Biology subjects

Van De Ville, D.

Publications and source records attributed to Van De Ville, D..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Spine-Prints: Transposing Brain Fingerprints to the Spinal Cord

Functional connectivity (FC) patterns in the human brain form a reproducible, individual-specific "fingerprint" that allows reliable identification of the same participant across scans acquired over different sessions. While brain fingerprinting is robust across healthy individuals and neuroimaging modalities, little is known about whether the fingerprinting principle extends beyond the brain. Here, we used multiple functional magnetic resonance imaging (fMRI) datasets acquired at different sites to examine whether a fingerprint can be revealed from FCs of the cervical region of the human spinal cord. Our results demonstrate that the functional organisation of this region also exhibits individual-specific properties, suggesting the potential existence of a spine-print within the same acquisition session. Although the spine-print scores are not directly comparable to those observed in the brain, this discrepancy may in part reflect the intrinsic limitations of imaging this region with fMRI, where where the signals are more susceptible to noise and effective resolution relative to structure size, and tSNR are markedly lower than in the brain.This study provides the first evidence of a spinal cord connectivity fingerprint, underscoring the importance of considering a more comprehensive view of the entire central nervous system. Eventually, these spine-specific signatures could contribute to identifying individualized biomarkers of neuronal connectivity, with potential clinical applications in neurology and neurosurgery.

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bioRxiv2025

Connectome-based brain fingerprints predict early cognitive decline in Parkinson's patients with minor hallucinations

Individual variability in connectome organization offers a unique framework for capturing patient-specific alterations and advancing personalized models in medicine. Minor hallucinations (MH) affect up to 40% of Parkinsons disease (PD) patients and are early indicators of cognitive decline and dementia, hence crucial for early intervention. While previous studies focused on group-level differences, connectome-based brain fingerprinting enables deeper, individualized analysis of neural change. Applying this approach to PD patients with and without MH using resting-state fMRI, we show that each patient exhibited unique brain fingerprint, revealing rich quantifiable personalized features with medical relevance. MH-patients showed a loss of subject-specific features in brain networks linked to cognitive health, while somatosensory regions - typically less distinctive - became more prominent, emphasizing their role in MH pathogenesis. These differences enabled to identify - in an entirely data driven manner - patient-specific networks linked to early subclinical cognitive alterations, as well differential spatial fingerprinting organization linked to cortical densities of neurotransmitters. These findings reveal a distinct, patient-specific connectomic signature that differentiates PD patients with MH, uncovering early neural markers for precision medicine in PD.

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bioRxiv2025

Frequency-Specific Resting-State MEG Network Characteristics of Tinnitus Patients Revealed by Graph Learning

Tinnitus, the perception of sound without an external source, affects a significant portion of the population, yet its impact on brain communication diagram known as the functional connectome, remains limited. Traditional functional connectivity (FC) methods, such as Pearson correlation, phase lag index and coherence rely on pairwise comparisons and are therefore limited in providing a holistic encoding of FC. Here, we employ an alternative approach to estimate the entire connectivity structure by analyzing all time-courses simultaneously. This approach is robust even for short-duration recordings, facilitating faster functional connectome identification and real-time applications. Using resting-state MEG recordings from controls and individuals with tinnitus, we demonstrated that the learned connectomes outperform correlation-based connectomes in fingerprinting, that is, identifying an individual from test-retest acquisitions. Group-level analysis revealed distinct altered FC in tinnitus across multiple frequency bands, affecting the default mode, auditory, visual, and salience networks, suggesting a reorganization of these large-scale networks beyond auditory areas. Our study reveals that tinnitus presents highly individualized and heterogeneous whole-brain connectome profiles, highlighting the need to focus on individual variability rather than group-level differences to gain a more nuanced understanding of tinnitus. Personalized FC could enable patient-specific tinnitus models, optimizing treatment strategies for individualized care.

