Fibre orientation atlas guided rapid segmentation of white matter tracts.

Fibre tract delineation from diffusion magnetic resonance imaging (MRI) is a valuable clinical tool for neurosurgical planning and navigation, as well as in research neuroimaging pipelines. Several popular methods are used for this task, each with different strengths and weaknesses making them more or less suited to different contexts. For neurosurgical imaging, priorities include ease of use, computational efficiency, robustness to pathology and ability to generalise to new tracts of interest. Many existing methods use streamline tractography, which may require expert neuroimaging operators for setting parameters and delineating anatomical regions of interest, or suffer from as a lack of generalisability to clinical scans involving deforming tumours and other pathologies. More recently, data-driven approaches including deep-learning segmentation models and streamline clustering methods have improved reproducibility and automation, although they can require large amounts of training data and/or computationally intensive image processing at the point of application. We describe an atlas-based direct tract mapping technique called 'tractfinder', utilising tract-specific location and orientation priors. Our aim was to develop a clinically practical method avoiding streamline tractography at the point of application while utilising prior anatomical knowledge derived from only 10-20 training samples. Requiring few training samples allows emphasis to be placed on producing high quality, neuro-anatomically accurate training data, and enables rapid adaptation to new tracts of interest. Avoiding streamline tractography at the point of application reduces computational time, false positives and vulnerabilities to pathology such as tumour deformations or oedema. Carefully filtered training streamlines and track orientation distribution mapping are used to construct tract specific orientation and spatial probability atlases in standard space. Atlases are then transformed to target subject space using affine registration and compared with the subject's voxel-wise fibre orientation distribution data using a mathematical measure of distribution overlap, resulting in a map of the tract's likely spatial distribution. This work includes extensive performance evaluation and comparison with benchmark techniques, including streamline tractography and the deep-learning method TractSeg, in two publicly available healthy diffusion MRI datasets (from TractoInferno and the Human Connectome Project) in addition to a clinical dataset comprising paediatric and adult brain tumour scans. Tract segmentation results display high agreement with established techniques while requiring less than 3 min on average when applied to a new subject. Results also display higher robustness than compared methods when faced with clinical scans featuring brain tumours and resections. As well as describing and evaluating a novel proposed tract delineation technique, this work continues the discussion on the challenges surrounding the white matter segmentation task, including issues of anatomical definitions and the use of quantitative segmentation comparison metrics.

Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm.

AIM: To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. METHOD: This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). RESULTS: Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. INTERPRETATION: We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms. WHAT THIS PAPER ADDS: The focal cortical dysplasia detection algorithm collocated with the seizure-onset zone (SOZ) in 4 out of 19 patients. The algorithm changed the resection boundaries in 1 of 19 patients undergoing stereoelectroencephalography for drug-resistant epilepsy. The patient with an altered resection due to the algorithm was seizure-free 1 year after resective surgery. Overall, the algorithm did not increase the proportion of patients in whom SOZ was identified.

The incidence of movement disorder increases with age and contrasts with subtle and limited neuroimaging abnormalities in argininosuccinic aciduria.

Argininosuccinate lyase (ASL) is integral to the urea cycle detoxifying neurotoxic ammonia and the nitric oxide (NO) biosynthesis cycle. Inherited ASL deficiency causes argininosuccinic aciduria (ASA), a rare disease with hyperammonemia and NO deficiency. Patients present with developmental delay, epilepsy and movement disorder, associated with NO-mediated downregulation of central catecholamine biosynthesis. A neurodegenerative phenotype has been proposed in ASA. To better characterise this neurodegenerative phenotype in ASA, we conducted a retrospective study in six paediatric and adult metabolic centres in the UK in 2022. We identified 60 patients and specifically looked for neurodegeneration-related symptoms: movement disorder such as ataxia, tremor and dystonia, hypotonia/fatigue and abnormal behaviour. We analysed neuroimaging with diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) in an individual with ASA with movement disorders. We assessed conventional and DTI MRI alongside single photon emission computer tomography (SPECT) with dopamine analogue radionuclide 123 I-ioflupane, in Asl-deficient mice treated by hASL mRNA with normalised ureagenesis. Movement disorders in ASA appear in the second and third decades of life, becoming more prevalent with ageing and independent from the age of onset of hyperammonemia. Neuroimaging can show abnormal DTI features affecting both grey and white matter, preferentially basal ganglia. ASA mouse model with normalised ureagenesis did not recapitulate these DTI findings and showed normal 123 I-ioflupane SPECT and cerebral dopamine metabolomics. Altogether these findings support the pathophysiology of a late-onset movement disorder with cell-autonomous functional central catecholamine dysregulation but without or limited neurodegeneration of dopaminergic neurons, making these symptoms amenable to targeted therapy.

