Background Children and adolescents, family history positive (FH+) for alcoholism, exhibit differences in brain structure and functional activation when compared to family history negative (FH-) counterparts. Given that frontal brain regions, and associated reciprocal connections with limbic structures, undergo the most dramatic maturational changes during adolescence, the objective of this study was to compare functional brain activation during a frontally-mediated test of response inhibition in 32 adolescents separated into low-risk (FH-) and high-risk (FH+) groups. Methods Functional magnetic resonance (fMRI) blood oxygen level dependent (BOLD) data were acquired at 1.5 Tesla during performance of Stroop Color Naming, Word Reading and Interference. Preprocessing and statistical analyses, covaried for age, were conducted in SPM99 using a search territory that included superior, middle, and inferior frontal gyri (trigone region), anterior cingulate gyrus, and left and right amygdala. Results Significantly greater activation in the fronto-limbic search territory was observed in FH+ relative to FH- subjects during Stroop Interference. In addition, a significant regression between brain activation and family history density was observed, with a greater density being associated with increased activation in regions including middle frontal gyrus (BA9) and cingulate gyrus (BA24). Conclusions These data demonstrate a significant influence of FH status on brain activation during the performance of a response inhibition task, perhaps reflecting a neurobiological vulnerability associated with FH status that may include reduced neuronal efficiency and/or recruitment of additional neuronal resources. These findings are important given that the adolescent developmental period is already associated with reduced inhibitory capacity, even prior to the onset of alcohol use.

Current therapeutic development in amyotrophic lateral sclerosis (ALS) relies on individual randomized clinical trials to test a specific investigational product in a single patient population. This approach has intrinsic limitations, including cost, time, and lack of flexibility. Adaptive platform trials represent a novel approach to investigate several interventions for a single disease in a continuous manner. Already in use in oncology, this approach is now being employed more often in neurology. Here, we describe a newly launched platform trial for ALS. The Healey ALS Platform Trial is testing multiple investigational products concurrently in people with ALS, with the goal of rapidly identifying novel treatments, biomarkers, and trial endpoints.

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Platform trials allow efficient evaluation of multiple interventions for a specific disease. The HEALEY ALS Platform Trial is testing multiple investigational products in parallel and sequentially in persons with amyotrophic lateral sclerosis (ALS) with the goal of rapidly identifying novel treatments to slow disease progression. Platform trials have considerable operational and statistical efficiencies compared with typical randomized controlled trials due to their use of shared infrastructure and shared control data. We describe the statistical approaches required to achieve the objectives of a platform trial in the context of ALS. This includes following regulatory guidance for the disease area of interest and accounting for potential differences in outcomes of participants within the shared control (potentially due to differences in time of randomization, mode of administration, and eligibility criteria). Within the HEALEY ALS Platform Trial, the complex statistical objectives are met using a Bayesian shared parameter analysis of function and survival. This analysis serves to provide a common integrated estimate of treatment benefit, overall slowing in disease progression, as measured by function and survival while accounting for potential differences in the shared control group using Bayesian hierarchical modeling. Clinical trial simulation is used to provide a better understanding of this novel analysis method and complex design. ANN NEUROL 2023