Keywords: Neurofluids, Data Analysis, Arachnoid granulation, sleep, cerebrospinal fluid

Motivation: The high prevalence of sleep dysfunction in Parkinson disease (PD) leads to chronic dysregulation of the CSF circuits.

Goal(s): Hypotheses regarding arachnoid granulation (AG) hypertrophy in patients with PD and its relationship to sleep dysfunction were tested.

Approach: Sleep quality was assessed in PD patients. Using high resolution MRI and a novel deep-learning method, we assessed volumetrics measures of AG in the superior sagittal sinus.

Results: We found that increased AG volumetrics in PD are significantly correlated with an increase of self-reported sleep disturbance and diurnal sleepiness, as well as actigraphy-based metrics of decreased sleep efficiency and increased wake after sleep onset.

Impact: Findings suggest that sleep dysfunction plays a role intravenous AG morphology. This motivates future structural and functional imaging analysis of AG to understand how increased AG volume impacts patients with neurodegenerative proteinopathy and how dysfunctional sleep influences this relationship.

Keywords: Neurofluids, Segmentation, Perivascular space, Glymphatic system

Motivation: In general, 3D T2 is more sensitive than 3D T1 in quantitatively assessing MR-visible perivascular space in the whole brain. However, in clinical practice, it is common to have 3D T1 but not 3D T2.

Goal(s): In this study, we introduce an improved method for PVS quantification using 3D T1 alone.

Approach: We used a cascaded model to sequentially improve perivascular space visibility and segmentation accuracy using 3D T1 alone.

Results: The result of the proposed method, using the T1w approach, demonstrates high similarity to the results obtained with only T2w data.

Impact: This study introduces a method to segment perivascular spaces using T1-weighted images when T2-weighted images are not available. The method involves cascaded models and shows the potential for results similar to T2w-based segmentation. 

Keywords: Neurofluids, Neurofluids, Parasagittal dural space, arachnoid granulations, Cerebrospinal fluid

Motivation: Peri-sinus structures such as the parasagittal dural space (PSD) and intravenous arachnoid granulation (AG) play an important role in regulating the CSF circulation.

Goal(s): To investigate the volume of the PSD and AG in children and adults (age range=5-100 years).
 

Approach: We refined and applied a novel deep-learning algorithm to estimate volumetric measures of PSD and intravenous AG in a large dataset (n=1,815) using 3D T₂-weighted MRI.

Results: Data confirm sex effects on PSD and AG volumes and indicate a more rapid growth in early life with an increase of 0.9 cm3 and 0.64 mm3 per year before plateauing in mid-adulthood. 

Impact: Analysis provides new insight into PSD and AG changes in a large dataset of healthy control participants. Findings demonstrate developmental PSD and intravenous AG changes, which may serve as an exemplar for normal vs. unhealthy aging across the lifespan.

Keywords: Neurofluids, Neurofluids, Child development; diffusion MRI; perivascular spaces

Motivation: Perivascular spaces (PVS) play a critical role in fluid transfer and waste clearance in the brain, but few studies have explored how alterations to perivascular fluid flow may impact brain maturation and behavior

Goal(s): This study aims to characterize age-related alterations to perivascular and parenchymal fluid flow characteristics in typically developing children and assess their contribution to cognition.

Approach: We employ multi-compartment diffusion models to quantify free water diffusion characteristics within automatically defined perivascular spaces using enhanced PVS contrasts.

Results: Our findings show free water diffusion characteristics within the PVS and surrounding parenchyma are associated with age and cognitive scores.

Impact: Our findings suggest alterations to perivascular space function may occur as early as childhood. Variations in perivascular and parenchymal fluid properties may be predictive of cognitive outcomes in adolescents, thus underscoring the importance of waste clearance functionality on brain health.

Keywords: Neurofluids, Neurofluids, perivascular space, explained variance of CSF

Motivation: Estimating cerebrospinal fluid (CSF) in perivascular spaces (PVS) is essential to advance understanding of glymphatic clearance of cerebral waste products. 

Goal(s): This study aimed to explore a novel method for evaluating CSF in PVS in awake subjects, including healthy controls and those with mild cognitive impairment (MCI). 

Approach: We analyzed the explained variance of CSF components (CSF-derived R²) in resting-state fMRI images to determine the proportionate volume of PVS.

