Nikos Logothetis offers a long and detailed discussion
of what we can and cannot learn from brain imaging approaches. I'm giving a few clips from his discussion:
fMRI is not and will never be a mind reader, as some of the proponents of decoding-based methods suggest, nor is it a worthless and non-informative 'neophrenology' that is condemned to fail, as has been occasionally argued.
The principal advantages of fMRI lie in its noninvasive nature, ever-increasing availability, relatively high spatiotemporal resolution, and its capacity to demonstrate the entire network of brain areas engaged when subjects undertake particular tasks. One disadvantage is that, like all haemodynamic-based modalities, it measures a surrogate signal whose spatial specificity and temporal response are subject to both physical and biological constraints. A more important shortcoming is that this surrogate signal reflects neuronal mass activity.
Figure - Two slices of GE-EPI demonstrating the high functional signal-to-noise ratio (SNR) of the images, but also the strong contribution of macrovessels. The yellow areas (indicated with the green arrows) are pia vessels, an example of which is shown in the inset scanning electron microscopy image (total width of inset, 2 mm). For the functional images red indicates low and yellow indicates high.
MRI may soon provide us with images of a fraction of a millimetre (for example, 300 x 300 m2 with a couple of millimetres slice thickness or 500 x 500 x 500 m3 isotropic), which amount to voxel volumes of about two–three orders of magnitude smaller than those currently used in human imaging. With an increasing number of acquisition channels such resolution may ultimately be attained in whole-head imaging protocols, yielding unparalleled maps of distributed brain activity in great regional detail and with reasonable—a couple of seconds—temporal resolution. Would that be enough for using fMRI to understand brain function?
The answer obviously depends on the scientific question and the spatial scale at which this question could be addressed—"it makes no sense to read a newspaper with a microscope", as neuroanatomist Valentino Braitenberg once pointed out. To understand the functioning of the microcircuits in cortical columns or of the cell assemblies in the striosomes of basal ganglia, one must know a great deal about synapses, neurons and their interconnections. To understand the functioning of a distributed large-scale system, such as that underlying our memory or linguistic capacities, one must first know the architectural units that organize neural populations of similar properties, and the interconnections of such units. With 1010 neurons and 1014 connections in the cortex alone, attempting to study dynamic interactions between subsystems at the level of single neurons would probably make little sense, even if it were technically feasible. It is probably much more important to understand better the differential activity of functional subunits—whether subcortical nuclei, or cortical columns, blobs and laminae—and the instances of their joint or conditional activation. If so, whole-head imaging with a spatial resolution, say, of 0.7 0.7 mm2 in slices of 1-mm thickness, and a sampling time of a couple of seconds, might prove optimal for the vast majority of questions in basic and clinical research. More so, because of the great sensitivity of the fMRI signal to neuromodulation. Neuromodulatory effects, such as those effected by arousal, attention, memory, and so on, are slow and have reduced spatiotemporal resolution and specificity.
Logothesis offers a concluding perspective.
The limitations of fMRI are not related to physics or poor engineering, and are unlikely to be resolved by increasing the sophistication and power of the scanners; they are instead due to the circuitry and functional organization of the brain, as well as to inappropriate experimental protocols that ignore this organization. The fMRI signal cannot easily differentiate between function-specific processing and neuromodulation, between bottom-up and top-down signals, and it may potentially confuse excitation and inhibition. The magnitude of the fMRI signal cannot be quantified to reflect accurately differences between brain regions, or between tasks within the same region. The origin of the latter problem is not due to our current inability to estimate accurately cerebral metabolic rate of oxygen (CMRO2) from the BOLD signal, but to the fact that haemodynamic responses are sensitive to the size of the activated population, which may change as the sparsity of neural representations varies spatially and temporally. In cortical regions in which stimulus- or task-related perceptual or cognitive capacities are sparsely represented (for example, instantiated in the activity of a very small number of neurons), volume transmission (see Supplementary Information)—which probably underlies the altered states of motivation, attention, learning and memory—may dominate haemodynamic responses and make it impossible to deduce the exact role of the area in the task at hand. Neuromodulation is also likely to affect the ultimate spatiotemporal resolution of the signal.
This having been said, and despite its shortcomings, fMRI is currently the best tool we have for gaining insights into brain function and formulating interesting and eventually testable hypotheses, even though the plausibility of these hypotheses critically depends on used magnetic resonance technology, experimental protocol, statistical analysis and insightful modelling. Theories on the brain's functional organization (not just modelling of data) will probably be the best strategy for optimizing all of the above. Hypotheses formulated on the basis of fMRI experiments are unlikely to be analytically tested with fMRI itself in terms of neural mechanisms, and this is unlikely to change any time in the near future.
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