Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery

Philips, Gavin R; Daly, Janis J.; Prı́ncipe, José C.

doi:10.1186/s12984-017-0277-3

Cited by 60 publications

(62 citation statements)

References 96 publications

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“…Thresholds can be selected based on the statistics of the data distribution or by taking into account the sparsity of the resulting matrix (Philips et al, 2017). Here, we adopt the most widely used method where a proportional threshold is imposed on all the links within the network.…”

Section: Thresholdingmentioning

confidence: 99%

Emotional valence modulates the topology of the parent-infant inter-brain network

Santamaría¹,

Noreika²,

Georgieva³

et al. 2019

Preprint

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Emotional communication between parents and children is crucial during early life, yet little is known about its neural underpinnings. Here, we adopt a dual-brain connectivity approach to assess how emotional valence modulates the parent-infant neural network.Fifteen mothers modelled positive and negative emotions toward pairs of objects during social interaction with their infants (aged 10.3 months) whilst their neural activity was concurrently measured using dual-EEG. Intra-brain and inter-brain network connectivity in the 6-9 Hz (infant Alpha) range was computed during maternal expression of positive and negative emotions using directed (partial directed coherence) and non-directed (phaselocking value) connectivity metrics. Graph theoretical metrics were used to quantify differences in network topology as a function of emotional valence. Inter-brain network indices (Density, Strength and Divisibility) consistently revealed that the integration of parents' and childrens' neural processes was significantly stronger during maternal demonstrations of positive than negative emotions. Further, directed inter-brain metrics indicated that mother-to-infant directional influences were stronger during the expression of positive than negative emotions. These results suggest that the parent-infant inter-brain network is modulated by the emotional quality and tone of dyadic social interactions, and that inter-brain graph metrics may be successfully applied to examine these changes in interpersonal network topology. (200/200 words) Keywords:EEG hyperscanning, network connectivity, graph theory, emotional expression, mother-infant interaction atypical patterns of EEG asymmetry, commonly showing higher right frontal EEG activity than controls (Gotlib et al., 1998). Recent research has also started to examine intraindividual network topology during emotion processing using graph-theoretic measures. For example, a recent study with adults showed that EEG graph-theoretic features performed better than traditionally used EEG features (such as spectral power and asymmetry) on the automatic classification of affective neural states (Gupta et al. , 2016).Behavioral and neuroimaging studies into early development suggest that the neural architecture for the detection and prioritized processing of emotional expressions, such as fear, emerges sometime during the first year of life (Hoehl, 2013; Hoehl et al.

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Section: Thresholdingmentioning

confidence: 99%

Emotional valence modulates the topology of the parent-infant inter-brain network

Santamaría¹,

Noreika²,

Georgieva³

et al. 2019

Preprint

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“…15 In particular, the thalamus-which receives direct input from the NTS-and the limbic system have both been implicated in the therapeutic mechanism of VNS. [19][20][21] Given converging evidence of a neuronal basis for VNS responsiveness and increasing application of machine learning to predict outcomes on an individual patient level, we perform the first multimodal connectomic profiling of individuals undergoing VNS. 18 In other conditions, brain connectomic profiling has been successfully used to predict poststroke motor recovery and response to psychiatric pharmacotherapy.…”

mentioning

confidence: 99%

“…18 In other conditions, brain connectomic profiling has been successfully used to predict poststroke motor recovery and response to psychiatric pharmacotherapy. [19][20][21] Given converging evidence of a neuronal basis for VNS responsiveness and increasing application of machine learning to predict outcomes on an individual patient level, we perform the first multimodal connectomic profiling of individuals undergoing VNS. In the current report, we perform combined structural and functional connectivity analyses to identify connectomic differences between children who do and do not respond to VNS.…”

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confidence: 99%

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation

Mithani

Mikhail

Morgan

et al. 2019

Annals of Neurology

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Objective: Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no preoperative means of identifying good candidates. This study aimed to predict VNS response using structural and functional connectomic profiling. Methods: Fifty-six children, comprising discovery (n = 38) and validation (n = 18) cohorts, were recruited from 3 separate institutions. Diffusion tensor imaging was used to identify group differences in white matter microstructure, which in turn informed beamforming of resting-state magnetoencephalography recordings. The results were used to generate a support vector machine learning classifier, which was independently validated. This algorithm was compared to a second classifier generated using 31 clinical covariates. Results: Treatment responders demonstrated greater fractional anisotropy in left thalamocortical, limbic, and association fibers, as well as greater connectivity in a functional network encompassing left thalamic, insular, and temporal nodes (p < 0.05). The resulting classifier demonstrated 89.5% accuracy and area under the receiver operating characteristic (ROC) curve of 0.93 on 10-fold cross-validation. In the external validation cohort, this model demonstrated an accuracy of 83.3%, with a sensitivity of 85.7% and specificity of 75.0%. This was significantly superior to predictions using clinical covariates alone, which exhibited an area under the ROC curve of 0.57 (p < 0.008). Interpretation: This study provides the first multi-institutional, multimodal connectomic prediction algorithm for VNS, and provides new insights into its mechanism of action. Reliable identification of VNS responders is critical to mitigate surgical risks for children who may not benefit, and to ensure cost-effective allocation of health care resources. ANN NEUROL 2019;86:743-753 N early one-third of children with epilepsy are refractory to medications. 1,2 Persistent seizures are associated with mortality, disability, psychosocial isolation, and diminished quality of life. 3-6 Vagus nerve stimulation (VNS) is an effective, safe, and well-tolerated intervention for a subset of patients with treatment-resistant epilepsy. 7-10 Although the goal of VNS is not complete resolution of seizures, many children will show a significant reduction in seizure frequency, as well as a reduction in hospitalizations and psychosocial comorbidities. 11,12 View this article online at wileyonlinelibrary.com.

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“…Electrooculography and electromyography signals were then removed via independent component analysis using the EEGlab toolbox [25]. The common average reference value was then applied to improve the quality of the signal-to-noise ratio, and we investigated the relationships between the patterns of activity in the EEG brain networks (beta band, 13-30 Hz) [26].…”

Section: Analysis Of Eeg Signalsmentioning

confidence: 99%

“…To analyze the functional connectivity among the different regions of each brain network, we utilized the phase locking value(PLV), which quantifies the frequency-specific synchronization of two regions [28]. Data were then extracted from 0.25 to 1.25 s (the period of motor execution) and graph theory was used to characterize the estimated network parameters [22,26].…”

Section: Analysis Of Eeg Signalsmentioning

confidence: 99%

Electroencephalography-based functional brain network analysis according to lesion location and motor function in stroke patients

Kim¹,

Kwon²,

Park³

et al. 2020

Preprint

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Background. While numerous studies have investigated changes in brain activation after stroke, limited information exists on the association between functional brain networks and lesion location in stroke patients. Methods. We compared the characteristics of brain networks among patients with cortico-subcortical lesions (n = 5), subcortical lesions (n = 7), and age-matched healthy controls (n = 12) during the execution of hand movements. Functional brain networks were analyzed based on network parameters in beta frequency electroencephalography (EEG) bands. Results. Our results indicated that while the healthy control group had appropriate compensatory patterns on the brain network with an aging effect, the two stroke lesion groups exhibited different hyper-connected characteristics in the brain network within the sensorimotor regions, particularly the contralesional M1, during motor execution. In addition, the betweenness centrality on the contralesional motor area was identified as a promising biomarker for motor functional ability associated with stroke. Our findings further allowed us to identify the characteristics of the stroke lesion that could not be found with EEG power by using the EEG brain network on the cerebral cortex. Conclusions. We anticipate that our study will improve the understanding of the complex changes that occur in the brain network as a result of stroke, and support the development of more effective and efficient rehabilitation programs based on lesion location for stroke patients.

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Topographical measures of functional connectivity as biomarkers for post-stroke motor recovery

Cited by 60 publications

References 96 publications

Emotional valence modulates the topology of the parent-infant inter-brain network

Emotional valence modulates the topology of the parent-infant inter-brain network

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation

Electroencephalography-based functional brain network analysis according to lesion location and motor function in stroke patients

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