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119 results for “brain evolution”
Multilevel atlas comparisons reveal divergent evolution of the primate brain
<p>Nifti files of 20 mammalian atlases modified into a Common Multilevel Segmentation.</p> <p>(See Figure 1 in Multilevel atlas comparisons reveal divergent evolution of the primate brain; https://www.pnas.org/doi/full/10.1073/pnas.2202491119#sec-3)</p> <p>These nifti files are based on the brain atlases from 18 mammalian species, that were published between the years 2013 and 2021 (see list).</p> <p>The Python script to re-segment the "original" atlases into the modified version (that is shared here) is also available:</p> <p>see Modify_atlases.py</p> <p>Each species folder contains 5 nifti files: 1 for each level of segmentation and 1 for the brain segmentation.</p> <p>The other txt files are the volumetric output extracted using AFNI on each nifti file.</p> <p>Please read the Readme.txt file to credit and cite accordingly all the authors.</p> <p> </p> <p> </p> <p> </p> <p> </p>
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.
Data from: Predation drives the evolution of brain cell proliferation and brain allometry in male Trinidadian killifish, Rivulus hartii
<p>The external environment influences brain cell proliferation, and this might contribute to brain plasticity underlying adaptive behavioural changes. Additionally, internal genetic factors influence brain cell proliferation rate. However, to date, researchers have not examined the importance of environmental vs. genetic factors in causing natural variation in brain cell proliferation. Here, we examine brain cell proliferation and brain growth trajectories in free-living populations of Trinidadian killifish, Rivulus hartii, exposed to contrasting predation environments. Compared to populations without predators, populations in high predation environments exhibited higher rates of brain cell proliferation and a steeper brain growth trajectory (relative to body size). To test whether these differences in the wild persist in a common garden environment, we reared first generation fish originating from both predation environments in uniform laboratory conditions. Just as in the wild, brain cell proliferation and brain growth in the common garden were greater in high predation populations than in no predation populations. The similar results in field and common garden studies indicate that population differences in these brain features are intrinsic, probably genetic, differences arising from natural selection acting on overall brain growth and life history rather than differences arising through phenotypic plasticity.</p>
Figure 11. Nervous system and brain. A in Systematics, evolution and phylogeny of Annelida - a morphological perspective
Figure 11. Nervous system and brain. A. Nervous system of the trunk with longitudinal and segmental circular nerves exemplified by Parapodrilus psammophilus (Dorvilleidae). Ventral cord consists of unpaired median (mn) and main paired nerves (mvn). B-D. Anti α-tubulin immunoreactivity; dotted lines indicate segment borders. B. Polygordius appendiculatus (Polygordiidae), ventral nerve cord (green) comprising three closely apposed neurite bundles, serotonergic perikarya (red) in a repetitive pattern although distinct ganglia are absent (medullary cord). Note high number of segmental nerves. C-D. Brania clavata (Syllidae); depth coding images. C. Brain (b) and ventral nerve cord in ventral view, ventral cord consists of several closely apposed nerves forming 3 bundles behind 1st ganglion (g1), 4 segmental nerves (arrowheads, ppn) in each segment; brain gives rise to several stomatogastric nerves (sn). D. Ventral cord in the trunk region. F. General diagram of the cephalic nervous system in polychaetes, numerals refer to palp nerve roots, somata stippled. E-H. Nereis sp. (Nereididae). E Ventral nerve cord in basiepithelial position (arrowheads refer to epidermal extracellular matrix). F. Parasagittal section with mushroom bodies (mb), note subepithelial position of brain; arrowheads point to cerebral ganglia. H Enlargement of anterior part of mushroom body with stalks of globuli cells (gc). – br = brain, cc = circumoesophageal connective, dcdr = dorsal commissure of drcc, dcvr = dorsal commissure of vrcc, dlln = dorsolateral longitudinal nerve, drcc = dorsal root of cc, ecm = extracellular matrix, ep = epidermis, g1 = 1st ganglion, gc = globuli cell, in = intestine, lln = lateral longitudinal nerve, mb = mushroom body, mn = median nerve of ventral cord, mvn = main nerve of ventral cord, nla = nerve of lateral antenna, nma = nerve of median antenna, no = nuchal organ, np = neuropil, obm = oblique muscle, pn = palp nerve, ppn = parapodial nerve, sn = stomatogastric nerve, so = somata of neurites, sog = suboesophageal ganglion, vbv = ventral blood vessel, vcdr = ventral commissure of drcc, vcvr = ventral commissure of vrcc, vlm = ventral longitudinal muscle, vrcc = ventral root of cc. A, F: modified from Müller and Orrhage (2005). Micrographs; B C: Lehmacher, C, D: M. Kuper, Osnabrück.
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>
Figure 8 in Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England
Figure 8. Virtual endocranial cast of Phaethon rubricauda showing close morphological similarity to Prophaethon shrubsolei. A, left lateral and B, dorsal aspects. Note the absence of a vallecula and presence of well-marked impressions of the fissura cerebelli. Virtual endocranial cast reconstructed from publicly available data at http://www.digimorph.org/ specimens/Phaethon_rubricauda_melanorhynchos/ using MIMICS 8.13. See text for list of anatomical abbreviations.
