fMRI assessment of language lateralization: an objective approach.

Language lateralization based on functional magnetic resonance imaging (fMRI) is often used in clinical neurological settings. Currently, interpretation of the distribution, pattern and extent of language activation can be heavily dependent on the chosen statistical threshold. The aim of the present study was to 1) test the robustness of adaptive thresholding of fMRI data to yield a fixed number of active voxels, and to 2) develop a largely threshold-independent method of assessing when individual patients have statistically atypical language lateralization.

Cortical reintegration of a replanted hand and an osseointegrated thumb prosthesis.

Background: Following a peripheral nerve repair the injured nerve has to re-innervate its original cortical area, a process, which is poorly understood. Errors in this cortical re-innervation have been suggested as one key reason for the generally poor clinical outcome following nerve injuries in the hand.

Method: Functional magnetic resonance imaging (fMRI) was used to assess cortical reintegration following amputation and reattachment of bodyparts in two different situations: a patient with a hand amputation followed by immediate surgical replantation and a patient with an osseointegrated thumb prosthesis.

Cortical sensory and motor response in a patient whose hand has been replanted: one-year follow up with functional magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) was used to study how a replanted hand regained its cortical territory parallel to recovery. The cortical response to sensory stimulation shifts from an ipsilateral to a bilateral pattern, and then to a predominantly contralateral activation. The cortical response to motor stimulation was normal from the first investigation.

Flexible statistical modelling detects clinical functional magnetic resonance imaging activation in partially compliant subjects.

Clinical functional magnetic resonance imaging (fMRI) occasionally fails to detect significant activation, often due to variability in task performance. The present study seeks to test whether a more flexible statistical analysis can better detect activation, by accounting for variance associated with variable compliance to the task over time. Experimental results and simulated data both confirm that even at 80% compliance to the task, such a flexible model outperforms standard statistical analysis when assessed using the extent of activation (experimental data), goodness of fit (experimental data), and area under the operator characteristic curve (simulated data).