Biology subjects

Marinazzo, D.

Publications and source records attributed to Marinazzo, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural integrity moderates the cognitive bearing of dopamine clearance in aging

Age-related cognitive decline in humans is well established, yet how neurobiological variations influence brain dynamics, and subsequently behavioral performance, remains insufficiently explored. Microscale accounts of cognitive aging revolve around attenuations in dopamine (DA) receptors and transporters (DAT). In contrast, macroscale accounts associate individual declines in cognitive performance with functional brain networks' reorganization and white-matter tracts' deterioration. We endeavor to bridge this gap with an amortized dopaminergic aging model (ADAM) which assembles aging brain twins by coupling dopaminergic modulation with structural connectomes to constrain emergent whole-brain dynamics. ADAM thereby augments established DAT decline with personalized estimates, through a reusable and generalizable estimator capable of disentangling neuromodulatory from structural contributions to age-related decline within each individual. To that end, we embed structural connectomes into a low-dimensional space exhibiting high reconstruction fidelity and -pseudonymized- individual identifiability. The spectrum of structural embeddings is subsequently updated with fMRI observations to estimate DA clearance timescales in 3,650 adults aged 36 to 95 years across five independent aging cohorts. Our analysis reveals an age-related elevation of DA clearance timescales. Critically, low performers exhibit elevated clearance in adults with marked structural degradation, and only within sliding age windows centred between 56 and 76 years. This supports a contribution of dopamine to aging, and reveals a structure- and age-conditional association between its clearance and cognitive performance. Our findings suggest that structural connectivity gates the macroscale reach of dopaminergic modulation, thus rendering the gain-control framework of neuromodulation mechanistically explicit across individuals and cohorts. ADAM therefore provides a biologically plausible framework that anchors interindividual variability in cognitive aging to perturbations in the dopaminergic system by learning cohort-scale mappings which can be shared across sites, establishing a transferable testbed for precision neuromodulation and future in-silico pharmacological trials.

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bioRxiv2026

From periodic/aperiodic to static/dynamic: rethinking the decomposition of neural power spectra

The power spectrum of electrophysiological signals exhibits both narrowband peaks and a broadband 1/f-like slope. The two are routinely treated as separable components: a "periodic" foreground and an "aperiodic" background; and each has been related to behaviour, cognition, and clinical status. This paper argues, and illustrates with simulations and case studies, that the boundary between the two is less sharp than the standard decomposition assumes: non-stationary amplitude modulation of an oscillation can inflate the apparent spectral trend (whereas clean periodic modulation does not, its sidebands being absorbed into the peak), and a broadband process viewed through any narrow band produces an envelope that mimics amplitude modulation. The two phenomena are mirror images of the same underlying property, that the temporal envelope and the spectral slope are coupled. I show what this means in practice with a set of illustrative scenarios and three case studies (a simulated alpha lateralisation paradigm, an auditory oddball ERP, and simultaneous scalp EEG and ECoG under rest and propofol sedation). I argue that the question "is the observed change a change in the aperiodic component" cannot in general be answered from time-series analysis alone, and that the temporal envelope of detected oscillations carries information that the static power spectrum loses. I ultimately argue that the conventional periodic/aperiodic partition of neural power spectra conflates a more fundamental distinction: between processes whose amplitude is stationary over the analysis window and processes whose amplitude changes. Transferring the dichotomy from the spectral domain to the time domain, from periodic/aperiodic to static/dynamic, reveals structure that the power spectrum alone cannot recover.

