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423 results for “Brain aging”
Emotion regulation in the Ageing Brain, University of Reading, BBSRC
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Protecting the Aging Brain, Case-Study
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Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes
<p><strong>Abstract</strong></p> <p>Brain ageing is a highly variable, spatially and temporally heterogeneous process, marked by numerous structural and functional changes. These can cause discrepancies between individuals’ chronological age and the apparent age of their brain, as inferred from neuroimaging data. Machine learning models, and particularly Convolutional Neural Networks (CNNs), have proven adept in capturing patterns relating to ageing induced changes in the brain. The differences between the predicted and chronological ages, referred to as brain age deltas, have emerged as useful biomarkers for exploring those factors which promote accelerated ageing or resilience, such as pathologies or lifestyle factors. However, previous studies rely only on structural neuroimaging for predictions, overlooking potentially informative functional and microstructural changes. Here we show that multiple contrasts derived from different MRI modalities can predict brain age, each encoding bespoke brain ageing information. By using 3D CNNs and UK Biobank data, we found that 57 contrasts derived from structural, susceptibility-weighted, diffusion, and functional MRI can successfully predict brain age. For each contrast, different patterns of association with non-imaging phenotypes were found, resulting in a total of 191 unique, statistically significant associations. Furthermore, we found that ensembling data from multiple contrasts results in both higher prediction accuracies and stronger correlations to non-imaging measurements. Our results demonstrate that other 3D contrasts and modalities, which have not been considered so far for the task of brain age prediction, encode different information about the ageing brain. We envision our work as being the starting point for future investigations into the causal links underpinning the observed brain age deltas and non-imaging measurement associations. For instance, drug effects can be monitored, given that certain medications correlated with accelerated brain ageing. Furthermore, continued development of brain age models could facilitate their deployment in clinical trials for recruitment and monitoring, and hospitals for diagnostic and screening tasks.</p> <p><strong>Data Description</strong></p> <p>This dataset contains the full correlation results with all nIDPs in the UK Biobank. These are presented in datasets split by sex in Female and Male subjects. For easier data manipulation, two smaller datasets have also been made available, containing just those correlation which pass the False Discovery Rate (FDR) threshold. </p> <p>As experiments were also conducted for ensembles using multiple contrasts, similar datasets are provided for those.</p> <p>Finally, global datasets are also provided. These are the concatenation of the associations contained in the Male and Female datasets.</p> <p><strong>Paper & Code</strong></p> <p>The original paper for this article can be accessed here:</p> <ul> <li><a href="https://ieeexplore.ieee.org/abstract/document/10196736">https://ieeexplore.ieee.org/abstract/document/10196736</a></li> </ul> <p>To access the codes relevant for this project, please access the project GitHub Repos:</p> <ul> <li><a href="https://github.com/AndreiRoibu/AgeMapper">https://github.com/AndreiRoibu/AgeMapper</a></li> </ul> <p>If using this work, please cite it based on the above paper, or using the following BibTex:</p> <pre><code class="language-markdown">@inproceedings{roibu2023brain, title={Brain Ages Derived from Different MRI Modalities are Associated with Distinct Biological Phenotypes}, author={Roibu, Andrei-Claudiu and Adaszewski, Stanislaw and Schindler, Torsten and Smith, Stephen M and Namburete, Ana IL and Lange, Frederik J}, booktitle={2023 10th IEEE Swiss Conference on Data Science (SDS)}, pages={17--25}, year={2023}, organization={IEEE}, doi={10.1109/SDS57534.2023.00010} }</code></pre> <p> </p> <p><strong>Data Access</strong></p> <p>The data for this project is freely available upon application at the UK Biobank. For more information regarding the individual nIDPs, please access the UK Biobank Showcase website at: https://biobank.ctsu.ox.ac.uk/showcase/search.cgi</p> <p><strong>Funding</strong></p> <p>ACR is supported by EPSRC Grant EP/S024093/1, F. Hoffmann-La Roche AG and a 2021 Industrial Fellowship offered by the Royal Commission for the Exhibition of 1851. SMS is supported by a Wellcome Trust Collaborative Award 215573/Z/19/Z. AILN is grateful for support from the Academy of Medical Sciences under the Springboard Awards scheme (SBF005/1136), and the Bill and Melinda Gates Foundation. FJL is supported by a Wellcome Trust Collaborative Award (215573/Z/19/Z). The WIN is supported by core funding from the Wellcome Trust (203139/Z/16/Z). The computational aspects were supported by the Wellcome Trust (203141/Z/16/Z) and the NIHR Oxford BRC. Corresponding authors: ACR (andreiroibu@icloud.com), SA (stanislaw.adaszewski@roche.com) and AILN (ana.namburete@cs.ox.ac.uk).</p>
