With increasing interest in functional MRI (fMRI) and functional connectivity networks to understand and diagnose neurodegenerative diseases such as Parkinson’s and Alzheimer’s, advanced methods for image processing are necessary to produce highly structured scans. fMRI is a convenient and effective analysis tool to understand the presence and progression of neurodegeneration, as it is a noninvasive in-vivo method for measuring neural activity through blood-oxygenation-leveldependent (BOLD) signal. Unstructured noise reduction is an essential step in fMRI processing for removing non-BOLD noise to reveal underlying neural function; however, no intuitive methods currently exist for this step in fMRI processing. Conventional processes such as spatio-temporal smoothing cannot effectively differentiate BOLD signal and noise, causing reduced image quality, blurring of cortical structures, and shifted neural signal. Due to this inconsistent image processing method, use of fMRI for clinical diagnosis is difficult and commonly avoided. Therefore, this study developed a novel process for differentiating and retaining neural signal more effectively using a Wishart Filter, which utilizes a Wishart noise eigenspectrum subtraction to remove unstructured eigenvalues that pollute the fMRI and connectome’s PCA eigenspectrum. Instead of spreading signal intensity across the brain volume like spatio-temporal smoothing, this novel method utilizes dimensionality reductions to specifically target unstructured signals in fMRI and isolate the BOLD. This study analyzed Wishart Filtering’s effects on random noise, connectivity, and gradients in fMRI and connectomes. Variations of the Wishart Filter were compared to a temporal low-pass filter and spatial smoothing to test for improvements in image quality. This study showed that Wishart Filtering significantly reduces noise and improves connectivity and gradient visibility in fMRI brain scans more effectively than spatio-temporal smoothing without causing additional side-effects. Thus, this novel tool in biomedical image processing and analysis has the potential to improve fMRI as a more effective clinical diagnostic technique for neurodegeneration.