NeuroImage xxx (2013) xxx–xxx

YNIMG-10542; No. of pages: 13; 4C: 5, 6, 7, 8, 9, 10, 11

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NeuroImage

j ourna l homepage: www.e lsev ie r .com/ locate /yn img

Review

Enhancing dual-task performance with verbal and spatial working memory training:Continuous monitoring of cerebral hemodynamics with NIRS

Ryan McKendrick a,⁎, Hasan Ayaz b, Ryan Olmstead a, Raja Parasuraman a

a Center of Excellence in Neuroergonomics, Technology, & Cognition (CENTEC), George Mason University, 4400 University Drive, Fairfax, VA 2230, USAb School of Biomedical Engineering, Science & Health Systems, Drexel University, 3141 Chestnut Street, Philadelphia, PA 19104, USA

⁎ Corresponding author at: George Mason University,Fairfax, VA 2230, USA.

E-mail address: rmckend2@gmu.edu (R. McKendrick

1053-8119/$ – see front matter © 2013 Elsevier Inc. Allhttp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Please cite this article as: McKendrick, R., et amonitoring of cerebral hemodynamics with

a b s t r a c t

a r t i c l e i n f o

Article history:Accepted 23 May 2013Available online xxxx

Keywords:Working memory trainingNear infrared spectroscopyDorsolateral prefrontal cortexVentrolateral prefrontal cortexHemodynamics

To better understand the mechanisms by which working memory training can augment human performancewe continuously monitored trainees with near infrared spectroscopy (NIRS) while they performed a dualverbal–spatial working memory task. Linear mixed effects models were used to model the changes incerebral hemodynamic response as a result of time spent training working memory. Nonlinear increases inleft dorsolateral prefrontal cortex (DLPFC) and right ventrolateral prefrontal cortex (VLPFC) were observedwith increased exposure to working memory training. Adaptive and yoked training groups also showeddifferential effects in rostral prefrontal cortex with increased exposure to working memory training. Therewas also a significant negative relationship between verbal working memory performance and bilateralVLPFC activation. These results are interpreted in terms of decreased proactive interference, increased neuralefficiency, reduced mental workload for stimulus processing, and increased working memory capacity withtraining.

© 2013 Elsevier Inc. All rights reserved.

Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Working memory tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0NIRS data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Statistical model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Analysis, multiple comparison corrections, and contrasts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Behavioral performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Training day effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Training day by training condition interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Behavioral performance by hemodynamic response correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0

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Introduction

The augmentation of human performance and its transfer toimproved functioning at work or in everyday settings via alteration ofunderlying neurocognitive processes is a prime goal of neuroergonomics

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

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(Parasuraman, 2011; Parasuraman et al., 2012). Training to increaseworking memory capacity (WMC) represents one potential method forsuch neurocognitive enhancement. Working memory represents acapacity limited process for the encoding, manipulation, and storage oftask relevant information for use by higher order cognitive processes(Baddeley, 1986). Individualswith highWMChave been found to exhibitsuperior focused visual attention (Engle, 2002), improved time criticaldecision making (Endsley, 1995) and even enhanced supervisorycontrol of unmanned aerial vehicles (de Visser et al., 2010; McKendricket al., 2011). A growing body of research also suggests that WMCcan be improved in healthy adults, so that normal age-related ordegeneratively-linked declines in cognitive function may possibly beminimized (see Buschkuehl et al., 2012; Klingberg, 2010; Chein andMorrison, 2010 for reviews).

It is well established that repeated practice on simple span workingmemory recall and working memory updating improves performanceon such tasks. After such training trainees are able to recall spans ofgreater length and manipulate working memory information with lessforgetting. However, whether such improvement transfers to othertasks, including higher functions required for everyday cognitive func-tioning, has provenmore difficult to show. In other words, “far transfer”of working memory training remains controversial. Early studiesshowed robust transfer effects to othermeasures of general intelligencesuch as Raven's progressive matrices (Klingberg et al., 2002). Howeverrecent reviews (Hulme and Melby-Lervåg, 2012; Shipstead et al.,2012) and a meta-analysis (Melby-Lervåg and Hulme, 2013) suggestthat the evidence for far transfer is minimal at best. Suboptimal transfereffects may be a result of the use of simple span and updating workingmemory tasks, rather than complex tasks, as well as the usage ofsuboptimal training parameters. It has been suggested that the use ofcomplex span tasks, training on different variants of working memory,increased practice span length, and more liberal criteria for adaptivetraining advancement, are factors that need to be accounted for to affordmore efficientWMC improvement and far transfer (Gibson et al., 2012).