Protecting the Aging Brain (PAgB)
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Emotion regulation in the Ageing Brain, University of Reading, BBSRC
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Consensus molecular environment of schizophrenia risk genes in co-expression networks shifting across age and brain regions
<p>This is the online data repository accompanying the following manuscript:<br><strong>Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions</strong></p> <p><em>Giulio Pergola<sup>1,2,3,*</sup>, Madhur Parihar<sup>1</sup>, Leonardo Sportelli<sup>1,2</sup>, Rahul Bharadwaj<sup>1</sup>, Christopher Borcuk<sup>2</sup>, Eugenia Radulescu<sup>1</sup>, Loredana Bellantuono<sup>2,5</sup>, Giuseppe Blasi<sup>2,4</sup>, Qiang Chen<sup>1</sup>, Joel E. Kleinman<sup>1,3</sup>, Yanhong Wang<sup>1</sup>, Srinidhi Rao Sripathy<sup>1</sup>, Brady J. Maher<sup>1,3,7</sup>, Alfonso Monaco<sup>5,9</sup>, Fabiana Rossi<sup>1,2</sup>, Joo Heon Shin<sup>1</sup>, Thomas M. Hyde<sup>1,3,6</sup>, Alessandro Bertolino<sup>2,4,*</sup>, Daniel R. Weinberger<sup>1,7,8,*</sup></em></p> <p> </p> <p><strong>Affiliations:</strong></p> <p><em>1)Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (USA)<br>2)Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy<br>3)Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>4)Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy<br>5)Istituto Nazionale di Fisica Nucleare (INFN), Bari, Italy<br>6)Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>7)Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>8)Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland<br>9)Dipartimento Interateneo di fisica, Università degli Studi di Bari Aldo Moro, Bari, Italy</em></p> <p> </p> <p><strong>Abstract:</strong></p> <p><em>Schizophrenia is a neurodevelopmental brain disorder whose genetic risk is associated with shifting clinical phenomena across the life span. We investigated the convergence of putative schizophrenia risk genes in brain coexpression networks in postmortem human prefrontal cortex (DLPFC), hippocampus, caudate nucleus, and dentate gyrus granule cells, parsed by specific age periods (total N = 833). The results support an early prefrontal involvement in the biology underlying schizophrenia and reveal a dynamic interplay of regions in which age parsing explains more variance in schizophrenia risk compared to lumping all age periods together. Across multiple data sources and publications, we identify 28 genes that are the most consistently found partners in modules enriched for schizophrenia risk genes in DLPFC; twenty-three are previously unidentified associations with schizophrenia. In iPSC-derived neurons, the relationship of these genes with schizophrenia risk genes is maintained. The genetic architecture of schizophrenia is embedded in shifting coexpression patterns across brain regions and time, potentially underwriting its shifting clinical presentation.</em></p> <p> </p> <p><strong>Citation:</strong> <em>Giulio Pergola et al. ,Consensus molecular environment of schizophrenia risk genes in coexpression networks shifting across age and brain regions.Sci. Adv.9, eade2812(2023).DOI:10.1126/sciadv.ade2812</em></p> <p> </p> <p><strong>Data Files:<br>DLPFC hit.genes_kb_200__online.version.zip: </strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For Sankey plots, hover mouse over the links to see the list of genes. Also supports zoom, drag and selection.<br><strong>DLPFC hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>DLPFC all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes<br><strong>DLPFC all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed DLPFC networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP hit.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only<br><strong>HP hit.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with SCZ genes (200 kbp list) only. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>HP all.genes_kb_200__online.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes<br><strong>HP all.genes_kb_200__paper.version.zip:</strong><br>Interactive Sankey plot for age-parsed Hippocampus networks with all genes. For paper version of the figure, smaller modules are merged into a macro-module (lightgrey color)<br><strong>Modulewise SCZ enrichment(1.0).xlsx:</strong><br>Excel file contains module level SCZ enrichment results for all networks<br><strong>wide_form_test_slidingwindow_NC_SchizoNew(v1.4)_final.xlsx:</strong><br>Excel file contains WGCNA output for sliding window networks<br><strong>wide_form_WGCNA(v3.7.1)_final.xlsx:</strong><br>Excel file contains WGCNA output for our generated networks and from previously published networks<br><strong>libdnetworks(NC).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for age-parsed/nonparsed NC networks (DLPFC, HP, CAUDATE, DENTATE). For fixed window and sliding window study.<br><strong>libdnetworks(SCZ).preprocessed.exp.RData: </strong><br>Preprocessed ranknormalised expression assay for nonparsed SCZ networks (DLPFC, HP, CAUDATE, DENTATE). For the sliding window study.<br><strong>sample_matched_HP_DG_qsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. QSVA removed pipeline. For Cell population enrichment study.<br><strong>sample_matched_HP_DG_noqsva(NC).preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the sample-matched HP-DG. No QSVA removed pipeline. For Cell population enrichment study.<br><strong>stemcell.preprocessed.exp.RData:</strong><br>Preprocessed ranknormalised expression assay for the iPSC network. For replication in human iPSC data study. Neuronal samples averaged for each “RealGenome”.<br><strong>SCZ.ref.list.sciadv.ade2812.rds</strong>: List of All Biotypes/ Protein Coding Schizophrenia reference genelist for following bins: PGC3, 0 kbp, 20 kbp, 50 kbp, 100 kbp, 150 kbp, 200 kbp, 250 kbp, 500 kbp.</p> <p> </p> <p>Accompanying code can be found at: <a href="https://github.com/LieberInstitute/Brain_WGCNA">https://github.com/LieberInstitute/Brain_WGCNA</a><br>Data from this repository is also available at: <a href="https://nets.libd.org/age_wgcna/">https://nets.libd.org/age_wgcna/</a></p> <p> </p> <p>For any data inquiries please contact:<br><strong>Giulio Pergola: </strong><a href="mailto:Giulio.Pergola@libd.org"><strong>Giulio.Pergola@libd.org</strong></a></p> <p> </p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 100 Schizophrenia Patients to 48 Patients with Major Depression -Figure 4. Histogram age distributions of the 100 Schizophrenia patients illustrating clusters of patients at 20, 30, 40, and 50 years old.
<p>The figure below is a histogram distribution of the ages of one hundred Schizophrenia<br> patients in this study. There appears to be a cyclical peak every ten-years cycles at 20, 30, 40, and<br> 50-year-old patients. This may suggest a recent finding that CD8 T cells, which play a pivotal role<br> in mediating long-term immunity to Toxoplasma, are down-regulated in schizophrenia patients<br> (Bhadra et al., 2013). Based on the aforementioned statements, a vaccine for Toxoplasmosis may be<br> difficult to achieve, but the treatment and screening possibilities in patients with psychosis are<br> available today.</p>
BRAIN Journal-The Presence and Activity on Facebook of the Informative Travel Organizations in Romania-Figure 4. Facebook adoption rate by age group in Romania
<p>Also, according to Facebrands statistics of 15 October 2015, the Facebook penetration rate in the population is 39.76% and in the total number of Romanian Internet users is 82.97%. The statistics infirm the preconceived ideas of skeptics that the websites of socialization have no relevance for tourism organizations because the contained information in these websites is unstructured, inconsistent in terms of content and irrelevant for tourism, or newer, the ideas of those who believe that the majority of Facebook users are young and very young (under 18). From Figure 4 we can see that the segment of major users (over 18 years) totals 88.3% of the total number of Facebook users and these users may have at any time the quality of tourists.</p>
Effects of brain maintenance and cognitive reserve on age-related decline in three cognitive abilities
<p><strong>Objectives </strong></p> <p>Age-related cognitive changes can be influenced by both brain maintenance (BM), which refers to the relative absence over time of changes in neural resources or neuropathologic changes, and cognitive reserve (CR), which encompasses brain processes that allow for better-than-expected behavioral performance given the degree of life-course related brain changes. This study evaluated the effects of age, BM, and CR on longitudinal changes over two visits, 5 years apart, in three cognitive abilities that capture most of age-related variability.</p> <p><strong>Method </strong></p> <p>Participants included 254 healthy adults aged 20–80 years at recruitment. Potential BM was estimated using whole brain cortical thickness and white matter mean diffusivity at both visits. Education and IQ (estimated with AMNART) were tested as moderating factors for cognitive changes in the three cognitive abilities.