The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain

Thompson, William Hedley; Fransson, Peter

doi:10.1016/j.neuroimage.2015.07.022

Cited by 109 publications

(86 citation statements)

References 89 publications

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“…Such differences are in line with known alterations of the frequencies in neuronal firing rates in those regions33. Furthermore, dependency of network topology on frequency34 was recently reported. The dependency of functional connectivity on phases (besides a phase of π) however, was not introduced before.…”

supporting

confidence: 84%

Frequency-phase analysis of resting-state functional MRI

Goelman

Dan

Růžička

et al. 2017

Sci Rep

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We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience.

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supporting

confidence: 84%

Frequency-phase analysis of resting-state functional MRI

Goelman

Dan

Růžička

et al. 2017

Sci Rep

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“…67 However, spatial variance might be less of an issue in twin studies, given that brain structural aspects such as area-specific brain volumes are strongly heritable. 68 Further, analyzing what is commonly referred to as 'static' RS connectivity, that is, temporal correlations across the entire scan, this study is not taking the dynamic alterations in RS connectivity into account that have recently been demonstrated to be informative in general 62,69 and specifically informative in ASD. 70 Testing differences of dynamic RS connectivity patterns in relation to autistic traits in twins would therefore be an interesting avenue for future research.…”

Section: Discussionmentioning

confidence: 99%

Alterations in resting state connectivity along the autism trait continuum: a twin study

et al. 2017

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Autism spectrum disorder (ASD) has been found to be associated with alterations in resting state (RS) functional connectivity, including areas forming the default mode network (DMN) and salience network (SN). However, insufficient control for confounding genetic and environmental influences and other methodological issues limit the generalizability of previous findings. Moreover, it has been hypothesized that ASD might be marked by early hyper-connectivity followed by later hypo-connectivity. To date, only a few studies have explicitly tested age-related influences on RS connectivity alterations in ASD. Using a within-twin pair design (N = 150 twins; 8-23 years), we examined altered RS connectivity between core regions of the DMN and SN in relation to autistic trait severity and age in a sample of monozygotic (MZ) and dizygotic (DZ) twins showing typical development, ASD or other neurodevelopmental conditions. Connectivity between core regions of the SN was stronger in twins with higher autistic traits compared to their co-twins. This effect was significant both in the total sample and in MZ twins alone, highlighting the effect of non-shared environmental factors on the link between SN-connectivity and autistic traits. While this link was strongest in children, we did not identify differences between age groups for the SN. In contrast, connectivity between core hubs of the DMN was negatively correlated with autistic traits in adolescents and showed a similar trend in adults but not in children. The results support hypotheses of age-dependent altered RS connectivity in ASD, making altered SN and DMN connectivity promising candidate biomarkers for ASD.Molecular Psychiatry advance online publication, 1 August 2017; doi:10.1038/mp.2017.160 INTRODUCTION Altered patterns of brain connectivity have been suggested as a key neurobiological correlate of the behavioral characteristics of autism spectrum disorder (ASD). However, as findings are inconsistent, the precise brain connectivity characteristics of ASD remain unclear. A large number of resting-state (RS) functional magnetic resonance imaging (MRI) brain connectivity studies have shown a complex pattern of both hypo-and hyper-connectivity in ASD.1-3 Notably, a large multi-site classification study including ASD and typically developing individuals (N = 252, 6-36 years) showed that of the top 100 biomarkers of brain connectivity achieving together 90.8% accuracy, 45% were related to hyperconnectivity and the remaining 55% to hypo-connectivity. 4 It is likely that the observed inconsistencies in classical brain connectivity investigations are limited by methodological issues, such as insufficient control for head motion or the inclusion of global signal regression which can modulate or even invert the direction of functional connectivity differences. [5][6][7] Moreover, gender has recently been shown to modulate the direction of connectivity differences in ASD. 8 In contrast, another study found default mode network (DMN) hypo-connectivity in both genders.

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“…To address the multiple comparisons issue, we used the network-based statistic (NBS) to identify network components that have ROI-ROI pairs with greater or lesser functional connectivity as a function of age or fitness and physical activity. The NBS algorithm has been well-validated (Zalesky et al, 2012; Zalesky et al, 2010) and widely applied (Bai et al, 2012; Cocchi et al, 2012; Thompson and Fransson, 2015; Wang et al, 2013) as a tool to provide both statistical power and protection against multiple comparisons when examining effects proposed to occur across distributed sub-systems of network nodes. Based on the literature, there is evidence that there are important sub-networks or sub-systems within the broader cortical systems that are vulnerable to normal aging (e.g., Andrews-Hanna et al, 2007; Damoiseaux et al, 2008).…”

Section: Methodsmentioning

confidence: 99%

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

et al. 2016

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Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the Default Mode Network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks.

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The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain

Cited by 109 publications

References 89 publications

Frequency-phase analysis of resting-state functional MRI

Frequency-phase analysis of resting-state functional MRI

Alterations in resting state connectivity along the autism trait continuum: a twin study

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

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