According to the predictive coding theory, the brain predicts future sensory inputs based on previous experiences. When there is a mismatch between the expected and the actual stimulus, a mismatch response is transmitted from low to high cortical levels to adapt the predictive model. An important cortical area for somatosensory mismatch responses is the secondary somatosensory (S2) cortex, which has reciprocal connections with the cerebellum. This study aims to characterize the role of the cortico-cerebellar interactions in the modulation of S2 cortex responses according to the predictability of afferent tactile and proprioceptive somatosensory inputs. We enrolled 20 right-handed healthy adults who underwent three functional magnetic resonance imaging (fMRI) runs (6 min each, block-design) consisting of twelve 30-s alternating blocks (10 brain volumes/block, 120 brain volumes/session) of tactile oddball paradigms. The fMRI signals within the contralateral S1 (cS1), ipsilateral S2 (iS2), and contralateral S2 (cS2) cortices; within the cortical areas involved in multimodal sensory mismatch detection (i.e., the right anterior insula (AIns), temporoparietal junction (TPJ), middle frontal gyrus (MFG), and supplementary motor are/anterior cingular cortex (SMA/ACC)); and within the ipsilateral cerebellar lobule 8 (iCL8) and 6 (iCL6) were extracted using region-of-interest (ROI) analyses and compared using ANOVA. The modulation of cortico-cerebellar functional connectivity by afferent stimuli predictability was studied using psychophysiological interaction (PPI) analyses. Predictable tactile stimuli were associated with significantly lower fMRI signals within cS1 and bilateral S2 cortices, and the right AIns, TPJ, and MFG compared to random tactile stimuli. PPI analyses showed that predictable tactile stimuli were associated with a significant increase in the functional connectivity (negative correlation) between cS2 cortex and iCL8 BOLD levels, with no significant correlation during random tactile stimulation. This effect, identified by the PPI analyses, occurred solely between the cerebellum and cS2 cortex and not with the other cortical mismatch areas. This study provides evidence for a cerebello-cortical interplay between iCL8 and the cS2 cortex in tactile somatosensory mismatch responses. The lower BOLD response in the S2 cortex observed for predictable tactile stimuli is likely mediated by an inhibitory influence of the cerebellum on the somatosensory cortex when tactile inputs are predictable.

Declarative memory formation critically relies on the synchronization of brain oscillations in the theta frequency band (4-8 Hz) within specific brain networks. The development of this capacity is closely linked to the functional organization of these networks already at rest. However, the relationship between theta-band resting-state functional connectivity and declarative memory abilities remains unexplored in children. Here, using magnetoencephalography, we examined the association between declarative memory performance and pre-learning resting-state functional connectivity across frequency bands in 32 school-aged children. Declarative memory was assessed as the percentage of correct retrieval of 50 new associations between non-objects and magical functions, while resting-state functional connectivity was measured through power envelope correlation of the theta, alpha, low beta, and high beta frequency bands. We found that stronger theta-band resting-state functional connectivity within occipito-temporo-frontal networks correlated with better declarative memory retrieval, while no correlation was observed in the alpha and beta frequency bands. These findings suggest that the functional brain architecture at rest, specifically involving theta-band oscillations, supports declarative memory in children. This mechanism may facilitate the subsequent rapid transformation of sensory input into visuo-semantic representations, highlighting the critical role of theta-band connectivity in early cognitive development.

Characterizing the early development of the human brain is critical from both fundamental and clinical perspectives. However, existing neuroimaging techniques are either not well suited to infants or have limited spatial or temporal resolution. The advent of optically pumped magnetometers (OPMs) has revolutionized magnetoencephalography (MEG) by enabling wearable and thus more naturalistic recordings while maintaining excellent sensitivity and spatiotemporal resolution. Nevertheless, its adaptation to studying neural activity in infancy poses several challenges. In this work, we present an original close-to-scalp OPM-MEG setup that successfully recorded brain responses to sounds in newborns. We exposed 1-month-old infants to continuous streams of tones and observed significant evoked responses, which peaked ~250 ms poststimulus at bilateral auditory cortices. When tones were presented at a steady fixed pace with an oddball tone every fourth tone, significant neural responses were found both at the frequency of the standard tones (3 Hz) and of the oddball tones (0.75 Hz). The latter reflects the ability of the newborn brain to detect auditory change and synchronize to regular auditory patterns. Additional analyses support the added value of triaxial OPMs to increase the number of channels on small heads. Finally, OPM-MEG responses were validated with those obtained from the same participants using an adult-sized cryogenic MEG. This study demonstrates the applicability of OPM-MEG to study early postnatal periods; a crucial step towards future OPM investigations of typical and pathological early brain development.

Magnetoencephalography (MEG) measures brain function via assessment of magnetic fields generated by neural currents. Conventional MEG uses superconducting sensors, which place significant limitations on performance, practicality, and deployment; however, the field has been revolutionised in recent years by the introduction of optically-pumped magnetometers (OPMs). OPMs enable measurement of the MEG signal without cryogenics, and consequently the conception of “OPM-MEG” systems which ostensibly allow increased sensitivity and resolution, lifespan compliance, free subject movement, and lower cost. However, OPM-MEG is in its infancy with existing limitations on both sensor and system design. Here, we report a new OPM-MEG design with miniaturised and integrated electronic control, a high level of portability, and improved sensor dynamic range. We show that this system produces equivalent measures compared with an established OPM-MEG instrument; specifically, when measuring task-induced beta-band, gamma-band, and evoked neuro-electrical responses, source localisations from the two systems were comparable and temporal correlation of measured brain responses was >0.7 at the individual level and >0.9 for groups. Using an electromagnetic phantom, we demonstrate improved dynamic range by running the system in background fields up to 8 nT. We show that the system is effective in gathering data during free movement (including a sitting-to-standing paradigm) and that it is compatible with simultaneous electroencephalography (EEG). Finally, we demonstrate portability by moving the system between two laboratories. Overall, our new system is shown to be a significant step forward for OPM-MEG and offers an attractive platform for next generation functional medical imaging.

The oscillatory nature of intrinsic brain networks is largely taken for granted in the systems neuroscience community. However, the hypothesis that brain rhythms—and by extension transient bursting oscillations—underlie functional networks has not been demonstrated per se. Electrophysiological measures of functional connectivity are indeed affected by the power bias, which may lead to artefactual observations of spectrally specific network couplings not genuinely driven by neural oscillations, bursting or not. We investigate this crucial question by introducing a unique combination of a rigorous mathematical analysis of the power bias in frequency-dependent amplitude connectivity with a neurobiologically informed model of cerebral background noise based on hidden Markov modeling of resting-state magnetoencephalography (MEG). We demonstrate that the power bias may be corrected by a suitable renormalization depending nonlinearly on the signal-to-noise ratio, with noise identified as non-bursting oscillations. Applying this correction preserves the spectral content of amplitude connectivity, definitely proving the importance of brain rhythms in intrinsic functional networks. Our demonstration highlights a dichotomy between spontaneous oscillatory bursts underlying network couplings and non-bursting oscillations acting as background noise but whose function remains unsettled.