The CMU Array. A 3D Nano-Printed, Fully Customizable Ultra-High-Density Microelectrode Array
Microelectrode arrays (MEAs) provide the means to record electrophysiological activity fundamental to both basic and clinical neuroscience (e.g. brain computer interfaces). Despite recent advances, current MEAs have significant limitations, including recording density, fragility, expense, and the inability to optimize the probe to individualized study or patient needs. Here we address the technological limitations through the utilization of the newest developments in 3D nanoparticle printing. Our CMU Arrays possess previously impossible electrode densities (> 6000 channels/cm2) with tip diameters as small as 10um. Most importantly, the probes are entirely customizable owing to the adaptive manufacturing process. Any combination of individual shank lengths, impedances, and layouts are possible. This is achieved in part via our new multi-layer, multi material, custom 3D-printed circuit boards, a fabrication advancement in itself. This device design enables new experimental avenues of targeted, large-scale recording of electrical signals from a variety of biological tissues.
https://www.biorxiv.org/content/10.1101/742346v1
Microelectrode arrays (MEAs) provide the means to record electrophysiological activity fundamental to both basic and clinical neuroscience (e.g. brain computer interfaces). Despite recent advances, current MEAs have significant limitations, including recording density, fragility, expense, and the inability to optimize the probe to individualized study or patient needs. Here we address the technological limitations through the utilization of the newest developments in 3D nanoparticle printing. Our CMU Arrays possess previously impossible electrode densities (> 6000 channels/cm2) with tip diameters as small as 10um. Most importantly, the probes are entirely customizable owing to the adaptive manufacturing process. Any combination of individual shank lengths, impedances, and layouts are possible. This is achieved in part via our new multi-layer, multi material, custom 3D-printed circuit boards, a fabrication advancement in itself. This device design enables new experimental avenues of targeted, large-scale recording of electrical signals from a variety of biological tissues.
https://www.biorxiv.org/content/10.1101/742346v1
bioRxiv
The CMU Array: A 3D Nano-Printed, Fully Customizable Ultra-High-Density Microelectrode Array Platform
Microelectrode arrays (MEAs) provide the means to record electrophysiological activity fundamental to both basic and clinical neuroscience (e.g. brain-computer interfaces). Despite recent advances, current MEAs have significant limitations – including recording…
Low-frequency Neural Activity Reflects Rule-based Chunking during Speech Listening
Cortical activity tracks the rhythms of phrases and sentences during speech comprehension, which has been taken as strong evidence that the brain groups words into multi-word chunks. It has prominently been argued, in contrast, that the tracking phenomenon could be explained as the neural tracking of word properties. Here we distinguish these two hypotheses based on novel tasks in which we dissociate word properties from the chunk structure of a sequence. Two tasks separately require listeners to group semantically similar or semantically dissimilar words into chunks. We demonstrate that neural activity actively tracks task-related chunks rather than passively reflecting word properties. Furthermore, without an explicit 'chunk processing task,' neural activity barely tracks chunks defined by semantic similarity - but continues to robustly track syntactically well-formed meaningful sentences. These results suggest that cortical activity tracks multi-word chunks constructed by either long-term syntactic rules or temporary task-related rules. The properties of individual words are likely to contribute only in a minor way, contrary to recent claims.
https://www.biorxiv.org/content/10.1101/742585v1
Cortical activity tracks the rhythms of phrases and sentences during speech comprehension, which has been taken as strong evidence that the brain groups words into multi-word chunks. It has prominently been argued, in contrast, that the tracking phenomenon could be explained as the neural tracking of word properties. Here we distinguish these two hypotheses based on novel tasks in which we dissociate word properties from the chunk structure of a sequence. Two tasks separately require listeners to group semantically similar or semantically dissimilar words into chunks. We demonstrate that neural activity actively tracks task-related chunks rather than passively reflecting word properties. Furthermore, without an explicit 'chunk processing task,' neural activity barely tracks chunks defined by semantic similarity - but continues to robustly track syntactically well-formed meaningful sentences. These results suggest that cortical activity tracks multi-word chunks constructed by either long-term syntactic rules or temporary task-related rules. The properties of individual words are likely to contribute only in a minor way, contrary to recent claims.
