Age-related Changes In Functional Connectivity Research Articles

Investigating Associations Between Maternal Behavior and the Development of Functional Connectivity During the Transition From Late Childhood to Early Adolescence

BackgroundParenting behavior is thought to affect child brain development, with implications for mental health. However, longitudinal studies that use whole-brain approaches are lacking. In this study, we investigated associations between parenting behavior, age-related changes in whole-brain functional connectivity, and psychopathology symptoms in children and adolescents. MethodsTwo hundred forty (126 female) children underwent resting-state functional magnetic resonance imaging at up to two time points, providing a total of 398 scans covering the age range 8 to 13 years. Parenting behavior was self-reported at baseline. Parenting factors (positive parenting, inattentive parenting, and harsh and inconsistent discipline) were identified based on a factor analysis of self-report parenting questionnaires. Longitudinal measures of child internalizing and externalizing symptoms were collected. Network-based R-statistics was used to identify associations between parenting and age-related changes in functional connectivity. ResultsHigher maternal inattentive behavior was associated with lower decreases in connectivity over time, particularly between regions of the ventral attention and default mode networks and frontoparietal and default mode networks. However, this association was not significant after strict correction for multiple comparisons. ConclusionsWhile results should be considered preliminary, they suggest that inattentive parenting may be associated with a reduction in the normative pattern of increased network specialization that occurs with age. This may reflect a delayed development of functional connectivity.

Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks.

Geometry-inspired notions of discrete Ricci curvature have been successfully used as markers of disrupted brain connectivity in neuropsychiatric disorders, but their ability to characterize age-related changes in functional connectivity is unexplored. We apply Forman-Ricci curvature and Ollivier-Ricci curvature to compare functional connectivity networks of healthy young and older subjects from the Max Planck Institute Leipzig Study for Mind-Body-Emotion Interactions (MPI-LEMON) dataset (N = 225). We found that both Forman-Ricci curvature and Ollivier-Ricci curvature can capture whole-brain and region-level age-related differences in functional connectivity. Meta-analysis decoding demonstrated that those brain regions with age-related curvature differences were associated with cognitive domains known to manifest age-related changes-movement, affective processing, and somatosensory processing. Moreover, the curvature values of some brain regions showing age-related differences exhibited correlations with behavioral scores of affective processing. Finally, we found an overlap between brain regions showing age-related curvature differences and those brain regions whose non-invasive stimulation resulted in improved movement performance in older adults. Our results suggest that both Forman-Ricci curvature and Ollivier-Ricci curvature correctly identify brain regions that are known to be functionally or clinically relevant. Our results add to a growing body of evidence demonstrating the sensitivity of discrete Ricci curvature measures to changes in the organization of functional connectivity networks, both in health and disease.

Mapping of Structure-Function Age-Related Connectivity Changes on Cognition Using Multimodal MRI

The relationship between age-related changes in brain structural connectivity (SC) and functional connectivity (FC) with cognition is not well understood. Furthermore, it is not clear whether cognition is represented via a similar spatial pattern of FC and SC or instead is mapped by distinct sets of distributed connectivity patterns. To this end, we used a longitudinal, within-subject, multimodal approach aiming to combine brain data from diffusion-weighted MRI (DW-MRI), and functional MRI (fMRI) with behavioral evaluation, to better understand how changes in FC and SC correlate with changes in cognition in a sample of older adults. FC and SC measures were derived from the multimodal scans acquired at two time points. Change in FC and SC was correlated with 13 behavioral measures of cognitive function using Partial Least Squares Correlation (PLSC). Two of the measures indicate an age-related change in cognition and the rest indicate baseline cognitive performance. FC and SC—cognition correlations were expressed across several cognitive measures, and numerous structural and functional cortical connections, mainly cingulo-opercular, dorsolateral prefrontal, somatosensory and motor, and temporo-parieto-occipital, contributed both positively and negatively to the brain-behavior relationship. Whole-brain FC and SC captured distinct and independent connections related to the cognitive measures. Overall, we examined age-related function-structure associations of the brain in a comprehensive and integrated manner, using a multimodal approach. We pointed out the behavioral relevance of age-related changes in FC and SC. Taken together, our results highlight that the heterogeneity in distributed FC and SC connectivity patterns provide unique information about the variable nature of healthy cognitive aging.

Age-Related Changes in Functional Connectivity during the Sensorimotor Integration Detected by Artificial Neural Network.

