Rachel L. C. Barrett

The increasing prevalence of large-scale neuroimaging initiatives has created a need for efficient and anatomically accurate methods for analysing tractography data. Here, we present MegaTrack, a framework designed to combine the anatomical precision of manual dissections with the efficiency required for big data analysis. At its core, MegaTrack operates by normalising individual tractography datasets to a standard space, concatenating them into a single "mega" tractography dataset the user manually dissects before automatically extracting subject-specific tracts and metrics in native space. This approach permits identifying and extracting tracts not yet available in existing templates or automatic dissection tools. We validated this approach using multiple datasets and applications. Comparison with individual manual dissections showed high spatial agreement (weighted dice coefficient > 0.95) and strong correlations in tract-specific metrics (R2 > 0.9). In a longitudinal dataset, MegaTrack demonstrated improved reproducibility across time points and higher inter-rater reliability compared to manual dissections. In a motor neuron disease cohort, MegaTrack replicated known group differences in corticospinal tract microstructure with comparable sensitivity to manual dissections (p = 0.001) but with significantly reduced processing time. Finally, we showcase MegaTrack's scalability by creating a comprehensive white matter atlas from 140 healthy adults and demonstrate its application for lesion analysis in stroke patients. This framework is accompanied by an interactive online tool that allows users to explore these atlases and perform customised lesion analyses. The MegaTrack framework is a time-efficient solution for the anatomically precise analysis of large tractography datasets, enabling the rapid simultaneous dissection of multiple subjects while preserving individual anatomical features. The framework's ability to efficiently process large datasets makes it an excellent tool for generating training data for machine learning and deep learning algorithms in tractography analysis. This approach has broad applications in both research and clinical settings, from group comparisons to personalised lesion mapping, and can significantly contribute to the development of advanced automated tractography methods.

Ex vivo diffusion imaging can be used to study healthy and pathological tissue microstructure in the rodent brain with high resolution, providing a link between in vivo MRI and ex vivo microscopy techniques. Major challenges for the successful acquisition of ex vivo diffusion imaging data however are changes in the relaxivity and diffusivity of brain tissue following perfusion fixation.In this study we address this question by examining the combined effects of tissue preparation factors that influence signal‐to‐noise ratio (SNR) and consequently image quality, including fixative concentration, contrast agent concentration and tissue rehydration time. We present an optimization strategy combining these factors to manipulate the and of fixed tissue and maximize SNR efficiency.We apply this strategy in the rat brain, for a diffusion‐weighted spin echo protocol with TE = 27 ms on a 9.4 T scanner with a 39 mm volume coil and 660 mT/m 114 mm gradient insert. We used a reduced fixative concentration of 2% paraformaldehyde (PFA), rehydration time more than 20 days, 15 mM Gd‐DTPA in perfusate and TR 250 ms. This resulted in a doubling of SNR and an increase in SNR per unit time of 135% in cortical grey matter and 88% in white matter compared with 4% PFA and no contrast agent.This improved SNR efficiency enabled the acquisition of excellent‐quality high‐resolution (78 m isotropic voxel size) diffusion data with b = 4000 s/mm , 30 diffusion directions and a field of view of mm³ in less than 4 days. It was also possible to achieve comparable data quality for a standard resolution (150 m) diffusion dataset in h.In conclusion, the tissue optimization strategy presented here may be used to improve SNR, increase spatial resolution and/or allow faster acquisitions in preclinical ex vivo diffusion MRI experiments.

Ex vivo diffusion imaging can be used to study healthy and pathological tissue microstructure in the rodent brain with microscopic resolution, providing a link between in vivo MRI and ex vivo microscopy techniques. A major challenge for the successful acquisition of ex vivo diffusion imaging data however are changes in the relaxivity and diffusivity of brain tissue following perfusion fixation. In this study we address this question by examining the combined effects of tissue preparation factors that influence image quality, including tissue rehydration time, fixative concentration and contrast agent concentration. We present an optimisation strategy combining these factors to manipulate the T1 and T2 of fixed tissue and maximise signal-to-noise ratio (SNR) efficiency. Applying this strategy in the rat brain resulted in a doubling of SNR and an increase in SNR per unit time by 135% in grey matter and 88% in white matter. This enabled the acquisition of excellent quality high-resolution (78 µm isotropic voxel size) diffusion data in less than 4 days, with a b -value of 4000 s/mm ² , 30 diffusion directions and a field of view of 40 × 13 × 18 mm, using a 9.4 Tesla scanner with a standard 39 mm volume coil and a 660 mT/m 114 mm gradient insert. It was also possible to achieve comparable data quality for a standard resolution (150 µm) diffusion dataset in 2 ¹ / ₄ hours. In conclusion, the optimisation strategy presented here may be used to improve signal quality, increase spatial resolution and/or allow faster acquisitions in preclinical ex vivo diffusion MRI experiments.

The frontal lobe is central to distinctive aspects of human cognition and behavior. Some comparative studies link this to a larger frontal cortex and even larger frontal white matter in humans compared with other primates, yet others dispute these findings. The discrepancies between studies could be explained by limitations of the methods used to quantify volume differences across species, especially when applied to white matter connections. In this study, we used a novel tractography approach to demonstrate that frontal lobe networks, extending within and beyond the frontal lobes, occupy 66% of total brain white matter in humans and 48% in three monkey species: vervets (Chlorocebus aethiops), rhesus macaque (Macaca mulatta) and cynomolgus macaque (Macaca fascicularis), all male. The simian–human differences in proportional frontal tract volume were significant for projection, commissural, and both intralobar and interlobar association tracts. Among the long association tracts, the greatest difference was found for tracts involved in motor planning, auditory memory, top-down control of sensory information, and visuospatial attention, with no significant differences in frontal limbic tracts important for emotional processing and social behaviour. In addition, we found that a nonfrontal tract, the anterior commissure, had a smaller volume fraction in humans, suggesting that the disproportionally large volume of human frontal lobe connections is accompanied by a reduction in the proportion of some nonfrontal connections. These findings support a hypothesis of an overall rearrangement of brain connections during human evolution.SIGNIFICANCE STATEMENTTractography is a unique tool to map white matter connections in the brains of different species, including humans. This study shows that humans have a greater proportion of frontal lobe connections compared with monkeys, when normalized by total brain white matter volume. In particular, tracts associated with language and higher cognitive functions are disproportionally larger in humans compared with monkeys, whereas other tracts associated with emotional processing are either the same or disproportionally smaller. This supports the hypothesis that the emergence of higher cognitive functions in humans is associated with increased extended frontal connectivity, allowing human brains more efficient cross talk between frontal and other high-order associative areas of the temporal, parietal, and occipital lobes.

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.