Research
|
|
|
|
Sili D., De Giorgi C., Pizzuti A., Spezialetti M., de Pasquale F. & Betti V.
ABSTRACT.
In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements supports the adaptability and flexibility of natural behavior is unknown. Through a sensorized glove, we collected kinematics data from thirty-six participants preparing and having breakfast in naturalistic conditions. By means of an unbiased analysis, we identified a repertoire of hand states. Then, we tracked their transitions over time. We found that manual behavior can be described in space through a complex organization of basic configurations. These, even in an unconstrained experiment, recurred across subjects. A specific temporal structure, highly consistent within the sample, seems to integrate such identified hand shapes to realize skilled movements. These findings suggest that the simplification of the motor commands unravels in the temporal dimension more than in the spatial one.
REF.: Sci Rep 13, 9451 (2023)
DOI: https://doi.org/10.1038/s41598-023-36280-4
In everyday activities, humans move alike to manipulate objects. Prior works suggest that hand movements are built by a limited set of basic building blocks consisting of a set of common postures. However, how the low dimensionality of hand movements supports the adaptability and flexibility of natural behavior is unknown. Through a sensorized glove, we collected kinematics data from thirty-six participants preparing and having breakfast in naturalistic conditions. By means of an unbiased analysis, we identified a repertoire of hand states. Then, we tracked their transitions over time. We found that manual behavior can be described in space through a complex organization of basic configurations. These, even in an unconstrained experiment, recurred across subjects. A specific temporal structure, highly consistent within the sample, seems to integrate such identified hand shapes to realize skilled movements. These findings suggest that the simplification of the motor commands unravels in the temporal dimension more than in the spatial one.
REF.: Sci Rep 13, 9451 (2023)
DOI: https://doi.org/10.1038/s41598-023-36280-4
|
|
Rosati G., Cisotto G., Sili D., Compagnucci L., De Giorgi C., Pavone E. F., Paccagnella A. & Betti V. (2021)
ABSTRACT.
The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93–95% for flexion and extension, respectively.
REF.: Sci Rep 11, 14938 (2021)
DOI: https://doi.org/10.1038/s41598-021-94526-5
The use of surface electromyography (sEMG) is rapidly spreading, from robotic prostheses and muscle computer interfaces to rehabilitation devices controlled by residual muscular activities. In this context, sEMG-based gesture recognition plays an enabling role in controlling prosthetics and devices in real-life settings. Our work aimed at developing a low-cost, print-and-play platform to acquire and analyse sEMG signals that can be arranged in a fully customized way, depending on the application and the users’ needs. We produced 8-channel sEMG matrices to measure the muscular activity of the forearm using innovative nanoparticle-based inks to print the sensors embedded into each matrix using a commercial inkjet printer. Then, we acquired the multi-channel sEMG data from 12 participants while repeatedly performing twelve standard finger movements (six extensions and six flexions). Our results showed that inkjet printing-based sEMG signals ensured significant similarity values across repetitions in every participant, a large enough difference between movements (dissimilarity index above 0.2), and an overall classification accuracy of 93–95% for flexion and extension, respectively.
REF.: Sci Rep 11, 14938 (2021)
DOI: https://doi.org/10.1038/s41598-021-94526-5
Grounded Cognition: nuove prospettive
Marucci M., Betti V. (2021)
ABSTRACT.
Through the use of body schema manipulation techniques and by exploiting realistic but controlled simulations, virtual reality has emerged over the past decade as a valuable tool for the empirical investigation of grounded theories of cognition. Here we discuss how the ability to map one’s actions onto an increasingly realistic avatar in real time in an artificial environment provides an ideal testbed for «embodied» theories. Not only will it be possible to conduct social interaction experiments with agents that are nearly indistinguishable from a human, but also, thanks to a graphics and physics engine, to endow artificial intelligence with a changing body and senses that are refined over time in a graphic engine. This could be useful on the one hand to test hypotheses about brain architectures, but also through evolutionary algorithms to investigate the evolution of a neural system Grou with a given body in a given environment from generation to generation.
DOI: 10.12832/102771
Through the use of body schema manipulation techniques and by exploiting realistic but controlled simulations, virtual reality has emerged over the past decade as a valuable tool for the empirical investigation of grounded theories of cognition. Here we discuss how the ability to map one’s actions onto an increasingly realistic avatar in real time in an artificial environment provides an ideal testbed for «embodied» theories. Not only will it be possible to conduct social interaction experiments with agents that are nearly indistinguishable from a human, but also, thanks to a graphics and physics engine, to endow artificial intelligence with a changing body and senses that are refined over time in a graphic engine. This could be useful on the one hand to test hypotheses about brain architectures, but also through evolutionary algorithms to investigate the evolution of a neural system Grou with a given body in a given environment from generation to generation.
DOI: 10.12832/102771
The impact of multisensory integration and perceptual load in virtual reality settings on performance, workload and presence |
|
Marucci M, Di Flumeri G, Borghini G, Sciaraffa N, Scandola M, Pavone EF, Babiloni F, Arico P., Betti V (2021)
ABSTRACT.
