149 research outputs found
Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification
There is no consensus on how to construct structural brain networks from
diffusion MRI. How variations in pre-processing steps affect network
reliability and its ability to distinguish subjects remains opaque. In this
work, we address this issue by comparing 35 structural connectome-building
pipelines. We vary diffusion reconstruction models, tractography algorithms and
parcellations. Next, we classify structural connectome pairs as either
belonging to the same individual or not. Connectome weights and eight
topological derivative measures form our feature set. For experiments, we use
three test-retest datasets from the Consortium for Reliability and
Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare
pairwise classification results to a commonly used parametric test-retest
measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure
Normal Modes of the Structural Connectome
Division of the human cortex into distinct regions is of high importance to neuroscientific inquiry. Fully-automated, multi-modal schemes of achieving such parcellation on an individual subject basis are particularly advantageous, however difficulties in inter-modal and inter-subject registration of brain images, as well as obstacles in preserving group-level correspondence of individual parcellation maps have slowed progress in this area. In parallel, there exists a relative dearth of data-driven parcellation schemes that incorporate high resolution structural connectivity metrics; the majority of widely-accepted parcellation maps in the literature have primarily used functional connectivity. Here, a fully data-driven, automated routine based on structural geometry and connectivity which achieves subject-specific cortical parcellation maps while maintaining group-level correspondence of maps is presented and optimized. Using high resolution white matter surface meshes and advanced fiber tracking techniques, a novel vertex-wise structural connectivity graph is constructed for each of 10 unrelated subjects, and the first k eigenvectors of the Laplacian Matrix of the graph's adjacency matrix are calculated. These eigenvectors represent the steady-state modes of vibration of the manifold described by this graph, and thus provide subject-specific maps of modes of connectivity in white matter. In order to obtain parcellations at varying levels of coarseness of the cortex from these eigenvectors, hierarchical agglomerative clustering is then performed on the surface mesh, where each vertex's feature vector is its profile in spectral space. Further, a multi-layer graph of all subjects is constructed, and individual parcellations with group level correspondence are obtained by agglomerative clustering of the eigenvectors of the laplacian matrix of the multi-layer graph.Bachelor of Scienc
Relationship between large-scale structural and functional brain connectivity in the human lifespan
The relationship between the anatomical structure of the brain and its functional organization
is not straightforward and has not been elucidated yet, despite the growing interest this topic
has received in the last decade. In particular, a new area of research has been defined in these
years, called \u2019connectomics\u2019: this is the study of the different kinds of \u2019connections\u2019 existing
among micro- and macro-areas of the brain, from structural connectivity \u2014 described by
white matter fibre tracts physically linking cortical areas \u2014 to functional connectivity \u2014
defined as temporal correlation between electrical activity of different brain regions \u2014 to
effective connectivity\u2014defining causal relationships between functional activity of different
brain areas. Cortical areas of the brain physically linked by tracts of white matter fibres
are known to exhibit a more coherent functional synchronization than areas which are not
anatomically linked, but the absence of physical links between two areas does not imply a
similar absence of functional correspondence. Development and ageing, but also structural
modifications brought on by malformations or pathology, can modify the relation between
structure and function.
The aim of my PhD work has been to further investigate the existing relationship between
structural and functional connectivity in the human brain at different ages of the human
lifespan, in particular in healthy adults and both healthy and pathological neonates and
children. These two \u2019categories\u2019 of subjects are very different in terms of the analysis
techniques which can be applied for their study, due to the different characteristics of the data
obtainable from them: in particular, while healthy adult data can be studied with the most
advanced state-of-the-art methods, paediatric and neonatal subjects pose hard constraints to
the acquisition methods applicable, and thus to the quality of the data which can be analysed.
During this PhD I have studied this relation in healthy adult subjects by comparing structural
connectivity from DWI data with functional connectivity from stereo-EEG recordings
of epileptic patients implanted with intra-cerebral electrodes. I have then focused on the
paediatric age, and in particular on the challenges posed by the paediatric clinical environment
to the analysis of structural connectivity. In collaboration with the Neuroradiology
Unit of the Giannina Gaslini Hospital in Genova, I have adapted and tested advanced DWI analysis methods for neonatal and paediatric data, which is commonly studied with less
effective methods. We applied the same methods to the study of the effects of a specific brain
malformation on the structural connectivity in 5 paediatric patients.