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bioRxiv2026

A spectral partial information decomposition framework for quantifying information about cognitive variables in oscillatory brain networks

Neural oscillations are thought to play a central role in encoding and transmitting cognitive information across large-scale brain networks, yet the relative contributions of phase synchrony and amplitude co-modulations to distributed coding remain unclear. A key obstacle is the absence of tools that can simultaneously quantify task-relevant information in the frequency domain and disentangle its phase and amplitude components across pairwise and higher-order interactions. Here, we introduce a spectral partial information decomposition framework (named NeOPID) for quantifying information about cognitive variables in power and phase contributions, and to quantify redundant and synergistic information in brain relations, from pairwise to higher-order interactions. We validated the approach on Kuramoto and Stuart-Landau oscillator networks, including a whole-brain model constrained by macaque anatomical connectivity. NeOPID accurately recovers ground-truth encoding schemes and reveals that phase relations and amplitude co-modulations act as complementary coding channels with both redundant and synergistic components. NeOPID further extends this decomposition to higher-order functional interactions enabling the characterization of how cognitive information is collectively distributed across multiple oscillatory edges via redundant and synergistic encoding. To illustrate biological applicability, we applied NeOPID to local field potentials (LFPs) recorded from the macaque fronto-parietal network during a working memory task. In this dataset, NeOPID identified beta-band amplitude co-modulations as the primary carrier of stimulus information, and revealed that higher-order phase interactions exhibit both redundant and synergistic structure during the memory delay. These results establish NeOPID as a principled tool for dissecting the informational architecture about cognitive processes of oscillatory brain networks.

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bioRxiv2026

Bridging Higher-Order Information Theory and Neuroimaging: A Voxel-Wise O-Information Framework

The brains functional organization has been extensively studied through pairwise connectivity analyses. While these approaches have provided important insights into brain network organization, they fall short in capturing the complexity of high-order functional interactions (HOI). Particularly relevant is the investigation of redundancy and synergy patterns -not addressable with pairwise interactions-, revealing fundamental mechanisms of brain integration and information processing across various cognitive functions and clinical conditions. Conventional neuroimaging software packages are primarily designed for classical (general linear model-like) analyses but lack native support for HOI metrics. To address this gap, this study introduces a novel framework that bridges high-order information theory with conventional neuroimaging analysis pipelines and is subsequently applied to resting-state functional MRI to demonstrate its practical utility. By representing HOI into voxel-level metrics, our approach allows standard neuroimaging analyses to probe complex multivariate dependencies. Moreover, voxel-level group-comparison analyses show age differences linked with reduced redundancy in default mode network interactions. These findings advance our understanding of the complex relationship between multivariate functional interactions, voxel-level neuroimaging, and behavior, highlighting novel analytic strategies to study high-order information processing underlying cognitive function and its alterations in pathological conditions.

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bioRxiv2026

Assessing whole-cortex excitability from electromagnetic brain signals

Cortical excitability, the propensity of neural circuits to respond to internal or external perturbations, is a fundamental property of brain functioning, shaped by local microcircuitry, large-scale networks, and neurochemical architecture. In humans, excitability is inferred from multiple indirect metrics derived from spontaneous and stimulus-driven electromagnetic activity, yet it remains unclear whether these measures reflect a common underlying construct or distinct physiological processes. Here, we systematically compared ten previously validated excitability metrics using whole-head magnetoencephalography (MEG) recorded at rest and during 40 Hz auditory stimulation in a large sample of healthy adults. The measures spanned stimulus-driven synchronization, spectral power, 1/f activity (hereafter defined as aperiodic), signal complexity, long-range temporal correlations, and phase-gamma synchronization. Hierarchical clustering revealed six separable excitability dimensions with limited redundancy, demonstrating that cortical excitability is inherently multidimensional. Aperiodic and alpha-band measures showed the strongest mutual coupling and the furthest spatial correspondence to stimulus-related responses, whereas temporal, complexity, and synchronization-based metrics were largely independent. The similarity between measures varied across cortical regions and functional networks, with maximal cluster separability in integrative and internally driven regions. These regions included orbitofrontal, temporal, anterior cingulate, and default mode areas, as well as visual and somatomotor networks. Finally, excitability dimensions showed distinct relationships with cortical morphology and neurotransmitter receptor density, implicating heterogeneous neurobiological substrates. Together, these findings provide a unified framework for interpreting excitability metrics and highlight the need for multimodal approaches when probing cortical excitability in health and disease.

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bioRxiv2025

Brain structural modules associated to functional high-order interactions in the human brain,

The brains modular organization, ranging from microcircuits to large-scale networks, has been extensively studied in terms of its structural and functional properties. Particularly insightful has been the investigation of the coupling between structural connectivity (SC) and functional connectivity (FC), whose analysis has revealed important insights into the brains efficiency and adaptability related to various cognitive functions. Interestingly, links in SC are intrinsically pairwise but this is not the case for FC; and while recent work demonstrates the relevance of the brains high-order interactions (HOI), the coupling of between SC and functional HOI remains unexplored. To address this gap, this study leverages functional MRI and diffusion weighted imaging to delineate the brains modular structure by investigating the coupling between SC and functional HOI. Our results demonstrates that structural networks can be associated with both redundant and synergistic functional interactions. In particular, SC exhibits both positive and negative correlations with redundancy, it shows consistent positive correlations with synergy, indicating that a higher density of structural connections is linked to increased synergistic interactions. These findings advance our understanding of the complex relationship between structural and high-order functional properties, shedding light on the brains architecture underlying its modular organization. Significance StatementUnderstanding the intricate relationship between the brain structural and functional connectivity is instrumental for enabling a richer characterization of cognitive functions. This challenge is complex, since it involves comparing pairwise links representing white-matter fibers with higher-order functional interactions that can involve multiple brain regions sharing information -- indeed, these interactions can be redundant in presence of shared information, or synergistic when novel information arises in the whole subsystem. Here we advance the investigation of the coupling between brain structure and high-order interactions, offering new insights into how structural properties are associated with both redundant and synergistic functional modules. Furthermore, the approach presented here opens new doors to investigate how this coupling between structural connectivity and functional HOI is modulated during development or under diverse brain pathologies.

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bioRxiv2024

Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning

Goal-directed learning arises from distributed neural circuits including the prefrontal, posterior parietal and temporal cortices. However, the role of cortico-cortical functional interactions remains unclear. To address this question, we integrated information dynamics analysis with magnetoencephalography to investigate the encoding of learning signals through neural interactions. Our findings revealed that information gain (the reduction in uncertainty about the causal relationship between actions and outcomes) is represented over the visual, parietal, lateral prefrontal and ventromedial/orbital prefrontal cortices. Cortico-cortical interactions encoded information gain synergistically at the level of pairwise and higher-order relations, such as triplets and quadruplets. Higher-order synergistic interactions were characterized by long-range relationships centered in the ventromedial and orbitofrontal cortices, which served as key receivers in the broadcast of information gain across cortical circuits. Overall, this study provides evidence that information gain is encoded through synergistic and higher-order functional interactions and is broadcast to prefrontal reward circuits.

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bioRxiv2024

The influence of the head model on magnetoencephalography-derived functional connectivity fingerprinting

Functional connectivity (FC)-based neural fingerprinting is an approach that promises to identify and/or differentiate subjects within a cohort on the basis of the patterns of statistical dependencies between time series recorded mostly if not always noninvasively, with electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). The message is that brain activity is what differentiates subjects, or what makes a neural fingerprint "unique". In EEG- and MEG-derived FC fingerprinting, the activity recorded at the sensors is usually projected back into cortical sources by means of an inverse model depending on head and brain shapes, sensor locations, and tissue conductivity, and further reduced in dimension to obtain time series of regional activity, used to compute FC. We think that the role of the head model in fingerprinting has been so far dismissed by means of suboptimal or incomplete tests. Here we employed a set of experiments aimed to decouple recorded activity and head model for each subject, and we found that the head model has a strong influence on both identification and differentiation.

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bioRxiv2023

Aperiodic component of EEG power spectrum and cognitive performance in aging: the role of education

Aging is associated with changes in the oscillatory -periodic-brain activity in the alpha band (8-12 Hz), as measured with resting-state EEG (rsEEG); it is characterized by a significantly lower alpha frequency and power. Aging influences the aperiodic component of the power spectrum: at a higher age the slope flattens, which is related with lower cognitive efficiency. It is not known whether education, a cognitive reserve proxy recognized for its modulatory role on cognition, influences such relationship. N=179 healthy participants of the LEMON dataset (Babayan et al., 2019) were grouped based on age and education: young adults with high education and older adults with high and low education. Eyes-closed rsEEG power spectrum was parametrized at the occipital level. Lower IAPF, exponent, and offset in older adults were shown, compared to younger adults. Visual attention and working memory were differently predicted by the aperiodic component across education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, with an opposite trend in those with lower education. Further investigation is needed; the study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.