Real-world experience is typically multimodal. Evidence indicates that the facilitation in the detection of multisensory stimuli is modulated by the perceptual load, the amount of information involved in the processing of the stimuli. Here, we used a realistic virtual reality environment while concomitantly acquiring Electroencephalography (EEG) and Galvanic Skin Response (GSR) to investigate how multisensory signals impact target detection in two conditions, high and low perceptual load. Different multimodal stimuli (auditory and vibrotactile) were presented, alone or in combination with the visual target. Results showed that only in the high load condition, multisensory stimuli significantly improve performance, compared to visual stimulation alone. Multisensory stimulation also decreases the EEG-based workload. Instead, the perceived workload, according to the “NASA Task Load Index” questionnaire, was reduced only by the trimodal condition (i.e., visual, auditory, tactile). This trimodal stimulation was more effective in enhancing the sense of presence, that is the feeling of being in the virtual environment, compared to the bimodal or unimodal stimulation. Also, we show that in the high load task, the GSR components are higher compared to the low load condition. Finally, the multimodal stimulation (Visual-Audio-Tactile—VAT and Visual-Audio—VA) induced a significant decrease in latency, and a significant increase in the amplitude of the P300 potentials with respect to the unimodal (visual) and visual and tactile bimodal stimulation, suggesting a faster and more effective processing and detection of stimuli if auditory stimulation is included. Overall, these findings provide insights into the relationship between multisensory integration and human behavior and cognition.
REF.: Scientific Reports, 11(1), 1-15.
DOI: 10.1038/s41598-021-84196-8 (IF 3.998)
ABSTRACT.
Real-world experience is typically multimodal. Evidence indicates that the facilitation in the detection of multisensory stimuli is modulated by the perceptual load, the amount of information involved in the processing of the stimuli. Here, we used a realistic virtual reality environment while concomitantly acquiring Electroencephalography (EEG) and Galvanic Skin Response (GSR) to investigate how multisensory signals impact target detection in two conditions, high and low perceptual load. Different multimodal stimuli (auditory and vibrotactile) were presented, alone or in combination with the visual target. Results showed that only in the high load condition, multisensory stimuli significantly improve performance, compared to visual stimulation alone. Multisensory stimulation also decreases the EEG-based workload. Instead, the perceived workload, according to the “NASA Task Load Index” questionnaire, was reduced only by the trimodal condition (i.e., visual, auditory, tactile). This trimodal stimulation was more effective in enhancing the sense of presence, that is the feeling of being in the virtual environment, compared to the bimodal or unimodal stimulation. Also, we show that in the high load task, the GSR components are higher compared to the low load condition. Finally, the multimodal stimulation (Visual-Audio-Tactile—VAT and Visual-Audio—VA) induced a significant decrease in latency, and a significant increase in the amplitude of the P300 potentials with respect to the unimodal (visual) and visual and tactile bimodal stimulation, suggesting a faster and more effective processing and detection of stimuli if auditory stimulation is included. Overall, these findings provide insights into the relationship between multisensory integration and human behavior and cognition.
REF.: Scientific Reports, 11(1), 1-15.
DOI: 10.1038/s41598-021-84196-8 (IF 3.998)
Spontaneous beta band rhythms in the predictive coding of natural stimuli |
|
Betti V, Della Penna S, de Pasquale F, Corbetta M (2021)
ABSTRACT.
The regularity of the physical world and the biomechanics of the human body movements generate distributions of highly probable states that are internalized by the brain in the course of a lifetime. In Bayesian terms, the brain exploits prior knowledge, especially under conditions when sensory input is unavailable or uncertain, to predictively anticipate the most likely outcome of upcoming stimuli and movements. These internal models, formed during development, yet still malleable in adults, continuously adapt through the learning of novel stimuli and movements.
Traditionally, neural beta (β) oscillations are considered essential for maintaining sensorimotor and cognitive representations, and for temporal coding of expectations. However, recent findings show that fluctuations of β band power in the resting state strongly correlate between cortical association regions. Moreover, central (hub) regions form strong interactions over time with different brain regions/networks (dynamic core). β band centrality fluctuations of regions of the dynamic core predict global efficiency peaks suggesting a mechanism for network integration. Furthermore, this temporal architecture is surprisingly stable, both in topology and dynamics, during the observation of ecological natural visual scenes, whereas synthetic temporally scrambled stimuli modify it. We propose that spontaneous β rhythms may function as a long-term “prior” of frequent environmental stimuli and behaviors.
REF.: The Neuroscientist, 27(2), 184-201
DOI: 10.1177/1073858420928988 (IF 6.837).
Investigating the Impact of Signal-to-Noise Ratio on EEG Resting-State source reconstruction |
|
Leone F., Caporali A., Pascarella A., Perciballi C., Maddaluno O., Basti A., Belardinelli P., Marzetti L., Di Lorenzo G., Betti V.
ABSTRACT.
The impact of Signal-To-Noise Ratio (SNR) on source localization accuracy is commonly assessed in relation to task-evoked cortical activity. The evaluation of the SNR effect is more challenging in the resting-state condition, i.e., in the absence of stimulus or task, because of the signal’s low amplitude and the lack of external stimuli. In this study, we explore the impact of varying SNR values on source estimation performance (SNR LOC) of EEG resting-state activities, using Minimum Norm Estimation (MNE) and a realistic head model. We simulated synthetic resting-state EEG signals with different known SNRs for three neural networks: the Motor Network (MN), the Visual Network (VN), and the Dorsal Attention Network (DAN). The synthetic source-based signal was 1 min long and sampled at 256 Hz. We considered eight non-linear sources for the MN, six for the VN, and fourteen for the DAN. For each network, the sources presented a time delay of 15 ms [1]. In addition, fifty uncorrelated noise sources were randomly distributed over the whole cortex. Using a boundary element method (BEM) volume conduction model based on the New York head model, we obtained the inverse solution through MNE on brain Independent Components (ICs), with different regularization parameters (defined as λ proportional to 1/SNR2). The SNR of the simulated EEG signal was set equal to [1, 5, 10, 15, 20, 30, 40, 50, 75, 100]. Differently, the SNR LOC values varied across [0.1, 3, 10, 100]. The performance was assessed using three metrics: localization error, source extension, and source fragmentation. The results of all three networks indicated that the localization error decreases as the SNR LOC increases, following a 1/SNR trend. Regarding source extension, higher SNR LOC values resulted in more focal sources. Additionally, when evaluating source fragmentation using the dbscan algorithm, the number of clusters was significantly higher for an SNR LOC of 100 compared to SNR LOC values of 0.1, 3, and 10. The statistical analysis based on the repeated-measures ANOVA (α=0.05), with Bonferroni correction for post-hoc multiple comparisons, revealed that 10 for SNR loc represents a good trade-off between the three metrics, as it provides a focal source reconstruction and ensures a low localization error. In conclusion, the SNR LOC significantly impacts the spatial resolution of the source-level analysis.
CONGRESS OF THE ITALIAN SOCIETY OF APPLIED AND INDUSTRIAL MATHEMATICS (SIMAI), MATERA, ITALY AUGUST 28 - SEPTEMBER 1 2023
The impact of Signal-To-Noise Ratio (SNR) on source localization accuracy is commonly assessed in relation to task-evoked cortical activity. The evaluation of the SNR effect is more challenging in the resting-state condition, i.e., in the absence of stimulus or task, because of the signal’s low amplitude and the lack of external stimuli. In this study, we explore the impact of varying SNR values on source estimation performance (SNR LOC) of EEG resting-state activities, using Minimum Norm Estimation (MNE) and a realistic head model. We simulated synthetic resting-state EEG signals with different known SNRs for three neural networks: the Motor Network (MN), the Visual Network (VN), and the Dorsal Attention Network (DAN). The synthetic source-based signal was 1 min long and sampled at 256 Hz. We considered eight non-linear sources for the MN, six for the VN, and fourteen for the DAN. For each network, the sources presented a time delay of 15 ms [1]. In addition, fifty uncorrelated noise sources were randomly distributed over the whole cortex. Using a boundary element method (BEM) volume conduction model based on the New York head model, we obtained the inverse solution through MNE on brain Independent Components (ICs), with different regularization parameters (defined as λ proportional to 1/SNR2). The SNR of the simulated EEG signal was set equal to [1, 5, 10, 15, 20, 30, 40, 50, 75, 100]. Differently, the SNR LOC values varied across [0.1, 3, 10, 100]. The performance was assessed using three metrics: localization error, source extension, and source fragmentation. The results of all three networks indicated that the localization error decreases as the SNR LOC increases, following a 1/SNR trend. Regarding source extension, higher SNR LOC values resulted in more focal sources. Additionally, when evaluating source fragmentation using the dbscan algorithm, the number of clusters was significantly higher for an SNR LOC of 100 compared to SNR LOC values of 0.1, 3, and 10. The statistical analysis based on the repeated-measures ANOVA (α=0.05), with Bonferroni correction for post-hoc multiple comparisons, revealed that 10 for SNR loc represents a good trade-off between the three metrics, as it provides a focal source reconstruction and ensures a low localization error. In conclusion, the SNR LOC significantly impacts the spatial resolution of the source-level analysis.
CONGRESS OF THE ITALIAN SOCIETY OF APPLIED AND INDUSTRIAL MATHEMATICS (SIMAI), MATERA, ITALY AUGUST 28 - SEPTEMBER 1 2023
ENCODING DEXTERITY IN THE HUMAN BRAIN |
|
ABSTRACT.
Introduction: The hand plays a pivotal role in human beings' everyday life: we use our hands to manipulate objects andinteract with the surrounding environment. Manual skills emerge during infancy and refine during development[1], however individuals differ in their level of manual ability (i.e., manual dexterity). One feature of a proficient motor behavior is a clear pattern of flexibility in the motor schemas – that ensures a good performance –parallel with a certain degree of stability that embeds learned behaviors and motor memories. It is well knownthat training and learning processes induce changes in the brain [2-4]. These changes are evident even in thespontaneous activity [5] (i.e., brain activity that occur without any external input). An open question remains onhow interindividual motor variability is encoded in the large-scale organization of the resting brain to ensure theflexibility of motor behavior. Network segregation/integration mechanisms support behavioral performance [6].However, whether the topological organization of the brain, network modularity and nodal centrality vary withmanual dexterity levels it is still unknown.
