Biology subjects

Diez, I.

Publications and source records attributed to Diez, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Bridging Higher-Order Information Theory and Neuroimaging: A Voxel-Wise O-Information Framework

The brains functional organization has been extensively studied through pairwise connectivity analyses. While these approaches have provided important insights into brain network organization, they fall short in capturing the complexity of high-order functional interactions (HOI). Particularly relevant is the investigation of redundancy and synergy patterns -not addressable with pairwise interactions-, revealing fundamental mechanisms of brain integration and information processing across various cognitive functions and clinical conditions. Conventional neuroimaging software packages are primarily designed for classical (general linear model-like) analyses but lack native support for HOI metrics. To address this gap, this study introduces a novel framework that bridges high-order information theory with conventional neuroimaging analysis pipelines and is subsequently applied to resting-state functional MRI to demonstrate its practical utility. By representing HOI into voxel-level metrics, our approach allows standard neuroimaging analyses to probe complex multivariate dependencies. Moreover, voxel-level group-comparison analyses show age differences linked with reduced redundancy in default mode network interactions. These findings advance our understanding of the complex relationship between multivariate functional interactions, voxel-level neuroimaging, and behavior, highlighting novel analytic strategies to study high-order information processing underlying cognitive function and its alterations in pathological conditions.

neuroscience↗

bioRxiv2026

Resting-state Compensatory Remapping in Patients with Brain Tumour Before and After Surgery

Brain tumours invade neural tissue, disrupting the functional organisation of neural networks. This disruption can trigger compensatory neuroplastic mechanisms that help preserve cognitive function despite pathological burden. Resting-state functional connectivity (rs-FC) provides a valuable approach for examining these alterations, yet the scope and trajectory of network-level changes remain unclear. In this study, we used rs-FC to characterise compensatory responses induced by left-hemisphere brain tumours affecting the integrity of the language network. The comparison of connectivity patterns at diagnosis and after surgery allowed us to track the temporal progression of these patterns. Relative to healthy participants, patients showed significant deviations in pre-surgical rs-FC, with widespread changes involving the dorsal attention, sensorimotor, frontoparietal, default mode, and cerebellar networks. These differences persisted post-surgically, with no significant longitudinal modifications. Network-level alterations also influenced topological properties: patients showed increased segregation while preserving global integration relative to controls. These properties changed significantly from pre- to postoperative period, with a postoperative increase in segregation and integration towards a pattern more closely resembling that of healthy controls. Furthermore, preoperative network segregation was identified as a predictor of postoperative cognitive recovery. These findings suggest that brain tumours induce network remapping, affecting functional connectivity far beyond the regions strictly related to the affected region. The observed changes in network topological organisation likely reflect the adaptive role of domain-general networks in facilitating homeostatic processes. Surgical strategies aimed at preserving these changes seem to promote sustained cognitive function, regardless of malignancy grade or lesion extent. Key pointsO_LIPresurgical patients show widespread inter- and intrahemispheric rs-FC alterations, reflecting tumour-driven network remapping. C_LIO_LINetwork integration and segregation show longitudinal recovery, approaching neurotypical topology four months post-surgery. C_LIO_LIPreoperative network segregation predicts postoperative cognitive recovery. C_LI Importance of the StudyBrain tumours disrupt large-scale brain networks, yet the extent, temporal dynamics, and cognitive relevance of these alterations remain poorly understood. Using resting-state functional connectivity, this study provides a longitudinal, network-level characterisation of tumour-induced neuroplasticity in patients with left-hemisphere lesions affecting language-related regions. Compared with prior work focused on local effects or single networks, our results demonstrate widespread inter- and intrahemispheric connectivity changes involving multiple domain-general resting-state networks, highlighting extensive network remapping beyond lesion boundaries. Importantly, we show that postoperative recovery is accompanied by a rebalancing of network integration and segregation toward a neurotypical configuration. Critically, preoperative network segregation emerges as a predictor of postoperative cognitive recovery, identifying a potential biomarker of resilience. These findings have direct translational relevance, suggesting that preserving and supporting large-scale network organisation during surgical planning may promote cognitive outcomes independently of tumour grade or lesion extent.

neuroscience↗

bioRxiv2025

Brain structural modules associated to functional high-order interactions in the human brain,

