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  • Frontiers Media SA  (4)
  • 1
    Online Resource
    Online Resource
    More than just statics: Static and temporal dynamic changes in intrinsic brain activity in unilateral temporal lobe epilepsy
    Frontiers Media SA ; 2022
    In:  Frontiers in Human Neuroscience Vol. 16 ( 2022-8-31)
    Details
    In: Frontiers in Human Neuroscience, Frontiers Media SA, Vol. 16 ( 2022-8-31)
    Abstract: Temporal lobe epilepsy (TLE) is the most prevalent refractory focal epilepsy and is more likely accompanied by cognitive impairment. The fully understanding of the neuronal activity underlying TLE is of great significance. Objective This study aimed to comprehensively explore the potential brain activity abnormalities affected by TLE and detect whether the changes were associated with cognition. Methods Six static intrinsic brain activity (IBA) indicators [amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree centrality (DC), global signal correlation (GSCorr), and voxel-mirrored homotopic connectivity (VMHC)] and their corresponding dynamic indicators, such as dynamic ALFF (dALFF), dynamic fALFF (dfALF F), dynamic ReHo (dReHo), dynamic DC (dDC), dynamic VMHC (dVMHC), and dynamic GSCorr (dGSCorr), in 57 patients with unilateral TLE and 42 healthy volunteers were compared. Correlation analyses were also performed between these indicators in areas displaying group differences and cognitive function, epilepsy duration, and severity. Results Marked overlap was present among the abnormal brain regions detected using various static and dynamic indicators, primarily including increased ALFF/dALFF/fALFF in the bilateral medial temporal lobe and thalamus, decreased ALFF/dALFF/fALFF in the frontal lobe contralateral to the epileptogenic side, decreased fALFF, ReHo, dReHo, DC, dDC, GSCorr, dGSCorr, and VMHC in the temporal neocortex ipsilateral to the epileptogenic foci, decreased dReHo, dDC, dGSCorr, and dVMHC in the occipital lobe, and increased ALFF, fALFF, dfALFF, ReHo, and DC in the supplementary motor area ipsilateral to the epileptogenic foci. Furthermore, most IBA indicators in the abnormal brain region significantly correlated with the duration of epilepsy and several cognitive scale scores ( P & lt; 0.05). Conclusion The combined application of static and dynamic IBA indicators could comprehensively reveal more real abnormal neuronal activity and the impairment and compensatory mechanisms of cognitive function in TLE. Moreover, it might help in the lateralization of epileptogenic foci and exploration of the transmission and inhibition pathways of epileptic activity.
    Type of Medium: Online Resource
    ISSN: 1662-5161
    URL: Article
    DOI: 10.3389/fnhum.2022.971062
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2425477-0
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  • 2
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    Online Resource
    Data_Sheet_1.pdf
    Frontiers Media SA ; 2021
    In:  Frontiers in Human Neuroscience Vol. 15 ( 2021-12-8)
    Details
    In: Frontiers in Human Neuroscience, Frontiers Media SA, Vol. 15 ( 2021-12-8)
    Abstract: It is well established that epilepsy is characterized by the destruction of the information capacity of brain network and the interference with information processing in regions outside the epileptogenic focus. However, the potential mechanism remains poorly understood. In the current study, we applied a recently proposed approach on the basis of resting-state fMRI data to measure altered local neural dynamics in mesial temporal lobe epilepsy (mTLE), which represents how long neural information is stored in a local brain area and reflect an ability of information integration. Using resting-state-fMRI data recorded from 36 subjects with mTLE and 36 healthy controls, we calculated the intrinsic neural timescales (INT) of neural signals by summing the positive magnitude of the autocorrelation of the resting-state brain activity. Compared to healthy controls, the INT values were significantly lower in patients in the right orbitofrontal cortices, right insula, and right posterior lobe of cerebellum. Whereas, we observed no statistically significant changes between patients with long- and short-term epilepsy duration or between left-mTLE and right-mTLE. Our study provides distinct insight into the brain abnormalities of mTLE from the perspective of the dynamics of the brain activity, highlighting the significant role of intrinsic timescale in understanding neurophysiological mechanisms. And we postulate that altered intrinsic timescales of neural signals in specific cortical brain areas may be the neurodynamic basis of cognitive impairment and emotional comorbidities in mTLE patients.
