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Issues in fMRI

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Functional magnetic resonance imaging, also known as ƒMRI, is a neuroimaging technique that measures the blood oxygenation, also called BOLD, that accompanies neuronal activity. The blood oxygenation level dependent fMRI has relatively high spatial resolution (of the order of a few millimetres), but lower temporal resolution (a few seconds), in comparison to other neuroimaging methods.[1] In recent years, ƒMRI usage has increased exponentially. Like other research and medical techniques, functional magnetic resonance imaging too has theoretical and methodological limitations, some shared in common with other brain-imaging methods such as EEG, MEG and single-electrode recordings, and some different from them.

Limitations on experimental design and data acquisitionEdit


If the baseline condition is too close to maximum activation, certain processes may not be represented appropriately.[2] Another limitation on experimental design is head motion, which can lead to artificial intensity changes of the fMRI signal.[2]

Block versus event-related designEdit

In a block design, two or more conditions are alternated in blocks. Each block will have a duration of a certain number of fMRI scans and within each block only one condition is presented. By making the conditions differ in only the cognitive process of interest, the fMRI signal that differentiates the conditions should represent this cognitive process of interest. This is known as the subtraction paradigm.[3] The increase in fMRI signal in response to a stimulus is additive. This means that the amplitude of the hemodynamic response (HDR) increases when multiple stimuli are presented in rapid succession. When each block is alternated with a rest condition in which the HDR has enough time to return to baseline, a maximum amount of variability is introduced in the signal. As such, we conclude that block designs offer considerable statistical power[4][5] There are however severe drawbacks to this method, as the signal is very sensitive to signal drift, such as head motion, especially when only a few blocks are used. Another limiting factor is a poor choice of baseline, as it may prevent meaningful conclusions from being drawn. There are also problems with many tasks lacking the ability to be repeated. Since within each block only one condition is presented, randomization of stimulus types is not possible within a block. This makes the type of stimulus within each block very predictable. As a consequence, participants may become aware of the order of the events.[4][5]

Event-related designs allow more real world testing, however, the statistical power of event related designs is inherently low, because the signal change in the BOLD fMRI signal following a single stimulus presentation is small.[6]

Both block and event-related designs are based on the subtraction paradigm, which assumes that specific cognitive processes can be added selectively in different conditions. Any difference in blood flow (the BOLD signal) between these two conditions is then assumed to reflect the differing cognitive process. In addition, this model assumes that a cognitive process can be selectively added to a set of active cognitive processes without affecting them.[3]

Baseline versus activity conditionsEdit

The brain is never completely at rest. It never stops functioning and firing neuronal signals, as well as using oxygen as long as the person in questions are alive. In fact, in Stark and Squire’s, 2001 study[7] When zero is not zero: The problem of ambiguous baseline conditions in fMRI, activity in the medial temporal lobe (as well as in other brain regions) was substantially higher during rest than during several alternative baseline conditions. The effect of this elevated activity during rest was to reduce, eliminate, or even reverse the sign of the activity during task conditions relevant to memory functions. These results demonstrate that periods of rest are associated with significant cognitive activity and are therefore not an optimal baseline for cognition tasks. In order to discern baseline and activation conditions it is necessary to interpret a lot of information. This includes situations as simple as breathing. Periodic blocks may result in identical data of other variance in the data if the person breathes at a regular rate of 1 breath/5sec, and the blocks occur every 10s, thus impairing the data.

Constraints on inferences supported by fMRIEdit

Reverse inferenceEdit

Neuroimaging methods such as fMRI offer a measure of the activation of certain brain areas in response to cognitive tasks engaged in during the scanning process. Data obtained during this time allow cognitive neuroscientists to gain information regarding the role of particular brain regions in cognitive function.[8] However, an issue arises when certain brain regions are alleged by researchers to identify the activation of previously labeled cognitive processes.[9] Poldrack[10] clearly describes this issue:

The usual kind of inference that is drawn from neuroimaging data is of the form ‘if cognitive process X is engaged, then brain area Z is active.’ Perusal of the discussion sections of a few fMRI articles will quickly reveal, however, an epidemic of reasoning taking the following form:
(1) In the present study, when task comparison A was presented, brain area Z was active.
(2) In other studies, when cognitive process X was putatively engaged, then brain area Z was active.
(3) Thus, the activity of area Z in the present study demonstrates engagement of cognitive process X by task comparison A.
This is a ‘reverse inference’, in that it reasons backwards from the presence of brain activation to the engagement of a particular cognitive function.

