1
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Gavenas J, Rutishauser U, Schurger A, Maoz U. Slow ramping emerges from spontaneous fluctuations in spiking neural networks. Nat Commun 2024; 15:7285. [PMID: 39179554 PMCID: PMC11344096 DOI: 10.1038/s41467-024-51401-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 08/05/2024] [Indexed: 08/26/2024] Open
Abstract
The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.
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Affiliation(s)
- Jake Gavenas
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Center for Neural Science and Medicine, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Aaron Schurger
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA
- INSERM U992, Cognitive Neuroimaging Unit, NeuroSpin Center, Gif sur Yvette, 91191, France
- Commissariat à l'Energie Atomique, Direction des Sciences du Vivant, I2BM, NeuroSpin Center, Gif sur Yvette, 91191, France
| | - Uri Maoz
- Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA.
- Fowler School of Engineering, Chapman University, Orange, CA, USA.
- Anderson School of Management, University of California, Los Angeles, CA, USA.
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2
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Ventura B, Çatal Y, Wolman A, Buccellato A, Cooper AC, Northoff G. Intrinsic neural timescales exhibit different lengths in distinct meditation techniques. Neuroimage 2024; 297:120745. [PMID: 39069224 DOI: 10.1016/j.neuroimage.2024.120745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/12/2024] [Accepted: 07/18/2024] [Indexed: 07/30/2024] Open
Abstract
Meditation encompasses a range of practices employing diverse induction techniques, each characterized by a distinct attentional focus. In Mantra meditation, for instance, practitioners direct their attention narrowly to a given sentence that is recursively repeated, while other forms of meditation such as Shoonya meditation are induced by a wider attentional focus. Here we aimed to identify the neural underpinnings and correlates associated with this spectrum of distinct attentional foci. To accomplish this, we used EEG data to estimate the brain's intrinsic neural timescales (INTs), that is, its temporal windows of activity, by calculating the Autocorrelation Window (ACW) of the EEG signal. The autocorrelation function measures the similarity of a timeseries with a time-lagged version of itself by correlating the signal with itself on different time lags, consequently providing an estimation of INTs length. Therefore, through using the ACW metric, our objective was to explore whether there is a correspondence between the length of the brain's temporal windows of activity and the width of the attentional scope during various meditation techniques. This was performed on three groups of highly proficient practitioners belonging to different meditation traditions, as well as a meditation-naïve control group. Our results indicated that practices with a wider attentional focus, like Shoonya meditation, exhibit longer ACW durations compared to practices requiring a narrower attentional focus, such as Mantra meditation or body-scanning Vipassana meditation. Together, we demonstrated that distinct meditation techniques with varying widths of attentional foci exhibit unique durations in their brain's INTs. This may suggest that the width of the attentional scope during meditation relates and corresponds to the width of the brain's temporal windows in its neural activity. SIGNIFICANCE STATEMENT: Our research uncovered the neural mechanisms that underpin the attentional foci in various meditation techniques. We revealed that distinct meditation induction techniques, featured by their range of attentional widths, are characterized by varying lengths of intrinsic neural timescales (INTs) within the brain, as measured by the Autocorrelation Window function. This finding may bridge the gap between the width of attentional windows (subjective) and the width of the temporal windows in the brain's neural activity (objective) during different meditation techniques, offering a new understanding of how cognitive and neural processes are related to each other. This work holds significant implications, especially in the context of the increasing use of meditation in mental health and well-being interventions. By elucidating the distinct neural foundations of different meditation techniques, our research aims to pave the way for developing more tailored and effective meditation-based treatments.
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Affiliation(s)
- Bianca Ventura
- School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa K1N 6N5, ON, Canada.
| | - Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa K1Z 7K4, ON, Canada.
| | - Angelika Wolman
- School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa K1N 6N5, ON, Canada.
| | - Andrea Buccellato
- Padova Neuroscience Center, University of Padova, Via Orus 2/B, Padova 35129, Italy; Department of General Psychology, University of Padova, Via Venezia, 8, 35131 Padova, Italy.
| | - Austin Clinton Cooper
- Integrated Program of Neuroscience, Room 302, Irving Ludmer Building, 1033 Pine Avenue W., McGill University, Montreal, QC H3A 1A1, Canada.
| | - Georg Northoff
- The Royal's Institute of Mental Health Research & University of Ottawa, Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa K1Z 7K4, ON, Canada.
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3
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Kramer MA, Chu CJ. A General, Noise-Driven Mechanism for the 1/f-Like Behavior of Neural Field Spectra. Neural Comput 2024; 36:1643-1668. [PMID: 39028955 DOI: 10.1162/neco_a_01682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/25/2024] [Indexed: 07/21/2024]
Abstract
Consistent observations across recording modalities, experiments, and neural systems find neural field spectra with 1/f-like scaling, eliciting many alternative theories to explain this universal phenomenon. We show that a general dynamical system with stochastic drive and minimal assumptions generates 1/f-like spectra consistent with the range of values observed in vivo without requiring a specific biological mechanism or collective critical behavior.
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Affiliation(s)
- Mark A Kramer
- Department of Mathematics and Statistics, and Center for Systems Neuroscience, Boston University, Boston, MA 02214, U.S.A.
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, U.S.A.
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4
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Gavenas J, Rutishauser U, Schurger A, Maoz U. Slow ramping emerges from spontaneous fluctuations in spiking neural networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.27.542589. [PMID: 37398452 PMCID: PMC10312459 DOI: 10.1101/2023.05.27.542589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
1. We reveal a mechanism for slow-ramping signals before spontaneous voluntary movements. 2. Slow synapses stabilize spontaneous fluctuations in spiking neural network. 3. We validate model predictions in human frontal cortical single-neuron recordings. 4. The model recreates the readiness potential in an EEG proxy signal. 5. Neurons that ramp together had correlated activity before ramping onset. The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ∼2 seconds before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.
