Corticokinematic coherence (CKC) is the phase coupling between limb kinematics and cortical neurophysiological signals reflecting cortical processing of proprioceptive afference, and is reproducible when estimated with magnetoencephalography (MEG). However, feasibility and reproducibility of CKC based on electroencephalography (EEG) is still unclear and is the primary object of the present report. Thirteen healthy right-handed volunteers (7 females, 21.7 ± 4.3 years) participated two separate EEG sessions 12.6±1.3 months apart. Participants' dominant and non-dominant index finger was continuously moved at 3 Hz for 4 min separately using a pneumatic-movement actuator. Coherence was computed between finger acceleration and three derivations of EEG signals: (1) average reference, (2) bipolar derivations, and (3) surface Laplacian. CKC strength was defined as the peak coherence value at the movement frequency. Intraclass-correlation coefficient values (0.74-0.93) indicated excellent inter-session reproducibility for CKC strength for all derivations and moved fingers. CKC strength obtained with EEG was ~2 times lower compared to MEG but the values were positively correlated across the participants. CKC strength was significantly (p<0.01) higher for bipolar (session-1 0.19±0.09; session-2 0.20±0.10) and surface Laplacian (session-1 0.22±0.09; session-2 0.21±0.09) derivations than for the average reference (session 1 0.10±0.04; session 2, 0.11±0.05). We demonstrated that CKC is feasible and reproducible tool to monitor proprioception using EEG recordings, although the strength of CKC was twice lower for EEG compared to MEG. Laplacian and bipolar (CP3-C1/CP3-C3 and CP4-C2/C4-FC2) EEG derivation(s) are recommended for future research and clinical use of CKC method.

Corticokinematic coherence (CKC) between limb kinematics and magnetoencephalographic (MEG) signals reflects cortical processing of proprioceptive afference. However, it is unclear whether strength of CKC is reproducible across measurement sessions. We thus examined reproducibility of CKC in a follow-up study. Thirteen healthy right-handed volunteers (7 females, 21.7 ± 4.3 yrs) were measured using MEG in two separate sessions 12.6 ± 1.3 months apart. The participant was seated and relaxed while his/her dominant or non-dominant index finger was continuously moved at 3 Hz (4 min for each hand) using a pneumatic movement actuator. Finger kinematics were recorded with a 3-axis accelerometer. Coherence was computed between finger acceleration and MEG signals. CKC strength was defined as the peak coherence value at 3 Hz form a single sensor among 40 pre-selected Rolandic gradiometers contralateral to the movement. Pneumatic movement actuator provided stable proprioceptive stimuli and significant CKC responses peaking at the contralateral Rolandic sensors. In the group level, CKC strength did not differ between the sessions in dominant (Day-1 0.40 ± 0.19 vs. Day-2 0.41 ± 0.17) or non-dominant (0.35 ± 0.16 vs. 0.36 ± 0.17) hand, nor between the hands. Intraclass-correlation coefficient (ICC) values indicated excellent inter-session reproducibility for CKC strength for both dominant (0.86) and non-dominant (0.97) hand. However, some participants showed pronounced inter-session variability in CKC strength, but only for the dominant hand. CKC is a promising tool to study proprioception in long-term longitudinal studies in the group level to follow, e.g., integrity of cortical proprioceptive processing with motor functions after stroke.

Several functional and morphological brain measures are partly under genetic control. The identification of direct links between neuroimaging signals and corresponding genetic factors can reveal cellular-level mechanisms behind the measured macroscopic signals and contribute to the use of imaging signals as probes of genetic function. To uncover possible genetic determinants of the most prominent brain signal oscillation, the parieto-occipital 10-Hz alpha rhythm, we measured spontaneous brain activity with magnetoencephalography in 210 healthy siblings while the subjects were resting, with eyes closed and open. The reactivity of the alpha rhythm was quantified from the difference spectra between the two conditions. We focused on three measures: peak frequency, peak amplitude and the width of the main spectral peak. In accordance with earlier electroencephalography studies, spectral peak amplitude was highly heritable (h 2 > 0.75). Variance component-based analysis of 28 000 single-nucleotide polymorphism markers revealed linkage for both the width and the amplitude of the spectral peak. The strongest linkage was detected for the width of the spectral peak over the left parieto-occipital cortex on chromosome 10 (LOD = 2.814, nominal P < 0.03). This genomic region contains several functionally plausible genes, including GRID1 and ATAD1 that regulate glutamate receptor channels mediating synaptic transmission, NRG3 with functions in brain development and HRT7 involved in the serotonergic system and circadian rhythm. Our data suggest that the alpha oscillation is in part genetically regulated, and that it may be possible to identify its regulators by genetic analyses on a realistically modest number of samples.

Neural processes are explored through macroscopic neuroimaging and microscopic molecular measures, but the two levels remain primarily detached. The identification of direct links between the levels would facilitate use of imaging signals as probes of genetic function and, vice versa, access to molecular correlates of imaging measures. Neuroimaging patterns have been mapped for a few isolated genes, chosen based on their connection with a clinical disorder. Here we propose an approach that allows an unrestricted discovery of the genetic basis of a neuroimaging phenotype in the normal human brain. The essential components are a subject population that is composed of relatives and selection of a neuroimaging phenotype that is reproducible within an individual and similar between relatives but markedly variable across a population. Our present combined magnetoencephalography and genome-wide linkage study in 212 healthy siblings demonstrates that auditory cortical activation strength is highly heritable and, specifically in the right hemisphere, regulated oligogenically with linkages to chromosomes 2q37, 3p12, and 8q24. The identified regions delimit as candidate genes TRAPPC9, operating in neuronal differentiation, and ROBO1, regulating projections of thalamocortical axons. Identification of normal genetic variation underlying neurophysiological phenotypes offers a non-invasive platform for an in-depth, concerted capitalization of molecular and neuroimaging levels in exploring neural function.