Arterial Spin-Labeling Perfusion Metrics in Pediatric Posterior Fossa Tumor Surgery.

BACKGROUND AND PURPOSE: Pediatric posterior fossa tumors often present with hydrocephalus; postoperatively, up to 25% of patients develop cerebellar mutism syndrome. Arterial spin-labeling is a noninvasive means of quantifying CBF and bolus arrival time. The aim of this study was to investigate how changes in perfusion metrics in children with posterior fossa tumors are modulated by cerebellar mutism syndrome and hydrocephalus requiring pre-resection CSF diversion. MATERIALS AND METHODS: Forty-four patients were prospectively scanned at 3 time points (preoperatively, postoperatively, and at 3-month follow-up) with single- and multi-inflow time arterial spin-labeling sequences. Regional analyses of CBF and bolus arrival time were conducted using coregistered anatomic parcellations. ANOVA and multivariable, linear mixed-effects modeling analysis approaches were used. The study was registered at clinicaltrials.gov (NCT03471026). RESULTS: CBF increased after tumor resection and at follow-up scanning (P = .045). Bolus arrival time decreased after tumor resection and at follow-up scanning (P = .018). Bolus arrival time was prolonged (P = .058) following the midline approach, compared with cerebellar hemispheric surgical approaches to posterior fossa tumors. Multivariable linear mixed-effects modeling showed that regional perfusion changes were more pronounced in the 6 children who presented with symptomatic obstructive hydrocephalus requiring pre-resection CSF diversion, with hydrocephalus lowering the baseline mean CBF by 20.5 (standard error, 6.27) mL/100g/min. Children diagnosed with cerebellar mutism syndrome (8/44, 18.2%) had significantly higher CBF at follow-up imaging than those who were not (P = .040), but no differences in pre- or postoperative perfusion parameters were seen. CONCLUSIONS: Multi-inflow time arterial spin-labeling shows promise as a noninvasive tool to evaluate cerebral perfusion in the setting of pediatric obstructive hydrocephalus and demonstrates increased CBF following resolution of cerebellar mutism syndrome.

Structural cortical network reorganization associated with early conversion to multiple sclerosis.

Brain structural covariance networks (SCNs) based on pairwise statistical associations of cortical thickness data across brain areas reflect underlying physical and functional connections between them. SCNs capture the complexity of human brain cortex structure and are disrupted in neurodegenerative conditions. However, the longitudinal assessment of SCN dynamics has not yet been explored, despite its potential to unveil mechanisms underlying neurodegeneration. Here, we evaluated the changes of SCNs over 12 months in patients with a first inflammatory-demyelinating attack of the Central Nervous System and assessed their clinical relevance by comparing SCN dynamics of patients with and without conversion to multiple sclerosis (MS) over one year. All subjects underwent clinical and brain MRI assessments over one year. Brain cortical thicknesses for each subject and time point were used to obtain group-level between-area correlation matrices from which nodal connectivity metrics were obtained. Robust bootstrap-based statistical approaches (allowing sampling with replacement) assessed the significance of longitudinal changes. Patients who converted to MS exhibited significantly greater network connectivity at baseline than non-converters (p = 0.02) and a subsequent connectivity loss over time (p = 0.001-0.02), not observed in non-converters' network. These findings suggest SCN analysis is sensitive to brain tissue changes in early MS, reflecting clinically relevant aspects of the condition. However, this is preliminary work, indicated by the low sample sizes, and its results and conclusions should be treated with caution and confirmed with larger cohorts.

Image analysis and statistical inference in neuroimaging with R.

R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R.

White matter integrity and cognition in childhood and old age.

OBJECTIVE: To test the hypothesis that white matter integrity, as measured by diffusion tensor and magnetization transfer MRI is significantly associated with cognitive ability measured in youth and old age. METHODS: Forty, nondemented, surviving participants of the Scottish Mental Survey of 1932 underwent brain MRI and a battery of psychometric tests covering major cognitive domains and tests of information processing efficiency. IQ scores were available from age 11. Mean diffusivity, fractional anisotropy (FA), and magnetization transfer ratio (MTR) were measured in frontal and parieto-occipital white matter and centrum semiovale. RESULTS: Centrum semiovale FA correlated (r = 0.36 to 0.56; p < 0.02) with contemporaneous (age 83) scores on psychometric tests of nonverbal reasoning, working memory, executive function, and information processing efficiency. Centrum semiovale FA also correlated with IQ at age 11 (r = 0.37; p = 0.02). Controlling for IQ at age 11 and information processing at age 83 attenuated the association between centrum semiovale FA and general cognitive ability by approximately 85%. MTR, largely, did not show significant correlations with cognitive test scores. CONCLUSIONS: These data support the information processing efficiency hypothesis of cognitive aging and suggest one foundation for individual differences in processing efficiency. They also suggest that studies of imaging and cognition in the elderly should take into account prior mental ability rather than assuming that any associations between imaging parameters and cognitive test scores are the result of age-related changes.