Recent resting-state functional connectivity fMRI (RS-fcMRI) research has demonstrated that head motion during fMRI acquisition systematically influences connectivity estimates despite bandpass ...filtering and nuisance regression, which are intended to reduce such nuisance variability. We provide evidence that the effects of head motion and other nuisance signals are poorly controlled when the fMRI time series are bandpass-filtered but the regressors are unfiltered, resulting in the inadvertent reintroduction of nuisance-related variation into frequencies previously suppressed by the bandpass filter, as well as suboptimal correction for noise signals in the frequencies of interest. This is important because many RS-fcMRI studies, including some focusing on motion-related artifacts, have applied this approach. In two cohorts of individuals (n=117 and 22) who completed resting-state fMRI scans, we found that the bandpass–regress approach consistently overestimated functional connectivity across the brain, typically on the order of r=.10–.35, relative to a simultaneous bandpass filtering and nuisance regression approach. Inflated correlations under the bandpass–regress approach were associated with head motion and cardiac artifacts. Furthermore, distance-related differences in the association of head motion and connectivity estimates were much weaker for the simultaneous filtering approach. We recommend that future RS-fcMRI studies ensure that the frequencies of nuisance regressors and fMRI data match prior to nuisance regression, and we advocate a simultaneous bandpass filtering and nuisance regression strategy that better controls nuisance-related variability.
•Bandpass filtering and nuisance regression are intended to reduce noise in RS-fMRI.•When RS-fMRI data are filtered, but regressors are not, noise is poorly controlled.•In addition, this approach reintroduces synchronous noise into RS-fMRI data.•Such noise leads to systematically inflated estimates of functional connectivity.•Simultaneous bandpass filtering and regression eliminates this source of bias.
Cognitive control, which continues to mature throughout adolescence, is supported by the ability for well-defined organized brain networks to flexibly integrate information. However, the development ...of intrinsic brain network organization and its relationship to observed improvements in cognitive control are not well understood. In the present study, we used resting state functional magnetic resonance imaging (RS-fMRI), graph theory, the antisaccade task, and rigorous head motion control to characterize and relate developmental changes in network organization, connectivity strength, and integration to inhibitory control development. Subjects were 192 10-26-y-olds who were imaged during 5 min of rest. In contrast to initial studies, our results indicate that network organization is stable throughout adolescence. However, cross-network integration, predominantly of the cingulo-opercular/salience network, increased with age. Importantly, this increased integration of the cingulo-opercular/salience network significantly moderated the robust effect of age on the latency to initiate a correct inhibitory control response. These results provide compelling evidence that the transition to adult-level inhibitory control is dependent upon the refinement and strengthening of integration between specialized networks. Our findings support a novel, two-stage model of neural development, in which networks stabilize prior to adolescence and subsequently increase their integration to support the cross-domain incorporation of information processing critical for mature cognitive control.
is a package for
that facilitates complex latent variable analyses in
involving comparisons among many models and parameters. More specifically,
provides tools to accomplish three objectives: to ...create and manage
syntax for groups of related models; to automate the estimation of many models; and to extract, aggregate, and compare fit statistics, parameter estimates, and ancillary model outputs. We provide an introduction to the package using applied examples including a large-scale simulation study. By reducing the effort required for large-scale studies, a broad goal of
is to support methodological developments in structural equation modeling using
.
