Biology subjects

Rodriguez-Cruces, R.

Publications and source records attributed to Rodriguez-Cruces, R..

33 records · Page 2Linked to original sources

bioRxiv2024

HippoMaps: Multiscale cartography of the human hippocampal formation

The hippocampus has a unique microarchitecture, is situated at the nexus of multiple macroscale functional networks, contributes to numerous cognitive as well as affective processes, and is highly susceptible to brain pathology across common disorders. These features make the hippocampus a model to understand how brain structure covaries with function, in both health and disease. Here, we introduce HippoMaps, an open access toolbox and online data warehouse for the mapping and contextualization of subregional hippocampal data in the human brain (http://hippomaps.readthedocs.io). HippoMaps capitalizes on a unified hippocampal unfolding approach as well as shape intrinsic registration capabilities to allow for cross-subject and cross-modal data aggregation. We initialize this repository with an unprecedented combination of hippocampal data spanning 3D ex-vivo histology, ex-vivo 9.4 Tesla MRI, as well as in-vivo structural MRI and resting-state functional MRI (rsfMRI) obtained at 3 and 7 Tesla, together with intracranial encephalography (iEEG) recordings in epilepsy patients. HippoMaps also contains validated tools for spatial map association analysis in the hippocampus that correct for autocorrelation. All code and data are compliant with community standards, and comprehensive online tutorials facilitate broad adoption. Applications of this work span methodologies and modalities, spatial scales, as well as clinical and basic research contexts, and we encourage community feedback and contributions in the spirit of open and iterative scientific resource development.

neuroscience↗

bioRxiv2023

Cerebral perfusion alterations in temporal lobe epilepsy: Structural underpinnings and network disruptions

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSOO_SCPLOWBJECTIVEC_SCPLOWC_ST_ABSNeuroimaging has been the prevailing method to study brain networks in temporal lobe epilepsy (TLE), showing widespread alterations beyond the mesiotemporal lobe. Despite the critical role of the cerebrovascular system in maintaining whole-brain structure and function, changes in cerebral blood flow (CBF) remain incompletely understood in the disease. MO_SCPLOWETHODSC_SCPLOWWe studied 24 individuals with pharmaco-resistant TLE and 38 healthy adults using multimodal 3T magnetic resonance imaging. We compared regional CBF changes in patients relative to controls and related our perfusion findings to morphological and microstructural metrics. We further probed inter-regional vascular networks in TLE, using graph theoretical CBF covariance analysis. Finally, we assessed the effects of disease duration to study progressive changes. RO_SCPLOWESULTSC_SCPLOWCompared to controls, individuals with TLE showed widespread CBF reductions, predominantly in fronto-temporal regions, with 83% of patients showing more marked decreases ipsilateral than contralateral to the seizure focus. Parallel structural profiling and network-based models showed that cerebral hypoperfusion may be partly constrained by grey and white matter changes and topologically segregated from whole-brain perfusion networks. Negative effects of progressive disease duration further targeted regional CBF profiles in patients. Findings were confirmed in a subgroup of patients who remained seizure-free after surgery. IO_SCPLOWNTERPRETATIONC_SCPLOWOur multimodal findings provide insights into vascular contributions to TLE pathophysiology and highlight their clinical potential in seizure lateralization.

