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      Multi-scale neural decoding and analysis

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          Abstract

          Objective.

          Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment.

          Approach.

          We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes.

          Main results.

          We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches.

          Significance.

          This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

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          Most cited references268

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          Theta oscillations in the hippocampus.

          Theta oscillations represent the "on-line" state of the hippocampus. The extracellular currents underlying theta waves are generated mainly by the entorhinal input, CA3 (Schaffer) collaterals, and voltage-dependent Ca(2+) currents in pyramidal cell dendrites. The rhythm is believed to be critical for temporal coding/decoding of active neuronal ensembles and the modification of synaptic weights. Nevertheless, numerous critical issues regarding both the generation of theta oscillations and their functional significance remain challenges for future research.
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            The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes.

            Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.
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              Fully integrated silicon probes for high-density recording of neural activity

              Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal–oxide–semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.
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                Author and article information

                Journal
                101217933
                32339
                J Neural Eng
                J Neural Eng
                Journal of neural engineering
                1741-2560
                1741-2552
                3 February 2022
                16 August 2021
                16 August 2021
                16 August 2022
                : 18
                : 4
                : 10.1088/1741-2552/ac160f
                Affiliations
                [1 ]The University of Texas at Austin, Biomedical Engineering, Austin, TX, United States of America
                [2 ]The University of Texas at Austin, Psychology, Austin, TX, United States of America
                [3 ]Rice University, Electrical and Computer Engineering, Houston, TX, United States of America
                [4 ]The University of Texas at Austin, Mechanical Engineering, Austin, TX, United States of America
                [5 ]University of Zurich, Neuroeconomics and Social Neuroscience, Zurich, Switzerland
                [6 ]The University of Texas at Austin, Neurology, Austin, TX, United States of America
                [7 ]University of Washington, Electrical and Computer Engineering, Seattle, WA, United States of America
                [8 ]University of Washington, Bioengineering, Seattle, WA, United States of America
                [9 ]The University of Texas at Austin, Institute for Neuroscience, Austin, TX, United States of America
                [10 ]Washington National Primate Research Center, Seattle, WA, United States of America
                Author notes
                [* ]Author to whom any correspondence should be addressed.: srsantacruz@ 123456utexas.edu
                Author information
                http://orcid.org/0000-0002-8951-773X
                http://orcid.org/0000-0003-4131-5781
                http://orcid.org/0000-0002-7352-5081
                Article
                NIHMS1773145
                10.1088/1741-2552/ac160f
                8840800
                34284369
                cd252cd3-5de9-4560-b57d-0050b6996593

                Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence.

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                Categories
                Article

                multi-scale analyses,neural decoding,electrophysiology,functional imaging

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