This study provides the first intra-subject comparison of fMRI-deactivation during two different cognitive tasks. The main findings are: a) VA and WM tasks commonly deactivate a network that includes the frontal, temporal, occipital, and limbic lobes; b) although WM tasks caused lower overall activation, they produced larger overall deactivation than VA tasks; and c) specific regions in the frontal lobes (PreCG, and PCL) deactivated during WM, but activated during VA tasks.
These findings suggest that global CBF is not constant across the tasks for two reasons: First, VA tasks produced greater activation but lesser amplitude of deactivation than WM tasks (, , and ); therefore the BOLD−in the deactivated network is not proportional to BOLD+ the in the activated network, thus Δ+ ≠ kΔ− across tasks; and Second, while cross-correlation of load responses in the activated network were similar during VA and WM tasks (), in the deactivated network the load responses cross-correlated better for VA tasks than for WM tasks (; ). The correlation-differences between tasks result from the lower dynamic range of negative BOLD signals during WM tasks, compared to VA tasks. Similarly, in deactivated brain regions, the Gaussian distribution of BOLD responses has different FWHM () for WM and VA tasks; in activated regions, however, the FWHM is the same for both tasks. This is also inconsistent with proportional blood flow changes in the activated and the deactivated networks across tasks.
Differently, the parametric increases of VA-load enhanced the positive BOLD signals in the IPL, SOG, IOG, thalamus, cerebellum, and the left DLPFC (IFG, and MFG), and the negative BOLD signals in the PCG, PHG, and the precuneus (see , and , and , and ). Similarly, parametric increases of WM-load enhanced brain activation bilaterally in the PFC, and left IPL, and brain deactivation in the CG, and the insula (see , and , and and ). These corresponding increases of activation and deactivation support that global CBF is constant across conditions of the same task. Our results are supported by one previous fMRI study that used an auditory target detection task with parametric changes of task difficulty(McKiernan, et al. 2003
), and that found increasing task difficulty results in greater degrees of brain deactivation in the ACG, SFG, MFG, PCG, SPL, and precuneus. This study suggested that the ongoing internal information processing during the conscious resting state is suspended during the task to allow for reallocation of processing resources. Therefore, the present study suggests that the hemodynamic responses in activated and deactivated networks are proportional across load conditions, but not across different tasks
(WM, and VA).
There are two potential mechanisms underlying deactivation on BOLD fMRI: Model 1: local reduction of rCBF in less active brain regions to compensate for rCBF increases in activated brain regions, without central involvement (“blood stealing”), and Model 2: stimulus-correlated, centrally mediated inhibition of neural processes in task-irrelevant brain regions.
Model 1 relates primarily to shunting of blood flow to activated brain regions. Since increased neural activity in the activated network would require increased rCBF and oxygen consumption (CMRO2), and since the total metabolism of the brain is approximately constant over a wide range of mental and motor activities (Raichle and Gusnard 2002
), increased rCBF in the activated network might require a synchronous decrease of rCBF in adjacent regions of the brain (i.e. a hemodynamic response). Consequently, these adjacent task-irrelevant regions might present negative, rather than positive, BOLD responses. In this purely hemodynamic model, regions with fMRI deactivation would reflect a transition from decreased rCBF-supply during “task” periods to normal rCBF-supply during “resting” periods. Despite greater global activation during VA tasks in this study, the PCL, PreCG, and precuneus deactivated during WM but activated during VA. This is inconsistent with the Model 1 since the cerebrovasculature is constant in anatomy and location within each subject; therefore, these areas also should deactivate during VA since all brain areas activated by WM tasks also activated during VA tasks, within the same cerebrovasculature in each subject.
