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Abstract Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawback, we propose a machine-learning-based approach to predict cortical-thalamic functional connectivity using cortical fNIRS data. We applied graph convolutional networks (GCN) to two datasets obtained from healthy adults and neonates with early brain injuries, respectively. Each dataset contained fNIRS connectivity data as input to the predictive models, while the connectivity from functional magnetic resonance imaging (fMRI) served as training targets. GCN models performed better compared to conventional methods, such as support vector machine and feedforward fully connected artificial neural networks, on both identifying the connections as binary classification tasks, and regressing the quantified strengths of connections. We also propose the addition of inter-subject connections into the GCN kernels could improve performance and that GCN models are resilient to noise in fNIRS data. Our results show it is feasible to identify subcortical activity from cortical fNIRS recordings. The findings can potentially extend the use of fNIRS in clinical settings for brain monitoring in critically ill patients.

Abstract Intraventricular hemorrhage (IVH) is a common neurological injury following very preterm birth. Resting-state functional connectivity (RSFC) using functional magnetic resonance imaging (fMRI) is associated with injury severity; yet, fMRI is impractical for use in intensive care settings. Functional near-infrared spectroscopy (fNIRS) measures RSFC through cerebral hemodynamics and has greater bedside accessibility than fMRI. We evaluated RSFC in preterm neonates with IVH using fNIRS and fMRI at term-equivalent age, and compared fNIRS connectivity between healthy newborns and those with IVH. Sixteen very preterm born neonates were scanned with fMRI and fNIRS. Additionally, fifteen healthy newborns were scanned with fNIRS. In preterms with IVH, fNIRS and fMRI connectivity maps were compared using Euclidean and Jaccard distances. The severity of IVH in relation to fNIRS-RSFC strength was examined using generalized linear models. fNIRS and fMRI RSFC maps showed good correspondence. Connectivity strength was significantly lower in healthy newborns (p-value = 0.023) and preterm infants with mild IVH (p-value = 0.026) compared to infants with moderate/severe IVH. fNIRS has potential to be a new bedside tool for assessing brain injury and monitoring cerebral hemodynamics, as well as a promising biomarker for IVH severity in very preterm born infants.

Abstract This paper describes a methodology for the assessment of training simulator‐based computer‐assisted intervention skills on an AR/VR‐guided procedure making use of CT axial slice views for a neurosurgical procedure: external ventricular drain (EVD) placement. The task requires that trainees scroll through a stack of axial slices and form a mental representation of the anatomical structures in order to subsequently target the ventricles to insert an EVD. The process of observing the 2D CT image slices in order to build a mental representation of the 3D anatomical structures is the skill being taught, along with the cognitive control of the subsequent targeting, by planned motor actions, of the EVD tip to the ventricular system to drain cerebrospinal fluid (CSF). Convergence is established towards the validity of this assessment methodology by examining two objective measures of spatial reasoning, along with one subjective expert ranking methodology, and comparing these to AR/VR guidance. These measures have two components: the speed and accuracy of the targeting, which are used to derive the performance metric. Results of these correlations are presented for a population of PGY1 residents attending the Canadian Neurosurgical “Rookie Bootcamp” in 2019.

Abstract Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify biomarkers of brain health. Functional near-infrared spectroscopy (fNIRS) can assess resting-state functional connectivity (RSFC) at the bedside. We acquired resting-state fNIRS data from 21 neonates with HIE (postmenstrual age [PMA] = 39.96), in 19 neonates the scans were acquired post-therapeutic hypothermia (TH), and from 20 term-born healthy newborns (PMA = 39.93). Twelve HIE neonates also underwent resting-state functional magnetic resonance imaging (fMRI) post-TH. RSFC was calculated as correlation coefficients amongst the time courses for fNIRS and fMRI data, respectively. The fNIRS and fMRI RSFC maps were comparable. RSFC patterns were then measured with graph theory metrics and compared between HIE infants and healthy controls. HIE newborns showed significantly increased clustering coefficients, network efficiency and modularity compared to controls. Using a support vector machine algorithm, RSFC features demonstrated good performance in classifying the HIE and healthy newborns in separate groups. Our results indicate the utility of fNIRS-connectivity patterns as potential biomarkers for HIE and fNIRS as a new bedside tool for newborns with HIE.

