Biology subjects

Mohsenzadeh, Y.

Publications and source records attributed to Mohsenzadeh, Y..

17 recordsLinked to original sources

bioRxiv2025

Reverse Spatiotemporal Hierarchy during Cross-modal Memory Recall and Imagery

Recalling past events is often accompanied by mental imagery of those experiences. Based on previous research, this process engages memory- and sensory-related brain areas. However, the underlying spatiotemporal dynamics remain poorly investigated. Here, we used naturalistic videos of audiovisual events and recorded fMRI data during the tasks in which human participants recalled visual contents when hearing associated sounds and recalled sounds when watching silent videos, after they had well memorized the video contents. With time-resolved fMRI multivariate pattern analyses, we observed reverse spatiotemporal hierarchy during the visual memory recall and imagery: the neural activity in primary visual cortex was delayed compared with high-order visual areas. A similar pattern was found during auditory memory recall and imagery, where the bottom-up progression from the mid-level planum temporale to the high-level superior temporal gyrus observed during auditory perception was absent. However, the primary auditory area was not involved, suggesting modality differences in the role of primary sensory areas in corresponding memory recall. We also observed the activity of the hippocampus, the parahippocampal cortex, the retrosplenial cortex, and the precuneus and examined their temporal dynamics. Overall, our study provided both spatial and temporal accounts of neural activity during the cross-modal memory recall and imagery.

neuroscience↗

bioRxiv2025

Distinct perceptual and conceptual representations of natural actions along the lateral and dorsal visual streams: an EEG-fMRI fusion study

Actions are the building blocks of our dynamic visual world, yet the neural computations supporting action perception are not well understood. How does perceptual and conceptual information unfold in the brain when we observe what others are doing? We collected EEG and fMRI data while participants viewed short videos and sentences depicting naturalistic actions. Using representational similarity analysis, we found a posterior-to-anterior gradient along the lateral pathway, from perceptual features to conceptual, modality-invariant representations. Among conceptual features, the target of actions (i.e. whether the action was directed at an object, a person, or the self) explained the most unique variance in EEG responses. In fMRI, we found distinct conceptual representations along the ventral, dorsal, and lateral pathways, with the target of actions specifically encoded in lateral occipitotemporal cortex (LOTC) and posterior superior temporal sulcus (pSTS). Finally, EEG-fMRI fusion revealed rapid processing along the lateral and dorsal pathways. Together, our results disentangle the perceptual and conceptual components of action understanding and characterize the underlying spatiotemporal dynamics in the human brain.

neuroscience↗

bioRxiv2024

Neural Processing of Naturalistic Audiovisual Events in Space and Time

Our brain seamlessly integrates distinct sensory information to form a coherent percept. However, when real-world audiovisual events are perceived, the specific brain regions and timings for processing different levels of information remain less investigated. To address that, we curated naturalistic videos and recorded fMRI and EEG data when participants viewed videos with accompanying sounds. Our findings reveal early asymmetrical cross-modal interaction, with acoustic information represented in both early visual and auditory regions, while visual information only identified in visual cortices. The visual and auditory features were processed with similar onset but different temporal dynamics. High-level categorical and semantic information emerged in multi-modal association areas later in time, indicating late cross-modal integration and its distinct role in converging conceptual information. Comparing neural representations to a two-branch deep neural network model highlighted the necessity of early fusion to build a biologically plausible model of audiovisual perception. With EEG-fMRI fusion, we provided a spatiotemporally resolved account of neural activity during the processing of naturalistic audiovisual stimuli.

neuroscience↗

bioRxiv2023

Using Machine Learning to Identify Neural Mechanisms Underlying the Development of Cognition in Children and Adolescents with ADHD

Children and adolescents diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) often show deficits in various measures of higher-level cognition, such as, memory and executive functioning. Poorer high-level cognitive functioning in children with ADDH has been associated with differences in functional connectivity across the brain, including the frontoparietal network. However, little is known about the developmental changes to cortical functional connectivity profiles associated with higher-order cognitive abilities in this cohort. To characterize changes in the functional brain connectivity profiles related to higher-order cognitive functioning, we analyzed a large dataset(n=479) from the publicly available Healthy Brain Network which included fMRI data collected while children and adolescents between the ages of 6 and 16 watched a short movie-clip. The cohort was divided into two groups, neurotypical youth (n=106), and children and adolescents with ADHD (n=373). We applied machine learning models to functional connectivity profiles generated from the fMRI data to identify patterns of network connectivity that differentially predict cognitive abilities in our cohort of interest. We found, using out-of-sample cross validation, models using functional connectivity profiles in response to movie-watching successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children ages 6 to 11, but not in adolescents with ADHD. The models identified connections with the default mode, memory retrieval, and dorsal attention networks as driving prediction during early and middle childhood, but connections with the somatomotor, cingulo-opercular, and frontoparietal networks were more important in middle childhood. This work demonstrated that computational models applied to neuroimaging data in response to naturalistic stimuli can identify distinct neural mechanisms associated with cognitive abilities at different developmental stages in children and adolescents with ADHD.

