Whole-brain estimates of directed connectivity for human connectomics

Connectomics is essential for understanding large-scale brain networks but requires that individual connection estimates are neurobiologically interpretable. In particular, a principle of brain organization is that reciprocal connections between cortical areas are functionally asymmetric. This is a challenge for fMRI-based connectomics in humans where only undirected functional connectivity estimates are routinely available. By contrast, whole-brain estimates of effective (directed) connectivity are computationally challenging, and emerging methods require empirical validation. Here, using a motor task at 7T, we demonstrate that a novel generative model can infer known connectivity features in a whole-brain network (>200 regions, >40,000 connections) highly efficiently. Furthermore, graph-theoretical analyses of directed connectivity estimates identify functional roles of motor areas more accurately than undirected functional connectivity estimates. These results, which can be achieved in an entirely unsupervised manner, demonstrate the feasibility of inferring directed connections in whole-brain networks and open new avenues for human connectomics

The Neurobiology of Human Vocalization: A Quantitative Meta-Analytic Approach

Vocalization is critical to communication and understanding the neural mechanisms that control voice is a critical scientific and clinical endeavor. Studies have used a variety of neuroimaging techniques to investigate the neural correlates of vocal control using perturbation tasks. These studies have provided substantial evidence that there is a critical role of the Superior Temporal Gyrus (STG) in error detection/correction during vocalization. The STG appears to function as a regulatory region within a complex network of brain areas that control human vocalization. The aims of this study were to 1) Use Activation Likelihood Estimation (ALE) analyses to substantiate the neural regions activation during vocalization; 2) To determine the functional significance of the neural regions activated during vocalization, as characterized by the BrainMap database; 3) To parcellate the bilateral STG by means of Connectivity Based Parcellation (CBP) and functionally characterize any discreate subregions found. Results of the vocalization ALE analysis revealed activation of the bilateral STG, right supplementary motor area, bilateral precentral gyrus, right inferior frontal gyrus, right pallidum, left putamen and right cerebellum (VI), which largely substantiates previous findings of the vocalization network. Results of CBP revealed six distinct subregions of the left and right STG, with major functional characterization in the domains of perception, action, and cognition and in the specific tasks of music production and stimulus monitoring/discrimination

Parcellation of the human sensorimotor cortex: a resting-state fMRI study

The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system

Brain circuits involved in self-paced motion: the influence of 0.1 Hz waves

The neural mechanisms behind human voluntary motion are not fully characterized yet, in spite of numerous research studies. Slow ( 0.1 Hz) brain oscillations are known to have a powerful modulatory effect on several cognitive and physiological phenomena, including free movement. This study is based on fMRI data acquired from 25 young, healthy subjects. The tasks were: rest, self-paced motion, motion paced by a periodic 0.1 Hz stimulus. The temporal resolution was finer than standard fMRI protocols (TR=871 ms). After preprocessing, the signal from brain regions of interest was extracted, and functional connectivity was computed between brain regions using wavelet phase coherence. Complementarily, effective connectivity was measured using Granger causality. The final output was Phase-Locking (PL) and Granger Causality (GC) matrices reflecting inter-regional phase coherence and causal interactions, respectively, around 0.1 Hz. Using the GraphVar toolbox, inter-task and inter-group comparisons were performed. In inter-task comparisons PL matrices showed encouraging results unlike GC matrices. Pairs of regions for which PL differs significantly between rest and self-paced movement were identified. These include mainly the Postcentral gyrus, Putamen, the Anterior Cingulum, the Precentral gyrus, the Calcarine, the Lingual and the Insula (all in the left hemisphere). Topological changes in the brain wiring were identified across the tasks by computing the node degree and global efficiency. Inter-group comparisons took into account the inter movement interval and the coupling between BOLD and heart rate beatto-beat interval signals and showed changes in brain activity depending on the regularity of movement intervals and specific connectivity patterns for neural BOLD oscillations, respectively. This methodological approach allowed to make a contribution towards the characterization of the functional connectivity of brain circuits related to voluntary motor behavior

Altered Anatomical Network in Early Blindness Revealed by Diffusion Tensor Tractography

