Improved tractography using asymmetric fibre orientation distributions

Diffusion MRI allows us to make inferences on the structural organisation of the brain by mapping water diffusion to white matter microstructure. However, such a mapping is generally ill-defined; for instance, diffusion measurements are antipodally symmetric (diffusion along x and –x are equal), whereas the distribution of fibre orientations within a voxel is generally not symmetric. Therefore, different sub-voxel patterns such as crossing, fanning, or sharp bending, cannot be distinguished by fitting a voxel-wise model to the signal. However, asymmetric fibre patterns can potentially be distinguished once spatial information from neighbouring voxels is taken into account. We propose a neighbourhood-constrained spherical deconvolution approach that is capable of inferring asymmetric fibre orientation distributions (A-fods). Importantly, we further design and implement a tractography algorithm that utilises the estimated A-fods, since the commonly used streamline tractography paradigm cannot directly take advantage of the new information. We assess performance using ultra-high resolution histology data where we can compare true orientation distributions against sub-voxel fibre patterns estimated from down-sampled data. Finally, we explore the benefits of A-fods-based tractography using in vivo data by evaluating agreement of tractography predictions with connectivity estimates made using different in-vivo modalities. The proposed approach can reliably estimate complex fibre patterns such as sharp bending and fanning, which voxel-wise approaches cannot estimate. Moreover, histology-based and in-vivo results show that the new framework allows more accurate tractography and reconstruction of maps quantifying (symmetric and asymmetric) fibre complexity

Building connectomes using diffusion MRI: why, how and but

Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically-relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the WU-Minn-Ox HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The HCP-style neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium

Intermittent Theta Burst Stimulation: Application to Spinal Cord Injury Rehabilitation and Computational Modeling

Loss of motor function from spinal cord injuries (SCI) results in loss of independence. Rehabilitation efforts are targeted to enhance the ability to perform activities of daily living (ADLs), but outcomes from physical therapy alone are often insufficient. Neuromodulation techniques that induce neuroplasticity may push the limits on recovery. Neuromodulation by intermittent theta burst transcranial magnetic stimulation (iTBS) induces neuroplasticity by increasing corticomotor excitability, though this has most frequently been studied with motor targets and on individuals not in need of rehabilitation. Increased corticomotor excitability is associated with motor learning. The response to iTBS, however, is highly variable and unpredictable, while the mechanisms are not well understood. Studies have proposed brain anatomy and individual subject differences as a source of variability but have not quantified the effects. Existing models have not incorporated known neurotransmitter changes at the synaptic level to pair mechanisms to cell output in a neural circuit. To use iTBS in practical rehabilitative efforts, the technique must either be consistent, have a predictable responsiveness, or present with enough mechanistic understanding to improve its efficacy. To that effect, this study has two primary objectives for the improvement of rehabilitation techniques. The first is to establish how iTBS affects both a motor target and population that typically undergoes physical rehabilitation often with unsatisfactory outcomes, in this case the biceps brachii in individuals with SCI and relate the empirical effects of iTBS to individual anatomy. This will establish the consistency of the technique and predictability of its effects, relevant to rehabilitative efforts. The secondary objective is to create the foundation of a model that exhibits circuit organization, which would start the development of a motor neuroplasticity functional unit with simulation of the synaptic long-term potentiation (LTP) like effects of iTBS. Summary of Methods: iTBS was performed targeting the biceps, on multiple cohorts, with changes in motor evoked potential amplitude (MEP) tracked after sham and active intervention. This was compared between nonimpaired individuals and those with SCI. Furthermore, iTBS of both biceps and first dorsal interosseus (FDI) was compared to simulation of TMS on MRI derived head models to establish the impact of individualized neuroanatomy. Finally, a motor canonical neural circuit was programmed to display fundamental physiological spiking behavior of membrane potentials. Summary of Results: iTBS did facilitate corticomotor excitability in the biceps of nonimpaired individuals and in those with SCI. iTBS had no group-wide effect on the FDI, highlighting the variability in response to the protocol. TMS response (motor thresholds) and iTBS response (change in MEPs) both were related to parameters extracted from MRI-derived head models representing variations in individual neuroanatomy. The neural circuit model represents a canonical networked unit. In the future, this can be further tuned to exhibit biological variability and generate population-based values being run in parallel, while matching the understood mechanisms of neuroplasticity: disinhibition and LTP. Conclusion: These studies provide missing information of iTBS responsivity by (1) determining group-wide responsiveness in a clinically relevant target; (2) establishing individual level influences that affect responsivity which can be measured prior to iTBS; and (3) beginning design of a tool to test a single neural circuit and its mechanistic responses