Abnormal default-mode network homogeneity in patients with temporal lobe epilepsy

Gao, Yujun; Zheng, Jinou; Li, Yaping; Guo, Danni; Wang, Mingli; Cui, Xiaoxiao; Ye, Wei

doi:10.1097/md.0000000000011239

Cited by 19 publications

(15 citation statements)

References 53 publications

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“…These brain networks can be derived from resting state functional magnetic resonance imaging (rsfMRI), and are supporting a variety of sensory, cognitive and behavioural functions [7,8]. Perturbed functionality of RSNs contributes to a range of brain diseases including epilepsy [9], Alzheimer's disease [10], autism [11], depression [12] and schizophrenia [13]. Although alterations of RSNs have been subject to numerous studies, characterization of their complex dynamics remains an open question in the brain sciences [1,[14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Omidvarnia

Zalesky

Mansour

et al. 2019

Preprint

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In 2014, McDonough and Nashiro [1] derived multiscale entropy -a marker of signal complexity-from resting state functional MRI data (rsfMRI), and found that functional brain networks displayed unique multiscale entropy fingerprints. This is a finding with potential impact as an imaging-based marker of normal brain function, as well as pathological brain dysfunction. Nevertheless, a limitation of this study was that rsfMRI data from only 20 healthy individuals was used for analysis. To overcome this limitation, we aimed to replicate McDonough and Nashiro's finding in a large cohort of healthy subjects. We used rsfMRI from the Human Connectome Project (HCP) comprising 936 gender-matched healthy young adults aged 22-35, each with 4 × 14.4-minute rsfMRI data from 100 brain regions. We quantified multiscale entropy of rsfMRI time series averaged at different cortical and sub-cortical regions. We also performed a test-retest analysis on the data of four recording sessions in 10 previously reported resting state networks (RSNs). Given that the morphology of multiscale entropy patterns is affected by the choice of the tolerance parameter (r ), we performed the analyses at two r values: 0.5, similar to the original study and 0.15, a commonly used option in the literature. Our results were similar to previous findings by McDonough and Nashiro emphasising high temporal complexity in the default mode network and fronto-parietal networks, and low temporal complexity in the cerebellum. We also investigated the effect of temporal resolution (determined by fMRI repetition time) by downsampling rsfMRI time series. At a low temporal resolution, we observed increased entropy and variance across datasets likely due to fewer data points in the multiscale entropy analysis. Test-retest analysis showed that findings were likely reproducible across individuals over four rsfMRI runs, especially for r = 0.5. We also showed a positive relationship between temporal complexity of RSNs and fluid intelligence (people's capacity to reason and think flexibly), suggesting that complex dynamics is an important attribute of optimized brain function.

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Section: Introductionmentioning

confidence: 99%

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Omidvarnia

Zalesky

Mansour

et al. 2019

Preprint

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“…All the participants were instructed to lie still and stay awake with their eyes closed. The scanning parameters were as follows: repetition time/echo time (TR/TE) 2,000/30 ms; 31 slices; 90° flip angle; 220 × 220 mm field of view; 5 mm slice thickness; and 1 mm pitch [the detailed steps were excerpted from our previous study ( 32 ), and detailed information can be found in the Supplementary Material ].…”

Section: Methodsmentioning

confidence: 99%

“…Several spurious covariates were removed, such as the signal from a region centered in the white matter and the signal from a ventricular seed-based region of interest, as well as the six head-motion parameters obtained by rigid body correction. The global signal was retained during the processing of the resting-state functional connectivity data [the detailed steps were excerpted from our previous study ( 32 ), and detailed information can be found in the Supplementary Material ].…”