The brains modular organization, ranging from microcircuits to large-scale networks, has been extensively studied in terms of its structural and functional properties. Particularly insightful has been the investigation of the coupling between structural connectivity (SC) and functional connectivity (FC), whose analysis has revealed important insights into the brains efficiency and adaptability related to various cognitive functions. Interestingly, links in SC are intrinsically pairwise but this is not the case for FC; and while recent work demonstrates the relevance of the brains high-order interactions (HOI), the coupling of between SC and functional HOI remains unexplored. To address this gap, this study leverages functional MRI and diffusion weighted imaging to delineate the brains modular structure by investigating the coupling between SC and functional HOI. Our results demonstrates that structural networks can be associated with both redundant and synergistic functional interactions. In particular, SC exhibits both positive and negative correlations with redundancy, it shows consistent positive correlations with synergy, indicating that a higher density of structural connections is linked to increased synergistic interactions. These findings advance our understanding of the complex relationship between structural and high-order functional properties, shedding light on the brains architecture underlying its modular organization. Significance StatementUnderstanding the intricate relationship between the brain structural and functional connectivity is instrumental for enabling a richer characterization of cognitive functions. This challenge is complex, since it involves comparing pairwise links representing white-matter fibers with higher-order functional interactions that can involve multiple brain regions sharing information -- indeed, these interactions can be redundant in presence of shared information, or synergistic when novel information arises in the whole subsystem. Here we advance the investigation of the coupling between brain structure and high-order interactions, offering new insights into how structural properties are associated with both redundant and synergistic functional modules. Furthermore, the approach presented here opens new doors to investigate how this coupling between structural connectivity and functional HOI is modulated during development or under diverse brain pathologies.

neuroscience↗

bioRxiv2025

Mechanical strain modulates Min patterning and division in E. coli filaments

Bacteria often encounter physico-chemical stresses that disrupt division, leading to filamentation, where cells elongate without dividing. While this adaptive response enhances survival, it also exposes filaments to significant mechanical strain, raising questions about the mechanochemical feedback in bacterial systems. In this study, we investigate how mechanical strain influences the Min oscillatory system, a reaction-diffusion network central to division in Escherichia coli. Through a multidisciplinary approach combining quantitative fluorescence microscopy, biophysical modeling, microfluidics, and patterned growth substrates, we demonstrate that filamentous E. coli undergoes growth-induced buckling instability. This phenomenon alters the diffusivity of membrane proteins and modulates the spatiotemporal patterning of the Min system. Moreover, we show that this mechanochemical interplay determines division site positioning after stress relief, effectively creating a mechanical "memory" for cytokinesis. Our findings underscore the critical role of mechanical forces in bacterial filamentation and provide new insights into the functional implications of mechanobiology in microbial systems.

biophysics↗

bioRxiv2023

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain

The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions. Addressing this issue, we provide open datasets and code enabling the examination of the correspondence between structural and functional connectivities at different scales. We present a module-level strategy that overcomes region-level approaches in understanding the structure-function correspondence. Moreover, we also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

neuroscience↗

bioRxiv2022

Autism excitation-inhibition imbalance linked to brain hyperconnectivity: An analysis based on 657 autistic subjects

The large heterogeneity in autism spectrum disorder (ASD) is a major drawback for the development of therapies. Here, we apply consensus-subtyping strategies based on functional connectivity patterns to a population of N=657 quality-assured autistic subjects. We found two major subtypes (each divided hierarchically into several minor subtypes): Subtype 1 exhibited hypoconnectivity (less average connectivity than typically developing controls) and subtype 2, hyperconnectivity. The two subtypes did not differ in structural imaging metrics in any of the regions analyzed (64 cortical and 14 subcortical), nor in any of the behavioral scores (including Intelligence Quotient, ADI and ADOS). Finally, we used the Allen Human Brain Atlas of gene transcription to show that subtype 2, corresponding with about 42% of all patients, had significant enrichment (after multiple comparisons correction) to excitation-inhibition (E/I) imbalance, a leading reported mechanism in the developmental pathophysiology of ASD. Altogether, our results support a link between E/I imbalance and brain hyperconnectivity in ASD, an association that does not exist in hypoconnected autistic subjects.

