intermittent “real-time” fmri feedback is superior to continuous presentation for a motor...

Experimental Laboratory Research

Intermittent “Real-time” fMRI Feedback Is Superior to ContinuousPresentation for a Motor Imagery Task: A Pilot Study

Kevin A. Johnson, PhD, Karen Hartwell, MD, Todd LeMatty, BA, Jeffrey Borckardt, PhD, Paul S. Morgan, PhD,Koushik Govindarajan, MS, Kathleen Brady, MD, PhD, Mark S. George, MDFrom the CNS Division, Department of Psychiatry (KH, TL, KB), Brain Stimulation Laboratory, Department of Psychiatry (KAJ, JB, MSG), Center for Advanced Imaging Research(KAJ, PSM, KG, MSG), and Department of Radiology (PSM), Medical University of South Carolina, Charleston, SC; Ralph H. Johnson VA Medical Center, Charleston, SC (KH,KB, MSG); and Systems Neuroscience and Pain Laboratory, Department of Anesthesiology, Stanford University, Palo Alto, CA (KAJ).

Keywords: fMRI, neurofeedback, real-time fMRI, motor imagery.

Acceptance: Received February 18,2010, and in revised form June 25, 2010.Accepted for publication August 8, 2010.

Correspondence: Address correspon-dence to Kevin A. Johnson, Ph.D.,Systems Neuroscience and Pain Labo-ratory, Department of Anesthesiology,Stanford University, 780 Welch RoadNo. 208, Palo Alto, CA 94304. E-mail:[email protected].

Conflicts of Interest: The authors haveno conflicts of interest to disclose. Thisstudy is supported in part by NIH/NIDA1R21DA026085-01 (Brady, George).

J Neuroimaging 2012;22:58-66.DOI: 10.1111/j.1552-6569.2010.00529.x

A B S T R A C T

BACKGROUNDReal-time functional MRI feedback (RTfMRIf) is a developing technique, with unansweredmethodological questions. Given a delay of seconds between neural activity and themeasurable hemodynamic response, one issue is the optimal method for presentation ofneurofeedback to subjects. The primary objective of this preliminary study was to comparethe methods of continuous and intermittent presentation of neural feedback on targetedbrain activity.METHODSThirteen participants performed a motor imagery task and were instructed to increaseactivation in an individually defined region of left premotor cortex using RTfMRIf. ThefMRI signal change was compared between real and false feedback for scans with eithercontinuous or intermittent feedback presentation.RESULTSMore individuals were able to increase their fMRI signal with intermittent feedback,while some individuals had decreased signal with continuous feedback. The evaluationof feedback itself activated an extensive amount of brain regions, and false feedbackresulted in brain activation outside of the individually defined region of interest.CONCLUSIONSAs implemented in this study, intermittent presentation of feedback is more effectivethan continuous presentation in promoting self-modulation of brain activity. Furthermore,it appears that the process of evaluating feedback involves many brain regions that canbe isolated using intermittent presentation.

Introduction“Real-time” functional MRI (RTfMRI) is used to describe theanalysis of data while scans are being acquired, as opposed tothe more common approach of analyzing data at some time fol-lowing scanning. It has been proposed that such real-time anal-ysis may be useful for quality monitoring, for brain-computerinterfaces, and for neurofeedback.1-5 RTfMRI feedback (RTfM-RIf) provides individuals neurofeedback regarding their ownbrain function, thus theoretically allowing a subject or patient todynamically self-manipulate brain activity during mental pro-cesses. There are a number of proposed research and clini-cal applications of RTfMRIf,4,6 yet fundamental questions sur-rounding the optimal procedures for RTfMRIf have not beensystematically explored. Such questions include how to accountfor scanner signal drift and physiologic noise over time duringa session, how best to select and quantify the signal to feedback,

and, perhaps most important, how to best provide the feedbackto the subject.1-4,7-9

