Altered Cerebro-Cerebellar Dynamic Functional Connectivity in Alcohol Use Disorder: a Resting-State fMRI Study.

Alcohol use disorder (AUD) is widely associated with cerebellar dysfunction and altered cerebro-cerebellar functional connectivity (FC) that lead to cognitive impairments. Evidence for this association comes from resting-state functional magnetic resonance imaging (rsfMRI) studies that assess time-averaged measures of FC across the duration of a typical scan. This approach, however, precludes the assessment of potentially FC dynamics happening at faster timescales. In this study, using rsfMRI data, we aim at exploring cerebro-cerebellar FC dynamics in AUD patients (N = 18) and age- and sex-matched controls (N = 18). In particular, we quantified group-level differences in the temporal variability of FC between the posterior cerebellum and large-scale cognitive systems, and we investigated the role of the cerebellum in large-scale brain dynamics in terms of the temporal flexibility and integration of its regions. We found that, relative to controls, the AUD group exhibited significantly greater FC variability between the cerebellum and both the frontoparietal executive control (F1,31 = 7.01, p(FDR) = 0.028) and ventral attention (F1,31 = 7.35, p(FDR) = 0.028) networks. Moreover, the AUD group exhibited significantly less flexibility (F1,31 = 8.61, p(FDR) = 0.028) and greater integration (F1,31 = 9.11, p(FDR) = 0.028) in the cerebellum. Finally, in an exploratory analysis, we found distributed changes in the dynamics of canonical large-scale networks in AUD. Overall, this study brings evidence of AUD-related alterations in dynamic FC within major cerebro-cerebellar networks. This pattern has implications for explaining the development and maintenance of this disorder and improving our understating of the cerebellum's involvement in addiction.

Involuntary and voluntary recall of musical memories: A comparison of temporal accuracy and emotional responses.

Comparisons between involuntarily and voluntarily retrieved autobiographical memories have revealed similarities in encoding and maintenance, with differences in terms of specificity and emotional responses. Our study extended this research area into the domain of musical memory, which afforded a unique opportunity to compare the same memory as accessed both involuntarily and voluntarily. Specifically, we compared instances of involuntary musical imagery (INMI, or "earworms")-the spontaneous mental recall and repetition of a tune-to deliberate recall of the same tune as voluntary musical imagery (VMI) in terms of recall accuracy and emotional responses. Twenty participants completed two 3-day tasks. In an INMI task, participants recorded information about INMI episodes as they occurred; in a VMI task, participants were prompted via text message to deliberately imagine each tune they had previously experienced as INMI. In both tasks, tempi of the imagined tunes were recorded by tapping to the musical beat while wearing an accelerometer and additional information (e.g., tune name, emotion ratings) was logged in a diary. Overall, INMI and VMI tempo measurements for the same tune were strongly correlated. Tempo recall for tunes that have definitive, recorded versions was relatively accurate, and tunes that were retrieved deliberately (VMI) were not recalled more accurately in terms of tempo than spontaneous and involuntary instances of imagined music (INMI). Some evidence that INMI elicited stronger emotional responses than VMI was also revealed. These results demonstrate several parallels to previous literature on involuntary memories and add new insights on the phenomenology of INMI.

BAASTA: Battery for the Assessment of Auditory Sensorimotor and Timing Abilities.

The Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA) is a new tool for the systematic assessment of perceptual and sensorimotor timing skills. It spans a broad range of timing skills aimed at differentiating individual timing profiles. BAASTA consists of sensitive time perception and production tasks. Perceptual tasks include duration discrimination, anisochrony detection (with tones and music), and a version of the Beat Alignment Task. Perceptual thresholds for duration discrimination and anisochrony detection are estimated with a maximum likelihood procedure (MLP) algorithm. Production tasks use finger tapping and include unpaced and paced tapping (with tones and music), synchronization-continuation, and adaptive tapping to a sequence with a tempo change. BAASTA was tested in a proof-of-concept study with 20 non-musicians (Experiment 1). To validate the results of the MLP procedure, less widespread than standard staircase methods, three perceptual tasks of the battery (duration discrimination, anisochrony detection with tones, and with music) were further tested in a second group of non-musicians using 2 down / 1 up and 3 down / 1 up staircase paradigms (n = 24) (Experiment 2). The results show that the timing profiles provided by BAASTA allow to detect cases of timing/rhythm disorders. In addition, perceptual thresholds yielded by the MLP algorithm, although generally comparable to the results provided by standard staircase, tend to be slightly lower. In sum, BAASTA provides a comprehensive battery to test perceptual and sensorimotor timing skills, and to detect timing/rhythm deficits.

