Maurizio Corbetta

YNIMG Journal 2026 Journal Article

Slow wave generation and propagation in a model of brain lesions

• Gianluca Gaglioti
• Leonardo Dalla Porta
• Michele Angelo Colombo
• Simone Russo
• Thierry Nieus
• Gustavo Deco
• Maurizio Corbetta
• Simone Sarasso

Slow waves (SWs), the hallmark of non-rapid eye movement (NREM) sleep, reflect the periodic occurrence of transient silent periods in cortical neurons (Down states). During NREM, SWs and Down states physiologically disrupt large-scale network interactions. Since early EEG studies, SWs have also been observed in awake patients after brain injury. Emerging evidence indicates that these intrusions of sleep-like activity interfere with ongoing network activity and contribute to motor and cognitive deficits; yet, the mechanisms governing the generation and spread of post-lesional SWs remain unclear. Here, we extend a neural mass model of EEG to capture transitions between wake-like and sleep-like dynamics and embed it in connectome-based networks with virtual lesions. This model supports that local disfacilitation, topology-dependent propagation, and synchrony-dependent amplification throughout the connectome are sufficient to produce post-lesional SWs. These mechanisms reproduce the spatial gradients of post-lesional SWs previously reported in patient studies, and identify actionable targets for neuromodulation and rehabilitation.

YNIMG Journal 2025 Journal Article

How spontaneous brain activity encodes the observation of grasping movements

• Cristina Perciballi
• Lorenzo Pini
• Daniele Sili
• Yara El Rassi
• Lu Zhang
• Giacomo Handjaras
• Federico Giove
• Emiliano Ricciardi

Spontaneous brain activity forms correlated networks resembling task-evoked activation patterns, yet its functional relevance remains debated. The representational hypothesis suggests that resting-state networks (RSNs) encode frequent behaviors, but whether these representations are motor-based or cognitive is unclear. Here, we used fMRI to examine RSNs activity during the observation of reach-to-grasp movements with either regular (common) or perturbed (uncommon) kinematics. We found that the dorsal attention network (DAN) exhibited greater similarity between rest and task patterns for common movements, whereas sensory networks showed no significant effects. While DAN is classically associated with attention mechanisms, these results suggest that it may also contribute to tracking the location or motion of the hand. Furthermore, uncommon movements elicited stronger activation in parietal and premotor areas, likely reflecting adaptive updating of internal models. Our findings support the role of spontaneous brain activity in maintaining cognitive representations of frequent behaviors, optimizing motor planning and perception.

YNICL Journal 2023 Journal Article

A common low dimensional structure of cognitive impairment in stroke and brain tumors

• Silvia Facchini
• Chiara Favaretto
• Marco Castellaro
• Andrea Zangrossi
• Margherita Zannin
• Antonio Luigi Bisogno
• Valentina Baro
• Maria Giulia Anglani

INTRODUCTION: Neuropsychological studies infer brain-behavior relationships from focal lesions like stroke and tumors. However, these pathologies impair brain function through different mechanisms even when they occur at the same brain's location. The aim of this study was to compare the profile of cognitive impairment in patients with brain tumors vs. stroke and examine the correlation with lesion location in each pathology. METHODS: Patients with first time stroke (n = 77) or newly diagnosed brain tumors (n = 76) were assessed with a neuropsychological battery. Their lesions were mapped with MRI scans. Test scores were analyzed using principal component analysis (PCA) to measure their correlation, and logistic regression to examine differences between pathologies. Next, with ridge regression we examined whether lesion features (location, volume) were associated with behavioral performance. RESULTS: The PCA showed a similar cognitive impairment profile in tumors and strokes with three principal components (PCs) accounting for about half of the individual variance. PC1 loaded on language, verbal memory, and executive/working memory; PC2 loaded on general performance, visuo-spatial attention and memory, and executive functions; and, PC3 loaded on calculation, reading and visuo-spatial attention. The average lesion distribution was different, and lesion location was correlated with cognitive deficits only in stroke. Logistic regression found language and calculation more affected in stroke, and verbal memory and verbal fluency more affected in tumors. CONCLUSIONS: A similar low dimensional set of behavioral impairments was found both in stroke and brain tumors, even though each pathology caused some specific deficits in different domains. The lesion distribution was different for stroke and tumors and correlated with behavioral impairment only in stroke.

YNICL Journal 2022 Journal Article

Diffusion-based microstructure models in brain tumours: Fitting in presence of a model-microstructure mismatch

• Umberto Villani
• Erica Silvestri
• Marco Castellaro
• Simona Schiavi
• Mariagiulia Anglani
• Silvia Facchini
• Elena Monai
• Domenico D'Avella

Diffusion-based biophysical models have been used in several recent works to study the microenvironment of brain tumours. While the pathophysiological interpretation of the parameters of these models remains unclear, their use as signal representations may yield useful biomarkers for monitoring the treatment and the progression of this complex and heterogeneous disease. Up to now, however, no study was devoted to assessing the mathematical stability of these approaches in cancerous brain regions. To this end, we analyzed in 11 brain tumour patients the fitting results of two microstructure models (Neurite Orientation Dispersion and Density Imaging and the Spherical Mean Technique) and of a signal representation (Diffusion Kurtosis Imaging) to compare the reliability of their parameter estimates in the healthy brain and in the tumoral lesion. The framework of our between-tissue analysis included the computation of 1) the residual sum of squares as a goodness-of-fit measure 2) the standard deviation of the models' derived metrics and 3) models' sensitivity functions to analyze the suitability of the employed protocol for parameter estimation in the different microenvironments. Our results revealed no issues concerning the fitting of the models in the tumoral lesion, with similar goodness of fit and parameter precisions occurring in normal appearing and pathological tissues. Lastly, with the aim of highlight possible biomarkers, in our analysis we briefly discuss the correlation between the metrics of the three techniques, identifying groups of indices which are significantly collinear in all tissues and thus provide no additional information when jointly used in data-driven analyses.