Large-scale functional connectivity is an important indicator of the brain’s normal functioning. The abnormalities in the connectivity pattern can be used as a diagnostic tool to detect various neurological disorders. The present paper describes the functional connectivity assessment based on artificial intelligence to reveal age-related changes in neural response in a simple motor execution task. Twenty subjects of two age groups performed repetitive motor tasks on command, while the whole-scalp EEG was recorded. We applied the model based on the feed-forward multilayer perceptron to detect functional relationships between five groups of sensors located over the frontal, parietal, left, right, and middle motor cortex. Functional dependence was evaluated with the predicted and original time series coefficient of determination. Then, we applied statistical analysis to highlight the significant features of the functional connectivity network assessed by our model. Our findings revealed the connectivity pattern is consistent with modern ideas of the healthy aging effect on neural activation. Elderly adults demonstrate a pronounced activation of the whole-brain theta-band network and decreased activation of frontal–parietal and motor areas of the mu-band. Between-subject analysis revealed a strengthening of inter-areal task-relevant links in elderly adults. These findings can be interpreted as an increased cognitive demand in elderly adults to perform simple motor tasks with the dominant hand, induced by age-related working memory decline.

Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults 24.5±2.7 years and 12 older 72.5±3.2 years adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

Cerebral functional networks during sleep in young and older individuals

Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Age differences in ventromedial prefrontal cortex functional connectivity during socioemotional content processing

Self-relevance effects are often confounded by the presence of emotional content, rendering it difficult to determine how brain networks functionally connected to the ventromedial prefrontal cortex (vmPFC) are affected by the independent contributions of self-relevance and emotion. This difficulty is complicated by age-related changes in functional connectivity between the vmPFC and other default mode network regions, and regions typically associated with externally oriented networks. We asked groups of younger and older adults to imagine placing emotional and neutral objects in their home or a stranger's home. An age-invariant vmPFC cluster showed increased activation for self-relevant and emotional content processing. Functional connectivity analyses revealed age × self-relevance interactions in vmPFC connectivity with the anterior cingulate cortex. There were also age × emotion interactions in vmPFC functional connectivity with the anterior insula, orbitofrontal gyrus, inferior frontal gyrus, and supramarginal gyrus. Interactions occurred in regions with the greatest differences between the age groups, as revealed by conjunction analyses. Implications of the findings are discussed.

4386 Age-related Changes in the Functional Connectivity within the Default Mode Network

OBJECTIVES/GOALS: To evaluate whether the default mode network experiences age-related changes in functional connectivity and to identify these patterns of progression across seven decades of life. The overall goal is to evaluate whether quantifying these functional changes can serve as potential neurobiomarkers of aging for further quantitative genetic analyses. METHODS/STUDY POPULATION: Scans were performed at the RII on a 3T Siemens Trio scanner with an 8-channel head coil. Whole-brain, rsfMR imaging was performed using a gradient-echo EPI sequence sensitive to the BOLD effect (TE/TR = 30/3000 ms; flip angle = 90°; isotropic 1.72 mm2). Subjects were instructed to lie in dimmed light with their eyes open and try not to fall asleep. Image analysis was performed with FMRIB’s Software Library tools (www.fmrib.ox.ac.uk/fsl). Preprocessing of resting state data includes motion correction, brain extraction, spatial smoothing, and high-pass temporal filtering. Time series data was extracted from 9 DMN ROIs using FSL’s Featquery tool with 6mm radius spherical ROI masks created in Mango. After extraction, DMN connectivity was assess using structural equation modeling implemented in Amos 22.0 (IBM, Inc.). RESULTS/ANTICIPATED RESULTS: The exploratory SEM (eSEM) default mode network (DMN) model used consists of 9 regions of interest and 13 functional connectivity paths. The eSEM DMN model exhibited exceptional model fit to a primary resting state data set of 1169 subjects from the Genetics of Brain Structure project (1R01MH078111-01, David Glahn PI) with an RMSEA of 0.037. This model also had excellent model fit in 7 cohorts that were grouped by decade age (10s – RMSEA: 0.058, 20s – 0.051, 30s – 0.045, 40s – 0.048, 50s – 0.043, 60s – 0.035, 70s – 0.037). Analysis of the decade group-wise path coefficients identified 7 of the 13 paths (pC -> LMTG, pC -> PCC, PCC -> MPFG, PCC -> vACC, MPFG -> vACC, LIPL -> RIPL, LMTG -> RMTG) significantly negatively correlated with age-related changes. As early as the 1st decade of life, the functional connectivity within the DMN decreases. DISCUSSION/SIGNIFICANCE OF IMPACT: The DMN experiences progressive age-related decreases in connectivity, beginning in the first decade of life. Our results suggest that DMN path coefficients can serve as biomarkers of cognitive aging, which can then be used as quantitative traits for genetic analyses to identify genes associated with normal aging and age-related cognitive diseases.

