Biology subjects

Kujala, J.

Publications and source records attributed to Kujala, J..

11 recordsLinked to original sources

bioRxiv2026

Feeling the Music: Preceding Vibroacoustic Stimulation Modulates Oscillatory Brain Dynamics During Music Listening

Background: Although typically considered an auditory experience, music listening engages multiple sensory systems, including somatosensory and motor pathways, making it an inherently multisensory phenomenon. However, research has predominantly examined the influence of music on other sensory systems, while the reciprocal question - how the existing state of a sensory system modulates the music listening experience- has received considerably less attention. To address this gap, we examined neural activity during music listening in two somatosensory states: one preceded by vibroacoustic stimulation (VAS) and one preceded by rest alone. Methods: Forty participants completed two MEG sessions in a within-subject crossover design. In one session, they received 20 minutes of 40 Hz VAS before listening to 10 minutes of self-selected relaxing music (VAS_ML); in the other, they lay on the same mattress without stimulation (NoVAS_ML). Oscillatory and aperiodic activity were estimated using DICS beamforming and FOOOF decomposition for the whole music period and for early and late listening segments. Results: Across the full listening period, the VAS condition was associated with reduced alpha power in the posterior temporal lobe and increased low-gamma power in the medial somatosensory and motor cortices compared to the NoVAS condition, suggesting enhanced cortical excitability and stronger auditory-motor engagement. Over time, both music listening conditions showed increases in alpha and beta power, consistent with habituation to the musical stimulus, though the spatial distribution differed qualitatively: changes were widespread across temporal and occipital regions in the NoVAS condition but remained localized to temporal areas after VAS. Additionally, VAS uniquely increased temporal-lobe theta power over time, whereas the NoVAS condition showed a decrease in the aperiodic exponent. Subjectively, participants reported stronger emotional intensity during music listening after VAS. Conclusion: These findings suggest that preceding VAS induces a more engaged neural state and qualitatively alters the temporal dynamics of music processing.

neuroscience↗

bioRxiv2026

Biobehavioral synchrony across species: Evidence for multi-level regulatory dynamics in human–canine dyads

Coordinated dynamics between individuals are a hallmark of social interaction, yet the temporal structure and physiological basis of such coupling beyond human species remain poorly understood. Here, we investigated cross-species biobehavioral synchrony by simultaneously quantifying motion dynamics and autonomic activity with hyperscanning of human-canine dyads. We observed both spontaneous and task-related synchrony across motion dynamics, heart rate, and heart rate variability at multiple timescales. Importantly, synchrony was modulated by individual and relational factors. Task-related autonomic synchrony was affected by the human temperament, whereas greater familiarity within the dyad altered the leader-follower dynamics, shifting directional influence from human-led toward canine-driven coordination. Motion synchrony emerged with minimal delay, whereas cardiac synchrony unfolded across longer timescales, suggesting coordinated processes underlying the shared activity, arousal, and autonomic regulation. Our findings extend current models of social synchrony beyond human interactions and reveal that regulatory dynamics underlying coordinated behavior operate across species boundaries.