Model 2 explains deactivation as a consequence of cross-modal inhibition mechanisms that reduce potentially distracting neural processes (Laurienti, et al. 2002
). With respect to the present study, the Model 2 advocates that deactivation in the posterior insula, PCL, ACG, MTG, CG, PHG, PCG, and precuneus during WM but less so during VA is a result of direct neural inhibition. This inhibition may serve the purpose of optimizing performance by minimizing interference. Deactivation of these brain areas during rapid visual information processing (Lawrence, et al. 2003
) and auditory target detection tasks (McKiernan, et al. 2003
) were associated with the need for focused attention towards more difficult tasks. For instance, competing neural processes such as those produced by the stimulation of the auditory cortices (from scanner noise) or by attention to introspective or emotional factors (i.e. anxiety during fMRI) could interfere with cognitive task performance. Consequently, neural processing in task-irrelevant networks might be partially inhibited (during tasks periods, but not during rest periods) to increase efficiency of the task-activated network. In this model, deactivation reflects the transition from an inhibited neural state (during “task” periods) to a less inhibited state (during “resting” periods). Several regions in the deactivated networks in our study appear to be related to auditory and emotional tasks. For instance, passive music listening (Brown, et al. 2004
) and facial emotion processing (Gur, et al. 2002
; Kircher, et al. 2000
; Lennox, et al. 2004
; Pessoa, et al. 2002
; Pierce, et al. 2004
) engage the same regions in the limbic and paralimbic systems (insula, ACG, CG, PCG, PHG, MTG, and the retrosplenial cortex). In addition, event-related fMRI studies on inhibition using go/no-go tasks have shown that errors during the tasks are associated to activation of the CG, suggesting an important function of the CG in the dynamic control of behavior (Fassbender, et al. 2004
; Garavan, et al. 2003
; Garavan, et al. 2002
). Therefore, the spatial specificity of deactivated brain regions in our current study makes a simple redistribution of blood supply unlikely, since these regions support neural processing that can interfere with attention processing. Furthermore, brain deactivation during WM tasks correlated with the behavioral data (RT and performance accuracy during the fMRI tasks; ); the linear increase of deactivation with task difficulty might reflect greater suppression of neural processes during more demanding tasks, which is consistent with the inhibition model. Recent studies in the rodent somatosensory cortex, however, have failed to reveal any neuronal correlate of negative hemodynamic responses, and it did not support neuronal inhibition as the origin of brain deactivation (Devor, et al. 2005
In addition to neural inhibition, brain deactivation might also be due to a simple reduction of neuronal activity in deactivated regions as other brain regions become more active (McKiernan, et al. 2003
). Furthermore, deactivation could also result form subjects’ anxiety and discomfort in the MRI environment. Our MRI system is based on an older 4 Tesla magnet that has a very long (3 meters) bore, which increases the risk for claustrophobic reactions (two other subjects did not perform the fMRI study for this reason), and produces loud sound pressure levels of acoustic noise (98 dB at the entrance of the tube). fMRI studies on emotional pain modulation have shown that anxiety about pain activates the CG, posterior insula, and the hippocampus (Ploghaus, et al. 2001
). PET studies on anticipatory anxiety (painful shocks to subject’s fingers) found that activation at the ACG correlates linearly with the anxiety ratings, suggesting that the rCBF in the medial PFC might reflect a combined effect of attentional demands causing reductions of rCBF, and accompanying performance anxiety that attenuate those reductions (Simpson, et al. 2001
). Therefore, during the resting periods, neural processing in the limbic regions might have been enhanced due to greater awareness of the confined MR scanner environment. During the task periods, however, the subjects might have inhibited the interfering neural processing in the limbic system while focusing their attention on the tasks. Similarly, during the resting periods, neural processing in the auditory cortices (the posterior insula adjacent to the primary auditory cortex, BA 41) might have been enhanced by the loud scanner noise. During task periods, the interfering auditory processing might have been partially inhibited to maximize attention to the tasks.
Limitations of the study
Our study could have been improved by using an additional task that activates the insula and the limbic lobe (for instance an emotional task) to test if the areas activated for this task are the same “interfering” areas deactivated by cognitive tasks (working memory and visual attention tasks). In addition, matching the number and duration of tasks and resting blocks could have minimized the number of design feature differences between the tasks. We did not make these improvements because the study was based on a re-analysis of existing data. Furthermore, the order of task-difficulty levels was not counterbalanced (0-, 1-, and 2-back for WM; 2-, 3-, and 4-balls for VA; although the WM/VA-order was counterbalanced) in this work to minimize variance due to differential practice effects. (Tomasi, et al. 2004
) This approach, however, could have reduced the effect of cognitive load (WM-load and VA-load).(Tomasi, et al. 2004
In summary, the findings of this study (the first intra-subject comparison of fMRI-deactivation during different cognitive tasks) are multiple. First, distinct cognitive paradigms (WM(Chang, et al. 2001
) and VA(Chang, et al. 2004
)) commonly deactivated a network that comprises the frontal (SFG, PreCG, ACG, PCL, and posterior insula), temporal (MTG), occipital (precuneus), and limbic (CG, PHG, and PCG) lobes. Second, WM tasks produced larger deactivation than VA tasks. Third, the PreCG, and the PCL deactivated during WM tasks, but activated during VA tasks. WM and VA tasks both activated a network that includes prefrontal, parietal, and occipital cortices, thalamus and the cerebellum, as reported previously. In this network, positive BOLD signals probably reflect increased local oxygen consumption and increased rCBF. The larger deactivation during MW tasks compared to VA tasks suggests that global CBF is task-dependent. Brain deactivation appears to occur predominantly in brain regions that potentially interfere with or are unimportant for performing the required tasks and is probably a compensatory response to optimize task performance due to limitations in processing bandwidth.