</p> <p><strong> Results </strong></p> <p>Consistent with BM—after accounting for age, sex, and baseline performance—individual differences in the preservation of mean diffusivity and cortical thickness were independently associated with relative preservation in the three abilities. Consistent with CR—after accounting for age, sex, baseline performance, and structural brain changes—higher IQ, but not education, was associated with reduced 5-year decline in Reasoning (𝛽=0.387, p=0.002), and education was associated with reduced decline in Speed (𝛽=0.237, p=0.039). <strong> </strong></p> <p><strong>Discussion</strong></p> <p>These results demonstrate that both CR and BM can moderate cognitive changes in healthy aging and that the two mechanisms can make differential contributions to preserved cognition.</p>
Effects of brain maintenance and cognitive reserve on age-related decline in three cognitive abilities
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Data from: Sleep, inflammation, and cognitive behavior of aged wild-type mice subjected to diffuse brain injury and aged 3xTg-AD mice as a model of Alzheimer's disease
<p>Identifying differential responses between sexes following traumatic brain injury (TBI) can elucidate the mechanisms behind disease pathology. Peripheral and central inflammation in the pathophysiology of TBI can increase sleep in male rodents, but this remains untested in females. We hypothesized that diffuse TBI would increase inflammation and sleep in males more so than in females. Diffuse TBI was induced in C57BL/6J mice and serial blood samples were collected (baseline, 1, 5, 7 days post‐injury [DPI]) to quantify peripheral immune cell populations and sleep regulatory cytokines. Brains and spleens were harvested at 7DPI to quantify central and peripheral immune cells, respectively. Mixed‐effects regression models were used for data analysis. Female TBI mice had 77%–124% higher IL‐6 levels than male TBI mice at 1 and 5DPI, whereas IL‐1β and TNF‐α levels were similar between sexes at all timepoints. Despite baseline sex differences in blood‐measured Ly6Chigh monocytes (females had 40% more than males), TBI reduced monocytes by 67% in TBI mice at 1DPI. Male TBI mice had 31%–33% more blood‐measured and 31% more spleen‐measured Ly6G+ neutrophils than female TBI mice at 1 and 5DPI, and 7DPI, respectively. Compared with sham, TBI increased sleep in both sexes during the first light and dark cycles. Male TBI mice slept 11%–17% more than female TBI mice, depending on the cycle. Thus, sex and TBI interactions may alter the peripheral inflammation profile and sleep patterns, which might explain discrepancies in disease progression based on sex.</p>
Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer's disease
<p>Connectome, traits and behavior data for APOE234 mice.</p> <ul> <li>1. connectome.zip: mouse brain structural connectivity matrices from diffusion MRI.</li> <li>2. FAGNN_Phenotype.csv: a sheet of trait information of mice used in the study.</li> </ul> <p>columns: winding numbers, total distance, normalized NE time, normalized NE distance, normalized NW time, normalized NW distance, normalized SE time, normalized SE distance, normlaized SW time, normalized SW distance, island latency to first entry, island entries, normalized thigmataxis time, and normalized thigmotaxis distance</p> <div>rows: 4 trials for each day from day 1 to day 5 with 1 probing test each at day 5 and day 8</div> <ul> <li>3. mouse_anatomy.csv: brain region information regarding the connectivity matrix.</li> <li>4. behavior.zip: behavioral data for each mouse from Morris Water Maze experiments.</li> </ul>
Brain–muscle tissue communication prevents muscle aging by maintaining daily physiology
<p>A network of molecular clocks is crucial for coordinating daily physiology and maintaining organismal health. However, the mechanisms underlying the interactions between these clocks and the significance of intra-tissue clock networks in muscle tissue maintenance have remained elusive. To uncover this network structure, we established a minimal clock module with the central clock (suprachiasmatic nucleus/brain) and/or a peripheral clock (muscle) in arrhythmic mice with premature aging. We find that reconstituting the brain-muscle clock network alone is sufficient to preserve fundamental daily homeostatic functions and prevent premature muscle aging. However, achieving whole muscle daily physiology requires the contribution of other peripheral clocks. Mechanistically, the muscle peripheral clock acts as a gatekeeper, selectively suppressing signals from the central clock that could be detrimental to muscle function if left uncontrolled while also integrating important muscle homeostatic functions. Our findings unveil the reciprocal interactions between central and peripheral clocks crucial for daily muscle function and highlight the significant influence of eating patterns on these interactions. These insights have implications for promoting healthier aging and reversing age-related muscle pathologies.</p>
