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Physiologists often express the change in the value of a measurement made on two occasions as a ratio of the initial value. This is usually motivated by an assumption that the absolute change fails to capture the true extent of the alteration that has occurred in attaining the final value – if there is initial variation among individual cases. While it may appear reasonable to use ratios to standardize the magnitude of change in this way, the perils of doing so have been widely documented. Ratios frequently have intractable statistical properties, both when taken in isolation and when analysed using techniques such as regression. A new method of computing a standardized metric of change, based on principal components analysis (PCA), is described. It exploits the collinearity within sets of initial, absolute change and final values. When these sets define variables subjected to PCA, the standardized measure of change is obtained as the product of the loading of absolute change onto the first principal component (PC1) and the eigenvalue of PC1. It is demonstrated that a sample drawn from a population of these standardized measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range; and preserves the rank order of the ratios. It is also shown that this method can be used to express the magnitude of a physiological response in an experimental condition relative to that obtained in a control condition. Key points: The intractable statistical properties of ratios and the perils of using ratios to standardize the magnitude of change are well known.A new method of computing a standardized metric, based on principal components analysis (PCA), is described, which exploits the collinearity within sets of initial, absolute change and final values.A sample drawn from a population of these PCA‐derived measures: approximates a normal distribution (unlike the corresponding ratios); lies within the same range as the ratios; and preserves the rank order of the ratios.The method can also be applied to express the magnitude of a physiological response in an experimental condition relative to a control condition. [ABSTRACT FROM AUTHOR]

It is a paradox of neurological rehabilitation that, in an era in which preclinical models have produced significant advances in our mechanistic understanding of neural plasticity, there is inadequate support for many therapies recommended for use in clinical practice. When the goal is to estimate the probability that a specific form of therapy will have a positive clinical effect, the integration of mechanistic knowledge (concerning ‘the structure or way of working of the parts in a natural system’) may improve the quality of inference. This is illustrated by analysis of three contemporary approaches to the rehabilitation of lateralized dysfunction affecting people living with stroke: constraint‐induced movement therapy; mental practice; and mirror therapy. Damage to ‘cross‐road’ regions of the structural (white matter) brain connectome generates deficits that span multiple domains (motor, language, attention and verbal/spatial memory). The structural integrity of these regions determines not only the initial functional status, but also the response to therapy. As structural disconnection constrains the recovery of functional capability, ‘disconnectome’ modelling provides a basis for personalized prognosis and precision rehabilitation. It is now feasible to refer a lesion delineated using a standard clinical scan to a (dis)connectivity atlas derived from the brains of other stroke survivors. As the individual disconnection pattern thus obtained suggests the functional domains most likely be compromised, a therapeutic regimen can be tailored accordingly. Stroke is a complex disorder that burdens individuals with distinct constellations of brain damage. Mechanistic knowledge is indispensable when seeking to ameliorate the behavioural impairments to which such damage gives rise. [ABSTRACT FROM AUTHOR]

Human motor behaviour is continually modified on the basis of errors between desired and actual movement outcomes. It is emerging that the role played by the primary motor cortex (M1) in this process is contingent upon a variety of factors, including the nature of the task being performed, and the stage of learning. Here we used repetitive TMS to test the hypothesis that M1 is intimately involved in the initial phase of sensorimotor adaptation. Inhibitory theta burst stimulation was applied to M1 prior to a task requiring modification of torques generated about the elbow/forearm complex in response to rotations of a visual feedback display. Participants were first exposed to a 30 degree clockwise (CW) rotation (Block A), then a 60 degree counterclockwise rotation (Block B), followed immediately by a second block of 30 degree CW rotation (A2). In the STIM condition, participants received 20 s of continuous theta burst stimulation (cTBS) prior to the initial A Block. In the conventional (CON) condition, no stimulation was applied. The overt characteristics of performance in the two conditions were essentially equivalent with respect to the errors exhibited upon exposure to a new variant of the task. There were however, profound differences between the conditions in the latency of response preparation, and the excitability of corticospinal projections from M1, which accompanied phases of de-adaptation and re-adaptation (during Blocks B and A2). Upon subsequent exposure to the A rotation 24 h later, the rate of re-adaptation was lower in the stimulation condition than that present in the conventional condition. These results support the assertion that primary motor cortex assumes a key role in a network that mediates adaptation to visuomotor perturbation, and emphasise that it is engaged functionally during the early phase of learning. (Contains 8 figures.)