Biology subjects

Chunharas, C.

Publications and source records attributed to Chunharas, C..

6 recordsLinked to original sources

bioRxiv2026

The Prefrontal Cortex Organizes Visual Evidence by Representational Abstraction

Perceptual decisions often depend on both lower-level visual features and higher-level object representations, yet natural images entangle these levels of abstraction. We used deep neural network features to dissociate lower- and higher-level visual similarity during a naturalistic image-similarity judgment task. Participants relied on both forms of evidence, with greater weighting of higher-level similarity. Across prefrontal cortex, visual evidence was systematically organized by representational level: lower- and higher-level similarity were expressed in distinct prefrontal regions during comparison and remained spatially differentiated as they were transformed into decision signals. This organization was behaviorally relevant, with prefrontal activation predicting which level of evidence guided choice when the two conflicted. Together, these findings identify representational level as an organizing principle of prefrontal cortex during visual decisions.

neuroscience↗

bioRxiv2026

Do Symptoms Matter? Investigating Symptom-Based Lesion Network Mapping.

Lesion network mapping (LNM) describes a group of methods using normative functional connectivity data to map disparate brain lesions and stimulation sites onto common brain networks. Van den Heuvel and colleagues recently showed that these methods lack disease specificity, instead producing maps that converge toward intrinsic properties of the normative connectome dataset. Here, we investigate symptom LNM (sLNM), a recent advancement in the method which attempts to increase the robustness of results by incorporating symptom severity and incorporating replication across multiple datasets and prediction of clinical outcomes. Using clinical datasets of depression and Brocas aphasia, we show that sLNM maps from unrelated disorders nonetheless converge despite using null models which break the specific lesion-symptom structure in the datasets. Using simulated datasets with a known ground-truth disease network, we show that sLNM results are systematically biased towards the normative connectomes first principal component (PC1), which drives spurious convergence across unrelated datasets. We further show that the apparent clinical predictive capability of these maps are non-specific: network maps derived from unrelated disorders such as migraine and aphasia predict brain stimulation improvement in depression as well as -- or better than -- the cohorts own sLNM map. However, controlling for PC1 reduces spurious convergence across unrelated datasets and improves clinical prediction specificity, supporting the notion that disease-specific signal exists within sLNM but is confounded by the globally present PC1 signal in the normative connectome. These findings offer a practical correction applicable to existing and future sLNM studies.

neuroscience↗

bioRxiv2025

Visual representations in the human brain rely on a reference frame that is in between allocentric and retinocentric coordinates

Visual information in our everyday environment is anchored to an allocentric reference frame - a tall building remains upright even when you tilt your head, which changes the projection of the building on your retina from a vertical to a diagonal orientation. Does retinotopic cortex represent visual information in an allocentric or retinocentric reference frame? Here, we investigate which reference frame the brain uses by dissociating allocentric and retinocentric reference frames via a head tilt manipulation combined with electroencephalography (EEG). Nineteen participants completed between 1728-2880 trials during which they briefly viewed (150 ms) and then remembered (1500 ms) a randomly oriented target grating. In interleaved blocks of trials, the participants head was either kept upright, or tilted by 45{o} using a custom rotating chinrest. The target orientation could be decoded throughout the trial (using both voltage and alpha-band signals) when training and testing within head-upright blocks, and within head-tilted blocks. Importantly, we directly addressed the question of reference frames via cross-generalized decoding: If target orientations are represented in a retinocentric reference frame, a decoder trained on head-upright trials would predict a 45{o} offset in decoded orientation when tested on head-tilted trials (after all, a vertical building becomes diagonal on the retina after head tilt). Conversely, if target representations are allocentric and anchored to the real world, no such offset should be observed. Our analyses reveal that from the earliest stages of perceptual processing all the way throughout the delay, orientations are represented in between an allocentric and retinocentric reference frame. These results align with previous findings from physiology studies in non-human primates, and are the first to demonstrate that the human brain does not rely on a purely allocentric or retinocentric reference frame when representing visual information.

neuroscience↗

bioRxiv2023

A gradual transition from veridical to categorical representations along the visual hierarchy during working memory, but not perception.

The ability to stably maintain visual information over brief delays is central to healthy cognitive functioning, as is the ability to differentiate such internal representations from external inputs. One possible way to achieve both is via multiple concurrent mnemonic representations along the visual hierarchy that differ systematically from the representations of perceptual inputs. To test this possibility, we examine orientation representations along the visual hierarchy during perception and working memory. Human participants directly viewed, or held in mind, oriented grating patterns, and the similarity between fMRI activation patterns for different orientations was calculated throughout retinotopic cortex. During direct viewing of grating stimuli, similarity was relatively evenly distributed amongst all orientations, while during working memory the similarity was higher around oblique orientations. We modeled these differences in representational geometry based on the known distribution of orientation information in the natural world: The "veridical" model uses an efficient coding framework to capture hypothesized representations during visual perception. The "categorical" model assumes that different "psychological distances" between orientations result in orientation categorization relative to cardinal axes. During direct perception, the veridical model explained the data well. During working memory, the categorical model gradually gained explanatory power over the veridical model for increasingly anterior retinotopic regions. Thus, directly viewed images are represented veridically, but once visual information is no longer tethered to the sensory world there is a gradual progression to more categorical mnemonic formats along the visual hierarchy.

neuroscience↗

bioRxiv2021

An explainable self-attention deep neural network for detecting mild cognitive impairment using multi-inputbdigital drawing tasks

Mild cognitive impairment (MCI) is an early stage of age-inappropriate cognitive decline, which could develop into dementia - an untreatable neurodegenerative disorder. An early detection of MCI is a crucial step for timely prevention and intervention. To tackle this problem, recent studies have developed deep learning models to detect MCI and various types of dementia using data obtained from the classic clock-drawing test (CDT), a popular neuropsychological screening tool that can be easily and rapidly implemented for assessing cognitive impairments in an aging population. While these models succeed at distinguishing severe forms of dementia, it is still difficult to predict the early stage of the disease using the CDT data alone. Also, the state-of-the-art deep learning techniques still face the black-box challenges, making it questionable to implement them in the clinical setting. Here, we propose a novel deep learning modeling framework that incorporates data from multiple drawing tasks including the CDT, cube-copying, and trail-making tasks obtained from a digital platform. Using self-attention and soft-label methods, our model achieves much higher classification performance at detecting MCI compared to those of a well-established convolutional neural network model. Moreover, our model can highlight features of the MCI data that considerably deviate from those of the healthy aging population, offering accurate predictions for detecting MCI along with visual explanation that aids the interpretation of the deep learning model.

neuroscience↗

bioRxiv2018

Simultaneous representation of sensory and mnemonic information in human visual cortex

Traversing sensory environments requires keeping relevant information in mind while simultaneously processing new inputs. Visual information is kept in working memory via feature selective responses in early visual cortex, but recent work had suggested that new sensory inputs wipe out this information. Here we show region-wide multiplexing abilities in classic sensory areas, with population-level response patterns in visual cortex representing the contents of working memory concurrently with new sensory inputs.

neuroscience↗