Reverse inference demonstrates the logical fallacy of affirming what you just found, although this logic could be supported by instances where a certain outcome is generated solely by a specific occurrence. With regard to the brain and brain function it is seldom that a particular brain region is activated solely by one cognitive process.[10] Some suggestions to improve the legitimacy of reverse inference have included both increasing the selectivity of response in the brain region of interest and increasing the prior probability of the cognitive process in question.[10] However, Poldrack[8] suggests that reverse inference should be used merely as a guide to direct further inquiry rather than a direct means to interpret results.

Forward inferenceEdit

Forward inference is a data driven method that uses patterns of brain activation to distinguish between competing cognitive theories. It shares characteristics with cognitive psychology’s dissociation logic and philosophy’s forward chaining. For example, Henson[11] discusses forward inference’s contribution to the “single process theory vs. dual process theory” debate with regard to recognition memory. Forward inference supports the dual process theory by demonstrating that there are two qualitatively different brain activation patterns when distinguishing between “remember vs. know judgments”. The main issue with forward inference is that it is a correlational method. Therefore one cannot be completely confident that brain regions activated during cognitive process are completely necessary for that execution of those processes.[8] In fact, there are many known cases that demonstrate just that. For example, the hippocampus has been shown to be activated during classical conditioning,[12] however lesion studies have demonstrated that classical conditioning can occur without the hippocampus.[13]

See alsoEdit


  1. Jezzard P., Matthews P.M., Smith S.M. (2001). Functional MRI: an introduction to methods. “Oxford: Oxford University Press”
  2. 2.0 2.1 Haller, S., Bartsch, A. (2009). Pitfalls in fMRI. European Radiology. 19, 2689-2706.
  3. 3.0 3.1 Grabowski, T., and Damasio, A.” (2000). Investigating language with functional neuroimaging. San Diego, CA, US: Academic Press. 14, 425-461.
  4. 4.0 4.1 Aguirre, G. and D'Esposito, M. (2000). Experimental design for brain fMRI. In C.Moonen & T. W. Bandettini (Eds.), Functional MRI (pp. 369-380). Heidelberg: Springer-Verlag Berlin.
  5. 5.0 5.1 Donaldson, D., and Bucknar, R. (2001). Effective paradigm design. In P.Jezzard, P. M. Matthews, & S. M. Smith (Eds.), Functional MRI: An introduction to methods (pp. 177-195). New York: Oxford University Press Inc.
  6. D'Esposito, M., Zarahn, E., and Aguirre, G. (1999). Event-related functional MRI: Implications for cognitive psychology. Psychological Bulletin. 125(1), 155-164.
  7. Stark, C., Squire, L. (2001). When zero is not zero: The problem of ambiguous baseline conditions in fMRI. “Proceedings of the National Academy of Science. 98(22, 12760-12766).
  8. 8.0 8.1 8.2 Poldrack, R. (2008). The role of fMRI in cognitive neuroscience: where do we stand? Current Opinions in Neurobiology. 18, 223-227.
  9. Harrison, G. (2008). Neuroeconomics: A Rejoiner. Economics and Philosophy. 24, 533-544.
  10. 10.0 10.1 10.2 Poldrack, R. (2006). Can cognitive processes be inferred from neuroimaging data? Trends in Cognitive Sciences. 10(2), 59-63.
  11. Henson, R. (2006). Forward inference in functional neuroimaging: dissociations vs associations. Trends in Cognitive Science. 10, 64-69.
  12. Knight, D., Smith, C., Cheng, D., Stein, E., and Helmstetter, F. (2004). Amygdala and hippocampal activity during acquisition and extinction of human fear conditioning.Cognitive, Affective and Behavioral Neuroscience. 4,317-325.
  13. Gabrieli, J., Carrillo, M., Cermak, L., Mcglinchey-Berroth, R., Gluck, M., and Disterhoft, J. (1995). Intact delay–eyeblink classical conditioning in amnesia. Behavioral Neuroscience. 109,819-827.
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