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bioRxiv2023

Assessing cortical excitability with electroencephalography: a pilot study with EEG-iTBS.

Cortical excitability measures neural reactivity to stimuli, usually delivered via Transcranial Magnetic Stimulation (TMS). Excitation/inhibition balance (E/I) is the ongoing equilibrium between excitatory and inhibitory activity of neural circuits. According to some studies, E/I could be estimated in-vivo and non-invasively through the modeling of electroencephalography (EEG) signals. Several measures have been proposed (phase consistency in the gamma band, sample entropy, exponent of the power spectral density 1/f curve, E/I index extracted from detrend fluctuation analysis, and alpha power). It remains to be investigated to what extent they scale with excitability and how they relate to each other. Intermittent theta burst stimulation (iTBS) of the primary motor cortex (M1) is a non-invasive neuromodulation technique allowing controlled and focal enhancement of cortical excitability and E/I of the stimulated hemisphere. M1 excitability and several E/I estimates extracted from resting state EEG recordings were assessed before and after iTBS in a cohort of healthy subjects. Enhancement of M1 excitability, as measured through motor-evoked potentials (MEPs), and phase consistency of the cortex in high gamma band correlated with each other. Other measures of E/I showed some expected results, but no correlation with TMS excitability measures or consistency with each other. EEG E/I estimates offer an intriguing opportunity to map cortical excitability non-invasively, with high spatio-temporal resolution and with a stimulus independent approach. While different EEG E/I estimates may reflect the activity of diverse excitatory-inhibitory circuits, spatial phase synchrony in the gamma band is the measure that best captures excitability changes in the primary motor cortex.

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bioRxiv2022

Intra-V1 functional networks predict observed stimuli

Several studies suggest that the pattern of co-fluctuations of neural activity within V1 (measured with fMRI) changes with variations in attention/perceptual organization of observed stimuli. Here we used multivariate pattern analysis of intra-V1 correlation matrices to predict the level and shape of the observed Navon letters. We examined the inter-individual stability of network topologies and then tested if they contained intra-individual information about stimulus shape or level that was tolerant to changes in the irrelevant feature. The inter-individual classification was accurate for all specific level and letter-shape tests. These results indicate that the association of V1 topologies and perceptual states is stable across participants. Intra-participant cross-classification of level (ignoring shape) was accurate but failed for shape (ignoring level). Cross-classification of stimulus level was more accurate when the stimulus-evoked response was suppressed in the fMRI time series and not present for correlations based on raw time series, stimulus-evoked beta-series, or simulations of the effects of eye movements measured in a control group. Furthermore, cross-classification weight maps evinced asymmetries of link strengths across the visual field that mirrored perceptual asymmetries. We hypothesize that feedback about level information drives the intra-V1 networks based on fMRI background activity. These intra-V1 networks can shed light on the neural basis of attention and perceptual organization.

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bioRxiv2022

Task matters: individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magnetoencephalography (MEG) recordings. However, to what extent MEG signatures constitute a marker of human identifiability when engaged in task-related behavior remains an open question. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

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bioRxiv2022

Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex

During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity--within the visual cortex--in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations--within each occipital source--were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state. Author summaryOne of the strongest signals that can be measured using EEG are so called alpha rhythms. These are neural oscillations that fall within the 8-12Hz frequency range. Alpha rhythms are most prominent when the eyes are closed and are seen at the electrodes placed at the back of the head. In this study, we studied the mechanism of alpha rhythms changes when going from eyes-open to an eyes-closed state. We used a biologically plausible model including different neural populations. We focused on modelling connections within and between different neural sources of the visual cortex and how they are modulated when going from eyes-open to an eyes closed state. We found evidence that inhibitory neurons play an important role in alpha rhythms.