Figure 3 in Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England
Figure 3. Virtual endocast of Odontopteryx toliapica in A, dorsal; B, rostral, and C, left lateral views. See text for list of anatomical abbreviations.
Figure 10 in Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England
Figure 10. Relative size of brain regions in selected modern bird species. A, B; pigeon (Columbia livia; Columbiiformes) in A, dorsal, and B, right lateral views. The wide rostrally positioned eminentia sagittalis with poor dorsal expansion is among the poorest developed in living birds. C, D; woodcock (Scolopax rusticola; Charadriiformes) in C, dorsal, and D, right lateral views. Note caudally positioned wide eminentia sagittalis with moderate dorsal expansion and moderate tectum mesencephali. E, F; tawny owl (Strix aluco; Strigiformes) in E, dorsal and F, right lateral views. Note the exceptionally well-developed (dorsally and laterally) rostral eminentia sagittalis, comparatively small cerebellum and moderately sized tectum mesencephali. G, H; macaw (Ara sp.; Psittaciformes) in G, dorsal and H, lateral views. Psittaciform brains are characterized by great lateral expansion of a caudally positioned eminentia sagittalis, and a very large telencephalon relative to the size of the cerebellum. The tectum mesencephali is particularly small relative to the telencephalon. I, J; raven (Corvus corax; Passeriformes) in I, dorsal and J, lateral views. The large, wide and dorsally well-developed rostrally positioned eminentia sagittalis of Passeriformes is exemplified in brains of Corvidae. The cerebellum is small relative to the exceptionally well-developed telencephalon. Of the species figured here, the charadriiform (Scolopax) (C, D) is typical in possessing the largest bulbus olfactorius. A, B adapted from Dubbeldam (1998); C, J adapted from Stingelin (1957). See text for abbreviations.
Figure 6 in Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England
Figure 6. Virtual endocast of Prophaethon shrubsolei in A, dorsal; B, rostral, and C, left lateral views. See text for list of anatomical abbreviations.
Figure 9 in Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England
Figure 9. Reconstruction of the left osseous labyrinth of Prophaethon shrubsolei in A, rostral; B, lateral; C, caudal, and D, medial views. See text for list of anatomical abbreviations.
Figure 5 in Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England
Figure 5. Reconstruction of the left osseous labyrinth of Odontopteryx toliapica in A, rostral; B, lateral; C, caudal, and D, medial views. See text for list of anatomical abbreviations.
Figure 2 in Avian brain evolution: new data from Palaeogene birds (Lower Eocene) from England
Figure 2. Transparent computed tomographic (CT) segmentation of two Lower Eocene skulls in right lateral views, revealing the virtual endocasts of the brain and osseous labyrinth. Compare positions of the canalis semicircularis horizontali indicating the in vivo alert head positions. A, Odontopteryx toliapica; all sutures are fully obliterated except that between the frontals and parietals. B, Prophaethon shrubsolei.
Data from: A brain-wide analysis maps structural evolution to distinct anatomical modules
<p>Brain anatomy is highly variable and it is widely accepted that anatomical variation impacts brain function and ultimately behavior. The structural complexity of the brain, including differences in volume and shape, presents an enormous barrier to define how variability underlies differences in function. In this study, we sought to investigate the evolution of brain anatomy in relation to brain region volume and shape across the brain of a single species with variable genetic and anatomical morphs. We generated a high-resolution brain atlas for the blind Mexican cavefish and coupled the atlas with automated computational tools to directly assess variability in brain region shape and volume across all populations. We measured the volume and shape of every neuroanatomical region of the brain and assessed correlations between anatomical regions in surface fish, cavefish, and surface to cave F2 hybrids, whose phenotypes span the range of surface to cave. We find that dorsal regions of the brain are contracted in cavefish, while ventral regions have expanded. This trend is true for both volume and shape, suggesting that these two parameters share developmental mechanisms necessary for remodeling the entire brain. Given the high conservation of brain anatomy and function among vertebrate species, we expect these data to reveal generalized principles of brain evolution and show that Astyanax provides a system for functionally determining basic principles of brain evolution by utilizing the independent genetic diversity of different morphs, to test how genes influence early patterning events to drive brain-wide anatomical evolution. </p>
Data from: Predation drives the evolution of brain cell proliferation and brain allometry in male Trinidadian killifish, Rivulus hartii
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Nutritional constraints on brain evolution: sodium and nitrogen limit brain size
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Data from: A brain-wide analysis maps structural evolution to distinct anatomical modules
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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.
