UMass professor Hava Siegelmann used fMRI data from tens of thousands of patients to understand how thought arises from brain structure. This resulted in a geometry-based method meant to advance the identification and treatment of brain disease. It can also be used to improve deep learning systems, and her lab is now creating a “massively recurrent deep learning network.”
Siegelmann found that cognitive function and abstract thought exist as an agglomeration of many cortical sources, from those close to sensory cortices to those far deeper along the brain connector. Her data-driven analyses defined a hierarchically ordered connectome, revealing a related continuum of cognitive function.
Siegelmann claims that “with a slope (geometrical algorithm) identifier, behaviors could now be ordered by their relative depth activity with no human intervention or bias.”
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MIT‘s John Gabrieli is investigating the use of neuroimaging to predict future behavior to customize brain health treatments.
Professor Gabrieli believes that neuromarkers, determined by fMRI, can be used to develop personalized interventions to improve education, health, addiction, criminal behavior and to analyze responses to drug or behavioral treatments.
According to Gabrieli, “Presently, we often wait for failure, in school or in mental health, to prompt attempts to help, but by then a lot of harm has occurred. If we can use neuroimaging to identify individuals at high risk for future failure, we may be able to help those individuals avoid such failure altogether.”
The cost of fMRI could pose a challenge for implementation. Cheaper, quicker, mobile EEG solutions could complement this research, and help bring imaging to the forefront of treatment.
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Virginia Tech Carilion Research Institute professor P. Read Montague has developed a brain imaging technique that may be able to identify autism in children. Current diagnosis is a long an unquantifiable process based on clinical judgment.
The study demonstrates that a perspective tracking response can be used to determine whether someone has autism spectrum disorder. It investigates how the middle cingulate cortex response differs in individuals at different developmental levels.
Children were shown 15 images of themselves and 15 images of a child matched for age and gender for four seconds per image in a random order. The control children had a high response in the middle cingulate cortex when viewing their own pictures. Children with autism spectrum disorder had a significantly diminished response.
According to Montague, “the single-stimulus functional MRI could also open the door to developing MRI-based applications for screening of other cognitive disorders.” Scientists can link the function of mental disorders to the disrupted mechanisms of neural tissue through mathematical approaches, such as brain scans. Doctors then can use measurable data for earlier diagnosis and treatment.
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Brain stimulation treatments can alter neural circuits electrically instead of chemically. However, understanding what brain regions should be targeted, by condition, remains a challenge, particularly in non-invasive rTMS. A Beth Israel Deaconess study suggests that brain networks – the interconnected pathways that link brain circuits to one another– can help guide site selection for brain stimulation therapies.
According to author Michael Fox, “Although different types of brain stimulation are currently applied in different locations, we found that the targets used to treat the same disease are nodes in the same connected brain network.”
Brain stimulation treatment data for 14 conditions, including addiction, Alzheimer’s, depression, dystonia, epilepsy, essential tremor, Huntington’s, and Parkinson’s were studied. The researchers listed the stimulation sites, deep in the brain and near the surface, thought to be effective for the treatment of each disease.
Through a data set of fMRI images of people’s brains at rest, the team found correlated fluctuations in spontaneous brain activity, illustrating which sites were functionally connected. A map of connections from deep brain stimulation sites to the surface of the brain was created. When the research team compared the map to sites on the brain surface that work for noninvasive brain stimulation, the two matched.
University of Liège professor Steven Laureys‘ recent study shows that PET scans fMRI were more reliable predictors of brain injury recovery than standardized bedside assessments by doctors.
Of 126 patients in the trial, 41 were in a persistent vegetative state, 81 were in a minimally conscious state and 4 had locked-in syndrome. PET correctly predicted the extent of recovery in the following year in 74% of patients, and fMRI in 56% of patients.
A third of the patients had been previously misdiagnosed. Of 41 patients whose doctors had diagnosed a vegetative state, 13 were found by a PET scan to have some level of consciousness. Of the 13, 9 regained consciousness within the year, 3 died of other causes, and only one was still in a vegetative state.
It is not yet possible to detect hidden levels of consciousness with EEG. If developed, this would be an inexpensive way to continuously monitor patients. Because of their size and cost, fMRI and PET scans cannot provide continuous monitoring.
Using fMRI, Tel Aviv University and Sourasky Medical Center’s Haggai Sharon, Yotam Pasternak, Talma Hendler and colleagues have shown that the brains of patients in a vegetative state emotionally react to photographs of people they know, as though they recognize them.
“We showed that patients in a vegetative state can react differently to different stimuli in the environment depending on their emotional value,” said Dr. Sharon. “It’s not a generic thing; it’s personal and autobiographical. We engaged the person, the individual, inside the patient.”
Research focused on the “emotional awareness” of patients in a vegetative state is relatively new. The researchers hope to eventually contribute to improved care and treatment. They are also working with patients in a minimally conscious state to better understand how regions of the brain interact in response to familiar cues.
Until now, chronic traumatic encephalopathy, caused by repetive head injury, could only be identified after a victim died. CTE is linked to depression, dementia, and memory loss.
A new imaging method, developed by Pittsburgh Steelers physician Julian Bailes and UCLA researchers, can for the first time spot signs of the condition in the living brain. It could help players avoid the degenerative condition by limiting their exposure, and it may help scientists develop better protective gear and treatments.
The technology is based on a positron emission tomography scans. UCLA researchers developed a radioactive compound that can be injected intravenously. The compound circulates through the bloodstream and into the brain, where it gloms onto tau proteins, which can then be measured in a PET scanner. The test takes about an hour.
The radioactive compound also sticks to amyloid proteins. Aggregations of both amyloid and tau are considered culprits in Alzheimer’s disease, whereas tau is the main indicator for CTE. Bailes and colleagues say the regions of the brain that are highlighted in PET scans of patients with Alzheimer’s differ from the scans of patients with CTE.
In related research, Israeli company ElMindA is developing non-invasive BNA (brain network activation) technology. Patients sit at a computer for 15 to 30 minutes, performing a specific task many times while the device maps network activation points in the brain. The repetition allows the device to sift out brain activity unrelated to the task. The result is a three-dimensional image of nerve cell connectivity and synchronization that is highly sensitive, specific and reproducible. The tool is sensitive enough to show subtle differences in the severity of the condition from one day to another, according to the company. They claim that it can also optimize drug dosing by monitoring the changes in brain network activities as the drug takes effect.
The “midbrain periaqueductal gray region,” or PAG, is extraordinarily difficult to investigate in humans because of its size and intricate structure. Northeastern University researcher Ajay Satpute is uses state-of-the art imaging to capture this complex neural activity. His technique increases the spatial resoluion of fMRI. As fMRI lacks temporal resolution, there is much room for improvement.
Satpute’s goal is to help scientists explore the grounds of human emotion. “The PAG’s functional properties occur at such small spatial scales that we need to capture its activity at very high resolution in order to understand it,” he explained.
Until recently, neuroimaging studies have been done with fMRI, providing data for understanding how the different areas respond to different stimuli. When those areas become sufficiently small and complicated, their resolution falls short. In the case of the tiny PAG, this problem is paramount because the PAG wraps around a hollow core, or “aqueduct,” containing cerebrospinal fluid, Satpute said. Traditional fMRI instruments cannot distinguish neural activity occurring in the PAG from that occurring in the CS fluid. Even more difficult is identifying where within the PAG itself specific responses originate.
Collaborating with researchers at Massachusetts General Hospital, Satpute used a seven Tesla magnet fMRI. Coupled with manual data analyses, he was able to resolve activity in sub-regions of the PAG with more precision than ever before. The research team showed 11 human subjects images of burn victims, gory injuries, and other content related to threat, harm, and loss while keeping tabs on the PAG’s activity. The subjects also viewed neutral images. The researchers compared results between the two scenarios. The proof-of-concept study showed emotion-related activity concentrated in particular areas of the PAG. While similar results have been demonstrated in animal models, nothing like it had previously been shown in human brains.
Using this methodology, the researchers said they would not only gain a better understanding of the PAG but also be able to investigate a range of brain-related research questions beyond this particular structure.