Biology subjects

Tavor, I.

Publications and source records attributed to Tavor, I..

12 recordsLinked to original sources

bioRxiv2026

Evolution and Human Neural Individuality

Individuality is a defining feature of human biology. The functional network architecture of the human brain harbors person-specific qualities and forms individualized connectivity profiles that function as a neural fingerprint, both stable and unique across time. Here, using fMRI data from 431 Human Connectome Project participants, we examined whether neural individuality is more strongly exhibited in brain regions bearing signatures of recent human evolution. We calculated region-wise fingerprinting accuracy and associated it with four properties of evolutionary cortical organization: cortical expansion, myelin content estimate (T1w/T2w), human-specific gene-expression profiles, and functional homology to other primates. Across all four measures, neural individuality was strongest in cortical areas showing greater evolutionary novelty in humans, particularly frontoparietal control and default mode networks, and weaker in more conserved primary regions. Our findings connect evolutionary variation across species with stable functional variation among individuals.

neuroscience↗

bioRxiv2026

Visual and auditory deep learning models capture neural representations of naturalistic social interaction in the superior temporal sulcus

The superior temporal sulcus (STS) is selectively responsive to multimodal social interactions. Yet studies so far have relied on pre-defined, simplified stimuli or features to uncover the type of information that drives STS activity. We hypothesized that high-dimensional representations from visual and auditory deep learning models would better predict STS responses to naturalistic social interactions. We used self-supervised visual and auditory deep learning models to extract representations of movie frames and audio, respectively, of a TV series participants watched during fMRI scanning. Voxel-wise encoding models of a joint visual-auditory representation outperformed human-made social-affective annotations in predicting STS. Variance partition further revealed visual-auditory posterior-to-anterior gradient within the STS. To interpret what these models encode, we applied Principal Component Analysis to the encoding model weights. In both the visual and auditory models the first dimension tracked social interaction and peaked in the STS, indicating that social interaction is a dominant dimension of STS representation across both modalities. We conclude that the STS represents naturalistic social interaction in a multimodal manner, integrating visual and auditory information, and that visual and auditory deep learning models capture key representational properties of these responses.

neuroscience↗

bioRxiv2026

Musical training-induced plasticity reflects melodic rather than instrumental expertise

Musical training produces behavioral gains that extend beyond the practiced context. Yet, it is unclear whether such generalization is accompanied by broadly transferable neural changes or whether training-induced plasticity remains tied to the specific features of the learned material. In this study, 80 musically naive adults completed four piano-training sessions and underwent functional MRI before and after training. Twenty untrained participants served as passive controls. Participants performed a passive listening task in which they listened to the trained melody (Fur Elise) and an untrained melody (Ode to Joy), each played on a trained musical instrument (piano) and an untrained musical instrument (saxophone). Following training, participants demonstrated improved accuracy and rhythmic performance when playing an unfamiliar melody, indicating behavioral generalization. Training-induced neural changes, however, were predominantly associated with the learned melodic content. Listening to Fur Elise elicited increased activity in the intraparietal and supplementary motor cortex and in the cerebellum, as well as decreased activity in the superior temporal cortex. Consistently, pre- and post-training scans were most accurately classified based on brain activity to Fur Elise played on piano or saxophone, rather than brain response to Ode to Joy, suggesting that neural reorganization was driven more strongly by the practiced melody than by the trained instrumental timbre. Training also enhanced the neural separability of both melodic and instrumental information. Critically, pre-training responses to Fur Elise played on the piano predicted subsequent playing accuracy, whereas pre-training sensitivity to instrumental timbre predicted rhythmic performance in a novel melodic context. Together, these findings suggest that while acquired musical skills behaviorally transfer to novel material, practice-induced functional plasticity remains predominantly melody-specific. We conclude that musical skill acquisition reflects complementary contributions of pre-existing neural characteristics and learning-induced plasticity, with baseline auditory representations potentially supporting successful behavioral transfer.