Genetic and environmental influences on functional connectivity within and between canonical cortical resting-state networks throughout adolescent development in boys and girls

The human brain is active during rest and hierarchically organized into intrinsic functional networks. These functional networks are largely established early in development, with reports of a shift from a local to more distributed organization during childhood and adolescence. It remains unknown to what extent genetic and environmental influences on functional connectivity change throughout adolescent development. We measured functional connectivity within and between eight cortical networks in a longitudinal resting-state fMRI study of adolescent twins and their older siblings on two occasions (mean ages 13 and 18 years). We modelled the reliability for these inherently noisy and head-motion sensitive measurements by analyzing data from split-half sessions. Functional connectivity between resting-state networks decreased with age whereas functional connectivity within resting-state networks generally increased with age, independent of general cognitive functioning. Sex effects were sparse, with stronger functional connectivity in the default mode network for girls compared to boys, and stronger functional connectivity in the salience network for boys compared to girls. Heritability explained up to 53% of the variation in functional connectivity within and between resting-state networks, and common environment explained up to 33%. Genetic influences on functional connectivity remained stable during adolescent development. In conclusion, longitudinal age-related changes in functional connectivity within and between cortical resting-state networks are subtle but wide-spread throughout adolescence. Genes play a considerable role in explaining individual variation in functional connectivity with mostly stable influences throughout adolescence.

Age-related changes in neural mechanisms of prospective memory.

The capability to remember and execute intentions in the future - termed prospective memory (PM) - may be of special significance for older adults to enable successful completion of important activities of daily living. Despite the importance of this cognitive function, mixed findings have been obtained regarding age-related decline in PM, and, currently, there is limited understanding of potential contributing mechanisms. In the current study, older (N=41) and younger adults (N=47) underwent task-functional MRI during performance of PM conditions that encouraged either spontaneous retrieval (Focal) or sustained attentional monitoring (Non-focal) to detect PM targets. Older adults exhibited a reduction in PM-related sustained activity within the anterior prefrontal cortex (aPFC) and associated dorsal frontoparietal cognitive control network, due to an increase in non-specific sustained activation in (no-PM) control blocks (i.e., an age-related compensatory shift). Transient PM-trial specific activity was observed in both age groups within a ventral parietal memory network that included the precuneus. However, within a left posterior inferior parietal node of this network, transient PM-related activity was selectively reduced in older adults during the non-focal condition. These age differences in sustained and transient brain activity statistically mediated age-related declines in PM performance, and were potentially linked via age-related changes in functional connectivity between the aPFC and precuneus. Together, they support an account consistent with the Dual Mechanisms of Control framework, in which age-related PM declines are due to neural mechanisms that support proactive cognitive control processes, such as sustained attentional monitoring, while leaving reactive control mechanisms relatively spared.

Progressive Bidirectional Age-Related Changes in Default Mode Network Effective Connectivity across Six Decades.

The default mode network (DMN) is a set of regions that is tonically engaged during the resting state and exhibits task-related deactivation that is readily reproducible across a wide range of paradigms and modalities. The DMN has been implicated in numerous disorders of cognition and, in particular, in disorders exhibiting age-related cognitive decline. Despite these observations, investigations of the DMN in normal aging are scant. Here, we used blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) acquired during rest to investigate age-related changes in functional connectivity of the DMN in 120 healthy normal volunteers comprising six, 20-subject, decade cohorts (from 20–29 to 70–79). Structural equation modeling (SEM) was used to assess age-related changes in inter-regional connectivity within the DMN. SEM was applied both using a previously published, meta-analytically derived, node-and-edge model, and using exploratory modeling searching for connections that optimized model fit improvement. Although the two models were highly similar (only 3 of 13 paths differed), the sample demonstrated significantly better fit with the exploratory model. For this reason, the exploratory model was used to assess age-related changes across the decade cohorts. Progressive, highly significant changes in path weights were found in 8 (of 13) paths: four rising, and four falling (most changes were significant by the third or fourth decade). In all cases, rising paths and falling paths projected in pairs onto the same nodes, suggesting compensatory increases associated with age-related decreases. This study demonstrates that age-related changes in DMN physiology (inter-regional connectivity) are bidirectional, progressive, of early onset and part of normal aging.

Age-related changes in functional connectivity between young adulthood and late adulthood

The functional brain network in late adulthood has been found to show a significant difference from that in young adulthood using a variety of network metrics.

Age-related changes in functional network connectivity associated with high levels of verbal fluency performance

The relative preservation of receptive language abilities in older adults has been associated with adaptive changes in cerebral activation patterns, which have been suggested to be task-load dependent. However, the effects of aging and task demands on the functional integration of neural networks contributing to speech production abilities remain largely unexplored. In the present functional neuroimaging study, data-driven spatial independent component analysis and hierarchical measures of integration were used to explore age-related changes in functional connectivity among cortical areas contributing to semantic, orthographic, and automated word fluency tasks in healthy young and older adults, as well as to assess the effect of age and task demands on the functional integration of a verbal fluency network. The results showed that the functional integration of speech production networks decreases with age, while at the same time this has a marginal effect on behavioral outcomes in high-performing older adults. Moreover, a significant task demand/age interaction was found in functional connectivity within the anterior and posterior subnetworks of the verbal fluency network. These results suggest that local changes in functional integration among cortical areas supporting lexical speech production are modulated by age and task demands.

