Biomarkers of brain diseases
This addresses the problem of ineffective biomarker identification in brain disease diagnosis and prognosis for clinicians and researchers, but is incremental as it builds on existing critiques and data methods.
The paper argues that current approaches to identifying brain disease biomarkers are insufficient due to the degeneracy of brain features, and proposes using multimodal and longitudinal data to group patients before defining biomarkers.
Despite the diversity of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment, we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patient versus healthy controls using single data type, we should use multimodal (e.g. brain activity, neurotransmitters, neuromodulators, brain imaging) and longitudinal brain data to guide the grouping before defining multidimensional biomarkers for brain diseases.