BrainDancer(TM)

What is BrainDancer(TM)?

Our patented dynamic phantom, BrainDancer, was designed to remove scanner induced artifact from resting-state fMRI data. By producing a “brain-like” fMRI signal, BrainDancer can determine the degree to which measured fMRI time-series accurately track ground-truth dynamics.  Using the difference between true and measured dynamics, the phantom then uses AI to learn distortion patterns and corrects for them, dramatically increasing signal/noise across sessions, scanners, and sites.

The video shown above provides a brief overview of BrainDancer design and operation. To view, please click on the image.

For more information, see:

Rajat Kumar, Liang Tan, Alan Kriegstein, Andrew Lithen, Jonathan R. Polimeni, Lilianne R. Mujica-Parodi, Helmut Strey. Ground-truth resting-state signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics.

Daniel J. DeDora, Sanja Nedic, Pratha Katti, Shafique Arnab, Lawrence L. Wald, Atsushi Takahashi, Koene R. A. Van Dijk, Helmut H. Strey and Lilianne R. Mujica-Parodi. Signal Fluctuation Sensitivity: An Improved Metric for Optimizing Detection of Resting-State fMRI Networks.

BrainDancer(TM) is sold through ALA Scientific Instruments. For a demonstration and quote, please click HERE.

Why does fMRI require a dynamic phantom?

As neuroimaging transitions from activation maps to connections between nodes we not only produce a conceptual shift with respect to the role of functional neuroimaging, but also radically increase dependence upon time-series dynamics. Intuitively, activation maps compare the amplitude of a signal against a background of undesired physiological, thermal, and scanner noise present in all fMRI studies. Thus, for task-based fMRI, subtracting noise from signal is straightforward, since a task activates the brain reliably more under one condition (signal) than another (noise). However, for task-free analyses, the ‘baseline’ fluctuations themselves also include the ‘signal.’ This means that one needs an independent ground-truth for signal and noise in order to remove one from the other. In neuroimaging, an instrument that produces a known MR signal—and therefore can provide this ground-truth—is called a phantom.

Unlike BrainDancer, the only other commercially available phantoms are static: typically, a sphere or cylinder of electrolyte solution with embedded geometric features. These are designed primarily for structural MRI, but can also be used in functional MRI in order to assess and minimize spontaneous scanner fluctuations due to noise. However, task-free fMRI depends not only upon suppressing fluctuations due to noise, but equally upon promoting fluctuations due to signal, which can only be assessed by a phantom that produces a known and changing (dynamic) signal. The importance of a dynamic phantom is that it is the only calibration method that can quantifiably assess the most basic assumption underlying all task-free fMRI: fidelity between input (brain) dynamics and output (fMRI time-series) dynamics. Because a dynamic phantom is uniquely capable of dissociating between signal fluctuations and noise fluctuations, it can be designed to increase detection sensitivity, accuracy, and reliability for the task-free paradigms that will increasingly dominate the clinical neuroimaging field.

With generous support from the NIH and NSF, we have partnered with ALA Scientific Instruments, Inc. for the manufacture and commercialization of BrainDancer.

Specific applications include:

• Quantitative feedback on Standardized Signal-to-Noise ratio (ST-SNR), Dynamic Fidelity between phantom-produced ground-truth and measured output, non-linearity in scanner-response, and scanner multiplicative noise. These metrics allow for fMRI-specific scanner health monitoring for quality assurance and optimization of acquisition parameters for a new study or scanner.

• Removing scanner-induced variance from human data for neuroimaging at the single-subject level, as required for clinical applications.

• Normalization with respect to scanner variance for multi-site studies.

• fMRI-driven computational modeling, as increased ST-SNR provides improved parameter estimation with narrower confidence intervals.