Biology subjects

Krzeminski, D.

Publications and source records attributed to Krzeminski, D..

4 recordsLinked to original sources

bioRxiv2023

Systematic annotation of a complete adult male Drosophila nerve cord connectome reveals principles of functional organisation

Nervous systems function as ensembles of neurons communicating via synaptic connections, and a functional understanding of nervous systems requires extensive knowledge of their connectomes. In a companion paper (Takemura et al., 2023), we describe the acquisition of a complete fruit fly nerve cord connectome, the first for an animal that can walk or fly. Here, to efficiently navigate and to appreciate the biological significance of this connectome, we categorise and name nearly all neurons systematically and link them to the experimental literature. We employ a system of hierarchical coarse annotations and group similar neurons across the midline and across segments, then define systematic cell types for sensory neurons, intrinsic neurons, ascending neurons, and non-motor efferent neurons. Stereotyped arrays of neuroblasts generate related neuron populations called hemilineages that repeat across the segments of the nerve cord. We confirm that larval-born neurons from a given hemilineage generally express the same neurotransmitter but find that earlier born neurons often express a different one. We match over 35% of intrinsic, ascending, and non-motor efferent neurons across segments, defining serial sets which were crucial for systematic typing of motor neurons and sensory neurons. We assign a sensory modality to over 5000 sensory neurons, cluster them by connectivity, and identify serially homologous cell types and a layered organisation likely corresponding to peripheral topography. Finally, we present selected examples of sensory circuits predicated on programmatic analysis of a complete VNC connectome. Our annotations are critical for analysing the structure of descending input to the nerve cord and of motor output, both described in a third companion paper (Cheong et al., 2023). These annotations are being released as part of the neuprint.janelia.org and clio.janelia.org web applications and also serve as the basis for programmatic analysis of the connectome through dedicated tools that we describe in this paper.

neuroscience↗

bioRxiv2021

Imperfect integration: sensory congruency between multiple sources modulates selective decision-making processes

AO_SCPLOWBSTRACTC_SCPLOWDecision making on the basis of multiple information sources is common. However, to what extent such decisions differ from those with a single source remains unclear. Here, we combined cognitive modelling and neural-mass modelling to characterise the neurocognitive process underlying decisionmaking with single or double information sources. Ninety-four human participants performed binary decisions to discriminate the coherent motion direction averaged across two independent apertures. Regardless the angular distance of the apertures, separating motion information into two apertures resulted in a reduction in accuracy. Our modelling results further showed that the addition of the second information source led to a lower signal-to-noise ratio of evidence accumulation with two congruent information sources, and a change in the decision strategy of speed-accuracy trade-off with two incongruent sources. Thus, our findings support a robust behavioural change in relation to multiple information sources, which have congruency-dependent impacts on selective decision-making subcomponents.

neuroscience↗

bioRxiv2019

Reward certainty and preference bias selectively shape voluntary decisions

Choosing between equally valued options can be a conundrum, for which classical decision theories predicted a prolonged response time (RT). Paradoxically, a rational decision-maker would need no deliberative thinking in this scenario, as outcomes of alternatives are indifferent. How individuals choose between equal options remain unclear. Here, we characterized the neurocognitive processes underlying such voluntary decisions, by integrating advanced cognitive modelling and EEG recording in a probabilistic reward task, in which human participants chose between pairs of cues associated with identical reward probabilities at different levels. We showed that higher reward certainty accelerated RT. At each certainty level, participants preferred to choose one cue faster and more frequently over the other. The behavioral effects on RT persisted in simple reactions to reward cues. By using hierarchical Bayesian parameter estimation for an accumulator model, we showed that the certainty and preference effects were independently associated with the rate of evidence accumulation during decisions, but not with visual encoding or motor execution latencies. Time-resolved multivariate pattern classification of EEG evoked response identified significant representations of reward certainty and preference choices as early as 120 ms after stimulus onset, with spatial relevance patterns maximal in middle central and parietal electrodes. Furthermore, EEG-informed computational modelling showed that the rate of change between N100 and P300 event-related potentials reflected changes in the model-derived rate of evidence accumulation on a trial-by-trial basis. Our findings suggested that reward certainty and preference collectively shaped voluntary decisions between equal options, providing a mechanism to prevent indecision or random behavior.

neuroscience↗

bioRxiv2019

Frontoparietal network dynamics impairments in juvenile myoclonic epilepsy revealed by MEG energy landscape

Juvenile myoclonic epilepsy (JME) is a form of idiopathic generalized epilepsy affecting brain activity. It is unclear to what extent JME leads to abnormal network dynamics across functional networks. Here, we proposed a method to characterise network dynamics in MEG resting-state data, combining a pairwise maximum entropy model (pMEM) and the associated energy landscape analysis. Fifty-two JME patients and healthy controls underwent a resting-state MEG recording session. We fitted the pMEM to the oscillatory power envelopes in theta (4-7 Hz), alpha (8-13 Hz), beta (15-25 Hz) and gamma (30-60 Hz) bands in three source-localised resting-state networks: the frontoparietal network (FPN), the default mode network (DMN), and the sensorimotor network (SMN). The pMEM provided an accurate fit to the MEG oscillatory activity in both patient and control groups, and allowed estimation of the occurrence probability of each network state, with its regional activity and pairwise regional co-activation constrained by empirical data. We used energy values derived from the pMEM to depict an energy landscape of each network, with a higher energy state corresponding to a lower occurrence probability. When comparing the energy landscapes between groups, JME patients showed fewer local energy minima than controls and had elevated energy values for the FPN within the theta, beta and gamma-bands. Furthermore, numerical simulation of the fitted pMEM showed that the proportion of time the FPN was occupied within the basins of characteristic energy minima was shortened in JME patients. These network alterations were confirmed by a significant leave-one-out classification of individual participants based on a support vector machine employing the energy values of pMEM as features. Our findings suggested that JME patients had altered multi-stability in selective functional networks and frequency bands in the frontoparietal cortices.\n\nHighlightsO_LIAn energy landscape analysis characterises the dynamics of MEG oscillatory activity\nC_LIO_LIPatients with JME exhibit fewer local minima of the energy in their energy landscapes\nC_LIO_LIJME affects the network dynamics in the frontoparietal network.\nC_LIO_LIEnergy landscape measures allow good single-patient classification.\nC_LI

neuroscience↗