Biology subjects

Neudorf, J.

Publications and source records attributed to Neudorf, J..

9 recordsLinked to original sources

bioRxiv2026

Physical activity promotes longitudinal white matter brain adaptations that support fluid intelligence with age

The aging population continues to grow, and along with it the need to better understand the unique characteristics of the aging brain. Despite the cortical thinning and myelin degeneration that occurs in later life, many individuals are resilient to these changes and are relatively spared from cognitive decline (so called "super-agers"). Longitudinal magnetic resonance imaging data from the Human Connectome Project allowed us to probe the underlying white matter connections that are strengthened or pruned to support fluid intelligence in younger and older adults, while also investigating physical activity as a potential behavioural intervention to promote these network reorganizations. Multivariate partial least squares analysis and the recent method of correlational tractography identified parts of the white matter structural connectivity network, measured with diffusion magnetic resonance imaging, where strengthening or pruning were associated with positive fluid intelligence trajectories and promoted by physical activity. This research improves our understanding of how the brain network adapts in later life, identifies physical activity as an accessible intervention to promote this adaptation, and enriches our fundamental models of what the brains neural network is capable of.

neuroscience↗

bioRxiv2025

Young and old adult brains experience opposite effects of acute sleep restriction on the functional connectivity network

Chronic, long-term sleep loss is detrimental to brain health and cognitive ability. However, older adults are affected differently by acute, short-term loss of sleep than young and middle-aged adults. Older adults are more resilient to the effects of acute sleep loss and, depending on the cognitive domain, may be completely unaffected while younger adults suffer. To elucidate the brain network responses to sleep loss underlying these cognitive differences between age groups, we investigated the static and dynamic functional connectivity effects of sleep restriction (sleep limited to 3 hours) and how these effects differ between younger adults (20-30 years) and older adults (65-75 years). We found a functional connectivity subnetwork that was primarily strengthened in younger adults after sleep restriction but weakened in older adults after sleep restriction. Similar crossover interactions were consistently observed in further analyses of functional connectivity degree, modularity, and dynamic functional connectivity state fractional occupancy. Our findings demonstrate that the effect of sleep restriction on older adults is fundamentally different from younger adults. These results most strongly support the compensation theory of aging, which predicts a fundamental shift in the effects of sleep loss, rather than a mere dampening of the sleep benefits experienced by younger adults.

neuroscience↗

bioRxiv2024

Examining Relationships between Functional and Structural Brain Network Architecture, Age, and Attention Skills in Early Childhood

Early childhood is a critical period showing experience-dependent changes in brain structure and function. The complex link between the structural connectivity (SC) and functional connectivity (FC) of the brain is of particular interest, however, its relationship with both age and attention in early childhood is not well understood. In this study, children between the ages of 4 and 7, and at a one-year follow-up visit, underwent neuroimaging (diffusion-weighted and passive-viewing functional magnetic resonance imaging) and assessments for selective, sustained, and executive attention. We examined regional graph theory metrics and SC-FC coupling of the structural and functional networks. Partial least squares (PLS) was used to investigate longitudinal brain measure changes and cross-sectional associations with age and attention. We observed longitudinal changes in functional graph theory metrics and age-related decreases in SC modularity. Region-wise graph theory analyses revealed variable brain-behaviour relationships across the brain, highlighting regions where structural topology is linked to age and attentional performance. Furthermore, we identified SC as a dominant predictor of age when compared to FC and SC-FC coupling. The findings emphasise how early childhood is a dynamic period where cognitive functioning is intricately and predominantly linked to structural network features. Significance StatementThis study investigates early childhood brain development, particularly the changes in structural and functional connectivity of the brain and the relationship between them. We examined children aged 4 to 7 over a year, and used graph theory analyses to characterise variable developmental changes and brain-behaviour relationships across the brain. Our findings emphasise the nuanced and regionally specific relationship between brain network structure and behaviour in early childhood, highlighting regions where structural topology correlates with attentional performance. This research underscores the dynamic nature of childhood brain development and the predominant role of SC in cognitive maturation, providing valuable insights into typical developmental trajectories and potential targets for early intervention strategies.

neuroscience↗

bioRxiv2024

Dynamic network features of functional and structural brain networks support visual working memory in aging adults

In this work, we investigated the relationship between structural connectivity and the dynamics of functional connectivity and how this relationship changes with age to benefit cognitive functions. Visual working memory (VWM) is an important brain function that allows us to maintain a mental representation of the world around us, but its capacity and precision peaks by around 20 years old and decreases steadily throughout the rest of our lives. This research examined the functional brain network dynamics associated with VWM throughout the lifespan and found that Default Mode Network and Fronto-Parietal Network states were more well represented in individuals with better VWM. Furthermore, transitions between the Visual/Somatomotor Network state and the Attention Network state were more well-represented in older adults, and a network control theory simulation demonstrated that structural connectivity differences supporting this transition were associated with better VWM, especially in middle-aged individuals. The structural connectivity of regions from all states was important for supporting this transition in younger adults, while regions within the Visual/Somatomotor and Attention Network states were more important in older adults. These findings demonstrate that structural connectivity supports flexible, functional dynamics that allow for better VWM with age and may lead to important interventions to uphold healthy VWM throughout the lifespan.

neuroscience↗

bioRxiv2023

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine if age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals. Author SummaryWe utilized network neuroscience methods to investigate why some older adults suffer tremendous cognitive decline while others are mostly spared. Past functional research found that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether structural connectivity reorganizes to preserve cognitive ability. We analyzed age and fluid intelligence as a function of structural connectivity and regional graph theory measures using partial least squares. Some differences with age were associated with poorer cognitive outcomes, but other reorganizations spared cognitive ability. Beneficial reorganizations included strengthened local intrahemispheric connectivity and increased nodal efficiency of focal regions for older adults, as well as widespread increased local efficiency for middle-aged individuals.

