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Hippocampal-Prefrontal Connectivity Predicts Midfrontal Oscillations and Long-Term Memory Performance
Current Biology 21, 1900–1905, November 22, 2011 ª2011 Elsevier Ltd All rights reserved
DOI 10.1016/j.cub.2011.09.036
Report Hippocampal-Prefrontal Connectivity Predicts Midfrontal Oscillations and Long-Term Memory Performance Michael X Cohen1,2,* 1Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam 1018 XA, The Netherlands 2Department of Physiology, University of Arizona, Tucson, AZ 85721, USA
Summary The hippocampus and prefrontal cortex interact to support working memory (WM) and long-term memory [1–3]. Neurophysiologically, WM is thought to be subserved by reverberatory activity of distributed networks within the prefrontal cortex (PFC) [2, 4–8], which become synchronized with reverberatory activity in the hippocampus [1, 4]. This electrophysiological synchronization is difficult to study in humans because noninvasive electroencephalography (EEG) cannot measure hippocampus activity. Here, using a novel integration of EEG and diffusion-weighted imaging, it is shown that individuals with relatively stronger anatomical connectivity linking the hippocampus to the right ventrolateral PFC (ventral Brodmann area 46) exhibited slower frequency neuronal oscillations during a WM task. Furthermore, subjects with stronger hippocampus-PFC connectivity were better able to encode the complex pictures used in the WM task into long-term memory. These findings are consistent with models suggesting that electrophysiological oscillations provide a mechanism of long-range interactions [9] and link hippocampus-PFC structural connectivity to PFC rhythmic electrical dynamics and memory performance. More generally, these results highlight the importance of incorporating individual differences when linking structure and function to cognition. Results and Discussion Neuronal oscillations reflect rhythmic fluctuations of population-level activity and have been linked to myriad synaptic, cellular, and systems-level processes [9–11]. Memory processes recruit a wide range of frequency bands, ranging from delta (w2–4 Hz) to theta (w4–8 Hz) to alpha (w8–12 Hz) to beta and gamma (w20–80 Hz) [12, 13], although the theta and alpha bands seem most prominent [5–8, 14, 15]. Although data are typically averaged across subjects, thus treating intersubject variability as noise, individual differences in oscillation characteristics may be related to individual differences in cognitive function. In particular, oscillation peak frequency (i.e., the precise frequency at which each subject’s power spectra has a local maximum) may be a meaningful characteristic of oscillations related to cognitive and neurobiological processes. For example, individual peak oscillation frequency correlates with memory performance [16], visual stimulus detection [17], reaction time [18], and time perception [19]. Neurobiologically, individual differences in peak oscillation frequency have also been correlated with brain GABA
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concentration and functional magnetic resonance imaging (fMRI) activation amplitude [20]. From a theoretical perspective, slower oscillations have been hypothesized to facilitate transmission or processing of more information during working memory (WM) [21]. Empirical studies showing slower oscillations in subjects with better memory or with increasing load [16, 22] support this idea. In addition to oscillation characteristics, individual differences in brain white matter have been linked to memory performance. Local white-matter integrity in the medial temporal lobe predicts memory encoding [23, 24], and longrange connectivity strength correlates with memory [25], personality [26], and performance on visual perceptual tasks [27]. Because oscillations are thought to be a mechanism for the functional integration of large networks that span multiple physically separate neural populations [9, 28], and because white-matter pathways form the physical pathways that connect distributed brain regions, one would expect structural connectivity to correlate with functional connectivity. Whether individual differences in structural connectivity between hippocampus and prefrontal cortex (PFC) are related to memory-related cortical oscillations remains unknown. The purpose of the present study was two-fold: (1) to test whether individual differences in peak oscillatory characteristics during WM maintenance are related to structural connectivity between the hippocampus and PFC, and (2) to test whether such individual differences in brain function and brain structure relate to long-term memory performance. Twenty subjects performed a WM task with complex line drawings [5, 29] while scalp electroencephalography (EEG) was recorded. In the task, subjects had to maintain a representation of a visual stimulus for 4–6 s and then judge whether the probe stimulus was tilted slightly clockwise or counterclockwise (Figure 1A). The WM task was designed to be challenging to keep subjects engaged. The tilt varied from 210 to +10 , and on average, subjects correctly identified the angle rotation on 77% of trials (standard error 2.15%). One hundred trials were recorded over two separate sessions. After each session, a long-term memory test was given in which subjects were asked to report whether each of 72 images (50 old and 22 new) was seen during the WM task. In a third session, subjects underwent diffusion tensor imaging (DTI) scanning that, after standard preprocessing using FSL software [30], provided quantitative estimates of individual differences in structural white matter connectivity (see Experimental Procedures for details). WM Cortical Oscillations and Hippocampus-PFC Connectivity fMRI and lesion studies have implicated lateral prefrontal cortical regions in WM and related control processes [31, 32]. Seemingly inconsistently, several EEG studies show that midfrontal rather than lateral frontal scalp electrodes (e.g., centered on FCz) are active during WM tasks [5, 6, 33]. Because each EEG electrode is sensitive to electrical fields generated by large populations of neurons that may be spatially distributed, there is not necessarily a one-to-one mapping between scalp electrode location and the source of
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A Task design
B Group-averaged IC “Rotated left or right?”
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D Subject-specific delay period Hz
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Figure 1. Task Design and Electrophysiological Results (A) Overview of the working memory (WM) task design and stimulus timing. (B) Group-averaged independent component (IC) topographical weightings (see Figure S1 for template and single-subject maps). (C) Group-averaged time-frequency activity from the IC (see Figure S2 for single-subject data). The gray box indicates the window used for analysis. Black lines outline time-frequency regions in which activity across subjects exceeded a p < 0.01 threshold for at least 50 ms and two frequency bands. (D) Distribution of WM delay-related peak oscillation frequencies.
