Showing papers by "John M. Beggs published in 2020"

One-Stop Microfluidic Assembly of Human Brain Organoids To Model Prenatal Cannabis Exposure

Abstract: Prenatal cannabis exposure (PCE) influences human brain development, but it is challenging to model PCE using animals and current cell culture techniques. Here, we developed a one-stop microfluidic platform to assemble and culture human cerebral organoids from human embryonic stem cells (hESC) to investigate the effect of PCE on early human brain development. By incorporating perfusable culture chambers, air-liquid interface, and one-stop protocol, this microfluidic platform can simplify the fabrication procedure and produce a large number of organoids (169 organoids per 3.5 cm × 3.5 cm device area) without fusion, as compared with conventional fabrication methods. These one-stop microfluidic assembled cerebral organoids not only recapitulate early human brain structure, biology, and electrophysiology but also have minimal size variation and hypoxia. Under on-chip exposure to the psychoactive cannabinoid, Δ-9-tetrahydrocannabinol (THC), cerebral organoids exhibited reduced neuronal maturation, downregulation of cannabinoid receptor type 1 (CB1) receptors, and impaired neurite outgrowth. Moreover, transient on-chip THC treatment also decreased spontaneous firing in these organoids. This one-stop microfluidic technique enables a simple, scalable, and repeatable organoid culture method that can be used not only for human brain organoids but also for many other human organoids including liver, kidney, retina, and tumor organoids. This technology could be widely used in modeling brain and other organ development, developmental disorders, developmental pharmacology and toxicology, and drug screening.

Differential effects of propofol and ketamine on critical brain dynamics.

Abstract: Whether the brain operates at a critical "tipping" point is a long standing scientific question, with evidence from both cellular and systems-scale studies suggesting that the brain does sit in, or near, a critical regime. Neuroimaging studies of humans in altered states of consciousness have prompted the suggestion that maintenance of critical dynamics is necessary for the emergence of consciousness and complex cognition, and that reduced or disorganized consciousness may be associated with deviations from criticality. Unfortunately, many of the cellular-level studies reporting signs of criticality were performed in non-conscious systems (in vitro neuronal cultures) or unconscious animals (e.g. anaesthetized rats). Here we attempted to address this knowledge gap by exploring critical brain dynamics in invasive ECoG recordings from multiple sessions with a single macaque as the animal transitioned from consciousness to unconsciousness under different anaesthetics (ketamine and propofol). We use a previously-validated test of criticality: avalanche dynamics to assess the differences in brain dynamics between normal consciousness and both drug-states. Propofol and ketamine were selected due to their differential effects on consciousness (ketamine, but not propofol, is known to induce an unusual state known as "dissociative anaesthesia"). Our analyses indicate that propofol dramatically restricted the size and duration of avalanches, while ketamine allowed for more awake-like dynamics to persist. In addition, propofol, but not ketamine, triggered a large reduction in the complexity of brain dynamics. All states, however, showed some signs of persistent criticality when testing for exponent relations and universal shape-collapse. Further, maintenance of critical brain dynamics may be important for regulation and control of conscious awareness.

Differential Effects of Propofol and Ketamine on Critical Brain Dynamics

Abstract: Whether the brain operates at a critical “tipping” point is a long standing scientific question, with evidence from both cellular and systems-scale studies suggesting that the brain does sit in, or near, a critical regime. Neuroimaging studies of humans in altered states of consciousness have prompted the suggestion that maintenance of critical dynamics is necessary for the emergence of consciousness and complex cognition, and that reduced or disorganized consciousness may be associated with deviations from criticality. Unfortunately, many of the cellular-level studies reporting signs of criticality were performed in non-conscious systems (in vitro neuronal cultures) or unconscious animals (e.g. anaesthetized rats). Here we attempted to address this knowledge gap by exploring critical brain dynamics in invasive ECoG recordings from multiple sessions with a single macaque as the animal transitioned from consciousness to unconsciousness under different anaesthetics (ketamine and propofol). We use a previously-validated test of criticality: avalanche dynamics to assess the differences in brain dynamics between normal consciousness and both drug-states. Propofol and ketamine were selected due to their differential effects on consciousness (ketamine, but not propofol, is known to induce an unusual state known as “dissociative anaesthesia”). Our analyses indicate that propofol dramatically restricted the size and duration of avalanches, while ketamine allowed for more awake-like dynamics to persist. In addition, propofol, but not ketamine, triggered a large reduction in the complexity of brain dynamics. All states, however, showed some signs of persistent criticality when testing for exponent relations and universal shape-collapse. Further, maintenance of critical brain dynamics may be important for regulation and control of conscious awareness. Author summary Here we explore how different anaesthetic drugs change the nature of brain dynamics, from sub-dural electrophysiological arrays implanted in a macaque brain. Previous research has suggested that loss of consciousness under anaesthesia is associated with a movement away from critical brain dynamics, towards a less flexible regime. When comparing ketamine and propofol, two anaesthetics with largely different effects on consciousness, we find that propofol, but not ketamine, produces a dramatic reduction in the complexity of brain activity and restricts the range of scales where critical dynamics are plausible. These results suggest that maintenance of critical dynamics may be important for regulation and control of conscious awareness.