While diffusion weighted imaging (DWI) is recognised as the best method to compute
structural connectivity in the human brain, the most common methods for estimating functional
connectivity data \u2014 functional MRI (fMRI) and electroencephalography (EEG) \u2014
suffer from different limitations: fMRI has good spatial resolution but low temporal resolution,
while EEG has a better temporal resolution but the localisation of each signal\u2019s
originating area is difficult and not always precise. Stereo-EEG (SEEG) combines strong
spatial and temporal resolution with a high signal-to-noise ratio and allows to identify the
source of each signal with precision, but is not used for studies on healthy subjects because
of its invasiveness.
Functional connectivity in children can be computed with either fMRI, EEG or SEEG,
as in adult subjects. On the other hand, the study of structural connectivity in the paediatric
age is met with obstacles posed by the specificity of this data, especially for the application
of the advanced DWI analysis techniques commonly used in the adult age. Moreover, the
clinical environment introduces even more constraints on the quality of the available data,
both in children and adults, further limiting the possibility of applying advanced analysis
methods for the investigation of connectivity in the paediatric age.
Our results on adult subjects showed a positive correlation between structural and functional
connectivity at different granularity levels, from global networks to community structures
to single nodes, suggesting a correspondence between structural and functional organization
which is maintained at different aggregation levels of brain units. In neonatal and
paediatric subjects, we successfully adapted and applied the same advanced DWI analysis
method used for the investigation in adults, obtaining white matter reconstructions more
precise and anatomically plausible than with methods commonly used in paediatric clinical
environments, and we were able to study the effects of a specific type of brain malformation
on structural connectivity, explaining the different physical and functional manifestation
of this malformation with respect to similar pathologies. This work further elucidates the
relationship between structural and functional connectivity in the adult subject, and poses
the basis for a corresponding work in the neonatal and paediatric subject in the clinical
environment, allowing to study structural connectivity in the healthy and pathological child
with clinical data
Organization and hierarchy of the human functional brain network lead to a chain-like core
The brain is a paradigmatic example of a complex system: its functionality emerges as a global property of local mesoscopic and microscopic interactions. Complex network theory allows to elicit the functional architecture of the brain in terms of links (correlations) between nodes (grey matter regions) and to extract information out of the noise. Here we present the analysis of functional magnetic resonance imaging data from forty healthy humans at rest for the investigation of the basal scaffold of the functional brain network organization. We show how brain regions tend to coordinate by forming ahighly hierarchical chain-like structure of homogeneously clustered anatomical areas. A maximum spanning tree approach revealed the centrality of the occipital cortex and the peculiar aggregation of
cerebellar regions to form a closed core. We also report the hierarchy of network segregation and the level of clusters integration as a function of the connectivity strength between brain regions
Examining the development of brain structure in utero with fetal MRI, acquired as part of the Developing Human Connectome Project
The human brain is an incredibly complex organ, and the study of it traverses many scales across space and time. The development of the brain is a protracted process that begins embryonically but continues into adulthood. Although neural circuits have the capacity to adapt and are modulated throughout life, the major structural foundations are laid in utero during the fetal period, through a series of rapid but precisely timed, dynamic processes. These include neuronal proliferation, migration, differentiation, axonal pathfinding, and myelination, to name a few. The fetal origins of disease hypothesis proposed that a variety of non-communicable diseases emerging in childhood and adulthood could be traced back to a series of risk factors effecting neurodevelopment in utero (Barker 1995). Since this publication, many studies have shown that the structural scaffolding of the brain is vulnerable to external environmental influences and the perinatal developmental window is a crucial determinant of neurological health later in life. However, there remain many fundamental gaps in our understanding of it. The study of human brain development is riddled with biophysical, ethical, and technical challenges. The Developing Human Connectome Project (dHCP) was designed to tackle these specific challenges and produce high quality open-access perinatal MRI data, to enable researchers to investigate normal and abnormal neurodevelopment (Edwards et al., 2022). This thesis will focus on investigating the diffusion-weighted and anatomical (T2) imaging data acquired in the fetal period, between the second to third trimester (22 – 37 gestational weeks). The limitations of fetal MR data are ill-defined due to a lack of