ORCID Profile
0000-0003-1367-8606
Current Organisation
Stanford University
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Astronomical and Space Sciences | Cosmology and Extragalactic Astronomy
Expanding Knowledge in the Physical Sciences | Expanding Knowledge in the Information and Computing Sciences | The Media | Expanding Knowledge in the Mathematical Sciences |
Publisher: Society for Neuroscience
Date: 03-11-2021
DOI: 10.1523/JNEUROSCI.1311-21.2021
Abstract: Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior (e.g., hearing a siren, we expect to see an ambulance and quickly make way). While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling, and machine learning, we show that inferred “causal” relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audiovisual delayed match-to-s le task which elicited predictions. Predictive β feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, an NMDAR blocker, but not the control drug dexmedetomidine, perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex, and pre-activation of sensory templates in higher-order sensory cortex. This study suggests that predictions depend on alpha activity in higher-order cortex, β feedback, and NMDARs, and ketamine blocks access to learned predictive information. SIGNIFICANCE STATEMENT We learn the statistical regularities around us, creating associations between sensory stimuli. These associations can be exploited by generating predictions, which enable fast and efficient behavior. When predictions are perturbed, it can negatively influence perception and even contribute to psychiatric disorders, such as schizophrenia. Here we show that the frontal lobe generates predictions and sends them to posterior brain areas, to activate representations of predicted sensory stimuli before their appearance. Oscillations in neural activity (α and β waves) are vital for these predictive mechanisms. The drug ketamine blocks predictions and the underlying mechanisms. This suggests that the generation of predictions in the frontal lobe, and the feedback pre-activating sensory representations in advance of stimuli, depend on NMDARs.
Publisher: Cold Spring Harbor Laboratory
Date: 09-04-2020
DOI: 10.1101/2020.04.07.030429
Abstract: Major theories of consciousness disagree on the key neural substrates. In Global Neuronal Workspace Theory and Higher-order Theories, consciousness depends on frontal cortex, whereas Integrated Information Theory and Recurrent Processing Theory highlight posterior contributions. Most theories omit subcortical influences. To test these theories, we performed simultaneous frontal, parietal, striatal and thalamic recordings from awake, sleeping and anesthetized macaques, further manipulating consciousness with deep-brain thalamic stimulation. Information theoretic measures and machine learning approaches suggested parietal cortex, striatum and thalamus contribute more to consciousness level than frontal cortex. While these findings provide greater support for Integrated Information Theory than the others, the theory does not incorporate subcortical structures such as the striatum. We therefore propose that thalamo-striatal circuits have a cause-effect structure to generate integrated information. Parietal, but not frontal, circuits incorporating striatum and thalamus predict consciousness.
Publisher: Elsevier BV
Date: 04-2020
Publisher: Cold Spring Harbor Laboratory
Date: 10-2019
DOI: 10.1101/776591
Abstract: Consciousness is the capacity to experience one’s environment and internal states. The minimal mechanisms sufficient to produce this experience, the neural correlates of consciousness (NCC), are thought to involve thalamocortical and intracortical interactions, but the key operations and circuit paths are unclear. We simultaneously recorded neural activity in central thalamus and across layers of fronto-parietal cortex in awake, sleeping and anesthetized macaques. Spiking activity was selectively reduced in deep cortical layers and thalamus during unconsciousness, as were intracolumnar and interareal interactions at alpha and gamma frequencies. Gamma-frequency stimulation, when focused on the central lateral thalamus of anesthetized macaques, counteracted these neural changes and restored consciousness. These findings suggest that the NCC involve both corticocortical feedforward and feedback pathways coordinated with intracolumnar and thalamocortical loops. Stimulation of central lateral thalamus counters anesthesia to restore wake cortical dynamics and consciousness.
