Biology subjects

Negi, A.

Publications and source records attributed to Negi, A..

6 recordsLinked to original sources

bioRxiv2025

Optimizing Language Model Embeddings to Voxel Activity Improves Brain Activity Predictions

Recent studies have shown that contextual semantic embeddings from language models can accurately predict human brain activity during language processing. However, most studies use contextual embeddings with the same context length and model layer for all voxels, potentially overlooking meaningful variations across the brain. In this study, we investigate whether optimizing contextual embeddings for individual voxels improves their ability to predict brain activity during reading. We optimize embeddings for each voxel by selecting the best-predicting context length, model layer, or both. We perform this optimization with two different types of stimuli (isolated sentences and narratives), and quantify the performance gains of optimized embeddings over standard fixed embeddings. Our results show that voxel-specific optimization substantially improves the prediction accuracy of contextual semantic embeddings. These findings demonstrate that voxel-specific contextual tuning provides a more accurate and nuanced account of how the contextual semantic information is represented across the cortex.

neuroscience↗

bioRxiv2025

Natural variation in pigmentation caused by cis-regulatory changes affecting expression of the ebony gene in Drosophila elegans

Repetitive evolution has offered valuable insights into how similar traits can arise across different populations and species. The dimorphic body color morphs (brown and black) of geographically isolated Drosophila elegans Bock & Wheeler, 1972, along with its closely related species Drosophila gunungcola Sultana et al., 1999, which exhibits black morph, raise intriguing questions about whether these traits have emerged through independent evolution, introgression, or shared ancestral variants. To investigate the genetic and evolutionary origins of these differences, we perform quantitative trait locus (QTL) mapping on recombinant hybrids generated from reciprocal crosses between brown and black morphs D. elegans. QTL mapping reveals significant contributions from the X chromosome and Muller element E to intraspecific pigmentation polymorphism, with ebony identified as one candidate gene. Analysis of ebony expression, including allele-specific expression in F1 hybrids, and sequence variation suggests that the intra- and interspecific pigmentation differences within D. elegans species subgroup may have evolved independently from the lineage-specific region upstream of ebony. By identifying a shared candidate regulatory region associated with both intra- and interspecific variation, our findings suggest that recurrent use of the same genetic region may contribute to the emergence of similar traits across populations and species.

evolutionary biology↗

bioRxiv2025

Brain-Informed Fine-Tuning for Improved Multilingual Understanding in Language Models

Recent studies have demonstrated that fine-tuning language models with brain data can improve their semantic understanding, although these findings have so far been limited to English. Interestingly, similar to the shared multilingual embedding space of pretrained multilingual language models, human studies provide strong evidence for a shared semantic system in bilingual individuals. Here, we investigate whether fine-tuning language models with bilingual brain data changes model representations in a way that improves them across multiple languages. To test this, we fine-tune monolingual and multilingual language models using brain activity recorded while bilingual participants read stories in English and Chinese. We then evaluate how well these representations generalize to the bilingual participants first language, their second language, and several other languages that the participants are not fluent in. We assess the fine-tuned language models on brain encoding performance and downstream NLP tasks. Our results show that bilingual brain-informed fine-tuned language models outperform their vanilla (pretrained) counterparts in both brain encoding performance and most downstream NLP tasks across multiple languages. These findings suggest that brain-informed fine-tuning improves multilingual understanding in language models, offering a bridge between cognitive neuroscience and NLP research. We make our code publicly available. 2

neuroscience↗

bioRxiv2025

How forgiving are M/EEG inverse solutions to noise level misspecification? An excursion into the BSI-Zoo.

AO_SCPLOWBSTRACTC_SCPLOWBrain source imaging (BSI), also known as source localization, from magneto- and electroencephalographic (M/EEG) data, is a challenging ill-posed inverse problem. Accurate source estimation is sensitive to modeling parameters, such as regularization strength and noise level, where misconfigurations can lead to under- or overfitting. Different BSI methods, however, may vary in their robustness to suboptimal parameter choices. Here we conducted extensive simulations of brain sources superimposed by varying degrees of sensor noise to study the ranges of noise misspecification within which different BSI approaches can still localize well. Using the Earth Movers Distance (EMD) and other metrics, we compare the performance of smooth linear inverse solutions with that of sparse non-linear Bayesian learning solutions. Additionally, we assess the effectiveness of various noise estimation and cross-validation techniques to select hyperparameters close to those achieving optimal localization. Methods and experiments are made available within the BSI-Zoo Python package.

neuroscience↗

bioRxiv2024

The Virtual Brain links transcranial magnetic stimulation evoked potentials and inhibitory neurotransmitter changes in major depressive disorder

BackgroundTranscranial magnetic stimulation evoked potentials (TEPs) show promise as a biomarker in major depressive disorder (MDD), but the origin of the increased TEP amplitude in these patients remains unclear. Gamma aminobutyric acid (GABA) may be involved, as TEP peak amplitude is known to increase with GABAergic activity in healthy controls. We employed a computational modeling approach to investigate this phenomenon. MethodsWhole-brain simulations in The Virtual Brain (thevirtualbrain.org), employing the Jansen and Rit neural mass model, were optimized to simulate TEPs of healthy individuals (Nsubs=20, 14 females, 24.5{+/-}4.9 years). To mimic MDD-like impaired inhibition, a GABAergic deficit was introduced to the simulations by altering one of two selected inhibitory parameters, the inhibitory synaptic decay rate b or the number of inhibitory synapses C4. The TEP amplitude was quantified and compared for all simulations. ResultsThe inhibitory synaptic decay rate showed a quadratic correlation (r=0.99, p<0.001) and the number of inhibitory synapses a negative exponential correlation (r=0.99, p<0.001) with the TEP amplitude. Moreover, significant correlations between these simulation-derived values and all TEP peaks and troughs were detected (p<0.001). Thus, under local parameter changes, we were able to alter the TEP amplitude towards pathological levels, i.e. creating an MDD-like increase of the global mean field amplitude in line with empirical results. ConclusionsOur model suggests specific GABAergic deficits as the cause of increased TEP amplitude in MDD patients, which may serve as therapeutic targets. This work highlights the potential of whole-brain simulations in the investigation of neuropsychiatric diseases.

neuroscience↗

bioRxiv2019

Interpretation of Phase Boundary Fluctuation Spectra in Biological Membranes with Nanoscale Organization

In this work, we use Support Vector Machine algorithm to detect simple and complex interfaces in atomistic and coarse-grained molecular simulation trajectories of phase separating lipid bilayer systems. We show that the power spectral density of the interfacial height fluctuations and in turn the line tension of the lipid bilayer systems depend on the order parameter used to identify the intrinsic interface. To highlight the effect of artificial smoothing of the interface on the fluctuation spectra and the ensuing line tension calculations, we perform a convolution of the boundaries identified at molecular resolution with a 2D Gaussian function of variance{varepsilon} 2 equal to the resolution limit, (1/2{pi}{varepsilon}2)exp(-|r|2/2{varepsilon}2). The convolution function is given by h{otimes}g, where h is the instantaneous height fluctuation and g is the Gaussian function. This is similar to the effect of point spread functions in experiments. We find that the region of fluctuation spectra that scales according to capillary wave theory formalism depends on the complexity of the interfacial geometry, which may not always be detected at experimental resolutions. We propose that the different q-regimes in the fluctuation spectra can be used to characterize mode dependent inter-facial tensions to understand the interfaces beyond the linear line tension calculations. This could also be useful in interpretation of fluctuating boundaries in out-of-equilibrium in-vivo membrane systems that carry information about the nature of non-thermal (active) fluctuations in these systems.

biophysics↗