Biology subjects

Karimi-Rouzbahani, H.

Publications and source records attributed to Karimi-Rouzbahani, H..

13 recordsLinked to original sources

bioRxiv2026

Bridging Sensory Extraction and Decision Dynamics: Face Pareidolia Emergent from Bottom-Up and Top-Down Interactions

Face pareidolia-the illusory perception of faces in everyday objects-provides a unique window into how the human brain resolves visual ambiguity. Whether pareidolia arises from bottom-up sensory processing or top-down, expectation-driven feedback, is currently debated. Here we recorded electroencephalography (EEG) while participants viewed rapid serial visual presentation (RSVP) streams of images, each of which was an object-to-face morph generated along a 21-image continuum via text-to-image diffusion models. Time-resolved representational similarity analyses revealed a clear spatiotemporal dissociation, in which early occipitotemporal areas tracked physical feature changes linearly, while later frontocentral channels collapsed this structural gradient into a sharp, sigmoidal profile reflecting discrete categorical decisions (face-like versus object-like). To characterise information transmission between these regions, we implemented a novel transformation-based representational connectivity analysis. These analyses uncovered an initial feedforward flow of information that transferred and transformed posterior linear traces into anterior sigmoidal representations. Crucially, partitioning the image continuum revealed that unambiguous stimuli (faces or objects) were predominantly resolved via feedforward information flow, whereas ambiguous pareidolic images recruited prolonged top-down feedback. Furthermore, perceptual decision sensitivity was biased by animacy priors and directly predicted individual behavioural reaction times during the task. Together, the findings help reconcile prior debate by establishing that face pareidolia is neither a feedforward process nor an isolated top-down projection, but an active, multi-stage recurrent process driven by feedforward feature extraction and top-down categorical resolution.

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bioRxiv2025

Dynamic Reversal of IT-PFC Information Flow Orchestrates Visual Categorization Under Perceptual Uncertainty

Categorization relies on a dynamic interplay between sensory representation and cognitive control, yet the classical view posits a fixed, feed-forward information flow from the inferotemporal (IT) cortex to the prefrontal cortex (PFC). Whether this hierarchical directionality adapts to cognitive context, such as perceptual certainty, remains a fundamental question in systems neuroscience. We investigated this by recording intracranial neural activity in monkeys performing a delayed match-to-category task. We developed a novel connectivity framework, Model-Based Representational Connectivity Analysis (RCA), to simultaneously track the content, timing, and directionality of information flow between IT and PFC while controlling for common task-general representations. Our results revealed that while both areas rapidly encoded task-relevant information, the directionality of information flow was highly modulated by stimulus certainty. For high-certainty stimuli (far from the category boundary), we observed the classical feed-forward flow from IT to PFC. However, for low-certainty stimuli (near the category boundary), this hierarchy dynamically reversed, with a dominant, early feedback flow from PFC to IT preceding the feed-forward sweep. This feedback signal carried content-specific information related to the ambiguous stimuli, suggesting a top-down mechanism recruited to refine sensory representations. These findings challenge fixed-hierarchy models of visual processing, providing mechanistic evidence that the brain dynamically reconfigures the interactions between sensory and executive areas as a function of perceptual difficulty. We propose that the PFC initiates a top-down biasing signal to the IT cortex when sensory evidence is ambiguous, serving as an adaptive, context-driven control mechanism.