neuroscience↗

bioRxiv2023

Atypical connectome topography and signal flow in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is one of the most common pharmaco-resistant epilepsies in adults. While hippocampal pathology is the hallmark of this condition, emerging evidence indicates that brain alterations extend beyond the mesiotemporal epicenter and affect macroscale brain function and cognition. We studied macroscale functional reorganization in TLE, explored structural substrates, and examined cognitive associations. We investigated a multisite cohort of 95 patients with pharmaco-resistant TLE and 95 healthy controls using state-of-the-art multimodal 3T magnetic resonance imaging (MRI). We quantified macroscale functional topographic organization using connectome dimensionality reduction techniques and estimated directional functional flow using generative models of effective connectivity. We observed atypical functional topographies in patients with TLE relative to controls, manifesting as reduced functional differentiation between sensory/motor networks and transmodal systems such as the default mode network, with peak alterations in bilateral temporal and ventromedial prefrontal cortices. TLE-related topographic changes were consistent in all three included sites and reflected reductions in hierarchical flow patterns between cortical systems. Integration of parallel multimodal MRI data indicated that these findings were independent of TLE-related cortical grey matter atrophy, but mediated by microstructural alterations in the superficial white matter immediately beneath the cortex. The magnitude of functional perturbations was robustly associated with behavioral markers of memory function. Overall, this work provides converging evidence for macroscale functional imbalances, contributing microstructural alterations, and their associations with cognitive dysfunction in TLE.

neuroscience↗

bioRxiv2023

The Human Brain Connectome Weighted by the Myelin Content and Total Intra-Axonal Cross-Sectional Area of White Matter Tracts

A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features. Computational models support in vivo investigation of the mechanisms mediating this relationship but currently lack the requisite biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features to assess their potential joint utilization in computational models. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, tractometry-derived networks weighted by either tensor-based metrics or neurite density show no relationship with whole-brain functional connectivity. We conclude that structure-function brain models are likely to be improved by the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content. We anticipate that the proposed microstructure-weighted computational modeling approach will support mechanistic understanding of the structure-function relationship of the human brain. AUTHOR SUMMARYFor computational network models to provide mechanistic links between brain structure and function, they must be informed by networks in which edge weights quantify structural features relevant to brain function. Here, we characterized several weighted structural networks capturing multiscale features of white matter connectivity. We describe these networks in terms of edge weight distribution, variance and network topology, as well as their relationships with each other, edge length and function. Overall, these findings support the joint use of structural networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts in structure-function models. This thorough characterization serves as a benchmark for future investigations of weighted structural brain networks.

neuroscience↗

bioRxiv2022

Cortical microstructural gradients capture memory network reorganization in temporal lobe epilepsy

Temporal lobe epilepsy (TLE), one of the most common pharmaco-resistant epilepsies, is associated with pathology of paralimbic brain regions, particularly in the mesiotemporal lobe. Cognitive dysfunction in TLE is frequent, and particularly affects episodic memory. Crucially, these difficulties challenge the quality of life of patients, sometimes more than seizures, underscoring the need to assess neural processes of cognitive dysfunction in TLE to improve patient management. Our work harnessed a novel conceptual and analytical approach to assess spatial gradients of microstructural differentiation between cortical areas based on high-resolution MRI analysis. Gradients track region-to-region variations in intracortical lamination and myeloarchitecture, serving as a system-level measure of structural and functional reorganization. Comparing cortex-wide microstructural gradients between 21 patients and 35 healthy controls, we observed a contracted gradient in TLE driven by reduced microstructural differentiation between paralimbic cortices and the remaining cortex with marked abnormalities in ipsilateral temporopolar and dorsolateral prefrontal regions. Findings were replicated in an independent cohort. Using an independent post mortem dataset, we observed that in vivo findings reflected topographical variations in cortical lamination patterns, confirming that TLE-related changes in the microstructural gradient reflected increased proximity of regions with more dissimilar laminar structure. Disease-related transcriptomics could furthermore show specificity of our findings to TLE over other common epilepsy syndromes. Finally, microstructural dedifferentiation was associated with cognitive network reorganization seen during an episodic memory functional MRI paradigm, and correlated with inter-individual differences in task accuracy. Collectively, our findings showing a pattern of reduced microarchitectural differentiation between paralimbic regions and the remaining cortex provide a parsimonious explanation for functional network reorganization and cognitive dysfunction characteristic of TLE.

