Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure

Automated Spatial Brain Normalization and Hindbrain White Matter Reference Tissue Give Improved [F-18]-Florbetaben PET Quantitation in Alzheimer's Model Mice

Preclinical PET studies of 13-amyloid (A beta) accumulation are of growing importance, but comparisons between research sites require standardized and optimized methods for quantitation. Therefore, we aimed to evaluate systematically the (1) impact of an automated algorithm for spatial brain normalization, and (2) intensity scaling methods of different reference regions for A beta-PET in a large dataset of transgenic mice. PS2APP mice in a 6 week longitudinal setting (N = 37) and another set of PS2APP mice at a histologically assessed narrow range of A beta burden (N = 40) were investigated by florbetaben PET Manual spatial normalization by three readers at different training levels was performed prior to application of an automated brain spatial normalization and inter -reader agreement was assessed by Fleiss Kappa (kappa). For this method the impact of templates at different pathology stages was investigated. Four different reference regions on brain uptake normalization were used to calculate frontal cortical standardized uptake value ratios (SUVRc-rx/REF) relative to raw SUVCTX. Results were compared on the basis of longitudinal stability (Cohen's d), and in reference to gold standard histopathological quantitation (Pearson's R). Application of an automated brain spatial normalization resulted in nearly perfect agreement (all If kappa >= 0.99) between different readers, with constant or improved correlation with histology. Templates based on inappropriate pathology stage resulted in up to 2.9% systematic bias for SUVRc-Fx, /REF " All SUVRG-Fx, /REF methods performed better than SUVGTx both with regard to longitudinal stability (d >= 1.21 vs. d = 0.23) and histological gold standard agreement (R >= 0.66 vs. R >= 0.31). Voxel-wise analysis suggested a physiologically implausible longitudinal decrease by global mean scaling. The hindbrain white matter reference (R-mean = 0.75

Etablierung einer standardisierten Analyse longitudinaler Aβ-PET Scans zur effektiven Evaluation der BACE-Inhibition im transgenen Alzheimer-Mausmodell

