Biology subjects

Richiardi, J.

Publications and source records attributed to Richiardi, J..

7 recordsLinked to original sources

bioRxiv2026

Frontoparietal effective connectivity supports working memory in early childhood

Working memory (WM), a core executive function that supports learning and reasoning, undergoes rapid development during early childhood years. Neural activation patterns underlying WM have been extensively studied in both adults and children, revealing increasing involvement of the frontoparietal network (FPN) with age and coordinated interactions with the default mode network (DMN). However, relatively little is known about how these network interactions develop in children, particularly within the visuospatial domain. The present study investigated the influence of age and task performance on effective connectivity within the FPN and its interactions with DMN hubs during a visuospatial WM task in early childhood. Functional MRI data were collected during a visual n-back task with two WM load levels (0-back and 2-back) in 120 typically developing children aged 5-8 years. Dynamic Causal Modeling (DCM) with parametric empirical Bayes was used to estimate effective connectivity among eight regions of interest spanning the FPN and DMN. Behaviorally, age-related gains were steepest under high-load for accuracy, and under low-load for reaction time, suggesting WM capacity is a demand-sensitive marker of development at this age. At the network level, high-load robustly engaged the FPN, whereas low-load was associated with relatively greater engagement of DMN and episodic memory-related regions. Baseline effective connectivity was dominated by parietal-to-frontal drive and strong self-inhibition in the anterior cingulate cortex. Age-related effects on this architecture were sparse, involving asymmetric interhemispheric frontal coupling and reduced parieto-cingulate coupling, whereas accuracy-related effects were larger in magnitude, particularly at self-connections. More accurate children showed greater excitability in frontal and parietal FPN nodes alongside increased self-inhibition in the precuneus, a DMN hub, consistent with a neural efficiency account in which better performance reflects more selective, rather than more global, network engagement. Task load further shaped connectivity in distinct ways: low-load elicited widespread inter-regional modulation, whereas high-load predominantly modulated self-connections, with age and accuracy effects on this modulation dissociating by load. These findings suggest that, in primary school children, current task performance was associated with more pronounced differences in frontoparietal effective connectivity than was age, and that WM-related network engagement becomes increasingly selective and load-dependent across this developmental window.

neuroscience↗

bioRxiv2026

Mapping corpus callosum architecture: developmental, genetic, and cognitive correlates in youth

The corpus callosum (CC), the largest interhemispheric white-matter tract, plays a central role in higher-order cognitive functions and is frequently altered in neurodevelopmental and psychiatric conditions. However, most diffusion MRI studies rely on tract-averaged measures, which obscure spatially specific microstructural variation along the tract that may hold biologically and functionally meaningful information. This study aimed to provide a fine-grained characterisation of spatial variation in callosal structure and to determine its developmental, genetic, and cognitive correlates. This study leveraged multimodal data from the Philadelphia Neurodevelopmental Cohort (PNC), comprising 1342 participants aged 8-21 years. Diffusion MRI and tractography-based segmentation were used to extract fine-grained spatial variation in diffusion metrics along seven callosal tracts. A candidate gene approach targeted four genes (DCC, CDH2, AKT3, and GLI3) previously linked to the neurobiological mechanisms underlying CC formation. Five cognitive factors were derived from the Penn Computerized Neurocognitive Battery using factor analysis. A two-stage functional data analysis approach to examine global and local associations between along-tract diffusion metrics and age, candidate genetic variants, and behavioural outcomes. Results revealed distinct midline-to-cortical variations of age-related diffusion metrics change across callosal subdivisions. Frontal and parietal heteromodal callosal pathways showed pronounced distal-segment maturation (F = 13 - 23, p [≤] 2.8x10e-16), whereas posterior sensorimotor and occipital callosal tracts exhibited more stable age associations along their lengths (F = 4.2 - 4.3, p = 10e-3). Genetic variations in callosal axon-guidance genes (ROBO1, IQCJ-SCHIP1, NRP1 and DCC) were associated with spatial variation in callosal diffusion metrics, particularly in anterior (rostrum) and posterior callosal subdivisions (isthmus and splenium) (F = 4.29 - 18.61, p-values = 2.2e-16 - 1.4e-04). These regions correspond to early-forming callosal compartments, suggesting that prenatal axon- guidance mechanisms leave enduring spatial patterns on callosal organisation. Finally, spatial variation in callosal microstructure was significantly associated with behavioural performance (F = 2.9 - 21.3, P = 2.8x10e-16 - 0.04), with the strongest and most spatially heterogeneous effects observed for complex cognition and executive functioning. Across all analyses, functional data models generally outperformed tract-averaged linear models, supporting the value of explicitly preserving spatial variation along callosal tracts. Our findings converge on the CC as a spatially differentiated structure in which early genetic modulators and developmental constraints shape region- and segment-specific microstructural architecture that is behaviourally relevant through childhood and adolescence.