Methods: We analyzed data from the Human Connectome Project collected from 51 participants (age range 21-35 yo)while they performed a motor task (i.e., a finger tapping) or during visual fixation. We computed theleakage-corrected band-limited power [7,8] correlation across 164 node regions - parcelled into 10 networks -in α (6.3-16.5 Hz), low β (12.5-29 Hz), and high β band (22.5-39 Hz). We then used a k-means algorithm totest, with a data driven approach, if participants have different connectivity profiles based on their level ofdexterity. We computed Louvain modularity and participation index as measure of centrality. We used the Nine-hole peg test scores as an index of finger dexterity [9].
Results: First, our results show that finger tapping decreases connectivity, but in alpha the decrease is smaller than inthe beta bands (p<0.001). Moreover, in alpha, brain topology reorganization involves fewer links, especially inthe Motor Network. Second, we demonstrate how the clustering algorithm splits participants in 2 groups withdistinct connectivity profiles: a group exhibits an overall decrease of functional connectivity (FC) aftertask performance; the other group shows a greater stability of FC between rest and task, especially in theMotor Network and in the Dorsal Attention Network, with an increase of FC in other networks. Notably, the 2groups (from now on high and low performers) differed in terms of manual dexterity. Thus, the lack of capacityof reorganization seems to be dysfunctional in terms of performance. Third, the 2 groups exhibit different segregation/integration mechanisms. Dexterous individuals exhibit higher modularity during finger tapping thanduring rest in parallel with a decrease of nodal centrality – especially in the Motor Network and ControlNetwork. Conversely, modularity decreases in low performers while nodal centrality increases. This oppositemodulation is significant in the Motor Network.
Conclusions: The individual level of skills leads to different pattern of FC reorganization. To reach an efficient behavior, wemust have a specific balance between stability/flexibility of network communication. Indeed, during the task,high performers efficiently reorganize their whole-brain topology; while low performers have a stable,dysfunctional connectivity pattern, especially in the task-evoked networks that prevent a good performance. Crucially, this scheme of flexibility/stability is observable only in the alpha band that is thus a clear marker ofmanual dexterity. Our hypothesis is that in the spontaneous activity is coded a scaffold of connectivity builtthrough experience, but this scaffold must be flexible enough to reorganize itself to enhance and supportperformance. Otherwise, a stability of the connectivity pattern turns out to be dysfunctional in terms of behavior.
29TH MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING MONTREAL, CANADA 22-26 JULY 2023 (OHBM 2023)
Introduction: The hand plays a pivotal role in human beings' everyday life: we use our hands to manipulate objects andinteract with the surrounding environment. Manual skills emerge during infancy and refine during development[1], however individuals differ in their level of manual ability (i.e., manual dexterity). One feature of a proficient motor behavior is a clear pattern of flexibility in the motor schemas – that ensures a good performance –parallel with a certain degree of stability that embeds learned behaviors and motor memories. It is well knownthat training and learning processes induce changes in the brain [2-4]. These changes are evident even in thespontaneous activity [5] (i.e., brain activity that occur without any external input). An open question remains onhow interindividual motor variability is encoded in the large-scale organization of the resting brain to ensure theflexibility of motor behavior. Network segregation/integration mechanisms support behavioral performance [6].However, whether the topological organization of the brain, network modularity and nodal centrality vary withmanual dexterity levels it is still unknown.
Methods: We analyzed data from the Human Connectome Project collected from 51 participants (age range 21-35 yo)while they performed a motor task (i.e., a finger tapping) or during visual fixation. We computed theleakage-corrected band-limited power [7,8] correlation across 164 node regions - parcelled into 10 networks -in α (6.3-16.5 Hz), low β (12.5-29 Hz), and high β band (22.5-39 Hz). We then used a k-means algorithm totest, with a data driven approach, if participants have different connectivity profiles based on their level ofdexterity. We computed Louvain modularity and participation index as measure of centrality. We used the Nine-hole peg test scores as an index of finger dexterity [9].
Results: First, our results show that finger tapping decreases connectivity, but in alpha the decrease is smaller than inthe beta bands (p<0.001). Moreover, in alpha, brain topology reorganization involves fewer links, especially inthe Motor Network. Second, we demonstrate how the clustering algorithm splits participants in 2 groups withdistinct connectivity profiles: a group exhibits an overall decrease of functional connectivity (FC) aftertask performance; the other group shows a greater stability of FC between rest and task, especially in theMotor Network and in the Dorsal Attention Network, with an increase of FC in other networks. Notably, the 2groups (from now on high and low performers) differed in terms of manual dexterity. Thus, the lack of capacityof reorganization seems to be dysfunctional in terms of performance. Third, the 2 groups exhibit different segregation/integration mechanisms. Dexterous individuals exhibit higher modularity during finger tapping thanduring rest in parallel with a decrease of nodal centrality – especially in the Motor Network and ControlNetwork. Conversely, modularity decreases in low performers while nodal centrality increases. This oppositemodulation is significant in the Motor Network.