IntroductionRegistration to a standardized template (i.e. “normalization”) is a critical step when performing neuroimaging studies. We present a comparative study involving the evaluation of general-purpose registration algorithms for pediatric patients with shunt treated hydrocephalus. Our sample dataset presents a number of intersecting challenges for registration, representing the potentially large deformations to both brain structures and overall brain shape, artifacts from shunts, and morphological differences corresponding to age. The current study assesses the normalization accuracy of shunt-treated hydrocephalus patients using freely available neuroimaging registration tools.MethodsAnatomical neuroimages from eight pediatric patients with shunt-treated hydrocephalus were normalized. Four non-linear registration algorithms were assessed in addition to the preprocessing steps of skull-stripping and bias-correction. Registration accuracy was assessed using the Dice Coefficient (DC) and Hausdorff Distance (HD) in subcortical and cortical regions.ResultsA total of 592 registrations were performed. On average, normalizations performed using the brain extracted and bias-corrected images had a higher DC and lower HD compared to full head/ non-biased corrected images. The most accurate registration was achieved using SyN by ANTs with skull-stripped and bias corrected images. Without preprocessing, the DARTEL Toolbox was able to produce normalized images with comparable accuracy. The use of a pediatric template as an intermediate registration did not improve normalization.DiscussionUsing structural neuroimages from patients with shunt-treated pediatric hydrocephalus, it was demonstrated that there are tools which perform well after specified pre-processing steps were taken. Overall, these results provide insight to the performance of registration programs that can be used for normalization of brains with complex pathologies.

Smart haptic gloves are a new technology emerging in Virtual Reality (VR) with a promise to enhance sensory feedback in VR. This paper presents one of the first attempts to explore its application to surgical training for neurosurgery trainees using VR-based surgery simulators. We develop and evaluate a surgical simulator for External Ventricular Drain Placement (EVD), a common procedure in the field of neurosurgery. Haptic gloves are used in combination with a VR environment to augment the experience of burr hole placement, and flexible catheter manipulation. The simulator was integrated into the training curriculum at the 2022 Canadian Neurosurgery Rookie Bootcamp. Thirty neurosurgery residents used the simulator where objective performance metrics and subjective experience scores were acquired. We provide the details of the simulator development, as well as the user study results and draw conclusions on the benefits added by the haptic gloves and future directions.

Objective: The objective of this study was to use probabilistic tractography in combination with white matter microstructure metrics to characterize differences in white matter networks between shunt-treated pediatric hydrocephalus patients relative to healthy controls. We were also able to explore the relationship between these white matter networks and postoperative ventricle volume. Methods: Network-based statistics was used in combination with whole-brain probabilistic tractography to determine dysregulated white matter networks in a sample of patients with pediatric hydrocephalus (n = 8), relative to controls (n = 36). Metrics such as streamline count (SC), as well as the mean of the fractional anisotropy along a tract, axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) were assessed. In networks that were found to be significantly different for patients with hydrocephalus, tracts were evaluated to assess their relationship with postoperative lateral ventricle volume. Results: Patients with pediatric hydrocephalus had various networks that were either upregulated or downregulated relative to controls across all white matter measures. Predominately, network dysregulation occurred in tracts involving structures located outside of the frontal lobe. Furthermore tracts with values suggesting decreased white matter integrity were not only found between subcortical structures, but also cortical structures. While there were various tracts with white matter metrics that were initially predicted by lateral ventricle volume, only two tracts remained significant following multiple comparisons. Conclusions: This cross-sectional study in pediatric patients with hydrocephalus and healthy controls demonstrated using whole-brain probabilistic tractography that there are various networks with dysregulated white matter integrity in hydrocephalus patients relative to controls. These dysregulated networks have tracts connecting structures throughout the brain, and the regions were predominately located centrally and posteriorly. Postoperative ventricle volume did not predict the white matter integrity of many tracts. Future studies with larger sample sizes are needed to further understand these results.

