Temporal modes of hub synchronization at rest

Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity

The human hand possesses both consolidated motor skills and remarkable flexibility in adapting to ongoing task demands. However, the underlying mechanisms by which the brain balances stability and flexibility remain unknown. In the absence of external input or behavior, spontaneous (intrinsic) brain connectivity is thought to represent a prior of stored memories. In this study, we investigated how manual dexterity modulates spontaneous functional connectivity in the motor cortex during hand movement. Using magnetoencephalography, in 47 human participants (both sexes), we examined connectivity modulations in the α and β frequency bands at rest and during two motor tasks (i.e., finger tapping or toe squeezing). The flexibility and stability of such modulations allowed us to identify two groups of participants with different levels of performance (high and low performers) on the nine-hole peg test, a test of manual dexterity. In the α band, participants with higher manual dexterity showed distributed decreases of connectivity, specifically in the motor cortex, increased segregation, and reduced nodal centrality. Participants with lower manual dexterity showed an opposite pattern. Notably, these patterns from the brain to behavior are mirrored by results from behavior to the brain. Indeed, when participants were divided using the median split of the dexterity score, we found the same connectivity patterns. In summary, this experiment shows that a long-term motor skill-manual dexterity-influences the way the motor systems respond during movements.

Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas

The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.

A recurrent Hopfield network for estimating meso-scale effective connectivity in MEG

The architecture of communication within the brain, represented by the human connectome, has gained a paramount role in the neuroscience community. Several features of this communication, e.g., the frequency content, spatial topology, and temporal dynamics are currently well established. However, identifying generative models providing the underlying patterns of inhibition/excitation is very challenging. To address this issue, we present a novel generative model to estimate large-scale effective connectivity from MEG. The dynamic evolution of this model is determined by a recurrent Hopfield neural network with asymmetric connections, and thus denoted Recurrent Hopfield Mass Model (RHoMM). Since RHoMM must be applied to binary neurons, it is suitable for analyzing Band Limited Power (BLP) dynamics following a binarization process. We trained RHoMM to predict the MEG dynamics through a gradient descent minimization and we validated it in two steps. First, we showed a significant agreement between the similarity of the effective connectivity patterns and that of the interregional BLP correlation, demonstrating RHoMM's ability to capture individual variability of BLP dynamics. Second, we showed that the simulated BLP correlation connectomes, obtained from RHoMM evolutions of BLP, preserved some important topological features, e.g, the centrality of the real data, assuring the reliability of RHoMM. Compared to other biophysical models, RHoMM is based on recurrent Hopfield neural networks, thus, it has the advantage of being data-driven, less demanding in terms of hyperparameters and scalable to encompass large-scale system interactions. These features are promising for investigating the dynamics of inhibition/excitation at different spatial scales.

Dynamic brain states in spatial neglect after stroke

Previous studies indicated that spatial neglect is characterized by widespread alteration of resting-state functional connectivity and changes in the functional topology of large-scale brain systems. However, whether such network modulations exhibit temporal fluctuations related to spatial neglect is still largely unknown. This study investigated the association between brain states and spatial neglect after the onset of focal brain lesions. A cohort of right-hemisphere stroke patients (n = 20) underwent neuropsychological assessment of neglect as well as structural and resting-state functional MRI sessions within 2 weeks from stroke onset. Brain states were identified using dynamic functional connectivity as estimated by the sliding window approach followed by clustering of seven resting state networks. The networks included visual, dorsal attention, sensorimotor, cingulo-opercular, language, fronto-parietal, and default mode networks. The analyses on the whole cohort of patients, i.e., with and without neglect, identified two distinct brain states characterized by different degrees of brain modularity and system segregation. Compared to non-neglect patients, neglect subjects spent more time in less modular and segregated state characterized by weak intra-network coupling and sparse inter-network interactions. By contrast, patients without neglect dwelt mainly in more modular and segregated states, which displayed robust intra-network connectivity and anti-correlations among task-positive and task-negative systems. Notably, correlational analyses indicated that patients exhibiting more severe neglect spent more time and dwelt more often in the state featuring low brain modularity and system segregation and vice versa. Furthermore, separate analyses on neglect vs. non-neglect patients yielded two distinct brain states for each sub-cohort. A state featuring widespread strong connections within and between networks and low modularity and system segregation was detected only in the neglect group. Such a connectivity profile blurred the distinction among functional systems. Finally, a state exhibiting a clear separation among modules with strong positive intra-network and negative inter-network connectivity was found only in the non-neglect group. Overall, our results indicate that stroke yielding spatial attention deficits affects the time-varying properties of functional interactions among large-scale networks. These findings provide further insights into the pathophysiology of spatial neglect and its treatment.

