Biology subjects

Perkins, D. O.

Publications and source records attributed to Perkins, D. O..

4 recordsLinked to original sources

bioRxiv2024

Unique functional neuroimaging signatures of genetic versus clinical high risk for psychosis

Background22q11.2 Deletion Syndrome (22qDel) is a copy number variant (CNV) associated with psychosis and other neurodevelopmental disorders. Adolescents at clinical high risk for psychosis (CHR) have subthreshold psychosis symptoms without known genetic risk factors. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped results to biological pathways. MethodsWe analyzed two large multi-site cohorts with resting-state functional MRI (rs-fMRI): 1) 22qDel (n=164, 47% female) and typically developing (TD) controls (n=134, 56% female); 2) CHR individuals (n=244, 41% female) and TD controls (n=151, 46% female) from the North American Prodrome Longitudinal Study-2. We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions, testing case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation. ResultsBSV, LC, and GBC are significantly disrupted in 22qDel compared with TD controls (False Discovery Rate q<0.05). Spatial maps of BSV and LC differences are highly correlated with each other, unlike GBC. In CHR, only LC is significantly altered versus controls, with a different spatial pattern compared to 22qDel. Group differences map onto biological gradients, with 22qDel effects strongest in regions with high predicted blood flow and metabolism. Conclusion22qDel and CHR exhibit divergent effects on fMRI temporal variability and multi-scale functional connectivity. In 22qDel, strong and convergent disruptions in BSV and LC not seen in CHR individuals suggest distinct functional brain alterations.

neuroscience↗

bioRxiv2020

Frisky CALF outruns LASSO

Regularized regression analysis is a mature analytic approach to identify weighted sums of variables that predict outcomes. Typically, the number of subjects (N) is smaller than the number of predictors (p). Here, we present a novel coarse approximation linear function (CALF) to frugally select important predictors, build linear models, and discover causality. CALF is a linear regression strategy applied to normalized data that employs only a few (such as 2 to 20) nonzero weights, each +1 or -1. Metrics can be Welch t-test p-value, area under curve (AUC) of receiver operating characteristic, or Pearson correlation, depending upon data type and user preferences. For quantitative approximations, a linear fit (adding an intercept value and rescaling {+/-}1 weights by a common multiplier) can be added to optimize mean squared error (MSE). Real medical data of five types were used to generate examples with goals of binary classification or real variable approximation. Predictors considered were real, sets of integers, or ternary values of single nucleotide polymorphisms. When applied to real data, CALF approximations outperformed in p-value, AUC, correlation, or MSE a popular regularized linear regression algorithm, namely, basic LASSO. It appears that using LASSO without considering CALF might risk wasting resources. AvailabilityR version: Comprehensive R Archive (CRAN): https://cran.r-project.org/web/packages/CALF/index.html Python 3.x version: GitHub: https://github.com/jorufo/CALF_Python Contactclark_jeffries@med.unc.edu

bioinformatics↗

bioRxiv2019

Reliability of Mismatch Negativity Event-Related Potentials in a Multisite, Traveling Subjects Study

ObjectiveMismatch negativity (MMN) is an auditory event-related potential (ERP) used to study schizophrenia and psychosis risk. MMN reliability from a multisite, traveling subjects study was compared using different ERP referencing, averaging, and scoring techniques.\n\nMethodsReliability of frequency, duration, and double (frequency+duration) MMN was determined from eight traveling subjects, tested on two occasions at eight EEG laboratory sites. Deviant-specific variance components were estimated for MMN peak amplitude and latency measures using different ERP processing methods. Generalizability (G) coefficients were calculated using two-facet (site, occasion), fully-crossed models and single-facet (occasion) models within each laboratory to assess MMN reliability.\n\nResultsG-coefficients calculated from two-facet models indicated fair (0.4 0.5). Reliability of MMN amplitude was greater than latency, and reliability with mastoid referencing significantly outperformed nose-referencing.\n\nConclusionsEEG preprocessing methods have a significant impact on the reliability of MMN amplitude. Within site MMN reliability can be excellent, consistent with prior single site studies.\n\nSignificanceWith standardized data collection and ERP processing, MMN can be reliably obtained in multisite studies, providing larger samples sizes within rare patient groups.

neuroscience↗

bioRxiv2018

Contribution of rare copy number variants to bipolar disorder risk is limited to schizoaffective cases

BackgroundGenetic risk for bipolar disorder (BD) is conferred through many common alleles, while a role for rare copy number variants (CNVs) is less clear. BD subtypes schizoaffective disorder bipolar type (SAB), bipolar I disorder (BD I) and bipolar II disorder (BD II) differ according to the prominence and timing of psychosis, mania and depression. The factors contributing to the combination of symptoms within a given patient are poorly understood.\n\nMethodsRare, large CNVs were analyzed in 6353 BD cases (3833 BD I [2676 with psychosis, 850 without psychosis], 1436 BD II, 579 SAB) and 8656 controls. Measures of CNV burden were integrated with polygenic risk scores (PRS) for schizophrenia (SCZ) to evaluate the relative contributions of rare and common variants to psychosis risk.\n\nResultsCNV burden did not differ in BD relative to controls when treated as a single diagnostic entity. Burden in SAB was increased compared to controls (p-value = 0.001), BD I (p-value = 0.0003) and BD II (p-value = 0.0007). Burden and SCZ PRS were higher in SAB compared to BD I with psychosis (CNV p-value = 0.0007, PRS p-value = 0.004) and BD I without psychosis (CNV p-value = 0.0004, PRS p-value = 3.9 x 10-5). Within BD I, psychosis was associated with higher SCZ PRS (p-value = 0.005) but not with CNV burden.\n\nConclusionsCNV burden in BD is limited to SAB. Rare and common genetic variants may contribute differently to risk for psychosis and perhaps other classes of psychiatric symptoms.

genetics↗