because peak frequency from electrode FCz (i.e., not using ICA) showed a 2 4 6 8 10 12 14 16 18 20 similar variability of peak frequencies 2 −1 (Figure S3). 3 4 probe Cue Delay 1 2 Peak frequency (Hz) Time (s) To test the hypothesis that individual differences in WM-related oscillatory characteristics are related to hippocampus-PFC connectivity, subject-specific the underlying neural activity. Therefore, combining EEG with probabilistic white-matter tractography was estimated from a high-spatial-resolution technique such as DTI help may the bilateral hippocampus. As expected, the hippocampi resolve differences between fMRI brain localization and EEG exhibited connectivity to widespread areas of PFC, temporal, parietal, and occipital cortices (Figure S4). Connectivity data topographical localization of working memory processes. To isolate working memory-related oscillatory activity and were linked to the EEG-measured oscillatory activity by correbased on previous EEG studies, independent components lating, at each voxel in which there was connectivity across analysis (ICA) was used to identify a midfrontal topographical subjects, hippocampus-seeded tract strength with individual pattern of activity for each subject (Figure 1B; see also Fig- differences in the peak (strongest positive response) oscillaure S1 available online). Component selection was done auto- tion frequency during the WM delay period. Statistics were matically based on maximal spatial correlation between the performed via combined voxel- and cluster-level permutation components and a template [34], which comprised a Gaussian testing, each at p < 0.001 (see Experimental Procedures). surrounding midfrontal electrode FCz (see Figure S1). Visual Results revealed a region in the right ventrolateral PFC (in inspection confirmed that all components reasonably approx- ventral Brodmann area 46; see also Figure S10) in which imated the template. Note that component selection was subjects with stronger structural connectivity with the hippobased exclusively on topographical weighting; no oscillatory campus had slower midfrontal WM delay-related oscillations or task-related information was used during selection. This (Figure 2A; see Table S1 for Montreal Neurological Institute procedure ensured that each subject’s data reflected midfron- [MNI] coordinates), as well as a positive correlation along the tal scalp dynamics without constraining the search according postcentral gyrus (Figure S6). Individual differences in oscillatory power at each subject’s peak frequency also correlated to oscillatory characteristics. Time-frequency dynamics of this component were ex- with hippocampus connectivity in the middle frontal gyrus tracted via complex wavelet convolution (see Experimental (Figure S7). Several control analyses confirm the specificity of these Procedures). Sustained WM delay-period theta and alpha oscillatory activity is visually observable in the group-aver- findings to connectivity between the hippocampus and PFC. aged time-frequency map in the theta and alpha bands First, the PFC regions in which correlations were significant (4–13 Hz; Figure 1C). However, this sustained activity did not were used as seed regions in a confirmatory ‘‘backtracking’’ exceed a statistical threshold of p < 0.01 as a result of a large analysis, in which the PFC regions defined in the previous analamount of interindividual variability in the frequency of the ysis were used as seeds and connectivity with the hipposustained response (Figure 1D). Visual inspection of subject- campus was correlated with peak oscillation frequency. This specific data reveals a sustained increase of oscillatory was done because probabilistic fiber tracking results in activity at different frequency bands across subjects (Fig- many fiber pathways emanating from the hippocampus, and ure 1D; Figure S2). Although this may seem like a large amount obtaining the same results within the hippocampus when of variability that extends outside the expected canonical tracking from the PFC provides a statistically stronger link theta-alpha range, because single-subject results are not between the two regions. As expected, connectivity with the typically displayed and because in other studies narrow hippocampus correlated with WM delay peak frequencies frequency bands are sometimes selected, it is difficult to and oscillation power at peak frequency (p < 0.001; Figure S8). assess how this variability compares to that of other studies. Second, correlating hippocampus connectivity with occipital However, memory-related oscillations have been observed in stimulus-evoked responses yielded no significant correlations several frequency bands, ranging from delta to gamma [5–8, in the PFC (Figure S5), demonstrating that the main findings 12–15]. This variability was not due to the ICA procedure, could not be attributed to general brain activation or general 4
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Delay period peak frequency (Hz)
A Correlation: Hippocampus connectivity and peak oscillation frequency 20 16
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Figure 2. Correlations among Hippocampus-PFC Connectivity, Frontal WM-Related Oscillations, and Long-Term Memory Encoding (A) Anatomical connectivity between the hippocampus and prefrontal cortex (ventral Brodmann area 46) predicted WM delay-related midfrontal peak oscillation frequencies. The scatter plot shows data averaged across all suprathreshold voxels. (B) Subjects with slower delay-related oscillation peak frequency (left) and stronger hippocampus connectivity to ventrolateral PFC performed better on the long-term memory (LTM) test.
brain rhythm speed. Third, no significant correlations were observed between WM delay-related midfrontal peak frequencies and fractional anisotropy or gray matter (Figure S9). Fractional anisotropy and gray-matter estimates at each voxel are unrelated to seed regions and therefore do not provide specific information regarding connectivity between the hippocampus and PFC. These nonsignificant correlations suggest that individual differences in connectivity could not be attributed to differences in geometric brain shape. Finally, hippocampal connectivity with all Brodmann area 46 voxels was also significantly correlated with WM delayrelated peak frequency (Figure S10). Together, these control analyses confirm that the connectivity-EEG correlations reflect a specific link between the hippocampus and ventrolateral PFC and cannot be explained by differences in local geometry. Long-Term Memory, WM Oscillations, and HippocampusPrefrontal Connectivity Following the experiment and after the EEG cap was removed (w15 min after the end of the EEG WM test), subjects performed a long-term memory test in which they indicated whether they saw or did not see a given picture during the WM task, or whether they were unsure. This long-term memory test was included to test whether subjects’ ability to encode the pictures into long-term memory was related to their
cortical oscillatory activity during the WM delay and to hippocampus-PFC connectivity. In general, subjects found the long-term memory test difficult: there was no group-level significant difference between hits (correct identification of previously seen pictures) and false alarms (reporting that a new stimulus was presented in the WM task) (t19 = 1.39, p = 0.18). However, subjects with slower WM delay peak frequencies performed better on the long-term memory task (r = 20.62, p = 0.0037; Figure 2C). Indeed, the ten subjects with below-median oscillation frequency (7.827 Hz) performed significantly better than chance (hits versus false alarms, t9 = 2.99, p = 0.015), whereas the ten subjects with above-median oscillation frequency did not (t9 = 20.99, p = 0.344). As expected, long-term memory performance also correlated with the strength of hippocampus-ventrolateral PFC connectivity (r = 0.645, p = 0.003). Power at the peak frequency did not significantly correlate with long-term memory performance (r = 0.022, p = 0.926). Conclusions These findings support a close interplay between the hippocampus and ventrolateral PFC during WM maintenance and long-term memory. Although it was traditionally thought that the hippocampus contributes only to long-term memory and not to WM, other findings suggest that the hippocampus facilitates WM particularly when the memoranda are difficult, complex, or abstract [3]. Indeed, studies of hemodynamic [29, 35, 36] and electrophysiological [1] activity of the human hippocampus confirm that WM maintenance-related activity contributes to subsequent long-term memory encoding. The present findings are consistent with a theoretical model suggesting that slower oscillation speeds support maintenance of complex information [21]. The findings are also consistent with empirical studies linking peak oscillation frequency to memory performance [16, 22]. It is tempting to speculate that subjects with stronger physical connections bridging the hippocampus to the PFC have slower PFC oscillations to accommodate a greater bandwidth and/or higher fidelity of information transfer. Indeed, previous theoretical and empirical studies suggest that slower oscillations provide a better opportunity for memory maintenance [16, 37]. Additional research is needed to delineate the contributions of myelination, axon density, and other biological properties that lead to regional and individual differences in probabilistic tractography. Nonetheless, the correlation with long-term memory performance observed here, as well as recent findings linking probabilistic tractography to individual differences in visual perceptual tasks [27], personality [26], neural dynamics [26, 38], and large-scale oscillatory synchronization [39], provide strong support for the relevance of fiber tracking to a better characterization of how brain structure shapes brain function. The present results help bridge the scalp EEG and magnetoencephalography (MEG) source reconstruction/fMRI literatures. Whereas scalp EEG research implicates midfrontal regions in WM [5, 6, 40], source reconstruction from MEG and fMRI studies show WM-related activations in lateral PFC (e.g., [25, 41]). Without any spatial filtering such as current source density or source estimation, it is difficult or impossible to link EEG topographical location to brain region. Therefore, the correlation between oscillation frequency measured at FCz and hippocampus connectivity to the ventrolateral PFC helps link the midfrontal EEG memory-related oscillations to underlying neuroanatomical networks.
