Biology subjects

Abdelhack, M.

Publications and source records attributed to Abdelhack, M..

5 recordsLinked to original sources

bioRxiv2024

Neurostructural and cognitive signatures of novel polygenic risk scores for molecular brain aging

The world population is shifting sharply toward an older-age demographic. To navigate the escalating burden of physical and cognitive decline common to aging, and heightened risk of neurodegenerative and neuropsychiatric disease, we require advances in treatment and prevention interventions. These advances are predicated on attaining a deeper understanding of the molecular processes underlying brain aging. Here, we employed novel GWAS and cis-eQTL-based polygenic risk scores (GWASAGE-PRS and cis-eQTLAGE-PRS) indexing genetic risk for accelerated molecular brain aging, and examined their associations with cortical thickness and performance in age-sensitive cognitive domains in 31 384 participants (16 392 women, age 64.1{+/-}7.65) from the UK Biobank. While GWASAGE-PRS was nominally associated with lower cortical thickness in frontotemporal regions, cis-eQTLAGE-PRS displayed robust associations with greater cortical thickness in age-sensitive frontal, temporal, and parietal regions, including the left and right precentral (pFDR<0.0001, pFDR=0.05), left insula (pFDR=0.05), as well as the right supramarginal (pFDR=0.05) and precuneus (pFDR=0.05) regions. Similar pFDR trending associations occurred bilaterally in the caudal middle frontal (pFDR=0.052, pFDR=0.078) and right insula (pFDR=0.071). These structural findings co-occurred alongside increased executive function performance on the Trail Making Test B (pFDR=0.035), suggesting a potential neurostructural and cognitive reserve phenotype. This resilience profile may reflect previously uncharacterized pathways of brain reserve in age-related pathology, informing future translational research identifying novel treatment and prevention targets.

neuroscience↗

bioRxiv2023

Reconstructing visual illusory experiences from human brain activity

Visual illusions provide significant insights into the brains interpretation of the world given sensory inputs. However, the precise manner in which brain activity translates into illusory experiences remains largely unknown. Here we leverage a brain decoding technique combined with deep neural network (DNN) representations to reconstruct illusory percepts as images from brain activity. The reconstruction model was trained on natural images to establish a link between brain activity and perceptual features and then tested on two types of illusions: illusory lines and neon color spreading. Reconstructions revealed lines and colors consistent with illusory experiences, which varied across the source visual cortical areas. This framework offers a way to materialize subjective experiences, shedding new light on the brains internal representations of the world.

neuroscience↗

bioRxiv2023

Sleep-insomnia superposition: opposing brain signatures of sleep in task-based and resting-state conditions

Sleep and depression have a complex, bidirectional relationship, with sleep-associated alterations in brain dynamics and structure impacting a range of symptoms and cognitive abilities. Previous work describing these relationships has provided an incomplete picture by investigating only one or two types of sleep measures, depression, or neuroimaging modalities in parallel. We analyzed the correlations between task and resting-state brain-wide signatures of sleep, cognition, and depression in over 30,000 individuals. Neural signatures of insomnia and depression were negatively correlated with neural signatures of sleep duration in the task condition but positively correlated in the resting-state condition, showing that resting-state neural signatures of insomnia and depression resemble that of rested wakefulness. This was further supported by our finding of hypoconnectivity in task but hyperconnectivity in resting-state data in association with insomnia and depression This information disputes conventional assumptions about the neurofunctional manifestations of hyper- and hypo-somnia, and may explain inconsistent findings in the literature.

neuroscience↗

bioRxiv2019

Conflicting Bottom-up and Top-down Signals during Misrecognition of Visual Objects

Visual recognition involves integrating visual information with other sensory information and prior knowledge. In accord with Bayesian inference under conditions of unreliable visual input, the brain relies on the prior as a source of information to achieve the inference process. This drives a top-down process to improve the neural representation of visual input. However, the extent to which non-stimulus-driven top-down information affects processing in the ventral stream is still unclear. We conducted a perceptual decision-making task using blurred images, while conducting functional magnetic resonance imaging. We then transformed brain activity into deep neural network features to distinguish bottom-up and top-down signals. We found that top-down information unrelated to the stimulus had a minimal effect on lower-level visual processes. The neural representations of degraded stimuli that were misrecognized were still correlated with the correct object category in the lower levels of processing. In contrast, activity in the higher cognitive areas was more strongly correlated with recognition reported by the subjects. The results indicated a discrepancy between the results of processing at the lower and higher levels, indicating the existence of a stimulus-independent top-down signal flowing back down the hierarchy. These findings suggest that integration between bottom-up and top-down information takes the form of competing evidence in higher visual areas between prior-driven top-down and stimulus-driven bottom-up signals. These findings could provide important insight into the different modes of integration of neural signals in the visual cortex that contribute to the visual inference process.

neuroscience↗

bioRxiv2017

Top-down sharpening of hierarchical visual feature representations

The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up and top-down pathways that process the visual input in concordance with stored prior information. The interaction mechanism by which they integrate visual input and prior information is still enigmatic. We present a new approach using deep neural network (DNN) representation to reveal the effects of such integration on degraded visual inputs. We transformed measured human brain activity resulting from viewing blurred images to the hierarchical representation space derived from a feedforward DNN. Transformed representations were found to veer towards the original non-blurred image and away from the blurred stimulus image. This indicated deblurring or sharpening in the neural representation, and possibly in our perception, from top-down modulation. We anticipate these results will help unravel the interplay mechanism between bottom-up and top-down pathways, leading to more comprehensive models of vision.

neuroscience↗