neuroscience↗

bioRxiv2022

Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: Diffusion versus shortest path routing

The relationship between structural and functional connectivity in the human brain is a core question in network neuroscience, and a topic of paramount importance to our ability to meaningfully describe and predict functional outcomes. Graph theory has been used to produce measures based on the structural connectivity network that are related to functional connectivity. These measures are commonly based on either the shortest path routing model or the diffusion model, which carry distinct assumptions about how information is transferred through the network. Unlike shortest path routing, which assumes the most efficient path is always known, the diffusion model makes no such assumption, and lets information diffuse in parallel based on the number of connections to other regions. Past research has also developed hybrid measures that use concepts from both models, which have better predicted the functional connectivity from structural connectivity than shortest path length alone. We examined the extent to which each of these models can account for the structure-function relationship of interest using graph theory measures that are exclusively based on each model. This analysis was performed on multiple parcellations of the Human Connectome Project using multiple approaches, which all converged on the same finding. We found that the diffusion model accounts for much more variance in functional connectivity than the shortest path routing model, suggesting that the diffusion model is better suited to describing the structure-function relationship in the human brain at the macroscale.

neuroscience↗

bioRxiv2021

Considering unique, shared, and dominant brain activation in the Visual Word Form Area and Lateral Occipital Cortex: A comparison of separate and combined word reading and picture naming

Identifying printed words and pictures concurrently is ubiquitous in daily tasks, and so it is important to consider the extent to which reading words and naming pictures may share a cognitive-neurophysiological functional architecture. Two functional magnetic resonance imaging (fMRI) experiments examined whether reading along the left ventral occipitotemporal region (vOT; often referred to as a visual word form area, VWFA) has activation that is overlapping with referent pictures (i.e., both conditions significant and shared, or with one significantly more dominant) or unique (i.e., one condition significant, the other not), and whether picture naming along the right lateral occipital complex (LOC) has overlapping or unique activation relative to referent words. Experiment 1 used familiar regular and exception words (to force lexical reading) and their corresponding pictures in separate naming blocks, and showed dominant activation for pictures in the LOC, and shared activation in the VWFA for exception words and their corresponding pictures (regular words did not elicit significant VWFA activation). Experiment 2 controlled for visual complexity by superimposing the words and pictures and instructing participants to either name the word or the picture, and showed primarily shared activation in the VWFA and LOC regions for both word reading and picture naming, with some dominant activation for pictures in the LOC. Overall, these results highlight the importance of including exception words to force lexical reading when comparing to picture naming, and the significant shared activation in VWFA and LOC serves to challenge specialized models of reading or picture naming.

neuroscience↗

bioRxiv2021

Structure Can Predict Function in the Human Brain: A Graph Neural Network Deep Learning Model of Functional Connectivity and Centrality based on Structural Connectivity

Although functional connectivity and associated graph theory measures (e.g., centrality; how centrally important to the network a region is) are widely used in brain research, the full extent to which these functional measures are related to the underlying structural connectivity is not yet fully understood. Graph neural network deep learning methods have not yet been applied for this purpose, and offer an ideal model architecture for working with connectivity data given their ability to capture and maintain inherent network structure. This model applied here to predict functional connectivity and centrality from structural connectivity accounted for 89% of the variance in mean functional connectivity, 56% of the variance in individual-level functional connectivity, 99% of the variance in mean functional centrality, and 81% of the variance in individual-level functional centrality. This model provides a new benchmark for performance and represents a novel finding that functional centrality can be robustly predicted from structural connectivity. Regions of particular importance to the models performance as determined through lesioning are discussed, whereby regions with higher centrality have a higher impact on model performance. Future research on models of patient, demographic, or behavioural data can also benefit from this graph neural network method as it is ideally-suited for capturing connectivity and centrality in brain networks. These results have set a new benchmark for prediction of functional connectivity from structural connectivity, and models like this may ultimately lead to a way to predict functional connectivity in individuals who are unable to do fMRI tasks (e.g., non-responsive patients).

neuroscience↗

bioRxiv2019

Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal

The complexity of brain activity has recently been investigated using the Hurst (H) exponent, which describes the extent to which functional magnetic resonance imaging (fMRI) blood oxygen-level dependent (BOLD) activity is self-similar vs. complex. For example, research has demonstrated that fMRI activity is more complex before than after consumption of alcohol and during task than resting state. The measurement of H in fMRI is a novel method that requires the investigation of additional factors contributing to complexity. Graph theory metrics of centrality can assess how centrally important to the brain network each region is, based on diffusion tensor imaging (DTI) counts of probabilistic white matter (WM) tracts. DTI derived centrality was hypothesized to account for the complexity of functional activity, based on the supposition that more sources of information to integrate should result in more complex activity. FMRI BOLD complexity as measured by H was associated with five brain region centrality measures: degree, eigenvector, PageRank, current flow betweenness, and current flow closeness centrality. Multiple regression analyses demonstrated that degree centrality was the most robust predictor of complexity, whereby greater centrality was associated with increased complexity (lower H). Regions known to be highly connected, including the thalamus and hippocampus, notably were among the highest in centrality and complexity. This research has led to a greater understanding of how brain region characteristics such as DTI centrality relate to the novel Hurst exponent approach for assessing brain activity complexity, and implications for future research that employ these measures are discussed.

neuroscience↗