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Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures-bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration-were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = -0.11 (95% confidence interval -0.13 to -0.10, p = 3 × 10-56, FDR = 6 × 10-55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

Major depressive disorder (MDD) has an enormous impact on global disease burden, affecting millions of people worldwide and ranking as a leading cause of disability for almost three decades. Past molecular studies of MDD employed bulk homogenates of postmortem brain tissue, which obscures gene expression changes within individual cell types. Here we used single-nucleus transcriptomics to examine ~80,000 nuclei from the dorsolateral prefrontal cortex of male individuals with MDD (n = 17) and of healthy controls (n = 17). We identified 26 cellular clusters, and over 60% of these showed differential gene expression between groups. We found that the greatest dysregulation occurred in deep layer excitatory neurons and immature oligodendrocyte precursor cells (OPCs), and these contributed almost half (47%) of all changes in gene expression. These results highlight the importance of dissecting cell-type-specific contributions to the disease and offer opportunities to identify new avenues of research and novel targets for treatment.Single-nucleus transcriptomics reveal brain alterations associated with major depression. Deep layer excitatory cells and immature oligodendrocytes showed most changes, involving synaptic plasticity, immune function and steroid hormones.

Background Genome-wide association studies (GWAS) have indicated moderate genetic overlap between Alzheimer’s disease (AD) and related dementias (ADRD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS), neurodegenerative disorders traditionally considered etiologically distinct. However, the specific genetic variants and loci underlying this overlap remain almost entirely unknown. Methods We leveraged state-of-the-art GWAS for ADRD, PD, and ALS. For each pair of disorders, we examined each of the GWAS hits for one disorder and tested whether they were also significant for the other disorder, applying Bonferroni correction for the number of variants tested. This approach rigorously controls the family-wise error rate for both disorders, analogously to genome-wide significance. Results Eleven loci with GWAS hits for one disorder were also associated with one or both of the other disorders: one with all three disorders (the MAPT / KANSL1 locus), five with ADRD and PD (near LCORL , CLU , SETD1A / KAT8 , WWOX , and GRN ), three with ADRD and ALS (near GPX3 , HS3ST5 / HDAC2 / MARCKS , and TSPOAP1 ), and two with PD and ALS (near GAK / TMEM175 and NEK1 ). Two of these loci ( LCORL and NEK1 ) were associated with an increased risk of one disorder but decreased risk of another. Colocalization analysis supported a shared causal variant between ADRD and PD at the CLU , WWOX , and LCORL loci, between ADRD and ALS at the TSPOAP1 locus, and between PD and ALS at the NEK1 and GAK / TMEM175 loci. To address the concern that ADRD is an imperfect proxy for AD and that the ADRD and PD GWAS have overlapping participants (nearly all of which are from the UK Biobank), we confirmed that all our ADRD associations had nearly identical odds ratios in an AD GWAS that excluded the UK Biobank, and all but one remained nominally significant ( p  < 0.05) for AD. Conclusions In one of the most comprehensive investigations to date of pleiotropy between neurodegenerative disorders, we identify eleven genetic risk loci shared among ADRD, PD, and ALS. These loci support lysosomal/autophagic dysfunction ( GAK / TMEM175 , GRN , KANSL1 ), neuroinflammation/immunity ( TSPOAP1 ), oxidative stress ( GPX3 , KANSL1 ), and the DNA damage response ( NEK1 ) as transdiagnostic processes underlying multiple neurodegenerative disorders.

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination ( SEMA3A ), neurite elongation and guidance ( NUAK1 , STRN , DPYSL2 , EPHA3 , SEMA3A , HGF , SHTN1 ), neural cell proliferation and differentiation ( GMNC , CELF4 , HGF ), neuronal migration ( CCDC88C ), cytoskeletal organization ( CTTNBP2 , MAPT , DAAM1 , MYO16 , PLEC ), and brain metal transport ( SLC39A8 ). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function. The structural connectome is the complete set of anatomical connections between brain cells. Here, the authors perform a genome-wide association study of white-matter structural connectivity in the human brain, finding 30 variants influencing the density of myelinated connections between brain regions.