A variety of approaches have been used to present RTfMRIf,such as display of whole-brain activity,10 verbal feedback,7,11

a scrolling graph display,8,12 visual scales,13,14 and combina-tions of feedback display approaches.6,15 The first publishedreport of RTfMRIf used intermittent feedback, updating a func-tional map after each rest-task block.10 Following EEG feed-back findings,8,16 many RTfMRIf studies have used contin-uous feedback, in which the visual display is updated aftereach acquired volume.6,8,12,13,15 It is important to note thatthere are temporal differences between EEG and fMRI mea-surements of brain activity. The sampling rate of EEG (∼100samples/second) is orders of magnitude faster than that of fMRI(∼.5 samples/second). Also, the EEG signal is tightly linked toneural activity in time, while fMRI measures a hemodynamic

58 Copyright ◦C 2010 by the American Society of Neuroimaging

response that follows seconds after neural activity.17 The aimof this study was to directly compare an intermittent versus acontinuous approach for providing feedback with RTfMRI totest whether this matters and to aid our group and others infuture RTfMRIf study design.

Continuous feedback theoretically may have some advan-tages. The more feedback that is given, the more opportunitiesare available to modify thoughts and brain activity to best ma-nipulate brain function. Also, continuous feedback may providegreater interest or engagement in participating in the feedbackparadigm and ensure greater attention. However, there may besome disadvantages to continuous feedback. Given the slow andvariable hemodynamic response measured by fMRI, it may bechallenging to link feedback with thoughts that occurred sev-eral seconds prior. Instructions and training about the delay,along with scrolling graphs, have been employed to deal withthis challenge.6,12,13 In addition, as noise in the fMRI signalis typically dealt with by traditional approaches of filtering andsignal averaging, constant feedback must employ nontraditionalapproaches to prevent noise from impacting continuous feed-back.2,3 Additionally and perhaps most importantly, the visualattention and cognitive load of evaluating feedback while si-multaneously engaged in the experimental paradigm may beconfounding and actually distract from the task under primarystudy. Too much feedback may distract from the main task athand.

Because of these considerations, intermittent feedback mayhave some advantages over continuous feedback in RTfMRIneurofeedback procedures. By providing feedback at the endof a block of time, the participant does not need to be awareof any hemodynamic delay and more time points are availablefor filtering and signal averaging. Furthermore, experimentaltask performance and the evaluation of feedback are separablein time (and can be more concretely isolated for further whole-brain analysis).

In this study, we directly compared a continuous and anintermittent approach to providing RTfMRIf in a movementimagery task. Our primary hypothesis was that intermittentRTfMRIf would be more effective for increasing brain functionin a defined region of interest (ROI) than would continuousfeedback. We further aimed to explore whole brain differencesevaluating feedback continuously versus intermittently, and weused the intermittent paradigm to characterize brain regionsinvolved in evaluating feedback.

MethodsParticipants

Healthy nonsmoking, right-handed volunteers, age of 18-60years, were eligible to participate in this study. After provid-ing informed consent as approved by the Institutional ReviewBoard of the Medical University of South Carolina, participantswere screened for conditions contraindicated to MRI scanning,current DSM-IV Axis 1 psychiatric disorders, substance depen-dence, substance abuse within the past 30 days, and significantmedical problems or medications that would interfere with thehemodynamic response.

Table 1. Order of Scanning for the 15 Participants

Participant Scan 1 Scan 2 Scan 3 Break Scan 4 Scan 5 Scan 6

1 B1 IR IF B2 CF CR2 B1 IF IR B2 CR CF3 B1 CF CR B2 IF IR4 B1 CR CF B2 IR IF5 B1 IR IF B2 CR CF6 B1 IR IF B2 CF CR7 B1 CF CR B2 IR IF8 B1 IR IF B2 CR CF9 B1 CF CR B2 IF IR

10 B1 CF CR B2 IF IR11 B1 CR CF B2 IR IF12 B1 IR IF B2 CR CF13 B1 CF CR B2 IF IR14 B1 IF IR B2 CF CR15 B1 IR IF B2 CR CF

B1 and B2 = no feedback (first or second baseline ROI localizer); I = intermittentparadigm; C = continuous paradigm; R = real feedback; F = false feedback.Shading indicates scans that were not completed or were excluded based onquality checks.

