Biology subjects

Gracia-Tabuenca, Z.

Publications and source records attributed to Gracia-Tabuenca, Z..

9 recordsLinked to original sources

bioRxiv2024

Developmental changes in hippocampal neurite colocalize with the expression of genes involved in modulating low-theta oscillations

The hippocampus is a critical brain structure supporting memory encoding and retrieval, yet the development of its microstructure in humans remains unknown. Understanding this development may provide insight into the mechanisms underlying memory and their disruption in disease. To address this, we non-invasively estimated the density and branching complexity of neurite (dendrites, axons, glial processes) using diffusion-weighted MRI in 364 participants aged 8-21. With development, we observed large increases in neurite density and branching complexity that persisted until approximately 15 years of age before stabilizing at adult-like values. Increases in neurite density were relatively homogenous across hippocampal axes, whereas increases in branching complexity were heterogeneous; increasing primarily in CA1, SRLM, subiculum, and anterior hippocampus. To assess whether this development may be attributable to specific cell-types, we tested for spatial overlap between age-related change in neurite and the cell-type composition of hippocampal tissue via cross-reference with an out-of-sample gene-expression atlas. We found age-related changes in neurite density spatially overlapped with a granule cell component; whereas age-related changes in neurite branching complexity overlapped with a pyramidal neuron component. These results provide the first glimpse at the nonlinear maturation of hippocampal microstructure and the cell-type composition of hippocampal tissue underlying these changes.

neuroscience↗

bioRxiv2023

Enhanced efficiency in the bilingual brain through the inter-hemispheric cortico-cerebellar pathway in early second language acquisition

The bilingual experience has a profound impact on the functional and structural organization of the brain, but it is not yet well known how this experience influences whole-brain functional network connectivity. We examined a well-characterized large sample (151 participants) of monolinguals and bilinguals with varied age of second language acquisition, who underwent resting-state functional magnetic brain imaging. We constructed comprehensive functional brain networks for each participant, encompassing cortical, subcortical, and cerebellar regions of interest. Whole-brain analyses revealed that bilingual individuals exhibit higher global efficiency than monolinguals, indicating enhanced functional integration in the brain. Moreover, the age at which the second language was acquired correlated with this increased efficiency, suggesting that earlier exposure to a second language has lasting positive effects on brain functional organization. Further investigation through the network-based statistics (NBS) approach indicates that this effect is primarily driven by heightened functional connectivity between association networks and the cerebellum. This work shows that early learning enhances global whole-brain efficiency and that the timing of learning of two languages has an impact on functional brain organization. Significance statementLong-term learning impacts brain organization at different spatial scales, and this may be particularly enhanced during early stages of life. Bilingualism offers a unique opportunity to test long-term learning effects in the human brain, given that exposure to a second language can occur from birth or later in life, and can be maintained over long periods of time. We found that second language acquisition in early childhood (before five years of age) enhances brain network efficiency, and that this effect goes beyond the language and cognitive control regions, in fact, the interhemispheric cortico-cerebellar circuit plays a key role. This work shows that the timing of bilingual learning experience alters the brain functional organization at the global and local levels.

neuroscience↗

bioRxiv2023

The developmental trajectory of 1H-MRS brain metabolites from childhood to adulthood.

Human brain development is ongoing throughout childhood, with for example myelination of nerve fibres and refinement of synaptic connections continuing until early adulthood. 1H-Magnetic Resonance Spectroscopy (1H-MRS) can be used to quantify the concentrations of endogenous metabolites (e.g., glutamate and {gamma}-aminobutyric acid (GABA)) in the human brain in vivo and so can provide valuable, tractable insight into the biochemical processes that support postnatal neurodevelopment. This can feasibly provide new insight into and aid management of neurodevelopmental disorders by providing chemical markers of atypical development. This study aims to characterize the normative developmental trajectory of various brain metabolites, as measured by 1H-MRS from a midline posterior parietal voxel. We find significant non-linear trajectories for GABA+, Glx, tNAA and tCr concentrations. Glx and GABA+ concentrations steeply decrease across childhood. tNAA concentrations are relatively stable in childhood but gradually decrease from early adulthood, while tCr concentrations increase from childhood to early adulthood. tCho was the only metabolite to have a strictly linear association with age. Trajectories likely reflect fundamental neurodevelopmental processes (including local circuit refinement) which occur from childhood to early adulthood and can be associated with cognitive development; we find GABA+ concentrations significantly positively correlate with recognition memory scores across post-natal development.