neuroscience↗

bioRxiv2026

Altering facial preferences with context

Facial features are known to play a key role in how we judge a persons traits. However, in many scenarios people do not evaluate a person in isolation but are required to compare and choose between multiple candidates. Yet, the principles that govern these decisions remain unknown. In economic literature, decisions are known to be influenced by the context of available options, but it is unclear to what extent these principles apply to social decisions about naturalistic stimuli such as human faces. Here, we bridged between these two fields of research - face evaluations and economic decisions - to investigate whether facial preferences can be systematically shifted by context, exhibiting a well-known choice context effect, the "decoy effect". We asked participants to choose the person they think is more trustworthy from either a set of two ("target" and "competitor") or three faces (including a "decoy"). Based on economic theory, we carefully selected face sets using their trustworthiness rankings and perceptual similarity, and tested them across several exploratory samples (n=100-400) and one large, preregistered experiment (n=1099). We found that adding a decoy face to the choice set increased participants choices of the target option by up to 7%, while the decoy face was rarely selected, consistent with a typical decoy effect. These results provide direct evidence that social judgements are affected by the same context-dependent principles known from economic choices, and that the decoy effect can be generalized to complex naturalistic stimuli such as human faces, relevant to various real-world applications. Significance statementPeople often rely on facial judgements when choosing between candidates, like politicians or potential partners. These judgments are typically made in comparison to available alternatives rather than in isolation. In economic literature, decisions are known to depend on the context of alternatives, but it remains unclear whether social evaluations follow the same principles. Here, tested whether the "decoy effect", a well-known choice context effect, applies to social judgments. Participants were asked to choose the more trustworthy individual from either two (decoy-absent) or three faces (decoy-present). Adding a decoy face increased selection of the target face by up to 7%, while the decoy itself was rarely chosen, showing that social judgments are governed by the same context-dependent principles as economic choices.

neuroscience↗

bioRxiv2025

Latent dimensions in neural representations predict choice context effects

Choices are affected by the context of available alternatives, a phenomenon termed choice context effects. Current models of context effects require options to be described by two explicit numerical attributes. However, decision-makers might represent these options by additional latent attributes, which are hard to define a-priori. We propose to use participants neural representations to access the full attribute set they consider and predict context effects without modelling any explicit attributes. We first estimated the context effects elicited by lotteries using a behavioral sample. Then we recruited two fMRI samples with preregistered design to estimate the neural representations of each lottery without the context of choice. We predicted the context effects using only the similarity in neural representations between the individual lotteries, improving both out-of-sample and in-sample predictions compared to traditional methods. These neural representations encoded a mixture of explicit and latent attributes, previously inaccessible to researchers using only behavioral methods.

neuroscience↗

bioRxiv2024

Continuous structural neuroplasticity during motor learning - a diffusion MRI study

How does our brain transform when we encounter a new task? To fully answer this question, comparing brain states before and after learning may not be enough, but rather an on-going, continuous monitoring of brain changes during learning is required. While such continuous examinations of functional learning-induced changes are widely available using functional magnetic resonance imaging (fMRI), a continuous investigation of microstructural brain modifications during learning is yet to be reported. Here, we continuously acquire diffusion MRI images during task performance. We then compute the mean diffusivity (MD) using a sliding-window approach, resulting in a continuous measure of microstructural changes throughout learning. We demonstrate the utility of this method on a motor sequence learning (finger tapping) task (n=58). MD decrease was detected in task-related brain regions, including the parahippocampal gyrus, hippocampus, inferior temporal gyrus, and cerebellum. Analysis of the temporal patterns of decrease revealed a rapid MD reduction in the right temporal gyrus after 11 minutes of learning, with additional decrease in the right parahippocampal gyrus and left cerebellum after 22 minutes. We further computed "neuroplasticity networks" of brain areas showing similar change patterns and detected similarities between these networks and canonical functional connectivity networks. Our findings offer novel insights on the spatio-temporal dynamics of microstructural neuroplasticity by demonstrating continuous modifications during the encoding phase of learning itself, rather than comparing pre- and post-learning states.

neuroscience↗

bioRxiv2022

Characterizing habit learning in the human brain at the individual and group levels: a multi-modal MRI study