neuroscience↗

bioRxiv2021

High-order functional interactions in ageing explained via alterations in the connectome in a whole-brain model

The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brains connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the age differences in high-order functional interactions can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modelling more complex forms of pathological ageing or cognitive deficits. Author summaryModern neuroimaging techniques allow us to study how the human brains anatomical architecture (a.k.a. structural connectome) changes under different conditions or interventions. Recently, using functional neuroimaging data, we have shown that complex patterns of interactions between brain areas change along the lifespan, exhibiting increased redundant interactions in the older population. However, the mechanisms that underlie these functional differences are still unclear. Here, we extended this work and hypothesized that the variations of functional patterns can be explained by the dynamics of the brains anatomical networks, which are known to degenerate as we age. To test this hypothesis, we implemented a whole-brain model of neuronal activity, where different brain regions are anatomically wired using real connectomes from 161 participants with ages ranging from 10 to 80 years old. Analyzing different functional aspects of brain activity when varying the empirical connectomes, we show that the increased redundancy found in the older group can indeed be explained by precise rules affecting anatomical connectivity, thus emphasizing the critical role that the brain connectome plays for shaping complex functional interactions and the efficiency in the global communication of the human brain.

neuroscience↗

bioRxiv2021

Amyloid-β and tau pathologies relate to distinctive brain dysconnectomics in preclinical autosomal-dominant Alzheimer's disease

The human brain is composed of functional networks that have a modular topology, where brain regions are organized into communities that form internally dense (segregated) and externally sparse (integrated) subnetworks that underlie higher-order cognitive functioning. It is hypothesized that amyloid-{beta} and tau pathology in preclinical Alzheimers disease (AD) spread through functional networks, disrupting neural communication that results in cognitive dysfunction. We used high-resolution (voxel-level) graph-based network analyses to test whether in vivo amyloid-{beta} and tau burden was associated with the segregation and integration of brain functional connections, and episodic memory, in cognitively-unimpaired Presenilin-1 E280A carriers who are expected to develop early-onset AD dementia in approximately 13 years on average. Compared to non-carriers, mutation carriers exhibited less functional segregation and integration in posterior default-mode network (DMN) regions, particularly the precuneus, and in the retrospenial cortex, which has been shown to link medial temporal regions and cortical regions of the DMN. Mutation carriers also showed greater functional segregation and integration in regions connected to the salience network, including the striatum and thalamus. Greater tau burden was associated with lower segregated and integrated functional connectivity of DMN regions, particularly the precuneus and medial prefrontal cortex. In turn, greater tau pathology was related to higher segregated and integrated functional connectivity in the retrospenial cortex, and the anterior cingulate cortex, a hub of the salience network. These findings enlighten our understanding of how AD-related pathology distinctly alters the brains functional architecture in the preclinical stage, possibly contributing to pathology propagation and ultimately resulting in dementia. SIGNIFICANCE STATEMENTAmyloid-{beta} and tau pathologies in Alzheimers disease (AD) are hypothesized to propagate through functional networks. Research is needed to elucidate how AD-related pathologies alter the brains functional connections years before symptom-onset to help predict pathology accumulation and dementia risk. Using high-resolution (voxel-level) graph-based network analyses and PET imaging, we showed that greater tau burden was associated with largely dysconnectivity of posterior default-mode network brain regions, and increased connectivity of structures associated with the salience network and others that are critical for integrating information across neural systems in cognitively-unimpaired Presenilin-1 E280A carriers. Findings enlighten how amyloid-{beta} and tau relate to distinct patterns of functional connectivity in regions that underlie memory functioning and are critical for information processing, helping predict disease progression.

neuroscience↗

bioRxiv2021

PhenoExam: an R package and Web application for the examination of phenotypes linked to genes and gene sets

