The natural frequencies of the resting human brain: an MEG-based atlas

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Furthermore, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.

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Increasing and decreasing interregional brain coupling increases and decreases oscillatory activity in the human brain

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2100652118 ◽

2021 ◽

Vol 118 (37) ◽

pp. e2100652118

Author(s):

Alejandra Sel ◽

Lennart Verhagen ◽

Katharina Angerer ◽

Raluca David ◽

Miriam C. Klein-Flügge ◽

...

Keyword(s):

Human Brain ◽

Alpha Rhythm ◽

Primary Motor Cortex ◽

Premotor Cortex ◽

Motor Task ◽

Brain Regions ◽

Spike Timing ◽

Oscillatory Activity ◽

The Impact ◽

The Brain

The origins of oscillatory activity in the brain are currently debated, but common to many hypotheses is the notion that they reflect interactions between brain areas. Here, we examine this possibility by manipulating the strength of coupling between two human brain regions, ventral premotor cortex (PMv) and primary motor cortex (M1), and examine the impact on oscillatory activity in the motor system measurable in the electroencephalogram. We either increased or decreased the strength of coupling while holding the impact on each component area in the pathway constant. This was achieved by stimulating PMv and M1 with paired pulses of transcranial magnetic stimulation using two different patterns, only one of which increases the influence exerted by PMv over M1. While the stimulation protocols differed in their temporal patterning, they were comprised of identical numbers of pulses to M1 and PMv. We measured the impact on activity in alpha, beta, and theta bands during a motor task in which participants either made a preprepared action (Go) or withheld it (No-Go). Augmenting cortical connectivity between PMv and M1, by evoking synchronous pre- and postsynaptic activity in the PMv–M1 pathway, enhanced oscillatory beta and theta rhythms in Go and No-Go trials, respectively. Little change was observed in the alpha rhythm. By contrast, diminishing the influence of PMv over M1 decreased oscillatory beta and theta rhythms in Go and No-Go trials, respectively. This suggests that corticocortical communication frequencies in the PMv–M1 pathway can be manipulated following Hebbian spike-timing–dependent plasticity.

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Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

10.1101/245530 ◽

2018 ◽

Author(s):

Marijn van Vliet ◽

Mia Liljeström ◽

Susanna Aro ◽

Riitta Salmelin ◽

Jan Kujala

Keyword(s):

Functional Connectivity ◽

Spectral Power ◽

Brain Regions ◽

Dynamic Imaging ◽

Oscillatory Activity ◽

Complete Analysis ◽

Cortical Mapping ◽

Group Level ◽

Connectivity Analysis ◽

Level Statistics

AbstractCommunication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex-and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data.

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Human brain regions responsive to visual motion stimuli: determination by magnetoencephalography and three dimensional MRI

Neuroscience Research ◽

10.1016/s0168-0102(00)81117-7 ◽

2000 ◽

Vol 38 ◽

pp. S45

Author(s):

M Bundo

Keyword(s):

Human Brain ◽

Visual Motion ◽

Three Dimensional ◽

Brain Regions

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EEG correlates of cognitive functions and neuropsychiatric disorders: A review of oscillatory activity and neural synchrony abnormalities

Current Psychiatry Research and Reviews ◽

10.2174/2666082216999201209130117 ◽

2020 ◽

Vol 16 ◽

Author(s):

Meysam Amidfar ◽

Yong-Ku Kim

Keyword(s):

Psychiatric Disorders ◽

Cognitive Functions ◽

Spectral Power ◽

Large Body ◽

Oscillatory Activity ◽

Brain Oscillations ◽

Emotional Symptoms ◽

Obsessive Compulsive ◽

Neuropsychiatric Diseases ◽

Obsessive Compulsive Disorders

Background: A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms. Objective: Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for treatment of cognitive impairments. Methods: This review includes description of brain oscillations in Alzheimer’s disease, bipolar disorder, attentiondeficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism. Results: The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations. Conclusion: In this article we examined nature of brain oscillations, association of brain rhythms with cognitive functions and relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

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An Analysis of the External Validity of EEG Spectral Power in an Uncontrolled Outdoor Environment during Default and Complex Neurocognitive States

Brain Sciences ◽

10.3390/brainsci11030330 ◽

2021 ◽

Vol 11 (3) ◽

pp. 330

Author(s):

Dalton J. Edwards ◽

Logan T. Trujillo

Keyword(s):

