Biology subjects

Berman, K. F.

Publications and source records attributed to Berman, K. F..

8 recordsLinked to original sources

bioRxiv2026

Sex Differences in Human Subcortical Anatomy are Robust and Replicable in Adulthood, Already Evident Prepubertally, and Aligned with Sex Chromosomal and Gonadal Influences

The existence and mechanistic bases of sex effects on human subcortical anatomy remain unclear. To address these gaps, we combined structural neuroimaging of sex differences in subcortical volume and shape across three independent cohorts (combined N=2,886) with neuroimaging maps of sex chromosomal and endocrine effects from three clinical cohorts (combined N=313, including sex chromosome aneuploidies and isolated gonadotropin-releasing hormone deficiency). We found that human subcortical anatomy shows focal sex differences of moderate-strong effect size during young adulthood (male-biased surface area overlying lateral/ventral putamen and pallidum, nucleus accumbens, ventral posterior complex of thalamus, anterior hippocampus, and medial and central nuclei of amygdala; female-biased surface area overlying caudate head, posterior/dorsal pallidum, mediodorsal, lateral dorsal, lateral geniculate, and medial geniculate nuclei of thalamus, posterior hippocampus, and basolateral and lateral nuclei of amygdala). These regional sex differences show strong within-sample reproducibility at modest sample sizes (N[≥]200) and are replicable in a fully independent mid-adulthood cohort. Adult subcortical sex differences are already evident in nascent form by pre/early puberty (age 9 years), and the same holds for cortical sex differences. Comparison with neuroimaging data from rare disorder cohorts reveals a statistically significant spatial and directional congruence of subcortical sex differences with the anatomical effects of X-chromosome dosage in the striatum and effects of testicular hormones in the amygdala. Our findings provide a reference map for the fine-grained spatial patterning of sex differences in adult subcortical shape, demonstrating its replicability, establishment before pubertal progression, and spatial concordance with potentially causal sex chromosomal and gonadal effects.

neuroscience↗

bioRxiv2023

Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function.

Schizophrenia (SCZ) is characterized by a polygenic risk architecture implicating diverse molecular pathways important for synaptic function. However, how polygenic risk funnels through these pathways to translate into syndromic illness is unanswered. To evaluate biologically meaningful pathways of risk, we used tensor decomposition to characterize gene co-expression in post-mortem brain (of neurotypicals: N=154; patients with SCZ: N=84; and GTEX samples N=120) from caudate nucleus (CN), hippocampus (HP), and dorsolateral prefrontal cortex (DLPFC). We identified a CN-predominant gene set showing dopaminergic selectivity that was enriched for genes associated with clinical state and for genes associated with SCZ risk. Parsing polygenic risk score for SCZ based on this specific gene set (parsed-PRS), we found that greater pathway-specific SCZ risk predicted greater in vivo striatal dopamine synthesis capacity measured by [18F]-FDOPA PET in three independent cohorts of neurotypicals and patients (total N=235) and greater fMRI striatal activation during reward anticipation in two additional independent neurotypical cohorts (total N=141). These results reveal a bench to bedside translation of dopamine-linked genetic risk variation in driving in vivo striatal neurochemical and hemodynamic phenotypes that have long been implicated in the pathophysiology of SCZ.

neuroscience↗

bioRxiv2021

Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance

Neurons derived from human induced pluripotent stem cells (hiPSCs) have been used to model basic cellular aspects of neuropsychiatric disorders, but the relationship between the emergent phenotypes and the clinical characteristics of donor individuals has been unclear. We analyzed RNA expression and indices of cellular function in hiPSC-derived neural progenitors and cortical neurons generated from 13 individuals with high polygenic risk scores (PRS) for schizophrenia and a clinical diagnosis of schizophrenia, along with 15 neurotypical individuals with low PRS. We identified electrophysiological measures associated with diagnosis that implicated altered Na+ channel function and GABA-ergic neurotransmission. Importantly, electrophysiological measures predicted cardinal clinical and cognitive features found in these schizophrenia patients. The identification of basic neuronal physiological properties related to core clinical characteristics of illness is a potentially critical step in generating leads for novel therapeutics.

neuroscience↗

bioRxiv2020

Beyond linearity in neuroimaging: Capturing nonlinear relationships with application to longitudinal studies

The ubiquitous adoption of linearity for quantitative explanatory variables in statistical modeling is likely attributable to its advantages of straightforward interpretation and computational feasibility. The linearity assumption may be a reasonable approximation especially when the variable is confined within a narrow range, but it can be problematic when the variables effect is non-monotonic or complex. Furthermore, visualization and model assessment of a linear fit are usually omitted because of challenges at the whole brain level in neuroimaging. By adopting a principle of learning from the data in the presence of uncertainty to resolve the problematic aspects of conventional polynomial fitting, we introduce a flexible and adaptive approach of multilevel smoothing splines (MSS) to capture any nonlinearity of a quantitative explanatory variable for population-level neuroimaging data analysis. With no prior knowledge regarding the underlying relationship other than a parsimonious assumption about the extent of smoothness (e.g., no sharp corners), we express the unknown relationship with a sufficient number of smoothing splines and use the data to adaptively determine the specifics of the nonlinearity. In addition to introducing the theoretical framework of MSS as an efficient approach with a counterbalance between flexibility and stability, we strive to (a) lay out the specific schemes for population-level nonlinear analyses that may involve task (e.g., contrasting conditions) and subject-grouping (e.g., patients vs controls) factors; (b) provide modeling accommodations to adaptively reveal, estimate and compare any nonlinear effects of an explanatory variable across the brain, or to more accurately account for the effects (including nonlinear effects) of a quantitative confound; (c) offer the associated program 3dMSS to the neuroimaging community for whole-brain voxel-wise analysis as part of the AFNI suite; and (d) demonstrate the modeling approach and visualization processes with a longitudinal dataset of structural MRI scans.