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More generally, these findings demonstrate the importance of individual differences analyses, as well as multimethodological approaches, in trying to elucidate the functional architecture of neurocognitive processes. Combining MEG/EEG and DTI provides a new opportunity to uncover fundamental structure-functional relationships and how they underlie cognition. Indeed, this multimodal approach helps overcome one of the main limitations of EEG, the inability to measure deep brain structures. The present findings demonstrate that brain hardware determines, to some extent, the rhythms of its software. Experimental Procedures Subjects Twenty subjects (ten male; average [standard deviation] age 22 [2.5] years) volunteered in exchange for course credit or money (V48). Subjects had normal or corrected-to-normal vision and no reported history of psychosis, brain disease, psychiatric illness, or significant drug usage. The local ethics committee at the University of Amsterdam approved the experiment, and subjects provided informed consent prior to the start of the experiment. Data were collected over three sessions: one MRI session and two EEG sessions. Task The task was programmed in MATLAB using the Psychophysics Toolbox [42]. The basic design and timing overview is presented in Figure 1A. Subjects were instructed to maintain a visual representation of the cue stimulus during the WM delay and to report with a mouse button press whether the second stimulus (probe) was tilted clockwise (to the right) or counterclockwise (to the left) relative to the cue. The probe stimulus was always the same as the cue stimulus. The tilt varied randomly between 210 and +10 . Subjects were informed that they should remember the stimuli for a long-term memory task that would follow the experiment. Explicit encoding was used instead of incidental encoding because there were two separate test sessions. Stimulus order and assignment to WM or long-term memory test was randomly varied across subjects. Note that the results presented in Figure 1A are cue locked; because the probe could appear between 4 and 6 s after cue offset (‘‘delay’’), the average probe onset time (5 s after the start of the delay) is indicated on the x axis (‘‘probe’’). During the long-term memory task, subjects were presented a single picture in the center of the screen and asked whether that picture was seen (‘‘old’’) or not seen during the WM task, or whether they were unsure. A fourth option (‘‘very sure old’’) was subsequently removed because all subjects reported that the long-term memory task was too difficult. Each trial lasted until the subject gave a response on the keyboard. Hits were taken as the proportion of responses in which subjects pressed ‘‘old’’ or ‘‘unsure’’ on pictures that had been presented during the WM task, and false alarms were taken as the proportion of responses in which subjects pressed ‘‘old’’ or ‘‘unsure’’ on pictures that had not been presented during the WM task. EEG Data Collection and Processing EEG data were acquired at 512 Hz from 64 channels placed according to the international 10-20 system, from perioccular and electromyographic electrodes on the thumbs, and from both earlobes (used as reference). Offline, EEG data were high-pass filtered at 0.5 Hz and then epoched from 21 to +8 s surrounding each cue onset (ending 2 s after the last possible probe onset). All trials were visually inspected, and those containing electromyography (EMG) or other artifacts not related to blinks were manually removed. Data analyses focused on independent component activity. Independent components analysis (ICA) is a data decomposition procedure that identifies a spatial weighting matrix such that independent sources of variance across electrodes can be separated. Independent components were computed using the EEGLAB toolbox [6] for MATLAB. The infomax algorithm was used, and 64 components were returned. Independent components analyses were performed separately for each testing session, because minor differences in cap placement or other state variables may affect component estimation. Component selection was performed automatically based on maximal spatial correlation between the components and templates. The template was defined as a Gaussian surrounding
electrode FCz. After component selection, data were averaged for each subject across the two sessions. ICA has the advantage in the present context of allowing selection of topographical activity for each subject without any constraints regarding oscillation characteristics. EEG Analyses All analyses were performed in MATLAB software, as in our previous studies [26, 34]. Briefly, a wavelet decomposition analysis was performed in which all trials were concatenated in time, the power spectrum of which (obtained from the fast Fourier transform) was multiplied by the power spectrum of 2 * 2 a complex Morlet wavelet [ei2ptf e 2 t =ð2 s Þ , where t is time; f is frequency, which increased from 1.7 to 50 Hz in 40 logarithmically spaced steps; and s defines the width of each frequency band, set according to l/(2pf), where l is a vector that increased logarithmically from 3 to 12 as a function of frequency], and the inverse fast Fourier transform was taken. This is equivalent to but more efficient than single-trial convolution. This procedure was performed separately for each channel. From the resulting complex signal from each convolution, an estimate of frequency-band-specific power at each time point is defined as the squared magnitude of the result of the convolution Z (real[z(t)]2 + imag[z(t)]2). Power was normalized using a decibel (dB) transform (dB power = 10 3 log10[power/baseline]), where the baseline activity was taken as the average power at each frequency band, averaged across conditions, from 2500 to 2200 ms prestimulus. DB conversion ensures that all frequencies, time points, electrodes, and subjects are in the same scale and thus visually and statistically comparable. To find each subject’s peak oscillation frequency, time-frequency data from 1 to 4 s postcue were averaged across time, and the frequency with the maximum power was taken. This time period extends from 1 s postcue offset until the earliest possible probe onset and therefore excludes transient responses related to visual stimuli. Power at this frequency was taken as the average power across the time window. Pixel-based statistics on the time-frequency map in Figure 1C were performed by testing the power value at each pixel across subjects