Imaging early consolidation of perceptual learning with face stimuli during rest

Brain and Cognition 85 (2014) 170–179

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Imaging early consolidation of perceptual learning with face stimuli during rest J.S. Vilsten, M.E. Mundy ⇑ School of Psychology and Psychiatry, Building 17, Clayton Campus, Monash University, Victoria 3800, Australia

a r t i c l e

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Article history: Accepted 16 December 2013 Available online 4 January 2014 Keywords: Consolidation Faces Perceptual learning Memory fMRI Resting

a b s t r a c t Studies investigating visual perceptual learning (VPL) have traditionally used simple visual tasks and focused on assessing the active (online) processes of learning and memory: encoding and retrieval. The assessment of complex stimuli and the passive (offline) process of consolidation is, however, necessary for a full understanding of the development of VPL and has received little direct analysis. In the current study, 30 young adults completed a VPL task with face stimuli while undergoing an fMRI scan. Activity was assessed within offline rest breaks both during and after the learning task. Changes in baseline activity within functionally-relevant regions were identified during these rest periods. Furthermore, differences in consolidation-related resting activity were evident between individuals who performed well on the active task, and those who performed less well. These findings provide preliminary evidence that activity during offline rest breaks, which immediately follow the active task, is associated with consolidation and learning, in VPL. Ó 2013 Elsevier Inc. All rights reserved.

1. Introduction Conscious attention and awareness are defining features of explicit learning whereas implicit learning can occur without awareness of both what was learned and even that learning occurred (e.g., Janacsek & Nemeth, 2012). Visual perceptual learning (VPL) is the mechanism responsible for improvement in discrimination accuracy, following experience with a visual task, and has traditionally been considered to be an explicit process (e.g., Ahissar & Hochstein, 1993). It is becoming increasingly evident, however, that implicit mechanisms are also necessary (e.g., Sasaki, Nanez, & Watanabe, 2010). Most research in VPL has focused on simple tasks, including Gabor patches and Vernier acuity measurements (e.g., Fahle, 1997; Mukai et al., 2007; Shiu & Pashler, 1992). Such research has shown that task-related learning activates early visual systems which are responsible for processing specific stimulus features such as texture, orientation and motion direction (e.g., Crist, Kapadia, Westheimer, & Gilbert, 1997; Karni & Sagi, 1991). In addition, such learning has also been shown to be specific to location on the retinal field (e.g., Schoups, Vogels, & Orban, 1995). Together, these findings indicate the presence of VPL related plasticity in early visual areas. This form of research is limited, however, as analyses of simple stimuli do not provide a complete representation of VPL. It ⇑ Corresponding author. Fax: +61 399053948. E-mail address: [email protected] (M.E. Mundy). 0278-2626/$ - see front matter Ó 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.bandc.2013.12.005

is known, for example, that perception, discrimination and learning of more complex stimuli requires higher-order visual areas (see Mundy, Downing, Dwyer, Honey, & Graham, 2013; Mundy, Downing, & Graham, 2012)1. The difference between VPL of simple and more complex stimuli is underlined in a theory of perceptual learning, which proposes cortically distinct processes for feature learning (higher-order areas), and for stimulus noise discrimination (earlier visual regions; see Mollon & Danilova, 1996). Recent functional imaging studies investigating perceptual learning of complex images have shown that stimuli with different features or meanings are processed in different cortical regions (see Mundy, Graham, Downing, Honey, & Dwyer, 2009a; Mundy et al., 2009b, 2012, 2013; see also Graham, Barense, & Lee, 2010 for a review). Such regions include the fusiform face area (FFA) and perirhinal cortex in the medial temporal lobe, which have been shown to be selective for perceptual learning of faces (Mundy et al., 2009a,b, 2013), and the parahippocampal place area (PPA) and posterior hippocampus, which are selective for scene learning (Mundy et al., 2013). Importantly, activity in these regions has been shown to vary with visual discrimination performance. When the to-be-learned stimuli contain many overlapping features, making discrimination difficult, accurate VPL with faces modulates activity

1 Stimulus complexity is defined here, by the relative proportion of individual features or elements and the degree of overlapping features between stimulus exemplars. Stimuli that share a large number of overlapping features are thus harder to differentiate from one-another.

