Biology subjects

Melin, M.

Publications and source records attributed to Melin, M..

5 recordsLinked to original sources

bioRxiv2025

Altered Use of Prior Expectations and Modified Neural Dynamics in a Mouse Model of Autism

In dynamic environments, updating beliefs based on past experiences (priors) is essential for optimal decision-making. Prior utilization is often impaired in psychiatric disorders, affecting perception and behavior. We investigate how Neurexin1 (Nrxn1 ) loss-of-function disrupts this process, providing insight into circuit deficits underlying sensorimotor dysfunction. While the synaptic role of Nrxn1 role is well studied, its impact on network dynamics and decision-making behavior remain unclear. Using widefield calcium imaging, we assess cortex-wide activity in mice performing a two-choice task to probe how priors influence visually-guided decisions. This task requires the mouse to combine sensory evidence with the prior probability over the stimulus side. We find Nrxn1 KO mice underutilized priors and were slower to update choices based on feedback. During decision-making, cortex-wide cortical activity is both elevated and increasingly correlated in Nrxn1 KO mice, independent of task period. Moreover, a larger fraction of cortical variance was explained by movement variables, consistent with stronger coupling of cortical activity to motor signals and a bias toward movement-related dynamics. These findings suggest that core computations underlying decision-making, such as integrating past experience with current evidence, depend on intact synaptic mechanisms shaped by genes like Nrxn1.

neuroscience↗

bioRxiv2024

Predicting woodland bird species habitat with multi-temporal and multisensor remote sensing data

Remote sensing data capture ecologically important information that can be used to characterize, model and predict bird habitat. This study implements fusion techniques using Random Forests (RF) with spectral Landsat data and structural airborne laser scanning (ALS) data to scale habitat attributes through time and to characterize habitat for four bird species in dynamic young forest environments in the United Kingdom. We use multi-temporal (2000, 2005, 2012/13, 2015) multi-sensor (Landsat and ALS) data to (i) predict structural attributes via pixel-level fusion at 30 metre spatial resolution, (ii) model bird habitat via object-level fusion and compare with models based on ALS, Landsat and predicted structural attributes, and (iii) predict bird habitat through time (i.e., predict 2015 habitat based on 2000-2012 data). First, we found that models predicting mean height from spectral information had the highest accuracy, whilst maximum height, standard deviation of heights, foliage height diversity, canopy cover and canopy relief ratio had good accuracy, and entropy had low accuracy. The green band and the normalized burn ratio (NBR) were consistently important for prediction, with the red and shortwave infrared (SWIR) 1 bands also important. For all structural variables, high values were underpredicted and low values were overpredicted. Second, for Blue Tit (Cyanistes caeruleus) and Chaffinch (Fringilla coelebs), the most accurate model employed Landsat data, while object-level fusion performed best for Chiffchaff (Phylloscopus collybita) and Willow Warbler (Phylloscopus trochilus). ALS mean, maximum and standard deviation of heights and Landsat tasseled cap transformations (TCT) (i.e., wetness, greenness and brightness) were ranked as important to all species across various models. Third, we used our models to predict presence in 2015 and implemented a spatial intersection approach to assess the predictive accuracy for each species. Blue Tit and Willow Warbler presences were well predicted with the Landsat, ALS, and objectlevel fusion models. Chaffinch and Chiffchaff presences were best predicted with the ALS model. Predictions based on pixel-level predicted structure surfaces had low accuracy but were acceptable for Chaffinch and Willow Warbler. This study is significant as it provides guidance for Landsat and ALS data application and fusion in habitat modelling. Our results highlight the need to use appropriate remote sensing data for each study species based on their ecology. Object-level data fusion improved habitat characterization for all species relative to ALS, but not to Landsat for Blue Tit and Chaffinch. Pixel-level fusion for predicting structural attributes in years where ALS data are note available is increasingly being used in modelling but may not adequately represent within-patch wildlife habitat. Finally, incorporating predicted surfaces generated through pixel-level fusion in our habitat models yielded low accuracy. HighlightsO_LIWe used object- and pixel-level fusion with ALS and Landsat to examine bird habitat C_LIO_LIPixel-level fusion predicted surfaces yielded low accuracy in habitat models C_LIO_LIBest models: Landsat (Blue Tit, Chaffinch); fusion (Chiffchaff, Willow Warbler) C_LIO_LIBest prediction: ALS (Chaffinch, Chiffchaff) C_LIO_LIBest prediction: ALS, Landsat, object-level fusion (Blue Tit, Willow Warbler) C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/625964v1_ufig1.gif" ALT="Figure 1"> View larger version (77K): org.highwire.dtl.DTLVardef@c52aaeorg.highwire.dtl.DTLVardef@75133dorg.highwire.dtl.DTLVardef@420d1aorg.highwire.dtl.DTLVardef@6a5f8a_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗

bioRxiv2023

Large scale, simultaneous chronic neural recordings from multiple brain areas

Understanding how brain activity is related to animal behavior requires measuring multi-area interactions on multiple timescales. However, methods to perform chronic, simultaneous recordings of neural activity from many brain areas are lacking. Here, we introduce a novel approach for independent chronic probe implantation that enables flexible, simultaneous interrogation of neural activity from many brain regions during head restrained or freely moving behavior. The approach, that we called indie (independent dovetail implants for electrophysiology), enables repeated retrieval and reimplantation of probes. The chronic implantation approach can be combined with other modalities such as skull clearing for cortex wide access and optogenetics with optic fibers. Using this approach, we implanted 6 probes chronically in one hemisphere of the mouse brain. The implant is lightweight, allows flexible targeting with different angles, and offers enhanced stability. Our approach broadens the applications of chronic recording while retaining its main advantages over acute recordings (superior stability, longitudinal monitoring of activity and freely moving interrogations) and provides an appealing venue to study processes not accessible by acute methods, such as the neural substrate of learning across multiple areas.

neuroscience↗

bioRxiv2023

Complex genomic landscape of inversion polymorphism in Europe's most destructive forest pest

In many species, polymorphic inversions underlie complex phenotypic polymorphisms and facilitate local adaptation in the face of gene flow. Multiple polymorphic inversions can co-occur in a genome, but the prevalence, evolutionary significance, and limits to complexity of genomic inversion landscapes remain poorly understood. Here, we examine genome-wide variation in one of Europes most destructive forest pests, the spruce bark beetle Ips typographus, scan for polymorphic inversions, and test whether inversions are involved in key adaptations in this species. We analyzed 240 individuals from 18 populations across the species European range and, using a whole-genome resequencing approach, identified 27 polymorphic inversions covering approximately 28% of the genome. The inversions vary in size and in levels of intra-inversion recombination, are highly polymorphic across the species range, and often overlap, forming a complex genomic architecture. We test several mechanisms, including directional selection, overdominance and associative overdominance that can contribute to the maintenance of inversion polymorphisms in the genome. We show that the heterogeneous inversion landscape is likely maintained by the combined action of several evolutionary forces and that inversions are enriched in odorant receptor genes encoding key elements of recognition pathways for host plants, mates, and symbiotic fungi. Our results indicate that the genome of this major forest pest of growing social, political, and economic importance harbors one of the most complex inversion landscapes described to date posing a question about limits of genomic architecture complexity.

evolutionary biology↗

bioRxiv2023

Engaged decision-makers align spontaneous movements to stereotyped task demands

Existing work demonstrates that animals alternate between engaged and disengaged states during perceptual decision-making. To understand the neural signature of these states, we performed cortex-wide measurements of neural activity in mice making auditory decisions. The trial-averaged magnitude of neural activity was similar in the two states. However, the trial-to-trial variance in neural activity was higher during disengagement. To understand this increased variance, we trained separate linear encoding models on neural data from each state. The models demonstrated that although task variables and task-aligned movements impacted neural activity similarly during the two states, movements that are independent of task events explained more variance during disengagement. Behavioral analyses uncovered that during disengagement, movements become uncoupled to task events. Taken together, these results argue that the neural signature of disengagement, though obscured in trial-averaged neural activity, is evident in trial-to-trial variability driven by changing patterns of spontaneous movements.

neuroscience↗