Results: We observed a decline in CSF-derived R² with aging in healthy controls. Conversely, elevated CSF-derived R² in MCI participants suggests enlargement of PVS, which may implicate altered glymphatic function in cognitive disorders.

Impact: The CSF-derived R² metric from resting-state fMRI images offers a quantifiable assessment of CSF volume in perivascular spaces of the gray matter, holding potential as a biomarker for investigating glymphatic system efficiency.

Keywords: Traumatic Brain Injury, Epilepsy

Motivation: Perivascular spaces (PVSs) are small fluid-filled spaces between blood vessels and pia mater. PVSs may appear differently in traumatic brain injury (TBI) patients with and without post-traumatic epilepsy (PTE) due to their role in waste removal.

Goal(s): To investigate characteristics of PVSs in PTE relative to TBI without PTE and healthy controls.

Approach: A semi-automated workflow for segmenting PVSs was established and applied to images from a 7T MRI study, including 8 TBI patients with PTE, 18 TBI patients without PTE, and 22 healthy controls.

Results: Larger median PVS equivalent diameter was observed in TBI, particularly with PTE, suggesting enlarged PVSs in TBI.

Impact: Identification of increased median perivascular space equivalent diameter in traumatic brain injury, particularly cases that develop post-traumatic epilepsy as shown in this work, may improve diagnosis and prognostication for post-traumatic epilepsy.

Keywords: Neurofluids, Quantitative Imaging, Autism Spectrum Disorder

Motivation:  To date, the assessment of cerebral parasagittal dura (PSD), a tissue lining the superior sagittal sinus, remains unexplored in young children with autism spectrum disorder (ASD).

Goal(s): Our goal was to quantify the volume of PSD using 3D-T2 Fluid Attenuated Inversion Recovery (3D-FLAIR) in young children with ASD and investigate correlations between PSD volume and both morphological and clinical variables.

Approach: We employed a customized U-net for the automatic segmentation of PSD.

Results: The study revealed a significant positive correlation between PSD volume and extra-axial cerebrospinal fluid volume, and a significant negative correlation with the degree of developmental delay in children with ASD.

Impact: Our findings indicate that PSD volume may play a key role in neurodevelopment by affecting cerebrospinal fluid dynamics. This highlights the need for further research to understand alterations in the dynamics of neurofluids in the developing brain and in ASD.

Keywords: Neurofluids, Neurofluids, Perivascular spaces, Waste clearance, Ocular glymphatics, Tau, Eye

Motivation: Preclinical research suggests an ocular glymphatic system similar to the cerebral system, driven by intraocular pressure (IOP). However, human studies are scarce.

Goal(s): To explore the eye-brain connection by investigating tear total-tau as a potential early marker of  cerebral glymphatics and consider the link between IOP (driver of ocular glymphatics) and impaired cerebral waste clearance.

Approach: MRI-visible PVS were scored on 7T images and related to IOP and tear total-tau.

Results: Higher tear total-tau and lower IOP were associated with more PVS, implying a connection to impaired cerebral waste clearance and aligning with the potential presence of a human ocular glymphatic system.

Impact: Our exploratory results suggest that higher tear-tau and a reduced driving force of ocular waste clearance are connected to impaired cerebral waste clearance. Thereby, this study bridges the gap between the potential human ocular glymphatic system and cerebral waste clearance.

Keywords: Neurofluids, Neurofluids

Motivation: Perivascular space (PVS) is a pial-lined, fluid-filled structure that accompany penetrating arteries or draining veins from the cerebral cortex. PVS occupies a large portion of the cerebral tissue. The effect of PVS on the brain's functional connectivity has not yet been fully investigated.

Goal(s): In particular, our goal was to determine the link between PVS, sleep and brain functional connectivity.

Approach: We utilized the structural MRI and rs-fMRI data from the HCP-Aging dataset.

Results: Results demonstrated that BG-PVS volume fraction was positively associated with FC of the right anterior medial temporal gyrus and a cluster in temporal regions.

Impact: These findings suggest that PVS morphology may reflect changes in neural connectivity involved in memory-related processing and open a new PVS research field (structure to function) for investigation.

Keywords: Neurofluids, Alzheimer's Disease, Biomarker

Motivation: Perivascular space (PVS) enlargement is used to estimate the severity of glymphatic clearance dysfunction in Alzheimer’s disease. There is a lack of imaging tool to estimate the cerebral cortical PVS load.

