98%
921
2 minutes
20
Relative brain sizes in birds can rival those of primates, but large-scale patterns and drivers of avian brain evolution remain elusive. Here, we explore the evolution of the fundamental brain-body scaling relationship across the origin and evolution of birds. Using a comprehensive dataset sampling> 2,000 modern birds, fossil birds, and theropod dinosaurs, we infer patterns of brain-body co-variation in deep time. Our study confirms that no significant increase in relative brain size accompanied the trend toward miniaturization or evolution of flight during the theropod-bird transition. Critically, however, theropods and basal birds show weaker integration between brain size and body size, allowing for rapid changes in the brain-body relationship that set the stage for dramatic shifts in early crown birds. We infer that major shifts occurred rapidly in the aftermath of the Cretaceous-Paleogene mass extinction within Neoaves, in which multiple clades achieved higher relative brain sizes because of a reduction in body size. Parrots and corvids achieved the largest brains observed in birds via markedly different patterns. Parrots primarily reduced their body size, whereas corvids increased body and brain size simultaneously (with rates of brain size evolution outpacing rates of body size evolution). Collectively, these patterns suggest that an early adaptive radiation in brain size laid the foundation for subsequent selection and stabilization.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.cub.2020.03.060 | DOI Listing |
IEEE J Biomed Health Inform
September 2025
Vision Transformer (ViT) applied to structural magnetic resonance images has demonstrated success in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, three key challenges have yet to be well addressed: 1) ViT requires a large labeled dataset to mitigate overfitting while most of the current AD-related sMRI data fall short in the sample sizes. 2) ViT neglects the within-patch feature learning, e.
View Article and Find Full Text PDFJ Vis
September 2025
Neuroscience Program, Western University, London, ON, Canada.
Studies of visual face processing often use flat images as proxies for real faces due to their ease of manipulation and experimental control. Although flat images capture many features of a face, they lack the rich three-dimensional (3D) structural information available when binocularly viewing real faces (e.g.
View Article and Find Full Text PDFStroke
September 2025
Brain Language Laboratory, Freie Universität Berlin, Germany (A.-T.P.J., M.R.O., A.S., F.P.).
Background: Intensive language-action therapy treats language deficits and depressive symptoms in chronic poststroke aphasia, yet the underlying neural mechanisms remain underexplored. Long-range temporal correlations (LRTCs) in blood oxygenation level-dependent signals indicate persistence in brain activity patterns and may relate to learning and levels of depression. This observational study investigates blood oxygenation level-dependent LRTC changes alongside therapy-induced language and mood improvements in perisylvian and domain-general brain areas.
View Article and Find Full Text PDFMult Scler
September 2025
Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, VA Medical Center, TN Valley Healthcare System, Nashville, TN, USA.
Background: There is limited knowledge on the post-glymphatic structures such as the parasagittal dural (PSD) space and the arachnoid granulations (AGs) in multiple sclerosis (MS).
Objectives: To evaluate differences in volume and macromolecular content of PSD and AG between people with newly diagnosed MS (pwMS), clinically isolated syndrome (pwCIS), or radiologically isolated syndrome (pwRIS) and healthy controls (HCs) and their associations with clinical and radiological disease measures.
Methods: A total of 69 pwMS, pwCIS, pwRIS, and HCs underwent a 3.
Front Comput Neurosci
August 2025
Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
Artificial neural networks are limited in the number of patterns that they can store and accurately recall, with capacity constraints arising from factors such as network size, architectural structure, pattern sparsity, and pattern dissimilarity. Exceeding these limits leads to recall errors, eventually leading to catastrophic forgetting, which is a major challenge in continual learning. In this study, we characterize the theoretical maximum memory capacity of single-layer feedforward networks as a function of these parameters.
View Article and Find Full Text PDF