neuroscience↗

bioRxiv2023

Predicting depression risk in early adolescence via multimodal brain imaging

Depression is an incapacitating psychiatric disorder with high prevalence in adolescent populations that is influenced by many risk factors, including family history of depression. The ability to predict who may develop depression before adolescence, when rates of depression increase markedly, is important for early intervention and prevention. Using a large longitudinal sample from the Adolescent Brain Cognitive Development (ABCD) Study (2658 participants after imaging quality control, between 9-10 years at baseline), we applied machine learning methods on a set of comprehensive multimodal neuroimaging features to predict depression risk at the two-year follow-up from the baseline visit. Features include derivatives from structural MRI, diffusion tensor imaging, and task and rest functional MRI. A rigorous cross-validation method of leave-one-site-out was used. Additionally, we tested the prediction models in a high-risk group of participants with parental history of depression (N=625). The results showed all brain features had prediction scores significantly better than expected by chance. When predicting depression onset in the high-risk group, brain features from resting-state functional connectomes showed the best classification performance, outperforming other brain features based on structural MRI and task-based fMRI. Results demonstrate that the functional connectivity of the brain can predict the risk of depression in early adolescence better than other univariate neuroimaging derivatives, highlighting the key role of the interacting elements of the connectome capturing more individual variability in psychopathology compared to measures of single brain regions.

neuroscience↗

bioRxiv2021

Development of the Functional Connectome Topology in Adolescence: evidence from Topological Data Analysis

Adolescence is a crucial developmental period in terms of behavior and mental health. Therefore, understanding how the brain develops during this stage is a fundamental challenge for neuroscience. Recent studies have modelled the brain as a network or connectome, mainly applying measures from graph theory, showing a change in its functional organization such as an increase in its segregation and integration. Topological Data Analysis (TDA) complements such modelling by extracting high-dimensional features across the whole range of connectivity values, instead of exploring a fixed set of connections. This study enquiries into the developmental trajectories of such properties using a longitudinal sample of typically developing participants (N = 98; 53/45 F/M; 6.7-18.1 years), applying TDA into their functional connectomes. In addition, we explore the effect of puberty on the individual developmental trajectories. Results showed that compared to random networks, the adolescent brain is more segregated at the global level, but more densely connected at the local level. Furthermore, developmental effects showed nonlinear trajectories for the integration of the whole brain and fronto-parietal networks, with an inflection point and increasing trajectories after puberty onset. These results add to the insights in the development of the functional organization of the adolescent. Significance StatementTopological Data Analysis may be used to explore the topology of the brain along the whole range of connectivity values instead of selecting only a fixed set of connectivity thresholds. Here, we explored some properties of the topology of the brain functional connectome, and how they develop in adolescence. First, we show that developmental trajectories are nonlinear and better explained by the puberty status than chronological age, with an inflection point around the puberty onset. The greatest effect is the increase in functional integration for the whole brain, and particularly for the Fronto-Parietal Network when exploring functional subnetworks.

neuroscience↗

bioRxiv2020

NBR: Network-based R-statistics for (unbalanced) longitudinal samples

Network neuroscience models the brain as interacting elements. However, a large number of elements imply a vast number of interactions, making it difficult to assess which connections are relevant and which are spurious. Zalesky et al. (2010) proposed the Network-Based Statistics (NBS), which identifies clusters of connections and tests their likelihood via permutation tests. This framework shows a better trade-off of Type I and II errors compared to conventional multiple comparison corrections. NBS uses General Linear Hypothesis Testing (GLHT), which may underestimate the within-subject variance structure when dealing with longitudinal samples with a varying number of observations (unbalanced samples). We implemented NBR, an R-package that extends the NBS framework adding (non)linear mixed-effects (LME) models. LME models the within-subject variance in more detail, and deals with missing values more flexibly. To illustrate its advantages, we used a public dataset of 333 human participants (188/145 females/males; age range: 17.0-28.4 y.o.) with two (n=212) or three (n=121) sessions each. Sessions include a resting-state fMRI scan and psychometric data. State anxiety scores and connectivity matrices between brain lobes were extracted. We tested their relationship using GLHT and LME models for balanced and unbalanced datasets, respectively. Only the LME approach found a significant association between state anxiety and a subnetwork that includes the cingulum, frontal, parietal, occipital, and cerebellum. Given that missing data is very common in longitudinal studies, we expect that NBR will be very useful to explore unbalanced samples. Significant StatementLongitudinal studies are increasing in neuroscience, providing new insights into the brain under treatment, development, or aging. Nevertheless, missing data is highly frequent in those studies, and conventional designs may discard incomplete observations or underestimate the within-subject variance. We developed a publicly available software (R package: NBR) that implements mixed-effect models into every possible connection in a sample of networks, and it can find significant subsets of connections using non-parametric permutation tests. We demonstrate that using NBR on larger unbalanced samples has higher statistical power than when exploring the balanced subsamples. Although this method is applicable in general network analysis, we anticipate this method being potentially useful in systems neuroscience considering the increase of longitudinal samples in the field.

