Biology subjects

De Baene, W.

Publications and source records attributed to De Baene, W..

4 recordsLinked to original sources

bioRxiv2026

A Multiverse Approach to Brain Age Prediction: Exploring the Impact of Modality, Bias Adjustment, and Atlas

Brain age, an individual's age predicted from neuroimaging data, is a promising biomarker of brain health. Yet brain age estimates may depend on methodological choices within the prediction pipeline. We used a multiverse approach to systematically investigate effects of data modality, bias-adjustment method, and brain parcellation on brain-age prediction using data from 560 healthy adults (18.6-89 years) from the CamCAN dataset. Across 84 pipeline conditions, chronological age was predicted from connectivity features with elastic net regression, and performance was evaluated using mean absolute error (MAE). A multilevel model tested the effects of the pipeline factors and their interactions; moreover, cross-atlas consistency of the most predictive connections was assessed. MAEs varied substantially across conditions (5.26-18.30 years). Modality and bias adjustment had significant effects on MAE and interacted significantly. Concatenating structural and functional connectivity performed similarly to structural connectivity alone, and structure-function coupling produced the highest MAE in most conditions. Beheshti-based bias adjustment outperformed Cole-based adjustment, while combining adjustment procedures did not consistently improve performance. Atlas choice had no main effect on MAE, its interaction with modality was significant but negligible in size, and finer parcellations did not systematically improve performance. However, the network-level features driving predictions showed limited spatial overlap across atlases, and importance was distributed over many features. Brain-age prediction is thus sensitive to methodological choices, particularly modality and bias-adjustment strategy. Atlas choice appears less important for predictive accuracy but may affect which connections appear most predictive. These findings highlight the importance of systematically evaluating analytical choices in brain-age research.

neuroscience↗

bioRxiv2026

Light on Broken Networks: Resting-State fNIRS as a Tool for Connectivity Mapping

Resting-state functional connectivity (RSFC) and networks (RSNs) provide insight into large-scale brain organization and its disruption in neurological disease. RSNs are most commonly assessed using fMRI, yet its translational use is constrained by high cost, motion sensitivity, and limited feasibility for repeated measurements. Functional near-infrared spectroscopy (fNIRS) offers a portable alternative, but its reliability for RSFC and RSN mapping remains insufficiently established. Near whole-head fNIRS data and fMRI-BOLD signals of corresponding cortical regions were extracted, based on which RSN organization was compared across two independent cohorts of 31 participants each. Cross-modal convergence and divergence were assessed using bivariate and partial correlations across multiple network levels. Edgewise analyses revealed substantial modality differences with bivariate correlations (50-61% of edges), which were markedly reduced using partial correlations (<3%). Group-level connectivity patterns showed moderate cross-modal similarity (r {approx} 0.37). At nodal level, net strength, local efficiency, and path-length differed substantially between modalities, while normalized strength and assortativity were largely comparable. Across nodes, group-level graph-metric distributions were broadly similar for normalized strength, assortativity, local efficiency, and path length (rho {approx} 0.27-0.5). At network-level, fNIRS-derived modules significantly overlapped with fMRI modules, particularly based on bivariate correlations, identifying default mode, attentional, executive, salience, sensorimotor, and visual networks (Jaccard {approx} 0.27-0.5). Overall, fNIRS captured key features of large-scale RSFC and RSN organization observed with fMRI, supporting meaningful cross-modal correspondence and translational utility. While partial correlations enhanced edge-level agreement, they attenuated nodal and modular recovery, suggesting greater suitability for targeted connectivity analyses rather than whole-network characterization.

neuroscience↗

bioRxiv2023

Can structure predict function at individual level in the human connectome?

Several studies predicting Functional Connectivity (FC) from Structural Connectivity (SC) at an individual level have been published in recent years, each promising increased performance and utility. We investigated three of these studies, analyzing whether the results truly represent a meaningful individual-level mapping from SC to FC. Using data from the Human Connectome Project shared accross the three studies, we constructed a predictor by averaging FC of training data and analyzed its performance in the same way. In each case, we found that this group average FC is an equivalent or better predictor of individual FC than the predictive models in terms of raw prediction performance. Furthermore, we showed that additional analyses performed by the authors of the three studies, in which they attempt to show that their predicted FC has value beyond raw prediction performance, could also be reproduced using the group average FC predictor. This makes it unclear whether any of the three methods represent a meaningful individual-level predictive model. We conclude that either the applied methods are not appropriate for the data, that the sample size is too small, or that the data itself does not contain sufficient information to learn a mapping from SC to FC e.g. due to the amount of noise in MRI measurements. We advise future individual-level studies to always explicitly report their results in comparison to the performance of the group average, and carefully demonstrate that their predictions contain meaningful individual-level information. Finally, we believe that investigating alternatives for the construction of SC and FC may improve the chances of developing a meaningful individual-level mapping from SC to FC.

neuroscience↗

bioRxiv2019

Metacognitive awareness of difficulty in action selection: the role of the cingulo-opercular network

The question whether and how we are able to monitor our own cognitive states (metacognition) has been a matter of debate for decades. Do we have direct access to our cognitive processes or can we only infer them indirectly based on their consequences? In the current study, we wanted to investigate the brain circuits that underlie the metacognitive experience of fluency in action selection. To manipulate action-selection fluency we used a subliminal response priming paradigm. On each trial, both male and female human participants additionally engaged in the metacognitive process of rating how hard they felt it was to respond to the target stimulus. Despite having no conscious awareness of the prime, results showed that participants rated incompatible trials (during which subliminal primes interfered with the required response) to be more difficult than compatible trials (where primes facilitated the required response) reflecting metacognitive awareness of difficulty. This increased sense of subjective difficulty was mirrored by increased activity in the rostral cingulate zone (RCZ) and the anterior insula, two regions that are functionally closely connected. Importantly, this reflected activations that were unique to subjective difficulty ratings and were not explained by reaction times or prime-response compatibility. We interpret these findings in light of a possible grounding of the metacognitive judgement of fluency in action selection in interoceptive signals resulting from increased effort.

neuroscience↗