https://www.biorxiv.org/content/10.1101/742585v1
bioRxiv
Low-frequency Neural Activity Reflects Rule-based Chunking during Speech Listening
Cortical activity tracks the rhythms of phrases and sentences during speech comprehension, which has been taken as strong evidence that the brain groups words into multi-word chunks. It has prominently been argued, in contrast, that the tracking phenomenon…
Human Neocortical Neurosolver (HNN): A new software tool for interpreting the cellular and network origin of human MEG/EEG data
Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.
https://www.biorxiv.org/content/10.1101/740597v1
Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators is a limitation that constrains using MEG/EEG to reveal novel principles of information processing or to translate findings into new therapies for neuropathology. To address this problem, we built Human Neocortical Neurosolver (HNN, https://hnn.brown.edu) software. HNN has a graphical user interface designed to help researchers and clinicians interpret the neural origins of MEG/EEG. HNN's core is a neocortical circuit model that accounts for biophysical origins of electrical currents generating MEG/EEG. Data can be directly compared to simulated signals and parameters easily manipulated to develop/test hypotheses on a signal's origin. Tutorials teach users to simulate commonly measured signals, including event related potentials and brain rhythms. HNN's ability to associate signals across scales makes it a unique tool for translational neuroscience research.
https://www.biorxiv.org/content/10.1101/740597v1
bioRxiv
Human Neocortical Neurosolver (HNN): A new software tool for interpreting the cellular and network origin of human MEG/EEG data
Magneto- and electro-encephalography (MEG/EEG) non-invasively record human brain activity with millisecond resolution providing reliable markers of healthy and disease states. Relating these macroscopic signals to underlying cellular- and circuit-level generators…
Frequency specific network effective connectivity: ERP analysis of recognition memory process by directed connectivity estimators
Various processes occur in memory retrieval in recognition memory and it is necessary to investigate memory brain function. Most of the research in past decades have focused on particular brain region function, but the interaction between these has a major role in human cognition. In this study, we used the memory retrieval task to investigate the underlying mechanism of recognition memory. The connectivity between brain regions is estimated from scalp electroencephalography signals that were recorded from twenty-three healthy subject participated in recognition memory task to correctly classify old/new words. Multivariate autoregressive models (MVAR) are used for the determination of Granger causality to estimate the effective connectivity in the time-frequency domain. We use GPDC and dDTF methods because they have almost resolved the previous problems in estimations. Results show that brain regions in the old condition have greater global connectivity in the theta and gamma band compared to the new words retrieval. Connectivity within and between the brain hemisphere may be related to correct rejection. The left frontal has a crucial role in recollection. theta and gamma specific connectivity pattern between temporal, parietal and frontal cortex may disclose the retrieval mechanism. old/new comparison resulted in the different patterns of network connection. These results and other evidence emphasize the role of frequency of causal network interactions in the memory process.
https://www.biorxiv.org/content/10.1101/739573v1
Various processes occur in memory retrieval in recognition memory and it is necessary to investigate memory brain function. Most of the research in past decades have focused on particular brain region function, but the interaction between these has a major role in human cognition. In this study, we used the memory retrieval task to investigate the underlying mechanism of recognition memory. The connectivity between brain regions is estimated from scalp electroencephalography signals that were recorded from twenty-three healthy subject participated in recognition memory task to correctly classify old/new words. Multivariate autoregressive models (MVAR) are used for the determination of Granger causality to estimate the effective connectivity in the time-frequency domain. We use GPDC and dDTF methods because they have almost resolved the previous problems in estimations. Results show that brain regions in the old condition have greater global connectivity in the theta and gamma band compared to the new words retrieval. Connectivity within and between the brain hemisphere may be related to correct rejection. The left frontal has a crucial role in recollection. theta and gamma specific connectivity pattern between temporal, parietal and frontal cortex may disclose the retrieval mechanism. old/new comparison resulted in the different patterns of network connection. These results and other evidence emphasize the role of frequency of causal network interactions in the memory process.
https://www.biorxiv.org/content/10.1101/739573v1
bioRxiv
Frequency specific network effective connectivity: ERP analysis of recognition memory process by directed connectivity estimators
Various processes occur in memory retrieval in recognition memory and it is necessary to investigate memory brain function. Most of the research in past decades have focused on particular brain region function, but the interaction between these has a major…
Characterization of the brain functional architecture of psychostimulant withdrawal using single-cell whole brain imaging
Numerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive. To address this question, we used male C57BL/6J mice and performed single-cell whole-brain imaging of neural activity during withdrawal from cocaine, methamphetamine, and nicotine. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical- to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway that leads to addiction.
