Biology subjects

Coutanche, M. N.

Publications and source records attributed to Coutanche, M. N..

9 recordsLinked to original sources

bioRxiv2025

Accessing Meaning During Encoding Shapes Subsequent Memory Reactivation in the Brain

Semantic access can occur in many forms, ranging from retrieving specific exemplars to linking concepts with related meanings. We hypothesized that accessing meaning at different levels during encoding would lead to distinct neural reactivation patterns during later memory retrieval. Human participants learned novel word-image associations through prompts that queried item-specific, categorical, or thematic features, while undergoing fMRI. Multivariate pattern analysis shows that encoding histories leave distinct neural signatures even when behavioral performances is equivalent. Encoding history could be decoded by classifiers from recognition activity across the ventral visual stream and higher-order regions. Category-based encoding reduced item-specific reinstatement in perceptual regions, while item-based encoding modified cross-phase item-level similarity in the anterior temporal lobe, suggesting a transition from detailed to integrative conceptual representations. Together, these findings demonstrate that semantic access during encoding shapes how memories are neurally reinstated to integrate perceptual and conceptual information across systems supporting semantic and episodic memory.

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bioRxiv2025

Reduced Dependence on Sensorimotor Processing in the Brain is Associated with Higher Math Skills in Adults

Number processing is crucial for math competence, yet the neural underpinnings remain unclear. In this study, we used functional magnetic resonance imaging (fMRI) to 1) identify key brain regions involved in symbolic and embodied number processing, and 2) examine their relations with math abilities in children and adults. We collected fMRI data from 104 adults (Mage = 23.25 years) and 88 4th graders (Mage = 9.75) as they performed number comparison and phonological tasks with numeral (symbolic) and hand (embodied) numerical stimuli. Participants math abilities were assessed using the math subtests of the Woodcock-Johnson Tests of Achievement. Univariate analyses revealed number processing regions in occipital, parietal and temporal cortices including inferior parietal, sensorimotor, supramarginal and insular areas. In adults, less involvement of sensorimotor processing regions during number compared to phonological processing is associated with enhanced math skills. These findings not only elucidate the neural mechanisms underlying number processing and math abilities but also raise questions about the emphasis on embodied number presentation in math learning experiences. Significance StatementChildren and adults show substantial individual differences in math competence, and understanding the underlying neural mechanisms is crucial. By employing functional magnetic resonance imaging (fMRI), we identified key brain regions involved in symbolic and embodied number processing and explored their relations with math skills. Our findings reveal that reduced sensorimotor involvement during number processing correlates with enhanced math performance in adults. This study not only elucidates the neural mechanisms underlying number processing, offering critical insights into the cognitive architecture that supports math abilities, but also raises questions about the emphasis on embodied number representations in math learning experiences.

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bioRxiv2025

The Neural Consequences of Semantic Composition

Humans can create completely new concepts through semantic composition. These conceptual combinations can be created by attributing the features of one concept to another (e.g., a lemon flamingo might be a yellow flamingo), or drawing on a relationship between concepts (e.g., a lemon flamingo might consume lemons). We ask how semantic composition modulates the neural representations of underlying concepts. Combining functional magnetic resonance imaging (fMRI) with multivariate pattern analysis, we interrogate neural patterns for concepts before and after they were subjected to semantic composition. We observe a post-composition shift in neural patterns underlying weakly constrained concepts in the visual system. The composition of strongly constrained combinations draws on a network of semantic regions that include the right inferior frontal gyrus, left angular gyrus, left lateral anterior temporal lobe, and posterior cingulate cortex. Finally, a subset of the semantic network, in left parahippocampal gyrus, distinguishes the manner of composition: relational or attributive. These findings reveal that semantic composition has neural consequences for the composed concepts, and that the manner of composition affects how the brains semantic network is deployed.

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bioRxiv2025

Semantic Memory Traces Reflect How They Were Last Retrieved

Episodic memories are known to change with each act of retrieval. We hypothesize that accessing semantic knowledge in different ways during episodic retrieval--from unique perceptual features to taxonomic and thematic context--shapes how those memories are subsequently represented during recognition. In this functional magnetic resonance imaging (fMRI) study, human participants learned novel word-image pairs and underwent re-exposure that required accessing semantic knowledge at one of three different levels: item, category, or theme. Participants then completed a recognition task. Although behavioral memory performance was matched across conditions, neural activity during recognition varied based on prior semantic access history. Recognition patterns could be classified according to prior semantic access in early visual cortex, ventral temporal cortex and the hippocampus for remembered concepts. A whole-brain searchlight analysis revealed bilateral clusters along the ventral visual stream where semantic access history was decodable. To further characterize how prior semantic access history shaped neural representations during recognition, we tested whether memories accessed at the same semantic level were expressed more similarly or distinctly in the brain. Category-level access increased similarity among items in early visual cortex, while item-level access led to more differentiated representations in the visual word form area. Together, these findings show that how we access semantic knowledge during episodic retrieval leaves measurable traces in subsequent neural representations, revealing how semantic-episodic interactions shape memory representations.

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bioRxiv2025

Identifying Networks within an fMRI Multivariate Searchlight Analysis

There is great interest in understanding how different brain regions represent information across space and time. Information-based searchlight analyses systematically examine the information encoded within clusters of functional magnetic resonance imaging (fMRI) voxels across the brain. Significant searchlights contain information that can be used to decode conditions of interest, but significant discriminability can be achieved in a variety of ways. We have developed and report on a new analysis method that can identify sub-networks of searchlights. Notably, unlike methods that collapse trials by condition, such as Representational Similarity Analysis, our method groups searchlights based on them having similar temporal changes in information. We present this method and apply it to fMRI data collected as participants viewed words, faces, shapes, and numbers. After running a searchlight analysis with a 4-way Gaussian Naive Bayes (GNB) classifier, the accuracy vector was submitted to a multi-subject Independent Component Analysis (ICA) to group searchlights based on their decoding timeseries. The ICA identified seven components (sub-networks) of searchlights. These networks identified sets of brain areas that have been commonly associated with the processing of faces, words, shapes and numbers. For instance, two of the components drew strongly on the face-processing network, including fusiform cortex. Switching the classification scheme to faces versus non-faces reconfigured the observed network to reflected face-related systems. These results demonstrate that this method can divide searchlight maps into meaningful components.

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bioRxiv2021

Schema support for forming inferences in the human brain

Associative inference, the process of drawing novel links between existing knowledge to rapidly integrate associated information, is supported by the hippocampus and neocortex. Within the neocortex, the medial prefrontal cortex (mPFC) has been implicated in the rapid cortical learning of new information that is congruent with an existing framework of knowledge, or schema. How the brain integrates associations to form inferences, specifically how inferences are represented, is not well understood. In this study, we investigate how the brain uses schemas to facilitate memory integration in an associative inference paradigm (A-B-C-D). We conducted two event-related fMRI experiments in which participants retrieved previously learned direct (AB, BC, CD) and inferred (AC, AD) associations between word pairs for items that are schema congruent or incongruent. Additionally, we investigated how two factors known to affect memory, a delay with sleep, and reward, modulate the neural integration of associations within, and between, schema. Schema congruency was found to benefit the integration of associates, but only when retrieval immediately follows learning. RSA revealed that neural patterns of inferred pairs (AC) in the PHc, mPFC, and posHPC were more similar to their constituents (AB and BC) when the items were schema congruent, suggesting that schema facilitates the assimilation of paired items into a single inferred unit containing all associated elements. Furthermore, a delay with sleep, but not reward, impacted the assimilation of inferred pairs. Our findings reveal that the neural representations of overlapping associations are integrated into novel representations through the support of memory schema. Significance StatementOur ability to draw novel links between pieces of existing knowledge allows us to understand how information in memory is related. Existing knowledge (memory schema) can facilitate learning, and then integration, of new related information. We ask how the human brain uses schema to form links between related pieces of information, and how sleep and reward affect this process. Our results suggest that memory schema helps pieces of knowledge become a single inferred unit in the brains memory systems. A delay with sleep between learning and retrieval, though not reward, is important for how schema achieve this.

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bioRxiv2020

Representational Connectivity Analysis: Identifying Networks of Shared Changes in Representational Strength through Jackknife Resampling

The structure of information in the brain is crucial to cognitive function. The representational space of a brain region can be identified through Representational Similarity Analysis (RSA) applied to functional magnetic resonance imaging (fMRI) data. In its classic form, RSA collapses the time-series of each condition, eliminating fluctuations in similarity over time. We propose a method for identifying representational connectivity (RC) networks, which share fluctuations in representational strength, in an analogous manner to functional connectivity (FC), which tracks fluctuations in BOLD signal, and informational connectivity, which tracks fluctuations in pattern discriminability. We utilize jackknife resampling, a statistical technique in which observations are removed in turn to determine their influence. We applied the jackknife technique to an existing fMRI dataset collected as participants viewed videos of animals (Nastase et al., 2017). We used ventral temporal cortex (VT) as a seed region, and compared the resulting network to a second-order RSA, in which brain regions representational spaces are compared, and to the network identified through FC. The novel representational connectivity analysis identified a network comprising regions associated with lower-level visual processing, spatial cognition, perceptual-motor integration, and visual attention, indicating that these regions shared fluctuations in representational similarity strength with VT. RC, second-order RSA and FC identified areas unique to each method, indicating that analyzing shared fluctuations in the strength of representational similarity reveals previously undetectable networks of regions. The RC analysis thus offers a new way to understand representational similarity at the network level.

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bioRxiv2019

Image memorability is predicted by activity across different stages of convolutional neural networks and the human ventral stream

The features of an image can be represented at multiple levels - from its low-level visual properties to high-level meaning. What drives some images to be memorable while others are forgettable? We address this question across two behavioral experiments. In the first, different layers of a convolutional neural network (CNN), which represent progressively higher levels of features, were used to select the images that would be shown to 100 participants through a form of prospective assignment. Here, the discriminability/similarity of an image with others, according to different CNN layers dictated the images presented to different groups, who made a simple indoor vs. outdoor judgment for each scene. We find that participants remember more scene images that were selected based on their low-level discriminability or high-level similarity. A second experiment replicated these results in an independent sample of fifty participants, with a different order of post-encoding tasks. Together, these experiments provide evidence that both discriminability and similarity, at different visual levels, predict image memorability.

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bioRxiv2018

Fast Memory Integration Facilitated by Schema Consistency

Many of our everyday decisions are based not only on memories of direct experiences, but on memories that are integrated across multiple distinct experiences. Sometimes memory integration between existing memories and newly learnt information occurs rapidly, without requiring inference at the time of a decision. Such fast memory integration is known to be supported by the hippocampus but not the neocortex. In this study, we explore an alternative mechanism of fast memory integration, through related prior knowledge (i.e., schema), which is associated with neocortical learning. Paired associates were selected to be schema consistent or inconsistent, and confirmed with a latent semantic analysis of text corpora. We observed that after enabling fast learning by using material that is consistent with a schema, faster memory integration can occur. This result suggests that the hippocampus-mediated integration of new information is not the only available mechanism that supports fast memory integration.

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