against zero (thus, the null hypothesis is no group-level change in oscillation power compared to prestimulus baseline). Clusters in which pixels were significant at p < 0.01 for at least 50 ms and two frequency bins are outlined in black. Note that the assumption underlying such pixel-based tests is that each time-frequency point is functionally the same for all subjects, analogous to how fMRI voxels are assumed to cover the same functional and/or anatomical area across subjects in group-level analyses. MRI Data Collection and Processing DTI data were acquired on a 3 T Phillips MRI scanner at the Academic Medical Center of the University of Amsterdam. Parameters were as follows: repetition time (TR) 9.1948, echo time (TE) 65, 60 2 mm thick slices, matrix size 128 3 128, in-plane resolution 2 3 2 mm2. For diffusion directions, 50 equidistant points along a unit sphere were created. Images with no diffusion were also acquired. Four repetitions of this scan sequence were taken to maximize signal-to-noise ratio. In total, DTI scanning lasted about 45 min per subject. In the same session, a high-resolution T1 scan was also acquired. Preprocessing of DTI data was performed according to standard protocols in FSL software, primarily bedpostX [30] (http://www.fmrib.ox.ac. uk/fsl). Nonoverlapping results from these MRIs have been reported elsewhere [39]. Linking DTI and EEG Data Probabilistic tractography was computed from a hippocampus mask in the Juelich atlas, which is available in the FSL software package. Because this is a probabilistic atlas that was back normalized to each subject’s native DTI space, tractography results likely include some connectivity from neighboring medial temporal lobe regions. Tractography was performed separately for the left and right hippocampus, but results were combined because there were no hypotheses about laterality. The result of the tractography analysis is a brain volume of probability values that correspond to the number of pathways starting in the seed region and passing through that voxel. Five thousand paths from each voxel in the seed region were generated. The resulting tract strength volume was used in a voxel-based analysis, in which tract strength was correlated across subjects with EEG characteristics (independent-component WM delay-related peak frequencies, and power at those frequencies) using Spearman’s rank-order correlation (tract strength values are nonnormally distributed, so nonparametric correlations are appropriate). Parametric Pearson’s correlations after log10 transforming tract values yielded the same pattern of results. These tract strength volumes were transformed to MNI space with 3 mm isotropic
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voxels and smoothed with a three-dimensional 6 mm Gaussian kernel. Correlations were tested only at voxels for which all 20 subjects had nonzero paths. This resulted in testing 27.89% of brain voxels. Significance testing was carried out via a two-stage permutation testing [43]. At the first stage (voxel level), the mapping of peak frequency and subject identity was randomly shuffled and the Spearman’s correlation was computed again at each voxel. This was repeated 1,000 times, generating a distribution of correlation coefficients at each voxel under the null hypothesis of no relationship between hippocampus connectivity and peak frequency. Statistical Z values were taken as the normalized distance of the real correlation coefficient compared to the null distribution. Voxels with a Z value greater than 3.09 (p < 0.001) were retained as being significant at the voxel level. In the second stage (cluster level), Z values were computed based on one of the 1,000 random permutation iterations, and the statistical map was thresholded again. This time, the number of voxels in the largest suprathreshold cluster was stored. This was repeated 500 times, generating a distribution of maximum cluster sizes under the null hypothesis. The cluster threshold was defined as the standardized distance from the mean of the maximum cluster distribution corresponding to p < 0.001 (25 contiguous voxels). These analyses were conducted in MATLAB. Significant correlation clusters were then projected back onto an MRI and displayed using fslview (MRI viewing program in the FSL package). Thresholded correlation maps were upsampled to 1 mm isotropic and shown on the standard MNI brain for visualization (analyses and statistical thresholding were performed on the 3 mm3 images). Supplemental Information Supplemental Information includes ten figures and one table and can be found with this article online at doi:10.1016/j.cub.2011.09.036. Acknowledgments This research was funded by a Vidi grant from the Netherlands Organization for Scientific Research (NWO). Thanks to Jessica Buitenweg and James F. Cavanagh for assistance with data collection and preprocessing, Charan Ranganath for critical comments and discussions, and anonymous reviews for comments and suggestions for control analyses. Received: June 22, 2011 Revised: August 30, 2011 Accepted: September 21, 2011 Published online: November 3, 2011 References 1. Axmacher, N., Schmitz, D.P., Wagner, T., Elger, C.E., and Fell, J. (2008). Interactions between medial temporal lobe, prefrontal cortex, and inferior temporal regions during visual working memory: a combined intracranial EEG and functional magnetic resonance imaging study. J. Neurosci. 28, 7304–7312. 2. Fuster, J.M. (2009). Cortex and memory: emergence of a new paradigm. J. Cogn. Neurosci. 21, 2047–2072. 3. Ranganath, C. (2006). Working memory for visual objects: complementary roles of inferior temporal, medial temporal, and prefrontal cortex. Neuroscience 139, 277–289. 4. Funahashi, S., Bruce, C.J., and Goldman-Rakic, P.S. (1989). Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349. 5. Khader, P.H., Jost, K., Ranganath, C., and Ro¨sler, F. (2010). Theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding. Neurosci. Lett. 468, 339–343. 6. Onton, J., Delorme, A., and Makeig, S. (2005). Frontal midline EEG dynamics during working memory. Neuroimage 27, 341–356. 7. Klimesch, W., Schack, B., and Sauseng, P. (2005). The functional significance of theta and upper alpha oscillations. Exp. Psychol. 52, 99–108. 8. Jensen, O., Gelfand, J., Kounios, J., and Lisman, J.E. (2002). Oscillations in the alpha band (9-12 Hz) increase with memory load during retention in a short-term memory task. Cereb. Cortex 12, 877–882. 9. Fries, P. (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. (Regul. Ed.) 9, 474–480.
