Analysis of surface folding patterns of diccols using the GPU-Optimized geodesic field estimate

Localization of cortical regions of interests (ROIs) in the human brain via analysis of Diffusion Tensor Imaging (DTI) data plays a pivotal role in basic and clinical neuroscience. In recent studies, 358 common cortical landmarks in the human brain, termed as Dense Indi- vidualized and Common Connectivity-based Cortical Landmarks (DICCCOLs), have been identified. Each of these DICCCOL sites has been observed to possess fiber connection patterns that are consistent across individuals and populations and can be regarded as predictive of brain function. However, the regularity and variability of the cortical surface fold patterns at these DICCCOL sites have, thus far, not been investigated. This paper presents a novel approach, based on intrinsic surface geometry, for quantitative analysis of the regularity and variability of the cortical surface folding patterns with respect to the structural neural connectivity of the human brain. In particular, the Geodesic Field Estimate (GFE) is used to infer the relationship between the structural and connectional DTI features and the complex surface geometry of the human brain. A parallel algorithm, well suited for implementation on Graphics Processing Units (GPUs), is also proposed for efficient computation of the shortest geodesic paths between all cortical surface point pairs. Based on experimental results, a mathematical model for the morphological variability and regularity of the cortical folding patterns in the vicinity of the DICCCOL sites is proposed. It is envisioned that this model could be potentially applied in several human brain image registration and brain mapping applications

Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment

Due to the difficulties in establishing correspondences between functional regions across individuals and populations, systematic elucidation of functional connectivity alterations in mild cognitive impairment (MCI) in comparison with normal controls (NC) is still a challenging problem. In this paper, we assessed the functional connectivity alterations in MCI via novel, alternative predictive models of resting state networks (RSNs) learned from multimodal resting state fMRI (R-fMRI) and diffusion tensor imaging (DTI) data. First, ICA-clustering was used to construct RSNs from R-fMRI data in NC group. Second, since the RSNs in MCI are already altered and can hardly be constructed directly from R-fMRI data, structural landmarks derived from DTI data were employed as the predictive models of RSNs for MCI. Third, given that the landmarks are structurally consistent and correspondent across NC and MCI, functional connectivities in MCI were assessed based on the predicted RSNs and compared with those in NC. Experimental results demonstrated that the predictive models of RSNs based on multimodal R-fMRI and DTI data systematically and comprehensively revealed widespread functional connectivity alterations in MCI in comparison with NC

Connectome-scale assessments of structural and functional connectivity in MCI: Structural and Functional Connectivity in MCI

Mild cognitive impairment (MCI) has received increasing attention not only because of its potential as a precursor for Alzheimer's disease (AD), but also as a predictor of conversion to other neurodegenerative diseases. Although MCI has been defined clinically, accurate and efficient diagnosis is still challenging. While neuroimaging techniques hold promise, compared to commonly-used biomarkers including amyloid plaques, tau protein levels and brain tissue atrophy, neuroimaging biomarkers are less well validated. In the present paper, we propose a connectomes-scale assessment of structural and functional connectivity in MCI via two independent multimodal DTI/fMRI datasets. We first used DTI-derived structural profiles to explore and tailor the most common and consistent landmarks, then applied them in a whole-brain functional connectivity analysis. The next step fused the results from two independent datasets together and resulted in a set of functional connectomes with the most differentiation power, hence named as “connectome signatures”. Our results indicate that these “connectome signatures” have significantly high MCI-vs-controls classification accuracy, at more than 95%. Interestingly, through functional meta-analysis, we found that the majority of “connectome signatures” are mainly derived from the interactions among different functional networks, e.g., cognition-perception and cognition-action domains, rather than from within a single network. Our work provides support for using functional “connectome signatures” as neuroimaging biomarkers of MCI

Brain Connectivity After Concussion

Mild traumatic brain injury (mTBI) accounts for over one million emergency visits in the United States each year. While most mTBI patients have normal findings in clinical neuroimaging, alterations in brain structure and functional connectivity have frequently been reported. In this study, we investigated the large-scale brain structural and functional connectivity using diffusion MRI and resting-state fMRI data. Data from 40 mTBI patients was acquired at the acute stage (within 24 hrs after injury). 35 patients returned for data acquisition at a follow-up (4-6 weeks after injury). Data was also collected from a cohort of 58 healthy subjects, 36 of whom returned for data acquisition at the second time point, 4-6 weeks later. All data was collected at Wayne State University, Detroit, Michigan, USA. We also evaluated the relationship between functional connectivity findings at the acute stage and neurocognitive symptoms at follow up to assess the feasibility of using neuroimaging data to predict neurocognitive complications after mTBI. Moreover, we developed the connectivity domain, a new analysis method which can potentially improve reproducibility and ability to compare findings across datasets

Fine-Granularity Functional Interaction Signatures for Characterization of Brain Conditions

In the human brain, functional activity occurs at multiple spatial scales. Current studies on functional brain networks and their alterations in brain diseases via resting-state functional magnetic resonance imaging (rs-fMRI) are generally either at local scale (regionally confined analysis and inter-regional functional connectivity analysis) or at global scale (graph theoretic analysis). In contrast, inferring functional interaction at fine-granularity sub-network scale has not been adequately explored yet. Here our hypothesis is that functional interaction measured at fine-granularity subnetwork scale can provide new insight into the neural mechanisms of neurological and psychological conditions, thus offering complementary information for healthy and diseased population classification. In this paper, we derived fine-granularity functional interaction (FGFI) signatures in subjects with Mild Cognitive Impairment (MCI) and Schizophrenia by diffusion tensor imaging (DTI) and rsfMRI, and used patient-control classification experiments to evaluate the distinctiveness of the derived FGFI features. Our experimental results have shown that the FGFI features alone can achieve comparable classification performance compared with the commonly used inter-regional connectivity features. However, the classification performance can be substantially improved when FGFI features and inter-regional connectivity features are integrated, suggesting the complementary information achieved from the FGFI signatures

Bayesian Methods in Brain Connectivity Change Point Detection with EEG Data and Genetic Algorithm

Human brain is processing a great amount of information everyday, and our brain regions are organized optimally for this information processing. There have been increasing number of studies focusing on functional or effective connectivity in human brain regions in the last decade. In this dissertation, Bayesian methods in Brain connectivity change point detection are discussed. First, a review of state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI) data is carried out, three methods are reviewed and compared. Second, the Bayesian connectivity change point model is extended to change point analysis in electroencephalogram (EEG) data, and the ability of EEG measures of frontal and temporo-parietal activity during mindfulness therapy to track response to dysfunctional anxiety patients\u27 treatment is tested successfully. Then an optimized method for Bayesian connectivity change point model with genetic algorithm (GA) is proposed and proved to be more efficient in change point detection. And due to the good parallel performance of GA, the change point detection method can be parallelized in GPU or multi-processor computers as a future work. Furthermore, a more advanced Bayesian bi-cluster connectivity change point model is developed to simultaneously detect change point of each subject within a group, and cluster subjects into different groups according to their change point distribution and connectivity dynamics. The method is also validated on experimental datasets. After discussing brain change point detection, a review of Bayesian analysis of complex mutations in HBV HCV and HIV studies is also included as part of my Ph.D. work. Finally, conclusions are drawn and future work is discussed

Consensus clustering framework for analysing fMRI datasets.

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonNeuroimaging of humans has gained a position of status within neuroscience. Modern functional magnetic resonance imaging (fMRI) technique provides neuroscientists with a powerful tool to depict the complex architecture of human brains. fMRI generates large amount of data and many analysis methods have been proposed to extract useful information from the data. Clustering technique has been one of the most popular data-driven techniques to study brain functional connectivity, which excels when traditional model-based approaches are difficult to implement. However, the reliability and consistency of many findings are jeopardised by too many analysis methods, parameters, and sometimes too few samples used. In this thesis, a consensus clustering analysis framework for analysing fMRI data has been developed, aiming at overcoming the clustering algorithm selection problem as well as reliability issues in neuroimaging. The framework is able to identify groups of voxels representing brain regions that consistently exhibiting correlated BOLD activities across many experimental conditions by integrating clustering results from multiple clustering algorithms and various parameters such as the number of clusters . In the framework, the individual clustering result generation is aided by high performance grid computing technique to reduce the overall computational time. The integration of clustering results is implemented by a technique named binarisation of consensus partition matrix (Bi-CoPaM) adapted and enhanced for fMRI data analysis. The whole framework has been validated and is robust to participants’ individual variability, yielding most complete and reproducible clusters compared to the traditional single clustering approach. This framework has been applied to two real fMRI studies that investigate brain responses to listening to the emotional music with different preferences. In the first fMRI study, three brain structures related to visual, reward, and auditory processing are found to have intrinsic temporal patterns of coherent neuroactivity during affective processing, which is one of the few data-driven studies that have observed. In the second study, different levels of engagement, i.e. intentional to unintentional, with music have unique effects on the auditory- limbic connectivity when listening to music, which has not been investigated and understood well in euro science of music field. We believe the work in this thesis has demonstrated an effective and competent approach to address the reliability and consistency concerns in fMRI data analysis

Implication de la connectivité anatomique dans les caractéristiques des fuseaux de sommeil

