Effective connectivity

Recent large-scale community effort has produced comprehensive mapping of neurons and chemical synapses in both the Drosophila larva and adult brains (‘connectomes’). In addition, the neurotransmitter expression of each neuron can be predicted using a machine learning algorithm. However, interpreting connectomics data has proven challenging: most neurons connect to tens/hundreds of upstream/downstream neurons, and removing weak connections eliminates most connections in the brain. This could lead to biassed usage of connectomics data, favouring neurons previously studied, or those accessible w

…and their relationship to circuit connectivity. It was previously proposed to use the covariance eigenvalue distribution, or spectrum, which can be analytically derived in random recurrent networks, as a robust measure for describe the shape of neural population activity beyond the dimension (Hu \& Sompolinsky 2022). Applications of the theoretical spectrum have bro

…However, we do not know what connectivity structures support this capability, nor the mechanism for selecting among the dynamics required for different tasks. We develop an analytical theory describing a network model capable of multi-task computation, where the connectivity is generated as a sum over a large number of low-rank matrices, each of which is specialized to perform one low-dimen

…co-activation strengthens effective connectivity via Hebbian-like plasticity. Linear network models reveal that these temporal dependencies between signal and noise correlations emerge only when Hebbian learning balances stochastic synaptic changes, preventing functional degradation

…that combines input “noise” and effective connectivity as key ingredients. Her

The analysis of effective connectivity in neuronal networks has become an essential tool for understanding the underlying mechanisms of information transfer in neural circuits. However, its interpretation is not always straightforward, and it can be masked by the presence of artifacts.We scrutinized calcium imaged data from in vitro neuronal cultures grown of PDMS-engineered patterns using a transfer entropy algorithm [1]. Initial analyses revealed the presence of spurious correlations which hampered the actual underlying network. The origin of these artifacts lays on the delicate interplay be

…intracranial EEG (iEEG) can help probe effective connectivity over time and brain states through the measurement of cortico-cortical evoked potentials (CCEPs).In 15 epilepsy patients undergoing iEEG for diagnostic reasons, we probed effective connectivity with single-pulse electrical stimulations (1ms, 3mA). For any given pair of iEEG contacts at shorter (<20mm) or longer (≥20mm) range, effective connectivity was expressed as a probability of observing a single-trial CCEP in one

Aims: Social cognition hinges on the fundamental capability to perceive the presence of others. Research indicates that the mere presence of conspecifics can enhance task performance, particularly in simpler tasks, and is closely associated with the activity of context-dependent neural subpopulations. While attentional modulation (attentional-conflict theory) has long been posited as the primary mechanism driving social facilitation, the literature on the neurobiological underpinnings of this modulation remains scant at best. Understanding these mechanisms could yield critical insights into so

High-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective evidence-based therapy for Parkinson's disease, however, its effects on the motor network are unclear. In anaesthetized mice we studied the layer- and frequency-specific effects of DBS on the connectivity between STN and rostral and caudal forelimb area (RFA and CFA) layers, the respective homologs to human premotor and motor cortex. Multi-site extracellular recordings served to quantify local field potential-driven activity at rest and during burst STN stimulation at multiple stimulation-freqeuncies f

…While these approaches have been effective in many cases, we will discuss how neglecting the internal dynamics of the brain (e.g., spontaneous activity, global dynamics, effective connectivity) and its underlying computational principles may hinder our progress in understanding and modeling brain functions. By reexamining evidence from our previous and ongoing work, we will propose new hypotheses and direction

Behavior-related neural dynamics in the frontal cortex is an emergent property of network connectivity. The network structure at the level of individual neurons, and its relationship to neural coding are largely unknown. We developed an optical method for rapid (500k pairwise connections / 30 minutes) mapping of effective connectivity in the neocortex in vivo. To this end, we combined 2-photon optogenetic stimulation of individual excitatory neurons and measurement of responses in non-stimulated neurons (‘effective connection’) using 2-photon volumetric calcium imaging. We applied this method

…this modelling rests upon functional connectivity. The dynamics of brain connectivity has recently attracted a lot of attention among brain mappers. I will also show a novel method to identify dynamic effective connectivity using spectral DCM. Further, I will summarise the development of the next generation of DCMs towards large-scale, whole-

To prevail in a dynamic and noisy environment, the brain must create reliable and meaningful representations from sensory inputs that are often ambiguous or corrupt. Since only information that permeates the cortical hierarchy can influence sensory perception and decision-making, it is critical that noisy external stimuli are encoded and propagated through different processing stages with minimal signal degradation. Here we hypothesize that stimulus-specific pathways akin to cortical topographic maps may provide the structural scaffold for such signal routing. We investigate whether the featur