White matter impairment in the speech network of individuals with autism spectrum disorder

AbstractImpairments in language and communication are core features of Autism Spectrum Disorder (ASD), and a substantial percentage of children with ASD do not develop speech. ASD is often characterized as a disorder of brain connectivity, and a number of studies have identified white matter impairments in affected individuals. The current study investigated white matter integrity in the speech network of high-functioning adults with ASD. Diffusion tensor imaging (DTI) scans were collected from 18 participants with ASD and 18 neurotypical participants. Probabilistic tractography was used to estimate the connection strength between ventral premotor cortex (vPMC), a cortical region responsible for speech motor planning, and five other cortical regions in the network of areas involved in speech production. We found a weaker connection between the left vPMC and the supplementary motor area in the ASD group. This pathway has been hypothesized to underlie the initiation of speech motor programs. Our results indicate that a key pathway in the speech production network is impaired in ASD, and that this impairment can occur even in the presence of normal language abilities. Therapies that result in normalization of this pathway may hold particular promise for improving speech output in ASD

The Role of Long-Range Connectivity for the Characterization of the Functional–Anatomical Organization of the Cortex

This review focuses on the role of long-range connectivity as one element of brain structure that is of key importance for the functional–anatomical organization of the cortex. In this context, we discuss the putative guiding principles for mapping brain function and structure onto the cortical surface. Such mappings reveal a high degree of functional–anatomical segregation. Given that brain regions frequently maintain characteristic connectivity profiles and the functional repertoire of a cortical area is closely related to its anatomical connections, long-range connectivity may be used to define segregated cortical areas. This methodology is called connectivity-based parcellation. Within this framework, we investigate different techniques to estimate connectivity profiles with emphasis given to non-invasive methods based on diffusion magnetic resonance imaging (dMRI) and diffusion tractography. Cortical parcellation is then defined based on similarity between diffusion tractograms, and different clustering approaches are discussed. We conclude that the use of non-invasively acquired connectivity estimates to characterize the functional–anatomical organization of the brain is a valid, relevant, and necessary endeavor. Current and future developments in dMRI technology, tractography algorithms, and models of the similarity structure hold great potential for a substantial improvement and enrichment of the results of the technique

Modelling individual variations in brain structure and function using multimodal MRI

Every brain is different. Understanding this variability is crucial for investigating the neural substrate underlying individuals’ unique behaviour and developing personalised diagnosis and treatments. This thesis presents novel computational approaches to study individual variability in brain structure and function using magnetic resonance imaging (MRI) data. It comprises three main chapters, each addressing a specific challenge in the field. In Chapter 3, the thesis proposes a novel Image Quality Transfer (IQT) technique, HQ-augmentation, to accurately localise a Deep Brain Stimulation (DBS) target in low-quality clinical-like data. Leveraging high-quality diffusion MRI datasets from the Human Connectome Project (HCP), the HQ-augmentation approach is robust to corruptions in data quality while preserving the individual anatomical variability of the DBS target. It outperforms existing alternatives and generalises to unseen low-quality diffusion MRI datasets with different acquisition protocols, such as the UK Biobank (UKB) dataset. In Chapter 4, the thesis presents a framework for enhancing prediction accuracy of individual task-fMRI activation profiles using the variability of resting-state fMRI. Assuming resting-state functional modes underlie task-evoked activity, this chapter demonstrates that shape and intensity of individualised task activations can be separately modelled. This chapter introduced the concept of "residualisation" and showed that training on residuals leads to better individualised predictions. The framework’s prediction accuracy, validated on HCP and UKB data, is on par with task-fMRI test-retest reliability, suggesting potential for supplementing traditional task localisers. In Chapter 5, the thesis presents a novel framework for individualised retinotopic mapping using resting-state fMRI, from the primary visual cortex to visual cortex area 4. The proposed approach reproduces task-elicited retinotopy and captures individual differences in retinotopic organisation. The proposed framework delineates borders of early visual areas more accurately than group-average parcellation and is effective with both high-field 7T and more common 3T resting-state fMRI data, providing a valuable alternative to resource-intensive retinotopy task-fMRI experiments. Overall, this thesis demonstrates the potential of advanced MRI analysis techniques to study individual variability in brain structure and function, paving the way for improved clinical applications tailored to individual patients and a better understanding of neural mechanisms underlying unique human behaviour

End to End Brain Fiber Orientation Estimation Using Deep Learning

In this work, we explore the various Brain Neuron tracking techniques, one of the most significant applications of Diffusion Tensor Imaging. Tractography is a non-invasive method to analyze underlying tissue micro-structure. Understanding the structure and organization of the tissues facilitates a diagnosis method to identify any aberrations which can occur within tissues due to loss of cell functionalities, provides acute information on the occurrences of brain ischemia or stroke, the mutation of certain neurological diseases such as Alzheimer, multiple sclerosis and so on. Under all these circumstances, accurate localization of the aberrations in efficient manner can help save a life. Following up with the limitations introduced by the current Tractography techniques such as computational complexity, reconstruction errors during tensor estimation and standardization, we aim to elucidate these limitations through our research findings. We introduce an End to End Deep Learning framework which can accurately estimate the most probable likelihood orientation at each voxel along a neuronal pathway. We use Probabilistic Tractography as our baseline model to obtain the training data and which also serve as a Tractography Gold Standard for our evaluations. Through experiments we show that our Deep Network can do a significant improvement over current Tractography implementations by reducing the run-time complexity to a significant new level. Our architecture also allows for variable sized input DWI signals eliminating the need to worry about memory issues as seen with the traditional techniques. The advantage of this architecture is that it is perfectly desirable to be processed on a cloud setup and utilize the existing multi GPU frameworks to perform whole brain Tractography in minutes rather than hours. The proposed method is a good alternative to the current state of the art orientation estimation technique which we demonstrate across multiple benchmarks

Altered Cortico-Cortical Brain Connectivity During Muscle Fatigue

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50 maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greater insights into the dynamic adaptations of int

Connecting brain and behavior in clinical neuroscience: A network approach

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior

Altered Cortico-Cortical Brain Connectivity During Muscle Fatigue

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50 maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greater insights into the dynamic adaptations of int