295 research outputs found
MR connectomics: a conceptual framework for studying the developing brain
The combination of advanced neuroimaging techniques and major developments in complex network science, have given birth to a new framework for studying the brain: âconnectomics.â This framework provides the ability to describe and study the brain as a dynamic network and to explore how the coordination and integration of information processing may occur. In recent years this framework has been used to investigate the developing brain and has shed light on many dynamic changes occurring from infancy through adulthood. The aim of this article is to review this work and to discuss what we have learned from it. We will also use this body of work to highlight key technical aspects that are necessary in general for successful connectome analysis using today's advanced neuroimaging techniques. We look to identify current limitations of such approaches, what can be improved, and how these points generalize to other topics in connectome research
Une nouvelle approche pour lâidentification des Ă©tats dynamiques de la parcellisation fonctionnelle cĂ©rĂ©brale individuelle
Les parcellations cérébrales sont appliquées en neuroimagerie pour aider les chercheurs à ré-
duire la haute dimensionnalitĂ© des donnĂ©es dâIRM fonctionnelle. Lâobjectif principal est une
meilleure comprĂ©hension de lâorganisation fonctionnelle du cerveau tant chez les sujets sains
que chez les sujets souffrant de troubles neurologiques, dont la maladie dâAlzheimer. MalgrĂ©
la vague dâapproches de parcellations prĂ©cĂ©dentes, les mesures de performance doivent en-
core ĂȘtre amĂ©liorĂ©es pour gĂ©nĂ©rer des parcellations fiables, mĂȘme avec de longues acquisitions.
Autrement dit, une reproductibilité plus élevée qui permet aux chercheurs de reproduire des
parcellations et de comparer leurs Ă©tudes. Il est Ă©galement important de minimiser la perte
dâinformations entre les donnĂ©es compressĂ©es et les donnĂ©es brutes pour reprĂ©senter avec
prĂ©cision lâorganisation dâun cerveau individuel. Dans cette thĂšse, jâai dĂ©veloppĂ© une nou-
velle approche pour parcellaire le cerveau en reconfigurations spatiales distinctes appelées
«états dynamiques de parcellations». Jâai utilisĂ© une mĂ©thode dâagrĂ©gation de cluster simple
DYPAC1.0 de parcelles basĂ©es sur des semences sur plusieurs fenĂȘtres de temps. Jâai Ă©mis
lâhypothĂšse que cette nouvelle façon de formaliser le problĂšme de parcellisation amĂ©liorera
les mesures de performance par rapport aux parcellations statiques. Le premier chapitre
de ce document est une introduction générale au contexte des réseaux à grande échelle du
cerveau humain. Je montre Ă©galement lâimportance des parcellations pour une meilleure
comprĂ©hension du cerveau humain Ă lâaide de connectomes fonctionnels afin de prĂ©dire les
schĂ©mas de progression de la maladie. Ensuite, jâexplique pourquoi le problĂšme de parcelli-
sation cérébrale est difficile et les différentes questions de recherche ouvertes associées à ce
domaine. Mes contributions à la recherche sont subdivisées en deux articles. Les deuxiÚme
et troisiÚme chapitres sont consacrés au premier article principal et à son supplément publié
dans Network Neuroscience Journal. Le quatriÚme chapitre représente le deuxiÚme document
en préparation. Le cinquiÚme chapitre conclut mes contributions et ses implications dans le
domaine de la neuroimagerie, ainsi que des orientations de recherche ouvertes. En un mot,
la principale conclusion de ce travail est lâexistence de reconfigurations spatiales distinctes
dans tout le cerveau avec des scores de reproductibilité presque parfaits sur les données de
test-retest (jusquâĂ 0,9 coefficient de corrĂ©lation de Pearson). Un algorithme dâagrĂ©gation
de cluster simple et évolutif appelé DYPAC 1.0 est expliqué pour identifier ces reconfigu-
rations ou «états dynamiques de parcellations» pour des sous-réseaux de départ spécifiques
(deuxiĂšme chapitre). Lâanalyse de ces Ă©tats a montrĂ© lâexistence dâun rĂ©pertoire plus riche
«dâĂ©tats dynamiques» dans le cas des cortex hĂ©tĂ©romodaux (ex: cortex cingulaire postĂ©-
rieur et cortex cingulaire antérieur dorsal) par rapport aux cortex unimodaux (ex: cortex
visuel). En outre, les rĂ©sultats de lâanalyse de reproductibilitĂ© ont montrĂ© que DYPAC 1.0 a
de meilleurs résultats de reproductibilité (en termes de corrélation de Pearson) par rapport