Paradigm

Study subjects participated in six fMRI scans on the same day.Each scan involved a block-design “imagine movement” task.Participants were instructed to imagine moving their right handwhen the word “IMAGINE” was visually displayed (imaginedactivities such as writing, playing a musical instrument, or com-pleting a sports-related movement were suggested), and to en-gage in nonmovement thoughts when the word “REST” wasdisplayed. A tight, molded foam wrist/hand brace was placedon the participant’s right hand, wrist, and forearm to limit move-ment during scanning. Following all scanning, participants wereasked to describe the mental strategies that they used during themotor imagery task, were asked to rate their confidence in per-forming the task, and were asked to rank order the four scanswith feedback based on their perceived performance.

The scanning was divided into two sessions for comfort pur-poses, to allow the participant a break from laying in the scanner(see Table 1). Each of the two scan sessions began with a motorimagery functional scan with no feedback, which was used toindividually localize a ROI for generating RTfMRIf in the nexttwo scans. Participants had four motor imagery scans with inter-mittent or continuous feedback, and with either real feedbackor false feedback (ie, intermittent real, intermittent false, con-tinuous real, and continuous false feedback scans). Scan orderwas randomized with either continuous or intermittent pairs ofscans first. Within each pair, scan order was also randomized forreal or false feedback. Using this cross-over design to controlfor order effects, “no feedback” ROI localizer scans for eachparticipant were followed by two continuous-feedback in onesession and two intermittent-feedback scans in the other ses-sion. One of the two feedback scans within each session used“real feedback” (based on actual fMRI signal) and the otherused “false feedback” (fixed randomized feedback not basedon actual fMRI signal, used as a control condition). Participantswere aware that scans would have different kinds of feedback,but they were not aware that some would be false feedback.

Johnson et al: Presentation of real-time fMRI feedback 59

All scans lasted for 280 image volumes (616 seconds). Thefirst 60 volumes were “REST,” allowing time for the operator toconfigure the real-time software and for drift of MRI signal in-tensities to stabilize. Next “IMAGINE” and “REST” alternatedfor blocks of 10 volumes. For the scans used to functionally lo-calize the ROI, no feedback was presented (although an inactivethermometer was displayed to orient participants). Feedbackwas provided to the participants as a thermometer (see Fig S1)with five increments above baseline and five increments belowbaseline (each increment was equal to .4% signal change forreal feedback). As activation changed, the thermometer read-ings moved incrementally both up and down. During feedbackscans, participants were instructed to attempt to maximally in-crease a thermometer display (ie, switch imagined activities iflittle or no positive activity; increase imagined activity if somepositive activity). For continuous-feedback scans, an active ther-mometer was shown throughout the “IMAGINE” condition (aninactive thermometer was shown with “REST”), updated everyvolume. Participants were instructed that there was a delayin the feedback, and it was suggested that a strategy be main-tained for several seconds in order to receive relevant feedback.For intermittent-feedback scans, no thermometer was displayedduring the “IMAGINE” and “REST” conditions. The displayfor the last volume of the IMAGINE condition and the first vol-ume of the REST condition were replaced with a thermometer(thus the block design had 3 conditions: 9 volumes “IMAG-INE,” 2 volumes “FEEDBACK,” and 9 volumes “REST”).

RTfMRIf Scanning

Scanning was performed using a 3T MRI Trio (Siemens Medi-cal, Erlangen, Germany). Each fMRI scan was acquired using astandard multislice single-shot gradient echo echo planar imag-ing (EPI) sequence with the following parameters: TR = 2.2seconds, TE = 35 ms, 64 × 64 matrix, parallel imaging factorof 2, 3 × 3 × 3 mm voxels, 280 volumes, 36 ascending trans-verse slices with approximate anterior commissure-posteriorcommissure (AC-PC) alignment. After each volume was ac-quired, it was automatically exported in DICOM format fromthe MRI scanner computer to a separate computer for in-scanprocessing.

Turbo-BrainVoyager (TBV) 2.0 software (Maastricht, TheNetherlands) was used to perform in-scan processing. Real-time prestatistical processing included motion correction andspatial smoothing using a Gaussian kernel of 8.0 mm full widthhalf maximum (FWHM). No feedback motor imagery fMRIscans were acquired using a block design paradigm to pro-vide participant-specific activation, to guide ROI placement forthe following RTfMRIf acquisitions. Toward the end of the nofeedback motor imagery scan, a 7-slice ROI was selected foractivation in the left-hemisphere (using a t value threshold of3, with cluster threshold size of 4), visually approximated to bepremotor cortex (see Fig 2 and Fig S2). The following settingswere used for generating neurofeedback: average feedback val-ues to calculate feedback value = 2 timepoints for continuousfeedback paradigm and 6 timepoints for intermittent feedbackparadigm, maximum percent signal change (PSC) of feedbackbar = 2, general linear model (ROI-GLM) baseline enabledfor stable baseline estimation, dynamic ROI enabled using best

voxel selection of top 33% (effectively creates a sub-ROI to givebetter signal extraction from a coarse anatomical ROI selectionand with small alignment errors within and between scans).

The experimental paradigm and feedback were presentedwith a mirrored-projector system, using EPrime 2.0 software(Psychology Software Tools, Pittsburgh, PA). Thermometerbar images were exported from the analysis computer (run-ning TBV software) to the presentation computer (runningEPrime software) for the real feedback conditions, or ther-mometer bar images were taken from a precreated folder onthe presentation computer (full-range set of thermometer im-ages selected by fixed randomization) for the false feedbackconditions.

Data Quality Checks

Data were excluded if motion greater than 3 mm or if no acti-vation was seen in the no feedback ROI localizer scan duringpost-hoc fMRI analysis.

ROI Analysis

As TBV is an operational software with limited capacity forpost-hoc analysis, time series extraction was performed us-ing FSL 4.1.5 (Oxford Centre for Functional MRI of theBrain, Oxford, UK). Two approaches were used to extracttime series, using parameters to approximate the TBV set-tings and to characterize data from all unfiltered voxels in theROI.

To characterize data from all voxels in an ROI without tem-poral filtering, 4-dimensional (4D) fMRI scans were motioncorrected using FSL MCFLIRT (using the first scan of the vol-ume as the reference scan for alignment) and spatially smoothed(using a Gaussian kernel of 8.0 mm FWHM). These volumeswere then masked by the individual ROI created in TBV, anda timecourse of mean intensities from all voxels in the ROI wasextracted.

To characterize data using parameters approximate to theTBV settings, the 4D fMRI scans were motion corrected usingFSL MCFLIRT (using the first scan of the volume as the ref-erence scan for alignment). These volumes were then maskedby the individual ROI created in TBV. An FSL FEAT analysiswas then run on the masked data using preprocessing (spatialsmoothing using a Gaussian kernel of 8.0 mm FWHM andhigh-pass temporal filtering with 44 seconds cutoff) and statis-tical analysis (GLM with temporal derivative). A timecourse ofsignal intensities was created from the voxel with the highestz -score.