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bioRxiv2025

Functional organization of the neonatal thalamus across development depicted by functional MRI

The thalamus is a central component of the brain that is involved in a variety of functions, from sensory processing to high-order cognition. Its structure and function in the first weeks of extrauterine life, including its connections to different cortical and subcortical areas, have not yet been widely explored. Here, we used resting state functional magnetic resonance imaging data of 730 newborns from the developing Human Connectome Project to study the functional organization of the thalamus from 37 to 44 post-conceptual weeks. We introduce KNIT: K-means for Nuclei in Infant Thalamus. The framework employs a highly granular vector space of 40 features, each corresponding to functional connectivity to a brain region, using k-means clustering and uncertainty quantification through bootstrapping to delineate thalamic units. Although the different clusters showed common patterns of increased connectivity to the superior temporal gyrus, the parietal, and the frontal cortex, implying an expected decrease in specialization at that age, they also show some specificity. That is, a pulvinar unit was identified, similar to the adult thalamus. Ventrolateral motor and medial salience units were also highlighted. The latter appeared around 41 weeks of age, while the former showed at least from 37 weeks, but had a decrease in volume through age, replaced mostly by a dominant dorsal thalamic unit. We also observed an increase in clustering robustness and in hemispheric bilateral symmetry with age, suggesting more specialized functional units. We also found a burst in global thalamic connectivity around 41 weeks. Finally, we demonstrate the benefits of this method in terms of granularity compared to the more conventional winner-takes-all approach.

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bioRxiv2025

EPISeg: Automated segmentation of the spinal cord on echo planar images using open-access multi-center data

Functional magnetic resonance imaging (fMRI) of the spinal cord is relevant for studying sensation, movement, and autonomic function. Preprocessing of spinal cord fMRI data involves segmentation of the spinal cord on gradient-echo echo planar imaging (EPI) images. Current automated segmentation methods do not work well on these data, due to the low spatial resolution, susceptibility artifacts causing distortions and signal drop-out, ghosting, and motion-related artifacts. Consequently, this segmentation task demands a considerable amount of manual effort which takes time and is prone to user bias. In this work, we (i) gathered a multi-center dataset of spinal cord gradient-echo EPI with ground-truth segmentations and shared it on OpenNeuro https://openneuro.org/datasets/ds005143/versions/1.3.0, and (ii) developed a deep learning-based model, EPISeg, for the automatic segmentation of the spinal cord on gradient-echo EPI data. We observe a significant improvement in terms of segmentation quality compared to other available spinal cord segmentation models. Our model is resilient to different acquisition protocols as well as commonly observed artifacts in fMRI data. The training code is available at https://github.com/sct-pipeline/fmri-segmentation/, and the model has been integrated into the Spinal Cord Toolbox as a command-line tool.

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bioRxiv2024

Multiscale excitation-inhibition balance dynamics: integrating metabolite kinetics with time-varying executive networks

The balance between neural excitation and inhibition (EIB) is an essential mechanism supporting cognitive processes. Yet, little is understood about how EIB shifts with cognitive load and its impact on functional connectivity dynamics. In this study, we investigate temporal profiles of the reciprocal modulation between EIB and functional network dynamics during working memory tasks, revealing that EIB prefrontal kinetics scale with increasing cognitive load. Notably, prefrontal EIB kinetics correlated with cognitive load, impacting stability of networks crucial for cognitive function. On one hand, brain dynamics adapt to meet increasing cognitive challenges with a shift towards more focused and sustained neural activity patterns in terms of connectivity. On the other, imbalances favouring excitation may hinder cognitive adaptability. Importantly, this experimental approach demonstrates a link between EIB kinetics, brain network dynamics and cognitive performance, defining the groundwork for exploring healthy and aberrant cognitive states. TeaserHighly focused or less responsive? Chemical signalling and network dynamics are coupled to produce persistent cognitive states.