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5
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Brake N, Duc F, Rokos A, Arseneau F, Shahiri S, Khadra A, Plourde G. A neurophysiological basis for aperiodic EEG and the background spectral trend. Nat Commun 2024; 15:1514. [PMID: 38374047 PMCID: PMC10876973 DOI: 10.1038/s41467-024-45922-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 02/06/2024] [Indexed: 02/21/2024] Open
Abstract
Electroencephalograms (EEGs) display a mixture of rhythmic and broadband fluctuations, the latter manifesting as an apparent 1/f spectral trend. While network oscillations are known to generate rhythmic EEG, the neural basis of broadband EEG remains unexplained. Here, we use biophysical modelling to show that aperiodic neural activity can generate detectable scalp potentials and shape broadband EEG features, but that these aperiodic signals do not significantly perturb brain rhythm quantification. Further model analysis demonstrated that rhythmic EEG signals are profoundly corrupted by shifts in synapse properties. To examine this scenario, we recorded EEGs of human subjects being administered propofol, a general anesthetic and GABA receptor agonist. Drug administration caused broadband EEG changes that quantitatively matched propofol's known effects on GABA receptors. We used our model to correct for these confounding broadband changes, which revealed that delta power, uniquely, increased within seconds of individuals losing consciousness. Altogether, this work details how EEG signals are shaped by neurophysiological factors other than brain rhythms and elucidates how these signals can undermine traditional EEG interpretation.
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Affiliation(s)
- Niklas Brake
- Quantiative Life Sciences PhD Program, McGill University, Montreal, Canada
- Department of Physiology, McGill University, Montreal, Canada
| | - Flavie Duc
- Department of Anesthesia, McGill University, Montreal, Canada
| | - Alexander Rokos
- Department of Anesthesia, McGill University, Montreal, Canada
| | | | - Shiva Shahiri
- School of Nursing, McGill University, Montreal, Canada
| | - Anmar Khadra
- Department of Physiology, McGill University, Montreal, Canada.
| | - Gilles Plourde
- Department of Anesthesia, McGill University, Montreal, Canada.
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6
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Zhang Y, Wang Y, Cheng H, Yan F, Li D, Song D, Wang Q, Huang L. EEG spectral slope: A reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia. Neuroimage 2023; 283:120426. [PMID: 37898378 DOI: 10.1016/j.neuroimage.2023.120426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 10/17/2023] [Accepted: 10/25/2023] [Indexed: 10/30/2023] Open
Abstract
The level of consciousness undergoes continuous alterations during anesthesia. Prior to the onset of propofol-induced complete unconsciousness, degraded levels of behavioral responsiveness can be observed. However, a reliable index to monitor altered consciousness levels during anesthesia has not been sufficiently investigated. In this study, we obtained 60-channel EEG data from 24 healthy participants during an ultra-slow propofol infusion protocol starting with an initial concentration of 1 μg/ml and a stepwise increase of 0.2 μg/ml in concentration. Consecutive auditory stimuli were delivered every 5 to 6 s, and the response time to the stimuli was used to assess the responsiveness levels. We calculated the spectral slope in a time-resolved manner by extracting 5-second EEG segments at each auditory stimulus and estimated their correlation with the corresponding response time. Our results demonstrated that during slow propofol infusion, the response time to external stimuli increased, while the EEG spectral slope, fitted at 15-45 Hz, became steeper, and a significant negative correlation was observed between them. Moreover, the spectral slope further steepened at deeper anesthetic levels and became flatter during anesthesia recovery. We verified these findings using an external dataset. Additionally, we found that the spectral slope of frontal electrodes over the prefrontal lobe had the best performance in predicting the response time. Overall, this study used a time-resolved analysis to suggest that the EEG spectral slope could reliably track continuously altered consciousness levels during propofol anesthesia. Furthermore, the frontal spectral slope may be a promising index for clinical monitoring of anesthesia depth.
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Affiliation(s)
- Yun Zhang
- School of Life Science and Technology, Xidian University, No.2 TaiBai South Road, Xi'an 710061, China
| | - Yubo Wang
- School of Life Science and Technology, Xidian University, No.2 TaiBai South Road, Xi'an 710061, China
| | - Huanhuan Cheng
- School of Life Science and Technology, Xidian University, No.2 TaiBai South Road, Xi'an 710061, China
| | - Fei Yan
- Department of Anesthesiology & Center for Brain Science, the First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an 710061, China
| | - Dingning Li
- School of Life Science and Technology, Xidian University, No.2 TaiBai South Road, Xi'an 710061, China
| | - Dawei Song
- Department of Anesthesiology & Center for Brain Science, the First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an 710061, China
| | - Qiang Wang
- Department of Anesthesiology & Center for Brain Science, the First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta West Road, Xi'an 710061, China.
| | - Liyu Huang
- School of Life Science and Technology, Xidian University, No.2 TaiBai South Road, Xi'an 710061, China.
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7
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Kramer MA, Chu CJ. The 1/f-like behavior of neural field spectra are a natural consequence of noise driven brain dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.532077. [PMID: 37214869 PMCID: PMC10197559 DOI: 10.1101/2023.03.10.532077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Consistent observations across recording modalities, experiments, and neural systems find neural field spectra with 1/f-like scaling, eliciting many alternative theories to explain this universal phenomenon. We show that a general dynamical system with stochastic drive and minimal assumptions generates 1/f-like spectra consistent with the range of values observed in vivo, without requiring a specific biological mechanism or collective critical behavior.
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Affiliation(s)
- Mark A Kramer
- Department of Mathematics and Statistics, and Center for Systems Neuroscience, Boston University, Boston MA, 02214
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston MA, 02114
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8
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Pei L, Northoff G, Ouyang G. Comparative analysis of multifaceted neural effects associated with varying endogenous cognitive load. Commun Biol 2023; 6:795. [PMID: 37524883 PMCID: PMC10390511 DOI: 10.1038/s42003-023-05168-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 07/24/2023] [Indexed: 08/02/2023] Open
Abstract
Contemporary neuroscience has firmly established that mental state variation concurs with changes in neural dynamic activity in a complex way that a one-to-one mapping cannot describe. To explore the scenario of the multifaceted changes in neural dynamics associated with simple mental state variation, we took cognitive load - a common cognitive manipulation in psychology - as a venue to characterize how multiple neural dynamic features are simultaneously altered by the manipulation and how their sensitivity differs. Electroencephalogram was collected from 152 participants performing stimulus-free tasks with different demands. The results show that task demand alters wide-ranging neural dynamic features, including band-specific oscillations across broad frequency bands, scale-free dynamics, and cross-frequency phase-amplitude coupling. The scale-free dynamics outperformed others in indexing cognitive load variation. This study demonstrates a complex relationship between cognitive dynamics and neural dynamics, which points to a necessity to integrate multifaceted neural dynamic features when studying mind-brain relationship in the future.
