Biology subjects

Harel, Y.

Publications and source records attributed to Harel, Y..

3 recordsLinked to original sources

bioRxiv2024

Mapping the developmental structure of stereotyped and individual-unique behavioral spaces in C. elegans

Developmental patterns of behavior are variably organized in time and among different individuals. However, long-term behavioral diversity was previously studied using pre-defined behavioral parameters, representing a limited fraction of the full individuality structure. Here, we continuously extracted [~]1.2 billion body postures of [~]2,200 single C. elegans individuals throughout their full development time, to create a complete developmental atlas of stereotyped and individual-unique behavioral spaces. Unsupervised inference of low-dimensional movement modes of each single individual revealed a dynamic developmental trajectory of stereotyped behavioral spaces and exposed unique behavioral trajectories of individuals that deviate from the stereotyped patterns. Moreover, classification of behavioral spaces within tens of neuromodulatory- and environmentally-perturbed populations efficiently uncovered plasticity in the temporal structures of stereotyped behavior and individuality. These results present a comprehensive atlas of continuous behavioral dynamics across development time and a general framework for unsupervised dissection of shared and unique developmental signatures of behavior.

neuroscience↗

bioRxiv2022

Early-life experience reorganizes neuromodulatory regulation of stage-specific behavioral responses and individuality types during development

Early-life experiences may promote stereotyped behavioral alterations that are dynamic across development time, but also behavioral responses that are variable among individuals, even when initially exposed to the same stimulus. Here, by utilizing longitudinal monitoring of C. elegans individuals throughout development we show that behavioral effects of early-life starvation are exposed during early and late developmental stages and buffered during intermediate stages of development. We further found that both dopamine and serotonin shape the discontinuous behavioral responses by opposite and temporally segregated functions across development time. While dopamine buffers behavioral responses during intermediate developmental stages, serotonin promotes behavioral sensitivity to stress during early and late stages. Interestingly, unsupervised analysis of individual biases across development uncovered multiple individuality types that coexist within stressed and unstressed populations and further identified experience-dependent effects on their composition. These results provide insight into the complex temporal regulation of behavioral plasticity across developmental timescales, structuring shared and unique individual responses to early-life experiences.

neuroscience↗

bioRxiv2022

Class imbalance should not throw you off balance: Choosing classifiers and performance metrics for brain decoding with imbalanced data

Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbalanced classes is a particularly common problem, and it can have severe consequences if not adequately addressed. With the neuroscience ML user in mind, this paper provides a didactic assessment of the class imbalance problem and illustrates its impact through systematic manipulation of data imbalance ratios in (i) simulated data and (ii) brain data recorded with electroencephalography (EEG) and magnetoencephalography (MEG). Our results illustrate how the widely-used Accuracy (Acc) metric, which measures the overall proportion of successful predictions, yields misleadingly high performances, as class imbalance increases. Because Acc weights the per-class ratios of correct predictions proportionally to class size, it largely disregards the performance on the minority class. A binary classification model that learns to systematically vote for the majority class will yield an artificially high decoding accuracy that directly reflects the imbalance between the two classes, rather than any genuine generalizable ability to discriminate between them. We show that other evaluation metrics such as the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), and the less common Balanced Accuracy (BAcc) metric - defined as the arithmetic mean between sensitivity and specificity, provide more reliable performance evaluations for imbalanced data. Our findings also highlight the robustness of Random Forest (RF), and the benefits of using stratified cross-validation and hyperprameter optimization to tackle data imbalance. Critically, for neuroscience ML applications that seek to minimize overall classification error, we recommend the routine use of BAcc, which in the specific case of balanced data is equivalent to using standard Acc, and readily extends to multi-class settings. Importantly, we present a list of recommendations for dealing with imbalanced data, as well as open-source code to allow the neuroscience community to replicate and extend our observations and explore alternative approaches to coping with imbalanced data.

neuroscience↗