TurboFIRE for Researchers

• Preprocessing of multi-echo Echo-Planar_Imaging (EPI) data, including T2*mapping and weighted echo averaging for optimum sensitivity
• Spatial normalization in reference to the MNI atlas-based regions-of-interest (ROI)s
• Up to 12 simultaneous sliding window correlation analyses for task-based and resting-state fMRI
• General Linear Model analysis
• PCA-based regression of motion and confounding signals (CSF, white matter)
• Inter-regional connectivity matrix
• Spatially aggregated  probabilistic pattern classification
• Automatic quantification using cluster analysis
• Interfaces for neurofeedback

• Know if your data is good before your subject leaves the scanner - real-time scan quality monitoring by tracking of motion artifacts, signal dropouts and lack of patient compliance. 
• Sub-second temporal resolution to understand neural dynamics
• 1mm isotropic whole-brain imaging with TR as low as 100 ms
• Neurofeedback interface - Built-in interface for neuro-feedback applications allowing real-time brain-state-dependent experimental paradigms based on task activation and resting state connectivity
• Ideal for single-subject analysis - patient-specific maps of resting state connectivity makes TurboFIRE highly suitable for single-subject studies and individualized clinical research
• Customizable solutions to meet the needs of clinicians and researchers seeking tailored data analysis pipelines for specific research questions. 

Functional MRI based  on blood-oxygenation-level-dependent (BOLD) contrast is a technology that has found widespread application in cognitive neuroscience. At the  same time, the sensitivity of data acquisition methodology has evolved  to the point that brain activation can be detected in single trials and  there is increasing interest in mapping brain activity in individual  subjects for the purpose of understanding inter-individual differences  in cognitive processing. Clinical applications for presurgical mapping  and interactive brain-imaging-guided exams of patients suffering from  psychiatric and neurological disorders are foreseeable.

Real-time  fMRI is a variant of fMRI that enables monitoring of changes in brain  activation during the ongoing scan. It is characterized by steady-state  image reconstruction, preprocessing and statistical analysis in a time  frame that is short with respect to the time to acquire a volume fMRI  data set, and with a time delay from data acquisition that is shorter  than the hemodynamic response delay, which is on the order of several  seconds. Real-time fMRI offers new intriguing opportunities for  monitoring brain processes related to thoughts and emotions. Using novel  highly sensitive real-time data acquisition methods based on multi-echo  Echo-Planar-Imaging (EPI) and real-time sliding-window correlation  analysis, we have shown that it is possible to monitor dynamic changes  in brain activation during brief motor, visual, auditory and cognitive  tasks with an effective temporal resolution of just a few seconds.  Recent real-time fMRI studies have demonstrated the feasibility of  modulating brain activity in localized areas for the purpose of  accelerated learning, to develop novel brain-computer interfaces for  communication and for controlling pain perception in patients with  chronic pain.

Our technology development is aimed at innovative  individualized designs of fMRI experiments, which include, but are not  limited to, (a) interactive brain-imaging-guided interview of patients  suffering from psychiatric and neurological disorders that are  refractory to conventional diagnosis and treatment, and (b)  individualized training of mental abilities and control of brain  activation patterns through the use of experimental feedback. The first  approach is of importance in situations where the subject is either  unable (e.g., stroke victims, babies and young children, many  schizophrenic patients, many patients with major depression) or  unwilling (e.g., in situations where deception is used) to accurately  report his/her mental experience. The second approach is of interest for  developing individually tailored training strategies for operators of  complex machine-human interfaces (e.g., automobile driver, pilots) and  for developing individually tailored mental learning strategies. Such  capabilities would constitute a breakthrough in cognitive neuroscience,  because they open the elusive world of human thought processes to  rigorous neural systems level analysis.