George Wallis

Inter- and intra-individual variability in alpha peak frequency

Converging electrophysiological evidence suggests that the alpha rhythm plays an important and active role in cognitive processing. Here, we systematically studied variability in posterior alpha peak frequency both between and within subjects. We recorded brain activity using MEG in 51 healthy human subjects under three experimental conditions — rest, passive visual stimulation and an N-back working memory paradigm, using source reconstruction methods to separate alpha activity from parietal and occipital sources. We asked how alpha peak frequency differed within subjects across cognitive conditions and regions of interest, and looked at the distribution of alpha peak frequency between subjects. In both regions we observed an increase of alpha peak frequency from resting state and passive visual stimulation conditions to the N-back paradigm, with a significantly higher alpha peak frequency in the 2-back compared to the 0-back condition. There was a trend for a greater increase in alpha peak frequency during the N-back task in the occipital vs. parietal cortex. The average alpha peak frequency across all subjects, conditions, and regions of interest was 10.3 Hz with a within-subject SD of 0.9 Hz and a between-subject SD of 2.8 Hz. We also measured beta peak frequencies, and except in the parietal cortex during rest, found no indication of a strictly harmonic relationship with alpha peak frequencies. We conclude that alpha peak frequency in posterior regions increases with increasing cognitive demands, and that the alpha rhythm operates across a wider frequency range than the 8–12 Hz band many studies tend to include in their analysis. Thus, using a fixed and limited alpha frequency band might bias results against certain subjects and conditions.

Resting GABA and glutamate concentrations do not predict visual gamma frequency or amplitude

Significance In vitro and modeling studies have indicated that GABAergic signaling underlies gamma oscillations. It would be valuable to measure this correlation between GABA and gamma oscillations in the human brain, and a recent study [Muthukumaraswamy SD, et al. (2009) Proc Natl Acad Sci USA 106(20):8356–8361] indicated that this is possible, using magnetoencephalography and magnetic resonance spectroscopy. If true, such a correlation would make the gamma peak frequency a useful surrogate marker of cortical excitability for studies investigating clinical populations and/or the effects of pharmacological agents. However, magnetic resonance spectroscopy does not measure synaptic GABA specifically, and the results from the current study (n = 50) indicate that GABA, as measured with magnetic resonance spectroscopy, does not correlate with gamma peak frequency. Gamma band oscillations arise in neuronal networks of interconnected GABAergic interneurons and excitatory pyramidal cells. A previous study found a correlation between visual gamma peak frequency, as measured with magnetoencephalography, and resting GABA levels, as measured with magnetic resonance spectroscopy (MRS), in 12 healthy volunteers. If true, this would allow studies in clinical populations testing modulation of this relationship, but this finding has not been replicated. We addressed this important question by measuring gamma oscillations and GABA, as well as glutamate, in 50 healthy volunteers. Visual gamma activity was evoked using an established gratings paradigm, and we applied a beamformer spatial filtering technique to extract source-reconstructed gamma peak frequency and amplitude from the occipital lobe. We determined gamma peak frequency and amplitude from the location with maximal activation and from the location of the MRS voxel to assess the relationship of GABA with gamma. Gamma peak frequency was estimated from the highest value of the raw spectra and by a Gaussian fit to the spectra. MRS data were acquired from occipital cortex. We did not replicate the previously found correlation between gamma peak frequency and GABA concentration. Calculation of a Bayes factor provided strong evidence in favor of the null hypothesis. We also did not find a correlation between gamma activity and glutamate or between gamma and the ratio of GABA/glutamate. Our results suggest that cortical gamma oscillations do not have a consistent, demonstrable relationship to excitatory/inhibitory network activity as proxied by MRS measurements of GABA and glutamate.

Temporal dynamics of attention during encoding versus maintenance of working memory: Complementary views from event-related potentials and alpha-band oscillations

Working memory (WM) is strongly influenced by attention. In visual WM tasks, recall performance can be improved by an attention-guiding cue presented before encoding (precue) or during maintenance (retrocue). Although precues and retrocues recruit a similar frontoparietal control network, the two are likely to exhibit some processing differences, because precues invite anticipation of upcoming information whereas retrocues may guide prioritization, protection, and selection of information already in mind. Here we explored the behavioral and electrophysiological differences between precueing and retrocueing in a new visual WM task designed to permit a direct comparison between cueing conditions. We found marked differences in ERP profiles between the precue and retrocue conditions. In line with precues primarily generating an anticipatory shift of attention toward the location of an upcoming item, we found a robust lateralization in late cue-evoked potentials associated with target anticipation. Retrocues elicited a different pattern of ERPs that was compatible with an early selection mechanism, but not with stimulus anticipation. In contrast to the distinct ERP patterns, alpha-band (8–14 Hz) lateralization was indistinguishable between cue types (reflecting, in both conditions, the location of the cued item). We speculate that, whereas alpha-band lateralization after a precue is likely to enable anticipatory attention, lateralization after a retrocue may instead enable the controlled spatiotopic access to recently encoded visual information.