Gene set based phenotype enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) can improve the rate of genetic diagnoses amongst other research purposes. To facilitate diverse phenotype analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases. PhenoExam achieves these tasks by integrating databases or resources such as the HPO, MGD, CRISPRbrain, CTD, ClinGen, CGI, OrphaNET, UniProt, PsyGeNET, and Genomics England Panel App. PhenoExam accepts both human and mouse genes as input. We developed PhenoExam to assist a variety of users, including clinicians, computational biologists and geneticists. It can be used to support the validation of new gene-to-disease discoveries, and in the detection of differential phenotypes between two gene sets (a phenotype linked to one of the gene set but no to the other) that are useful for differential diagnosis and to improve genetic panels. We validated PhenoExam performance through simulations and its application to real cases. We demonstrate that PhenoExam is effective in distinguishing gene sets or Mendelian diseases with very similar phenotypes through projecting the disease-causing genes into their annotation-based phenotypic spaces. We also tested the tool with early onset Parkinsons disease and dystonia genes, to show phenotype-level similarities but also potentially interesting differences. More specifically, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy. Therefore, PhenoExam effectively discovers links between phenotypic terms across annotation databases through effective integration. The R package is available at https://github.com/alexcis95/PhenoExam and the Web tool is accessible at https://snca.atica.um.es/PhenoExamWeb/.

bioinformatics↗

bioRxiv2020

Genes involved in cholesterol cascades are linked to brain connectivity in one third of autistic patients

The authors have withdrawn their manuscript because after a peer-review process of four different reviewers, several important points were raised, which made us go over all concerns. One major concern involved a gene-wise statistical control for multiple testing, which, after implementation, changed the main conclusion of the study. Other points included a more restrictive filtering of subjects with large head-motion, the exploration of subtyping at different resolutions, a subtyping stability analysis, and the consideration of spatial autocorrelations in the associations between brain connectivity and transcriptomics data. Therefore, the authors do not wish the current work here to be cited as reference for the project anymore. If you have further questions, please contact the corresponding author.

neuroscience↗

bioRxiv2020

Transcriptional Signatures of Synaptic Vesicle Genes Define Myotonic Dystrophy Type I Neurodegeneration

Despite significant research, the biological mechanisms underlying the brain degeneration in Myotonic Dystrophy Type I (DM1) remain largely unknown. Here we have assessed brain degeneration by measuring the volume loss (VL) and cognitive deficits (CD) in two cohorts of DM1 patients, and associating them to the large-scale brain transcriptome maps provided by the Allen Human Brain Atlas (AHBA). From a list of preselected hypothesis-driven genes, three of them appear to play a major role in degeneration: dystrophin (DMD), alpha-synuclein (SNCA) and the microtubule-associated protein tau (MAPT). Moreover, a purely data-driven strategy identified gene clusters enriched for key biological processes in the central nervous system, such as synaptic vesicle recycling, localization, endocytosis and exocytosis, and the serotonin and dopamine neurotransmitter pathways. Therefore, by combining large-scale transcriptome interactions with brain imaging and cognitive function, we provide a new more comprehensive understanding of DM1 that might help define future therapeutic strategies and research into this condition.

neuroscience↗

bioRxiv2020

A controlled thermoalgesic stimulation device to identify novel pain perception biomarkers

Objective To develop a new device that will help identify physiological markers of pain perception by reading the brain’s electrical activity and the bodies hemodynamic interactions while applying thermoalgesic stimulation. Methods: We designed a compact prototype that generates well-controlled thermal stimuli using a computer driven Peltier cell while simultaneously capturing electroencephalography (EEG) and photoplethysmography (PPG) signals as the stimuli are varied. The study was performed on 35 healthy subjects (mean age 30.46 years, SD 4.93 years; 20 males, 15 females) and to account for the inter-subject variability in the tolerance to thermal pain, we first determined the heat pain threshold (HPT) for each subject, defined as the maximum temperature that the subject can withstand when the Peltier cell gradually increases the temperature. Subsequently, we defined the pain parameters associated with a stimulation temperature equivalent to 90% of the HPT, comparing this to the no-pain state (control) in the absence of thermoalgesic stimulation. Results: Both the one-dimensional and the two-dimensional spectral entropy (SE) obtained from both the EEG and PPG signals could differentiate the condition of pain. In particular, the PPG SE was significantly reduced in association with pain, while the SE for EEG increased slightly. Moreover, significant discrimination occurred within a specific range of frequencies, 26-30 Hz for EEG and about 5-10 Hz for PPG. Conclusion: Hemodynamics, brain dynamics and their interactions can discriminate thermal pain perception. Significance: The possibility of monitoring on-line variations in thermal pain perception using a similar device and algorithms may be of interest to study different pathologies that affect the peripheral nervous system, such as small fiber neuropathies, fibromyalgia or painful diabetic neuropathy.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗

bioRxiv2020

High-order interdependencies in the aging brain

Brain interdependencies can be studied either from a structural/anatomical perspective ("structural connectivity", SC) or by considering statistical interdependencies ("functional connectivity", FC). Interestingly, while SC is typically pairwise (white-matter fibers start in a certain region and arrive at another), FC is not; however, most FC analyses focus only on pairwise statistics and neglect high-order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and redundancy in groups of three or more interacting variables. In this paper we used the O-Information to investigate how high-order statistical interdependencies are affected by age. For this, we analised functional magnetic resonance imaging (fMRI) data at rest obtained from 164 healthy participants, ranging from 10 to 80 years old. Our results show that older subjects (age ranging from 60 to 80 years) exhibit a higher predominance of redundant dependencies than younger subjects; moreover, this effect seems to be pervasive, taking place at all interaction orders. Additionally, we found that these effects are highly heterogeneous across brain regions, and suggest the existence of a "redundancy core" formed by the prefrontal and motor cortices - thus involving functions such as working memory, executive and motor functions. Our methodology to assess high-order interdependencies in fMRI data has unlimited applications. The code to calculate these metrics is freely available.

neuroscience↗

bioRxiv2020

Low variability of dynamical functional connectivity in cerebellar networks

Brain networks can be defined and explored using different types of connectivity. Here, we studied P=48 healthy participants with neuroimaging state-of-the-art techniques and analyzed the relationship between the actual structural connectivity (SC) networks (between 2514 regions of interest covering the entire brain and brainstem) and the dynamical functional connectivity (DFC) among the same regions. To do so, we focused on a combination of two metrics: the first one measures the degree of SC-DFC similarity -i.e. how much functional correlations can be explained by structural pathways- and the second one, the intrinsic variability of DFC networks across time. Overall, we found that cerebellar networks have smaller DFC variability than other networks in the cerebrum. Moreover, our results clearly evidence the internal structure of the cerebellum, which is divided in two differentiated networks, the posterior and anterior parts, the latter also being connected to the brain stem. The mechanism for keeping the DFC variability low in the posterior part of the cerebellum is consistent with another finding, namely, it exhibits the highest SC-DFC similarity among all other sub-networks, i.e. its structure constrains very strongly its dynamics. On the other hand, the anterior part of the cerebellum, which also exhibits a low level of DFC variability, has the lowest SC-DFC similarity, suggesting very different dynamical mechanisms. It is likely that its connections with the brain stem -which regulates sleep cycles, cardiac and respiratory functioning- might have a critical role in DFC variations in the anterior part. A lot is known about cerebellar networks, such as having extremely rich and complex anatomy and functionality, connecting to the brainstem, and cerebral hemispheres, and participating in a large variety of cognitive functions, such as movement control and coordination, executive function, visual-spatial cognition, language processing, and emotional regulation. However, as far as we know, our findings of low variability in the dynamical functional connectivity of cerebellar networks and its possible relation with the above functions, have not been reported so far. Further research is still needed to shed light on these findings.

neuroscience↗

bioRxiv2019

The value of non-motor features and genetic variants of Parkinson's disease for clustering Lewy body diseases

IntroductionThe use of non-motor Parkinsons disease (PD) features and genetic PD variants for clustering analyses may refine the phenotypic classification of idiopathic Lewy body (LB) diseases. MethodsOne-hundred participants [n=7 E46K-SNCA (n=5 symptomatic and n=2 asymptomatic), n=4 PARK2, n=3 LRRK2, n=8 dementia with Lewy bodies (DLB), n=48 idiopathic PD (iPD), n=30 healthy controls (HC)] underwent a comprehensive evaluation of non-motor and motor PD features. Non-motor features were used to perform a hierarchical clustering analysis with patients and HC using a Scikit-learn toolkit. ResultsClustering analysis suggested three clusters of subjects including Cluster 1 or "Normal-to-mild": young iPD (< 60 years) classified together with most HC and the variable LB burden genetic PD variants (PARK2 and LRRK2) characterized by having normal-to-mild cognitive disabilities and mild-to-moderate motor disability with few axial symptoms; Cluster 2 or "Mild-to-moderate": old iPD patients (>60 years) classified together with the lowest symptomatic E46K-SNCA, PARK2 carriers and HCs, characterizing by having mild-to-moderate cognitive and motor disabilities with few axial symptoms; and Cluster 3 or "Severe": old iPD (>60 years) classified together with all DLB and the most symptomatic E46K-SNCA carriers, characterized by having severe pattern-specific cognitive disabilities (visual attention, perception, processing speed, memory and executive functions) and severe motor PD manifestations with marked axial symptoms. ConclusionsOur study supports the potential value of incorporating genetic PD variants in data-driven iPD classification algorithms and the usefulness of non-motor PD features, especially visual cognition abnormalities, to facilitate the identification of aggressive LB diseases.