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bioRxiv2022

Investigation on How Dynamic Effective Connectivity Patterns Encode the Fluctuating Pain Intensity in Chronic Migraine

Chronic migraine is characterised by persistent headaches for more than 15 days per month; the intensity of the pain is fluctuating over time. Here, we explored the dynamic interplay of connectivity patterns between regions known to be related to pain processing and their relation to the ongoing dynamic pain experience. We recorded EEG from 80 sessions (20 chronic migraine patients in 4 separate sessions of 25 minutes). The patients were asked to continuously rate the intensity of their endogenous headache. On different time-windows, a dynamic causal model (DCM) of cross spectral responses was inverted to estimate connectivity strengths. For each patient and session, the evolving dynamics of effective connectivity were related to pain intensities and to pain intensity changes by using a Bayesian linear model. Hierarchical Bayesian modelling was further used to examine which connectivity-pain relations are consistent across sessions and across patients. The results reflect the multi-facetted clinical picture of the disease. Across all sessions, each patient with chronic migraine exhibited a distinct pattern of pain intensity-related cortical connectivity. The diversity of the individual findings are accompanied by inconsistent relations between the connectivity parameters and pain intensity or pain intensity changes at group level. This suggests a rejection of the idea of a common neuronal core problem for chronic migraine.

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bioRxiv2022

Theta-phase connectivity between medial prefrontal and posterior areas underlies novel instructions implementation

Implementing novel instructions is a complex and uniquely human cognitive ability, that requires the rapid and flexible conversion of symbolic content into a format that enables the execution of the instructed behavior. Preparing to implement novel instructions, as opposed to their mere maintenance, involves the activation of the instructed motor plans, and the binding of the action information to the specific context in which this should be executed. Recent evidence and prominent computational models suggest that this efficient configuration of the system might involve a central role of frontal theta oscillations in establishing top-down long-range synchronization between distant and task-relevant brain areas. In the present EEG study (human subjects, 30 females, 4 males), we demonstrate that proactively preparing for the implementation of novels instructions, as opposed to their maintenance, involves a strengthened degree of connectivity in the theta frequency range between medial prefrontal and motor/visual areas. Moreover, we replicated previous results showing oscillatory features associated specifically with implementation demands, and extended on them demonstrating the role of theta oscillations in mediating the effect of task demands on behavioral performance. Taken together, these findings support our hypothesis that the modulation of connectivity patterns between frontal and task-relevant posterior brain areas is a core factor in the emergence of a behavior-guiding format from novel instructions. Significance statementEveryday life requires the use and manipulation of currently available information to guide behavior and reach specific goals. In the present study we investigate how the same instructed content elicits different neural activity depending on the task being performed. We show that medial prefrontal theta oscillations are larger when novel instructions have to be implemented, rather than simply maintained. Crucially, connectivity between medial prefrontal cortex and posterior areas is strengthened during instructions implementation, suggesting that theta oscillations play a role in setting up a dynamic and flexible network of task-relevant regions optimized for the execution of the instructed behavior.

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bioRxiv2020

How hot is the hot zone? Computational modelling clarifies the role of parietal and frontoparietal connectivity during anaesthetic-induced loss of consciousness

