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The principle of Representational Similarity Analysis (RSA) posits that neural representations reflect the structure of encoded information, allowing exploration of spatial and temporal organization of brain information processing. Traditional RSA when applied to EEG or MEG data faces challenges in accessing activation time series at the brain source level due to modeling complexities and insufficient geometric/anatomical data.To overcome this, we introduce Representational Dissimilarity Component Analysis (ReDisCA), a method for estimating spatial–temporal components in EEG or MEG responses aligned with a target representational dissimilarity matrix (RDM). ReDisCA yields informative spatial filters and associated topographies, offering insights into the location of ”representationally relevant” sources. Applied to evoked response time series, ReDisCA produces temporal source activation profiles with the desired RDM. Importantly, while ReDisCA does not require inverse modeling its output is consistent with EEG and MEG observation equation and can be used as an input to rigorous source localization procedures.Demonstrating ReDisCA’s efficacy through simulations and comparison with conventional methods, we show superior source localization accuracy and apply the method to real EEG and MEG datasets, revealing physiologically plausible representational structures without inverse modeling. ReDisCA adds to the family of inverse modeling free methods such as independent component analysis (Makeig, 1995), Spatial spectral decomposition (Nikulin, 2011), and Source power comodulation (Dähne, 2014) designed for extraction sources with desired properties from EEG or MEG data. Extending its utility beyond EEG and MEG analysis, ReDisCA is likely to find application in fMRI data analysis and exploration of representational structures emerging in multilayered artificial neural networks.

Abstract Although more people are engaging in meditation practices that require specialized training, few studies address the issues associated with nervous activity pattern changes brought about by such training. For beginners, it remains unclear how much practice is needed before objective physiological changes can be detected, whether or not they are similar across the novices and what are the optimal strategies to track these changes. To clarify these questions we recruited individuals with no prior meditation experience. The experimental group underwent an eight-week Taoist meditation course administered by a professional, while the control group listened to audiobooks. Both groups participated in audio-guided, 34-min long meditation sessions before and after the 8-week long intervention. Their EEG, photoplethysmogram, respiration, and skin conductance were recorded during the mediation and resting state periods. Compared to the control group, the experimental group exhibited band-specific topically organized changes of the resting state brain activity and heart rate variability associated with sympathetic system activation. Importantly, no significant changes were found during the meditation process prior and post the 8-week training in either of the groups. The absence of notable changes in CNS and ANS activity indicators during meditation sessions, for both the experimental and control groups, casts doubt on the effectiveness of wearable biofeedback devices in meditation practice. This finding redirects focus to the importance of monitoring resting state activity to evaluate progress in beginner meditators. Also, 16 h of training is not enough for forming individual objectively different strategies manifested during the meditation sessions. Our results contributed to the development of tools to objectively monitor the progress in novice meditators and the choice of the relevant monitoring strategies. According to our findings, in order to track early changes brought about by the meditation practice it is preferable to monitor brain activity outside the actual meditation sessions.

BACKGROUND: Motor disorders of the upper limbs often develop after stroke. The use of traditional meth-ods to restore the motor functions of the upper limbs has limited effectiveness thus making the search and development of new rehabilitation approaches important. We propose to use a proactive selection of a purposeful movement as such an approach. AIM: to investi-gate the influence of different strategies of the upper limb movement regulation on the mo-tor decision making and hand kinematics parameters in stroke patients. METHODS: Ten pa-tients with stroke participated in the study during their hospital stay. They were divided into 2 groups and performed the task of selecting a movement target and performing arm movements for 10 sessions of 10 minutes each. The sessions were performed on separate days. When performing the task, patients selected one of the targets, either the near target or the distant one. The groups differed in the type of presentation of the distant target; the near target was stationary. The stimuli were presented on a touch screen arranged hori-zontally. A patient sat in front of the screen and placed the hand with a foam ball fixed in it on the starting area. The patient’s forearm was supported by means of a pendant. The pa-tient selected a target to reach in each attempt and then performed the movement by swip-ing the ball on the touch screen. The frequency of the target selection to perform the movement, the range of the selected target, the latencies of the movement onset, the hand speed and acceleration, and the accuracy in reaching the target were assessed. RESULTS: The results indicate that amidst the overall effects represented by a reduced movement on-set time [F (9.72)=8.59; p 0.001], a greater movement speed in reaching the distant tar-get [F (9.72)=2.79; p=0.007], a lower selection rate [F (9.72)=2.78; p=0.008] and a re-duced mean distance of the selected distant target [F (9.72)=2.19; p=0.033], the differ-ences in the distant target presentation between the groups have an impact on the hand movement dynamics in patients. Presenting a target at a random distance results in a greater selected target distance [F (1.8)=17.04; p=0.003], and an increased hand speed [F (9.72)=3.03; p=0.004] compared to the adaptive presentation. There is also a greater decrease in the movement onset time [F (9.72)=2.71; p=0.009] in the group of patients with the adaptive presentation of the distant target compared to the group with the random presentation. CONCLUSION: The differences in the target presentation strategy are reflected in the motor decision making and hand movement dynamics of stroke patients. A random-ized range of target presentation results in a higher amplitude arm movement closer to the activation threshold of the stretch reflex. The results of the study may be useful for selecting the rehabilitation strategies for stroke patients.