White matter (WM) continues to mature through adolescence in parallel with gains in cognitive ability. To date, developmental changes in human WM microstructure have been inferred using analyses of ...cross-sectional or two time-point follow-up studies, limiting our understanding of individual developmental trajectories. The aims of the present longitudinal study were to characterize the timing of WM growth and investigate how sex and behavior are associated with different developmental trajectories. We utilized diffusion tensor imaging (DTI) in 128 individuals aged 8–28, who received annual scans for up to 5years and completed motor and cognitive tasks. Flexible nonlinear growth curves indicated a hierarchical pattern of WM development. By late childhood, posterior cortical–subcortical connections were similar to adults. During adolescence, WM microstructure reached adult levels, including frontocortical, frontosubcortical and cerebellar connections. Later to mature in adulthood were major corticolimbic association tracts and connections at terminal gray matter sites in cortical and basal ganglia regions. These patterns may reflect adolescent maturation of frontal connectivity supporting cognitive abilities, particularly the protracted refinement of corticolimbic connectivity underlying cognition–emotion interactions. Sex and behavior also played a large role. Males showed continuous WM growth from childhood through early adulthood, whereas females mainly showed growth during mid-adolescence. Further, earlier WM growth in adolescence was associated with faster and more efficient responding and better inhibitory control whereas later growth in adulthood was associated with poorer performance, suggesting that the timing of WM growth is important for cognitive development.
•We find hierarchical white matter (WM) development in a longitudinal sample.•Maturation of most frontal connectivity in adolescence supports cognitive function.•Late maturation of corticolimbic connectivity supports socioemotional interactions.•Males showed WM growth from childhood to adulthood, females mainly in mid-adolescence.•Delayed maturation of WM was associated with poorer cognitive performance.
Functional hubs are brain regions that play a crucial role in facilitating communication among parallel, distributed brain networks. The developmental emergence and stability of hubs, however, is not ...well understood. The current study used measures of network topology drawn from graph theory to investigate the development of functional hubs in 99 participants, 10-20 years of age. We found that hub architecture was evident in late childhood and was stable from adolescence to early adulthood. Connectivity between hub and non-hub ("spoke") regions, however, changed with development. From childhood to adolescence, the strength of connections between frontal hubs and cortical and subcortical spoke regions increased. From adolescence to adulthood, hub-spoke connections with frontal hubs were stable, whereas connectivity between cerebellar hubs and cortical spoke regions increased. Our findings suggest that a developmentally stable functional hub architecture provides the foundation of information flow in the brain, whereas connections between hubs and spokes continue to develop, possibly supporting mature cognitive function.
Acute risk of death by suicide manifests in heightened suicidal ideation in certain contexts and time periods. These increases are thought to emerge from complex and mutually reinforcing ...relationships between dispositional vulnerability factors and individually suicidogenic short-term stressors. Together, these processes inform clinical safety planning and our therapeutic tools accommodate a reasonable degree of idiosyncrasy when we individualize interventions. Unraveling these multifaceted factors and processes on a quantitative level, however, requires estimation frameworks capable of representing idiosyncrasies relevant to intervention and psychotherapy. Using, data from a 21-day ambulatory assessment protocol that included six random prompts per day, we developed personalized (i.e., idiographic) models of interacting risk factors and suicidal ideation via Group Iterative Multiple Model Estimation (GIMME) in a sample of people diagnosed with borderline personality disorder (N = 95) stratified for a history of high lethality suicide attempts. Our models revealed high levels of heterogeneity in state risk factors related to suicidal ideation, with no features shared among the majority of participants or even among relatively homogenous clusters of participants (i.e., empirically derived subgroups). We discuss steps toward clinical implementation of personalized models, which can eventually capture suicidogenic changes in proximal risk factors and inform safety planning and interventions.
•Acute suicide risk results from volatile, contextualized dynamic processes.•Idiographic models depict within-person dynamics of suicide risk.•Models reveal high levels of heterogeneity across individuals.•More work is needed before personalized models can be applied in clinical settings.
Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as ...Alzheimer’s disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
Abstract Background Connectivity between the amygdala and ventromedial prefrontal cortex (vmPFC) is compromised in multiple psychiatric disorders, many of which emerge during adolescence. To identify ...to what extent the deviations in amygdala-vmPFC maturation contribute to the onset of psychiatric disorders, it is essential to characterize amygdala-vmPFC connectivity changes during typical development. Methods Using an accelerated cohort longitudinal design (1–3 time points, 10–25 years old, n = 246), we characterized developmental changes of the amygdala-vmPFC subregion functional and structural connectivity using resting-state functional magnetic resonance imaging and diffusion-weighted imaging. Results Functional connectivity between the centromedial amygdala and rostral anterior cingulate cortex (rACC), anterior vmPFC, and subgenual cingulate significantly decreased from late childhood to early adulthood in male and female subjects. Age-associated decreases were also observed between the basolateral amygdala and the rACC. Importantly, these findings were replicated in a separate cohort (10–22 years old, n = 327). Similarly, structural connectivity, as measured by quantitative anisotropy, significantly decreased with age in the same regions. Functional connectivity between the centromedial amygdala and the rACC was associated with structural connectivity in these same regions during early adulthood (22–25 years old). Finally, a novel time-varying coefficient analysis showed that increased centromedial amygdala-rACC functional connectivity was associated with greater anxiety and depression symptoms during early adulthood, while increased structural connectivity in centromedial amygdala-anterior vmPFC white matter was associated with greater anxiety/depression during late childhood. Conclusions Specific developmental periods of functional and structural connectivity between the amygdala and the prefrontal systems may contribute to the emergence of anxiety and depressive symptoms and may play a critical role in the emergence of psychiatric disorders in adolescence.
Working memory (WM), the ability to hold information on-line to guide planned behavior, improves through adolescence in parallel with continued maturation of critical brain systems supporting ...cognitive control. Initial developmental neuroimaging studies with one or two timepoints have provided important though varied results limiting our understanding of which and how neural systems change during this transition into mature WM. In this study, we leverage functional magnetic resonance imaging (fMRI) longitudinal data spanning up to 9 years in 129 normally developing individuals to identify which systems demonstrate growth changes that accompany improvements in WM performance. We used a memory guided saccade task that allowed us to probe encoding, pure maintenance, and retrieval neural processes of WM. Consistent with prior research, we found that WM performance continued to improve into the early 20's. fMRI region of interest (ROI) analyses revealed developmental (1) increases in sensorimotor-related (encoding/retrieval) activity in visual cortex from childhood through early adulthood that were associated with WM accuracy and (2) decreases in sustained (maintenance) activity in executive regions from childhood through mid-adolescence that were associated with response latency in childhood and early adolescence. Together these results provide compelling evidence that underlying the maturation of WM is a transition from reliance on executive systems to specialized regions related to the domain of mnemonic requirements of the task leading to optimal performance.
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•Protracted development of working memory and underlying neural processes into 20's.•Increases in encoding/retrieval activity in visual cortex.•Decreases in maintenance activity in prefrontal/subcortical regions.•Association of precision error and encoding activity reflecting mnemonic processing.•Association of latency and maintenance activity reflecting executive processing.
Understanding patterns of symptom co-occurrence is one of the most difficult challenges in psychopathology research. Do symptoms co-occur because of a latent factor, or might they directly and ...causally influence one another? Motivated by such questions, there has been a surge of interest in network analyses that emphasize the putatively direct role symptoms play in influencing each other. In this critical paper, we highlight conceptual and statistical problems with using centrality measures in cross-sectional networks. In particular, common network analyses assume that there are no unmodeled latent variables that confound symptom co-occurrence. The traditions of clinical taxonomy and test development in psychometric theory, however, greatly increase the possibility that latent variables exist in symptom data. In simulations that include latent variables, we demonstrate that closeness and betweenness are vulnerable to spurious covariance among symptoms that connect subgraphs (e.g., diagnoses). We further show that strength is redundant with factor loading in several cases. Finally, if a symptom reflects multiple latent causes, centrality metrics reflect a weighted combination, undermining their interpretability in empirical data. Our results suggest that it is essential for network psychometric approaches to examine the evidence for latent variables prior to analyzing or interpreting patterns at the symptom level. Failing to do so risks identifying spurious relationships or failing to detect causally important effects. Altogether, we argue that centrality measures do not provide solid ground for understanding the structure of psychopathology when latent confounding exists.
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