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bioRxiv2025

Identifying Explainable and Generalizable Features for MEG Decoding

Sensory neural coding, the brains process of transforming inputs into informative patterns of neural activity, generates complex and multiplexed neural codes which are hard to interpret. Although decoding methods have facilitated the interpretation of these codes, the specific features of neural activity that generalize across individuals, along with their precise timing, largely remain elusive. To address this gap, we investigated the potential of interpretable time-series features in magnetoencephalography (MEG) for decoding visual stimulus attributes (spatial frequency and orientation) and their generalizability across individuals. We extracted a comprehensive set of highly informative features from 18 subjects engaged in a visual task and performed a time-resolved decoding analysis. Our findings revealed that particular features, especially those capturing rapid changes in neural activity within the first 200 milliseconds of stimulus presentation, yielded high decoding accuracy. Notably, these early, transient features exhibited robust cross-subject generalizability, suggesting a shared neural coding mechanism during the initial processing of visual inputs. Furthermore, these features outperformed previously successful electroencephalography (EEG) wavelet features when applied to MEG data. While within-subject decoding demonstrated sustained above-chance performance, cross-subject generalization diminished after the initial 200 milliseconds, indicating more individualized processing at later stages. Our results underscore the importance of systematic, data-driven evaluation of neural signals for elucidating neural codes and for developing more transparent and generalizable Brain-Computer Interface (BCI) systems that capitalize on these reliable neural signatures.

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bioRxiv2024

Spatiotemporal characterisation of information coding and exchange in the multiple demand network

The multiple-demand network (MDN), a brain-wide system with nodes near sensory and higher-order cognitive regions, has been suggested to integrate and exchange task-related information across the brain, supporting cognitive task performance. However, the profile of information coding and the role of each node within this network in information exchange remain unclear. To address this, we combined fMRI and MEG data in a challenging stimulus-response mapping task. Using multivariate pattern analysis (MVPA), we decoded various forms of task information, including coarse and fine stimulus details, motor responses, and stimulus-response mapping rules, across the MDN and visual regions. Early in the task, visual regions responded to large physical differences in stimuli, while later on, fine stimulus information and rules were encoded across the MDN. To assess information exchange between regions, we developed Fusion-RCA, a novel connectivity analysis method based on fMRI-MEG fusion profiles. Our findings revealed significant transfer of fine stimulus information, rules, and responses, but little evidence for the transfer of coarse stimulus information. These results highlight distinct information encoding patterns within MDN nodes and suggest that the anterior cingulate cortex (ACC) plays a key role in distributing task-relevant information. This study offers new insights into the dynamic function of the MDN and introduces Fusion-RCA as a powerful tool for exploring brain-wide information transfer.

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bioRxiv2023

Evidence for multiscale multiplexed representation of visual features in EEG

Distinct neural processes are often encoded across distinct time scales of neural activations. However, it has remained unclear if this multiscale coding strategy is also implemented for separate features of the same process. One difficulty is that the conventional methods of time scale analysis provide imperfect estimations of time scales when several components are active during a single process. Developing a novel decoding-based time scale estimation method, we detected distinct time scales for simultaneously present features of visual stimuli in electroencephalography. We observed that orientation and colour of grating stimuli were encoded in shorter whereas the spatial frequency and contrast of those stimuli were encoded in longer time scales. The conventional autocorrelation-based estimation of time scale was unable to detect these distinguishable time scales. These results provide new evidence for a multiscale multiplexed neural code in the human visual system and introduces a flexible method for estimating neural time scales.

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bioRxiv2021

Caveats and nuances of model-based and model-free representational connectivity analysis

Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarising activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions and transformation of representational structure across brain regions. The paper is accompanied by scripts that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

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bioRxiv2021

When the whole is less than the sum of its parts: maximum object category information and behavioral prediction in multiscale activation patterns

Neural codes are reflected in complex, temporally and spatially specific patterns of activation. One popular approach to decode neural codes in electroencephalography (EEG) is multivariate decoding. This approach examines the discriminability of activity patterns across experimental conditions to test if EEG contains information about those conditions. However, conventional decoding analyses ignore aspects of neural activity which are informative. Specifically, EEG data can be decomposed into a large number of mathematically distinct features (e.g., entropy, Fourier and Wavelet coefficients) which can reflect different aspects of neural activity. We previously compared 30 such features of EEG data, and found that visual category, and participant behavior, can be more accurately predicted using multiscale spatiotemporally sensitive Wavelet coefficients than mean amplitude (Karimi-Rouzbahani et al., 2021b). Here, we considered that even this larger set of features may only partially capture the underlying neural code, because the brain could use a combination of encoding protocols within a single trial which is not reflected in any one mathematical feature alone. To check, we combined those mathematical features using state-of-the-art supervised and unsupervised feature selection procedures (n = 17). Across 3 datasets, we compared decoding of visual object category between these 17 sets of combined features, and between combined and individual features. Object category could be robustly decoded using the combined features from all of the 17 algorithms. However, the combination of features, which were equalized in dimension to the individual features, were outperformed in most of the time points by the most informative individual feature (Wavelet coefficients). Moreover, the Wavelet coefficients also explained the behavioral performance more accurately than the combined features. These results suggest that a single but multiscale encoding protocol may capture the neural code better than any combination of features. Our findings put new constraints on the models of neural information encoding in EEG.

