees, G., Friston, K., and Koch, C A direct quantitative relationship between the functional properties of human and macaque V5. Nat. Neurosci. 3: 716–723. Heeger, D. J., Huk, A. C., Geisler, W. S., and Albrecht, D. G Spikes versus BOLD: What does neuroimaging tell us about neuronal activity? Nat. Neurosci. 3: 631– spikes/sec -> 1% BOLD

Logothetis et al. (2001) “Neurophysiological investigation of the basis of the fMRI signal” Nature, 412,

Relationship to Neuronal Activity Muthukumaraswamy, S. D., Singh, K. D. (2008) NeuroImage 40 (4), pp

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Logothetis et al. (2001) “Neurophysiological investigation of the basis of the fMRI signal” Nature, 412, S. M. Rao et al, (1996) “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex.” J. Cereb. Blood Flow and Met. 16, Linearity

measured linear Signal (%) time (s) Stimulus OFF duration (s) Linearity linear measured Brief “ off ” periods produce smaller decreases than expected. R.M. Birn, P. A. Bandettini, NeuroImage, 27, (2005) measured linear Signal Brief “ on ” periods produce larger increases than expected. Linearity Stimulus Duration (s) linear R. M. Birn, Z. Saad, P. A. Bandettini, NeuroImage, 14: , (2001) time (s) Linearity

Varying the Duty Cycle % ON 50% ON 75% ON 8% ON time (s) Signal Deconvolved Response 25% 50% 75% Brief ON Brief OFF Duty Cycle Linearity R.M. Birn, P. A. Bandettini, NeuroImage, 27, (2005) Linearity

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

task

P. A. Bandettini, Functional MRI temporal resolution in "Functional MRI" (C. Moonen, and P. Bandettini., Eds.), p , Springer - Verlag,

P. A. Bandettini, (1999) "Functional MRI" Magnitude Latency + 2 sec - 2 sec Venogram Latency Variation…

Number of runs 8 sec on/off 16 sec on/off Smallest latency Variation Detectable (ms) (p < 0.001) t 1 run: Number delay estimate (ms)  delay = 107ms 11 1% Noise 4% BOLD 256 time pts /run 1 second TR

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue. How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current

Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current Yacoub et al. NeuroImage 24 (3), pp

Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current Yacoub et al. NeuroImage 37 (4), pp

Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current H. Lu et al. Magnetic Resonance in Medicine 50 (2), pp

Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current K. Miller, et al Proceedings of the National Academy of Sciences of the United States of America 104 (52), pp D. Le Bihan, et al Proceedings of the National Academy of Sciences of the United States of America 103 (21), pp

Other controversial contrast mechanisms: a.T2 contrast – Spin-echo b.Blood Volume (VASO) c.Diffusion d.Neuronal Current Intracellular Current Magnetic Field Adapted from: J.P. Wikswo Jr et al. J Clin Neurophy 8(2): , 1991 Surface Field Distribution Across Spatial Scales

Power decrease between PRE & TTX EEG : ~ 81% Decrease between PRE & TTX MR phase: ~ 70% Decrease between PRE & TTX MR magnitude: ~ 8% N. Petridou, D. Plenz, A. C. Silva, J. Bodurka, M. Loew, P. A. Bandettini, Proc. Nat'l. Acad. Sci. USA. 103, (2006). TTX EEG MR (7T)

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Over the past year, a heated discussion about ‘circular’ or ‘nonindependent’ analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in <– 100 words per question. Although divergent views remain on some of the questions, there is also a substantial convergence of opinion, which we have summarized in a consensus box. The box provides the best current answers that the five authors could agree upon.

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Multi-sensory integration Visual Auditory Multisensory M.S. Beauchamp et al.,

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Boynton (2005), News & Views on Kamitani & Tong (2005) and Haynes & Rees (2005)

Kamitani & Tong (2005) Lower spatial frequency clumping

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state

Seed Based Correlation from the Posterior Cingulate Cortex (PCC) Greicius M D et al. PNAS 2003;100: De Luca, Neuroimage 29(4) 2006 Visual Visuospatial Executive Sensory Auditory Dorsal Pathway Ventral Pathway Seed Voxel ICA

Identifying the brain’s most globally connected regions, M. W. Cole, S. Pathak, W. Schneider, NeuroImage 49, 2010, Hub Maps

Some Basic Limitations Seed voxel: -Inefficient: Only assesses a fraction of the possible networks. -Subjective: Iterative and user-biased choice of what looks best. ICA: -Random component ranking. Difficult to separate real vs artifactual. Hub Map: - Determines regions that are most globally connected, not specific networks. All of these methods usually only consider time chunks of 5 min or more and do not consider changes in correlation over time.

Two other issues with imaging resting state fluctuations: 1.Global signal correction or not? 2.Short range correlations may be scanner-related.

The issue of global signal regression K. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, P. A. Bandettini, NeuroImage, 44, (2009)

…the global regression story is not so simple…

You can observe a lot by watching --- Yogi Berra

He and Liu, NIMG 2012

Delta and Theta Alpha = EEG vigilance Klimesch. Brain Res Rev alpha Delta theta Relative EEG Hz

Relation across Subjects and Runs EEG spectra for three representative subjects. Caffeine session pre-dose (blue), control session pre- dose (red) /post-dose sections (green)

Relative EEG Higher vigilance -> lower GS -> more anticorrelation Pre Post

More diversified neural activity Tononi et al. BMC Neurosci Anti-correlation Global signal amplitude Vigilance

Summary Global signal amplitude decreases with increases in vigilance. Relationship between vigilance, global signal amplitude, and DMN-TPN anticorrelation may hold across multiple time scales Neural activity may become more spatially diversified and less globally synchronized as we become more alert Preprocessing steps that remove the effects of global signal differences may reduce the effect of connectivity differences that are related to changes in vigilance

The issue of correlation across voxels due scanner instabilities N. Kriegeskorte, J. Bodurka, P. Bandettini, International Journal of Imaging Systems and Technology, 18 (5-6), (2008)

What do these low frequencies really mean? What do the low frequencies “do?” How can we better remove ALL the non-neuronal noise? Other Open Questions on Resting State

The Undershoots Negative signal changes Relationship to neuronal activity Linearity Temporal precision fMRI contrast mechanisms and sequences Analysis circularity issue How to handle high resolution Basis of the decoding signal How to best process/interpret resting state