Psilocybin desynchronizes the human brain.

A single dose of psilocybin, a psychedelic that acutely causes distortions of space-time perception and ego dissolution, produces rapid and persistent therapeutic effects in human clinical trials1-4. In animal models, psilocybin induces neuroplasticity in cortex and hippocampus5-8. It remains unclear how human brain network changes relate to subjective and lasting effects of psychedelics. Here we tracked individual-specific brain changes with longitudinal precision functional mapping (roughly 18 magnetic resonance imaging visits per participant). Healthy adults were tracked before, during and for 3 weeks after high-dose psilocybin (25 mg) and methylphenidate (40 mg), and brought back for an additional psilocybin dose 6-12 months later. Psilocybin massively disrupted functional connectivity (FC) in cortex and subcortex, acutely causing more than threefold greater change than methylphenidate. These FC changes were driven by brain desynchronization across spatial scales (areal, global), which dissolved network distinctions by reducing correlations within and anticorrelations between networks. Psilocybin-driven FC changes were strongest in the default mode network, which is connected to the anterior hippocampus and is thought to create our sense of space, time and self. Individual differences in FC changes were strongly linked to the subjective psychedelic experience. Performing a perceptual task reduced psilocybin-driven FC changes. Psilocybin caused persistent decrease in FC between the anterior hippocampus and default mode network, lasting for weeks. Persistent reduction of hippocampal-default mode network connectivity may represent a neuroanatomical and mechanistic correlate of the proplasticity and therapeutic effects of psychedelics.

Structure-function coupling in macroscale human brain networks.

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

A shared accretion instability for black holes and neutron stars.

Accretion disks around compact objects are expected to enter an unstable phase at high luminosity1. One instability may occur when the radiation pressure generated by accretion modifies the disk viscosity, resulting in the cyclic depletion and refilling of the inner disk on short timescales2. Such a scenario, however, has only been quantitatively verified for a single stellar-mass black hole3-5. Although there are hints of these cycles in a few isolated cases6-10, their apparent absence in the variable emission of most bright accreting neutron stars and black holes has been a continuing puzzle11. Here we report the presence of the same multiwavelength instability around an accreting neutron star. Moreover, we show that the variability across the electromagnetic spectrum-from radio to X-ray-of both black holes and neutron stars at high accretion rates can be explained consistently if the accretion disks are unstable, producing relativistic ejections during transitions that deplete or refill the inner disk. Such a new association allows us to identify the main physical components responsible for the fast multiwavelength variability of highly accreting compact objects.

A persistent ultraviolet outflow from an accreting neutron star binary transient.

All disc-accreting astrophysical objects produce powerful disc winds. In compact binaries containing neutron stars or black holes, accretion often takes place during violent outbursts. The main disc wind signatures during these eruptions are blue-shifted X-ray absorption lines, which are preferentially seen in disc-dominated 'soft states'1,2. By contrast, optical wind-formed lines have recently been detected in 'hard states', when a hot corona dominates the luminosity3. The relationship between these signatures is unknown, and no erupting system has as yet revealed wind-formed lines between the X-ray and optical bands, despite the many strong resonance transitions in this ultraviolet (UV) region4. Here we report that the transient neutron star binary Swift J1858.6-0814 exhibits wind-formed, blue-shifted absorption lines associated with C IV, N V and He II in time-resolved UV spectroscopy during a luminous hard state, which we interpret as a warm, moderately ionized outflow component in this state. Simultaneously observed optical lines also display transient blue-shifted absorption. Decomposing the UV data into constant and variable components, the blue-shifted absorption is associated with the former. This implies that the outflow is not associated with the luminous flares in the data. The joint presence of UV and optical wind features reveals a multi-phase and/or spatially stratified evaporative outflow from the outer disc5. This type of persistent mass loss across all accretion states has been predicted by radiation-hydrodynamic simulations6 and helps to explain the shorter-than-expected duration of outbursts7.

Copper-mediated synthesis of drug-like bicyclopentanes.

Multicomponent reactions are relied on in both academic and industrial synthetic organic chemistry owing to their step- and atom-economy advantages over traditional synthetic sequences1. Recently, bicyclo[1.1.1]pentane (BCP) motifs have become valuable as pharmaceutical bioisosteres of benzene rings, and in particular 1,3-disubstituted BCP moieties have become widely adopted in medicinal chemistry as para-phenyl ring replacements2. These structures are often generated from [1.1.1]propellane via opening of the internal C-C bond through the addition of either radicals or metal-based nucleophiles3-13. The resulting propellane-addition adducts are then transformed to the requisite polysubstituted BCP compounds via a range of synthetic sequences that traditionally involve multiple chemical steps. Although this approach has been effective so far, a multicomponent reaction that enables single-step access to complex and diverse polysubstituted drug-like BCP products would be more time efficient compared to current stepwise approaches. Here we report a one-step three-component radical coupling of [1.1.1]propellane to afford diverse functionalized bicyclopentanes using various radical precursors and heteroatom nucleophiles via a metallaphotoredox catalysis protocol. This copper-mediated reaction operates on short timescales (five minutes to one hour) across multiple (more than ten) nucleophile classes and can accommodate a diverse array of radical precursors, including those that generate alkyl, α-acyl, trifluoromethyl and sulfonyl radicals. This method has been used to rapidly prepare BCP analogues of known pharmaceuticals, one of which is substantially more metabolically stable than its commercial progenitor.

Genomic loci influence patterns of structural covariance in the human brain.

Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.

WASp controls oriented migration of endothelial cells to achieve functional vascular patterning.

Endothelial cell migration and proliferation are essential for the establishment of a hierarchical organization of blood vessels and optimal distribution of blood. However, how these cellular processes are quantitatively coordinated to drive vascular network morphogenesis remains unknown. Here, using the zebrafish vasculature as a model system, we demonstrate that the balanced distribution of endothelial cells, as well as the resulting regularity of vessel calibre, is a result of cell migration from veins towards arteries and cell proliferation in veins. We identify the Wiskott-Aldrich Syndrome protein (WASp) as an important molecular regulator of this process and show that loss of coordinated migration from veins to arteries upon wasb depletion results in aberrant vessel morphology and the formation of persistent arteriovenous shunts. We demonstrate that WASp achieves its function through the coordination of junctional actin assembly and PECAM1 recruitment and provide evidence that this is conserved in humans. Overall, we demonstrate that functional vascular patterning in the zebrafish trunk is established through differential cell migration regulated by junctional actin, and that interruption of differential migration may represent a pathomechanism in vascular malformations.

Svep1 stabilises developmental vascular anastomosis in reduced flow conditions.

Molecular mechanisms controlling the formation, stabilisation and maintenance of blood vessel connections remain poorly defined. Here, we identify blood flow and the large extracellular protein Svep1 as co-modulators of vessel anastomosis during developmental angiogenesis in zebrafish embryos. Both loss of Svep1 and blood flow reduction contribute to defective anastomosis of intersegmental vessels. The reduced formation and lumenisation of the dorsal longitudinal anastomotic vessel (DLAV) is associated with a compensatory increase in Vegfa/Vegfr pERK signalling, concomittant expansion of apelin-positive tip cells, but reduced expression of klf2a. Experimentally, further increasing Vegfa/Vegfr signalling can rescue the DLAV formation and lumenisation defects, whereas its inhibition dramatically exacerbates the loss of connectivity. Mechanistically, our results suggest that flow and Svep1 co-regulate the stabilisation of vascular connections, in part by modulating the Vegfa/Vegfr signalling pathway.

Regression and alignment for functional data and network topology.