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bioRxiv2024

Multiscale Quantification of Hemispheric Asymmetry in Cortical Maps Using Geometric Eigenmodes

Hemispheric asymmetry is a universal property of brain organization with wide implications into brain function and structure, and diseases. This study presents a laterality index for characterizing hemispheric asymmetries that underlie cortical maps using geometric eigenmodes derived from human cortical surfaces.We develop a generalized design to quantify asymmetries across various cortical spatial scales. While the design is individual-specific, we implement normalization steps to enable unbiased comparisons across individuals. As a proof of concept, we validated the method on cortical maps of 545 subjects across two datasets, using fMRI maps of healthy individuals and tau-PET maps of patients across the Alzheimers disease continuum. Our results reveal that cortical regions in different canonical functional networks have connectivity patterns that entail different degrees of hemispheric asymmetry. Moreover, aggregates of the pathological tau protein manifest subtle asymmetries at varying spatial scales along the disease continuum.

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bioRxiv2024

On reconstruction of cortical functional maps using subject-specific geometric and connectome eigenmodes

Understanding the interplay between human brain structure and function is crucial to discern neural dynamics. This study explores the relation between brain structure and macroscale functional activity using subject-specific structural connectome eigenmodes, complementing prior work that focused on group-level models and geometry. Leveraging data from the Human Connectome Project, we assess accuracy in reconstructing various functional MRI-based cortical maps using individualised eigenmodes, specifically, across a range of connectome construction parameters. Our results show only minor differences in performance between surface geometric eigenmodes, a local neighborhood graph, a highly smoothed null model, and individual and group-level connectomes at modest smoothing and density levels. Furthermore, our results suggest that spatially smooth eigenmodes best explain functional data. The absence of improvement of individual connectomes and surface geometry over smoothed null models calls for further methodological innovation to better quantify and understand the degree to which brain structure constrains brain function.

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bioRxiv2024

Functional and causal neural mechanisms of human voice perception in noisy situations

Human communication entails an efficient way of simultaneously processing voice and reducing the impact of environmental noise. By manipulating background noise, we aimed at clarifying the neural mechanisms allowing voice comprehension in noisy situations. Our results point to spatial and temporal coexistence of lateral and medial temporal cortex networks when voice is easily detected in highly noisy conditions, revealing the necessary neural underpinnings of human communication in realistic situations.

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bioRxiv2024

Mindfulness training impacts brain network dynamics linked to stress response in young adolescents.

Mindfulness-based interventions (MBI) may lead to lower levels of psychological distress, including depression, anxiety, and stress in adolescents. Past research has advanced the discovery of neural architecture recruited by MBI. However, the brain mechanisms through which mindfulness exerts more resilient responses to social stressors in teens remain unclear. Here, we examined how MBI modulates changes in brain network dynamics following social stress with different affective valence (i.e., neutral, negative, and positive). For this aim, we carried out a longitudinal randomized controlled trial in which non-clinical adolescents underwent MBI for 8 weeks. They completed a psychosocial stress task before and following MBI. Functional magnetic resonance imaging (fMRI) and self-reported measurements of psychological distress were collected in both measurement points (i.e., "pre" and "post" MBI). We computed co-activation patterns on fMRI data to characterize dynamic functional connectivity within whole-brain networks. The results depicted how MBI modulates transient co-activation changes in dorsal medial regions of the brain default network (DN) following the experience of stress. However, these brain changes were not specific to the affective valence of stressful stimuli. The relationship between the DN dynamics and the measurements of psychological distress was mediated by MBI. Globally, our findings support a model in which MBI causally mediate brain-behavior interactions related to psychosocial stress in adolescents.

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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.

