Biology subjects

Schaefer, S. M.

Publications and source records attributed to Schaefer, S. M..

5 recordsLinked to original sources

bioRxiv2025

Associations Between Age, Heart Rate Variability, and BOLD fMRI Signal Variability

Numerous studies report that BOLD fMRI signal variance (SDBOLD) decreases with age. However, these associations may partly reflect cardiovascular contributions to the BOLD signal. For example, heart rate variability (HRV) has been positively associated with Resting State Fluctuation Amplitude (RSFA), which captures low frequency components of BOLD fMRI variability. HRV is also negatively associated with age, which could potentially confound age-SDBOLD associations. Yet, limited research has examined HRV-SDBOLD associations or tested within-person HRV-SDBOLD coupling using sliding window analyses of simultaneous HRV and SDBOLD. We analyzed resting-state fMRI data from two independent Midlife in the United States (MIDUS) samples: Core at M3 (n=115) and Refresher at MR1 (n=101). Partial Least Squares (PLS) analyses revealed significant positive HRV-SDBOLD associations (Core: permutation p=0.018; Refresher: permutation p<0.001). Whole brain age-SDBOLD PLS associations were non-significant via permutation tests across several models (Core: permutation p=0.201; Refresher: permutation p=0.121). We found age-related decreases in SDBOLD across [~]70% of voxels in both samples. Concordance analyses showed 67-69% of brain voxels exhibited negative age-SDBOLD but positive HRV-SDBOLD relationships, suggesting that regions showing age-related decreases in SDBOLD also showed HRV-related increases in SDBOLD. Sliding-window analyses demonstrated robust positive within-person associations between person-centered HRV and SDBOLD via different HRV metrics: SDNN (Core: p < 0.001; Refresher: p < 0.001), RMSSD (Core: p = 0.072; Refresher: p = 0.009), and low frequency (Core: p < 0.001; Refresher: p < 0.001), with non-significant effects of high frequency (Core: p = 0.516; Refresher: p = 0.12) HRV. Thus, regardless of baseline levels, windows with higher HRV corresponded to higher SDBOLD, suggesting that cardiovascular factors partially explain age-SDBOLD associations and HRV may mechanistically influence SDBOLD. These results suggest that controlling for HRV, especially low-frequency HRV or SDNN, may be necessary when analyzing SDBOLD to isolate neural effects.

neuroscience↗

bioRxiv2023

Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years

Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual alpha peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of alpha oscillations and the relative balance of excitatory:inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection timepoints. Post-hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging. Significance StatementMeasures of intrinsic brain function at rest assessed noninvasively from the scalp using electroencephalography (EEG) show promise as predictors of cognitive decline in humans. Using data from 235 participants from the Midlife in the United States (MIDUS) longitudinal study, we found two resting EEG markers (individual peak alpha frequency and aperiodic exponent) interacted to predict cognitive decline over a span of 10 years. Follow-up analyses revealed that "mismatched" markers (i.e., high in one and low in the other) predicted greater cognitive decline compared to "matching" markers. Because of the low cost and ease of collecting EEG data at rest, the current research provides evidence for possible scalable clinical applications for identifying individuals at risk for accelerated cognitive decline.

neuroscience↗

bioRxiv2022

Biological age and brain age in midlife: relationship to multimorbidity and mental health

Multimorbidity, co-occurrence of two or more chronic conditions, is one of the top priorities in global health research and has emerged as the gold standard approach to study disease accumulation. As aging underlies the development of many chronic conditions, surrogate aging biomarkers are not disease-specific and capture health at the whole person level, having the potential to improve our understanding of multimorbidity. Biological age has been examined in recent years as a surrogate biomarker to capture the process of aging. However, relatively few studies have investigated the relationship between biological age and multimorbidity. More research is needed to quantify biological age using a broad range of biological markers and multimorbidity based on a comprehensive set of chronic conditions. Brain age estimated by neuroimaging data and machine learning models is another surrogate aging biomarker predictive of a wide range of health outcomes. Little is known about the relationship between brain age and multimorbidity. To answer these questions, our study investigates whether elevated biological age and accelerated brain age are associated with an increased risk of multimorbidity using a large dataset from the Midlife in the United States (MIDUS) Refresher study. Ensemble learning is utilized to combine multiple machine learning models to estimate biological age using a comprehensive set of biological markers. Brain age is obtained using convolutional neural networks and neuroimaging data. Our study is the first to examine the relationship between accelerated brain age and multimorbidity and presents the first effort to test whether sex moderates the relationship between these surrogate aging biomarkers and multimorbidity. Furthermore, it is the first attempt to explore how biological age and brain age are related to multimorbidity in mental health. Our findings hold the potential to advance the understanding of the accumulation of physical and mental health conditions, which may contribute to new strategies to improve the treatment of multimorbidity and detection of at-risk individuals.

neuroscience↗

bioRxiv2019

Optimizing the intrinsic parallel diffusivity in NODDI: an extensive empirical evaluation

PurposeNODDI is widely used in parameterizing microstructural brain properties. The model includes three signal compartments: intracellular, extracellular, and free water. The neurite compartment intrinsic parallel diffusivity (d||) is set to 1.7 {micro}m2*ms-1, though the effects of this assumption have not been extensively explored. This work seeks to optimize d|| by minimizing the model residuals.\n\nMethodsThe model residuals were evaluated in function of d|| over the range from 0.5 to 3.0 {micro}m2*ms-1. This was done with respect to tissue type (i.e., white matter versus gray matter), sex, age (infancy to late adulthood), and diffusion-weighting protocol (maximum b-value). Variation in the estimated parameters with respect to d|| was also explored.\n\nResultsResults show the optimum d|| is significantly lower for gray matter relative to 1.7 {micro}m2*ms-1 and to white matter. Infants showed significantly decreased optimum d|| in gray and white matter. Minor optimum d|| differences were observed versus diffusion protocol. No significant sex effects were observed. Additionally, changes in d|| resulted in significant changes to the estimated NODDI parameters.\n\nConclusionFuture implementations of NODDI would benefit from d|| optimization, particularly when investigating young populations and/or gray matter.

biophysics↗

bioRxiv2017

Behavioral and neural indices of affective coloring for neutral social stimuli

Emotional processing often continues beyond the presentation of emotionally evocative stimuli, which can result in affective biasing or coloring of subsequently encountered events. Here, we describe neural correlates of affective coloring and examine how individual differences in affective style impact the magnitude of affective coloring. We conducted functional magnetic resonance imaging in 117 adults who passively viewed negative, neutral, and positive pictures presented 2s prior to neutral faces. Brain responses to neutral faces were modulated by the valence of preceding pictures, with greater activation for faces following negative (vs. positive) pictures in the amygdala, dorsomedial and lateral prefrontal cortex, ventral visual cortices, posterior superior temporal sulcus, and angular gyrus. Three days after the MRI scan, participants rated their memory and liking of previously encountered neutral faces. Individuals higher in trait positive affect and emotional reappraisal rated faces as more likable when preceded by emotionally arousing (negative or positive) pictures. Additionally, greater amygdala responses to neutral faces preceded by positively valenced pictures were associated with greater memory for these faces three days later. Collectively, these results reveal individual differences in how emotions spill over onto the processing of unrelated social stimuli, resulting in persistent and affectively biased evaluations of such stimuli.

neuroscience↗