Search

We report the case of a 65-year-old woman, clinically diagnosed with the logopenic variant of primary progressive aphasia (PPA), and carrier of C9ORF72 expansion, despite cerebrospinal fluid biomarkers suggesting Alzheimer's disease (AD). She underwent structural MRI, metabolic PET, and amyloid PET imaging using florbetapir. Comparison with healthy controls revealed widespread hypometabolism, left sided cortical atrophy, and an increased cortical amyloid load. No difference in amyloid binding was found between the patient and predemential AD patients. This case provides evidence of amyloidopathy in a carrier of C9ORF72 expansion exhibiting a clinical profile of the logopenic variant of PPA.

Skin-brain signalling in itch reactions has been demonstrated with neuroimaging techniques showing specific brain activation. With positron emission tomography (PET), the itch model used must be adapted to technical and practical constraints. The technique of itch induction by histamine iontophoresis enables modulation of the sensation via the electrical charge applied. This itch model was validated on normal forearm skin of 56 subjects, with itch visual analogue scores peaking to approximately 1.0 cm after 3-4 min, falling to 0.2 cm at 15 min, with no influence of sex, zone, or order. Subsequently, the model was used in a PET study on 14 male volunteers, comparing histamine with physiological saline (control). The results show that the brain is able to discriminate these two conditions, with activated areas similar to those described previously, with, in addition, the anterior cingulate cortex and the insula being positively correlated with the intensity of the sensation.

The T1 head template model used in Statistical Parametric Mapping Version 2000 (SPM2), was segmented into five layers (scalp, skull, CSF, grey and white matter) and implemented in 2 mm voxels. We designed a resistor mesh model (RMM), based on the finite volume method (FVM) to simulate the electrical properties of this head model along the three axes for each voxel. Then, we introduced four dipoles of high eccentricity (about 0.8) in this RMM, separately and simultaneously, to compute the potentials for two sets of conductivities. We used the direct cortical imaging technique (CIT) to recover the simulated dipoles, using 60 or 107 electrodes and with or without addition of Gaussian white noise (GWN). The use of realistic conductivities gave better CIT results than standard conductivities, lowering the blurring effect on scalp potentials and displaying more accurate position areas when CIT was applied to single dipoles. Simultaneous dipoles were less accurately localized, but good qualitative and stable quantitative results were obtained up to 5% noise level for 107 electrodes and up to 10% noise level for 60 electrodes, showing that a compromise must be found to optimize both the number of electrodes and the noise level. With the RMM defined in 2 mm voxels, the standard 128-electrode cap and 5% noise appears to be the upper limit providing reliable source positions when direct CIT is used. The admittance matrix defining the RMM is easy to modify so as to adapt to different conductivities. The next step will be the adaptation of individual real head T2 images to the RMM template and the introduction of anisotropy using diffusion imaging (DI).

A resistor mesh model (RMM) has been validated with reference to the analytical model by consideration of a set of four dipoles close to the cortex. The application of the RMM to scalp potential interpolation was detailed in Part 1. Using the RMM and the same four dipoles, the different methods of cortical mapping were compared and have shown the potentiality of this RMM for obtaining current and potential cortical distributions. The lead-field matrices are well-adapted tools, but the use of a square matrix of high dimension does not permit the inverse solution to be improved in the presence of noise, as a regularisation technique is necessary with noisy data. With the RMM, the transfer matrix and the cortical imaging technique proved to be easy to implement. Further development of the RMM will include application to more realistic head models with more accurate conductivities.