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bioRxiv2024

Eigenmodes of the brain: revisiting connectomics and geometry

Eigenmodes can be derived from various structural brain properties, including cortical surface geometry1 and interareal axonal connections comprising an organisms connectome2. Pang and colleagues map geometric and connectome eigenmodes to spatial patterns of human brain activity, assessing whether brain connectivity or geometry provide greater explanatory power of brain function3. The authors find that geometric eigenmodes are superior predictors of cortical activity compared to connectome eigenmodes. They conclude that this supports the predictions of neural field theory (NFT)4, in that "brain activity is best represented in terms of eigenmodes derived directly from the shape of the cortex, thus emphasizing a fundamental role of geometry in constraining dynamics". The experimental comparisons favoring geometric eigenmodes over connectome eigenmodes, in conjunction with specific statements regarding the relative efficacy of geometry in representing brain activity, have been widely interpreted to mean that geometry imposes stronger constraints on cortical dynamics than connectivity5-9. Here, we reconsider the comparative experimental evidence focusing on the impact of connectome mapping methodology. Utilizing established methods to mitigate connectome construction limitations, we map new connectomes for the same dataset, finding that eigenmodes derived from these connectomes reach comparable accuracy in explaining brain activity to that of geometric eigenmodes. We conclude that the evidence presented to support the comparative proposition that "eigenmodes derived from brain geometry represent a more fundamental anatomical constraint on dynamics than the connectome" may require reconsideration in light of our findings. Pang and colleagues present compelling evidence for the important role of geometric constraints on brain function, but their findings should not be interpreted to mean that geometry has superior explanatory power over the connectome.

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bioRxiv2024

Cerebro-spinal somatotopic organization uncoveredthrough functional connectivity mapping

Somatotopy, the topographical arrangement of sensorimotor pathways corresponding to distinct body parts, is a fundamental feature of the human central nervous system (CNS). Traditionally, investigations into brain and spinal cord somatotopy have been conducted independently, primarily utilizing body stimulations or movements. To date, however, no study has probed the somatotopic arrangement of cerebro-spinal functional connections in vivo in humans. In this study, we used simultaneous brain and cervical spinal cord functional magnetic resonance imaging (fMRI) to demonstrate how the coordinated activities of these two CNS levels at rest can reveal their shared somatotopy. Using functional connectivity analyses, we mapped preferential correlation patterns between each spinal cord segment and distinct brain regions, revealing a somatotopic gradient within the cortical sensorimotor network. We then validated this large-scale somatotopic organization through a complementary data-driven analysis, where we effectively identified spinal cord segments through the connectivity profiles of their voxels with the sensorimotor cortex. These findings underscore the potential of resting-state cerebro-spinal cord fMRI to probe the large-scale organization of the human sensorimotor system with minimal experimental burden, holding promise for gaining a more comprehensive understanding of normal and impaired somatosensory-motor functions.

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bioRxiv2023

Lumbosacral spinal cord functional connectivity at rest: From feasibility to reliability

In the past decade, exploration of spontaneous blood-oxygen-level-dependent (BOLD) signal fluctuations has expanded beyond the brain to include the spinal cord. While most studies have predominantly focused on the cervical region, the lumbosacral segments play a crucial role in motor control and sensory processing of the lower limbs. Addressing this gap, the aims of the current study were two-fold: first, confirming the presence and nature of organized spontaneous BOLD signals in the human lumbosacral spinal cord; second, systematically assessing the impact of various denoising strategies on signal quality and functional connectivity (FC) patterns. Given the susceptibility of spinal cord fMRI to noise, this step is pivotal to ensure the robustness of intrinsic FC. Our findings uncovered bilateral FC between the ventral horns. Importantly, these patterns were consistently observed across denoising methods and demonstrating fair to excellent reliability. Conversely, no other significant connectivity patterns were identified across the remaining horns. Importantly, the evaluation of diverse denoising strategies highlighted the efficacy of PNM-based pipelines in cleaning the signal while preserving the strength and reliability of connectivity estimates. Together, our results provide evidence of robust FC patterns in the lumbosacral spinal cord, thereby paving the way for future studies probing caudal spinal activity.