The success of working memory training and its potential fortransfer to higher order functioning has generated interest in under-standing its underlying neural mechanisms. Various brain imagingmodalities, including both structural (Magnetic Resonance Imaging;MRI) and functional (Functional Magnetic Resonance Imaging; fMRI,Electroencephalogram; EEG) imaging, have been used to uncoverthe underlying neural plasticity. The literature suggests that trainingcan alter the brain in multiple ways, such as increased gray mattervolume (Draganski et al., 2004, 2006; Hamzei et al., 2012; Taubertet al., 2010), increased and decreased white matter fiber tract density(Scholz et al., 2009; Taubert et al., 2010), increases and decreases inthe BOLD fMRI response (Dahlin et al., 2008; Jonides, 2004; Mooreet al., 2006; Olesen et al., 2004), and increased frontal-midline EEGtheta power (Dopplemayr et al., 2008; Gevins et al., 1997; Smith et al.,1999). Short term working memory training is associated withdecreases in hemodynamic response in dorsolateral prefrontal cortex(DLPFC) (Garavan et al., 2000; Jansma et al., 2001; Landau et al., 2004,2007; Sayala et al., 2006; Schneiders et al., 2011), while prolonged train-ing has been linked to an increase in hemodynamic response of DLPFC(Dahlin et al., 2008; Jolles et al., 2010; Jonides, 2004; Olesen et al.,2004; Westerberg and Klingberg, 2007). Working memory traininghas also been linked to increases in white matter fiber tract density inthe anterior body of the corpus callosum which connects bilaterallythe DLPFC, thereby potentially improving information transfer betweenthe left and right DLPFC (Takeuchi et al., 2010). It is believed thatincreases in DLPFC activity diminish inhibitory signals to intraparietalsulcus (IPS) and mediate an increase in WMC (Edin et al., 2009).

Training related changes inDLPFC are also accompanied by evidencefor an increase in the ventrolateral prefrontal cortex (VLPFC) BOLDresponse and neuronal recruitment during themaintenance of workingmemory representations (Meyer et al., 2011; Moore et al., 2006; Qiet al., 2011). These effects are consistent with other research in which

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

VLPFC activation has been implicated in the resolution of proactiveinterference betweenworkingmemory representations and concurrentdistracter representations (Badre andWagner, 2007; Badre et al., 2005).Therefore increased hemodynamic response in VLPFC and DLPFC isexpected as a result of working memory training.

Previous studies of workingmemory training have typically used apre- and post-training design, in which neurocognitive changes areassessed before and after training. One reason for the use of such adesign is due to the high cost of multiple fMRI scans. However, neuralchanges are likely to occur continuously throughout training, and itwould be of interest to see how such changes are linked to performance.Accordingly, the present study focused on continuous changes incerebral hemodynamics, using a method that is well suited to repeatedimaging, near infrared spectroscopy (NIRS). NIRS uses specific wave-lengths of light to measure changes in oxygenated and deoxygenatedhemoglobin and the NIRS signal is correlated with the fMRI BOLD asboth measure hemodynamic response, especially in brain regionsmore proximal to the scalp such as the frontal cortex (Cui et al., 2011).

To accurately isolate changes in the hemodynamic response as aresult of working memory performance compared to changes due tonon-specific increases in mental effort, we compared two training con-ditions: A traditional adaptive condition whose working memory loadwas adjusted based on the trainee's performance, and a yoked conditionwhoseworkingmemory loadwas adjusted based on theperformance oftrainees in the adaptive condition. Since task demands are not matchedto the capabilities of yoked-trainees we would expect them to expendmore mental effort in order to perform the task. Evidence for thiswould be represented as an increase in hemodynamic response in PFCfor the yoked training condition. At the same time, task demands arematched to the capabilities of adaptive-trainees, therefore we wouldexpect to see them exhibit a decrease or little change in hemodynamicresponse in PFC due to minimal changes in required mental effort.

In addition, to improve the efficacy of the working memory trainingdesign, we implemented the suggestions for optimal training proposedby Gibson et al. (2012). Our task tested two components of workingmemory; spatial working memory which requires the encoding andretrieval of spatial locations, and verbal working memory whichrequires the encoding and retrieval of semantic content. Memory forthe two working memory components was temporally combined inorder to tax the updating and executive control components associatedwith working memory (Baddeley, 1986). In order to avoid ceilingeffects and challenge trainees, load for verbal and spatialworkingmem-ory was set to a range beyond what is considered the average capacitylimit (spatial: 4 chunks, verbal: 7 chunks). We expected an increase inverbal working memory performance over time, with moderatechanges in spatial working memory performance (McKendrick andParasuraman, 2012).

In summary, as an effect of working memory training, we anticipatedan increase in verbalworkingmemory performance alongwithmoderatechanges in spatial working memory performance as training progressed.We also expected to observe an inverse relationship in PFChemodynamicresponse between the two training conditions as an effect of training.Finally orthogonal to the hypothesized condition by training interac-tion, we expected an increase in hemodynamic response in bothDLPFC and VLPFC as an effect of working memory training.

Methods

Participants

Ten right handed adults were recruited for participation in workingmemory training sessions over five days. All had normal or corrected tonormal vision and signed an informed consent form approved by theGeorge Mason Institutional Review Board before participating in thestudy. Five participants were randomly assigned to each of two trainingconditions.

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

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Working memory tasks

Participants received training and performed the working memorytask on a desktop PC running Matlab and Psychophysics Toolbox. Theexperimental task consisted of two concurrently presented workingmemory span tasks, verbal span and spatial span. Stimuli for the verbalspan task consisted of a string of gray numbers presented on a blackbackground. The length of the string varied across training sessions.All participants began with verbal span load being randomly assignedas seven, eight, or nine digit strings for a given trial, trainees sawthree examples of each load level within a training block. Each digitwithin the string was also randomly selected and ranged from 1 to 9.All the numbers in the string were presented simultaneously andordered left to right across the screen. The display duration of the stringwas yoked to the length of the string. Each digit contributed .4 s to thedisplay duration of the string. Verbal span performance was defined asthe number of digits reported correctly and in the correct order. Input-ting more digits than initially specified in the string to be rememberedwas penalized. If a participant was presented with seven digits andinputted 8 digits, the number of presented digits was divided by thenumber of inputted digits, the quotient was then multiplied by thenumber of digits correctly reported to arrive at the trainee's perfor-mance score for that trial.

The stimuli for the spatial task consisted of black circles presentedsimultaneously over a graybackground. Thenumber of circles presentedvaried across training conditions. All participants began with spatialspan load being randomly assigned as five, six, or seven circles for agiven trial, trainees saw three examples of each load level within a train-ing block The spatial location for each circle was randomly chosen withthe only caveat being that the center of one circle had to be greater than150 pixels from the center of any other circle displayed. All the circleswere presented simultaneously across the screen. The spatial taskstimuli were presented for 1 s. Spatial span performance was definedas the number of circles reported correctly and in the correct location.Inputting more circles than initially specified in the display to beremembered was penalized. If a participant was presented with fivecircles and inputted six circles, the number of presented circles wasdivided by the number of inputted circles, the quotient was then multi-plied by the number of circles correctly reported to arrive at the trainee'sperformance score for that trial.

Each trial began with the presentation of the verbal stimulifollowed directly by the presentation of the spatial stimuli. The spatial

Fig. 1. Dual verbal–spatial working memory task representation: (1) verbal span stimuli, dduration: 1.0 s., (4) random noise mask, duration: 4.0 s., (5) response to spatial span, (6) r

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

stimuli were followed by a random noise mask that was displayed for4 s. Following the mask participants were instructed to respond to thespatial stimuli by using a standard computer mouse and pressing theleft mouse button on the location where a circle had been presented.Participants were instructed to reproduce the pattern of spatial stimulilocations exactly, and to press the space bar once they had finishedresponding. After responding to the spatial stimuli participantsresponded to the verbal stimuli by pressing the number keys at thetop of a QWERTY keyboard. Participants were instructed to reproducethe span exactly, and to press the space bar when they were finished(Fig. 1).

Training

Participants trained for 2 h each day for five consecutive days,resulting in 10 total hours of training. Daily training was separatedinto two 1 h sessions with a 15 min break between training sessions.Within a given training sessions participants performed 10 trainingblocks and each training block consisted of nine trials of the dualworking memory task.

Participantswere randomly assigned to two training conditions, anadaptive and a yoked condition. In the adaptive condition the spandifficulty that participants were exposed to on subsequent days oftraining was a product of their previous days' performance. If partici-pants in the adaptive condition responded with 80% or greater perfor-mance in a component of the dual working memory task workingmemory load of that component was increased by dropping the low-est span load and replacing it with a load level one greater than theprevious highest load. For example if an adaptive-trainee on the firstday of training correctly reported 85% of the digits presented in theverbal span task averaged across load than their working memoryload was increased on the second day of training. Since this was thefirst day of training the trainee was seeing loads of five, six, andseven digits, on the second day of training the trainee would bepresented with loads of six, seven, and eight digits. Participants inthe yoked condition saw the same difficulty levels as those seen bythe adaptive condition but the difficulty changes were dictated notby their performance but by the performance of the individual in theadaptive condition to which they were yoked. Yoked pairings wereconstrained by trainee gender, with males being yoked only to malesand females only to females.

uration: 2.8–4.0 s., (2) spatial span fixation, duration: 0.2 s., (3) spatial span stimuli,esponse to verbal span, duration: trainee controlled.