neuroscience↗

bioRxiv2020

Development of the Brain Functional Connectome Follows Puberty-Dependent Nonlinear Trajectories

Adolescence is a developmental period that dramatically impacts body and behavior, with pubertal hormones playing an important role not only in the morphological changes in the body but also in brain structure and function. Understanding brain development during adolescence has become a priority in neuroscience because it coincides with the onset of many psychiatric and behavioral disorders. However, little is known about how puberty influences the brain functional connectome. In this study, taking a longitudinal human sample of typically developing children and adolescents (of both sexes), we demonstrate that the development of the brain functional connectome better fits pubertal status than chronological age. In particular, centrality, segregation, efficiency, and integration of the brain functional connectome increase after the onset of the pubertal markers. We found that these effects are stronger in attention and task control networks. Lastly, after controlling for this effect, we showed that functional connectivity between these networks is related to better performance in cognitive flexibility. This study points out the importance of considering longitudinal nonlinear trends when exploring developmental trajectories, and emphasizes the impact of puberty on the functional organization of the brain in adolescence. Significance StatementUnderstanding the brain organization along development is a crucial challenge for Neuroscience. In particular, during adolescence there is a great impact in body and cognitive functions as well as substantial incidence of mental health disruptions. Here, we tested how brain organization changes along this period based on the properties of the functional connectome in a longitudinal pediatric sample. We found a nonlinear increase in the connectivity and the brain network efficiency, particularly after the onset of puberty. These effects were more prominent in association networks. In addition, higher connectivity in those areas was associated with better performance in cognitive flexibility. These results demonstrate the importance of considering pubertal assessment as well as nonlinear trends in developmental studies.

neuroscience↗

bioRxiv2019

The Time Varying Networks of the Interoceptive Attention and Rest

Focused attention to spontaneous sensations is a phenomenon that demands interoceptive abilities and a dynamic character of attentive processes. The lack of its control has been linked to neuropsychiatric disorders, such as illness-anxiety disorder. Regulatory strategies, like focused attention meditation, may enhance the ability to control attention particularly to body sensations, which can be reflected on functional neuroanatomy. Adopting a systems-level approach, we aimed at estimating the recurring fMRI functional connectivity (FC) patterns between regions of the dorsal attention network, default mode network, and frontoparietal network during 20 minutes of an attentional task to spontaneous sensations (Task), and at rest, before (Pre-task rs) and after the task (Post-task rs), in fifteen experienced meditators. Dynamic functional connectivity analysis was performed using sliding windows and k-means clustering on the grouped data finding five FC patterns. In both rest conditions the subjects remain longer in a low connectivity state, in contrast, during the task a higher proportion of time spent in complex organization states was preferred. Moreover, an impact over the post-task rs FC was observed as an effect of the preceding interoceptive task performance, with this remaining effect probably taking an active role in the learning process linked to cognitive training.Competing Interest StatementThe authors have declared no competing interest.View Full Text

neuroscience↗

bioRxiv2019

Topological Data Analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in Attention-Deficit/Hyperactivity Disorder

The functional organization of the brain network (connectome) has been widely studied as a graph; however, methodological issues may affect the results, such as the brain parcellation scheme or the selection of a proper threshold value. Instead of exploring the brain in terms of a static connectivity threshold, this work explores its algebraic topology as a function of the filtration value (i.e., the connectivity threshold), a process termed the Rips filtration in Topological Data Analysis. Specifically, we characterized the transition from all nodes being isolated to being connected into a single component as a function of the filtration value, in a public dataset of children with attention-deficit/hyperactivity disorder (ADHD) and typically developing children. Results were highly congruent when using four different brain segmentations (atlases), and exhibited significant differences for the brain topology of children with ADHD, both at the whole brain network and at the functional sub-network levels, particularly involving the frontal lobe and the default mode network. Therefore, this approach may contribute to identify the neurophysio-pathology of ADHD, reducing the bias of connectomics-related methods.\n\nHighlightsO_LITopological Data Analysis was implemented in functional connectomes.\nC_LIO_LIBetti curves were assessed based on the area under the curve, slope and kurtosis.\nC_LIO_LIThe explored variables were robust along four different brain atlases.\nC_LIO_LIADHD showed lower areas, suggesting decreased functional segregation.\nC_LIO_LIFrontal and default mode networks showed the greatest differences between groups.\nC_LI\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=168 HEIGHT=200 SRC=\"FIGDIR/small/751008v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (51K):\norg.highwire.dtl.DTLVardef@50e91corg.highwire.dtl.DTLVardef@1f837aforg.highwire.dtl.DTLVardef@10fe920org.highwire.dtl.DTLVardef@1923ead_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