YNICL Journal 2022 Journal Article

Edge-centric analysis of stroke patients: An alternative approach for biomarkers of lesion recovery

• Sebastian Idesis
• Joshua Faskowitz
• Richard F. Betzel
• Maurizio Corbetta
• Olaf Sporns
• Gustavo Deco

Most neuroimaging studies of post-stroke recovery rely on analyses derived from standard node-centric functional connectivity to map the distributed effects in stroke patients. Here, given the importance of nonlocal and diffuse damage, we use an edge-centric approach to functional connectivity in order to provide an alternative description of the effects of this disorder. These techniques allow for the rendering of metrics such as normalized entropy, which describes the diversity of edge communities at each node. Moreover, the approach enables the identification of high amplitude co-fluctuations in fMRI time series. We found that normalized entropy is associated with stroke lesion severity and continually increases across the time of patients' recovery. Furthermore, high amplitude co-fluctuations not only relate to the lesion severity but are also associated with patients' level of recovery. The current study is the first edge-centric application for a clinical population in a longitudinal dataset and demonstrates how a different perspective for functional data analysis can further characterize topographic modulations of brain dynamics.

YNICL Journal 2022 Journal Article

Inferring the dynamical effects of stroke lesions through whole-brain modeling

• Sebastian Idesis
• Chiara Favaretto
• Nicholas V. Metcalf
• Joseph C. Griffis
• Gordon L. Shulman
• Maurizio Corbetta
• Gustavo Deco

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

YNIMG Journal 2022 Journal Article

Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions

• Elvira Pirondini
• Nawal Kinany
• Cécile Le Sueur
• Joseph C. Griffis
• Gordon L. Shulman
• Maurizio Corbetta
• Dimitri Van De Ville

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.

YNICL Journal 2022 Journal Article

Presurgical predictors of early cognitive outcome after brain tumor resection in glioma patients

• Andrea Zangrossi
• Erica Silvestri
• Marta Bisio
• Alessandra Bertoldo
• Serena De Pellegrin
• Antonino Vallesi
• Alessandro Della Puppa
• Domenico D'Avella

Gliomas are commonly characterized by neurocognitive deficits that strongly impact patients' and caregivers' quality of life. Surgical resection is the mainstay of therapy, and it can also cause cognitive impairment. An important clinical problem is whether patients who undergo surgery will show post-surgical cognitive impairment above and beyond that present before surgery. The relevant rognostic factors are largely unknown. This study aims to quantify the cognitive impairment in glioma patients 1-week after surgery and to compare different pre-surgical information (i.e., cognitive performance, tumor volume, grading, and lesion topography) towards predicting early post-surgical cognitive outcome. We retrospectively recruited a sample of N = 47 patients affected by high-grade and low-grade glioma undergoing brain surgery for tumor resection. Cognitive performance was assessed before and immediately after (∼1 week) surgery with an extensive neurocognitive battery. Multivariate linear regression models highlighted the combination of predictors that best explained post-surgical cognitive impairment. The impact of surgery on cognitive functioning was relatively small (i.e., 85% of test scores across the whole sample indicated no decline), and pre-operative cognitive performance was the main predictor of early post-surgical cognitive outcome above and beyond information from tumor topography and volume. In fact, structural lesion information did not significantly improve the accuracy of prediction made from cognitive data before surgery. Our findings suggest that post-surgery neurocognitive deficits are only partially explained by preoperative brain damage. The present results suggest the possibility to make reliable, individualized, and clinically relevant predictions from relatively easy-to-obtain information.

YNIMG Journal 2022 Journal Article

Variability of regional glucose metabolism and the topology of functional networks in the human brain

• Alessandro Palombit
• Erica Silvestri
• Tommaso Volpi
• Marco Aiello
• Diego Cecchin
• Alessandra Bertoldo
• Maurizio Corbetta

F-FDG PET, and the topology of RSNs, measured through graph analysis applied to fMRI resting-state functional connectivity (FC). We found a moderate linear relationship between the strength (STR) of pairwise regional FC and metabolism. Moreover, the linear correlation between SUVR and STR grew stronger as we considered more connected regions (hubs). Regions connecting different RSNs, or connector hubs, showed higher SUVR than regions connecting nodes within the same RSN, or provincial hubs. Our results show that functional connections as probed by fMRI are related to glucose metabolism, especially in a system of provincial and connector hubs.

