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At least 19 recordsLinked to original sources
PubMed2026

Is impulsivity simply a failure of self-control? Evidence based on multi-omics analyses of genomics, metabolomics and brain imaging.

High impulsivity-a hallmark of various adverse life outcomes such as substance abuse, impulsive buying, violence, and crime-has typically been considered as a failure of self-control. However, is impulsivity simply a failure of self-control? To address this issue, we employed multi-omics combined with brain imaging approach in a large-scale sample (Nbrain imaging=1524, Ngenomics=835, Nmetabolomics=946) to elucidate the relationship between impulsivity and self-control. Mendelian randomization showed a bidirectional association between impulsivity and self-control, suggesting that they influenced each other. Partial least squares analysis highlighted that self-control primarily implicates the frontal lobe regions (e.g., superior frontal gyrus), whereas impulsivity involves the amygdala, insula, and basal ganglia. The cerebellum, superior frontal gyrus, and middle frontal gyrus were identified as shared areas in impulsivity and self-control. Furthermore, gene-based association analysis identified heterochromatin protein 1 binding protein 3 as specifically related to impulsivity, while pathway enrichment analysis demonstrated that arginine and proline metabolism was a common metabolic pathway associated with both impulsivity and self-control. Overall findings demonstrate that impulsivity and self-control involve both shared and distinct brain regions, genetic and metabolic foundations. The brain imaging results suggest that impulsivity is related not only to self-control-related processes but also to the motivation to pursue rewards. Together, this large-scale integrative study firstly provides a side-by-side map of genomic, metabolic, and limbic-network signatures of impulsivity distinct from self-control, offering a foundation for mechanism-driven biomarker and intervention research in maladaptive impulsivity.

Impulsive Behavior↗
PubMed2026

Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing genome-wide association study (GWAS) results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. The imaging phenotypes covered structural magnetic resonance imaging (MRI), diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task-based functional MRI. Results were included in the primary analysis when the imaging phenotype had positive single-nucleotide polymorphism (SNP) heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests (ACATs) were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Statistical power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of the 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were classified as biological imaging phenotypes. No individual phenotype survived false discovery rate correction. The smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute genetic correlation of 0.0338. ACAT provided no evidence of an aggregate association across all biological imaging phenotypes (P = 0.302), and no predefined imaging category survived multiple-testing correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were fully contained within the interval [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under the stringent heritability quality-control definition. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of genome-wide genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Nevertheless, small, localized, mixed-direction, or developmentally specific genetic effects remain possible.

Journal Article↗
PubMed1979

The role of radionuclide brain imaging and computerized tomography in the early diagnosis of herpes simplex encephalitis.

We have reviewed the medical records and radiographic examinations of 12 patients with herpes simplex encephalitis to assess the role of RN and CT in the early diagnosis of this disease. The initial RN study was positive in 83% (10/12) of cases while the initial CT study was positive in 75% (9/12) of cases. The earliest positive RN was seen on the second day after the onset of neurologic signs or symptoms while the earliest positive CT was seen on the third day. We describe various abnormal patterns encountered in HSE and discuss their diagnostic reliability. We make recommendations for the diagnosis of HSE based on our findings and on the information available in the literature

Adult↗
PubMed1978

Design considerations for a positron emission transverse tomograph (PETT V) for imaging of the brain.

Imaging of the brain by positron emission tomography can be optimized for sensitivity by dedicating the design of the tomograph to this application. We have designed a multislice positron emission tomograph (PETT V) for imaging the human brain and the whole body of small experimental animals. The detector system of PETT V consists of a circular array of 48 NaI(Tl) scintillation detectors, each fitted with two photomultiplier tubes, with one dimensional positioning capability. Suitable sampling is achieved by rotation of the circular array of detectors and by a wobbling motion of the detector circle. The proposed system is capable of providing seven slices simultaneously, with a spatial resolution in the plane of the slice from 7 to 15 mm and with slice thicknesses of 7 and 14 mm. The minimum scanning time is 1 sec. The estimated overall sensitivity of PETT V is 350,000 counts/sec/mCi in a 20 cm diameter phantom for a resolution of approximately 1.5 x 1.5 cm. The system is under construction.

Animals↗
PubMed1975

The diagnostic value of serial brain scanning.

A survey of the literature pertaining to several serial brain scanning procedures has been presented. These procedures include rapid brain imaging, sequential brain imaging, delayed from imaging, and follow-up brain imaging. Applications of these techniques to specific clinical problems have been stressed and the reported results reviewed. Thus, it has been indicated that rapid brain imaging is most useful in detecting lesions secondary to cerebrovascular disease but may also provide some helpful information pertaining to the differential diagnosis of other C.N.S. lesions demonstrated on subsequent static brain scans. Sequential brain imaging is a time-consuming adjunctive procedure which, however, can be extraordinarily helpful in a highly selected group of problem cases which present with relatively small lesions adjacent to normal anatomic structures which themselves have considerable radioactivity. Delayed brain imaging has the distinction of detecting the greatest number of intracranial lesions but is attended by tactical problems in maintaining an optimal patient flow through the department and also has the undesirable consequence of reduced information density and diminished image quality, unless greater radiation doses are injected. Follow-up brain imaging is useful in the differential diagnosis of cerebrovascular and neoplastic disease and in the assessment of effectiveness of radiation therapy.

Adenoma, Chromophobe↗
PubMed2026

Phenotypic and Genetic Characterization of 64 Egyptian Children With Neuronal Ceroid Lipofuscinosis.

BACKGROUND: Neuronal ceroid lipofuscinoses (NCLs) are the most common neurodegenerative diseases in childhood. This study aimed to investigate the phenotypic and genetic spectrum of NCLs in Egypt. METHODS: This descriptive study involved children with NCLs diagnosed and managed at five Egyptian centers between 2019 and 2024. Demographic, clinical, brain imaging, and genetic data were systematically evaluated. Identified variants in NCL-related genes were classified following the American College of Medical Genetics and Genomics guidelines. RESULTS: The cohort included 64 Egyptian children (from 57 families) with eight NCL types. The most commonly identified genotype was CLN2 (17/64, 27%), followed by CLN1 and CLN7 (12/64, 19% each). Patients generally exhibited the classic manifestations of NCLs, particularly motor regression (64/64, 100%), cognitive decline (64/64, 100%), language impairment (64/64, 100%), epilepsy (57/64, 89%), and vision loss (47/64, 73%). Notably, developmental regression (12/17, 71%) was the predominant presenting symptom for CLN2. Brain imaging generally showed typical cerebral and cerebellar atrophy in 95% (61/64) and 84% (54/64) of cases, respectively. Nevertheless, thalamic abnormalities were observed in only 16% (10/64) of cases. A total of 46 distinct variants were identified across eight NCL-related genes, including 23 novel ones, with the majority (33/46, 72%) being private. There was a median diagnostic delay of 2 years, and none of the patients received specific therapy. CONCLUSIONS: This study reports the largest cohort of children with NCLs from Egypt, including 12 patients with the less-commonly reported CLN7 subtype, which expands the demographic, clinical, and molecular spectrum of these diseases.

Humans↗
PubMed2026

Characterizing the impact of plasma protein levels on human brain structure and disorders leveraging integrative multi-omics analysis.

With recent advances in high-throughput proteomic technologies, population-scale plasma proteomics datasets, often linked to extensive genetic and phenotypic information, have become increasingly accessible. Yet the relationships between circulating protein levels, brain imaging phenotypes, and risk for neurological and psychiatric disorders remain largely unexplored. Proteome-wide association studies offer a promising approach for elucidating biological mechanisms that connect genetic variation to complex brain-related traits and diseases. In this study, we integrated protein quantitative trait loci (pQTLs) from the two largest plasma proteomic resources (the UK Biobank Pharma Proteomics Project [UKB-PPP] and Ferkingstad et al. [deCODE]) with genome-wide association studies of brain imaging-derived phenotypes in UK Biobank using Mendelian randomization and colocalization analyses. We identified 120 cis and 20 trans associations between plasma proteins and imaging phenotypes and validated these findings using brain tissue-derived proteomic and transcriptomic datasets. Multivariable Mendelian randomization revealed eleven plasma proteins (coding genes APOE, ARL3, MICB, NSF, RHOC, RSPO3, ENPP2, BTN2A1, EIF2AK3, MRVI1, and OPLAH) with significant direct effects on the risk of Alzheimer's disease, Parkinson's disease, multiple sclerosis, bipolar disorder, and schizophrenia. Single-cell expression and pathway enrichment analyses further revealed cell-type-specific effects and distinct biological processes underlying these protein-disease associations. Together, these findings demonstrate robust links between plasma protein variation and brain structure, delineate protein-disease pathways, and highlight the cellular and molecular mechanisms that contribute to neurobiological diversity and pathology.

Journal Article↗
PubMed2026

Thyroid-stimulating hormone receptor mediates peripheral-central neuroimmune crosstalk in autoimmune thyroid diseases.

BACKGROUND: Organ-specific autoimmune diseases, particularly Graves' disease (GD) and its extrathyroidal manifestation, Graves' orbitopathy (GO), are characterized by systemic autoimmunity that may extend its impact to the central nervous system (CNS). While thyroid-stimulating hormone receptor (TSHR) is the primary driver of pathological remodeling in the thyroid and orbital tissues, emerging evidence suggests it is also expressed in the brain and may participate in neuroimmune signaling. However, the molecular mechanisms linking peripheral TSHR-driven autoimmunity to these extended systemic features remain unclear. Thus, GD and GO provide a unique window to investigate how peripheral autoantibodies influence CNS involvement as part of its broader pathological spectrum. METHODS: Genome-wide association studies (GWAS) and post-GWAS analyses were integrated with bulk RNA sequencing, single-cell and spatial transcriptomics, and brain imaging phenotypes to comprehensively characterize peripheral and central alterations in GD and GO. Mendelian randomization was applied to test causal relationships between genetic variants and brain signatures. Structural biology analyses were further conducted including protein-protein docking, small-molecule docking, and normal mode dynamics to identify prospective modulators of TSHR. Immunofluorescence staining was performed in a GO mouse model to validate the colocalization of potential interacted proteins in the specific brain region. RESULTS: Brain imaging-derived phenotypes (IDPs) alterations in GO and GO were systematically analyzed to identify neuroanatomical and functional alterations. TSHR was further identified as a shared genetic driver across peripheral and central compartments. TSHR was expressed in spiny projection neurons, microglia, and peripheral T cells, with cell-cell communication analyses highlighting TSHR-mediated interactions among neurons, endothelial cells, and microglia. Immunofluorescence staining in a GO mouse model confirmed the colocalization of TSHR with FN1 and GNAS in the basal ganglia, providing tissue-level validation of the computationally predicted ligand-receptor interactions. Immune profiling further showed immune alterations in GD and GO. Structural modeling supported plausible physical interfaces between TSHR and interacting proteins, and small-molecule screening identified three repurposable compounds - venetoclax, irinotecan, and dutasteride - with predicted favorable docking scores and stable binding poses in our simulations. CONCLUSIONS: These findings demonstrate that TSHR acts as a molecular hub mediating peripheral-central neuroimmune crosstalk in GD and GO. The results support a broader "disease-molecule axis" framework that links genetic susceptibility with multi-level immune and neural mechanisms. This work provides mechanistic insights relevant to the development of TSHR-targeted therapies, with implications for both peripheral immune modulation and central regulation. However, the limited sample size, lack of longitudinal follow-up, and absence of in vivo validation warrant cautious interpretation and further investigation.

Receptors, Thyrotropin↗
PubMed2025

S-GMAS: Genome-Wide Mediation Analysis With Brain Subcortical Shape Mediators.

Mediation analysis is widely utilized in neuroscience to investigate the role of brain image phenotypes in the neurological pathways from genetic exposures to clinical outcomes. However, it is still difficult to conduct mediation analyses with whole genome-wide exposures and brain subcortical shape mediators due to several challenges including (i) large-scale genetic exposures, that is, millions of single-nucleotide polymorphisms (SNPs); (ii) nonlinear Hilbert space for shape mediators; and (iii) statistical inference on the direct and indirect effects. To tackle these challenges, this paper proposes a genome-wide mediation analysis framework with brain subcortical shape mediators. First, to address the issue caused by the high dimensionality in genetic exposures, a fast genome-wide association analysis is conducted to discover potential genetic variants with significant genetic effects on the clinical outcome. Second, the square-root velocity function representations are extracted from the brain subcortical shapes, which fall in an unconstrained linear Hilbert subspace. Third, to identify the underlying causal pathways from the detected SNPs to the clinical outcome implicitly through the shape mediators, we utilize a shape mediation analysis framework consisting of a shape-on-scalar model and a scalar-on-shape model. Furthermore, the bootstrap resampling approach is adopted to investigate both global and spatial significant mediation effects. Finally, our framework is applied to the corpus callosum shape data from the Alzheimer's Disease Neuroimaging Initiative.

Humans↗
PubMed2026

Big data and psychiatry: advances, constraints and future directions.

Early work in psychiatry research, often involving single sites, small samples, and limited variables, has shifted to contemporary research involving multiple sites, large samples, and many variables. Such research raises important questions, including concerns about data quality and methodological rigor, uncertainty about its key lessons, issues regarding clinical relevance, and questions about how to optimize future advances. Here we consider these questions and concerns against the context of big data work on community and register-based surveys, cohort and biobank studies, electronic health records, digital phenotyping, brain imaging, genomics and other -omics, and randomized controlled trials. The development of large datasets allowing well-powered analyses is a major milestone, but sample size alone does not guarantee more precise estimates, and ongoing attention to the quality and rigor of big data collation and analysis is needed. Big data research has fostered trans-disciplinarity and given insights into mechanisms underlying psychiatric disorders, but also emphasizes the intricacy, heterogeneity and variability of such mechanisms, and the importance of triangulating between large-scale and small-scale research. The complexity of psychiatric phenotypes and psychobiological mechanisms contributes to the difficulty in bridging from big data to clinical application; big data research reinforces the importance of holding our diagnoses of psychiatric disorders lightly and providing explanations of these conditions humbly; and future work needs to be more attentive to clinical issues. There is enormous scope for further building databases relevant to psychiatry, but advances in conceptual models and asking the right questions are equally valuable. The full impact of big data, including artificial intelligence analyses, remains to be seen, but overenthusiastic support should be tempered by a better understanding of its strengths and limitations. At its best, such work will contribute in an iterative and integrative way to advancing our knowledge of psychiatric disorders and mental health.

Big data↗