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Affiliation(s)
- Leisi Pei
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Georg Northoff
- Institute of Mental Health Research, Mind, Brain Imaging and Neuroethics Research Unit, University of Ottawa, Ottawa, Canada
| | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, China.
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9
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Aggarwal S, Ray S. Slope of the power spectral density flattens at low frequencies (<150 Hz) with healthy aging but also steepens at higher frequency (>200 Hz) in human electroencephalogram. Cereb Cortex Commun 2023; 4:tgad011. [PMID: 37334259 PMCID: PMC10276190 DOI: 10.1093/texcom/tgad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Indexed: 06/20/2023] Open
Abstract
The power spectral density (PSD) of the brain signals is characterized by two distinct features: oscillations, which are represented as distinct "bumps," and broadband aperiodic activity, that reduces in power with increasing frequency and is characterized by the slope of the power falloff. Recent studies have shown a change in the slope of the aperiodic activity with healthy aging and mental disorders. However, these studies analyzed slopes over a limited frequency range (<100 Hz). To test whether the PSD slope is affected over a wider frequency range with aging and mental disorder, we analyzed the slope till 800 Hz in electroencephalogram data recorded from elderly subjects (>49 years) who were healthy (n = 217) or had mild cognitive impairment (MCI; n = 11) or Alzheimer's Disease (AD; n = 5). Although the slope reduced up to ~ 150 Hz with healthy aging (as shown previously), surprisingly, at higher frequencies (>200 Hz), it increased with age. These results were observed in all electrodes, for both eyes open and eyes closed conditions, and for different reference schemes. However, slopes were not significantly different in MCI/AD subjects compared with healthy controls. Overall, our results constrain the biophysical mechanisms that are reflected in the PSD slopes in healthy and pathological aging.
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Affiliation(s)
- Srishty Aggarwal
- Department of Physics, Indian Institute of Science, Bengaluru 560012, India
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bengaluru 560012, India
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10
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Pei L, Zhou X, Leung FKS, Ouyang G. Differential associations between scale-free neural dynamics and different levels of cognitive ability. Psychophysiology 2023; 60:e14259. [PMID: 36700291 DOI: 10.1111/psyp.14259] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 12/14/2022] [Accepted: 01/08/2023] [Indexed: 01/27/2023]
Abstract
As indicators of cognitive function, scale-free neural dynamics are gaining increasing attention in cognitive neuroscience. Although the functional relevance of scale-free dynamics has been extensively reported, one fundamental question about its association with cognitive ability remains unanswered: is the association universal across a wide spectrum of cognitive abilities or confined to specific domains? Based on dual-process theory, we designed two categories of tasks to analyze two types of cognitive processes-automatic and controlled-and examined their associations with scale-free neural dynamics characterized from resting-state electroencephalography (EEG) recordings obtained from a large sample of human adults (N = 102). Our results showed that resting-state scale-free neural dynamics did not predict individuals' behavioral performance in tasks that primarily engaged the automatic process but did so in tasks that primarily engaged the controlled process. In addition, by fitting the scale-free parameters separately in different frequency bands, we found that the cognitive association of scale-free dynamics was more strongly manifested in higher-band EEG spectrum. Our findings indicate that resting-state scale-free dynamics are not universal neural indicators for all cognitive abilities but are mainly associated with high-level cognition that entails controlled processes. This finding is compatible with the widely claimed role of scale-free dynamics in reflecting properties of complex dynamic systems.
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Affiliation(s)
- Leisi Pei
- Faculty of Education, The University of Hong Kong, Hong Kong, China
| | - Xinlin Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | | | - Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, China
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11
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Seymour RA, Alexander N, Maguire EA. Robust estimation of 1/f activity improves oscillatory burst detection. Eur J Neurosci 2022; 56:5836-5852. [PMID: 36161675 PMCID: PMC9828710 DOI: 10.1111/ejn.15829] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 09/13/2022] [Indexed: 02/06/2023]
Abstract
Neural oscillations often occur as transient bursts with variable amplitude and frequency dynamics. Quantifying these effects is important for understanding brain-behaviour relationships, especially in continuous datasets. To robustly measure bursts, rhythmical periods of oscillatory activity must be separated from arrhythmical background 1/f activity, which is ubiquitous in electrophysiological recordings. The Better OSCillation (BOSC) framework achieves this by defining a power threshold above the estimated background 1/f activity, combined with a duration threshold. Here we introduce a modification to this approach called fBOSC, which uses a spectral parametrisation tool to accurately model background 1/f activity in neural data. fBOSC (which is openly available as a MATLAB toolbox) is robust to power spectra with oscillatory peaks and can also model non-linear spectra. Through a series of simulations, we show that fBOSC more accurately models the 1/f power spectrum compared with existing methods. fBOSC was especially beneficial where power spectra contained a 'knee' below ~.5-10 Hz, which is typical in neural data. We also found that, unlike other methods, fBOSC was unaffected by oscillatory peaks in the neural power spectrum. Moreover, by robustly modelling background 1/f activity, the sensitivity for detecting oscillatory bursts was standardised across frequencies (e.g., theta- and alpha-bands). Finally, using openly available resting state magnetoencephalography and intracranial electrophysiology datasets, we demonstrate the application of fBOSC for oscillatory burst detection in the theta-band. These simulations and empirical analyses highlight the value of fBOSC in detecting oscillatory bursts, including in datasets that are long and continuous with no distinct experimental trials.
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Affiliation(s)
- Robert A. Seymour
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Nicholas Alexander
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Eleanor A. Maguire
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of NeurologyUniversity College LondonLondonUK
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12
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Evertz R, Hicks DG, Liley DTJ. Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes. PLoS Comput Biol 2022; 18:e1010012. [PMID: 35427355 PMCID: PMC9045666 DOI: 10.1371/journal.pcbi.1010012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 04/27/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022] Open
Abstract
The dynamical and physiological basis of alpha band activity and 1/fβ noise in the EEG are the subject of continued speculation. Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/fβ noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling (β) of the power spectrum (1/fβ). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/fβ noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG. The resting human electroencephalogram (EEG) exhibits two dominant spectral features: the alpha rhythm (8–13 Hz) and its associated attenuation between eyes-closed and eyes-open resting state (alpha blocking), and the 1/fβ scaling of the power spectrum. While these phenomena are well studied a thorough understanding of their respective generative processes remains elusive. By employing a theoretical approach that follows from neural population models of EEG we demonstrate that it is possible to economically account for both of these phenomena using a singular mechanistic framework: resting EEG is assumed to arise from the summed activity of multiple uncorrelated, stochastically driven, damped alpha band linear oscillatory processes having a distribution of relaxation rates or dampings. By numerically estimating these damping distributions from eyes-closed and eyes-open EEG data, in a total of 136 participants, it is found that such damping distributions are predominantly bimodal in shape. The most weakly damped mode is found to account for alpha band power, with alpha blocking being driven by an increase in the damping of this weakly damped mode, whereas the second, and more heavily damped mode, is able to explain 1/fβ scaling present in the resting state EEG spectra.
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Affiliation(s)
- Rick Evertz
- Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria, Australia
- * E-mail: (RE); (DGH); (DTJL)
| | - Damien G. Hicks
- Optical Sciences Centre, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Department of Physics and Astronomy, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Bioinformatics Division, Walter & Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
- * E-mail: (RE); (DGH); (DTJL)
| | - David T. J. Liley
- Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
- * E-mail: (RE); (DGH); (DTJL)
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13
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Çatal Y, Gomez-Pilar J, Northoff G. Intrinsic dynamics and topography of sensory input systems. Cereb Cortex 2022; 32:4592-4604. [PMID: 35094077 PMCID: PMC9614113 DOI: 10.1093/cercor/bhab504] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 02/01/2023] Open
Abstract
The brain is continuously bombarded by external stimuli, which are processed in different input systems. The intrinsic features of these sensory input systems remain yet unclear. Investigating topography and dynamics of input systems is the goal of our study in order to better understand the intrinsic features that shape their neural processing. Using a functional magnetic resonance imaging dataset, we measured neural topography and dynamics of the input systems during rest and task states. Neural dynamics were probed by scale-free activity, measured with the power-law exponent (PLE), as well as by order/disorder as measured with sample entropy (SampEn). Our main findings during both rest and task states are: 1) differences in neural dynamics (PLE, SampEn) between regions within each of the three sensory input systems 2) differences in topography and dynamics among the three input systems; 3) PLE and SampEn correlate and, as demonstrated in simulation, show non-linear relationship in the critical range of PLE; 4) scale-free activity during rest mediates the transition of SampEn from rest to task as probed in a mediation model. We conclude that the sensory input systems are characterized by their intrinsic topographic and dynamic organization which, through scale-free activity, modulates their input processing.
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Affiliation(s)
- Yasir Çatal
- The Royal's Institute of Mental Health Research & University of Ottawa. Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa, Ottawa, 145 Carling Avenue, Rm. 6435, Ottawa, Ontario K1Z 7K4, Canada
| | - Javier Gomez-Pilar
- Biomedical Engineering Group, Higher Technical School of Telecommunications Engineering, University of Valladolid, Valladolid 47011, Spain,Centro de Investigación Biomédica en Red—Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), Madrid 28029, Spain
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14
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Pathania A, Euler MJ, Clark M, Cowan R, Duff K, Lohse KR. Resting EEG spectral slopes are associated with age-related differences in information processing speed. Biol Psychol 2022; 168:108261. [PMID: 34999166 DOI: 10.1016/j.biopsycho.2022.108261] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 01/03/2022] [Accepted: 01/05/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND Previous research has shown the slope of the EEG power spectrum differentiates between older and younger adults in various experimental cognitive tasks. We extend that work, assessing the relation between the EEG power spectrum and performance on the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). METHODS Twenty-one younger and twenty-three older adults completed the RBANS with EEG data collected at rest. Using spectral parameterization, we tested the mediating effect of the spectral slope on differences in subsequent cognitive task performance. RESULTS Older adults performed reliably worse on the RBANS overall, and on the Attention and Delayed Memory domains specifically. However, evidence of mediation was only found for the Coding subtest. CONCLUSIONS The slope of the resting EEG power spectrum mediated age-related differences in cognition, but only in a task requiring speeded processing. Mediation was not statistically significant for delayed memory, even though age-related differences were present.
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Affiliation(s)
- A Pathania
- Department of Health and Kinesiology, University of Utah
| | - M J Euler
- Department of Psychology, University of Utah
| | - M Clark
- Department of Health and Kinesiology, University of Utah
| | - R Cowan
- Department of Health and Kinesiology, University of Utah
| | - K Duff
- Department of Neurology, University of Utah
| | - K R Lohse
- Department of Health and Kinesiology, University of Utah; Program in Physical Therapy and Department of Neurology, Washington University School of Medicine in Saint Louis
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15
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Broadband Dynamics Rather than Frequency-Specific Rhythms Underlie Prediction Error in the Primate Auditory Cortex. J Neurosci 2021; 41:9374-9391. [PMID: 34645605 DOI: 10.1523/jneurosci.0367-21.2021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 09/15/2021] [Accepted: 09/20/2021] [Indexed: 11/21/2022] Open
Abstract
Detection of statistical irregularities, measured as a prediction error response, is fundamental to the perceptual monitoring of the environment. We studied whether prediction error response is associated with neural oscillations or asynchronous broadband activity. Electrocorticography was conducted in three male monkeys, who passively listened to the auditory roving oddball stimuli. Local field potentials (LFPs) recorded over the auditory cortex underwent spectral principal component analysis, which decoupled broadband and rhythmic components of the LFP signal. We found that the broadband component captured the prediction error response, whereas none of the rhythmic components were associated with statistical irregularities of sounds. The broadband component displayed more stochastic, asymmetrical multifractal properties than the rhythmic components, which revealed more self-similar dynamics. We thus conclude that the prediction error response is captured by neuronal populations generating asynchronous broadband activity, defined by irregular dynamic states, which, unlike oscillatory rhythms, appear to enable the neural representation of auditory prediction error response.SIGNIFICANCE STATEMENT This study aimed to examine the contribution of oscillatory and asynchronous components of auditory local field potentials in the generation of prediction error responses to sensory irregularities, as this has not been directly addressed in the previous studies. Here, we show that mismatch negativity-an auditory prediction error response-is driven by the asynchronous broadband component of potentials recorded in the auditory cortex. This finding highlights the importance of nonoscillatory neural processes in the predictive monitoring of the environment. At a more general level, the study demonstrates that stochastic neural processes, which are often disregarded as neural noise, do have a functional role in the processing of sensory information.