Section: Methodsmentioning

confidence: 99%

Abnormal Fractional Amplitude of Low-Frequency Fluctuation as a Potential Imaging Biomarker for First-Episode Major Depressive Disorder: A Resting-State fMRI Study and Support Vector Machine Analysis

et al. 2021

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Objective: Major depressive disorder (MDD) is a psychiatric disorder with serious negative health outcomes; however, there is no reliable method of diagnosis. This study explored the clinical diagnostic value of the fractional amplitude of low-frequency fluctuation (fALFF) based on the support vector machine (SVM) method for the diagnosis of MDD.Methods: A total of 198 first-episode MDD patients and 234 healthy controls were involved in this study, and all participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Imaging data were analyzed with the fALFF and SVM methods.Results: Compared with the healthy controls, the first-episode MDD patients showed higher fALFF in the left mid cingulum, right precuneus, and left superior frontal gyrus (SFG). The increased fALFF in these three brain regions was positively correlated with the executive control reaction time (ECRT), and the increased fALFF in the left mid cingulum and left SFG was positively correlated with the 17-item Hamilton Rating Scale for Depression (HRSD-17) scores. The SVM results showed that increased fALFF in the left mid cingulum, right precuneus, and left SFG exhibited high diagnostic accuracy of 72.92% (315/432), 71.76% (310/432), and 73.84% (319/432), respectively. The highest diagnostic accuracy of 76.39% (330/432) was demonstrated for the combination of increased fALFF in the right precuneus and left SFG, along with a sensitivity of 84.34% (167/198), and a specificity of 70.51% (165/234).Conclusion: Increased fALFF in the left mid cingulum, right precuneus, and left SFG may serve as a neuroimaging marker for first-episode MDD. The use of the increased fALFF in the right precuneus and left SFG in combination showed the best diagnostic value.

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“…TLE is increasingly thought to be a disorder involving abnormal epileptogenic networks, rather than a single focal epileptogenic source ( 16 – 18 ). Accumulating evidence has shown that TLE exists in several networks disturbances, including alertness network ( 19 ), attention network ( 20 ) and default mode network (DMN) ( 13 , 21 , 22 ). However, there are only a few reports concerning the DMN and its function in patients with TLE.…”

Section: Introductionmentioning

confidence: 99%

The Bilateral Precuneus as a Potential Neuroimaging Biomarker for Right Temporal Lobe Epilepsy: A Support Vector Machine Analysis

Huang

Zhou²,

Zhong

et al. 2022

Front. Psychiatry

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Background and ObjectiveWhile evidence has demonstrated that the default-mode network (DMN) plays a key role in the broad-scale cognitive problems that occur in right temporal lobe epilepsy (rTLE), little is known about alterations in the network homogeneity (NH) of the DMN in TLE. In this study, we used the NH method to investigate the NH of the DMN in TLE at rest, and an support vector machine (SVM) method for the diagnosis of rTLE.MethodsA total of 43 rTLE cases and 42 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Imaging data were analyzed with the NH and SVM methods.ResultsrTLE patients have a decreased NH in the right inferior temporal gyrus (ITG) and left middle temporal gyrus (MTG), but increased NH in the bilateral precuneus (PCu) and right inferior parietal lobe (IPL), compared with HCs. We found that rTLE had a longer performance reaction time (RT). No significant correlation was found between abnormal NH values and clinical variables of the patients. The SVM results showed that increased NH in the bilateral PCu as a diagnostic biomarker distinguished rTLE from HCs with an accuracy of 74.12% (63/85), a sensitivity 72.01% (31/43), and a specificity 72.81% (31/42).ConclusionThese findings suggest that abnormal NH of the DMN exists in rTLE, and highlights the significance of the DMN in the pathophysiology of cognitive problems occurring in rTLE, and the bilateral PCu as a neuroimaging diagnostic biomarker for rTLE.

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Abnormal default-mode network homogeneity in patients with temporal lobe epilepsy

Cited by 19 publications

References 53 publications

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Temporal complexity of fMRI is reproducible and correlates with higher order cognition

Abnormal Fractional Amplitude of Low-Frequency Fluctuation as a Potential Imaging Biomarker for First-Episode Major Depressive Disorder: A Resting-State fMRI Study and Support Vector Machine Analysis

The Bilateral Precuneus as a Potential Neuroimaging Biomarker for Right Temporal Lobe Epilepsy: A Support Vector Machine Analysis

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