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in perirhinal cortex, and accurate VPL with scenes modulates posterior hippocampus (Mundy et al., 2013). When discrimination is less difficult (but still demanding), there is reduced involvement of these medial temporal lobe regions and instead, face VPL accuracy modulates activity in the FFA and occipital face area (OFA), whereas scene accuracy modulates activity in PPA (Mundy, 2013; Mundy & Chen, 2013; see also Mundy et al., 2009a,b, 2012; Pitcher, Walsh, Yovel, & Duchaine, 2007). Additionally, preliminary evidence, comparing the brain structure of good face learners with that of poor face learners, reveals grey matter density and cortical volume differences in regions including FFA, superior temporal and medial temporal lobes (Mundy, 2013; see also Teipel et al., 2007). Taken together, such findings provide evidence of a role for higherorder visual regions in VPL, which is not simply a by-product of stimulus type. Instead, it appears there is a network of regions along the ventral visual stream that contribute to accurate VPL, according in part to stimulus complexity. In addition to material specific areas which are utilised for VPL, activity within a number of material independent regions also appears to be necessary for perceptual learning of featurally complex stimuli. These are areas which are active regardless of stimulus type but for which the strength of blood oxygen level dependent (BOLD) modulation correlates with the degree of learning. The early visual pathway (including V1–V4) is an example of one such area (Mundy et al., 2009a,b, 2013). Interestingly, participants who perform the VPL task well show different patterns of activity within this pathway as a result of learning compared to those who perform the task less accurately (Mundy et al., 2009a,b; see also Mukai et al., 2007); a distinction which the authors suggest reflects different implicit learning strategies between the two groups. As a general rule, although shown with simple visual stimuli (i.e., those with few elements, or few overlapping elements across exemplars), activity within early visual areas reduces as a function of task performance after a period of learning (e.g., Lewis, Baldassarre, Committeri, Romani, & Corbetta, 2009; Mukai et al., 2007) and is thought to correspond to improved efficiency in task completion as a result of neuronal sharpening (e.g., Schoups, Vogels, Qian, & Orban, 2001). Attention networks, which include the frontal and supplementary eye fields and the dorsolateral prefrontal cortex, are also implicated in VPL and also have decreased activation after learning (Lewis et al., 2009; Mukai et al., 2007; Mundy et al., 2009a,b); a result which suggests a decreased need for focused attention after a period of learning (Lewis et al., 2009). Additionally, areas within the default mode network (DMN), active when individuals are not focused on the external environment (e.g., Buckner, Andrews-Hanna, & Schacter, 2008), are activated to a greater extent while individuals complete trained compared to untrained tasks (e.g., Lewis et al., 2009) and suggests that participants focus less on the active task and their surroundings as learning increases. While research in VPL with complex stimuli is growing, there exists another large aspect of VPL which has seen limited attention. The system of learning and subsequent memory involves three broad processes: encoding, consolidation and retrieval of information (e.g., Robertson, 2009). Encoding and retrieval require ‘‘online’’ activity, activity associated with a goal directed task, and have been the focus of the majority of VPL research. The process of consolidation, which occurs during ‘‘offline’’ or resting periods, while an individual is not focussed on any active task, has however seen little exploration. In addition, as with VPL research in general, investigations have primarily used simple stimuli rather than those which are more featurally complex. Investigations into the process of consolidation are, nevertheless, essential for a complete understanding of VPL and will form the crux of the research presented here. Consolidation itself has three phases which can overlap in their processes but also have distinct features. These include, early

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consolidation (which occurs in the time period directly after the encoding of learning), sleep dependent consolidation (which occurs during sleep and can enhance the degree of consolidation in some situations) and late consolidation (which occurs in the weeks and years following learning; e.g., Nadel & Moscovitch, 1997; Robertson, 2009). Our research focuses on the first of these. Behavioural research has shown that the process of early VPL consolidation has two primary detectable effects: it strengthens the original encoding of learning (e.g., Janacsek & Nemeth, 2012; Robertson, 2009; Sasaki et al., 2010) and it is responsible for offline task improvement (e.g., Janacsek & Nemeth, 2012; Robertson, 2009). When first encoded, learning is fragile and without primary consolidation it is prone to disruption as a result of interference from new information (e.g., Hung & Seitz, 2011; Seitz et al., 2005; Yotsumoto, Chang, Watanabe, & Sasaki, 2009). Experiments testing this phenomenon have determined that the first hour after learning a VPL task is critical to strengthen learning sufficiently to prevent learning disruption (e.g., Seitz et al., 2005; Yotsumoto et al., 2009). It is important to note, however, that it is likely that the time when learning can no longer be interfered with represents a period at which a threshold degree of early consolidation has taken place. Consequently, the process of consolidation must progressively occur during this time period. The phenomenon of offline or resting-time task improvement has seen very little assessment with regard to perceptual learning. Control groups of VPL/sleep consolidation studies, using featurally simple tasks, have shown mixed results with regards to learning improvement after 12 h (see Gais, Plihal, Wagner, & Born, 2000; Matarazzo, Franko, Maquet, & Vogels, 2008), however it is possible that differences in results occurred due to consolidation interference during this long period. Specifically, as individuals were not kept from visual stimuli in this time frame, due to continued visual activity in trained visual areas, consolidation could have been disturbed. Importantly, there is good evidence to suggest that VPL improvement can be identified over much shorter intervals (e.g., Honey, Mundy, & Dwyer, 2012; Mundy, Honey, & Dwyer, 2007). The detection of early consolidation over short time intervals is particularly interesting as it suggests that controlled functional imaging investigations may be able to detect cortical activity relating to the process of consolidation as it occurs (e.g., Albert, Robertson, & Miall, 2009; Hasson, Nusbaum, & Small, 2009). Research using functional imaging with visual stimuli has, however, focussed on functional connectivity changes, during offline periods, that occur as a result of learning (e.g., Lewis et al., 2009; Stevens, Buckner, & Schacter, 2010). Such research is good for providing information on the product of consolidation; however the process of consolidation can only be inferred from these studies. As with research into the encoding and retrieval process of VPL, changes in functional connectivity have been detected between early visual and attention networks as a product of several days of VPL using featurally simple tasks (e.g., Lewis et al., 2009). In addition, the extent of learning has been shown to correlate with the degree of change in functional connectivity (Lewis et al., 2009). Importantly, functional connectivity changes are also evident on shorter timescales (e.g., 9 min) and with socially relevant stimuli of scenes and faces (Stevens et al., 2010). Crucially, the PPA and FFA, which are selective for scene and face VPL respectively, have been shown to exhibit increased functional connectivity with areas within the inferior frontal gyrus as a result of stimulus observation (Stevens et al., 2010). Stevens and colleagues also showed that areas, for which faces or scenes were preferentially active, overlapped with areas in which functional connectivity changes would later be seen (Stevens et al., 2010). Together, these findings support the use of offline assessments of activity as measures of the consolidation process. Critically, such results indicate that consolidationrelated activity may be observed directly. The direct assessment of

