In the early 1960s, reports began to surface that some children living in remote villages in East Africa were suffering mysterious episodes of “head nodding.” The condition, now named nodding syndrome, is recognized as a rare and devastating form of epilepsy. There were hints that the syndrome might be caused by a parasitic worm called Onchocerca volvulus, which is transmitted through the bites of blackflies. But no one had been able to tie the parasitic infection directly to the nodding heads.
Now, NIH researchers and their international colleagues think they’ve found the missing link. The human immune system turns out to be a central player. After analyzing blood and cerebrospinal fluid of kids with nodding syndrome, they detected a particular antibody at unusually high levels . Further studies suggest the immune system ramps up production of that antibody to fight off the parasite. The trouble is those antibodies also react against a protein in healthy brain tissue, apparently leading to progressive cognitive dysfunction, neurological deterioration, head nodding, and potentially life-threatening seizures.
The findings, published in Science Translational Medicine, have important implications for the treatment and prevention of not only nodding syndrome, but perhaps other autoimmune-related forms of epilepsy. As people in the United States and around the globe today observe the 10th anniversary of international Rare Disease Day, this work provides yet another example of how rare disease research can shed light on more common diseases and fundamental aspects of human biology.
Tags: Africa, antibody, autoimmune disease, autoimmunity, childhood infectious diseases, cognitive dysfunction, epilepsy, global health, immunity, infectious disease, ivermectin, leiomodin-1, neglected tropical diseases, neurons, Nodding Syndrome, Onchocerca volvulus, Onchocerciasis, parasite, parasitic worm, rare disease, Rare Disease Day, River Blindness, seizures, South Sudan, Tanzania, Uganda, worm
For many of the 65 million people around the world with epilepsy, modern medications are able to keep the seizures under control. When medications fail, as they do in about one-third of people with epilepsy, surgery to remove affected brain tissue without compromising function is a drastic step, but offers a potential cure. Unfortunately, not all drug-resistant patients are good candidates for such surgery for a simple reason: their brains appear normal on traditional MRI scans, making it impossible to locate precisely the source(s) of the seizures.
Now, in a small study published in Science Translational Medicine , NIH-funded researchers report progress towards helping such people. Using a new MRI method, called GluCEST, that detects concentrations of the nerve-signaling chemical glutamate in brain tissue , researchers successfully pinpointed seizure-causing areas of the brain in four of four volunteers with drug-resistant epilepsy and normal traditional MRI scans. While the findings are preliminary and must be confirmed by larger studies, researchers are hopeful that GluCEST, which takes about 30 minutes, may open the door to new ways of treating this type of epilepsy.
Tags: 7 Tesla MRI, brain, brain imaging, CEST, chemical exchange saturation transfer, drug-resistant epilepsy, epilepsy, GluCest, glutamate, hippocampi, hipppocampus, MRI, neurological disorders, seizures, temporal lobe
For millions of people with epilepsy, life comes with too many restrictions. If they just had a reliable way to predict when their next seizure will come, they could have a chance at leading more independent and productive lives.
That’s why it is so encouraging to hear that researchers have developed a new algorithm that can predict the onset of a seizure correctly 82 percent of the time. Until recently, the best algorithm was hardly better than flipping a coin, leading some to speculate that seizures are random neurological events that can’t be predicted at all. But the latest leap forward shows that seizures certainly can be predicted, and our research efforts are headed in the right direction to make them even more predictable. The other big news is how this new algorithm was developed: it’s the product of a crowdsourcing competition.