Das erste Ziel der Promotionsarbeit war es eine optimale und standardisierte Analyse der Positron Emissions Tomographie in einem transgenen Kleintiermodell der Amyloid Anreicherung zu etablieren. Hierzu erfolgte die Evaluation einer automatisierten räumlichen Normalisierung und einer Intensitätsskalierung zu verschiedenen Referenzregionen. Insgesamt wurden 114 Aβ PET Scans des Mausmodells PS2APP mit dem Tracer FBB angefertigt. Darunter waren 37 Mäuse mit einem Baseline Scan (BL) im Alter von 8 Monaten und einem kurzfristigen Folgescan (FU) im Alter von 9,5 Monate, sowie 40 Scans im Alter von 13-16 Monaten (TER). Die so akquirierten microPET Dateien wurden durch eine starre, manuelle Fusion auf eine zerebrale MRT Vorlage durch drei Analysten mit unterschiedlichem Erfahrungsniveau untersucht. Im Anschluss wurden die Bilder durch Verwendung einer automatisierten, nicht-linearen Transformation auf altersabhängige Schablonen räumlich normalisiert und die Übereinstimmung der Analysten mittels Fleiss Kappa ermittelt. Zur Berechnung der zerebralen Amyloid Last definierten wir, neben dem reinen SUV (CTX), vier Referenzregionen mit den entsprechenden SUVR. Als Referenzregionen bestimmten wir den Hirnstamm (BST), das Zerebellum (CBL), die weiße Substanz (WM) und das gesamte Gehirn (GLM). Die Ergebnisse wurden anhand der longitudinalen Stabilität und der Übereinstimmung mit dem histologischen Goldstandart verglichen. Die räumliche Normalisierung resultierte in einer nahezu perfekten Übereinstimmung der Analysen. Die Korrelation zur Histologie blieb konstant oder verbesserte sich (alle κ ≥ 0.99). Alle SUVR waren der reinen SUV (CTX) überlegen (d ≥1.21 vs. d = 0.23 und R ≥ 0.66 vs. R ≥ 0.31). Eine Analyse der Skalierung zum GBM auf Voxelebene erbrachte einen physiologisch nicht erklärbaren Abfall der SUVR im Bereich des Rautenhirns. Die optimale Referenzregion definierten wir anhand der Korrelation mit der Histologie, der artifizielle Anhebung der Varianz in der Gruppe, der Effektgröße der longitudinalen kortikalen Amyloid Progression, den Effekt der Pathologie in der Referenzregion und den Einfluss einzelner Tiere. In Zusammenschau der untersuchten Qualitäten war die Intensitätsskalierung zur WM den übrigen SUVR überlegen. Auf Basis dieser Ergebnisse erfolgte im Anschluss, als zweiter Teil, eine multimodale Interventionsstudie zur Untersuchung der Amyloidose unter Therapie mit dem BACE-Inhibitor RO5508887. Hierzu wurden 26 weibliche PS2APP-Swe (TG) und 22 weibliche C57BL/6 (WT) Mäuse im Alter von 9,5 Monaten in einen Placeboarm (TG-VEH) und einen Interventionsarm (TG-BSI) randomisiert. Nach initialer Anfertigung eines Baseline [18F] -FBB PET Scans erfolgte die zweimal tägliche orale Applikation von RO5508887 oder einer entsprechenden Placebolösung über einen Zeitraum von 4 Monaten. Nach 10 und nach 18,5 Wochen wurde jeweils ein FU Scan angefertigt. Im Anschluss an den finalen Scan wurden die Gehirne der Tiere zur histologischen und biochemischen Quantifizierung und Differenzierung entnommen. Der Amyloid Progress war 15.3 ± 4.4% in der Placebo Gruppe und 8.4 ± 2.2% in der Therapiegruppe. Durch die Therapie konnte der Anstieg der Amyloidose signifikant reduziert werden (p<0.001). Diesen Effekt konnte die histologische Analyse in Bezug auf die Plaque-Belastung (-9,7%, p < 0,05) und die Plaque-Dichte (-9,9%, p <0,05) bestätigen. Auch die biochemische Analyse zeiget eine Reduktion des löslichen und unlöslichen Aβ40 ( -47% und -10%), sowie des löslichen und unlöslichen Aβ42 (-61% und -40%). Die akute BACE Inhibition konnte durch eine Reduktion des sAPP-β bei Anstieg des sAPP-α und unveränderten sAPP nachgewiesen werden. Außerdem stellte sich eine negative Korrelation zwischen dem Ausmaß der Amyloidose bei Therapiebeginn und dem Progress unter Therapie dar. Zusätzlich beobachteten wir eine weitere inverse Korrelation zwischen der naiven Amyloid Akkumulation und dem Therapieeffekt. So war die absolute Reduktion der Aβ Deposition unter Behandlung 7,6 ± 1,7% in hoch akkumulierenden Regionen (≥ 10%) und 4,8 ± 2,0% in niedrig akkumulierenden Regionen (<10%). Der relative Behandlungseffekt war 49 ± 11% in hochakkumulierenden Hirnregionen und 69 ± 28% in Regionen mit geringer naiver Akkumulation. In Regionen mit niedrigem, naivem Progresse konnte ein Schwellenwert der regionale Depositionsrate von 3,7%/18,5 Wochen bestimmt werden, unterhalb dem eine vollständige Blockierung der weiteren Signalzunahme angenommen werden kann. Zusammenfassend stellten wir die Überlegenheit einer Analyse von longitudinalen Aβ PET Daten im Mausmodell durch Verwendung einer räumlichen Normalisierung und einer SUVR mit Skalierung zur WM dar. Auf Basis dieser Erkenntnisse konnten wir im Anschluss zeigen, dass der Therapieeffekt einer BACE Inhibition auf den Progress der zerebralen Amyloidose von der Aβ-Last bei Therapiebeginn abhängig ist

A Standardized [18F]-FDG-PET Template for Spatial Normalization in Statistical Parametric Mapping of Dementia

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Impact of Global Mean Normalization on Regional Glucose Metabolism in the Human Brain