Paediatric subarachnoid haemorrhage and severe vasospasm secondary to traumatic pseudoaneurysm of a fenestrated vertebral artery: a case report and review of the literature.

Paediatric intracranial aneurysms are rare entities accounting for less than 5% of all age intracranial aneurysms. Traumatic aneurysms are more common in children and have an association with anatomical variations such as arterial fenestrations. Here, we present a case of a child initially presenting with traumatic subarachnoid haemorrhage who returned to baseline and was discharged home only to return within 2 weeks with diffuse subarachnoid and intraventricular re-haemorrhage. A dissecting aneurysm of a duplicated (fenestrated) V4 vertebral artery segment was identified as a rare cause of rebleeding. We describe a course complicated by severe vasospasm delaying aneurysm detection and treatment. Dissecting aneurysms in children should be considered in all cases of delayed post-traumatic cranial rebleeding, particularly where there is anomalous arterial anatomy.

High-resolution microscopic diffusion anisotropy imaging in the human hippocampus at 3T.

PURPOSE: Several neurological conditions are associated with microstructural changes in the hippocampus that can be observed using DWI. Imaging studies often use protocols with whole-brain coverage, imposing limits on image resolution and worsening partial-volume effects. Also, conventional single-diffusion-encoding methods confound microscopic diffusion anisotropy with size variance of microscopic diffusion environments. This study addresses these issues by implementing a multidimensional diffusion-encoding protocol for microstructural imaging of the hippocampus at high resolution. METHODS: The hippocampus of 8 healthy volunteers was imaged at 1.5-mm isotropic resolution with a multidimensional diffusion-encoding sequence developed in house. Microscopic fractional anisotropy (µFA) and normalized size variance (CMD ) were estimated using q-space trajectory imaging, and their values were compared with DTI metrics. The overall scan time was 1 hour. The reproducibility of the protocol was confirmed with scan-rescan experiments, and a shorter protocol (14 minutes) was defined for situations with time constraints. RESULTS: Mean µFA (0.47) was greater than mean FA (0.20), indicating orientation dispersion in hippocampal tissue microstructure. Mean CMD was 0.17. The reproducibility of q-space trajectory imaging metrics was comparable to DTI, and microstructural metrics in the healthy hippocampus are reported. CONCLUSION: This work shows the feasibility of high-resolution microscopic anisotropy imaging in the human hippocampus at 3 T and provides reference values for microstructural metrics in a healthy hippocampus.

Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study.

OBJECTIVE: This retrospective, cross-sectional study evaluated the feasibility and potential benefits of incorporating deep-learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug-resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. METHODS: A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier-predicted lesions was considered colocalization. RESULTS: In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity = 100%). Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. The algorithm correctly detected seven of eight of these FCDs (86%). In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted. SIGNIFICANCE: There was a high degree of colocalization between automated lesion detection and sEEG. We have created a framework for incorporation of deep-learning-based MRI lesion detection into sEEG implantation planning. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories.

Natural biological variation of white matter microstructure is accentuated in Huntington's disease.