https://www.biorxiv.org/content/10.1101/743799v1
Numerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive. To address this question, we used male C57BL/6J mice and performed single-cell whole-brain imaging of neural activity during withdrawal from cocaine, methamphetamine, and nicotine. We used hierarchical clustering and graph theory to identify similarities and differences in brain functional architecture. Although methamphetamine and cocaine shared some network similarities, the main common neuroadaptation between these psychostimulant drugs was a dramatic decrease in modularity, with a shift from a cortical- to subcortical-driven network, including a decrease in total hub brain regions. These results demonstrate that psychostimulant withdrawal produces the drug-dependent remodeling of functional architecture of the brain and suggest that the decreased modularity of brain functional networks and not a specific set of brain regions may represent the final common pathway that leads to addiction.
https://www.biorxiv.org/content/10.1101/743799v1
bioRxiv
Characterization of the brain functional architecture of psychostimulant withdrawal using single-cell whole brain imaging
Numerous brain regions have been identified as contributing to addiction-like behaviors, but unclear is the way in which these brain regions as a whole lead to addiction. The search for a final common brain pathway that is involved in addiction remains elusive.…
Neocortical activity tracks syllable and phrasal structure of self-produced speech
To gain novel insights into how the human brain processes self-produced auditory information during reading aloud, we investigated the coupling between neuromagnetic activity and the temporal envelope of the heard speech sounds (i.e., speech brain tracking) in a group of adults who 1) read a text aloud, 2) listened to a recording of their own speech (i.e., playback), and 3) listened to another speech recording. Coherence analyses revealed that, during reading aloud, the reader's brain tracked the slow temporal fluctuations of the speech output. Specifically, auditory cortices tracked phrasal structure (<1 Hz) but to a lesser extent than during the two speech listening conditions. Also, the tracking of syllable structure (4-8 Hz) occurred at parietal opercula during reading aloud and at auditory cortices during listening. Directionality analyses based on renormalized partial directed coherence revealed that speech brain tracking at <1 Hz and 4-8 Hz is dominated by speech-to-brain directional coupling during both reading aloud and listening, meaning that speech brain tracking mainly entails auditory feedback processing. Nevertheless, brain-to-speech directional coupling at 4-8 Hz was enhanced during reading aloud compared with listening, likely reflecting speech monitoring before production. Altogether, these data bring novel insights into how auditory verbal information is tracked by the human brain during perception and self-generation of connected speech.
https://www.biorxiv.org/content/10.1101/744151v1
To gain novel insights into how the human brain processes self-produced auditory information during reading aloud, we investigated the coupling between neuromagnetic activity and the temporal envelope of the heard speech sounds (i.e., speech brain tracking) in a group of adults who 1) read a text aloud, 2) listened to a recording of their own speech (i.e., playback), and 3) listened to another speech recording. Coherence analyses revealed that, during reading aloud, the reader's brain tracked the slow temporal fluctuations of the speech output. Specifically, auditory cortices tracked phrasal structure (<1 Hz) but to a lesser extent than during the two speech listening conditions. Also, the tracking of syllable structure (4-8 Hz) occurred at parietal opercula during reading aloud and at auditory cortices during listening. Directionality analyses based on renormalized partial directed coherence revealed that speech brain tracking at <1 Hz and 4-8 Hz is dominated by speech-to-brain directional coupling during both reading aloud and listening, meaning that speech brain tracking mainly entails auditory feedback processing. Nevertheless, brain-to-speech directional coupling at 4-8 Hz was enhanced during reading aloud compared with listening, likely reflecting speech monitoring before production. Altogether, these data bring novel insights into how auditory verbal information is tracked by the human brain during perception and self-generation of connected speech.
