Review of the Midbrain Ascending Arousal Network Nuclei and Implications for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), Gulf War Illness (GWI) and Postexertional Malaise (PEM).
James N BaraniukPublished in: Brain sciences (2022)
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS and Gulf War Illness (GWI) share features of post-exertional malaise (PEM), exertional exhaustion, or postexertional symptom exacerbation. In a two-day model of PEM, submaximal exercise induced significant changes in activation of the dorsal midbrain during a high cognitive load working memory task (Washington 2020) (Baraniuk this issue). Controls had no net change. However, ME/CFS had increased activity after exercise, while GWI had significantly reduced activity indicating differential responses to exercise and pathological mechanisms. These data plus findings of the midbrain and brainstem atrophy in GWI inspired a review of the anatomy and physiology of the dorsal midbrain and isthmus nuclei in order to infer dysfunctional mechanisms that may contribute to disease pathogenesis and postexertional malaise. The nuclei of the ascending arousal network were addressed. Midbrain and isthmus nuclei participate in threat assessment, awareness, attention, mood, cognition, pain, tenderness, sleep, thermoregulation, light and sound sensitivity, orthostatic symptoms, and autonomic dysfunction and are likely to contribute to the symptoms of postexertional malaise in ME/CFS and GWI.
Keyphrases
- working memory
- sleep quality
- neuropathic pain
- physical activity
- spinal cord
- high intensity
- transcranial direct current stimulation
- depressive symptoms
- pulmonary artery
- chronic obstructive pulmonary disease
- chronic pain
- attention deficit hyperactivity disorder
- heat stress
- bipolar disorder
- case report
- spinal cord injury
- heart rate variability
- oxidative stress
- resistance training
- pain management
- electronic health record
- mild cognitive impairment
- multiple sclerosis
- coronary artery
- body composition
- deep learning
- intensive care unit