Post-hemorrhagic ventricular dilatation (PHVD) is characterized by a build-up of cerebral spinal fluid (CSF) in the ventricles, which increases intracranial pressure and compresses brain tissue. Clinical interventions (i.e., ventricular taps, VT) work to mitigate these complications through CSF drainage; however, the timing of these procedures remains imprecise. This study presents Neonatal NeuroMonitor (NNeMo), a portable optical device that combines broadband near-infrared spectroscopy (B-NIRS) and diffuse correlation spectroscopy (DCS) to provide simultaneous assessments of cerebral blood flow (CBF), tissue saturation (StO2), and the oxidation state of cytochrome c oxidase (oxCCO). In this study, NNeMo was used to monitor cerebral hemodynamics and metabolism in PHVD patients selected for a VT. Across multiple VTs in four patients, no significant changes were found in any of the three parameters: CBF increased by 14.6 ± 37.6% (p = 0.09), StO2 by 1.9 ± 4.9% (p = 0.2), and oxCCO by 0.4 ± 0.6 µM (p = 0.09). However, removing outliers resulted in significant, but small, increases in CBF (6.0 ± 7.7%) and oxCCO (0.1 ± 0.1 µM). The results of this study demonstrate NNeMo’s ability to provide safe, non-invasive measurements of cerebral perfusion and metabolism for neuromonitoring applications in the neonatal intensive care unit.

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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.

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OBJECTIVEEpilepsy affects neural processing and often causes intra- or interhemispheric language reorganization, rendering localization solely based on anatomical landmarks (e.g., Broca’s area) unreliable. Preoperative brain mapping is necessary to weigh the risk of resection with the risk of postoperative deficit. However, the use of conventional mapping methods (e.g., somatosensory stimulation, task-based functional MRI [fMRI]) in pediatric patients is technically difficult due to low compliance and their unique neurophysiology. Resting-state fMRI (rs-fMRI), a “task-free” technique based on the neural activity of the brain at rest, has the potential to overcome these limitations. The authors hypothesized that language networks can be identified from rs-fMRI by applying functional connectivity analyses.METHODSCases in which both task-based fMRI and rs-fMRI were acquired as part of the preoperative clinical protocol for epilepsy surgery were reviewed. Task-based fMRI consisted of 2 language tasks and 1 motor task. Resting-state fMRI data were acquired while the patients watched an animated movie and were analyzed using independent component analysis (i.e., data-driven method). The authors extracted language networks from rs-fMRI data by performing a similarity analysis with functionally defined language network templates via a template-matching procedure. The Dice coefficient was used to quantify the overlap.RESULTSThirteen children underwent conventional task-based fMRI (e.g., verb generation, object naming), rs-fMRI, and structural imaging at 1.5T. The language components with the highest overlap with the language templates were identified for each patient. Language lateralization results from task-based fMRI and rs-fMRI mapping were comparable, with good concordance in most cases. Resting-state fMRI–derived language maps indicated that language was on the left in 4 patients (31%), on the right in 5 patients (38%), and bilateral in 4 patients (31%). In some cases, rs-fMRI indicated a more extensive language representation.CONCLUSIONSResting-state fMRI–derived language network data were identified at the patient level using a template-matching method. More than half of the patients in this study presented with atypical language lateralization, emphasizing the need for mapping. Overall, these data suggest that this technique may be used to preoperatively identify language networks in pediatric patients. It may also optimize presurgical planning of electrode placement and thereby guide the surgeon’s approach to the epileptogenic zone.