Search bioRxiv⌕ Search

Biology subjects

Valizadeh, A.

Publications and source records attributed to Valizadeh, A..

7 recordsLinked to original sources

A single-nucleus multiome analysis of transcriptome and chromatin accessibility reveals cell-type-specific immune modulation for chronic cannabis use among people with HIV infection

As cannabis use continues to rise among people with HIV (PWH), understanding its impact on immune function in this population is becoming increasingly important. To provide new insights on how cannabis modulates immune function, we analyzed single-nucleus multi-omic profiles of peripheral blood mononuclear cells (PBMCs) from PWH to characterize the changes in gene expression and chromatin accessibility associated with chronic cannabis exposure. We identified numerous differentially expressed genes (DEGs) between cannabis users and non-users in each cell type, approximately half of which were unique to individual cell types. Changes in pro- and anti-inflammatory gene expression associated with cannabis use are dependent on cell lineage and type. We identified hundreds of differential chromatin accessibility regions in each cell type, including cis-regulatory elements correlated with cell-type-dependent DEGs (e.g. NFKBIA in CD4+ T cells and CCL3L1 in classical monocytes). Multiple cannabis-associated transcription factors (e.g., NFKB1, FOS, and TCF7) emerge as regulators of the differentially expressed inflammatory genes. Furthermore, cannabis altered the communication between classical monocytes and lymphocytes. These findings indicate that cannabis-induced immunomodulatory effects are profound, dynamic and complex among cell types and that transcriptional changes are regulated at least in part by epigenetic mechanisms.

genomics↗

Delay selection by spike-timing-dependent plasticity shapes efficient networks for signal transmission

Information processing in the brain relies on efficient communication between different brain regions. Brain oscillations can control signal transmission in brain networks by modulating the timing and excitability of sender and receiver areas. For effective transmission, signals should arrive at target areas when their excitability is maximized. For reciprocally connected neural populations, this mechanism works if the transmission delay matches the period of their evoked oscillation. However, the mechanisms underlying such development of the connections with matched delays remain elusive. While transmission delays in brain networks change during development, the process by which delays are tuned for efficient transmission is unknown. Here, we demonstrate that the well-known Hebbian learning rule can provide a mechanism for selecting connections with delays that match the period of network oscillations. We consider a reciprocally connected bi-layer network of excitatory and inhibitory neurons that generate network-level oscillations spontaneously or in response to external stimuli. When exposed to spiketiming-dependent plasticity (STDP), the network self-organizes to potentiate connections with delays matching the oscillation period, while depressing those with non-matching delays. Our findings shed light on how transmission delays may evolve during learning and development to optimize the organization of brain networks for efficient signal transmission.

neuroscience↗

Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity

The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulations can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks.

neuroscience↗

Effect of Stimulation of Subthalamic Nucleus on Beta Oscillations and Thalamus Tremor Activity in a Computational Model of Parkinson's Disease

Parkinsons disease (PD) is associated with abnormal {beta} band oscillations (13-30 Hz) in the cortico-basal ganglia circuits. Abnormally increased striato-pallidal inhibition and strengthening the synaptic coupling between subthalamic nucleus (STN) and globus pallidus externa (GPe), due to the loss of dopamine, are considered as the potential sources of {beta} oscillations in the basal ganglia. Deep brain stimulation (DBS) of the basal ganglia subregions is known as a way to reduce the pathological {beta} oscillations and motor deficits related to PD. Despite the success of the DBS, its underlying mechanism is poorly understood and, there is controversy about the inhibitory or excitatory role of the DBS in the literature. Here, we utilized a computational network model of basal ganglia which consists of STN, GPe, globus pallidus interna (GPi), and thalamic neuronal population. This model can reproduce healthy and pathological {beta} oscillations similar to what has been observed in experimental studies. Using this model, we investigated the effect of DBS to understand whether its effect is excitatory or inhibitory. Our results show that the excitatory DBS (EDBS) is able to quench the pathological synchrony and {beta} oscillations, while, applying inhibitory DBS (IDBS) failed to quench the PD signs. In light of simulation results, we conclude that the effect of the DBS on its target is excitatory.

neuroscience↗

Frequency-resolved functional connectivity: Role of delay and the strength of connections