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activity is important, as changes in activity may underlie neuronal changes associated with consolidation. Equally, changes in activity may mediate changes in connectivity within other areas. Furthermore, as with learning and perception, where learning can be considered an emergent property of perception (Graham et al., 2010), it can be difficult to separate the process of consolidation from encoding, as research into the field has shown that many of the same areas are active during both phenomena. Consequently, one could make the case for early consolidation being an extended period of encoding rather than a separate process. Thus, as offline periods represent a time in which stimulus perception and task execution effects are removed but consolidation mediated learning persists, the assessment of activity in this time can allow for a comparison of areas involved in consolidation with areas known to be involved in the active learning task. In accordance with this, the current study aims to shed some light on the process which underlies early consolidation in VPL of complex visual stimuli. It will assess activity during short breaks between an active face discrimination learning task, while participants are expecting further learning to take place. In addition, it will measure activity during a long break after learning, while participants are resting with no expectations of future learning. Brain areas active during these resting periods are likely to be those already shown to be involved in VPL, encoding and retrieval of face stimuli, as well as areas which have been implicated via functional connectivity changes resulting from consolidation. These include non-material specific early visual areas as well as stimulus-specific higher-order visual regions. Finally, to show that areas active during post-task rest are indeed associated with learning and performance, and are not simply a by-product of task completion, a further assessment is conducted. We will compare resting post-task cortical activity between behaviourally-defined good and bad performers. If this offline activity is associated with learning consolidation, as is proposed, good and bad learners will have distinct patterns of activation in functionally relevant areas.

2. Materials and methods 2.1. Participants Thirty healthy, young adults (mean age ± standard deviation [SD] = 23.1 ± 4.1; range = 21–32; 15 males/15 females), with normal or corrected-to-normal visual acuity and no history of psychiatric, neurological, or other medical illness participated in the research. The research was approved by the Monash University Human Research Ethics Committee (MUHREC) and all participants were reimbursed for their participation and gave written informed consent prior to participation, in accordance with the guidelines of MUHREC and Monash Biomedical Imaging (MBI). 2.2. Materials The cognitive task which preceded the downtime periods of interest involved discrimination learning for face stimuli (see example in Fig. 1, left panel). The detailed imaging results of this active task are beyond the scope of the current paper and will be reported separately. The active task was modelled on a previous study by Mundy et al. (2009a,b see also Mundy et al., 2013) and a full description of stimuli and behavioural task used is provided in that paper. Briefly, face stimuli were eight confusable pairs of colour photographs of faces, created using Morpheus Photo Morpher 1.85 (ACD Systems, Saanichiton, British Columbia, Canada; see Mundy et al., 2007, for detailed information about the morphing procedure). 2.3. Experimental design Visual stimuli were presented to participants using Presentation software (Neurobehavioral Systems Inc., Albany, California, USA) run on a Acer Timeline laptop computer, projected onto a screen positioned at the head of the MRI scanner bore using a JVC (model SX21/s) D-ILA projector, and viewed by participants via a mirror attached to the head coil. In a single trial, one face pair

Fig. 1. Left Panel: Example trial and a timeline of the experimental procedure. An example of one of the face pairs is shown, along with the timing of a single discrimination trial from the active study. There were 32 of these trials in each of the four active task blocks, and each face pair (8 total) was tested four times per block. The experiment duration was 30 min. Baseline = 7 min resting state scan; light grey = short downtime, 1 min bocks (d1–d4); white = active task; LD = long downtime, 7 min scan. Right Panel: Behavioural data showing overall percentage correct (upper graph) and reaction time (lower graph), split across the four blocks of the active face discrimination task (each block precedes a short downtime rest). Error bars = SEM.