Correlated activity favors synergistic processing in local cortical networks in vitro at synaptically relevant timescales.

Abstract: Neural information processing is widely understood to depend on correlations in neuronal activity. However, whether correlation is favorable or not is contentious. Here, we sought to determine how ...

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints.

Abstract: Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. Although previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the generalized linear model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from the mouse somatosensory cortex. Here, our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.NEW & NOTEWORTHY Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

Network structure of cascading neural systems predicts stimulus propagation and recovery.

Abstract: Objective Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network's local and global connectivity to these patterns and information processing remains largely unknown. Approach Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory. Main results In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks. Significance Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

Abstract: Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron’s type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research. New & Noteworthy Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here we develop an extension of a Generalized Linear Model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks, and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

One-stop Microfluidic Assembly of Human Brain Organoids to Model Prenatal Cannabis Exposure

Abstract: Prenatal cannabis exposure (PCE) influences human brain development, but it is challenging to model PCE using animals and current cell culture techniques. Here, we developed a one-stop microfluidic platform to assemble and culture human cerebral organoids from human embryonic stem cells (hESC) to investigate the effect of PCE on early human brain development. By incorporating perfusable culture chambers, air-liquid interface, and one-stop protocol, this microfluidic platform can simplify the fabrication procedure, and produce a large number of organoids (169 organoids per 3.5 cm x 3.5 cm device area) without fusion, as compared with conventional fabrication methods. These one-stop microfluidic assembled cerebral organoids not only recapitulate early human brain structure, biology, and electrophysiology but also have minimal size variation and hypoxia. Under on-chip exposure to the psychoactive cannabinoid, delta-9-tetrahydrocannabinol (THC), cerebral organoids exhibited reduced neuronal maturation, downregulation of cannabinoid receptor type 1 (CB1) receptors, and impaired neurite outgrowth. Moreover, transient on-chip THC treatment also decreased spontaneous firing in microfluidic assembled brain organoids. This one-stop microfluidic technique enables a simple, scalable, and repeatable organoid culture method that can be used not only for human brain organoids, but also for many other human organoids including liver, kidney, retina, and tumor organoids. This technology could be widely used in modeling brain and other organ development, developmental disorders, developmental pharmacology and toxicology, and drug screening.

A computational examination of the two-streams hypothesis: which pathway needs a longer memory?

Abstract: The two visual streams hypothesis is a robust framework that has inspired many studies in the past three decades. One of the well-studied claims of this hypothesis is the idea that the dorsal visual pathway is involved in visually guided motor behavior, and it is operating with a short memory. Conversely, this hypothesis claims that the ventral visual pathway is involved in object classification, and it works using a long-term memory. In this study, we tested these claims by training identical recurrent neural networks to either perform viewpoint-invariant object classification (a task attributed to dorsal stream) or orientation classification (a task attributed to dorsal stream) and measured how much they rely on their memory in each task. Using a modified leaky-integrator echo state recurrent network, we found that object classification requires a longer memory compared to orientation classification. However, when we used long-short-term memory (LSTM) networks, we observed that object classification requires longer memory only in larger datasets. Accordingly, our results suggest that having a longer memory is advantageous in performing ventral stream9s tasks more than their dorsal counterparts, as was originally suggested by the two-streams hypothesis.

Synergistic neural computation is greater downstream of recurrent connectivity in organotypic cortical cultures

Abstract: Cortical information processing requires synergistic integration of input. Understanding the determinants of synergistic integration--a form of computation--in cortical circuits is therefore a critical step in understanding the functional principles underlying cortical information processing. We established previously that synergistic integration varies directly with the strength of feedforward connectivity. What relationship recurrent and feedback connectivity have with synergistic integration remains unknown. To address this, we analyzed the spiking activity of hundreds of well-isolated neurons in organotypic cultures of mouse somatosensory cortex, recorded using a high-density 512-channel microelectrode array. We asked how empirically observed synergistic integration, quantified through partial information decomposition, varied with local functional network structure. Toward that end, local functional network structure was categorized into motifs with varying recurrent and feedback connectivity. We found that synergistic integration was elevated in motifs with greater recurrent connectivity and was decreased in motifs with greater feedback connectivity. These results indicate that the directionality of local connectivity, beyond feedforward connections, has distinct influences on neural computation. Specifically, more upstream recurrence predicts greater downstream computation, but more feedback predicts lesser computation.