Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter

Huang, Yali; Wei, Penghu; Xu, Longzhou; Chen, Desheng; Yang, Yanfeng; Song, Wenqiang; Yi, Yangyang; Jia, Xiaoli; Wu, Guowei; Fan, Qing; Cui, Zaixu; Zhao, Guoguang

doi:10.1038/s41467-023-39067-3

Cited by 9 publications

(4 citation statements)

References 61 publications

(77 reference statements)

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“…On the other hand, while gray matter is a common focus in fMRI studies, recent studies have consistently found that BOLD signals can be reliably detected in white matter, and brain functional connectivity has been organized into distributed networks in white matter ( 60 , 61 ). Our result on unidirectional causality echoes the existing findings and indicates that neuronal activity in V1 and PPA might lead to the information transfer activity in the white matter, which connects regions that send and receive signals.…”

Section: Resultsmentioning

confidence: 99%

Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis

Deng,

Sun,

Scheel

et al. 2023

PNAS Nexus

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Convergent cross mapping (CCM) has attracted increased attention recently due to its capability in detecting causality in non-separable systems under deterministic settings, which may not be covered by the traditional Granger causality. From an information-theoretic perspective, causality is often characterized as the directed information (DI) flowing from one side to the other. As information is essentially nondeterministic, a natural question is: does CCM measure directed information flow? Here we first causalize CCM so that it aligns with the presumption in causality analysis—the future values of one process cannot influence the past of the other, and then establish and validate the approximate equivalence of causalized CCM (cCCM) and directed information (DI) under Gaussian variables through both theoretical derivations and fMRI-based brain network causality analysis. Our simulation result indicates that, in general, cCCM tends to be more robust than DI in causality detection. The underlying argument is that DI relies heavily on probability estimation, which is sensitive to data size as well as the digitization procedures; cCCM, on the other hand, gets around this problem through geometric cross-mapping between the manifolds involved. Overall, our analysis demonstrates that cross-mapping provides an alternative way to evaluate directed information and is potentially an effective technique for identifying both linear and nonlinear causal coupling in brain neural network and other settings, either random or deterministic, or both.

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Section: Resultsmentioning

confidence: 99%

Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis

Deng,

Sun,

Scheel

et al. 2023

PNAS Nexus

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“…Using the first node reduction method, we found that patients with JME have 21 different brain functional connectivity networks in the resting state. After running the code 500 times, we observed that these 21 different types of connection networks were distributed between ( Abe et al, 2007 ; Vollmar et al, 2012 ; Chin and Ratnavelu, 2016 ; Poleon and Szaflarski, 2017 ; Lee and Park, 2019 ; Qin et al, 2019 ; Ma et al, 2022 ; Samandari Bahraseman et al, 2022 ; Huang et al, 2023 ; Ke et al, 2023 ). As the number of runs increased, the probability of each type of connection network appearing gradually approached 1/21.…”

Section: Resultsmentioning

confidence: 99%

“…A previous study found that anatomical fiber tracts underlie functional synchronization in white matter ( Huang et al, 2023 ). The study further incorporated the tracts of curving and projection fibers of the core brain region in the functional connectivity network composition.…”

Section: Resultsmentioning

confidence: 99%

Differences in the distribution of triggers among resting state networks in patients with juvenile myoclonic epilepsy explained by network analysis

Luo,

Liu,

Zhang

et al. 2023

Front. Neurosci.

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BackgroundJuvenile myoclonus epilepsy (JME) is an idiopathic generalized epilepsy syndrome. Functional connectivity studies based on graph theory have demonstrated changes in functional connectivity among different brain regions in patients with JME and healthy controls. However, previous studies have not been able to clarify why visual stimulation or increased cognitive load induces epilepsy symptoms in only some patients with JME.MethodsThis study constructed a small-world network for the visualization of functional connectivity of brain regions in patients with JME, based on system mapping. We used the node reduction method repeatedly to identify the core nodes of the resting brain network of patients with JME. Thereafter, a functional connectivity network of the core brain regions in patients with JME was established, and it was analyzed manually with white matter tracks restriction to explain the differences in symptom distribution in patients with JME.ResultsPatients with JME had 21 different functional connections in their resting state, and no significant differences in their distribution were noted. The thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal lobe were the core brain regions that comprised the functional connectivity network in patients with JME during their resting state. The betweenness centrality of the prefrontal lobe and the visual cortex in the core functional connectivity network of patients with JME was lower than that of the other brain regions.ConclusionThe functional connectivity and node importance of brain regions of patients with JME changed dynamically in the resting state. Abnormal discharges originating from the thalamus, cerebellum, basal ganglia, supplementary motor area, visual cortex, and prefrontal cortex are most likely to lead to seizures in patients with JME. Further, the low average value of betweenness centrality of the prefrontal and visual cortices explains why visual stimulation or increased cognitive load can induce epileptic symptoms in only some patients with JME.

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“…In the field of genomics, studies have identified inter-subject variabilities in WM-FC and elucidated the genetic basis for the distribution patterns observed from macroscale imaging (Li et al, 2021), and unveiled WM dysfunction in psychiatric disorders correlated with the distributions of disease-related neurotransmitters and the expression maps of specific genes (Ji et al, 2023). Furthermore, a recent study also presented evidence which elucidates the electrophysiological and structural foundation of BOLD WM-FC from the perspective of electrophysiological synchronization (Huang et al, 2023). The role of white matter signal in the field of rs-fMRI studies is increasingly recognized, and research has investigated the alteration of WM function in neuro-psychiatric disorders (Chen et al, 2023b;Cui et al, 2021;Fan et al, 2023;Jiang et al, 2019;Wen et al, 2022;Zhang et al, 2021b), which reveals disease related-abnormalities, provides new insights into brain pathophysiology, and potentially yields novel clinical biomarkers for brain diseases.…”

Section: Introductionmentioning

confidence: 97%

Static and Dynamic Connectivity Structure of White-Matter Functional Network across the Adult Lifespan

Linli,

Hu,

Guo

et al. 2024

Preprint

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Background. The maturation and ageing of the human brain involve intricate biological processes that result in complex changes in brain structure and function. Although the effects of aging on functional connectivity within gray matter (GM) regions have been extensively studied, the investigation of white matter (WM) functional changes remains limited. Methods. In this study, our aim was to investigate age-related trajectory in WM functional dynamics using resting-state functional magnetic resonance imaging (rs-fMRI) data from a large lifespan sample of 494 individuals. First, GM and WM functional networks (FNs) were identified by k-means clustering. Next, we performed static and dynamic analysis of WM functional network connectivity (FNC) to explore age effect on WM-FNs. Furthermore, we investigated recurrent patterns of dynamic FNC. Finally, we conducted several validation analyses to ensure replicability. Results. We identified 9 reliable WM and 12 GM FNs. The findings revealed age-related effects on WM FNC strength and WM-GM FNC dynamics, primarily including linear positive and U-shaped age trajectories in static FNC strength, as well as linear negative and inverted U-shaped age trajectories in FNC temporal variability. Additionally, we identified three distinct brain states with significant age-related differences. The aforementioned findings were largely replicated in the validation analysis. Conclusion. High integration and low temporal variability in WN-GM FNC may reflect a less efficient network system in older adults. These findings enhance our understanding of brain aging processes and provide insights into the trajectory of WM functional dynamics during normal ageing.

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Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter

Cited by 9 publications

References 61 publications

Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis

Causalized convergent cross-mapping and its approximate equivalence with directed information in causality analysis

Differences in the distribution of triggers among resting state networks in patients with juvenile myoclonic epilepsy explained by network analysis

Static and Dynamic Connectivity Structure of White-Matter Functional Network across the Adult Lifespan

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