Frontoparietal and cingulo-opercular networks play dissociable roles in control of working memory

We used magnetoencephalography to characterize the spatiotemporal dynamics of cortical activity during top–down control of working memory (WM). fMRI studies have previously implicated both the frontoparietal and cingulo-opercular networks in control over WM, but their respective contributions are unclear. In our task, spatial cues indicating the relevant item in a WM array occurred either before the memory array or during the maintenance period, providing a direct comparison between prospective and retrospective control of WM. We found that in both cases a frontoparietal network activated following the cue, but following retrocues this activation was transient and was succeeded by a cingulo-opercular network activation. We also characterized the time course of top–down modulation of alpha activity in visual/parietal cortex. This modulation was transient following retrocues, occurring in parallel with the frontoparietal network activation. We suggest that the frontoparietal network is responsible for top–down modulation of activity in sensory cortex during both preparatory attention and orienting within memory. In contrast, the cingulo-opercular network plays a more downstream role in cognitive control, perhaps associated with output gating of memory.

Modulation of hippocampal theta and hippocampal-prefrontal cortex function by a schizophrenia risk gene.

Hippocampal theta‐band oscillations are thought to facilitate the co‐ordination of brain activity across distributed networks, including between the hippocampus and prefrontal cortex (PFC). Impairments in hippocampus‐PFC functional connectivity are implicated in schizophrenia and are associated with a polymorphism within the ZNF804A gene that shows a genome‐wide significant association with schizophrenia. However, the mechanisms by which ZNF804A affects hippocampus‐PFC connectivity are unknown. We used a multimodal imaging approach to investigate the impact of the ZNF804A polymorphism on hippocampal theta and hippocampal network coactivity. Healthy volunteers homozygous for the ZNF804A rs1344706 (A[risk]/C[nonrisk]) polymorphism were imaged at rest using both magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI). A dual‐regression approach was used to investigate coactivations between the hippocampal network and other brain regions for both modalities, focusing on the theta band in the case of MEG. We found a significant decrease in intrahippocampal theta (using MEG) and greater coactivation of the superior frontal gyrus with the hippocampal network (using fMRI) in risk versus nonrisk homozygotes. Furthermore, these measures showed a significant negative correlation. Our demonstration of an inverse relationship between hippocampal theta and hippocampus‐PFC coactivation supports a role for hippocampal theta in coordinating hippocampal‐prefrontal activity. The ZNF804A‐related differences that we find in hippocampus‐PFC coactivation are consistent with previously reported associations with functional connectivity and with these changes lying downstream of altered hippocampal theta. Changes in hippocampal‐PFC co‐ordination, driven by differences in oscillatory activity, may be one mechanism by which ZNF804A impacts on brain function and risk for psychosis. Hum Brain Mapp 36:2387–2395, 2015.

Behavioral and Neural Markers of Flexible Attention over Working Memory in Aging

Working memory (WM) declines as we age and, because of its fundamental role in higher order cognition, this can have highly deleterious effects in daily life. We investigated whether older individuals benefit from flexible orienting of attention within WM to mitigate cognitive decline. We measured magnetoencephalography (MEG) in older adults performing a WM precision task with cues during the maintenance period that retroactively predicted the location of the relevant items for performance (retro-cues). WM performance of older adults significantly benefitted from retro-cues. Whereas WM maintenance declined with age, retro-cues conferred strong attentional benefits. A model-based analysis revealed an increase in the probability of recalling the target, a lowered probability of retrieving incorrect items or guessing, and an improvement in memory precision. MEG recordings showed that retro-cues induced a transient lateralization of alpha (8–14 Hz) and beta (15–30 Hz) oscillatory power. Interestingly, shorter durations of alpha/beta lateralization following retro-cues predicted larger cueing benefits, reinforcing recent ideas about the dynamic nature of access to WM representations. Our results suggest that older adults retain flexible control over WM, but individual differences in control correspond to differences in neural dynamics, possibly reflecting the degree of preservation of control in healthy aging.

Reward boosts working memory encoding over a brief temporal window

Selection mechanisms for WM are ordinarily studied by explicitly cueing a subset of memory items. However, we might also expect the reward associations of stimuli we encounter to modulate their probability of being represented in working memory (WM). Theoretical and computational models explicitly predict that reward value should determine which items will be gated into WM. For example, a model by Braver and colleagues in which phasic dopamine signalling gates WM updating predicts a temporally-specific but not item-specific reward-driven boost to encoding. In contrast, Hazy and colleagues invoke reinforcement learning in cortico-striatal loops and predict an item-wise reward-driven encoding bias. Furthermore, a body of prior work has demonstrated that reward-associated items can capture attention, and it has been shown that attentional capture biases WM encoding. We directly investigated the relationship between reward history and WM encoding. In our first experiment, we found an encoding benefit associated with reward-associated items, but the benefit generalized to all items in the memory array. In a second experiment this effect was shown to be highly temporally specific. We speculate that in real-world contexts in which the environment is sampled sequentially with saccades/shifts in attention, this mechanism could effectively mediate an item-wise encoding bias, because encoding boosts would occur when rewarded items were fixated.