The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across functionally specialized areas. Functional networks are not static and change over time and in different brain states, enabling the nervous system to engage and disengage different local areas in specific tasks on demand. Due to the low temporal resolution, however, BOLD signals do not allow the exploration of spectral properties of the brain dynamics over different frequency bands which are known to be important in cognitive processes. Recent studies using imaging tools with a high temporal resolution has made it possible to explore the correlation between the regions at multiple frequency bands. These studies introduce the frequency as a new dimension over which the functional networks change, enabling brain networks to transmit multiplex of information at any time. In this computational study, we explore the functional connectivity at different frequency ranges and highlight the role of the distance between the nodes in their correlation. We run the generalized Kuramoto model with delayed interactions on top of the brains connectome and show that how the transmission delay and the strength of the connections, affect the correlation between the pair of nodes over different frequency bands.

neuroscience↗

Frequency-dependent organization of the brain’s functional network through delayed-interactions

The structure of the brain network shows modularity at multiple spatial scales. The effect of the modular structure on the brain dynamics has been the focus of several studies in recent years but many aspects remain to be explored. For example, it is not well-known how the delays in the transmission of signals between the neurons and the brain regions, interact with the modular structure to determine the brain dynamics. In this paper, we show an important impact of the delays on the collective dynamics of the brain network with modular structure; that is, the degree of the synchrony between different brain regions is dependent on the frequency. In particular, we show that increasing the frequency the network transits from a global synchrony state to an asynchronous state, through a transition region over which the local synchrony inside the modules is stronger than the global synchrony. When the delays are dependent on the distance between the nodes, the modular structure of different spatial scales appears in the correlation matrix over different specific frequency bands, so that, finer spatial modular structure reveal in higher frequency bands. The results are justified by a simple theoretical argument and elaborated by simulations on several simplified modular networks and the connectome with different spatial resolutions.

neuroscience↗

Facilitating the propagation of spiking activity in feedforward networks by including feedback

Transient oscillations in the network activity upon sensory stimulation have been reported in different sensory areas. These evoked oscillations are the generic response of networks of excitatory and inhibitory neurons (EI-networks) to a transient external input. Recently, it has been shown that this resonance property of EI-networks can be exploited for communication in modular neuronal networks by enabling the transmission of sequences of synchronous spike volleys ( pulse packets), despite the sparse and weak connectivity between the modules. The condition for successful transmission is that the pulse packet (PP) intervals match the period of the modules resonance frequency. Hence, the mechanism was termed communication through resonance (CTR). This mechanism has three sever constraints, though. First, it needs periodic trains of PPs, whereas single PPs fail to propagate. Second, the inter-PP interval needs to match the network resonance. Third, transmission is very slow, because in each module, the network resonance needs to build-up over multiple oscillation cycles. Here, we show that, by adding appropriate feedback connections to the network, the CTR mechanism can be improved and the aforementioned constraints relaxed. Specifically, we show that adding feedback connections between two upstream modules, called the resonance pair, in an otherwise feedforward modular network can support successful propagation of a single PP throughout the entire network. The key condition for successful transmission is that the sum of the forward and backward delays in the resonance pair matches the resonance frequency of the network modules. The transmission is much faster, by more than a factor of two, than in the original CTR mechanism. Moreover, it distinctly lowers the threshold for successful communication by synchronous spiking in modular networks of weakly coupled networks. Thus, our results suggest a new functional role of bidirectional connectivity for the communication in cortical area networks.\n\nAuthor summaryThe cortex is organized as a modular system, with the modules (cortical areas) communicating via weak long-range connections. It has been suggested that the intrinsic resonance properties of population activities in these areas might contribute to enabling successful communication. A modules intrinsic resonance appears in the damped oscillatory response to an incoming spike volley, enabling successful communication during the peaks of the oscillation. Such communication can be exploited in feedforward networks, provided the participating networks have similar resonance frequencies. This, however, is not necessarily true for cortical networks. Moreover, the communication is slow, as it takes several oscillation cycles to build up the response in the downstream network. Also, only periodic trains of spikes volleys (and not single volleys) with matching intervals can propagate. Here, we present a novel mechanism that alleviates these shortcomings and enables propagation of synchronous spiking across weakly connected networks with not necessarily identical resonance frequencies. In this framework, an individual spike volley can propagate by local amplification through reverberation in a loop between two successive networks, connected by feedforward and feedback connections: the resonance pair. This overcomes the need for activity build-up in downstream networks, causing the volley to propagate distinctly faster and more reliably.

neuroscience↗