Search

Depressive symptoms and stress exposure fluctuate over time in community‐dwelling older adults, but they are frequently assessed using one‐time retrospective self‐report measures. Social support viewed as a multifaceted construct can play diverse moderating roles in this association although it is typically gauged through the measure of perceived social support. This study aims to explore the relationships between stress, social support, and depressive symptoms among older adults by utilizing the longitudinal data collected through a smartphone application and supplemented by annual face‐to‐face interviews conducted over a 2‐year period. Using longitudinal multilevel analysis, we analyzed the data on PHQ‐9, stress exposure, and four distinct measures of social support collected from 354 community‐dwelling older adults in South Korea. The results demonstrated that 59% of the variability in depressive symptoms was attributable to differences between individuals. Stress exposure was a strong predictor (γ = 3.01 ∗∗∗, 95% CI = 2.34–3.67). As expected, positive functional social support alleviated the effects of stress on depression (γ = −1.12 ∗∗, 95% CI = −1.92 ~ −0.32) while negative functional social support (γ = 2.36 ∗∗∗, 95% CI = 1.29–3.44) and negative structural social support (γ = 3.22 ∗, 95% CI = 0.79–5.64) worsened the effects of stress on depression. A notable finding is that stress‐amplifying effects from the negative functional and structural social support, in addition to well‐known stress‐buffering effects from positive functional social support, should be regarded as indispensable components in safeguarding the mental health of older adults. Considering the decline in social interactions and the lower probability of older adults establishing new social connections, it is essential to consider approaches that prevent a lack of functional and structural social support and foster a high‐quality of functional and structural social support, particularly for those facing greater stressors, as a preventative method against depressive symptoms. [ABSTRACT FROM AUTHOR]

Topological data analysis (TDA) is increasingly recognized as a promising tool in the field of neuroscience, unveiling the underlying topological patterns within brain signals. However, most TDA related methods treat brain signals as if they were static, i.e., they ignore potential non-stationarities and irregularities in the statistical properties of the signals. In this study, we develop a novel fractal dimension-based testing approach that takes into account the dynamic topological properties of brain signals. By representing EEG brain signals as a sequence of Vietoris-Rips filtrations, our approach accommodates the inherent non-stationarities and irregularities of the signals. The application of our novel fractal dimension-based testing approach in analyzing dynamic topological patterns in EEG signals during an epileptic seizure episode exposes noteworthy alterations in total persistence across 0, 1, and 2-dimensional homology. These findings imply a more intricate influence of seizures on brain signals, extending beyond mere amplitude changes. [ABSTRACT FROM AUTHOR]

We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information. Author summary: The paper introduces a new data-driven topological data analysis (TDA) method for studying dynamically changing human functional brain networks obtained from the resting-state functional magnetic resonance imaging (rs-fMRI). Leveraging persistent homology, a multiscale topological approach, we present a framework that incorporates the temporal dimension of brain network data. This allows for a more robust estimation of the topological features of dynamic brain networks. The method employs the Wasserstein distance to measure the topological differences between networks and demonstrates greater efficiency and performance than the commonly used k-means clustering in defining the state spaces of dynamic brain networks. Our method maintains robust performance across different scales and is especially suited for dynamic brain networks. In addition to the methodological advancement, the paper applies the proposed technique to analyze the heritability of overall brain network topology using a twin study design. The study investigates whether the dynamic pattern of brain networks is a genetically influenced trait, an area previously underexplored. By examining the state change patterns in twin brain networks, we make significant strides in understanding the genetic factors underlying dynamic brain network features. Furthermore, the paper makes its method accessible by providing MATLAB codes, contributing to reproducibility and broader application. [ABSTRACT FROM AUTHOR]

Introduction: Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each nonamnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods: Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results: Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the nonamnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion: We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in nonamnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread. [ABSTRACT FROM AUTHOR]

Developing reliable methods to discriminate different transient brain states that change over time is a key neuroscientific challenge in brain imaging studies. Topological data analysis (TDA), a novel framework based on algebraic topology, can handle such a challenge. However, existing TDA has been somewhat limited to capturing the static summary of dynamically changing brain networks. We propose a novel dynamic-TDA framework that builds persistent homology over a time series of brain networks. We construct a Wasserstein distance based inference procedure to discriminate between time series of networks. The method is applied to the resting-state functional magnetic resonance images of the human brain. We demonstrate that our proposed dynamic-TDA approach can distinctly discriminate between the topological patterns of male and female brain networks. [ABSTRACT FROM AUTHOR]

Over the last two decades, topological data analysis (TDA) has emerged as a very powerful data analytic approach that can deal with various data modalities of varying complexities. One of the most commonly used tools in TDA is persistent homology (PH), which can extract topological properties from data at various scales. The aim of this article is to introduce TDA concepts to a statistical audience and provide an approach to analyzing multivariate time series data. The application's focus will be on multivariate brain signals and brain connectivity networks. Finally, this paper concludes with an overview of some open problems and potential application of TDA to modeling directionality in a brain network, as well as the casting of TDA in the context of mixed effect models to capture variations in the topological properties of data collected from multiple subjects. [ABSTRACT FROM AUTHOR]

Cognitive deficits have been reported in children who experienced early neglect, especially children raised in institutionalized settings. Previous research suggests that early neglect may differentially affect the directional organization of white matter in the prefrontal cortex (PFC). This may be one mechanism to explain cognitive deficits associated with neglect. To test this idea, properties of white matter and neurocognitive performance were assessed in children who suffered early neglect and those raised in typical environments ("n" = 63, M [subscript age] = 11.75 years). As predicted, prefrontal white matter microstructure was affected, consistent with more diffuse organization, in children that suffered early neglect and this was related to neurocognitive deficits. Such findings underscore how early adversity may affect the PFC and explain cognitive deficits associated with neglect.

Understanding the common topological characteristics of the human brain network across a population is central to understanding brain functions. The abstraction of human connectome as a graph has been pivotal in gaining insights on the topological properties of the brain network. The development of group-level statistical inference procedures in brain graphs while accounting for the heterogeneity and randomness still remains a difficult task. In this study, we develop a robust statistical framework based on persistent homology using the order statistics for analyzing brain networks. The use of order statistics greatly simplifies the computation of the persistent barcodes. We validate the proposed methods using comprehensive simulation studies and subsequently apply to the resting-state functional magnetic resonance images. We found a statistically significant topological difference between the male and female brain networks. [ABSTRACT FROM AUTHOR]

Purpose: The anatomic origin for prepubertal vowel acoustic differences between male and female subjects remains unknown. The purpose of this study is to examine developmental sex differences in vocal tract (VT) length and its oral and pharyngeal portions. Method: Nine VT variables were measured from 605 imaging studies (magnetic resonance imaging and computed tomography) of subjects between birth and age 19 years. Given sex differences in growth rate (Vorperian et al., 2009), assessment of sex differences was done through use of a localized comparison window of 60 months. Analysis entailed applying this comparison window first to 4 discrete age cohorts, followed by a progressive assessment in which this comparison window was moved in 1-month increments from birth across all ages. Results: Findings document significant postpubertal sex differences in both the oral and pharyngeal portions of the VT. They also document periods of significant prepubertal sex differences in the oral region first, followed by segments in the pharyngeal region. Conclusions: Assessment of developmental sex differences using localized age ranges is effective in unveiling sex differences that growth rate differences may conceal. Findings on the presence of prepubertal sex differences in the oral region of the VT may clarify, in part, the anatomic basis of documented prepubertal acoustic differences. Key Words: sexual dimorphism, vocal tract, development, As the vocal tract (VT) increases in length during development, its formant frequencies decrease (Fant, 1960). Fant (1966) also noted that physiologically induced differences in formant patterns between male and [...]

We present a new tensor-based morphometric framework that quantifies cortical shape variations using a local area element. The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation, which generalizes the traditional SPHARM as a special case. For a specific choice of weights, the weighted-SPHARM is shown to be the least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims of this paper are to present the weighted-SPHARM and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in the group of high functioning autistic subjects. Index Terms--Cortical surface, spherical harmonic (SPHARM), tensor-based morphometry.

We present a novel weighted Fourier series (WFS) representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a self-adjoint partial differential equation and the traditional spherical harmonic (SPHARM) representation. To reduce steep computational requirements, a new iterative residual fitting (IRF) algorithm is developed. Its computational and numerical implementation issues are discussed in detail. The computer codes are also available at http://www.stat.wisc.edu/~mchung/softwares/weighted-SPHARM /weighted-SPHARM.html. As an illustration, the WFS is applied in quantifying the amount of gray matter in a group of high functioning autistic subjects. Within the WFS framework, cortical thickness and gray matter density are computed and compared. Index Terms--Cortical thickness, diffusion smoothing , gray matter density, iterative residual fitting, SPHARM, spherical harmonics.

This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace–Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator. We define spectral filters via the LB operator on a graph and explore the feasibility of Chebyshev, Laguerre, and Hermite polynomials to approximate LB-based spectral filters. We then update the LB operator for pooling in the LB-CNN. We employ the brain image data from Alzheimer's Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS) to demonstrate the use of the proposed LB-CNN. Based on the cortical thickness of two datasets, we showed that the LB-CNN slightly improves classification accuracy compared to the spectral graph-CNN. The three polynomials had a similar computational cost and showed comparable classification accuracy in the LB-CNN or spectral graph-CNN. The LB-CNN trained via the ADNI dataset can achieve reasonable classification accuracy for the OASIS dataset. Our findings suggest that even though the shapes of the three polynomials are different, deep learning architecture allows us to learn spectral filters such that the classification performance is not dependent on the type of the polynomials or the operators (graph Laplacian and LB operator). [ABSTRACT FROM AUTHOR]