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Achard, S.

Publications and source records attributed to Achard, S..

11 recordsLinked to original sources

Space partitioning by self-organized epithelial networks

Branching epithelia build supracellular networks that must simultaneously ensure connectivity, mechanical integrity, and efficient space partitioning, yet a general framework for how such networks form beyond the endothelial lineage has been lacking. Here we show that epithelial cells from multiple branching organs spontaneously self-organize into extended reticulate networks in simplified environments, revealing a conserved network-forming capacity. Combining wide-field lensless holographic imaging, deep-learning segmentation (EpiNet), and graph-theoretic analysis, we resolve a reproducible assembly sequence - contact initiation, clustering, percolation, and post-percolation relaxation - and quantify its geometry across scales. Actomyosin contractility controls both the growth of connectivity and the relaxation dynamics that set network geometry, tuning the effective cost of forming connections and thereby selecting between tree-like and reticulate topologies. After percolation, epithelial networks behave as active tension networks that progressively refine space partitioning toward centroidal, near-optimal configurations while maintaining a characteristic mesh size through continuous edge nucleation. These findings establish epithelial network formation as a generic, physically regulated mode of tissue self-organization and provide a quantitative framework linking single-cell mechanics, network topology, and space-partitioning dynamics, with implications for branching morphogenesis, organoid models, and tissue engineering.

biophysics↗

Divergent disruption of brain networks following total and chronic sleep loss: a longitudinal fMRI study

Study objectivesSleep loss significantly disrupts cognitive and emotional functioning, yet the neural consequences of different types of sleep deprivation remain unclear. MethodsIn a within-subject resting-state fMRI study, we examined how acute total sleep deprivation (TSD) and chronic sleep restriction (CSR) alter intrinsic functional brain organization in 28 healthy adults scanned under three conditions: rested wakefulness (RW), after one night of TSD, and after five nights of CSR. To quantify network-level disruption, we applied graph-theoretical analyses, including a novel within-subject adaptation of the Hub Disruption Index and Covariate-Constrained Manifold Learning (CCML), an unsupervised embedding technique sensitive to subject-level covariates. Moreover, we assessed subjective sleep quality, sleepiness, and circadian traits. ResultsBoth TSD and CSR were associated with a consistent reorganization of graph topology relative to RW. Furthermore, direct comparisons revealed that TSD and CSR affect different brain hubs. Regional changes in degree, closeness, and clustering coefficients were most prominent in subsystems of the default mode network, frontoparietal network, and cerebellum. These differences were also captured in CCML embeddings, supporting the hypothesis that acute and chronic sleep deprivation exert divergent effects on brain connectivity. Findings were robust across graph thresholds, brain atlases, and nodal metrics. Moreover, these results were further supported by the subjective measures - sleepiness was associated with reduced network integration in RW, and circadian phenotype emerged as a key determinant of individual sensitivity to sleep loss. ConclusionsOur results show that TSD and CSR induce distinct alterations in brain functional organization, offering new insights into their neural impact. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=157 HEIGHT=200 SRC="FIGDIR/small/681651v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1f24790org.highwire.dtl.DTLVardef@1387233org.highwire.dtl.DTLVardef@d18e97org.highwire.dtl.DTLVardef@1e88a15_HPS_FORMAT_FIGEXP M_FIG C_FIG Statement of significanceSleep deprivation is a growing public health concern, yet it remains unclear whether total and chronic sleep loss affect the brain in the same way. In this study, participants underwent one sleepless night and five consecutive days of reduced sleep (as in working week). We found that these two forms of sleep loss disrupt brain networks in fundamentally different ways. This distinction closes a critical gap in understanding how the brain responds to the total and chronic sleep loss. Recognizing that not all sleep deprivation is alike has important implications for managing fatigue in occupational, educational, and clinical contexts. Our findings highlight the need for personalized strategies to protect brain health and performance under conditions of sleep disruption.

neuroscience↗

Structure-Function Dynamics in Healthy Cognitive Aging: A Graph Signal Processing Approach

Network neuroscience has significantly advanced our understanding of how structural and functional connectivity evolve during healthy neurocognitive aging. Yet, integrative studies linking structural and functional brain organization with cognitive performance remain relatively limited. In this study, we analyzed resting-state functional MRI and diffusion-weighted imaging data from 600 healthy adults aged 18 to 88, drawn from the CamCAN dataset. Using a graph signal processing framework, we investigated how structural connectivity constrains functional brain signals across the adult lifespan. Our results reveal that control and semantic cognitive systems exhibit distinct age-related patterns of structure-function reorganization, potentially reflecting a shift in integrative processing during midlife. Notably, our findings suggest that structurally-coupled sensorimotor integration plays a crucial role for regulating these systems. Until midlife, it accompanies structurally-decoupled activity in transmodal cortices to sustain cognitive control. In parallel, it likely supports the formation of embodied internal models that leverage more structurally-decoupled semantic processes, thus contributing to maintain lexical production skills for longer in older adulthood. Taken together, our study offer new multimodal insights into how sensory-driven processes help reconfigure the healthy aging brain to support controlled semantic cognition.