YNICL Journal 2021 Journal Article

Lesion Quantification Toolkit: A MATLAB software tool for estimating grey matter damage and white matter disconnections in patients with focal brain lesions

• Joseph C. Griffis
• Nicholas V. Metcalf
• Maurizio Corbetta
• Gordon L. Shulman

Lesion studies are an important tool for cognitive neuroscientists and neurologists. However, while brain lesion studies have traditionally aimed to localize neurological symptoms to specific anatomical loci, a growing body of evidence indicates that neurological diseases such as stroke are best conceptualized as brain network disorders. While researchers in the fields of neuroscience and neurology are therefore increasingly interested in quantifying the effects of focal brain lesions on the white matter connections that form the brain's structural connectome, few dedicated tools exist to facilitate this endeavor. Here, we present the Lesion Quantification Toolkit, a publicly available MATLAB software package for quantifying the structural impacts of focal brain lesions. The Lesion Quantification Toolkit uses atlas-based approaches to estimate parcel-level grey matter lesion loads and multiple measures of white matter disconnection severity that include tract-level disconnection measures, voxel-wise disconnection maps, and parcel-wise disconnection matrices. The toolkit also estimates lesion-induced increases in the lengths of the shortest structural paths between parcel pairs, which provide information about changes in higher-order structural network topology. We describe in detail each of the different measures produced by the toolkit, discuss their applications and considerations relevant to their use, and perform example analyses using real behavioral data collected from sub-acute stroke patients. We show that analyses performed using the different measures produced by the toolkit produce results that are highly consistent with results that have been reported in the prior literature, and we demonstrate the consistency of results obtained from analyses conducted using the different disconnection measures produced by the toolkit. We anticipate that the Lesion Quantification Toolkit will empower researchers to address research questions that would be difficult or impossible to address using traditional lesion analyses alone, and ultimately, lead to advances in our understanding of how white matter disconnections contribute to the cognitive, behavioral, and physiological consequences of focal brain lesions.

YNIMG Journal 2021 Journal Article

Multi-band MEG signatures of BOLD connectivity reorganization during visuospatial attention

• Chiara Favaretto
• Sara Spadone
• Carlo Sestieri
• Viviana Betti
• Angelo Cenedese
• Stefania Della Penna
• Maurizio Corbetta

The functional architecture of the resting brain, as measured with the blood oxygenation level-dependent functional connectivity (BOLD-FC), is slightly modified during task performance. In previous work, we reported behaviorally relevant BOLD-FC modulations between visual and dorsal attention regions when subjects performed a visuospatial attention task as compared to central fixation (Spadone et al., 2015). Here we use magnetoencephalography (MEG) in the same group of subjects to identify the electrophysiological correlates of the BOLD-FC modulation found in our previous work. While BOLD-FC topography, separately at rest and during visual attention, corresponded to neuromagnetic Band-Limited Power (BLP) correlation in the alpha and beta bands (8-30 Hz), BOLD-FC modulations evoked by performing the visual attention task (Spadone et al. 2015) did not match any specific oscillatory band BLP modulation. Conversely, following the application of an orthogonal spatial decomposition that identifies common inter-subject co-variations, we found that attention-rest BOLD-FC modulations were recapitulated by multi-spectral BLP-FC components. Notably, individual variability of alpha connectivity between Frontal Eye Fields and visual occipital regions, jointly with decreased interaction in the Visual network, correlated with visual discrimination accuracy. In summary, task-rest BOLD connectivity modulations match multi-spectral MEG BLP connectivity.

YNIMG Journal 2021 Journal Article

Spectral signature of attentional reorienting in the human brain

• Sara Spadone
• Viviana Betti
• Carlo Sestieri
• Vittorio Pizzella
• Maurizio Corbetta
• Stefania Della Penna

As we move in the environment, attention shifts to novel objects of interest based on either their sensory salience or behavioral value (reorienting). This study measures with magnetoencephalography (MEG) different properties (amplitude, onset-to-peak duration) of event-related desynchronization/synchronization (ERD/ERS) of oscillatory activity during a visuospatial attention task designed to separate activity related to reorienting vs. maintaining attention to the same location, controlling for target detection and response processes. The oscillatory activity was measured both in fMRI-defined regions of interest (ROIs) of the dorsal attention (DAN) and visual (VIS) networks, previously defined as task-relevant in the same subjects, or whole-brain in a pre-defined set of cortical ROIs encompassing the main brain networks. Reorienting attention (shift cues) as compared to maintaining attention (stay cues) produced a temporal sequence of ERD/ERS modulations at multiple frequencies in specific anatomical regions/networks. An early (∼330 ms), stronger, transient theta ERS occurred in task-relevant (DAN, VIS) and control networks (VAN, CON, FPN), possibly reflecting an alert/reset signal in response to the cue. A more sustained, behaviorally relevant, low-beta band ERD peaking ∼450 ms following shift cues (∼410 for stay cues) localized in frontal and parietal regions of the DAN. This modulation is consistent with a control signal re-routing information across visual hemifields. Contralateral vs. ipsilateral shift cues produced in occipital visual regions a stronger, sustained alpha ERD (peak ∼470 ms) and a longer, transient high beta/gamma ERS (peak ∼490 ms) related to preparatory visual modulations in advance of target occurrence. This is the first description of a cascade of oscillatory processes during attentional reorienting in specific anatomical regions and networks. Among these processes, a behaviorally relevant beta desynchronization in the FEF is likely associated with the control of attention shifts.

YNICL Journal 2021 Journal Article

Stroke-related alterations in inter-areal communication

• Michele Allegra
• Chiara Favaretto
• Nicholas Metcalf
• Maurizio Corbetta
• Andrea Brovelli

Beyond causing local ischemia and cell damage at the site of injury, stroke strongly affects long-range anatomical connections, perturbing the functional organization of brain networks. Several studies reported functional connectivity abnormalities parallelling both behavioral deficits and functional recovery across different cognitive domains. FC alterations suggest that long-range communication in the brain is altered after stroke. However, standard FC analyses cannot reveal the directionality and time scale of inter-areal information transfer. We used resting-state fMRI and covariance-based Granger causality analysis to quantify network-level information transfer and its alteration in stroke. Two main large-scale anomalies were observed in stroke patients. First, inter-hemispheric information transfer was significantly decreased with respect to healthy controls. Second, stroke caused inter-hemispheric asymmetries, as information transfer within the affected hemisphere and from the affected to the intact hemisphere was significantly reduced. Both anomalies were more prominent in resting-state networks related to attention and language, and they correlated with impaired performance in several behavioral domains. Overall, our findings support the hypothesis that stroke provokes asymmetries between the affected and spared hemisphere, with different functional consequences depending on which hemisphere is lesioned.

YNIMG Journal 2020 Journal Article

Damage to the shortest structural paths between brain regions is associated with disruptions of resting-state functional connectivity after stroke

• Joseph C. Griffis
• Nicholas V. Metcalf
• Maurizio Corbetta
• Gordon L. Shulman

Focal brain lesions disrupt resting-state functional connectivity, but the underlying structural mechanisms are unclear. Here, we examined the direct and indirect effects of structural disconnections on resting-state functional connectivity in a large sample of sub-acute stroke patients with heterogeneous brain lesions. We estimated the impact of each patient's lesion on the structural connectome by embedding the lesion in a diffusion MRI streamline tractography atlas constructed using data from healthy individuals. We defined direct disconnections as the loss of direct structural connections between two regions, and indirect disconnections as increases in the shortest structural path length between two regions that lack direct structural connections. We then tested the hypothesis that functional connectivity disruptions would be more severe for disconnected regions than for regions with spared connections. On average, nearly 20% of all region pairs were estimated to be either directly or indirectly disconnected by the lesions in our sample, and extensive disconnections were associated primarily with damage to deep white matter locations. Importantly, both directly and indirectly disconnected region pairs showed more severe functional connectivity disruptions than region pairs with spared direct and indirect connections, respectively, although functional connectivity disruptions tended to be most severe between region pairs that sustained direct structural disconnections. Together, these results emphasize the widespread impacts of focal brain lesions on the structural connectome and show that these impacts are reflected by disruptions of the functional connectome. Further, they indicate that in addition to direct structural disconnections, lesion-induced increases in the structural shortest path lengths between indirectly structurally connected region pairs provide information about the remote functional disruptions caused by focal brain lesions.

YNIMG Journal 2020 Journal Article

Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

• Giulia Prando
• Mattia Zorzi
• Alessandra Bertoldo
• Maurizio Corbetta
• Marco Zorzi
• Alessandro Chiuso

Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration.

YNIMG Journal 2019 Journal Article

Archetypes of human cognition defined by time preference for reward and their brain correlates: An evolutionary trade-off approach

• Giorgia Cona
• Loren Koçillari
• Alessandro Palombit
• Alessandra Bertoldo
• Amos Maritan
• Maurizio Corbetta

Biological systems carry out multiple tasks in their lifetime, which, in the course of evolution, may lead to trade-offs. In fact phenotypes (different species, individuals within a species, circuits, bacteria, proteins, etc.) cannot be optimal at all tasks, and, according to Pareto optimality theory, lay into a well-defined geometrical distribution (polygons and/or polyhedrons) in the space of traits. The vertices of this distribution contain archetypes, namely phenotypes that are specialists at one of the tasks, whereas phenotypes toward the center of the geometrical distribution show average performance across tasks. We applied this theory to the variability of cognitive and behavioral scores measured in 1206 individuals from the Human Connectome Project. Among all possible combinations of pairs of traits, we found the best fit to Pareto optimality when individuals were plotted in the trait-space of time preferences for reward, evaluated with the Delay Discounting Task (DDT). The DDT measures subjects' preference in choosing either immediate smaller rewards or delayed larger rewards. Time preference for reward was described by a triangular distribution in which each of the three vertices included individuals who used a particular strategy to discount reward. These archetypes accounted for variability on many cognitive, personality, and socioeconomic status variables, as well as differences in brain structure and functional connectivity, with only a weak influence of genetics. In summary, time preference for reward reflects a core variable that biases human phenotypes via natural and cultural selection.

YNIMG Journal 2019 Journal Article

Brain controllability: Not a slam dunk yet

• Samir Suweis
• Chengyi Tu
• Rodrigo P. Rocha
• Sandro Zampieri
• Marzo Zorzi
• Maurizio Corbetta

In our recent article [1] published in this journal we provide quantitative evidence to show that there are warnings and caveats in the way Gu and collaborators [2] define controllability of brain networks and measure the contribution of each of its nodes. The comment by Pasqualetti et al. [3] confirms the need to go beyond the methodology and approach presented in Gu et al. ’s original work. In fact, they recognize that “the source of confusion is due to the fact that assessing controllability via numerical analysis typically leads to ill-conditioned problems, and thus often generates results that are difficult to interpret”. This is indeed the first warning we discussed in [1]: our work was not meant to prove that brain networks are not controllable from one node, rather we wished to highlight that the one node controllability framework and all consequent results were not properly justified based on the methodology presented in Gu et al. [2]. We used in our work the same method of Gu et al. not because we believe it is the best methodology, but because we extensively investigated it with the aim of replicating, testing, and extending their results. The warning and caveats we have proposed are the results of this investigation. Indeed, on the basis of our controllability analyses of multiple human brain networks datasets, we concluded: “The λ min (W K ) are statistically compatible with zero and thus the associated controllability Gramian cannot be inverted 1. These results show that it is not possible to infer one node controllability of the brain numerically”. Hence both groups agree that one node controllability cannot be inferred numerically.

