Biology subjects

Ivanova, A. A.

Publications and source records attributed to Ivanova, A. A..

10 recordsLinked to original sources

bioRxiv2026

Scaling laws for group-constrained, subject-specific task fMRI analyses

Functional MRI provides a powerful way to characterize the architecture of the human brain. Yet individual brains often vary in the exact anatomic locations of functionally specific regions; as a result of this variability, traditional random-effects group analyses (RFX) often require unrealistically large sample sizes to converge onto stable results. Here, we explore the scaling patterns for an alternative fMRI analysis framework that accounts for inter-individual topographic variability while still capturing spatial similarities--group-constrained, subject-specific (GcSS) analysis. Using sixteen contrasts from four Human Connectome Project tasks (spanning language, social cognition, motor, and working memory), we first show that the GcSS approach yields much larger effect sizes than traditional RFX analyses. We then investigate the impact of sample size (N=10-450) on the estimation of three key GcSS outputs: (1) group probabilistic maps for the contrast of interest, (2) group-level parcels that serve as anatomical constraints for subsequent functional region of interest (fROI) definitions in individual subjects, and (3) effect sizes of fROI responses to task conditions. For most contrasts, the probabilistic maps show good reliability at N=100 (intraclass correlation, ICC=.75) and excellent reliability at N=200 (ICC=.90). Most group parcels also achieve substantial agreement at N=100 (Dice coefficient, DC=.80). Critically, once robust group parcels are established, the subject-specific portion of the analysis can proceed with much smaller sample sizes: even samples of N=10 participants yield accurate effect size estimates within subject-specific fROIs, detecting 80% of practically meaningful effects (effect size [≥] 0.2); with N=20, this increases to 90%. The scaling patterns we observed held not only in the cortex, but also in the cerebellum. Our results challenge the view that fMRI research requires large samples: once the broad region of interest is established (with N=100 or higher), fROI-based analyses can yield generalizable results with small sample sizes (N=10 or N=20).

neuroscience↗

bioRxiv2025

Semantic reasoning takes place largely outside the language network

The brains language network is often implicated in the representation and manipulation of abstract semantic knowledge. However, this view is inconsistent with a large body of evidence suggesting that language processing is neurally distinct from the rest of cognition. Here, we use precision brain imaging to uncover a set of brain regions, separate from the language network, that are engaged in semantic processing of both linguistic and pictorial stimuli. In three fMRI experiments, participants (total n=41 tested across 49 sessions) viewed sentences and pictures depicting simple events. In separate blocks, they performed either a semantic task or a difficulty-matched perceptual task. Across all three experiments, several areas in left lateral prefrontal cortex, left temporo-parietal cortex, and right cerebellum responded to semantic tasks for both sentences and pictures. These semantic processing areas are spatially and functionally distinct from the nearby language-selective areas, as well as from the multiple demand and default mode networks, exhibiting a unique response profile. Our results provide evidence for a new kind of selectivity in the human brain and pave the way for future explorations of the neural mechanisms that underlie semantic reasoning.

neuroscience↗

bioRxiv2025

The language network responds robustly to sentences across diverse tasks

A network of left frontal and temporal brain areas supports language comprehension and production, implementing computations related to word retrieval and combinatorial linguistic processing. Here, we ask: to what extent are responses to language in this language network stable across task contexts, and how does this stability compare to task sensitivity in the domain-general multiple demand (MD) network? Participants (n=52) read sentences and nonword lists under six task conditions, including passive reading, reading with a memory probe after each stimulus, and reading and answering questions that require deep semantic engagement. The sentences>nonwords contrast isolated the same set of language-responsive voxels across all tasks; the locations of those voxels were participant-specific, highlighting the value of individual-specific functional localization. We therefore conclude that language localization is robust to task variation. We then examined the magnitudes and fine-grained activation patterns in these language-responsive voxels (the language network) and in the domain-general MD network, to test whether task demands modulate linguistic computations and/or recruit a distinct brain system. The language network responded robustly to sentences across all tasks, with somewhat higher responses to semantically engaging tasks. In contrast, the MD network responded to both sentences and nonwords in the presence of a task, which warrants caution when using language paradigms that include task demands, as such paradigms engage two independent networks. A multivariate analysis further revealed that stimulus information is more easily decodable in the language network, whereas task information is more decodable in the MD network. These results suggest that the language and MD networks perform complementary functions during task-driven language comprehension, with the language network primarily extracting information from linguistic input and the MD network determining the appropriate response to the task.

neuroscience↗

bioRxiv2022

Temporal dynamics of the multi-omic response to endurance exercise training across tissues

