MRI is central to neurologic diagnosis and treatment planning, with a rich history of innovations drawing on knowledge and insights from clinical neuroscience experts.1–3 Recent advances in physiologic imaging, quantitative methods, informatics, artificial intelligence, and scan-time reduction are yielding more information with which to make early clinical diagnoses, but also resulting in greater complexity and the need for strong multidisciplinary communication among subspecialty professionals. At least a rudimentary technical literacy of MRI is important for all clinicians who regularly refer patients for MRI. Although there are only a few procedure codes to consider when ordering an MRI, there should be an appreciation for the clinical detail required to select from the many MRI scanning protocols, sequences, and parameters that should be tailored precisely to answer the clinical question for that individual patient.4 Furthermore, knowledge of fundamentals enables the ordering provider to ask about the availability of special techniques that may improve conspicuity of suspected pathologic condition, set expectations for the patient experience, and take advantage of readily available images to enhance shared decision making with patients. Ultimately, understanding the language of MRI permits a more successful collaboration between patient care and diagnostic imaging professionals. The purpose of this article is to provide an overview of currently available MRI capabilities, and those in the process of becoming mainstream, to help the practicing clinician achieve these goals. A complete didactic is outside of the scope possible within this article, so the reader is advised to consult readily available papers, books, videos, and illustrations for additional detail on MRI techniques5–9; safety considerations related to contrast use, implanted devices or cosmetics10–13; and appropriate utilization guidelines used by payers and regulatory agencies.

BACKGROUND: Alterations in the dynamic coordination of widespread brain networks are proposed to underlie cognitive symptoms of schizophrenia. However, there is limited understanding of the temporal evolution of these networks and how they relate to cognitive impairment. The current study was designed to explore dynamic patterns of network connectivity underlying cognitive features of schizophrenia. METHODS: In total, 21 inpatients with schizophrenia and 28 healthy control participants completed a cognitive task while electroencephalography data were simultaneously acquired. For each participant, Pearson cross-correlation was applied to electroencephalography data to construct correlation matrices that represent the static network (averaged over 1200 ms) and dynamic network (1200 ms divided into four windows of 300 ms) in response to cognitive stimuli. Global and regional network measures were extracted for comparison between groups. RESULTS: Dynamic network analysis identified increased global efficiency; decreased clustering (globally and locally); reduced strength (weighted connectivity) around the frontal, parietal, and sensory-motor areas; and increased strength around the occipital lobes (a peripheral hub) in patients with schizophrenia. Regional network measures also correlated with clinical features of schizophrenia. Network differences were prominent 900 ms following the cognitive stimuli before returning to levels comparable to those of healthy control participants. CONCLUSIONS: Patients with schizophrenia exhibited altered dynamic patterns of network connectivity across both global and regional measures. These network differences were time sensitive and may reflect abnormalities in the flexibility of the network that underlies aspects of cognitive function. Further research into network dynamics is critical to better understanding cognitive features of schizophrenia and identification of network biomarkers to improve diagnosis and treatment models.

Schizophrenia (SCH) is a complex and devastating psychiatric disorder whose underlying neurobiological mechanisms are still unknown. Cognitive dysfunction is a multifaceted and complex feature of SCH and is commonly associated with poor treatment outcomes (1). Many of these cognitive processes rely on brain circuitry such as the frontal and parietal regions (2), the same regions altered in SCH (3,4). Therefore, there appears to be an intricate relationship between cognitive impairment and the pathophysiology of SCH. The current study was designed to examine altered network connectivity patterns underlying various cognitive features of SCH and to explore the dynamic nature of these network anomalies. Recently, topological measures that apply network analysis based on graph theory to neuroimaging data have been used to characterize global network properties of the brain (5–7). This approach is particularly pertinent to the study of SCH, which is described as the prototypical disease of brain dysconnectivity (8,9). Indeed, a growing number of studies have revealed network abnormalities in patients with SCH such as altered network measures of connectivity, efficiency, and integration (10). While these network findings are largely based on a static network representation of the SCH brain, there is a growing interest in the dynamic changes of network connectivity (11–13). Static network representations are derived from a network constructed by encapsulating neuroimaging data from an entire scan session (resting state or task activated). However, higher-order brain functions, such as executive function, require dynamic brain coordination that can occur on the order of milliseconds (14). To examine the dynamic connectivity changes that underlie specific features of cognition, recent studies in healthy control (HC) participants have applied network analysis to shorter time intervals and constructed functional networks for each of these time intervals to quantify how these networks change over time (15,16). Using this approach, recent functional magnetic resonance imaging studies have demonstrated dynamic reconfiguration of network connectivity patterns following administration of cognitive stimuli (13,17,18), while transient changes (on the order of seconds and milliseconds) in network states have been detected by magnetoencephalography (19) and electroencephalography (EEG) (14,20,21). Although network analysis traditionally has been performed on functional magnetic resonance imaging data, these EEG and magnetoencephalography studies highlight the temporal benefits of using physiological techniques to explore the rapid reconfiguration of functional brain networks underlying various aspects of cognition (20).