Biology subjects

Jagannathan, S. R.

Publications and source records attributed to Jagannathan, S. R..

6 recordsLinked to original sources

bioRxiv2023

A consensus cell type atlas from multiple connectomes reveals principles of circuit stereotypy and variation

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the flys success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper1, this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this [~]130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database2. Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the "hemibrain" connectome3. In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

neuroscience↗

bioRxiv2023

Multivariate classification of multichannel long-term electrophysiology data identifies different sleep stages in fruit flies

Sleep is observed in most animals, which suggests it subserves a fundamental process associated with adaptive biological functions. However, the evidence to directly associate sleep with a specific function is lacking, in part because sleep is not a single process in many animals. In humans and other mammals, different sleep stages have traditionally been identified using electroencephalograms (EEGs), but such an approach is not feasible in different animals such as insects. Here, we perform long-term multichannel local field potential (LFP) recordings in the brains of behaving flies undergoing spontaneous sleep bouts. We developed protocols to allow for consistent spatial recordings of LFPs across multiple flies, allowing us to compare the LFP activity across awake and sleep periods and further compare the same to induced sleep. Using machine learning, we uncover the existence of distinct temporal stages of sleep and explore the associated spatial and spectral features across the fly brain. Further, we analyze the electrophysiological correlates of micro-behaviours associated with certain sleep stages. We confirm the existence of a distinct sleep stage associated with rhythmic proboscis extensions and show that spectral features of this sleep-related behavior differ significantly from those associated with the same behavior during wakefulness, indicating a dissociation between behavior and the brain states wherein these behaviors reside.

neuroscience↗

bioRxiv2021

Fluctuations in Neural Complexity During Wakefulness Relate To Conscious Level and Cognition

There has been considerable recent progress in measuring conscious level using neural complexity measures. For instance, such measures can reliably distinguish healthy awake from asleep subjects and vegetative state patients. However, this line of research has never explored the dynamics of conscious level during normal wakefulness. Being able to capture meaningful differences in conscious level during wakefulness may provide a vital new insight into the nature of consciousness, by demonstrating what biological, behavioural and cognitive factors relate to such differences. Here we take advantage of a large MEG and fMRI dataset of healthy adults, to examine within-subject conscious level fluctuations during resting state and tasks, by using a range of complexity measures. We first establish the validity of this approach in both neuroimaging domains by relating neural complexity measures to pre-existing techniques for capturing transitions of consciousness from full wakefulness into drowsiness and the earliest stages of sleep, finding decreased complexity as participants become increasingly drowsy. We further demonstrate that neural complexity measures in both MEG and fMRI change both within and between tasks, and relate to performance on an executive task, with higher complexity associated with better performance and faster reaction times. This approach provides a powerful new route to further explore the cognitive and neural underpinnings of consciousness.

neuroscience↗

bioRxiv2021

Different underlying mechanisms for high and low arousal in probabilistic learning in humans

Humans are uniquely capable of adapting to highly changing environments by updating relevant information and adjusting ongoing behaviour accordingly. Here we show how this ability --termed cognitive flexibility-- is differentially modulated by high and low arousal fluctuations. We implemented a probabilistic reversal learning paradigm in healthy participants as they transitioned towards sleep or physical extenuation. The results revealed, in line with our pre-registered hypotheses, that low arousal leads to diminished behavioural performance through increased decision volatility, while performance decline under high arousal was attributed to increased perseverative behaviour. These findings provide evidence for distinct patterns of maladaptive decision-making on each side of the arousal inverted u-shaped curve, differentially affecting participants ability to generate stable evidence-based strategies, and introduces wake-sleep and physical exercise transitions as complementary experimental models for investigating neural and cognitive dynamics.

neuroscience↗

bioRxiv2020

Alertness modulates perceptual decision-making

The ability to make decisions based on external information, prior knowledge and evidence, is a crucial aspect of cognition and may determine the success and survival of an organism. Despite extensive work on decision-making mechanisms/models, understanding the effects of alertness on neural and cognitive processes remain limited. Here we use electroencephalography and behavioural modelling to characterise cognitive and neural dynamics of perceptual decision-making in awake/low alertness periods in humans (14 male, 18 female) and characterise the compensatory mechanisms as alertness decreases. Well-rested human participants, changing between full-wakefulness and low alertness, performed an auditory tone-localisation task and its behavioural dynamics was quantified with psychophysics, signal detection theory and drift-diffusion modelling, revealing slower reaction times, inattention to the left side of space, and a lower rate of evidence accumulation in periods of low alertness. Unconstrained multivariate pattern analysis (decoding) showed a ~280ms delayed onset driven by low alertness of the neural signatures differentiating between left and right decision, with a spatial reconfiguration from centro-parietal to lateral frontal regions 150-360ms. To understand the neural compensatory mechanisms with decreasing alertness, we connected the evidence-accumulation behavioural parameter to the neural activity, showing in the early periods (125-325ms) a shift in the associated patterns from right parietal regions in awake, to right fronto-parietal during low alertness. This change in the neurobehavioural dynamics for central accumulation related cognitive processes define a clear reconfiguration of the brain networks regions and dynamics needed for the implementation of decision-making, revealing mechanisms of resilience of cognition when challenged by decreased alertness. Significance statementMost living organisms make multiple daily decisions and these require a degree of evidence from both the environment and the internal milieu. Such decisions are usually studied under sequential sampling models and involve making a behavioural choice based on sensory encoding, central accumulation, and motor implementation processes. Since there is little research on how decreasing alertness affects such cognitive processes, this study has looked at the cognitive and neural dynamics of perceptual decision-making in people while fully awake and in drowsy periods. Using computational modelling of behaviour and neural dynamics on human participants performing an auditory tone-localisation task, we reveal how low alertness modulates evidence accumulation related processes and its corresponding compensatory neural signatures.

neuroscience↗

bioRxiv2017

Tracking wakefulness as it fades: micro-measures of Alertness

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method based on frequency and sleep graphoelements, which is capable of detecting micro variations in alertness. The validity of the automated method was verified by training and testing the algorithm using a dataset where participants are known to fall asleep. In addition, we tested generalizability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience.

neuroscience↗