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was tested for discrimination. A face stimulus would appear on screen for 350 ms, followed by a 500 ms blank screen, at which point a second face would appear for an additional 350 ms. This second face was either the paired partner of the first face (i.e., a ‘different’ response) or a repeat of the first face (a ‘same’ response). Participants were required to indicate as quickly and as accurately as they could, whether they thought each pair of presented stimuli were the same, or different, using a two-button response box (see Fig. 1, left panel). Following a trial, there was a random length inter trial interval between 2 and 10 s (mean ITI = 4.4 s). The behavioural task involved sixteen repetitions, of each of the eight image pairs in randomised order (i.e., 128 discrimination trials total), grouped into four blocks (i.e., 32 trials per block). Within a block, sixteen trials required ‘same’ responses, sixteen required a ‘different’ response. The four blocks each lasted 3 min and were separated by 1 min rest intervals in which individuals were instructed to close their eyes, while the computer displayed a black screen. This time period was increased from the 16 s rest intervals used in previous research so that more power could be obtained in the downtime rest period with which to detect any activity related to early consolidation, and to minimise the effects of carry-over from task-related activity. In addition, as the haemodynamic response function after an active task returns to normal 15–20 s after stimulus offset (Glover, 1999) the inter trial interval between the end of a block and beginning of the rest periods was fixed at 15 s. Thus task-related activity ‘bleed’ into downtime was also minimised in this way. A further 1 min rest interval was conducted after the final set of images was presented. Together, these four intervals represent the short ‘intra-task’ downtime breaks (d1–d4). Importantly, while no active task assessment occurred after d4, participants were not aware of this. This means d4 can be treated as representing rest activity with the expectation of future learning, in a similar way to d1–d3. Prior to the active task, individuals remained in a passive state with their eyes closed for a period of 7 min. Neural activity in this time was used as the pre-task resting baseline, and should thus include no task-related consolidation activity. Equally, after completion of the task, individuals were asked to once again close their eyes for a period of 7 min. This was treated as a ‘post-task’ long downtime rest (LD). This experimental structure is summarised in Fig. 1. When their eyes were closed, participants were alerted to the beginning of the next active task via audible tone and in-bore speakers. Prior to entering the scanner, instructions for the behavioural task were explained to participants by the experimenters. These instructions were also contained on-screen within the task itself. Participants were naive to the assessment of offline resting activity and were debriefed on this after completion of the scan. Participants’ behavioural response data was later scored for discrimination accuracy on the active task. 2.4. Data acquisition Participants were scanned at the Monash Biomedical Imaging centre (MBI) using a 3T Siemens Skyra Scanner (Siemens Medical Solutions, Erlangen, Germany) equipped with a 32-channel phased array whole-head coil. Cushions and clamps were used to minimise head movement during scanning. Functional imaging data was obtained continuously throughout the testing. During this time a whole brain volume containing 50 slices was obtained every 3 s and prescribed 30 degrees inclined from the AC–PC plane (in order to maximise signal coverage in the medial temporal lobe). The first four of these brain volumes were deleted to allow for scanner ‘warm up’. Detailed scanning parameters were: repetition time/ echo time (TR/TE) 3000/35 ms; flip angle (FA) 90 degrees; slice thickness 3.8 mm (no gap); acquisition matrix 64  64; in-plane field of view 22 cm. For anatomic localisation, a structural scan

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was acquired for each subject using a T1-weighted sequence (3DFSPGR). Scanning parameters were: TR/TE7.9/3.0 ms; FA 20 degrees; acquisition matrix 256  256  176, field of view 256  256  176 mm, 1 mm isotropic resolution. 2.5. Data pre-processing Data pre-processing and statistical analysis of MRI data was carried out using FEAT (fMRI Expert Analysis Tool) Version 5.63, part of FSL (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). Pre-statistics processing involved motion correction using MCFLIRT (Jenkinson, Bannister, Brady, & Smith, 2002); non-brain removal using BET (Smith, 2002); spatial smoothing using a Gaussian kernel of FWHM 5 mm; mean-based intensity normalisation of all volumes by the same factor; high pass temporal filtering (Gaussianweighted least-squares straight line fitting, with sigma = 20.0 s). Time series statistical analysis was carried out using FILM with local auto correlation correction (Woolrich, Ripley, Brady, & Smith, 2001). Registration to high resolution 3D anatomical T1 scans (per participant) and to a standard Montreal Neurological Institute (MNI) template image (for group average) was carried out using FLIRT (Jenkinson, Bannister, Brady, & Smith, 2002; Jenkinson & Smith, 2001). 2.6. Data analysis After pre-processing each individual subject’s fMRI time series the data were submitted to a (random effects) general linear model, with one predictor that was convolved with a standard model of the haemodynamic response function (HRF) for each condition. The event-types (regressors) were defined by the offline, downtime condition. Thus, there were regressors for pre-task baseline and each downtime period (d1, d2, d3, d4 and LD). The short downtime periods (d1–d4) were subsequently combined to create a single Short Downtime (ShD) regressor2. The parameter estimates relating to the height of the HRF response to each regressor were calculated on a voxel by voxel basis, via a multiple linear regression of the response time course, to create one beta image for each event-type per participant. These parameter estimates, characterising the extent to which a region was activated by the event-type, were used as the basis for our analyses by including them in a higher-level (group) FLAME analysis (FMRIB’s Local Analysis of Mixed Effects; Beckmann, Jenkinson, & Smith, 2003; Woolrich, Behrens, Beckmann, Jenkinson, & Smith, 2004). For each participant, two contrasts were made. Activity during the Short Downtime (ShD) contrasted to baseline, creating a single difference from baseline statistical image for short downtime rest activity (Baseline versus ShD). A baseline to LD contrast was also completed for each individual. These individual-level contrasts were then used perform two enquiries: In the first enquiry, two group-level contrasts were created by pooling each of the two individual-level difference contrasts for downtime intervals. The group-level contrasts were thus: Baseline versus ShD; and Baseline versus LD. These whole-brain analyses tested for significant clusters of activity that differed from pre-task baseline and (intra-task) short downtime rest and (post-task) long downtime rest conditions, respectively. A third contrast tested for differences between short and long downtime (i.e., ShD versus LD). FEAT’s group (Gaussianised) statistics were thresholded using clusters determined by z value greater than 2.96 and a (corrected) cluster significance threshold of p = 0.05. Maximally active voxels within each identified cluster were then determined in FSLView.