Because the human brain consumes a disproportionate fraction of the resting body’s energy, positron emission tomography (PET) measurements of absolute glucose metabolism (CMRglc) can serve as disease biomarkers. Global mean normalization (GMN) of PET data reveals disease-based differences from healthy individuals as fractional changes across regions relative to a global mean. To assess the impact of GMN applied to metabolic data, we compared CMRglc with and without GMN in healthy awake volunteers with eyes closed (i.e., control) against specific physiological/clinical states, including healthy/awake with eyes open, healthy/awake but congenitally blind, healthy/sedated with anesthetics, and patients with disorders of consciousness. Without GMN, global CMRglc alterations compared to control were detected in all conditions except in congenitally blind where regional CMRglc variations were detected in the visual cortex. However, GMN introduced regional and bidirectional CMRglc changes at smaller fractions of the quantitative delocalized changes. While global information was lost with GMN, the quantitative approach (i.e., a validated method for quantitative baseline metabolic activity without GMN) not only preserved global CMRglc alterations induced by opening eyes, sedation, and varying consciousness but also detected regional CMRglc variations in the congenitally blind. These results caution the use of GMN upon PET-measured CMRglc data in health and disease

Multimodal analysis in normal aging, mild cognitive impairment, and Alzheimer's disease: group differentiation, baseline cognition, and prediction of future cognitive decline

Thesis (Ph.D.)--Boston UniversityAlzheimer's disease (AD) is a progressive neurodegenerative disease with an insidious onset that makes it difficult to distinguish from normal aging. It begins with an impairment of memory that develops into amnestic mild cognitive impairment (aMCI) and later to dementia as deficits become apparent in other cognitive domains. Effective biomarkers that differentiate normal aging, MCI, and AD and predict future cognitive decline are needed. Potential biomarkers have been studied in isolation, but their impact when combined is not understood. The goal of this project is to determine the optimal combination of CSF biomarkers, MRI morphometry, FDG PET metabolism, and neuropsychological test scores to differentiate between normal aging subjects and those with MCI and AD. This study addresses: 1) the optimal normalization region and partial volume correction method to quantify FDG PET analysis, 2) the effects of adjusting MRI-based cortical thickness measures for differences in gray/white matter tissue contrast in normal aging and disease, 3) whether multimodal multivariate stepwise logistic regression models can predict group membership, and 4) whether multimodal multivariate stepwise linear regression models can determine which imaging and CSF biomarker variables best predict future cognitive decline. The results indicate that normalizing FDG PET to the cerebellum along with using a gray matter mask for partial volume correction provides optimal prediction. In contrast, age-associated changes in gray/white matter intensity ratio did not differentiate between the groups and only slightly improved the efficacy of cortical thickness as a biomarker. MRI morphometry of the gray matter and neuropsychological test scores were better able to discriminate between the groups than FDG PET or CSF biomarker concentrations. Combining all modalities significantly improved the index of discrimination, especially at the earliest stages of the disease. MRI gray matter morphometry variables were more highly associated with baseline cognitive function and best predicted future cognitive decline compared to other variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores, and CSF biomarkers provides significantly better discrimination than any modality alone. Hence, the variables important for discriminating between the groups may be candidates for biomarkers in human clinical interventional trials

