96 research outputs found
TMSmap – Software for Quantitative Analysis of TMS Mapping Results
The use of the MRI-navigation system ensures accurate targeting of TMS. This, in turn, results in TMS motor mapping becoming a routinely used procedure in neuroscience and neurosurgery. However, currently, there is no standardized methodology for assessment of TMS motor-mapping results. Therefore, we developed TMSmap—free standalone graphical interface software for the quantitative analysis of the TMS motor mapping results (http://tmsmap.ru/). In addition to the estimation of standard parameters (such as the size of cortical muscle representation and the center of gravity location), it allows estimation of the volume of cortical representations, excitability profile of the cortical surface map, and the overlap between cortical representations. The input data for the software includes the coordinates of the coil position (or electric field maximum) and the corresponding response in each stimulation point. TMSmap has been developed for versatile assessment and comparison of TMS maps relating to different experimental interventions including, but not limited to longitudinal, pharmacological and clinical studies (e.g., stroke recovery). To illustrate the use of TMSmap we provide examples of the actual TMS motor-mapping analysis of two healthy subjects and one chronic stroke patient
Generalized Cross-Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies
Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g., within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging. Here we present a new method for the robust extraction of cross-frequency phase-to-phase synchronized components. Generalized Cross-Frequency Decomposition (GCFD) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to the whole range of frequencies: it works for any f1 and f2 whenever f1:f2 is a rational number. GCFD gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. We successfully validated the new method in simulations and tested it with real EEG recordings including resting state data and steady state visually evoked potentials (SSVEP)
Aberrant Long-Range Temporal Correlations in Depression Are Attenuated after Psychological Treatment
The spontaneous oscillatory activity in the human brain shows long-range
temporal correlations (LRTC) that extend over time scales of seconds to
minutes. Previous research has demonstrated aberrant LRTC in depressed
patients; however, it is unknown whether the neuronal dynamics normalize after
psychological treatment. In this study, we recorded EEG during eyes-closed
rest in depressed patients (N = 71) and healthy controls (N = 25), and
investigated the temporal dynamics in depressed patients at baseline, and
after attending either a brief mindfulness training or a stress reduction
training. Compared to the healthy controls, depressed patients showed stronger
LRTC in theta oscillations (4–7 Hz) at baseline. Following the psychological
interventions both groups of patients demonstrated reduced LRTC in the theta
band. The reduction of theta LRTC differed marginally between the groups, and
explorative analyses of separate groups revealed noteworthy topographic
differences. A positive relationship between the changes in LRTC, and changes
in depressive symptoms was observed in the mindfulness group. In summary, our
data show that aberrant temporal dynamics of ongoing oscillations in
depressive patients are attenuated after treatment, and thus may help uncover
the mechanisms with which psychotherapeutic interventions affect the brain
Identification of spatial patterns with maximum association between power of resting state neural oscillations and trait anxiety
Anxiety affects approximately 5-10% of the adult population worldwide, placing a large burden on the health systems. Despite its omnipresence and impact on mental and physical health, most of the individuals affected by anxiety do not receive appropriate treatment. Current research in the field of psychiatry emphasizes the need to identify and validate biological markers relevant to this condition. Neurophysiological preclinical studies are a prominent approach to determine brain rhythms that can be reliable markers of key features of anxiety. However, while neuroimaging research consistently implicated prefrontal cortex and subcortical structures, such as amygdala and hippocampus, in anxiety, there is still a lack of consensus on the underlying neurophysiological processes contributing to this condition. Methods allowing non-invasive recording and assessment of cortical processing may provide an opportunity to help identify anxiety signatures that could be used as intervention targets.
In this study, we apply Source-Power Comodulation (SPoC) to electroencephalography (EEG) recordings in a sample of participants with different levels of trait anxiety. SPoC was developed to find spatial filters and patterns whose power comodulates with an external variable in individual
participants. The obtained patterns can be interpreted neurophysiologically. Here, we extend the use of SPoC to a multi-subject setting and test its validity using simulated data with a realistic head model. Next, we apply our SPoC framework to resting state EEG of 43 human participants for whom trait anxiety scores were available. SPoC inter-subject analysis of narrow frequency band data reveals neurophysiologically meaningful spatial patterns in the theta band (4-7 Hz) that are negatively correlated with anxiety. The outcome is specific to the theta band and not observed in the alpha (8-12 Hz) or beta (13-30 Hz) frequency range. The theta-band spatial pattern is primarily localised to the superior frontal gyrus.
