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Static, Low-Frequency, and Pulsed Magnetic Fields
of passive listening distraction and frontal oculomotor regions in the case of saccadic
distraction. Within these regions, the neural interference effects are specific to voxels
that show delay-period activity on unfilled memory trials. Individual differences were
also predicted in the magnitude of the behavioral interference effect. These results indicate that nonvisual processes supported by nonvisual brain areas contribute importantly to visual working memory performance.
Debas et al. (2010) investigated, through fMRI, the neuronal substrates associated with the consolidation process of two motor skills: (1) motor sequence learning
(MSL) and (2) motor adaptation (MA). Four groups of healthy young individuals were
assigned to either a night/sleep condition, in which they were scanned while practicing a finger sequence learning task or an eight-target adaptation pointing task in
the evening (test) and were scanned again 12 hours later in the morning (retest), or
a day/awake condition, in which they were scanned on the MSL or the MA tasks in
the morning and were rescanned 12 hours later in the evening. As expected and consistent with the behavioral results, the functional data revealed increased test–retest
changes in activity in the striatum for the night/sleep group compared with the day/
awake group in the MSL task. By contrast, results of the MA task did not show any
difference in test–retest activity between the night/sleep and day/awake groups. When
the two MA task groups were combined, however, increased test–retest activity was
found in lobule VI of the cerebellar cortex. Together, these findings highlighted the
presence of both functional and structural dissociations reflecting the off-line consolidation processes of MSL and MA. The authors suggested that MSL consolidation
is sleep dependent and reflected by a differential increase in neural activity within
the corticostriatal system, whereas MA consolidation necessitates either a period of
daytime or sleep and is associated with increased neuronal activity within the corticocerebellar system.
Impedance-weighted MRI was obtained during applications of external oscillating magnetic fields, which induce impedance-dependent eddy currents in a sample
(Ueno and Iriguchi 1998). In another study, spatial distribution of electrical impedance was obtained from electric current distributions by using an iterative algorithm
(Khang et al. 2002). The apparent diffusion coefficient reflects electrical conductivity
of a tissue, which enables an estimation of anisotropic conductivity of that tissue
(Tuch et al. 2001; Sekino, Inoue, and Ueno 2004). This method was applied to the
imaging of electrical conductivity in the human brain. Several regions in the white
matter, such as the corpus callosum and the internal capsule, exhibited high anisotropy in conductivity. The magnitude and phase of MR signals are affected by tissue
permittivity (Sekino et al. 2005). A distinctive signal inhomogeneity arises in the
images of objects whose dimension is comparable to the wavelength of the EMF at
the resonant frequency. This phenomenon, dielectric resonance, particularly appears
in scanners using high SMFs.
The MRI may not be applied widely in brain function research; however, high-quality
impedance MRI for impedance and admittance scanning in vivo may lead to the development of a new research field of impedance physiology. It is obvious that information
regarding impedance distributions is important for studying magnetic stimulation and
MEG inverse problems.