Topic: structural connectivity

Understanding the causal connectivity within a network is crucial for unraveling its functional dynamics. However，the inferred causal connections are fundamentally influenced by the choice of causality measure employed, which may not always align with the actual structural connectivity of the network. The relationship between causal and structural connectivity, especially how different causality measures affect the inferred causal links, requires further exploration. In this talk, we examine nonlinear networks characterized by pulse signal outputs, such as spiking neural networks, using four p

Psychotic disorders are characterized by heterogeneity in etiopathology, clinical presentation and individual trajectory, complicating diagnosis and treatment. Despite the increasing amount of studies investigating significant alterations, the mechanisms underlying the emergence and progression of these disorders remains unclear. It is therefore fundamental to improve our ability to differentiate between clinical subgroups and to move towards a more personalised approach. Here, we aim to highlight critical neural correlates in early stages of psychosis, and to identify relevant biomarkers corr

Which nodes in a brain network causally influence one another, and how do such interactions utilize the underlying structural connectivity? One of the fundamental goals of neuroscience is to pinpoint such causal relations. Conventionally, these relationships are established by manipulating a node while tracking changes in another node. A causal role is then assigned to the first node if this intervention led to a significant change in the state of the tracked node. In this presentation, I use a series of intuitive thought experiments to demonstrate the methodological shortcomings of the curren

The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function. However, quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state r

Accessing structural connectivity information is crucial for understanding intricate functions of complex networks. In situations where direct measurement of structural connectivity is limited, like in cortical neuronal networks, causal inference tools such as transfer entropy are employed to reconstruct structural connectivity from network activity data. However, the causal connectivity inferred may not always align with the network's underlying structural connectivity, leaving the interpretation of inferred causal connectivity open for further clarification. Specifically, the relationship be