Whole brain functional connectivity predicted by indirect structural connections

Røge, Rasmus; Ambrosen, Karen S; Albers, Kristoffer Jon; Eriksen, Casper T.; Liptrot, Matthew G.; Schmidt, Mikkel N.; Madsen, Kristoffer Hougaard; Mørup, Morten

doi:10.1109/prni.2017.7981496

Cited by 7 publications

(12 citation statements)

References 17 publications

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“…They concluded that functional connectivity in the brain may be driven by cortico-cortical WM connections and that functional patterns can be retained in the presence of indirect structural connections (O’Reilly et al, 2013). More recently, the incorporation of indirect structural pathways have also been shown to be more robust predictors of functional connectivity (Røge et al, 2017). Another possible reason for the discrepancy shown between functional and structural connectivity is simply that the analysis methods used in this study may not be sensitive enough to tease out all functional-structural relationships between GM and WM (further discussed in Section 4.3).…”

Section: Discussionmentioning

confidence: 99%

Resting-state white matter-cortical connectivity in non-human primate brain

Wang

et al. 2019

NeuroImage

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Numerous studies have used functional magnetic resonance imaging (fMRI) to characterize functional connectivity between cortical regions by analyzing correlations in blood oxygenation level dependent (BOLD) signals in a resting state. However, to date, there have been only a handful of studies reporting resting state BOLD signals in white matter. Nonetheless, a growing number of reports has emerged in recent years suggesting white matter BOLD signals can be reliably detected, though their biophysical origins remain unclear. Moreover, recent studies have identified robust correlations in a resting state between signals from cortex and specific white matter tracts. In order to further validate and interpret these findings, we studied a non-human primate model to investigate resting-state connectivity patterns between parcellated cortical volumes and specific white matter bundles. Our results show that resting-state connectivity patterns between white and gray matter structures are not randomly distributed but share notable similarities with diffusion- and histology-derived anatomic connectivities. This suggests that resting-state BOLD correlations between white matter fiber tracts and the gray matter regions to which they connect are directly related to the anatomic arrangement and density of WM fibers. We also measured how different levels of baseline neural activity, induced by varying levels of anesthesia, modulate these patterns. As anesthesia levels were raised, we observed weakened correlation coefficients between specific white matter tracts and gray matter regions while key features of the connectivity pattern remained similar. Overall, results from this study provide further evidence that neural activity is detectable by BOLD fMRI in both gray and white matter throughout the resting brain. The combined use of gray and white matter functional connectivity could also offer refined full-scale functional parcellation of the entire brain to characterize its functional architecture.

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Section: Discussionmentioning

confidence: 99%

Resting-state white matter-cortical connectivity in non-human primate brain

Wang

et al. 2019

NeuroImage

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“…To fully characterize the functional relationship between A and C , however, we may wish to consider other nodes’ time series that can make indirect functional connections, or alternate paths, between them. Previous research has shown that, although direct structural connections alone do not predict functional connectivity well, incorporating indirect structural paths can improve predictions of functional connectivity (Deligianni et al, ; Røge et al, ). It is possible that, analogously, predictions of behavior can be improved by taking into account nodes on indirect functional paths along with direct functional paths.…”

Section: Methodsmentioning

confidence: 99%

An information network flow approach for measuring functional connectivity and predicting behavior

et al. 2019

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Introduction Connectome‐based predictive modeling (CPM) is a recently developed machine‐learning‐based framework to predict individual differences in behavior from functional brain connectivity (FC). In these models, FC was operationalized as Pearson's correlation between brain regions’ fMRI time courses. However, Pearson's correlation is limited since it only captures linear relationships. We developed a more generalized metric of FC based on information flow. This measure represents FC by abstracting the brain as a flow network of nodes that send bits of information to each other, where bits are quantified through an information theory statistic called transfer entropy. Methods With a sample of individuals performing a sustained attention task and resting during functional magnetic resonance imaging (fMRI) ( n = 25), we use the CPM framework to build machine‐learning models that predict attention from FC patterns measured with information flow. Models trained on n − 1 participants’ task‐based patterns were applied to an unseen individual's resting‐state pattern to predict task performance. For further validation, we applied our model to two independent datasets that included resting‐state fMRI data and a measure of attention (Attention Network Task performance [ n = 41] and stop‐signal task performance [ n = 72]). Results Our model significantly predicted individual differences in attention task performance across three different datasets. Conclusions Information flow may be a useful complement to Pearson's correlation as a measure of FC because of its advantages for nonlinear analysis and network structure characterization.

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“…Honey and colleagues 36 reasoned that this unambiguity might be explained by indirect connections, mediated by a third brain region. As well, Roge and colleagues 37 and Deligianni and colleagues 38 demonstrated that functional connections can be predicted by indirect struc-tural connections up to the second order (i.e., indirect pathways with 2 internodes). In line with these findings, we investigated structural pathways between the left dlPFC and the cingulate cortex with up to 2 internodes.…”

Section: Discussionmentioning

confidence: 89%

“…Validation of this measure in replication studies is therefore highly recommended. In accordance with Roge and colleagues 37 and Deligianni and colleagues, 38 we studied pathways up to 2 internodes. Given that more internodes results in more potential pathways, the direction of neuronal communication (orthodromic versus antidromic) cannot be derived from dMRI data.…”

Section: Limitationsmentioning

confidence: 92%

Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder

Klooster

Vos

Caeyenberghs

et al. 2020

JPN

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Background: Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD), but its clinical efficacy remains rather modest. One reason for this could be that the propagation of rTMS effects via structural connections from the stimulated area to deeper brain structures (such as the cingulate cortices) is suboptimal. Methods: We investigated whether structural connectivity-derived from diffusion MRI data-could serve as a biomarker to predict treatment response. We hypothesized that stronger structural connections between the patient-specific stimulation position in the left dorsolateral prefrontal cortex (dlPFC) and the cingulate cortices would predict better clinical outcomes. We applied accelerated intermittent theta burst stimulation (aiTBS) to the left dlPFC in 40 patients with MDD. We correlated baseline structural connectivity, quantified using various metrics (fractional anisotropy, mean diffusivity, tract density, tract volume and number of tracts), with changes in depression severity scores after aiTBS. Results: Exploratory results (p < 0.05) showed that structural connectivity between the patient-specific stimulation site and the caudal and posterior parts of the cingulate cortex had predictive potential for clinical response to aiTBS. Limitations: We used the diffusion tensor to perform tractography. A main limitation was that multiple fibre directions within voxels could not be resolved, which might have led to missing connections in some patients. Conclusion: Stronger structural frontocingular connections may be of essence to optimally benefit from left dlPFC rTMS treatment in MDD. Even though the results are promising, further investigation with larger numbers of patients, more advanced tractography algorithms and classic daily rTMS treatment paradigms is warranted.

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Whole brain functional connectivity predicted by indirect structural connections

Cited by 7 publications

References 17 publications

Resting-state white matter-cortical connectivity in non-human primate brain

Resting-state white matter-cortical connectivity in non-human primate brain

An information network flow approach for measuring functional connectivity and predicting behavior

Indirect frontocingulate structural connectivity predicts clinical response to accelerated rTMS in major depressive disorder

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