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Farrher E, Grinberg F, Khechiashvili T, Neuner I, Konrad K, Shah NJ. Spatiotemporal Patterns of White Matter Maturation after Pre-Adolescence: A Diffusion Kurtosis Imaging Study. Brain Sci 2024; 14:495. [PMID: 38790472 PMCID: PMC11119177 DOI: 10.3390/brainsci14050495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/03/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024] Open
Abstract
Diffusion tensor imaging (DTI) enables the assessment of changes in brain tissue microstructure during maturation and ageing. In general, patterns of cerebral maturation and decline render non-monotonic lifespan trajectories of DTI metrics with age, and, importantly, the rate of microstructural changes is heterochronous for various white matter fibres. Recent studies have demonstrated that diffusion kurtosis imaging (DKI) metrics are more sensitive to microstructural changes during ageing compared to those of DTI. In a previous work, we demonstrated that the Cohen's d of mean diffusional kurtosis (dMK) represents a useful biomarker for quantifying maturation heterochronicity. However, some inferences on the maturation grades of different fibre types, such as association, projection, and commissural, were of a preliminary nature due to the insufficient number of fibres considered. Hence, the purpose of this follow-up work was to further explore the heterochronicity of microstructural maturation between pre-adolescence and middle adulthood based on DTI and DKI metrics. Using the effect size of the between-group parametric changes and Cohen's d, we observed that all commissural fibres achieved the highest level of maturity, followed by the majority of projection fibres, while the majority of association fibres were the least matured. We also demonstrated that dMK strongly correlates with the maxima or minima of the lifespan curves of DTI metrics. Furthermore, our results provide substantial evidence for the existence of spatial gradients in the timing of white matter maturation. In conclusion, our data suggest that DKI provides useful biomarkers for the investigation of maturation spatial heterogeneity and heterochronicity.
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Affiliation(s)
- Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
| | - Tamara Khechiashvili
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
| | - Irene Neuner
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
| | - Kerstin Konrad
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Child Neuropsychology Section, Department of Child and Adolescent Psychiatry and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
- Institute of Neuroscience and Medicine 3, INM-3, Forschungszentrum Jülich, 52425 Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
| | - N. Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, 52425 Jülich, Germany; (F.G.); (T.K.); (I.N.); (N.J.S.)
- Department of Neurology, RWTH Aachen University, 52074 Aachen, Germany
- JARA—BRAIN—Translational Medicine, 52074 Aachen, Germany;
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, 52425 Jülich, Germany
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Song Q, Dong W, Tian S, Xie L, Chen L, Wei Q, Liu A. Diffusion kurtosis imaging with multiple quantitative parameters for predicting microsatellite instability status of endometrial carcinoma. Abdom Radiol (NY) 2023; 48:3746-3756. [PMID: 37740047 DOI: 10.1007/s00261-023-04041-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/24/2023]
Abstract
PURPOSE To explore the value of Diffusion kurtosis imaging (DKI) with multiple quantitative parameters in predicting microsatellite instability (MSI) status in endometrial carcinoma (EC). METHODS Data of 38 patients with EC were retrospectively analyzed, including 12 MSI and 26 microsatellite stability (MSS). All patients underwent preoperative 1.5T MR examination. The quantitative values of the DKI sequence in the tumor parenchyma of the two groups, including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), fractional anisotropy of kurtosis (FAk), mean diffusivity (MD), axial diffusivity (Da), and radial diffusivity (Dr) were measured by two observers, respectively. RESULTS The MK, Ka, Kr, FA, FAk, MD, Da, and Dr values of the MSI group were 1.074 ± 0.162, 1.253 ± 0.229, 0.886 ± 0.205, 0.207 ± 0.041, 0.397 ± 0.129, 0.890 ± 0.158 μm2/ms, 1.083 ± 0.218 μm2/ms, and 0.793 ± 0.133 μm2/ms, and 0.956 (0.889,1.002), 1.048 ± 0.211, 0.831 ± 0.099, 0.188 ± 0.061, 0.334 (0.241,0.410), 1.043 ± 0.217 μm2/ms, 1.235 ± 0.229 μm2/ms, and 0.946 ± 0.215 μm2/ms in the MSS group. The MK and Ka values of the MSI group were higher than those of the MSS group (P<0.05), while the MD and Dr values were lower than those of the MSS group (P<0.05). The AUC of MK, Ka, MD, and Dr values in predicting MSI status of EC was 0.763, 0.729, 0.731, 0.748, respectively. The sensitivity was 58.3%, 50.0%, 65.4%, 61.5%, and the specificity was 96.2%, 92.3%, 75.0%, 83.3%, respectively. CONCLUSION DKI can provide multiple quantitative parameters for predicting the MSI status of EC, and assist gynecologist to optimize the treatment plan for the patients.
