Connectivity between the cerebrum and cerebellum during social and non-social sequencing using dynamic causal modelling

This analysis explores the effective connectivity of the cerebellum with the cerebral cortex during the generation of correct sequences of social and non-social events, using dynamic causal modelling (DCM). Our hypothesis is that during human evolution, the cerebellum’s function evolved from a mere coordinator of fluent sequences of motions and actions, to an interpreter of action sequences without overt movements that are important for social understanding. This requires efficient neural communication between the cerebellum and cerebral cortex. In a functional magnetic resonance imaging (fMRI) study, participants generated the correct chronological order of (non-)social events, including stories involving mechanical and social scripts, and true or false beliefs. Across all stories, a DCM analysis of these data revealed, as predicted, bidirectional (closed-loop) connections linking the bilateral posterior cerebellum with the bilateral temporo-parietal junction (TPJ) associated with behavior understanding, and this connectivity pattern was almost entirely significant. There was also a unidirectional connection from the right posterior cerebellum to the precuneus, but no direct connections with the dorsomedial prefrontal cortex (dmPFC). Moreover, all connections emanating from the bilateral posterior cerebellum were negative, indicative of some kind of error signal. Within the cerebral cortex, there were unidirectional connections from the bilateral TPJ to the dmPFC, as well as bidirectional connections between the precuneus and dmPFC, and between the bilateral TPJ. These results confirm that the effective connectivity between the posterior cerebellum and mentalizing areas in the cerebral cortex play a critical role in the understanding and construction of the correct order of social and non-social action sequences

The role of the cerebellum in social and non-social action sequences : a preliminary LF-rTMS study

An increasing number of studies demonstrated the involvement of the cerebellum in (social) sequence processing. The current preliminary study is the first to investigate the causal involvement of the cerebellum in sequence generation, using low-frequency repetitive transcranial magnetic stimulation (LF-rTMS). By targeting the posterior cerebellum, we hypothesized that the induced neuro-excitability modulation would lead to altered performance on a Picture and Story sequencing task, which involve the generation of the correct chronological order of various social and non-social stories depicted in cartoons or sentences. Our results indicate that participants receiving LF-rTMS over the cerebellum, as compared to sham participants, showed a stronger learning effect from pre to post stimulation for both tasks and for all types of sequences (i.e. mechanical, social scripts, false belief, true belief). No differences between sequence types were observed. Our results suggest a positive effect of LF-rTMS on sequence generation. We conclude that the cerebellum is causally involved in the generation of sequences of social and nonsocial events. Our discussion focuses on recommendations for future studies

Consensus Paper: Cerebellum and Social Cognition.

The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social "mirroring") which is very close to its original role in motor learning, as well as in social understanding of other individuals' mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social "mentalizing"). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions

DEVELOPMENT OF A CEREBELLAR MEAN FIELD MODEL: THE THEORETICAL FRAMEWORK, THE IMPLEMENTATION AND THE FIRST APPLICATION

Brain modeling constantly evolves to improve the accuracy of the simulated brain dynamics with the ambitious aim to build a digital twin of the brain. Specific models tuned on brain regions specific features empower the brain simulations introducing bottom-up physiology properties into data-driven simulators. Despite the cerebellum contains 80 % of the neurons and is deeply involved in a wide range of functions, from sensorimotor to cognitive ones, a specific cerebellar model is still missing. Furthermore, its quasi-crystalline multi-layer circuitry deeply differs from the cerebral cortical one, therefore is hard to imagine a unique general model suitable for the realistic simulation of both cerebellar and cerebral cortex. The present thesis tackles the challenge of developing a specific model for the cerebellum. Specifically, multi-neuron multi-layer mean field (MF) model of the cerebellar network, including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells, was implemented, and validated against experimental data and the corresponding spiking neural network microcircuit model. The cerebellar MF model was built using a system of interdependent equations, where the single neuronal populations and topological parameters were captured by neuron-specific inter- dependent Transfer Functions. The model time resolution was optimized using Local Field Potentials recorded experimentally with high-density multielectrode array from acute mouse cerebellar slices. The present MF model satisfactorily captured the average discharge of different microcircuit neuronal populations in response to various input patterns and was able to predict the changes in Purkinje Cells firing patterns occurring in specific behavioral conditions: cortical plasticity mapping, which drives learning in associative tasks, and Molecular Layer Interneurons feed-forward inhibition, which controls Purkinje Cells activity patterns. The cerebellar multi-layer MF model thus provides a computationally efficient tool that will allow to investigate the causal relationship between microscopic neuronal properties and ensemble brain activity in health and pathological conditions. Furthermore, preliminary attempts to simulate a pathological cerebellum were done in the perspective of introducing our multi-layer cerebellar MF model in whole-brain simulators to realize patient-specific treatments, moving ahead towards personalized medicine. Two preliminary works assessed the relevant impact of the cerebellum on whole-brain dynamics and its role in modulating complex responses in causal connected cerebral regions, confirming that a specific model is required to further investigate the cerebellum-on- cerebrum influence. The framework presented in this thesis allows to develop a multi-layer MF model depicting the features of a specific brain region (e.g., cerebellum, basal ganglia), in order to define a general strategy to build up a pool of biology grounded MF models for computationally feasible simulations. Interconnected bottom-up MF models integrated in large-scale simulators would capture specific features of different brain regions, while the applications of a virtual brain would have a substantial impact on the reality ranging from the characterization of neurobiological processes, subject-specific preoperative plans, and development of neuro-prosthetic devices

Right Neural Substrates of Language and Music Processing Left Out: Activation Likelihood Estimation (ALE) and Meta-Analytic Connectivity Modelling (MACM)

Introduction: Language and music processing have been investigated in neuro-based research for over a century. However, consensus of independent and shared neural substrates among the domains remains elusive due to varying neuroimaging methodologies. Identifying functional connectivity in language and music processing via neuroimaging meta-analytic methods provides neuroscientific knowledge of higher cognitive domains and normative models may guide treatment development in communication disorders based on principles of neural plasticity. Methods: Using BrainMap software and tools, the present coordinate-based meta-analysis analyzed 65 fMRI studies investigating language and music processing in healthy adult subjects. We conducted activation likelihood estimates (ALE) in language processing, music processing, and language + music (Omnibus) processing. Omnibus ALE clusters were used to elucidate functional connectivity by use of meta-analytic connectivity modelling (MACM). Paradigm Class and Behavioral Domain analyses were completed for the ten identified nodes to aid functional MACM interpretation. Results: The Omnibus ALE revealed ten peak activation clusters (bilateral inferior frontal gyri, left medial frontal gyrus, right superior temporal gyrus, left transverse temporal gyrus, bilateral claustrum, left superior parietal lobule, right precentral gyrus, and right anterior culmen). MACM demonstrates an interconnected network consisting of unidirectional and bidirectional connectivity. Subsequent analyses demonstrated nodal involvement across 44 BrainMap paradigms and 32 BrainMap domains. Discussion: These findings demonstrate functional connectivity among Omnibus areas of activation in language and music processing. We analyze ALE and MACM outcomes by comparing them to previously observed roles in cognitive processing and functional network connectivity. Finally, we discuss the importance of translational neuroimaging and need for normative models guiding intervention

Vocal Imitation in Sensorimotor Learning Models: a Comparative Review

International audienceSensorimotor learning represents a challenging problem for natural and artificial systems. Several computational models have been proposed to explain the neural and cognitive mechanisms at play in the brain. In general, these models can be decomposed in three common components: a sensory system, a motor control device and a learning framework. The latter includes the architecture, the learning rule or optimisation method, and the exploration strategy used to guide learning. In this review, we focus on imitative vocal learning, that is exemplified in song learning in birds and speech acquisition in humans. We aim to synthesise, analyse and compare the various models of vocal learning that have been proposed, highlighting their common points and differences. We first introduce the biological context, including the behavioural and physiological hallmarks of vocal learning and sketch the neural circuits involved. Then, we detail the different components of a vocal learning model and how they are implemented in the reviewed models