딥러닝 기반 군집화 방법을 이용하여 FDG PET에서 알츠하이머병의 공간적 뇌 대사 패턴의 특징적 아형 분류

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 분자의학 및 바이오제약학과, 2022.2. 이동수.알츠하이머병은 아밀로이드와 타우 침착과 같은 병리학적 특징을 공유함에도 불구하고 광범위한 임상병리학적 특성을 보인다. 본 연구에서는 딥러닝 기반 군집화 방법을 이용하여 FDG PET 영상에서 알츠하이머병 특징적 아형을 분류하여 신경 퇴행의 공간적 뇌 대사 패턴을 이해하고자 하였으며, 공간적 뇌 대사 패턴에 의해 정의된 아형의 임상병리학적 특징을 밝히고자 하였다. Alzheimer’s Disease Neuroimaging Initiative(ADNI) 데이터베이스로부터 첫번째 방문 및 추적 방문을 포함한 알츠하이머병, 경도인지장애, 인지 정상군의 총 3620개의 FDG 뇌 양전자단층촬영(PET) 영상을 수집하였다. 알츠하이머병에서 질병의 진행 외의 뇌 대사 패턴을 나타내는 표현(representation)을 찾기 위하여, 조건부 변이형 오토인코더(conditional variational autoencoder)를 사용하였으며, 인코딩된 표현으로부터 군집화(clustering)를 시행하였다. 알츠하이머병의 뇌 FDG PET (n=838)과 CDR-SB(Clinical Demetria Rating Scale Sum of Boxes) 점수가 cVAE 모델의 입력값으로 사용되었으며, 군집화에는 k-means 알고리즘이 사용되었다. 훈련된 딥러닝 모델은 경도인지장애군 (n=1761)의 뇌 FDG PET에 전이(transfer)되어 각 아형의 서로 다른 궤적(trajectory)과 예후를 밝히고자 하였다. 통계적 파라미터 지도작성법(Statistical Parametric Mapping, SPM)을 이용하여 각 군집의 공간적 패턴을 시각화 하였으며, 각 군집의 임상적 및 생물학적 특징을 비교하였다. 또한 아형 별 경도인지장애로부터 알츠하이머병으로 전환되는 비율을 비교하였다. 딥러닝 기반 군집화 방법으로 4개의 특징적 아형이 분류되었다. (i) S1 (angular): 모이랑(angular gyrus)에서 현저한 대사 저하를 보이며 분산된 피질의 대사 저하 패턴, 남성에서 빈도 높음, 더 많은 아밀로이드 침착, 더 적은 타우 침착, 더 심한 해마 위축, 초기 단계의 인지 저하의 특징을 보였다. (ii) S2 (occipital): 후두엽(occipital) 피질에서 현저한 대사 저하를 보이며 후부 우세한 대사 저하 패턴, 더 적은 연령, 더 많은 타우, 더 적은 해마 위축, 더 낮은 집행 및 시공간 점수, 경도인지장애로부터 알츠하이머병으로의 빠른 전환의 특징을 보였다. (iii) S3(orbitofrontal): 안와전두(orbitofrontal) 피질에서 현저한 대사 저하를 보이며 전방 우세한 대사 저하 패턴, 더 높은 연령, 더 적은 아밀로이드 침착, 더 심한 해마 위축, 더 높은 집행 및 시공간 점수의 특징을 보였다. (iv) S4(minimal): 최소의 대사 저하를 보임, 여성에서 빈도 높음, 더 적은 아밀로이드 침착, 더 많은 타우 침착, 더 적은 해마 위축, 더 높은 인지기능 점수의 특징을 보였다. 결론적으로, 본 연구에서 우리는 서로 다른 뇌 병리 및 임상 특성을 가진 알츠하이머병의 특징적 아형을 분류하였다. 또한 우리 딥러닝 모델은 경도인지장애군에 성공적으로 전이되어 아형 별 경도인지장애로부터 알츠하이머병으로 전환되는 예후를 예측할 수 있었다. 본 결과는 FDG PET에서 알츠하이머병의 특징적 아형은 개인의 임상 결과에 영향을 미칠 수 있고, 병태생리학 측면에서 알츠하이머병의 광범위한 스펙트럼을 이해하는데 단서를 제공할 수 있음을 시사한다.Alzheimer’s disease (AD) presents a broad spectrum of clinicopathologic profiles, despite common pathologic features including amyloid and tau deposition. Here, we aimed to identify AD subtypes using deep learning-based clustering on FDG PET images to understand distinct spatial patterns of neurodegeneration. We also aimed to investigate clinicopathologic features of subtypes defined by spatial brain metabolism patterns. A total of 3620 FDG brain PET images with AD, mild cognitive impairment (MCI), and cognitively normal controls (CN) at baseline and follow-up visits were obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. In order to identify representations of brain metabolism patterns different from disease progression in AD, a conditional variational autoencoder (cVAE) was used, followed by clustering using the encoded representations. FDG brain PET images with AD (n=838) and Clinical Demetria Rating Scale Sum of Boxes (CDR-SB) scores were used as inputs of cVAE model and the k-means algorithm was applied for the clustering. The trained deep learning model was also transferred to FDG brain PET image with MCI (n=1761) to identify differential trajectories and prognosis of subtypes. Statistical parametric maps were generated to visualize