Huntington's disease (HD) is a monogenic neurodegenerative disorder caused by a CAG-repeat expansion in the Huntingtin gene. Presence of this expansion signifies certainty of disease onset, but only partly explains age at which onset occurs. Genome-wide association studies have shown that naturally occurring genetic variability influences HD pathogenesis and disease onset. Investigating the influence of biological traits in the normal population, such as variability in white matter properties, on HD pathogenesis could provide a complementary approach to understanding disease modification. We have previously shown that while white matter diffusivity patterns in the left sensorimotor network were similar in controls and HD gene-carriers, they were more extreme in the HD group. We hypothesized that the influence of natural variation in diffusivity on effects of HD pathogenesis on white matter is not limited to the sensorimotor network but extends to cognitive, limbic, and visual networks. Using tractography, we investigated 32 bilateral pathways within HD-related networks, including motor, cognitive, and limbic, and examined diffusivity metrics using principal components analysis. We identified three independent patterns of diffusivity common to controls and HD gene-carriers that predicted HD status. The first pattern involved almost all tracts, the second was limited to sensorimotor tracts, and the third encompassed cognitive network tracts. Each diffusivity pattern was associated with network specific performance. The consistency in diffusivity patterns across both groups coupled with their association with disease status and task performance indicates that naturally-occurring patterns of diffusivity can become accentuated in the presence of the HD gene mutation to influence clinical brain function.

Structural and functional brain network correlates of depressive symptoms in premanifest Huntington's disease.

Depression is common in premanifest Huntington's disease (preHD) and results in significant morbidity. We sought to examine how variations in structural and functional brain networks relate to depressive symptoms in premanifest HD and healthy controls. Brain networks were constructed using diffusion tractography (70 preHD and 81 controls) and resting state fMRI (92 preHD and 94 controls) data. A sub-network associated with depression was identified in a data-driven fashion and network-based statistics was used to investigate which specific connections correlated with depression scores. A replication analysis was then performed using data from a separate study. Correlations between depressive symptoms with increased functional connectivity and decreased structural connectivity were seen for connections in the default mode network (DMN) and basal ganglia in preHD. This study reveals specific connections in the DMN and basal ganglia that are associated with depressive symptoms in preHD. Hum Brain Mapp 38:2819-2829, 2017. © 2017 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Long-term white matter tract reorganization following prolonged febrile seizures.

OBJECTIVE: Diffusion magnetic resonance imaging (MRI) studies have demonstrated acute white matter changes following prolonged febrile seizures (PFS), but their longer-term evolution is unknown. We investigated a population-based cohort to determine white matter diffusion properties 8 years after PFS. METHODS: We used diffusion tensor imaging (DTI) and applied Tract-Based Spatial Statistics for voxel-wise comparison of white matter microstructure between 26 children with PFS and 27 age-matched healthy controls. Age, gender, handedness, and hippocampal volumes were entered as covariates for voxel-wise analysis. RESULTS: Mean duration between the episode of PFS and follow-up was 8.2 years (range 6.7-9.6). All children were neurologically normal, and had normal conventional neuroimaging. On voxel-wise analysis, compared to controls, the PFS group had (1) increased fractional anisotropy in early maturing central white matter tracts, (2) increased mean and axial diffusivity in several peripheral white matter tracts and late-maturing central white matter tracts, and (3) increased radial diffusivity in peripheral white matter tracts. None of the tracts had reduced fractional anisotropy or diffusivity indices in the PFS group. SIGNIFICANCE: In this homogeneous, population-based sample, we found increased fractional anisotropy in early maturing central white matter tracts and increased mean and axial diffusivity with/without increased radial diffusivity in several late-maturing peripheral white matter tracts 8 years post-PFS. We propose disruption in white matter maturation secondary to seizure-induced axonal injury, with subsequent neuroplasticity and microstructural reorganization as a plausible explanation.

Impact of induction chemotherapy, hyperfractionated accelerated radiotherapy and high-dose thiotepa on brain volume loss and functional status of children with primitive neuroectodermal tumour.

BACKGROUND: The introduction of aggressive chemo-radiotherapy regimens has improved overall survival in children with primitive neuroectodermal tumours (PNET). However, these combinations may result in neurotoxicity. Previously reported magnetic resonance imaging abnormalities in children receiving intensive sequential chemotherapy, hyperfractionated accelerated radiotherapy (HART) and high-dose thiotepa prompted us to investigate the degree of brain volume loss and patients' functional status after therapy. METHODS: We retrospectively reviewed clinico-radiological data of children with PNET treated in this way at our centre. RESULTS: We studied 14 children treated between December 2009 and April 2013. Data were not complete for one child. Performance status was severely restricted in four children, and mildly to moderately impaired in 7 of the 13 children. Eleven of 13 children showed mild-to-severe generalised neuroparenchymal atrophy, in 7 of whom neuroparenchymal volume loss was moderate to severe. Of these seven, six had received high-dose thiotepa. There was no correlation between brain volume loss and Lansky performance status. However, unexpected neurotoxicities, such as symptoms of transverse myelitis, were observed. CONCLUSION: Measurement of brain volume loss in patients treated with HART and high-dose thiotepa may not be sufficient to predict function. However, correlation of brain volume loss due to late neurotoxicity with performance decline may be more obvious over longer period of follow-up. The combination of HART and myeloablative courses of thiotepa is associated with severe neurotoxicity and subsequent decline in performance status in a significant proportion of patients.

NODDI reproducibility and variability with magnetic field strength: A comparison between 1.5 T and 3 T.

Diffusion models are advantageous for examining brain microstructure non-invasively and their validation is important for transference into the clinical domain. Neurite Orientation Dispersion and Density Imaging (NODDI) is a promising model for estimating multiple diffusion compartments from MRI data acquired in a clinically feasible time. As a relatively new model, it is necessary to examine NODDI under certain experimental conditions, such as change in magnetic field-strength, and assess it in relation to diffusion tensor imaging (DTI), an established model that is largely understood by the neuroimaging community. NODDI measures (intracellular volume fraction, νic , and orientation distribution, OD) were compared with DTI at 1.5 and 3 T data in healthy adults in whole-brain tissue masks and regions of white- and deep grey-matter. Within-session reproducibility and between-subject differences of NODDI with field-strength were also investigated. Field-strength had a significant effect on NODDI measures, suggesting careful interpretation of results from data acquired at 1.5 and 3 T. It was demonstrated that NODDI is feasible at 1.5 T, but with lower νic in white-matter regions compared with 3 T. Furthermore, the advantages of NODDI over DTI in a region of complex microstructure were shown. Specifically, in the centrum-semiovale where FA is typically as low as in grey-matter, νic was comparable to other white-matter regions yet accompanied by an OD similar to deep grey-matter. In terms of reproducibility, NODDI measures varied more than DTI. It may be that NODDI is more susceptible to noisier parameter estimates when compared with DTI, conversely it may have greater sensitivity to true within- and between-subject heterogeneity. Hum Brain Mapp 37:4550-4565, 2016. © 2016 Wiley Periodicals, Inc.

Natural variation in sensory-motor white matter organization influences manifestations of Huntington's disease.

While the HTT CAG-repeat expansion mutation causing Huntington's disease (HD) is highly correlated with the rate of pathogenesis leading to disease onset, considerable variance in age-at-onset remains unexplained. Therefore, other factors must influence the pathogenic process. We asked whether these factors were related to natural biological variation in the sensory-motor system. In 243 participants (96 premanifest and 35 manifest HD; 112 controls), sensory-motor structural MRI, tractography, resting-state fMRI, electrophysiology (including SEP amplitudes), motor score ratings, and grip force as sensory-motor performance were measured. Following individual modality analyses, we used principal component analysis (PCA) to identify patterns associated with sensory-motor performance, and manifest versus premanifest HD discrimination. We did not detect longitudinal differences over 12 months. PCA showed a pattern of loss of caudate, grey and white matter volume, cortical thickness in premotor and sensory cortex, and disturbed diffusivity in sensory-motor white matter tracts that was connected to CAG repeat length. Two further major principal components appeared in controls and HD individuals indicating that they represent natural biological variation unconnected to the HD mutation. One of these components did not influence HD while the other non-CAG-driven component of axial versus radial diffusivity contrast in white matter tracts were associated with sensory-motor performance and manifest HD. The first component reflects the expected CAG expansion effects on HD pathogenesis. One non-CAG-driven component reveals an independent influence on pathogenesis of biological variation in white matter tracts and merits further investigation to delineate the underlying mechanism and the potential it offers for disease modification. Hum Brain Mapp 37:4615-4628, 2016. © 2016 Wiley Periodicals, Inc.

Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington's disease.

Huntington's disease can be predicted many years before symptom onset, and thus makes an ideal model for studying the earliest mechanisms of neurodegeneration. Diffuse patterns of structural connectivity loss occur in the basal ganglia and cortex early in the disease. However, the organizational principles that underlie these changes are unclear. By understanding such principles we can gain insight into the link between the cellular pathology caused by mutant huntingtin and its downstream effect at the macroscopic level. The 'rich club' is a pattern of organization established in healthy human brains, where specific hub 'rich club' brain regions are more highly connected to each other than other brain regions. We hypothesized that selective loss of rich club connectivity might represent an organizing principle underlying the distributed pattern of structural connectivity loss seen in Huntington's disease. To test this hypothesis we performed diffusion tractography and graph theoretical analysis in a pseudo-longitudinal study of 50 premanifest and 38 manifest Huntington's disease participants compared with 47 healthy controls. Consistent with our hypothesis we found that structural connectivity loss selectively affected rich club brain regions in premanifest and manifest Huntington's disease participants compared with controls. We found progressive network changes across controls, premanifest Huntington's disease and manifest Huntington's disease characterized by increased network segregation in the premanifest stage and loss of network integration in manifest disease. These regional and whole brain network differences were highly correlated with cognitive and motor deficits suggesting they have pathophysiological relevance. We also observed greater reductions in the connectivity of brain regions that have higher network traffic and lower clustering of neighbouring regions. This provides a potential mechanism that results in a characteristic pattern of structural connectivity loss targeting highly connected brain regions with high network traffic and low clustering of neighbouring regions. Our findings highlight the role of the rich club as a substrate for the structural connectivity loss seen in Huntington's disease and have broader implications for understanding the connection between molecular and systems level pathology in neurodegenerative disease.

Basal ganglia-cortical structural connectivity in Huntington's disease.

Huntington's disease is an incurable neurodegenerative disease caused by inheritance of an expanded cytosine-adenine-guanine (CAG) trinucleotide repeat within the Huntingtin gene. Extensive volume loss and altered diffusion metrics in the basal ganglia, cortex and white matter are seen when patients with Huntington's disease (HD) undergo structural imaging, suggesting that changes in basal ganglia-cortical structural connectivity occur. The aims of this study were to characterise altered patterns of basal ganglia-cortical structural connectivity with high anatomical precision in premanifest and early manifest HD, and to identify associations between structural connectivity and genetic or clinical markers of HD. 3-Tesla diffusion tensor magnetic resonance images were acquired from 14 early manifest HD subjects, 17 premanifest HD subjects and 18 controls. Voxel-based analyses of probabilistic tractography were used to quantify basal ganglia-cortical structural connections. Canonical variate analysis was used to demonstrate disease-associated patterns of altered connectivity and to test for associations between connectivity and genetic and clinical markers of HD; this is the first study in which such analyses have been used. Widespread changes were seen in basal ganglia-cortical structural connectivity in early manifest HD subjects; this has relevance for development of therapies targeting the striatum. Premanifest HD subjects had a pattern of connectivity more similar to that of controls, suggesting progressive change in connections over time. Associations between structural connectivity patterns and motor and cognitive markers of disease severity were present in early manifest subjects. Our data suggest the clinical phenotype in manifest HD may be at least partly a result of altered connectivity.

Spherical convolutional neural networks can improve brain microstructure estimation from diffusion MRI data.

