The frequency gradient of human resting-state brain oscillations follows cortical hierarchies

AbstractThe human cortex is characterized by local morphological features such as cortical thickness, myelin content and gene expression that change along the posterior-anterior axis. We investigated if these structural gradients are associated with a similar gradient in a prominent feature of brain activity – namely the frequency of brain oscillations. In resting-state MEG recordings from healthy participants (N=187), we found that the strongest peak frequency in a brain area decreases significantly, gradually and robustly along the posterior-anterior axis following the global hierarchy from early sensory to higher-order areas. This spatial gradient of peak frequency was significantly anticorrelated with the cortical thickness of corresponding areas representing a proxy of the cortical hierarchical level. This result indicates that the intrinsic ‘resonance’ frequency decreases systematically from early sensory to higher-order areas and establishes a new structure-function relationship pertaining to brain oscillations as a core organizational principle that may underlie hierarchical specialization in the brain.

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The frequency gradient of human resting-state brain oscillations follows cortical hierarchies

eLife ◽

10.7554/elife.53715 ◽

2020 ◽

Vol 9 ◽

Cited By ~ 3

Author(s):

Keyvan Mahjoory ◽

Jan-Mathijs Schoffelen ◽

Anne Keitel ◽

Joachim Gross

Keyword(s):

Cortical Thickness ◽

Resting State ◽

Brain Area ◽

Peak Frequency ◽

Hierarchical Level ◽

Spatial Gradient ◽

Brain Oscillations ◽

Mixed Effect ◽

Frequency Changes ◽

Posterior Anterior

The human cortex is characterized by local morphological features such as cortical thickness, myelin content, and gene expression that change along the posterior-anterior axis. We investigated if some of these structural gradients are associated with a similar gradient in a prominent feature of brain activity - namely the frequency of oscillations. In resting-state MEG recordings from healthy participants (N = 187) using mixed effect models, we found that the dominant peak frequency in a brain area decreases significantly along the posterior-anterior axis following the global hierarchy from early sensory to higher order areas. This spatial gradient of peak frequency was significantly anticorrelated with that of cortical thickness, representing a proxy of the cortical hierarchical level. This result indicates that the dominant frequency changes systematically and globally along the spatial and hierarchical gradients and establishes a new structure-function relationship pertaining to brain oscillations as a core organization that may underlie hierarchical specialization in the brain.

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Brain activity at rest: a multiscale hierarchical functional organization

Journal of Neurophysiology ◽

10.1152/jn.00895.2010 ◽

2011 ◽

Vol 105 (6) ◽

pp. 2753-2763 ◽

Cited By ~ 187

Author(s):

Gaëlle Doucet ◽

Mikaël Naveau ◽

Laurent Petit ◽

Nicolas Delcroix ◽

Laure Zago ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Functional Organization ◽

Brain Activity ◽

Correlation Coefficients ◽

Temporal Correlation ◽

Hierarchical Level ◽

Spontaneous Brain Activity ◽

Dorsal Attentional Network

Spontaneous brain activity was mapped with functional MRI (fMRI) in a sample of 180 subjects while in a conscious resting-state condition. With the use of independent component analysis (ICA) of each individual fMRI signal and classification of the ICA-defined components across subjects, a set of 23 resting-state networks (RNs) was identified. Functional connectivity between each pair of RNs was assessed using temporal correlation analyses in the 0.01- to 0.1-Hz frequency band, and the corresponding set of correlation coefficients was used to obtain a hierarchical clustering of the 23 RNs. At the highest hierarchical level, we found two anticorrelated systems in charge of intrinsic and extrinsic processing, respectively. At a lower level, the intrinsic system appears to be partitioned in three modules that subserve generation of spontaneous thoughts (M1a; default mode), inner maintenance and manipulation of information (M1b), and cognitive control and switching activity (M1c), respectively. The extrinsic system was found to be made of two distinct modules: one including primary somatosensory and auditory areas and the dorsal attentional network (M2a) and the other encompassing the visual areas (M2b). Functional connectivity analyses revealed that M1b played a central role in the functioning of the intrinsic system, whereas M1c seems to mediate exchange of information between the intrinsic and extrinsic systems.