aux parcelles statiques (deuxiÚme chapitre). Plusieurs analyses démontrent que DYPAC 1.0
est robuste au choix de ses paramĂštres (troisiĂšme chapitre). Ces rĂ©sultats et lâĂ©volutivitĂ©
de DYPAC 1.0 ont motivé une analyse complÚte du niveau cérébral. Je présente DYPAC
2.0 comme une approche au niveau cérébral complet pour fragmenter le cerveau en «états
dynamiques de parcellations». Des reconfigurations spatiales distinctes et se chevauchant ou
«états dynamiques» sont identifiées pour différentes régions du cerveau (quatriÚme chapitre).
Ces Ă©tats ont des scores de compression prometteurs qui montrent une faible perte dâinfor-
mations entre les cartes de stabilitĂ© dâĂ©tat rĂ©duit et les donnĂ©es dâorigine dans les cortex
cĂ©rĂ©braux, câest-Ă -dire jusquâĂ seulement 20% de perte de la variance expliquĂ©e. Cette thĂšse
présente ainsi de nouvelles contributions dans le domaine de la parcellisation fonctionnelle
qui pourraient avoir un impact sur la maniÚre dont les chercheurs modélisent les interactions
riches et dynamiques entre les réseaux cérébraux dans la santé et la maladie.Brain parcellations are applied in neuroimaging to help researchers reduce the high dimen-
sionality of the functional MRI data. The main objective is a better understanding of the
brain functional organization in both healthy subjects and subjects having neurological dis-
orders, including Alzheimer disease. Despite the flurry of previous parcellation approaches,
the performance measures still need improvement to generate reliable parcellations even with
long acquisitions. That is, a higher reproducibility that allows researchers to replicate par-
cellations and compare their studies. It is also important to minimize the information loss
between the compressed data and the raw data to accurately represent the organization of
an individual brain. In this thesis, I developed a new approach to parcellate the brain into
distinct spatial reconfigurations called âdynamic states of parcellationsâ. I used a simple
cluster aggregation method DYPAC1.0 of seed based parcels over multiple time windows. I
hypothesized this new way to formalize the parcellation problem will improve performance
measures over static parcellations. The first chapter of this document is a general context
introduction to the human brain large scale networks. I also show the importance of par-
cellations for a better understanding of the human brain using functional connectomes in
order to predict patterns of disease progression. Then, I explain why the brain parcellation
problem is hard and the different open research questions associated with this field. My
research contributions are subdivided into two papers. The second and the third chapters
are dedicated to the first main paper and its supplementary published in Network Neuro-
science Journal. The fourth chapter represents the second paper under preparation. The
fifth chapter concludes my contributions and its implications in the neuroimaging field, along
with open research directions. In a nutshell, the main finding of this work is the existence of
distinct spatial reconfigurations throughout the brain with near perfect reproducibility scores
across test-retest data (up to .9 Pearson correlation coefficient). A simple and scalable clus-
ter aggregation algorithm called DYPAC 1.0 is explained to identify these reconfigurations
or âdynamic states of parcellationsâ for specific seed subnetworks (second chapter). The
analysis of these states showed the existence of a richer repertoire of âdynamic statesâ in the
case of heteromodal cortices (e.g., posterior cingulate cortex and the dorsal anterior cingulate
cortex) compared to unimodal cortices (e.g., visual cortex). Also, the reproducibility analysis
results showed that DYPAC 1.0 has better reproducibility results (in terms of Pearson corre-
lation) compared to static parcels (second chapter). Several analyses demonstrate DYPAC
1.0 is robust to the choice of its parameters (third chapter). These findings and the scalabil-
ity of DYPAC 1.0 motivated a full brain level analysis. I present DYPAC 2.0 as the full brain
level approach to parcellate the brain into âdynamic states of parcellationsâ. Distinct and
overlapping spatial reconfigurations or âdynamic statesâ are identified for different regions
throughout the brain (fourth chapter). These states have promising compression scores that
show low information loss between the reduced state stability maps and the original data
throughout the cerebral cortices, i.e. up to only 20% loss in explained variance. This thesis
thus presents new contributions in the functional parcellation field that may impact how
researchers model the rich and dynamic interactions between brain networks in health and
disease
Default mode resting-state functional connectivity of the aging brain
The term functional connectivity is used to describe which parts of the brain work together on a process, and might aid the understanding of how the processing systems in the human brain are fundamentally organised. The default mode network (DMN) is a constellation of cortical structures that has shown remarkable reliability as a resting-state network(RSN). It has often been referred to as a task-negative network, because it typically exhibits amplified activation patterns during rest. Numerous studies have documented DMN alterations
in various clinical conditions, including mild cognitive impairment and Alzheimerâs disease. However, little is known about the impact of normal aging on this network. The present study investigates ageâdifferences in DMN functional connectivity and further, whether the effects of age are modulated by the allelic variation of the alipoprotein E-gene, APOE. Based on current theories of cognitive aging and the few existing previous studies on restingstate patterns and APOE, we have two hypotheses: 1) an elevated co-activation in the DMN with increasing age, and 2) different effects for Δ4-carriers compared to non-carriers in the MTL structures, including the hippocampus. We tested these hypotheses on resting-state functional magnetic resonance imaging (fMRI) data from 182 healthy participants aged 20-78 years, including 63 carriers of the Δ4-allele. Using a combination of independent component analysis (ICA) and dual-regression, we document regionally specific escalations in DMN synchronicity with increased age, especially in frontal brain areas. Additionally, we observed a moderate negative effect of the Δ4-allele in the posterior cingulate cortex (PCC) of the posterior parts of the DMN, indicating lower co-activity in carriers compared to non-carriers in areas spanning core parts of the DMN. These findings are discussed in light of theories of cognitive aging, and we argue that the amplified DMN functional connectivity with age is indicative of
an age-related decrease in neural differentiation manifested as decreased decoupling between task-negative and task-positive brain networks during rest
Big Data Analytics and Information Science for Business and Biomedical Applications II
The analysis of big data in biomedical, business and financial research has drawn much attention from researchers worldwide. This collection of articles aims to provide a platform for an in-depth discussion of novel statistical methods developed for the analysis of Big Data in these areas. Both applied and theoretical contributions to these areas are showcased
Social Cognitive and Affective Neural Substrates of Adolescent Transdiagnostic Symptoms
Indiana University-Purdue University Indianapolis (IUPUI)The social cognitive ability to identify anotherâs internal state and social affective
ability to share anotherâs emotional experience, known as empathy, are integral to healthy
social functioning. During tasks, neural systems active when adolescents empathize
include cognitive (medial prefrontal cortex and posterior cingulate cortex with the
dorsolateral prefrontal cortex) and affective (anterior insula and anterior cingulate cortex)
regions that are consistent with the adult task-based literature implicating the default mode,
salience, and frontoparietal networks. However, task-based studies are limited to
examining neural regions probed by the task; thus, do not capture broader patterns of
information processing associated with complex processes, such as empathy. Methods of
functional connectivity capture broader patterns of information processing at the level of
network connectivity. Although it has clear advantages in identifying neural vulnerabilities
to disorder, functional connectivity has yet to be used in adolescent investigations of
empathy. Via parent- and self-report, deficits in either cognitive or affective processes
central to empathy associate with the most widely agreed on classifications of behavioral
disorders in adolescents â transdiagnostic symptoms of internalizing and externalizing.