YNIMG Journal 2019 Journal Article

Distinct modes of functional connectivity induced by movie-watching

• Murat Demirtaş
• Adrian Ponce-Alvarez
• Matthieu Gilson
• Patric Hagmann
• Dante Mantini
• Viviana Betti
• Gian Luca Romani
• Karl Friston

A fundamental question in systems neuroscience is how endogenous neuronal activity self-organizes during particular brain states. Recent neuroimaging studies have demonstrated systematic relationships between resting-state and task-induced functional connectivity (FC). In particular, continuous task studies, such as movie watching, speak to alterations in coupling among cortical regions and enhanced fluctuations in FC compared to the resting-state. This suggests that FC may reflect systematic and large-scale reorganization of functionally integrated responses while subjects are watching movies. In this study, we characterized fluctuations in FC during resting-state and movie-watching conditions. We found that the FC patterns induced systematically by movie-watching can be explained with a single principal component. These condition-specific FC fluctuations overlapped with inter-subject synchronization patterns in occipital and temporal brain regions. However, unlike inter-subject synchronization, condition-specific FC patterns were characterized by increased correlations within frontal brain regions and reduced correlations between frontal-parietal brain regions. We investigated these condition-specific functional variations as a shorter time scale, using time-resolved FC. The time-resolved FC showed condition-specificity over time; notably when subjects watched both the same and different movies. To explain self-organisation of global FC through the alterations in local dynamics, we used a large-scale computational model. We found that condition-specific reorganization of FC could be explained by local changes that engendered changes in FC among higher-order association regions, mainly in frontal and parietal cortices.

YNIMG Journal 2018 Journal Article

Distinct phase-amplitude couplings distinguish cognitive processes in human attention

• Ravi V. Chacko
• Byungchan Kim
• Suh Woo Jung
• Amy L. Daitch
• Jarod L. Roland
• Nicholas V. Metcalf
• Maurizio Corbetta
• Gordon L. Shulman

Spatial attention is the cognitive function that coordinates the selection of visual stimuli with appropriate behavioral responses. Recent studies have reported that phase-amplitude coupling (PAC) of low and high frequencies covaries with spatial attention, but differ on the direction of covariation and the frequency ranges involved. We hypothesized that distinct phase-amplitude frequency pairs have differentiable contributions during tasks that manipulate spatial attention. We investigated this hypothesis with electrocorticography (ECoG) recordings from participants who engaged in a cued spatial attention task. To understand the contribution of PAC to spatial attention we classified cortical sites by their relationship to spatial variables or behavioral performance. Local neural activity in spatial sites was sensitive to spatial variables in the task, while local neural activity in behavioral sites correlated with reaction time. We found two PAC frequency clusters that covaried with different aspects of the task. During a period of cued attention, delta-phase/high-gamma (DH) PAC was sensitive to cue direction in spatial sites. In contrast, theta-alpha-phase/beta-low-gamma-amplitude (TABL) PAC robustly correlated with future reaction times in behavioral sites. Finally, we investigated the origins of TABL PAC and found it corresponded to behaviorally relevant, sharp waveforms, which were also coupled to a low frequency rhythm. We conclude that TABL and DH PAC correspond to distinct mechanisms during spatial attention tasks and that sharp waveforms are elements of a coupled dynamical process.

YNIMG Journal 2018 Journal Article

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

• Matthieu Gilson
• Gustavo Deco
• Karl J. Friston
• Patric Hagmann
• Dante Mantini
• Viviana Betti
• Gian Luca Romani
• Maurizio Corbetta

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. However, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

YNIMG Journal 2018 Journal Article

Warnings and caveats in brain controllability

• Chengyi Tu
• Rodrigo P. Rocha
• Maurizio Corbetta
• Sandro Zampieri
• Marco Zorzi
• S. Suweis

A recent article by Gu et al. (Nat. Commun. 6, 2015) proposed to characterize brain networks, quantified using anatomical diffusion imaging, in terms of their “controllability”, drawing on concepts and methods of control theory. They reported that brain activity is controllable from a single node, and that the topology of brain networks provides an explanation for the types of control roles that different regions play in the brain. In this work, we first briefly review the framework of control theory applied to complex networks. We then show contrasting results on brain controllability through the analysis of five different datasets and numerical simulations. We find that brain networks are not controllable (in a statistical significant way) by one single region. Additionally, we show that random null models, with no biological resemblance to brain network architecture, produce the same type of relationship observed by Gu et al. between the average/modal controllability and weighted degree. Finally, we find that resting state networks defined with fMRI cannot be attributed specific control roles. In summary, our study highlights some warning and caveats in the brain controllability framework.

YNIMG Journal 2017 Journal Article

Frequency-specific electrophysiologic correlates of resting state fMRI networks

• Carl D. Hacker
• Abraham Z. Snyder
• Mrinal Pahwa
• Maurizio Corbetta
• Eric C. Leuthardt

Resting state functional MRI (R-fMRI) studies have shown that slow (<0. 1Hz), intrinsic fluctuations of the blood oxygen level dependent (BOLD) signal are temporally correlated within hierarchically organized functional systems known as resting state networks (RSNs) (Doucet et al. , 2011). Most broadly, this hierarchy exhibits a dichotomy between two opposed systems (Fox et al. , 2005). One system engages with the environment and includes the visual, auditory, and sensorimotor (SMN) networks as well as the dorsal attention network (DAN), which controls spatial attention. The other system includes the default mode network (DMN) and the fronto-parietal control system (FPC), RSNs that instantiate episodic memory and executive control, respectively. Here, we test the hypothesis, based on the spectral specificity of electrophysiologic responses to perceptual vs. memory tasks (Klimesch, 1999; Pfurtscheller and Lopes da Silva, 1999), that these two large-scale neural systems also manifest frequency specificity in the resting state. We measured the spatial correspondence between electrocorticographic (ECoG) band-limited power (BLP) and R-fMRI correlation patterns in awake, resting, human subjects. Our results show that, while gamma BLP correspondence was common throughout the brain, theta (4–8Hz) BLP correspondence was stronger in the DMN and FPC, whereas alpha (8–12Hz) correspondence was stronger in the SMN and DAN. Thus, the human brain, at rest, exhibits frequency specific electrophysiology, respecting both the spectral structure of task responses and the hierarchical organization of RSNs.

YNICL Journal 2016 Journal Article

Exploring the physiological correlates of chronic mild traumatic brain injury symptoms

• Serguei V. Astafiev
• Kristina L. Zinn
• Gordon L. Shulman
• Maurizio Corbetta

We report on the results of a multimodal imaging study involving behavioral assessments, evoked and resting-state BOLD fMRI, and DTI in chronic mTBI subjects. We found that larger task-evoked BOLD activity in the MT+/LO region in extra-striate visual cortex correlated with mTBI and PTSD symptoms, especially light sensitivity. Moreover, higher FA values near the left optic radiation (OR) were associated with both light sensitivity and higher BOLD activity in the MT+/LO region. The MT+/LO region was localized as a region of abnormal functional connectivity with central white matter regions previously found to have abnormal physiological signals during visual eye movement tracking (Astafiev et al., 2015). We conclude that mTBI symptoms and light sensitivity may be related to excessive responsiveness of visual cortex to sensory stimuli. This abnormal sensitivity may be related to chronic remodeling of white matter visual pathways acutely injured.

YNIMG Journal 2016 Journal Article

Magnetic stimulation of visual cortex impairs perceptual learning

• Antonello Baldassarre
• Paolo Capotosto
• Giorgia Committeri
• Maurizio Corbetta

The ability to learn and process visual stimuli more efficiently is important for survival. Previous neuroimaging studies have shown that perceptual learning on a shape identification task differently modulates activity in both frontal-parietal cortical regions and visual cortex (Sigman et al. , 2005; Lewis et al. , 2009). Specifically, fronto-parietal regions (i. e. intra parietal sulcus, pIPS) became less activated for trained as compared to untrained stimuli, while visual regions (i. e. V2d/V3 and LO) exhibited higher activation for familiar shape. Here, after the intensive training, we employed transcranial magnetic stimulation over both visual occipital and parietal regions, previously shown to be modulated, to investigate their causal role in learning the shape identification task. We report that interference with V2d/V3 and LO increased reaction times to learned stimuli as compared to pIPS and Sham control condition. Moreover, the impairment observed after stimulation over the two visual regions was positive correlated. These results strongly support the causal role of the visual network in the control of the perceptual learning.

YNICL Journal 2015 Journal Article

Filling in the gaps: Anticipatory control of eye movements in chronic mild traumatic brain injury

• Mithun Diwakar
• Deborah L. Harrington
• Jun Maruta
• Jamshid Ghajar
• Fady El-Gabalawy
• Laura Muzzatti
• Maurizio Corbetta
• Ming-Xiong Huang

A barrier in the diagnosis of mild traumatic brain injury (mTBI) stems from the lack of measures that are adequately sensitive in detecting mild head injuries. MRI and CT are typically negative in mTBI patients with persistent symptoms of post-concussive syndrome (PCS), and characteristic difficulties in sustaining attention often go undetected on neuropsychological testing, which can be insensitive to momentary lapses in concentration. Conversely, visual tracking strongly depends on sustained attention over time and is impaired in chronic mTBI patients, especially when tracking an occluded target. This finding suggests deficient internal anticipatory control in mTBI, the neural underpinnings of which are poorly understood. The present study investigated the neuronal bases for deficient anticipatory control during visual tracking in 25 chronic mTBI patients with persistent PCS symptoms and 25 healthy control subjects. The task was performed while undergoing magnetoencephalography (MEG), which allowed us to examine whether neural dysfunction associated with anticipatory control deficits was due to altered alpha, beta, and/or gamma activity. Neuropsychological examinations characterized cognition in both groups. During MEG recordings, subjects tracked a predictably moving target that was either continuously visible or randomly occluded (gap condition). MEG source-imaging analyses tested for group differences in alpha, beta, and gamma frequency bands. The results showed executive functioning, information processing speed, and verbal memory deficits in the mTBI group. Visual tracking was impaired in the mTBI group only in the gap condition. Patients showed greater error than controls before and during target occlusion, and were slower to resynchronize with the target when it reappeared. Impaired tracking concurred with abnormal beta activity, which was suppressed in the parietal cortex, especially the right hemisphere, and enhanced in left caudate and frontal-temporal areas. Regional beta-amplitude demonstrated high classification accuracy (92%) compared to eye-tracking (65%) and neuropsychological variables (80%). These findings show that deficient internal anticipatory control in mTBI is associated with altered beta activity, which is remarkably sensitive given the heterogeneity of injuries.

YNIMG Journal 2013 Journal Article

Dynamic functional connectivity: Promise, issues, and interpretations

• R. Matthew Hutchison
• Thilo Womelsdorf
• Elena A. Allen
• Peter A. Bandettini
• Vince D. Calhoun
• Maurizio Corbetta
• Stefania Della Penna
• Jeff H. Duyn

The brain must dynamically integrate, coordinate, and respond to internal and external stimuli across multiple time scales. Non-invasive measurements of brain activity with fMRI have greatly advanced our understanding of the large-scale functional organization supporting these fundamental features of brain function. Conclusions from previous resting-state fMRI investigations were based upon static descriptions of functional connectivity (FC), and only recently studies have begun to capitalize on the wealth of information contained within the temporal features of spontaneous BOLD FC. Emerging evidence suggests that dynamic FC metrics may index changes in macroscopic neural activity patterns underlying critical aspects of cognition and behavior, though limitations with regard to analysis and interpretation remain. Here, we review recent findings, methodological considerations, neural and behavioral correlates, and future directions in the emerging field of dynamic FC investigations.

YNIMG Journal 2013 Journal Article

Function in the human connectome: Task-fMRI and individual differences in behavior

• Deanna M. Barch
• Gregory C. Burgess
• Michael P. Harms
• Steven E. Petersen
• Bradley L. Schlaggar
• Maurizio Corbetta
• Matthew F. Glasser
• Sandra Curtiss

The primary goal of the Human Connectome Project (HCP) is to delineate the typical patterns of structural and functional connectivity in the healthy adult human brain. However, we know that there are important individual differences in such patterns of connectivity, with evidence that this variability is associated with alterations in important cognitive and behavioral variables that affect real world function. The HCP data will be a critical stepping-off point for future studies that will examine how variation in human structural and functional connectivity play a role in adult and pediatric neurological and psychiatric disorders that account for a huge amount of public health resources. Thus, the HCP is collecting behavioral measures of a range of motor, sensory, cognitive and emotional processes that will delineate a core set of functions relevant to understanding the relationship between brain connectivity and human behavior. In addition, the HCP is using task-fMRI (tfMRI) to help delineate the relationships between individual differences in the neurobiological substrates of mental processing and both functional and structural connectivity, as well as to help characterize and validate the connectivity analyses to be conducted on the structural and functional connectivity data. This paper describes the logic and rationale behind the development of the behavioral, individual difference, and tfMRI batteries and provides preliminary data on the patterns of activation associated with each of the fMRI tasks, at both group and individual levels.

YNICL Journal 2013 Journal Article

Impaired and facilitated functional networks in temporal lobe epilepsy

• Luigi Maccotta
• Biyu J. He
• Abraham Z. Snyder
• Lawrence N. Eisenman
• Tammie L. Benzinger
• Beau M. Ances
• Maurizio Corbetta
• R. Edward Hogan

How epilepsy affects brain functional networks remains poorly understood. Here we investigated resting state functional connectivity of the temporal region in temporal lobe epilepsy. Thirty-two patients with unilateral temporal lobe epilepsy underwent resting state blood-oxygenation level dependent functional magnetic resonance imaging. We defined regions of interest a priori focusing on structures involved, either structurally or metabolically, in temporal lobe epilepsy. These structures were identified in each patient based on their individual anatomy. Our principal findings are decreased local and inter-hemispheric functional connectivity and increased intra-hemispheric functional connectivity ipsilateral to the seizure focus compared to normal controls. Specifically, several regions in the affected temporal lobe showed increased functional coupling with the ipsilateral insula and immediately neighboring subcortical regions. Additionally there was significantly decreased functional connectivity between regions in the affected temporal lobe and their contralateral homologous counterparts. Intriguingly, decreased local and inter-hemispheric connectivity was not limited or even maximal for the hippocampus or medial temporal region, which is the typical seizure onset region. Rather it also involved several regions in temporal neo-cortex, while also retaining specificity, with neighboring regions such as the amygdala remaining unaffected. These findings support a view of temporal lobe epilepsy as a disease of a complex functional network, with alterations that extend well beyond the seizure onset area, and the specificity of the observed connectivity changes suggests the possibility of a functional imaging biomarker for temporal lobe epilepsy.

YNIMG Journal 2013 Journal Article

Resting state network estimation in individual subjects

• Carl D. Hacker
• Timothy O. Laumann
• Nicholas P. Szrama
• Antonello Baldassarre
• Abraham Z. Snyder
• Eric C. Leuthardt
• Maurizio Corbetta

Resting state functional magnetic resonance imaging (fMRI) has been used to study brain networks associated with both normal and pathological cognitive functions. The objective of this work is to reliably compute resting state network (RSN) topography in single participants. We trained a supervised classifier (multi-layer perceptron; MLP) to associate blood oxygen level dependent (BOLD) correlation maps corresponding to pre-defined seeds with specific RSN identities. Hard classification of maps obtained from a priori seeds was highly reliable across new participants. Interestingly, continuous estimates of RSN membership retained substantial residual error. This result is consistent with the view that RSNs are hierarchically organized, and therefore not fully separable into spatially independent components. After training on a priori seed-based maps, we propagated voxel-wise correlation maps through the MLP to produce estimates of RSN membership throughout the brain. The MLP generated RSN topography estimates in individuals consistent with previous studies, even in brain regions not represented in the training data. This method could be used in future studies to relate RSN topography to other measures of functional brain organization (e. g. , task-evoked responses, stimulation mapping, and deficits associated with lesions) in individuals. The multi-layer perceptron was directly compared to two alternative voxel classification procedures, specifically, dual regression and linear discriminant analysis; the perceptron generated more spatially specific RSN maps than either alternative.

YNIMG Journal 2012 Journal Article

Data-driven analysis of analogous brain networks in monkeys and humans during natural vision

• Dante Mantini
• Maurizio Corbetta
• Gian Luca Romani
• Guy A. Orban
• Wim Vanduffel

Inferences about functional correspondences between functional networks of human and non-human primates largely rely on proximity and anatomical expansion models. However, it has been demonstrated that topologically correspondent areas in two species can have different functional properties, suggesting that anatomy-based approaches should be complemented with alternative methods to perform functional comparisons. We have recently shown that comparative analyses based on temporal correlations of sensory-driven fMRI responses can reveal functional correspondent areas in monkeys and humans without relying on spatial assumptions. Inter-species activity correlation (ISAC) analyses require the definition of seed areas in one species to reveal functional correspondences across the cortex of the same and other species. Here we propose an extension of the ISAC method that does not rely on any seed definition, hence a method void of any spatial assumption. Specifically, we apply independent component analysis (ICA) separately to monkey and human data to define species-specific networks of areas with coherent stimulus-related activity. Then, we use a hierarchical cluster analysis to identify ICA-based ISAC clusters of monkey and human networks with similar timecourses. We implemented this approach on fMRI data collected in monkeys and humans during movie watching, a condition that evokes widespread sensory-driven activity throughout large portions of the cortex. Using ICA-based ISAC, we detected seven monkey–human clusters. The timecourses of several clusters showed significant correspondences either with the motion energy in the movie or with eye-movement parameters. Five of the clusters spanned putative homologous functional networks in either primary or extrastriate visual regions, whereas two clusters included higher-level visual areas at topological locations that are not predicted by cortical surface expansion models. Overall, our ICA-based ISAC analysis complemented the findings of our previous seed-based investigations, and suggested that functional processes can be executed by brain networks in different species that are functionally but not necessarily anatomically correspondent. Overall, our method provides a novel approach to reveal evolution-driven functional changes in the primate brain with no spatial assumptions.

YNIMG Journal 2012 Journal Article

Why use a connectivity-based approach to study stroke and recovery of function?

• Alex R. Carter
• Gordon L. Shulman
• Maurizio Corbetta

The brain is organized into a set of widely distributed networks. Therefore, although structural damage from stroke is focal, remote dysfunction can occur in regions connected to the area of lesion. Historically, neuroscience has focused on local processing due in part to the absence of tools to study the function of distributed networks. In this article we discuss how a more comprehensive understanding of the effects of stroke can be attained using resting state functional connectivity BOLD magnetic resonance imaging (resting state fcMRI). Resting state fcMRI has a number of advantages over task-evoked fMRI for studying brain network reorganization in response to stroke, including the ability to image subjects with a broad range of impairments and the ability to study multiple networks simultaneously. We describe our rationale for using resting state connectivity as a tool for investigating the neural substrates of stroke recovery in a heterogeneous population of stroke patients and discuss the main questions we hope to answer, in particular whether resting state fcMRI measures in the acute phase of stroke can predict subsequent recovery. Early results suggest that disruption of inter-hemispheric connectivity in the somatomotor network and the dorsal attention network is more strongly associated with behavioral impairment in those domains than is intra-hemispheric connectivity within either the lesioned or unaffected hemisphere. We also observe in the somatomotor network an interesting interaction between corticospinal tract damage and decreased inter-hemispheric connectivity that suggests that both processes combine to contribute to neuromotor impairment after stroke. A connectivity-based approach will provide greater insight into network reorganization in the acute and chronic phases after stroke and will contribute to improving prognostic ability and the development of therapeutic interventions.

YNIMG Journal 2011 Journal Article

Functional connectivity in resting-state fMRI: Is linear correlation sufficient?

• Jaroslav Hlinka
• Milan Paluš
• Martin Vejmelka
• Dante Mantini
• Maurizio Corbetta

Functional connectivity (FC) analysis is a prominent approach to analyzing fMRI data, especially acquired under the resting state condition. The commonly used linear correlation FC measure bears an implicit assumption of Gaussianity of the dependence structure. If only the marginals, but not all the bivariate distributions are Gaussian, linear correlation consistently underestimates the strength of the dependence. To assess the suitability of linear correlation and the general potential of nonlinear FC measures, we present a framework for testing and estimating the deviation from Gaussianity by means of comparing mutual information in the data and its Gaussianized counterpart. We apply this method to 24 sessions of human resting state fMRI. For each session, matrix of connectivities between 90 anatomical parcel time series is computed using mutual information and compared to results from its multivariate Gaussian surrogate that conserves the correlations but cancels any nonlinearity. While the group-level tests confirmed non-Gaussianity in the FC, the quantitative assessment revealed that the portion of mutual information neglected by linear correlation is relatively minor—on average only about 5% of the mutual information already captured by the linear correlation. The marginality of the non-Gaussianity was confirmed in comparisons using clustering of the parcels—the disagreement between clustering obtained from mutual information and linear correlation was attributable to random error. We conclude that for this type of data, practical relevance of nonlinear methods trying to improve over linear correlation might be limited by the fact that the data are indeed almost Gaussian.

YNIMG Journal 2000 Journal Article

Temporal dynamics of visual attention: Spatial expectancy vs. target detection, as revealed by ANOVA based event-related fMRI

• Maurizio Corbetta
• Michelle Kincade
• J.M. Ollinger
• Mark McAvoy
• Gordon Shulman