Biology subjects

Preti, M. G.

Publications and source records attributed to Preti, M. G..

9 recordsLinked to original sources

bioRxiv2026

Higher-order topology uncovers the multidimensional dynamics of naturalistic emotion

Emotions are thought to emerge from co-activation among distributed brain systems, yet traditional fMRI analyses typically examine localized responses and pairwise connections, potentially overlooking interactions among groups of regions. Here, we use time-resolved higher-order topology to characterize these group interactions during naturalistic viewing of 14 films totaling over 2.5 h, continuously annotated across 50 affective features. The homological scaffold, representing evolving group-level topology, most closely tracks recurrent emotional states and best predicts fine-grained affective profiles. This sensitivity diminishes when emotion is compressed into dimensions of valence, arousal, and power, where pairwise connectivity captures the dominant arousal signal. Across three independent datasets, arousal predictions transfer most robustly through pairwise connectivity, while the relative geometry of broad affective states remains conserved across film narratives despite poor valence generalization. These findings reveal complementary neural representations of emotion: higher-order topology captures fine-grained affective structure, whereas pairwise connectivity provides a portable readout of broad arousal.

neuroscience↗

bioRxiv2026

Individual connectome fingerprints reveal early stabilization and long-term circuit remodeling after stroke

Stroke is one of the leading causes of global disability, yet the principles governing how focal brain injury disrupts large-scale neural connectivity over time remain poorly understood. Here, we leverage a longitudinal multimodal dataset to track the evolution of individual-specific connectivity patterns, or brain fingerprints, over the first year after stroke. Despite a persistent shift from healthy architecture, we demonstrate that each patients unique functional connectome fingerprint is remarkably resilient and stabilizes within three weeks. This early global stabilization masks a protracted system-specific reorganization of brain circuits, which is characterized by an initial increase in connectivity within sensory and attention systems, followed by a decline across higher-level association networks. A joint structure-function embedding further shows that recovery involves a gradual shift toward the normative healthy range, driven primarily by functional reconfiguration atop a stable structural lesion. Crucially, a multivariate prediction model reveals that early functional signatures selectively forecast long-term impairment in language, executive function, and attention. Together, our results define the post-stroke brain as a shifting but constrained dynamical system, identifying early-stabilized brain patterns as biomarkers for individual recovery profiles and targets for personalized neurorehabilitation.

neuroscience↗

bioRxiv2026

Directed Brain Connectomics Revealed by Bicommunity Structure

The brain is a complex, interconnected system in which structural wiring underpins the flow of information between functional units. After two decades of advances in connectomics, the asymmetry of edge structures in large-scale brain networks remains largely unexplored. Here, we build a directed connectome combining structure and function, and dissect its organization into bicommunities where sending and associated receiving sets of nodes, through clustering of directed-edge information, are fully acknowledged. Our findings reveal a primary directional axis from sensory to association cortices, indicative of bottom-up information flow. Validation with invasive electrophysiological measures demonstrates that these bicommunities capture an intermediate organizational scale at which connection asymmetry aligns across modalities. Finally, sets of distinct known anatomical fiber bundles and their functional mapping are recovered as specific bicommunities. This work identifies asymmetric pathways onto which neural computation can be expressed, and establishes directed connectomics as a novel framework for understanding brain organization.

neuroscience↗

bioRxiv2024

Structure-Function Relationship in Electrical and Hemodynamic Brain Networks: Insights from EEG and fNIRS during Rest and Task States

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, we characterize global and local structure-function coupling using source-reconstructed Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.

neuroscience↗

bioRxiv2023

Hippocampal timestamp for goals

Our brain must manage multiple goals that differ in their temporal proximity. Some goals require immediate attention, while others have already been accomplished, or will be relevant later in time. Here, we examined how the hippocampus represents the temporal distance to different goals using a novel space-themed paradigm during 7T functional MRI (n=31). The hippocampus has an established role in mental time travel and a system in place to stratify information along its longitudinal axis on the basis of representational granularity. Previous work has documented a functional transformation from fine-grained, detail rich representations in the posterior hippocampus to coarse, gist-like representations in the anterior hippocampus. We tested whether the hippocampus uses this long axis system to dissociate goals based upon their temporal distance from the present. We hypothesized that the hippocampus would distinguish goals relevant for ones current needs from those that are removed in time along the long axis, with temporally removed past and future goals eliciting increasingly anterior activation. We sent participants on a mission to Mars where they had to track goals that differed in when they needed to be accomplished. We observed a long-axis dissociation, where temporally removed past and future goals activated the left anterior hippocampus and current goals activated the left posterior hippocampus. Altogether, this study demonstrates that the timestamp attached to a goal is a key driver in where the goal is represented in the hippocampus. This work extends the scope of the hippocampus long axis system to the goal-mapping domain.