Abstract Germinal Matrix-Intraventricular Hemorrhage (GMH-IVH) remains a significant cause of adverse neurodevelopment in preterm infants. Current management relies on 2-dimensional cranial ultrasound (2D cUS) ventricular measurements. Reliable biomarkers are needed to aid in the early detection of posthemorrhagic ventricular dilatation (PHVD) and subsequent neurodevelopment. In a prospective cohort study, we incorporated 3-dimensional (3D) cUS and functional near-infrared spectroscopy (fNIRS) to monitor neonates with GMH-IVH. Preterm neonates (≤ 32 weeks' gestation) were enrolled following a GMH-IVH diagnosis. Neonates underwent sequential measurements: 3D cUS images were manually segmented using in-house software, and the ventricle volumes (VV) were extracted. Multichannel fNIRS data were acquired using a high-density system, and spontaneous functional connectivity (sFC) was calculated. Of the 30 neonates enrolled in the study, 19 (63.3%) had grade I–II and 11 (36.7%) grade III–IV GMH-IVH; of these, 7 neonates (23%) underwent surgical interventions to divert cerebrospinal fluid (CSF). In infants with severe GMH-IVH, larger VV were significantly associated with decreased |sFC|. Our findings of increased VV and reduced sFC suggest that regional disruptions of ventricular size may impact the development of the underlying white matter. Hence, 3D cUS and fNIRS are promising bedside tools for monitoring the progression of GMH-IVH in preterm neonates.

We develop a hybrid model-based data-driven seizure detection algorithm called Mutual Information-based CNN-Aided Learned factor graphs (MICAL) for detection of eclectic seizures from EEG signals. Our proposed method contains three main components: a neural mutual information (MI) estimator, 1D convolutional neural network (CNN), and factor graph inference. Since during seizure the electrical activity in one or more regions in the brain becomes correlated, we use neural MI estimators to measure inter-channel statistical dependence. We also design a 1D CNN to extract additional features from raw EEG signals. Since the soft estimates obtained as the combined features from the neural MI estimator and the CNN do not capture the temporal correlation between different EEG blocks, we use them not as estimates of the seizure state, but to compute the function nodes of a factor graph. The resulting factor graphs allows structured inference which exploits the temporal correlation for further improving the detection performance. On public CHB-MIT database, We conduct three evaluation approaches using the public CHB-MIT database, including 6-fold leave-four-patients-out cross-validation, all patient training; and per patient training. Our evaluations systematically demonstrate the impact of each element in MICAL through a complete ablation study and measuring six performance metrics. It is shown that the proposed method obtains state-of-the-art performance specifically in 6-fold leave-four-patients-out cross-validation and all patient training, demonstrating a superior generalizability.

Abstract A major concern with preterm birth is the risk of neurodevelopmental disability. Poor cerebral circulation leading to periods of hypoxia is believed to play a significant role in the etiology of preterm brain injury, with the first three days of life considered the period when the brain is most vulnerable. This study focused on monitoring cerebral perfusion and metabolism during the first 72 h after birth in preterm infants weighing less than 1500 g. Brain monitoring was performed by combining hyperspectral near-infrared spectroscopy to assess oxygen saturation and the oxidation state of cytochrome c oxidase (oxCCO), with diffuse correlation spectroscopy to monitor cerebral blood flow (CBF). In seven of eight patients, oxCCO remained independent of CBF, indicating adequate oxygen delivery despite any fluctuations in cerebral hemodynamics. In the remaining infant, a significant correlation between CBF and oxCCO was found during the monitoring periods on days 1 and 3. This infant also had the lowest baseline CBF, suggesting the impact of CBF instabilities on metabolism depends on the level of blood supply to the brain. In summary, this study demonstrated for the first time how continuous perfusion and metabolic monitoring can be achieved, opening the possibility to investigate if CBF/oxCCO monitoring could help identify preterm infants at risk of brain injury.

