Metabolic Changes in the Visual Cortex Are Linked to Retinal Nerve Fiber Layer Thinning in Multiple Sclerosis

OBJECTIVE: To investigate the damage to the retinal nerve fiber layer as part of the anterior visual pathway as well as an impairment of the neuronal and axonal integrity in the visual cortex as part of the posterior visual pathway with complementary neuroimaging techniques, and to correlate our results to patients' clinical symptoms concerning the visual pathway. DESIGN, SUBJECTS AND METHODS: Survey of 86 patients with relapsing-remitting multiple sclerosis that were subjected to retinal nerve fiber layer thickness (RNFLT) measurement by optical coherence tomography, to a routine MRI scan including the calculation of the brain parenchymal fraction (BPF), and to magnetic resonance spectroscopy at 3 tesla, quantifying N-acetyl aspartate (NAA) concentrations in the visual cortex and normal-appearing white matter. RESULTS: RNFLT correlated significantly with BPF and visual cortex NAA, but not with normal-appearing white matter NAA. This was connected with the patients' history of a previous optic neuritis. In a combined model, both BPF and visual cortex NAA were independently associated with RNFLT. CONCLUSIONS: Our data suggest the existence of functional pathway-specific damage patterns exceeding global neurodegeneration. They suggest a strong interrelationship between damage to the anterior and the posterior visual pathway

Neuropsychosocial profiles of current and future adolescent alcohol misusers

A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Hydrido-Ruthenium Cluster Complexes as Models for Reactive Surface Hydrogen Species of Ruthenium Nanoparticles. Solid-State 2 H NMR and Quantum Chemical Calculations

International audienc

Correlation of RNFLT with BPF and ¹H-MRS parameters.

<p>a) Depicted is the average RNFLT, every symbol representing a single eye examined together with the corresponding BPF values. The symbols represent the patient's previous history of optic neuritis (open circles – no previous optic neuritis, grey squares - unilateral optic neuritis, black triangles – bilateral optic neuritis) A linear correlation function was calculated by a Generalised Linear Model to account for intra-individual inter-eye relationships (p = 0.001). b) Mean BPF was calculated for three groups that were defined based on their previous history of optic neuritis (white bar– no previous optic neuritis, grey bar - unilateral optic neuritis, black bar – bilateral optic neuritis). The (-) symbol indicates a trend, but a missing significant correlation of group differences as calculated by ANOVA (p = 0.055). Error bars represent 2× standard error of the mean (SEM). c) RNFLT averages are shown in relation to corresponding NAA concentrations in the visual cortex (VC). The symbols are coded as in a). The correlation is significant (p = 0.047). d) Mean visual cortex voxel (VC) NAA and the significance of group differences was calculated for optic neuritis groups as in b). The asterisk indicates statistically significant (p = 0.046) group differences. Error bars represent 2× standard error of the mean (SEM). e) RNFLT averages are shown in relation to corresponding NAA concentrations in normal-appearing white matter (NAWM). The symbols are coded as in a). No significant correlation was found (p = 0.531). f) Mean NAA in normal-appearing white matter (NAWM) and the significance of group differences was calculated for optic neuritis groups as in b) (p = 0.429). Error bars represent 2× standard error of the mean (SEM).</p

Statistical data for GEE and combined model GEE (NAWM = normal-appearing white matter, VC = visual cortex).

<p>Statistical data for GEE and combined model GEE (NAWM = normal-appearing white matter, VC = visual cortex).</p