Systems Biology↗

bioRxiv2024

Heart-brain coupling and its relevance for individual trait characteristics

A bulk of recent neurophysiological research has focused on how bodily functions are intertwined with neural activity, but the dynamic heart-brain coupling and its relevance for individual trait characteristics remains largely unexamined. Thus, our aim was to investigate how ongoing oscillatory brain activity is modulated by the natural fluctuations in heart rate variability (HRV). We further explored whether heart-brain coupling is associated with individual trait characteristics. Magnetoencephalography (MEG) together with electrocardiography (ECG) were used to record neural activity and HRV during rest. Self-reported trait characteristics were examined using Behavioral Inhibition and Activation Systems Scale (BIS/BAS) and attunement to internal bodily sensations using Body Vigilance Scale (BVS). Statistically significant increases were observed for low HRV vs. high HRV state in alpha and beta power (p < 0.05) indicating that oscillatory brain activity is modulated by fluctuations in HRV. Moreover, we demonstrated that heart-brain coupling was associated with self-reported behavioral approach and avoidance tendencies. The results of the moderator analysis further indicated that the relationship between heart-brain coupling and trait characteristics is at least partly moderated by the attunement to internal bodily sensations. Our findings bring insights to the intricate interplay between cardiac and neural signaling and its relationship with individual trait characteristics. HighlightsO_LIOscillatory brain activity is modulated by the natural fluctuations in HRV C_LIO_LIAlpha and beta power increase during states of lower parasympathetic activity C_LIO_LIHeart-brain coupling is linked with self-reported individual trait characteristics C_LIO_LIThis association is influenced by the attunement to the internal bodily milieu C_LI

neuroscience↗

bioRxiv2023

Test-retest reliability of MEG functional brain connectivity related to language processing

The number of studies examining changes in functional connectivity of the human brain is increasing rapidly. In this magnetoencephalography (MEG) study, we examined the reliability of dynamic connectivity related to language processing in a picture naming test-retest paradigm, using data collected from the same participants on two separate days. We determined the connections that were reliable across both days and also examined the behavioral, functional, and structural properties underlying this reliability. A particularly salient finding among a rich set of results was a reliable pattern of beta connectivity increase in the left motor and frontal regions (0-400 ms and 400-800 ms after stimulus onset) and gamma connectivity decrease in the bilateral motor regions (800-1200 ms) which we suggest to represent the motor preparation of speech production. Furthermore, the reliable connections tended to be more frequently associated with the behavioral performance than the non-reliable ones. Finally, the reliable connections were also linked to stronger functional connectivity, as well as to stronger structural connectivity and shorter structural path length, as determined through diffusion MRI (magnetic resonance imaging). Overall, this study defines reliable language-related functional connectivity and introduces practices that may increase reliability. AUTHOR SUMMARYResearch applying connectivity metrics in neuroimaging has increased rapidly during recent years. Hence, the focus has also been to define the best methods for increasing the reliability of connectivity estimation. This study determined reliable functional connectivity from MEG data related to language processing. Moreover, we defined what makes a connection reliable by studying the behavioral, functional, and structural properties underlying the reliable connections.

neuroscience↗

bioRxiv2023

Neural activity is modulated by spontaneous and volitionally controlled breathing

Recent studies have provided evidence regarding respiration-brain coupling, but how continuously varying dynamics of breathing modulate neural activity is not known. We examined whether the neural state differs between spontaneous and volitionally controlled breathing and across the phases of breathing, inspiration and expiration. Magnetoencephalography with a respiratory belt was used to record cortical oscillatory activity during spontaneous, deep, and square breathing (n=33). Alpha power was suppressed during inspiration and increased during expiration (p<0.01) indicating dynamically fluctuating neural states across the respiratory cycle. Compared to spontaneous and square breathing, alpha power increased during deep breathing (p<0.01). We also observed a steeper aperiodic slope and a broadband shift in the power spectrum during square breathing in comparison with spontaneous breathing suggesting that spectral characteristics of neural activity are modulated by the rate, depth, and pattern of breathing. Altogether, we demonstrate that neural activity is modulated by breathing techniques and phases of breathing.

neuroscience↗

bioRxiv2023

Beta- and gamma-band oscillatory connectivity support naturalistic reading of continuous text

Large-scale integration of information across cortical structures, building on neural connectivity, has been proposed to be a key element in supporting human cognitive processing. In electrophysiological neuroimaging studies of reading, quantification of neural interactions has been limited to the level of isolated words or sentences due to artefacts induced by eye movements. Here, we combined magnetoencephalography recording with advanced artefact rejection tools to investigate both cortico-cortical coherence and directed neural interactions during naturalistic reading of full-page texts. Our results show that reading vs. visual scanning of text was associated with wide-spread increases of cortico-cortical coherence in the beta- and gamma-bands. We further show that the reading task was linked with increased directed neural interactions compared to the scanning task across a sparse set of connections within a wide range of frequencies. Together, the results demonstrate that neural connectivity flexibly builds on different frequency bands to support continuous natural reading.