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Fig. 2. Registration of 16 optodes (measurement locations) across prefrontal cortex(Ayaz et al., 2012).

Table 2Hemodynamic changes as a function of day and training condition.

Source Main effects Polynomial contrasts

dfnum dfden F Linear Quadratic Cubic

Optode 3 HbO2

Day 4.00 891.57 3.49⁎⁎ 1.43 −0.65 3.30⁎⁎

Condition 1.00 8.00 1.13Day × condition 4.00 891.59 2.44

Optode 3 total HbDay 4.00 891.51 3.66⁎⁎ 2.44⁎ −1.27 2.24⁎

Condition 1.00 8.00 1.09Day × condition 4.00 891.53 2.77

Optode 4 HbRDay 4.00 944.93 1.94Condition 1.00 7.86 1.28Digit performance 1.00 528.46 7.98⁎⁎

Day × condition 4.00 942.79 7.53⁎⁎⁎

Adaptive 2.47⁎ 2.48⁎ −1.79Yoked 0.68 −4.44⁎⁎⁎ 0.65

Optode 4 Hbo2Day 4.00 946.04 1.53Condition 1.00 7.92 0.23Digit performance 1.00 781.69 9.66⁎⁎

Day × condition 4.00 944.12 4.60⁎⁎

Adaptive 2.29⁎ 1.02 −0.10Yoked 0.30 −2.97⁎⁎ 1.51

Optode 5 HbO2

Day 4.00 844.79 6.17⁎⁎⁎ 2.69⁎⁎ −1.19 3.85⁎⁎⁎

Condition 1.00 8.00 0.26Day × condition 4.00 844.82 0.71

Optode 5 total HbDay 4.00 845.83 3.33⁎ 2.33⁎ −0.96 2.42⁎

Condition 1.00 8.00 0.52Day × condition 4.00 845.87 1.12

Optode 9 HbRDay 4.00 877.50 0.33Condition 1.00 8.00 0.25Day × condition 4.00 877.50 5.28⁎⁎⁎

Adaptive 1.74 2.35⁎ −1.57Yoked −0.84 −1.69 2.52⁎

Optode 9 HbO2

Day 4.00 878.31 3.58⁎⁎ 1.50 −1.67 3.07⁎⁎

Condition 1.00 8.00 0.18Day × condition 4.00 878.33 3.97⁎⁎

Adaptive 3.09⁎⁎ 0.08 0.92Yoked −0.85 −2.40⁎ 3.37⁎⁎⁎

Optode 9 total HbDay 4.00 876.32 1.93Condition 1.00 8.00 0.34Day × condition 4.00 876.34 6.18⁎⁎⁎

Adaptive 3.25⁎⁎ 1.30 −0.14⁎⁎ ⁎⁎

4 R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

Imaging

We used a continuous wave fNIR Device model 1100 fNIR system(fNIR Devices LLC, Photomac MD; www.fnirdevices.com) to imagecerebral hemodynamics of prefrontal cortex. At the beginning of eachtraining block, the participant was connected to the fNIR system andtheir baselines were taken while participants visually fixated on acentral cross presented on the computer screen. Activation of eachparticipant's prefrontal cortex was monitored throughout the entiretime participants were trained on the dual working memory task. Thesensor had a temporal resolution of 500 ms per scan with 2.5 cmsource–detector separation allowing for approximately 1.25 cm pene-tration depth. The dual-wavelength light emitting diodes (LEDs) wereactivated in turn one light source and wavelength at a time and thefour surrounding photodetectors around the active source were sam-pled. The positioning of light source and detectors on the sensor padyielded a total of 16 active optodes (Fig. 2). COBI Studio software (DrexelUniversity) was used for data acquisition and visualization (Ayaz et al.,2011).

NIRS data processing

For each participant, raw fNIR data (16 optodes × 2 wavelengths)were low-pass filtered with a finite impulse response, linear phasefilter with order 20 and cut-off frequency of 0.1 Hz to attenuate the

Table 1Behavioral changes as a function of day and training condition.

Source Main effects Polynomial contrasts

dfnum dfden F Linear Quadratic Cubic

Verbal span performanceDay 4.00 981.00 60.18⁎⁎⁎ 14.26⁎⁎⁎ −4.20⁎⁎⁎ 4.35⁎⁎⁎

Condition 1.00 8.00 0.14Day × condition 4.00 981.00 15.37⁎⁎⁎

Adaptive 13.82⁎⁎⁎ −1.29 3.23⁎⁎

Yoked 5.53⁎⁎⁎ −5.15⁎⁎⁎ 2.92⁎⁎

Spatial span performanceDay 4.00 985.00 22.77⁎⁎⁎ 5.54⁎⁎⁎ −7.14⁎⁎⁎ −0.11

Polynomial contrasts = least squares t-ratios.Denominator degrees of freedom calculated with Kenward–Rogers corrections.Random effects specified as random intercept for each participant.⁎⁎ p b .01.

⁎⁎⁎ p b .001.

Yoked −1.04 −2.59 3.27

Optode 11 HbRDay 4.00 890.81 0.96Condition 1.00 7.98 0.16Day × condition 4.00 891.13 8.67⁎⁎⁎

Adaptive 1.95 3.81⁎⁎⁎ −1.97⁎

Yoked −2.38⁎ −2.55⁎ 1.10

Optode 11 HbO2

Day 4.00 889.60 0.22Condition 1.00 8.00 0.02Day × condition 4.00 889.72 5.89⁎⁎⁎

Adaptive 2.93⁎⁎ 1.33 −0.44Yoked −2.65⁎⁎ −1.57 0.91

Optode 11 total HbDay 4.00 889.59 0.17Condition 1.00 8.00 0.01Day × condition 4.00 889.71 11.53⁎⁎⁎

Adaptive 3.75⁎⁎⁎ 3.52⁎⁎⁎ −1.61Yoked −3.18⁎⁎ −2.38⁎ 1.21

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performance with verbal and spatial workingmemory training: Continuousmonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), http://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Table 2 (continued)

Source Main effects Polynomial contrasts

dfnum dfden F Linear Quadratic Cubic

Optode 12 HbRDay 4.00 868.50 0.87Condition 1.00 7.99 0.25Day × condition 4.00 868.63 6.87⁎⁎⁎

Adaptive 0.91 3.61⁎⁎⁎ −1.23Yoked −1.45 −2.43⁎ 2.93⁎⁎

Optode 12 HbO2

Day 4.00 869.81 0.22Condition 1.00 8.00 0.01Day × condition 4.00 869.90 5.34⁎⁎⁎

Adaptive 2.31⁎ 0.83 −0.57Yoked −2.67⁎⁎ −1.00 1.96

Optode 12 total HbDay 4.00 868.94 0.37Condition 1.00 8.00 0.01Day × condition 4.00 869.04 8.41⁎⁎⁎

Adaptive 2.07⁎ 2.56⁎ −1.38Yoked −2.86⁎⁎ −1.86 2.89⁎⁎

Optode 14 total HbDay 4.00 855.20 3.77⁎⁎ 3.08⁎⁎ −2.02⁎ 0.90Condition 1.00 8.00 0.12Day × condition 4.00 855.60 3.05

Optode 16 HbRDay 4.00 947.06 4.05⁎⁎ 2.16⁎ −2.67⁎⁎ 2.10⁎

Condition 1.00 8.00 0.96Day × condition 4.00 947.06 0.83

Optode 16 HbO2

Day 4.00 955.13 10.39⁎⁎⁎ 3.71⁎⁎⁎ −4.91⁎⁎⁎ 2.62⁎⁎

Digit performance 1.00 836.60 10.07⁎⁎

Optode 16 total HbDay 4.00 954.34 11.98⁎⁎⁎ 3.99⁎⁎⁎ −4.98⁎⁎⁎ 3.32⁎⁎⁎

Digit performance 1.00 807.96 7.92⁎⁎

Polynomial contrasts = least squares t-ratios.Denominator degrees of freedom calculated with Kenward–Rogers corrections.Random effects specified as random intercept for each participant.

⁎ p b .05.⁎⁎ p b .01.

⁎⁎⁎ p b .001.

Fig. 3. Average number of correct digits reported for the verbal span task for a training blorepresentative of mixed model estimate for significant polynomial factors.

