Biology subjects

Super, H.

Publications and source records attributed to Super, H..

4 recordsLinked to original sources

bioRxiv2026

Cognitive Vergence and Pupillary Responses as Functional Oculomotor Signatures to Differentiate AT(N) Biological Profiles

BackgroundThe AT(N) biological framework classifies Alzheimers disease (AD) pathology using CSF biomarkers, with the A+T+ profile defining biological AD and the A-T+ profile representing a biologically distinct entity consistent with suspected non-Alzheimers pathophysiology, such as primary age-related tauopathy. Functional assessment capable of differentiating these profiles non-invasively remains limited. This study investigates whether cognitive vergence and pupillary temporal dynamics during a visual oddball task can distinguish A-T+ from A+T+ biological profiles in individuals with mild cognitive impairment (MCI). MethodsThirty-eight participants with MCI (12 A-T+, 26 A+T+) classified by CSF biomarkers completed a visual oddball task (80% distractors, 20% targets) under continuous eye-tracking. Linear mixed-effects models examined profile x condition interactions on full time series and six trial-level temporal features. Participant-level differentiation was assessed using binomial logistic regression, adjusting for age, sex, and MMSE. ResultsBoth profiles showed comparable overall oculomotor response magnitudes but diverged markedly in temporal organization. Significant profile x condition interactions emerged for cognitive vergence global slope, time to peak, and pupillary time to peak. Logistic regression confirmed that timing features discriminated biological profiles at the participant level, with differentiation reversing direction between distractor and target conditions. A-T+ participants also maintained superior target detection accuracy (89.3% vs. 82.4%, p = 0.001). ConclusionCognitive Vergence and pupillary temporal dynamics during an oddball task provide condition-dependent functional oculomotor signatures that systematically differentiate AT(N) biological profiles in MCI, suggesting that oculomotor assessment may offer an accessible, non-invasive complement to CSF-based profile characterization.

neuroscience↗

bioRxiv2026

Cognitive Vergence and Pupil Response During Oddball Task are Associated With Alzheimers Disease Cerebrospinal Fluid Neurodegenerative Biomarkers

BackgroundAlzheimers disease (AD) can be diagnosed using cerebrospinal fluid (CSF) biomarkers reflecting amyloid and tau pathology. However, it provides no information about functional network status. We aimed to determine whether CSF biomarkers (A{beta}42, p-Tau, t-Tau, and A{beta}42/p-Tau ratio) are associated with altered stimulus differentiation in vergence and pupil responses during an oddball task, and to evaluate oculomotor metrics as predictors of CSF core AD biomarkers in patients at mild cognitive impairment (MCI) stage. MethodsThirty-eight participants with abnormal CSF core AD biomarkers at MCI stage completed a visual oddball task while oculomotor responses were recorded. Linear mixed-effects models examined condition x biomarker interactions, controlling for sex, age, and MMSE. Temporal and magnitude features were tested as predictors using linear regression. ResultsHigher p-Tau levels were negatively associated with target-distractor differentiation in cognitive vergence ({beta} = -0.035, p < 0.001) and pupil responses ({beta} = - 0.060, p < 0.001). Higher A{beta}42 and A{beta}42/p-Tau showed positive associations with vergence differentiation but opposite effects on pupil responses. Oculomotor features predicted p-Tau levels (R2 = 0.20-0.21). ConclusionOculomotor differentiation metrics capture functional signatures of tau-related network dysfunction, positioning them as accessible biomarkers complementing CSF measures for detecting network disruption at MCI stage.

neuroscience↗

bioRxiv2026

Gray matter Volume Modulates the Effect of Acute Physical Activity on Reading Comprehension and Cognitive Load in Adolescents. The Cogni-Action Project

BackgroundPhysical activity has been associated with better reading comprehension and reduces cognitive load (CL), but the role of brain volume in modulating this relationship remains unclear. Therefore, this study aims to determine whether the gray matter volume in key regions modulates the effects of different physical activity modalities on reading comprehension and associated CL. MethodsThirteen male adolescents (12-13 years). Adolescents with MRI data participated in a randomized cross-over trial comparing three conditions: 1) sedentary behavior (SC, emulating a school class), 2) moderate-intensity continuous training (MICT), and 3) cooperative high-intensity interval training (C-HIIT), with physical activity conditions duration adjusted to match SC energy expenditure. Gray matter volumes were measured in the bilateral hippocampus, left pars opercularis, and the brainstem. CL was assessed via pupil dilation during reading using eye-tracking. Reading comprehension was measured through seven-question multiple-choice tests with expert-validated items. ResultsC-HIIT demonstrated superior effects on both CL and reading comprehension compared to MICT and SC, with significant brain volume modulation effects across all examined regions. Brain volume interactions with physical activity modalities systematically modified the pattern of cognitive responses, with C-HIIT consistently benefiting from these modulations, whereas the effects of MICT were generally attenuated. ConclusionThis study suggests that selecting the appropriate physical activity modality may be relevant for cognitive outcomes during reading in adolescents. C-HIIT yielded lower CL and better reading comprehension, and these effects were not explained by brain volume alone but by its interaction with exercise modality.

neuroscience↗

bioRxiv2020

Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

The visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a predictive coding mechanism, which reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process, which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast. Author summaryWe hardly notice that what we see is often different from the physical world "outside" of the brain. This means that the visual experience that the brain actively constructs may be different from the actual physical properties of objects in the world. In this work, we propose a hypothesis about how the visual system of the brain may construct a representation for achromatic images. Since this process is not unambiguous, sometimes we notice "errors" in our perception, which cause visual illusions. The challenge for theorists, therefore, is to propose computational principles that recreate a large number of visual illusions and to explain why they occur. Notably, our proposed mechanism explains a broader set of visual illusions than any previously published proposal. We achieved this by trying to suppress predictable information. For example, if an image contained repetitive structures, then these structures are predictable and would be suppressed. In this way, non-predictable structures stand out. Predictive coding mechanisms act as early as in the retina (which enhances luminance changes but suppresses uniform regions of luminance), and our computational model holds that this principle also acts at the next stage in the visual system, where representations of perceived luminance (brightness) are created.

neuroscience↗