Reduced local diffusion homogeneity as a biomarker for temporal lobe epilepsy

Liu, Hui-hua; Wang, Jun; Li, Jianping; Ye, Wei; Zheng, Jinou

doi:10.1097/md.0000000000004032

Cited by 11 publications

(12 citation statements)

References 30 publications

(35 reference statements)

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“…To our best knowledge, this is the first study that comprehensively compare LDH and FA in a imaging-based clinical research, and the first study using LDH for DWI-based brain alteration detection in T2DM (American Diabetes, 2010; Liu et al, 2016, 2017; Zhuo et al, 2016). In this study, voxel-based intra-voxel (FA) and inter-voxel diffusivity metrics (LDH) were used in a whole-brain exploratory study to explore and compare their sensitivity in between-group comparison and brain-clinical association analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, Kendall’s coefficient of concordance is used to quantify such an overall diffusivity similarity between a centered voxel and those of all its nearest neighborhood (Gong, 2013). For example, a higher LDH value may indicate local coherence enhancement of the fibers, possibly caused by changes in fiber myelination, diameter, or density (Gong, 2013; Liu et al, 2016, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Local Diffusion Homogeneity Provides Supplementary Information in T2DM-Related WM Microstructural Abnormality Detection

et al. 2019

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Objectives: We aimed to investigate whether an inter-voxel diffusivity metric (local diffusion homogeneity, LDH), can provide supplementary information to traditional intra-voxel metrics (i.e., fractional anisotropy, FA) in white matter (WM) abnormality detection for type 2 diabetes mellitus (T2DM).Methods: Diffusion tensor imaging was acquired from 34 T2DM patients and 32 healthy controls. Voxel-based group-difference comparisons based on LDH and FA, as well as the association between the diffusion metrics and T2DM risk factors [i.e., body mass index (BMI) and systolic blood pressure (SBP)], were conducted, with age, gender and education level controlled.Results: Compared to the controls, T2DM patients had higher LDH in the pons and left temporal pole, as well as lower FA in the left superior corona radiation (p < 0.05, corrected). In T2DM, there were several overlapping WM areas associated with BMI as revealed by both LDH and FA, including right temporal lobe and left inferior parietal lobe; but the unique areas revealed only by using LDH included left inferior temporal lobe, right supramarginal gyrus, left pre- and post-central gyrus (at the semiovale center), and right superior radiation. Overlapping WM areas that associated with SBP were found with both LDH and FA, including right temporal pole, bilateral orbitofrontal area (rectus gyrus), the media cingulum bundle, and the right cerebellum crus I. However, the unique areas revealed only by LDH included right inferior temporal lobe, right inferior occipital lobe, and splenium of corpus callosum.Conclusion: Inter- and intra-voxel diffusivity metrics may have different sensitivity in the detection of T2DM-related WM abnormality. We suggested that LDH could provide supplementary information and reveal additional underlying brain changes due to diabetes.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Local Diffusion Homogeneity Provides Supplementary Information in T2DM-Related WM Microstructural Abnormality Detection

et al. 2019

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“…The design of most of the molecular, electrophysiologic, or MRI bio-marker studies performed in epilepsy populations indicates that the objective has been to discover sensitive and specific biomarkers differentiating the epilepsy population from controls (Table 3). These studies report plasma/serum/CSF miRNA and protein biomarkers, plasma exosome miRNA biomarkers, cortical brain tissue miRNA biomarkers, brain diffusion tensor imaging/diffusion-weighted imaging-MRI biomarkers, and electrophysiologic biomarkers that differentiate patients with epilepsy (Wang et al, 2015b;Wang et al, 2016aWang et al, , 2016b, temporal lobe epilepsy (TLE) (Raoof et al, 2017), medial TLE (mTLE) (Avansini et al, 2017;Liu et al, 2016), mTLE with hippocampal sclerosis (Yan et al, 2017), idiopathic generalized epilepsy (Won et al, 2017), drugrefractory epilepsy (An et al, 2016;Wang et al, 2015a), drug-resistant focal epilepsy (Pollard et al, 2012), drug-refractory TLE (Walker et al, 2017), or focal cortical dysplasia with refractory TLE (Sun et al, 2016b;Wang et al, 2015) from controls ( Table 3). Many of these diagnostic epilepsy biomarkers have an AUC > 0.800, suggesting acceptable sensitivity and specificity to make an epilepsy diagnosis without evidence of the occurrence of a seizure.…”

