Biology subjects

Dahan, A.

Publications and source records attributed to Dahan, A..

3 recordsLinked to original sources

bioRxiv2026

From Channel-Pair Connectivity to Brain Networks: An Open Graph Theoretical Pipeline for fNIRS Hyperscanning

Despite the growing use of functional near-infrared spectroscopy (fNIRS) hyperscanning to record brain activity simultaneously from interacting individuals in naturalistic settings, most analyses quantify functional connectivity separately for each channel pair. The resulting collection of pairwise estimates is difficult to integrate into a network-level characterization of intra- and inter-brain organization. Here, we present an open, configuration-driven Python toolkit that transforms preprocessed fNIRS hyperscanning time series into functional connectivity graphs. The toolkit constructs a bipartite inter-brain network for each dyad and separate intra-brain networks for each participant, computes node- and graph-level measures, and exports adjacency matrices, edge lists, analysis-ready summary tables, reproducibility metadata, and standardized visualizations. Dataset-specific parameters, including directory structure, participant naming, channel selection, epoch extraction, and edge-retention criteria, are defined in a human-readable YAML configuration file, enabling the same workflow to accommodate differently organized datasets without changes to the source code. We illustrate the pipeline using a representative recording from a mother-infant fNIRS hyperscanning dataset and present the resulting network outputs. The toolkit provides a reproducible framework for moving from pairwise functional connectivity estimates to network-level analyses of dyadic and individual brain organization.

neuroscience↗

bioRxiv2021

Different brain systems support the aversive and appetitive sides of human pain-avoidance learning

Both unexpected pain and unexpected pain absence can drive avoidance learning, but whether they do so via shared or separate neural and neurochemical systems is largely unknown. To address this issue, we combined an instrumental pain-avoidance learning task with computational modeling, functional magnetic resonance imaging (fMRI) and pharmacological manipulations of the dopaminergic (100 mg levodopa) and opioidergic (50 mg naltrexone) systems (N=83). Computational modeling provided evidence that untreated participants learned more from received than avoided pain. Our dopamine and opioid manipulations negated this learning asymmetry by selectively increasing learning rates for avoided pain. Furthermore, our fMRI analyses revealed that pain prediction errors were encoded in subcortical and limbic brain regions, whereas no-pain prediction errors were encoded in frontal and parietal cortical regions. However, we found no effects of our pharmacological manipulations on the neural encoding of prediction errors. Together, our results suggest that human pain-avoidance learning is supported by separate threat- and safety-learning systems, and that dopamine and endogenous opioids specifically regulate learning from successfully avoided pain.

neuroscience↗

bioRxiv2017

Clearing the Fungal FoG: Perseverance, a property distinct from resistance, is associated with clinical persistence

Drug susceptibility, defined by the minimal inhibitory concentration (MIC), often does not predict whether fungal infections will respond to therapy in the clinic. Tolerance at supra-MIC antifungal drug concentrations is rarely quantified and current clinical recommendations suggest it be ignored. Here, we measured and characterized drug-response variables that could influence the outcomes of fungal infections and be generalizable across major clades of Candida albicans, one of the most frequently isolated human fungal pathogens. We quantified antifungal tolerance as the fraction of growth (FoG) above the MIC and found that it is clearly distinct from susceptibility/resistance measured as MIC. Instead, tolerance is due to the slow growth of subpopulations of cells that overcome drug stress more efficiently than the rest of the population, and correlates inversely with the accumulation of intracellular drug. Importantly, many adjuvant drugs used together with fluconazole, a fungistatic drug, reduce tolerance without affecting resistance. These include inhibitors of major stress response hubs such as Hsp90, calcineurin, PKC1 and TOR. Accordingly, in an invertebrate infection model, adjuvant combination therapy was significantly more effective than fluconazole alone in treating highly tolerant isolates and did not improve the treatment of isolates with low tolerance levels. Furthermore, isolates recovered from immunocompetent patients with persistent candidemia displayed significantly higher tolerance than isolates that were readily cleared by fluconazole. Thus, tolerance correlates with the response to fluconazole therapy in patients and may help predict whether infections will respond to fluconazole alone. Similarly, measuring tolerance may provide a useful clinical parameter for choosing appropriate therapeutic strategies to overcome persistent clinical candidemia.

microbiology↗