Lifeboat Foundation

Scientists have developed a novel computational approach that incorporates individual patients’ brain activity to calculate optimal, personalized brain stimulation treatment for Alzheimer’s disease. Lazaro Sanchez-Rodriguez of the University of Calgary, Canada, and colleagues present their new framework in PLOS Computational Biology.

Electrical stimulation of certain parts of the brain could help promote healthy activity in neural circuits impaired by Alzheimer’s disease, a neurodegenerative condition. This experimental treatment has shown some promise in clinical trials. However, all patients currently receive identical treatment protocols, potentially leading to different outcomes according to individual variations in brain signaling.

To investigate the possibility of personalized brain stimulation, Sanchez-Rodriguez and colleagues took a theoretical approach. They built a computational tool that incorporates patients’ MRI scans and physiological brain signaling measurements to calculate optimal brain stimulation signals, with the goal of delivering efficient, effective personalized treatment.

Brain stimulation treatments, like electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS), are often effective for the treatment of depression. Like antidepressant medications, however, they typically have a delayed onset. For example, a patient may receive several weeks of regular ECT treatments before a full response is achieved.

Thus, there is an impetus to develop antidepressant treatments that act to rapidly improve mood.

Low field magnetic stimulation (LFMS) is one such potential new treatment with rapid mood-elevating effects, as reported by researchers at Harvard Medical School and Weill Cornell Medical College.

From the time of Hippocrates, physicians have suspected a link between epilepsy and depression. Now, for the first time, scientists at Rutgers University-New Brunswick and Columbia University have found evidence that seizures and mood disorders such as depression may share the same genetic cause in some people with epilepsy, which may lead to better screening and treatment to improve patients’ quality of life.

The scientists studied dozens of unusual families with multiple relatives who had epilepsy, and compared the family members’ lifetime prevalence of mood disorders with that of the U.S. population.

They found an increased incidence of mood disorders in persons who suffer from a type of the condition called focal epilepsy, in which seizures begin in just one part of the brain. But mood disorders were not increased in people with generalized epilepsy, in which seizures start on both sides of the brain.

New research in humans demonstrates the potential to improve memory with a non-invasive brain stimulation technique delivered during sleep. The results, published in JNeurosci, come from a project funded by the United States Department of Defense that aims to better understand the process of memory consolidation, which could translate into improved memory function in both healthy and patient populations.

The transfer of memories from the hippocampus to the neocortex for long-term storage is thought to be enabled by synchronization of these parts of the brain during sleep. Nicholas Ketz, Praveen Pilly, and colleagues at University of New Mexico sought to enhance this natural process of overnight reactivation or neural replay to improve memory with a closed-loop transcranial alternating current stimulation system matching the phase and frequency of ongoing slow-wave oscillations during sleep.

Participants were trained and tested on a realistic visual discrimination task in which they had to detect potentially threatening hidden objects and people such as explosive devices and enemy snipers. The researchers found that when participants received stimulation during overnight visits to their sleep laboratory, they showed improved performance in detecting targets in similar but novel situations the next day compared to when they did not receive the stimulation, suggesting an integration of recent experience into a more robust and general memory. Overnight memory changes correlated with stimulation-induced neural changes, which could be used to optimize stimulation in future applications. These findings provide a method for enhancing memory consolidation without disturbing sleep.

More than three times as many people may have died worldwide as a result of the pandemic than official COVID-19 death records suggest, according to an analysis published in The Lancet.

While the official COVID-19 death toll was 5.9 million between January 1, 2020, and December 31, 2021, the new study estimates 18.2 million excess deaths occurred over the same period, suggesting the full impact of the pandemic may have been far greater.

Excess deaths—the difference between the number of recorded deaths from all causes and the number expected based on past trends—are a key measure of the true death toll of the pandemic. While there have been several attempts to estimate excess mortality from COVID-19, most have been limited in geographical scope by the availability of data.

Moore’s Law is dead, right? Not if we can get working photonic computers.

Lightmatter is building a photonic computer for the biggest growth area in computing right now, and according to CEO Nick Harris, it can be ordered now and will ship at the end of this year. It’s already much faster than traditional electronic computers a neural nets, machine learning for language processing, and AI for self-driving cars.

Continue reading “Beating Moore’s Law: This photonic computer is 10X faster than NVIDIA GPUs using 90% less energy” »

Photonic computing processes information using light, whilst neuromorphic computing attempts to emulate the human brain. Bring the two together, and we may have the perfect platform for next generation AI, as this video explores.

If you like this video, you may also enjoy my previous episodes on:

Continue reading “Photonic Neuromorphic Computing: The Future of AI?” »

Researchers created a mathematical framework to examine the genome and detect signatures of natural selection, deciphering the evolutionary past and future of non-coding DNA.

Despite the sheer number of genes that each human cell contains, these so-called “coding” DNA sequences comprise just 1% of our entire genome. The remaining 99% is made up of “non-coding” DNA — which, unlike coding DNA, does not carry the instructions to build proteins.

One vital function of this non-coding DNA, also called “regulatory” DNA, is to help turn genes on and off, controlling how much (if any) of a protein is made. Over time, as cells replicate their DNA to grow and divide, mutations often crop up in these non-coding regions — sometimes tweaking their function and changing the way they control gene expression. Many of these mutations are trivial, and some are even beneficial. Occasionally, though, they can be associated with increased risk of common diseases, such as type 2 diabetes, or more life-threatening ones, including cancer.