5R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

high frequency noise, respiration and cardiac cycle effects (Ayaz et al.,2010; Izzetoglu et al., 2005). Each participant's data was checked forany potential saturation (when light intensity at the detector washigher than the analog-to-digital converter limit) and motion artifactcontamination by means of a coefficient of variation based assessment(Ayaz et al., 2010). It was of particular importance to control for motionbased artifacts in the present study, as theNIRS signal in VLPFC has beenshown to be sensitive to task concurrent motion (Schecklmann et al.,2010). fNIR data for each training block were extracted using timesynchronizationmarkers received through serial port during the exper-iment and hemodynamic changes for each of 16 optodes during eachtrial block were calculated separately using the Modified Beer LambertLaw (MBLL). The hemodynamic response at each optode was averagedacross time for each trial block to provide a mean hemodynamicresponse at each optode for each block. The final output of each optodewasmean block deoxygenated hemoglobin (HbR), mean block oxygen-ated hemoglobin (HbO2), and the sum of the first to measures repre-sented as mean block total hemoglobin (Total Hb).

Statistical model selection

We used linear mixed effects models to estimate effects of trainingon cerebral hemodynamics and behavioral performance. Linear mixedeffects models offer advantages over repeated measures ANOVA whenmodeling hemodynamic change over time. They do not require anequal number of observations per participant. Linear mixed effectsmodels allow for the estimation of parameters unique to individualparticipants. Furthermore linear mixed effects models allow for timeto be modeled as a continuous variable, therefore temporal changecan also be modeled nonlinearly (Baayen et al., 2008; Krueger andTian, 2004; Laird andWare, 1982). Models only containing fixed effectswere fitted first. Behavioral performance and hemodynamic responseswere specified as dependent variables. The Akaike information criterioncorrected for sample size (AICc)was used to select themost parsimoni-ousmodel (Akaike, 1973) of fixed effects; if there was less than a differ-ence of two in AICc (Burnham and Anderson, 2002) between thetwo most parsimonious models, the simpler model containing fewerparameters was selected. After the most parsimonious fixed effectswere determined, random effects were selected once again using AICc

ck as a factor of training group (A—adaptive, B—yoked), day, and trainee. Line of fit is

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Fig. 4. Average number of correct locations reported for a training block as a factor of day, and trainee. Line of fit is representative of mixed model estimate for significant polynomialfactors.

6 R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

to assess model fit. If there was competition between the top twomodels the model with fewer parameters was selected for furtheranalysis.

Results

Analysis, multiple comparison corrections, and contrasts

To test for changes in behavioral performance and hemodynamics,finalmodelswere analyzed as linearmixed effectsmodelswith restrictedmaximum likelihood (REML) using lme4 in R (Bates and Sarkar, 2007).F testswere approximatedwithKenward–Roger corrections for denom-inator degrees of freedom (Kenward and Roger, 1997). Benjamini–Hockberg corrections with q specified at .05 were applied to effects fora given hemodynamic response across optodes to control for false dis-covery error rate (Benjamini and Hochberg, 1995; Verhoeven et al.,2005). Orthogonal polynomial contrasts were applied to significanteffects to further model behavioral and hemodynamic changes overtime. The results of the behavioral analyses are summarized in

Fig. 5. Optode-3 average HbO2 levels for a training block as a factor of day, and trainee. Li

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

Table 1, and the results of the hemodynamic analyses are presented inTable 2.

Behavioral performance

Performance on the verbal span task increased over training day,being best modeled as a cubic function. Differences between traininggroups were modeled by a significant negative quadratic componentfor the yoked training condition, representing a slowing of skill devel-opment on the third and fourth days of training relative to the adaptivecondition (Fig. 3). Performance on the spatial span task was bestmodeled as a negative quadratic function for both training conditions(Fig. 4).

Training day effects

Both training conditions showed an increase in hemodynamicresponse in optodes 3, 5, 14 and 16, being best modeled as a cubic func-tion (Fig. 5–8). During the first two days of training all four optodes

ne of fit is representative of mixed model estimate for significant polynomial factors.

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Fig. 6. Optode-5 average HbO2 levels for a training block as a factor of day, and trainee. Line of fit is representative of mixed model estimate for significant polynomial factors.

7R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

showed a linear increase in hemodynamic response. Optodes 3, 14, and16 showed an increase continuing to day three, and specifically optode14 showed an increase in response up until the fourth day of training.Optode 5 showed a decrease in response on days three and four andoptodes 3 and 16 showed a similar response but only for day four. Re-sponse in optodes 3, 5, 14, and 16 increased again on the final day oftraining.

Training day by training condition interactions

Significant day by training condition interactions were found inoptodes 4, 9, 11, and 12 (Fig. 9–12). The hemodynamic response ofthe adaptive condition had either positive linear and or quadraticcomponents: the response tended to decrease over the first threedays of training and then increase on the fourth and fifth days. Incontrast, the hemodynamic response of the yoked condition in optodes4, 9, 11, and 12 was best modeled with negative linear and or quadraticcomponents: the response of this group tended to increase after thefirstday of training until the third day, after which the response declined onthe fourth day.