neuroscience↗

bioRxiv2023

Multivariate Pattern Analysis of EEG Reveals Neural Mechanism of Naturalistic Target Processing in Attentional Blink

The human brain has inherent limitations in consciously processing visual information. When individuals monitor a rapid sequence of images for detecting two targets, they often miss the second target (T2) if it appears within a short time frame of 200-500ms after the first target (T1), a phenomenon known as the attentional blink (AB). The neural mechanism behind AB remains unclear, largely due to the use of simplistic visual items such as letters and digits in conventional AB experiments, which differ significantly from naturalistic vision. This study employed advanced multivariate pattern analysis (MVPA) of human EEG data to explore the neural representations associated with target processing within a naturalistic paradigm under conditions where AB does or does not occur. Our MVPA analysis successfully decoded the identity of target images from EEG data. Moreover, in the AB condition, characterized by a limited time between targets, T1 processing coincided with T2 processing, resulting in the suppression of late representational markers of both T1 and T2. Conversely, in the condition with longer inter-target interval, neural representations endured for a longer duration. These findings suggest that the attentional blink can be attributed to the suppression of neural representations in the later stages of target processing. Significance StatementWithin a naturalistic paradigm, we investigated the phenomenon known as attentional blink, where individuals struggle to identify a second target in a rapid sequence when the first target precedes it too closely. Attentional blink is purported to reflect an apparent bottleneck in the attention systems ability to rapidly redirect attentional resources; however, the mechanism underlying this phenomenon remains hotly debated. Our findings reveal that during a rapid presentation of natural images, a short temporal gap between targets results in reduced neural representations of targets and the occurrence of attentional blink. Conversely, when a greater temporal gap exists between targets, neural representations are preserved. This study provides valuable insights into how the human brain perceives the ever-changing visual world around us.

neuroscience↗

bioRxiv2023

Unveiling the neural dynamics of conscious perception in rapid object recognition

Our brain excels at recognizing objects, even when they flash by in a rapid sequence. However, the neural processes determining whether a target image in a rapid sequence can be recognized or not remained elusive. We used electroencephalography (EEG) to investigate the temporal dynamics of brain processes that shape perceptual outcomes in these challenging viewing conditions. Using naturalistic images and advanced multivariate pattern analysis (MVPA) techniques, we probed the brain dynamics governing conscious object recognition. Our results show that although initially similar, the processes for when an object can or cannot be recognized diverge around 180ms post-appearance, coinciding with feedback neural processes. Decoding analyses indicate that object categorization can occur at [~]120ms through feedforward mechanisms. In contrast, object identification is resolved at [~]190ms after target onset, suggesting involvement of recurrent processing. These findings underscore the importance of recurrent neural connections in object recognition and awareness in rapid visual presentations.

neuroscience↗

bioRxiv2023

Shared representations of human actions across vision and language

Humans can recognize and communicate about many actions performed by others. How are actions organized in the mind, and is this organization shared across vision and language? We collected similarity judgments of human actions depicted through naturalistic videos and sentences, and tested four models of action categorization, defining actions at different levels of abstraction ranging from specific (action verb) to broad (action target: whether an action is directed towards an object, another person, or the self). The similarity judgments reflected a shared organization of action representations across videos and sentences, determined mainly by the target of actions, even after accounting for other semantic features. Language model embeddings predicted the behavioral similarity of action videos and sentences, and captured information about the target of actions alongside unique semantic information. Together, our results show how action concepts are organized in the human mind and in large language model representations.

neuroscience↗

bioRxiv2023

Investigating task-free functional connectivity patterns in newborns using functional near-infrared spectroscopy

SignificanceResting-state networks (RSN), particularly the sensorimotor network, begin to develop in the third trimester of pregnancy and mature extensively by term age. The integrity and structure of these networks have been linked to neurological health outcomes in neonates, highlighting the significance of monitoring RSN development. To this end, functional near-infrared spectroscopy (fNIRS) has emerged as a neuroimaging technique that utilizes near-infrared light to indirectly measure neural activity by detecting changes in oxygenated (HbO) and deoxygenated (HbR) hemoglobin concentrations. Compared to other imaging methods, fNIRS is non-invasive and allows for naturalistic monitoring of neural activity at the bedside, particularly in awake infants. AimUse fNIRS to expand on previous findings regarding the development of functional networks in awake neonates. ApproachfNIRS was acquired in 41 term-born neonates (17 females, gestational age range=36+0 to 42+1 weeks) within the first 48 hours after birth. ResultsGroup level analysis of functional connectivity showed strong positive connectivity in most channel-pairs over the sensorimotor network, especially the left hemisphere (q < 0.05). Next, we examined the relationship between functional connectivity, gestational age and postnatal age, while controlling for sex and subject effects. Both gestational and postnatal age were found to be positively associated with an increase in functional connectivity in the sensorimotor RSN, especially in channels covering the posterior portion. ConclusionsOur findings emphasize the importance of considering developmental changes in functional networks in awake infants. Moreover, our study demonstrates the potential of fNIRS as a valuable tool for studying neural activity in naturalistic settings in neonates.