External Validity ◽

Spectral Power ◽

Brain Activity ◽

Outdoor Environment ◽

Oscillatory Activity ◽

Brain State ◽

Beta Band ◽

Eeg Spectral Power ◽

Eeg Power ◽

Human Participants

Traditionally, quantitative electroencephalography (QEEG) studies collect data within controlled laboratory environments that limit the external validity of scientific conclusions. To probe these validity limits, we used a mobile EEG system to record electrophysiological signals from human participants while they were located within a controlled laboratory environment and an uncontrolled outdoor environment exhibiting several moderate background influences. Participants performed two tasks during these recordings, one engaging brain activity related to several complex cognitive functions (number sense, attention, memory, executive function) and the other engaging two default brain states. We computed EEG spectral power over three frequency bands (theta: 4–7 Hz, alpha: 8–13 Hz, low beta: 14–20 Hz) where EEG oscillatory activity is known to correlate with the neurocognitive states engaged by these tasks. Null hypothesis significance testing yielded significant EEG power effects typical of the neurocognitive states engaged by each task, but only a beta-band power difference between the two background recording environments during the default brain state. Bayesian analysis showed that the remaining environment null effects were unlikely to reflect measurement insensitivities. This overall pattern of results supports the external validity of laboratory EEG power findings for complex and default neurocognitive states engaged within moderately uncontrolled environments.

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Visual predictions, neural oscillations and naïve physics

Scientific Reports ◽

10.1038/s41598-021-95295-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Blake W. Saurels ◽

Wiremu Hohaia ◽

Kielan Yarrow ◽

Alan Johnston ◽

Derek H. Arnold

Keyword(s):

Brain Activity ◽

Dynamic Environment ◽

Predictive Performance ◽

Brain Regions ◽

Internal Models ◽

Oscillatory Activity ◽

Alpha Band ◽

Core Property ◽

Ball Tracking ◽

Band Activity

AbstractPrediction is a core function of the human visual system. Contemporary research suggests the brain builds predictive internal models of the world to facilitate interactions with our dynamic environment. Here, we wanted to examine the behavioural and neurological consequences of disrupting a core property of peoples’ internal models, using naturalistic stimuli. We had people view videos of basketball and asked them to track the moving ball and predict jump shot outcomes, all while we recorded eye movements and brain activity. To disrupt people’s predictive internal models, we inverted footage on half the trials, so dynamics were inconsistent with how movements should be shaped by gravity. When viewing upright videos people were better at predicting shot outcomes, at tracking the ball position, and they had enhanced alpha-band oscillatory activity in occipital brain regions. The advantage for predicting upright shot outcomes scaled with improvements in ball tracking and occipital alpha-band activity. Occipital alpha-band activity has been linked to selective attention and spatially-mapped inhibitions of visual brain activity. We propose that when people have a more accurate predictive model of the environment, they can more easily parse what is relevant, allowing them to better target irrelevant positions for suppression—resulting in both better predictive performance and in neural markers of inhibited information processing.

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Alcohol use disorder causes global changes in splicing in the human brain

Translational Psychiatry ◽

10.1038/s41398-020-01163-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Derek Van Booven ◽

Mengying Li ◽

J. Sunil Rao ◽

Ilya O. Blokhin ◽

R. Dayne Mayfield ◽

...

Keyword(s):

Alcohol Use ◽

Human Brain ◽

Alcohol Use Disorder ◽

Brain Regions ◽

Gene Set Enrichment Analysis ◽

Splicing Factor ◽

Central Nucleus ◽

Global Changes ◽

Aberrant Expression ◽

Altered Expression

AbstractAlcohol use disorder (AUD) is a widespread disease leading to the deterioration of cognitive and other functions. Mechanisms by which alcohol affects the brain are not fully elucidated. Splicing constitutes a nuclear process of RNA maturation, which results in the formation of the transcriptome. We tested the hypothesis as to whether AUD impairs splicing in the superior frontal cortex (SFC), nucleus accumbens (NA), basolateral amygdala (BLA), and central nucleus of the amygdala (CNA). To evaluate splicing, bam files from STAR alignments were indexed with samtools for use by rMATS software. Computational analysis of affected pathways was performed using Gene Ontology Consortium, Gene Set Enrichment Analysis, and LncRNA Ontology databases. Surprisingly, AUD was associated with limited changes in the transcriptome: expression of 23 genes was altered in SFC, 14 in NA, 102 in BLA, and 57 in CNA. However, strikingly, mis-splicing in AUD was profound: 1421 mis-splicing events were detected in SFC, 394 in NA, 1317 in BLA, and 469 in CNA. To determine the mechanism of mis-splicing, we analyzed the elements of the spliceosome: small nuclear RNAs (snRNAs) and splicing factors. While snRNAs were not affected by alcohol, expression of splicing factor heat shock protein family A (Hsp70) member 6 (HSPA6) was drastically increased in SFC, BLA, and CNA. Also, AUD was accompanied by aberrant expression of long noncoding RNAs (lncRNAs) related to splicing. In summary, alcohol is associated with genome-wide changes in splicing in multiple human brain regions, likely due to dysregulation of splicing factor(s) and/or altered expression of splicing-related lncRNAs.

