Multi-shanks SiNAPS Active Pixel Sensor CMOS probe: 1024 simultaneously recording channels for high-density intracortical brain mapping

AbstractThe advent of implantable active dense CMOS neural probes opened a new era for electrophysiology in neuroscience. These single shank electrode arrays, and the emerging tailored analysis tools, provide for the first time to neuroscientists the neurotechnology means to spatiotemporally resolve the activity of hundreds of different single-neurons in multiple vertically aligned brain structures. However, while these unprecedented experimental capabilities to study columnar brain properties are a big leap forward in neuroscience, there is the need to spatially distribute electrodes also horizontally. Closely spacing and consistently placing in well-defined geometrical arrangement multiple isolated single-shank probes is methodologically and economically impractical. Here, we present the first high-density CMOS neural probe with multiple shanks integrating thousand’s of closely spaced and simultaneously recording microelectrodes to map neural activity across 2D lattice. Taking advantage from the high-modularity of our electrode-pixels-based SiNAPS technology, we realized a four shanks active dense probe with 256 electrode-pixels/shank and a pitch of 28 µm, for a total of 1024 simultaneously recording channels. The achieved performances allow for full-band, whole-array read-outs at 25 kHz/channel, show a measured input referred noise in the action potential band (300-7000 Hz) of 6.5 ± 2.1µVRMS, and a power consumption <6 µW/electrode-pixel. Preliminary recordings in awake behaving mice demonstrated the capability of multi-shanks SiNAPS probes to simultaneously record neural activity (both LFPs and spikes) from a brain area >6 mm2, spanning cortical, hippocampal and thalamic regions. High-density 2D array enables combining large population unit recording across distributed networks with precise intra- and interlaminar/nuclear mapping of the oscillatory dynamics. These results pave the way to a new generation of high-density and extremely compact multi-shanks CMOS-probes with tunable layouts for electrophysiological mapping of brain activity at the single-neurons resolution.

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What Neuroscientific Studies Tell Us about Inhibition of Return

Vision ◽

10.3390/vision3040058 ◽

2019 ◽

Vol 3 (4) ◽

pp. 58 ◽

Cited By ~ 2

Author(s):

Jason Satel ◽

Nicholas R. Wilson ◽

Raymond M. Klein

Keyword(s):

Inhibition Of Return ◽

Brain Activity ◽

Critical Role ◽

Direct Methods ◽

Brain Structures ◽

Developmental Studies ◽

Unit Recording ◽

Wide Range ◽

Lesion Studies

An inhibitory aftermath of orienting, inhibition of return (IOR), has intrigued scholars since its discovery about 40 years ago. Since then, the phenomenon has been subjected to a wide range of neuroscientific methods and the results of these are reviewed in this paper. These include direct manipulations of brain structures (which occur naturally in brain damage and disease or experimentally as in TMS and lesion studies) and measurements of brain activity (in humans using EEG and fMRI and in animals using single unit recording). A variety of less direct methods (e.g., computational modeling, developmental studies, etc.) have also been used. The findings from this wide range of methods support the critical role of subcortical and cortical oculomotor pathways in the generation and nature of IOR.

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High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

Nature Communications ◽

10.1038/s41467-021-25876-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Lucy L. W. Owen ◽

Thomas H. Chang ◽

Jeremy R. Manning

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Activity Patterns ◽

Dynamic Network ◽

Brain Structures ◽

High Order ◽

Dynamic Correlations ◽

Neuroimaging Data ◽

Human Participants ◽

High Level

AbstractOur thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

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The impact of D-cycloserine and sarcosine on in vivo frontal neural activity in a schizophrenia-like model

BMC Psychiatry ◽

10.1186/s12888-019-2306-1 ◽

2019 ◽

Vol 19 (1) ◽

Author(s):

Lulu Yao ◽

Zongliang Wang ◽

Di Deng ◽

Rongzhen Yan ◽

Jun Ju ◽

...