For both time series extraction approaches, intensity valueswere converted to PSC using baseline defined as the average ofvolumes 51-60 (end of first REST period). The hemodynamicresponse to the “IMAGINE” period was temporally defined bythe average time series (from the voxel with the highest z -score)of the no feedback ROI localizer scans (positive PSC values, lessone volume as the intermittent imagine period was one volumeshorter). For each condition of feedback type (continuous orintermittent), the average PSC per block was compared pairwisefor each participant between real feedback and false feedback.Slopes for each scan were calculated as the change in PSC overthe 11 blocks, and slopes were compared pairwise between real

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feedback and false feedback for feedback methods (continuousor intermittent).

Whole-Brain Analysis

For each scan, a standard FSL FEAT analysis was performedusing preprocessing (motion correction, brain extraction usingFSL BET, spatial smoothing using a Gaussian kernel of 8.0 mmFWHM, high-pass temporal filtering with 44 seconds cutoff) sta-tistical analysis (FILM prewhitening, motion parameters addedto model, and GLM with temporal derivative). Two conditionswere defined for the no feedback ROI localizer and continuousscans (rest and imagine), and 3 conditions were defined for theintermittent scans (rest, imagine, and feedback). Higher levelanalysis were performed in FSL using fixed effects for within-subject comparisons and mixed effects (FLAME 1 + 2) forbetween-subject comparisons. All statistical results were thresh-olded using clusters determined by Z > 2.3 and a correctedcluster significance of P = .05.

ResultsParticipants

Fifteen participants (8 men and 7 women) enrolled in the study,but scanning was not completed for 1 male (due to claustro-phobia) and 1 female (nausea during scanning). The averageage of the 13 included participants was 31.6 years (SD = 10.7years). All participants were high school graduates and the ma-jority had college degrees (2 some college, 7 college degrees,and 1 postgraduate degree). Commonly reported strategies em-ployed during the imagined movement periods included typing(n = 6), sports activity such as bouncing a ball, swimming, orkarate (n = 6), playing a musical instrument (n = 5), and writ-ing (n = 2). On a scale from 1 to 10 (1-not at all confident,10 extremely confident) participants rated an average of 7.2(SD = 1.3) in their ability to do the task. In ranking the fourfeedback scans based on the participants’ perception of theirown performance (1 = best, 4 = worst), intermittent real feed-back had the best average ranking of 2.0 (SD = 1.1), continuousreal feedback followed having an average of 2.5 (SD = 1.1), andboth continuous false and intermittent false feedback had theworst averages of 2.8 (SD = 1.2). The intermittent real feedbackrankings were significantly better than the continuous real feed-back rankings, and the continuous real feedback rankings weresignificantly better than both the intermittent and continuousfalse feedback rankings (Wilcoxon signed-rank tests, P = .05).

Quality Checks

Of 26 total comparative sessions (a no feedback ROI localizer,real feedback, and false feedback scans), three comparative ses-sions were excluded due to at least one scan with motion greaterthan 3 mm. Five comparative sessions were excluded due tolack of activation with the no feedback ROI localizer scan. Thisyielded 10 usable continuous feedback sessions and 8 usableintermittent half-sessions (see Table 1).

ROI Analysis

Figure 1 shows the mean PSC from all voxels in the individ-ually selected regions of interest, without temporal filtering.

With time series extracted from all voxels, the mean PSC (SD)were continuous no feedback = .25 (.52), continuous real feed-back = .48 (.54), continuous false feedback = .44 (.45), intermit-tent no feedback = .38 (.20), intermittent real feedback = .76(.31), and intermittent false feedback = .22 (.93). With contin-uous feedback (comparing real feedback to false feedback), 3participants performed significantly better with real feedback,3 participants had no significant difference with real feedback,and 4 participants performed significantly worse with real feed-back (significance levels of P = .05). With intermittent feedback(comparing real feedback to false feedback), 4 participants per-formed significantly better with real feedback, 4 participantshad no significant difference with real feedback, and no partic-ipants performed significantly worse with real feedback (signif-icance levels of P = .05).