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Affiliation(s)
- Qingling Song
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang district, Zhongshan road, No.222, Dalian, 116011, China
| | - Wan Dong
- Department of Radiology, Wuhan Children's Hospital, Tongji Medical College of Huazhong University of Science & Technology, Jiang'an District Wuhan Hong Kong Road No.100, Wuhan, 430019, China
| | - Shifeng Tian
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang district, Zhongshan road, No.222, Dalian, 116011, China
| | - Lizhi Xie
- GE Healthcare, MR Research, Beijing, 100024, China
| | - Lihua Chen
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang district, Zhongshan road, No.222, Dalian, 116011, China
| | - Qiang Wei
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang district, Zhongshan road, No.222, Dalian, 116011, China
| | - Ailian Liu
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Xigang district, Zhongshan road, No.222, Dalian, 116011, China.
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Brain Diffusion Weighted Imaging Study of Mongolian Idiopathic Epilepsy. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6978116. [PMID: 36478789 PMCID: PMC9722273 DOI: 10.1155/2022/6978116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 11/13/2022] [Accepted: 11/14/2022] [Indexed: 11/29/2022]
Abstract
Objective To evaluate the effectiveness of diffusion-weighted imaging in the assessment of idiopathic epilepsy in Mongolian. Methods One hundred Mongolian idiopathic epilepsy patients were enrolled as the observation group and 100 healthy Mongolian volunteers as the control group. All the subjects underwent routine MRI, diffusion kurtosis imaging (DKI), and intra-voxel incoherent motion (IVIM) examination on a 3.0 T scanner. Mean kurtosis (MK), mean diffusivity (MD), fractional anisotropy (FA), true water molecular diffusion coefficient (D), mean diffusion coefficient (MD), pseudo-diffusion coefficient (D∗), and perfusion fraction (f) of each region of interest in the brain were measured. Count data were expressed as rates, and the chi-square test was performed for comparison between groups. Measurement data were first assessed by a normality test, and the t test for independent samples was performed for comparison between groups if they met the normal distribution; for non-normal distribution, the Mann-Whitney U test was performed for comparison between groups. A ROC curve analysis was performed to test the effectiveness of each parameter. Results MK values of the hippocampus, thalamus, and white matter of the temporal lobe in the observation group were significantly higher than those in the control group, while D and F values were significantly lower (all P < 0.05). ROC curve analysis showed that MK, D, and F values of the hippocampus, thalamus, and white matter of the temporal lobe had moderate to good diagnostic efficacy for idiopathic epilepsy (AUC = 0.617-0.749, all P < 0.001). Conclusion DKI and IVIM can more accurately represent the abnormal changes of brain tissue in patients with epilepsy, and it may have important implications for the clinical diagnosis of Mongolian epileptic patients.
