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 O 2  CBF  O 2 …But… if perfectly regulated: O 2 should match demand eg glucose Roy&Sherrington 1890; Fox&Raichle 1986 Anaerobic metabolism hypothesis Compensation for inefficient O 2 diffusion

Neurovascular coupling Vascular density correlates with # of synapses

1. How ‘Neural activity’ is measured and quantified: fMRI: simultaneous activity of MANY neurons in a LARGE region of cortex (millimeters) over a LONG period (seconds). What component of the neural activity most predicts the fMRI signal? Average firing rate of all / a subpopulation of neurons? Degree of synchronous spiking? The Local Field Potential (LFP), believed to reflect dendritic currents? The Multi Unit Activity (MUA), believed to reflect spiking near the electrode tip? The current source density? Some measure of local average synaptic activity? Some measure of subthreshold electrical activity? All the above may correlate with each other under some circumstances, but can also vary independently of each other.

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 Glutamate  calcium wave  PLA2  PG  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 PSBellgowan 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 tsts 

Hemodynamic Response vs. ISI

Modelling Non-linear effects

Balloon/Windkessel Model-Buxton ‘98 Non-linear coupling: rCBF & BOLD Spm_fx_HRF Friston 2000 Mechelli 2001

Delayed Compliance model

Comparing different subjects

Variability of HRF: Evidence Aguirre, 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 sessionsame subject, different session

Variability of HRF: Implications Aguirre, 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 _____________________________________________________ __________________ Copyright (C) 2005 Wellcome Department of Imaging Neuroscience

% Karl Friston % $Id: spm_hrf.m :20:52Z john $ % global parameter % global defaults if ~isempty(defaults), fMRI_T = defaults.stats.fmri.t; else, fMRI_T = 16; end; % default parameters % p = [ ]; 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);

Negative BOLD

Resting fluctuations