https://www.biorxiv.org/content/10.1101/744151v1
bioRxiv
Neocortical activity tracks syllable and phrasal structure of self-produced speech during reading aloud
To gain novel insights into how the human brain processes self-produced auditory information during reading aloud, we investigated the coupling between neuromagnetic activity and the temporal envelope of the heard speech sounds (i.e., speech brain tracking)…
Phase-amplitude markers of synchrony and noise: A resting-state and TMS-EEG study of schizophrenia
The electroencephalogram (EEG) of schizophrenia patients exhibits several well-known abnormalities. For brain responses evoked by a stimulus in a variety of behavioral paradigms, these alterations appear as a reduction of signal-to-noise ratio and of phase-locking, and as a facilitated excitability. Here we observe these effects in EEG using transcranial magnetic stimulation (TMS)-evoked potentials, comparing also to the resting-state EEG. To ensure veracity of our results we used three weekly sessions and analyzed both resting state and TMS-EEG data. In addition to confirming the known results for the stimulus response we also show a broadening of the amplitude distribution in the resting-state EEG of patients relative to that of controls, indicative of lower signal to noise. Specifically, we evaluated the instantaneous amplitude in narrow-band filtered EEG, using a form of mean-normalized variation (quartile coefficient, QC) as a marker for amplitude fluctuations. We find that on time scales of seconds to tens of seconds, amplitude fluctuations in the alpha and beta frequency bands in the resting state of healthy controls are larger than their fluctuations in schizophrenia patients. According to our marker, the narrow-band-filtered amplitude fluctuations in the patient group are more similar to the theoretical limit of narrow-band Gaussian noise. Our results thus support the neuronal noise hypothesis of schizophrenia, which states that the ability of neuronal populations to form locally and temporally correlated activity is reduced in schizophrenia due to inherent noise in all brain activity.
https://www.biorxiv.org/content/10.1101/740621v1
The electroencephalogram (EEG) of schizophrenia patients exhibits several well-known abnormalities. For brain responses evoked by a stimulus in a variety of behavioral paradigms, these alterations appear as a reduction of signal-to-noise ratio and of phase-locking, and as a facilitated excitability. Here we observe these effects in EEG using transcranial magnetic stimulation (TMS)-evoked potentials, comparing also to the resting-state EEG. To ensure veracity of our results we used three weekly sessions and analyzed both resting state and TMS-EEG data. In addition to confirming the known results for the stimulus response we also show a broadening of the amplitude distribution in the resting-state EEG of patients relative to that of controls, indicative of lower signal to noise. Specifically, we evaluated the instantaneous amplitude in narrow-band filtered EEG, using a form of mean-normalized variation (quartile coefficient, QC) as a marker for amplitude fluctuations. We find that on time scales of seconds to tens of seconds, amplitude fluctuations in the alpha and beta frequency bands in the resting state of healthy controls are larger than their fluctuations in schizophrenia patients. According to our marker, the narrow-band-filtered amplitude fluctuations in the patient group are more similar to the theoretical limit of narrow-band Gaussian noise. Our results thus support the neuronal noise hypothesis of schizophrenia, which states that the ability of neuronal populations to form locally and temporally correlated activity is reduced in schizophrenia due to inherent noise in all brain activity.
https://www.biorxiv.org/content/10.1101/740621v1
bioRxiv
Phase-amplitude markers of synchrony and noise: A resting-state and TMS-EEG study of schizophrenia
The electroencephalogram (EEG) of schizophrenia patients exhibits several well-known abnormalities. For brain responses evoked by a stimulus in a variety of behavioral paradigms, these alterations appear as a reduction of signal-to-noise ratio and of phase…
Stimjim: open source hardware for precise electrical stimulation
Electrical stimulation is a simple and powerful tool to perturb and evoke neuronal activity in order to understand the function of neurons and neural circuits. Despite this, devices that can provide precise current or voltage stimulation are expensive and closed-source. Here, we introduce Stimjim, a capable and inexpensive ($200 USD) open-source instrument for electrical stimulation that combines both function generation and electrical isolation. Stimjim provides microsecond temporal resolution with microampere or millivolt scale precision on two electrically isolated output channels. We demonstrate Stimjim's utility both in vitro by precisely stimulating brain slices, and in vivo by training mice to perform intracranial self-stimulation (ICSS) for brain stimulation reward. During ICSS, Stimjim enables the experimenter to smoothly tune the strength of reward-seeking behavior by varying either the output frequency or amplitude. We envision Stimjim will enable new kinds of experiments due to its open-source and scalable nature.
