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Skaar : son of Hulk - the complete collection
Born in fire. Raised by monsters. Destined to smash! On an alien planet shattered by war, no one is stronger than Skaar -- the savage Son of Hulk! But as a warlord and a princess spread chaos through the wastelands, will Skaar save the puny survivors -- or eat them? Skaar seeks the mysterious Old Power, but can even he stop the coming of the Silver Surfer-and Galactus the Devourer? The soothsayers sing: One day, monsters will clash -- the boy will confront the man who abandoned him. When the Son of Hulk seeks vengeance on his father, will Earth be turned into Planet Skaar?
BOOK
Large-scale single-neuron speech sound encoding across the depth of human cortex
Understanding the neural basis of speech perception requires that we study the human brain both at the scale of the fundamental computational unit of neurons and in their organization across the depth of cortex. Here we used high-density Neuropixels arrays
1
–
3
to record from 685 neurons across cortical layers at nine sites in a high-level auditory region that is critical for speech, the superior temporal gyrus
4
,
5
, while participants listened to spoken sentences. Single neurons encoded a wide range of speech sound cues, including features of consonants and vowels, relative vocal pitch, onsets, amplitude envelope and sequence statistics. Neurons at each cross-laminar recording exhibited dominant tuning to a primary speech feature while also containing a substantial proportion of neurons that encoded other features contributing to heterogeneous selectivity. Spatially, neurons at similar cortical depths tended to encode similar speech features. Activity across all cortical layers was predictive of high-frequency field potentials (electrocorticography), providing a neuronal origin for macroelectrode recordings from the cortical surface. Together, these results establish single-neuron tuning across the cortical laminae as an important dimension of speech encoding in human superior temporal gyrus.
High-density single-neuron recordings show diverse tuning for acoustic and phonetic features across layers in human auditory speech cortex.
Journal Article
A hippocampal network for spatial coding during immobility and sleep
In the mammalian navigational system, neurons have been identified in the CA2 region of the hippocampus that keep track of position when an animal is not moving.
How does an animal know where it is when it stops moving? Hippocampal place cells fire at discrete locations as subjects traverse space, thereby providing an explicit neural code for current location during locomotion. In contrast, during awake immobility, the hippocampus is thought to be dominated by neural firing representing past and possible future experience. The question of whether and how the hippocampus constructs a representation of current location in the absence of locomotion has been unresolved. Here we report that a distinct population of hippocampal neurons, located in the CA2 subregion, signals current location during immobility, and does so in association with a previously unidentified hippocampus-wide network pattern. In addition, signalling of location persists into brief periods of desynchronization prevalent in slow-wave sleep. The hippocampus thus generates a distinct representation of current location during immobility, pointing to mnemonic processing specific to experience occurring in the absence of locomotion.
Knowing your place
Decades of research have shown that the mammalian navigational system, based in and around the hippocampus, keeps track of an animal's location during locomotion. Yet it was not clear whether and how the brain tracks location when animals stop moving. This study shows that the brain keeps track of location at all times, on the move and at rest. Loren Frank and colleagues identify a distinct population of hippocampal neurons in the CA2 region of the hippocampus that signal current location not only during awake immobility, but also during sleep.
Journal Article
Ultraflexible electrode arrays for months-long high-density electrophysiological mapping of thousands of neurons in rodents
Penetrating flexible electrode arrays can simultaneously record thousands of individual neurons in the brains of live animals. However, it has been challenging to spatially map and longitudinally monitor the dynamics of large three-dimensional neural networks. Here we show that optimized ultraflexible electrode arrays distributed across multiple cortical regions in head-fixed mice and in freely moving rats allow for months-long stable electrophysiological recording of several thousand neurons at densities of about 1,000 neural units per cubic millimetre. The chronic recordings enhanced decoding accuracy during optogenetic stimulation and enabled the detection of strongly coupled neuron pairs at the million-pair and millisecond scales, and thus the inference of patterns of directional information flow. Longitudinal and volumetric measurements of neural couplings may facilitate the study of large-scale neural circuits.
Optimized ultraflexible electrode arrays enable months-long electrophysiological recordings of several thousand neurons at densities of up to 1,000 neural units per cubic millimetre.
Journal Article
Hippocampal replay of experience at real-world speeds
Representations related to past experiences play a critical role in memory and decision-making processes. The rat hippocampus expresses these types of representations during sharp-wave ripple (SWR) events, and previous work identified a minority of SWRs that contain ‘replay’ of spatial trajectories at ∼20x the movement speed of the animal. Efforts to understand replay typically make multiple assumptions about which events to examine and what sorts of representations constitute replay. We therefore lack a clear understanding of both the prevalence and the range of representational dynamics associated with replay. Here, we develop a state space model that uses a combination of movement dynamics of different speeds to capture the spatial content and time evolution of replay during SWRs. Using this model, we find that the large majority of replay events contain spatially coherent, interpretable content. Furthermore, many events progress at real-world, rather than accelerated, movement speeds, consistent with actual experiences.