Regular exercise promotes whole-body health and prevents disease, yet the underlying molecular mechanisms throughout a whole organism are incompletely understood. Here, the Molecular Transducers of Physical Activity Consortium (MoTrPAC) profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome, and immunome in whole blood, plasma, and 18 solid tissues in Rattus norvegicus over 8 weeks of endurance exercise training. The resulting data compendium encompasses 9466 assays across 19 tissues, 25 molecular platforms, and 4 training time points in young adult male and female rats. We identified thousands of shared and tissue- and sex-specific molecular alterations. Temporal multi-omic and multi-tissue analyses demonstrated distinct patterns of tissue remodeling, with widespread regulation of immune, metabolism, heat shock stress response, and mitochondrial pathways. These patterns provide biological insights into the adaptive responses to endurance training over time. For example, exercise training induced heart remodeling via altered activity of the Mef2 family of transcription factors and tyrosine kinases. Translational analyses revealed changes that are consistent with human endurance training data and negatively correlated with disease, including increased phospholipids and decreased triacylglycerols in the liver. Sex differences in training adaptation were widespread, including those in the brain, adrenal gland, lung, and adipose tissue. Integrative analyses generated novel hypotheses of disease relevance, including candidate mechanisms that link training adaptation to non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health, and tissue injury and recovery. The data and analysis results presented in this study will serve as valuable resources for the broader community and are provided in an easily accessible public repository (https://motrpac-data.org/). HighlightsO_LIMulti-tissue resource identifies 35,439 analytes regulated by endurance exercise training at 5% FDR across 211 combinations of tissues and molecular platforms. C_LIO_LIInterpretation of systemic and tissue-specific molecular adaptations produced hypotheses to help describe the health benefits induced by exercise. C_LIO_LIRobust sex-specific responses to endurance exercise training are observed across multiple organs at the molecular level. C_LIO_LIDeep multi-omic profiling of six tissues defines regulatory signals for tissue adaptation to endurance exercise training. C_LIO_LIAll data are available in a public repository, and processed data, analysis results, and code to reproduce major analyses are additionally available in convenient R packages. C_LI

molecular biology↗

bioRxiv2021

Suppression of Fatty Acid Oxidation by Thioesterase Superfamily Member 2 in Skeletal Muscle Promotes Hepatic Steatosis and Insulin Resistance

Thioesterase superfamily member 2 (Them2) is highly expressed in oxidative tissues where it hydrolyzes long chain fatty acyl-CoA esters to free fatty acids and CoA. Although mice globally lacking Them2 (Them2-/-) are protected against diet-induced obesity, insulin resistance and hepatic steatosis, liver-specific Them2-/- mice remain susceptible. To explore the contribution of Them2 in extrahepatic tissues, we created mice with Them2 deleted in skeletal muscle (S-Them2-/-), cardiac muscle (C-Them2-/-) or adipose tissue (A-Them2-/-). When fed a high-fat diet, S-Them2-/- but not C-Them2-/- or A-Them2-/- mice exhibited reduced weight gain. Only S-Them2-/- mice exhibited improved glucose homeostasis together with improved insulin sensitivity in skeletal muscle. Increased rates of fatty acid oxidation in skeletal muscle of S-Them2-/- mice were reflected in alterations in skeletal muscle metabolites, including short chain fatty acids, branched chain amino acids and the pentose phosphate pathway. Protection from diet-induced hepatic steatosis in S-Them2-/- mice was attributable to increased VLDL triglyceride secretion rates in support of demands of increased muscle fatty acid utilization. These results reveal a key role for skeletal muscle Them2 in the pathogenesis of diet-induced obesity, insulin resistance and hepatic steatosis.

molecular biology↗

bioRxiv2021

Is it that simple? Linear mapping models in cognitive neuroscience

Many cognitive neuroscience studies use large feature sets to predict and interpret brain activity patterns. Feature sets take many forms, from human stimulus annotations to representations in deep neural networks. Of crucial importance in all these studies is the mapping model, which defines the space of possible relationships between features and neural data. Until recently, most encoding and decoding studies have used linear mapping models. Increasing availability of large datasets and computing resources has recently allowed some researchers to employ more flexible nonlinear mapping models instead; however, the question of whether nonlinear mapping models can yield meaningful scientific insights remains debated. Here, we discuss the choice of a mapping model in the context of three overarching desiderata: predictive accuracy, interpretability, and biological plausibility. We show that, contrary to popular intuition, these desiderata do not map cleanly onto the linear/nonlinear divide; instead, each desideratum can refer to multiple research goals, each of which imposes its own constraints on the mapping model. Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models. We show that, in many cases, complexity provides a more accurate reflection of restrictions imposed by various research goals. Finally, we outline several complexity metrics that can be used to effectively evaluate mapping models.