neuroscience↗

bioRxiv2023

Connectome embedding in multidimensional graph-invariant spaces

The topological organization of brain networks, or connectomes, can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to ten node-level graph theoretical invariants. Nodal invariants are intrinsic nodal properties which reflect the topological characteristics of the nodes with respect to the whole network, including segregation (e.g., clustering coefficient) and centrality (e.g., betweenness centrality) measures. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated multiple types of connectomes (structural/functional networks and binary/weighted networks) and embedded the corresponding network nodes (brain regions) into multidimensional graph spaces defined by the invariants. First, we observed that nodal invariants are correlated between them (i.e., they carry similar network information) at a whole-brain and subnetwork level. Second, we conducted a machine learning analysis to test whether brain regions embedded in multidimensional graph spaces can be accurately classified into higher order (association, subcortical and cerebellar) and lower order (visual, somatomotor, attention) areas. Brain regions of higher order and lower order brain circuits were classified with an 80-87% accuracy in a 10-dimensional (10D) space. 10D graph metrics performed better than 2D and 3D graph metrics, and non-linear Gaussian kernels performed better than linear kernels. This suggests a non-linear brain network information gain in a high-dimensional graph space. Finally, we quantified the inter-subject Euclidean distance of each brain region embedded in the multidimensional graph space. The inter-individual distance was largest for regions of the default mode and frontoparietal networks, providing a new avenue for subject-specific network coordinates in a multidimensional space. To conclude, we propose a new framework for quantifying connectome features in multidimensional spaces defined by graph invariants, providing a new avenue for subject-specific network coordinates and inter-individual distance analyses.

neuroscience↗

bioRxiv2023

Brain fingerprinting using fMRI spectral signatures on high-resolution cortical graphs

Resting-state fMRI has proven to entail subject-specific signatures that can serve as a fingerprint to identify individuals. Conventional methods are based on building a connectivity matrix based on correlation between the average time course of pairs of brain regions. This approach, first, disregards the exquisite spatial detail manifested by fMRI due to working on average regional activities, second, cannot disentangle correlations associated to cognitive activity and underlying noise, and third, does not account for cortical morphology that spatially constraints function. Here we propose a method to address these shortcomings via leveraging principles from graph signal processing. We build high spatial resolution cortical graphs that encode each individuals cortical morphology and treat region-specific, whole-hemisphere fMRI maps as signals that reside on the graphs. fMRI graph signals are then decomposed using systems of graph spectral kernels to extract structure-informed functional signatures, which are in turn used for fingerprinting. Results on 100 subjects showed the overall superior subject differentiation power of the proposed signatures over the conventional method. Moreover, placement of the signatures within canonical functional brain networks revealed the greater contribution of high-level cognitive networks in subject identification.

neuroscience↗

bioRxiv2021

Structure-function interplay as signature for brain decoding and fingerprinting

Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the potential of GSP to introduce new imaging-based biomarkers to characterize tasks and individuals. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structurefunction coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, that is the one decoupled from structure. A network mainly involving cortico-subcortical connections showed the strongest correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum. Highlights- The relation of brain function with the underlying structural wiring is complex - We propose new structure-informed graph signal processing (GSP) of functional data - GSP-derived features allow accurate task decoding and individual fingerprinting - Functional connectivity from filtered data is more unique to subject and cognition - The role of structurally aligned and liberal graph frequencies is elucidated

neuroscience↗

bioRxiv2021

When makes you unique: temporality of the human brain fingerprint

The extraction of "fingerprints" from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability have not been addressed yet. In other words, what temporal features make our brains more "identifiable"? We here explore the dynamics of brain fingerprints (or brainprints) along two complementary axes: 1) what is the optimal time scale at which brainprints integrate sufficient information, 2) when best identification happens. Using dynamic identifiability, we show that the best identification emerges at longer time scales (~300s); however, short transient "bursts of identifiability" persist even when looking at shorter functional interactions. We find that these bursts of identifiability might be strongly associated with neuronal activity. Furthermore, we report evidence that different parts of connectome fingerprints relate to different time scales: i.e., more visual-somatomotor at short temporal windows, more frontoparietal-DMN driven by increasing temporal windows. Finally, using a meta-analytic approach, we show that there is a broad spectrum of associations between brainprints and behavior. At faster time scales, human brain fingerprints are linked to multisensory stimulation, eye movements, affective processing, visuospatial attention. At slower time scales instead, we find higher-cognitive functions, such as language and verbal semantics, awareness, declarative and working memory, social cognition. We hope that this first investigation of the temporality of the human brain fingerprint will pave the way towards a better understanding of what and when makes our brains unique.

neuroscience↗