Relative to time series extracted from all voxels without tem-poral filtering, there was less signal drift over time for the analy-sis approximating TBV settings. With time series extracted fromthe voxels of highest z-score, the mean PSC (SD) were contin-uous no feedback = .40 (.36), continuous real feedback = .18(.31), continuous false feedback = .29 (.27), intermittent no feed-back = .30 (.14), intermittent real feedback = .48 (.16), andintermittent false feedback = .31 (.19). With continuous feed-back (comparing real feedback to false feedback), 2 participantsperformed significantly better with real feedback, 4 participantshad no significant difference with real feedback, and 4 partici-pants performed significantly worse with real feedback (signif-icance levels of P = .05). With intermittent feedback (compar-ing real feedback to false feedback), 4 participants performedsignificantly better with real feedback, 4 participants had nosignificant difference with real feedback, and no participantsperformed significantly worse with real feedback (significancelevels of P = .05).

With time series extracted from all voxels, the mean slopes(SD) were continuous no feedback = −.033 (.069), continuousreal feedback = .053 (.090), continuous false feedback = .028(.054), intermittent no feedback = −.005 (.042), intermittent realfeedback = .060 (.061), and intermittent false feedback = −.010(.129). With time series extracted from the voxels of high-est z-score, the mean slopes (SD) were continuous no feed-back = −.015 (.024), continuous real feedback = .005 (.039),continuous false feedback = −.014 (.015), intermittent no feed-back = −.010 (.012), intermittent real feedback = .003 (.025),and intermittent false feedback = −.009 (.022). Paired t-testfailed to find any significant differences (P = .05) between realand false feedback, for either feedback type in either analysisapproach.

Whole-Brain Analysis

The whole brain activation pattern of no feedback ROI lo-calizer scans for the contrast of “Imagine Movement—Rest” isshown in Figure 2. The analysis included 11 individuals with 1or 2 scans, for a total of 18 scans; analyzed using a multisession(fixed effects) and multisubject (mixed effects) three-level anal-ysis. Brain regions with significant activation include bilateralmiddle frontal gyrus, left parietal cortex, left frontal regions, andright frontal and insula regions (clusters and local maximum ofactivation are listed in Table S1).


Fig 1. Unfiltered percent signal change from all voxels in the individually selected region of interests. A region of interest was selected foreach individual from a no feedback baseline scan. The mean percent signal change, with no temporal filtering, from all voxels in the individualregion of interest is plotted for the continuous feedback paradigm (A) and for the intermittent feedback paradigm (B). The hemodynamic rest(and intermittent feedback) periods are shaded and the “Imagine Movement” periods are unshaded in the plots, comparing the no feedbackbaseline scans (dotted thin line), false feedback scans (thin solid line), and real feedback scans (thick solid line).

Fig 2. No feedback baseline scans of imagine movement task for ROI localization. Pattern of activation for “Imagine Movement—Rest”contrast (11 individuals with one or two scans, for a total of 18 scans). Scans were analyzed using a multisession (fixed effects) andmultisubject (mixed effects) three level analysis, and thresholded using clusters determined by Z > 2.3 and a corrected cluster significance ofP = .05. The yellow square demonstrates the manually circumscribed region, from which the ROI was dynamically defined.


Fig 3. Continuous feedback. Pattern of activation for “Imagine Movement—Rest” contrast (10 paired scan sets). Scans were analyzedusing a tripled two-group difference analysis (mixed effects), and thresholded using clusters determined by Z > 2.3 and a corrected clustersignificance of P = .05.

For continuous feedback, contrasts of “real feedback > nofeedback,” “real feedback > false feedback,” and “false feed-back > real feedback” are shown in Figure 3 (from lower levelcontrast of “Imagine Movement – Rest”). The analysis included10 scan sessions (30 total scans), analyzed using the FSL tripledtwo-group difference analysis (mixed effects). Results includea relatively small cluster of activation in right frontal regionsfor “real feedback > no feedback,” no significant activationfor “real feedback > false feedback,” and relatively extensiveactivation with maximum in right frontal regions for “false feed-back > real feedback” (clusters and local maximum are listedin Table S2).