Le sommeil est un état de conscience distinct de l’éveil et nécessaire à diverses fonctions du cerveau allant de la métabolisation des déchets dans le système nerveux central jusqu’à la plasticité cérébrale, la mémoire et la performance cognitive. Les fuseaux de sommeil (FS), ces oscillations fusiformes ayant une fréquence qui varie entre 12 et 16 Hz, constituent un marqueur de synchronie neuronale principalement observé dans le sommeil lent. Ils font partie de ces oscillations qui ont été associées à la préservation du sommeil, la consolidation en mémoire et à l’intelligence. Les FS montrent une très grande variabilité intra- et interindividuelle quant à leurs caractéristiques, celles-ci étant d’ailleurs influencées par des facteurs tels que l’âge et le sexe. Les mécanismes neurophysiologiques impliqués dans ces variations demeurent toutefois méconnus à ce jour. Il a été démontré que la génération et la propagation des FS dépendent de la communication entre le thalamus et le cortex et reposeraient sur les fibres de matière blanche (MB) du cerveau. Le but de cette thèse est donc d’investiguer l’implication de la connectivité anatomique par l’analyse de la MB du cerveau, dans la variabilité interindividuelle des caractéristiques des FS. Nous évaluerons également si les différences d’âge et de sexe dans les caractéristiques des FS peuvent être expliquées par la MB. La première étude a évalué si l’intégrité de la MB du cerveau pouvait expliquer les changements d’amplitude et de densité des FS au cours du vieillissement. Une meilleure intégrité de la MB dans les principaux faisceaux connectant le thalamus au cortex frontal a été associée à une plus grande amplitude des FS et de l’activité électroencéphalographique dans la bande de fréquences sigma. Ces résultats ont été observés exclusivement chez les sujets jeunes, suggérant que d’autres facteurs pourraient expliquer les changements de FS au cours du vieillissement. La deuxième étude avait, quant à elle, pour but d’évaluer si la longueur des faisceaux de fibres thalamo-corticales (TC) prédisait la variation interindividuelle de la fréquence et de l’amplitude des FS. Il a été démontré que de plus courts faisceaux de fibres entre le thalamus et les régions frontales prédisaient une fréquence des FS plus rapide. De plus, une analyse de médiation a permis de démontrer que la différence sexuelle observée pour la fréquence des FS était complètement expliquée par l’effet indirect du sexe sur la longueur des faisceaux de fibres de MB. Nos résultats suggèrent donc que l’amplitude et la fréquence des FS reflèteraient des aspects spécifiques des projections de MB sous-jacentes à la boucle TC. De fait, l’amplitude des FS a été associée à l’intégrité des connexions neuronales et à la synchronie des décharges électriques alors que la fréquence des FS a été associée au temps requis à l’influx nerveux pour parcourir la boucle TC et à des mesures quantitatives des projections entre le thalamus et le cortex cérébral. Cette thèse propose donc une première hypothèse neuroanatomique tentant d’expliquer les variations interindividuelles et sexuelles des caractéristiques des FS.Sleep is a state of consciousness distinct from waking and necessary in multiple brain functions ranging from the metabolism of waste products in the central nervous system to brain plasticity, memory, and cognition. Sleep spindles (SS), these fusiform oscillations with a frequency which varies between 12 and 16 Hz, constitute a marker of neuronal synchrony prominently observed during non-rapid eye movement sleep. SS are one of these brain oscillations associated with sleep maintenance, memory consolidation, and intelligence. SS characteristics show an important intra- and inter-individual variability, and are known to be affected by factors such as age and sex. However, the neurophysiological mechanisms implicated in this variability are yet to be discovered. The generation and the propagation of SS depend on the communication between the thalamus and the cerebral cortex which rely on white matter (WM) fibre bundles. The goal of this thesis is to investigate the implication of the anatomical connectivity as assessed through WM, in the inter-individual variability of SS characteristics. We will also evaluate whether the age and sex differences in SS characteristics could be explained by the WM. The first study evaluated whether WM integrity could explain age-related changes in SS amplitude and density. Increased WM integrity in the main WM tracts connecting the thalamus to the frontal cortex was associated with an increased SS amplitude and electroencephalographic signal power in the sigma frequency band. These results were observed exclusively in young subjects suggesting that other factors could explain age-related changes in SS. The second study aimed at evaluating whether the length of the thalamo-cortical (TC) fiber bundles would predict the inter-individual variability of SS frequency and amplitude. We found that shorter fiber bundles between the thalamus and the frontal regions of the brain predicted a faster SS frequency. Moreover, a mediation analysis showed that the sex-related differences in SS frequency was completely explained by the indirect effect of sex on the length of the WM fiber bundles. Our results suggest that SS amplitude and frequency reflect specific aspect of the WM projections underlying the TC loop. Indeed, SS amplitude was associated with the integrity of neuronal connections and the synchrony of nerve impulses, whereas SS frequency was associated with the timing of the nerve impulses in the TC loop and to quantitative measures of WM projections between the thalamus and the cerebral cortex. This thesis therefore brings a first neuroanatomical hypothesis in explaining the inter-individual and sex-related variability of SS characteristics