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DOI 10.1016/j.cub.2011.09.036
Report Hippocampal-Prefrontal Connectivity Predicts Midfrontal Oscillations and Long-Term Memory Performance Michael X Cohen1,2,* 1Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam 1018 XA, The Netherlands 2Department of Physiology, University of Arizona, Tucson, AZ 85721, USA
Summary The hippocampus and prefrontal cortex interact to support working memory (WM) and long-term memory [1–3]. Neurophysiologically, WM is thought to be subserved by reverberatory activity of distributed networks within the prefrontal cortex (PFC) [2, 4–8], which become synchronized with reverberatory activity in the hippocampus [1, 4]. This electrophysiological synchronization is difficult to study in humans because noninvasive electroencephalography (EEG) cannot measure hippocampus activity. Here, using a novel integration of EEG and diffusion-weighted imaging, it is shown that individuals with relatively stronger anatomical connectivity linking the hippocampus to the right ventrolateral PFC (ventral Brodmann area 46) exhibited slower frequency neuronal oscillations during a WM task. Furthermore, subjects with stronger hippocampus-PFC connectivity were better able to encode the complex pictures used in the WM task into long-term memory. These findings are consistent with models suggesting that electrophysiological oscillations provide a mechanism of long-range interactions [9] and link hippocampus-PFC structural connectivity to PFC rhythmic electrical dynamics and memory performance. More generally, these results highlight the importance of incorporating individual differences when linking structure and function to cognition. Results and Discussion Neuronal oscillations reflect rhythmic fluctuations of population-level activity and have been linked to myriad synaptic, cellular, and systems-level processes [9–11]. Memory processes recruit a wide range of frequency bands, ranging from delta (w2–4 Hz) to theta (w4–8 Hz) to alpha (w8–12 Hz) to beta and gamma (w20–80 Hz) [12, 13], although the theta and alpha bands seem most prominent [5–8, 14, 15]. Although data are typically averaged across subjects, thus treating intersubject variability as noise, individual differences in oscillation characteristics may be related to individual differences in cognitive function. In particular, oscillation peak frequency (i.e., the precise frequency at which each subject’s power spectra has a local maximum) may be a meaningful characteristic of oscillations related to cognitive and neurobiological processes. For example, individual peak oscillation frequency correlates with memory performance [16], visual stimulus detection [17], reaction time [18], and time perception [19]. Neurobiologically, individual differences in peak oscillation frequency have also been correlated with brain GABA
*Correspondence: [email protected]
concentration and functional magnetic resonance imaging (fMRI) activation amplitude [20]. From a theoretical perspective, slower oscillations have been hypothesized to facilitate transmission or processing of more information during working memory (WM) [21]. Empirical studies showing slower oscillations in subjects with better memory or with increasing load [16, 22] support this idea. In addition to oscillation characteristics, individual differences in brain white matter have been linked to memory performance. Local white-matter integrity in the medial temporal lobe predicts memory encoding [23, 24], and longrange connectivity strength correlates with memory [25], personality [26], and performance on visual perceptual tasks [27]. Because oscillations are thought to be a mechanism for the functional integration of large networks that span multiple physically separate neural populations [9, 28], and because white-matter pathways form the physical pathways that connect distributed brain regions, one would expect structural connectivity to correlate with functional connectivity. Whether individual differences in structural connectivity between hippocampus and prefrontal cortex (PFC) are related to memory-related cortical oscillations remains unknown. The purpose of the present study was two-fold: (1) to test whether individual differences in peak oscillatory characteristics during WM maintenance are related to structural connectivity between the hippocampus and PFC, and (2) to test whether such individual differences in brain function and brain structure relate to long-term memory performance. Twenty subjects performed a WM task with complex line drawings [5, 29] while scalp electroencephalography (EEG) was recorded. In the task, subjects had to maintain a representation of a visual stimulus for 4–6 s and then judge whether the probe stimulus was tilted slightly clockwise or counterclockwise (Figure 1A). The WM task was designed to be challenging to keep subjects engaged. The tilt varied from 210 to +10 , and on average, subjects correctly identified the angle rotation on 77% of trials (standard error 2.15%). One hundred trials were recorded over two separate sessions. After each session, a long-term memory test was given in which subjects were asked to report whether each of 72 images (50 old and 22 new) was seen during the WM task. In a third session, subjects underwent diffusion tensor imaging (DTI) scanning that, after standard preprocessing using FSL software [30], provided quantitative estimates of individual differences in structural white matter connectivity (see Experimental Procedures for details). WM Cortical Oscillations and Hippocampus-PFC Connectivity fMRI and lesion studies have implicated lateral prefrontal cortical regions in WM and related control processes [31, 32]. Seemingly inconsistently, several EEG studies show that midfrontal rather than lateral frontal scalp electrodes (e.g., centered on FCz) are active during WM tasks [5, 6, 33]. Because each EEG electrode is sensitive to electrical fields generated by large populations of neurons that may be spatially distributed, there is not necessarily a one-to-one mapping between scalp electrode location and the source of
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A Task design
B Group-averaged IC “Rotated left or right?”
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ITI: 0.8-1.5 s Probe: 1 s Delay: 4-6 s Cue: 1 s
D Subject-specific delay period Hz
C Group-averaged time-frequency 1
18 Power (dB)
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Figure 1. Task Design and Electrophysiological Results (A) Overview of the working memory (WM) task design and stimulus timing. (B) Group-averaged independent component (IC) topographical weightings (see Figure S1 for template and single-subject maps). (C) Group-averaged time-frequency activity from the IC (see Figure S2 for single-subject data). The gray box indicates the window used for analysis. Black lines outline time-frequency regions in which activity across subjects exceeded a p < 0.01 threshold for at least 50 ms and two frequency bands. (D) Distribution of WM delay-related peak oscillation frequencies.
