From Brain Connectivity, 9 October 2015. [Epub ahead of print].
Abnormal Resting-State Functional Connectivity in Patients with Chronic Fatigue Syndrome: Results of Seed and Data-Driven Analyses.
Gay C(1), Robinson ME(2), Lai S(3), O’Shea A(4), Craggs J(5), Price DD(6), Staud R(7).
1) University of Florida, Gainesville, Florida, United States; email@example.com.
2) University of Florida, Gainesville, Florida, United States; firstname.lastname@example.org.
3) University of Florida, Gainesville, Florida, United States; email@example.com.
4) University of Florida, Gainesville, Florida, United States; firstname.lastname@example.org.
5) University of Missouri, Columbia, Missouri, United States; email@example.com.
6) University of Florida, Gainesville, Florida, United States; firstname.lastname@example.org.
7) University of Florida, Medicine , PO Box 100221 , Gainesville, Florida, United States, 32610-0221; email@example.com.
Although altered resting-state functional connectivity is a characteristic of many chronic pain conditions it has not yet been evaluated in patients with chronic fatigue. Our objective was to investigate the association between fatigue and altered resting-state functional connectivity in myalgic-encephalomyelitis/chronic fatigue syndrome (ME/CFS).
Thirty-six female subjects, 19 ME/CFS and 17 healthy controls completed a fatigue inventory before undergoing functional magnetic-resonance imaging. Two methods, 1) data driven and 2) model-based, were used to estimate and compare the intra-regional functional connectivity between both groups during the resting state (RS).
The first approach using independent-component analysis was applied to investigate five RS-networks: the default mode network (DMN), salience network (SN), left and right fronto-parietal networks (LFPN, RFPN), and sensory-motor network (SMN).
The second approach used a-priori selected seed regions demonstrating abnormal regional cerebral blood-flow (rCBF) in ME/CFS patients at rest.
In ME/CFS patients, Method-1 identified decreased intrinsic connectivity among regions within the LFPN. Furthermore, the functional connectivity of the left anterior mid-cingulate with the SMN and the connectivity of the left posterior-cingulate cortex with the SN were significantly decreased.
For Method-2, five distinct clusters within the right parahippocampus and occipital lobes, demonstrating significant rCBF reductions in ME/CFS patients were used as seeds. The parahippocampal seed and three occipital-lobe seeds showed altered functional connectivity with other brain regions. The degree of abnormal connectivity correlated with the level of self-reported fatigue.
Our results confirm altered RS functional connectivity in patients with ME/CFS which was significantly correlated with the severity of their chronic fatigue.
From Neuroendocrinology Letters, 12 September 2015 [Epub ahead of print].
A new case definition of Neuro-Inflammatory and Oxidative Fatigue (NIOF), a neuroprogressive disorder, formerly known as chronic fatigue syndrome or Myalgic Encephalomyelitis: results of multivariate pattern recognition methods and external validation by neuro-immune biomarkers.
IMPACT Strategic Research Center, Barwon Health, Deakin University, Geelong, Vic, Australia.
Chronic fatigue syndrome (CFS) or Myalgic Encephalomyelitis (ME) is characterized by neuro-psychiatric (e.g. depression, irritability, sleep disorders, autonomic symptoms and neurocognitive defects) and physio-somatic (fatigue, a flu-like malaise, hyperalgesia, irritable bowel, muscle pain and tension) symptoms. New ME/CFS case definitions based on consensus criteria among experts are largely inadequate, e.g. those of the US Institute of Medicine.
The aim of the present study was to delineate a new case definition of ME/CFS based on pattern recognition methods and using neuro-immune, inflammatory, oxidative and nitrosative stress (neuro-IO&NS) biomarkers as external validating criteria.
We measured the 12-item Fibromyalgia and Chronic Fatigue Syndrome Rating (FF) Scale in 196 subjects with CFS (CDC criteria) and 83 with chronic fatigue. The “Neuro-IO&NS” biomarkers were: IgM / IgA responses against LPS of gut commensal bacteria (leaky gut), IgM responses to O&NS modified neoepitopes, autoimmunity to serotonin, plasma interleukin-1 (IL-1) and serum neopterin.
Cluster analysis showed the presence of two well-separated clusters with highly significant differences in symptoms and biomarkers. The cluster with higher scores on all FF items was externally validated against all IO&NS biomarkers and therefore this diagnostic group was labeled “Neuro-IO&NS Fatigue” or “Neuro-Inflammatory and Oxidative Fatigue” (NIOF).
An algorithm was constructed which defined NIOF as chronic fatigue and 4 or more of the following 6 symptoms: muscle tension, memory disturbances, sleep disorders, irritable bowel, headache or a flu-like malaise. There was a significant overlap between NIOF and CFS although NIOF criteria were much more restrictive. Factor analysis showed two factors, the first a fatigue-hyperalgesia (fibromyalgic complaints) and the second a fatigue-depression factor.
From the Journal of Neurology and Neurobiology (link goes to full paper), 17 September 2015.
Factor Analysis of the DePaul Symptom Questionnaire: Identifying Core Domains
Leonard A. Jason(1*), Madison Sunnquist(1), Abigail Brown(1), Jacob Furst(1), Marjoe Cid(1), Jillianna Farietta(1), Bobby Kot(1), Craig Bloomer(1), Laura Nicholson(1), Yolonda Williams(1), Rachel Jantke(1), Julia L. Newton(2) and Elin Bolle Strand(3)/
1) DePaul University, USA
2) Newcastle University, UK
3) Oslo University Hospital, Norway
￼The present study attempted to identify critical symptom domains of individuals with Myalgic Encephalomyelitis (ME) and chronic fatigue syndrome (CFS). Using patient and control samples collected in the United States, Great Britain, and Norway, exploratory factor analysis (EFA) was used to establish the underlying factor structure of ME and CFS symptoms.
The EFA suggested a four-factor solution: post-exertional malaise, cognitive dysfunction, sleep difficulties, and a combined factor consisting of neuroendocrine, autonomic, and immune dysfunction symptoms. The use of empirical methods could help better understand the fundamental symptom domains of this illness.