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146 results for “Brain size”
Butterfly heavy metal content, wing size, egg count, and brain mass in the Minneapolis-St. Paul (MSP) Metropolitan Area
We collected 26 common species of butterflies across a gradient of lead pollution in the Twin Cities metropolitan area (Minneapolis and St. Paul, MN, USA). We measured their thorax lead concentrations and their body condition including wing area, number of eggs, and brain mass. We also quantified lead in the soil, host plant leaves, and air (through lichen bio-monitors) at sites where the butterflies were collected.
Nutritional constraints on brain evolution: sodium and nitrogen limit brain size
Nutrition has been hypothesized as an important constraint on brain evolution. However, it is unclear whether the availability of specific nutrients or the difficulty of locating high quality diets limits brain evolution, especially over long periods of time. We show that dietary nutrient content predicted brain size across 42 species of butterflies. Brain size, relative to body size, was associated with the sodium and nitrogen content of a species' diet. There was no evidence that host plant apparency (measured by plant height) was related to brain evolution. The timing of diet shifts varied from 3.5 to 90 million years ago, but nutritional constraints did not lessen over time as species adapted to a diet. While nutrition was linked to overall brain volume, there was no evidence that nutrition was related to the relative size of individual brain regions. Lab rearing experiments confirmed the underlying assumption of most comparative studies that the majority of interspecific trait variation stems from species differences rather than an individual's current developmental environment. This study highlights a novel role of sodium and nitrogen in brain evolution, which is additionally interesting given current anthropogenic change in the availability of these nutrients.
Figure 2 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 2. Three-dimensional reconstructions of Diprotodon optatum (A), Zygomaturus trilobus (B), Neohelos stirtoni (C) and Propalorchestes sp. (D) showing the extent of the auditory, squamosal, parietal and frontal sinuses in blue, and brain endocast in red. Skulls are shown in dorsal (left) and lateral (right) views. Scale bars represent 10 cm. Bone is 70% transparent.
Figure 3 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 3. Three-dimensional reconstructions of Vombatus ursinus (A), Lasiorhinus latifrons (B) and Phascolarctos cinereus (C) showing the extent of the frontal sinuses in blue and brain endocast in red. Skulls are shown in dorsal (left) and lateral (right) views. Scale bars represent 10 cm. Bone is 70% transparent.
Figure 1 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 1. Three-dimensional digital reconstruction of Zygomaturus trilobus cranium, QVM1992 GFV73 from CT scans. Each fragment of the specimen was scanned separately and reconstructed to form the complete cranium on the right.
Figure 5 in A quantitative comparative analysis of the size of the frontoparietal sinuses and brain in vombatiform marsupials
Figure 5. Frontal CT slices showing the braincase (BC), diploe (DIP) and parietal sinuses (PAS) inDiprotodon (A),Neohelos (B),Lasiorhinus (C) and Phascolarctos (D). Scale bars are 3 cm.
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 2. Human body sizes for men/women.
<p>We propose an efficient, simple and robust human body feature extraction based on the front and side images of a human body. Description of anthropometric data - men/women: Dataset based on an experiment is used to test the system data describing the anthropometric features of men, includes 12 sizes of the human body, which are presented in figure 2. </p>
BRAIN Journal-Image Finder Mobile Application Based on Neural Networks-Figure 6. Reducing size of Sketch
<p>As previously described, there are a total of 1600 integers, which can be the input to the dataset for neural networks. But it is a huge number, so in order to minimize the size of inputs to the neural network the 40x40 matrix is reduced to 20x20 by skipping odd rows and columns of the original matrix. Figure 6 shows a matrix containing green and red rows and columns. If this was the 40x40 matrix, then the red part of this matrix would be skipped to convert it into a 20x20 sized matrix. Now there are only 20x20=400 values, which is a reasonable input size for the neural network. </p> <p>The other part of the developed approach is to collect the data about the same two objects of the real pictures taken by the camera. These images are converted into black and white pictures and then treated the same way as the sketches, i.e. black and white pictures are also converted into integers based on the color of each pixel. </p>
Complete numerical solutions for "Inference of ecological and social drivers of human brain-size evolution" by Mauricio González-Forero and Andy Gardner
