oh BOLD where art thou? Evidence for a “mm-scale” match between intracortical and fMRI measures.

A frequently discussed problem with functional magnetic resonance imaging is that we don’t really understand how the hemodynamic ‘activations’ measured by the technique relate to actual neuronal phenomenon. This is because fMRI measures the Blood-Oxygenation-Level Dependent (BOLD) signal, a complex vascular response to neuronal activity. As such, neuroscientists can easily get worried about all sorts of non-neural contributions to the BOLD signal, such as subjects gasping for air, pulse-related motion artefacts, and other generally uninteresting effects. We can even start to worry that out in the lab, the BOLD signal may not actually measure any particular aspect of neuronal activity, but rather some overly diluted, spatially unconstrained filter that simply lacks the key information for understanding brain processes.

Given that we generally use fMRI over neurophysiological methods (e.g. M/EEG) when we want to say something about the precise spatial generators of a cognitive process, addressing these ambiguities is of utmost importance. Accordingly a variety of recent papers have utilized multi-modal techniques, for example combining optogenetics, direct recordings, and FMRI, to assess particularly which kinds of neural events contribute to alterations in the BOLD signal and it’s spatial (mis)localization. Now a paper published today in Neuroimage addresses this question by combining high resolution 7-tesla fMRI with Electrocorticography (ECoG) to determine the spatial overlap of finger-specific somatomotor representations captured by the measures. Starting from the title’s claim that “BOLD matches neuronal activity at the mm-scale”, we can already be sure this paper will generate a great deal of interest.

From Siero et al (In Press)

As shown above, the authors managed to record high resolution (1.5mm) fMRI in 2 subjects implanted with 23 x 11mm intracranial electrode arrays during a simple finger-tapping task. Motor responses from each finger were recorded and used to generate somatotopic maps of brain responses specific to each finger. This analysis was repeated in both ECoG and fMRI, which were then spatially co-registered to one another so the authors could directly compare the spatial overlap between the two methods. What they found appears at first glance, to be quite impressive:
From Siero et al (In Press)

Here you can see the color-coded t-maps for the BOLD activations to each finger (top panel, A), the differential contrast contour maps for the ECOG (middle panel, B), and the maximum activation foci for both measures with respect to the electrode grid (bottom panel, C), in two individual subjects. Comparing the spatial maps for both the index and thumb suggests a rather strong consistency both in terms of the topology of each effect and the location of their foci. Interestingly the little finger measurements seem somewhat more displaced, although similar topographic features can be seen in both. Siero and colleagues further compute the spatial correlation (Spearman’s R) across measures for each individual finger, finding an average correlation of .54, with a range between .31-.81, a moderately high degree of overlap between the measures. Finally the optimal amount of shift needed to reduce spatial difference between the measures was computed and found to be between 1-3.1 millimetres, suggesting a slight systematic bias between ECoG and fMRI foci.

Are ‘We the BOLD’ ready to breakout the champagne and get back to scanning in comfort, spatial anxieties at ease? While this is certainly a promising result, suggesting that the BOLD signal indeed captures functionally relevant neuronal parameters with reasonable spatial accuracy, it should be noted that the result is based on a very-best-case scenario, and that a considerable degree of unique spatial variance remains for the two methods. The data presented by Siero and colleagues have undergone a number of crucial pre-processing steps that are likely to influence their results: the high degree of spatial resolution, the manual removal of draining veins, the restriction of their analysis to grey-matter voxels only, and the lack of spatial smoothing all render generalizing from these results to the standard 3-tesla whole brain pipeline difficult. Indeed, even under these best-case criteria, the results still indicate up to 3mm of systematic bias in the fMRI results. Though we can be glad the bias was systematic and not random– 3mm is still quite a lot in the brain. On this point, the authors note that the stability of the bias may point towards a systematic miss-registration of the ECoG and FMRI data and/or possible rigid-body deformations introduced by the implantation of the electrodes), issues that could be addressed in future studies. Ultimately it remains to be seen whether similar reliability can be obtained for less robust paradigms than finger wagging, obtained in the standard sub-optimal imaging scenarios. But for now I’m happy to let fMRI have its day in the sun, give or take a few millimeters.