We discuss the relevance of our spatial pattern results for the search of biomarkers for anxiety and their application in neurofeedback studies
Disruption of Boundary Encoding During Sensorimotor Sequence Learning: An MEG Study study
Music performance relies on the ability to learn and execute actions and their associated sounds. The process of learning these auditory-motor contingencies depends on the proper encoding of the serial order of the actions and sounds. Among the different serial positions of a behavioral sequence, the first and last (boundary) elements are particularly relevant. Animal and patient studies have demonstrated a specific neural representation for boundary elements in prefrontal cortical regions and in the basal ganglia, highlighting the relevance of their proper encoding. The neural mechanisms underlying the encoding of sequence boundaries in the general human population remain, however, largely unknown. In this study, we examined how alterations of auditory feedback, introduced at different ordinal positions (boundary or within-sequence element), affect the neural and behavioral responses during sensorimotor sequence learning. Analysing the neuromagnetic signals from 20 participants while they performed short piano sequences under the occasional effect of altered feedback (AF), we found that at around 150–200 ms post-keystroke, the neural activities in the dorsolateral prefrontal cortex (DLPFC) and supplementary motor area (SMA) were dissociated for boundary and within-sequence elements. Furthermore, the behavioral data demonstrated that feedback alterations on boundaries led to greater performance costs, such as more errors in the subsequent keystrokes. These findings jointly support the idea that the proper encoding of boundaries is critical in acquiring sensorimotor sequences. They also provide evidence for the involvement of a distinct neural circuitry in humans including prefrontal and higher-order motor areas during the encoding of the different classes of serial order
Robust Statistical Detection of Power-Law Cross-Correlation
We show that widely used approaches in statistical physics incorrectly
indicate the existence of power-law cross-correlations between financial stock
market fluctuations measured over several years and the neuronal activity of
the human brain lasting for only a few minutes. While such cross-correlations
are nonsensical, no current methodology allows them to be reliably discarded,
leaving researchers at greater risk when the spurious nature of cross-
correlations is not clear from the unrelated origin of the time series and
rather requires careful statistical estimation. Here we propose a theory and
method (PLCC-test) which allows us to rigorously and robustly test for power-
law cross-correlations, correctly detecting genuine and discarding spurious
cross-correlations, thus establishing meaningful relationships between
processes in complex physical systems. Our method reveals for the first time
the presence of power-law cross-correlations between amplitudes of the alpha
and beta frequency ranges of the human electroencephalogram
Pre-stimulus Alpha Oscillations and Inter-subject Variability of Motor Evoked Potentials in Single- and Paired-Pulse TMS Paradigms
Inter- and intra-subject variability of the motor evoked potentials (MEPs) to
TMS is a well-known phenomenon. Although a possible link between this
variability and ongoing brain oscillations was demonstrated, the results of
the studies are not consistent with each other. Exploring this topic further
is important since the modulation of MEPs provides unique possibility to
relate oscillatory cortical phenomena to the state of the motor cortex probed
with TMS. Given that alpha oscillations were shown to reflect cortical
excitability, we hypothesized that their power and variability might explain
the modulation of subject-specific MEPs to single- and paired-pulse TMS
(spTMS, ppTMS, respectively). Neuronal activity was recorded with multichannel
electroencephalogram. We used spTMS and two ppTMS conditions: intracortical
facilitation (ICF) and short-interval intracortical inhibition (SICI).
Spearman correlations were calculated within and across subjects between MEPs
and the pre-stimulus power of alpha oscillations in low (8–10 Hz) and high
(10–12 Hz) frequency bands. Coefficient of quartile variation was used to
measure variability. Across-subject analysis revealed no difference in the
pre-stimulus alpha power among the TMS conditions. However, the variability of
high-alpha power in spTMS condition was larger than in the SICI condition. In
ICF condition pre-stimulus high-alpha power variability correlated positively
with MEP amplitude variability. No correlation has been observed between the
pre-stimulus alpha power and MEP responses in any of the conditions. Our
results show that the variability of the alpha oscillations can be more
predictive of TMS effects than the commonly used power of oscillations and we
provide further support for the dissociation of high and low-alpha bands in
predicting responses produced by the stimulation of the motor cortex
Resting-State Theta Oscillations and Reward Sensitivity in Risk Taking
Females demonstrate greater risk aversion than males on a variety of tasks, but the underlying neurobiological basis is still unclear. We studied how theta (4–7 Hz) oscillations at rest related to three different measures of risk taking. Thirty-five participants (15 females) completed the Bomb Risk Elicitation Task (BRET), which allowed us to measure risk taking during an economic game. The Domain-Specific Risk-Taking Scale (DOSPERT) was used to measure self-assessed risk attitudes as well as reward and punishment sensitivities. In addition, the Barratt Impulsiveness Scale (BIS11) was included to quantify impulsiveness. To obtain measures of frontal theta asymmetry and frontal theta power, we used magnetoencephalography (MEG) acquired prior to task completion, while participants were at rest. Frontal theta asymmetry correlated with average risk taking during the game but only in the female sample. By contrast, frontal theta power correlated with risk taking as well as with measures of reward and punishment sensitivity in the joint sample. Importantly, we showed that reward sensitivity mediated a correlation between risk taking and the power of theta oscillations localized to the anterior cingulate cortex. In addition, we observed significant sex differences in source- and sensor-space theta power, risk taking during the game, and reward sensitivity. Our findings suggest that sensitivity to rewards, associated with resting-state theta oscillations in the anterior cingulate cortex, is a trait that potentially contributes to sex differences in risk taking
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