Conclusions: The individual level of skills leads to different pattern of FC reorganization. To reach an efficient behavior, wemust have a specific balance between stability/flexibility of network communication. Indeed, during the task,high performers efficiently reorganize their whole-brain topology; while low performers have a stable,dysfunctional connectivity pattern, especially in the task-evoked networks that prevent a good performance. Crucially, this scheme of flexibility/stability is observable only in the alpha band that is thus a clear marker ofmanual dexterity. Our hypothesis is that in the spontaneous activity is coded a scaffold of connectivity builtthrough experience, but this scaffold must be flexible enough to reorganize itself to enhance and supportperformance. Otherwise, a stability of the connectivity pattern turns out to be dysfunctional in terms of behavior.
29TH MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING MONTREAL, CANADA 22-26 JULY 2023 (OHBM 2023)
Investigating the Impact of Signal-to-Noise Ratio on EEGResting-State source reconstruction |
|
Leone F., Perciballi C., Caporali A., Basti A., Belardinelli P., Di Lorenzo G., Marzetti L., Betti V.
ABSTRACT.
Introduction: Identifying sources of electroencephalography (EEG) activity is a complex problem that requires models of thehead and tissues [1,2]. The effect of Signal-To-Noise Ratio (SNR) on source localization accuracy is oftenevaluated considering the task-evoked cortical activity [3]. However, elucidating spontaneous activation of thebrain, i.e., in the absence of a stimulus or task, is not immediate as the signal is of low amplitude and theunderlying neural sources are challenging to examine [4]. In the EEG resting-state signal, the effect of SNR iscritical to be determined as prior information. Moreover, many studies have used spherical heads to investigatethe localization errors of dipoles [5]. Here, we present a simulation study to investigate the effect of differentSNR values on the performance of source estimation (SNR LOC) using the Minimum Norm Estimation (MNE)[6] and a realistic head model.
Methods: We simulated synthetic resting-state EEG signals with different known SNRs [7]. The signal was 1 min longand sampled at 256 Hz. It was generated from synthetic source time courses, using two non-linear dipolarcoupled sources located in the primary motor cortex and fifty uncorrelated noise sources randomly distributedover the whole cortex. The two non-linearly coupled sources, with quadratic nonlinearity, presented a timedelay of 15 ms [8]. Using a BEM volume conductor model based on the New York Head model [9] andimposing the EEG electrode locations, the leadfield matrix for the simulated sources was computed accordingto [10]. The source space consisted of a cortical layer of 10016 distributed points registered to a commontemplate. The SNR of the simulated EEG signal was set equal to [1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100].The simulated EEG data were analyzed using Independent Component Analysis (ICA) to remove artifacts andretain ICs of brain origin. The inverse solution was obtained using MNE on brain ICs, with differentregularization parameters (defined as λ proportional to 1/SNR2) that were used to balance the accuracy andsmoothness of the solution. The variation of the SNR LOC values [0.1, 1:10, 50, 100] influences the numericalsolution of the inverse problem in terms of the spread and position of the source reconstruction. Theperformance was evaluated using three different metrics: the localization error, the measure of the sourceextension, and the source fragmentation. The localization error was defined as the distance between theinverse solution peak and the true location of the generating source. The source extension was measured asthe Euclidean distance between the point of the source with the highest intensity and all the points of a Regionof Interest, i.e., where the inverse problem solution is higher than 80% of the solution range. To evaluate thesource fragmentation, we apply the K-means clustering with the Calinski-Harabasz criterion, to the outliers ofthe distances from the peak of the highest intensity.
Results: Fig.1A shows the localization error: this decreases as the SNR LOC increases, with a 1/SNR trend. Fig.2Bshows the distribution of median distances between the peak of the inverse solution and the true location of thegenerated source. As the SNR LOC increases, the sources become narrower. The repeated-measure ANOVA,with a four-level within-subject factor, indicates statistically significant differences (p<0.05) between thedistributions, and a post-hoc test was carried out with Bonferroni correction. Regarding the sourcefragmentation (Figure 2), the number of clusters for SNR LOC equal to 100 was significantly higher than forSNR LOC equal to 0.1, 3, and 10, which present no statistically significant differences.
Conclusions: Evaluating the effect of SNR LOC strongly influences the spatial resolution of the source-level analysis: anSNR loc value of 10 appears to be a good trade-off between the three metrics, as it provides a focused sourcereconstruction and ensures a low localization error.
29TH MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING MONTREAL, CANADA 22-26 JULY 2023 (OHBM 2023)
Introduction: Identifying sources of electroencephalography (EEG) activity is a complex problem that requires models of thehead and tissues [1,2]. The effect of Signal-To-Noise Ratio (SNR) on source localization accuracy is oftenevaluated considering the task-evoked cortical activity [3]. However, elucidating spontaneous activation of thebrain, i.e., in the absence of a stimulus or task, is not immediate as the signal is of low amplitude and theunderlying neural sources are challenging to examine [4]. In the EEG resting-state signal, the effect of SNR iscritical to be determined as prior information. Moreover, many studies have used spherical heads to investigatethe localization errors of dipoles [5]. Here, we present a simulation study to investigate the effect of differentSNR values on the performance of source estimation (SNR LOC) using the Minimum Norm Estimation (MNE)[6] and a realistic head model.