because peak frequency from electrode FCz (i.e., not using ICA) showed a 2 4 6 8 10 12 14 16 18 20 similar variability of peak frequencies 2 −1 (Figure S3). 3 4 probe Cue Delay 1 2 Peak frequency (Hz) Time (s) To test the hypothesis that individual differences in WM-related oscillatory characteristics are related to hippocampus-PFC connectivity, subject-specific the underlying neural activity. Therefore, combining EEG with probabilistic white-matter tractography was estimated from a high-spatial-resolution technique such as DTI help may the bilateral hippocampus. As expected, the hippocampi resolve differences between fMRI brain localization and EEG exhibited connectivity to widespread areas of PFC, temporal, parietal, and occipital cortices (Figure S4). Connectivity data topographical localization of working memory processes. To isolate working memory-related oscillatory activity and were linked to the EEG-measured oscillatory activity by correbased on previous EEG studies, independent components lating, at each voxel in which there was connectivity across analysis (ICA) was used to identify a midfrontal topographical subjects, hippocampus-seeded tract strength with individual pattern of activity for each subject (Figure 1B; see also Fig- differences in the peak (strongest positive response) oscillaure S1 available online). Component selection was done auto- tion frequency during the WM delay period. Statistics were matically based on maximal spatial correlation between the performed via combined voxel- and cluster-level permutation components and a template [34], which comprised a Gaussian testing, each at p < 0.001 (see Experimental Procedures). surrounding midfrontal electrode FCz (see Figure S1). Visual Results revealed a region in the right ventrolateral PFC (in inspection confirmed that all components reasonably approx- ventral Brodmann area 46; see also Figure S10) in which imated the template. Note that component selection was subjects with stronger structural connectivity with the hippobased exclusively on topographical weighting; no oscillatory campus had slower midfrontal WM delay-related oscillations or task-related information was used during selection. This (Figure 2A; see Table S1 for Montreal Neurological Institute procedure ensured that each subject’s data reflected midfron- [MNI] coordinates), as well as a positive correlation along the tal scalp dynamics without constraining the search according postcentral gyrus (Figure S6). Individual differences in oscillatory power at each subject’s peak frequency also correlated to oscillatory characteristics. Time-frequency dynamics of this component were ex- with hippocampus connectivity in the middle frontal gyrus tracted via complex wavelet convolution (see Experimental (Figure S7). Several control analyses confirm the specificity of these Procedures). Sustained WM delay-period theta and alpha oscillatory activity is visually observable in the group-aver- findings to connectivity between the hippocampus and PFC. aged time-frequency map in the theta and alpha bands First, the PFC regions in which correlations were significant (4–13 Hz; Figure 1C). However, this sustained activity did not were used as seed regions in a confirmatory ‘‘backtracking’’ exceed a statistical threshold of p < 0.01 as a result of a large analysis, in which the PFC regions defined in the previous analamount of interindividual variability in the frequency of the ysis were used as seeds and connectivity with the hipposustained response (Figure 1D). Visual inspection of subject- campus was correlated with peak oscillation frequency. This specific data reveals a sustained increase of oscillatory was done because probabilistic fiber tracking results in activity at different frequency bands across subjects (Fig- many fiber pathways emanating from the hippocampus, and ure 1D; Figure S2). Although this may seem like a large amount obtaining the same results within the hippocampus when of variability that extends outside the expected canonical tracking from the PFC provides a statistically stronger link theta-alpha range, because single-subject results are not between the two regions. As expected, connectivity with the typically displayed and because in other studies narrow hippocampus correlated with WM delay peak frequencies frequency bands are sometimes selected, it is difficult to and oscillation power at peak frequency (p < 0.001; Figure S8). assess how this variability compares to that of other studies. Second, correlating hippocampus connectivity with occipital However, memory-related oscillations have been observed in stimulus-evoked responses yielded no significant correlations several frequency bands, ranging from delta to gamma [5–8, in the PFC (Figure S5), demonstrating that the main findings 12–15]. This variability was not due to the ICA procedure, could not be attributed to general brain activation or general 4
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Delay period peak frequency (Hz)
A Correlation: Hippocampus connectivity and peak oscillation frequency 20 16
R2 = 0.75 p = <0.001
12 8 4 0 0
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R2 = 0.43 p = 0.002
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Figure 2. Correlations among Hippocampus-PFC Connectivity, Frontal WM-Related Oscillations, and Long-Term Memory Encoding (A) Anatomical connectivity between the hippocampus and prefrontal cortex (ventral Brodmann area 46) predicted WM delay-related midfrontal peak oscillation frequencies. The scatter plot shows data averaged across all suprathreshold voxels. (B) Subjects with slower delay-related oscillation peak frequency (left) and stronger hippocampus connectivity to ventrolateral PFC performed better on the long-term memory (LTM) test.
brain rhythm speed. Third, no significant correlations were observed between WM delay-related midfrontal peak frequencies and fractional anisotropy or gray matter (Figure S9). Fractional anisotropy and gray-matter estimates at each voxel are unrelated to seed regions and therefore do not provide specific information regarding connectivity between the hippocampus and PFC. These nonsignificant correlations suggest that individual differences in connectivity could not be attributed to differences in geometric brain shape. Finally, hippocampal connectivity with all Brodmann area 46 voxels was also significantly correlated with WM delayrelated peak frequency (Figure S10). Together, these control analyses confirm that the connectivity-EEG correlations reflect a specific link between the hippocampus and ventrolateral PFC and cannot be explained by differences in local geometry. Long-Term Memory, WM Oscillations, and HippocampusPrefrontal Connectivity Following the experiment and after the EEG cap was removed (w15 min after the end of the EEG WM test), subjects performed a long-term memory test in which they indicated whether they saw or did not see a given picture during the WM task, or whether they were unsure. This long-term memory test was included to test whether subjects’ ability to encode the pictures into long-term memory was related to their
cortical oscillatory activity during the WM delay and to hippocampus-PFC connectivity. In general, subjects found the long-term memory test difficult: there was no group-level significant difference between hits (correct identification of previously seen pictures) and false alarms (reporting that a new stimulus was presented in the WM task) (t19 = 1.39, p = 0.18). However, subjects with slower WM delay peak frequencies performed better on the long-term memory task (r = 20.62, p = 0.0037; Figure 2C). Indeed, the ten subjects with below-median oscillation frequency (7.827 Hz) performed significantly better than chance (hits versus false alarms, t9 = 2.99, p = 0.015), whereas the ten subjects with above-median oscillation frequency did not (t9 = 20.99, p = 0.344). As expected, long-term memory performance also correlated with the strength of hippocampus-ventrolateral PFC connectivity (r = 0.645, p = 0.003). Power at the peak frequency did not significantly correlate with long-term memory performance (r = 0.022, p = 0.926). Conclusions These findings support a close interplay between the hippocampus and ventrolateral PFC during WM maintenance and long-term memory. Although it was traditionally thought that the hippocampus contributes only to long-term memory and not to WM, other findings suggest that the hippocampus facilitates WM particularly when the memoranda are difficult, complex, or abstract [3]. Indeed, studies of hemodynamic [29, 35, 36] and electrophysiological [1] activity of the human hippocampus confirm that WM maintenance-related activity contributes to subsequent long-term memory encoding. The present findings are consistent with a theoretical model suggesting that slower oscillation speeds support maintenance of complex information [21]. The findings are also consistent with empirical studies linking peak oscillation frequency to memory performance [16, 22]. It is tempting to speculate that subjects with stronger physical connections bridging the hippocampus to the PFC have slower PFC oscillations to accommodate a greater bandwidth and/or higher fidelity of information transfer. Indeed, previous theoretical and empirical studies suggest that slower oscillations provide a better opportunity for memory maintenance [16, 37]. Additional research is needed to delineate the contributions of myelination, axon density, and other biological properties that lead to regional and individual differences in probabilistic tractography. Nonetheless, the correlation with long-term memory performance observed here, as well as recent findings linking probabilistic tractography to individual differences in visual perceptual tasks [27], personality [26], neural dynamics [26, 38], and large-scale oscillatory synchronization [39], provide strong support for the relevance of fiber tracking to a better characterization of how brain structure shapes brain function. The present results help bridge the scalp EEG and magnetoencephalography (MEG) source reconstruction/fMRI literatures. Whereas scalp EEG research implicates midfrontal regions in WM [5, 6, 40], source reconstruction from MEG and fMRI studies show WM-related activations in lateral PFC (e.g., [25, 41]). Without any spatial filtering such as current source density or source estimation, it is difficult or impossible to link EEG topographical location to brain region. Therefore, the correlation between oscillation frequency measured at FCz and hippocampus connectivity to the ventrolateral PFC helps link the midfrontal EEG memory-related oscillations to underlying neuroanatomical networks.
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More generally, these findings demonstrate the importance of individual differences analyses, as well as multimethodological approaches, in trying to elucidate the functional architecture of neurocognitive processes. Combining MEG/EEG and DTI provides a new opportunity to uncover fundamental structure-functional relationships and how they underlie cognition. Indeed, this multimodal approach helps overcome one of the main limitations of EEG, the inability to measure deep brain structures. The present findings demonstrate that brain hardware determines, to some extent, the rhythms of its software. Experimental Procedures Subjects Twenty subjects (ten male; average [standard deviation] age 22 [2.5] years) volunteered in exchange for course credit or money (V48). Subjects had normal or corrected-to-normal vision and no reported history of psychosis, brain disease, psychiatric illness, or significant drug usage. The local ethics committee at the University of Amsterdam approved the experiment, and subjects provided informed consent prior to the start of the experiment. Data were collected over three sessions: one MRI session and two EEG sessions. Task The task was programmed in MATLAB using the Psychophysics Toolbox [42]. The basic design and timing overview is presented in Figure 1A. Subjects were instructed to maintain a visual representation of the cue stimulus during the WM delay and to report with a mouse button press whether the second stimulus (probe) was tilted clockwise (to the right) or counterclockwise (to the left) relative to the cue. The probe stimulus was always the same as the cue stimulus. The tilt varied randomly between 210 and +10 . Subjects were informed that they should remember the stimuli for a long-term memory task that would follow the experiment. Explicit encoding was used instead of incidental encoding because there were two separate test sessions. Stimulus order and assignment to WM or long-term memory test was randomly varied across subjects. Note that the results presented in Figure 1A are cue locked; because the probe could appear between 4 and 6 s after cue offset (‘‘delay’’), the average probe onset time (5 s after the start of the delay) is indicated on the x axis (‘‘probe’’). During the long-term memory task, subjects were presented a single picture in the center of the screen and asked whether that picture was seen (‘‘old’’) or not seen during the WM task, or whether they were unsure. A fourth option (‘‘very sure old’’) was subsequently removed because all subjects reported that the long-term memory task was too difficult. Each trial lasted until the subject gave a response on the keyboard. Hits were taken as the proportion of responses in which subjects pressed ‘‘old’’ or ‘‘unsure’’ on pictures that had been presented during the WM task, and false alarms were taken as the proportion of responses in which subjects pressed ‘‘old’’ or ‘‘unsure’’ on pictures that had not been presented during the WM task. EEG Data Collection and Processing EEG data were acquired at 512 Hz from 64 channels placed according to the international 10-20 system, from perioccular and electromyographic electrodes on the thumbs, and from both earlobes (used as reference). Offline, EEG data were high-pass filtered at 0.5 Hz and then epoched from 21 to +8 s surrounding each cue onset (ending 2 s after the last possible probe onset). All trials were visually inspected, and those containing electromyography (EMG) or other artifacts not related to blinks were manually removed. Data analyses focused on independent component activity. Independent components analysis (ICA) is a data decomposition procedure that identifies a spatial weighting matrix such that independent sources of variance across electrodes can be separated. Independent components were computed using the EEGLAB toolbox [6] for MATLAB. The infomax algorithm was used, and 64 components were returned. Independent components analyses were performed separately for each testing session, because minor differences in cap placement or other state variables may affect component estimation. Component selection was performed automatically based on maximal spatial correlation between the components and templates. The template was defined as a Gaussian surrounding
electrode FCz. After component selection, data were averaged for each subject across the two sessions. ICA has the advantage in the present context of allowing selection of topographical activity for each subject without any constraints regarding oscillation characteristics. EEG Analyses All analyses were performed in MATLAB software, as in our previous studies [26, 34]. Briefly, a wavelet decomposition analysis was performed in which all trials were concatenated in time, the power spectrum of which (obtained from the fast Fourier transform) was multiplied by the power spectrum of 2 * 2 a complex Morlet wavelet [ei2ptf e 2 t =ð2 s Þ , where t is time; f is frequency, which increased from 1.7 to 50 Hz in 40 logarithmically spaced steps; and s defines the width of each frequency band, set according to l/(2pf), where l is a vector that increased logarithmically from 3 to 12 as a function of frequency], and the inverse fast Fourier transform was taken. This is equivalent to but more efficient than single-trial convolution. This procedure was performed separately for each channel. From the resulting complex signal from each convolution, an estimate of frequency-band-specific power at each time point is defined as the squared magnitude of the result of the convolution Z (real[z(t)]2 + imag[z(t)]2). Power was normalized using a decibel (dB) transform (dB power = 10 3 log10[power/baseline]), where the baseline activity was taken as the average power at each frequency band, averaged across conditions, from 2500 to 2200 ms prestimulus. DB conversion ensures that all frequencies, time points, electrodes, and subjects are in the same scale and thus visually and statistically comparable. To find each subject’s peak oscillation frequency, time-frequency data from 1 to 4 s postcue were averaged across time, and the frequency with the maximum power was taken. This time period extends from 1 s postcue offset until the earliest possible probe onset and therefore excludes transient responses related to visual stimuli. Power at this frequency was taken as the average power across the time window. Pixel-based statistics on the time-frequency map in Figure 1C were performed by testing the power value at each pixel across subjects against zero (thus, the null hypothesis is no group-level change in oscillation power compared to prestimulus baseline). Clusters in which pixels were significant at p < 0.01 for at least 50 ms and two frequency bins are outlined in black. Note that the assumption underlying such pixel-based tests is that each time-frequency point is functionally the same for all subjects, analogous to how fMRI voxels are assumed to cover the same functional and/or anatomical area across subjects in group-level analyses. MRI Data Collection and Processing DTI data were acquired on a 3 T Phillips MRI scanner at the Academic Medical Center of the University of Amsterdam. Parameters were as follows: repetition time (TR) 9.1948, echo time (TE) 65, 60 2 mm thick slices, matrix size 128 3 128, in-plane resolution 2 3 2 mm2. For diffusion directions, 50 equidistant points along a unit sphere were created. Images with no diffusion were also acquired. Four repetitions of this scan sequence were taken to maximize signal-to-noise ratio. In total, DTI scanning lasted about 45 min per subject. In the same session, a high-resolution T1 scan was also acquired. Preprocessing of DTI data was performed according to standard protocols in FSL software, primarily bedpostX [30] (http://www.fmrib.ox.ac. uk/fsl). Nonoverlapping results from these MRIs have been reported elsewhere [39]. Linking DTI and EEG Data Probabilistic tractography was computed from a hippocampus mask in the Juelich atlas, which is available in the FSL software package. Because this is a probabilistic atlas that was back normalized to each subject’s native DTI space, tractography results likely include some connectivity from neighboring medial temporal lobe regions. Tractography was performed separately for the left and right hippocampus, but results were combined because there were no hypotheses about laterality. The result of the tractography analysis is a brain volume of probability values that correspond to the number of pathways starting in the seed region and passing through that voxel. Five thousand paths from each voxel in the seed region were generated. The resulting tract strength volume was used in a voxel-based analysis, in which tract strength was correlated across subjects with EEG characteristics (independent-component WM delay-related peak frequencies, and power at those frequencies) using Spearman’s rank-order correlation (tract strength values are nonnormally distributed, so nonparametric correlations are appropriate). Parametric Pearson’s correlations after log10 transforming tract values yielded the same pattern of results. These tract strength volumes were transformed to MNI space with 3 mm isotropic
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voxels and smoothed with a three-dimensional 6 mm Gaussian kernel. Correlations were tested only at voxels for which all 20 subjects had nonzero paths. This resulted in testing 27.89% of brain voxels. Significance testing was carried out via a two-stage permutation testing [43]. At the first stage (voxel level), the mapping of peak frequency and subject identity was randomly shuffled and the Spearman’s correlation was computed again at each voxel. This was repeated 1,000 times, generating a distribution of correlation coefficients at each voxel under the null hypothesis of no relationship between hippocampus connectivity and peak frequency. Statistical Z values were taken as the normalized distance of the real correlation coefficient compared to the null distribution. Voxels with a Z value greater than 3.09 (p < 0.001) were retained as being significant at the voxel level. In the second stage (cluster level), Z values were computed based on one of the 1,000 random permutation iterations, and the statistical map was thresholded again. This time, the number of voxels in the largest suprathreshold cluster was stored. This was repeated 500 times, generating a distribution of maximum cluster sizes under the null hypothesis. The cluster threshold was defined as the standardized distance from the mean of the maximum cluster distribution corresponding to p < 0.001 (25 contiguous voxels). These analyses were conducted in MATLAB. Significant correlation clusters were then projected back onto an MRI and displayed using fslview (MRI viewing program in the FSL package). Thresholded correlation maps were upsampled to 1 mm isotropic and shown on the standard MNI brain for visualization (analyses and statistical thresholding were performed on the 3 mm3 images). Supplemental Information Supplemental Information includes ten figures and one table and can be found with this article online at doi:10.1016/j.cub.2011.09.036. Acknowledgments This research was funded by a Vidi grant from the Netherlands Organization for Scientific Research (NWO). Thanks to Jessica Buitenweg and James F. Cavanagh for assistance with data collection and preprocessing, Charan Ranganath for critical comments and discussions, and anonymous reviews for comments and suggestions for control analyses. Received: June 22, 2011 Revised: August 30, 2011 Accepted: September 21, 2011 Published online: November 3, 2011 References 1. Axmacher, N., Schmitz, D.P., Wagner, T., Elger, C.E., and Fell, J. (2008). Interactions between medial temporal lobe, prefrontal cortex, and inferior temporal regions during visual working memory: a combined intracranial EEG and functional magnetic resonance imaging study. J. Neurosci. 28, 7304–7312. 2. Fuster, J.M. (2009). Cortex and memory: emergence of a new paradigm. J. Cogn. Neurosci. 21, 2047–2072. 3. Ranganath, C. (2006). Working memory for visual objects: complementary roles of inferior temporal, medial temporal, and prefrontal cortex. Neuroscience 139, 277–289. 4. Funahashi, S., Bruce, C.J., and Goldman-Rakic, P.S. (1989). Mnemonic coding of visual space in the monkey’s dorsolateral prefrontal cortex. J. Neurophysiol. 61, 331–349. 5. Khader, P.H., Jost, K., Ranganath, C., and Ro¨sler, F. (2010). Theta and alpha oscillations during working-memory maintenance predict successful long-term memory encoding. Neurosci. Lett. 468, 339–343. 6. Onton, J., Delorme, A., and Makeig, S. (2005). Frontal midline EEG dynamics during working memory. Neuroimage 27, 341–356. 7. Klimesch, W., Schack, B., and Sauseng, P. (2005). The functional significance of theta and upper alpha oscillations. Exp. Psychol. 52, 99–108. 8. Jensen, O., Gelfand, J., Kounios, J., and Lisman, J.E. (2002). Oscillations in the alpha band (9-12 Hz) increase with memory load during retention in a short-term memory task. Cereb. Cortex 12, 877–882. 9. Fries, P. (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. (Regul. Ed.) 9, 474–480.
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