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Causal modulation of right hemisphere fronto-parietal phase synchrony with Transcranial Magnetic Stimulation during a conscious visual detection task

Scientific Reports ◽

10.1038/s41598-020-79812-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chloé Stengel ◽

Marine Vernet ◽

Julià L. Amengual ◽

Antoni Valero-Cabré

Keyword(s):

Transcranial Magnetic Stimulation ◽

Visual Perception ◽

Frequency Band ◽

Magnetic Stimulation ◽

Visual Detection ◽

Detection Task ◽

Oscillatory Activity ◽

Top Down ◽

The Right ◽

High Beta

AbstractCorrelational evidence in non-human primates has reported increases of fronto-parietal high-beta (22–30 Hz) synchrony during the top-down allocation of visuo-spatial attention. But may inter-regional synchronization at this specific frequency band provide a causal mechanism by which top-down attentional processes facilitate conscious visual perception? To address this question, we analyzed electroencephalographic (EEG) signals from a group of healthy participants who performed a conscious visual detection task while we delivered brief (4 pulses) rhythmic (30 Hz) or random bursts of Transcranial Magnetic Stimulation (TMS) to the right Frontal Eye Field (FEF) prior to the onset of a lateralized target. We report increases of inter-regional synchronization in the high-beta band (25–35 Hz) between the electrode closest to the stimulated region (the right FEF) and right parietal EEG leads, and increases of local inter-trial coherence within the same frequency band over bilateral parietal EEG contacts, both driven by rhythmic but not random TMS patterns. Such increases were accompained by improvements of conscious visual sensitivity for left visual targets in the rhythmic but not the random TMS condition. These outcomes suggest that high-beta inter-regional synchrony can be modulated non-invasively and that high-beta oscillatory activity across the right dorsal fronto-parietal network may contribute to the facilitation of conscious visual perception. Our work supports future applications of non-invasive brain stimulation to restore impaired visually-guided behaviors by operating on top-down attentional modulatory mechanisms.

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Physiology, Pharmacology, and Topography of Cholinergic Neocortical Oscillations In Vitro

Journal of Neurophysiology ◽

10.1152/jn.1997.77.5.2427 ◽

1997 ◽

Vol 77 (5) ◽

pp. 2427-2445 ◽

Cited By ~ 61

Author(s):

Heath S. Lukatch ◽

M. Bruce Maciver

Keyword(s):

Current Source ◽

Brain Slices ◽

Receptor Activation ◽

Brain Regions ◽

Oscillatory Activity ◽

Cortical Layers ◽

Eeg Activity ◽

Layer 2

Lukatch, Heath S. and M. Bruce MacIver. Physiology, pharmacology, and topography of cholinergic neocortical oscillations in vitro. J. Neurophysiol. 77: 2427–2445, 1997. Rat neocortical brain slices generated rhythmic extracellular field [microelectroencephalogram (micro-EEG)] oscillations at theta frequencies (3–12 Hz) when exposed to pharmacological conditions that mimicked endogenous ascending cholinergic and GABAergic inputs. Use of the specific receptor agonist and antagonist carbachol and bicuculline revealed that simultaneous muscarinic receptor activation and γ-aminobutyric acid-A (GABAA)-mediated disinhibition werenecessary to elicit neocortical oscillations. Rhythmic activity was independent of GABAB receptor activation, but required intact glutamatergic transmission, evidenced by blockade or disruption of oscillations by 6-cyano-7-nitroquinoxaline-2,3-dione and (±)-2-amino-5-phosphonovaleric acid, respectively. Multisite mapping studies showed that oscillations were localized to areas 29d and 18b (Oc2MM) and parts of areas 18a and 17. Peak oscillation amplitudes occurred in layer 2/3, and phase reversals were observed in layers 1 and 5. Current source density analysis revealed large-amplitude current sinks and sources in layers 2/3 and 5, respectively. An initial shift in peak inward current density from layer 1 to layer 2/3 indicated that two processes underlie an initial depolarization followed by oscillatory activity. Laminar transections localized oscillation-generating circuitry to superficial cortical layers and sharp-spike-generating circuitry to deep cortical layers. Whole cell recordings identified three distinct cell types based on response properties during rhythmic micro-EEG activity: oscillation-on (theta-on) and -off (theta-off) neurons, and transiently depolarizing glial cells. Theta-on neurons displayed membrane potential oscillations that increased in amplitude with hyperpolarization (from −30 to −90 mV). This, taken together with a glutamate antagonist-induced depression of rhythmic micro-EEG activity, indicated that cholinergically driven neocortical oscillations require excitatory synaptic transmission. We conclude that under the appropriate pharmacological conditions, neocortical brain slices were capable of producing localized theta frequency oscillations. Experiments examining oscillation physiology, pharmacology, and topography demonstrated that neocortical brain slice oscillations share many similarities with the in vivo and in vitro theta EEG activity recorded in other brain regions.

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