Diffusion magnetic resonance imaging is sensitive to the microstructural properties of brain tissue. However, estimating clinically and scientifically relevant microstructural properties from the measured signals remains a highly challenging inverse problem that machine learning may help solve. This study investigated if recently developed rotationally invariant spherical convolutional neural networks can improve microstructural parameter estimation. We trained a spherical convolutional neural network to predict the ground-truth parameter values from efficiently simulated noisy data and applied the trained network to imaging data acquired in a clinical setting to generate microstructural parameter maps. Our network performed better than the spherical mean technique and multi-layer perceptron, achieving higher prediction accuracy than the spherical mean technique with less rotational variance than the multi-layer perceptron. Although we focused on a constrained two-compartment model of neuronal tissue, the network and training pipeline are generalizable and can be used to estimate the parameters of any Gaussian compartment model. To highlight this, we also trained the network to predict the parameters of a three-compartment model that enables the estimation of apparent neural soma density using tensor-valued diffusion encoding.

The CONNECT project: Combining macro- and micro-structure.

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.

Effect of growth hormone deficiency on brain structure, motor function and cognition.

The growth hormone-insulin-like growth factor-1 axis plays a role in normal brain growth but little is known of the effect of growth hormone deficiency on brain structure. Children with isolated growth hormone deficiency (peak growth hormone <6.7 µg/l) and idiopathic short stature (peak growth hormone >10 µg/l) underwent cognitive assessment, diffusion tensor imaging and volumetric magnetic resonance imaging prior to commencing growth hormone treatment. Total brain, corpus callosal, hippocampal, thalamic and basal ganglia volumes were determined using Freesurfer. Fractional anisotropy (a marker of white matter structural integrity) images were aligned and tract-based spatial statistics performed. Fifteen children (mean 8.8 years of age) with isolated growth hormone deficiency [peak growth hormone <6.7 µg/l (mean 3.5 µg/l)] and 14 controls (mean 8.4 years of age) with idiopathic short stature [peak growth hormone >10 µg/l (mean 15 µg/l) and normal growth rate] were recruited. Compared with controls, children with isolated growth hormone deficiency had lower Full-Scale IQ (P < 0.01), Verbal Comprehension Index (P < 0.01), Processing Speed Index (P < 0.05) and Movement-Assessment Battery for Children (P < 0.008) scores. Verbal Comprehension Index scores correlated significantly with insulin-like growth factor-1 (P < 0.03) and insulin-like growth factor binding protein-3 (P < 0.02) standard deviation scores in isolated growth hormone deficiency. The splenium of the corpus callosum, left globus pallidum, thalamus and hippocampus (P < 0.01) were significantly smaller; and corticospinal tract (bilaterally; P < 0.045, P < 0.05) and corpus callosum (P < 0.05) fractional anisotropy were significantly lower in the isolated growth hormone deficiency group. Basal ganglia volumes and bilateral corticospinal tract fractional anisotropy correlated significantly with Movement-Assessment Battery for Children scores, and corpus callosum fractional anisotropy with Full-Scale IQ and Processing Speed Index. In patients with isolated growth hormone deficiency, white matter abnormalities in the corpus callosum and corticospinal tract, and reduced thalamic and globus pallidum volumes relate to deficits in cognitive function and motor performance. Follow-up studies that investigate the course of the structural and cognitive deficits on growth hormone treatment are now required to confirm that growth hormone deficiency impacts significantly on brain structure, cognitive function and motor performance.

Accuracy of robot-assisted stereotactic MRI-guided laser ablation in children with epilepsy.