Journal Article
Bidirectional propagation of low frequency oscillations over the human hippocampal surface
The hippocampus is diversely interconnected with other brain systems along its axis. Cycles of theta-frequency activity are believed to propagate from the septal to temporal pole, yet it is unclear how this one-way route supports the flexible cognitive capacities of this structure. We leveraged novel thin-film microgrid arrays conformed to the human hippocampal surface to track neural activity two-dimensionally in vivo. All oscillation frequencies identified between 1–15 Hz propagated across the tissue. Moreover, they dynamically shifted between two roughly opposite directions oblique to the long axis. This predominant propagation axis was mirrored across participants, hemispheres, and consciousness states. Directionality was modulated in a participant who performed a behavioral task, and it could be predicted by wave amplitude topography over the hippocampal surface. Our results show that propagation directions may thus represent distinct meso-scale network computations, operating along versatile spatiotemporal processing routes across the hippocampal body.
New microgrid recordings on the human hippocampal surface reveal that oscillations travel in reversing directions. The route of travel at a given moment was related to behavior and topographic patterns of activity strength, suggesting directions may be biomarkers of hippocampal cognitive processes.
Journal Article
Regulation of glucose homeostasis through a XBP-1–FoxO1 interaction
Insulin dials down endogenous hepatic glucose production after a meal by deactivating the transcription factor FoxO1. In a mouse model of insulin resistance, Umut Ozcan and his colleagues now show that hepatic overexpression of Xbp-1s, a factor involved in the cell stress response, leads to the protein degradation of FoxO1, thus reducing serum glucose levels. These results suggest a way to bypass one aspect of insulin resistance.
To date, the only known role of the spliced form of X-box–binding protein-1 (XBP-1s) in metabolic processes has been its ability to act as a transcription factor that regulates the expression of genes that increase the endoplasmic reticulum (ER) folding capacity, thereby improving insulin sensitivity. Here we show that XBP-1s interacts with the Forkhead box O1 (FoxO1) transcription factor and directs it toward proteasome-mediated degradation. Given this new insight, we tested modest hepatic overexpression of XBP-1s
in vivo
in mouse models of insulin deficiency or insulin resistance and found it improved serum glucose concentrations, even without improving insulin signaling or ER folding capacity. The notion that XBP-1s can act independently of its role in the ER stress response is further supported by our finding that in the severely insulin resistant
ob/ob
mouse strain a DNA-binding–defective mutant of XBP-1s, which does not have the ability to increase ER folding capacity, is still capable of reducing serum glucose concentrations and increasing glucose tolerance. Our results thus provide the first evidence to our knowledge that XBP-1s, through its interaction with FoxO1, can bypass hepatic insulin resistance independent of its effects on ER folding capacity, suggesting a new therapeutic approach for the treatment of type 2 diabetes.
Journal Article
DREDge: robust motion correction for high-density extracellular recordings across species
High-density microelectrode arrays have opened new possibilities for systems neuroscience, but brain motion relative to the array poses challenges for downstream analyses. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from action potential data, DREDge enables automated, high-temporal-resolution motion tracking in local field potential data. In human intraoperative recordings, DREDge’s local field potential-based tracking reliably recovered evoked potentials and single-unit spike sorting. In recordings of deep probe insertions in nonhuman primates, DREDge tracked motion across centimeters of tissue and several brain regions while mapping single-unit electrophysiological features. DREDge reliably improved motion correction in acute mouse recordings, especially in those made with a recent ultrahigh-density probe. Applying DREDge to recordings from chronic implantations in mice yielded stable motion tracking despite changes in neural activity between experimental sessions. These advances enable automated, scalable registration of electrophysiological data across species, probes and drift types, providing a foundation for downstream analyses of these rich datasets.
DREDge is a software tool for motion correction of high-density electrophysiology recordings. It can handle action potential or local field potential data and is demonstrated on a variety of acute or chronic recordings from humans, nonhuman primates and mice.
Journal Article
Distinct hippocampal-cortical memory representations for experiences associated with movement versus immobility
While ongoing experience proceeds continuously, memories of past experience are often recalled as episodes with defined beginnings and ends. The neural mechanisms that lead to the formation of discrete episodes from the stream of neural activity patterns representing ongoing experience are unknown. To investigate these mechanisms, we recorded neural activity in the rat hippocampus and prefrontal cortex, structures critical for memory processes. We show that during spatial navigation, hippocampal CA1 place cells maintain a continuous spatial representation across different states of motion (movement and immobility). In contrast, during sharp-wave ripples (SWRs), when representations of experience are transiently reactivated from memory, movement- and immobility-associated activity patterns are most often reactivated separately. Concurrently, distinct hippocampal reactivations of movement- or immobility-associated representations are accompanied by distinct modulation patterns in prefrontal cortex. These findings demonstrate a continuous representation of ongoing experience can be separated into independently reactivated memory representations.
Journal Article
Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery
Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, protein structures have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective and are considered the gold standard, they are very resource-intensive and time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology and artificial intelligence (AI), the field of protein prediction has been revolutionized. Innovations like AlphaFold and RoseTTAFold enable protein structure predictions to be made directly from amino acid sequences with remarkable speed and accuracy. Despite the enormous enthusiasm associated with these newly developed AI-approaches, their true potential in structure-based drug discovery remains uncertain. In fact, although these algorithms generally predict overall protein structures well, essential details for computational ligand docking, such as the exact location of amino acid side chains within the binding pocket, are not predicted with the necessary accuracy. Additionally, docking methodologies are considered more as a hypothesis generator rather than a precise predictor of ligand–target interactions, and thus, usually identify many false-positive hits among only a few correctly predicted interactions. In this paper, we are reviewing the latest development in this cutting-edge field with emphasis on the GPCR target class to assess the potential role of AI approaches in structure-based drug discovery.
Journal Article