Posts tagged brain imaging

A multicenter research team led by Cedars-Sinai neurologist Nancy Sicotte, MD, an expert in multiple sclerosis and state-of-the-art imaging techniques, used a new, automated technique to identify shrinkage of a mood-regulating brain structure in a large sample of women with MS who also have a certain type of depression.

In the study, women with MS and symptoms of “depressive affect” – such as depressed mood and loss of interest – were found to have reduced size of the right hippocampus. The left hippocampus remained unchanged, and other types of depression – such as vegetative depression, which can bring about extreme fatigue – did not correlate with hippocampal size reduction, according to an article featured on the cover of the January 2014 issue of Human Brain Mapping.

The research supports earlier studies suggesting that the hippocampus may contribute to the high frequency of depression in multiple sclerosis. It also shows that a computerized imaging technique called automated surface mesh modeling can readily detect thickness changes in subregions of the hippocampus. This previously required a labor-intensive manual analysis of MRI images.

Sicotte, the article’s senior author, and others have previously found evidence of tissue loss in the hippocampus, but the changes could only be documented in manual tracings of a series of special high-resolution MRI images. The new approach can use more easily obtainable MRI scans and it automates the brain mapping process.

“Patients with medical disorders – and especially those with inflammatory diseases such as MS – often suffer from depression, which can cause fatigue. But not all fatigue is caused by depression. We believe that while fatigue and depression often co-occur in patients with MS, they may be brought about by different biological mechanisms. Our studies are designed to help us better understand how MS-related depression differs from other types, improve diagnostic imaging systems to make them more widely available and efficient, and create better, more individualized treatments for our patients,” said Sicotte, director of Cedars-Sinai’s Multiple Sclerosis Program and the Neurology Residency Program. She received a $506,000 grant from the National Multiple Sclerosis Society last year to continue this research.

(Source: newswise.com)

A new brain-imaging technique enables people to ‘watch’ their own brain activity in real time and to control or adjust function in pre-determined brain regions. The study from the Montreal Neurological Institute and Hospital – The Neuro, McGill University and the McGill University Health Centre, published in NeuroImage, is the first to demonstrate that magnetoencephalography (MEG) can be used as a potential therapeutic tool to control and train specific targeted brain regions. This advanced brain-imaging technology has important clinical applications for numerous neurological and neuropsychiatric conditions.

MEG is a non-invasive imaging technology that measures magnetic fields generated by nerve cell circuits in the brain. MEG captures these tiny magnetic fields with remarkable accuracy and has unrivaled time resolution - a millisecond time scale across the entire brain. “This means you can observe your own brain activity as it happens,” says Dr. Sylvain Baillet, acting Director of the Brain Imaging Centre at The Neuro and lead investigator on the study. “We can use MEG for neurofeedback – a process by which people can see on-going physiological information that they aren’t usually aware of, in this case, their own brain activity, and use that information to train themselves to self-regulate. Our ultimate hope and aim is to enable patients to train specific regions of their own brain, in a way that relates to their particular condition. For example neurofeedback can be used by people with epilepsy so that they could train to modify brain activity in order to avoid a seizure.”

In this proof of concept study, participants had nine sessions in the MEG and used neurofeedback to reach a specific target. The target was to look at a coloured disc on a display screen and find  their own strategy to change the disc’s colour from dark red to bright yellow white, and to maintain that bright colour for as long as possible. The disc colour was indexed on a very specific aspect of their ongoing brain activity: the researchers had set it up so that the experiment was accessing predefined regions of the motor cortex in the participants’ brain. The colour presented was changing according to a predefined combination of slow and faster brain activity within these regions. This was possible because the researchers combined MEG with MRI, which provides information on the brain’s structures, known as magnetic source imaging (MSI).

“The remarkable thing is that with each training session, the participants were able to reach the target aim faster, even though we were raising the bar for the target objective in each session, the way you raise the bar each time in a high jump competition. These results showed that participants were successfully using neurofeedback to alter their pattern of brain activity according to a predefined objective in specific regions of their brain’s motor cortex, without moving any body part. This demonstrates that MEG source imaging can provide brain region-specific real time neurofeedback and that longitudinal neurofeedback training is possible with this technique.”

These findings pave the way for MEG as an innovative therapeutic approach for treating patients. To date, work with epilepsy patients has shown the most promise but there is great potential to use MEG to investigate other neurological syndromes and neuropsychiatric disorders (e.g., stroke, dementia, movement disorders, chronic depression, etc). MEG has potential to reveal dynamics of brain activity involved in perception, cognition and behaviour: it has provided unique insight on brain functions (language, motor control, visual and auditory perception, etc.) and dysfunctions (movement disorders, tinnitus, chronic pain, dementia, etc.).

Dr. Baillet and his team are collaborating presently with Prof. Isabelle Peretz at Université de Montréal to use this technique with people that have amusia, a disorder that makes them unable to process musical pitch. It is hypothesized that amusia results from poor connectivity between the auditory cortex and prefrontal regions in the brain. In an ongoing study, the team is measuring the intensity of functional connectivity between these brain regions in amusic patients and aged-matched healthy controls. Using MEG-neurofeedback, they hope to take advantage of the brain’s plasticity to reinforce the functional connectivity between the target brain regions. If the approach demonstrates an improvement in pitch discrimination in participants, that will demonstrate the clinical and rehabilitative applications of this approach. The baseline measurements have been taken already, and the training sessions will take place over this year.

(Source: mcgill.ca)

When an MRI scan uncovers an unusual architecture or shape in a child’s brain, it’s cause for concern: The malformation may be a sign of disease. But deciding whether that odd-looking anatomy is worrisome or harmless can be difficult. To help doctors reach the right decision, Johns Hopkins researchers are building a detailed digital library of MRI scans collected from children with normal and abnormal brains. The goal, the researchers say, is to give physicians a Google-like search system that will enhance the way they diagnose and treat young patients with brain disorders.

This cloud-computing project, being developed by a team of engineers and radiologists, should allow physicians to access thousands of pediatric scans to look for some that resemble their own patient’s image. The project is supported by a three-year $600,000 grant from the National Institutes of Health.

"We’re creating a pediatric brain data bank that will let doctors look at MRI brain scans of children who have already been diagnosed with illnesses like epilepsy or psychiatric disorders," said Michael I. Miller, a lead investigator on the project. "It will provide a way to share important new discoveries about how changes in brain structures are linked to brain disorders. For the medical imaging world, this system will do what a search engine like Google does when you ask it to look for specific information on the Web."

Miller, a pioneer in the field of computational anatomy, the technology used for “brain parsing,” is the Herschel and Ruth Seder Professor of Biomedical Engineering at Johns Hopkins and director of the university’s Center for Imaging Science. He also is a core faculty member in the university’s Institute for Computational Medicine.

The new pediatric brain imaging data bank, Miller said, will be useful in at least two ways.

"If doctors aren’t sure which disease is causing a child’s condition, they could search the data bank for images that closely match their patient’s most recent scan," he said. "If a diagnosis is already attached to an image from the data bank that could steer the physician in the right direction. Also, the scans in our library may help a physician identify a change in the shape of a brain structure that occurs very early in the course of a disease, even before clinical symptoms appear. That could allow the physician get an early start on the treatment."

Miller’s co-lead investigator on the project is Susumu Mori, a professor of radiology in the Johns Hopkins School of Medicine. One of Mori’s primary research interests is studying the anatomy of brain structures captured in MRI scans. 

Mori points out that such a “biobank” has the potential to impact doctors’ workflow dramatically.

"We empirically know that a certain type of anatomical abnormality is related to specific brain diseases," he said. "This relationship, however, is not always clear and often is compounded by anatomical changes during the normal course of brain development. Therefore, neuro-radiologists need extensive training to accumulate the knowledge. We hope our brain imaging data bank will not only assist such a learning process but also enhance the physician’s ability to understand the pathology and reach the best medical decision."

Mori and his collaborator, Thierry Huisman, a professor of radiology and pediatrics and the director of pediatric radiology at the Johns Hopkins Children’s Center, have been working for more than four years to establish a clinical database of more than 5,000 whole-brain MRI scans of children treated at Johns Hopkins. The patients’ names and other identifying information were withheld, but details related to their medical conditions were included. The computer software indexed anatomical information involving up to 1,000 structural measurements in 250 regions of the brain. These images were also sorted into 22 brain disease categories, including chromosomal abnormalities, congenital malformations, vascular diseases, infections, epilepsy and psychiatric disorders.

According to Huisman, the new data bank now under development not only facilitates recognition and correct classification of pediatric brain disorders, but the more objective image analysis also allows identification of injury and disease that may go undetected by the classical, more subjective radiological “eyeballing” of MR images. Furthermore, he said, recognition of distinct patterns of injury and the subsequent grouping of these children based upon their characteristic patterns of MRI findings allow recognition and identification of new diseases as well as reclassification of previously unclassified diseases. Finally, he added, the data acquisition is free of ionizing radiation, allowing doctors to study the most vulnerable, youngest patients and perhaps to help initiate disease-specific treatment before irreversible injury to the developing brain occurs.

Beyond the brain imaging data bank for children, the researchers have begun building a similar MRI brain image library with Marilyn Albert, a Johns Hopkins neurology professor. This library focuses on brain disorders commonly found in elderly patients. That project is associated with the National Institute of Aging’ Alzheimer’s Disease Research Center.

With all of this data in place, physicians will be able to conduct a Google-like search for images associated with normal and abnormal pediatric and aging brain conditions. For example, a physician who is uncertain about a child’s diagnosis could submit that patient’s latest brain scan and request the medical records of children with similar images. Alternatively, for studying neurodegenerative diseases such as Alzheimer’s in aging patients, a physician might ask to see the medical records associated with all images that display neurofibrillary tangles in the temporal lobe, a condition seen in his or her patient’s scan.

Jonathan Lewin, the chairman and radiologist-in-chief of the Johns Hopkins Department of Radiology and Radiological Science, noted that this approach could help patients with both common and uncommon diseases. “This research is one of the first real applications of ‘Big Data’ analytics, taking medical information from large numbers of patients, removing anything that would identify specific individuals, and then bringing the data into the ‘cloud’ to allow very high-powered analysis,” Lewin said. “This has been a goal of the medical community for almost a decade, and professors Miller and Mori have found a way to implement this technology in a manner that can bring its benefit to our patients, and can assist in the classification and identification of rare and subtle brain disorders as well as uncommon manifestations of more common diseases of the brain.”

Currently, the pilot pediatric brain imaging data bank is limited to physicians and patients within the Johns Hopkins medical system, but the researchers say the data bank could be expanded or replicated elsewhere in coming years.

(Source: hopkinschildrens.org)

Many of us have steeled ourselves for those ‘needle in a haystack’ tasks of finding our vehicle in an airport car park, or scouring the supermarket shelves for a favourite brand.

A new scientific study has revealed that our understanding of how the human brain prepares to perform visual search tasks of varying difficulty may now need to be revised.

When people search for a specific object, they tend to hold in mind a visual representation of it, based on key attributes like shape, size or colour. Scientists call this ‘advanced specification’. For example, we might search for a friend at a busy railway station by scanning the platform for someone who is very tall or who is wearing a green coat, or a combination of these characteristics.

Researchers from the School of Psychology at the University of Lincoln, UK, set out to better explain how these abstract visual representations are formed. They used fMRI scanners to record neural activity when volunteers prepared to search for a target object: a coloured letter amid a screen of other coloured letters.

Their findings, published in the journal ‘Brain Research’, are the first to fully isolate the different areas of the human brain involved in this ‘prepare to search’ function. Surprisingly, they show that the advanced frontal areas of the brain, usually key to advanced cognitive tasks, appear to take a backseat. Instead it is the basic back areas of the brain and the sub-cortical areas that do the work.

Dr Patrick Bourke from the University of Lincoln’s School of Psychology, who led the study, said: “Up until now, when researchers have studied visual search tasks they have also found that frontal areas of the brain were active. This has been assumed to indicate a control system: an ‘executive’ that largely resides in the advanced front of the brain which sends signals to the simpler back of the brain, activating visual memories. Here, when we isolated the ‘prepare’ part of the task from the actual search and response phase we found that this activation in the front was no longer present.”

This finding has important implications for understanding the fundamental brain processes involved. It was previously thought that the Intra-parietal region of the brain, which is linked to visual attention, was the central component of the supposed ‘front-back’ control network, relaying useful information (such as a shape or colour bias) from frontal areas of the brain to the back, where simple visual representations of the object are held. If the frontal areas are not activated in the preparation phase, this cannot be the case.

The study also showed that the pattern of brain activation varied depending on the anticipated difficulty of the search task, even when the target object was the same. This indicates that rather than holding in mind a single representation of an object, a new target is constructed each time, depending on the nature of the task.

Dr Bourke added: “While consistent with previous brain imaging work on visual search, these results change the interpretations and assumptions that have been applied previously. Notably, they highlight a difference between studies of animals’ brains and those of humans. Studies with monkeys convincingly show the front-back control system and we thought we understood how this worked. At the same time our findings are consistent with a growing body of brain imaging work in humans that also shows no frontal brain activation when short term memories are held.”

(Source: lincoln.ac.uk)

When faced with a choice, the brain retrieves specific traces of memories, rather than a generalized overview of past experiences, from its mental Rolodex, according to new brain-imaging research from The University of Texas at Austin.

Led by Michael Mack, a postdoctoral researcher in the departments of psychology and neuroscience, the study is the first to combine computer simulations with brain-imaging data to compare two different types of decision-making models.

In one model — exemplar — a decision is framed around concrete traces of memories, while in the other model — prototype — the decision is based on a generalized overview of all memories lumped into a specific category.

Whether one model drives decisions more than the other has remained a matter of debate among scientists for more than three decades. But according to the findings, the exemplar model is more consistent with decision-making behavior.

The study was published this month in Current Biology. The authors include Alison Preston, associate professor in the Department of Psychology and the Center for Learning and Memory; and Bradley Love, a professor at University College London.

In the study, 20 respondents were asked to sort various shapes into two categories. During the task their brain activity was observed using functional magnetic resonance imaging (fMRI), allowing researchers to see how the respondents associate shapes with past memories.

According to the findings, behavioral research alone cannot determine whether a subject uses the exemplar or prototype model to make decisions. With brain-imaging analysis, researchers found that the exemplar model accounted for the majority of participants’ decisions. The results show three different regions associated with the exemplar model were activated during the learning task: occipital (visual perception), parietal (sensory) and frontal cortex (attention).

While processing new information, the brain stores concrete traces of experiences, allowing it to make different kinds of decisions, such as categorization information (is that a dog?), identification (is that John’s dog?) and recall (when did I last see John’s dog?).

To illustrate, Mack says: Imagine having a conversation with a friend about buying a new car. When you think of the category “car,” you’re likely to think of an abstract concept of a car, but not specific details. However, abstract categories are composed of memories from individual experiences. So when you imagine “car,” the abstract mental picture is actually derived from experiences, such as your friend’s white sedan or the red sports car you saw on the morning commute.

“We flexibly memorize our experiences, and this allows us to use these memories for different kinds of decisions,” Mack says. “By storing concrete traces of our experiences, we can make decisions about different types of cars and even specific past experiences in our life with the same memories.”

Mack says this new approach to model-based cognitive neuroscience could lead to discoveries in cognitive research.

“The field has struggled with linking theories of how we behave and act to the activation measures we see in the brain,” Mack says. “Our work offers a method to move beyond simply looking at blobs of brain activation. Instead, we use patterns of brain activation to decode the algorithms underlying cognitive behaviors like decision making.”

(Source: utexas.edu)