Biology subjects

Rogers, T. T.

Publications and source records attributed to Rogers, T. T..

11 recordsLinked to original sources

bioRxiv2026

Multidimensional semantic representations emerge from multi-frequency representational similarity learning

Current theories of semantic representation are agnostic about how fine-grained neurophysiological activity corresponds to representation of multidimensional semantic information. By applying an innovative multivariate technique (representational similarity learning; RSL), we adjudicated three hypotheses: (1) that multidimensional semantic structure is represented within a single frequency range (e.g., gamma/high gamma); (2) that each semantic dimension is independently represented within a different frequency range; and (3) that multidimensional semantic information is "transfrequency" (at least some information emerges only when multiple frequencies are considered together). RSL was applied to time-frequency power and phase data extracted from electrocorticography (ECoG) grid electrodes on the surface of human ventral anterior temporal lobe (vATL). We found significant decoding of graded, multidimensional semantic information from a wide range of frequencies (4 - 200 Hz), but not from individual frequency bands, providing clear evidence that multidimensional semantic information is coded in a transfrequency fashion.

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bioRxiv2025

Adjudicating competing theories of semantic representation with 7T-fMRI and multivariate decoding

The anterior temporal lobes (ATLs) are known to support semantic cognition, but theories about their precise representational coding vary. We collected 7T-fMRI data with a novel acquisition sequence designed to improve signal quality in the ATLs, then employed a pioneering analytical approach (comparative multivariate decoding) to adjudicate between theories. Specifically, we applied multiple decoding methods, each making different assumptions about the content, nature or location of representations within the ATLs, then used the pattern of results across methods to adjudicate competing hypotheses. The results suggest that the ATLs represent domain-general semantic information via a multidimensional inchoate vector-space code that is anatomically clustered within and across individuals, and that posterior temporal and occipitotemporal regions utilise a similar domain-general, inchoate vector-space code. More generally, the comparative multivariate analytical framework utilised here has the potential to reveal how the brain represents, not just semantic knowledge, but any kind of information.

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bioRxiv2025

All spectral frequencies of neural activity reveal semantic representation in the human anterior ventral temporal cortex

Intracranial electrophysiology offers a unique insight into the nature of information representation in the brain - it can be used to disentangle information encoded in local neuronal activity (gamma and high gamma frequencies) from information encoded via long-range interactions (lower frequencies). We used regularised logistic regression to decode animacy from time-frequency power and phase extracted from electrocorticography (ECoG) grid electrode data recorded on the surface of human vATL. Power in gamma (30 - 60 Hz) and high gamma (60 - 200 Hz) produced reliable decoding, indicating that semantic information is indeed expressed by local populations in vATL. However, power from a wide range of frequencies (4 - 200 Hz) produced significantly higher decoding accuracy and also exhibited the same rapidly-changing dynamic code previously observed when decoding voltage. These findings support the theory that semantic information is encoded by a local vATL "hub" that interacts with distributed cortical "spokes".

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bioRxiv2025

Optimising 7T-fMRI for imaging regions of magnetic susceptibility

The temporal signal-to-noise ratio (tSNR) of functional magnetic resonance imaging (fMRI) is particularly poor in ventral anterior temporal and orbitofrontal regions because of B0 and B1+ magnetic field inhomogeneity, a problem that is exacerbated at higher field strengths. In this 7T-fMRI study we compared three methods of improving sensitivity in these areas: parallel transmit, which uses multiple transmit elements, controlled independently, to homogenise the flip angle experienced by the tissue; multi-echo, which entails collection of multiple volumes at different echo times following a single radiofrequency pulse; and multiband, in which multiple slices are acquired simultaneously. We found that parallel transmit and multi-echo increased the magnitude of the BOLD signal change, but only multi-echo increased BOLD magnitude in areas prone to susceptibility artefacts. Multiband and denoising of multi-echo data with independent components analysis (ICA) both improved precision of GLM fit. Exploratory results suggested that multi-echo and ICA denoising can both benefit multivariate analyses. In conclusion, a multi-echo, multiband sequence improved fMRI quality in areas prone to susceptibility artefacts while maintaining sensitivity across the whole brain. We recommend this approach for studies investigating the functional roles of ventral temporal and orbitofrontal regions with 7T fMRI.

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bioRxiv2022

Representational similarity learning reveals a graded multi-dimensional semantic space in the human anterior temporal cortex

Neurocognitive models of semantic memory have proposed that the ventral anterior temporal lobes (vATLs) encode a graded and multidimensional semantic space--yet neuroimaging studies seeking brain regions that encode semantic structure rarely identify these areas. In simulations we show that this discrepancy may arise from a crucial mismatch between theory and analysis approach. Utilizing an analysis recently formulated to investigate graded multidimensional representations, representational similarity learning (RSL), we decoded semantic structure from ECoG data collected from the vATL cortical surface while participants named line drawings of common items. The results reveal a graded, multidimensional semantic space encoded in neural activity across the vATL, which evolves over time and simultaneously expresses both broad and finer-grained semantic structure amongst animate and inanimate concepts. The work resolves the apparent discrepancy within the semantic cognition literature and, more importantly, suggests a new approach to discovering representational structure in neural data more generally. Conflict of interestThe Department of Epilepsy, Movement Disorders, and Physiology, Kyoto University Graduate School of Medicine conducts Industry-Academia Collaboration Courses, supported by Eisai Co., Ltd, Nihon Kohden Corporation, Otsuka Pharmaceutical Co., and UCB Japan Co., Ltd.