For intermittent feedback, contrasts of “real feedback > nofeedback,” “real feedback > false feedback,” and “false feed-back > real feedback” are shown in Figure 4 (from lower levelcontrast of “Imagine Movement – Rest”). The analysis included8 scan sessions (24 total scans), analyzed using the FSL tripledtwo-group difference analysis (mixed effects). Results includea relatively small cluster of activation in right visual regionsfor “real feedback > no feedback,” no significant activation for“real feedback > false feedback,” and relatively extensive ac-tivation with maximum in right visual, right caudate, and leftputamen regions for “false feedback > real feedback” (clustersand local maximum are listed in Table S3).

With intermittent feedback scans only, the lower level con-trast of “Feedback (2 volume blocks)—Rest (9 volume blocks)”is shown in Figure 5 for higher level contrasts of “all inter-mittent scans,” “real feedback > false feedback,” and “falsefeedback > real feedback.” The analysis included 8 scan ses-sions (16 scans total), analyzed using a multisession (fixed ef-fects) and multisubject (mixed effects) three level analysis for“all intermittent scans” (top); and a two-sample paired t-test

(mixed effects) for “real feedback > false feedback” and “falsefeedback > real feedback” contrasts. Results include a rela-tively extensive cluster of activation for all intermittent scans,no significant activation for “real feedback > false feedback,”and activation with maximum in right cingulate, right frontal,right temporal, and right parietal regions for “false feedback> real feedback” (clusters and local maximum are listed inTable S4).

DiscussionOur main hypothesis was that participants would generategreater activation in premotor cortex when given intermittentfeedback than they would when given continuous feedback.Using a post-hoc analysis similar to the real-time processing, 4of 8 participants had significantly higher PSC with intermittentfeedback (real feedback compared to the false feedback controlcondition). This compares to only 2 of 10 participants havinghigher PSC with continuous feedback, and additionally 4 of10 participants having significantly worse PSC with continuousfeedback. For continuous feedback, the significant decreasesin PSC with real feedback relative to false feedback may bedue in part to incorrect interpretation of feedback. The falsefeedback may have provided use feedback at times by randomchance, whereas real feedback could be consistently unhelpful,if the hemodynamic delay is no properly accounted for by theparticipant.

Another advantage of the intermittent approach is that thebrain regions involved in evaluating feedback can be uniquelyseparated in time from task performance (see Fig 5). Giventhe extensive brain activation implicated in evaluated feed-back, continuous feedback during task performance could be


Fig 4. Intermittent feedback. Pattern of activation for “Imagine Movement—Rest” contrast (eight paired scan sets). Scans were analyzedusing a tripled two-group difference analysis (mixed effects), and thresholded using clusters determined by Z > 2.3 and a corrected clustersignificance of P = .05.

confounding and interfere with RTfMRIf objectives. The phe-nomenon of evaluation feedback itself may be a worthwhileresearch area. Notably false feedback generated much brainactivation relative to real feedback, potentially related to taskswitching, and feedback appraisal. Task and appraisal pro-cesses occur simultaneous with continuous feedback paradigms,

whereas these features may be separable with the intermittentfeedback paradigms. Future work focused on feedback process-ing, correlating factors of accuracy (when feedback matchesbrain activity, whether from real data or randomly generateddata) and direction (positive feedback vs. negative feedback),could also aid in isolating feedback components.

Fig 5. Intermittent feedback component. Pattern of activation for “Feedback—Rest” contrast (eight paired scan sets). Scans were analyzedusing a multisession (fixed effects) and multisubject (mixed effects) three level analysis for all intermittent scans (top); and a two-sample pairedt-test (mixed effects) for “real feedback > false feedback” and “false feedback > real feedback” contrasts. All contrasts were thresholded usingclusters determined by Z > 2.3 and a corrected cluster significance of P = .05.