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Guo M, Shen B, Li J, Huang X, Hu J, Wei X, Wang S, Yuan R, He C, Li Y. Diffusion Abnormality in Temporal Lobe Epilepsy Patients With Sleep Disorders: A Diffusion Kurtosis Imaging Study. Front Psychiatry 2022; 13:885477. [PMID: 35693954 PMCID: PMC9177985 DOI: 10.3389/fpsyt.2022.885477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/13/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Patients with temporal lobe epilepsy (TLE) frequently complain of poor sleep quality, which is a condition that clinicians are typically neglecting. In this study, Epworth Sleepiness Scale (ESS), Pittsburgh Sleep Quality Index (PSQI), and Athens Insomnia Scale (AIS) were used to assess the sleep status of patients with temporal lobe epilepsy (TLE). Simultaneously diffusion kurtosis imaging (DKI) was applied to examine the white matter microstructure abnormalities in patients with TLE and sleep disorders. METHODS TLE patients who have been diagnosed in the cardio-cerebrovascular ward of the Yanan University Affiliated Hospital from October 2020 to August 2021 were recruited. Finally, 51 patients and 30 healthy controls were enrolled in our study, with all subjects completing the sleep evaluation questionnaire and undergoing a DKI examination. Using independent sample t-test, analysis of variance (ANOVA), and Mann-Whitney U test to compare groups. RESULTS Thirty patients (58.82%) complained of long-term sleep difficulties. The overall differences among the evaluation of AIS, ESS, and PSQI are significant (P = 0.00, P = 0.00, P = 0.03). The scores of AIS, ESS in Left and Right-TLE (L/R-TLE) with sleep disorders, as well as PSQI in L-TLE, are statistically higher than the control group (P = 0.00, P = 0.00, P = 0.00, P = 0.00, P = 0.02). L-TLE with sleep disorders showed decreased MK on affected sides (P = 0.01). However, statistical differences in MD and FA have not been observed (P = 0.34, P = 0.06); R-TLE with sleep disorders showed significantly decreased MK and increased MD on affected sides (P = 0.00, P = 0.00), but FA's statistical difference has not been observed (P = 0.20). CONCLUSIONS TLE patients with sleep disorders have different DKI parameters than individuals who do not have sleep issues. During this process, the kurtosis parameter (MK) was more sensitive than the tensor parameters (MD, FA) in detecting the patient's aberrant white matter diffusion. DKI may be a better choice for in vivo investigation of anomalous craniocerebral water diffusion.
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Affiliation(s)
- Min Guo
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
| | - Boxing Shen
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
| | - Jinhong Li
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
| | - Xiaoqi Huang
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
| | - Jie Hu
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
| | | | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, China
| | - Ruohan Yuan
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
| | - Chengcheng He
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
| | - Yanjing Li
- Department of Radiology, Yanan University Affiliated Hospital, Yanan, China
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Kang L, Chen J, Huang J, Zhang T, Xu J. Identifying epilepsy based on machine-learning technique with diffusion kurtosis tensor. CNS Neurosci Ther 2021; 28:354-363. [PMID: 34939745 PMCID: PMC8841295 DOI: 10.1111/cns.13773] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 11/11/2021] [Accepted: 11/14/2021] [Indexed: 12/20/2022] Open
Abstract
Introduction Epilepsy is a serious hazard to human health. Minimally invasive surgery is an extremely effective treatment to refractory epilepsy currently if the location of epileptic foci is given. However, it is challenging to locate the epileptic foci since a multitude of patients are MRI‐negative. It is well known that DKI (diffusion kurtosis imaging) can analyze the pathological changes of local tissues and other regions of epileptic foci at the molecular level. In this article, we propose a new localization way for epileptic foci based on machine‐learning method with kurtosis tensor in DKI. Methods We recruited 59 children with hippocampus epilepsy and 70 age‐ and sex‐matched normal controls; their T1‐weighted images and DKI were collected simultaneously. Then, the hippocampus in DKI is segmented based on a mask as a local brain region, and DKE is utilized to estimate the kurtosis tensor of each subject's hippocampus. Finally, the kurtosis tensor is fed into SVM (support vector machine) to identify epilepsy. Results The classifier produced 95.24% accuracy for patient versus normal controls, which is higher than that obtained with FA (fractional anisotropy) and MK (mean kurtosis). Experimental results show that the kurtosis tensor is a kind of remarkable feature to identify epilepsy, which indicates that DKI images can act as an important biomarker for epilepsy from the view of clinical diagnosis. Conclusion Although the classification task for epileptic patients and normal controls discussed in this article did not directly achieve the location of epileptic foci and only identified epilepsy on certain brain region, the epileptic foci can be located with the results of identifying results on other brain regions.
