Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank.

Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.

The impact of REM sleep loss on human brain connectivity.

Brain function is vulnerable to the consequences of inadequate sleep, an adverse trend that is increasingly prevalent. The REM sleep phase has been implicated in coordinating various brain structures and is hypothesized to have potential links to brain variability. However, traditional imaging research have encountered challenges in attributing specific brain region activity to REM sleep, remained understudied at the whole-brain connectivity level. Through the spilt-night paradigm, distinct patterns of REM sleep phases were observed among the full-night sleep group (n = 36), the early-night deprivation group (n = 41), and the late-night deprivation group (n = 36). We employed connectome-based predictive modeling (CPM) to delineate the effects of REM sleep deprivation on the functional connectivity of the brain (REM connectome) during its resting state. The REM sleep-brain connectome was characterized by stronger connectivity within the default mode network (DMN) and between the DMN and visual networks, while fewer predictive edges were observed. Notably, connections such as those between the cingulo-opercular network (CON) and the auditory network, as well as between the subcortex and visual networks, also made significant contributions. These findings elucidate the neural signatures of REM sleep loss and reveal common connectivity patterns across individuals, validated at the group level.

Whole exome sequencing analysis identifies genes for alcohol consumption.

Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.

Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Investigating grey matter volumetric trajectories through the lifespan at the individual level.

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to limited large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages, and the neurobiological basis underlying individual heterogeneity remains poorly understood. Here we identify, using the IMAGEN adolescent cohort followed up over 9 years (14-23 y), three groups of adolescents characterized by distinct developmental patterns of whole-brain gray matter volume (GMV). Group 1 show continuously decreasing GMV associated with higher neurocognitive performances than the other two groups during adolescence. Group 2 exhibit a slower rate of GMV decrease and lower neurocognitive performances compared with Group 1, which was associated with epigenetic differences and greater environmental burden. Group 3 show increasing GMV and lower baseline neurocognitive performances due to a genetic variation. Using the UK Biobank, we show these differences may be attenuated in mid-to-late adulthood. Our study reveals clusters of adolescent neurodevelopment based on GMV and the potential long-term impact.

Imitating and exploring the human brain's resting and task-performing states via brain computing: scaling and architecture.

A computational human brain model with the voxel-wise assimilation method was established based on individual structural and functional imaging data. We found that the more similar the brain model is to the biological counterpart in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex (V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart, despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework for a digital twin brain for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical and lesioning approaches that would otherwise be unfeasible in real subjects.

A neural signature for the subjective experience of threat anticipation under uncertainty.

Uncertainty about potential future threats and the associated anxious anticipation represents a key feature of anxiety. However, the neural systems that underlie the subjective experience of threat anticipation under uncertainty remain unclear. Combining an uncertainty-variation threat anticipation paradigm that allows precise modulation of the level of momentary anxious arousal during functional magnetic resonance imaging (fMRI) with multivariate predictive modeling, we train a brain model that accurately predicts subjective anxious arousal intensity during anticipation and test it across 9 samples (total n = 572, both gender). Using publicly available datasets, we demonstrate that the whole-brain signature specifically predicts anxious anticipation and is not sensitive in predicting pain, general anticipation or unspecific emotional and autonomic arousal. The signature is also functionally and spatially distinguishable from representations of subjective fear or negative affect. We develop a sensitive, generalizable, and specific neuroimaging marker for the subjective experience of uncertain threat anticipation that can facilitate model development.

Irritable Bowel Syndrome Is Associated With Brain Health by Neuroimaging, Behavioral, Biochemical, and Genetic Analyses.

BACKGROUND: Irritable bowel syndrome (IBS) interacts with psychopathology in a complex way; however, little is known about the underlying brain, biochemical, and genetic mechanisms. METHODS: To clarify the phenotypic and genetic associations between IBS and brain health, we performed a comprehensive retrospective cohort study on a large population. Our study included 171,104 participants from the UK Biobank who underwent a thorough assessment of IBS, with the majority also providing neuroimaging, behavioral, biochemical, and genetic information. Multistage linked analyses were conducted, including phenome-wide association analysis, polygenic risk score calculation, and 2-sample Mendelian randomization analysis. RESULTS: The phenome-wide association analysis showed that IBS was linked to brain health problems, including anxiety and depression, and poor cognitive performance. Significantly lower brain volumes associated with more severe IBS were found in key areas related to emotional regulation and higher-order cognition, including the medial orbitofrontal cortex/ventromedial prefrontal cortex, anterior insula, anterior and mid-cingulate cortices, dorsolateral prefrontal cortex, and hippocampus. Higher triglycerides, lower high-intensity lipoprotein, and lower platelets were also related (p < 1 × 10-10) to more severe IBS. Finally, Mendelian randomization analyses demonstrated potential causal relationships between IBS and brain health and indicated possible mediating effects of dyslipidemia and inflammation. CONCLUSIONS: For the first time, this study provides a comprehensive understanding of the relationship between IBS and brain health phenotypes, integrating perspectives from neuroimaging, behavioral performance, biochemical factors, and genetics, which is of great significance for clinical applications to potentially address brain health impairments in patients with IBS.

