JNeurosci: Findings from Quintana-Sarti et al. help explain how targeting microRNA-134 in mice can reduce seizure activity and support the continued development of this novel RNA-based approach for the treatment of epilepsy.

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The multifactorial pathophysiology of acquired epilepsies lends itself to a multitargeting therapeutic approach. MicroRNAs (miRNA) are short noncoding RNAs that individually can negatively regulate dozens of protein-coding transcripts. Previously, we reported that central injection of antisense oligonucleotides targeting microRNA-134 (Ant-134) shortly after status epilepticus potently suppressed the development of recurrent spontaneous seizures in rodent models of temporal lobe epilepsy. The mechanism(s) of these antiseizure effects remain, however, incompletely understood. Here we show that intracerebroventricular microinjection of Ant-134 in male mice with preexisting epilepsy caused by intra-amygdala kainic acid-induced status epilepticus potently reduces the occurrence of spontaneous seizures.

Parkinson’s disease (PD) is a chronic neurodegenerative condition affecting more than 1% of people over 65 years old. It is characterized by the preferential degeneration of nigrostriatal dopaminergic neurons, which is responsible for the motor symptoms of PD patients. The pathogenesis of this multifactorial disorder is still elusive, hampering the discovery of therapeutic strategies able to suppress the disease’s progression. While redox alterations, mitochondrial dysfunctions, and neuroinflammation are clearly involved in PD pathology, how these processes lead to the preferential degeneration of dopaminergic neurons is still an unanswered question. In this context, the presence of dopamine itself within this neuronal population could represent a crucial determinant.

Researchers used a microscopic model of human nerves and muscles to show that Alzheimer’s disease directly damages peripheral nerves. This physical damage happens independently of cognitive decline and does not improve with standard medications for the illness.

Scientists who use imaging to understand the brain’s complexity often focus on the strongest signals and ignore the rest. But this strategy, researchers warn, may reveal only the tip of the iceberg. A study published in Nature Human Behavior reveals that connections routinely overlooked as “noise” during neuroimaging data analysis can predict behavior with remarkable accuracy—and implicate entirely different brain networks. The finding could open many new targets for treating psychiatric illness, the researchers say.

“Many studies that rely on techniques like feature selection—which simplifies the brain down to a narrow slice—might only uncover a small part of the true neurobiology that underlies a given behavior,” says lead author Brendan Adkinson, Ph.D., an MD-Ph. D. student at Yale School of Medicine.

“Our study suggests that there may be multiple, non-overlapping networks capable of predicting a given behavior just as well.”