neuroscience↗

bioRxiv2019

Parietal prefrontal feedforward connectivity in association with schizophrenia genetic risk and delusions

BackgroundConceptualizations of delusion formation implicate, in part, deficits at feed-forward information transfer across posterior to prefrontal cortices, resulting in dysfunctional integration of new information in favor of over-familiar prior beliefs. Here, we used functional MRI and machine learning models to examine feedforward parietal-prefrontal information transfer in schizophrenia patients in relation to delusional thinking, and polygenic risk for schizophrenia.\n\nMethodsWe studied 66 schizophrenia patients and 143 healthy controls as they performed context updating during working memory (WM). Dynamic causal models of effective connectivity were focused on prefrontal and parietal cortex, where we examined parietal-prefrontal connectivity in relation to delusions in patients. We further tested for an effect of polygenic risk for schizophrenia on connectivity in healthy individuals. We then leveraged support vector regression models to define optimal normalized target connectivity tailored for each patient, and tested the extent to which deviation from this target predicted individual variation in delusion severity.\n\nResultsIn schizophrenia patients, updating and manipulating context information was disproportionately less accurate than was WM maintenance, with a task accuracy-by-diagnosis interaction. Also, patients with delusions tended to have relatively reduced feedforward effective connectivity during context updating in WM manipulation. The same parietal-prefrontal feedforward prefrontal effective connectivity was adversely influenced by polygenic risk for schizophrenia in healthy subjects. Individual patients deviation from predicted normal feedforward connectivity based on the support vector models correlated with delusional severity.\n\nConclusionsThese computationally-derived observations support a role for feed-forward parietal-prefrontal information processing deficits in delusional psychopathology, and in genetic risk for schizophrenia.

neuroscience↗

bioRxiv2019

Dysfunctional and compensatory brain networks underlying math fluency

Poor math fluency, or timed calculation (TC) performance, is a characteristic of dyscalculia, a common cause of poor educational and occupational outcomes. Here, we examined neural substrates of dysfunctional math fluency and potential compensatory mechanisms. We performed functional MRI scans of participants with divergent performance on an event-related TC paradigm (poor TC, <0.5 accuracy, n=34; vs. controls, accuracy>0.8, n=34). Individuals with poor TC had decreased intraparietal sulcus (IPS) engagement, and decreased IPS-striatal and IPS-prefrontal effective connectivity. We next examined an independent well-performing sample (TC accuracy>0.8, n=100), stratified according to relatively low-versus high-IPS activation during TC. Relatively reduced IPS engagement, or patterns of IPS-related effective connectivity similar to those with poor TC, appeared to be compensated for by increased engagement of effective connectivity involving fusiform gyrus, angular gyrus, inferior frontal gyrus and striatum. Neural connectivity involving high-level visual processing in fusiform gyrus and related ventral cortical networks may be relevant in compensatory function ameliorating aspects of dyscalculia and mathematical difficulty.

neuroscience↗

bioRxiv2018

Dissecting transcriptomic signatures of neuronal differentiation and maturation using iPSCs

Human induced pluripotent stem cells (hiPSCs) are a powerful model of neural differentiation and maturation. We present a hiPSC transcriptomics resource on corticogenesis from 5 iPSC donor and 13 subclonal lines across nine time points over 5 broad conditions: self-renewal, early neuronal differentiation, neural precursor cells (NPCs), assembled rosettes, and differentiated neuronal cells that were validated using electrophysiology. We identified widespread changes in the expression of individual transcript features and their splice variants, gene networks, and global patterns of transcription. We next demonstrated that co-culturing human NPCs with rodent astrocytes resulted in mutually synergistic maturation, and that cell type-specific expression data can be extracted using only sequencing read alignments without potentially disruptive cell sorting. We lastly developed and validated a computational tool to estimate the relative neuronal maturity of iPSC-derived neuronal cultures and human brain tissue, which were maturationally heterogeneous but contained subsets of cells most akin to adult human neurons.

genomics↗

bioRxiv2017

Placental gene expression mediates the interaction between obstetrical history and genetic risk for schizophrenia

Defining the environmental context in which genes enhance susceptibility can provide insight into the pathogenesis of complex disorders, like schizophrenia. Here we show that the intrauterine and perinatal environment modulates the association of schizophrenia with genomic risk, as measured with polygenic risk scores (PRS) based primarily on GWAS significant variants. Genomic risk interacts with intrauterine and perinatal complications (Early Life Complications, ELCs) in each of three independent samples from USA, Italy and Germany (overall n= 1693, p= 6e-05). In each sample, the liability of schizophrenia explained by PRS is nominally more than five times greater in the presence of a history of ELCs compared with its absence. In each sample, patients with positive ELC histories have higher PRS than patients without ELCs, which is further confirmed in two additional patient samples from Germany and Japan (overall n=2038, p= 1e-04). The gene set based on the schizophrenia loci interacting with ELCs is highly expressed in multiple placental compartments and dynamically regulated in placenta from complicated in comparison with normal pregnancies. The same genes are differentially up-regulated in placentae from male compared with female offspring. The interaction between genomic risk and ELCs is mainly driven by GWAS significant loci enriched for genes highly expressed in the various placenta samples. Molecular pathway analyses based on the genes not driving this interaction reflect previous analyses about schizophrenia risk-genes, while genes highly and differentially expressed in placentae implicate an orthogonal biology involving cellular stress. These results suggest that the most significant genetic variants detected by current schizophrenia GWAS contribute to risk in part by converging on a developmental trajectory sensitive to events affecting placentation, which may underlie the male preponderance of schizophrenia and offer new insights into primary prevention.

genetics↗