Methods: We simulated synthetic resting-state EEG signals with different known SNRs [7]. The signal was 1 min longand sampled at 256 Hz. It was generated from synthetic source time courses, using two non-linear dipolarcoupled sources located in the primary motor cortex and fifty uncorrelated noise sources randomly distributedover the whole cortex. The two non-linearly coupled sources, with quadratic nonlinearity, presented a timedelay of 15 ms [8]. Using a BEM volume conductor model based on the New York Head model [9] andimposing the EEG electrode locations, the leadfield matrix for the simulated sources was computed accordingto [10]. The source space consisted of a cortical layer of 10016 distributed points registered to a commontemplate. The SNR of the simulated EEG signal was set equal to [1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100].The simulated EEG data were analyzed using Independent Component Analysis (ICA) to remove artifacts andretain ICs of brain origin. The inverse solution was obtained using MNE on brain ICs, with differentregularization parameters (defined as λ proportional to 1/SNR2) that were used to balance the accuracy andsmoothness of the solution. The variation of the SNR LOC values [0.1, 1:10, 50, 100] influences the numericalsolution of the inverse problem in terms of the spread and position of the source reconstruction. Theperformance was evaluated using three different metrics: the localization error, the measure of the sourceextension, and the source fragmentation. The localization error was defined as the distance between theinverse solution peak and the true location of the generating source. The source extension was measured asthe Euclidean distance between the point of the source with the highest intensity and all the points of a Regionof Interest, i.e., where the inverse problem solution is higher than 80% of the solution range. To evaluate thesource fragmentation, we apply the K-means clustering with the Calinski-Harabasz criterion, to the outliers ofthe distances from the peak of the highest intensity.
Results: Fig.1A shows the localization error: this decreases as the SNR LOC increases, with a 1/SNR trend. Fig.2Bshows the distribution of median distances between the peak of the inverse solution and the true location of thegenerated source. As the SNR LOC increases, the sources become narrower. The repeated-measure ANOVA,with a four-level within-subject factor, indicates statistically significant differences (p<0.05) between thedistributions, and a post-hoc test was carried out with Bonferroni correction. Regarding the sourcefragmentation (Figure 2), the number of clusters for SNR LOC equal to 100 was significantly higher than forSNR LOC equal to 0.1, 3, and 10, which present no statistically significant differences.
Conclusions: Evaluating the effect of SNR LOC strongly influences the spatial resolution of the source-level analysis: anSNR loc value of 10 appears to be a good trade-off between the three metrics, as it provides a focused sourcereconstruction and ensures a low localization error.
29TH MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING MONTREAL, CANADA 22-26 JULY 2023 (OHBM 2023)
Spectral connectivity fingerprint in the resting human brain: a MEG study
|
|
Maddaluno O., Guidotti R., Vettoruzzo A., Marzetti L., Cisotto G., Betti V.
ABSTRACT.
Intrinsic functional connectivity reflects spontaneous neuronal fluctuations that are functionally coupled and organized in networks (the so-called resting-state networks, RSNs) in the absence of active tasks. Functional magnetic resonance imaging (fMRI) studies suggest that the coherent spontaneous activity accounts for variability in event-related BOLD responses, and it predicts inter-individual differences in brain responses during task performance. Recent evidence shows that spontaneous activity patterns are related to inter-individual differences in cognition, personality traits, and behavioral performance. In the present research, we investigated how intrinsic functional connectivity predicts the inter-individual difference in task-evoked connectivity in the alpha and beta bands, the neurophysiological correlates of RSNs. We analyzed MEG data from the Human Connectome Project collected from 51 subjects (age range 22-35 yo) during visual fixation and during a motor task (i.e., finger tapping or toe squeezing). We computed the leakage-corrected band-limited power (BLP) correlation across 164 node regions - parcelled into 10 networks - in alpha (6.3-16.5 Hz), low beta (12.5-29 Hz), and high beta band (22.5-39 Hz). Then, we used a univariate model to explore if the variance of functional connectivity can be explained in terms of the band, subject, and task. Finally, we used a leave-one-out approach to predict task-evoked connectivity patterns from the connectivity at rest. We evaluated how accurate is the model in predicting the single subject’s connectivity pattern using the identification rate (ID rate). Our results show that 1) the frequency band and the subject account for most of the explained variance (41.13% and 22.32%, respectively), while the task explains only 6.02% of the variance. 2) ID rate values are higher (above 58%) in the Motor Network (MN) and the Visual Network (VIS) in alpha and low beta bands for both tasks (i.e., finger tapping and toe squeezing). In all the other RSNs and frequency bands, the ID rate was lower than 50%. Our findings show that inter-individual variability modulates functional connectivity. Moreover, we demonstrate that from intrinsic connectivity patterns, it is possible to draw inferences about the connectivity patterns during a motor task, at the level of the single subject, in specific RSNs (MN and VIS) and frequency bands. This evidence supports the existence of a MEG connectivity fingerprint already present at rest. This fingerprint is unique for each individual and can accurately predict the connections reorganization during a motor task.
SFN MEETING 2022, San Diego, CA, USA 12-16 NOVEMBER
Intrinsic functional connectivity reflects spontaneous neuronal fluctuations that are functionally coupled and organized in networks (the so-called resting-state networks, RSNs) in the absence of active tasks. Functional magnetic resonance imaging (fMRI) studies suggest that the coherent spontaneous activity accounts for variability in event-related BOLD responses, and it predicts inter-individual differences in brain responses during task performance. Recent evidence shows that spontaneous activity patterns are related to inter-individual differences in cognition, personality traits, and behavioral performance. In the present research, we investigated how intrinsic functional connectivity predicts the inter-individual difference in task-evoked connectivity in the alpha and beta bands, the neurophysiological correlates of RSNs. We analyzed MEG data from the Human Connectome Project collected from 51 subjects (age range 22-35 yo) during visual fixation and during a motor task (i.e., finger tapping or toe squeezing). We computed the leakage-corrected band-limited power (BLP) correlation across 164 node regions - parcelled into 10 networks - in alpha (6.3-16.5 Hz), low beta (12.5-29 Hz), and high beta band (22.5-39 Hz). Then, we used a univariate model to explore if the variance of functional connectivity can be explained in terms of the band, subject, and task. Finally, we used a leave-one-out approach to predict task-evoked connectivity patterns from the connectivity at rest. We evaluated how accurate is the model in predicting the single subject’s connectivity pattern using the identification rate (ID rate). Our results show that 1) the frequency band and the subject account for most of the explained variance (41.13% and 22.32%, respectively), while the task explains only 6.02% of the variance. 2) ID rate values are higher (above 58%) in the Motor Network (MN) and the Visual Network (VIS) in alpha and low beta bands for both tasks (i.e., finger tapping and toe squeezing). In all the other RSNs and frequency bands, the ID rate was lower than 50%. Our findings show that inter-individual variability modulates functional connectivity. Moreover, we demonstrate that from intrinsic connectivity patterns, it is possible to draw inferences about the connectivity patterns during a motor task, at the level of the single subject, in specific RSNs (MN and VIS) and frequency bands. This evidence supports the existence of a MEG connectivity fingerprint already present at rest. This fingerprint is unique for each individual and can accurately predict the connections reorganization during a motor task.
SFN MEETING 2022, San Diego, CA, USA 12-16 NOVEMBER
Distinct MEG functional connectivity and topology changes distinguish between voluntary hand and foot movements |
|
Pizzuti A, Della Penna S, Spezialetti M, Corbetta M, Betti V
ABSTRACT.
Hand and foot movements are topographically represented in specific patches of the sensorimotor cortex and associated to strong reductions of beta (β) oscillations during preparation and execution in parallel to increments of β power occurring 300 to 1000 ms, after their end, as evident through Magneto- and Electro-Encephalography (M/EEG) (Cheyne, 2013). In the last two decades, studies of functional connectivity have revealed that brain regions that process behaviorally relevant visual stimuli or motor tasks are functionally coupled, i.e., continue to communicate, even at rest (i.e., in absence of any stimulus, response or task) (Deco & Corbetta, 2011). The topography of these resting state networks (RSNs) is resilient despite significant perturbations of connectivity strength and topology induced by cognitive paradigms (e.g., Cole et al., 2014). Whereas significant knowledge exists about local changes produced by voluntary movements, a fundamental issue is to understand how hand and foot movements modulate the large-scale organization of the resting brain to support the flexibility of motor behavior.
26TH ANNUAL MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING (OHBM 2020)
ABSTRACT.
Hand and foot movements are topographically represented in specific patches of the sensorimotor cortex and associated to strong reductions of beta (β) oscillations during preparation and execution in parallel to increments of β power occurring 300 to 1000 ms, after their end, as evident through Magneto- and Electro-Encephalography (M/EEG) (Cheyne, 2013). In the last two decades, studies of functional connectivity have revealed that brain regions that process behaviorally relevant visual stimuli or motor tasks are functionally coupled, i.e., continue to communicate, even at rest (i.e., in absence of any stimulus, response or task) (Deco & Corbetta, 2011). The topography of these resting state networks (RSNs) is resilient despite significant perturbations of connectivity strength and topology induced by cognitive paradigms (e.g., Cole et al., 2014). Whereas significant knowledge exists about local changes produced by voluntary movements, a fundamental issue is to understand how hand and foot movements modulate the large-scale organization of the resting brain to support the flexibility of motor behavior.
26TH ANNUAL MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING (OHBM 2020)
On the contribution of visual statistics to changes of MEG functional connectivity patterns |
|
Chiara De Giorgi , Sara Petrichella , Paolo Papale , Andrea Leo , Emilano Ricciardi , Pietro Pietrini , Maurizio
Corbetta , Stefania Della Penna , Viviana Betti
ABSTRACT.
Real-world events unfold at different spatial and temporal scales. Neuroscientic studies suggest that cognitive and neural processes likewise occur at different spatial and temporal scales. Neurons along the visual cortical pathways show increasingly larger spatial receptive fields and an analogous hierarchy has been suggested for the temporal responses: early visual cortex is sensitive to short temporal patterns while higher-order visual regions prefer longer temporal stimuli. Recent MEG studies showed that, as compared to the state of rest, viewing natural scenes (e.g., movies) not only affects the local processing within the visual system, but also reorganizes the overall topology, the dynamics of functional connectivity (FC) and node centrality, at neural networks level, more strongly in alpha (α) than in beta (β) band. We hypothesize that the statistics of visual stimuli play a role in shifting task-related FC patterns in space, time and frequency domains predicting a hierarchical relationship: low-level visual features impact early sensory regions, and high-level information changes high-order control regions.