neuroscience↗

A Neurocognitive Shift in Midlife: Linking Cognitive Flexibility and Functional-Metabolic Adaptation with the SENECA model

Understanding how the older adult brain sustains cognitive flexibility remains a central question in aging research. Here, we analyzed resting-state fMRI data from the population-based CamCAN database (N = 628; age 18-88) and applied structural balance theory to measure functional network energy, a graph-theoretical proxy of network flexibility. In line with the SENECA model, our findings highlight midlife as a critical transition period: network energy is redistributed along the sensory-transmodal hierarchy, shifting from higher-level networks (DMN-FPN) to lower-level networks (SMN, CON, Auditory, Visual, Language). This reorganization (i) helps preserve a global wiring economy across the lifespan, hinting at an allostatic mechanism (i.e., stability through change); and (ii) may support embodied semantic strategies in older adulthood, leveraging more predictive processing to sustain cognitive flexibility at lower costs. Taken together, our study reframes healthy neurocognitive aging as an allostatic process and provides a reference for extending the SENECA model to metabolism and neuropathology.

neuroscience↗

Early alterations of motor learning and corticostriatal network activity in a Huntington's disease mouse model

Huntingtons disease (HD) is a neurodegenerative disorder that presents motor, cognitive and psychiatric symptoms as it progresses. Prior to motor symptoms onset, alterations and dysfunctions in the corticostriatal projections have been described along with cognitive deficits, but the sequence of early defects of brain circuits is largely unknown. There is thus a crucial need to identify early alterations that precede symptoms and that could be used as potential early disease markers. Using an HD knock-In mouse model (HdhCAG140/+) that recapitulates the human genetic alterations and that show a late and progressive appearance of anatomical and behavior deficits, we identified early alterations in the motor learning abilities of young mice, long before any motor coordination defects. In parallel, ex vivo two-photon calcium recordings revealed that young HD mice have altered basal activity patterns in both dorsomedial and dorsolateral parts of the striatum. In addition, while wild-type mice display specific reorganization of the activity upon motor training, network alterations present in the basal state of non-trained mice are not affected by motor training of HD mice. Our results thus identify early behavioral deficits and network alterations that could serve as early markers of the disease.

neuroscience↗

Lifespan Oscillatory Dynamics in Lexical Production: A Population-based MEG Resting-State Analysis

Lexical production remains relatively preserved across the lifespan, but cognitive control demands increase with age to support efficient semantic access. It suggests a domain-general and a language-specific component. Current neurocognitive models suggest the Default Mode Network (DMN) may drive the interplay between these components, impacting the trajectory of production performance with a pivotal shift around midlife. However, the corresponding time-varying architecture still needs clarification. Here, we leveraged MEG resting-state data from healthy adults aged 18-88 from a CamCAN population-based sample. We found that DMN temporal dynamics shift from anterior-ventral to posterior-dorsal states until midlife to mitigate word-finding challenges. Similarly, sensorimotor integration along this posterior path enhances cross-talk with lower-level circuitry as the dynamic information flow with more anterior, higher-order cognitive states gets compromised. It suggests a bottom-up, exploitation-based form of cognitive control in the aging brain, highlighting the interplay between abstraction, control, and perceptive-motor systems in preserving lexical production. HighlightsO_LIMidlife is a pivotal period for time-varying functional connectivity C_LIO_LIDMN activation and deactivation drive the resting-state oscillatory architecture C_LIO_LIEnhanced posterior DMN temporal dynamics mitigates lexical production decline C_LI

neuroscience↗

Dynamics of White Matter Architecture in Lexical Production among Middle-Aged Adults

This study aimed to elucidate the white matter changes associated with lexical production (LP) difficulties that typically emerge in middle age, resulting in increased naming latencies. To delay the onset of LP decline, middle-aged adults may rely on domain-general (DG) and language-specific (LS) compensatory mechanisms as proposed by the LARA model (Lexical Access and Retrieval in Aging). However, our knowledge of the white matter changes supporting these mechanisms remains incomplete. Based on a sample of 155 middle-aged adults from the CAMCAN cohort, we combined dimensionality reduction techniques with multivariate statistical methods to jointly examine the relationships between diffusion-weighted imaging and LP-related neuropsychological data. Our findings (i) show that midlife constitutes a pivotal period marked by a discontinuity in brain structure within distributed networks within dorsal, ventral, and anterior cortico-subcortical pathways, and (ii) reveal that this discontinuity signals a neurocognitive transition around age 53-54, marking the onset of LP decline. Indeed, our results propose that middle-aged adults may initially adopt a "semantic strategy" to compensate for initial LP challenges. Still, this strategy may be compromised when late middle-aged adults (age 55-60) lose the ability to exert cognitive control over semantic representations (i.e., reduced semantic control). In summary, our study advances our comprehension of brain structure changes that underpin the neurocognitive profile of LP in middle age. Specifically, we underscore the importance of considering the interplay between DG and LS processes when studying the trajectory of LP performance in healthy aging. Furthermore, these findings offer valuable insights into identifying predictive biomarkers related to the compensatory dynamics observed in midlife, which can help understand language-related neurodegenerative pathologies. HighlightsO_LIMidlife constitutes a pivotal period characterized by a discontinuity in brain structure. C_LIO_LIEarly middle-aged adults (age 45-55) adopt a "semantic strategy" to facilitate semantic access and sustain lexical production (LP) performances. C_LIO_LILate middle-aged adults (age 55-60) gradually lose the ability to exert cognitive control over semantic representations, marking the onset of LP decline. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/579645v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@85346dorg.highwire.dtl.DTLVardef@13515b7org.highwire.dtl.DTLVardef@24a1fforg.highwire.dtl.DTLVardef@1078f4c_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Modeling the Neurocognitive Dynamics of Language across the Lifespan