<p>This zip file contains the complete numerical solutions across the parameter sweep over the P parameters for the six cases considered.</p> <p>/1RatioForm/ -> solutions for power competence.<br> /2DiffForm/ -> solutions for exponential competence.</p> <p>/1RatioForm/1BenchmarkFromSimpleInitialGuess -> solution for the step 1 of initialization (section 5 of the SI).<br> /1RatioForm/2Benchmark/ -> solution for the step 2 of the initialization (section 5 of the SI).<br> /1RatioForm/3AdditiveCoop/ -> solutions across the P parameter combinations for additive cooperation.<br> /1RatioForm/4MultCoop/ -> solutions across the P parameter combinations for multiplicative cooperation.<br> /1RatioForm/5SubMultCoop/ -> solutions across the P parameter combinations for submultiplicative cooperation.</p> <p>The contents of /2DiffForm/ are analogous.</p> <p>The P vector is written in these files in the order (etas,etac,etaC,etag), where<br> etas -> P1<br> etac -> P3<br> etaC -> P2<br> etag -> P4</p> <p>The files 1runNotesACMC.pdf and 2runNotesSC.pdf contain the tree structure of the parameter sweep, specifying which parameter combination was used as the resident and which combinations converged to an uninvadable strategy (those with a checkmark).</p> <p>The file 3runNotesMaternalCareOptimization.pdf contains the 10 parameter combinations that yielded the best adult fit, which then were subject to variation in the parameter phi to find the combination that yielded the best ontogenetic fit.</p> <p>The file 4runNotesDuplicates.pdf gives the parameter combinations that were not run because they are equivalent to other parameter combinations.</p> <p>Running [T,N1,run,Tshort,N1short,runShort]=etaCombinations in Matlab and typing run.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i (where i is a number 1,2,...) for PC-AC, EC-MC, PC-SC, and EC-SC. The meaning of "next" is explained in step 4 of the parameter sweep (section 5 of the SI). Typing runShort.seed{i}.parallel{:} gives the "next" parameter combinations from parameter combination i for PC-MC and EC-AC.</p> <p>The terminal folders contain the solutions and have the following files:<br> brainNashDeep.m -> the master file launching the iteration of best responses.<br> brainMainDeep.m -> the file launching one iteration solving the optimal control problem to find best response.<br> brainMainTestRunDeep.m -> runs a test to check if there are infeasibility warnings.<br> brainContinuous.m -> specifies the dynamic constraints.<br> brainEndpoint.m -> specifies the terminal constraints.<br> parameters.m -> specifies the parameter values and rescales them to rescale units as specified in section 5 of the SI.<br> getSolution.m -> extracts solution.<br> plots.m -> plots solutions over best response iterations.<br> brainPlot.m -> plots solution of a given best response iteration.<br> guessDeep.mat -> initial guess and resident used.<br> solutionNashDeep.mat -> solutions over best response iterations.<br> solutionDeep.mat -> solution of last best response iteration.</p> <p> </p>
Does brain size affect mate choice? An experimental examination in pygmy halfbeaks
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Size of brain, heart, liver, alimentary tract, and kidneys along altitudinal gradients in Asiatic toad
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Nutritional constraints on brain evolution: sodium and nitrogen limit brain size
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Brain size predicts bees’ tolerance to urban environments
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Brain size and life history variables in birds
<p>The database contains information on brain size, body mass, life-history traits and development mode for a total of 620 bird species. The taxonomy follows Jetz et. al. (2012). For life-history the database includes information for the following six variables: clutch size, egg size, incubation period, fledging age, maximum longevity; as well as development mode (altricial, semialtricial, precocial and semiprecocial). Additionally, in most cases there is information about the origin or the sampled specimen (captivity vs wild origin), quality control, and a nominal estimate of the sample size, all referring to the maximum longevity data.</p>
Data from: Pattern and process in hominin brain size evolution are scale-dependent
A large brain is a defining feature of modern humans, yet there is no consensus regarding the patterns, rates, and processes involved in hominin brain size evolution. We use a reliable proxy for brain size in fossils, endocranial volume (ECV), to better understand how brain size evolved at both clade- and lineage-level scales. For the hominin clade overall, the dominant signal is consistent with a gradual increase in brain size. This gradual trend appears to have been generated primarily by processes operating within hypothesized lineages – 64% or 88% depending on whether one uses a more or less speciose taxonomy, respectively. These processes were supplemented by the appearance in the fossil record of larger-brained Homo species and the subsequent disappearance of smaller-brained Australopithecus and Paranthropus taxa. When the estimated rate of within-lineage ECV increase is compared to an exponential model that operationalizes generation-scale evolutionary processes, it suggests that the observed data were the result of episodes of directional selection interspersed with periods of stasis and/or drift; all of this occurs on too fine a time scale to be resolved by the current human fossil record, thus producing apparent gradual trends within lineages. Our findings provide a quantitative basis for developing and testing scale-explicit hypotheses about the factors that led brain size to increase during hominin evolution.
