Free Water Volume Fraction: An Imaging Biomarker to Characterize Moderate-to-Severe Traumatic Brain Injury

Free water imaging in Parkinson's disease and atypical parkinsonian disorders

BACKGROUND

Free water (FW)-corrected diffusion measures are more precise compared to standard diffusion measures. This study comprehensively evaluates FW and corrected diffusion metrics for whole brain white and deep gray matter (WM, GM) structures in patients with Parkinson's disease (PD), progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) and attempts to ascertain the probable patterns of WM abnormalities.

METHOD

Diffusion MRI was acquired for subjects with PD (n = 133), MSA (n = 25), PSP (n = 30) and matched healthy controls (HC) (n = 99, n = 24, n = 12). Diffusion metrics of FA, MD, AD, RD were generated and FW, corrected FA maps were calculated using a bi-tensor model. TBSS was carried out at 5000 permutations with significance at p < 0.05. For GM, diffusivity maps were extracted from the basal ganglia, and analyzed at an FDR with p < 0.05.

RESULTS

Compared to HC, PD showed focal changes in FW. MSA showed changes in the cerebellum and brainstem, and PSP showed increase in FW involving supratentorial WM and midbrain. All three showed increased substantia nigra FW. MSA, PSP demonstrated increased FW in bilateral putamen. PD showed increased FW in left GP externa, and bilateral thalamus. Compared to HC, MSA had increased FW in bilateral GP interna, and left thalamic. PSP had an additional increase in FW of the right GP externa, right GP interna, and bilateral thalamus.

CONCLUSION

The present study demonstrated definitive differences in the patterns of FW alterations between PD and atypical parkinsonian disorders suggesting the possibility of whole brain FW maps being used as markers for diagnosis of these disorders.

Evidence of Ongoing Cerebral Microstructural Reorganization in Children With Persisting Symptoms Following Mild Traumatic Brain Injury: A NODDI DTI Analysis

Approximately 300-550 children per 100,000 sustain a mild traumatic brain injury (mTBI) each year, of whom ∼25-30% have long-term cognitive problems. Following mTBI, free water (FW) accumulation occurs in white matter (WM) tracts. Diffusion tensor imaging (DTI) can be used to investigate structural integrity following mTBI. Compared with conventional DTI, neurite orientation dispersion and density imaging (NODDI) orientation dispersion index (ODI) and fraction of isolated free water (FISO) metrics may allow a more advanced insight into microstructural damage following pediatric mTBI. In this longitudinal study, we used NODDI to explore whole-brain and tract-specific differences in ODI and FISO in children with persistent symptoms after mTBI (n = 80) and in children displaying clinical recovery (n = 32) at 1 and 2-3 months post-mTBI compared with healthy controls (HCs) (n = 21). Two-way repeated measures analysis of variance (ANOVA) and voxelwise two-sample t tests were conducted to compare whole-brain and tract-specific diffusion across groups. All results were corrected at positive false discovery rate (pFDR) <0.05. We also examined the association between NODDI metrics and clinical outcomes, using logistical regression to investigate the value of NODDI metrics in predicting future recovery from mTBI. Whole-brain ODI was significantly increased in symptomatic participants compared with HCs at both 1 and 2 months post-injury, where the uncinate fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF) were particularly implicated. Using region of interest (ROI) analysis in significant WM, bilateral IFOF and UF voxels, symptomatic participants had the highest ODI in all ROIs. ODI was lower in asymptomatic participants, and HCs had the lowest ODI in all ROIs. No changes in FISO were found across groups or over time. WM ODI was moderately correlated with a higher youth-reported post-concussion symptom inventory (PCSI) score. With 87% predictive power, ODI (1 month post-injury) and clinical predictors (age, sex, PCSI score, attention scores) were a more sensitive predictor of recovery at 2-3 months post-injury than fractional anisotropy (FA) and clinical predictors, or clinical predictors alone. FISO could not predict recovery at 2-3 months post-injury. Therefore, we found that ODI was significantly increased in symptomatic children following mTBI compared with HCs at 1 month post-injury, and progressively decreased over time alongside clinical recovery. We found no significant differences in FISO between groups or over time. WM ODI at 1 month was a more sensitive predictor of clinical recovery at 2-3 months post-injury than FA, FISO, or clinical measures alone. Our results show evidence of ongoing microstructural reorganization or neuroinflammation between 1 and 2-3 months post-injury, further supporting delayed return to play in children who remain symptomatic. We recommend future research examining the clinical utility of NODDI following mTBI to predict recovery or persistence of post-concussion symptoms and thereby inform management of mTBI.