Whole-transcriptome sequencing identifies neuroinflammation, metabolism and blood-brain barrier related processes in the hippocampus of aged mice during perioperative period
<p><span><strong>Aim</strong>:</span><span> Perioperative neurocognitive disorders (PND) occur frequently after surgery and anesthesia, especially in aged patients. Previous studies have shown multiple PND related mechanisms in the hippocampus, however, their relationships remain unclear. Meanwhile, the perioperative neuropathological processes are sophisticated and changeable, single period study could not reveal the accurate mechanisms. Thus, multiperiod whole-transcriptome study is necessary to elucidate the gene expression patterns during perioperative period.</span></p> <p><span><strong>Methods</strong>: </span><span>Aged</span><span> C57BL/6 mice were subjected to exploratory laparotomy under sevoflurane anesthesia. Whole-transcriptome sequencing (RNA-seq analysis) was performed on the hippocampi from control condition (Con), 30 minutes (Day0), 2 days (Day2) and 7 days (Day7) after surgery. Gene Ontology</span><span>/Kyoto Encyclopedia of Genes and Genomes analyses,</span><span> quantitative Real-Time PCR, immunofluorescence and fear conditioning test were also performed to elucidate the pathological processes and modulation networks during the period.</span></p> <p><span><strong>Results</strong>: </span><span>Through RNA-seq analysis, 328, 3597 and 4179 differentially expressed genes (DEGs) were screened out in intraoperative period (Day0 vs Con), early postoperative period (Day2 vs Day0) and late postoperative period (Day7 vs Day2). The involved GO biological processes were divided into 9 categories, and positive-regulated processes were more than negative-regulated ones. Seventy-four transcription factors were highlighted. The potential synaptic and neuroinflammatory pathways were constructed for Neurotransmitter, Synapse and Neuronal alteration categories with 9 DEGs (<em>Htr1a, Rims1, Ezh2,</em> etc.). The metabolic and mitochondrial pathways were constructed for Metabolism, Oxidative stress and Biological rhythm categories with 9 DEGs (<em>Gpld1, Sirt1, Cry2, </em>etc.). The blood-brain barrier and neurotoxicity related pathways were constructed for Blood-brain barrier, Neurotoxicity and Cognitive function categories with 10 DEGs (<em>Mmp2, Itpr1, Nrf1, </em>etc.).</span></p> <p><span><strong>Conclusion</strong>:</span><span> The results revealed gene expression patterns and modulation networks in the aged hippocampus during perioperative period, which provide insights into overall mechanisms and potential therapeutic targets for prevention and treatment of perioperative central nervous system diseases, such as PND, from the genetic level.</span></p>
Microglia protect against age-associated brain pathologies - all scRNAseq datasets
<p>Zipped cellranger_matrices file contains the filtered_feature_bc_matrices and the raw_feature_bc_matrices obtained from cellranger for the young (E...), middle-aged(S...), old (...vo...) and thalamic (TH...) datasets. </p> <p> The cellinfo csv files give the metadata for each cell kept in the analysis, with a relationship between each barcode and information such as the umi counts or the cell type annotation.</p> <p>The metadata file gives information about each sample, to trace back the sample names used in cellranger to the biological samples. On other tabs it also has qc information such as the thresholds used for each sample.</p> <p>The raw fastq files and RDS files are available at GEO: GSE267545 and GEO:GSE215440 with the same metadata as here as well as the SingleCellExperiment in form of RDS objects (with information such as normalised reads and dimensional reduction embedings) available at GEO: GSE267545</p> <p>The code used to do the analysis is available in Anna-Williams GitHub and also in ZENODO: </p> <p> https://github.com/Anna-Williams/David-young (10.5281/zenodo.11199128) for the young dataset</p> <p> https://github.com/Anna-Williams/David-old (10.5281/zenodo.11199278) for the middle-aged dataset</p> <p>https://github.com/Anna-Williams/David-vold (10.5281/zenodo.11199322) for the old dataset<br>https://github.com/Anna-Williams/David-Thalamus (10.5281/zenodo.11199349) for the thalamic dataset<br>https://github.com/Anna-Williams/David-AgeIntegration (10.5281/zenodo.11199367) for the integration between the young, middle-aged and old datasets.