Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.

Efforts to decipher chronic lung disease and to reconstitute functional lung tissue through regenerative medicine have been hampered by an incomplete understanding of cell-cell interactions governing tissue homeostasis. Because the structure of mammalian lungs is highly conserved at the histologic level, we hypothesized that there are evolutionarily conserved homeostatic mechanisms that keep the fine architecture of the lung in balance. We have leveraged single-cell RNA sequencing techniques to identify conserved patterns of cell-cell cross-talk in adult mammalian lungs, analyzing mouse, rat, pig, and human pulmonary tissues. Specific stereotyped functional roles for each cell type in the distal lung are observed, with alveolar type I cells having a major role in the regulation of tissue homeostasis. This paper provides a systems-level portrait of signaling between alveolar cell populations. These methods may be applicable to other organs, providing a roadmap for identifying key pathways governing pathophysiology and informing regenerative efforts.

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Conscious perception occurs within less than 1 s. To study events on this time scale we used direct electrical recordings from the human cerebral cortex during a conscious visual perception task. Faces were presented at individually titrated visual threshold for 9 subjects while measuring broadband 40-115 Hz gamma power in a total of 1621 intracranial electrodes widely distributed in both hemispheres. Surface maps and k-means clustering analysis showed initial activation of visual cortex for both perceived and non-perceived stimuli. However, only stimuli reported as perceived then elicited a forward-sweeping wave of activity throughout the cerebral cortex accompanied by large-scale network switching. Specifically, a monophasic wave of broadband gamma activation moves through bilateral association cortex at a rate of approximately 150 mm/s and eventually reenters visual cortex for perceived but not for non-perceived stimuli. Meanwhile, the default mode network and the initial visual cortex and higher association cortex networks are switched off for the duration of conscious stimulus processing. Based on these findings, we propose a new "switch-and-wave" model for the processing of consciously perceived stimuli. These findings are important for understanding normal conscious perception and may also shed light on its vulnerability to disruption by brain disorders.

Using fMRI and functional connectivity analyses, it is possible to establish a functional connectome for an individual. The extent to which functional connectomes from adolescents and young adults remain identifiable across many years has not been investigated. Here we show in three publically available longitudinal resting-state fMRI datasets that connectome-based identification of adolescents and young adults scanned 1-3 years apart is possible at levels well above chance using whole-brain functional connectivity data. When we restrict the identification process to specific edges, we find that edges in the frontal, parietal, and temporal cortices tend to lead to the highest identification rates. We also demonstrate that highly unique edges contributing the most to a successful ID tend to connect nodes in these same cortical regions, while edges contributing the least tend to connect cross-hemispheric homologs. These results suggest that despite developmental changes, adolescent and young adult subjects have unique and stable functional connectomes and that the frontal, parietal, and temporal cortices are important in defining individual uniqueness in younger subjects.

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1–12. So far, work modelling linear brain–phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain–phenotype relationships. To this end, here we related brain activity to phenotype using predictive models—trained and tested on independent data to ensure generalizability15—and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18–20 on the interpretation and utility of resulting brain–phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

BackgroundNeisseria meningitidis expresses type four pili (Tfp) which are important for colonisation and virulence. Tfp have been considered as one of the most variable structures on the bacterial surface due to high frequency gene conversion, resulting in amino acid sequence variation of the major pilin subunit (PilE). Meningococci express either a class I or a class II pilE gene and recent work has indicated that class II pilins do not undergo antigenic variation, as class II pilE genes encode conserved pilin subunits. The purpose of this work was to use whole genome sequences to further investigate the frequency and variability of the class II pilE genes in meningococcal isolate collections.ResultsWe analysed over 600 publically available whole genome sequences of N. meningitidis isolates to determine the sequence and genomic organization of pilE. We confirmed that meningococcal strains belonging to a limited number of clonal complexes (ccs, namely cc1, cc5, cc8, cc11 and cc174) harbour a class II pilE gene which is conserved in terms of sequence and chromosomal context. We also identified pilS cassettes in all isolates with class II pilE, however, our analysis indicates that these do not serve as donor sequences for pilE/pilS recombination. Furthermore, our work reveals that the class II pilE locus lacks the DNA sequence motifs that enable (G4) or enhance (Sma/Cla repeat) pilin antigenic variation. Finally, through analysis of pilin genes in commensal Neisseria species we found that meningococcal class II pilE genes are closely related to pilE from Neisseria lactamica and Neisseria polysaccharea, suggesting horizontal transfer among these species.ConclusionsClass II pilins can be defined by their amino acid sequence and genomic context and are present in meningococcal isolates which have persisted and spread globally. The absence of G4 and Sma/Cla sequences adjacent to the class II pilE genes is consistent with the lack of pilin subunit variation in these isolates, although horizontal transfer may generate class II pilin diversity. This study supports the suggestion that high frequency antigenic variation of pilin is not universal in pathogenic Neisseria.

A recent study by Waller and colleagues evaluated the reliability, specificity, and generalizability of using functional connectivity data to identify individuals from a group. The authors note they were able to replicate identification rates in a larger version of the original Human Connectome Project (HCP) dataset. However, they also report lower identification accuracies when using historical neuroimaging acquisitions with low spatial and temporal resolution. The authors suggest that their results indicate connectomes derived from historical imaging data may be similar across individuals, to the extent that this connectome-based approach may be inappropriate for precision psychiatry and the goal of drawing inferences based on subject-level data. Here we note that the authors did not take into account factors affecting data quality and hence identification rates, independent of whether a low spatiotemporal resolution acquisition or a high spatiotemporal resolution acquisition is used. Specifically, we show here that the amount of data collected per subject and in-scanner motion are the predominant factors influencing identification rates, not the spatiotemporal resolution of the acquisition. To do this, we investigated identification rates in the HCP dataset as a function of the amount of data and motion. Using a dataset from the Consortium for Reliability and Reproducibility (CoRR), we investigated the impact of multiband versus non-multiband imaging parameters; that is, high spatiotemporal resolution versus low spatiotemporal resolution acquisitions. We show scan length and motion affect identification, whereas the imaging protocol does not affect these rates. Our results suggest that motion and amount of data per subject are the primary factors impacting individual connectivity profiles, but that within these constraints, individual differences in the connectome are readily observable.