The genetic architecture of the human hypothalamus and its involvement in neuropsychiatric behaviours and disorders.

Despite its crucial role in the regulation of vital metabolic and neurological functions, the genetic architecture of the hypothalamus remains unknown. Here we conducted multivariate genome-wide association studies (GWAS) using hypothalamic imaging data from 32,956 individuals to uncover the genetic underpinnings of the hypothalamus and its involvement in neuropsychiatric traits. There were 23 significant loci associated with the whole hypothalamus and its subunits, with functional enrichment for genes involved in intracellular trafficking systems and metabolic processes of steroid-related compounds. The hypothalamus exhibited substantial genetic associations with limbic system structures and neuropsychiatric traits including chronotype, risky behaviour, cognition, satiety and sympathetic-parasympathetic activity. The strongest signal in the primary GWAS, the ADAMTS8 locus, was replicated in three independent datasets (N = 1,685-4,321) and was strengthened after meta-analysis. Exome-wide association analyses added evidence to the association for ADAMTS8, and Mendelian randomization showed lower ADAMTS8 expression with larger hypothalamic volumes. The current study advances our understanding of complex structure-function relationships of the hypothalamus and provides insights into the molecular mechanisms that underlie hypothalamic formation.

Dopaminergic-related Anatomical Pattern of Dorsal Striatum in Schizophrenia.

Striatal dopaminergic overactivity was hypothesized as the core pathophysiology of schizophrenia. However, morphological alterations of striatum in schizophrenia remains exclusive, largely because brain regional heterogeneity limited traditional group-mean based approach. Leveraging third-party brain maps of neurotransmitter and cognition behaviours, we developed a pattern-based representation feature score (ReFS) to investigate structural spatial pattern variation in schizophrenia. Structural ReFS of subcortical regions, particularly the striatum, were linked to schizophrenia diagnosis, symptom severity, and genetic susceptibility. Dopaminergic-ReFS of striatum was increased in schizophrenia patients and reliably reproduced across 13 datasets. The pattern-based ReFS effectively captured the shared genetic pathways underlying both schizophrenia and striatum. The results provide convergent, multimodal suggest the central role of striatal spatial patterns in schizophrenia psychopathologies and and open new avenues to develop individualized treatments for psychotic disorders.

Fractionation of neural reward processing into independent components by novel decoding principle.

How to retrieve latent neurobehavioural processes from complex neurobiological signals is an important yet unresolved challenge. Here, we develop a novel approach, orthogonal-Decoding multi-Cognitive Processes (DeCoP), to reveal underlying latent neurobehavioural processing and show that its performance is superior to traditional non-orthogonal decoding in terms of both false inference and robustness. Processing value and salience information are two fundamental but mutually confounded pathways of reward reinforcement essential for decision making. During reward/punishment anticipation, we applied DeCoP to decode brain-wide responses into spatially overlapping, yet functionally independent, evaluation and readiness processes, which are modulated differentially by meso­limbic vs nigro-striatal dopamine systems. Using DeCoP, we further demonstrated that most brain regions only encoded abstract information but not the exact input, except for dorsal anterior cingulate cortex and insula. Furthermore, we anticipate our novel analytical principle to be applied generally in decoding multiple latent neurobehavioral processes and thus advance both the design and hypothesis testing for cognitive tasks.

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Structural neurodevelopment at the individual level - a life-course investigation using ABCD, IMAGEN and UK Biobank data.