Expectations induced by natural-like temporal fluctuations are independent of attention decrement: evidence from behavior and early visual evoked potentials.

Temporal expectations and attention decrement affect human behavior in opposing ways: the former positively, the latter negatively yet both exhibit similar neural signatures - i.e., reduction in the early event-related potential components' amplitude - despite different underlying mechanisms. Furthermore, there is a significant and growing debate in the literature regarding the putative role of attention in the encoding of expectations in perception. The question then arises as to what are the behavioral and neural consequences, if any, of attention decrement on temporal expectations and related enhancement of sensory information processing. Here, we investigated behavioral performance and visual N1a, N1p and P1 components during a sustained attention reaction time task inducing attention decrement under two conditions. In one condition, the inter-stimulus intervals (ISIs) were randomly distributed to impede expectation effects while for the other, the ISI exhibited natural-like long-term correlations supposed to induce temporal expectations. Behavioral results show that natural-like fluctuations in ISI indeed induced faster RT due to temporal expectations. These temporal expectations were beneficial even under attention decrement circumstances. Further, temporal expectations were associated with reduced N1a amplitude while attention decrement was associated with reduced N1p amplitude. Our findings provide evidence that the effects of temporal expectations and attention decrement induced in a single task can be independent at the behavioral level, and are supported at separate information processing stages at the neural level in vision.

Tunes stuck in your brain: The frequency and affective evaluation of involuntary musical imagery correlate with cortical structure.

Recent years have seen a growing interest in the neuroscience of spontaneous cognition. One form of such cognition is involuntary musical imagery (INMI), the non-pathological and everyday experience of having music in one's head, in the absence of an external stimulus. In this study, aspects of INMI, including frequency and affective evaluation, were measured by self-report in 44 subjects and related to variation in brain structure in these individuals. Frequency of INMI was related to cortical thickness in regions of right frontal and temporal cortices as well as the anterior cingulate and left angular gyrus. Affective aspects of INMI, namely the extent to which subjects wished to suppress INMI or considered them helpful, were related to gray matter volume in right temporopolar and parahippocampal cortices respectively. These results provide the first evidence that INMI is a common internal experience recruiting brain networks involved in perception, emotions, memory and spontaneous thoughts.

The speed of our mental soundtracks: Tracking the tempo of involuntary musical imagery in everyday life.

The study of spontaneous and everyday cognitions is an area of rapidly growing interest. One of the most ubiquitous forms of spontaneous cognition is involuntary musical imagery (INMI), the involuntarily retrieved and repetitive mental replay of music. The present study introduced a novel method for capturing temporal features of INMI within a naturalistic setting. This method allowed for the investigation of two questions of interest to INMI researchers in a more objective way than previously possible, concerning (1) the precision of memory representations within INMI and (2) the interactions between INMI and concurrent affective state. Over the course of 4 days, INMI tempo was measured by asking participants to tap to the beat of their INMI with a wrist-worn accelerometer. Participants documented additional details regarding their INMI in a diary. Overall, the tempo of music within INMI was recalled from long-term memory in a highly veridical form, although with a regression to the mean for recalled tempo that parallels previous findings on voluntary musical imagery. A significant positive relationship was found between INMI tempo and subjective arousal, suggesting that INMI interacts with concurrent mood in a similar manner to perceived music. The results suggest several parallels between INMI and voluntary imagery, music perceptual processes, and other types of involuntary memories.

A dataset of acoustic measurements from soundscapes collected worldwide during the COVID-19 pandemic.