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Affiliation(s)
- Li Kang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
| | - Jin Chen
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
| | - Jianjun Huang
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
| | - Tijiang Zhang
- The Affiliate Hospital of Zunyi Medical University, Zunyi, China
| | - Jiahui Xu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.,The Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, China
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White matter tracts in Bipolar Disorder patients: A comparative study based on diffusion kurtosis and tensor imaging. J Affect Disord 2021; 292:45-55. [PMID: 34098469 DOI: 10.1016/j.jad.2021.05.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 05/12/2021] [Accepted: 05/20/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Diffusion kurtosis imaging (DKI), an extension of diffusion tensor imaging (DTI), is a powerful tool for studying human brain.The purpose is to investigate differences between DKI and DTI by comparing parameters in same analysis methods with bipolar disorder (BD) patients. METHODS In this study, we attained in 47 BD patients and 49 age-, sex-, and education-matched healthy controls, complimented DTI and DKI scanning and got Fractional Anisotropy (FA), Mean Diffusion (MD) and Mean Kurtosis (MK). Voxel-wise statistical analysis was performed by the tract-based spatial statistics (TBSS) analysis and atlas-based regional data analysis. RESULTS TBSS analysis showed more widespread regions and higher fidelity in DKI parameters than DTI parameters with the same p-value threshold, and DKI parameters showed significant alterations after Family-Wise Error correction. The DKI-FA value in the corpus callosum, bilateral cingulum (cingulate gyrus), bilateral superior corona radiata, left anterior corona radiata and left posterior corona radiata of BD patients was negatively correlated with the duration of illness. In the atlas-based regional data analysis, the effect size of DTI-FA, DTI-MD, DKI-FA and DKI-MD were quantified using Cohen's d value. DKI-FA and DKI-MD demonstrated more between-group different regions and the higher (p < 0.001) absolute Cohen's d value than DTI-FA. LIMITATIONS This study did not consider the difference between sub-types of BD. CONCLUSIONS Compared to DTI parameters, DKI parameters were more sensitive and stable to probe the local microstructure, and particularly powerful to exploit cerebral alterations in BD patients.
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Henriques RN, Jespersen SN, Jones DK, Veraart J. Toward more robust and reproducible diffusion kurtosis imaging. Magn Reson Med 2021; 86:1600-1613. [PMID: 33829542 PMCID: PMC8199974 DOI: 10.1002/mrm.28730] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 01/20/2021] [Accepted: 01/24/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values. THEORY AND METHODS A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit. RESULTS The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast. CONCLUSION Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.
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Affiliation(s)
| | - Sune N. Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLabDepartment of Clinical MedicineAarhus UniversityAarhusDenmark
- Department of Physics and AstronomyAarhus UniversityAarhusDenmark
| | - Derek K. Jones
- CUBRICSchool of PsychologyCardiff UniversityCardiffUK
- Mary MacKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneVictoriaAustralia
| | - Jelle Veraart
- Center for Biomedical ImagingNew York University Grossman School of MedicineNew YorkNYUSA
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Zhang H, Shen Z, Zhao Q, Yan L, Du L, Deng Z. Dynamic Transitions of Epilepsy Waveforms Induced by Astrocyte Dysfunction and Electrical Stimulation. Neural Plast 2020; 2020:8867509. [PMID: 33281896 PMCID: PMC7685866 DOI: 10.1155/2020/8867509] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 10/14/2020] [Accepted: 10/22/2020] [Indexed: 11/17/2022] Open
Abstract
Experimental studies have shown that astrocytes participate in epilepsy through inducing the release of glutamate. Meanwhile, considering the disinhibition circuit among inhibitory neuronal populations with different time scales and the feedforward inhibition connection from thalamic relay nucleus to cortical inhibitory neuronal population, here, we propose a modified thalamocortical field model to systematically investigate the mechanism of epilepsy. Firstly, our results show that rich firing activities can be induced by astrocyte dysfunction, including high or low saturated state, high- or low-frequency clonic, spike-wave discharge (SWD), and tonic. More importantly, with the enhancement of feedforward inhibition connection, SWD and tonic oscillations will disappear. In other words, all these pathological waveforms can be suppressed or eliminated. Then, we explore the control effects after different external stimulations applying to thalamic neuronal population. We find that single-pulse stimulation can not only suppress but also induce pathological firing patterns, such as SWD, tonic, and clonic oscillations. And we further verify that deep brain stimulation can control absence epilepsy by regulating the amplitude and pulse width of stimulation. In addition, based on our modified model, 3 : 2 coordinated reset stimulation strategies with different intensities are compared and a more effective and safer stimulation mode is proposed. Our conclusions are expected to give more theoretical insights into the treatment of epilepsy.
