Biology subjects

Cauda, F.

Publications and source records attributed to Cauda, F..

8 recordsLinked to original sources

bioRxiv2026

Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

Cognitive decline is characterized by substantial inter-individual variability across the lifespan, even among clinically healthy adults. However, it is unclear how structural and functional brain organization jointly relate to differences in cognitive performance within the typical populations. We considered a multimodal brain age framework by predicting for each subject its age from either structural MRI or resting-state functional MRI, after training on the 1000BRAINS cohort (901 participants, 18-85 years). Following, a structural and a functional brain age gap markers were defined as the deviations with respect to the real chronological age. We then used the markers to characterize the interplay between structure and function in shaping cognitive performances. Our findings reveal that the functional marker is as important to predict global cognitive performance as the structural marker. The most accurate prediction was found for global cognition, executive functions and episodic memory, followed by working memory, attention and language. In particular, the structural marker was contributing to all domains, except for working memory, whereas the functional marker had the highest contribution. Overall, structural and functional brain age models together provide a meaningful subject-level summary of brain information for predicting cognition, supporting a multidimensional view of typical aging.

neuroscience↗

bioRxiv2022

A minimum Bayes Factor based threshold for activation likelihood estimation

Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical p-value selected. However, this is not informative in terms of probabilities of the validity of the hypotheses. Here, we describe an innovative thresholding procedure based on the concept of minimum Bayes factor (mBF). The use of the Bayesian framework allows to consider different levels of probability, each of these being equally significant. In order to simplify the translation between the common ALE practice and the proposed approach, we analised six task-fMRI/VBM datasets and determined the mBF values equivalent to the currently recommended frequentist thresholds based on Family Wise Error (FWE). Sensitivity and robustness toward spurious findings were also analyzed. Results showed that the cutoff log10(mBF)=5 is equivalent to the FWE threshold, often referred as voxel-level threshold, while the cutoff log10(mBF)=2 is equivalent to the cluster-level FWE (c-FWE) threshold. However, only in the latter case voxels spatially far from the blobs of effect in the c-FWE ALE map survived. Therefore, when using the Bayesian thresholding the cutoff log10(mBF)=5 should be preferred. However, being in the Bayesian framework, lower values are all equally significant, while suggesting weaker level of force for that hypothesis. Hence, results obtained through less conservative thresholds can be legitimately discussed without losing statistical rigor. The proposed technique adds therefore a powerful tool to the human-brain-mapping field.

neuroscience↗

bioRxiv2021

A meta-analysis of areas of structural variation in grey matter in individuals with Autism Spectrum Disorder (ASD) in relation to gene expression of candidate ASD genes

Autism Spectrum Disorder (ASD) is a set of developmental pathologies with a strong genetic basis and high heritability. Although neuroimaging studies have indicated anatomical changes in grey matter (GM) morphometry, their associations with gene expression remain elusive. In the present study, we aim to understand how gene expression correlates with structural brain aberration in ASD and how it distributes in a functional network perspective. First, we performed an activation likelihood estimation (ALE) meta-analysis to determine GM alteration in the brain, then we selected genes from the SHANK, NRXN, NLGN family and MECP2, which have been implicated with ASD, particularly in regards to altered synaptic transmission. Gene expression maps were built. We then assessed the correlation between the gene expression maps and the GM alteration maps. We found that the default mode network regions were the most significantly correlated with gene expression of selected genes in both areas of GM decrease and increase. The dorsal attention and the cerebellar network regions are significantly correlated with ASD genes. Different networks, namely somatomotor, limbic and basal ganglia/thalamus network - were found in the increase; for each of these networks, however, only a few genes were significant. Our approach allowed to combine the well beaten path of genetic and brain imaging in a novel way, to specifically investigate the relation between gene expression and brain with structural damage, and individuate genes of interest for further investigation in specific functional networks.

neuroscience↗

bioRxiv2021

Default Mode Network spatial configuration varies across task domains

Recent developments in network neuroscience suggest reconsidering what we thought we knew about the Default Mode Network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation maps were then computed. An additional map of task-induced deactivations was also created. A Multidimensional Scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to the involvement of lateral cortices in more external tasks. Further investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the variability of task-induced DMN anatomic redistribution was hard to recapitulate, as none of the maps, or any linear combination of them, could represent the whole space of its dynamical reconfiguration. Altogether, our findings suggest that the DMN may be characterized by a richer functional diversity and a more spatial complexity than previously suggested.

neuroscience↗

bioRxiv2020

A meta-analytic approach to mapping co-occurrent grey matter volume increases and decreases in psychiatric disorders

