New Frontiers in Neuroimaging
A portfolio of imaging technologies are providing researchers with new insight into the human brain. Since it first became commercially available in the 1980s, the MRI has been an invaluable tool to study the central nervous system.
In the ensuing decades, new sequences and modalities, and advances in computational science have transformed this technology, enabling researchers to study not only the structure, but the brain’s function and complex networks of connections.
Created in 2004, the University of Rochester (UR) Center for Advanced Brain Imaging and Neurophysiology (CABIN) houses a state-of-the-art Siemens 3T whole-body horizontal-bore Prisma magnet. CABIN, led by John Foxe, Ph.D., and Jianhui Zhong, Ph.D., serves as a hub for neuroscientists, technicians, biostatisticians, data scientists, and biomedical engineers. These multidisciplinary teams are necessary to not only develop study protocols, but also interpret the mountains of data that are providing scientists with an ever more detailed portrait of the human brain.
More than 30 principal investigators are currently engaged in research using the imaging resources in CABIN to study a wide range of neurological disorders such as Batten disease, Alzheimer’s, Rett syndrome, schizophrenia, chronic pain, stroke, muscular dystrophy, traumatic brain injury, brain tumors, and seizure disorders. UR researchers are also employing neuroimaging technologies to understand brain development and sensory processing.
Feng (Vankee) Lin, Ph.D., an associate professor in the School of Nursing, is harnessing neuroimaging technologies in an effort to develop new diagnostic tools for early detection of Alzheimer’s disease and dementia, identify new therapeutic targets that hold the potential to slow the progression of the diseases, and understand the cognitive decline associated with normal aging.
The current gold standard for Alzheimer’s diagnosis is PET imaging, but the high cost of the technology severely restricts its use as a screening tool. One of the goals of Lin’s research is to identify cost-effective MRI-based imaging biomarkers that could detect the disease before cognitive symptoms appear.
Lin has zeroed in on the anterior cingulate cortex (ACC), a region that serves as a hub with connections to many other areas of the brain. ACC is associated with a broad range of behaviors and cognitive processes and its function deteriorates during both Alzheimer’s and the natural aging process. Her research has shown that a small population of older adults — known as “supernormals” — who are able to maintain memory and cognitive function as they age have higher levels of ACC activity and connectivity. Lin is testing whether a regimen of computerized tasks that are specific to ACC function and other non-invasive neuro-stimulation approaches can improve working memory, strengthen the connections between the ACC and other brain regions, and thwart cognitive decline.
Giovanni Schifitto, M.D., M.S., a professor in the Department of Neurology, is employing neuroimaging to study inflammation in the brain and its association with cerebral small vessel disease, stroke, and cognitive impairment.
While many factors — including hypertension and diabetes — can trigger damage in the vascular system in the brain, one of the culprits that Schifitto and his colleague are most interested in is the immune system. When the body’s immune response is triggered due to infection or injury, this can activate monocytes which then cross the blood brain barrier, promote inflammation, and damage the network of micro vessels that supply blood to the brain. This causes local tissue damage and can disrupt communication between different areas of the brain, leading to cognitive problems.
Schifitto and his colleagues employ several MRI modalities, including diffusion weighted imaging (DWI) and resting state fMRI, to assess impact on the brain’s connections and integrity of the cerebrovascular system. His lab also employs a new imaging modality called magnetic resonance elastography (MRE), which gently vibrates the head during an MRI scan and measures the relative stiff ness of brain tissue. Changes in brain elasticity are the result of neuro-inflammation associated with diseases such as HIV, multiple sclerosis, vascular diseases, brain tumors, and neurodegenerative disorders such as Alzheimer’s. Collectively, these technologies enable researchers to measure changes in the brain and correlate these with cognitive performance. Further, by linking changes with inflammatory markers in the peripheral blood system, this research could lead to new methods to diagnose and measure the effectiveness of experimental therapies.
People with schizophrenia often have difficulty empathizing, recognizing, and responding to the unspoken cues that allow us to navigate the world around us and form social connections. David Dodell-Feder, Ph.D., an assistant professor in the Department of Psychology, is employing imaging technologies in the field of psychotic spectrum disorders, such as schizophrenia, to see if the social deficits in these patients can be overcome by “training” specific areas of the brain.
Dodell-Feder is focusing on a network of regions of the brain in the medial temporal cortex and medial prefrontal cortex that studies have shown to be important for allowing us to understand and interpret another person’s knowledge, beliefs, emotions, and intentions. His research, along with others, have found abnormalities in these brain networks in people with schizophrenia, specifically they tend to be under recruited when performing social tasks. The most widely used behavioral intervention for people with schizophrenia is social skills training, typically performed in a group setting. While beneficial, this approach has limitations and does not address the underlying deficits in the brain.
The question that Dodell-Feder’s research is attempting to address is whether these areas of the brain can be targeted in a more direct and mechanistic way. He employs a technology called real time fMRI, which allows study subjects to view their own neural activity as it occurs. While in the scanner, participants are asked to focus on activating specific areas of the brain. The neural activity from the fMRI is gathered and displayed in real time, providing participants feedback during the exercises. The idea is that by giving someone a window into his or her own brain, they have the opportunity to gain control over these regions and that skill could lead to cognitive or behavioral changes in the real world.
A common thread across the field of neuroimaging is data — specifically, how to store, share, and analyze the vast quantities of information produced by a technology that is constantly advancing and yielding increasingly detailed images of brain structure and function. Zhengwu Zhang, Ph.D., an assistant professor of Biostatistics and Computational Biology, who collaborates with many CABIN investigators, identifies data as one of the greatest challenges and opportunities in the field of neuroimaging. New public and private initiatives are creating vast imaging resources for data sharing and machine learning and artificial intelligence are allowing researchers to accelerate the process extracting information from images and data. These advances, along with scanners that are producing ever-higher resolution images, are poised to revolutionize the field in the coming years.