2 Initial analyses revealed no significant differences in activity between individual downtime periods, so a combined regressor was made.

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In the second enquiry, activity in a series of face-specific regions of interest was measured within each of the downtime rest periods, and correlated with behavioural performance. In each participant OFA, FFA and fSTS were defined for the purposes of analysing the active task data, not reported here, using a separate (orthogonal) functional localiser sequence (see Mundy et al., 2009a,b, 2013 for more detail). Briefly, participants viewed a 15 min sequence of alternating face and scrambled face stimulus blocks. Cortical activity during these blocks was contrasted (face blocks minus scrambled face blocks) and the resulting data was used to create region of interest (ROI) masks for face-sensitive loci. The mean group-level locations of independently localised OFA, FFA and fSTS (OFA: 40, 79, 16; 39, 79, 17; FFA: 40, 50, 20; 38, 52, 18; fSTS: 55, 45, 1; 50, 50, 2) were very similar to those present in the downtime analysis (see Table 1) and corresponded well with previously published co-ordinates for these locations (e.g., Harris, Young, & Andrews, 2012; Peelen & Downing, 2005; Rossion, Hanseeuw, & Dricot, 2012). This ROI analysis allowed us to measure the strength of any relationship between resting activity in these higher-order visual processing regions, which may relate to learning consolidation, and subsequent behavioural performance. In the third enquiry, individuals were separated into two groups, based on behavioural score in the active task, in order to evaluate the effect of performance more generally. Overall percentage correct for the active task was calculated for all participants (i.e., percent correct for face discriminations, spread out across all 128 trials). ‘Good’ and ‘Bad’ performers were grouped via mean spilt, such that individuals who performed above the mean of all participants (72.15% correct discrimination) were judged as ‘good’ performers (N = 15, mean performance 84.26%) and those falling below this level were placed in the ‘bad’ performers grouping (N = 15, mean performance 60.04%). A t-test confirmed a significant difference in performance between the groups (t(28) = 4.43, p = 0.001). The corresponding difference from baseline contrasts for the ShD and LD periods for each individual were then pooled according to group and these pools were subsequently contrasted Good versus Bad. FEAT’s group statistics were thresholded using the same parameters as the first enquiry. The focus in this analysis was on significant clusters of activity that differed between good and bad learners. FSLView was then used to identify the local maxima in significant clusters resulting from the good versus bad contrast.

3. Results and discussion 3.1. Behavioural data Behavioural data are presented in the right panel of Fig. 1. Participants’ accuracy improved across each of the active blocks. ANOVA confirmed a main effect of block (F (3, 87) = 48.2, p < 0.01), with a significant linear trend (F(1, 28) = 9.53, p = .01). Simple effects analysis indicated that performance in each block was significantly higher than the preceding block (minimum F(3, 27) = 8.90, p < 0.01). A second AVOVA revealed that reaction time did not differ by block (F < 1), confirming no speed-accuracy trade-off in this data. 3.2. Functional MRI (whole brain) Whole-brain contrasts tested for highly active cortical regions in downtime rest periods following VPL, compared with pre-task baseline. Peak voxel coordinates, for all analyses, are presented in MNI 152 standard space. A number of regions exhibited changes in activity during the offline breaks compared to baseline after face learning and activation levels within these areas are summarised in Table 1. In addition, significant clusters of activity during the downtime periods are represented visually in Fig. 2. Readers will note that both short and long downtime periods produced very similar clusters of activation; differences were only evident in magnitude of response and not location. A contrast of short versus long downtimes confirmed this observation, yielding no further regions of activity. For the sake of clarity, both short and long downtime data are included here, but there appears to be little functional difference between the conditions. As can be seen in Fig. 2, in line with expectations, face stimuli increased the BOLD response signal in regions which correspond to the early visual stream (lingual gyrus/intracalcarine cortex and occipital fusiform gyrus) compared with baseline. This finding supports the implication of previous analyses with simpler stimuli, which suggest that these areas are modulated during resting periods, as a result of task-related consolidation (Lewis et al., 2009). It also supports the hypothesis that learning requires continued activity, during rest, within areas associated with the active task (see Mundy et al., 2009a,b). Adding to this argument, regions well known to mediate face-specific processing (e.g., Mundy et al.,

Table 1 Significant clusters (local maxima) of activity resulting from the contrasts short downtime > baseline and long downtime > baseline (opposite contrasts revealed no significant clusters). Activated region

Lingual gyrus/ICC R occipital fusiform gyrus L occipital fusiform gyrus R pSTS L pSTS L posterior fusiform gyrus R inferior frontal gyrus L inferior frontal gyrus R DLPFC L DLPFC Dorsal anterior cingulate R anterior insula Ventral anterior cingulate Precuneus Precuneus/cuneus

ShD

Peak voxel

(z-scores)

x

4.45 4.49 4.04 3.31 3.97 3.77 4.09 3.93 4.19 4.12 4.23 4.16 4.66 4.18 4.72

y 1 37 35 57 55 38 44 32 22 21 3 32 3 3 6

z 73 78 79 45 50 52 20 14 54 48 25 21 39 50 79

3 16 19 0 9 15 16 19 2 14 16 10 11 63 46

LD

Peak voxel

(z-scores)

x

4.1 4.17 4.06 2.67 2.98 3.22 3.66 3.42 4.12 3.59 3.86 4.09 3.97 4.03 4.56

y 1 36 35 56 49 40 47 32 29 28 9 32 7 2 6

z 72 76 80 45 50 51 10 13 51 52 36 21 41 50 80

4 16 15 1 8 18 18 18 7 4 19 9 5 64 46

Note: Positive z-scores represent an increase in BOLD signal from baseline and z-scores exceeding 3.3 equate to p < .001, while z –scores exceeding 2.6 equate to p < .01, two tailed, corrected. L = left; R = right; ShD = short downtimes; LD = long downtime. ICC = intracalcarine cortex; pSTS = posterior superior temporal sulcus; DLPFC = dorsolateral prefrontal cortex. N = 30.

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Fig. 2. Activity change from baseline during downtime periods. Each column (A–F) represents activity within a single brain slice over the downtime periods (ShD = short downtime; LD = long downtime). Each slice position is indicated by x, y or z coordinates at the top of the figure. The left side of the brain is on the right side of axial slices. Activity shown corresponds to z-scores such that positive scores represent an increase in activity from baseline. DLPFC = dorsolateral prefrontal cortex; pSTS = posterior superior temporal sulcus (containing fSTS); IFG = inferior frontal gyrus; OFG = occipital fusiform gyrus (containing OFA); LiG/ICC = lingual gyrus/intracalcarine cortex; Pc/C = precuneus/cuneus; Pc = precuneus; dAC = dorsal anterior cingulate cortex; vAC = ventral anterior cingulate cortex; PFG = posterior fusiform gyrus (containing FFA); AIn = anterior insula.

2013; Peelen & Downing, 2005; Rossion et al., 2012) were also activated during these downtime rest periods. Areas included regions within the occipital and posterior fusiform gyrus, and posterior superior temporal sulcus, which directly cross-matched with functionally defined OFA, FFA and face-specific STS at group level from the active task (see Section 3.3 for further analysis). Much commentary has been made regarding the function of these regions within face processing (e.g., Kanwisher & Yovel, 2006; Rossion et al., 2012), and whilst the current study cannot directly address such questions, activity in these regions goes some way in support of our hypothesis that consolidation in material specific higher-order perception areas occurs during rest, to facilitate the learning of faces. This whole brain analysis also revealed that activity within the inferior frontal gyrus (IFG) was increased. Functional connectivity between the MTL and IFG has been previously shown to increase with both face and scene observation (Stevens et al., 2010) and, consequently, activity in this area during downtime periods may underlie future functional connectivity changes. It has been suggested that frontal regions, such as IFG, supply the MTL with information which is then synthesised with new informational input so that new memories can be created (Buckner, Kelley, & Petersen, 1999). Non-visual areas also exhibited increased activity during the downtime breaks. The dorsolateral prefrontal cortex (DLPFC), which is part of the attention network, exhibited increased activity as a result of resting after learning. Previous studies have also linked this area with discrimination learning (e.g., Mukai et al., 2007; Mundy et al., 2013), and have further shown it to be modulated by task-related success. There was also evidence of increased activity within salience and decision-making regions (dorsal anterior cingulate cortex and anterior insula; e.g., Seeley et al., 2007). Such activity may reflect the consolidation or adjustment of top-down information flows, which contribute to faster and more accurate detection of stimulus differences, and the filtering of unattended features (c.f., Lewis et al., 2009). The right anterior insula, specifically, has been shown to be associated with controlling the switch between central executive and default mode networks (Sridharan, Levitin, & Menon, 2008). Areas known to be within the DMN (ventral anterior cingulate cortex and precuneus (e.g., Fox et al., 2005; Greicius, Krasnow, Reiss, & Menon, 2003; Koch et al., 2012; Raichle et al., 2001) also exhibited increases in activa-

tion over normal resting baseline. The DMN naturally has higher levels of activity during offline periods and lower activity during active tasks (e.g., Singh & Fawcett, 2008). Thus, it may seem difficult to understand why regions associated with it would further increase past baseline during rest after learning. One possible interpretation for this comes from the change in DMN activity of Alzheimer’s disease patients. Specifically, areas within the DMN show decreased activity in individuals with Alzheimer’s disease (e.g., Koch et al., 2012; Persson et al., 2008). More importantly, the magnitude of DMN disruption appears to be related to cognitive deficit in these individuals (see Schwindt et al., 2013). Consequently, the converse may also be true: If reduced activity can be associated with decreased learning and memory, increased activity may lead to an increase in this capability. That is, increased DMN activity may facilitate consolidation of newly learned information. That said, differences in DMN-related activity were not observed between good and bad learners (see below), and thus may simply reflect a greater level of ‘rest’ undertaken in the downtime breaks, compared with baseline, as a result of recovery from task demands. 3.3. Face-specific regions of interest In order to further support the contention that offline resting activity in higher-order (material specific) visual areas reflects consolidation, correlations were performed between discrimination performance in the behavioural task and downtime activity in the face-sensitive regions of interest. If such rest activity relates to consolidation of preceding task-related learning, which then in turn contributes to improved learning in subsequent task blocks, then it seems reasonable to expect a relationship between activity seen during a given rest period and performance in the subsequent test block. This is particularly relevant given that behavioural performance was shown to significantly increase, block on block. Using FEATQUERY, parameter estimates (b) were measured within each participant’s independently localised OFA, FFA and fSTS, during each of the downtime periods. Looking firstly at the relationship between overall task performance (measured as mean precent correct) and each individual’s parameter estimates from the combined Short Downtime (SD) condition, a significant positive correlation is seen in FFA and fSTS, but not OFA (FFA: r = 0.534, p < 0.01; fSTS; r = 0.417, p = 0.022; OFA: r = 0.117,

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z-scores

Peak voxel x

Gooda > badb L lingual gyrus/intracalcarine cortex Short downtime 3.52 Long downtime 3.5 R lingual gyrus/intracalcarine cortex Short downtime 3.16 Long downtime 3.07 R posterior fusiform gyrus (FFA) Short downtime 3.29 Long downtime 3.21 Fig. 3. Correlation between behavioural discrimination performance and materialspecific regions of interest within the short downtime condition. Discrimination performance is measured as mean % correct. Parameter estimates are based on measurements from each individual’s region of interest (FFA = blue; OFA = red; fSTS = green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

p = 0.538; see Fig. 3)3. It does appear, therefore, that stronger activation during rest is related to better performance at test (at least within FFA and fSTS). Given this interesting pattern, further correlations were carried out, examining the relationship between parameter estimates in individual downtime rest periods, and the potential to predict subsequent performance in each of the testing blocks. As expected, there was no correlation between activity during resting baseline and task performance in the following initial test block (in any region: largest r = 0.114, p = 0.549). This makes sense, given no task has occurred before baseline rest. However, correlating performance in test block 2 with activity in the preceding short downtime (d1) revealed that downtime activity in each of the ROIs significantly predicts later behavioural performance (FFA: r = 0.458, p < 0.01; fSTS; r = 0.514, p < 0.01; OFA: r = 0.529, p < 0.01). This pattern was repeated in correlations between discrimination performance in block 3 and ROI activity during d2 (FFA: r = 0.513, p < 0.01; fSTS; r = 0.509, p < 0.01; OFA: r = 0.462, p < 0.01). Correlating performance between block 4 and d3 indicated that resting activity in both FFA and fSTS predicts subsequent performance in this final testing block (FFA: r = 0.548, p < 0.01; fSTS; r = 0.620, p < 0.01), but not activity in OFA (r = 0.102, p = 0.592). Activity in functionally defined face-specific processing regions, during rest intra-task rest periods, appears to predict subsequent behavioural performance. However, the extent to which each region is involved varies, with the relevance of OFA appearing to reduce over time. This observation sits well with the hypothesis that such activity represents task-specific consolidation, which contributes to the learning of faces.

3.4. Good versus bad performers The analysis of the difference between active areas in good and bad performers following VPL revealed a number of divisions between the two groups. Some areas had greater activity in good performers (Good > Bad) while other areas were preferentially activated by bad performers (Bad > Good). The results of this analysis are summarised in Table 2 and the range of activity levels for both good and bad performers within each of the significant regions can be seen in Fig. 4. The range of activity allows for an assessment regarding the origin of the difference between the two groups. 3 A similar, albeit weaker, pattern was seen for the long downtime (LD) condition (FFA: r = 0.375, p = 0.041; fSTS; r = 0.398, p = 0.029; OFA: r = 0.098, p = 0.606)

Bad > good L anterior hippocampus Short downtime Long downtime L amygdala Short downtime Long downtime

y

z

19 9

69 78

10 7

19 11

71 79

14 12

37 38

53 54

16 17

3.84 3.88

28 31

14 18

19 17

3.77 3.67

19 20

2 3

15 13

Note: Positive z-scores represent an increase in BOLD signal from baseline and zscores exceeding 2.96 equate to p < .05 (two tailed; corrected). L = left; R = right. a n = 15. b n = 15.

Table 2 indicates that good performers activated early visual areas (lingual gyrus and intracalcarine cortex) more than bad performers, during downtimes. By looking at the range of activity in this area it can be ascertained that this difference arises from greater activity by good performers rather than decreased activity from bad and thus implies that good performers may be engaging in higher levels of consolidation-related activity during rest, than bad performers. A region of posterior fusiform gyrus (corresponding to functionally-defined FFA, see above) also showed significantly more activation in good performers over poor performers. It therefore appears that good performers may also be consolidating face-specific components of the stimuli more effectively, particularly in light of the significant relationship between performance and intra-task resting activity in this area. Bad performers had significantly greater anterior hippocampus activity and this difference appears to occur due a decreasing trend in activation from good performers together with an increase from bad performers. The anterior hippocampus has long been implicated in visual associative recollection; and modulations in hippocampal activity have repeatedly been reported with reference to subsequent memory (e.g., Henke, Buck, Weber, & Wieser, 1997; Kirwan & Stark, 2004; Davachi, 2006). Such activity in bad learners may suggest that they are poor at inhibiting spurious recollections during the offline periods. Thus in good learners, greater levels of consolidation can take place without interference from the recall of irrelevant stimuli. Additionally, and potentially of more importance, increases in hippocampal activity have also been linked to subsequent memory failures (e.g., Davachi & Wagner, 2002; Henson, Rugg, Shallice, Josephs, & Dolan, 1999). The reasons for this relationship are not well examined, however recent evidence suggests that the association between hippocampal activity and subsequent mnemonic performance is not a simple one. High levels of hippocampal activity may inhibit memory formation, perhaps as a result of interfering irrelevant information (see Liu, Qin, Rijpkema, Luo, & Fernández, 2010). Furthermore, hippocampal activity which relates to improved consolidation is seen over longer timescales than those tested here (in the order days, opposed to minutes, e.g., Bosshardt et al., 2005). Bad performers also exhibited greater amygdala activation than good performers. As with the hippocampus, this difference is

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Fig. 4. Range of activity for good and bad performers during downtime periods. Positive z-scores represent an increase in BOLD signal from baseline and z-scores exceeding ±2.96 equate to p < .05 (two tailed; corrected). A = areas in which good learners exhibit greater BOLD signal increase. B = areas in which bad learners exhibit greater BOLD signal increase. L = left; R = right. ngood = 15; nbad = 15.

characterised by a decrease in activity from good performers and an increase from bad performers. The amygdala is known to mediate the effect of arousal on memory consolidation, with numerous studies suggesting that stressful or emotional events facilitate memory (see Paré, 2003 for review). Since all faces presented during the active task were emotionally neutral, amygdala activity in individuals who display poor learning may reflect spurious, interfering, recollections during downtime (in a similar way to the hippocampal activity seen). 4. Conclusion This study assessed the early consolidation process resulting from visual perceptual learning to face stimuli, and compared this activity between individuals who learned well, and those who learned poorly. Whole-brain BOLD data revealed regions of activity related to consolidation of visual perceptual learning: Early visual areas were associated with the consolidation of VPL, along with higher-order stimulus-specific processing areas. Activity in these higher-order regions during rest was shown to predict activity in subsequent test blocks. Regions within the DMN, attention and salience networks were also implicated. There were no significant differences between clusters associated with ‘short’ downtime activity (breaks nested between task blocks) and ‘long’ downtime (a rest period following the perceptual learning task). Taken together, these results suggest that (a) regions originally involved in perceptual discrimination learning are also involved in later consolidation of the learned information; and (b) this consolidationrelated activity can be seen in BOLD response both during and immediately after the active task. Analyses of differences in activity between good and bad learners revealed that good perceptual learners exhibit greater activation of both early and higher-order visual areas during downtime resting periods, whereas bad learners appear to display activity which may relate to interference in the consolidation process. This observation sits well with the hypothesis that accurate learning is supported by more effective consolidation during rest. An use of explicit visual or mnemonic strategies within the downtime breaks, which may contribute to the activity reported here, independently of VPL. Following the testing session participants were informally debriefed by the experimenters and asked about their thoughts during rest. This feedback indicated that participants generally did not think about the images, and expressed that their mind wandered, or that they were sleepy. However, in

future experiments this strategy needs to be formalised in order to rule out this potential confound, or downtime breaks need to be filled with a task which prevents explicit mnemonic or visual strategies. The current study relied on a relatively small set of stimuli, which were repeated across the active task. Future studies should consider the use of a larger stimulus set, in order to rule out explanations which rely on explicit, effortful encoding of individual stimuli. Additionally, our pre-task baseline resting condition may not be entirely comparable to the downtime conditions, as it did not follow an active task. Thus, it is possible that differences between pre-task baseline and downtime resting periods may reflect a more general task-level engagement. Although this is unlikely, particularly for the long downtime condition, this confound should be addressed with the use of an active (but irrelevant) task before baseline measurement. Previous research has suggested that resting-state BOLD modulations are associated with a functional role in the consolidation of recent learning (e.g., Albert et al., 2009; Hasson et al., 2009; Lewis et al., 2009; Stevens et al., 2010). Focusing mainly on changes in functional connectivity or low-frequency BOLD fluctuations, these studies provide evidence of a link between rest networks and those engaged during task performance. Our results support and extend these findings by demonstrating that not only is resting activity modulated by a preceding task, and in functionally-relevant regions, but this activity is also related to task performance. Critically, our results indicate that consolidation-related activity may be observed directly. This direct assessment of activity is important, as these changes in activity during rest may underlie neuronal changes associated with the process of new memory consolidation. This is particularly striking as areas active during rest are similar to those reported during the active task, and required for stimulus-specific processing. This further strengthens the contention that learning is an emergent property of perception (Graham et al., 2010). Ultimately, the current study provides further evidence for the activation of brain regions associated with the early consolidation process in VPL. Acknowledgments This work was supported by Monash Biomedical Imaging (MBI) and Monash School of Psychology and Psychiatry via Early Career Researcher grants to MEM. Portions of this work were submitted in partial fulfilment of the degree of Bachelor of Behavioural Neuroscience (Honours) by JV. We thank our colleagues at MBI,

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