Yilmazrivas4409

Excessive brain iron negatively affects working memory and related processes but the impact of cortical iron on task-relevant, cortical brain networks is unknown. We hypothesized that high cortical iron concentration may disrupt functional circuitry within cortical networks supporting working memory performance. Fifty-five healthy older adults completed an N-Back working memory paradigm while functional magnetic resonance imaging (fMRI) was performed. Participants also underwent quantitative susceptibility mapping (QSM) imaging for assessment of non-heme brain iron concentration. Additionally, pseudo continuous arterial spin labeling scans were obtained to control for potential contributions of cerebral blood volume and structural brain images were used to control for contributions of brain volume. Task performance was positively correlated with strength of task-based functional connectivity (tFC) between brain regions of the frontoparietal working memory network. However, higher cortical iron concentration was associated with lower tFC within this frontoparietal network and with poorer working memory performance after controlling for both cerebral blood flow and brain volume. Our results suggest that high cortical iron concentration disrupts communication within frontoparietal networks supporting working memory and is associated with reduced working memory performance in older adults.Brain connectivity profiles seeding from deep brain stimulation (DBS) electrodes have emerged as informative tools to estimate outcome variability across DBS patients. Given the limitations of acquiring and processing patient-specific diffusion-weighted imaging data, a number of studies have employed normative atlases of the human connectome. To date, it remains unclear whether patient-specific connectivity information would strengthen the accuracy of such analyses. Here, we compared similarities and differences between patient-specific, disease-matched and normative structural connectivity data and their ability to predict clinical improvement. Data from 33 patients suffering from Parkinson's Disease who underwent surgery at three different centers were retrospectively collected. Stimulation-dependent connectivity profiles seeding from active contacts were estimated using three modalities, namely patient-specific diffusion-MRI data, age- and disease-matched or normative group connectome data (acquired in heac connectivity may bear the potential of explaining slightly more variance than group connectomes. Furthermore, use of normative connectomes involves datasets with high signal-to-noise acquired on specialized MRI hardware, while clinical datasets as the ones used here may not exactly match their quality. Our findings support the role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming.Neuropsychological assessments are essential in diagnosing age-related neurocognitive disorders. However, they are lengthy in duration and can be unreliable at times. To this end, we explored a modified connectome-based predictive modeling approach to estimating individualized scores from multiple cognitive domains using structural connectivity (SC) and functional connectivity (FC) features. Multi-shell HARDI and resting-state functional magnetic resonance imaging scans, and scores from 10 cognitive measures were acquired from 91 older adults with mild cognitive impairment. click here SC and FC matrices were derived from these scans and, in various combinations, entered into models along with demographic covariates to predict cognitive scores. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (rpredicted-observed). Across all cognitive measures, significant rpredicted-observed (0.402 to 0.654) were observed from the best-predicting models. Six of these models consisted of multimodal features. For three cognitive measures, their best-predicting models' rpredicted-observed were similar to that of a model that included only demographic covariates- suggesting that SC and/or FC features did not contribute significantly on top of demographics. Cross-prediction models revealed that the best-predicting models were similarly accurate in predicting scores of related cognitive measures- suggesting their limited specificity in predicting cognitive scores. Generally, multimodal connectomes together with demographics, can be exploited as sensitive markers, though with limited specificity, to predict cognitive performance across a spectrum in multiple cognitive domains. In certain situations, it may not be worthwhile to acquire neuroimaging data, considering that demographics alone can be similarly accurate in predicting cognitive scores.The FMN-dependent NADH-indigo reductase gene from the thermophilic bacterium Bacillus smithii was overexpressed in Escherichia coli. The expressed enzyme functioned as a highly thermostable indigo reductase that retained complete activity even after incubation at 100 °C for 10 min. Furthermore, B. smithii indigo reductase exhibited high stability over a wider pH range and longer storage periods compared with indigo reductases previously identified from other sources. The enzyme catalyzed the reduction of various azo compounds and indigo carmine. The crystal structures of the wild-type enzyme in complex with FMN/N-cyclohexyl-2-aminoethanesulfonate (CHES) and the Y151F mutant enzyme in complex with FMN were determined by the molecular replacement method and refined at resolutions of 1.97 and 1.95 Å, respectively. Then, indigo carmine molecule was modeled into the active site using the molecular docking simulation and the binding mode of indigo carmine was elucidated. In addition, the structure of B. cohnii indigo reductase, which is relatively less stable than B. smithii indigo reductase, was constructed by homology modeling. The factor contributing to the considerably higher thermostability of B. smithii indigo reductase was analyzed by comparing its structure with that of B. cohnii indigo reductase, which revealed that intersubunit aromatic interactions (F105-F172' and F172-F105') may be responsible for the high thermostability of B. smithii indigo reductase. Notably, site-directed mutagenesis results showed that F105 plays a major role in the intersubunit aromatic interaction.