Neurofunctional Drift: A Systems Framework for Neural Reintegration After Structural Restoration

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Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-16
DOI
https://doi.org/10.5281/zenodo.22787768
Primary Topic
Nerve injury and regeneration
Type
preprint
Controls
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preprint

Neurofunctional Drift: A Systems Framework for Neural Reintegration After Structural Restoration

Carl Balog
Zenodo (CERN European Organization for Nuclear Research)
Nerve injury and regeneration
preprint

Neurofunctional Drift: A Systems Framework for Neural Reintegration After Structural Restoration

Carl Balog
preprint en

Abstract

A knee can be replaced, a nerve repaired, a limb engineered and wired into the nervous system. Structure is restored, and presumably function with it. They do not arrive together. It takes time, it takes a dynamic process, and, ultimately, reintegration to arrive at self. A mundane phenomenon, unnamed but familiar, without an instrument or measure to characterize it. Neurofunctional Drift is that term: the process by which the nervous system revises its internal model of a changed body — the approach rather than the arrival. Neuroplasticity is the substrate. It corrects quickly and stabilizes slowly, and a system built that way does not climb cleanly. Function overshoots and undershoots while that model is refitted. Endpoints cluster onto a few attractors rather than spreading along a gradient, and some sit above baseline. Because the shape follows the timing and not the tissue, the process would not discriminate biology from silicon — an engineered limb undergoing the same drift as a repaired nerve. Scope is deliberate: peripheral restoration, central apparatus intact. Three parameters make it measurable: how far function trails the repair, how long the trailing lasts, and where it comes to rest — Drift Magnitude, Convergence Lag, Adaptive Gap. And the account can fail: no overshoot, no clustering, or no shared form across tissues would end it. Sampled densely enough, early slopes should forecast the endpoint, turning "give it time" from a shrug into a prediction. Version 2 notes: Substantially revised and expanded. New material: a section examining five adjacent literatures and where each stops short of the interval; a section deriving the predicted shape of the trajectory from the dynamics of neuroplasticity; a section stating what observations would refute the framework; and explicit definitions of the three parameters, including a declared baseline, the derived index Cumulative Drift, and a settling criterion for Convergence Lag. Correction. Version 1 stated that the capacity-versus-performance distinction had not been made longitudinal. That is incorrect. It has been followed across annual visits in chronic obstructive pulmonary disease and month by month in outpatient neurorehabilitation, and daily knee range of motion has been reported alongside daily step count after arthroplasty. The claim is narrowed in this version to what remains unexamined: continuous real-world monitoring paired with elicited capacity testing at weekly or finer intervals following structural restoration of a limb. Adaptive Sufficiency is redefined by substrate limitation rather than by how well a patient copes, with reversibility as the empirical test distinguishing it from Maladaptive Convergence. All references verified against primary sources, persistent identifiers added, and the reference list reformatted to APA.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Nerve injury and regeneration
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