The dual-process theory of action control postulates that there are two competitive and complementary mechanisms that control our behavior: a goal-directed system that executes deliberate actions, explicitly aimed toward a particular outcome, and a habitual system that autonomously execute well-learned actions, typically following an encounter with a previously associated cue. In line with dual-process theory, animal studies have provided convincing evidence for dissociable neural mechanisms, mainly manifested in cortico-striatal regions, involved in goal-directed and habitual action control. While substantial progress has been made in characterizing the neural mechanism underlying habit learning in animals, we still lack knowledge on how habits are formed and maintained in the human brain. Thus far only one study, conducted more than a decade ago by Tricomi et al. (2009), has been able to induce habitual behavior in humans via extensive training. This study also implicated the posterior putamen in the process, using functional magnetic resonance imaging (fMRI). However, recent attempts to replicate the behavioral results of this study were not successful. This leaves the research of human habits, and particularly the research of their formation through extensive repetition, as well as their neural basis, limited and far behind the animal research in the field. This motivated us to (1) attempt to replicate the behavioral and imaging main findings of Tricomi et al., (2) identify further functional and microstructural neural modifications associated with habit formation and manifestation, and (3) investigate the relationships between functional and structural plasticity and individual differences in habit expression. To this end, in this registered report we used Tricomi et al.s free-operant task along with multi-modal MRI methods in a well-powered sample (n=123). In this task participants sensitivity to outcome devaluation (an index of goal-directed/habitual action control) is tested following either short or extensive training. In contrast to our hypothesis, we were not able to demonstrate habit formation as a function of training duration nor were we able to relate any functional or microstructural plasticity in the putamen with individual habit expression. We found that a pattern of increased activations in the left head of caudate that re-occurred across each days training is associated with goal directed behavior and that increased processing of devalued cues in low-level visual regions was indicative of goal-directed behavior (and vice versa). In a follow-up exploratory analysis comparing habitual and goal-directed subgroups within each experimental group, we found that elevated activations in frontoparietal regions during early stages of training, as well as increased reactivity towards still-valued cues in somatosensory and superior parietal regions were found in individuals that were more inclined to perform goal-directed behavior (compared with more habitual individuals). Taken together, regions commonly implicated in goal-directed behavior were most predictive of individual habit expression. Finally, we also found that differential patterns of training-related microstructural plasticity, as measured with diffusion MRI, in midbrain dopaminergic regions were associated with habit expression. This work provides new insights into the neural dynamics involved in individual habit formation/expression and encourages the development and testing of new, more sensitive, procedures for experimental habit induction in humans.

neuroscience↗

bioRxiv2021

Act Natural: Functional Connectivity from Naturalistic Stimuli fMRI Outperforms Resting-State in Predicting Brain Activity

The search for an ideal approach to investigate the functional connections in the human brain is an ongoing challenge for the neuroscience community. While resting-state functional magnetic resonance imaging (fMRI) has been widely used to study individual functional connectivity patterns, recent work has highlighted the benefits of collecting functional connectivity data while participants are exposed to naturalistic stimuli, such as watching a movie or listening to a story. For example, functional connectivity data collected during movie-watching were shown to predict cognitive and emotional scores more accurately than resting-state-derived functional connectivity. We have previously reported a tight link between resting-state functional connectivity and task-derived neural activity, such that the former successfully predicts the latter. In the current work we use data from the Human Connectome Project to demonstrate that naturalistic-stimulus-derived functional connectivity predicts task-induced brain activation maps more accurately than resting-state-derived functional connectivity. We then show that activation maps predicted using naturalistic stimuli are better predictors of individual intelligence scores than activation maps predicted using resting-state. We additionally examine the influence of naturalistic-stimulus type on prediction accuracy. Our findings emphasize the potential of naturalistic stimuli as a promising alternative to resting-state fMRI for connectome-based predictive modelling of individual brain activity and cognitive traits.

