Traumatic Brain Injury (TBI) affects 1.7 million people annually, yet the diagnosis and treatment can be highly ineffective. Using cutting edge, non-invasive, in vivo diffusion imaging techniques combined with HDFT analysis and visualization, the Schneider Lab is working toward more comprehensive diagnosis of TBI. Our lab’s goal is to create a fast, effective method for looking at white matter damage in individual patients, and eventually allow medical doctors to provide more useful prognosis for frustrated patients. HDFT has already been applied in a number of clinical and surgical cases here at UPMC, and holds much promise.
Please visit our dedicated site about HDFT TBI ( http://hdft.info ).
Mobile phones and portable internet tablets have become the most popular computing devices in human history. Mobile devices are changing the ways that we complete our daily tasks and interact with people in the same way PCs have in the past thirty years. The high penetration rate of mobile devices provides both challenges and opportunities in learning. On one hand, researchers have been exploring the usage of mobile phones and PDAs as new education vehicles and numerous publications have been created; On the other hand, many mobile learning projects treat mobile devices as "smaller/cheaper PCs" and a major portion of research efforts focus on porting the existing educational applications for PCs to mobile devices, hence enabling "education anytime, anywhere".
The emergence of cognitive neuroscience in the late 1980s and continued advances over the past ten years have yielded broad and highly significant insights into the neural basis of human cognition. But knowledge advances for one brain structure – the cerebellum – have not kept pace. This research will use an integrative approach that combines measures of functional activation, functional connectivity, and structural connectivity to address this knowledge gap. It will test the principle that the specific cerebro-cerebellar circuits that are engaged by a language task varies according to the type of information that must be processed to support task performance.
A principle focus of our laboratory is the optimization of imaging methods that map the white matter pathways of the brain. Rather than refining the way images are acquired, our group focuses on maximizing the potential of modeling and analytical methods in order to produce High Definition Fiber Tractography (HDFT). The goal is to produce in vivo technologies that have a comparable resolution to post-mortem histological methods, thus opening a new level of analysis for exploring the human brain.
In collaboration with neuropathology laboratories, both locally and across the world, we are focused on capturing the microstructure of white matter fiber connections. This is done by combining post-mortem HDFT with histological methods (e.g., fiber staining and polarized light imaging). This combined methodological approach produces rich, detailed maps of the network patterns in the human brain and provides the basis for probabilistic maps to be used in future white matter tracking techniques.
Using cutting edge, non-invasive, in vivo diffusion imaging techniques combined with HDFT analysis and visualization, our lab seeks to advance clinical research in the diagnosis and treatment of neurological pathology and trauma. We collaborate with the Neurological Surgery Department at UPMC to visualize fiber tracts within the brain in three dimensions in order to plan the most effective and least damaging pathways of tumor excision in patients suffering from various forms of brain cancer. Additionally, we are engaged in a DARPA funded HDFT project to localize the fiber breaks caused by traumatic brain injuries (TBI), which cannot be seen with the current standard computed axial tomography (CAT or CT) scans or magnetic resonance (MR) imaging, aiding the diagnosis and prognosis of patient brain trauma.
Through the Biologically Accelerated Learning Technologies (BALT) and Brian Fitness Training (BFT) projects we are using HDFT, fMRI, and NIRS to characterize how the structural and computational properties of brain networks change as various cognitive skills are acquired over the course of extensive training. The overarching goal of these projects are to optimize the efficacy of cognitive training paradigms in order to facilitate high level skill learning and develop a biological theory around effective instruction methods.