Political responses to the COVID-19 pandemic led to changes in city soundscapes around the globe. From March to October 2020, a consortium of 261 contributors from 35 countries brought together by the Silent Cities project built a unique soundscape recordings collection to report on local acoustic changes in urban areas. We present this collection here, along with metadata including observational descriptions of the local areas from the contributors, open-source environmental data, open-source confinement levels and calculation of acoustic descriptors. We performed a technical validation of the dataset using statistical models run on a subset of manually annotated soundscapes. Results confirmed the large-scale usability of ecoacoustic indices and automatic sound event recognition in the Silent Cities soundscape collection. We expect this dataset to be useful for research in the multidisciplinary field of environmental sciences.

A Strong and Simple Deep Learning Baseline for BCI Motor Imagery Decoding.

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredients from the literature, to serve as a standard for comparison. The proposed architecture is composed of standard layers, including 1D convolutions, batch normalisations, ReLU activation functions and pooling functions. EEG-SimpleConv architecture is accompanied by a straightforward and tailored training routine, which is subjected to an extensive ablation study to quantify the influence of its components. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We believe that using standard components and ingredients can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible.

Unravelling individual rhythmic abilities using machine learning.

Humans can easily extract the rhythm of a complex sound, like music, and move to its regular beat, like in dance. These abilities are modulated by musical training and vary significantly in untrained individuals. The causes of this variability are multidimensional and typically hard to grasp in single tasks. To date we lack a comprehensive model capturing the rhythmic fingerprints of both musicians and non-musicians. Here we harnessed machine learning to extract a parsimonious model of rhythmic abilities, based on behavioral testing (with perceptual and motor tasks) of individuals with and without formal musical training (n = 79). We demonstrate that variability in rhythmic abilities and their link with formal and informal music experience can be successfully captured by profiles including a minimal set of behavioral measures. These findings highlight that machine learning techniques can be employed successfully to distill profiles of rhythmic abilities, and ultimately shed light on individual variability and its relationship with both formal musical training and informal musical experiences.

Sound externalization in dynamic binaural listening: A comparative behavioral and EEG study.

Binaural reproduction aims at recreating a realistic sound scene at the ears of the listener using headphones. Unfortunately, externalization for frontal and rear sources is often poor (virtual sources are perceived inside the head, instead of outside the head). Nevertheless, previous studies have shown that large head-tracked movements could substantially improve externalization and that this improvement persisted once the subject had stopped moving his/her head. The present study investigates the relation between externalization and evoked response potentials (ERPs) by performing behavioral and EEG measurements in the same experimental conditions. Different degrees of externalization were achieved by preceding measurements with 1) head-tracked movements, 2) untracked head movements, and 3) no head movement. Results showed that performing a head movement, whether the head tracking was active or not, increased the amplitude of ERP components after 100 ms, which suggests that preceding head movements alters the auditory processing. Moreover, untracked head movements gave a stronger amplitude on the N1 component, which might be a marker of a consistency break in regards to the real world. While externalization scores were higher after head-tracked movements in the behavioral experiment, no marker of externalization could be found in the EEG results.

Deep learning models of cognitive processes constrained by human brain connectomes.

Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity over many trials. Recently, brain decoding with graph neural networks has been shown to scale at fine temporal resolution and on the full brain, achieving state-of-the-art performance on the human connectome project benchmark. The reason behind this success is likely the strong inductive connectome prior that enables the integration of distributed patterns of brain activity. Yet, the nature of such inductive bias is still poorly understood. In this work, we investigate the impact of the inclusion of multiple path lengths (through high-order graph convolution), the homogeneity of brain parcels (graph nodes), and the type of interactions (graph edges). We evaluate the decoding models on a large population of 1200 participants, under 21 different experimental conditions, acquired from the Human Connectome Project database. Our findings reveal that the optimal choice for large-scale cognitive decoding is to propagate neural dynamics within empirical functional connectomes and integrate brain dynamics using high-order graph convolutions. In this setting, the model exhibits high decoding accuracy and robustness against adversarial attacks on the graph architecture, including randomization in functional connectomes and lesions in targeted brain regions and networks. The trained model relies on biologically meaningful features for the prediction of cognitive states and generates task-specific graph representations resembling task-evoked activation maps. These results demonstrate that a full-brain integrative model is critical for the large-scale brain decoding. Our study establishes principles of how to effectively leverage human connectome constraints in deep graph neural networks, providing new avenues to study the neural substrates of human cognition at scale.