neuroscience↗

bioRxiv2020

Reconstructing feedback representations in ventral visual pathway with a generative adversarial autoencoder

While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway. Author summaryIt has been shown that the ventral visual cortex consists of a dense network of regions with feedforward and feedback connections. The feedforward path processes visual inputs along a hierarchy of cortical areas that starts in early visual cortex (an area tuned to low level features e.g. edges/corners) and ends in inferior temporal cortex (an area that responds to higher level categorical contents e.g. faces/objects). Alternatively, the feedback connections modulate neuronal responses in this hierarchy by broadcasting information from higher to lower areas. In recent years, deep neural network models which are trained on object recognition tasks achieved human-level performance and showed similar activation patterns to the visual brain. In this work, we developed a generative neural network model that consists of encoding and decoding sub-networks. By comparing this computational model with the human brain temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) response patterns, we found that the encoder processes resemble the brain feedforward processing dynamics and the decoder shares similarity with the brain feedback processing dynamics. These results provide an algorithmic insight into the spatiotemporal dynamics of feedforward and feedback processes in biological vision.

neuroscience↗

bioRxiv2020

Spatiotemporal Dynamics of Sound Representations Reveal a Hierarchical Progression of Category Selectivity

As the human brain transforms incoming sounds, it remains unclear whether semantic meaning is assigned via distributed, domain-general architectures or specialized hierarchical streams. Here we show that the spatiotemporal progression from acoustic to semantically dominated representations is consistent with a hierarchical processing scheme. Combining magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) patterns, we found superior temporal responses beginning ~80 ms post-stimulus onset, spreading to extratemporal cortices by ~130 ms. Early acoustically-dominated representations trended systematically toward semantic category dominance over time (after ~200 ms) and space (beyond primary cortex). Semantic category representation was spatially specific: vocalizations were preferentially distinguished in temporal and frontal voice-selective regions and the fusiform face area; scene and object sounds were distinguished in parahippocampal and medial place areas. Our results are consistent with an extended auditory processing hierarchy in which acoustic representations give rise to multiple streams specialized by category, including areas typically considered visual cortex.

neuroscience↗

bioRxiv2020

Emergence of Visual Center-Periphery Spatial Organization in Deep Convolutional Neural Networks

Research at the intersection of computer vision and neuroscience has revealed hierarchical correspondence between layers of deep convolutional neural networks (DCNNs) and cascade of regions along human ventral visual cortex. Recently, studies have uncovered emergence of human interpretable concepts within DCNNs layers trained to identify visual objects and scenes. Here, we asked whether an artificial neural network (with convolutional structure) trained for visual categorization would demonstrate spatial correspondences with human brain regions showing central/peripheral biases. Using representational similarity analysis, we compared activations of convolutional layers of a DCNN trained for object and scene categorization with neural representations in human brain visual regions. Results reveal a brain-like topographical organization in the layers of the DCNN, such that activations of layer-units with central-bias were associated with brain regions with foveal tendencies (e.g. fusiform gyrus), and activations of layer-units with selectivity for image backgrounds were associated with cortical regions showing peripheral preference (e.g. parahippocampal cortex). The emergence of a categorical topographical correspondence between DCNNs and brain regions suggests these models are a good approximation of the perceptual representation generated by biological neural networks.

neuroscience↗

bioRxiv2019

A neural correlate of image memorability

Some images are easy to remember while others are easily forgotten. While variation in image memorability is consistent across individuals, we lack a full account of its neural correlates. By analyzing data collected from inferotemporal cortex (IT) as monkeys performed a visual memory task, we demonstrate that a simple property of the visual encoding of an image, its population response magnitude, is strongly correlated with its memorability. These results establish a novel behavioral role for the magnitude of the IT response, which lies largely orthogonal to the coding scheme that IT uses to represent object identity. To investigate the origin of IT memorability modulation, we also probed convolutional neural network models trained to categorize objects. We found brain-analogous correlates of memorability that grew in strength across the hierarchy of these networks, suggesting that this memorability correlate is likely to arise from the optimizations required for visual as opposed to mnemonic processing.