neuroscience↗

bioRxiv2023

Human sensorimotor beta event characteristics and aperiodic signal are highly heritable

Individuals phenotypes, including the brains structure and function, are largely determined by genes and their interplay. The resting brain generates salient rhythmic patterns which can be characterized non-invasively using functional neuroimaging such as magnetoencephalography (MEG). One of these rhythms, the somatomotor ( rolandic) beta rhythm, shows intermittent high amplitude events which predict behavior across tasks and species. Beta rhythm is altered in neurological disease. The aperiodic ( 1/f) signal present in electrophysiological recordings is also modulated by some neurological conditions and aging. Both sensorimotor beta and aperiodic signal could thus serve as biomarkers of sensorimotor function. Knowledge about the extent to which these brain functional measures are heritable could shed light on the mechanisms underlying their generation. We investigated the heritability and variability of human spontaneous sensorimotor beta rhythm and aperiodic activity in 210 healthy adult siblings spontaneous MEG activity. Both the overall beta spectral power as well as time-resolved beta event amplitude parameters were highly heritable, whereas the heritabilities for peak frequency and measures of event duration remained nonsignificant. Interestingly, the most heritable trait was the aperiodic 1/f signal, with a heritability of 0.94 in the right hemisphere. Human sensorimotor neural activity can thus be dissected into different components with variable heritability. We postulate that differences in heritability in part reflect different underlying signal generating mechanisms. The 1/f signal and beta event amplitude measures may depend more on fixed, anatomical parameters, whereas beta event duration and its modulation reflect dynamic characteristics, guiding their use as potential disease biomarkers.

neuroscience↗

bioRxiv2023

Towards a more robust non-invasive assessment of functional connectivity

Non-invasive evaluation of functional connectivity, based on source-reconstructed estimates of phase-difference-based metrics, is notoriously non-robust. This is due to a combination of factors, ranging from a misspecification of seed regions to suboptimal baseline assumptions, and residual signal leakage. In this work, we propose a new analysis scheme of source level phase-difference-based connectivity, which is aimed at optimizing the detection of interacting brain regions. Our approach is based on the combined use of sensor subsampling and dual-source beamformer estimation of all-to-all connectivity on a prespecified dipolar grid. First, a pairwise two-dipole model, to account for reciprocal leakage in the estimation of the localized signals, allows for a usable approximation of the pairwise bias in connectivity due to residual leakage of third party noise. Secondly, using sensor array subsampling, the recreation of multiple connectivity maps using different subsets of sensors allows for the identification of consistent spatially localized peaks in the 6-dimensional connectivity maps, indicative of true brain region interactions. These steps are combined with the subtraction of null coherence estimates to obtain the final coherence maps. With extensive simulations, we compared different analysis schemes for their detection rate of connected dipoles, as a function of signal-to-noise ratio, phase difference and connection strength. We demonstrate superiority of the proposed analysis scheme in comparison to single-dipole models, or an approach that discards the zero phase difference component of the connectivity. We conclude that the proposed pipeline allows for a more robust identification of functional connectivity in experimental data, opening up new possibilities to study brain networks with mechanistically inspired connectivity measures in cognition and in the clinic.

neuroscience↗

bioRxiv2023

Spectral signatures of cross-modal attentional control in the adolescent brain and their link with physical activity and aerobic fitness levels