neuroscience↗

bioRxiv2021

Predicting Individual Traits from Unperformed Tasks

Relating individual differences in cognitive traits to brain functional organization is a long-lasting challenge for the neuroscience community. Individual intelligence scores were previously predicted from whole-brain connectivity patterns, extracted from functional magnetic resonance imaging (fMRI) data acquired at rest. Recently, it was shown that task-induced brain activation maps outperform these resting-state connectivity patterns in predicting individual intelligence, suggesting that a cognitively demanding environment improves prediction of cognitive abilities. Here, we use data from the Human Connectome Project to predict task-induced brain activation maps from resting-state fMRI, and proceed to use these predicted activity maps to further predict individual differences in a variety of traits. While models based on original task activation maps remain the most accurate, models based on predicted maps significantly outperformed those based on the resting-state connectome. Thus, we provide a promising approach for the evaluation of measures of human behavior from brain activation maps, that could be used without having participants actually perform the tasks.

neuroscience↗

bioRxiv2020

Brain volumetric changes in the general population following the COVID-19 outbreak and lockdown

The COVID-19 outbreak introduced unprecedented health-risks, as well as pressure on the financial, social, and psychological well-being due to the response to the outbreak. Here, we examined the manifestations of the COVID-19 outbreak on the brain structure in the healthy population, following the initial phase of the pandemic in Israel. We pre-registered our hypothesis that the intense experience of the outbreak potentially induced stress-related brain modifications. Volumetric changes in n = 50 participants who were scanned before and after the COVID-19 outbreak and lockdown, were compared with n = 50 control participants who were scanned twice prior to the pandemic. The pandemic provided a rare opportunity to examine brain plasticity in a natural experiment. We found volumetric increases in bilateral amygdalae, putamen, and the anterior temporal cortices. Changes in the amygdalae diminished as time elapsed from lockdown relief, suggesting that the intense experience associated with the pandemic outbreak induced transient volumetric changes in brain regions commonly associated with stress and anxiety.

neuroscience↗

bioRxiv2020

Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

BACKGROUNDPatients suffering from schizophrenia demonstrate abnormal brain activity, as well as alterations in patterns of functional connectivity assessed by functional magnetic resonance imaging (fMRI). Previous studies in healthy participants suggest a strong association between resting-state functional connectivity and task-evoked brain activity that could be detected at an individual level, and show that brain activation in various tasks could be predicted from task-free fMRI scans. In the current study we aimed to predict brain activity in patients diagnosed with schizophrenia, using a prediction model based on healthy individuals exclusively. This offers novel insights regarding the interrelations between brain connectivity and activity in schizophrenia. METHODSWe generated a prediction model using a group of 80 healthy controls that performed the well-validated N-back task, and used it to predict individual variability in task-evoked brain activation in 20 patients diagnosed with schizophrenia. RESULTSWe demonstrated a successful prediction of individual variability in the task-evoked brain activation based on resting-state functional connectivity. The predictions were highly sensitive, reflected by high correlations between predicted and actual activation maps (Median = 0.589, SD = 0.193) and specific, evaluated by a Kolomogrov-Smirnov test (D = 0.25, p < 0.0001). CONCLUSIONSA Successful prediction of brain activity from resting-state functional connectivity highlights the strong coupling between the two. Moreover, our results support the notion that even though resting-state functional connectivity and task-evoked brain activity are frequently reported to be altered in schizophrenia, the relations between them remains unaffected. This may allow to generate task activity maps for clinical populations without the need the actually perform the task.

neuroscience↗

bioRxiv2019

Short-Term Plasticity Following Motor Sequence Learning Revealed by Diffusion MRI

Current non-invasive methods to detect structural plasticity in humans are mainly used to study long-term changes. Diffusion MRI was recently proposed as a novel approach to reveal gray matter changes following spatial navigation learning and object-location memory tasks. In the present work we used diffusion MRI to investigate the short-term neuroplasticity that accompanies sequential motor learning. Following a 45-minutes training session in which participants learned to accurately play a short sequence on a piano keyboard, changes in diffusion properties were revealed mainly in motor system regions such as the premotor cortex and cerebellum. A second learning session taking place immediately afterwards shifted the attention of participants onto timing of key pressing instead of accuracy. This second session induced a different plasticity pattern, demonstrating the dynamic nature of this phenomenon, formerly thought to require months of training in order to be detectable. These results provide us with an important reminder that the brain is an extremely dynamic structure. Furthermore, diffusion MRI offers a novel measure to follow tissue plasticity particularly over short timescales, allowing new insights into the dynamics of structural brain plasticity.

neuroscience↗