Contrasting Identifying Assumptions of Average Causal Effects: Robustness and Semiparametric Efficiency

Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying assumptions may be plausible; an analyst has to make a delicate choice between these models. In this paper, we study three identifying assumptions based on the potential outcome framework: the back-door assumption, which uses pre-treatment covariates, the front-door assumption, which uses mediators, and the two-door assumption using pre-treatment covariates and mediators simultaneously. We provide the efficient influence functions and the corresponding semiparametric efficiency bounds that hold under these assumptions, and their combinations. We demonstrate that neither of the identification models provides uniformly the most efficient estimation and give conditions under which some bounds are lower than others. We show when semiparametric estimating equation estimators based on influence functions attain the bounds, and study the robustness of the estimators to misspecification of the nuisance models. The theory is complemented with simulation experiments on the finite sample behavior of the estimators. The results obtained are relevant for an analyst facing a choice between several plausible identifying assumptions and corresponding estimators. Our results show that this choice implies a trade-off between efficiency and robustness to misspecification of the nuisance models

Longitudinal association between hippocampus atrophy and episodic-memory decline in non-demented APOE ε4 carriers.

Introduction: The apolipoprotein E (APOE) ε4 allele is the main genetic risk factor for Alzheimer's disease (AD), accelerated cognitive aging, and hippocampal atrophy, but its influence on the association between hippocampus atrophy and episodic-memory decline in non-demented individuals remains unclear. Methods: We analyzed longitudinal (two to six observations) magnetic resonance imaging (MRI)-derived hippocampal volumes and episodic memory from 748 individuals (55 to 90 years at baseline, 50% female) from the European Lifebrain consortium. Results: The change-change association for hippocampal volume and memory was significant only in ε4 carriers (N = 173, r = 0.21, P = .007; non-carriers: N = 467, r = 0.073, P = .117). The linear relationship was significantly steeper for the carriers [t(629) = 2.4, P = .013]. A similar trend toward a stronger change-change relation for carriers was seen in a subsample with more than two assessments. Discussion: These findings provide evidence for a difference in hippocampus-memory association between ε4 carriers and non-carriers, thus highlighting how genetic factors modulate the translation of the AD-related pathophysiological cascade into cognitive deficits

Metoder för longitudinella hjärnavbildningsstudier med bortfall

One of the challenges in aging research is to understand the brain mechanisms that underlie cognitive development in older adults. Such aging processes are investigated in longitudinal studies, where the within-individual changes over time are observed. However, several methodological issues exist in longitudinal analyses.  One of them is loss of participants to follow-up, which occurs when individuals drop out from the study. Such dropout should be taken into account for valid conclusions from longitudinal investigations, and this is the focus of this thesis. The developed methods are used to explore brain aging and its relation to cognition within the Betula longitudinal study of aging. Papers I and II consider the association between changes in brain structure and cognition. In the first paper, regression analysis is used to establish the statistical significance of brain-cognition associations while accounting for dropout. Paper II develops interval estimators directly for an association as measured by partial correlation, when some data are missing. The estimators of Paper II may be used in longitudinal as well as cross-sectional studies and are not limited to brain imaging.  Papers III and IV study functional brain connectivity, which is the statistical dependency between the functions of distinct brain regions. Typically, only brain regions with associations stronger than a predefined threshold are considered connected. However, the threshold is often arbitrarily set and does not reflect the individual differences in the overall connectivity patterns.  Paper III proposes a mixture model for brain connectivity without explicit thresholding of associations and suggests an alternative connectivity measure. Paper IV extends the mixture modeling of Paper III to a longitudinal setting with dropout and investigates the impact of ignoring the dropout mechanism on the quality of the inferences made on longitudinal connectivity changes

Metoder för longitudinella hjärnavbildningsstudier med bortfall

One of the challenges in aging research is to understand the brain mechanisms that underlie cognitive development in older adults. Such aging processes are investigated in longitudinal studies, where the within-individual changes over time are observed. However, several methodological issues exist in longitudinal analyses.  One of them is loss of participants to follow-up, which occurs when individuals drop out from the study. Such dropout should be taken into account for valid conclusions from longitudinal investigations, and this is the focus of this thesis. The developed methods are used to explore brain aging and its relation to cognition within the Betula longitudinal study of aging. Papers I and II consider the association between changes in brain structure and cognition. In the first paper, regression analysis is used to establish the statistical significance of brain-cognition associations while accounting for dropout. Paper II develops interval estimators directly for an association as measured by partial correlation, when some data are missing. The estimators of Paper II may be used in longitudinal as well as cross-sectional studies and are not limited to brain imaging.  Papers III and IV study functional brain connectivity, which is the statistical dependency between the functions of distinct brain regions. Typically, only brain regions with associations stronger than a predefined threshold are considered connected. However, the threshold is often arbitrarily set and does not reflect the individual differences in the overall connectivity patterns.  Paper III proposes a mixture model for brain connectivity without explicit thresholding of associations and suggests an alternative connectivity measure. Paper IV extends the mixture modeling of Paper III to a longitudinal setting with dropout and investigates the impact of ignoring the dropout mechanism on the quality of the inferences made on longitudinal connectivity changes

