Biology subjects

Williams, S. D.

Publications and source records attributed to Williams, S. D..

6 recordsLinked to original sources

bioRxiv2025

The aging human brain exhibits reduced cerebrospinal fluid flow during sleep due to both neural and vascular factors

Aging reduces the quality and quantity of sleep, and greater sleep loss over the lifespan is predictive of neurodegeneration and cognitive decline. One mechanism by which sleep loss could contribute to impaired brain health is through disruption of cerebrospinal fluid (CSF) circulation. CSF is the primary waste transport system of the brain, and in young adults, CSF waves are largest during NREM sleep. However, whether sleep-dependent brain fluid physiology changes in aging is not known, due to the technical challenges of performing neuroimaging studies during sleep. We collected simultaneous fast fMRI and EEG data to measure large-scale CSF flow in healthy young and older adults and tested whether there were age-related changes to CSF dynamics during nighttime sleep. We found that sleep-dependent CSF flow was reduced in older adults, and this reduction was linked to impaired frontal EEG delta power and global hemodynamic oscillations during sleep. To identify mechanisms underlying reduced CSF flow, we used sensory and vasoactive stimuli to drive CSF flow in daytime task experiments, and found that both neural and cerebrovascular physiological changes contributed to the disruption of CSF flow during sleep. Finally, we found that this reduction in CSF flow was associated with gray matter atrophy in aging. Together, these results demonstrate that the aging human brain has reduced CSF flow during sleep, and identifies underlying neurovascular mechanisms that contribute to this age-related decline, suggesting targets for future interventions.

neuroscience↗

bioRxiv2024

Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness

The brain exhibits rich oscillatory dynamics that play critical roles in vigilance and cognition, such as the neural rhythms that define sleep. These rhythms continuously fluctuate, signaling major changes in vigilance, but the widespread brain dynamics underlying these oscillations are difficult to investigate. Using simultaneous EEG and fast fMRI in humans who fell asleep inside the scanner, we developed a machine learning approach to investigate which fMRI regions and networks predict fluctuations in neural rhythms. We demonstrated that the rise and fall of alpha (8-12 Hz) and delta (1-4 Hz) power-- two canonical EEG bands critically involved with cognition and vigilance--can be predicted from fMRI data in subjects that were not present in the training set. This approach also identified predictive information in individual brain regions across the cortex and subcortex. Finally, we developed an approach to identify shared and unique predictive information, and found that information about alpha rhythms was highly separable in two networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale primarily across the cortex. These results demonstrate that EEG rhythms can be predicted from fMRI data, identify large-scale network patterns that underlie alpha and delta rhythms, and establish a novel framework for investigating multimodal brain dynamics. Author summaryNeurons often fire in synchrony, generating rhythms that play major roles in brain functioning. These rhythms are hallmarks of different brain states of vigilance, such as sleep and wakefulness. Sleep disorders are extremely prevalent among adults, and studying neural rhythms associated with vigilance states is a key step towards understanding sleep disorders and how healthy sleep can be restored. Measuring how neural rhythms affect the brain, however, is difficult: the primary method used in humans, electroencephalography (EEG), can only measure neural activity close to the scalp. EEG can be combined with functional magnetic resonance imaging (fMRI), which is capable of measuring activity in deep brain regions, but fMRI data can be difficult to analyze, as it estimates neural activity indirectly by measuring changes in blood oxygenation. We developed an approach to analyze combined EEG-fMRI data using machine learning, and used it to investigate how fluctuations in neural rhythms across sleep and wakefulness are tied to changes in neural activity throughout the whole brain. Our results describe how different brain networks are coupled to alpha and delta rhythms, and provide a new approach for analyzing EEG-fMRI data that can be employed to investigate other neural rhythms necessary for healthy brain functioning.

neuroscience↗

bioRxiv2021

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

The moment of awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is not known. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei that preceded behavioral arousal after a period of inactivity, followed by widespread cortical deactivation. These thalamic dynamics were linked to whether participants remained awake or fell back asleep, with unified thalamic activation reflecting subsequent maintenance of awake behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.

neuroscience↗

bioRxiv2021

Interactions, information and emergence: Exploring task allocation in ant colonies using network analysis

In animal societies, individuals may take on different roles to fulfil their own needs and the needs of their groups. Ant colonies display high levels of organisational complexity, with ants fulfilling different roles at different timescales (what is known as task allocation). Factors affecting task allocation can be at the individual level (e.g., physiology), or at the group level (e.g., the network of interactions). We focus on group level processes by exploring the relationship between interaction networks, task allocation and task switching using a previously published dataset (Mersch et al., 2013) tracking the behaviour of six Camponotus fellah colonies over 41 days. In our new analyses, our goal was to better explain the noisy process of task switching beyond simple age polyethism. First, we investigated the architecture of interaction networks using node (individual) level network measures and their relation to the individuals task - foraging, cleaning or nursing - and whether or not the ant switched tasks. We then explored how noisy information propagation was among ants, as a function of the colony composition (how many ants carried out which tasks), through the information-theoretic metric of Effective Information. Our results show that interaction history was tied to task allocation: ants who switched to a task are more likely to have interacted with other ants carrying out that task. The degree to which interactions related to task allocation, as well as the noise in those interactions, depended on which groups of ants were interacting. Overall, we showed that colony cohesion was stable even as ant-level network measures varied more for ants when they switched functional groups; thus, ant colonies maintained a high level of information flow as determined by network analysis, and ant functional groups played different roles in maintaining colony cohesion through varied information flows. HighlightsO_LIWe analysed the interaction networks of six Camponotus fellah colonies C_LIO_LIWe tested how centrality and information flow were tied to task switching C_LIO_LINode-level network metrics and the information theoretic measure of Effective Information explained differences among functional groups C_LIO_LIInteractions were correlated with task switching, but the strength of the correlation differed across functional groups C_LI

animal behavior and cognition↗

bioRxiv2020

Sustained attention and spatial attention distinctly influence long-term memory encoding

Our attention is critically important for what we remember. Prior measures of the relationship between attention and memory, however, have largely treated "attention" as a monolith. Here, across three experiments, we provide evidence for two dissociable aspects of attention that influence encoding into long-term memory. Using spatial cues together with a sensitive continuous report procedure, we find that long-term memory response error is affected by both trial-by-trial fluctuations of sustained attention and prioritization via covert spatial attention. Furthermore, using multivariate analyses of EEG, we track both sustained attention and spatial attention prior to stimulus onset. Intriguingly, even during moments of low sustained attention, there is no decline in the representation of the spatially attended location, showing that these two aspects of attention have robust but independent effects on long term memory encoding. Finally, sustained and spatial attention predicted distinct variance in long-term memory performance across individuals. That is, the relationship between attention and long-term memory suggests a composite model, wherein distinct attentional subcomponents influence encoding into long-term memory. These results point towards a taxonomy of the distinct attentional processes that constrain our memories.

neuroscience↗

bioRxiv2020

Time scales of mixing in an imperforate scleractinian coelenteron

Coelentera are the largest components by volume in the gastrovascular system connecting polyps in a scleractinian colony. Thus to understand colony connectivity which is predicted to affect corals response to environmental change, we must first describe the dynamics inside these gastric cavities of individual polyps. We determined key time scales of mixing in coelentera by using microelectrodes to measure oxygen concentration after a light-to-dark transition in three polyps each of three colonies of Montastraea cavernosa in the laboratory. The gastrovascular system was modeled as an electrical network where voltage represents oxygen concentration, current represents oxygen flux, capacitors represent volume compartments, and resistors represent impedance to oxygen flux. The time constant of mixing, defined as the time needed for the system to disperse 63.2% of the fluid in the coelenteron, was determined from the oxygen dynamics in the coelenteron as modeled by a resistor-capacitor network. Time constants were on the order of three minutes and oxygen dynamics were well fit by the model prediction. However, as polyps depleted oxygen, we observed small magnitude (~ 0.1 ppm), high-frequency fluctuations in oxygen concentration. A power spectral density analysis identified two time scales of high-frequency mixing in the coelenteron. The greatest variance occurred at a period of 48.3 {+/-} 2.8 seconds, with a secondary peak seen at 35.9 {+/-} 2.3 seconds. The microenvironment within polyps of M. cavernosa can respond as fast or faster than their external environment can fluctuate, thus scleractinian polyps have the capacity to mediate their response to changing environmental conditions.

physiology↗