In the brain, functional connections form a network whose topological organization can be described by graph-theoretic network diagnostics. These include characterizations of the community structure, such as modularity and participation coefficient, which have been shown to change over the course of childhood and adolescence. To investigate if such changes in the functional network are associated with changes in cognitive performance during development, network studies often rely on an arbitrary choice of preprocessing parameters, in particular the proportional threshold of network edges. Because the choice of parameter can impact the value of the network diagnostic, and therefore downstream conclusions, we propose to circumvent that choice by conceptualizing the network diagnostic as a function of the parameter. As opposed to a single value, a network diagnostic curve describes the connectome topology at multiple scales-from the sparsest group of the strongest edges to the entire edge set. To relate these curves to executive function and other covariates, we use scalar-on-function regression, which is more flexible than previous functional data-based models used in network neuroscience. We then consider how systematic differences between networks can manifest in misalignment of diagnostic curves, and consequently propose a supervised curve alignment method that incorporates auxiliary information from other variables. Our algorithm performs both functional regression and alignment via an iterative, penalized, and nonlinear likelihood optimization. The illustrated method has the potential to improve the interpretability and generalizability of neuroscience studies where the goal is to study heterogeneity among a mixture of function- and scalar-valued measures.

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

Imaging chronic active lesions in multiple sclerosis: a consensus statement.

Chronic active lesions (CAL) are an important manifestation of chronic inflammation in multiple sclerosis and have implications for non-relapsing biological progression. In recent years, the discovery of innovative MRI and PET-derived biomarkers has made it possible to detect CAL, and to some extent quantify them, in the brain of persons with multiple sclerosis, in vivo. Paramagnetic rim lesions on susceptibility-sensitive MRI sequences, MRI-defined slowly expanding lesions on T1-weighted and T2-weighted scans, and 18-kDa translocator protein-positive lesions on PET are promising candidate biomarkers of CAL. While partially overlapping, these biomarkers do not have equivalent sensitivity and specificity to histopathological CAL. Standardization in the use of available imaging measures for CAL identification, quantification and monitoring is lacking. To fast-forward clinical translation of CAL, the North American Imaging in Multiple Sclerosis Cooperative developed a consensus statement, which provides guidance for the radiological definition and measurement of CAL. The proposed manuscript presents this consensus statement, summarizes the multistep process leading to it, and identifies the remaining major gaps in knowledge.

Reelin and stk25 have opposing roles in neuronal polarization and dendritic Golgi deployment.

The Reelin ligand regulates a Dab1-dependent signaling pathway required for brain lamination and normal dendritogenesis, but the specific mechanisms underlying these actions remain unclear. We find that Stk25, a modifier of Reelin-Dab1 signaling, regulates Golgi morphology and neuronal polarization as part of an LKB1-Stk25-Golgi matrix protein 130 (GM130) signaling pathway. Overexpression of Stk25 induces Golgi condensation and multiple axons, both of which are rescued by Reelin treatment. Reelin stimulation of cultured neurons induces the extension of the Golgi into dendrites, which is suppressed by Stk25 overexpression. In vivo, Reelin and Dab1 are required for the normal extension of the Golgi apparatus into the apical dendrites of hippocampal and neocortical pyramidal neurons. This demonstrates that the balance between Reelin-Dab1 signaling and LKB1-Stk25-GM130 regulates Golgi dispersion, axon specification, and dendrite growth and provides insights into the importance of the Golgi apparatus for cell polarization.

Sex differences in the functional topography of association networks in youth.

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Protein-protein interactions between tenascin-R and RPTPζ/phosphacan are critical to maintain the architecture of perineuronal nets.

Neural plasticity, the ability to alter the structure and function of neural circuits, varies throughout the age of an individual. The end of the hyperplastic period in the central nervous system coincides with the appearance of honeycomb-like structures called perineuronal nets (PNNs) that surround a subset of neurons. PNNs are a condensed form of neural extracellular matrix that include the glycosaminoglycan hyaluronan and extracellular matrix proteins such as aggrecan and tenascin-R (TNR). PNNs are key regulators of developmental neural plasticity and cognitive functions, yet our current understanding of the molecular interactions that help assemble them remains limited. Disruption of Ptprz1, the gene encoding the receptor protein tyrosine phosphatase RPTPζ, altered the appearance of nets from a reticulated structure to puncta on the surface of cortical neuron bodies in adult mice. The structural alterations mirror those found in Tnr-/- mice, and TNR is absent from the net structures that form in dissociated cultures of Ptprz1-/- cortical neurons. These findings raised the possibility that TNR and RPTPζ cooperate to promote the assembly of PNNs. Here, we show that TNR associates with the RPTPζ ectodomain and provide a structural basis for these interactions. Furthermore, we show that RPTPζ forms an identical complex with tenascin-C, a homolog of TNR that also regulates neural plasticity. Finally, we demonstrate that mutating residues at the RPTPζ-TNR interface impairs the formation of PNNs in dissociated neuronal cultures. Overall, this work sets the stage for analyzing the roles of protein-protein interactions that underpin the formation of nets.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging.

