Resting-state functional magnetic resonance imaging (fMRI) has enabled the mapping of functional brain networks and advanced understanding of psychiatric disorders such as depression, schizophrenia, and autism through functional connectivity (FC) studies. These studies have revealed that dysconnectivity is linked to clinical symptoms in schizophrenia. Schizophrenia is a complex disorder marked by hallucinations, delusions, and social withdrawal. Although dysconnectivity theories have evolved, the onset of schizophrenia remains multifactorial, involving neurodevelopmental, genetic, and etiological influences. This continues to present challenges with appropriate and timely diagnostic measures and treatment interventions for patients. Although fMRI has been widely utilized to understand patterns of dysconnectivity, another modality which also measures regional cerebral blood flow (rCBF) called single photon emission computed tomography (SPECT) has been seldomly explored in the context of dysconnectivity. To address this gap, this European dissertation examined the current literature through two systematic reviews and four empirical studies. In Chapter 1, we discussed the state of the neuroimaging field, fMRI and SPECT literature, and the rationale for utilizing SPECT imaging for clinical research. An initial systematic review (Chapter 2) surveys the schizophrenia literature as discussed through the lens of dysconnectivity and aberrantly connected brain networks in schizophrenia. To extend our understanding of dysconnectivity, another systematic review (Chapter 3) investigates face/emotion processing networks that are disrupted across schizophrenia, and first episode psychosis populations. Next, dysconnectivity is closely examined in SPECT networks (Chapter 4) through the same case/control datasets between schizophrenia vs. healthy controls by validating prior fMRI networks. In Chapter 5, these findings are validated by running a multi-classifier approach using the Python based Polyssifier, with the primary findings centered around Support Vector Machine (SVM) performance compared to other classification approaches. We then discuss creating and validating a novel NeuroMark SPECT template (Chapter 6) in the same small schizophrenia case/control sample from the previous chapter. Finally, we further demonstrate the reliability and robustness of the NeuroMark SPECT template in a large, heterogeneous clinical SPECT dataset (Chapter 7). The last chapter, Chapter 8, discusses and summarizes these findings, and provides next steps in translational clinical neuroscience.