Publisher: Elsevier BV
Date: 09-2021
Publisher: Public Library of Science (PLoS)
Date: 11-07-2022
DOI: 10.1371/JOURNAL.PCBI.1010294
Abstract: Anesthetic manipulations provide much-needed causal evidence for neural correlates of consciousness, but non-specific drug effects complicate their interpretation. Evidence suggests that thalamic deep brain stimulation (DBS) can either increase or decrease consciousness, depending on the stimulation target and parameters. The putative role of the central lateral thalamus (CL) in consciousness makes it an ideal DBS target to manipulate circuit-level mechanisms in cortico-striato-thalamic (CST) systems, thereby influencing consciousness and related processes. We used multi-microelectrode DBS targeted to CL in macaques while recording from frontal, parietal, and striatal regions. DBS induced episodes of abnormally long, vacant staring with low-frequency oscillations here termed vacant, perturbed consciousness (VPC). DBS modulated VPC likelihood in a frequency-specific manner. VPC events corresponded to decreases in measures of neural complexity (entropy) and integration (Φ*), proposed indices of consciousness, and substantial changes to communication in CST circuits. During VPC, power spectral density and coherence at low frequencies increased across CST circuits, especially in thalamo-parietal and cortico-striatal pathways. Decreased consciousness and neural integration corresponded to shifts in cortico-striatal network configurations that dissociated parietal and subcortical structures. Overall, the features of VPC and implicated networks were similar to those of absence epilepsy. As this same multi-microelectrode DBS method–but at different stimulation frequencies–can also increase consciousness in anesthetized macaques, it can be used to flexibly address questions of consciousness with limited confounds, as well as inform clinical investigations of other consciousness disorders.
Publisher: Cold Spring Harbor Laboratory
Date: 07-01-2020
DOI: 10.1101/2020.01.06.896589
Abstract: Learned associations between stimuli allow us to model the world and make predictions, crucial for efficient behavior e.g., hearing a siren, we expect to see an ambulance and quickly make way. While there are theoretical and computational frameworks for prediction, the circuit and receptor-level mechanisms are unclear. Using high-density EEG, Bayesian modeling and machine learning, we show that inferred “causal” relationships between stimuli and frontal alpha activity account for reaction times (a proxy for predictions) on a trial-by-trial basis in an audio-visual delayed match-to-s le task which elicited predictions. Predictive beta feedback activated sensory representations in advance of predicted stimuli. Low-dose ketamine, a NMDA receptor blocker – but not the control drug dexmedetomidine – perturbed behavioral indices of predictions, their representation in higher-order cortex, feedback to posterior cortex and pre-activation of sensory templates in higher-order sensory cortex. This study suggests predictions depend on alpha activity in higher-order cortex, beta feedback and NMDA receptors, and ketamine blocks access to learned predictive information.
Publisher: Elsevier BV
Date: 08-2023
Publisher: Copernicus GmbH
Date: 27-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-2459
Abstract: & & South-East Asia hosts the largest and most complicated subduction system of our planet, associated with extensive volcanism and seismicity. Obtaining high-resolution seismic images of South-East Asia can provide important constraints on the lateral variations of physical parameters such as density, composition, temperature, and viscosity of this dynamic patchwork. In turn, this has relevant implications on our ability to forecast its geodynamic evolution by numerical modeling. In this study, we join all the publicly-available seismic data distributed across the Malay Peninsula, Sumatra, Java, Sulawesi, South Borneo, and North Australia (amounting of 468 broad-band seismic receivers) with the continuous seismograms from 70 receivers recently installed in North Borneo, resulting in an unprecedented seismic coverage of the region. & br& We first use such data to extract Rayleigh and Love phase velocities based on both seismic ambient noise and teleseismic earthquakes. Overall, we retrieve 14,036 Rayleigh- and 12,005 Love-wave dispersion curves, covering surface-wave periods between 3 and 150 s and sensitive to both the shallow crust and the upper mantle. We then invert the dispersion curves for phase-velocity maps at different periods, using a linearized-inversion algorithm based on the ray theory with a roughness d ing constraint. In doing so, we adopt an adaptive parameterization, allowing for a finer resolution