MRI-based multivariate gray matter volumetric distance for predicting motor symptom progression in Parkinson's disease

While Parkinson's disease (PD)-related neurodegeneration is associated with structural changes in the brain, conventional magnetic resonance imaging (MRI) has proven less effective for clinical diagnosis due to its inability to reliably identify subtle changes early in the disease course. In this study, we aimed to develop a structural MRI-based biomarker to predict the rate of progression of motor symptoms in the early stages of PD. The study included 88 patients with PD and 120 healthy controls from the Parkinson's Progression Markers Initiative database; MRI at baseline and motor symptom scores assessed using the MDS-UPDRS-III at two time points (baseline and 48 months) were selected. Group-level volumetric analyses revealed that the volumetric reductions in the left striatum were associated with the decline in motor functioning. Then, we developed a patient-specific multivariate gray matter volumetric distance and demonstrated that it could significantly predict changes in motor symptom scores (P < 0.05). Further, we classified patients as relatively slower and faster progressors with 89% accuracy using a support vector machine classifier. Thus, we identified a promising structural MRI-based biomarker for predicting the rate of progression of motor symptoms and classifying patients based on motor symptom severity.

A novel MRI-based volumetric index for monitoring the motor symptoms in Parkinson's disease

BACKGROUND

Conventional MRI scans have limited usefulness in monitoring Parkinson's disease as they typically do not show any disease-specific brain abnormalities. This study aimed to identify an imaging biomarker for tracking motor symptom progression by using a multivariate statistical approach that can combine gray matter volume information from multiple brain regions into a single score specific to each PD patient.

METHODS

A cohort of 150 patients underwent MRI at baseline and had their motor symptoms tracked for up to 10 years using MDS-UPDRS-III, with motor symptoms focused on total and subscores, including rigidity, bradykinesia, postural instability, and gait disturbances, resting tremor, and postural-kinetic tremor. Gray matter volume extracted from MRI data was summarized into a patient-specific summary score using Mahalanobis distance, MGMV. MDS-UPDRS-III's progression and its association with MGMV were modeled via linear mixed-effects models over 5- and 10-year follow-up periods.

RESULTS

Over the 5-year follow-up, there was a significant increase (P < 0.05) in MDS-UPDRS-III total and subscores, except for postural-kinetic tremor. Over the 10-year follow-up, all MDS-UPDRS-III scores increased significantly (P < 0.05). A higher baseline MGMV was associated with a significant increase in MDS-UPDRS-III total, bradykinesia, postural instability and gait disturbances, and resting tremor (P < 0.05) over the 5-year follow-up, but only with total, bradykinesia, and postural instability and gait disturbances during the 10-year follow-up (P < 0.05).

CONCLUSIONS

Higher MGMV scores were linked to faster motor symptom progression, suggesting it could be a valuable marker for clinicians monitoring Parkinson's disease over time.

Longitudinal Abnormalities in White Matter Extracellular Free Water Volume Fraction and Neuropsychological Functioning in Patients with Traumatic Brain Injury

Traumatic brain injury is a global public health problem associated with chronic neurological complications and long-term disability. Biomarkers that map onto the underlying brain pathology driving these complications are urgently needed to identify individuals at risk for poor recovery and to inform design of clinical trials of neuroprotective therapies. Neuroinflammation and neurodegeneration are two endophenotypes potentially associated with increases in brain extracellular water content, but the nature of extracellular free water abnormalities after neurotrauma and its relationship to measures typically thought to reflect traumatic axonal injury are not well characterized. The objective of this study was to describe the relationship between a neuroimaging biomarker of extracellular free water content and the clinical features of a cohort with primarily complicated mild traumatic brain injury. We analyzed a cohort of 59 adult patients requiring hospitalization for non-penetrating traumatic brain injury of all severities as well as 36 healthy controls. Patients underwent brain magnetic resonance imaging (MRI) at 2 weeks (n = 59) and 6 months (n = 29) post-injury, and controls underwent a single MRI. Of the participants with TBI, 50 underwent clinical neuropsychological assessment at 2 weeks and 28 at 6 months. For each subject, we derived a summary score representing deviations in whole brain white matter extracellular free water volume fraction (VF) and free water-corrected fractional anisotropy (fw-FA). The summary specific anomaly score (SAS) for VF was significantly higher in TBI patients at 2 weeks and 6 months post-injury relative to controls. SAS for VF exhibited moderate correlation with neuropsychological functioning, particularly on measures of executive function. These findings indicate abnormalities in whole brain white matter extracellular water fraction in patients with TBI and are an important step toward identifying and validating noninvasive biomarkers that map onto the pathology driving disability after TBI.

Connectomic assessment of injury burden and longitudinal structural network alterations in moderate-to-severe traumatic brain injury

Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network-wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been relatively limited. In this study, we propose a novel connectomic measure that we call network normality score (NNS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate-to-severe TBI patients, we demonstrate that structural network topology of patients is more heterogeneous and significantly different than that of healthy controls at 3 months postinjury, where dissimilarity further increases up to 12 months. We also show that NNS captures injury burden as quantified by posttraumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally, we compare NNS to major graph theory measures used in TBI literature and demonstrate the superiority of NNS in characterizing the disease.