Cross-Sectional Accuracy Does Not Certify Within-Patient Tracking in Functional Connectivity

Resting-state functional connectivity supports two applications at once, classifying disease and identifying individuals from their connectivity patterns. A profile carries two kinds of feature, those that stay fixed within a person and differ between people, and those that change within a person from visit to visit. Which kind carries the separable disease information has not been asked, yet the answer decides what a connectivity marker can be used for. We first localise that information to the fixed component, which reads the diagnosis at $0.728$ against $0.734$ from the whole profile, and the localisation holds under seven alternative removal methods. We then show that centring a profile on its subject mean cancels the class-mean difference exactly whenever the label is constant within a subject, so a linear readout of the residual is at chance by construction and cannot settle the question, while a label that does vary within a subject is retained. The test that remains is whether change within a person carries the label, which we run directly on $57$ subjects with repeated Mini-Mental State Examination scores, where the subject mean predicts the level at a Spearman correlation of $0.364$ while the within-subject rate of change predicts the rate of cognitive change at $-0.186$, against a detection limit of $0.33$. The same placement further recurs on four of six cohorts spanning five disorders. A cross-sectional accuracy therefore certifies that patients differ from one another, not that connectivity detects change within one patient, and a marker meant to follow one patient has to be validated on within-subject change.