    Type of Medium: Online Resource
    ISSN: 1662-5161
    URL: Article
    URL: Article
    DOI: 10.3389/fnhum.2021.772365
    DOI: 10.3389/fnhum.2021.772365.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2425477-0
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  • 3
    Online Resource
    Online Resource
    Data_Sheet_1.docx
    Frontiers Media SA ; 2023
    In:  Frontiers in Neuroscience Vol. 17 ( 2023-8-1)
    Details
    In: Frontiers in Neuroscience, Frontiers Media SA, Vol. 17 ( 2023-8-1)
    Abstract: Initial precipitating injury (IPI) such as febrile convulsion and intracranial infection will increase the susceptibility to epilepsy. It is still unknown if the functional deficits differ between mesial temporal lobe epilepsy with IPI (mTLE-IPI) and without IPI (mTLE-NO). Methods We recruited 25 mTLE-IPI patients, 35 mTLE-NO patients and 33 healthy controls (HC). Static regional homogeneity (sReHo) and dynamic regional homogeneity (dReHo) were then adopted to estimate the alterations of local neuronal activity. One-way analysis of variance was used to analyze the differences between the three groups in sReHo and dReHo. Then the results were utilized as masks for further between-group comparisons. Besides, correlation analyses were carried out to detect the potential relationships between abnormal regional homogeneity indicators and clinical characteristics. Results When compared with HC, the bilateral thalamus and the visual cortex in mTLE-IPI patients showed an increase in both sReHo and variability of dReHo. Besides, mTLE-IPI patients exhibited decreased sReHo in the right cerebellum crus1/crus2, inferior parietal lobule and temporal neocortex. mTLE-NO patients showed decreased sReHo and variability of dReHo in the bilateral temporal neocortex compared with HC. Increased sReHo and variability of dReHo were found in the bilateral visual cortex when mTLE-IPI patients was compared with mTLE-NO patients, as well as increased variability of dReHo in the left thalamus and decreased sReHo in the right dorsolateral prefrontal cortex. Additionally, we discovered a negative correlation between the national hospital seizure severity scale testing score and sReHo in the right cerebellum crus1 in mTLE-IPI patients. Conclusion According to the aforementioned findings, both mTLE-IPI and mTLE-NO patients had significant anomalies in local neuronal activity, although the functional deficits were much severer in mTLE-IPI patients. The use of sReHo and dReHo may provide a novel insight into the impact of the presence of IPI on the development of mTLE.
    Type of Medium: Online Resource
    ISSN: 1662-453X
    URL: Article
    URL: Article
    DOI: 10.3389/fnins.2023.1226077
    DOI: 10.3389/fnins.2023.1226077.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2411902-7
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  • 4
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    Online Resource
    DataSheet1.docx
    Frontiers Media SA ; 2023
    In:  Frontiers in Earth Science Vol. 10 ( 2023-1-4)
    Details
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 10 ( 2023-1-4)
    Abstract: Salt structures are crucial targets in oil and gas seismic exploitation so that one fast, automatic and accurate method is necessary for accelerating salt structure identification in the exploitation process. With the development of machine-learning algorithms, geophysical scientists adopt machine-learning models to solve problems. Most machine-learning models in geophysics require mass data in the model training. However, the number of seismic images is limited and the class-imbalance is often existed in actuality, causing the machine-learning algorithms to be difficult to apply in exploitation projects. To overcome the challenge of the seismic images’ volume, this work collects a two-dimensional (2D) seismic images dataset and trains several U-net models with the methods of inversion and multiple distillation. Moreover, self-distillation is introduced to boost the model’s performance. A test using a public seismic dataset and the case of salt detection in the Hith evaporite in southern United Arab Emirates and western Oman shows the distillation method is able to identify salt structures automatically and accurately, which has great potential for application in actual exploitation.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    URL: Article
    URL: Article
    DOI: 10.3389/feart.2022.1071637
    DOI: 10.3389/feart.2022.1071637.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2741235-0
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