Gradients of connectivity as graph Fourier bases of brain activity.

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.

Beta and Theta Oscillations Correlate With Subjective Time During Musical Improvisation in Ecological and Controlled Settings: A Single Subject Study.

In this paper, we describe the results of a single subject study attempting at a better understanding of the subjective mental state during musical improvisation. In a first experiment, we setup an ecological paradigm measuring EEG on a musician in free improvised concerts with an audience, followed by retrospective rating of the mental state of the improviser. We introduce Subjective Temporal Resolution (STR), a retrospective rating assessing the instantaneous quantization of subjective timing of the improviser. We identified high and low STR states using Hidden Markov Models in two performances, and were able to decode those states using supervised learning on instantaneous EEG power spectrum, showing increases in theta and alpha power with high STR values. In a second experiment, we found an increase of theta and beta power when experimentally manipulating STR in a musical improvisation imagery experiment. These results are interpreted with respect to previous research on flow state in creativity, as well as with the temporal processing literature. We suggest that a component of the subjective state of musical improvisation may be reflected in an underlying mechanism related to the subjective quantization of time. We also demonstrate the feasibility of single case studies of musical improvisation using brain activity measurements and retrospective reports, by obtaining consistent results across multiple sessions.

Graph Fourier transform of fMRI temporal signals based on an averaged structural connectome for the classification of neuroimaging.

Graph signal processing (GSP) is a framework that enables the generalization of signal processing to multivariate signals described on graphs. In this paper, we present an approach based on Graph Fourier Transform (GFT) and machine learning for the analysis of resting-state functional magnetic resonance imaging (rs-fMRI). For each subject, we use rs-fMRI time series to compute several descriptive statistics in regions of interest (ROI). Next, these measures are considered as signals on an averaged structural graph built using tractography of the white matter of the brain, defined using the same ROI. GFT of these signals is computed using the structural graph as a support, and the obtained feature vectors are subsequently benchmarked in a supervised learning setting. Further analysis suggests that GFT using structural connectivity as a graph and the standard deviation of fMRI time series as signals leads to more accurate supervised classification using a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange) when compared to several other statistical metrics. Moreover, the proposed approach outperforms several approaches, based on using functional connectomes or complex functional network measures as features for classification.

Static and dynamic aspects of cerebro-cerebellar functional connectivity are associated with self-reported measures of impulsivity: A resting-state fMRI study.

Human and animal brain studies bring converging evidence of a possible role for the cerebellum and the cerebro-cerebellar system in impulsivity. However, the precise nature of the relation between cerebro-cerebellar coupling and impulsivity is far from understood. Characterizing functional connectivity (FC) patterns between large-scale brain networks that mediate different forms of impulsivity, and the cerebellum may improve our understanding of this relation. Here, we analyzed static and dynamic features of cerebro-cerebellar FC using a highly sampled resting-state functional magnetic resonance imaging (rs-fMRI) dataset and tested their association with two widely used self-reports of impulsivity: the UPPS-P impulsive behavior scale and the behavioral inhibition/approach systems (BIS/BAS) in a large group of healthy subjects (N = 134, ≈ 1 hr of rs-fMRI/subject). We employed robust data-driven techniques to identify cerebral and cerebellar resting-state networks and extract descriptive summary measures of static and dynamic cerebro-cerebellar FC. We observed evidence linking BIS, BAS, sensation seeking, and lack of premeditation to the total strength and temporal variability of FC within networks connecting regions of the prefrontal cortex, precuneus, posterior cingulate cortex, basal ganglia, and thalamus with the cerebellum. Overall, our findings improve the existing knowledge of the neural correlates of impulsivity and the behavioral correlates of the cerebro-cerebellar system.

Born to Speak and Sing: Musical Predictors of Language Development in Pre-schoolers.

