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Miller, M. B.

Publications and source records attributed to Miller, M. B..

21 records · Page 2Linked to original sources

Functional reorganization of brain networks across the human menstrual cycle

The brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterones impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterones widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded--and perhaps driven--by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brains functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain. HighlightsO_LIIntrinsic fluctuations in sex hormones shape the brains functional architecture. C_LIO_LIEstradiol facilitates tighter coherence within whole-brain functional networks. C_LIO_LIProgesterone has the opposite, reductive effect. C_LIO_LIOvulation (via estradiol) modulates variation in topological network states. C_LIO_LIEffects are pronounced in network hubs densely populated with estrogen receptors. C_LI

neuroscience↗

Evidence that APP gene copy number changes reflect recombinant vector contamination

Mutations that occur in cells of the body, called somatic mutations, cause human diseases including cancer and some neurological disorders1. In a recent study published in Nature, Lee et al.2 (hereafter \"the Lee study\") reported somatic copy number gains of the APP gene, a known risk locus of Alzheimers disease (AD), in the neurons of AD-patients and controls (69% vs 25% of neurons with at least one APP copy gain on average). The authors argue that the mechanism of these copy number gains was somatic integration of APP mRNA into the genome, creating what they called genomic cDNA (gencDNA). We reanalyzed the data from the Lee study, revealing evidence that APP gencDNA originates mainly from contamination by exogenous APP recombinant vectors, rather from true somatic retrotransposition of endogenous APP. Our reanalysis of two recent whole exome sequencing (WES) datasets--one by the authors of the Lee study3 and the other by Park et al.4--revealed that reads claimed to support APP gencDNA in AD samples resulted from contamination by PCR products and mRNA, respectively. Lastly, we present our own single-cell whole genome sequencing (scWGS) data that show no evidence for somatic APP retrotransposition in AD neurons or in neurons from normal individuals of various ages.

genomics↗

How Sample Size Influences The Reproducibility Of Task-Based fMRI

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven distinct tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

neuroscience↗