The topological architecture of the cerebral anatomical network reflects the structural organization of the human brain. Recently, topological measures based on graph theory have provided new approaches for quantifying large-scale anatomical networks. Diffusion MRI studies have revealed the efficient small-world properties and modular structure of the anatomical network in normal subjects. However, no previous study has used diffusion MRI to reveal changes in the brain anatomical network in early blindness. Here, we utilized diffusion tensor imaging to construct binary anatomical networks for 17 early blind subjects and 17 age- and gender-matched sighted controls. We established the existence of structural connections between any pair of the 90 cortical and sub-cortical regions using deterministic tractography. Compared with controls, early blind subjects showed a decreased degree of connectivity, a reduced global efficiency, and an increased characteristic path length in their brain anatomical network, especially in the visual cortex. Moreover, we revealed some regions with motor or somatosensory function have increased connections with other brain regions in the early blind, which suggested experience-dependent compensatory plasticity. This study is the first to show alterations in the topological properties of the anatomical network in early blindness. From the results, we suggest that analyzing the brain's anatomical network obtained using diffusion MRI data provides new insights into the understanding of the brain's re-organization in the specific population with early visual deprivation

The autonomic brain: multi-dimensional generative hierarchical modelling of the autonomic connectome

The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods — and data scales — hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system — a multidimensional, generative network — that renders its richness tractable within future models of its function in health and disease

The autonomic brain: Multi-dimensional generative hierarchical modelling of the autonomic connectome.

The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods-and data scales-hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system-a multidimensional, generative network-that renders its richness tractable within future models of its function in health and disease

Machine Learning for the Diagnosis of Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurological disorder. It refers to a wide range of behavioral and social abnormality and causes problems with social skills, repetitive behaviors, speech, and nonverbal communication. Even though there is no exact cure to ASD, an early diagnosis can help the patient take precautionary steps. Diagnosis of ASD has been of great interest recently, as researchers are yet to find a specific biomarker to detect the disease successfully. For the diagnosis of ASD, subjects need to go through behavioral observation and interview, which are not accurate sometimes. Also, there is a lack of dissimilarity between neuroimages of ASD subjects and healthy control (HC) subjects which make the use of neuroimages difficult for the diagnosis. So, machine learning-based approaches to diagnose ASD are becoming popular day by day. In the machine learning-based approach, features are extracted either from the functional MRI images or the structural MRI images to build the models. In this study at first, I created brain networks from the resting-state functional MRI (rs-fMRI) images, by using the 264 regions based parcellation scheme. These 264 regions capture the functional activity of the brain more accurately compared to regions defined in other parcellation schemes. Next, I extracted spectrum as a raw feature and combined it with other network based topological centralities: assortativity, clustering coefficient, the average degree. By applying a feature selection algorithm on the extracted features, I reduced the dimension of the features to cope up with overfitting. Then I used the selected features in support vector machine (SVM), K-nearest neighbor (KNN), linear discriminant analysis (LDA), and logistic regression (LR) for the diagnosis of ASD. Using the proposed method on Autism Brain Imaging Data Exchange (ABIDE) I achieved the classification accuracy of 78.4% for LDA, 77.0% for LR, 73.5% for SVM, and 73.8% for KNN. Next, I built a deep neural network model for the classification and feature selection using the autoencoder. In this approach, I used the previously defined features to build the DNN classifier. The DNN classifier is pre-trained using the autoencoder. Due to the pre-training, there has been a significant increase in the performance of the DNN classifier. I also proposed an autoencoder based feature selector. The latent space representation of the autoencoder is used to create a discriminate and compressed representation of the features. To make a more discriminate representation, the autoencoder is pre-trained with the DNN classifier. The classification accuracy of the DNN classifier and the autoencoder based feature selector is 79.2% and 74.6%, respectively. Finally, I studied the structural MRI images and proposed a convolutional autoencoder (CAE) based classification model. The T1-weighted MRI images without any pre-processing are used in this study. As the effect of age is very important when studying the structural images for the diagnosis of ASD, I used the ABIDE 1 dataset, which covers subjects with a wide range of ages. Using the proposed CAE based diagnosis method, I achieved a classification accuracy of 96.6%, which is better than any other study for the diagnosis of ASD using the ABIDE 1 dataset. The results of this thesis demonstrate that the spectrum of the brain networks is an essential feature for the diagnosis of ASD and rather than extracting features from the structural MRI image a more efficient way is to use the images directly into deep learning models. The proposed studies in this thesis can help to build an early diagnosis model for ASD