spatial patterns of clusters, and clinical and biological characteristics were compared among the clusters. The conversion rate from MCI to AD was also compared among the subtypes. Four distinct subtypes were identified by deep learning-based FDG PET clusters: (i) S1 (angular), showing prominent hypometabolism in the angular gyrus with a diffuse cortical hypometabolism pattern; frequent in males; more amyloid; less tau; more hippocampal atrophy; cognitive decline in the earlier stage. (ii) S2 (occipital), showing prominent hypometabolism in the occipital cortex with a posterior-predominant hypometabolism pattern; younger age; more tau; less hippocampal atrophy; lower executive and visuospatial scores; faster conversion from MCI to AD. (iii) S3 (orbitofrontal), showing prominent hypometabolism in the orbitofrontal cortex with an anterior-predominant hypometabolism pattern; older age; less amyloid; more hippocampal atrophy; higher executive and visuospatial scores. (iv) S4 (minimal), showing minimal hypometabolism; frequent in females; less amyloid; more tau; less hippocampal atrophy; higher cognitive scores. In conclusion, we could identify distinct subtypes in AD with different brain pathologies and clinical profiles. Also, our deep learning model was successfully transferred to MCI to predict the prognosis of subtypes for conversion from MCI to AD. Our results suggest that distinct AD subtypes on FDG PET may have implications for the individual clinical outcomes and provide a clue to understanding a broad spectrum of AD in terms of pathophysiology.1. Introduction 1 1.1 Heterogeneity of Alzheimer's disease 1 1.2 FDG PET as a biomarker of Alzheimer's disease 1 1.3 Biologic subtypes of Alzheimer's disease 2 1.4 Dimensionality reduction methods 5 1.5 Variational autoencoder for clustering 8 1.6 Final goal of the study 10 2. Methods 11 2.1 Subjects 11 2.2 FDG PET data acquisition and preprocessing 12 2.3 Deep learning-based model for representations of FDG PET in AD 12 2.4 Clustering method for AD subtypes on FDG PET 17 2.5 Transfer of deep learning-based FDG PET cluster model for MCI subtypes 17 2.6 Visualization of subtype-specific spatial brain metabolism pattern 21 2.7 Clinical and biological characterization 21 2.8 Prognosis prediction of MCI subtypes 22 2.9 Generation of subtype-specific FDG PET images 22 2.10 Statistical analysis 23 3. Results 24 3.1 Deep learning-based FDG PET clusters 24 3.2 Spatial brain metabolism pattern in AD subtypes 27 3.3 Clinical and biological characterization in AD subtypes 32 3.4 Subtype-specific spatial metabolism patterns resemble in MCI 43 3.5 Clinical and biological characterization in MCI subtypes 50 3.6 Prognosis prediction of subtypes for conversion from MCI to AD 56 3.7 Generating FDG PET images of AD subtypes 61 4. Discussion 66 4.1 Limitations of previous subtyping approach 68 4.2 Interpretation of results 68 4.3 Strength of our deep learning-based clustering approach 73 4.4 Strength of our deep learning-based AD subtypes 77 4.5 Limitations and future directions 82 5. Conclusion 83 References 84 Supplementary Figures 99 국문 초록 101박

Mechanisms of cognitive-behavioral therapy for obsessive-compulsive disorder involve robust and extensive increases in brain network connectivity.

Cognitive-behavioral therapy (CBT) is effective for obsessive compulsive disorder (OCD); however, little is understood about its mechanisms related to brain network connectivity. We examined connectivity changes from resting-state functional magnetic resonance imaging data pre-to-post-CBT in 43 OCD participants, randomized to receive either 4 weeks of intensive CBT or 4 weeks waitlist followed by 4 weeks of CBT, and 24 healthy controls before and after 4 weeks of no treatment. Network-based-statistic analysis revealed large-magnitude increases in OCD connectivity in eight networks. Strongest increases involved connectivity between the cerebellum and caudate/putamen, and between the cerebellum and dorsolateral/ventrolateral prefrontal cortices. Connectivity increases were associated with increased resistance to compulsions. Mechanisms of CBT may involve enhanced cross-network integration, both within and outside of classical cortico-striatal-thalamo-cortical regions; those involving cerebellar to striatal and prefrontal regions may reflect acquisition of new non-compulsive goal-directed behaviors and thought patterns. Our findings have implications for identifying targets for enhancing treatment efficacy and monitoring treatment progress

Quantitative Intensity Harmonization of Dopamine Transporter SPECT Images Using Gamma Mixture Models

PURPOSE: Differences in site, device, and/or settings may cause large variations in the intensity profile of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images. However, the current standard to evaluate these images, the striatal binding ratio (SBR), does not efficiently account for this heterogeneity and the assessment can be unequivalent across distinct acquisition pipelines. In this work, we present a voxel-based automated approach to intensity normalize such type of data that improves on cross-session interpretation. PROCEDURES: The normalization method consists of a reparametrization of the voxel values based on the cumulative density function (CDF) of a Gamma distribution modeling the specific region intensity. The harmonization ability was tested in 1342 SPECT images from the PPMI repository, acquired with 7 distinct gamma camera models and at 24 different sites. We compared the striatal quantification across distinct cameras for raw intensities, SBR values, and after applying the Gamma CDF (GDCF) harmonization. As a proof-of-concept, we evaluated the impact of GCDF normalization in a classification task between controls and Parkinson disease patients. RESULTS: Raw striatal intensities and SBR values presented significant differences across distinct camera models. We demonstrate that GCDF normalization efficiently alleviated these differences in striatal quantification and with values constrained to a fixed interval [0, 1]. Also, our method allowed a fully automated image assessment that provided maximal classification ability, given by an area under the curve (AUC) of AUC = 0.94 when used mean regional variables and AUC = 0.98 when used voxel-based variables. CONCLUSION: The GCDF normalization method is useful to standardize the intensity of DAT SPECT images in an automated fashion and enables the development of unbiased algorithms using multicenter datasets. This method may constitute a key pre-processing step in the analysis of this type of images.Instituto de Salud Carlos III FI14/00497 MV15/00034Fondo Europeo de Desarrollo Regional FI14/00497 MV15/00034ISCIII-FEDER PI16/01575Wellcome Trust UK Strategic Award 098369/Z/12/ZNetherland Organization for Scientific Research NWO-Vidi 864-12-00

Cerebral F18 -FDG PET CT in Children: Patterns during Normal Childhood and Clinical Application of Statistical Parametric Mapping

The first aim was to recruit and analyse a high quality dataset of cerebral FDG PET CT scans in neurologically normal children. Using qualitative, semi-quantitative and statistical parametric mapping (SPM) techniques, the results showed that a pattern of FDG uptake similar to adults does not occur by one year of age as was previously believed, but the regional FDG uptake changes throughout childhood driven by differing age related regional rates of increasing FDG uptake. The second aim was to use this normal dataset in the clinical analysis of cerebral FDG PET CT scans in children with epilepsy and Neurofibromatosis type 1 (NF1). The normal dataset was validated for single-subject-versus-group SPM analysis and was highly specific for identifying the epileptogenic focus likely to result in a good post-operative outcome in children with epilepsy. Qualitative, semi-quantitative and group-versus-group SPM analyses were applied to FDG PET CT scans in children with NF1. The results showed reduced metabolism in the thalami and medial temporal lobes compared to neurologically normal children. This thesis has produced novel findings that advance the understanding of childhood brain development and has developed SPM techniques that can be applied to cerebral FDG PET CT scans in children with neurological disorders