BackgroundVolumetric measurements of fetal brain maturation in the third trimester of pregnancy are key predictors of developmental outcomes. Improved understanding of fetal brain development trajectories may aid in identifying and clinically managing at-risk fetuses. Currently, fetal brain structures in magnetic resonance images (MRI) are often manually segmented, which requires both time and expertise. To facilitate the targeting and measurement of brain structures in the fetus, we compared the results of five segmentation methods applied to fetal brain MRI data to gold-standard manual tracings.MethodsAdult women with singleton pregnancies (n = 21), of whom five were scanned twice, approximately 3 weeks apart, were recruited [26 total datasets, median gestational age (GA) = 34.8, IQR = 30.9–36.6]. T2-weighted single-shot fast spin echo images of the fetal brain were acquired on 1.5T and 3T MRI scanners. Images were first combined into a single 3D anatomical volume. Next, a trained tracer manually segmented the thalamus, cerebellum, and total cerebral volumes. The manual segmentations were compared with five automatic methods of segmentation available within Advanced Normalization Tools (ANTs) and FMRIB’s Linear Image Registration Tool (FLIRT) toolboxes. The manual and automatic labels were compared using Dice similarity coefficients (DSCs). The DSC values were compared using Friedman’s test for repeated measures.ResultsComparing cerebellum and thalamus masks against the manually segmented masks, the median DSC values for ANTs and FLIRT were 0.72 [interquartile range (IQR) = 0.6–0.8] and 0.54 (IQR = 0.4–0.6), respectively. A Friedman’s test indicated that the ANTs registration methods, primarily nonlinear methods, performed better than FLIRT (p < 0.001).ConclusionDeformable registration methods provided the most accurate results relative to manual segmentation. Overall, this semi-automatic subcortical segmentation method provides reliable performance to segment subcortical volumes in fetal MR images. This method reduces the costs of manual segmentation, facilitating the measurement of typical and atypical fetal brain development.

Background: As pediatric BOLD Signal Variability (SV) analysis is relatively novel, there is a need to provide a foundational framework that gives researchers an entry point into engaging with the topic. This begins with clarifying the definition of BOLD signal variability by identifying and categorizing the various metrics utilized to measure BOLD SV. Methods: A systematic review of the literature was conducted. Inclusion criteria were restricted to studies utilizing any metric of BOLD SV and with individuals younger than 18 in the study population. The definition of BOLD SV was any measure of intra-individual variability in the BOLD signal. Five databases were searched: Psychinfo, Healthstar, MEDLINE, Embase, and Scopus. Results: A total of 17 observational studies, including male (n = 1796) and female (n = 1324) pediatric participants were included. Eight studies quantified variability as the amount of deviation from the average BOLD signal, seven used complexity-based metrics, three used correlation measures of variability, and one used the structure of the hemodynamic response function. In this study, 10 methods of quantifying signal variability were identified. Associations and trends in BOLD SV were commonly found with age, factors specific to mental and/or neurological disorders such as attention deficit disorder, epilepsy, psychotic symptoms, and performance on psychological and behavioral tasks. Conclusions: BOLD SV is a potential biomarker of neurodevelopmental and neurological conditions and symptom severity in mental disorders for defined pediatric populations. Studies that establish clinical trends and identify the mechanisms underlying BOLD SV with a low risk of bias are needed before clinical applications can be utilized by physicians.

The relationship between age-related changes in brain structural connectivity (SC) and functional connectivity (FC) with cognition is not well understood. Furthermore, it is not clear whether cognition is represented via a similar spatial pattern of FC and SC or instead is mapped by distinct sets of distributed connectivity patterns. To this end, we used a longitudinal, within-subject, multimodal approach aiming to combine brain data from diffusion-weighted MRI (DW-MRI), and functional MRI (fMRI) with behavioral evaluation, to better understand how changes in FC and SC correlate with changes in cognition in a sample of older adults. FC and SC measures were derived from the multimodal scans acquired at two time points. Change in FC and SC was correlated with 13 behavioral measures of cognitive function using Partial Least Squares Correlation (PLSC). Two of the measures indicate an age-related change in cognition and the rest indicate baseline cognitive performance. FC and SC—cognition correlations were expressed across several cognitive measures, and numerous structural and functional cortical connections, mainly cingulo-opercular, dorsolateral prefrontal, somatosensory and motor, and temporo-parieto-occipital, contributed both positively and negatively to the brain-behavior relationship. Whole-brain FC and SC captured distinct and independent connections related to the cognitive measures. Overall, we examined age-related function-structure associations of the brain in a comprehensive and integrated manner, using a multimodal approach. We pointed out the behavioral relevance of age-related changes in FC and SC. Taken together, our results highlight that the heterogeneity in distributed FC and SC connectivity patterns provide unique information about the variable nature of healthy cognitive aging.