Numerous studies have investigated gray matter (GM) volume changes in diverse patient groups. Reports of disorder-related GM reductions are common in such work, but many studies also report evidence for GM volume increases in patients. It is unclear whether these GM increases and decreases independent or related in some way. Here, we address this question using a novel meta-analytic network mapping approach. We used a coordinate-based meta-analysis of 64 voxel-based morphometry studies of psychiatric disorders to calculate the probability of finding a GM increase or decrease in one region given an observed change in the opposite direction in another region. Estimating this co-occurrence probability for every pair of brain regions allowed us to build a network of concurrent GM changes of opposing polarity. Our analysis revealed that disorder-related GM increases and decreases are not independent; instead, a GM change in one area is often statistically related to a change of opposite polarity in other areas, highlighting distributed yet coordinated changes in GM volume as a function of brain pathology. Most regions showing GM changes linked to an opposite change in a distal area were located in salience, executive-control and default mode networks, as well as the thalamus and basal ganglia. Moreover, pairs of regions showing coupled changes of opposite polarity were more likely to belong to different canonical networks than to the same one. Our results suggest that regional GM alterations in psychiatric disorders are often accompanied by opposing changes in distal regions that belong to distinct functional networks.

neuroscience↗

bioRxiv2019

Hubs of long-distance co-alteration in brain pathology

The exact mechanisms at the root of pathologic anatomical covariances are still unknown. It is nonetheless becoming clearer that the impact of brain diseases is more convincingly represented in terms of co-alterations rather than in terms of localization of alterations. According to this view, neurological and psychiatric conditions might be seen as whole-brain patterns of modifications. In this context, the physical distance between two co-altered areas may provide insightful information about how pathology develops across the brain, assuming that long-range co-alterations might be relevant features of pathological networks. To demonstrate this hypothesis, we calculated the probability of co-alteration between brain areas across a large database of voxel-based morphometry studies of psychiatric and neurological disorders, and we investigated the physical (Euclidean) distance of the edges of the resulting network. Such analysis produced a series of observations relevant for the understanding of pathology, which range from unanticipated results to the recognition of regions of well-known functional and clinical relevance. For instance, it emphasizes the importance of the anterior and dorsal prefrontal cortices in the distribution of the disease-related alterations, as well as a specular asymmetry of gray matter decreases and increases between the hemispheres. Also, the analyses of schizophrenia and Alzheimers disease show that long-distance co-alterations are able to identify areas involved in pathology and symptomatology. Moreover, the good concordance between the measure of the mean physical distance and that of functional degree centrality suggests that co-alterations and connectivity are intimately related. These findings highlight the importance of analyzing the physical distance in pathology, as the areas characterized by a long mean distance may be considered as hubs with a crucial role in the systems of alterations induced by brain diseases.

neuroscience↗

bioRxiv2019

Addressing reverse inference in structural brain alterations

In neuroimaging with reverse inferences we can infer the involvement of cognitive processes from certain patterns of brain activity. Still, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder". To assess the involvement of a certain alteration pattern in a brain disorder we used the Bayes factor technique on voxel-based morphometry data of schizophrenia and Alzheimers disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alteration spread associated with different pathologies. The Bayes factor values calculated on these simulated data were able to reveal that the areas which are more specific to a certain disease are also the ones to be early altered.

neuroscience↗

bioRxiv2017

Specific patterns of bold variability associated with the processing of pain stimuli

It is well known that the blood oxygen level dependent (BOLD) signal varies according to task performance and region specificity. This ongoing and fluctuating activity reflects the organization of functional brain networks. Peculiar dynamics of BOLD signal are therefore supposed to characterize brain activity in different conditions. Within this framework, we investigated through a multivoxel pattern analysis whether patterns of BOLD variability convey information that may allow an efficient discrimination between task (i.e., painful stimulation) and rest conditions. We therefore identified the most discriminative brain areas between the two conditions, which turned out to be the anterior insula, dorsal anterior cingulate cortex, posterior insula, the thalamus, and the periaqueductal gray. Then, on the basis of information theory, we calculated the entropy of their different time series. Entropy was found to distribute differently between these brain areas. The posterior insula was found to be is the smaller contributor to the entropy rate, whereas the system formed by the thalamus and periaqueductal gray was found to be the major contributor. Overall, the brain system reaches a higher level of entropy during the rest condition, which suggests that cerebral activity is characterized by a larger informational space when the brain is at rest than when it is engaged in a specific task. Thus, this study provides evidence that: i) the pattern of BOLD variance allow a good discrimination between the conditions of rest and pain stimulation; ii) the discriminative pattern resembles closely that of the functional network that has been called pain matrix; iii) brain areas with high and low variability are characterized by a different sample entropy; iv) the entropy rate of cerebral regions can be an insightful parameter to better understand the complex dynamics of the brain.

neuroscience↗