Marcie Zinn

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Marcie L. Zinn, Ph.D., (born 1951) is an experimental psychologist conducting myalgic encephalomyelitis (ME) research with her husband, Mark Zinn, and Dr. Leonard Jason at the DePaul Center for Community Research. Marcie's research interests include the study of myalgic encephalomyelitis as well as Performing Arts Neuroscience and Psychology. Her early research and career interests were in Performance Neuroscience and Psychology, but after being diagnosed in 2009 with viral encephalitis which became Myalgic Encephalomyelitis, her interest turned toward using her extensive qEEG and neuroscience skills for research in ME. Under the leadership of Dr. Jose Montoya, she conducted a pilot study to look at cognitive dysfunction using qEEG methods. She and her husband Mark have recently moved to Chicago to work with Dr. Leonard Jason and have published four articles, all of which illustrate qEEG methods in the study of ME brain dysfunction.

Open Letter to The Lancet[edit | edit source]

Two open letters to the editor of The Lancet urged the editor to commission a fully independent review of the PACE trial, which the journal had published in 2011. In 2016, Dr. Zinn, along with 41 colleagues in the ME/CFS field, signed the second letter.

Notable studies[edit | edit source]

  • 2016 - Intrinsic Functional Hypoconnectivity in Core Neurocognitive Networks Suggests Central Nervous System Pathology in Patients with Myalgic Encephalomyelitis: A Pilot Study (FULL TEXT)
    Abstract - Exact low resolution electromagnetic tomography (eLORETA) was recorded from nineteen EEG channels in nine patients with myalgic encephalomyelitis (ME) and 9 healthy controls to assess current source density and functional connectivity, a physiological measure of similarity between pairs of distributed regions of interest, between groups. Current source density and functional connectivity were measured using eLORETA software. We found significantly decreased eLORETA source analysis oscillations in the occipital, parietal, posterior cingulate, and posterior temporal lobes in Alpha and Alpha-2. For connectivity analysis, we assessed functional connectivity within Menon triple network model of neuropathology. We found support for all three networks of the triple network model, namely the central executive network (CEN), salience network (SN), and the default mode network (DMN) indicating hypo-connectivity in the Delta, Alpha, and Alpha-2 frequency bands in patients with ME compared to controls. In addition to the current source density resting state dysfunction in the occipital, parietal, posterior temporal and posterior cingulate, the disrupted connectivity of the CEN, SN, and DMN appears to be involved in cognitive impairment for patients with ME. This research suggests that disruptions in these regions and networks could be a neurobiological feature of the disorder, representing underlying neural dysfunction.[1]
  • 2016 - qEEG / LORETA in Assessment of Neurocognitive Impairment in a Patient with Chronic Fatigue Syndrome: A Case Report (FULL TEXT)
    Abstract - Importance: Chronic Fatigue Syndrome (CFS) is a chronic disease resulting in considerable and widespread cognitive deficits. Accurate and accessible measurement of the extent and nature of these deficits can aid healthcare providers and researchers in the diagnosis of this condition, choosing interventions and tracking treatment effects. Here, we present a case of a middle-aged man diagnosed with CFS which began following a typical viral illness. Observations: LORETA source density measures of surface EEG connectivity at baseline were performed on 3 minutes of eyes closed deartifacted19-channel qEEG. The techniques used to analyze the data are described along with the hypothesized effects of the deregulation found in this data set. Nearly all (>90%) patients with CFS complain of cognitive deficits such as slow thinking, difficulty in reading comprehension, reduced learning and memory abilities and an overall feeling of being in a “fog.”Therefore, impairment may be seen in deregulated connections with other regions (functional connectivity); this functional impairment may serve as one cause of the cognitive decline in CFS. Here, the functional connectivity networks of this patient were sufficiently deregulated to cause the symptoms listed above. Conclusions and significance: This case report increased our understanding of CFS from the perspective of brain functional networks by offering some possible explanations for cognitive deficits in patients with CFS. There are only a few reports of using source density analysis or qEEG connectivity analysis for cognitive deficits in CFS. While no absolute threshold exists to advise the physician as to when to conduct such analyses, the basis of his or her decision whether or not to use these tools should be a function of clinical judgment and experience. These analyses may potentially aid in clinical diagnosis, symptom management, treatment response and can alert the physician as to when intervention may be warranted.[2]
  • 2016 - Functional Neural Network Connectivity in Myalgic Encephalomyelitis (FULL TEXT)
    Abstract - Myalgic Encephalomyelitis (ME) is a chronic illness with debilitating neurocognitive impairment that remains poorly understood. Previous studies have characterized cognitive deficits as a process by which brain abnormalities are inferred from pre-established testing paradigms using neuroimaging with low temporal resolution. Unfortunately, this approach has been shown to provide limited predictive power, rendering it inadequate for the study of neuronal communication between synchronized regions. More recent developments have highlighted the importance of modeling spatiotemporal dynamic interactions within and between large-scale and small-scale neural networks on a millisecond time scale. Here, we focus on recent emergent principles of complex cortical systems, suggesting how subtle disruptions of network properties could be related to significant disruptions in cognition and behavior found in ME. This review, therefore, discusses how electrical neuroimaging methods with time-dependent metrics (e.g., coherence, phase, cross-frequency coupling) can be a useful approach for the understanding of the cognitive symptoms in ME. By providing a platform for utilizing real-time alterations of the perpetual signals as an outcome, the disruptions to higher-level cognition typically seen in ME can be readily identified, creating new opportunities for better diagnosis and targeted treatments.[3]
  • 2015 - Myalgic Encephalomyelitis: Symptoms and Biomarkers (FULL TEXT)
    Abstract - Myalgic Encephalomyelitis (ME) continues to cause significant morbidity worldwide with an estimated one million cases in the United States. Hurdles to establishing consensus to achieve accurate evaluation of patients with ME continue, fueled by poor agreement about case definitions, slow progress in development of standardized diagnostic approaches, and issues surrounding research priorities. Because there are other medical problems, such as early MS and Parkinson’s Disease, which have some similar clinical presentations, it is critical to accurately diagnose ME to make a differential diagnosis. In this article, we explore and summarize advances in the physiological and neurological approaches to understanding, diagnosing, and treating ME. We identify key areas and approaches to elucidate the core and secondary symptom clusters in ME so as to provide some practical suggestions in evaluation of ME for clinicians and researchers. This review, therefore, represents a synthesis of key discussions in the literature, and has important implications for a better understanding of ME, its biological markers, and diagnostic criteria. There is a clear need for more longitudinal studies in this area with larger data sets, which correct for multiple testing.[4]
  • 2014, Conference paper, EEG peak alpha frequency is associated with chronic fatigue syndrome: A case-control observational study (FULL TEXT)
    Abstract - The purpose of this study was to look at the electrophysiology of the brain in patients with chronic fatigue syndrome (CFS) and compare their electrophysiology to that of healthy controls. Using EEG peak alpha frequency (PAF) in a resting-state eyes-closed condition, EEG was recorded from 19 scalp locations using linked-ear reference from 50 patients and 50 matched controls age 28 to 74 years. The Multidimensional Fatigue Inventory (MFI-20) and the Fatigue Severity Scale (FSS) were used to evaluate fatigue. PAF was computed within the 8-12 Hz frequency band based on each participant’s EEG. Differences between patients diagnosed with the Fukuda criteria and healthy controls were evaluated using a mixed effects analysis of variance and regression analyses to evaluate the relationship between PAF and fatigue within each fatigue measure. A small but significant difference was observed with control PAF being higher than participants’ PAF. Bonferroni-corrected follow-up tests indicated significant differences in PAF at multiple (11) electrode sites (p<.05) in frontal, left temporal, central and parietal regions of interest (ROIs), consistently exhibiting a direct relationship between qEEG and fatigue. Linear regression model fits using PAF to predict each fatigue scale and each subscale of the MFI-20 were statistically significant (p=.000). These novel neuropsychological relationships attest to the use of qEEG as a novel and objective measure of fatigue and neurocognitive impairment (NCI) in CFS. Moreover, resting-state PAFs may indicate its potential for use in diagnosis and to evaluate treatment progress in the clinic.[5]

