Sanford, N. | Search bioRxiv

bioRxiv2022

Sex differences in predictors and regional patterns of brain-age-gap estimates

BackgroundThe brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data, and is considered a biomarker of brain health. Understanding sex-differences in brainAGE is a significant step toward precision medicine. MethodsGlobal and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex-differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. ResultsL-brainAGE showed sex-specific differences in brain ageing. In females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex-differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception for poor sleep quality, which was common to both. The most important predictor of higher G-brainAGE was non-white race in males and systolic blood pressure in females. ConclusionsThe results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain ageing and provide a foundation for targeted interventions.

neuroscience↗

bioRxiv2021

Brain Networks Detectable by fMRI during On-Line Self Report of Hallucinations in Schizophrenia

An analysis of an internationally shared functional magnetic resonance imaging (fMRI) data involving healthy participants and schizophrenia patients extracted brain networks involved in listening to radio speech and capture hallucination experiences. A multidimensional analysis technique demonstrated that for radio-speech sound files, a brain network matching known auditory perception networks emerged, and importantly, displayed speech-duration-dependent hemodynamic responses (HDRs), confirming fMRI detection of these speech events. In the hallucination-capture data, although a sensorimotor (response) network emerged, it did not show hallucination-duration-dependent HDRs. We conclude that although fMRI retrieved the brain network involved in generating the motor responses indicating the start and end of an experienced hallucination, the hallucination event itself was not detected. Previous reports on brain networks detected by fMRI during hallucination capture is reviewed in this context.

neuroscience↗