Biology subjects

McPherson, B. C.

Publications and source records attributed to McPherson, B. C..

4 recordsLinked to original sources

bioRxiv2025

Clinical profile impacts the replicability of multivariate brain-behavioural associations

Recent work suggests that thousands of individuals are required in multivariate brain-behaviour analyses to obtain consistently replicable results. Some believe, however, that smaller sample sizes may be sufficient if specific subpopulations are targeted. We investigate how sample size and cohort composition influence the replicability of Canonical Correlation Analysis (CCA) results using the UK Biobank (N=40,514). We apply CCA to diffusion-weighted magnetic resonance imaging (dMRI) phenotypes and cognitive assessment test scores. We define four participant cohorts based on clinical profile and find that, across all cohorts, sample sizes of {approx}500 are needed to obtain replicable canonical correlations and variable loadings. The most targeted cohort (comprising individuals with a history of psychoactive substance use) requires much fewer samples to achieve similar or greater correlations than the other cohorts. Our findings support the idea that moderate sample sizes from targeted cohorts can be sufficient for obtaining replicable brain-behaviour associations.

neuroscience↗

bioRxiv2025

Meta all the way down: An overview of neuroimaging meta-analyses

Meta-analyses are invaluable tools for navigating the rapidly expanding scientific literature. Given their high value, ensuring the quality of meta-analyses is paramount. We conducted a multifaceted overview, examining each step in a manual neuroimaging meta-analysis on a large scale. We used four novel datasets comprising over 14,000 papers, including fMRI meta-analyses, fMRI studies, studies included in meta-analyses, and studies associated with image data on NeuroVault. Regarding successes, two-thirds of meta-analyses stated that they followed PRISMA guidelines, and 65% included a flowchart describing their inclusion process. We point out several areas for improvement. Pre-registration was fairly rare (20%), and only half listed their exact search strategy. There could be a location bias in which papers are included, and many did not include enough studies to be robust against publication bias (68% of meta analyses have less than 30 studies included). We also offer ideas for future directions. As image based meta-analysis is the gold standard, we have indicated which topics have the most image data available. The potential redundancy of topics can be visualized in our paper, and we recommend future meta-analyses be in conversation with past ones by citing and discussing previous similar work. By addressing these findings, the neuroimaging community can collectively improve the field of neuroimaging meta-analyses.

neuroscience↗

bioRxiv2021

Deep learning and cross-species analysis identify the influences of sex and cannabinoidsignaling on cerebellar vermis morphology

The cerebellum is the only folded cortical structure present in both rodents and humans, allowing mechanistic studies of gyrification across species. In mice, the extent of anterior vermis folding and the shapes of white matter exhibit sex-dependent differences. We trained a deep convolutional neural network to predict biological sex from midline cerebellar MRI images, showing that human cerebellar midvermis contains sex-dependent anatomical features. Our comparative cross-species analysis shows that in both species, sex and anterior vermis folding patterns are not enough to predict an individuals behavior because the variability in performance between individuals is far greater than the differences between conditions. Finally, utilizing constitutive cannabinoid receptor 1 (CB1) knockout (KO) mice, we identify developmental cannabinoid signaling as a novel molecular mechanism limiting secondary fissure formation. TeaserMales and females display differences in the architecture of cerebellar folding shaped by cannabinoid signaling during development.

neuroscience↗

bioRxiv2020

A single-mode associates global patterns of brain network structure and behavior across the human lifespan

Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r=0.5808). Individual variability within the single association axis well predicted the age of the subject (r=0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach. [147] Data availabilityThe source data are provided by the Cambridge Aging Neuroscience Project https://camcan-archive.mrc-cbu.cam.ac.uk/. Brain data derived as part of this project and used as features for all the analyses are available on brainlife.io/pubs: Code availabilityCode is available on github at https://github.com/bcmcpher/cca_aging and as web services reproducing the analyes at

neuroscience↗