Fig. 7. Optode-14 average total Hb levels for a training block as a factor of day, and trainee.

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

Behavioral performance by hemodynamic response correlations

Behavioral response variables were included in models for optodeswith a significant effect of training daywhen they could parsimoniouslyexplain further variance in the hemodynamic response. A relationshipwas found in optodes 4 and 16 between the hemodynamic responseand behavioral performance on the verbal span task (Fig. 13–14). Inboth cases increases in performance on the verbal span task wereaccompanied by decreases in the hemodynamic response at optodes 4and 16.

Discussion

We continuously monitored cerebral hemodynamic changes inthe prefrontal cortex in two groups of participants—an adaptive anda yoked control training condition—while they were trained on adual verbal–spatial working memory task. As expected, verbal spanand spatial span increased with training: increases in verbal span laterin trainingwere accompanied bydecreases in spatial span as individualsfocused more on improving their verbal span. We believe that this

Line of fit is representative of mixed model estimate for significant polynomial factors.

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Fig. 8. Optode-16 average HbO2 levels for a training block as a factor of day, and trainee. Line of fit is representative of mixed model estimate for significant polynomial factors.

8 R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

pattern is due to skill acquisition being easier in the verbal task relativeto the spatial task (McKendrick and Parasuraman, 2012). Differences inthe behavioral performance of the training conditions did not becomeapparent until the final two days of training where the yoked controlgroup appeared to reach a performance limit and could no longerkeep pace with the adaptive group.

Behavioral differences between training conditions informed theinterpretation of the cerebral hemodynamic differences. As predicted,we observed an increase in hemodynamic response for the yokedcontrol condition. This was specifically observed in the right rostralprefrontal cortex during the first three days of training. In the sameregion, in the adaptive condition there was a decrease in hemodynamicresponse over the same time period. Following the third day theresponse in the yoked condition decreased and the response in theadaptive condition increased (Fig. 15). The rostral prefrontal cortex is

Fig. 9. Optode-4 average HbO2 levels for a training block as a factor of training group, day, andfactors.

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

believed to be involved in the monitoring and processing of sensorystimuli during multitasking (Burgess et al., 2005). The NIRS signal hasreduced sensitivity in this region due to increased scalp to cortexdistance (Haeussinger et al., 2011; Heinzel et al., 2013). While we didfind a robust interaction within rostral prefrontal cortex as predicted,there is a possibility that other effects in this region were obscured byreduced signal sensitivity.

Overall the interactions within rostral prefrontal cortex suggestthat in order to keep pace with the performance of the adaptive groupthe yoked group had to apply considerably more effort in maintainingand processing dual task representations. Furthermore, towards theend of training the adaptive group had to increase the effort appliedto processing dual task representations to improve their performance.At the same time the yoked group may have become fatigued due tothe high level of effort required on the first three days of training.

trainee. Line of fit is representative of mixed model estimate for significant polynomial

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Fig. 10. Optode-9 average HbO2 levels for a training block as a factor of training group, day, and trainee. Line of fit is representative of mixed model estimate for significantpolynomial factors.

9R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

Irrespective of condition differences, we observed changes in thehemodynamic response of left DLPFC and right VLPFC as a result oftraining (Fig. 16). As predicted, the hemodynamic response increasedwith time spent on training working memory. However, the changesnot only were linear but also contained significant linear, quadraticand cubic components. Importantly, non-linear changes over timewould not have been observed if a pre-/post-training design commonlyused in fMRI studies had been used. Hemodynamic increases in rightVLPFC suggest a reduction in proactive interference improving themaintenance of working memory representations (Badre and Wagner,

Fig. 11. Optode-11 average total Hb levels for a training block as a factor of training grouppolynomial factors.

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

2007; Moore et al., 2006; Qi et al., 2011). This effect could be a result ofthe dual-task nature of our training methodology. However right PFCactivation as a result of inhibition of irrelevant stimuli during workingmemory has been previously observed via NIRS (Schreppel et al.,2008). It is possible that increased training reduced the proactiveinterference between concurrent verbal and spatial working memoryrepresentations, making their simultaneous maintenance easier andfacilitating greater representation capacity. The increased hemodynamicresponse in left DLPFC may have been representative of the top downinfluence of the IPS, increasing its activity thereby increasing working

, day, and trainee. Line of fit is representative of mixed model estimate for significant

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Fig. 12. Optode-12 average total Hb levels for a training block as a factor of training group, day, and trainee. Line of fit is representative of mixed model estimate for significantpolynomial factors.