Section: Diagnostic Biomarkers For Epilepsy-mentioning

confidence: 99%

Epilepsy biomarkers – Toward etiology and pathology specificity

Pitkänen

Ndode-Ekane

Lapinlampi

et al. 2019

Neurobiology of Disease

136

131

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A biomarker is a characteristic that is measured as an indicator of normal biologic processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarker modalities include molecular, histologic, radiographic, or physiologic characteristics. In 2015, the FDA-NIH Joint Leadership Council developed the BEST Resource (Biomarkers, EndpointS, and other Tools) to improve the understanding and use of biomarker terminology in biomedical research, clinical practice, and medical product development. The BEST biomarker categories include: (a) susceptibility/risk biomarkers, (b) diagnostic biomarkers, (c) monitoring biomarkers, (d) prognostic biomarkers, (e) predictive biomarkers, (f) pharmacodynamic/response biomarkers, and (g) safety biomarkers. Here we review 30 epilepsy biomarker studies that have identified (a) diagnostic biomarkers for epilepsy, epileptogenesis, epileptogenicity, drug-refractoriness, and status epilepticus - some of the epileptogenesis and epileptogenicity biomarkers can also be considered prognostic biomarkers for the development of epilepsy in subjects with a given brain insult, (b) predictive biomarkers for epilepsy surgery outcome, and (c) a response biomarker for therapy outcome. The biomarker modalities include plasma/serum/exosomal and cerebrospinal fluid molecular biomarkers, brain tissue molecular biomarkers, imaging biomarkers, electrophysiologic biomarkers, and behavioral/cognitive biomarkers. Both single and combinatory biomarkers have been described. Most of the reviewed biomarkers have an area under the curve >0.800 in receiver operating characteristics analysis, suggesting high sensitivity and specificity. As discussed in this review, we are in the early phase of the learning curve in epilepsy biomarker discovery. Many of the seven biomarker categories lack epilepsy-related biomarkers. There is a need for epilepsy biomarker discovery using proper, statistically powered study designs with validation cohorts, and the development and use of novel analytical methods. A strategic roadmap to discuss the research priorities in epilepsy biomarker discovery, regulatory issues, and optimization of the use of resources, similar to those devised in the cancer and Alzheimer's disease research areas, is also needed.

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“…[14][15][16] LDH can, thus, provide valuable information complementary to those obtained using conventional DTI metrics. Here, we examined whether a combination of clinical assessment and DTI findings obtained in the acute phase of subcortical infarction could accurately predict the resolution of upper limb impairment within 3 months after onset.…”

Section: Strokementioning

confidence: 99%

“…Kendall coefficient concordance was used to quantify the similarity of each voxel diffusivity vector to those of the 26 nearest voxels. [14][15][16] For between-group comparisons, a permutationbased 2-sample unpaired t test using the general linear model (5000 permutations) with FSL randomize program was used to compare the FA, MD, and LDH differences. Age was included as a covariate of no interest.…”

Section: Tract-based Spatial Statistics Analysismentioning

confidence: 99%

Motor Recovery Prediction With Clinical Assessment and Local Diffusion Homogeneity After Acute Subcortical Infarction

Liu

Tan

Dang

et al. 2017

Stroke

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Background and Purpose-Initial clinical assessment or conventional diffusion tensor imaging parameters alone do not reliably predict poststroke recovery of motor function. Recently, local diffusion homogeneity (LDH) has been proposed to represent the local coherence of water molecule diffusion and can serve as a complementary marker for investigating white matter alterations of the brain. We aimed to determine whether a combination of initial clinical assessment and LDH could predict motor recovery after acute subcortical infarction. Methods-Standard upper extremity Fugl-Meyer assessment and diffusion tensor imaging were performed 1, 4, and 12 weeks after onset in 50 patients with subcortical infarction. Proportional recovery model residuals were used to assign patients to proportional recovery and poor recovery groups. Tract-based spatial statistics analysis was used to compare diffusion differences between proportional and poor recovery outcomes. Multivariate logistic regression model was used to identify the predictors of motor improvement within 12 weeks after stroke. Results-The poor recovery group had lower LDH than the proportional recovery group, mainly in the ipsilesional corticospinal tract in the superior corona radiate and posterior limb of internal capsule 1 week after stroke (P<0.005; family-wise error corrected). Multivariate logistic regression analysis indicated that both initial Fugl-Meyer assessment and LDH in the ipsilesional corticospinal tract in the superior corona radiate and posterior limb of internal capsule were predictors of motor improvement within 12 weeks after stroke (G=47.22; P<0.001). Leave-one-out cross-validation confirmed a positive predictive value of 0.818, a negative predictive value of 0.833, and an accuracy of 0.824 (P<0.00 001; permutation test). Conclusions-These

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Reduced local diffusion homogeneity as a biomarker for temporal lobe epilepsy

Abstract: Supplemental Digital Content is available in the text

Cited by 11 publications

References 30 publications

Local Diffusion Homogeneity Provides Supplementary Information in T2DM-Related WM Microstructural Abnormality Detection

Local Diffusion Homogeneity Provides Supplementary Information in T2DM-Related WM Microstructural Abnormality Detection

Epilepsy biomarkers – Toward etiology and pathology specificity

Motor Recovery Prediction With Clinical Assessment and Local Diffusion Homogeneity After Acute Subcortical Infarction

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