Goal(s): Investigate the association between the MR T2-relaxometry based CSF fraction (CSFF), a measure of total PVS (including both MR visible and invisible PVS), and PET imaging based Aβ deposition.

Approach: Use 6-echo FAST-T2 image to map CSFF and correlate it with Aβ deposition in both cognitive normal and mild cognitive impaired groups.

Results: CSFF is better than PVS load in association with Aβ deposition in MCI/AD subjects.

Impact: Parenchymal CSF fraction measured using MR T2-relaxometry is an estimate of total perivascular space, which reflect glymphatic clearance function. It has superior performance in correlation with Aβ deposition than MRI based PVS segmentation.

Keywords: Data Processing, Machine Learning/Artificial Intelligence, Multiple Sclerosis

Motivation: The Choroid Plexus (ChP) is a vascular structure involved in brain regulatory functions. The relation between ChP Volume and brain disorders raises the interest on this structure and the need for an accurate segmentation, questioning whether to introduce a preprocessing step.

Goal(s): This work studies the preprocessing impact on the ChP segmentation with Deep Neural Networks (DNN) ensemble.

Approach: Three different preprocessing steps (brain extraction, N4 intensity correction, combination of both) were applied to 128 T1-w MRI images before DNN training. These approaches performances were compared to that without preprocessing.

Results: The preprocessing step does not improve DNN performance for the ChP segmentation.

Impact: The preprocessing steps of brain extraction and N4 intensity normalization correction on T1-w MRI images do not have an impact on Deep Neural Networks performance during the automatic segmentation of Choroid Plexus on Multiple Sclerosis patients.

Keywords: Software Tools, Quantitative Imaging

Motivation:  Deep medullary veins (DMVs) stenosis may be one of the causes of small vessel disease, so non-invasive tool for its assessment is desired.

Goal(s): Developing automatic DMV segmentation and diameter quantification methods for assessing DMV stenosis.

Approach: We trained an automatic segmentation model and proposed a DMV diameter quantification method by analyzing the complex MRI signals at sub-voxel scale.

Results: The segmentation model achieved satisfactory performance. The accuracy of the diameter quantification method was verified in phantoms. The fitted DMV diameter distribution was close to earlier ex-vivo report and showed strong correlation with DMV susceptibility from quantitative susceptibility mapping.

Impact: Our approach can serve as a useful automatic pipeline to study the role of DMV stenosis in the pathogenesis of small vessel disease.

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, 3D MRI

Motivation: Automated detection for disproportionately enlarged subarachnoid-space hydrocephalus (DESH) using 3D MRIs.

Goal(s): We developed robust deep learning models for accurate DESH detection by automatically segmenting regions.

Approach: Utilized 3D U-Net for segmentation and multimodal convolutional neural network for classification. Achieved high accuracy, with mean Dice scores ranging 0.60 – 0.84 and softmax probability scores exceeding 0.95. All of the area under the curves exceeded 0.97.

Results: Successfully developed the highly accurate deep learning models in automatically segmentation of ventricles and regional subarachnoid spaces and in the detecting DESH, ventricular dilatation, tightened sulci in the high convexities, and Sylvian fissure dilatation.

Impact: Combining a 3D U-Net model and a multi-modal convolutional neural network model, disproportionately enlarged subarachnoid-space hydrocephalus (DESH) for idiopathic normal pressure hydrocephalus (iNPH) was automatically detected with automatically segmented regions from 3D T1- and T2-weighted MRIs.

Keywords: Diagnosis/Prediction, Brain, neuroanatomical

Motivation: Emerging evidence suggests that Ménière's disease (MD) may extend beyond the confines of the inner ear, and involved the central nervous system.

Goal(s): To investigate the neuroanatomical alterations associated with MD and to develop a machine learning classification model to effectively discriminate between MD patients and HC. 

Approach: A case-control morphometry study was performed to examine potential brain structural changes and delineate the diagnostic utility of these identified brain alterations.

Results: Distinctive alterations in gray matter volume and cortical thickness were identified in regions implicated in emotional processing and sensory integration. The classification model showcased a discriminative power with an impressive AUC value(0.92).

Impact: MD patients showed distinctive morphometry alterations, and were leveraged as potential biomarkers, facilitating the discrimination between MD and HC.These findings provide critical insights into the intricate neuroanatomical alterations in MD and highlight the diagnostic potential of advanced neuroimaging techniques.