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Peer reviewe

Effects of bright light treatment on psychomotor speed in athletes

Purpose: A recent study suggests that transcranial brain targeted light treatment via ear canals may have physiological effects on brain function studied by functional magnetic resonance imaging (fMRI) techniques in humans. We tested the hypothesis that bright light treatment could improve psychomotor speed in professional ice hockey players. Methods: Psychomotor speed tests with audio and visual warning signals were administered to a Finnish National Ice Hockey League team before and after 24 days of transcranial bright light or sham treatment. The treatments were given during seasonal darkness in the Oulu region (latitude 65 degrees north) when the strain on the players was also very high (10 matches during 24 days). A daily 12-min dose of bright light or sham (n = 11 for both) treatment was given every morning between 8–12 am at home with a transcranial bright light device. Mean reaction time and motor time were analyzed separately for both psychomotor tests. Analysis of variance for repeated measures adjusted for age was performed. Results: Time x group interaction for motor time with a visual warning signal was p = 0.024 after adjustment for age. In Bonferroni post-hoc analysis, motor time with a visual warning signal decreased in the bright light treatment group from 127 ± 43 to 94 ± 26 ms (p = 0.024) but did not change significantly in the sham group 121 ± 23 vs. 110 ± 32 ms (p = 0.308). Reaction time with a visual signal did not change in either group. Reaction or motor time with an audio warning signal did not change in either the treatment or sham group. Conclusion: Psychomotor speed, particularly motor time with a visual warning signal, improves after transcranial bright light treatment in professional ice-hockey players during the competition season in the dark time of the year

Using Coherence to Measure Regional Homogeneity of Resting-State fMRI Signal

In this study, we applied coherence to voxel-wise measurement of regional homogeneity of resting-state functional magnetic resonance imaging (RS-fMRI) signal. We compared the current method, regional homogeneity based on coherence (Cohe-ReHo), with previously proposed method, ReHo based on Kendall's coefficient of concordance (KCC-ReHo), in terms of correlation and paired t-test in a large sample of healthy participants. We found the two measurements differed mainly in some brain regions where physiological noise is dominant. We also compared the sensitivity of these methods in detecting difference between resting-state conditions [eyes open (EO) vs. eyes closed (EC)] and in detecting abnormal local synchronization between two groups [attention deficit hyperactivity disorder (ADHD) patients vs. normal controls]. Our results indicated that Cohe-ReHo is more sensitive than KCC-ReHo to the difference between two conditions (EO vs. EC) as well as that between ADHD and normal controls. These preliminary results suggest that Cohe-ReHo is superior to KCC-ReHo. A possible reason is that coherence is not susceptible to random noise induced by phase delay among the time courses to be measured. However, further investigation is still needed to elucidate the sensitivity and specificity of these methods

Mapping Transient Hyperventilation Induced Alterations with Estimates of the Multi-Scale Dynamics of BOLD Signal

Temporal blood oxygen level dependent (BOLD) contrast signals in functional MRI during rest may be characterized by power spectral distribution (PSD) trends of the form 1/fα. Trends with 1/f characteristics comprise fractal properties with repeating oscillation patterns in multiple time scales. Estimates of the fractal properties enable the quantification of phenomena that may otherwise be difficult to measure, such as transient, non-linear changes. In this study it was hypothesized that the fractal metrics of 1/f BOLD signal trends can map changes related to dynamic, multi-scale alterations in cerebral blood flow (CBF) after a transient hyperventilation challenge. Twenty-three normal adults were imaged in a resting-state before and after hyperventilation. Different variables (1/f trend constant α, fractal dimension Df, and, Hurst exponent H) characterizing the trends were measured from BOLD signals. The results show that fractal metrics of the BOLD signal follow the fractional Gaussian noise model, even during the dynamic CBF change that follows hyperventilation. The most dominant effect on the fractal metrics was detected in grey matter, in line with previous hyperventilation vaso-reactivity studies. The α was able to differentiate also blood vessels from grey matter changes. Df was most sensitive to grey matter. H correlated with default mode network areas before hyperventilation but this pattern vanished after hyperventilation due to a global increase in H. In the future, resting-state fMRI combined with fractal metrics of the BOLD signal may be used for analyzing multi-scale alterations of cerebral blood flow

Group-ICA Model Order Highlights Patterns of Functional Brain Connectivity

Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. Altering ICA dimensionality (model order) estimation can have a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Recent evidence from several neuroimaging studies suggests that the human brain has a modular hierarchical organization which resembles the hierarchy depicted by different ICA model orders. We hypothesized that functional connectivity between-group differences measured with ICA might be affected by model order selection. We investigated differences in functional connectivity using so-called dual regression as a function of ICA model order in a group of unmedicated seasonal affective disorder (SAD) patients compared to normal healthy controls. The results showed that the detected disease-related differences in functional connectivity alter as a function of ICA model order. The volume of between-group differences altered significantly as a function of ICA model order reaching maximum at model order 70 (which seems to be an optimal point that conveys the largest between-group difference) then stabilized afterwards. Our results show that fine-grained RSNs enable better detection of detailed disease-related functional connectivity changes. However, high model orders show an increased risk of false positives that needs to be overcome. Our findings suggest that multilevel ICA exploration of functional connectivity enables optimization of sensitivity to brain disorders

A characteristic time sequence of epileptic activity in EEG during dynamic penicillin-induced focal epilepsy—A preliminary study

AbstractPenicillin-induced focal epilepsy is a well-known model in experimental epilepsy. However, the dynamic evolution of waveforms, DC-level changes, spectral content and coherence are rarely reported. Stimulated by earlier fMRI findings, we also seek for the early signs preceding spiking activity from frequency domain of EEG signal. In this study, EEG data is taken from previous EEG/fMRI series (six pigs, 20–24kg) of an experimental focal epilepsy model, which includes dynamic induction of epileptic activity with penicillin (6000IU) injection into the somatosensory cortex during deep isoflurane anaesthesia. No ictal discharges were recorded with this dose. Spike waveforms, DC-level, time–frequency content and coherence of EEG were analysed. Development of penicillin induced focal epileptic activity was not preceded with specific spectral changes. The beginning of interictal spiking was related to power increase in the frequencies below 6Hz or 20Hz, and continued to a widespread spectral increase. DC-level and coherence changes were clear in one animal. Morphological evolution of epileptic activity was a collection of the low-amplitude monophasic, bipolar, triple or double spike-wave forms, with an increase in amplitude, up to large monophasic spiking. In conclusion, in the time sequence of induced epileptic activity, immediate shifts in DC-level EEG are plausible, followed by the spike activity-related widespread increase in spectral content. Morphological evolution does not appear to follow a clear continuum; rather, intermingled and variable spike or multispike waveforms generally lead to stabilised activity of high-amplitude monophasic spikes

Sampling Rate Effects on Resting State fMRI Metrics

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning

Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing

In resting state functional magnetic resonance imaging (fMRI) studies of autism spectrum disorders (ASDs) decreased frontal-posterior functional connectivity is a persistent finding. However, the picture of the default mode network (DMN) hypoconnectivity remains incomplete. In addition, the functional connectivity analyses have been shown to be susceptible even to subtle motion. DMN hypoconnectivity in ASD has been specifically called for re-evaluation with stringent motion correction, which we aimed to conduct by so-called scrubbing. A rich set of default mode subnetworks can be obtained with high dimensional group independent component analysis (ICA) which can potentially provide more detailed view of the connectivity alterations. We compared the DMN connectivity in high-functioning adolescents with ASDs to typically developing controls using ICA dual-regression with decompositions from typical to high dimensionality. Dual-regression analysis within DMN subnetworks did not reveal alterations but connectivity between anterior and posterior DMN subnetworks was decreased in ASD. The results were very similar with and without motion scrubbing thus indicating the efficacy of the conventional motion correction methods combined with ICA dual-regression. Specific dissociation between DMN subnetworks was revealed on high ICA dimensionality, where networks centered at the medial prefrontal cortex and retrosplenial cortex showed weakened coupling in adolescents with ASDs compared to typically developing control participants. Generally the results speak for disruption in the anterior-posterior DMN interplay on the network level whereas local functional connectivity in DMN seems relatively unaltered

Age-Related Differences in Functional Nodes of the Brain Cortex – A High Model Order Group ICA Study

Functional MRI measured with blood oxygen dependent (BOLD) contrast in the absence of intermittent tasks reflects spontaneous activity of so-called resting state networks (RSN) of the brain. Group level independent component analysis (ICA) of BOLD data can separate the human brain cortex into 42 independent RSNs. In this study we evaluated age-related effects from primary motor and sensory, and, higher level control RSNs. One hundred sixty-eight healthy subjects were scanned and divided into three groups: 55 adolescents (ADO, 13.2 ± 2.4 years), 59 young adults (YA, 22.2 ± 0.6 years), and 54 older adults (OA, 42.7 ± 0.5 years), all with normal IQ. High model order group probabilistic ICA components (70) were calculated and dual-regression analysis was used to compare 21 RSN's spatial differences between groups. The power spectra were derived from individual ICA mixing matrix time series of the group analyses for frequency domain analysis. We show that primary sensory and motor networks tend to alter more in younger age groups, whereas associative and higher level cognitive networks consolidate and re-arrange until older adulthood. The change has a common trend: both spatial extent and the low frequency power of the RSN's reduce with increasing age. We interpret these result as a sign of normal pruning via focusing of activity to less distributed local hubs