10 R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

memory capacity (Edin et al., 2009). We also note the lack of differencebetween training groups in their hemodynamic response in theseregions, suggesting that these changes are not representative of changesin mental effort.

Changes in prefrontal hemodynamic response as an effect of trainingwere accompanied by negative correlations between the verbal spanperformance and the hemodynamic response in bilateral VLPFC. Nega-tive relationships between performance and hemodynamic responsein frontal brain regions are generally interpreted as an increase in pro-cessing efficiency (Kelly and Garavan, 2005; Neubauer and Fink, 2009;Poldrack, 2000). This increase in efficiency during multitasking canpotentially manifest as an increase in automatic processing in taskspecific pathways, the creation of independent streams of processing

Fig. 13. Optode-4 average HbO2 levels for a training block as a factor of average number of comodel estimate.

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

for each task, or an increase in processing speed due to improvedresponse selection. The strongest support has been found for an im-provement in processing efficiency via an increase in processing speeddue to improved response selection (Dux et al., 2009). Therefore wetake the negative relationship between verbal performance and hemo-dynamic response to suggest an increase in the speed of retrieval forverbal working memory representations.

Conclusion

NIRS provides an efficient and effectiveway to continuouslymonitorhemodynamic changes over extended periods of time, as required intraining studies. In addition, portable NIRS systems are being developed

rrect digits reported on the verbal span task. Line of fit is representative of linear mixed

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

Fig. 15. Mean total Hb (B-spline interpolated) as a factor of day and training group.

Fig. 14. Optode-16 average HbO2 levels for a training block as a factor of average number of correct digits reported on the verbal span task. Line of fit is representative of linearmixed model estimate.

11R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

as part of mobile brain imaging (MoBI) initiatives (Makeig et al., 2009).Consistent with the goals of neuroergonomics (Parasuraman, 2011;Parasuraman et al., 2012), NIRS technologies could be used to measurethe effects of training in complex real world tasks where the use of fMRIwould be challenging or impossible. Furthermore, the inclusion of linearmixed effectsmodels to NIRSmeasurement affords a robust and power-ful means of cataloging hemodynamics over time.

Working memory training is a potentially efficacious method ofneurocognitive enhancement. The present study examined the effectsof such training and its underlying neural correlates using an optimal

Fig. 16. Mean HbO2 (B-spline int

Please cite this article as: McKendrick, R., et al., Enhancing dual-task performonitoring of cerebral hemodynamics with NIRS, NeuroImage (2013), ht

trainingmethodology, a complexworkingmemory task, and a continu-ous monitoring of cerebral hemodynamics over the course of training.The results suggests that adaptive working memory training improvesworking memory performance by one of at least four mechanisms:(1) a reduction in proactive interference during representation mainte-nance due to increased recruitment of VLPFC; (2) an increase inworking memory capacity via top down disinhibition of IPS by DLPFC;(3) an increased efficiency of retrieval due to increased processingspeed during response selection; and (4) not over taxing mentalresources during the monitoring and processing of relevant stimuli.

erpolated) as a factor of day.

mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103

12 R. McKendrick et al. / NeuroImage xxx (2013) xxx–xxx

Additional work is needed before firm conclusions can be reachedon the efficacy of working memory training as a method ofneuroenhancement. Future work should examine larger samples ofparticipants and greater periods of training. It would also be importantto investigate hemodynamic response on a trial by trial basis withinboth prefrontal and parietal cortices so that training effects on proactiveinterference, disinhibition, and response selection can be more directlyassessed. Finally, consistent with the neuroergonomic approach,evidence of transfer to complex and ecologically relevant tasks wouldneed to be obtained in order to establish the usefulness of workingmemory training.

Acknowledgments

This research was supported by Air Force Office of ScientificResearch grant FA9550-10-1-0385, and the Center of Excellence inNeuroergonomics, Technology, and Cognition (CENTEC). We thankEmilyMarszalkowski, RabiaMurtza, andMollyOwens for their assistancein data acquisition.We also thank Harry Haladjian for supplying the codethat became the spatial workingmemory component of our training taskand Patrick McKnight for comments on the data analysis. The views,opinions, and/orfindings contained in this article are those of the authorsand should not be interpreted as representing the official views orpolicies, either expressed or implied, of the funding agencies.

Disclosure statement

fNIRDevices, LLC manufactures the optical brain imaging instru-ment and licensed IP and know-how from Drexel University. H.Ayazwas involved in the technology development and thus offered aminor share in the new start up firm fNIRDevices, LLC.

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mance with verbal and spatial workingmemory training: Continuoustp://dx.doi.org/10.1016/j.neuroimage.2013.05.103