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Alpha peak frequency is linked to episodic memory impairment in pathological aging

10.1101/2021.06.18.448931 ◽

2021 ◽

Author(s):

Delphine Puttaert ◽

Vincent Wens ◽

Patrick Fery ◽

Antonin Rovai ◽

Nicola Trotta ◽

...

Keyword(s):

Episodic Memory ◽

Resting State ◽

Early Stage ◽

Brain Activity ◽

Peak Frequency ◽

Short Term ◽

Slowing Down ◽

Pathological Aging ◽

18Fdg Pet ◽

Alpha Peak

The Free and Cued Selective Reminding Test (FCSRT) is a largely validated neuropsychological test for the identification of amnestic syndrome from the early stage of Alzheimers disease (AD). Previous electrophysiological data suggested a slowing down of the alpha rhythm in the AD-continuum as well as a key role of this rhythmic brain activity for episodic memory processes. This study therefore investigates the link between alpha brain activity and alterations in episodic memory as assessed by the FCSRT. For that purpose, 37 patients with altered FCSRT performance underwent a comprehensive neuropsychological assessment, supplemented by 18F-fluorodeoxyglucose positron emission tomography/structural magnetic resonance imaging (18FDG-PET/MR), and 10 minutes of resting-state magnetoencephalography (MEG). The individual alpha peak frequency (APF) in MEG resting-state data was positively correlated with patients encoding efficiency as well as with the efficacy of semantic cues in facilitating patients retrieval of previous stored word. The APF also correlated positively with patients hippocampal volume and their regional glucose consumption in the posterior cingulate cortex. Overall, this study demonstrates that alterations in the ability to learn and store new information for a relatively short-term period are related to a slowing down of alpha rhythmic activity, possibly due to altered interactions in the extended mnemonic system. As such, a decreased APF may be considered as an electrophysiological correlate of short-term episodic memory dysfunction accompanying pathological aging.

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Decreased Alpha Peak Frequency Is Linked to Episodic Memory Impairment in Pathological Aging

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.711375 ◽

2021 ◽

Vol 13 ◽

Author(s):

Delphine Puttaert ◽

Vincent Wens ◽

Patrick Fery ◽

Antonin Rovai ◽

Nicola Trotta ◽

...

Keyword(s):

Episodic Memory ◽

Resting State ◽

Early Stage ◽

Brain Activity ◽

Peak Frequency ◽

Posterior Cingulate Cortex ◽

Short Term ◽

Slowing Down ◽

Pathological Aging ◽

Alpha Peak

The Free and Cued Selective Reminding Test (FCSRT) is a largely validated neuropsychological test for the identification of amnestic syndrome from the early stage of Alzheimer’s disease (AD). Previous electrophysiological data suggested a slowing down of the alpha rhythm in the AD-continuum as well as a key role of this rhythmic brain activity for episodic memory processes. This study therefore investigates the link between alpha brain activity and alterations in episodic memory as assessed by the FCSRT. For that purpose, 37 patients with altered FCSRT performance underwent a comprehensive neuropsychological assessment, supplemented by 18F-fluorodeoxyglucose positron emission tomography/structural magnetic resonance imaging (18FDG-PET/MR), and 10 min of resting-state magnetoencephalography (MEG). The individual alpha peak frequency (APF) in MEG resting-state data was positively correlated with patients’ encoding efficiency as well as with the efficacy of semantic cues in facilitating patients’ retrieval of previous stored word. The APF also correlated positively with patients’ hippocampal volume and their regional glucose consumption in the posterior cingulate cortex. Overall, this study demonstrates that alterations in the ability to learn and store new information for a relatively short-term period are related to a slowing down of alpha rhythmic activity, possibly due to altered interactions in the extended mnemonic system. As such, a decreased APF may be considered as an electrophysiological correlate of short-term episodic memory dysfunction accompanying pathological aging.