Metoder för longitudinella hjärnavbildningsstudier med bortfall

One of the challenges in aging research is to understand the brain mechanisms that underlie cognitive development in older adults. Such aging processes are investigated in longitudinal studies, where the within-individual changes over time are observed. However, several methodological issues exist in longitudinal analyses.  One of them is loss of participants to follow-up, which occurs when individuals drop out from the study. Such dropout should be taken into account for valid conclusions from longitudinal investigations, and this is the focus of this thesis. The developed methods are used to explore brain aging and its relation to cognition within the Betula longitudinal study of aging. Papers I and II consider the association between changes in brain structure and cognition. In the first paper, regression analysis is used to establish the statistical significance of brain-cognition associations while accounting for dropout. Paper II develops interval estimators directly for an association as measured by partial correlation, when some data are missing. The estimators of Paper II may be used in longitudinal as well as cross-sectional studies and are not limited to brain imaging.  Papers III and IV study functional brain connectivity, which is the statistical dependency between the functions of distinct brain regions. Typically, only brain regions with associations stronger than a predefined threshold are considered connected. However, the threshold is often arbitrarily set and does not reflect the individual differences in the overall connectivity patterns.  Paper III proposes a mixture model for brain connectivity without explicit thresholding of associations and suggests an alternative connectivity measure. Paper IV extends the mixture modeling of Paper III to a longitudinal setting with dropout and investigates the impact of ignoring the dropout mechanism on the quality of the inferences made on longitudinal connectivity changes

A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity : An Alternative to Thresholding

This article proposes a Bayesian hierarchical mixture model to analyze functional brain connectivity where mixture components represent "positively connected" and "non-connected" brain regions. Such an approach provides a data-informed separation of reliable and spurious connections in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population as well as for each individual subject. A new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions' activity, can be computed from the model fit. The posterior probability reflects the connectivity of a pair of regions relative to the overall connectivity pattern of an individual, which is overlooked in traditional correlation analyses. This article demonstrates that using the posterior probability might diminish the effect of spurious connections on inferences, which is present when a correlation is used as a connectivity measure. In addition, simulation analyses reveal that the sparsification of the connectivity matrix using the posterior probabilities might outperform the absolute thresholding based on correlations. Therefore, we suggest that posterior probability might be a beneficial measure of connectivity compared with the correlation. The applicability of the introduced method is exemplified by a study of functional resting-state brain connectivity in older adults

Aerobic fitness, but not physical activity, is associated with grey matter volume in adolescents

Higher levels of aerobic fitness and physical activity are linked to beneficial effects on brain health, especially in older adults. The generalizability of these earlier results to young individuals is not straightforward, because physiological responses (such as cardiovascular responses) to exercise may depend on age. Earlier studies have mostly focused on the effects of either physical activity or aerobic fitness on the brain. Yet, while physical activity indicates the amount of activity, aerobic fitness is an adaptive state or attribute that an individual has or achieves. Here, by measuring both physical activity and aerobic fitness in the same study, we aimed to differentiate the association between these two measures and grey matter volume specifically. Magnetic resonance imaging scans were used to study volumes of 30 regions of interest located in the frontal, motor and subcortical areas of 60 adolescents (12.7–16.2 years old). Moderate-to-vigorous intensity physical activity (MVPA) was measured with hip-worn accelerometers and aerobic fitness was assessed with a 20-m shuttle run. Multiple regression analyses revealed a negative association between aerobic fitness and left superior frontal cortex volume and a positive association between aerobic fitness and the left pallidum volume. No associations were found between MVPA and any brain region of interest. These results demonstrate unequal contribution of physical activity and aerobic fitness on grey matter volumes, with inherent or achieved capacity (aerobic fitness) showing clearer associations than physical activity.peerReviewe

Longitudinal association between hippocampus atrophy and episodic-memory decline

There is marked variability in both onset and rate of episodic-memory decline in aging. Structural magnetic resonance imaging studies have revealed that the extent of age-related brain changes varies markedly across individuals. Past studies of whether regional atrophy accounts for episodic-memory decline in aging have yielded inconclusive findings. Here we related 15-year changes in episodic memory to 4-year changes in cortical and subcortical gray matter volume and in white-matter connectivity and lesions. In addition, changes in word fluency, fluid IQ (Block Design), and processing speed were estimated and related to structural brain changes. Significant negative change over time was observed for all cognitive and brain measures. A robust brain-cognition change-change association was observed for episodic-memory decline and atrophy in the hippocampus. This association was significant for older (65-80 years) but not middle-aged (55-60 years) participants and not sensitive to the assumption of ignorable attrition. Thus, these longitudinal findings highlight medial-temporal lobe system integrity as particularly crucial for maintaining episodic-memory functioning in older age.

Physical activity and aerobic fitness in relation to local and interhemispheric functional connectivity in adolescents’ brains

Introduction Adolescents have experienced decreased aerobic fitness levels and insufficient physical activity levels over the past decades. While both physical activity and aerobic fitness are related to physical and mental health, little is known concerning how they manifest in the brain during this stage of development, characterized by significant physical and psychosocial changes. The aim of the study is to examine the associations between both physical activity and aerobic fitness with brains’ functional connectivity. Methods Here, we examined how physical activity and aerobic fitness are associated with local and interhemispheric functional connectivity of the adolescent brain (n = 59), as measured with resting‐state functional magnetic resonance imaging. Physical activity was measured by hip‐worn accelerometers, and aerobic fitness by a maximal 20‐m shuttle run test. Results We found that higher levels of moderate‐to‐vigorous intensity physical activity, but not aerobic fitness, were linked to increased local functional connectivity as measured by regional homogeneity in 13–16‐year‐old participants. However, we did not find evidence for significant associations between adolescents’ physical activity or aerobic fitness and interhemispheric connectivity, as indicated by homotopic connectivity. Conclusions These results suggest that physical activity, but not aerobic fitness, is related to local functional connectivity in adolescents. Moreover, physical activity shows an association with a specific brain area involved in motor functions but did not display any widespread associations with other brain regions. These results can advance our understanding of the behavior–brain associations in adolescents.peerReviewe