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16
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Complementary roles of neural synchrony and complexity for indexing consciousness and chances of surviving in acute coma. Neuroimage 2021; 245:118638. [PMID: 34624502 DOI: 10.1016/j.neuroimage.2021.118638] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 09/28/2021] [Accepted: 10/05/2021] [Indexed: 11/23/2022] Open
Abstract
An open challenge in consciousness research is understanding how neural functions are altered by pathological loss of consciousness. To maintain consciousness, the brain needs synchronized communication of information across brain regions, and sufficient complexity in neural activity. Coordination of brain activity, typically indexed through measures of neural synchrony, has been shown to decrease when consciousness is lost and to reflect the clinical state of patients with disorders of consciousness. Moreover, when consciousness is lost, neural activity loses complexity, while the levels of neural noise, indexed by the slope of the electroencephalography (EEG) spectral exponent decrease. Although these properties have been well investigated in resting state activity, it remains unknown whether the sensory processing network, which has been shown to be preserved in coma, suffers from a loss of synchronization or information content. Here, we focused on acute coma and hypothesized that neural synchrony in response to auditory stimuli would reflect coma severity, while complexity, or neural noise, would reflect the presence or loss of consciousness. Results showed that neural synchrony of EEG signals was stronger for survivors than non-survivors and predictive of patients' outcome, but indistinguishable between survivors and healthy controls. Measures of neural complexity and neural noise were not informative of patients' outcome and had high or low values for patients compared to controls. Our results suggest different roles for neural synchrony and complexity in acute coma. Synchrony represents a precondition for consciousness, while complexity needs an equilibrium between high or low values to support conscious cognition.
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17
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Abrevaya G, Dumas G, Aravkin AY, Zheng P, Gagnon-Audet JC, Kozloski J, Polosecki P, Lajoie G, Cox D, Dawson SP, Cecchi G, Rish I. Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks. Neural Comput 2021; 33:2087-2127. [PMID: 34310676 DOI: 10.1162/neco_a_01401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 02/19/2021] [Indexed: 01/16/2023]
Abstract
Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.
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Affiliation(s)
- Germán Abrevaya
- Departamento de Física, FCEyN, UBA and IFIBA, CONICET, 1428 Buenos Aires, Argentina
| | - Guillaume Dumas
- Mila-Quebec Artificial Intelligence Institute, and CHU Sainte-Justine Research Center, Department of Psychiatry, Universitéde Montréal, Montreal H3A OE8, Canada
| | | | - Peng Zheng
- University of Washington, Seattle, WA 98195, U.S.A.
| | | | - James Kozloski
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Pablo Polosecki
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Guillaume Lajoie
- Mila-Quebec Artificial Intelligence Institute, Universitéde Montréal, Montreal H3A OE8, Canada
| | - David Cox
- MIT-IBM Watson AI Lab, Cambridge, MA 02139, U.S.A.
| | - Silvina Ponce Dawson
- Departamento de Física, FCEyN, UBA and IFIBA, CONICET, 1428 Buenos Aires, Argentina
| | - Guillermo Cecchi
- IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, U.S.A.
| | - Irina Rish
- Mila-Quebec Artificial Intelligence Institute, Université de Montréal, Montreal H3A OE8, Canada
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18
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Spitzner FP, Dehning J, Wilting J, Hagemann A, P. Neto J, Zierenberg J, Priesemann V. MR. Estimator, a toolbox to determine intrinsic timescales from subsampled spiking activity. PLoS One 2021; 16:e0249447. [PMID: 33914774 PMCID: PMC8084202 DOI: 10.1371/journal.pone.0249447] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 03/18/2021] [Indexed: 11/23/2022] Open
Abstract
Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking activity, our toolbox is applicable to a wide range of systems where subsampling-the difficulty to observe the whole system in full detail-limits our capability to record. Applications range from epidemic spreading to any system that can be represented by an autoregressive process. In the context of neuroscience, the intrinsic timescale can be thought of as the duration over which any perturbation reverberates within the network; it has been used as a key observable to investigate a functional hierarchy across the primate cortex and serves as a measure of working memory. It is also a proxy for the distance to criticality and quantifies a system's dynamic working point.
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Affiliation(s)
- F. P. Spitzner
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. Dehning
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. Wilting
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - A. Hagemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. P. Neto
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - J. Zierenberg
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - V. Priesemann
- Max-Planck-Institute for Dynamics and Self-Organization, Göttingen, Germany
- Bernstein-Center for Computational Neuroscience (BCCN) Göttingen, Göttingen, Germany
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19
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Singh SH, Peterson SM, Rao RPN, Brunton BW. Mining naturalistic human behaviors in long-term video and neural recordings. J Neurosci Methods 2021; 358:109199. [PMID: 33910024 DOI: 10.1016/j.jneumeth.2021.109199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 04/07/2021] [Accepted: 04/19/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Recent technological advances in brain recording and machine learning algorithms are enabling the study of neural activity underlying spontaneous human behaviors, beyond the confines of cued, repeated trials. However, analyzing such unstructured data lacking a priori experimental design remains a significant challenge, especially when the data is multi-modal and long-term. NEW METHOD Here we describe an automated, behavior-first approach for analyzing simultaneously recorded long-term, naturalistic electrocorticography (ECoG) and behavior video data. We identify and characterize spontaneous human upper-limb movements by combining computer vision, discrete latent-variable modeling, and string pattern-matching on the video. RESULTS Our pipeline discovers and annotates over 40,000 instances of naturalistic arm movements in long term (7-9 day) behavioral videos, across 12 subjects. Analysis of the simultaneously recorded brain data reveals neural signatures of movement that corroborate previous findings. Our pipeline produces large training datasets for brain-computer interfacing applications, and we show decoding results from a movement initiation detection task. COMPARISON WITH EXISTING METHODS Spontaneous movements capture real-world neural and behavior variability that is missing from traditional cued tasks. Building beyond window-based movement detection metrics, our unsupervised discretization scheme produces a queryable pose representation, allowing localization of movements with finer temporal resolution. CONCLUSIONS Our work addresses the unique analytic challenges of studying naturalistic human behaviors and contributes methods that may generalize to other neural recording modalities beyond ECoG. We publish our curated dataset and believe that it will be a valuable resource for future studies of naturalistic movements.
