Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed

Ouyang, Guang; Hildebrandt, Andrea; Schmitz, Florian; Herrmann, Christoph S.

doi:10.1016/j.neuroimage.2019.116304

Cited by 201 publications

(252 citation statements)

References 71 publications

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“…However, because we observed the same pattern of effects for forward and backward conditions, our data cannot speak to why tracking is enhanced at lower frequencies, and whether this is mainly due to confounds with 1/f brain activity (cf. Ouyang et al, 2019). Still, we find these results striking, especially considering the accumulating evidence that the most acoustically prominent information appears to occur at the frequency of the syllable.…”

Section: Discussionsupporting

confidence: 48%

“…Note, however, that we found the same significant effects for comparisons between the backward conditions (phrase -word: Δ = 0.60, SE = 0.08, p < 0.01; phrase -syllable: Δ = 1.08, SE = 0.08, p < 0.01; word -syllable: Δ = 0.47, SE = 0.08, p < 0.01). Although the effects appear to be more pronounced for forward than for backward conditions, as evidenced by a significant positive Frequency * Direction interaction in the base model (χ 2 (2) = 26.29, p < 0.01; based on likelihood ratios), it is unclear whether increased MI at lower frequencies reflects increased sensitivity of the brain to information occurring at those frequencies, or whether these effects are due to properties of the acoustic signal and/or related to the 1/f frequency component (see Ouyang et al, 2019, for a more detailed discussion of the potential confounds of 1/f brain activity).…”

Section: Speech Tracking We Computed Mutual Information (Mi) Betweenmentioning

confidence: 99%

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Linguistic structure and meaning organize neural oscillations into a content-specific hierarchy

Kaufeld

Bosker

Alday

et al. 2020

Preprint

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Short title: Structure and meaning organize neural oscillationsThe authors declare no competing financial interests. Significance StatementBiological systems like the brain encode their environment not only by reacting in a series of stimulus-driven responses, but by combining stimulus-driven information with internally generated, inferential knowledge and meaning. Understanding language from speech is the human benchmark for this. Much research focusses on the purely stimulus-driven response, but here, we focus on the goal of language behavior: conveying structure and meaning. To that end, we use naturalistic stimuli that contrast acoustic-prosodic and lexical-semantic information to show that, during spoken language comprehension, oscillatory modulations reflect computations related to inferring structure and meaning from the acoustic signal. Our experiment provides the first evidence to date that compositional structure and meaning organize the oscillatory response, above and beyond acoustic and lexical controls.

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Section: Discussionsupporting

confidence: 48%

Section: Speech Tracking We Computed Mutual Information (Mi) Betweenmentioning

confidence: 99%

Linguistic structure and meaning organize neural oscillations into a content-specific hierarchy

Kaufeld

Bosker

Alday

et al. 2020

Preprint

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“…But not all brain studies of human cognition have argued against the existence of critical dynamics. An EEG study of 210 neurotypical adults undergoing an object recognition task showed that variation in 1/f noise was a robust predictor of cognitive processing speed (Ouyang et al, 2020). Moreover, the power-law exponent of the 1/f noise was most predictive of person-to-person processing speeds.…”

Section: Cognition Attention Learning and Autismmentioning

confidence: 99%

“…brain response to errors at a critical point (Cohen, 2016). In summary, evidence from both brain dynamics and behavioral dynamics points to some aspects of criticality in human cognition (Altamura et al, 2012;Cohen, 2016;Simola et al, 2017;Ouyang et al, 2020) while other studies do not (Tinker and Velazquez, 2014;Euler et al, 2016). Studies of human attention have centered on the roles of criticality in meditation and its possible applications to ADHD, as mentioned earlier.…”

Section: Cognition Attention Learning and Autismmentioning

confidence: 99%

Why Brain Criticality Is Clinically Relevant: A Scoping Review

Zimmern

2020

Front. Neural Circuits

133

118

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The past 25 years have seen a strong increase in the number of publications related to criticality in different areas of neuroscience. The potential of criticality to explain various brain properties, including optimal information processing, has made it an increasingly exciting area of investigation for neuroscientists. Recent reviews on this topic, sometimes termed brain criticality, make brief mention of clinical applications of these findings to several neurological disorders such as epilepsy, neurodegenerative disease, and neonatal hypoxia. Other clinicallyrelevant domains-including anesthesia, sleep medicine, developmental-behavioral pediatrics, and psychiatry-are seldom discussed in review papers of brain criticality. Thorough assessments of these application areas and their relevance for clinicians have also yet to be published. In this scoping review, studies of brain criticality involving human data of all ages are evaluated for their current and future clinical relevance. To make the results of these studies understandable to a more clinical audience, a review of the key concepts behind criticality (e.g., phase transitions, long-range temporal correlation, self-organized criticality, power laws, branching processes) precedes the discussion of human clinical studies. Open questions and forthcoming areas of investigation are also considered.

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“…This extends the previous discussion in suggesting that MEG and fMRI are not only complementary for prediction but also with regard to characterizing brain-behavior mappings. Moreover, it is enticing to speculate that the regional power of fast-paced and band brain rhythms allows one to capture fast-paced components of cognitive processes such as attentional sampling or adaptive attention (Gola et al, 2013;Clark et al, 2004), which, in turn might explain unique variance in certain cognitive facets, such as fluid intelligence (Ouyang et al, 2019) or visual short-term memory (Tallon-Baudry et al, 2001). On the other hand, functional connectivity between cortical areas and subcortical structures, in particular the hippocampus, may be key for depression and is well captured with fMRI (Stockmeier et al, 2004;Sheline et al, 2009;Rocca et al, 2015).…”

Section: Brain Age δ As Sensitive Index Of Normative Agingmentioning

confidence: 99%

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

Engemann¹,

Kozynets²,

Sabbagh³

et al. 2019

Preprint

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Electrophysiological methods, i.e., M/EEG provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as surrogate biomarker in 674 subjects from the Cam-CAN. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by source-topography of power spectra between 8 and 30 Hz. Finally, we demonstrate that the model maintains benefits of stacking when data is missing. The proposed framework hence enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.

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Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed

Cited by 201 publications

References 71 publications

Linguistic structure and meaning organize neural oscillations into a content-specific hierarchy

Linguistic structure and meaning organize neural oscillations into a content-specific hierarchy

Why Brain Criticality Is Clinically Relevant: A Scoping Review

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

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