neuroscience↗

bioRxiv2018

Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams

To build a representation of what we see, the human brain recruits regions throughout the visual cortex in cascading sequence. Recently, an approach was proposed to evaluate the dynamics of visual perception in high spatiotemporal resolution at the scale of the whole brain. This method combined functional magnetic resonance imaging (fMRI) data with magnetoencephalography (MEG) data using representational similarity analysis and revealed a hierarchical progression from primary visual cortex through the dorsal and ventral streams. To assess the replicability of this method, here we present results of a visual recognition neuro-imaging fusion experiment, and compare them within and across experimental settings. We evaluated the reliability of this method by assessing the consistency of the results under similar test conditions, showing high agreement within participants. We then generalized these results to a separate group of individuals and visual input by comparing them to the fMRI-MEG fusion data of Cichy et al (2016), revealing a highly similar temporal progression recruiting both the dorsal and ventral streams. Together these results are a testament to the reproducibility of the fMRI-MEG fusion approach and allows for the interpretation of these spatiotemporal dynamic in a broader context.

neuroscience↗

bioRxiv2018

The Perceptual Neural Trace of Memorable Unseen Scenes

Some scenes are more memorable than others: they cement in minds with consistencies across observers and time scales. While memory mechanisms are traditionally associated with the end stages of perception, recent behavioral studies suggest that the features driving these memorability effects are extracted early on, and in an automatic fashion. This raises the question: is the neural signal of memorability detectable during early perceptual encoding phases of visual processing? Using the high temporal resolution of magnetoencephalography (MEG), during a rapid serial visual presentation (RSVP) task, we traced the neural temporal signature of memorability across the brain. We found an early and prolonged memorability related signal recruiting a network of regions in both dorsal and ventral streams, detected outside of the constraints of subjective awareness. This enhanced encoding could be the key to successful storage and recognition.

neuroscience↗

bioRxiv2018

Ultra-Rapid Serial Visual Presentation Reveals Dynamics of Feedforward and Feedback Processes in the Ventral Visual Pathway

Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.

neuroscience↗

bioRxiv2018

Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion

Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information. Deep neural networks are shown to achieve human-level performance in these tasks, and explain the primate brain representation. On the other hand, object recognition under more challenging conditions (i.e. beyond the core recognition problem) is less characterized. One such example is object recognition under occlusion. It is unclear to what extent feedforward and recurrent processes contribute in object recognition under occlusion. Furthermore, we do not know whether the conventional deep neural networks, such as AlexNet, which were shown to be successful in solving core object recognition, can perform similarly well in problems that go beyond the core recognition. Here, we characterize neural dynamics of object recognition under occlusion, using magnetoencephalography (MEG), while participants were presented with images of objects with various levels of occlusion. We provide evidence from multivariate analysis of MEG data, behavioral data, and computational modelling, demonstrating an essential role for recurrent processes in object recognition under occlusion. Furthermore, the computational model with local recurrent connections, used here, suggests a mechanistic explanation of how the human brain might be solving this problem. Author SummaryIn recent years, deep-learning-based computer vision algorithms have been able to achieve human-level performance in several object recognition tasks. This has also contributed in our understanding of how our brain may be solving these recognition tasks. However, object recognition under more challenging conditions, such as occlusion, is less characterized. Temporal dynamics of object recognition under occlusion is largely unknown in the human brain. Furthermore, we do not know if the previously successful deep-learning algorithms can similarly achieve human-level performance in these more challenging object recognition tasks. By linking brain data with behavior, and computational modeling, we characterized temporal dynamics of object recognition under occlusion, and proposed a computational mechanism that explains both behavioral and the neural data in humans. This provides a plausible mechanistic explanation for how our brain might be solving object recognition under more challenging conditions.

neuroscience↗

bioRxiv2017

Decoding the orientation of contrast edges from MEG evoked and induced responses

1Visual gamma oscillations have been proposed to subserve perceptual binding, but their strong modulation by diverse stimulus features confounds interpretations of their precise functional role. Overcoming this challenge necessitates a comprehensive account of the relationship between gamma responses and stimulus features. Here we used multivariate pattern analyses on human MEG data to characterize the relationships between gamma responses and one basic stimulus feature, the orientation of contrast edges. Our findings confirmed we could decode orientation information from induced responses in two dominant frequency bands at 24-32 Hz and 50-58 Hz. Decoding was higher for cardinal than oblique orientations, with similar results also obtained for evoked MEG responses. In contrast to multivariate analyses, orientation information was mostly absent in univariate signals: evoked and induced responses in early visual cortex were similar in all orientations, with only exception an inverse oblique effect observed in induced responses, such that cardinal orientations produced weaker oscillatory signals than oblique orientations. Taken together, our results showed multivariate methods are well suited for the analysis of gamma oscillations, with multivariate patterns robustly encoding orientation information and predominantly discriminating cardinal from oblique stimuli.

neuroscience↗