In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs - the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) - to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 minutes of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior "hot zone", frontoparietal connections, and between-RSN connections. We estimate - for the first time - a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness. HighlightsO_LIModelling shows that connectivity within hot zone tracks change of conscious state C_LIO_LISeparately, frontoparietal connections support maintenance of conscious state C_LIO_LIStrength of frontoparietal connections predicts conscious state in unseen data C_LIO_LIBoth parietal hot zone and frontoparietal connectivity important for consciousness C_LI FundingThis work was supported by the UK Engineering and Physical Sciences Research Council (EP/P033199/1), Belgian National Funds for Scientific Research (FRS-FNRS), the University and University Hospital of Liege, the Fund Generet, the King Baudouin Foundation, the AstraZeneca Foundation, the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), DOCMA project (EU-H2020-MSCA-RISE-778234), the BIAL Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Center-TBI project (FP7-HEALTH-602150), the Public Utility Foundation Universite Europeenne du Travail, "Fondazione Europea di Ricerca Biomedica", the Mind Science Foundation, the European Commission, and the Special Research Fund of Ghent University. O.G. is research associate and S.L. is research director at the F.R.S-FNRS. Declaration of interestNone. Significance StatementVarious connectivity studies have suggested multiple network-level mechanisms driving changes in the state of consciousness, such as the posterior hot zone, frontal-, and large-scale frontoparietal networks. Here, we computationally evaluate evidence for these mechanisms using dynamic causal modeling for resting EEG recorded before and during propofol-anaesthesia, and demonstrate that, particularly, connectivity in the posterior hot zone is impaired during propofol-induced unconsciousness. With a robust cross-validation paradigm, we show that connectivity in the large-scale frontoparietal networks can consistently predict the state of consciousness and further generalise these findings to an unseen state of recovery. These results suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.

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bioRxiv2019

Objects seen as scenes: neural circuitry for attending whole or parts

Depending on our goals, we pay attention to the global shape of an object or to the local shape of its parts, since its difficult to do both at once. This typically effortless process can be impaired in disease. However, it is not clear which cortical regions carry the information needed to constrain shape processing to a chosen global/local level. Here, novel stimuli were used to dissociate functional MRI responses to global and local shapes. This allowed identification of cortical regions containing information about level (independent from shape). Crucially, these regions overlapped part of the cortical network implicated in scene processing. As expected, shape information (independent of level) was mainly located in category-selective areas specialized for object- and face-processing. Regions with the same informational profile were strongly linked (as measured by functional connectivity), but were weak when the profiles diverged. Specifically, in the ventral-temporal-cortex (VTC) regions favoring level and shape were consistently separated by the mid-fusiform sulcus (MFS). These regions also had limited crosstalk despite their spatial proximity, thus defining two functional pathways within VTC. We hypothesize that object hierarchical level is processed by neural circuitry that also analyses spatial layout in scenes, contributing to the control of the spatial-scale used for shape recognition. Use of level information tolerant to shape changes could guide whole/part attentional selection but facilitate illusory shape/level conjunctions under impoverished vision. Significance statementOne daily engages hierarchically organized objects (e.g. face-eyes-eyelashes). Their perception is commonly studied with global shapes composed by of local shapes. Seeing shape at one level is easy, but difficult for both at once. How can the brain guide attention to one level? Here using novel stimuli that dissociate different levels over time and examining local patterns of brain-activity, we found that the level and shape of visual objects were represented into segregated sets of cortical regions, each connected into their own pathway. Level information was found in part of the cortical network known to process scenes. Coding of object-level independently from shape could participate in guiding sustained attention within objects, eliminating interference from irrelevant levels. It could also help produce "illusory conjunctions" (perceptual migration of a shape to the wrong level) when attention is limited. HighlightsO_LIModified Navon figures allow dissociation in time of fMRI responses for the global/local levels. C_LIO_LIShape-invariant hierarchical level information was found in scenes selective areas, whereas level-invariant shape information was found in object- and faces- selective areas. C_LIO_LILevel and shape regions were divided by the mid-fusiform sulcus (MFS) in VTC cortex, and each type of region connected into its own pathway. C_LIO_LIHaving separate level/shape pathways could facilitate selective-attention, but foster illusory conjunctions. C_LI

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bioRxiv2019

Modeling brain dynamics after tumor resection using The Virtual Brain

Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, computational modeling of brain activity using so-called brain network models has been introduced as a promising tool for this purpose. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first \"virtual neurosurgery\" analyses to evaluate the potential of brain network modeling in predicting brain dynamics after tumor resection.\n\nWe find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. In addition, we identify several robust associations between individually optimized model parameters, structural network topology and cognitive performance from pre-to post-operative assessment. Concerning the virtual neurosurgery analyses, we obtain promising results in some patients, whereas the predictive accuracy of the currently applied model is poor in others. These findings reveal interesting avenues for future research, as well as important limitations that warrant further investigation.

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