| Autor: |
Baracchini G; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada., Zhou Y; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada., da Silva Castanheira J; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada., Hansen JY; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada., Rieck J; Health Canada, Ottawa, ON, Canada., Turner GR; Department of Psychology, York University, Toronto, ON, Canada., Grady CL; Rotman Research Institute at Baycrest, and Department of Psychiatry and Psychology, University of Toronto, Toronto, ON, Canada., Misic B; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada., Nomi J; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA., Uddin LQ; Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, USA., Spreng RN; Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada. |
| Abstrakt: |
Variability drives the organization and behavior of complex systems, including the human brain. Understanding the variability of brain signals is thus necessary to broaden our window into brain function and behavior. Few empirical investigations of macroscale brain signal variability have yet been undertaken, given the difficulty in separating biological sources of variance from artefactual noise. Here, we characterize the temporal variability of the most predominant macroscale brain signal, the fMRI BOLD signal, and systematically investigate its statistical, topographical and neurobiological properties. We contrast fMRI acquisition protocols, and integrate across histology, microstructure, transcriptomics, neurotransmitter receptor and metabolic data, fMRI static connectivity, and empirical and simulated magnetoencephalography data. We show that BOLD signal variability represents a spatially heterogeneous, central property of multi-scale multi-modal brain organization, distinct from noise. Our work establishes the biological relevance of BOLD signal variability and provides a lens on brain stochasticity across spatial and temporal scales. |