neuroscience↗

bioRxiv2018

Hyperconnectivity of the default mode network in multiorgan dysfunction syndrome

Multiple Organ Dysfunction Syndrome (MODS) is a systemic physiological disorder affecting two or more body organs triggered after an insult complication. Beyond the systemic failure, patients who survive MODS present cognitive and neurological impairments that remain stable even several years after Intensive Care Unit (ICU) discharge. Here, we focus on the specific situation of MODS patients with no apparent brain damage (NABD), where the mechanisms driving cognitive impairment at long term are not well-understood. We recruit N1 = 13 MODS patients with NABD at 6 months after ICU discharge, together with N2 = 13 healthy controls (matched by age, sex and years of education), and acquire functional magnetic resonance imaging at rest to find that, as compared to control, MODS patients with NABD present an overall increase of the functional connectivity (FC) at rest. In particular, we find that the default mode network (DMN) hyperconnects (increasing the node strength of the FC matrix) to three classes of networks: primary sensory (such as auditory, sensory-motor and visual), multimodal integration (such as dorsal attention and salience) and higher order cognition networks (such as fronto-parietal, language and executive control). Therefore, although these patients do not have an apparent structural damage after MODS, at the functional level, we found brain network alterations coexisting with hyperconnectivity of the DMN, that similar to what happens at the onset of other pathologies, might indicate a possible mechanism for brain compensation occurring after MODS.

neuroscience↗

bioRxiv2018

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

Interaction Information generalizes the univariate Shannon Entropy to triplets of variables, allowing the detection of redundant or synergetic interactions in dynamical networks. Here, we calculated interaction information from functional magnetic resonance imaging and asked whether redundancy or synergy vary across brain regions and along lifespan. We found high overlapping between the pattern of high redundancy and the default mode network, and this occurred along lifespan. The pattern of high values of synergy, more heterogeneous and variable along lifespan, was overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, the amount of redundancy and synergy seem to be balanced along lifespan, suggesting informational compensatory mechanisms in brain networks.

neuroscience↗

bioRxiv2017

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

Physiological ageing affects brain structure and function impacting its morphology, connectivity and performance. However, at which extent brain-connectivity metrics reflect the age of an individual and whether treatments or lifestyle factors such as physical activity influence the age-connectivity match is still unclear. Here, we assessed the level of physical activity and collected brain images from healthy participants (N=155) ranging from 10 to 80 years to build functional (resting-state) and structural (tractography) connectivity matrices that were combined as connectivity descriptors. Connectivity descriptors were used to compute a maximum likelihood age estimator that was optimized by minimizing the mean absolute error. The connectivity-based estimated age, i.e. the brain-connectome age (BCA), was compared to the chronological age (ChA). Our results were threefold. First, we showed that ageing widely affects the structural-functional connectivity of multiple structures, such as the anterior part of the default mode network, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus and temporal pole. Second, our analysis showed that the structure-function connectivity between basal ganglia and thalamus to orbitofrontal and frontal areas make a major contribution to age estimation. Third, we found that high levels of physical activity reduce BCA as compared to ChA, and vice versa, low levels increment it. In conclusion, the BCA model results highlight the impact of physical activity and the key role played by the connectivity between basal ganglia and thalamus to frontal areas on the process of healthy aging. Notably, the same methodology can be generally applied both to evaluate the impact of other factors and therapies on brain ageing, and to identify the structural-functional brain connectivity correlate of other biomarkers than ChA.

neuroscience↗