Biology subjects

Sweeney, J. A.

Publications and source records attributed to Sweeney, J. A..

5 recordsLinked to original sources

bioRxiv2026

Sleep Health in Lennox-Gastaut Syndrome impacts both Individuals with LGS and their Caregivers

Sleep disturbances are a significant but underrecognized burden for individuals with Lennox-Gastaut Syndrome (LGS) and their caregivers. We conducted a large LGS caregiver survey to assess sleep quality, duration, specific sleep-related issues, and management strategies. Quantitative analysis revealed a significant association between nocturnal seizures and overall seizure frequency, but general sleep metrics in individuals with LGS (e.g., sleep hours, sleep quality) were not associated with seizure burden. In contrast, caregiver sleep was frequently disrupted - particularly in families who co-slept or reported frequent seizures. Nearly 40% of caregivers experienced nightly sleep disturbances. Respondents reported a range of strategies to manage sleep problems, including medication, environmental adjustments, and behavioral routines. Many also expressed frustrations over the limited effectiveness of strategies and inadequate support for sleep issues. These findings underscore the dual impact of LGS-related sleep disturbances on patients and caregivers, and highlight the need for comprehensive, family-centered approaches to sleep health.

neuroscience↗

bioRxiv2020

Regression dynamic causal modeling for resting-state fMRI

AO_SCPLOWBSTRACTC_SCPLOW"Resting-state" functional magnetic resonance imaging (rs-fMRI) is widely used to study brain connectivity. So far, researchers have been restricted to measures of functional connectivity that are computationally efficient but undirected, or to effective connectivity estimates that are directed but limited to small networks. Here, we show that a method recently developed for task-fMRI - regression dynamic causal modeling (rDCM) - extends to rs-fMRI and offers both directional estimates and scalability to whole-brain networks. First, simulations demonstrate that rDCM faithfully recovers parameter values over a wide range of signal-to-noise ratios and repetition times. Second, we test construct validity of rDCM in relation to an established model of effective connectivity, spectral DCM. Using rs-fMRI data from nearly 200 healthy participants, rDCM produces biologically plausible results consistent with estimates by spectral DCM. Importantly, rDCM is computationally highly efficient, reconstructing whole-brain networks (>200 areas) within minutes on standard hardware. This opens promising new avenues for connectomics.

neuroscience↗

bioRxiv2020

Risperidone-induced changes in DNA methylation from peripheral blood in first-episode schizophrenia parallel neuroimaging and cognitive phenotype

Today, second generation anti-psychotics such as clozapine and risperidone are the favored treatment for schizophrenia. Yet, the absence of relevant biomarkers that can decode their neurobiological effect shackles our ability to accurately predict and track response to treatment. While researchers have investigated DNA methylation as a biomarker for schizophrenia risk, none have performed a systematic analysis of the effect of antipsychotics upon DNA methylation. We hypothesize that disease-related methylation changes occur before treatment, and that acute antipsychotic treatment may affect DNA methylation. We designed a longitudinal DNA methylation study to estimate risperidones effect on DNA methylation and how changes in DNA methylation might influence risperidones therapeutic effect on behavioral and neuroimaging phenotypes. Thirty-eight patients with first-episode drug-naive schizophrenia (FES) and 38 demographically-matched individuals (healthy controls) participated. We identified brain related pathways enriched in 8,204 FES-associated methylation sites. Risperidone administration altered methylation in 6,143 CpG DNA sites. Post-treatment FES associated with methylation in 6760 CpG sites. Majority of the DNA methylation changes were treatment effect in the overall CpG sites, the FES associated CpG sites, and risperidone associated CpG sites, except for the post-treatment FES associated CpG sites. There were 590 DNA methylation cites normalized by risperidone treatment. The methylation changes of these 590 CpG sites were related to alterations in symptom severity, spontaneous neurophysiological activity, and cognitive function. To our knowledge, this is the first longitudinal methylation study of drug treatment effect and side effect in psychiatric disorders to include parallel studies of neuroimaging and cognitive phenotypes. We identified FES-associated CpG sites not confounded by drug treatment as potential SCZ biomarkers. The normalization effect of risperidone monotherapy suggests that DNA methylation changes may serve as a predictive biomarker for treatment effect. The constructed methylation-phenotype network revealed a relationship between methylation and a wide range of biological and psychological variables.

pharmacology and toxicology↗

bioRxiv2017

Genetic Analysis of Deep Phenotyping Projects in Common Disorders

Several studies of complex psychotic disorders with large numbers of neurobiological phenotypes are currently under way, in living patients and controls, and on assemblies of brain specimens. Genetic analyses of such data typically present challenges, because of the choice of underlying hypotheses on genetic architecture of the studied disorders and phenotypes, large numbers of phenotypes, the appropriate multiple testing corrections, limited numbers of subjects, imputations required on missing phenotypes and genotypes, and the cross-disciplinary nature of the phenotype measures. Advances in genotype and phenotype imputation, and in genome-wide association (GWAS) methods, are useful in dealing with these challenges. As compared with the more traditional single-trait analyses, deep phenotyping with simultaneous genome-wide analyses serves as a discovery tool for previously unsuspected relationships of phenotypic traits with each other, and with specific molecular involvements.

genetics↗

bioRxiv2017

Higher genetic risk of schizophrenia is associated with lower cognitive performance in healthy individuals

Psychotic disorders including schizophrenia are commonly accompanied by cognitive deficits. Recent studies have reported negative genetic correlations between schizophrenia and indicators of cognitive ability such as general intelligence and processing speed. Here we compare the effect of the genetic risk of schizophrenia (PRSSCZ) on measures that differ in their relationships with psychosis onset: a measure of current cognitive abilities (the Brief Assessment of Cognition in Schizophrenia, BACS) that is greatly reduced in psychosis patients; a measure of premorbid intelligence that is minimally affected by psychosis (the Wide-Range Achievement Test, WRAT); and educational attainment (EY), which covaries with both BACS and WRAT. Using genome-wide SNP data from 314 psychotic and 423 healthy research participants in the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) Consortium, we investigated the association of PRSSCZ with BACS, WRAT and EY. Among apparently healthy individuals, greater genetic risk for schizophrenia (PRSSCZ) was associated with lower BACS scores (r = -0.19, p = 1 x 10-4 at PT = 1 x 10-4) but did not associate with WRAT or EY, suggesting that these areas of cognition vary in their etiologic relationships with schizophrenia. Among individuals with psychosis, PRSSCZ did not associate with variation in cognitive performance. These findings suggest that the same cognitive abilities that are disrupted in psychotic disorders are also associated with schizophrenia genetic risk in the general population. Specific cognitive phenotypes, independent of education or general intelligence, could be more deeply studied for insight into the specific processes affected by the genetic influences on psychosis.\n\nSignificancePsychotic disorders such as schizophrenia often involve profound cognitive deficits, the genetic underpinnings of which remain to be elucidated. Poor educational performance early in life is a well-known risk factor for future psychotic illness, potentially reflecting either shared genetic influences or other risk factors that are epidemiologically correlated. Here we show that, in apparently healthy individuals, common genetic risk factors for schizophrenia associate with lower performance in areas of cognition that are impaired in psychotic disorders but do not associate independently with educational attainment or more general measures of intelligence. These results suggest that specific cognitive phenotypes - independent of education or general intelligence - could be more deeply studied for insight into the processes affected by the genetic influences on psychosis.

genetics↗