The Quantitative Genetics of Neurodevelopment: A Magnetic Resonance Imaging Study of Childhood and Adolescence

Understanding the causes of individual differences in brain structure may give clues about the etiology of cognition, personality, and psychopathology, and also may identify endophenotypes for molecular genetic studies on brain development. We performed a comprehensive statistical genetic study of anatomic neuroimaging data from a large pediatric sample (N=600+) of twins and family members from the Child Psychiatry Branch at the NIMH. These analyses included variance decomposition of structural volumetric endophenotypes at several levels of resolution, voxel-level analysis of cortical thickness, assessment of gene by age interaction, several multivariate genetic analyses, and a search for genetically-mediated brain-behavioral relationships. These analyses found strong evidence for a genetic role in the generation of individual differences in brain volumes, with the exception of the cerebellum and the lateral ventricles. Subsequent multivariate analyses demonstrated that most of the genetic variance in large volumes shares a common source. More subtle analyses suggest that although this global genetic factor is the principal determinant of neuroanatomic variability, genetic factors also mediate regional variability in cortical thickness and are different for gray and white matter volumes. Models using graph theory show that brain structure follows small-world architectural rules, and that these relationships are genetically-determined. Structural homologues appeared to be strongly related genetically, which was further confirmed using novel methods for semi-multivariate quantitative genetic analysis at the voxel level. Studies on interactions with age were mixed. We found evidence of gene by age interaction on frontal and temporal lobar volumes, indicating that the role of genetic factors on these structures is dynamic during childhood. Analyses on cortical thickness at a finer scale, however, showed that environmental factors are more important in childhood, and environmental changes were responsible for most of the changes in heritability over this age range. When assessing the relationship between brain and behavior, we found weak negative genetic correlations and positive environmental correlations between IQ and cortical thickness, which appear to partially cancel each other out. More complex models allowing for age interactions suggest that high and low IQ groups have different patterns of gene by age interactions in concordance with prior literature on cortical phenotypes

Mapping the time-varying functional brain networks in response to naturalistic movie stimuli

One of human brain’s remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain’s dynamic changes during the processing of visual and auditory stimuli

Brain Functional and Structural Networks Underpinning Musical Creativity

Musical improvisation is one of the most complex forms of creative behavior, which offers a realistic task paradigm for the investigation of real-time creativity. Despite previous studies on the topics of musical improvisation, brain activations, and creativity, the main questions about the neural mechanisms for musical improvisation in efforts to unlocking the mystery of human creativity remain unanswered. What are the brain regions that are activated during the improvised performances of music? How do these brain areas coordinate activity among themselves and others during such performances? Whether and how does the brain connectivity structure encapsulate such creative skills? In attempts to contribute to answering these questions, this dissertation examines the brain activity dynamics during musical improvisation, explores white matter fiber architecture in advanced jazz improvisers using functional and structural magnetic resonance imaging (MRI) techniques. A group of advanced jazz musicians underwent functional and structural magnetic resonance brain imaging. While the functional MRI (fMRI) of their brains were collected, these expert improvisers performed vocalization and imagery improvisation and pre-learned melody tasks. The activation and connectivity analysis of the fMRI data showed that musical improvisation is characterized by higher brain activity with less functional connectivity compared to pre-learned melody in the brain network consisting of the dorsolateral prefrontal cortex (dlPFC), supplementary motor area (SMA), lateral premotor cortex (lPMC), Cerebellum (Cb) and Broca’s Area (BCA). SMA received a dominant causal information flow from dlPFC during improvisation and prelearned melody tasks. The deterministic fiber tractography analysis also revealed that the underlying white matter structure and fiber pathways in advanced jazz improvisers were enhanced in advanced jazz improvisers compared to the control group of nonmusicians, specifically the dlPFC - SMA network. These results point to the notion that an expert\u27s performance under real-time constraints is an internally directed behavior controlled primarily by a specific brain network, that has enhanced task-supportive structural connectivity. Overall, these findings suggest that a creative act of an expert is functionally controlled by a specific cortical network as in any internally directed attention and is encapsulated by the long-timescale brain structural network changes in support of the related cognitive underpinnings