Functional magnetic resonance imaging studies have documented the resting human brain to be functionally organized in multiple large‐scale networks, called resting‐state networks (RSNs). Other brain imaging techniques, such as electroencephalography (EEG) and magnetoencephalography (MEG), have been used for investigating the electrophysiological basis of RSNs. To date, it is largely unclear how neural oscillations measured with EEG and MEG are related to functional connectivity in the resting state. In addition, it remains to be elucidated whether and how the observed neural oscillations are related to the spatial distribution of the network nodes over the cortex. To address these questions, we examined frequency‐dependent functional connectivity between the main nodes of several RSNs, spanning large part of the cortex. We estimated connectivity using band‐limited power correlations from high‐density EEG data collected in healthy participants. We observed that functional interactions within RSNs are characterized by a specific combination of neuronal oscillations in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, which highly depend on the position of the network nodes. This finding may contribute to a better understanding of the mechanisms through which neural oscillations support functional connectivity in the brain.

Electroencephalography (EEG) is a technique for non‐invasively measuring neuronal activity in the human brain using electrodes placed on the participant's scalp. With the advancement of digital technologies, EEG analysis has evolved over time from the qualitative analysis of amplitude and frequency modulations to a comprehensive analysis of the complex spatiotemporal characteristics of the recorded signals. EEG is now considered a powerful tool for measuring neural processes in the same time frame in which they happen (i.e. the subsecond range). However, it is commonly argued that EEG suffers from low spatial resolution, which makes it difficult to localize the generators of EEG activity accurately and reliably. Today, the availability of high‐density EEG (hdEEG) systems, combined with methods for incorporating information on head anatomy and sophisticated source‐localization algorithms, has transformed EEG into an important neuroimaging tool. hdEEG offers researchers and clinicians a rich and varied range of applications. It can be used not only for investigating neural correlates in motor and cognitive neuroscience experiments, but also for clinical diagnosis, particularly in the detection of epilepsy and the characterization of neural impairments in a wide range of neurological disorders. Notably, the integration of hdEEG systems with other physiological recordings, such as kinematic and/or electromyography data, might be especially beneficial to better understand the neuromuscular mechanisms associated with deconditioning in ageing and neuromotor disorders, by mapping the neurokinematic and neuromuscular connectivity patterns directly in the brain. image

Transcranial alternating current stimulation (tACS) mechanisms remain poorly understood. While tACS is expected to modulate periodic electroencephalography (EEG) activity due to the imposition of oscillatory alternating currents, it may also modulate the aperiodic EEG component, suggesting alternative tACS mechanisms that have not yet been explored. This study examined whether 20-minute tACS session delivered at 40 Hz over C3/C4 can selectively modulate periodic and aperiodic components. We implemented a paired, counterbalanced, and sham-controlled design consisting of two sessions during which tACS was administered over the sensorimotor regions. EEG was recorded before and after tACS under three EEG conditions: resting state, active task (i.e., the serial reaction time task), and passive task (i.e., 40-Hz auditory stimulation). We found that tACS led to selective modulation of the aperiodic component, specifically a broadband downward shift in the power spectrum (i.e., reduction in the offset), independent of the EEG conditions and with no evidence of an effect on periodic activity. This result may reflect a general effect on neural spiking rather than direct modulation of oscillatory activity. Disentangling the contribution of the aperiodic component from periodic activity represents an opportunity to identify alternative neurophysiological mechanisms underlying neural modulation and provides a clearer interpretation of tACS mechanisms.

Objective. To spatio-temporally resolve cardiac signals in functional magnetic resonance imaging (fMRI) time-series of the human brain using neither external physiological measurements nor ad hoc modelling assumptions. Approach. Cardiac pulsation is a physiological confound of fMRI time-series that introduces spurious signal fluctuations in proximity to blood vessels. fMRI alone is not sufficiently fast to resolve cardiac pulsation. Depending on the ratio between the instantaneous heart-rate and the acquisition sampling frequency ( 1 / TR , with TR being the repetition time), the cardiac signal may alias into the frequency band of neural activation so that its removal through spectral filtering techniques is generally not possible. In this paper, we show that it is feasible to temporally and spatially resolve cardiac signals throughout the brain even when cardiac aliasing occurs by combining fMRI hyper-sampling with simultaneous multislice (SMS) imaging. The technique, which we name WHOle-brain CArdiac signal REgression from highly accelerated simultaneous multi-Slice fMRI acquisitions (WHOCARES), was developed on 695 healthy subjects selected from the Human Connectome Project and its performance validated against the RETROICOR, HAPPY and the pulse oxymeter signal regression methods. Main results. WHOCARES is capable of retrieving voxel-wise cardiac signal regressors. This is achieved without employing external physiological recordings nor through ad hoc modelling assumptions. The performance of WHOCARES was, on average, superior to RETROICOR, HAPPY and the pulse oxymeter regression methods. Significance. WHOCARES holds basis for the reliable mapping of cardiac activity in fMRI time-series. WHOCARES can be employed for the retrospective removal of cardiac noise in publicly available fMRI datasets where physiological recordings are not available. WHOCARES is freely available at https://github.com/gferrazzi/WHOCARES.

Schizophrenia (SZ) is a widespread psychiatric disorder that is traditionally characterized by positive and negative symptoms. However, recent focus has shifted to cognitive deficits as a crucial aspect. The cerebellum, conventionally tied to motor coordination, is now recognized as pivotal in the pathophysiology of SZ cognitive impairments. Proposed disruptions in the cortico-cerebellar-thalamic-cortico circuit contribute to these deficits. Despite evidence of cerebellar abnormalities, within-cerebellum functional connectivity is often overlooked. This study explores spontaneous functional interactions within the cerebellum and their link to cognitive deficits in SZ. Using a multi-domain task battery (MDTB) parcellation, fMRI data from SZ patients and healthy controls were analyzed. Significant differences in cerebellar connectivity emerged, particularly in regions related to attention, language, and memory processing. Correlations between connectivity values and SZ symptomatology were identified. A post hoc analysis, considering the patients’ hallucination vulnerability, revealed distinct connectivity patterns. Non-hallucinating and low-hallucinating SZ patients exhibited higher cerebellar connectivity than high-hallucinating patients, especially in language and motor control regions. These findings suggest a gradient of cerebellar connectivity alterations corresponding to hallucination vulnerability in SZ patients. This study offers novel insights into cerebellar impairments in SZ, highlighting the role of within-cerebellum connectivity in cognitive deficits. The observed connectivity patterns in language-related regions contribute to understanding language development and auditory verbal hallucinations in SZ.