White Matter Neuroinflammation in ME/CFS

Dr. Qiang Yu presented a study on white matter neuroinflammation in ME/CFS using an advanced diffusion magnetic resonance imaging (MRI) model. The starting point was the recognition that conventional diffusion tensor imaging (DTI) metrics yield inconsistent findings across studies and cannot distinguish between inflammation, oedema, and axonal damage. Prior work from the group also identified heterogeneity by disease onset type: increased diffusivity in post-infectious cases versus decreased diffusivity in those with gradual onset. To obtain more biologically interpretable markers, the team applied a diffusion-based neural inflammation (NI) model, which simulates the MRI diffusion signal as a linear combination of multiple anisotropic diffusion tensors and a spectrum of isotropic diffusion components. Three key parameters were derived: NIHR (non-restricted isotropic diffusion ratio, associated with extracellular water and vasogenic oedema), NIRF (restricted isotropic diffusion fraction, associated with cellularity and microglial activation), and fiber fraction (reflecting axonal packing density and extracellular space). In a cohort of 67 ME/CFS patients and 67 age-matched healthy controls, patients showed significantly reduced NIHR and NIRF values alongside increased fiber fraction. These findings were interpreted as indicative of cellular swelling, altered microglial states or chronic microglial downregulation, and reduced extracellular space. Crucially, the NI model parameters – but not conventional DTI metrics – showed significant associations with clinical measures, underscoring the added value of this approach. Dr. Yu acknowledged that the NI model provides only indirect evidence for neuroinflammation and that biological validation through biosamples (blood, cerebrospinal fluid, tissue) remains outstanding. The closing discussion highlighted the possible role of microglial dysregulation and glial fragmentation, with reference to post-mortem findings from Amsterdam, and pointed to the importance of future multimodal and cross-disciplinary validation studies.