Adolescents exhibit remarkable heterogeneity in the structural architecture of brain development. However, due to the lack of large-scale longitudinal neuroimaging studies, existing research has largely focused on population averages and the neurobiological basis underlying individual heterogeneity remains poorly understood. Using structural magnetic resonance imaging from the IMAGEN cohort (n=1,543), we show that adolescents can be clustered into three groups defined by distinct developmental patterns of whole-brain gray matter volume (GMV). Genetic and epigenetic determinants of group clustering and long-term impacts of neurodevelopment in mid-to-late adulthood were investigated using data from the ABCD, IMAGEN and UK Biobank cohorts. Group 1, characterized by continuously decreasing GMV, showed generally the best neurocognitive performances during adolescence. Compared to Group 1, Group 2 exhibited a slower rate of GMV decrease and worsened neurocognitive development, which was associated with epigenetic changes and greater environmental burden. Further, Group 3 showed increasing GMV and delayed neurocognitive development during adolescence due to a genetic variation, while these disadvantages were attenuated in mid-to-late adulthood. In summary, our study revealed novel clusters of adolescent structural neurodevelopment and suggested that genetically-predicted delayed neurodevelopment has limited long-term effects on mental well-being and socio-economic outcomes later in life. Our results could inform future research on policy interventions aimed at reducing the financial and emotional burden of mental illness.

Spectrally efficient multi-service fiber-wireless access in low-cost direct-detection THz system at 300†GHz.

This Letter demonstrates a novel, to the best of knowledge, overlapping single-sideband (OSSB) transmission scheme for spectrally efficient multi-service fiber-wireless (FiWi) access in a low-cost direct-detection (DD) THz system. Utilizing the proposed OSSB scheme, user data from different services can share the same spectrum resource yet can be successfully demodulated via one cost-effective DD THz receiver in conjunction with the Kramers-Kronig (KK) based SSB field reconstruction and look-up table (LUT) enabled signal separation algorithms. A proof-of-principle experiment is conducted. Based on an IQ modulator and a single THz zero-bias diode (ZBD), two independent 10-GBd quadrature phase shift keying (QPSK) signals with an overlapped spectrum are successfully demodulated after 20-km fiber and up to 3-m wireless transmission at the 300-GHz band. To the best of our knowledge, this is the first demonstration of multi-service FiWi access with an OSSB format in a 300-GHz DD THz system.

Association between vmPFC gray matter volume and smoking initiation in adolescents.

Smoking of cigarettes among young adolescents is a pressing public health issue. However, the neural mechanisms underlying smoking initiation and sustenance during adolescence, especially the potential causal interactions between altered brain development and smoking behaviour, remain elusive. Here, using large longitudinal adolescence imaging genetic cohorts, we identify associations between left ventromedial prefrontal cortex (vmPFC) gray matter volume (GMV) and subsequent self-reported smoking initiation, and between right vmPFC GMV and the maintenance of smoking behaviour. Rule-breaking behaviour mediates the association between smaller left vmPFC GMV and smoking behaviour based on longitudinal cross-lagged analysis and Mendelian randomisation. In contrast, smoking behaviour associated longitudinal covariation of right vmPFC GMV and sensation seeking (especially hedonic experience) highlights a potential reward-based mechanism for sustaining addictive behaviour. Taken together, our findings reveal vmPFC GMV as a possible biomarker for the early stages of nicotine addiction, with implications for its prevention and treatment.

Brain and molecular mechanisms underlying the nonlinear association between close friendships, mental health, and cognition in children.

Close friendships are important for mental health and cognition in late childhood. However, whether the more close friends the better, and the underlying neurobiological mechanisms are unknown. Using the Adolescent Brain Cognitive Developmental study, we identified nonlinear associations between the number of close friends, mental health, cognition, and brain structure. Although few close friends were associated with poor mental health, low cognitive functions, and small areas of the social brain (e.g., the orbitofrontal cortex, the anterior cingulate cortex, the anterior insula, and the temporoparietal junction), increasing the number of close friends beyond a level (around 5) was no longer associated with better mental health and larger cortical areas, and was even related to lower cognition. In children having no more than five close friends, the cortical areas related to the number of close friends revealed correlations with the density of µ-opioid receptors and the expression of OPRM1 and OPRK1 genes, and could partly mediate the association between the number of close friends, attention-deficit/hyperactivity disorder (ADHD) symptoms, and crystalized intelligence. Longitudinal analyses showed that both too few and too many close friends at baseline were associated with more ADHD symptoms and lower crystalized intelligence 2 y later. Additionally, we found that friendship network size was nonlinearly associated with well-being and academic performance in an independent social network dataset of middle-school students. These findings challenge the traditional idea of 'the more, the better,' and provide insights into potential brain and molecular mechanisms.