</p> <p> </p> <p>Microglia are brain-resident macrophages that contribute to central nervous system development, maturation, and preservation. Here, we examine the consequences of lifelong absence of microglia on ageing using the Csf1rΔFIRE/ΔFIRE mouse model. In juvenile Csf1rΔFIRE/ΔFIRE mice, we show that microglia are largely dispensable for the transcriptomic maturation of other brain cell types. In contrast, with advancing age, multiple pathologies accumulate in Csf1rΔFIRE/ΔFIRE brains, astrocytes and oligodendrocyte-lineage cells become increasingly dysregulated, and white matter integrity declines, mimicking many of the pathological features of human CSF1R-related leukoencephalopathy. The thalamus is particularly sensitive to neuropathological changes in the absence of microglia, with atrophy, neuron loss, vascular disturbances, macroglial dysregulation, and severe calcifications all detected in this region. Thalamic calcification formation, which often occurs with normal ageing, is dramatically accelerated in Csf1rΔFIRE/ΔFIRE brains but can be prevented via transplantation of wild-type microglia. Our results indicate that lifelong absence of microglia results in an age-related neurodegenerative condition that can be prevented by the transplantation of healthy microglia.</p>
Data from: Effects of maternal age and environmental enrichment on learning ability and brain size
<p>It is well known that maternal age at reproduction affects offspring lifespan and some other fitness-related traits, but it remains understudied whether maternal senescence affects how offspring respond to their environments. Early environment often plays a significant role in the development of an animal's behavioral phenotype. For example, complex environments can promote changes in cognitive ability and brain morphology in young animals. Here, we study whether and how maternal effect senescence influences offspring plasticity in cognition, group behavior, and brain morphology in response to environmental complexity. For this, juvenile three-spined sticklebacks from young and old mothers (i.e. 1-year and 2-years-old) were exposed to different levels of environmental enrichment and complexity (i.e. none, simple and complex), and their behavior, cognitive ability, and brain size were measured. Exposing fish to enriched conditions improved individual learning ability assessed by a repeated detour-reaching task, increased the size of the whole brain, and decreased aggressive interactions in the shoal. Maternal age did not influence the inhibitory control, learning ability, and group behavioral responses of offspring to the experimental environmental change. However, maternal age affected how some brain regions of offspring changed in response to environmental complexity. In offspring from old mothers, those exposed to the complex environment had larger telencephalons and cerebellums than those who experienced simpler environments. Our results suggest that maternal effect senescence may influence how offspring invest in brain functions related to cognition in response to environmental complexity.</p>
A 3D Adult Zebrafish Brain Atlas (AZBA) for the Digital Age
<p>Zebrafish have made significant contributions to our understanding of the vertebrate brain and the neural basis of behavior, earning a place as one of the most widely used model organisms in neuroscience. Their appeal arises from the marriage of low cost, early life transparency, and ease of genetic manipulation with a behavioral repertoire that becomes more sophisticated as animals transition from larvae to adults. To further enhance the use of adult zebrafish, we created the first fully segmented three-dimensional digital <u>a</u>dult <u>z</u>ebrafish <u>b</u>rain <u>a</u>tlas (AZBA). AZBA was built by combining tissue clearing, light-sheet fluorescence microscopy, and three-dimensional image registration of nuclear and antibody stains. These images were used to guide segmentation of the atlas into over 200 neuroanatomical regions comprising the entirety of the adult zebrafish brain. As an open source, online (azba.wayne.edu), updatable digital resource, AZBA will significantly enhance the use of adult zebrafish in furthering our understanding of vertebrate brain function in both health and disease.</p>
Review of Diagnostic Yield of MRI Brain Results in Children Under Age 3 Years
ClinicalTrials.gov study NCT03561168. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Brain Aging in Phenylketonuria
ClinicalTrials.gov study NCT06969209. IPD Sharing: YES. Countries: 1. Publications: 6.
Brain Aging and Treatment Response in Geriatric Depression
ClinicalTrials.gov study NCT01902004. IPD Sharing: NO. Countries: 1. Publications: 4.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.