Siero, J. C. W., Hermes, D., Hoogduin, H., Luijten, P. R., Ramsey, N. F., & Petridou, N. (2014). BOLD matches neuronal activity at the mm scale: A combined 7T fMRI and ECoG study in human sensorimotor cortex. NeuroImage. doi:10.1016/j.neuroimage.2014.07.002

 

8 thoughts on “oh BOLD where art thou? Evidence for a “mm-scale” match between intracortical and fMRI measures.

  1. Good post! If the bias really were just 3 mm, i.e. one voxel’s worth, then that’s not going to worry many people.

    But as you say, this is a best (best best) case scenario. It would be interesting to repeat the study at 1.5T and 3T. The errors might be much higher.

    • Thanks! Happy to be back at it, wrote this thinking I should get ot it before you did😉

      I agree, I don’t think many would be too worried about such a small systematic bias, and it seems quite reasonable (as the authors suggest) that it would be more related to registration error than BOLD variability. But I would be really interested to see what happens as you start getting closer to the standard paradigm – for example including voxels with veins is likely going to influence the outcome as it would act as a spatial filter (all of which is altered further at higher resolution/strength). However the authors actually consider this in the opposite direction, suggesting that modelling the BOLD variability in those voxels might actually improve the specificity do to capillary effects. So it’s going to be very interesting to see how resolution, noise measurement, and paradigm impact this result. I think it probably also matters that somatotopic activity is highly reliable (both in terms of neural activity and HRF variability i’d imagine). Would be interested to see if 3mm holds in PFC, where you may have greater HRF variability.

    • Yup, they almost certainly will be. We tend to think of BOLD as a single thing whereas it’s a complicated contrast mechanism with many, many dependencies. Changing B0, slice acquisition order (flow sensitivity), TE, spatial resolution (partial volume effects) and other parameters all affect the spatial *and* temporal characteristics of the BOLD signal you observe.

      I’ve always found it helpful to break BOLD into four components when thinking about its origins: extravascular, intravascular, small vessel and large vessel. (There is a continuum of vessel size but here I’m conceptualizing capillaries and venuoles versus draining veins, say.) At 1.5 T, with standard gradient echo EPI and 3-5 mm resolution, the majority of the strongest BOLD signals will come from voxels whose properties are dominated by extravascular large vessels. There will be contributions from the other compartments, but the dominant one is as stated. By 3 T (assume same task, same subject) the literature suggests about a 50:50 split of large versus small vessel-dominated voxels. Why? If we do a spin echo EPI instead of a gradient echo EPI acquisition, about half of the BOLD signals go away, indicating that they have a static dephasing that a spin echo can fix, hence long length-scale susceptibility effects. (Jochimsen et al., Mag Res Med 52, 724 (2004) for a good example.)

      The intravascular compartment can be virtually eliminated with flow crusher gradients within the EPI acquisition (spin echo or gradient echo). Hulvershorn did some clever separation of all the components back in 2005 (NeuroImage 24, 216). And one intriguing result of de Zwart et al. (NeuroImage 24, 667 (2005)) was that the time to peak varies sufficiently that one can use it as a crude “vein filter.” Signals peaking around 3-4 secs were generally located within parenchyma whereas those peaking around 5-6 secs were at the cortical surface.

      So, yeah, this current paper is good as far as it goes, but we can’t infer much for lower field strengths. And even at one field strength we must always recognize that the acquisition parameters and the pulse sequence change the nature of the BOLD signal we get. BOLD is a concept, not a universal constant.

      Good post, Micah.

      • Wow, I don’t have much to add, but thanks for your very interesting response! It’s clear we have a long way to go and that it’s going to be very important to continue clamping down on how exactly BOLD signals relate to underlying phenomenon. I’m always happy to read papers like these as I think they show it’s not nearly as simple as ‘BOLD is rubbish’ – it’s a very complex phenomenon that certainly has some use, all the more so the more we understand it!

        • Yep! It’s a rich subject. And understanding some aspect of neurovascular coupling definitely benefits from restricting the model carefully. A unified model across all fields, sequences and parameters seems unlikely, but I’m fine with that. There is rich information, and one person’s noise is another’s signal!

    • For sure, the authors themselves suggest that the 3mm bias is likely due to a misregistration between the ECoG and the fMRI data, or even due to a slight rigid-body displacement caused by the implantation.

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