The Hidden Spatial Dimension of Alpha: 10-Hz Perceptual Echoes Propagate as Periodic Traveling Waves in the Human Brain

Lozano-Soldevilla, Diego; VanRullen, Rufin

doi:10.1016/j.celrep.2018.12.058

Cited by 70 publications

(87 citation statements)

References 34 publications

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“…This apparent forward direction of travelling waves was also reported in studies of so-called "perceptual echoes", which constitute a direct index of sensory processing (VanRullen & Macdonald, 2012). Participants were stimulated with random (white-noise) luminance sequences, and the resulting impulse response function showed a long-lasting 10Hz oscillation (or perceptual echo); importantly, the spatial distribution of echo phase was organized as a travelling wave propagating from posterior to frontal sensors (Lozano-Soldevilla & VanRullen, 2019;Alamia & VanRullen, 2019). It thus seems that the directionality of travelling waves could be task-dependent.…”

Section: Introductionsupporting

confidence: 56%

“…In order to quantify the presence of travelling waves in EEG signals and assess the propagation direction, we adopted a wave quantification method from our previous studies (Alamia & VanRullen, 2019;Lozano-Soldevilla & VanRullen, 2019), which is described in Figure 3. For each subject, every trial (10s long with 0.5s baseline) was divided into 20 time-bins by a sliding window of 1 second (with 500ms overlap).…”

Section: Wave Quantificationmentioning

confidence: 99%

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Turning the Stimulus On and Off Dynamically Changes the Direction of Alpha Traveling Waves

Pang

Alamia

VanRullen

2020

Preprint

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Travelling waves have been studied to characterize the complex spatiotemporal dynamics of the brain. Several studies have suggested that the propagation direction of travelling waves can be task-dependent. For example, a recent EEG study from our group found that forward waves (i.e. occipital to frontal, FW waves) were observed during visual processing, whereas backward waves (i.e. frontal to occipital, BW waves) mostly occurred in the absence of sensory input. These EEG recordings, however, were obtained from different experimental sessions and different groups of subjects. To further examine how the waves' direction changes between task conditions, 13 participants were tested on a target detection task while EEG signals were recorded simultaneously. We alternated visual stimulation (5 s display of visual luminance sequences) and resting state (5 s of black screen) within each single trial, allowing us to monitor the moment-to-moment progression of travelling waves. As expected, the waves' direction was closely linked with task conditions (visual processing vs. rest state). First, FW waves from occipital to frontal regions, absent during rest, emerged as a result of visual processing, while BW waves in the opposite direction dominated in the absence of visual inputs, and were reduced (but not eliminated) by external visual inputs. Second, during visual stimulation (but not rest), both waves coexisted on average, but were negatively correlated. That is, when the FW waves were stronger than expected based on alpha amplitude alone, the BW waves tended to be weaker, and vice-versa. In summary, we conclude that the functional role of travelling waves is closely related with their propagating direction, with stimulus-evoked FW waves supporting visual processing and spontaneous BW waves involved more in top-down control.

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Section: Introductionsupporting

confidence: 56%

Section: Wave Quantificationmentioning

confidence: 99%

Turning the Stimulus On and Off Dynamically Changes the Direction of Alpha Traveling Waves

Pang

Alamia

VanRullen

2020

Preprint

Self Cite

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“…Although considered a 555 limitation here, the widespread nature of PAF's relationship to pain sensitivity may provide an 556 important clue to its identity. In line with findings that the alpha rhythm travels across the cortex in 557 "waves" (Zhang et al, 2018;Lozano-Soldevilla et al, 2019), PAF may reflect processes or sources whose 558 actions are distributed across the brain. The thalamus represents one obvious candidate given its 559 extensive cortical projections (e.g.…”

Section: Sensorimotor Paf Can Identify the Most Pain Sensitive Indivisupporting

confidence: 56%

Sensorimotor peak alpha frequency is a reliable biomarker of pain sensitivity

Furman

Prokhorenko

Keaser

et al. 2019

Preprint

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26Previous research has observed that individuals with chronic pain demonstrate slower alpha band 27 oscillations (8-12 Hz range) during resting electroencephalography (EEG) than do age-matched, healthy 28 controls. While this slowing may reflect pathological changes within the brain that occur during the 29 chronification of pain, an alternative explanation is that healthy individuals with slower alpha 30 frequencies are more sensitive to prolonged pain, and by extension, more susceptible to developing 31 chronic pain. To formally test this hypothesis, we examined the relationship between the pain-free, 32resting alpha frequency of healthy individuals and their subsequent sensitivity to two experimental 33 models of prolonged pain, Phasic Heat Pain and Capsaicin Heat Pain, at two testing visits separated by 8 34 weeks on average (n = 61 Visit 1, n = 46 Visit 2). We observed that the speed of an individual's pain-free 35 alpha oscillations was negatively correlated with sensitivity to both prolonged pain tests and that this 36 relationship was reliable across short (minutes) and long (weeks) timescales. Furthermore, we used the 37 speed of pain-free alpha oscillations to successfully identify those individuals most sensitive to 38prolonged pain, which we also validated on data from a separate, independent study. These results 39suggest that alpha oscillation speed is a reliable biomarker of prolonged pain sensitivity with the 40 potential to become a tool for prospectively identifying pain sensitivity in the clinic. 41

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“…The alpha increase that we observed was widespread across cortex. Alpha oscillations are traveling waves in the human neocortex, therefore the trial averaging employed in this analysis might obscure various metastable brain states that occur during BCI control (Zhang et al 2018;Lozano-Soldevilla and VanRullen 2019;Roberts et al 2019). In fact, a recent resting-state MEG study was able to decompose the DMN into anterior and posterior subcomponents which were active at different points in time (Vidaurre et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

Stieger

Engel

Jiang

et al. 2020

Preprint

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Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals' mindfulness practice as well as performance on a sustained attention task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

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The Hidden Spatial Dimension of Alpha: 10-Hz Perceptual Echoes Propagate as Periodic Traveling Waves in the Human Brain

Cited by 70 publications

References 34 publications

Turning the Stimulus On and Off Dynamically Changes the Direction of Alpha Traveling Waves

Turning the Stimulus On and Off Dynamically Changes the Direction of Alpha Traveling Waves

Sensorimotor peak alpha frequency is a reliable biomarker of pain sensitivity

Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

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