OBJECTIVE: Robot-assisted (RA) stereotactic MRI-guided laser ablation has been reported to be a safe and effective technique for the treatment of epileptogenic foci in children and adults. In this study the authors aimed to assess the accuracy of RA stereotactic MRI-guided laser fiber placement in children and to identify factors that might increase the risk of misplacement. METHODS: A retrospective single-institution review of all children from 2019 to 2022 who underwent RA stereotactic MRI-guided laser ablation for epilepsy was undertaken. Placement error was calculated at the target by measuring the Euclidean distance between the implanted laser fiber position and the preoperatively planned position. Collected data included age at surgery, sex, pathology, date of robot calibration, number of catheters, entry position, entry angle, extracranial soft-tissue thickness, bone thickness, and intracranial catheter length. A systematic review of the literature was also performed using Ovid Medline, Ovid Embase, and the Cochrane Central Register of Controlled Trials. RESULTS: In 28 children with epilepsy, the authors assessed 35 RA stereotactic MRI-guided laser ablation fiber placements. Twenty (71.4%) children had undergone ablation for hypothalamic hamartoma, 7 children (25.0%) for presumed insular focal cortical dysplasia, and 1 patient (3.6%) for periventricular nodular heterotopia. Nineteen children were male (67.9.%) and 9 were female (32.1%). The median age at the time of the procedure was 7.67 years (IQR 4.58-12.26 years). The median target point localization error (TPLE) was 1.27 mm (IQR 0.76-1.71 mm). The median offset error between the planned and actual trajectories was 1.04° (IQR 0.73°-1.46°). Patient age, sex, pathology and the time interval between date of surgery and robot calibration, entry position, entry angle, soft-tissue thickness, bone thickness, and intracranial length were not associated with the placement accuracy of the implanted laser fibers. However, the number of catheters placed did correlate with the offset angle error on univariate analysis (ρ = 0.387, p = 0.022). There were no immediate surgical complications. Meta-analysis indicated that the overall pooled mean TPLE was 1.46 mm (95% CI -0.58 to 3.49 mm). CONCLUSIONS: RA stereotactic MRI-guided laser ablation for epilepsy in children is highly accurate. These data will aid surgical planning.

Extent of piriform cortex resection in children with temporal lobe epilepsy.

OBJECTIVE: A greater extent of resection of the temporal portion of the piriform cortex (PC) has been shown to be associated with higher likelihood of seizure freedom in adults undergoing anterior temporal lobe resection (ATLR) for drug-resistant temporal lobe epilepsy (TLE). There have been no such studies in children, therefore this study aimed to investigate this association in a pediatric cohort. METHODS: A retrospective, neuroimaging cohort study of children with TLE who underwent ATLR between 2012 and 2021 was undertaken. The PC, hippocampal and amygdala volumes were measured on the preoperative and postoperative T1-weighted MRI. Using these volumes, the extent of resection per region was compared between the seizure-free and not seizure-free groups. RESULTS: In 50 children (median age 9.5 years) there was no significant difference between the extent of resection of the temporal PC in the seizure-free (median = 50%, n = 33/50) versus not seizure-free (median = 40%, n = 17/50) groups (p = 0.26). In a sub-group of 19 with ipsilateral hippocampal atrophy (quantitatively defined by ipsilateral-to-contralateral asymmetry), the median extent of temporal PC resection was greater in children who were seizure-free (53%) versus those not seizure-free (19%) (p = 0.009). INTERPRETATION: This is the first study demonstrating that, in children with TLE and hippocampal atrophy, more extensive temporal PC resection is associated with a greater chance of seizure freedom-compatible with an adult series in which 85% of patients had hippocampal sclerosis. In a combined group of children with and without hippocampal atrophy, the extent of PC resection was not associated with seizure outcome, suggesting different epileptogenic networks within this cohort.

IDEAL approach to the evaluation of machine learning technology in epilepsy surgery: protocol for the MAST trial.

Epilepsy and epilepsy surgery lend themselves well to the application of machine learning (ML) and artificial intelligence (AI) technologies. This is evidenced by the plethora of tools developed for applications such as seizure detection and analysis of imaging and electrophysiological data. However, few of these tools have been directly used to guide patient management. In recent years, the Idea, Development, Exploration, Assessment, Long-Term Follow-Up (IDEAL) collaboration has formalised stages for the evaluation of surgical innovation and medical devices, and, in many ways, this pragmatic framework is also applicable to ML/AI technology, balancing innovation and safety. In this protocol paper, we outline the preclinical (IDEAL stage 0) evaluation and the protocol for a prospective (IDEAL stage 1/2a) study to evaluate the utility of an ML lesion detection algorithm designed to detect focal cortical dysplasia from structural MRI, as an adjunct in the planning of stereoelectroencephalography trajectories in children undergoing intracranial evaluation for drug-resistant epilepsy.