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Brain Slices ◽

Unit Recording ◽

Glycine Transporter 1 ◽

Glycine Transporter ◽

Neural Spiking ◽

In Vivo Calcium Imaging ◽

The Impact

Abstract Background N-methyl-D-aspartate receptor (NMDAR) hypofunction has been proposed to underlie the pathogenesis of schizophrenia. Specifically, reduced function of NMDARs leads to altered balance between excitation and inhibition which further drives neural network malfunctions. Clinical studies suggested that NMDAR modulators (glycine, D-serine, D-cycloserine and glycine transporter inhibitors) may be beneficial in treating schizophrenia patients. Preclinical evidence also suggested that these NMDAR modulators may enhance synaptic NMDAR function and synaptic plasticity in brain slices. However, an important issue that has not been addressed is whether these NMDAR modulators modulate neural activity/spiking in vivo. Methods By using in vivo calcium imaging and single unit recording, we tested the effect of D-cycloserine, sarcosine (glycine transporter 1 inhibitor) and glycine, on schizophrenia-like model mice. Results In vivo neural activity is significantly higher in the schizophrenia-like model mice, compared to control mice. D-cycloserine and sarcosine showed no significant effect on neural activity in the schizophrenia-like model mice. Glycine induced a large reduction in movement in home cage and reduced in vivo brain activity in control mice which prevented further analysis of its effect in schizophrenia-like model mice. Conclusions We conclude that there is no significant impact of the tested NMDAR modulators on neural spiking in the schizophrenia-like model mice.

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Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

10.1101/2020.04.13.039081 ◽

2020 ◽

Cited By ~ 2

Author(s):

James R. Stieger ◽

Stephen Engel ◽

Haiteng Jiang ◽

Christopher C. Cline ◽

Mary Jo Kreitzer ◽

...

Keyword(s):

Neural Activity ◽

Resting State ◽

Brain Activity ◽

Large Population ◽

Body Awareness ◽

Mindfulness Practice ◽

Control Group ◽

Alpha Band ◽

Attention Task ◽

Band Activity

AbstractBrain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals’ mindfulness practice as well as performance on a sustained attention task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients’ control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

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High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays

10.1101/242693 ◽

2018 ◽

Cited By ~ 6

Author(s):

Jason E. Chung ◽

Hannah R. Joo ◽

Jiang Lan Fan ◽

Daniel F. Liu ◽

Alex H. Barnett ◽

...

Keyword(s):

Large Scale ◽

Brain Activity ◽

Brain Regions ◽

Electrode Arrays ◽

Long Term Stability ◽

Electrophysiological Recordings ◽

Spatial Coverage ◽

Recording Channels ◽

Distributed Circuits ◽

Polymer Electrodes

AbstractThe brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to months. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track 25% of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

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High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

10.1101/763821 ◽

2019 ◽

Cited By ~ 2

Author(s):

Lucy L. W. Owen ◽

Thomas H. Chang ◽

Jeremy R. Manning

Keyword(s):

Neural Activity ◽

Brain Activity ◽

Activity Patterns ◽

Dynamic Network ◽

Brain Structures ◽

High Order ◽

Dynamic Correlations ◽

Neuroimaging Data ◽

Human Participants ◽

High Level

AbstractOur thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. We tested the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We developed an approach to estimating high-order dynamic correlations in timeseries data, and we applied the approach to neuroimaging data collected as human participants either listened to a ten-minute story or listened to a temporally scrambled version of the story. We trained across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We found that classifiers trained to decode from high-order dynamic correlations yielded the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

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Mindfulness Improves Brain–Computer Interface Performance by Increasing Control Over Neural Activity in the Alpha Band

Cerebral Cortex ◽

10.1093/cercor/bhaa234 ◽

2020 ◽

Vol 31 (1) ◽

pp. 426-438

Author(s):

James R Stieger ◽

Stephen Engel ◽

Haiteng Jiang ◽

Christopher C Cline ◽

Mary Jo Kreitzer ◽

...

Keyword(s):

Stress Reduction ◽

Neural Activity ◽

Resting State ◽

Brain Activity ◽

Large Population ◽

Body Awareness ◽

Mindfulness Practice ◽

Control Group ◽

Alpha Band ◽

Band Activity

Abstract Brain–computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind–body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here, we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in electroencephalogram signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals’ mindfulness practice as well as performance on a breath counting task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients' control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

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Faculty Opinions recommendation of Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13386958.14754059 ◽

2011 ◽

Author(s):

Eberhard Fetz ◽

Andrew Richardson

Keyword(s):

Brain Activity ◽

Electrode Array ◽

High Density

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Consciousness, decision making, and volition: freedom beyond chance and necessity

Theory in Biosciences ◽

10.1007/s12064-021-00346-6 ◽

2021 ◽

Author(s):

Hans Liljenström

Keyword(s):

Decision Making ◽

Free Will ◽

Brain Activity ◽

Brain Structures ◽

Complex Process ◽

Neural Information ◽

Neural Information Processing ◽

Stochastic Population Model ◽

The Brain

AbstractWhat is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

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