Problem-solving skills are predicted by innovations in the wild and brain size in Passerines
<p>Code and dataset for the article "<strong>Problem-solving skills are predicted by innovations in the wild and brain size in Passerines</strong>"</p>
Coevolution of relative brain size and life expectancy in parrots
<p><span><span><span><span>Previous studies have demonstrated a correlation between longevity and brain size in a variety of taxa. Little research has been devoted to understanding this link in parrots; yet parrots are well-known for both their exceptionally long lives and cognitive complexity. We employed a large-scale comparative analysis that investigated the influence of brain size and life history variables on longevity in parrots. Specifically, we addressed two hypotheses for evolutionary drivers of longevity: the <em>Cognitive Buffer Hypothesis</em>, which proposes that increased cognitive abilities enable longer life spans, and the <em>Expensive Brain Hypothesis</em>, which holds that increases in life span are caused by prolonged developmental time of, and increased parental investment in, large-brained offspring<em>. </em>We estimated life expectancy from detailed zoo records for 133,818 individuals across 244 parrot species. Using a principled Bayesian approach that addresses data uncertainty and imputation of missing values, we found a consistent correlation between relative brain size and life expectancy in parrots. This correlation was best explained by a direct effect of relative brain size. Notably, we found no effects of developmental time, clutch size, or age at first reproduction. Our results suggest that selection for enhanced cognitive abilities in parrots have in turn promoted longer lifespans.</span></span></span></span></p>
Data from: Neuron numbers link innovativeness with both absolute and relative brain size in birds
<p>A long-standing issue in biology is whether the intelligence of animals can be predicted by absolute or relative brain size. However, progress has been hampered by an insufficient understanding of how neuron numbers shape internal brain organization and cognitive performance. Based on estimations of neuron numbers for 111 bird species, we show here that the number of neurons in the pallial telencephalon is positively associated with a major expression of intelligence: innovation propensity. The number of pallial neurons, in turn, is greater in brains that are larger in both absolute and relative terms, and positively co-varies with longer post-hatching development periods. Thus, our analyses show that neuron numbers link cognitive performance to both absolute and relative brain size through developmental adjustments. These findings help unify neuro-anatomical measures at multiple levels, reconciling contradictory views over the biological significance of brain expansion. The results also highlight the value of a life history perspective to advance our understanding of the evolutionary bases of the connections between brain and cognition.</p>
Sex-specific evolution of brain size, brain structure, and covariation with eye size in Trinidadian killifish
<p>Links between contrasting ecological conditions and evolutionary shifts in neurosensory components such as brain and eye size are accumulating. Whether selection operates differently on these traits between sexes is unclear. Trinidadian killifish (<em>Anablepsoides hartii</em>) are located in sites with and without predators. Male killifish from sites without predators have evolved larger brains and eyes than males from sites with predators. These differences in brain size are present early in life but disappear in adult size-classes. Here, we evaluated female brain growth allometries to determine if females exhibit similar size-specific brain size differences between sites that differ in predation intensity. We also quantified brain size, structure, and eye size to determine if these structures coevolve in a sex-specific manner. We found that female brain growth allometries did not differ across populations. Yet, female killifish from sites without predators exhibited a larger cerebellum, optic tectum, and dorsal medulla early in life (prior to maturation), but such differences disappeared in larger size-classes. Females from sites with predators exhibit similar patterns in brain growth as males in those sites, therefore shifts in brain size and structure are driven by differences between sexes in sites without predators. We also found evidence for covariation between brain and eye size in both sexes despite different levels of variation in both structures, suggesting that these structures may covary to fluctuating degrees in sex-specific ways. We conclude that differential investment in brain tissue in sites without predators may be linked to varying reproductive and cognitive demands across the sexes.</p>
Data for: Revealing intact neuronal circuitry in centimeter-sized formalin-fixed paraffin-embedded brain
<p>Tissue clearing and labeling techniques have revolutionized brain-wide imaging and analysis, yet their application to clinical formalin-fixed paraffin-embedded (FFPE) blocks remains challenging. We introduce HIF-Clear, a novel method for efficiently clearing and labeling centimeter-thick FFPE specimens using elevated temperature and concentrated detergents. HIF-Clear with multi-round immunolabeling reveals neuron circuitry regulating multiple neurotransmitter systems in a whole FFPE mouse brain, and is able to be used as the evaluation of disease treatment efficiency. HIF-Clear also supports expansion microscopy and can be performed on a non-sectioned 15-year-old FFPE specimen, as well as a 3-month formalin-fixed mouse brain. Thus, HIF-Clear represents a feasible approach for researching archived FFPE specimens for future neuroscientific and 3D neuropathological analyses.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.