A Brain-Wide Study of Age-Related Changes in Functional Connectivity

Aging affects functional connectivity between brain areas, however, a complete picture of how aging affects integration of information within and between functional networks is missing. We used complex network measures, derived from a brain-wide graph, to provide a comprehensive overview of age-related changes in functional connectivity. Functional connectivity in young and older participants was assessed during resting-state fMRI. The results show that aging has a large impact, not only on connectivity within functional networks but also on connectivity between the different functional networks in the brain. Brain networks in the elderly showed decreased modularity (less distinct functional networks) and decreased local efficiency. Connectivity decreased with age within networks supporting higher level cognitive functions, that is, within the default mode, cingulo-opercular and fronto-parietal control networks. Conversely, no changes in connectivity within the somatomotor and visual networks, networks implicated in primary information processing, were observed. Connectivity between these networks even increased with age. A brain-wide analysis approach of functional connectivity in the aging brain thus seems fundamental in understanding how age affects integration of information.

Reduced specificity of functional connectivity in the aging brain during task performance

The importance of studying connectivity in the aging brain is increasingly recognized. Recent studies have shown that connectivity within the default mode network is reduced with age and have demonstrated a clear relation of these changes with cognitive functioning. However, research on age-related changes in other functional networks is sparse and mainly focused on prespecified functional networks. Using functional magnetic resonance imaging, we investigated age-related changes in functional connectivity during a visual oddball task in a range of functional networks. It was found that compared with young participants, elderly showed a decrease in connectivity between areas belonging to the same functional network. This was found in the default mode network and the somatomotor network. Moreover, in all identified networks, elderly showed increased connectivity between areas within these networks and areas belonging to different functional networks. Decreased connectivity within functional networks was related to poorer cognitive functioning in elderly. The results were interpreted as a decrease in the specificity of functional networks in older participants.

Using a self-organizing map algorithm to detect age-related changes in functional connectivity during rest in autism spectrum disorders

Healthy individuals show robust functional connectivity during rest, which is stronger in adults than in children. Connectivity occurs between the posterior and anterior portions of the default network, a group of structures active in the absence of a task, including the posterior cingulate cortex and the superior frontal gyrus. Previous studies found weaker posterior–anterior connectivity in the default network in adults and adolescents with autism spectrum disorders (ASD). However, these studies used small a priori regions of interest (“seeds”) to calculate connectivity. Since seed location for all participants was chosen based on controls' brains, these studies' analyses are more tailored to controls than individuals with ASD. An alternative is to use a data-driven approach, such as self-organizing maps (SOM), to create a reference for each participant to calculate connectivity. We used individualized resting-state clusters identified by an SOM algorithm to corroborate previous findings of weaker posterior–anterior connectivity in the ASD group and examine age-related changes in the ASD and control groups. Thirty-nine adolescents with ASD and 41 controls underwent a 10-minute, eyes-open, resting-state functional MRI scan. The SOM analysis revealed that adolescents with ASD versus controls have weaker connectivity between the posterior hub of the default network and the right superior frontal gyrus. Additionally, controls have larger increases in connectivity with age compared to the ASD group. These findings indicate that SOM is a complementary method for calculating connectivity in a clinical population. Additionally, adolescents with ASD have a different developmental trajectory of the default network compared to controls.

Subcortical functional connectivity and verbal episodic memory in healthy elderly—A resting state fMRI study

Imaging research into age-related changes in episodic memory has mainly focused on changes in cortical areas in the medial temporal lobe and the hippocampus. However, several lines of evidence indicate that subcortical structures such as the basal ganglia and the thalamus are also involved in episodic memory function. Recent studies have revealed age-related changes in functional connectivity between different brain areas, as measured by resting state fMRI. It remains to be shown whether functional connectivity measures in the basal ganglia and the thalamus can be associated with age-related changes in memory function. Here, we investigate this question by applying high model order spatial independent component analysis to resting state fMRI data in a cohort of 100 healthy elderly and relate connectivity features to verbal episodic memory function as assessed by the California Verbal Learning Test (CVLT). We identified five components that were located within different parts of the thalamus and the basal ganglia. Two of these components demonstrated negative correlations between their functional connectivity level and CVLT performance. We also found negative correlations between connectional strength within subcortical structures and CVLT performance. These results indicate a previously undocumented role for the putamen and the thalamus in verbal episodic memory function in aging.