Biology subjects

Bogdan, P. C.

Publications and source records attributed to Bogdan, P. C..

5 recordsLinked to original sources

bioRxiv2025

Mapping concept and relational semantic representation in the brain using large language models

How the brain organizes semantic information is one of the most challenging and expansive questions in cognitive neuroscience. To shed light on this issue, prior studies have attempted to decode how the brain represents concepts. We instead examined how relational information is encoded, which we pursued by submitting texts to a contemporary large language model and extracting relational embeddings from the model. Using behavioral data (N = 636), we found these embeddings capture independent information about scenes and objects, along with relational information on their semantic links. Turning to fMRI data (N = 60), we leveraged these embeddings for representational similarity analysis: The occipitotemporal cortex represents concepts in isolation, whereas the dorsolateral prefrontal cortex and basal ganglia principally encode relational information. Relational coding within prefrontal and striatal areas also tracks how participants reason about scenes and objects. Altogether, this research maps how information progresses from concept-level to integrative forms and how this translates into behavior.

neuroscience↗

bioRxiv2025

Trial-Level Representational Similarity Analysis

Neural representation refers to the brain activity that stands in for ones cognitive experience, and in cognitive neuroscience, a prominent method of studying neural representations is representational similarity analysis (RSA). While there are several recent advances in RSA, the classic RSA (cRSA) approach examines the structure of representations across numerous items by assessing the correspondence between two representational similarity matrices (RSMs): usually one based on a theoretical model of stimulus similarity and the other based on similarity in measured neural data. However, because cRSA cannot weigh the contributions of individual trials (RSM rows/columns), it is fundamentally limited in its ability to assess subject-, stimulus-, and trial-level variances that all influence representation. Here, we formally introduce trial-level RSA (tRSA), an analytical framework that estimates the strength of neural representation for singular experimental trials and evaluates hypotheses using multi-level models. First, we verified the correspondence between tRSA and cRSA in quantifying the overall representation strength across all trials. Second, we compared the statistical inferences drawn from both approaches using simulated data that reflected a wide range of scenarios. Compared to cRSA, the multi-level framework of tRSA was both more theoretically appropriate and significantly sensitive to true effects. Third, using real fMRI datasets, we further demonstrated several issues with cRSA, to which tRSA was more robust. Finally, we presented some novel findings of neural representations that could only be assessed with tRSA and not cRSA. In summary, tRSA proves to be a robust and versatile analytical approach for cognitive neuroscience and beyond.

neuroscience↗

bioRxiv2025

Intrinsic fluctuations in global connectivity reflect transitions between states of high and low prediction error

While numerous researchers claim that the minimization of prediction error (PE) is a general force underlying most brain functions, others argue instead that PE minimization drives low-level, sensory-related neuronal computations but not high-order, abstract cognitive operations. We investigated this issue using behavioral, fMRI, and EEG data. Studies 1A/1B examined semantic- and reward-processing PE using task-fMRI, yielding converging evidence of PEs global effects on large-scale connectivity: high-PE states broadly upregulated ventral-dorsal connectivity, and low-PE states upregulated posterior-anterior connectivity. Investigating whether these global patterns characterize cognition generally, Studies 2A/2B used resting-state fMRI and showed that individuals continuously fluctuate between ventral-dorsal (high-PE) and posterior-anterior (low-PE) dynamic connectivity states. Additionally, individual differences in PE task responses track differences in resting-state fluctuations, further endorsing that these fluctuations represent PE minimization at rest. Finally, Study 3 combined fMRI and EEG data, and the study found that the fMRI fluctuation amplitude correlates most strongly with EEG power at 3-6 Hz, consistent with the PE network fluctuations occurring at Delta/Theta oscillation speeds. This whole-brain layout and timeline together are consistent with high/low-PE fluctuations playing a role in integrative and general sub-second cognitive operations.

neuroscience↗

bioRxiv2025

Local and distributed information coding in the ventral stream

Neuroscience is replete with evidence that cognitive representations are distributed across many cortical regions. Yet, the scale and content of such distributed processing is unclear. Do findings of widespread information coding suggest a large-scale "forest" of regions interacting to represent information or instead imply a multitude of small-scale trees, processing information as localized modules. To investigate this distinction, we used visual and semantic representational analysis of fMRI data from 60 participants viewing everyday objects in multiple task contexts, and we examined the relationships between regions in terms of information coding. We demonstrate that coding of visual content in the occipital lobe is overwhelmingly modular, such that different occipital structures show limited coordination and tend to encode information redundantly. By contrast, the coding of semantic content in the inferior temporal lobe involves a high degree of coordination between regions, which optimize their coding to collectively represent a large semantic space with minimal redundancy between regions. No other brain area - neither the parietal nor prefrontal cortices - shows the preference for large-scale coding seen in the inferior temporal lobe. Taken together, these results outline a framework of how the ventral stream transitions from small-scale to large-scale coding as information progresses from visual to semantic representations. Significance statementHow does the brain convert incoming signals into usable information? Many studies have investigated this question by attempting to clarify which brain regions encode what information (e.g., V4 encodes color information). We instead aimed to shed light on the degree of coordination among information coding regions. We find that the visual-to-semantic transition as information flows anteriorly in the occipitotemporal cortex is accompanied by a shift from modular to distributed coding. That is, occipital regions encode perceptual information relatively independently with redundancy, while inferior temporal lobe regions cooperate to most efficiently represent a large space of semantic information. By leveraging ideas from information theory, our work introduces coding scale as a new dimension for understanding the architecture of information coding.

neuroscience↗

bioRxiv2024

Cortico-hippocampal interactions underlie schema-supported memory encoding in older adults

Although episodic memory is typically impaired in older adults (OAs) compared to young adults (YAs), this deficit is attenuated when OAs can leverage their rich semantic knowledge, such as their knowledge of schemas. Memory is better for items consistent with pre-existing schemas and this effect is larger in OAs. Neuroimaging studies have associated schema use with the ventromedial prefrontal cortex (vmPFC) and hippocampus (HPC), but most of this research has been limited to YAs. This fMRI study investigated the neural mechanisms underlying how schemas boost episodic memory in OAs. Participants encoded scene-object pairs with varying congruency, and memory for the objects was tested the following day. Congruency with schemas enhanced object memory for YAs and, more substantially, for OAs. FMRI analyses examined how cortical modulation of HPC predicted subsequent memory. Congruency-related vmPFC modulation of left HPC enhanced subsequent memory in both age groups, while congruency-related modulation from angular gyrus (AG) boosted subsequent memory only in OAs. Individual differences in cortico-hippocampal modulations indicated that OAs preferentially used their semantic knowledge to facilitate encoding via an AG-HPC interaction, suggesting a compensatory mechanism. Collectively, our findings illustrate age-related differences in how schemas influence episodic memory encoding via distinct routes of cortico-hippocampal interactions.

neuroscience↗