The pitfalls of experimental methods in neuroscience have not all been worked out. I’ll say it again, no one result is reliable in science; what is convincing is a fabric of results not a string but a fabric. This is especially true in a new field.
Micah at neuroconscience blog has a posting on possible BOLD signal problems. (here)
Particularly in fMRI research, were all too familiar with certain regions that seem to pop up in study after study, regardless of experimental paradigm. When it comes to areas like the anterior cingulate cortex (ACC) and insula (AIC), the trend is glaringly obvious. Generally when I see the same brain region involved in a wide a variety of tasks, I think there must be some very general level function which encompasses these paradigms. Off the top of my head, the ACC and AIC are major players in cognitive control, pain, emotion, consciousness, salience, working memory, decision making, and interoception to name a few. Maybe on a bad day Ill look at a list like that and think, well localization is just all wrong, and really what we have is a big fat prefrontal cortex doing everything in conjunction. A paper published yesterday in Cerebral Cortex (Di, Kannurpatti, Rypma, Biswal: Calibrating BOLD fRMI Activations with Neurovascular and Anatomical Constraints) took my breath away and lead to a third, more sinister option: a serious methodological confound in a large majority of published fMRI papers.
An important line of research in neuroimaging focuses on noise in fMRI signals. The essential problem of fMRI is that, while it provides decent spatial resolution, the data is acquired slowly and indirectly via the blood-oxygenation level dependent (BOLD) signal. The BOLD signal is messy, slow, and extremely complex in its origins. Although we typically assume increasing BOLD signal equals greater neural activity, the details of just what kind of activity (e.g. excitatory vs inhibitory, post-synaptic vs local field) are murky at best. Advancements in multi-modal and optogenetic imaging hold a great deal of promise regarding the signals true nature, but sadly we are currently at a best guess level of understanding. This weakness means that without careful experimental design, it can be difficult to rule out non-neural contributors to our fMRI signal. Setting aside the worry about what neural activity IS measured by BOLD signal, there is still the very real threat of non-neural sources like respiration and cardiovascular function confounding the final result. This is a whole field of research in itself, and is far too complex to summarize here in its entirety. The basic issue is quite simple though.
Well, maybe not that simple go to the original posting for the physiology. The upshot is that it is really important to control for the subject holding their breath or breathing differently at different times in the protocol.
The authors conclude that (results) indicated that the adjustment tended to suppress activation in regions that were near vessels such as midline cingulate gyrus, bilateral anterior insula, and posterior cerebellum. It seems that indeed, our old friends the anterior insula and cingulate cortex are extremely susceptible to neurovascular confound.
What does this mean for cognitive neuroscience? For one, it should be clear that even well-controlled fMRI designs can exhibit such confounds. This doesnt mean we should throw the baby out with the bathwater though; some designs are better than others. Thankfully its pretty easy to measure respiration with most scanners, and so it is probably a good idea at minimum to check if ones experimental conditions do indeed create differential respiration patterns. Further, we need to be especially cautious in cases like meditation or clinical fMRI, where special participant groups may have different baseline respiration rates or stronger parasympathetic responses to stimuli.
Experimental methods in neuroscience are new enough and complicated enough to be misleading. It is reassuring to me that there are researchers looking at possible short-comings of these methods.