Biological Basis for the Blood

Oxygenation Level Dependent signal

What is BOLD?

blood FLOW blood VOLUME blood OXYGENATION.

What do we see? Believed to be:

Initial O2 CBF O2

…But… if perfectly regulated:

O2 should match demand eg glucose Roy&Sherrington 1890; Fox&Raichle 1986

Anaerobic metabolism hypothesis Compensation for inefficient O2 diffusion

Logothetis et.al. (Nature, 2001): simultaneous fMRI, LFPs and MUAs in rats. Concluded that BOLD fMRI signals “reflect the input and intracortical processing of a given area rather than its spiking output.”

GABAA agonist

COX-1 Glutamatecalcium wavePLA2PG IP3

Feed forward pro-active control

Haemodynamic Response function

Factors in Spatial resolution MRI technique: Hardware, diffusion limit of

water and motion artifact High resolution eg interleaved EPI

Haemodynamics:Overwatering hypothesis, vascular territory Initial undershoot

Large vessel effects SE vs GE, choice of CNR vs resolution

Best resolution=humans 1-2mm; Columnar resolution demonstrated in cat visual cortex

Temporal resolution SNR trade-off Safety: peripheral nerve stimulation TR>T1

Haemodynamic response to electrical activity 4-8s

Solution: Gating, fast sequences, EEG-fMRI

Resolution abt 500ms now, might be as little as 50ms with gating, ideal stimulus…Bellgowan PS et al PNAS 2003

Linearity assumption Linear Time Invariant system Boynton&Heeger Only accurate greater than 6s Actually increases according to compressive

nonlinear saturating function of stimulus energy Nonlinear component

Stimulus to neural signal Neural signal to BOLD

Linearity assumption 2 LFP, MUA, spike comparisons with BOLD Linear relationship over restricted ranges Stronger with LFPs than MUAs LFP-MUA dissociation

Non-linearity of BOLD Response

BOLD response vs. length of stimulation

BOLD response during rapidly-repeated stimulation

ts

Hemodynamic Response vs. ISI

Balloon/Windkessel Model-Buxton ‘98

Non-linear coupling:

rCBF & BOLD Spm_fx_HRF

Friston 2000

Mechelli 2001

Variability of HRF: EvidenceAguirre, Zarahn & D’Esposito, 1998• HRF shows considerable variability between subjects

• Within subjects, responses are more consistent, although there is still some variability between sessions

different subjects

same subject, same session same subject, different session

Variability of HRF: ImplicationsAguirre, Zarahn & D’Esposito, 1998• Generic HRF models (gamma functions) account for 70% of variance• Subject-specific models account for 92% of the variance (22% more!)• Poor modeling reduces statistical power• Less of a problem for block designs than event-related• Biggest problem with delay tasks where an inappropriate estimate of the initial and final components contaminates the delay component

• Possible solution: model the HRF individually for each subject

• Possible caveat: HRF may also vary between areas, not just subjects• Buckner et al., 1996:

• noted a delay of .5-1 sec between visual and prefrontal regions• vasculature difference?• processing latency?

• Bug or feature? • Menon & Kim – mental chronometry

returns a hemodynamic response function FORMAT [hrf,p] = spm_hrf(RT,[p]); RT - scan repeat time p - parameters of the response function (two gamma functions) defaults (seconds) p(1) - delay of response (relative to onset) 6 p(2) - delay of undershoot (relative to onset) 16 p(3) - dispersion of response 1 p(4) - dispersion of undershoot 1 p(5) - ratio of response to undershoot 6 p(6) - onset (seconds) 0 p(7) - length of kernel (seconds) 32 hrf - hemodynamic response function p - parameters of the response function

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Copyright (C) 2005 Wellcome Department of Imaging Neuroscience

% Karl Friston % $Id: spm_hrf.m 112 2005-05-04 18:20:52Z john $

% global parameter %----------------------------------------------------------------------- global defaults if ~isempty(defaults), fMRI_T = defaults.stats.fmri.t; else, fMRI_T = 16; end;

% default parameters %----------------------------------------------------------------------- p = [6 16 1 1 6 0 32]; if nargin > 1 p(1:length(P)) = P; end

% modelled hemodynamic response function - {mixture of Gammas} %----------------------------------------------------------------------- dt = RT/fMRI_T; u = [0:(p(7)/dt)] - p(6)/dt; hrf = spm_Gpdf(u,p(1)/p(3),dt/p(3)) - spm_Gpdf(u,p(2)/p(4),dt/p(4))/p(5); hrf = hrf([0:(p(7)/RT)]*fMRI_T + 1); hrf = hrf'/sum(hrf);