neuroscience↗

bioRxiv2022

Atypical intrinsic neural timescales in temporal lobe epilepsy

ObjectiveTemporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Here, we aimed to profile local neural function in TLE in vivo, building on prior evidence that has identified widespread structural alterations. Using multimodal MRI, we mapped intrinsic neural timescales (INT) at rest, examined associations to TLE-related structural compromise, and evaluated the clinical utility of INT. MethodsWe studied 46 TLE patients and 44 healthy controls from two independent sites, and mapped INT changes in patients relative to controls across hippocampal, subcortical, and neocortical regions. We examined region-specific associations to structural alterations and explored effects of age and epilepsy duration. A supervised machine learning paradigm assessed utility of INT for classifying patients-vs-controls and seizure focus lateralization. ResultsRelative to controls, TLE showed marked INT reductions across multiple regions bilaterally, indexing faster changing resting activity, with strongest effects in ipsilateral medial and lateral temporal regions, and sensorimotor cortices. Findings were consistent in each site and robust, albeit with reduced effect sizes, when correcting for structural alterations. TLE-related INT reductions increased with advancing disease duration, yet findings differed from aging effects seen in controls. Classifiers based on INT distinguished patients-vs-controls (balanced accuracy, 5-fold: 76{+/-}2.65%; cross-site, 72-83%) and lateralized the focus in TLE (balanced accuracy, 5-fold: 96{+/-}2.10%; cross-site, 95-97%) with high accuracies and generalization. ConclusionsOur findings robustly demonstrate atypical macroscale function in TLE in a topography that extends beyond mesiotemporal epicenters. INT measurements can assist in TLE diagnosis, seizure focus lateralization, and monitoring of disease progression, which suggests clinical utility.

neuroscience↗

bioRxiv2022

Micapipe: a pipeline for multimodal neuroimaging and connectome analysis

AO_SCPLOWBSTRACTC_SCPLOWMultimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain structure, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and different spatial scales. Here, we present micapipe, an open processing pipeline for BIDS-conform multimodal MRI datasets. micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. These matrices are routinely generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, and connectivity.

neuroscience↗

bioRxiv2022

BrainStat: A toolbox for brain-wide statistics and neuroscientific contextualization

AO_SCPLOWBSTRACTC_SCPLOWAnalysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

neuroscience↗

bioRxiv2021

A Riemannian approach to predicting brain function from the structural connectome

AO_SCPLOWBSTRACTC_SCPLOWOngoing brain function is largely determined by the underlying wiring of the brain, but the specific rules governing this relationship remain unknown. Emerging literature has suggested that functional interactions between brain regions emerge from the structural connections through mono-as well as polysynaptic mechanisms. Here, we propose a novel approach based on diffusion maps and Riemannian optimization to emulate this dynamic mechanism in the form of random walks on the structural connectome and predict functional interactions as a weighted combination of these random walks. Our proposed approach was evaluated in two different cohorts of healthy adults (Human Connectome Project, HCP; Microstructure-Informed Connectomics, MICs). Our approach outperformed existing approaches and showed that performance plateaus approximately around the third random walk. At macroscale, we found that the largest number of walks was required in nodes of the default mode and frontoparietal networks, underscoring an increasing relevance of polysynaptic communication mechanisms in transmodal cortical networks compared to primary and unimodal systems.

neuroscience↗

bioRxiv2021

Long-range connections mirror and link microarchitectural and cognitive hierarchies in the human brain

AO_SCPLOWBSTRACTC_SCPLOWCore features of higher-order cognition are hypothesized to be implemented via distributed cortical networks that are linked via long-range connections. However, these connections are biologically expensive, and it is unknown how the computational advantages long-range connections provide overcome the associated wiring costs. Our study investigated this question by exploring the relationship between long-range functional connections and local cortical microarchitecture. Specifically, our work (i) profiled distant cortical connectivity using resting-state fMRI and cortico-cortical geodesic distance mapping, (ii) assessed how long-range connections reflect local brain microarchitecture, and (iii) studied the microarchitectural similarity of regions connected through long-range connections. Analysis of two independent datasets indicated that sensory and motor areas had more clustered short-range connectivity patterns, while transmodal association cortices, including regions of the default mode network, were characterized by distributed, long-range connections. Confirmatory meta-analysis suggested that this topographical difference mirrored a shift in cognitive function, from perception/action towards emotional and social cognitive processing. Analysis of myelin-sensitive in vivo MRI in the same participants as well as post mortem histology and gene expression established that gradients in functional connectivity distance are paralleled by those present in cortical microarchitecture. Moreover, long-range connections were found to link together spatially remote regions of association cortex with an unexpectedly similar microarchitecture. These findings provide novel insights into how the organization of distributed functional networks in transmodal association cortex contribute to cognition, because they suggest that long-range connections link together distant islands of association cortex with similar microstructural features.