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bioRxiv2020

Informative Neural Codes to Separate Object Categories

In order to develop object recognition algorithms, which can approach human-level recognition performance, researchers have been studying how the human brain performs recognition in the past five decades. This has already in-spired AI-based object recognition algorithms, such as convolutional neural networks, which are among the most successful object recognition platforms today and can approach human performance in specific tasks. However, it is not yet clearly known how recorded brain activations convey information about object category processing. One main obstacle has been the lack of large feature sets, to evaluate the information contents of multiple aspects of neural activations. Here, we compared the information contents of a large set of 25 features, extracted from time series of electroencephalography (EEG) recorded from human participants doing an object recognition task. We could characterize the most informative aspects of brain activations about object categories. Among the evaluated features, event-related potential (ERP) components of N1 and P2a were among the most informative features with the highest information in the Theta frequency bands. Upon limiting the analysis time window, we observed more information for features detecting temporally informative patterns in the signals. The results of this study can constrain previous theories about how the brain codes object category information.

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bioRxiv2020

Temporal codes provide additional category-related information in object category decoding: a systematic comparison of informative EEG features

How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly use the "mean" neural activation within the analysis window to extract category information. Such temporal averaging overlooks the within-trial neural variability which is suggested to provide an additional channel for the encoding of information about the complexity and uncertainty of the sensory input. The richness of temporal variabilities, however, has not been systematically compared with the conventional "mean" activity. Here we compare the information content of 31 variability-sensitive features against the "mean" of activity, using three independent highly-varied datasets. In whole-trial decoding, the classical event-related potential (ERP) components of "P2a" and "P2b" provided information comparable to those provided by "Original Magnitude Data (OMD)" and "Wavelet Coefficients (WC)", the two most informative variability-sensitive features. In time-resolved decoding, the "OMD" and "WC" outperformed all the other features (including "mean"), which were sensitive to limited and specific aspects of temporal variabilities, such as their phase or frequency. The information was more pronounced in Theta frequency band, previously suggested to support feed-forward visual processing. We concluded that the brain might encode the information in multiple aspects of neural variabilities simultaneously e.g. phase, amplitude and frequency rather than "mean" per se. In our active categorization dataset, we found that more effective decoding of the neural codes corresponds to better prediction of behavioral performance. Therefore, the incorporation of temporal variabilities in time-resolved decoding can provide additional category information and improved prediction of behavior.

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bioRxiv2020

Perceptual difficulty modulates the direction of information flow in familiar face recognition

Humans are fast and accurate when they recognize familiar faces. Previous neurophysiological studies have shown enhanced representations for the dichotomy of familiar vs. unfamiliar faces. As familiarity is a spectrum, however, any neural correlate should reflect graded representations for more vs. less familiar faces along the spectrum. By systematically varying familiarity across stimuli, we show a neural familiarity spectrum using electroencephalography. We then evaluated the spatiotemporal dynamics of familiar face recognition across the brain. Specifically, we developed a novel informational connectivity method to test whether peri-frontal brain areas contribute to familiar face recognition. Results showed that feed-forward flow dominates for the most familiar faces and top-down flow was only dominant when sensory evidence was insufficient to support face recognition. These results demonstrate that perceptual difficulty and the level of familiarity influence the neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.