literature and therefore this thesis aims to explore the data through a series of critical and strategic analysis approaches that are mindful of the biophysical challenges associated with fetal imaging. A variety of analysis approaches are optimised to quantify structural brain development in utero, exploring avenues to relate the changes in MR signal to possible neurobiological correlates. In doing so, the work in this thesis aims to improve mechanistic understanding about how the human brain develops in utero, providing the clinical and medical imaging community with a normative reference point. To this aim, this thesis investigates fetal neurodevelopment with advanced in utero MRI methods, with a particular emphasis on diffusion MRI. Initially, the first chapter outlines a descriptive, average trajectory of diffusion metrics in different white matter fiber bundles across the second to third trimester. This work identified unique polynomial trajectories in diffusion metrics that characterise white matter development (Wilson et al., 2021). Guided by previous literature on the sensitivity of DWI to cellular processes, I formulated a hypothesis about the biophysical correlates of diffusion signal components that might underpin this trend in transitioning microstructure. This hypothesis accounted for the high sensitivity of the diffusion signal to a multitude of simultaneously occurring processes, such as the dissipating radial glial scaffold, commencement of pre-myelination and arborization of dendritic trees. In the next chapter, the methods were adapted to address this hypothesis by introducing another dimension, and charting changes in diffusion properties along developing fiber pathways. With this approach it was possible to identify compartment-specific microstructural maturation, refining the spatial and temporal specificity (Wilson et al., 2023). The results reveal that the dynamic fluctuations in the components of the diffusion signal correlate with observations from previous histological work. Overall, this work allowed me to consolidate my interpretation of the changing diffusion signal from the first chapter. It also serves to improve understanding about how diffusion signal properties are affected by processes in transient compartments of the fetal brain. The third chapter of this thesis addresses the hypothesis that cortical gyrification is influenced by both underlying fiber connectivity and cytoarchitecture. Using the same fetal imaging dataset, I analyse the tissue microstructural change underlying the formation of cortical folds. I investigate correlations between macrostructural surface features (curvature, sulcal depth) and tissue microstructural measures (diffusion tensor metrics, and multi-shell multi-tissue decomposition) in the subplate and cortical plate across gestational age, exploring this relationship both at the population level and within subjects. This study provides empirical evidence to support the hypotheses that microstructural properties in the subplate and cortical plate are altered with the development of sulci. The final chapter explores the data without anatomical priors, using a data-driven method to extract components that represent coordinated structural maturation. This analysis aims to examine if brain regions with coherent patterns of growth over the fetal period converge on neonatal functional networks. I extract spatially independent features from the anatomical imaging data and quantify the spatial overlap with pre-defined neonatal resting state networks. I hypothesised that coherent spatial patterns of anatomical development over the fetal period would map onto the functional networks observed in the neonatal period. Overall, this thesis provides new insight about the developmental contrast over the second to third trimester of human development, and the biophysical correlates affecting T2 and diffusion MR signal. The results highlight the utility of fetal MRI to research critical mechanisms of structural brain maturation in utero, including white matter development and cortical gyrification, bridging scales from neurobiological processes to whole brain macrostructure. their gendered constructions relating to women
Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy
Focal epilepsy is a devastating neurological disorder that affects an
overwhelming number of patients worldwide, many of whom prove resistant to
medication. The efficacy of current innovative technologies for the treatment
of these patients has been stalled by the lack of accurate and effective
methods to fuse multimodal neuroimaging data to map anatomical targets driving
seizure dynamics. Here we propose a parsimonious model that explains how
large-scale anatomical networks and shared genetic constraints shape
inter-regional communication in focal epilepsy. In extensive ECoG recordings
acquired from a group of patients with medically refractory focal-onset
epilepsy, we find that ictal and preictal functional brain network dynamics can
be accurately predicted from features of brain anatomy and geometry, patterns
of white matter connectivity, and constraints complicit in patterns of gene
coexpression, all of which are conserved across healthy adult populations.
Moreover, we uncover evidence that markers of non-conserved architecture,
potentially driven by idiosyncratic pathology of single subjects, are most
prevalent in high frequency ictal dynamics and low frequency preictal dynamics.