Analyzing the sensitivity of quantitative 3D MRI of longitudinal relaxation at very low field in Gd-doped phantoms

PURPOSE

Recently, new MRI systems working at magnetic field below 10 mT (Very and Ultra Low Field regime) have been developed, showing improved T1-contrast in projected 2D maps (i.e. images without slice selection). Moving from projected 2D to 3D maps is not trivial due to the low SNR of such devices. This work aimed to demonstrate the ability and the sensitivity of a VLF-MRI scanner operating at 8.9 mT in quantitatively obtaining 3D longitudinal relaxation rate (R1) maps and distinguishing between voxels intensities. We used phantoms consisting of vessels doped with different Gadolinium (Gd)-based Contrast Agent (CA) concentrations, providing a set of various R1 values. As CA, we used a commercial compound (MultiHance®, gadobenate dimeglumine) routinely used in clinical MRI.

METHODS

3D R1 maps and T1-weighted MR images were analysed to identify each vessel. R1 maps were further processed by an automatic clustering analysis to evaluate the sensitivity at the single-voxel level. Results obtained at 8.9 mT were compared with commercial scanners operating at 0.2 T, 1.5 T, and 3 T.

RESULTS

VLF R1 maps offered a higher sensitivity in distinguishing the different CA concentrations and an improved contrast compared to higher fields. Moreover, the high sensitivity of 3D quantitative VLF-MRI allowed an effective clustering of the 3D map values, assessing their reliability at the single voxel level. Conversely, in all fields, T1-weighted images were less reliable, even at higher CA concentrations.

CONCLUSION

In summary, with few excitations and an isotropic voxel size of 3 mm, VLF-MRI 3D quantitative mapping showed a sensitivity better than 2.7 s-1 corresponding to a concentration difference of 0.17 mM of MultiHance in copper sulfate doped water, and improved contrast compared to higher fields. Based on these results, future studies should characterize R1 contrast at VLF, also with other CA, in the living tissues.

Alpha rhythm modulations in the intraparietal sulcus reflect decision signals during item recognition

Theoretical work and empirical observations suggest a contribution of regions along the intraparietal sulcus to the process of evidence accumulation during episodic memory retrieval. In the present study, we recorded magnetoencephalographic signals in a group of healthy human participants to test whether the pattern of oscillatory modulations in the lateral parietal lobe is consistent with the mnemonic accumulator hypothesis. To this aim, the dynamic properties and the spatial distribution of MEG oscillatory power modulations were investigated during an item recognition task in which the amount of evidence for old vs. new memory decisions was manipulated across three levels. A data-driven approach was employed to identify brain nodes where oscillatory activity was sensitive to both retrieval success and the amount of evidence for old decisions. The analysis identified three nodes in the left lateral parietal lobe where the event-related desynchronization (ERD) in the alpha frequency band showed both effects. Further analyses revealed that the alpha ERD in the intraparietal sulcus, but not in other parietal nodes: i. showed modulation of duration in response to the amount of evidence for both old and new decisions, ii. was behaviorally significant, and iii. more accurately tracked the subjective memory judgment rather than the objective memory status. The present findings provide support for a recent anatomical-functional model of the parietal involvement in episodic memory retrieval and suggest that the alpha ERD in the intraparietal sulcus might represent a neural signature of the evidence accumulation process during simple memory-based decisions.

Frontal and parietal background connectivity and their dynamic changes account for individual differences in the multisensory representation of peripersonal space

Functional connectivity (FC) of brain networks dynamically fluctuates during both rest and task execution. Individual differences in dynamic FC have been associated with several cognitive and behavioral traits. However, whether dynamic FC also contributes to sensorimotor representations guiding body-environment interactions, such as the representation of peripersonal space (PPS), is currently unknown. PPS is the space immediately surrounding the body and acts as a multisensory interface between the individual and the environment. We used an audio-tactile task with approaching sounds to map the individual PPS extension, and fMRI to estimate the background FC. Specifically, we analyzed FC values for each stimulus type (near and far space) and its across-trial variability. FC was evaluated between task-relevant nodes of two fronto-parietal networks (the Dorsal Attention Network, DAN, and the Fronto-Parietal Network, FPN) and a key PPS region in the premotor cortex (PM). PM was significantly connected to specific task-relevant nodes of the DAN and the FPN during the audio-tactile task, and FC was stronger while processing near space, as compared to far space. At the individual level, less PPS extension was associated with stronger premotor-parietal FC during processing of near space, while the across-trial variability of premotor-parietal and premotor-frontal FC was higher during the processing of far space. Notably, only across-trial FC variability captured the near-far modulation of space processing. Our findings indicate that PM connectivity with task-relevant frontal and parietal regions and its dynamic changes participate in the mechanisms that enable PPS representation, in agreement with the idea that neural variability plays a crucial role in plastic and dynamic sensorimotor representations.