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Affiliation(s)
- Honghui Zhang
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zhuan Shen
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Qiangui Zhao
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Luyao Yan
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lin Du
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zichen Deng
- School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China
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Huang J, Xu J, Kang L, Zhang T. Identifying Epilepsy Based on Deep Learning Using DKI Images. Front Hum Neurosci 2020; 14:590815. [PMID: 33240068 PMCID: PMC7680804 DOI: 10.3389/fnhum.2020.590815] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/08/2020] [Indexed: 11/25/2022] Open
Abstract
Epilepsy is a serious hazard to human health. Minimally invasive surgery is currently an extremely effective treatment to refractory epilepsy. However, it is challenging to localize the lesion for most patients because they are MRI negative. The identification of epileptic foci in local brain region will be helpful to the localization of epileptic foci because we can infer whether there is a lesion from the results of the classification. For the sake of simplicity and the data we collected, only the hippocampus was segmented as a local brain region and classified in this paper. We recruited 59 children with hippocampus epilepsy and 70 age- and sex-matched normal controls, and diffusion kurtosis images (DKI) for all subjects were collected because DKI can understand the pathological changes of local tissues and other regions of epileptic foci at the molecular level. Then, a mask of hippocampus was made to segment the hippocampus of FA, MD, and MK images for all subjects, which are the parameter images of DKI and were used to perform the independent-sample t-test and the classification task. At last, a convolutional neural network (CNN) based on transfer learning technique was developed to extract features of FA, MD, MK, and the fusion of FA and MK, and support vector machine was employed to classify epilepsy and normal control. Finally, the classifier produced 90.8% accuracy for patient vs. normal controls. Experimental results showed that the features extraction based on CNN is very effective, and the high accuracy of classification means that FA and MK are two remarkable features to identify epilepsy, which indicates that DKI images can act as an important biomarker for epilepsy from the point of view of clinical diagnosis.
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Affiliation(s)
- Jianjun Huang
- College of Electrical and Information Engineering, Shenzhen University, Shenzhen, China
| | - Jiahui Xu
- College of Electrical and Information Engineering, Shenzhen University, Shenzhen, China
| | - Li Kang
- College of Electrical and Information Engineering, Shenzhen University, Shenzhen, China
| | - Tijiang Zhang
- Imaging Department, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
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White matter alterations in adult with autism spectrum disorder evaluated using diffusion kurtosis imaging. Neuroradiology 2019; 61:1343-1353. [PMID: 31209529 DOI: 10.1007/s00234-019-02238-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 05/29/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE Autism spectrum disorder (ASD) is related to impairment in various white matter (WM) pathways. Utility of the recently developed two-compartment model of diffusion kurtosis imaging (DKI) to analyse axial diffusivity of WM is restricted by several limitations. The present study aims to validate the utility of model-free DKI in the evaluation of WM alterations in ASD and analyse the potential relationship between DKI-evident WM alterations and personality scales. METHODS Overall, 15 participants with ASD and 15 neurotypical (NT) controls were scanned on a 3 T magnetic resonance (MR) scanner, and scores for autism quotient (AQ), systemising quotient (SQ) and empathising quotient (EQ) were obtained for both groups. Multishell diffusion-weighted MR data were acquired using two b-values (1000 and 2000 s/mm2). Differences in mean kurtosis (MK), radial kurtosis (RK) and axial kurtosis (AK) between the groups were evaluated using tract-based spatial statistics (TBSS). Finally, the relationships between the kurtosis indices and personality quotients were examined. RESULTS The ASD group demonstrated significantly lower AK in the body and splenium of corpus callosum than the NT group; however, no other significant differences were identified. Negative correlations were found between AK and AQ or SQ, predominantly in WM areas related to social-emotional processing such as uncinate fasciculus, inferior fronto-occipital fasciculus, and inferior and superior longitudinal fasciculi. CONCLUSIONS Model-free DKI and its indices may represent a novel, objective method for detecting the disease severity and WM alterations in patients with ASD.