Top-down attentional control seems to increase and suppress the activity of sensory cortices for relevant stimuli and to suppress activity for irrelevant ones. Higher physical activity (PA) and aerobic fitness (AF) levels have been associated with improved attention, but most studies have focused on unimodal tasks (e.g., visual stimuli only). The impact of higher PA or AF levels on the ability of developing brains to focus on certain stimuli while ignoring distractions remains unknown. The aim of this study was to examine the neural processes in visual and auditory sensory cortices during a cross-modal attention-allocation task using magnetoencephalography in 13-16-year-old adolescents (n = 51). During continuous and simultaneous visual (15 Hz) and auditory (40 Hz) noise-tagging stimulation, participants attended to either visual or auditory targets appearing on their left or right sides. High and low PA groups were formed based on seven-day accelerometer measurements, and high and low AF groups were determined based on the 20-m shuttle-run test. Steady-state (evoked) responses to the visual stimulus were observed in all the adolescents in the primary visual cortex, but some did not show responses in the primary auditory cortices to the auditory stimulus. The adolescents with auditory-tag-driven signals in the left temporal cortex were older than those who did not show responses. Visual cortices showed enhanced visual-tag-related activity with attention, but there was no cross-modal effect, perhaps due to the developmental effect observed in the temporal areas. The visual-tag-related responses in the occipital cortex were enhanced in the higher-PA group, irrespective of task demands. In summary, sensory cortices are unequally involved in cross-modal attention in the adolescent brain. This involvement seems to be enhanced by attention. Higher PA seems to be associated with a specific visual engagement benefit in the adolescent brain. Highlights- Visual and auditory cortices engagement differs in cross-modal processing in adolescence. - Adolescents with responses in the left temporal cortex are older than those without responses. - Physical activity, but not aerobic fitness, is associated with visual engagement benefits in the adolescent brain.

neuroscience↗

bioRxiv2020

Picture naming yields highly consistent cortical activation patterns: test-retest reliability of magnetoencephalography recordings

Reliable paradigms and imaging measures of individual-level brain activity are paramount when reaching from group-level research studies to clinical assessment of individual patients. Magnetoencephalography (MEG) provides a direct, non-invasive measure of cortical processing with high spatiotemporal accuracy, and is thus well suited for assessment of functional brain damage in patients with language difficulties. This MEG study aimed to identify, in a picture naming paradigm, source-localized evoked activity and modulations of cortical oscillations that show high test-retest reliability across measurement days in healthy individuals, demonstrating their applicability in clinical settings. For patients with a severe language disorder picture naming can be a challenging task. Therefore, we also determined whether a semantic judgement task ( is this item living) with a spoken response ( yes/no) would suffice to induce comparably consistent activity within brain regions related to language production. The MEG data was collected from 19 healthy participants on two separate days. In picture naming, evoked activity was consistent across measurement days (intraclass correlation coefficient (ICC)>0.4) in the left frontal (400-800 ms after image onset), sensorimotor (200-800 ms), parietal (200-600 ms), temporal (200-800 ms), occipital (400-800 ms) and cingulate (600-800 ms) regions, as well as the right temporal (600-800 ms) region. In the semantic judgement task, consistent evoked activity was spatially more limited, occurring in the left temporal (200-800 ms), sensorimotor (400-800 ms), occipital (400-600 ms) and subparietal (600-800 ms) regions, and the right supramarginal cortex (600-800 ms). The naming task showed typical beta oscillatory suppression in premotor and sensorimotor regions (800-1200 ms) but other consistent modulations of oscillatory activity were mostly observed in posterior cortical regions that have not typically been associated with language processing. The high test-retest consistency of MEG evoked activity in the picture naming task testifies to its applicability in clinical evaluations of language function, as well as in longitudinal MEG studies of language production in clinical and healthy populations.

neuroscience↗

bioRxiv2018

Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

Communication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex-and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 \"familiar vs. unfamiliar vs. scrambled faces\" dataset. The goal is to educate both novice and experienced data analysts with the \"tricks of the trade\" necessary to successfully perform this type of analysis on their own data.

neuroscience↗