https://bitbucket.org/natecermak/stimjim/src/master/
https://www.biorxiv.org/content/10.1101/757716v1
Electrical stimulation is a simple and powerful tool to perturb and evoke neuronal activity in order to understand the function of neurons and neural circuits. Despite this, devices that can provide precise current or voltage stimulation are expensive and closed-source. Here, we introduce Stimjim, a capable and inexpensive ($200 USD) open-source instrument for electrical stimulation that combines both function generation and electrical isolation. Stimjim provides microsecond temporal resolution with microampere or millivolt scale precision on two electrically isolated output channels. We demonstrate Stimjim's utility both in vitro by precisely stimulating brain slices, and in vivo by training mice to perform intracranial self-stimulation (ICSS) for brain stimulation reward. During ICSS, Stimjim enables the experimenter to smoothly tune the strength of reward-seeking behavior by varying either the output frequency or amplitude. We envision Stimjim will enable new kinds of experiments due to its open-source and scalable nature.
https://bitbucket.org/natecermak/stimjim/src/master/
https://www.biorxiv.org/content/10.1101/757716v1
bioRxiv
Stimjim: open source hardware for precise electrical stimulation
Electrical stimulation is a simple and powerful tool to perturb and evoke neuronal activity in order to understand the function of neurons and neural circuits. Despite this, devices that can provide precise current or voltage stimulation are expensive and…
Forwarded from Neuroeconomics
Interacting with volatile environments stabilizes hidden-state inference and its brain signatures
Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Time-resolved analyses of magnetoencephalographic (MEG) signals explained this behavioral difference by the neural interaction between inferred beliefs and incoming evidence, an effect originating from the medial temporal lobe (MTL).
https://www.biorxiv.org/content/10.1101/755223v1
Making accurate decisions in uncertain environments requires identifying the generative cause of sensory cues, but also the expected outcomes of possible actions. Although both cognitive processes can be formalized as Bayesian inference, they are commonly studied using different experimental frameworks, making their formal comparison difficult. Here, by framing a reversal learning task either as cue-based or outcome-based inference, we found that humans perceive the same volatile environment as more stable when inferring its hidden state by interaction with uncertain outcomes than by observation of equally uncertain cues. Time-resolved analyses of magnetoencephalographic (MEG) signals explained this behavioral difference by the neural interaction between inferred beliefs and incoming evidence, an effect originating from the medial temporal lobe (MTL).
https://www.biorxiv.org/content/10.1101/755223v1
Changes in cross-frequency coupling following closed-loop auditory stimulation in non-rapid eye movement sleep
The activity of different brain networks in non-rapid eye movement (NREM) sleep is regulated locally in an experience-dependent manner, reflecting the extent of the network load during wakefulness. In particular, improved task performance after sleep correlates with the local post-learning power increase of neocortical slow waves and faster oscillations such as sleep spindles and their temporal coupling. Recently, it was demonstrated that by targeting slow waves in a particular region at a particular phase with closed-loop auditory stimulation it is possible to locally manipulate slow-wave activity and interact with training-induced neuroplastic changes.
https://www.biorxiv.org/content/10.1101/810861v1
The activity of different brain networks in non-rapid eye movement (NREM) sleep is regulated locally in an experience-dependent manner, reflecting the extent of the network load during wakefulness. In particular, improved task performance after sleep correlates with the local post-learning power increase of neocortical slow waves and faster oscillations such as sleep spindles and their temporal coupling. Recently, it was demonstrated that by targeting slow waves in a particular region at a particular phase with closed-loop auditory stimulation it is possible to locally manipulate slow-wave activity and interact with training-induced neuroplastic changes.
https://www.biorxiv.org/content/10.1101/810861v1
Forwarded from NeuroIDSS
https://github.com/neuroidss/FreeEEG32-alpha1.5/blob/master/OpenVIBE/gdf/record-%5B2019.11.13-22.23.59%5D.edf
FreeEEG32 + alpha1.5 32 channels 5min eyes closed
FreeEEG32 + alpha1.5 32 channels 5min eyes closed
Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle.
https://doi.org/10.1038/s41467-019-08934-3
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle.
https://doi.org/10.1038/s41467-019-08934-3
👍1
Pulsed Near Infrared Transcranial and Intranasal Photobiomodulation Significantly Modulates Neural Oscillations
Transcranial photobiomodulation (tPBM) is the application of low levels of red or near-infrared (NIR) light to stimulate neural tissues. Here, we administer tPBM in the form of NIR light (810 nm wavelength) pulsed at 40 Hz to the default mode network (DMN), and examine its effects on human neural oscillations, in a randomized, sham-controlled, double-blinded trial. Using electroencephalography (EEG), we found that a single session of tPBM significantly increases the power of the higher oscillatory frequencies of alpha, beta and gamma and reduces the power of the slower frequencies of delta and theta in subjects in resting state. Furthermore, the analysis of network properties using inter-regional synchrony via weighted phase lag index (wPLI) and graph theory measures, indicate the effect of tPBM on the integration and segregation of brain networks.
https://doi.org/10.1038/s41598-019-42693-x
Transcranial photobiomodulation (tPBM) is the application of low levels of red or near-infrared (NIR) light to stimulate neural tissues. Here, we administer tPBM in the form of NIR light (810 nm wavelength) pulsed at 40 Hz to the default mode network (DMN), and examine its effects on human neural oscillations, in a randomized, sham-controlled, double-blinded trial. Using electroencephalography (EEG), we found that a single session of tPBM significantly increases the power of the higher oscillatory frequencies of alpha, beta and gamma and reduces the power of the slower frequencies of delta and theta in subjects in resting state. Furthermore, the analysis of network properties using inter-regional synchrony via weighted phase lag index (wPLI) and graph theory measures, indicate the effect of tPBM on the integration and segregation of brain networks.
https://doi.org/10.1038/s41598-019-42693-x
Forwarded from Andrey Samokhvalov
оч круто http://cnbr.skoltech.ru/bci_myths
cnbr.skoltech.ru
#CNBR_Open Seminar. Alexey Ossadtchi and Nikolai Smetanin
Demystifying BCIs
Forwarded from Elena Rybina
Привет!
Мы с Женей @ekalenkovich и Катей @katyatulit делаем первый в России ReproducibiliTea журнальный клуб — журнальный клуб вокруг открытой науки (правда, в основном психологической), https://reproducibilitea.org. Мы будем встречаться каждые 2-4 недели в неформальной обстановке, пить чай и обсуждать статьи про открытую и воспроизводимую науку.
Первая встреча пройдет 12 марта в 19:30 на ПсихФаке Вышки по адресу Армянский пер., 4с2, аудитория 118. Далее планируем мигрировать по разным лабораториям (предлагайте свою!)
Обсуждать будем статью “False-Positive Psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant” (Simmons, Nelson, and Simonsohn, 2011 https://osf.io/t8jsd/).
За первые 10-15 минут я вкратце напомню, про что была статья. Дальше будем все вместе обсуждать и пить чай. Такой формат предполагает, что участники знакомятся со статьей до встречи, поэтому лучше прочитать статью заранее — это делает обсуждение более осмысленным.
Пожалуйста, зарегистрируйтесь https://tinyurl.com/ws79279, чтобы мы понимали, сколько чашек для чая готовить, и нужно ли заказать пропуск. Чай и чашки мы организуем, приносите к чаю то, что вам захочется.
Также добавляйтесь в наш телеграм-канал (t.me/reproducibilitea_moscow), там будут появляться анонсы встреч, и оттуда же можно перейти в чат для обсуждения.
Приходите!
Мы с Женей @ekalenkovich и Катей @katyatulit делаем первый в России ReproducibiliTea журнальный клуб — журнальный клуб вокруг открытой науки (правда, в основном психологической), https://reproducibilitea.org. Мы будем встречаться каждые 2-4 недели в неформальной обстановке, пить чай и обсуждать статьи про открытую и воспроизводимую науку.
Первая встреча пройдет 12 марта в 19:30 на ПсихФаке Вышки по адресу Армянский пер., 4с2, аудитория 118. Далее планируем мигрировать по разным лабораториям (предлагайте свою!)
Обсуждать будем статью “False-Positive Psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant” (Simmons, Nelson, and Simonsohn, 2011 https://osf.io/t8jsd/).
За первые 10-15 минут я вкратце напомню, про что была статья. Дальше будем все вместе обсуждать и пить чай. Такой формат предполагает, что участники знакомятся со статьей до встречи, поэтому лучше прочитать статью заранее — это делает обсуждение более осмысленным.
Пожалуйста, зарегистрируйтесь https://tinyurl.com/ws79279, чтобы мы понимали, сколько чашек для чая готовить, и нужно ли заказать пропуск. Чай и чашки мы организуем, приносите к чаю то, что вам захочется.
Также добавляйтесь в наш телеграм-канал (t.me/reproducibilitea_moscow), там будут появляться анонсы встреч, и оттуда же можно перейти в чат для обсуждения.
Приходите!
ReproducibiliTea
ReproducibiliTea - Journal Clubs for Open Science
Brain network motifs are markers of loss and recovery of consciousness
Motifs are patterns of inter-connections between nodes of a complex network, and have been investigated as the basic building blocks of directed networks. This study explored the re-organization of 3-node (i.e. 3-electrode) network motifs during anesthetic-induced loss and recovery of consciousness. We hypothesized that motif frequency and topography would change across states of consciousness, and that the frequency and topography associated with consciousness would recover prior to the return of behavioral responsiveness. Methods: Nine healthy subjects underwent a 3-hour controlled anesthetic protocol (propofol induction; isoflurane maintenance) in an operating room, while brain activity was recorded through 128-channel electroencephalography (EEG). In the alpha (8-13 Hz) frequency band, five-minute epochs of EEG were extracted for: baseline; induction; unconsciousness.
https://www.biorxiv.org/content/10.1101/2020.03.16.993659v1
Motifs are patterns of inter-connections between nodes of a complex network, and have been investigated as the basic building blocks of directed networks. This study explored the re-organization of 3-node (i.e. 3-electrode) network motifs during anesthetic-induced loss and recovery of consciousness. We hypothesized that motif frequency and topography would change across states of consciousness, and that the frequency and topography associated with consciousness would recover prior to the return of behavioral responsiveness. Methods: Nine healthy subjects underwent a 3-hour controlled anesthetic protocol (propofol induction; isoflurane maintenance) in an operating room, while brain activity was recorded through 128-channel electroencephalography (EEG). In the alpha (8-13 Hz) frequency band, five-minute epochs of EEG were extracted for: baseline; induction; unconsciousness.
https://www.biorxiv.org/content/10.1101/2020.03.16.993659v1
Forwarded from Blue_Arrakis (March Slashcheva)
Я тут несу несколько лекций по самой передовой нейробиологии, свеженьких, майских, с самых фронтиров (спасибо ковиду за это).
Лекция Ёрла Миллера про рабочую память - очень крутая. Миллер это один из самых известных персонажей в исследованиях рабочей памяти, если вам нужен быстрый обзор того, как развивались представления о рабочей памяти и что там творится сейчас, плюс еще моделирование рабочей памяти, то вам сюда. Его группа работает с макаками, и задания, которым они учат макак, таки впечатляют. Это годы тяжелой работы, и у Миллера эта работа поставлена очень хорошо. Их хороший обзор - "Working memory 2.0". В лекции еще подчеркнуто различие кратковременной памяти (short-term memory) и рабочей (working memory), потому что последняя, в отличии от первой, требует волевых усилий для удержания куска информации. Интересно то, что даже если нам на уровне нейронов будет понятно, как кусок информации удерживается в памяти, волевое усилие - это еще один уровень, и как оно представлено в мозге совсем неясно.
https://vimeo.com/416435315
Вчера проходил онлайн симпозиум про нейросайнс и AI, на нем было три хороших keynote лектора:
- "Does the brain do something like back-propagation?" by Dr. Konrad Kording (University of Pennsylvania) - сразу говорю, что очень математично
- "Bridging scales of intelligence from biophysics to ConvNets" by Dr. Eilif Muller (formerly with Element AI and EPFL)
- "Biological dynamics and their role in computation and learning" by Dr. Adrienne Fairhall (University of Washington)
Они все есть на краудкасте, где вам нужно будет залогиниться и там есть навигация по разным лекциям, вот прямая ссылка на вторую лекцию. Чуть запарно с регистрацией, но кому надо - тот найдет
https://www.crowdcast.io/e/uss2020/3
Лекция Ёрла Миллера про рабочую память - очень крутая. Миллер это один из самых известных персонажей в исследованиях рабочей памяти, если вам нужен быстрый обзор того, как развивались представления о рабочей памяти и что там творится сейчас, плюс еще моделирование рабочей памяти, то вам сюда. Его группа работает с макаками, и задания, которым они учат макак, таки впечатляют. Это годы тяжелой работы, и у Миллера эта работа поставлена очень хорошо. Их хороший обзор - "Working memory 2.0". В лекции еще подчеркнуто различие кратковременной памяти (short-term memory) и рабочей (working memory), потому что последняя, в отличии от первой, требует волевых усилий для удержания куска информации. Интересно то, что даже если нам на уровне нейронов будет понятно, как кусок информации удерживается в памяти, волевое усилие - это еще один уровень, и как оно представлено в мозге совсем неясно.
https://vimeo.com/416435315
Вчера проходил онлайн симпозиум про нейросайнс и AI, на нем было три хороших keynote лектора:
- "Does the brain do something like back-propagation?" by Dr. Konrad Kording (University of Pennsylvania) - сразу говорю, что очень математично
- "Bridging scales of intelligence from biophysics to ConvNets" by Dr. Eilif Muller (formerly with Element AI and EPFL)
- "Biological dynamics and their role in computation and learning" by Dr. Adrienne Fairhall (University of Washington)
Они все есть на краудкасте, где вам нужно будет залогиниться и там есть навигация по разным лекциям, вот прямая ссылка на вторую лекцию. Чуть запарно с регистрацией, но кому надо - тот найдет
https://www.crowdcast.io/e/uss2020/3
Vimeo
Seminar: Earl K. Miller: Working Memory 2.0
A seminar by MIT neuroscientist Earl K. Miller, presented May 7, 2020.
Forwarded from NeuroIDSS
https://www.crowdsupply.com/neuroidss/freeeeg32
FreeEEG32 crowdfunding announced
FreeEEG32 crowdfunding announced
Crowd Supply
FreeEEG32
Affordable, stackable, open source, 32-channel EEG ADC powered by a Cortex-M7
Forwarded from Dmitry Sukhoruchkin
Лабораторию мирового уровня создадут в Сколтехе на грант РНФ
https://sk.ru/news/laboratoriyu-mirovogo-urovnya-sozdadut-v-skoltehe-na-grant-rnf/
https://sk.ru/news/laboratoriyu-mirovogo-urovnya-sozdadut-v-skoltehe-na-grant-rnf/
sk.ru
Лабораторию мирового уровня создадут в Сколтехе на грант
Российский научный фонд по итогам конкурса 2021 года поддержал крупным грантом идею профессора Сколтеха Михаила
Forwarded from Новости нейронаук и нейротехнологий
Загадочное и парадоксальное состояние сна: лекция Ивана Пигарева
Для чего нужен сон? Какое его функциональное назначение? Чем занят мозг пока вы спите?
Об этом — на лекции Ивана Н. Пигарёва в рамках весеннего периода CNBR_Open — проекта Центра Нейробиологии Сколтеха, объединяющего людей, интересующихся нейронауками и нейротехнологиями.
Иван Николаевич Пигарёв – доктор биологических наук, главный научный сотрудник Лаборатории передачи информации в сенсорных системах Института проблем передачи информации РАН. Специалист в области физиологии зрения и физиологии сна. Автор висцеральной теории сна, сильно отличающейся от мейнстримных теорий.
http://neuronovosti.ru/zagadochnoe-i-paradoksalnoe-sostoyanie-sna-lektsiya-ivana-pigareva/
Для чего нужен сон? Какое его функциональное назначение? Чем занят мозг пока вы спите?
Об этом — на лекции Ивана Н. Пигарёва в рамках весеннего периода CNBR_Open — проекта Центра Нейробиологии Сколтеха, объединяющего людей, интересующихся нейронауками и нейротехнологиями.
Иван Николаевич Пигарёв – доктор биологических наук, главный научный сотрудник Лаборатории передачи информации в сенсорных системах Института проблем передачи информации РАН. Специалист в области физиологии зрения и физиологии сна. Автор висцеральной теории сна, сильно отличающейся от мейнстримных теорий.
http://neuronovosti.ru/zagadochnoe-i-paradoksalnoe-sostoyanie-sna-lektsiya-ivana-pigareva/
Neuronovosti
Загадочное и парадоксальное состояние сна: лекция Ивана Пигарева - Neuronovosti
Для чего нужен сон? Какое его функциональное назначение? Чем занят мозг пока вы спите? Об этом — на лекции Ивана Н. Пигарёва в рамках весеннего...