Evaluating whole‐brain tissue‐property changes in MRI‐negative pharmacoresistant focal epilepsies using MR fingerprinting

Su, Ting-Yu; Tang, Yingying; Choi, Joon Yul; Hu, Siyuan; Sakaie, Ken; Murakami, Hiroatsu; Jones, Stephen E.; Blümcke, Ingmar; Najm, Imad; Ma, Dan; Wang, Irène

doi:10.1111/epi.17488

Cited by 8 publications

(14 citation statements)

References 48 publications

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“…Increasing evidence suggests that biologically distinct epilepsy syndromes may be characterized by shared disturbances in cortico-subcortical brain networks 7,8 and, specifically in focal epilepsies, cortical abnormalities extend spatially beyond the presumed epileptogenic focus 4 . In this work, we assessed whole brain cortical changes in children with drug-resistant focal epilepsy.…”

Section: Discussionmentioning

confidence: 99%

“…Focussing exclusively on childhood enabled us to gain a deeper insight into whether cortical alterations are the result of ongoing chronic disease-processes 4,18,19 and treatment-related side-effects 16,17 , or whether they emerge at an earlier disease-stage 13,27 . The marked heterogeneity in the sample in terms of lesion/focus location and underlying pathologies could be leveraged to further understand shared patterns of abnormalities.…”

Section: Discussionmentioning

confidence: 99%

“…A recent study 4 evaluated the distribution of whole-brain qT1 and qT2 changes in a sample of both children and adults with pharmaco-resistant focal epilepsy with no apparent lesions on conventional MRI. The authors reported multi-regional qT1 increases in patients, in both GM and WM ipsilateral to the epileptic origin, involving key structures of the limbic/paralimbic systems.…”

Section: Introductionmentioning

confidence: 99%

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Widespread, depth-dependent cortical microstructure alterations in paediatric focal epilepsy

Casella

Vecchiato

Cromb

et al. 2022

Preprint

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Our current understanding of focal epilepsy is evolving as increasing evidence suggests that tissue abnormalities may extend beyond the focus. In adults, widespread structural changes remote from the epileptic focus have been demonstrated with MRI, predominantly using morphological markers. However, the underlying pathophysiology of these changes is unclear, and it is not known whether these result from ongoing disease processes or treatment-related side-effects, or whether they emerge earlier. Few studies have focused on children, who typically have shorter disease duration. Fewer still have utilised quantitative MRI, which may provide a more sensitive and interpretable measure of tissue microstructural changes. In this study, we aimed to determine if there were common spatial modes of changes in cortical architecture in children with drug-resistant focal epilepsy, and secondarily if changes were related to disease severity. To assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 89 children - 43 with drug-resistant focal epilepsy [age-range=4-18 years] and 46 healthy controls [age-range=2-18 years]. We assessed depth-dependent qT1 and qT2 values across the cortex, as well as their gradient of change across cortical depths. As a post-hoc analysis, we determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically-defined abnormalities (MRI-positive) and healthy controls, and tested if this could classify patients without reported radiological abnormalities (MRI-negative). We detected depth-dependent qT1 and qT2 increases in widespread cortical areas, likely representing loss of structure such as altered cortical stratification, gliosis, myelin and iron alterations, oedema-associated increases in free-water, or a combination of these. Changes did not correlate with disease severity measures, suggesting they may appear during cerebral development and represent antecedent neurobiological alterations. Using a classifier trained with MRI-positive patients and controls, sensitivity was 62% at 100% specificity on held-out MRI-negative patients and controls. Our findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically-identified abnormalities, and possibly evident at a pre-symptomatic disease stage.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Widespread, depth-dependent cortical microstructure alterations in paediatric focal epilepsy

Casella

Vecchiato

Cromb

et al. 2022

Preprint

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“…The dictionary composes of fingerprint templates simulated according to the MR physics model for a wide range of discretized T 1 and T 2 values to ensure successful retrieval of acquired fingerprints. On a clinical 3 T MR system, this framework allows T 1 and T 2 mapping of entire human brain at an isotropic resolution of ∼1 mm using 144‐fold undersampled 3D MRF data acquired in below 10 min 7–9 . Leveraging parallel imaging techniques further reduces the whole‐brain scan time to 7.5 min 10 …”

Section: Introductionmentioning

confidence: 99%

“…On a clinical 3 T MR system, this framework allows T 1 and T 2 mapping of entire human brain at an isotropic resolution of ∼1 mm using 144-fold undersampled 3D MRF data acquired in below 10 min. [7][8][9] Leveraging parallel imaging techniques further reduces the whole-brain scan time to 7.5 min. 10 Besides clinical applications, neurological MR imaging of non-human primates is gaining increased interests.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐assisted preclinical MR fingerprinting for sub‐millimeter T₁ and T₂ mapping of entire macaque brain

Gu,

Pan,

Fang

et al. 2023

Magnetic Resonance in Med

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PurposePreclinical MR fingerprinting (MRF) suffers from long acquisition time for organ‐level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning‐assisted fast MRF framework for sub‐millimeter T1 and T2 mapping of entire macaque brain on a preclinical 9.4 T MR system.MethodsThree dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T1 and T2 mapping for each axial slice exploited a self‐attention assisted residual U‐Net to suppress aliasing‐induced quantification errors, and the transmit‐field (B1+) measurements for robustness against B1+ inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains.ResultsThe trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T1 and T2 distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35 0.35 1 mm3 can be realized via a 20‐min 3D MRF scan, which was sixfold faster than the baseline protocol.ConclusionIntroducing deep learning to MRF framework paves the way for efficient organ‐level high‐resolution quantitative MRI in preclinical applications.

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Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting

Su,

Choi,

et al. 2024

Annals of Neurology

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ObjectivesTo develop a multiparametric machine‐learning (ML) framework using high‐resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD).MaterialsWe included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age‐ and gender‐matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole‐brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z‐score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual‐level classification. Fivefold cross‐validation was performed and performance metrics were reported using receiver‐operating‐characteristic‐curve analyses.ResultsFCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF‐ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross‐validation trials for the magnetic resonance imaging negative group and MAP negative group. Type II versus non‐type‐II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases.InterpretationThe MRF‐ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024

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Evaluating whole‐brain tissue‐property changes in MRI‐negative pharmacoresistant focal epilepsies using MR fingerprinting

Cited by 8 publications

References 48 publications

Widespread, depth-dependent cortical microstructure alterations in paediatric focal epilepsy

Widespread, depth-dependent cortical microstructure alterations in paediatric focal epilepsy

Deep learning‐assisted preclinical MR fingerprinting for sub‐millimeter T₁ and T₂ mapping of entire macaque brain

Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting

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Evaluating whole‐brain tissue‐property changes in MRI‐negative pharmacoresistant focal epilepsies using MR fingerprinting

Cited by 8 publications

References 48 publications

Widespread, depth-dependent cortical microstructure alterations in paediatric focal epilepsy

Widespread, depth-dependent cortical microstructure alterations in paediatric focal epilepsy

Deep learning‐assisted preclinical MR fingerprinting for sub‐millimeter T1 and T2 mapping of entire macaque brain

Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting

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Deep learning‐assisted preclinical MR fingerprinting for sub‐millimeter T₁ and T₂ mapping of entire macaque brain