neuroscience↗

bioRxiv2021

Structural network alterations in focal and generalized epilepsy follow axes of epilepsy risk gene expression: An ENIGMA study

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem co-expression patterns of epilepsy risk genes. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1,328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-genetic signatures could guide diagnosis, and ultimately, tailor therapeutic approaches to specific epilepsy syndromes.

neuroscience↗

bioRxiv2021

An open MRI dataset for multiscale neuroscience

AO_SCPLOWBSTRACTC_SCPLOWMultimodal neuroimaging grants a powerful window into the structure and function of the human brain at multiple scales. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends (also referred to as gradients) in brain microstructure and connectivity, offering an integrative framework to study multiscale brain organization. Here, we share a multimodal MRI dataset for Microstructure-Informed Connectomics (MICA-MICs) acquired in 50 healthy adults (23 women; 29.54{+/-}5.62 years) who underwent high-resolution T1-weighted MRI, myelin-sensitive quantitative T1 relaxometry, diffusion-weighted MRI, and resting-state functional MRI at 3 Tesla. In addition to raw anonymized MRI data, this release includes brain-wide connectomes derived from i) resting-state functional imaging, ii) diffusion tractography, iii) microstructure covariance analysis, and iv) geodesic cortical distance, gathered across multiple parcellation scales. Alongside, we share large-scale gradients estimated from each modality and parcellation scale. Our dataset will facilitate future research examining the coupling between brain microstructure, connectivity, and macroscale function. MICA-MICs is available on the Canadian Open Neuroscience Platforms data portal (https://portal.conp.ca).

neuroscience↗

bioRxiv2021

Topographic divergence of atypical cortical asymmetry and regional atrophy patterns in temporal lobe epilepsy: a worldwide enigma study

AO_SCPLOWBSTRACTC_SCPLOWTemporal lobe epilepsy (TLE), a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter pathology in TLE relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multi-site ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 TLE patients and 1,418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in TLE, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity, and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of TLE-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of TLE and may inform future discovery and validation of complementary MRI biomarkers in TLE.

neuroscience↗

bioRxiv2020

Network-based atrophy modelling in the common epilepsies: a worldwide ENIGMA study

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1,021 adults with epilepsy compared to 1,564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy co-localized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Indices of progressive atrophy further revealed a strong influence of connectome architecture on disease progression in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients, and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided novel insights into the macroscale features that shape the pathophysiology of common epilepsies.

neuroscience↗

bioRxiv2019

Multidimensional Associations between Cognition and Connectome Organization in Temporal Lobe Epilepsy

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSOO_SCPLOWBJECTIVEC_SCPLOWC_ST_ABSTemporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. The current work sought to identify multidimensional associations between cognitive performance and structural network topology in TLE.\n\nMO_SCPLOWETHODSC_SCPLOWWe studied 34 drug-resistant TLE patients and 25 age- and sex-matched healthy controls. All participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies.\n\nRO_SCPLOWESULTSC_SCPLOWBoth methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized better the cognitive performance than morphometric measures. Thus, connectome characteristics featured as important markers of network reorganization and loss of inter-regional connectivity.\n\nCO_SCPLOWONCLUSIONSC_SCPLOWThe multivariate approach emphasized the close interplay between cognitive impairment and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy

neuroscience↗