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Xiong Y, Sui Y, Zhang S, Zhou XJ, Yang S, Fan Y, Zhang Q, Zhu W. Brain microstructural alterations in type 2 diabetes: diffusion kurtosis imaging provides added value to diffusion tensor imaging. Eur Radiol 2018; 29:1997-2008. [DOI: 10.1007/s00330-018-5746-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 08/03/2018] [Accepted: 09/10/2018] [Indexed: 12/25/2022]
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Zhao L, Wang Y, Jia Y, Zhong S, Sun Y, Zhou Z, Zhang Z, Huang L. Microstructural Abnormalities of Basal Ganglia and Thalamus in Bipolar and Unipolar Disorders: A Diffusion Kurtosis and Perfusion Imaging Study. Psychiatry Investig 2017; 14:471-482. [PMID: 28845175 PMCID: PMC5561406 DOI: 10.4306/pi.2017.14.4.471] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Revised: 06/23/2016] [Accepted: 07/25/2016] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Bipolar disorder (BD) is often misdiagnosed as unipolar depression (UD), leading to mistreatment and poor clinical outcomes. However, little is known about the similarities and differences in subcorticalgray matter regions between BD and UD. METHODS Thirty-five BD patients, 30 UD patients and 40 healthy controls underwent diffusional kurtosis imaging (DKI) and three dimensional arterial spin labeling (3D ASL). The parameters including mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (Da), radial diffusivity (Dr) and cerebral blood flow (CBF) were measured by using regions-of-interest analysis in the caudate, putamen and thalamus of the subcortical gray matter regions. RESULTS UD exhibited differences from controls for DKI measures and CBF in the left putamen and caudate. BD showed differences from controls for DKI measures in the left caudate. Additionally, BD showed lower Ka in right putamen, higher MD in right caudate compared with UD. Receiver operating characteristic analysis revealed the Kr of left caudate had the highest predictive power for distinguishing UD from controls. CONCLUSION The two disorders may have overlaps in microstructural abnormality in basal ganglia. The change of caudate may serve as a potential biomarker for UD.
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Affiliation(s)
- Lianping Zhao
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Radiology, Gansu Provincial Hospital, Gansu, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
- Clinical Experimental Center, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yao Sun
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Zhifeng Zhou
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
| | | | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou, China
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13
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Stimulus-induced Epileptic Spike-Wave Discharges in Thalamocortical Model with Disinhibition. Sci Rep 2016; 6:37703. [PMID: 27876879 PMCID: PMC5120301 DOI: 10.1038/srep37703] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Accepted: 11/03/2016] [Indexed: 12/17/2022] Open
Abstract
Epileptic absence seizure characterized by the typical 2–4 Hz spike-wave discharges (SWD) are known to arise due to the physiologically abnormal interactions within the thalamocortical network. By introducing a second inhibitory neuronal population in the cortical system, here we propose a modified thalamocortical field model to mathematically describe the occurrences and transitions of SWD under the mutual functions between cortex and thalamus, as well as the disinhibitory modulations of SWD mediated by the two different inhibitory interneuronal populations. We first show that stimulation can induce the recurrent seizures of SWD in the modified model. Also, we demonstrate the existence of various types of firing states including the SWD. Moreover, we can identify the bistable parametric regions where the SWD can be both induced and terminated by stimulation perturbations applied in the background resting state. Interestingly, in the absence of stimulation disinhibitory functions between the two different interneuronal populations can also both initiate and abate the SWD, which suggests that the mechanism of disinhibition is comparable to the effect of stimulation in initiating and terminating the epileptic SWD. Hopefully, the obtained results can provide theoretical evidences in exploring dynamical mechanism of epileptic seizures.
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14
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Glenn GR, Jensen JH, Helpern JA, Spampinato MV, Kuzniecky R, Keller SS, Bonilha L. Epilepsy-related cytoarchitectonic abnormalities along white matter pathways. J Neurol Neurosurg Psychiatry 2016; 87:930-6. [PMID: 27076491 DOI: 10.1136/jnnp-2015-312980] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Accepted: 03/28/2016] [Indexed: 11/04/2022]
Abstract
OBJECTIVE Temporal lobe epilepsy (TLE) is one of the most common forms of epilepsy. Unfortunately, the clinical outcomes of TLE cannot be determined based only on current diagnostic modalities. A better understanding of white matter (WM) connectivity changes in TLE may aid the identification of network abnormalities associated with TLE and the phenotypic characterisation of the disease. METHODS We implemented a novel approach for characterising microstructural changes along WM pathways using diffusional kurtosis imaging (DKI). Along-the-tract measures were compared for 32 subjects with left TLE and 36 age-matched and gender-matched controls along the left and right fimbria-fornix (FF), parahippocampal WM bundle (PWMB), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF) and cingulum bundle (CB). Limbic pathways were investigated in relation to seizure burden and control with antiepileptic drugs. RESULTS By evaluating measures along each tract, it was possible to identify abnormalities localised to specific tract subregions. Compared with healthy controls, subjects with TLE demonstrated pathological changes in circumscribed regions of the FF, PWMB, UF, AF and ILF. Several of these abnormalities were detected only by kurtosis-based and not by diffusivity-based measures. Structural WM changes correlated with seizure burden in the bilateral PWMB and cingulum. CONCLUSIONS DKI improves the characterisation of network abnormalities associated with TLE by revealing connectivity abnormalities that are not disclosed by other modalities. Since TLE is a neuronal network disorder, DKI may be well suited to fully assess structural network abnormalities related to epilepsy and thus serve as a tool for phenotypic characterisation of epilepsy.
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Affiliation(s)
- G Russell Glenn
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Jens H Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Joseph A Helpern
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Maria V Spampinato
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Ruben Kuzniecky
- Department of Neurology, New York University, New York City, New York, USA
| | - Simon S Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK Department of Clinical Neuroscience, Institute of Psychiatry, King's College London, London, UK
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA
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15
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Lanzafame S, Giannelli M, Garaci F, Floris R, Duggento A, Guerrisi M, Toschi N. Differences in Gaussian diffusion tensor imaging and non-Gaussian diffusion kurtosis imaging model-based estimates of diffusion tensor invariants in the human brain. Med Phys 2016; 43:2464. [DOI: 10.1118/1.4946819] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
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16
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Goveas J, O'Dwyer L, Mascalchi M, Cosottini M, Diciotti S, De Santis S, Passamonti L, Tessa C, Toschi N, Giannelli M. Diffusion-MRI in neurodegenerative disorders. Magn Reson Imaging 2015; 33:853-76. [PMID: 25917917 DOI: 10.1016/j.mri.2015.04.006] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2014] [Revised: 04/18/2015] [Accepted: 04/19/2015] [Indexed: 12/11/2022]
Abstract
The ability to image the whole brain through ever more subtle and specific methods/contrasts has come to play a key role in understanding the basis of brain abnormalities in several diseases. In magnetic resonance imaging (MRI), "diffusion" (i.e. the random, thermally-induced displacements of water molecules over time) represents an extraordinarily sensitive contrast mechanism, and the exquisite structural detail it affords has proven useful in a vast number of clinical as well as research applications. Since diffusion-MRI is a truly quantitative imaging technique, the indices it provides can serve as potential imaging biomarkers which could allow early detection of pathological alterations as well as tracking and possibly predicting subtle changes in follow-up examinations and clinical trials. Accordingly, diffusion-MRI has proven useful in obtaining information to better understand the microstructural changes and neurophysiological mechanisms underlying various neurodegenerative disorders. In this review article, we summarize and explore the main applications, findings, perspectives as well as challenges and future research of diffusion-MRI in various neurodegenerative disorders including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, Huntington's disease and degenerative ataxias.
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Affiliation(s)
- Joseph Goveas
- Department of Psychiatry and Behavioral Medicine, and Institute for Health and Society, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Laurence O'Dwyer
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt, Germany
| | - Mario Mascalchi
- Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy; Quantitative and Functional Neuroradiology Research Program at Meyer Children and Careggi Hospitals of Florence, Florence, Italy
| | - Mirco Cosottini
- Department of Translational Research and New Surgical and Medical Technologies, University of Pisa, Pisa, Italy; Unit of Neuroradiology, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Silvia De Santis
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Luca Passamonti
- Institute of Bioimaging and Molecular Physiology, National Research Council, Catanzaro, Italy; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Carlo Tessa
- Division of Radiology, "Versilia" Hospital, AUSL 12 Viareggio, Lido di Camaiore, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, Medical Physics Section, University of Rome "Tor Vergata", Rome, Italy; Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Marco Giannelli
- Unit of Medical Physics, Pisa University Hospital "Azienda Ospedaliero-Universitaria Pisana", Pisa, Italy.
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