Efficacy and safety of larotrectinib in TRK fusion-positive primary central nervous system tumors

BACKGROUND Larotrectinib is a first-in-class, highly selective tropomyosin receptor kinase (TRK) inhibitor approved to treat adult and pediatric patients with TRK fusion-positive cancer. The aim of this study was to evaluate the efficacy and safety of larotrectinib in patients with TRK fusion-positive primary central nervous system (CNS) tumors. METHODS Patients with TRK fusion-positive primary CNS tumors from two clinical trials (NCT02637687, NCT02576431) were identified. The primary endpoint was investigator-assessed objective response rate (ORR). RESULTS As of July 2020, 33 patients with TRK fusion-positive CNS tumors were identified (median age: 8.9 years; range: 1.3-79.0). The most common histologies were high-grade glioma (HGG; n = 19) and low-grade glioma (LGG; n = 8). ORR was 30% (95% confidence interval [CI]: 16-49) for all patients. In all patients, the 24-week disease control rate was 73% (95% CI: 54-87). Twenty-three of 28 patients (82%) with measurable disease had tumor shrinkage. The 12-month rates for duration of response, progression-free survival, and overall survival were 75% (95% CI: 45-100), 56% (95% CI: 38-74), and 85% (95% CI: 71-99), respectively. Median time to response was 1.9 months (range 1.0-3.8 months). Duration of treatment ranged from 1.2-31.3+ months. Treatment-related adverse events were reported for 20 patients, with Grade 3-4 in 3 patients. No new safety signals were identified. CONCLUSIONS In patients with TRK fusion-positive CNS tumors, larotrectinib demonstrated rapid and durable responses, high disease control rate, and a favorable safety profile

Permutation and parametric tests for effect sizes in voxel-based morphometry of grey matter volume in brain structural MRI

Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults. High resolution T1-weighted volume scans were acquired from 80 healthy adults aged 25–64 years. Using a validated VBM procedure and voxel-based permutation testing for Pearson product-moment coefficient, the effect sizes of changes in gray matter proportion with age were assessed using traditional parametric and permutation testing inference with 100, 500, 1000, 5000, 10000 and 20000 permutations. The statistical significance was set at P < 0.05 and false discovery rate (FDR) was used to correct for multiple comparisons. Clusters of voxels with statistically significant (PFDR < 0.05) declines in gray matter proportion with age identified with permutation testing inference (N ≈ 6000) were approximately twice the size of those identified with parametric inference (N = 3221 voxels). Permutation testing with 10000 (N = 6251 voxels) and 20000 (N = 6233 voxels) permutations produced clusters that were generally consistent with each other. However, with 1000 permutations there were approximately 20% more statistically significant voxels (N = 7117 voxels) than with ≥ 10000 permutations. Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences in gray matter proportion. Based on the results reported here, at least 10000 permutations should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings. Additional studies using permutation testing in large imaging databanks are required to address the impact of model complexity, multivariate analysis, number of observations, sampling bias and data quality on the accuracy with which subtle differences in brain structure associated with normal aging can be identified

Atypical Balance between Occipital and Fronto-Parietal Activation for Visual Shape Extraction in Dyslexia

Reading requires the extraction of letter shapes from a complex background of text, and an impairment in visual shape extraction would cause difficulty in reading. To investigate the neural mechanisms of visual shape extraction in dyslexia, we used functional magnetic resonance imaging (fMRI) to examine brain activation while adults with or without dyslexia responded to the change of an arrow’s direction in a complex, relative to a simple, visual background. In comparison to adults with typical reading ability, adults with dyslexia exhibited opposite patterns of atypical activation: decreased activation in occipital visual areas associated with visual perception, and increased activation in frontal and parietal regions associated with visual attention. These findings indicate that dyslexia involves atypical brain organization for fundamental processes of visual shape extraction even when reading is not involved. Overengagement in higher-order association cortices, required to compensate for underengagment in lower-order visual cortices, may result in competition for top-down attentional resources helpful for fluent reading.Ellison Medical FoundationMartin Richmond Memorial FundNational Institutes of Health (U.S.). (Grant UL1RR025758)National Institutes of Health (U.S.). (Grant F32EY014750-01)MIT Class of 1976 (Funds for Dyslexia Research

Estimated maximal and current brain volume predict cognitive ability in old age

AbstractBrain tissue deterioration is a significant contributor to lower cognitive ability in later life; however, few studies have appropriate data to establish how much influence prior brain volume and prior cognitive performance have on this association. We investigated the associations between structural brain imaging biomarkers, including an estimate of maximal brain volume, and detailed measures of cognitive ability at age 73 years in a large (N = 620), generally healthy, community-dwelling population. Cognitive ability data were available from age 11 years. We found positive associations (r) between general cognitive ability and estimated brain volume in youth (male, 0.28; females, 0.12), and in measured brain volume in later life (males, 0.27; females, 0.26). Our findings show that cognitive ability in youth is a strong predictor of estimated prior and measured current brain volume in old age but that these effects were the same for both white and gray matter. As 1 of the largest studies of associations between brain volume and cognitive ability with normal aging, this work contributes to the wider understanding of how some early-life factors influence cognitive aging

Variance in brain volume with advancing age: implications for defining the limits of normality

Background: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages. Materials and Methods: We acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer’s disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age. Results: In both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5th percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects. Conclusions: While more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease