Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.

Tracking Whole-Brain Connectivity Dynamics in the Resting State

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

The availability of neuroimaging technology has spurred a marked increase in the human cognitive neuroscience literature, including the study of cognitive ageing. Although there is a growing consensus that the ageing brain retains considerable plasticity of function, currently measured primarily by means of functional MRI, it is less clear how age differences in brain activity relate to cognitive performance. The field is also hampered by the complexity of the ageing process itself and the large number of factors that are influenced by age. In this Review, current trends and unresolved issues in the cognitive neuroscience of ageing are discussed.

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The cognitive neuroscience of ageing

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

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Behavioral interpretations of intrinsic connectivity networks

Treatments have been developed and tested to successfully reduce the symptoms and disabilities of many mental illnesses. Unfortunately, people distressed by these illnesses often do not seek out services or choose to fully engage in them. One factor that impedes care seeking and undermines the service system is mental illness stigma. In this article, we review the complex elements of stigma in order to understand its impact on participating in care. We then summarize public policy considerations in seeking to tackle stigma in order to improve treatment engagement. Stigma is a complex construct that includes public, self, and structural components. It directly affects people with mental illness, as well as their support system, provider network, and community resources. The effects of stigma are moderated by knowledge of mental illness and cultural relevance. Understanding stigma is central to reducing its negative impact on care seeking and treatment engagement. Separate strategies have evolved for counteracting the effects of public, self, and structural stigma. Programs for mental health providers may be especially fruitful for promoting care engagement. Mental health literacy, cultural competence, and family engagement campaigns also mitigate stigma's adverse impact on care seeking. Policy change is essential to overcome the structural stigma that undermines government agendas meant to promote mental health care. Implications for expanding the research program on the connection between stigma and care seeking are discussed.

The Impact of Mental Illness Stigma on Seeking and Participating in Mental Health Care

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.

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A Baseline for the Multivariate Comparison of Resting-State Networks

The President's New Freedom Commission (2003) called for the elimination of service disparities in rural areas by addressing barriers in access, acceptability and availability. Barriers to accessibility include unemployment, poverty, inability to pay for services, inadequate insurance, transportation, and lack of knowledge of the need for and availability of care (Fortney et al., 2001; Merwin, Snyder, & Katz, 2006). Barriers to acceptability include stigma, confidentiality concerns, attitudes of self-reliance, and lack of cultural competence among providers (Willging, Waitzkin, & Nicdao, 2008). This article focuses on the third barrier--availability of services related to rural disparities in workforce capacity. General disparities between rural and urban workforce capacities are well documented (Fortney et al., 2001; Merwin, Snyder, & Katz, 2006; Ellis et al., 2009; Konrad et al., 2009; Thomas et al., 2009). Disparities between different rural and urban environments and the impact of these disparities on service planning and program development remain an issue. Language: en

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Steven Adelsheim

Papers

A Baseline for the Multivariate Comparison of Resting-State Networks

Workforce Capacity for Reducing Rural Disparities in Public Mental Health Services for Adults with Severe Mental Illness.

Developing school mental health in a rural state. The New Mexico School Mental Health Initiative.