Search

There are growing concerns about potential delayed, neuropsychiatric consequences (e.g, cognitive decline, mood or anxiety disorders) of sports-related traumatic brain injury (TBI). Autopsy studies of brains from a limited number of former athletes have described characteristic, pathologic changes of chronic traumatic encephalopathy (CTE) leading to questions about the relationship between these pathologic and the neuropsychiatric disturbances seen in former athletes. Research in this area will depend on in vivo methods that characterize molecular changes in the brain, linking CTE and other sports-related pathologies with delayed emergence of neuropsychiatric symptoms. In this pilot project we studied former National Football League (NFL) players using new neuroimaging techniques and clinical measures of cognitive functioning. We hypothesized that former NFL players would show molecular and structural changes in medial temporal and parietal lobe structures as well as specific cognitive deficits, namely those of verbal learning and memory. We observed a significant increase in binding of [11C]DPA-713 to the translocator protein (TSPO), a marker of brain injury and repair, in several brain regions, such as the supramarginal gyrus and right amygdala, in 9 former NFL players compared to 9 age-matched, healthy controls. We also observed significant atrophy of the right hippocampus. Finally, we report that these same former players had varied performance on a test of verbal learning and memory, suggesting that these molecular and pathologic changes may play a role in cognitive decline. These results suggest that localized brain injury and repair, indicated by increased [11C]DPA-713 binding to TSPO, may be linked to history of NFL play. [11C]DPA-713 PET is a promising new tool that can be used in future study design to examine further the relationship between TSPO expression in brain injury and repair, selective regional brain atrophy, and the potential link to deficits in verbal learning and memory after NFL play.

Cerebrospinal fluid (CSF) biomarkers, such as α-synuclein (α-syn), amyloid beta peptide 1-42 (Aβ1-42), phosphorylated tau (181P) (p-tau), and total tau (t-tau), have long been associated with the development of Parkinson disease (PD) and other neurodegenerative diseases. In this investigation, we reported the assessment of CSF biomarkers and their correlations with vesicular monoamine transporter 2 (VMAT2) bindings measured with 18F-9-fluoropropyl-(+)-dihydrotetrabenazine (18F-AV133) that is being developed as a biomarker for PD. We test the hypothesis that monoaminergic degeneration was correlated with CSF biomarker levels in untreated PD patients.The available online data from the Parkinson's Progression Markers Initiative study (PPMI) project were collected and analyzed, which include demographic information, clinical evaluations, CSF biomarkers (α-syn, Aβ1-42, p-tau, and t-tau), 18F-AV133 brain PET, and T1 weighted MRIs. Region of interest (ROI) and voxel-wise Pearson correlation between standardized uptake value ratio (SUVR) and CSF biomarkers were calculated.Our major findings are: 1) Compared with controls, CSF α-syn and tau levels decreased significantly in PD; 2) α-syn was closely correlated with Aβ1-42 and tau in PD, especially in early-onset patients; and 3) hypothesis-driven ROI analysis found a significant negative correlation between CSF Aβ1-42 levels and VMAT2 densities in post cingulate, left caudate, left anterior putamen, and left ventral striatum in PDs. CSF t-tau and p-tau levels were significantly negatively related to VMAT2 SUVRs in substantia nigra and left ventral striatum, respectively. Voxel-wise analysis showed that left caudate, parahippocampal gyrus, insula and temporal lobe were negatively correlated with Aβ1-42. In addition, superior frontal gyrus and transverse temporal gyrus were negatively correlated with CSF p-tau levels.These results suggest that monoaminergic degeneration in PD is correlated with CSF biomarkers associated with cognitive impairment in neurodegenerative diseases including Alzheimer's disease. The association between loss of dopamine synaptic function and pathologic protein accumulations in PD indicates an important role of CSF biomarkers in PD development.

We describe a general and robust method for identification of an optimal non-linear mixed effects model. This includes structural, inter-individual random effects, covariate effects and residual error models using machine learning. This method is based on combinatorial optimization using genetic algorithm. [ABSTRACT FROM AUTHOR]