CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG

Hao, Zexuan; Zhai, Xiaoxue; Peng, Bo; Cheng, Dandan; Zhang, Yanlin; Pan, Yu; Dou, Weibei

doi:10.1016/j.neuroimage.2023.120405

Cited by 4 publications

(5 citation statements)

References 80 publications

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“…A cluster can be defined as the edges within a weakly connected component (WCC). A WCC is a subnetwork of a network, e.g., functional connectivity (FC); effective connectivity (EC), structural connectivity (SC), and edge-centric FC (eFC) (Faskowitz et al, 2020; Hao et al, 2023b), where all nodes are connected to each other by some edges, ignoring the direction.…”

Section: Methodsmentioning

confidence: 99%

“…Node and edge definitions 1 in brain networks vary across modalities and analysis goals (Bassett and Sporns, 2017; Betzel, 2022). A vast and growing body of research leverages network neuroscience tools to study task- related activations, development, nervous system anomalies, and brain–behavior associations, among others (Rubinov and Sporns, 2010; Bassett and Sporns, 2017; Betzel, 2022; Noble et al, 2022; Chai et al, 2023; Hao et al, 2023b). However, brain network studies often involve massive univariate tests on edge- or node-level measures (Helwegen et al, 2023; Hao et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

“…A vast and growing body of research leverages network neuroscience tools to study task-8 related activations, development, nervous system anomalies, 9 and brain-behavior associations, among others (Rubinov and 10 Sporns, 2010; Bassett and Sporns, 2017;Betzel, 2022;Chai et al, 2023;Hao et al, 2023b). However, brain network studies often involve massive univariate tests on edge-or node-level measures (Helwegen et al, 2023;Hao et al, 2023b). In statistical terminology, they suffer from multiple testing/comparisons problems.…”

Section: Introductionmentioning

confidence: 99%

“…• A cluster can be defined as the edges within a weakly con-270 nected component (WCC). A WCC is a subnetwork of a network, e.g., FC, EC, SC, and edge-centric FC (eFC) 272 (Faskowitz et al, 2020;Hao et al, 2023b), where all nodes • A cluster can be defined as the edges within a strongly 276 connected component (SCC). An SCC is a subnetwork of a directed network, e.g., EC, where every node can reach…”

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confidence: 99%

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Threshold-Free Network-Oriented Statistics in Neuroscience

Hao,

Wang,

Xia

et al. 2024

Preprint

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Network neuroscience has emerged as a powerful tool for studying brain structure and function. Currently, the network-based statistic (NBS) method is widely used to control the type I error rate for mass univariate tests on brain network edges. However, the NBS requires the selection of a cluster-forming threshold, which lacks objective guidelines. There is also an urgent need for a powerful and flexible statistical framework for analyzing brain networks in diverse contexts. Here, we introduce a permutation-based framework called “threshold-free network-oriented statistics” (TFNOS). It integrates two “threshold-free” pathways: traversing all cluster-forming thresholds (TT) and using predefined clusters (PC). The TT procedure, building upon the threshold-free network-based statistics, requires the setting of additional parameters. The PC procedures have six variants according to the way of pooling data, the form of the null distribution, and the controlled error rate. Using numerical simulations, we comprehensively evaluated the performance of the TT procedure under 600 parameter combinations, benchmarked TFNOS procedures and baselines across different topologies of effects, sample sizes, and effect sizes, and provided examples of application on real data. We offer recommended parameter values that can make the TT procedure stably maintain leading power while empirically controlling the false discovery rate (FDR) at an appropriate level. Analyses reveal that the recommended parameters available in the field are overly liberal. Furthermore, for the PC procedures, FDR-controlling variants showed improved power compared to FWER-controlling variants, some of which are simple but do not compromise power. The nonparametric PC procedures allow for the selection of any test statistics considered appropriate. Overall, we provide empirical and principled criteria for selecting statistical procedures and the TFNOS is a generalized framework for inference in various contexts on edges or nodes of undirected or directed brain networks.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Threshold-Free Network-Oriented Statistics in Neuroscience

Hao,

Wang,

Xia

et al. 2024

Preprint

Self Cite

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“…This gametheoretic approach quantifies the marginal contribution of each feature to the individual prediction using Shapley values (also referred to as SHAP values [51]). The average absolute SHAP value for each feature across subjects illustrates the magnitude of the contribution of each feature to the model output [52]. To this end, we developed deep neural network (DNN) models to appraise the predictive power of baseline clinical and neural features concerning patient outcomes.…”

Section: Feature Importance Analysismentioning

confidence: 99%

Uncovering Brain Network Insights for Prognosis in Disorders of Consciousness: EEG Source Space Analysis and Brain Dynamics

Hao,

Xia,

Pan

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Accurate prognostic prediction in patients with disorders of consciousness (DOC) is a core clinical concern and a formidable challenge in neuroscience. Resting-state EEG has shown promise in identifying electrophysiological prognostic markers and may be easily deployed at the bedside. However, the lack of brain dynamic modeling and the spatial mixture of signals in scalp EEG have constrained our exploration of biomarkers and comprehension of the mechanisms underlying consciousness recovery. Here, we introduce EEG source space analysis and brain dynamics to investigate the brain networks of patients with DOC (n = 178) with different outcomes (sixmonth follow-up), followed by graph theory and high-order topological analysis to explore the relationship between network structure and prognosis, and finally assess the importance of features. We show that a positive prognosis is associated with large-scale lower levels of low-frequency hypersynchrony. Moreover, we provide evidence that this pattern is driven not by all brain states but only by specific states. Analyses reveal that the positive prognosis is attributed to the network retaining lower segregation, higher integration, and stronger stability compared to the negative prognosis. Furthermore, our results highlight the importance of brain networks derived from brain dynamics in prognosis. The prognosis models based on clinical and neural features can achieve acceptable and even excellent performance under different outcome definitions (AUC = 0.714-0.893). Overall, our study offers new perspectives for the identification of prognostic biomarkers and provides avenues for profound insights into the mechanisms under-

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Lateralization of cortical activity, networks, and hemodynamic lag after stroke: A resting‐state fNIRS study

Xu,

Chen,

Yin

et al. 2024

Journal of Biophotonics

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Focal damage due to stroke causes widespread abnormal changes in brain function and hemispheric asymmetry. In this study, functional near‐infrared spectroscopy (fNIRS) was used to collect resting‐state hemoglobin data from 85 patients with subacute stroke and 26 healthy controls, to comparatively analyze the characteristics of lateralization after stroke in terms of cortical activity, functional networks, and hemodynamic lags. Higher intensity of motor cortical activity, lower hemispheric autonomy, and more abnormal hemodynamic leads or lags were found in the affected hemisphere. Lateralization metrics of the three aspects were all associated with the Fugl‐Meyer score. The results of this study prove that three lateralization metrics may provide clinical reference for stroke rehabilitation. Meanwhile, the present study piloted the use of resting‐state fNIRS for analyzing hemodynamic lag, demonstrating the potential of fNIRS to assess hemodynamic abnormalities in addition to the study of cortical neurological function after stroke.

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CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG

Cited by 4 publications

References 80 publications

Threshold-Free Network-Oriented Statistics in Neuroscience

Threshold-Free Network-Oriented Statistics in Neuroscience

Uncovering Brain Network Insights for Prognosis in Disorders of Consciousness: EEG Source Space Analysis and Brain Dynamics

Lateralization of cortical activity, networks, and hemodynamic lag after stroke: A resting‐state fNIRS study

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