Rethinking EEG biomarkers of brain disorders: a transdiagnostic dimensional view

Developing clinically useful brain-based biomarkers remains a central challenge in translational psychiatry and neurology. Traditional approaches focusing on disorder-specific signals have shown limited clinical utility. EEG, a scalable and non-invasive measure of brain function, illustrates the value of an alternative perspective: transdiagnostic and dimensional biomarker development. Here, we use low-frequency activity (LFA) as an illustrative example to demonstrate this framework. We synthesize evidence from 176 EEG studies across chronic pain, migraine, fatigue, and depression and identify increased low-frequency activity (LFA) as the most consistent alteration across studies. Crucially, this absence of disorder specificity does not diminish its clinical value. Instead, it points to shared neural dysfunction, consistent with frameworks of thalamo-cortical dysrhythmia and excitation-inhibition imbalance. These processes may underlie shared symptom dimensions, such as negative affect, cognitive dysfunction, and somatic manifestations. Accordingly, such transdiagnostic, dimensional markers could support prevention, monitoring, stratification, and neuromodulation across disorders, exemplifying precision neuroscience via mechanistically grounded, clinically actionable biomarkers.