Structural connectivity

…brain regions, its relationship to structural connectivity and homeostatic statistics remains unclear [1]. To address this, we explored how drift interacts with structural recurrency and noise in a recurrent neural network (RNN) model. By simulating synaptic-level noise through random connectivity manip

Understanding the structural and functional network architectures of nervous systems has been a key focus of neuroscience. Many features of brain-wide neuronal networks have been identified in mammals using noninvasive methods that lack cellular information. Here, we performed whole-brain calcium imaging in zebrafish larvae to investigate the structural and genetic basis of functional connectivity (FC) at single-cell resolution. Neural activity from $\sim$50,000 neurons was recorded in head-restrained transgenic larvae expressing a pan-neuronal calcium sensor, while monitoring their tail movem

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

…of long-range connections in cortical structural connectivity (SC). These damages manifest as losses in connections or demyelination of axons. When the SC of the brain is damaged, the functional connectivity (FC, i.e. the dynamic interactions and information exchange between distinct brain regions) is locally and globally perturbed. Despite the close correlation between SC and FC, they are not identical; FC exhibits greater flexibility and can reorganize in resp

…neurons, as morphology constrains the connectivity and impacts the functionality of brain networks. Biologically realistic dendrites within brain regions could be successfully synthesized using topological neuron synthesis (TNS), but inter-regional connectivity remains largely an open question which we address in this

…been widely used to estimate the microstructural integrity of white matter. However, the extensive fibrous structure of the white matter, varying widely across individuals, necessitates an analytic approach capable of capturing both its macroscopic and microscopic structures effectively. We hypothesized that a deep neural network approach could learn multi-scale features of white matter in an end-to-end manner. We analyzed data from the Adolescent Brain Cognitive Development

The aim of this study is to reveal structural connectivity patterns prevalent across a large population and whether any of these patterns differ between schizophrenia subjects (SCH) and healthy controls (HC).We use topological data analysis methods, namely persistent homology using the weight rank clique filtration on brain connectivity matrices obtained from a probabilistic fibre-tracking algorithm run on the Centre for Biomedical Research Excellence (COBRE) dataset (N = 44 SCH, N = 44 HC) to investigate group differences. We show that some of the connectivity structures differ in strength re

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

…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

…stimulus encoding and the underlying structural connectivity. In visual areas, it has been shown that correlated variability depends on orientation tuning. However, it is challenging to interpret this tuning dependence when the spikes are generated from a doubly stochastic Poisson process, which is commonly assumed. In this case, spikes can exhibit tuning-dependent noise correlation, e

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