Knowing left from right: asymmetric functional connectivity during resting state

Raemaekers, Mathijs; Schellekens, Wouter; Petridou, Natalia; Ramsey, Nick F.

doi:10.1007/s00429-017-1604-y

Cited by 51 publications

(67 citation statements)

References 48 publications

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“…This is in line with previous results that suggested an global integration role for PCUN as part of the default mode network [65]. In comparison, SF has more balanced (and weaker) input At the global level, previous work [66] showed asymmetries in the left and right within-365 hemisphere FC patterns. In contrasts, we observe for our data a symmetric communicability for the two hemispheres in Fig.…”

supporting

confidence: 92%

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

Gilson

Kouvaris

Deco

et al. 2018

Preprint

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Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often "static" despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity. 15 to evaluate global effects in recurrently-connected networks [9]. It has then been used to derive measures that describe the roles for nodes in networks [10] and to define a version for centrality in graphs [11,12]. In the context of neuroimaging, graph communicability has been applied to evaluate the contribution of SC topology in generating FC [13]. However, graph theory is often applied in an off-the-shelf manner and this type of approach is limited 20 for explaining the time-series nature of the fMRI measurements.In this context the present study aims to describe the fMRI-related functional associations between the nodes in the brain network, also known as regions of interest (ROIs), while properly taking time into account. We follow recent works that employed dynamic models of the brain activity to link SC and FC [14]. A great variety of network designs has been explored 25 to combine experimental data in various levels of detail [15,16,17,18,19, 20]. These dynamic models typically involve a ...

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supporting

confidence: 92%

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

Gilson

Kouvaris

Deco

et al. 2018

Preprint

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“…(Piai, 2019) fMRI experiments show asymmetric left lateralization patterns for language function in healthy subjects and thus weaken a possible hypothesis of an a priori bihemispheric configuration. (Raemaekers et al, 2018) In contrast to the rTMS ERs analysis, the SVM results do not show a proportional increase of ER in all areas, but rather an increment in specific right frontal and left parietal areas, such as Frontal_Inf_Tri_R, SupraMarginal_L, Parietal_Inf_L. This result directs us towards an interpretation of a functional shift, indicating a functional relevant involvement of the right frontal area in relation to aphasia.…”

Section: Discussioncontrasting

confidence: 57%

Transcranial magnetic stimulation language mapping analysis revisited: Machine learning classification of 90 patients reveals distinct reorganization pattern in aphasic patients

Wang

Fekonja

Dreyer

et al. 2020

Preprint

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Repetitive TMS (rTMS) allows to non-invasively and transiently disrupt local neuronal functioning. Its potential for mapping of language function is currently explored. Given the inter- individual heterogeneity of tumor impact on the language network and resulting rTMS derived functional mapping, we propose to use machine learning strategies to classify potential patterns of functional reorganization. We retrospectively included 90 patients with left perisylvian glioma tumors, world health organization (WHO) grade II-IV, affecting the language network. All patients underwent navigated rTMS language mappings. The severity of aphasia was assessed preoperatively using the Berlin Aphasia Score (BAS), which is adapted to the Aachener Aphasia Test (AAT). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each cortical area by automated anatomical labeling parcellation (AAL) and used support vector machine (SVM) as a classifier for significant areas in relation to aphasia. 29 of 90 (32.2%) patients suffered from aphasia. Univariate analysis revealed 11 perisylvian AVOIs' ERs (eight left, three right hemispheric) that were significantly higher in the aphasic than non-aphasic group (p < 0.05), depicting a broad, bihemispheric language network. After feeding the significant AVOIs into the SVM model, it showed that additional to age (w = 2.95), the ERs of right Frontal_Inf_Tri (w = 2.06) and left SupraMarginal (w = 2.05) and Parietal_Inf (w= 1.80) contributed more than other features to the model. The model's sensitivity was 89.7%, the specificity was 82.0%, the overall accuracy was 81.1% and AUC was 88.7%. Our results demonstrate an increased vulnerability of the right inferior frontal gyrus to rTMS in patients suffering from aphasia due to left perisylvian gliomas. This confirms a functional relevant involvement of the right frontal area in relation to aphasia. While age as a feature improved our SVM model the most, the tumor location feature didn't affect the SVM model. This finding indicates that general tumor induced network disconnection is relevant to aphasia and not necessarily related to specific lesion locations. Additionally, our results emphasize the decreasing potential for neuroplasticity with age.

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“…Reports more closely related to the approach used herein have compared resting-state connectivity and hemispheric activation asymmetries obtained during language production in healthy individuals (Joliot et al 2016) and epileptic patients (Doucet et al 2014), or during story listening (Raemaekers et al 2018). Other investigations have found correlations between the task-induced and intrinsic connectivity asymmetries measured in selected sets of hROIs (Liu et al 2009), such as the set of regions involved in a semantic decision task (Wang et al 2014).…”

Section: Discussionmentioning

confidence: 99%

A SENtence Supramodal Areas AtlaS (SENSAAS) based on multiple task-induced activation mapping and graph analysis of intrinsic connectivity in 144 healthy right-handers

et al. 2018

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We herein propose an atlas of 32 sentence-related areas based on a 3-step method combining the analysis of activation and asymmetry during multiple language tasks with hierarchical clustering of resting-state connectivity and graph analyses. 144 healthy right-handers performed fMRI runs based on language production, reading and listening, both with sentences and lists of over-learned words. Sentence minus word-list BOLD contrast and left-minus-right BOLD asymmetry for each task were computed in pairs of homotopic regions of interest (hROIs) from the AICHA atlas. Thirty-two hROIs were identified that were conjointly activated and leftward asymmetrical in each of the three language contrasts. Analysis of resting-state temporal correlations of BOLD variations between these 32 hROIs allowed the segregation of a core network, SENT_CORE including 18 hROIs. Resting-state graph analysis applied to SENT_CORE hROIs revealed that the pars triangularis of the inferior frontal gyrus and the superior temporal sulcus were hubs based on their degree centrality (DC), betweenness, and participation values corresponding to epicentres of sentence processing. Positive correlations between DC and BOLD activation values for SENT_CORE hROIs were observed across individuals and across regions regardless of the task: the more a SENT_CORE area is connected at rest the stronger it is activated during sentence processing. DC measurements in SENT_CORE may thus be a valuable index for the evaluation of inter-individual variations in language areas functional activity in relation to anatomical or clinical patterns in large populations. SENSAAS (SENtence Supramodal Areas AtlaS), comprising the 32 supramodal sentence areas, including SENT_CORE network, can be downloaded at http://www.gin.cnrs.fr/en/tools/.Electronic supplementary materialThe online version of this article (10.1007/s00429-018-1810-2) contains supplementary material, which is available to authorized users.

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Knowing left from right: asymmetric functional connectivity during resting state

Cited by 51 publications

References 48 publications

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability

Transcranial magnetic stimulation language mapping analysis revisited: Machine learning classification of 90 patients reveals distinct reorganization pattern in aphasic patients

A SENtence Supramodal Areas AtlaS (SENSAAS) based on multiple task-induced activation mapping and graph analysis of intrinsic connectivity in 144 healthy right-handers

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