of the resulting maps in the areas characterized by a relatively high density of measurements. At relatively short periods (& s), the phase-velocity maps are characterized by strong lateral heterogeneities. We find, for ex le, relatively low velocities in correspondence of the Central- and South-Sumatra Basin, ascribed to thick sedimentary layers, and higher velocities in the (adjacent) Barisan Mountains. Low velocities also characterize a large region approximately centered onto the Merapi volcano (Central Java), the Mentawai islands (in correspondence of the Mentawai Fault System), the Sahul Shelf (including the East Timor island), and the marine region between east Borneo and Sulawesi. Relatively high velocities are found below the Banda Sea. The litude of such lateral variations quickly decreases at larger periods and, among the most pronounced features, we observe relatively low velocities in the north-east of Borneo (as opposed to its south-western part), and high velocities in the Celebes Sea (north of the North-Sulawesi Trench). & br& At the time of writing, we are planning to translate the phase-velocity maps thus retrieved into shear-wave velocity (Vs) as a function of depth. Specifically, we plan to extract one Rayleigh- and one Love-wave phase-velocity profile for each grid cell constituting our phase-velocity maps, and (non-linearly) jointly invert them for Vs using the neighbourhood algorithm. The resulting 3-D tomographic model will thus be interpreted in light of the existing literature on the study area, involving (but not limited to) geodynamic and geologic models, geophysical, geochemical, and geodetic observations.& &
Publisher: Elsevier BV
Date: 04-2019
Publisher: Cold Spring Harbor Laboratory
Date: 27-07-2021
DOI: 10.1101/2021.07.27.453855
Abstract: Anesthetic manipulations provide much-needed causal evidence for neural correlates of consciousness, but non-specific drug effects complicate their interpretation. Evidence suggests that thalamic deep brain stimulation (DBS) can either increase or decrease consciousness, depending on the stimulation target and parameters. The putative role of the central lateral thalamus (CL) in consciousness makes it an ideal DBS target to manipulate circuit-level mechanisms in cortico-striato-thalamic (CST) systems, thereby influencing consciousness and related processes. We used multi-microelectrode DBS targeted to CL in macaques while recording from frontal, parietal, and striatal regions. DBS induced episodes reminiscent of absence epilepsy, here termed absence-like activity (ALA), with decreased behavior and vacant staring coinciding with low-frequency oscillations. DBS modulated ALA likelihood in a frequency-specific manner. ALA events corresponded to decreases in measures of neural complexity (entropy) and integration (Φ*), an index of consciousness, and substantial changes to communication in CST circuits. During ALA, power spectral density and coherence at low frequencies increased across CST circuits, especially in thalamo-parietal and cortico-striatal pathways. Decreased consciousness and neural integration corresponded to shifts in cortico-striatal network configurations that dissociated parietal and subcortical structures. Overall, the features of ALA and implicated networks were similar to those of absence epilepsy. As this same multi-microelectrode DBS method – but at different stimulation frequencies – can also increase consciousness in anesthetized macaques, it can be used to flexibly address questions of consciousness with limited confounds, as well as inform clinical investigations of absence epilepsy and other consciousness disorders. We use tailored, multi-microelectrode thalamic deep brain stimulation to reversibly decrease consciousness for otherwise healthy, wakeful animals in a stimulation frequency-dependent manner. This represents a bidirectional mechanism for controlling consciousness, as the same method can increase consciousness under certain conditions. Theories of consciousness debate the relative contribution of parietal and frontal lobes, and largely ignore subcortical contributions. In this study, changes in consciousness predominantly involve changes in subcortical and parietal regions, implying that they contribute more to consciousness than frontal regions. Further, decreases in consciousness (indexed by Φ*) coincide with decreased movement, staring, and low-frequency activity in the EEG, similar to absence epilepsy. Thus, the systems-level mechanisms for decreased consciousness in this study have broader clinical implications for absence epilepsy.
Publisher: Elsevier BV
Date: 04-2021
Start Date: 2011
End Date: 2014
Amount: $265,000.00
Funder: Australian Research Council
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