We did not provide feedback during rest periods to keepthe task simple for participants and to allow contrasts of “task—rest” to include feedback components. While analyzing the datawithout temporal filtering did not change our primary findings,there were some trends worth considering in future work. Base-line rest values, specifically for real feedback, tend to drift upthroughout the scan (Fig 1). Providing feedback during rest toreduce such drift could produce greater “task—rest” contrastvalues. Practice and learning effects may be important as tasksignal trended up, specifically through the real feedback scan.

There are many limitations of this pilot study. A considerablenumber of scans were excluded based on quality checks, andfuture RTfMRIf studies relying on functionally defined ROIsmay be limited if such defined ROIs are not reliably found. Ex-cluded studies also altered our counterbalanced design, so ourstudy may be susceptible to order effects. However, we did notnote obvious order effects in our limited sample. We did notuse EMG recordings to verify that participants were perform-ing motor imagery rather than actual movements. However,we took steps to minimize the possibility of actual movements(immobilization and instructions), blinded participants to falsefeedback conditions, and failed to find significant differencesin primary motor cortex in real versus false feedback fMRIcontrasts. It should also be noted that there are other ways toprovide feedback, such as a continuous timeline that cues par-ticipants to the relationship between what they are doing in themoment and the sluggish 3-6 seconds hemodynamic delay.8,12

Such approaches may require extensive training not requiredfor intermittent feedback. However, we tested only two specificfeedback strategies in our study and did not examine trainingeffects.

In summary, we have shown that participants can use in-termittent feedback to modulate premotor cortex activity dur-ing an imaginary movement task. Feedback displayed intermit-tently may be superior to feedback that is constantly updatingand continuously shown, at least for some tasks. As we onlytested motor imagery using a single ROI, it is difficult to knowif these findings generalize to other RTfMRIf applications. Thispilot study provides some interesting, albeit preliminary, datato guide future studies using RTfMRIf. Future methods work isneeded to refine and develop the most interesting new tool ofRTfMRIf.

The authors wish to thank Brian Dale at Siemens Medical for support inenabling the real-time MR image export, facilitated by a master researchagreement between Siemens Medical and the Medical University ofSouth Carolina. The authors also wish to thank Rainer Goebel fortechnical assistance with Turbo-BrainVoyager.

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Supporting InformationAdditional supporting information may be found in the onlineversion of this article:

Figure S1. Continuous and Intermittent FeedbackParadigms: (A) Continuous feedback is given by an activevertical-scaled bar every volume (2.2 seconds) during the“Imagine Movement” period (10 volumes or 22 seconds), fol-lowed a “Rest” period (10 volumes or 22 seconds) with aninactive scale. (B) Intermittent feedback is given during 2 vol-umes (4.4 seconds) following the “Imagine Movement” period(9 volumes or 19.8 seconds). The “Rest” period (9 volumes or19.8 seconds) follows the feedback.

Figure S2. Selected Regions of Interest: Each individualROI was spatially normalized to the MNI template. The binaryROIs were then added together, yielding highest intensities atvoxels common across individuals. The ROIs are then over-layed on the MNI template for Scan 1, the first no feedbackROI localizer (A); and for scan 4, the second no feedback ROIlocalizer (B).


Table S1. No feedback ROI localizer scans of imaginemovement task for ROI localization (for Fig 2).

Table S2. Continuous feedback (for Fig 3).Table S3. Intermittent feedback (for Fig 4).Table S4. Intermittent feedback component (for Fig 5).

Please note: Wiley-Blackwell are not responsible for thecontent or functionality of any supporting materials sup-plied by the authors. Any queries (other than missing mate-rial) should be directed to the corresponding author for thearticle.


intermittent “real-time” fmri feedback is superior to continuous presentation for a motor...

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