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Penalized decomposition using residuals (PeDecURe) for feature extraction in the presence of nuisance variables.

Neuroimaging data are an increasingly important part of etiological studies of neurological and psychiatric disorders. However, mitigating the influence of nuisance variables, including confounders, remains a challenge in image analysis. In studies of Alzheimer's disease, for example, an imbalance in disease rates by age and sex may make it difficult to distinguish between structural patterns in the brain (as measured by neuroimaging scans) attributable to disease progression and those characteristic of typical human aging or sex differences. Concerningly, when not properly accounted for, nuisance variables pose threats to the generalizability and interpretability of findings from these studies. Motivated by this critical issue, in this work, we examine the impact of nuisance variables on feature extraction methods and propose Penalized Decomposition Using Residuals (PeDecURe), a new method for obtaining nuisance variable-adjusted features. PeDecURe estimates primary directions of variation which maximize covariance between partially residualized imaging features and a variable of interest (e.g., Alzheimer's diagnosis) while simultaneously mitigating the influence of nuisance variation through a penalty on the covariance between partially residualized imaging features and those variables. Using features derived using PeDecURe's first direction of variation, we train a highly accurate and generalizable predictive model, as evidenced by its robustness in testing samples with different underlying nuisance variable distributions. We compare PeDecURe to commonly used decomposition methods (principal component analysis (PCA) and partial least squares) as well as a confounder-adjusted variation of PCA. We find that features derived from PeDecURe offer greater accuracy and generalizability and lower correlations with nuisance variables compared with the other methods. While PeDecURe is primarily motivated by challenges that arise in the analysis of neuroimaging data, it is broadly applicable to data sets with highly correlated features, where novel methods to handle nuisance variables are warranted.

Similarity-based multimodal regression.

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distance-based method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations between clinical variables and multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.

Effect of patient factors, center, and era on Fontan timing: An observational study using the Pediatric Health Information Systems Database.

BACKGROUND: There are no consensus guidelines defining optimal timing for the Fontan operation, the last planned surgery in staged palliation for single-ventricle heart disease. OBJECTIVES: Identify patient-level characteristics, center-level variation, and secular trends driving Fontan timing. METHODS: A retrospective observational study of subjects who underwent Fontan from 2007 to 2021 at centers in the Pediatric Health Information Systems database was performed using linear mixed-effects modeling in which age at Fontan was regressed on patient characteristics and date of operation with center as random effect. RESULTS: We included 10,305 subjects (40.4% female, 44% non-white) at 47 centers. Median age at Fontan was 3.4 years (IQR 2.6-4.4). Hypoplastic left heart syndrome (-4.4 months, 95%CI -5.5 to -3.3) and concomitant conditions (-2.6 months, 95%CI -4.1 to -1.1) were associated with younger age at Fontan. Subjects with technology-dependence (+4.6 months, 95%CI 3.1-6.1) were older at Fontan. Black (+4.1 months, 95%CI 2.5-5.7) and Asian (+8.3 months, 95%CI 5.4-11.2) race were associated with older age at Fontan. There was significant variation in Fontan timing between centers. Center accounted for 10% of variation (ICC 0.10, 95%CI 0.07-0.14). Center surgical volume was not associated with Fontan timing (P = .21). Operation year was associated with age at Fontan, with a 3.1 month increase in age for every 5 years (+0.61 months, 95%CI 0.48-0.75). CONCLUSIONS: After adjusting for patient-level characteristics there remains significant inter-center variation in Fontan timing. Age at Fontan has increased. Future studies addressing optimal Fontan timing are warranted.