25TH ANNUAL MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING (OHBM 2019)
Corbetta , Stefania Della Penna , Viviana Betti
ABSTRACT.
Real-world events unfold at different spatial and temporal scales. Neuroscientic studies suggest that cognitive and neural processes likewise occur at different spatial and temporal scales. Neurons along the visual cortical pathways show increasingly larger spatial receptive fields and an analogous hierarchy has been suggested for the temporal responses: early visual cortex is sensitive to short temporal patterns while higher-order visual regions prefer longer temporal stimuli. Recent MEG studies showed that, as compared to the state of rest, viewing natural scenes (e.g., movies) not only affects the local processing within the visual system, but also reorganizes the overall topology, the dynamics of functional connectivity (FC) and node centrality, at neural networks level, more strongly in alpha (α) than in beta (β) band. We hypothesize that the statistics of visual stimuli play a role in shifting task-related FC patterns in space, time and frequency domains predicting a hierarchical relationship: low-level visual features impact early sensory regions, and high-level information changes high-order control regions.
25TH ANNUAL MEETING OF THE ORGANIZATION FOR HUMAN BRAIN MAPPING (OHBM 2019)
How Signal-to-Noise Ratio affects source reconstruction with Minimum Norm: the case of high-density EEG resting state |
|
Pizzuti A., Basti A., Maddaluno O., Belardinelli P., Marzetti L., Di Lorenzo G., Betti V.
Awarded as best oral presentation
ABSTRACT.
High-density EEG (HD-EEG) is a powerful tool to study the neurophysiological correlates of cognitive brain functions. Contrary to its temporal resolution, achieving a high spatial resolution is still challenging due to the influence of several factors. For Minimum Norm Estimation (MNE) method, when the exploited norm corresponds to the Euclidean norm, the regularization parameter (λ) is critical in determining a balance between the accuracy and the smoothness of the solution. λ is usually estimated through its inverse relation with the square of the signal-to-noise ratio (SNR). Here, we investigate the impact of SNR on source estimation performance through MNE.
XXVIII edizione congresso della Società Italiana di Psicofisiologia e Neuroscienze Cognitive
Awarded as best oral presentation
ABSTRACT.
High-density EEG (HD-EEG) is a powerful tool to study the neurophysiological correlates of cognitive brain functions. Contrary to its temporal resolution, achieving a high spatial resolution is still challenging due to the influence of several factors. For Minimum Norm Estimation (MNE) method, when the exploited norm corresponds to the Euclidean norm, the regularization parameter (λ) is critical in determining a balance between the accuracy and the smoothness of the solution. λ is usually estimated through its inverse relation with the square of the signal-to-noise ratio (SNR). Here, we investigate the impact of SNR on source estimation performance through MNE.
XXVIII edizione congresso della Società Italiana di Psicofisiologia e Neuroscienze Cognitive
Representation of hand shape in the human resting-state activity |
|
El Rassi Y., Handjaras G., Leo A., Papale P., Corbetta M., Ricciardi E., Betti V.
ABSTRACT.
At rest, regions of similar functionality show spontaneous slow-frequency fluctuations that are temporally coherent. Recent studies, mainly in visual areas, suggest that these functional topographies at rest retain striking similarities with the patterns elicited by specific tasks, suggesting that ongoing activity encodes behaviorally relevant information, in the form of internal representations or prior expectations. Here we test whether a similar mechanism exists for the human hand area in the somatomotor cortex by measuring the coherence between topographies obtained at rest and during a visual task comprising pictures of the hand shape at decreasing levels of animacy. We expect a coherent internal representation strongest for the stimuli of natural hands, specifically in the cortical area devoted to hand representation.
XXVIII edizione congresso della Società Italiana di Psicofisiologia e Neuroscienze Cognitive
ABSTRACT.
At rest, regions of similar functionality show spontaneous slow-frequency fluctuations that are temporally coherent. Recent studies, mainly in visual areas, suggest that these functional topographies at rest retain striking similarities with the patterns elicited by specific tasks, suggesting that ongoing activity encodes behaviorally relevant information, in the form of internal representations or prior expectations. Here we test whether a similar mechanism exists for the human hand area in the somatomotor cortex by measuring the coherence between topographies obtained at rest and during a visual task comprising pictures of the hand shape at decreasing levels of animacy. We expect a coherent internal representation strongest for the stimuli of natural hands, specifically in the cortical area devoted to hand representation.
XXVIII edizione congresso della Società Italiana di Psicofisiologia e Neuroscienze Cognitive
papers IN preparation
Stability and flexibility of the intrinsic network connectivity associated with manual dexterity: a MEG study
Representation of the hand in human resting-state activity
States and transitions of everyday manual behavior
On the contribution of visual statistics to changes of MEG functional connectivity patterns
Follow us on social networks:
