Lieber Institute for Brain Development

In-scanner head motion is a recognized source of bias in structural magnetic resonance imaging (sMRI), yet it remains under-addressed in psychiatric neuroimaging, where structural differences in patient populations are considered foundational. We examined motion-related bias in grey matter volume estimates across eight independent cohorts comprising 9,664 individuals, including 8,979 neurotypical controls (NC), 497 patients with schizophrenia (SCZ), and 188 patients with bipolar disorder (BD). Motion estimates were derived from multiple fMRI scans acquired within the same scanning session and summarized using principal component analysis. In NC, motion accounted for 1-6% of regional grey matter variance, a magnitude comparable to reported psychiatric case-control effect sizes. Adjusting for motion attenuated SCZ-NC group differences, reducing effect sizes in 85% of brain regions and yielding 5% fewer significant ROIs (pFDR<0.05). In BD, motion correction reduced effect sizes in 97% of regions, with a 24% reduction in significant ROIs. Cross-diagnostic spatial patterns were significantly correlated (r = 0.63), explaining a sizable portion of the commonalities between SCZ and BD. Critically, a falsification analysis in UK Biobank (N=5,123) showed that stratifying NC by motion alone produced grey matter differences accounting for 45-62% of SCZ case-control effect magnitude, underscoring how difficult it is to interpret SCZ-like morphometric differences as tissue properties rather than as motion-driven patterns. These findings urge caution in interpretations of sMRI differences in patient-control comparisons and use of systematic fMRI based motion control as standard practice in sMRI analyses.

Suicide is a leading cause of death worldwide, yet the biological mechanisms underlying suicide remain poorly understood. A clearer understanding at the molecular level is essential for developing objective biomarkers and targeted interventions. In this study, we used transcriptomic profiling to investigate gene expression patterns associated with suicidal thoughts and behaviors across peripheral blood (n=264) and postmortem brain tissue from two prefrontal regions (dorsolateral prefrontal cortex, DLPFC; subgenual anterior cingulate cortex, sgACC) of individuals with and without psychiatric illness (n=249). Peripheral analyses revealed broad transcriptional changes associated with suicidal thoughts and behaviors, marked by dysregulated immune-related and inflammatory processes. Longitudinal modeling further revealed gene co-expression modules that predicted future suicide attempts over a 12-month follow-up, highlighting processes related to apoptosis, mitochondrial function, and immune regulation. By contrast, transcriptomic analyses of postmortem tissue derived from the DLPFC and sgACC revealed largely suppressed neuroimmune activity. Gene co-expression analyses in the brain identified suicide-associated modules enriched for synaptic plasticity, oxidative stress, and neuroimmune function, some of which displayed regional specificity. Cross-tissue comparison showed minimal gene-level overlap between brain and blood, although shared pathway-level themes emerged in immune, sensory, and cellular stress processes. Taken together, these findings suggest that suicide is associated with distinct but functionally convergent transcriptional alterations across brain and blood. By integrating tissue-specific and systems-level molecular signatures, this work provides insight into the biological architecture of suicide and lays the groundwork for developing novel biomarkers and therapeutic targets to improve prevention and treatment outcomes.

Alzheimer’s disease (AD), though defined as a cognitive disorder, often presents neuropsychiatric symptoms such as anxiety, depression, agitation and sleep disruptions years before the onset of frank memory impairment. An early pathological feature is the accumulation of hyperphosphorylated “pretangle” tau (pTau) in the locus coeruleus (LC), the brain’s primary source of norepinephrine (NE). While clinical studies link LC pTau burden to behavioral abnormalities, causal mechanisms remain unclear. We developed a translationally-relevant mouse model that recapitulates the ‘LC-first’ phenomenon using cell type-specific viral expression of pathogenic P364S mutant human tau in LC neurons. Three months post-infusion, pTau accumulation induced anxiety-and compulsive-like behaviors and reduced sleep spindles without altering overall sleep architecture. Consistent with the behavioral phenotypes, electrophysiological recordings revealed significant increases in spontaneous and evoked firing of LC neurons, accompanied by robust astrocytic reactivity with no apparent cell death. Transcriptomic analysis identified upregulation ofHcn2and downregulation ofClic6, suggesting changes in neuronal excitability. To further define molecular mechanisms, we developed a cell type-specific proteomics approach, which showed synaptic and metabolic alterations associated with LC-specific tau pathology. Early anxiety-like behaviors observed at 3 months diminished at later timepoints (6-9 months) and were replaced by anxiolytic characteristics. These findings demonstrate that pTau triggers phenotypes reflective of LC-NE hyperactivity in the early stages of AD pathogenesis, laying the foundation for the development of LC-based disease-modifying therapies to address neuropsychiatric manifestations.

Phosphorylation ranks among the most crucial post-translational modifications (PTMs), significantly influencing the conformation, activity, and functionality of proteins, and is intricately associated with numerous pathophysiological processes. Therefore, proposing a scientifically valid computational method for precise prediction of phosphorylation sites carries substantial importance. In this study, we propose and evaluate a novel method, PEGN-PSP, based on graph techniques and language models for the prediction of general phosphorylation sites. This method employs an adaptive feature fusion strategy, combining sequence embeddings and pretrained model embeddings to enhance the model's capability in representing features. The use of graph neural network attention mechanisms not only captures local patterns of protein sequences, but also effectively captures long-range dependency information between residues in protein sequences. Independent test results indicate that, in comparison to the current state-of-the-art methods for general phosphorylation site prediction, PEGN-PSP improves the Matthews correlation coefficient for S/T sites by 4.6% and for Y sites by 6.5%. Additionally, PEGN-PSP has good robustness in predicting lysine crotonylation sites, indicating that our method PEGN-PSP has strong potential in predicting other protein post-translational modification sites.

Abstract MicroRNAs (miRNAs) regulate nearly all protein-coding genes and play critical roles in gene regulation, yet the mechanisms governing miRNA regulation remain poorly understood. Here, we examined the genetic architecture of miRNA expression in 995 human brain tissues spanning four regions and two ancestries (African and European) from neurotypical controls and individuals with three psychiatric disorders (schizophrenia, major depressive disorder, and bipolar disorder). We found that miRNA expression is highly dynamic across brain regions, with region-specific differences exceeding those attributable to ancestry or psychiatric diagnosis. Through miRNA expression quantitative trait loci (miR-eQTL) analyses, we identified genetic variants associated with miRNA expression in a region- and ancestry-dependent manner. Overall, miRNAs exhibited heritability levels comparable to protein-coding genes and were positively co-regulated with their host transcripts. Notably, miRNA-regulating genetic variants were enriched in enhancers active in oligodendrocytes and in binding sites of the transcription factor OLIG2, suggesting miRNA-mediated gene regulation in oligodendrocyte lineage. Finally, we identified 15 miRNAs as likely causative factors for psychiatric and neurodegenerative disorders such as major depressive disorder and Alzheimer’s disease. Together, our results reveal the genetic underpinnings of miRNA regulation in the human brain and suggest that miRNAs serve as key intermediaries linking genetic variation to complex neuropsychiatric and neurological phenotypes.

MicroRNAs (miRNAs) are evolutionarily conserved post-transcriptional regulators that play critical roles in cellular development and differentiation across species. Although the importance of miRNAs in oligodendrocyte lineage cell (OLLC) differentiation has been extensively studied in rodent models, their roles in human OL development remain less understood. To address this gap, we used a human embryonic stem cell (hESC) reporter system designed to study human OLs and OL progenitor cells (OPCs). Using an optimized differentiation protocol, we used the reporter hESCs to generate and isolate well-characterized OLLCs at specific developmental stages and performed next-generation sequencing-based miRNA profiling to identify stage-specific miRNAs enriched during OL lineage specification and maturation. In addition to canonical miRNAs known to be enriched at various stages of OL development, our study identified several lesser-known miRNAs with distinct stage-specific enrichment patterns that may serve as useful molecular markers for classifying human CNS cell types in future studies. Target analysis of OPC- and OL-enriched miRNAs revealed key genes, including transcription factors ZNF488 and DLX1, cytoskeletal regulator CSNK2B, and potassium channel gene KCNJ1, along with key signaling pathways such as AKT, SMAD2/3, estrogen receptor, and insulin signaling, which regulate OPC and OL lineage function. These findings advance our understanding of the OLLC-specific miRNAs, and miRNA-mediated regulatory networks governing human OL differentiation and maturation and provide promising therapeutic targets for future studies aimed at restoring myelin integrity and improving outcomes in demyelinating diseases.

The entorhinal cortex (ERC) is implicated in early progression of Alzheimer’s disease (AD). Here we investigated the impact of established biological risk factors for AD, including APOE genotype (E2 versus E4 alleles), sex, and ancestry, on gene expression in the human ERC. We generated paired spatially-resolved transcriptomics (SRT) and single-nucleus RNA sequencing data (snRNA-seq) in postmortem human ERC tissue from middle aged brain donors with no history of AD. APOE-dependent changes in gene expression predominantly mapped to a transcriptionally-defined oligodendrocyte subtype, which varied substantially with ancestry, and suggested differences in oligodendrocyte differentiation and myelination. Integration of SRT and snRNA-seq data identified a common gene expression signature associated with APOE genotype, which we localized to the same oligodendrocyte subtype and a white matter spatial domain. This suggests that AD risk in ERC may be associated with disrupted oligodendrocyte function, potentially contributing to future neurodegeneration.

Abstract The placenta plays a central role in supporting fetal growth. Placental efficiency (PlE) defined as the birthweight-to-placental weight ratio proves to be a key measure of its capacity to adapt to the fetal developmental demands. Although the genetic architecture of birthweight (BW) and placental weight (PW) have been explored, the biology underlying PlE remains largely unknown. Here, we report the first genome-wide association study (GWAS) of PlE in 63,894 at term singleton births from the Norwegian Mother, Father and Child cohort (MoBa), complemented by maternal (N = 60,472) and paternal (N = 40,116) analyses. Across offspring and maternal genomes, we identified multiple genome-wide significant loci, with TSNAX-DISC1 consistently implicated across analyses. Comparative genetic analyses revealed strong overlap between PlE and PW, but minimal overlap with BW, suggesting that PlE captures distinct aspects of placental adaptation beyond overall growth. Gene-set enrichment highlighted significant involvement of monoaminergic pathways, particularly norepinephrine uptake and transport, while tissue-specific analyses demonstrated strong enrichment in placental tissue. Notably, mapped genes including SLC6A2, SLC22A2, and SLC22A3 link PlE to regulation of monoamine signaling, aligning with the placenta’s potential role in neurodevelopmental vulnerability. Together, these findings establish PlE as a genetically distinct phenotype, provide insight into the biology of placental adaptation, and suggest shared genetic pathways connecting placental function and offspring neurodevelopment.