neuroscience↗

bioRxiv2021

No evidence for a special role of language in feature-based categorization

The relationship between language and human thought is the subject of long-standing debate. One specific claim implicates language in feature-based categorization. According to this view, language resources facilitate object categorization based on a certain feature (e.g., color). Specifically, it is hypothesized that verbal labels help maintain focus on a relevant categorization criterion and reduce interference from irrelevant features. As a result, language impairment is expected to affect categorization of items grouped according to a single feature (low-dimensional categories, e.g., Things that are yellow), where many irrelevant features need to be inhibited, more than categorization of items that share many features (high-dimensional categories, e.g., Animals), where few irrelevant features need to be inhibited. In two behavioral studies with individuals with aphasia, we failed to find consistent support for the role of language in low-dimensional categorization. We also collected fMRI data from healthy adults and observed little activity in language-responsive brain regions during both low-dimensional and high-dimensional categorization. Combined, these results demonstrate that the language system is not implicated in object categorization. Our work adds to the growing evidence that, although language may assist in accessing task-relevant information (e.g., instructions), many cognitive tasks in adult brains proceed without recruiting the language system.

neuroscience↗

bioRxiv2020

Comprehension of computer code relies primarily on domain-general executive resources

Computer programming is a novel cognitive tool that has transformed modern society. What cognitive and neural mechanisms support this skill? Here, we used fMRI to investigate two candidate brain systems: the multiple demand (MD) system, typically recruited during math, logic, problem solving, and executive tasks, and the language system, typically recruited during linguistic processing. We examined MD and language system responses to code written in Python, a text-based programming language (Experiment 1) and in ScratchJr, a graphical programming language (Experiment 2); for both, we contrasted responses to code problems with responses to content-matched sentence problems. We found that the MD system exhibited strong bilateral responses to code in both experiments, whereas the language system responded strongly to sentence problems, but weakly or not at all to code problems. Thus, the MD system supports the use of novel cognitive tools even when the input is structurally similar to natural language.

neuroscience↗

bioRxiv2019

Arl13b regulates Sonic Hedgehog signaling from outside primary cilia

ARL13B is a regulatory GTPase highly enriched in cilia. Complete loss of Arl13b disrupts cilia architecture, protein trafficking and Sonic hedgehog signaling. To determine whether ARL13B is required within cilia, we knocked in a cilia-excluded variant of ARL13B (V358A) and showed it retains all known biochemical function. We found that ARL13BV358A protein was expressed but could not be detected in cilia, even when retrograde ciliary transport was blocked. We showed Arl13bV358A/V358A mice are viable and fertile with normal Shh signal transduction. However, in contrast to wild type cilia, Arl13bV358A/V358A cells displayed short cilia and lacked ciliary ARL3 and INPP5E. These data indicate that ARL13Bs role within cilia can be uncoupled from its function outside of cilia. Furthermore, these data imply that the cilia defects upon complete absence of ARL13B do not underlie the alterations in Shh transduction, which is unexpected given the requirement of cilia for Shh transduction.

developmental biology↗

bioRxiv2019

The language network is recruited but not required for non-verbal semantic processing

The ability to combine individual meanings into complex representations of the world is often associated with language. Yet people also construct combinatorial event-level representations from non-linguistic input, e.g. from visual scenes. Here, we test whether the language network in the human brain is involved in and necessary for semantic processing of nonverbal events. In Experiment 1, we scanned participants with fMRI while they performed a semantic plausibility judgment task vs. a difficult perceptual control task on sentences and line drawings that describe/depict simple agent-patient interactions. We found that the language network responded robustly during the semantic task but not during the perceptual control task. This effect was observed for both sentences and pictures (although the response to sentences was stronger). Thus, language regions in healthy adults are engaged during a semantic task performed on pictorial depictions of events. But is this engagement necessary? In Experiment 2, we tested two individuals with global aphasia, who have sustained massive damage to perisylvian language areas and display severe language difficulties, against a group of age-matched control participants. Individuals with aphasia were severely impaired on a task of matching sentences and pictures. However, they performed close to controls in assessing the plausibility of pictorial depictions of agent-patient interactions. Overall, our results indicate that the left fronto-temporal language network is recruited but not necessary for semantic processing of nonverbal events.

neuroscience↗