Finally, we find that ictal dynamics are better predicted by white matter
features and more poorly predicted by geometry and genetic constraints than
preictal dynamics, suggesting that the functional brain network dynamics
manifest in seizures rely on - and may directly propagate along - underlying
white matter structure that is largely conserved across humans. Broadly, our
work offers insights into the generic architectural principles of the human
brain that impact seizure dynamics, and could be extended to further our
understanding, models, and predictions of subject-level pathology and response
to intervention
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Anatomical and Functional Organization of Domain-General Brain Regions
How does complex brain activity organize thought and behaviour? Theoretical proposals have long emphasized that intelligent behaviour must be supported by a flexible control system. Numerous brain imaging studies identified a domain-general or “multiple-demand” (MD) brain system co-activated accompanying many tasks and is hypothesised to play a central role in cognitive control. However, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding its anatomy and physiology. To address these limitations, the experiments in chapters 2 and 3 capitalize on novel multi-modal magnetic resonance imaging (MRI) methods developed by the Human Connectome Project. Chapter 2 delineated nine cortical MD patches per hemisphere and subdivided them into 10 regions forming a core of most strongly activated and functionally interconnected regions, surrounded by a penumbra of 17 additional regions. MD activations were also identified in specific subcortical and cerebellar regions. Chapter 3 investigated the relation between the newly defined MD regions and previously identified sensory-biased cortical regions. Contrasting auditory and visual low working memory demands revealed the strongest sensory-biases are localized just outside of MD regions. And additional working memory demands revealed MD activations showed no sensory biases. Chapter 4 used human electrophysiological recordings from the lateral frontal cortex to functionally map cognitive control regions during awake neurosurgeries. By contrasting a hard vs easy cognitive demand, spectral analysis revealed localized power increases in the gamma range (>30 Hz) that overlap with a canonical mask of the fronto-parietal control network. These findings contrast with spatially non-specific power decreases in the beta range (12-30 Hz). Thus, using similar task difficulty manipulations, electrophysiology and MRI functional signals converged on localizing lateral frontal regions related to cognitive control and support their clinical potential for intraoperative mapping of cognitive control. All together, the distributed anatomical organization, mosaic functional preferences, and strong functional interconnectivity of MD regions, suggest a skeleton for integrating and organizing the diverse components of cognitive operations. The precise anatomical delineation of MD regions provides the groundwork for refined analyses of their functions
The individuality of shape asymmetries of the human cerebral cortex
Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences
Estimating EEG Source Dipole Orientation Based on Singular-value Decomposition for Connectivity Analysis.
In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity. One specific need and still open problem is how to represent the time- and frequency-content carried by hundreds of dipoles with diverging orientation in each ROI with one unique representative time-series. The main aim of this paper is to provide a method to compute a signal that explains most of the variability of the data contained in each ROI before computing, for instance, time-varying connectivity. As the representative time-series for a ROI, we propose to use the first singular vector computed by a singular-value decomposition of all dipoles belonging to the same ROI. We applied this method to two real datasets (visual evoked potentials and epileptic spikes) and evaluated the time-course and the frequency content of the obtained signals. For each ROI, both the time-course and the frequency content of the proposed method reflected the expected time-course and the scalp-EEG frequency content, representing most of the variability of the sources (~ 80%) and improving connectivity results in comparison to other procedures used so far. We also confirm these results in a simulated dataset with a known ground truth
Connectomics across development:towards mapping brain structure from birth to childhood
The brain is probably the most complex system of the human body, composed of numerous neural units interconnected at dierent scales. This highly structured architecture provides the ability to communicate, synthesize information and perform the analytical tasks of human beings. Its development starts during the transition between the embryonic and fetal periods, from a simple tubular to a highly complex folded structure. It is globally organized as early as birth. This developing process is highly vulnerable to antenatal adverse conditions. Indeed, extreme prematurity and intra uterine growth restriction are major risk factors for long-term morbidities, including developmental ailments such as cerebral palsy, mental retardation and a wide spectrum of learning disabilities and behavior disorders. In this context, the characterization of the brainâs normative wiring pattern is crucial for our understanding of its architecture and workings, as the origin of many neurological and neurobehavioral disorders is found in early structural brain development. Diusion magnetic resonance imaging (dMRI) allows the in vivo assessment of biological tissues at the microstructural level. It has emerged as a powerful tool to study brain connectivity and analyse the underlying substrate of the human brain, comprising its structurally integrated and functionally specialized architecture. dMRI has been widely used in adult studies. Nevertheless, due to technical constraints, this mapping at earlier stages of development has not yet been accomplished. Yet, this time period is of extreme importance to comprehend the structural and functional integrity of the brain. This thesis is motivated by this shortfall, and intends to fill the gap between the clinical and neuroscience demands and the methodological developments needed to fulfill them. In our work, we comprehensibly study the brain structural connectivity of children born extremely prematurely and/or with additional prenatal restriction at school-age. We provide evidence that brain systems that mature early in development are the most vulnerable to antenatal insults. Interestingly, the alterations highlighted in these systems correlate with the neurobehavioral and cognitive impairments seen in these children at school-age. The overall brain organization appear also altered after preterm birth and prenatal restriction. Indeed, these children show dierent brain network modular topology, with a reduction in the overall network capacity. What remains unclear is whether the alterations seen at school age are already present at birth and, if yes, to what extent. In this thesis we set the technical basis to enable the connectome analysis as early as at birth. This task is challenging when dealing with neonatal data. Indeed, most of the assumptions used in adult data processing methods do not hold, due to the inverted image contrast and other MRI artefacts such as motion, partial volume and intensity inhomogeneities. Here, we propose a novel technique for surface reconstruction, and provide a fully-automatic procedure to delineate the newborn cortical surface, opening the way to establish the newborn connectome
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