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bioRxiv2020

Vigilance decrements in the brain: neural signatures can predict behavioural errors before they happen

There are now many environments in which humans need to monitor moving displays and only rarely act, such as train control and driving autonomous vehicles; lapses of attention in these circumstances can have tragic consequences. Problematically, we know that it is difficult to sustain attention under these monitoring or vigilance conditions and performance drops: when target events are rare, we tend to miss them, or are slower to respond. This rare target effect becomes more marked with longer tasks, known as a vigilance decrement. Despite the importance, we still have limited understanding of how the brain processes information during monitoring, particularly with dynamic stimuli, and how this processing changes when attention lapses. Here, we designed a multiple-object monitoring (MOM) paradigm that required sustained attention to dynamic stimuli, and used multivariate analyses of magnetoencephalography (MEG) data to examine how the neural representation of the information in the display varied with target frequency and time on the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for the frequent target (active) condition. This change was mirrored in the neural results: under monitoring conditions, there was weaker coding of the critical distance between objects during time periods when vigilance decrements in performance occurred. There was also weaker informational connectivity between peri-occipital and peri-frontal brain areas in rare versus frequent target conditions. We developed a new analysis which used the strength of information decoding to predict whether the participant was going to miss the target on a given trial. We could predict behavioural errors more than a second before they occurred. This provides a first step in developing methods to predict and pre-empt behavioural errors due to lapses in attention and provides new insight into how vigilance decrements are reflected in information coding in the brain.

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bioRxiv2018

Spatiotemporal Analysis of Category and Target-related Information Processing in the Brain during Object Detection

To recognize a target object, the brain implements strategies which involve a combination of externally sensory-driven and internally task-driven mechanisms. While several studies have suggested a role for frontal brain areas in enhancing task-related representations in visual cortices, especially the lateral-occipital cortex, they remained silent about the type of information transferred to visual areas. However, the recently developed method of representational causality analysis, allowed us to track the movement of different types of information in the brain. Accordingly, we designed an EEG object detection experiment and evaluated the spatiotemporal dynamics of category- and target-related information across the brain using. Results showed that the prefrontal area initiated the processing of target-related information. This information was then transferred to posterior brain areas during stimulus presentation to facilitate object detection and to direct the decision-making procedure. We also observed that, as compared to category-related information, the target-related information could predict the behavioral detection performance more accurately, suggesting the dominant representation of internal compared to external information in brain signals. These results provided new evidence about the role of prefrontal cortices in the processing of task-related information the brain during object detection.

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bioRxiv2018

Three-stage processing of category information by interactive mechanisms of peri-occipital and peri-frontal cortices

Invariant object recognition, which refers to the ability of precisely and rapidly recognizing objects in the presence of variations, has been a central question in human vision research. The general consensus is that the ventral and dorsal visual streams are the major processing pathways which undertake category and variation encoding in entangled layers. This overlooks the mounting evidence which support the role of peri-frontal areas in category encoding. These recent studies, however, have left open several aspects of visual processing in peri-frontal areas including whether these areas contributed only in active tasks, whether they interacted with peri-occipital areas or processed information independently and differently. To address these concerns, a passive EEG paradigm was designed in which subjects viewed a set of variation-controlled object images. Using multivariate pattern analysis, noticeable category and variation information were observed in occipital, parietal, temporal and prefrontal areas, supporting their contribution to visual processing. Using task specificity indices, phase and Granger causality analyses, three distinct stages of processing were identified which revealed transfer of information between peri-frontal and peri-occipital areas suggesting their parallel and interactive processing of visual information. A brain-plausible computational model supported the possibility of parallel processing mechanisms in peri-occipital and peri-frontal areas. These findings, while advocating previous results on the role of prefrontal areas in object recognition, extend their contribution from active recognition, in which peri-frontal to peri-occipital feedback mechanisms are activated, to the general case of object and variation processing, which is an integral part of visual processing and play role even during passive viewing.

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