neuroscience↗

bioRxiv2024

Defacing biases visual quality assessments of structural MRI

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. This introduces undesired variability into downstream analysis and interpretation. This registered report investigated the degree to which the so-called defacing altered the quality assessment of T1-weighted images of the human brain from the openly available "IXI dataset". The effect of defacing on manual quality assessment was investigated on a single-site subset of the dataset (N=185). By comparing two linear mixed-effects models, we determined that four trained human raters perception of quality was significantly influenced by defacing by modeling their ratings on the same set of images in two conditions: "nondefaced" (i.e., preserving facial features) and "defaced". In addition, we investigated these biases on automated quality assessments by applying repeated-measures, multivariate ANOVA (rm-MANOVA) on the image quality metrics extracted with MRIQC on the full IXI dataset (N=581; three acquisition sites). This study found that defacing altered the quality assessments by humans and showed that MRIQCs quality metrics were mostly insensitive to defacing.

bioinformatics↗

bioRxiv2024

Do you see what I see? Diffusion imaging protocol heterogeneity biases ischemic core volume, location, and clinical associations in acute stroke

PurposeDiffusion-weighted magnetic resonance imaging (DWI) is essential for diagnosing ischemic stroke and identifying targets for emergency revascularization. Apparent diffusion coefficient (ADC) maps derived from DWI are commonly used to locate the infarct core, but they are not strictly quantitative and can vary across platforms and sites due to technical factors. This retrospective study was conducted to examine how differences in ADC map generation, resulting from varied protocols across platforms and sites, affect the determination of infarct core size, location, and related clinical outcomes in acute stroke. MethodsIn this retrospective study, 726 acute anterior circulation stroke patients admitted to the Lausanne University Hospital between May 2018 and January 2021 were selected. DWI data were used to generate ADC maps as they would appear from different protocols: two simulated with low and medium angular resolution (4 and 12 diffusion gradient directions) and one with high angular resolution (20 directions). Using a DEFUSE like criteria and image post-processing, ischemic cores were localized; core volume, location, and associations to National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS) scores were compared between the two imaging sequences. ResultsSignificant differences were observed in the ADC distribution within white matter, particularly in the kurtosis and skewness, with the segmented infarct core volume being higher in protocols with reduced angular resolution compared to the 20-directions data (7.63 ml vs. 3.78 ml). The volumetric differences persisted after correcting for age, sex, and type of intervention. Infarcted voxels locations varied significantly between the two protocols. This variability affected associations between infarct core volume and clinical scores, with lower associations observed for 4-directions data compared to 20-directions data for NIHSS at admission and after 24 hours, and mRS after 3 months, further confirmed by multivariate regression. ConclusionsImaging protocol heterogeneity leads to significant changes in the ADC distribution, ischemic core location, size, and association with clinical scores. Work is needed in standardizing imaging protocols to improve the reliability of ADC as an imaging biomarker in stroke management.protocols to improve the reliability of ADC as an imaging biomarker in stroke management. Key ResultsO_LIOrientation changes in diffusion imaging significantly impact ADC distribution and threshold-based infarct core volume determination, affecting multi-centric studies. C_LIO_LILower number of directions in DWI acquisitions weakens associations between infarct volume measurements and clinical scores. C_LIO_LIFindings emphasize the impact of DWI acquisition protocol heterogeneity and image processing on acute stroke workups. C_LI

neuroscience↗

bioRxiv2021

Preparing for a second attack: a lesion simulation study on network resilience after stroke

Background and PurposeDoes the brain become more resilient after a first stroke to reduce the consequences of a new lesion? Although recurrent strokes are a major clinical issue, whether and how the brain prepares for a second attack is unknown. This is due to the difficulties to obtain an appropriate dataset of stroke patients with comparable lesions, imaged at the same interval after onset. Furthermore, timing of the recurrent event remains unpredictable. MethodsHere we used a novel clinical lesion simulation approach to test the hypothesis that resilience in brain networks increases during stroke recovery. 16 patients with a lesion restricted to the primary motor cortex were recruited. At 3 time points of the index event (10 days, 3 weeks, 3 months), we mimicked recurrent infarcts by deletion of nodes in brain networks (resting-state fMRI). Graph measures were applied to determine resilience (global efficiency) and wiring cost (mean degree) of the network. ResultsAt 10 days and 3 weeks after stroke, resilience was similar in patients and controls. However, at 3 months, while motor function had fully recovered, resilience to clinically representative simulated lesions was higher compared to controls (cortical lesion p=0.012; subcortical: p=0.009; cortico-subcortical: p=0.009). Similar results were found after random (p=0.012) and targeted (p=0.015) attacks. ConclusionOur results suggest that, after a lesion, brain networks reconfigure to increase resilience to future insults. Lesion simulation is an innovative approach, which may have major implications for stroke therapy. Individualized neuromodulation strategies could be developed to foster resilient network reconfigurations after a first stroke to limit the consequences of future attacks.

neuroscience↗

bioRxiv2020

Genetic variation in CSMD1 affects amygdala connectivity and prosocial behavior

The amygdala is one of the most widely connected structures in the primate brain and plays a key role in social and emotional behavior. Here, we present the first genome-wide association study (GWAS) of whole-brain resting-state amygdala networks to discern whether connectivity in these networks could serve as an endophenotype for social behavior. Leveraging published resting-state amygdala networks as a priori endophenotypes in a GWAS meta-analysis of two adolescent cohorts, we identified a common polymorphism on chr.8p23.2 (rs10105357 A/G, MAF (G)=0.35) associated with stronger connectivity in the medial amygdala network (beta=0.20, p=2.97x10-8). This network contains regions that support reward processes and affiliative behavior. People carrying two copies of the minor allele for rs10105357 participate in more prosocial behaviors (t=2.644, p=0.008) and have higher CSMD1 expression in the temporal cortex (t=3.281, p=0.002) than people with one or no copy of the allele. In post-mortem brains across the lifespan, we found that CSMD1 expression is relatively high in the amygdala (2.79 fold higher than white matter, p=1.80x10-29), particularly so for nuclei in the medial amygdala, reaching a maximum in later stages of development. Amygdala network endophenotyping has the potential to accelerate genetic discovery in disorders of social function, such as autism, in which CSMD1 may serve as a diagnostic and therapeutic target.

neuroscience↗

bioRxiv2017

Distance Is Not Everything In Imaging Genomics Of Functional Networks: Reply To A Commentary On Correlated Gene Expression Supports Synchronous Activity In Brain Networks

Our 2015 paper (Richiardi et al., 2015), showed that transcriptional similarity of gene expression level is higher than expected by chance within functional brain networks (defined by functional magnetic resonance imaging), a relationship that is driven by around 140 genes. These results were replicated in vivo in adolescents, where we showed that SNPs of these genes where associated above chance with in-vivo fMRI connectivity, and in the mouse, where mouse orthologs of our genes showed above-chance association with meso-scale axonal connectivity. This paper has received a commentary on biorXiv (Pantazatos and Li, 2016), making several claims about our results and methods, mainly pointing out that Euclidean distance explains our results (\"...high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks...\"). Here we address these claims and their weaknesses, and show that our original results stand, contrary to the claims made in the commentary.

neuroscience↗