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bioRxiv2022

Late maturation of executive control promotes conceptual development

Control processes underpinned by the prefrontal cortex are critical for generating task-appropriate behaviour across cognitive domains, yet this region develops extremely late. Traditionally, this developmental pattern is considered negative but necessary. However, an alternative (yet perhaps complementary) view suggests that a developmental period without control could support learning, particularly in the semantic domain. Here, we exploit a recent computational model to test formally whether late development of the context-sensitive use of conceptual knowledge, or semantic control, would promote concept acquisition. Simulations show that late maturation of semantic control and anatomical connectivity conspire to promote conceptual learning. Delayed control speeds conceptual learning without compromising conceptual representations, particularly when control connects to intermediate layers. To assess whether semantic control also develops late in human children, we conducted a meta-analysis of the classic triadic matching task where participants decide which of two options best matches a third. Matching can be based on taxonomic or thematic relations. When these conflict, participants must exert semantic control to determine which relation is task appropriate. Context-sensitivity develops later than conceptual knowledge with large increases between 3 and 6 years. Thus, the protracted PFC development leads to a delay in acquiring semantic control processes, benefiting conceptual learning.

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bioRxiv2019

Reverse-Engineering the Cortical Architecture for Controlled Semantic Cognition

We present a reverse engineering approach to deconstruct cognition into neurocomputational mechanisms and their underlying cortical architecture, using controlled semantic cognition as a test case. By systematically varying the structure of a computational model and assessing the functional consequences, we identified architectural properties necessary for generating the core functions of the semantic system. Semantic cognition presents a challenging test case as the brain must achieve two seemingly contradictory functions: abstracting context-invariant conceptual representations across time and modalities, whilst producing specific context-sensitive behaviours appropriate for the immediate task. These functions were best achieved in models possessing a single, deep multimodal hub with sparse connections from modality-specific inputs, and control systems acting on peripheral rather than deep network layers. These architectural features correspond well with those suggested by neural data, strongly supporting the efficacy of the reverse engineering approach, and further generating novel hypotheses about the neuroanatomy of controlled semantic cognition.

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bioRxiv2019

Evidence for a deep, distributed and dynamic semantic code in human ventral anterior temporal cortex

How does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: semantic information is distributed across ventral temporal cortex in a dynamic code that possesses stable feature-like elements in posterior regions but with elements that change rapidly and nonlinearly in anterior regions. This pattern is consistent with the view that anterior temporal lobes serve as a deep cross-modal "hub" in an interactive semantic network, and more generally suggests that tertiary association cortices may adopt dynamic distributed codes difficult to detect with common brain imaging methods.

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bioRxiv2018

Connecting natural and artificial neural networks in functional brain imaging using structured sparsity

Artificial neural network models have long proven useful for understanding healthy, disordered, and developing cognition, but this work has often proceeded with little connection to functional brain imaging. We consider how analysis of functional brain imaging data is best approached if the representational assumptions embodied by neural networks are valid. Using a simple model to generate synthetic data, we show that four contemporary methods each have critical and complementary blind-spots for detecting distributed signal. The pattern suggests a new approach based on structured sparsity that, in simulation, retains the strengths of each method while avoiding its weaknesses. When applied to functional magnetic resonance imaging data the new approach reveals extensive distributed signal missed by the other methods, suggesting radically different conclusions about how brains encode cognitive information in the well-studied domain of visual face perception.

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bioRxiv2017

Human hippocampal replay during rest prioritizes weakly-learned information and predicts memory performance

There is now extensive evidence that the hippocampus replays experiences during quiet rest periods, and that this replay benefits subsequent memory. A critical open question is how memories are prioritized for replay during these offline periods. We addressed this question in an experiment in which participants learned the features of 15 objects and then underwent fMRI scanning to track item-level replay in the hippocampus using pattern analysis during a rest period. Objects that were remembered less well were replayed more during the subsequent rest period, suggesting a prioritization process in which weaker memories--memories most vulnerable to forgetting--are selected for wake replay. Participants came back for a second session, either after a night of sleep or a day awake, and underwent another scanned rest period followed by a second memory test. In the second session, more hippocampal replay of a satellite during the rest period predicted better subsequent memory for that satellite. Only in the group with intervening sleep did rest replay predict improvement from the first to second session. Our results provide the first evidence that replay of individual memories occurs during rest in the human hippocampus and that this replay prioritizes weakly learned information, predicts subsequent memory performance, and relates to memory improvement across a delay with sleep.

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bioRxiv2017

Sleep Benefits Memory For Semantic Category Structure While Preserving Exemplar-Specific Information

Semantic memory encompasses knowledge about both the properties that typify concepts (e.g. robins, like all birds, have wings) as well as the properties that individuate conceptually related items (e.g. robins, in particular, have red breasts). We investigate the impact of sleep on new semantic learning using a property inference task in which both kinds of information are initially acquired equally well. Participants learned about three categories of novel objects possessing some properties that were shared among category exemplars and others that were unique to an exemplar, with exposure frequency varying across categories. In Experiment 1, memory for shared properties improved and memory for unique properties was preserved across a night of sleep, while memory for both feature types declined over a day awake. In Experiment 2, memory for shared properties improved across a nap, but only for the lower-frequency category, suggesting a prioritization of weakly learned information early in a sleep period. The increase was significantly correlated with amount of REM, but was also observed in participants who did not enter REM, suggesting involvement of both sleep stages. The results provide the first evidence that sleep improves memory for the shared structure of object categories, while simultaneously preserving object-unique information.

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