Biology subjects

Aqil, M.

Publications and source records attributed to Aqil, M..

4 recordsLinked to original sources

bioRxiv2025

Psilocybin alters visual-contextual computations

Psilocybin alters perception and brain dynamics. Contextual computations are ubiquitous in the brain. Here, we investigate the effects of psilocybin using psychophysics, ultra-high field functional MRI, and computational modeling. We find that 1) psilocybin alters contextual perception in the Ebbinghaus illusion, 2) psilocybin alters contextual modulation in cortical responses to visual stimuli, and 3) we propose a computational model capable of capturing and linking these changes. Leveraging vision as a beachhead, our findings highlight the alteration of contextual computations as a potential general mechanism underlying psychedelic action. TeaserPsilocybin alters visual-contextual computations, a potential general computational mechanism for psychedelic effects in the human brain.

neuroscience↗

bioRxiv2025

DMT-induced shifts in criticality correlate with ego-dissolution

Psychedelics profoundly alter subjective experience and brain dynamics. Brain oscillations express signatures of near-critical dynamics, relevant for healthy function. Alterations in the proximity to criticality have been suggested to underlie the experiential and neurological effects of psychedelics. Here, we investigate the effects of a psychedelic substance (DMT) on the criticality of brain oscillations, and in relation to subjective experience. We find that DMT shifts the dynamics of brain oscillations away from criticality in alpha and adjacent frequency bands. In this context, entropy is increased while complexity is reduced. We find that the criticality shifts observed in alpha and theta bands correlate with the intensity ratings of ego-dissolution, a hallmark of psychedelic experience. Finally, using a recently developed metric, the functional excitatory-inhibitory ratio, we find that the DMT-induced criticality shift in brain oscillations is towards subcritical regimes. These findings have major implications for the understanding of psychedelic mechanisms of action in the human brain and for the neurological basis of altered states of consciousness.

neuroscience↗

bioRxiv2024

A retinotopic reference frame for space throughout human visual cortex

We perceive the world as stable despite our rapid eye movements. To explain our sense of visual stability, it has been suggested that the brain encodes the location of attended visual stimuli in an external, or spatiotopic, reference frame. However, such spatiotopy is seemingly at odds with the fundamental retinotopic organization of visual inputs. Here, we probe the spatial reference frame of vision using ultra-high-field (7T) fMRI and voxel-level receptive field modeling, while manipulating both gaze direction and spatial attention. To manipulate spatial attention, participants performed an equally demanding visual task on either a bar stimulus that traversed the visual field, or a small stimulus at fixation. To dissociate retinal stimulus position from its real-world position the entire stimulus array was placed at one of three distinct horizontal screen positions in each run. We found that population receptive fields in all cortical visual field maps are pinioned to the retina, irrespective of how spatial attention is deployed. This pattern of results is strong evidence for a fully retinotopic reference frame for visual-spatial processing. Reasoning that a spatiotopic reference frame could independently be computed at the population level of entire visual areas rather than in individual voxels, we additionally used Bayesian decoding of stimulus location from the BOLD response patterns in visual areas. We found that decoded stimulus locations also adhere to the retinotopic frame of reference. Again, this result holds for all visual areas and irrespective of the deployment of spatial attention. Our findings reorient the search for visual stability mechanisms toward transient sensorimotor interactions rather than static spatiotopic maps.

neuroscience↗

bioRxiv2020

Graph neural fields: a framework for spatiotemporal dynamical models on the human connectome

Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI); as an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome, and show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of BOLD fMRI data. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships. Author summaryThe human brain can be seen as an interconnected network of many thousands neuronal "populations"; in turn, each population contains thousands of neurons, and each is connected both to its neighbors on the cortex, and crucially also to distant populations thanks to long-range white matter fibers. This extremely complex network, unique to each of us, is known as the "human connectome graph". In this work, we develop a novel approach to investigate how the neural activity that is necessary for our life and experience of the world arises from an individual human connectome graph. For the first time, we implement a mathematical model of neuronal activity directly on a high-resolution connectome graph, and show that it can reproduce the spatial patterns of activity observed in the real brain with magnetic resonance imaging. This new kind of model, made of equations implemented directly on connectome graphs, could help us better understand how brain function is shaped by computational principles and anatomy, but also how it is affected by pathology and lesions.

neuroscience↗