The evolution of plasticity in brain morphology following colonization of an ecologically divergent habitat in Trinidadian guppies
<p>Natural environments are constantly changing. To survive, organisms will either need to rapidly adapt to new conditions or colonize new habitats. Colonization has been hypothesized to select for increased plasticity as well as increased brain size, though empirical tests of these effects have proven difficult to evaluate. In particular, the degree to which plasticity of brain morphology can evolve, and its subsequent ecological consequences have rarely been explored. Trinidadian guppies (<em>Poecilia reticulata</em>) are known for their repeated adaptation to ancestral high-predation (HP) and derived low-predation (LP) environments. We used this system to examine the evolution and plasticity of brain morphology. We exposed second-generation offspring of individuals collected from HP and LP sites to two different kinds of environmental treatments: predation cues and conspecific social environment. We found that guppies descended from a colonized LP habitat showed greater plasticity in brain morphology than descendants of their ancestral HP population, supporting the hypothesis that plasticity of brain morphology may increase fitness after colonization of a novel habitat. Additionally, we show sexual dimorphism in brain morphology plasticity. Overall, these results suggest the evolution of brain morphology plasticity as an important mechanism that allows for ecological diversification and colonization of novel habitats.</p>
Dataset of ""Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution"
<p>Dataset of <em>Statistical atlases and automatic labelling strategies to accelerate the analysis of social insect brain evolution</em> by Sara Arganda, Ignacio Arganda-Carreras, Darcy G. Gordon, Andrew P. Hoadley, Alfonso Pérez-Escudero, Martin Giurfa and James F. A. Traniello.</p> <p>In this dataset, we are presenting:</p> <ul> <li>10 confocal brain images from <em>Pheidole spadonia </em>minors (in the original confocal TIFF format and in the open NRRD format), with manually segmented labels of 8 subregions (Optic Lobes, OL; Antennal Lobes, AL; Mushroom Body Medial Calyx, MB-MC; Mushroom Body Lateral Calyx, MB-LC; Mushroom Body Peduncle, MB-P; Central Complex, CX; Subesophageal zone, SEZ; and Rest of Central Brain, ROCB – in NRRD format) from one expert annotator.</li> <li>12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from one expert annotator.</li> <li>5 confocal brain images from <em>Pheidole spadonia </em>minors (“test brains”), with five sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB) from three expert annotators (one set from annotator 1, one set from annotator 2 and three sets from annotator 3, to evaluate inter and intra person differences).</li> <li>1 group-wise template generated from the 10 confocal brain images from <em>Pheidole spadonia </em>minors, with three sets of manually segmented labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>5 group-wise templates generated from the 9 confocal brain images from <em>Pheidole spadonia </em>minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>1 group-wise template generated from 12 confocal brain images from <em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana</em> and <em>P. obtusospinosa</em> minors, with consensus labels of the same 8 subregions (OL; AL; MB-MC; MB-LC; MB-P; CX; SEZ; and ROCB).</li> <li>7 sets of automatic labels for the 5 “test brains”: 3 sets of “Direct Labels”, 3 sets of “Consensus Labels”, 1 set of “Multispecies Template Labels”.</li> </ul> <p>Brain of minor workers were dissected from the ant head capsule in ice cold HEPES-buffered saline and were fixed and immunohistochemically stained using SYNORF1 (a monoclonal <em>Drosophila</em> synapsin I antibody obtained from the Developmental Studies Hybridoma Bank, catalog 3C11) and secondarily stained using Alexa Fluor 488 for visualization of neuropil (slightly modified from Ott, 2008). Later, brains were mounted in methyl salicylate and imaged on an Olympus Fluoview BX50 laser scanning confocal microscope with a ×20 objective at a resolution of ~0.7 × 0.7 × 5µm/voxel. All brain tissue manipulation, staining and recording was performed by Darcy G. Gordon. Brain images were obtained in TIFF format by the confocal microscope and then opened and saved as Amira Mesh (.am) stack images in Amira (version 6.0). Manual segmentation of each brain was done using Amira (version 6.0 or 2019.2). Labels were traced on eight compartments in only one brain hemisphere, except for the CX, SEZ and ROCB, which lack a clear subdivision between hemispheres. Brain grey image stacks and labels were transformed to NRRD format for template construction using the Fiji plugin SaveAsGzipNrrd<a href="#_ftn1">[1]</a>. Volume and volume similarity of labels were calculated using the Fiji toolbox MorphoLibJ<a href="#_ftn2">[2]</a>.</p> <p><strong>Acknowledgements: </strong>We thank Ming Huang (from Dr. Diana Wheeler’s laboratory) who kindly provided access to colonies from four species of the hyperdiverse ant genus <em>Pheidole</em> (<em>P. spadonia</em>, <em>P. rhea</em>, <em>P. tepicana </em>and <em>P. obtusospinosa</em>). This research was supported by National Science Foundation grants IOS 1354291 and IOS 1953393 to JT, a Marie Skłodowska-Curie Individual Fellowship BrainiAnts-660976 and Ayudas destinadas a la atracción de talento investigador a la Comunidad de Madrid en centros de I+D. This work is supported in part by the University of the <a href="https://www.sciencedirect.com/topics/engineering/basque-country">Basque Country</a> UPV/EHU grant GIU19/027.</p> <p> </p> <p><a href="#_ftnref1">[1]</a> https://github.com/iarganda/tefor</p> <p><a href="#_ftnref2">[2]</a> https://imagej.net/plugins/morpholibj</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>
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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.