<i>B</i>‑Cyanodicarba-<i>closo</i>-dodecaboranes: Facile Synthesis and Spectroscopic Features

<i>B</i>-Cyanodicarba-<i>closo</i>-dodecaboranes are a poorly explored class of compounds due to their complex synthetic availability. Now, we report a fast, atom-efficient, and high-yielding synthesis. We obtained the cyano derivatives by reacting <i>B</i>-iododicarba-<i>closo</i>-dodecaboranes with copper­(I) cyanide under both palladium catalysis and microwave irradiation. We successfully applied this method to 9-iodo-<i>o</i>-, 9-iodo-<i>m</i>-, and 2-iodo-<i>p</i>-dicarba-<i>closo</i>-dodecaborane, obtaining the corresponding cyanides up to 89% isolated yield. The facile synthesis and evaluation of their spectroscopic properties reported herein will pave the way to exploring the chemistry and application of <i>B</i>-cyanodicarba-<i>closo</i>-dodecaborane clusters in more detail

Synthesis of Dicarba-<i>closo</i>-dodecaborane-1-carboxamides

Amide bond formation is one of the most important chemical reactions. In peptide and organic chemistry, the application of amide coupling reagents is a routine strategy, but surprisingly not in carborane chemistry. Thus, we now report a fast, safe, and robust protocol to couple amines to <i>m</i>- and <i>p</i>-dicarba-<i>closo</i>-dodecaborane-1-carboxylic acids. The procedure comprises the activation of carboxylic acid with the coupling reagent (1-cyano-2-ethoxy-2-oxoethylidenaminooxy)­(dimethylamino)­morpholinocarbenium hexafluorophosphate, extraction of the product using the hydrophobic nature of the cluster, and a straightforward chromatographic purification. The protocol allows access to a variety of carborane–organic hybrid molecules suitable for application in multiple areas

Deep brain stimulation restores frontostriatal network activity in obsessive-compulsive disorder

Little is known about the underlying neural mechanism of deep brain stimulation (DBS). We found that DBS targeted at the nucleus accumbens (NAc) normalized NAc activity, reduced excessive connectivity between the NAc and prefrontal cortex, and decreased frontal low-frequency oscillations during symptom provocation in patients with obsessive-compulsive disorder. Our findings suggest that DBS is able to reduce maladaptive activity and connectivity of the stimulated regio

Thalamic hyperconnectivity as neurophysiological signature of major depressive disorder in two multicenter studies

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. Resting-state functional magnetic resonance imaging data were obtained from the REST-meta-MDD (N=2338) and PsyMRI (N=1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN) and performance was evaluated using 5-fold cross-validation. Results were visualized using GCN-Explainer, an ablation study and univariate t-testing. Mean classification accuracy was 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. Whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies