Cruttenden, Corey2020-08-252020-08-252020-05https://hdl.handle.net/11299/215135University of Minnesota Ph.D. dissertation. May 2020. Major: Mechanical Engineering. Advisors: Rajesh Rajamani, Wei Chen. 1 computer file (PDF); x, 189 pages.The understanding of brain function in humans and animals can be greatly improved by simultaneous recording of electrical neural signals and acquisition of ultrahigh field (UHF) magnetic resonance images. Such simultaneous recordings will enable the study of neurovascular coupling with functional structure specificity and improve our understanding of the circuits and brain function changes associated with deep brain stimulation (DBS). However, integrating neural electrodes with UHF MRI is technically challenging due to electromagnetic interactions between the electrodes and the MRI magnetic fields. These interactions include magnetic field distortions by the electrodes that create image artifacts in MRI, as well as electromagnetic inductive coupling that introduces noise and interferences in the extracellular neural signal recordings made at UHF. This dissertation develops solutions to address both types of interferences. The MRI image artifact issue is addressed through novel neural electrode designs, and the electrical interferences in extracellular neural recordings are addressed using software filtering and estimation techniques. Two types of implantable neural electrodes are presented that significantly reduce artifacts in UHF MRI images due to their improved properties including magnetic susceptibility that better matches the surrounding brain tissue. The improved magnetic susceptibility match with brain tissue reduces distortions to the static magnetic field, which consequently reduces MRI image artifacts. First, carbon nanotube film electrodes are shown to substantially reduce image artifacts in UHF MRI, and second, a novel gold-aluminum microwire neural electrode is developed that is very easy to construct and provides multiple channels for recording or stimulation with reduced artifacts at UHF. The ease of constructing the gold-aluminum microwire electrode increases its potential impact to the field because it can be quickly adopted by other research groups and applied to any MRI field strength. The ability of both types of electrodes to reduce UHF MRI image artifacts is demonstrated using both phantom tissue and in vivo animal studies, and also verified using numerical computations of the magnetic field distortions around a two-dimensional electrode model embedded in a brain tissue substrate. Three major artifact removal algorithms are developed for cleaning neural signal recordings in a UHF MRI scanner. These are for the specific tasks of removing artifacts due to periodic motions including breathing and hardware vibration, eliminating severe scanning artifacts from the gradients of the MRI system during fMRI acquisition, and extracting action potential spike waveforms that are otherwise well below detection thresholds in an UHF 16.4 T MRI animal research scanner. First, reference-free adaptive filtering is implemented for removal of periodic motion induced interferences in extracellular local field potential (LFP) recordings at UHF. Second, a new approach to estimate severe fMRI gradient-induced artifacts using a coefficient shrinkage algorithm based on the first difference of the extracellular neural signal is presented. The first difference of the extracellular neural signal is found to have interesting statistical properties that benefit MRI artifact estimation including a Gaussian probability density function, a near-white power spectral density, an approximate Dirac delta function autocorrelation, and an upper bound on its singular value distribution. Finally, an adaptive virtual referencing approach based on adaptive least mean square (LMS) filters is shown to reduce the noise floor in extracellular unit recordings made both on the benchtop and at UHF. The reduced noise floor allows for identification of additional action potential waveforms that were previously below the detection threshold. This advancement allowed us to detect and classify action potential waveforms during fMRI data acquisition inside a 16.4 T MRI animal research scanner, the highest field strength horizontal-bore small animal scanner currently available. The artifact estimation and removal algorithms developed to improve the extracellular neural signal quality recorded during UHF fMRI could be beneficial in other applications as well. For example, periodic motion artifacts caused by breathing, heart pulsation, chewing, and blinking are often present in extracellular neural recordings made outside of MRI scanners, particularly if they are made in awake/behaving animals. Furthermore, the coefficient shrinkage algorithm used for removing fMRI gradient-induced artifacts could benefit the removal of stimulation artifacts from neural recordings made during electrical or optogenetic brain stimulation. The findings related to the properties of the first difference of the extracellular neural signal that provided statistical advantages in noise estimation and removal might apply to other signals with a 1/f power spectral density profile as well. Finally, adaptive virtual referencing for extraction of action potential spike waveforms is shown in this dissertation to benefit benchtop recordings in addition to recordings made at UHF. The technical contributions of this dissertation enable preclinical animal studies to be undertaken involving the simultaneous use of neural electrodes together with UHF MRI. Such studies can answer questions about the relationship between neuronal signaling and the functional MRI contrast with cortical layer and column specificity. Further, such studies will enable us to conduct fMRI imaging while using DBS electrodes which will help us understand the underlying mechanisms of how DBS successfully treats multiple brain disorders.enartifactextracellular recordingfMRIMRI-compatibleneural electrodesignal processingThesis or Dissertation