A pilot study of the effect of short-term escitalopram treatment on brain metabolites and gamma-oscillations in healthy subjects.

Understanding the neural substrates underlying pharmacological action on different neurotransmitter systems is a key facet of neuroscience research and critical in the development of new treatments in psychiatry (Matthews et al., 2006). The initial efforts to incorporate combined neuroimaging techniques, including magnetoencephalography (MEG) and proton magnetic resonance spectroscopy (MRS), into the study of brain functioning indicate a high potential of such an approach in advancing the research on pharmacotherapy of psychiatric disorders, including depression and anxiety disorders. For example, the possible sensitivity of MEG measures to γ-aminobutyric acid (GABA) mediated inhibition suggests that the measurement of induced gamma-oscillation frequency in an individual may be useful within the context of pharmacological manipulations (Muthukumaraswamy et al., 2009). In the current study, we applied for the first time both MEG and MRS measurements in healthy volunteers medicated with the highly selective serotonin re-uptake inhibitor (SSRI) antidepressant escitalopram. Fifteen right handed, healthy volunteers (seven females and eight males) with a mean age of 23.4 (range 19–40) years received escitalopram (10 mg/day) for a total of 7–10 days in open-label, not placebo controlled manner. MRS scanning following MEG measurements was performed twice: once before and once after medication in all participants on a 3T Siemens TIM Trio scanner (Erlangen, Germany) with a body coil transmitter and a 32-channel receive head array. Data were acquired from a 20 mm × 25 mm × 20 mm voxel located in the occipital cortex (OCC). The voxel was positioned manually by reference to an axial T1-weighted gradient echo image. SPECIAL data with water suppression were acquired (TE 8.5 s, 3200 ms, 16-step phase cycle, 128 averages for the occipital voxel). Following spectral processing, SPECIAL data were analysed with LCModel version 6.2-2B using a basis set that consisted of 21 simulated metabolite basis spectra. Basis spectra were generated using an in-house, MATLAB (Natick, MA, USA) based implementation of the density matrix formalism to simulate the effect of the SPECIAL pulse sequence on each of the 21 metabolite spin systems studied. For more information about the accuracy and reliability of SPECIAL in conjunction with the LCModel in the measurement of GABA concentrations see Near et al. (2013). MEG data were acquired from eight of the 15 mentioned above subjects using an Elekta Neuromag 306channel system (Elekta, Stockholm, Sweden; 204 planar gradiometers, 102 magnetometers). MEG data were first visually inspected to remove channels severely affected by noise, and then de-noised and corrected for head movements using Elekta’s Maxfilter Signal Space Separation algorithm (Taulu et al., 2004), making use of the spatiotemporal filter algorithm to detect noise components. The anatomical images from MRI were used to create single-shell MEG forward models fitted to each subject’s anatomy (Nolte, 2003). Using SPM’s spm_eeg_inv_ mesh a transformation was computed for each subject, mapping a set of canonical meshes for cortical surface, skull and scalp to the subject’s anatomy. The individual MEG forward models were derived from these transformations using Fieldtrip’s forward toolbox (Oostenveld et al., 2011). Data were band-pass filtered between 30 and 120Hz, and then transformed from sensor-space into source-space using a linearly constrained minimum variance beamformer (Woolrich et al., 2011). Each subject’s source-space map for left and right gratings was then visually inspected (using FMRIB Software Library’s viewer) to locate the voxels with the visual cortex with the strongest gamma activation to the stimulus. The gamma peak frequency was found by averaging the data over the time interval from 300ms to 1000ms relative to stimulus onset and compared with a baseline period from –1000ms to –200ms relative to the stimulus. The peak gamma frequency was found by computing the ratio between power in the stimulus period and baseline period for each frequency bin. The frequency bin with the largest relative change between baseline and stimulus period was taken as the gamma peak frequency for that subject. The effects of escitalopram treatment on MRS and MEG measurements were compared by paired t tests in SPPS v.20 (SPSS Inc., Chicago, IL, USA). A pilot study of the effect of short-term escitalopram treatment on brain metabolites and gamma-oscillations in healthy subjects

Constraining Theories of Working Memory with Biophysical Modeling

A debate about the basic unit of representation in working memory (WM) has recently been prominent in the field. Some argue that WM consists of a limited number of “slots.” These have been equated with object representations ([Luck and Vogel, 1997][1]), but could also arise if the neural coding