Close friendships are crucial during the transition from late childhood to adolescence as children become more independent from their parents and influenced by their peers. The brain undergoes a tremendous amount of development during this period, and it is also a time when mental health disorders often begin to emerge. Scientists are still learning about how friendships shape brain development and mental health during this transition. Maintaining friendships takes time and mental resources so there may be limits on how many friends are beneficial. Here, Shen, Rolls et al. show that the having more friends is not always directly related to better mental health and cognitive abilities. In the study, Shen, Rolls et al. analyzed data from nearly 7,500 young people between around 10 to 12 years old: this included, their number of close friends, their mental health and cognitive abilities such as working memory, attention and processing speed, and images of their brains. Data from a second set of about 16,000 young people were then analyzed to confirm the results. Shen, Rolls et al. found having a higher number of close friends was associated with improved mental health and cognitive ability. However, this association stopped once around five friends had been reached, after which having more friends was no longer linked to better mental health and was even correlated with lower cognition. Additionally, individuals with too few or too many friends had more symptoms of Attention-deficit/hyperactivity disorder (ADHD) and were less able to learn from their experiences. This non-linear relationship between number of friends and mental health and cognitive abilities can be partly explained by the structure of the brain. Shen, Rolls et al. found that brain regions associated with friendship were larger in individuals with more close friends, but did not increase any further once the number of friends a person had exceeded five individuals with around five close friends also had more of a receptor that is part of the opioid system, which may make them more responsive to laughter, friendly touch, or other positive social interactions. These findings challenge the idea that having more friends is always better. It also provides insights into how friendships affect brain health during the transition from late childhood to adolescence. Insights from this study may aid the development of interventions to support healthy brain development during youth.

Association between polygenic risk for Alzheimer's disease and brain structure in children and adults.

BACKGROUND: The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages. METHODS: This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life. RESULTS: Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD). CONCLUSIONS: In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.

A shared neural basis underlying psychiatric comorbidity.

Recent studies proposed a general psychopathology factor underlying common comorbidities among psychiatric disorders. However, its neurobiological mechanisms and generalizability remain elusive. In this study, we used a large longitudinal neuroimaging cohort from adolescence to young adulthood (IMAGEN) to define a neuropsychopathological (NP) factor across externalizing and internalizing symptoms using multitask connectomes. We demonstrate that this NP factor might represent a unified, genetically determined, delayed development of the prefrontal cortex that further leads to poor executive function. We also show this NP factor to be reproducible in multiple developmental periods, from preadolescence to early adulthood, and generalizable to the resting-state connectome and clinical samples (the ADHD-200 Sample and the Stratify Project). In conclusion, we identify a reproducible and general neural basis underlying symptoms of multiple mental health disorders, bridging multidimensional evidence from behavioral, neuroimaging and genetic substrates. These findings may help to develop new therapeutic interventions for psychiatric comorbidities.

Genome-wide association study of cerebellar white matter microstructure and genetic overlap with common brain disorders.

BACKGROUND: The cerebellum is recognized as being involved in neurocognitive and motor functions with communication with extra-cerebellar regions relying on the white matter integrity of the cerebellar peduncles. However, the genetic determinants of cerebellar white matter integrity remain largely unknown. METHODS: We conducted a genome-wide association analysis of cerebellar white matter microstructure using diffusion tensor imaging data from 25,415 individuals from UK Biobank. The integrity of cerebellar white matter microstructure was measured as fractional anisotropy (FA) and mean diffusivity (MD). Identification of independent genomic loci, functional annotation, and tissue and cell-type analysis were conducted with FUMA. The linkage disequilibrium score regression (LDSC) was used to calculate genetic correlations between cerebellar white matter microstructure and regional brain volumes and brain-related traits. Furthermore, the conditional/conjunctional false discovery rate (condFDR/conjFDR) framework was employed to identify the shared genetic basis between cerebellar white matter microstructure and common brain disorders. RESULTS: We identified 11 genetic loci (P < 8.3 × 10-9) and 86 genes associated with cerebellar white matter microstructure. Further functional enrichment analysis implicated the involvement of GABAergic neurons and cholinergic pathways. Significant polygenetic overlap between cerebellar white matter tracts and their anatomically connected or adjacent brain regions was detected. In addition, we report the overall genetic correlation and specific loci shared between cerebellar white matter microstructural integrity and brain-related traits, including movement, cognitive, psychiatric, and cerebrovascular categories. CONCLUSIONS: Collectively, this study represents a step forward in understanding the genetics of cerebellar white matter microstructure and its shared genetic etiology with common brain disorders.