The relationship between musical and linguistic skills has received particular attention in infants and school-aged children. However, very little is known about pre-schoolers. This leaves a gap in our understanding of the concurrent development of these skills during development. Moreover, attention has been focused on the effects of formal musical training, while neglecting the influence of informal musical activities at home. To address these gaps, in Study 1, 3- and 4-year-old children (n = 40) performed novel musical tasks (perception and production) adapted for young children in order to examine the link between musical skills and the development of key language capacities, namely grammar and phonological awareness. In Study 2, we investigated the influence of informal musical experience at home on musical and linguistic skills of young pre-schoolers, using the same evaluation tools. We found systematic associations between distinct musical and linguistic skills. Rhythm perception and production were the best predictors of phonological awareness, while melody perception was the best predictor of grammar acquisition, a novel association not previously observed in developmental research. These associations could not be explained by variability in general cognitive functioning, such as verbal memory and non-verbal abilities. Thus, selective music-related auditory and motor skills are likely to underpin different aspects of language development and can be dissociated in pre-schoolers. We also found that informal musical experience at home contributes to the development of grammar. An effect of musical skills on both phonological awareness and language grammar is mediated by home musical experience. These findings pave the way for the development of dedicated musical activities for pre-schoolers to support specific areas of language development.

A functional connectome phenotyping dataset including cognitive state and personality measures.

The dataset enables exploration of higher-order cognitive faculties, self-generated mental experience, and personality features in relation to the intrinsic functional architecture of the brain. We provide multimodal magnetic resonance imaging (MRI) data and a broad set of state and trait phenotypic assessments: mind-wandering, personality traits, and cognitive abilities. Specifically, 194 healthy participants (between 20 and 75 years of age) filled out 31 questionnaires, performed 7 tasks, and reported 4 probes of in-scanner mind-wandering. The scanning session included four 15.5-min resting-state functional MRI runs using a multiband EPI sequence and a hig h-resolution structural scan using a 3D MP2RAGE sequence. This dataset constitutes one part of the MPI-Leipzig Mind-Brain-Body database.

Gait improvement via rhythmic stimulation in Parkinson's disease is linked to rhythmic skills.

Training based on rhythmic auditory stimulation (RAS) can improve gait in patients with idiopathic Parkinson's disease (IPD). Patients typically walk faster and exhibit greater stride length after RAS. However, this effect is highly variable among patients, with some exhibiting little or no response to the intervention. These individual differences may depend on patients' ability to synchronize their movements to a beat. To test this possibility, 14 IPD patients were submitted to RAS for four weeks, in which they walked to music with an embedded metronome. Before and after the training, patients' synchronization was assessed with auditory paced hand tapping and walking to auditory cues. Patients increased gait speed and stride length in non-cued gait after training. However, individual differences were apparent as some patients showed a positive response to RAS and others, either no response, or a negative response. A positive response to RAS was predicted by the synchronization performance in hand tapping and gait tasks. More severe gait impairment, low synchronization variability, and a prompt response to a stimulation change foster a positive response to RAS training. Thus, sensorimotor timing skills underpinning the synchronization of steps to an auditory cue may allow predicting the success of RAS in IPD.

Effects of musically cued gait training in Parkinson's disease: beyond a motor benefit.

Auditory stimulation via rhythmic cues can be used successfully in the rehabilitation of motor function in patients with motor disorders. A prototypical example is provided by dysfunctional gait in patients with idiopathic Parkinson's disease (PD). Coupling steps to external rhythmic cues (the beat of music or the sounds of a metronome) leads to long-term motor improvements, such as increased walking speed and greater stride length. These effects are likely to be underpinned by compensatory brain mechanisms involving cerebellar-thalamocortical networks. Because these areas are also involved in perceptual and motor timing, parallel improvement in timing tasks is expected in PD beyond purely motor benefits. In keeping with this idea, we report here recent behavioral data showing beneficial effects of musically cued gait training (MCGT) on gait performance (i.e., increased stride length and speed), perceptual timing (e.g., discriminating stimulus durations), and sensorimotor timing abilities (i.e., in paced tapping tasks) in PD patients. Particular attention is paid to individual differences in timing abilities in PD, thus paving the ground for an individualized MCGT-based therapy.