Healthy aging is associated with a heterogeneous decline across cognitive functions, typically observed between language comprehension and language production (LP). Examining resting-state fMRI and neuropsychological data from 628 healthy adults (age 18-88) from the CamCAN cohort, we performed state-of-the-art graph theoretical analysis to uncover the neural mechanisms underlying this variability. At the cognitive level, our findings suggest that LP is not an isolated function but is modulated throughout the lifespan by the extent of inter-cognitive synergy between semantic and domain-general processes. At the cerebral level, we show that DMN (Default Mode Network) suppression coupled with FPN (Fronto-Parietal Network) integration is the way for the brain to compensate for the effects of dedifferentiation at a minimal cost, efficiently mitigating the age-related decline in LP. Relatedly, reduced DMN suppression in midlife could compromise the ability to manage the cost of FPN integration. This may prompt older adults to adopt a more cost-efficient compensatory strategy that maintains global homeostasis at the expense of LP performances. Taken together, we propose that midlife represents a critical neurocognitive juncture that signifies the onset of LP decline, as older adults gradually lose control over semantic representations. We summarize our findings in a novel SENECA model (Synergistic, Economical, Nonlinear, Emergent, Cognitive Aging), integrating connectomic and cognitive dimensions within a complex system perspective. HighlightsO_LILexical production (LP) relies on the interplay between domain-general and semantic processes throughout life. C_LIO_LIDMN (Default Mode Network) suppression cooperates with FPN (Fronto-Parietal Network) integration to maintain LP performance at a minimal cost. C_LIO_LIMidlife marks a neurocognitive shift, with reduced DMN suppression prompting a more cost-efficient compensatory strategy that prioritizes homeostasis over LP performance. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/547510v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@85939aorg.highwire.dtl.DTLVardef@19d8bbcorg.highwire.dtl.DTLVardef@40f5e8org.highwire.dtl.DTLVardef@d0713f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

Unraveling the functional attributes of the language connectome: crucial subnetworks, flexibility and variability.

Language processing is a highly integrative function, intertwining linguistic operations (processing the language code intentionally used for communication) and extra-linguistic processes (e.g., attention monitoring, predictive inference, long-term memory). This synergetic cognitive architecture requires a distributed and specialized neural substrate. Brain systems have mostly been examined at rest. However, task-related functional connectivity provides additional and valuable information about how information is processed when various cognitive states are involved. We gathered thirteen language fMRI tasks in a unique database of one hundred and fifty neurotypical adults (InLang database). The tasks were designed to assess a wide range of linguistic processes and subprocesses. From this database, we applied network theory as a computational tool to model the task-related functional connectome of language (LANG). The organization of this data-driven neurocognitive atlas of language is examined at multiple levels, uncovering its major components (or crucial subnetworks) and its anatomical and functional correlates. Furthermore, we estimate its reconfiguration as a function of linguistic demand (flexibility), or several factors such as age or gender (variability). By accounting for the multifaceted nature of language and modulating factors, this study can contribute to enrich and refine existing neurocognitive models of language. The LANG atlas can also be considered as a reference for comparative or clinical studies, involving a variety of patients and conditions.

neuroscience↗

Spatial and temporal autocorrelation weave human brain networks

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler low-dimensional statistics is largely unknown. To explore this question, we examine resting state fMRI (rs-fMRI) data using complex topology measures from network neuroscience. We show that spatial and temporal autocorrelation are reliable statistics which explain numerous measures of network topology. Surrogate timeseries with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely-used complexity measures may help link them to neurobiology.

neuroscience↗

Low dimensional morphospace of topological motifs in human fMRI brain networks

We present a low-dimensional morphospace of fMRI brain networks, where axes are defined in a data-driven manner based on the network motifs. The morphospace allows us to identify the key variations in healthy fMRI networks in terms of their underlying motifs and we observe that two principal components (PCs) can account for 97% of the motif variability. The first PC corresponds to the small-world axis and correlates strongly with the networks global efficiency. There is also some evidence that PC1 correlates with the average length of the 5% of longest edges in the network. Hence this axis represents the trade-off between the cost of long distance edges and their topological benefits. The second PC correlates with the networks assortativity. Finally, we show that the economical clustering generative model proposed by Vertes et al. can approximately reproduce the motif PC space of the real fMRI brain networks, in contrast to other generative models. Overall, the motif morphospace provides a powerful way to visualise the relationships between network properties and to study the driving forces behind the topology of fMRI brain networks.

neuroscience↗