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Faculty Opinions recommendation of [CONFERENCE PRESENTATION] Resting state brain activity changes with and predicts memory for recent experiences.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1488958.970057 ◽

2010 ◽

Author(s):

Maurizio Corbetta ◽

Carlo Sestieri

Keyword(s):

Resting State ◽

Brain Activity

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Faculty Opinions recommendation of Signature of consciousness in the dynamics of resting-state brain activity.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725298381.793517282 ◽

2016 ◽

Author(s):

Cheryl Grady

Keyword(s):

Resting State ◽

Brain Activity

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Abnormal Spontaneous Brain Activity in Left-Onset Parkinson Disease: A Resting-State Functional MRI Study

Frontiers in Neurology ◽

10.3389/fneur.2020.00727 ◽

2020 ◽

Vol 11 ◽

Author(s):

Kai Li ◽

Wen Su ◽

Min Chen ◽

Chun-Mei Li ◽

Xin-Xin Ma ◽

...

Keyword(s):

Parkinson Disease ◽

Functional Mri ◽

Resting State ◽

Brain Activity ◽

Spontaneous Brain Activity ◽

Mri Study

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Alzheimer’s Disease Progressively Reduces Visual Functional Network Connectivity

Journal of Alzheimer s Disease Reports ◽

10.3233/adr-210017 ◽

2021 ◽

pp. 1-14

Author(s):

Jie Huang ◽

Paul Beach ◽

Andrea Bozoki ◽

David C. Zhu

Keyword(s):

Visual Cortex ◽

Lower Order ◽

Resting State ◽

Visual Processing ◽

Network Connectivity ◽

Higher Order ◽

Functional Network ◽

Functional Networks ◽

The Face ◽

Processing Network

Background: Postmortem studies of brains with Alzheimer’s disease (AD) not only find amyloid-beta (Aβ) and neurofibrillary tangles (NFT) in the visual cortex, but also reveal temporally sequential changes in AD pathology from higher-order association areas to lower-order areas and then primary visual area (V1) with disease progression. Objective: This study investigated the effect of AD severity on visual functional network. Methods: Eight severe AD (SAD) patients, 11 mild/moderate AD (MAD), and 26 healthy senior (HS) controls undertook a resting-state fMRI (rs-fMRI) and a task fMRI of viewing face photos. A resting-state visual functional connectivity (FC) network and a face-evoked visual-processing network were identified for each group. Results: For the HS, the identified group-mean face-evoked visual-processing network in the ventral pathway started from V1 and ended within the fusiform gyrus. In contrast, the resting-state visual FC network was mainly confined within the visual cortex. AD disrupted these two functional networks in a similar severity dependent manner: the more severe the cognitive impairment, the greater reduction in network connectivity. For the face-evoked visual-processing network, MAD disrupted and reduced activation mainly in the higher-order visual association areas, with SAD further disrupting and reducing activation in the lower-order areas. Conclusion: These findings provide a functional corollary to the canonical view of the temporally sequential advancement of AD pathology through visual cortical areas. The association of the disruption of functional networks, especially the face-evoked visual-processing network, with AD severity suggests a potential predictor or biomarker of AD progression.

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Jumping over baselines with new methods to predict activation maps from resting-state fMRI

Scientific Reports ◽

10.1038/s41598-021-82681-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Eric Lacosse ◽

Klaus Scheffler ◽

Gabriele Lohmann ◽

Georg Martius

Keyword(s):

Resting State ◽

Brain Activity ◽

Resting State Fmri ◽

Behavioral Differences ◽

Large Variability ◽

Single Vertex ◽

Spontaneous Brain Activity ◽

New Methods ◽

Group Averaging ◽

Proposed Modification

AbstractCognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

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