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bioRxiv2023

Joint subject-identification and task-decoding from inferred functional brain graphs via a multi-task neural network

Functional connectivity (FC) between brain regions as manifested via fMRI entails signatures that can be used to differentiate individuals and decode cognitive tasks. In this work, we use methods from graph structure inference to estimate FC, which is in contrast to the conventional approach of deriving FC via correlation. Moreover, we infer FC graphs from seed-based co-activation patterns instead of raw fMRI data. We also propose a multi-task neural network architecture to jointly perform subject-identification and taskdecoding from inferred functional brain graphs. We validate the developed model on data from the Human Connectome Project across eight fMRI tasks. Most importantly, our results show the superior task-decoding performance of FC graphs inferred from seed-based activity maps over graphs inferred from raw fMRI data. Furthermore, via gradient-based back-projection, we derive a significance score for inputs to the neural network, and present results showing the differential role of brain connections in subject-identification and task-decoding.

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bioRxiv2023

TR(acking) individuals down: exploring the effect of temporal resolution in resting-state functional MRI fingerprinting

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisitions temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s. We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make substantial contributions to subject identifiability, whereas sensory-motor regions become influential only when integrating data from different TRs. We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signals sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

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bioRxiv2023

Transient brain activity dynamics discriminate levels of consciousness during anesthesia

The awake mammalian brain is functionally organized in terms of large-scale distributed networks that are constantly interacting. Loss of consciousness might disrupt this temporal organization leaving patients unresponsive. We hypothesized that characterizing brain activity in terms of transient events may provide a signature of consciousness. For this, we analyzed temporal dynamics of spatiotemporally overlapping functional networks obtained from fMRI transient activity across different states of consciousness. We first show a striking homology in spatial organization of networks between monkeys and humans, indicating cross-species similarities in resting- state fMRI structure. We then tracked how network organization shifts under different anesthesia conditions in macaque monkeys. While the spatial aspect of the networks was preserved, their temporal dynamics were highly affected by anesthesia. Networks expressed for longer durations and co-activated in an anesthetic-specific configuration. Additionally, hierarchical brain organization was disrupted with a consciousness-level- signature role of the default mode network. In conclusion, network temporal dynamics is a reliable and robust cortical signature of consciousness, paving the way to its clinical translation.

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bioRxiv2023

Thalamic contributions to psychosis susceptibility: Evidence from co-activation patterns accounting for intra-seed spatial variability (μCAPs)

The temporal variability of the thalamus in functional networks may provide valuable insights into the pathophysiology of schizophrenia. To address the complexity of the role of the thalamic nuclei in psychosis, we introduced micro-co-activation patterns (CAPs) by employing this method on the human genetic model of schizophrenia 22q11.2 deletion syndrome (22q11.2DS). Participants underwent resting-state functional MRI and a data-driven iterative process resulting in the identification of six whole-brain CAPs with specific activity patterns within the thalamus. Unlike conventional methods, CAPs extract dynamic spatial patterns that reveal partially overlapping and non-mutually exclusive functional subparts. Thus, the CAPs method detects finer foci of activity within the initial seed region, retaining valuable and clinically relevant temporal and spatial information. We found that a CAP showing co-activation of the mediodorsal thalamus with brain-wide cortical regions was significantly less frequent in patients with 22q11.2DS, and its occurrence negatively correlated with the severity of positive psychotic symptoms. Additionally, the activity within the auditory-visual cortex and their respective geniculate nuclei were expressed in two different CAPs. One of these auditory-visual CAPs co-activated with salience areas, while the other co-activated with the default mode network (DMN). A significant shift of occurrence from the salience+visuo-auditory-thalamus to the DMN+visuo-auditory-thalamus CAP was observed in patients with 22q11.2DS. Thus, our findings support existing research on the gatekeeping role of the thalamus for sensory information in the pathophysiology of psychosis and revisit the evidence of geniculate nuclei hyperconnectivity with the audio-visual cortex in 22q11.2DS in the context of dynamic functional connectivity as specific hyper-occurrence of these circuits with the task negative brain networks.

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