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Affiliation(s)
- Satpreet H Singh
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA
| | - Steven M Peterson
- Department of Biology, University of Washington, Seattle, USA; eScience Institute, University of Washington, Seattle, USA
| | - Rajesh P N Rao
- Department of Electrical and Computer Engineering, University of Washington, Seattle, USA; Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, USA; Center for Neurotechnology, University of Washington, Seattle, USA; University of Washington Institute for Neuroengineering, Seattle, USA
| | - Bingni W Brunton
- Department of Biology, University of Washington, Seattle, USA; eScience Institute, University of Washington, Seattle, USA; University of Washington Institute for Neuroengineering, Seattle, USA.
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20
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Heiney K, Huse Ramstad O, Fiskum V, Christiansen N, Sandvig A, Nichele S, Sandvig I. Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation. Front Comput Neurosci 2021; 15:611183. [PMID: 33643017 PMCID: PMC7902700 DOI: 10.3389/fncom.2021.611183] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/18/2021] [Indexed: 01/03/2023] Open
Abstract
It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed "neuronal avalanches." The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.
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Affiliation(s)
- Kristine Heiney
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Vegard Fiskum
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Nicholas Christiansen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Clinical Neuroscience, Umeå University Hospital, Umeå, Sweden
- Department of Neurology, St. Olav's Hospital, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Oslo Metropolitan University, Oslo, Norway
- Department of Holistic Systems, Simula Metropolitan, Oslo, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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21
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Schaworonkow N, Voytek B. Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life. Dev Cogn Neurosci 2020; 47:100895. [PMID: 33316695 PMCID: PMC7734223 DOI: 10.1016/j.dcn.2020.100895] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/30/2020] [Accepted: 12/05/2020] [Indexed: 12/26/2022] Open
Abstract
Neuronal oscillations emerge in early human development. These periodic oscillations are thought to rapidly change in infancy and stabilize during maturity. Given their numerous connections to physiological and cognitive processes, understanding the trajectory of oscillatory development is important for understanding healthy human brain development. This understanding is complicated by recent evidence that assessment of periodic neuronal oscillations is confounded by aperiodic neuronal activity, an inherent feature of electrophysiological recordings. Recent cross-sectional evidence shows that this aperiodic signal progressively shifts from childhood through early adulthood, and from early adulthood into later life. None of these studies, however, have been performed in infants, nor have they been examined longitudinally. Here, we analyzed longitudinal non-invasive EEG data from 22 typically developing infants, ranging between 38 and 203 days old. We show that the progressive flattening of the EEG power spectrum begins in very early development, continuing through the first months of life. These results highlight the importance of separating the periodic and aperiodic neuronal signals, because the aperiodic signal can bias measurement of neuronal oscillations. Given the infrequent, bursting nature of oscillations in infants, we recommend using quantitative time domain approaches that isolate bursts and uncover changes in waveform properties of oscillatory bursts. We assess oscillatory and aperiodic activity in longitudinal infant EEG recordings. Infant EEG activity is predominantly of aperiodic nature. The aperiodic exponent shows a strong decrease in the first half year of life. We confirm a developmental increase in alpha-frequency of infant oscillatory bursts.
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Affiliation(s)
- Natalie Schaworonkow
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA; Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA; Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA; Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA
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22
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Gao R, van den Brink RL, Pfeffer T, Voytek B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. eLife 2020; 9:e61277. [PMID: 33226336 PMCID: PMC7755395 DOI: 10.7554/elife.61277] [Citation(s) in RCA: 104] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 11/22/2020] [Indexed: 12/21/2022] Open
Abstract
Complex cognitive functions such as working memory and decision-making require information maintenance over seconds to years, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex and are relevant for cognition in both short and long terms, bridging microcircuit physiology with macroscale dynamics and behavior.
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Affiliation(s)
- Richard Gao
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
| | - Ruud L van den Brink
- Section Computational Cognitive Neuroscience, Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Thomas Pfeffer
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu FabraBarcelonaSpain
| | - Bradley Voytek
- Department of Cognitive Science, University of California, San DiegoLa JollaUnited States
- Halıcıoğlu Data Science Institute, University of California, San DiegoLa JollaUnited States
- Neurosciences Graduate Program, University of California, San DiegoLa JollaUnited States
- Kavli Institute for Brain and Mind, University of California, San DiegoLa JollaUnited States
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23
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Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proc Natl Acad Sci U S A 2020; 117:20890-20897. [PMID: 32817467 DOI: 10.1073/pnas.2003383117] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Multimodal evidence suggests that brain regions accumulate information over timescales that vary according to anatomical hierarchy. Thus, these experimentally defined "temporal receptive windows" are longest in cortical regions that are distant from sensory input. Interestingly, spontaneous activity in these regions also plays out over relatively slow timescales (i.e., exhibits slower temporal autocorrelation decay). These findings raise the possibility that hierarchical timescales represent an intrinsic organizing principle of brain function. Here, using resting-state functional MRI, we show that the timescale of ongoing dynamics follows hierarchical spatial gradients throughout human cerebral cortex. These intrinsic timescale gradients give rise to systematic frequency differences among large-scale cortical networks and predict individual-specific features of functional connectivity. Whole-brain coverage permitted us to further investigate the large-scale organization of subcortical dynamics. We show that cortical timescale gradients are topographically mirrored in striatum, thalamus, and cerebellum. Finally, timescales in the hippocampus followed a posterior-to-anterior gradient, corresponding to the longitudinal axis of increasing representational scale. Thus, hierarchical dynamics emerge as a global organizing principle of mammalian brains.
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24
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Lendner JD, Helfrich RF, Mander BA, Romundstad L, Lin JJ, Walker MP, Larsson PG, Knight RT. An electrophysiological marker of arousal level in humans. eLife 2020; 9:e55092. [PMID: 32720644 PMCID: PMC7394547 DOI: 10.7554/elife.55092] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 07/06/2020] [Indexed: 12/13/2022] Open
Abstract
Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, 'wake-like' EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.
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Affiliation(s)
- Janna D Lendner
- Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
- Department of Anesthesiology and Intensive Care Medicine, University Medical Center TuebingenTuebingenGermany
| | - Randolph F Helfrich
- Hertie-Institute for Clinical Brain ResearchTuebingenGermany
- Department of Neurology and Epileptology, University Medical Center TuebingenTuebingenGermany
| | - Bryce A Mander
- Department of Psychiatry and Human Behavior, University of California, IrvineIrvineUnited States
| | - Luis Romundstad
- Department of Anesthesiology, University of Oslo Medical CenterOsloNorway
| | - Jack J Lin
- Department of Neurology, University of California, IrvineIrvineUnited States
| | - Matthew P Walker
- Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
| | - Pal G Larsson
- Department of Neurosurgery, University of Oslo Medical CenterOsloNorway
| | - Robert T Knight
- Helen Wills Neuroscience Institute, University of California, BerkeleyBerkeleyUnited States
- Department of Psychology, University of California, BerkeleyBerkeleyUnited States
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25
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Antiepileptic drugs induce subcritical dynamics in human cortical networks. Proc Natl Acad Sci U S A 2020; 117:11118-11125. [PMID: 32358198 DOI: 10.1073/pnas.1911461117] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Cortical network functioning critically depends on finely tuned interactions to afford neuronal activity propagation over long distances while avoiding runaway excitation. This importance is highlighted by the pathological consequences and impaired performance resulting from aberrant network excitability in psychiatric and neurological diseases, such as epilepsy. Theory and experiment suggest that the control of activity propagation by network interactions can be adequately described by a branching process. This hypothesis is partially supported by strong evidence for balanced spatiotemporal dynamics observed in the cerebral cortex; however, evidence of a causal relationship between network interactions and cortex activity, as predicted by a branching process, is missing in humans. Here this cause-effect relationship is tested by monitoring cortex activity under systematic pharmacological reduction of cortical network interactions with antiepileptic drugs. This study reports that cortical activity cascades, presented by the propagating patterns of epileptic spikes, as well as temporal correlations decline precisely as predicted for a branching process. The results provide a missing link to the branching process theory of cortical network function with implications for understanding the foundations of cortical excitability and its monitoring in conditions like epilepsy.
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26
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Veerakumar A, Tiruvadi V, Howell B, Waters AC, Crowell AL, Voytek B, Riva-Posse P, Denison L, Rajendra JK, Edwards JA, Bijanki KR, Choi KS, Mayberg HS. Field potential 1/ f activity in the subcallosal cingulate region as a candidate signal for monitoring deep brain stimulation for treatment-resistant depression. J Neurophysiol 2019; 122:1023-1035. [PMID: 31314668 PMCID: PMC6766743 DOI: 10.1152/jn.00875.2018] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 07/02/2019] [Accepted: 07/02/2019] [Indexed: 01/22/2023] Open
Abstract
Subcallosal cingulate cortex deep brain stimulation (SCC-DBS) is an experimental therapy for treatment-resistant depression (TRD). Refinement and optimization of SCC-DBS will benefit from increased study of SCC electrophysiology in context of ongoing high-frequency SCC-DBS therapy. The study objective was a 7-mo observation of frequency-domain 1/f slope in off-stimulation local field potentials (SCC-LFPs) alongside standardized measurements of depression severity in 4 patients undergoing SCC-DBS. SCC was implanted bilaterally with a combined neurostimulation-LFP recording system. Following a 1-mo off-stimulation postoperative phase with multiple daily recordings, patients received bilateral SCC-DBS therapy (130 Hz, 90 μs) and weekly resting-state SCC-LFP recordings over a 6-mo treatment phase. 1/f slopes for each time point were estimated via linear regression of log-transformed Welch periodograms. General linear mixed-effects models were constructed to estimate pretreatment sources of 1/f slope variance, and 95% bootstrap confidence intervals were constructed to estimate treatment phase 1/f slope association with treatment response (50% decrease in preimplantation symptom severity). Results show the time of recording was a prominent source of pretreatment 1/f slope variance bilaterally, with increased 1/f slope magnitude observed during night hours (2300-0659). Increase in right 1/f slope was observed in the setting of treatment response, with bootstrap analysis supporting this observation in 3 of 4 subjects. We conclude that 1/f slope can be measured longitudinally in a combined SCC-DBS/LFP recording system and likely conforms to known 1/f circadian variability. The preliminary evidence of 1/f slope increase during treatment response suggests a potential utility as a candidate biomarker for ongoing development of adaptive TRD-neuromodulation strategies.NEW & NOTEWORTHY In four patients with treatment-resistant depression undergoing therapeutic deep brain stimulation (DBS), we present the first longitudinal observations of local field potentials (LFP) from the subcallosal cingulate region outside the postoperative period. Specifically, our results demonstrate that frequency-domain 1/f activity is measurable in a combined DBS-LFP recording system and that right hemisphere recordings appear sensitive to mood state, thus suggesting a potential readout suitable for consideration in ongoing efforts to develop adaptive DBS delivery systems.
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Affiliation(s)
- Ashan Veerakumar
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
| | - Vineet Tiruvadi
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia
| | - Bryan Howell
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Allison C Waters
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrea L Crowell
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
| | - Bradley Voytek
- Department of Cognitive Science, University of California San Diego, La Jolla, California
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
| | - Lydia Denison
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
| | - Justin K Rajendra
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Scientific and Statistical Computational Core. National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
| | - Johnathan A Edwards
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia
| | - Kelly R Bijanki
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Department of Neurosurgery, Emory University, Atlanta, Georgia
| | - Ki Sueng Choi
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Helen S Mayberg
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, Georgia
- Center for Advanced Circuit Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York
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27
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Okun M, Steinmetz NA, Lak A, Dervinis M, Harris KD. Distinct Structure of Cortical Population Activity on Fast and Infraslow Timescales. Cereb Cortex 2019; 29:2196-2210. [PMID: 30796825 PMCID: PMC6458908 DOI: 10.1093/cercor/bhz023] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/26/2019] [Accepted: 01/28/2019] [Indexed: 12/20/2022] Open
Abstract
Cortical activity is organized across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms-1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the structure of individual neurons' spiking activity and its coupling to local population rate and to arousal level across 0.01-100 Hz frequency range. In mouse medial prefrontal cortex, the spiking dynamics of individual neurons could be quantitatively captured by a combination of interspike interval and firing rate power spectrum distributions. The relative strength of coherence with local population often differed across timescales: a neuron strongly coupled to population rate on fast timescales could be weakly coupled on slow timescales, and vice versa. On slow but not fast timescales, a substantial proportion of neurons showed firing anticorrelated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of individual neurons' population coupling strength. These observations demonstrate how neurons simultaneously partake in fast local dynamics, and slow brain-wide dynamics, extending our understanding of infraslow cortical activity beyond the mesoscale resolution of fMRI.
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Affiliation(s)
- Michael Okun
- Centre for Systems Neuroscience and Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
- Institute of Neurology, University College London, London, UK
| | | | - Armin Lak
- Institute of Neurology, University College London, London, UK
| | - Martynas Dervinis
- Centre for Systems Neuroscience and Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
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28
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Demirtaş M, Burt JB, Helmer M, Ji JL, Adkinson BD, Glasser MF, Van Essen DC, Sotiropoulos SN, Anticevic A, Murray JD. Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics. Neuron 2019; 101:1181-1194.e13. [PMID: 30744986 PMCID: PMC6447428 DOI: 10.1016/j.neuron.2019.01.017] [Citation(s) in RCA: 203] [Impact Index Per Article: 40.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 12/04/2018] [Accepted: 01/10/2019] [Indexed: 01/20/2023]
Abstract
The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical levels of cortical organization. We developed a large-scale dynamical circuit model of human cortex that incorporates heterogeneity of local synaptic strengths, following a hierarchical axis inferred from magnetic resonance imaging (MRI)-derived T1- to T2-weighted (T1w/T2w) mapping and fit the model using multimodal neuroimaging data. We found that incorporating hierarchical heterogeneity substantially improves the model fit to functional MRI (fMRI)-measured resting-state functional connectivity and captures sensory-association organization of multiple fMRI features. The model predicts hierarchically organized higher-frequency spectral power, which we tested with resting-state magnetoencephalography. These findings suggest circuit-level mechanisms linking spatiotemporal levels of analysis and highlight the importance of local properties and their hierarchical specialization on the large-scale organization of human cortical dynamics.
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Affiliation(s)
- Murat Demirtaş
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Joshua B. Burt
- Department of Physics, Yale University, New Haven, CT, USA,These authors contributed equally
| | - Markus Helmer
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,These authors contributed equally
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Brendan D. Adkinson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Matthew F. Glasser
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, USA,St. Luke’s Hospital, Saint Louis, MO, USA
| | - David C. Van Essen
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, USA
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK,Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,Department of Physics, Yale University, New Haven, CT, USA,Lead Contact,Correspondence:
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29
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Zhang X, Su J, Gao C, Ni W, Gao X, Li Y, Zhang J, Lei Y, Gu Y. Progression in Vascular Cognitive Impairment: Pathogenesis, Neuroimaging Evaluation, and Treatment. Cell Transplant 2019; 28:18-25. [PMID: 30488737 PMCID: PMC6322135 DOI: 10.1177/0963689718815820] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Vascular cognitive impairment (VCI) defines an entire spectrum of neurologic disorders from mild cognitive impairment to dementia caused by cerebral vascular disease. The pathogenesis of VCI includes ischemic factors (e.g., large vessel occlusion and small vessel dysfunction); hemorrhagic factors (e.g., intracerebral hemorrhage and subarachnoid hemorrhage); and other factors (combined with Alzheimer's disease). Clinical evaluations of VCI mainly refer to neuropsychological testing and imaging assessments, including structural and functional neuroimaging, with different advantages. At present, the main treatment for VCI focuses on neurological protection, cerebral blood flow reconstruction, and neurological rehabilitation, such as pharmacological treatment, revascularization, and cognitive training. In this review, we discuss the pathogenesis, neuroimaging evaluation, and treatment of VCI.
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Affiliation(s)
- Xin Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Chao Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Xinjie Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Yu Lei and Yuxiang Gu, Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, No. 12 Middle Wulumuqi Road, Shanghai 200040, China. Emails: ;
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
- Yu Lei and Yuxiang Gu, Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, No. 12 Middle Wulumuqi Road, Shanghai 200040, China. Emails: ;
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30
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Kosmidis EK, Contoyiannis YF, Papatheodoropoulos C, Diakonos FK. Traits of criticality in membrane potential fluctuations of pyramidal neurons in the CA1 region of rat hippocampus. Eur J Neurosci 2018; 48:2343-2353. [PMID: 30117214 DOI: 10.1111/ejn.14117] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 07/05/2018] [Accepted: 08/07/2018] [Indexed: 01/19/2023]
Abstract
Evidence that neural circuits are operating near criticality has been provided at various levels of brain organisation with a presumed role in maximising information processing and multiscale activity association. Criticality has been linked to excitation at both the single-cell and network levels, as action potential generation marks an obvious phase transition from a resting to an excitable state. Using in vitro intracellular recordings, we examine irregular, small amplitude membrane potential fluctuations from CA1 pyramidal neurons of Wistar male rats. We show that these fluctuations, confounded with noise, carry information relevant to the neuronal state. The underlying dynamics exhibit intermittent characteristics shown to describe the temporal fluctuations of the order parameter of a macroscopic system at its critical point even in the absence of firing. An externally applied stimulus serves as the control parameter, driving the system in and out of its critical state. Based on our experimental observations we calculate the equivalent of the isothermal critical exponent δh finding a value which depends on the applied stimulus. For each neuron there is a stimulus amplitude for which the critical behaviour becomes most pronounced. The corresponding mean value of δh in the considered ensemble of neurons is δh ≈ 1.89, close to theoretical predictions for critical networks. Finally, we show that the firing rate of a neuron decreases exponentially with δh .
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Affiliation(s)
- Efstratios K Kosmidis
- Department of Medicine, Laboratory of Physiology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Yiannis F Contoyiannis
- Department of Electrical and Electronics Engineering, University of West Attica, Aigaleo, Athens, Greece
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