In the human neocortex coherent interlaminar theta oscillations are driven by deep cortical layers, suggesting neurons in these layers exhibit distinct electrophysiological properties. To characterize this potential distinctiveness, we use in vitro whole-cell recordings from cortical layers 2 and 3 (L2&3), layer 3c (L3c) and layer 5 (L5) of the human cortex. Across all layers we observe notable heterogeneity, indicating human cortical pyramidal neurons are an electrophysiologically diverse population. L5 pyramidal cells are the most excitable of these neurons and exhibit the most prominent sag current (abolished by blockade of the hyperpolarization activated cation current, I h ). While subthreshold resonance is more common in L3c and L5, we rarely observe this resonance at frequencies greater than 2 Hz. However, the frequency dependent gain of L5 neurons reveals they are most adept at tracking both delta and theta frequency inputs, a unique feature that may indirectly be important for the generation of cortical theta oscillations. The unique biophysical properties of human cortical neurons that may underlie interlaminar communication are explored. With a focus on I h and layers 2&3, 3c, and 5, the authors show that L5 pyramidal neurons are better adapted than their superficial layer counterparts to track delta and theta frequency inputs.

In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were \"class-driven,\" meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from PatchSeq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.

•Original virtual histology identifies cell types associated with MRI phenotypes.•Case-control virtual histology uses case-control gene expression maps.•Inferred loss of excitatory and inhibitory neurons in regions most thinned in Alzheimer's disease.•Different cell types are associated with Alzheimer's disease using case-control and original virtual histology. Many neuropsychiatric disorders are characterised by altered cortical thickness, but the cell types underlying these changes remain largely unknown. Virtual histology (VH) approaches map regional patterns of gene expression with regional patterns of MRI-derived phenotypes, such as cortical thickness, to identify cell types associated with case-control differences in those MRI measures. However, this method does not incorporate valuable information of case-control differences in cell type abundance. We developed a novel method, termed case-control virtual histology (CCVH), and applied it to Alzheimer's disease (AD) and dementia cohorts. Leveraging a multi-region gene expression dataset of AD cases (n = 40) and controls (n = 20), we quantified AD case-control differential expression of cell type-specific markers across 13 brain regions. We then correlated these expression effects with MRI-derived AD case-control cortical thickness differences across the same regions. Cell types with spatially concordant AD-related effects were identified through resampling marker correlation coefficients. Among regions thinner in AD, gene expression patterns identified by CCVH suggested fewer excitatory and inhibitory neurons, and greater proportions of astrocytes, microglia, oligodendrocytes, oligodendrocyte precursor cells, and endothelial cells in AD cases vs. controls. In contrast, original VH identified expression patterns suggesting that excitatory but not inhibitory neuron abundance was associated with thinner cortex in AD, despite the fact that both types of neurons are known to be lost in the disorder. Compared to original VH, cell types identified through CCVH are more likely to directly underlie cortical thickness differences in AD. Sensitivity analyses suggest our results are largely robust to specific analysis choices, like numbers of cell type-specific marker genes used and background gene sets used to construct null models. As more multi-region brain expression datasets become available, CCVH will be useful for identifying the cellular correlates of cortical thickness across neuropsychiatric illnesses.

Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.

Cannabis is known to produce acute, transient psychotic-like experiences. However, it is unclear whether cannabis disproportionately increases the risk of specific types of psychotic experiences and whether genetic predisposition influences the relationship between cannabis use and psychotic experiences. In this cross-sectional study of 109,308 UK Biobank participants, we examined how schizophrenia polygenic risk modulates the association between self-reported cannabis use and four types of self-reported psychotic experiences (auditory hallucinations, visual hallucinations, persecutory delusions, and delusions of reference). Cohort-wide, we found a strong, dose-dependent relationship between cannabis use and all four types of psychotic experiences, especially persecutory delusions. Cannabis users’ psychotic experiences tended to be earlier-onset and cause greater distress than non-users’, but were not more likely to lead to help-seeking. Participants with high schizophrenia polygenic risk scores showed stronger associations between cannabis use and auditory hallucinations, visual hallucinations, and delusions of reference, as well as psychotic experiences overall. For instance, cannabis ever-use was associated with 67% greater adjusted odds of delusions of reference among individuals in the top fifth of polygenic risk, but only 7% greater adjusted odds among the bottom fifth. Our results suggest that cannabis use is a predictive risk factor for psychotic experiences, including early-onset and distressing experiences. Individuals genetically predisposed to schizophrenia may be especially vulnerable to psychotic experiences as a result of using cannabis, supporting a long-postulated hypothesis. This study exemplifies the utility of population-scale biobanks for elucidating gene-by-environment interactions relating substance use to neuropsychiatric outcomes and points to the translational potential of using polygenic risk scores to inform personalized harm reduction interventions.