Talks & interviews[edit | edit source]

Online presence[edit | edit source]

Learn more[edit | edit source]

See also[edit | edit source]

References[edit | edit source]

  1. Zinn, Marcie; Zinn, Mark; Jason, Leonard (2016), "Intrinsic Functional Hypoconnectivity in Core Neurocognitive Networks Suggests Central Nervous System Pathology in Patients with Myalgic Encephalomyelitis: A Pilot Study", Applied Psychophysiology and Biofeedback, 41 (3): 283-300, doi:10.1007/s10484-016-9331-3, PMID 26869373 
  2. Zinn, Marcie; Zinn, Mark; Jason, Leonard (2016), "qEEG / LORETA in Assessment of Neurocognitive Impairment in a Patient with Chronic Fatigue Syndrome: A Case Report", Clinical Research: Open Access, 2 (1), doi:10.16966/2469-6714.110, PMID 26869373 
  3. Zinn, Marcie; Zinn, Mark; Jason, Leonard (2016), "Functional Neural Network Connectivity in Myalgic Encephalomyelitis", NeuroRegulation, 3 (1): 28-50, doi:10.15540/nr.3.1.28 
  4. Jason, Leonard; Zinn, Marcie; Zinn, Mark (2015), "Myalgic Encephalomyelitis: Symptoms and Biomarkers", Current Neuropharmacology, 13 (5): 701-34., doi:10.2174/1570159X13666150928105725, PMID 26411464 
  5. Zinn, Marcie L; Zinn, Mark A; Norris, Jane; Valencia, Ian; Montoya, Jose G; Maldonad, Jose R (2014), "EEG peak alpha frequency is associated with chronic fatigue syndrome: A case-control observational study", Conference: 2014 Stanford Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome Symposium 

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From MEpedia, a crowd-sourced encyclopedia of ME and CFS science and history