However, this evidence relies exclusively on self-report measures and research has yet to
examine the neural connectivity underlying transdiagnostic symptoms in relation to
cognitive and affective empathy. What has yet to be known is (1) how the social cognitive
and affective processes of empathy are functionally connected across a heterogeneous
sample of adolescents and (2) the association of cognitive, affective, and imbalanced empathy with transdiagnostic symptoms. Addressing these gaps in knowledge is an
important incremental step for specifying vulnerabilities not fully captured via subjective
report alone. This information can be used to improve prevention and intervention
strategies. The present study will examine the functional connectivity of neural networks
underlying empathy in early to mid-adolescents and their association with transdiagnostic
symptoms
Doctor of Philosophy
dissertationThe human brain is the seat of cognition and behavior. Understanding the brain mechanistically is essential for appreciating its linkages with cognitive processes and behavioral outcomes in humans. Mechanisms of brain function categorically represent rich and widely under-investigated biological substrates for neural-driven studies of psychiatry and mental health. Research examining intrinsic connectivity patterns across whole brain systems utilizes functional magnetic resonance imaging (fMRI) to trace spontaneous fluctuations in blood oxygen-level dependent (BOLD) signals. In the first study presented, we reveal patterns of dynamic attractors in resting state functional connectivity data corresponding to well-documented biological networks. We introduce a novel simulation for whole brain dynamics that can be adapted to either group-level analysis or single-subject level models. We describe stability of intrinsic functional architecture in terms of transient and global steady states resembling biological networks. In the second study, we demonstrate plasticity in functional connectivity following a minimum six-week intervention to train cognitive performance in a speed reading task. Long-term modulation of connectivity with language regions indicate functional connectivity as a candidate biomarker for tracking and measuring functional changes in neural systems as outcomes of cognitive training. The third study demonstrates utility of functional biomarkers in predicting individual differences in behavioral and cognitive features. We successfully predict three major domains of personality psychologyintelligence, agreeableness, and conscientiousnessin individual subjects using a large (N=475) open source data sample compiled by the National Institutes of Healths Human Connectome Project
Developmental Changes in the Organization of Functional Connections between the Basal Ganglia and Cerebral Cortex
The basal ganglia (BG) comprise a set of subcortical nuclei with sensorimotor, cognitive, and limbic subdivisions, indicative of functional organization. BG dysfunction in several developmental disorders suggests the importance of the healthy maturation of these structures. However, few studies have investigated the development of BG functional organization. Using resting-state functional connectivity MRI (rs-fcMRI), we compared human child and adult functional connectivity of the BG with rs-fcMRI-defined cortical systems. Because children move more than adults, customized preprocessing, including volume censoring, was used to minimize motion-induced rsfcMRI artifact. Our results demonstrated functional organization in the adult BG consistent with subdivisions previously identified in anatomical tracing studies. Group comparisons revealed a developmental shift in bilateral posterior putamen/pallidum clusters from preferential connectivity with the somatomotor âfaceâ system in childhood to preferential connectivity with control/attention systems (frontoparietal, ventral attention) in adulthood. This shift was due to a decline in the functional connectivity of these clusters with the somatomotor face system over development, and no change with control/attention systems. Applying multivariate pattern analysis, we were able to reliably classify individuals as children or adults based on BGâcortical system functional connectivity. Interrogation of the features driving this classification revealed, in addition to the somatomotor face system, contributions by the orbitofrontal, auditory, and somatomotor hand systems. These results demonstrate that BGâcortical functional connectivity evolves over development, and may lend insight into developmental disorders that involve BG dysfunction, particularly those involving motor systems (e.g., Tourette syndrome)
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