Diffuse structural connectivity loss occurs early in Huntington’s disease. However, the organizational principles underlying these changes are unclear. Using whole brain diffusion tractography and graph theoretical analysis, McColgan, Seunarine et al. identify a specific role for highly connected rich club regions as a substrate for structural connectivity loss in Huntington’s disease.

Camino is an open-source, object-oriented software package for processing diffusion MRI data. Camino implements a data processing pipeline, which allows for easy scripting and flexible integration with other software. This paper summarises the features of Camino at each stage of the pipeline from the raw data to the statistics used by clinicians and researchers. The paper also discusses the role of Camino in the paper “An Automated Approach to Connectivity-based Partitioning of Brain Structures”, published at MICCAI 2005.

BackgroundThe earliest white matter changes in Huntington’s disease are seen before disease onset in the premanifest stage around the striatum, within the corpus callosum, and in posterior white matter tracts. While experimental evidence suggests that these changes may be related to abnormal gene transcription, we lack an understanding of the biological processes driving this regional vulnerability.MethodsHere, we investigate the relationship between regional transcription in the healthy brain, using the Allen Institute for Brain Science transcriptome atlas, and regional white matter connectivity loss at three time points over 24 months in subjects with premanifest Huntington’s disease relative to control participants. The baseline cohort included 72 premanifest Huntington’s disease participants and 85 healthy control participants.ResultsWe show that loss of corticostriatal, interhemispheric, and intrahemispheric white matter connections at baseline and over 24 months in premanifest Huntington’s disease is associated with gene expression profiles enriched for synaptic genes and metabolic genes. Corticostriatal gene expression profiles are predominately associated with motor, parietal, and occipital regions, while interhemispheric expression profiles are associated with frontotemporal regions. We also show that genes with known abnormal transcription in human Huntington’s disease and animal models are overrepresented in synaptic gene expression profiles, but not in metabolic gene expression profiles.ConclusionsThese findings suggest a dual mechanism of white matter vulnerability in Huntington’s disease, in which abnormal transcription of synaptic genes and metabolic disturbance not related to transcription may drive white matter loss.

The growth hormone-insulin-like growth factor-1 axis plays a role in normal brain growth but little is known of the effect of growth hormone deficiency on brain structure. Children with isolated growth hormone deficiency (peak growth hormone <6.7 µg/l) and idiopathic short stature (peak growth hormone >10 µg/l) underwent cognitive assessment, diffusion tensor imaging and volumetric magnetic resonance imaging prior to commencing growth hormone treatment. Total brain, corpus callosal, hippocampal, thalamic and basal ganglia volumes were determined using Freesurfer. Fractional anisotropy (a marker of white matter structural integrity) images were aligned and tract-based spatial statistics performed. Fifteen children (mean 8.8 years of age) with isolated growth hormone deficiency [peak growth hormone <6.7 µg/l (mean 3.5 µg/l)] and 14 controls (mean 8.4 years of age) with idiopathic short stature [peak growth hormone >10 µg/l (mean 15 µg/l) and normal growth rate] were recruited. Compared with controls, children with isolated growth hormone deficiency had lower Full-Scale IQ (P < 0.01), Verbal Comprehension Index (P < 0.01), Processing Speed Index (P < 0.05) and Movement-Assessment Battery for Children (P < 0.008) scores. Verbal Comprehension Index scores correlated significantly with insulin-like growth factor-1 (P < 0.03) and insulin-like growth factor binding protein-3 (P < 0.02) standard deviation scores in isolated growth hormone deficiency. The splenium of the corpus callosum, left globus pallidum, thalamus and hippocampus (P < 0.01) were significantly smaller; and corticospinal tract (bilaterally; P < 0.045, P < 0.05) and corpus callosum (P < 0.05) fractional anisotropy were significantly lower in the isolated growth hormone deficiency group. Basal ganglia volumes and bilateral corticospinal tract fractional anisotropy correlated significantly with Movement-Assessment Battery for Children scores, and corpus callosum fractional anisotropy with Full-Scale IQ and Processing Speed Index. In patients with isolated growth hormone deficiency, white matter abnormalities in the corpus callosum and corticospinal tract, and reduced thalamic and globus pallidum volumes relate to deficits in cognitive function and motor performance. Follow-up studies that investigate the course of the structural and cognitive deficits on growth hormone treatment are now required to confirm that growth hormone deficiency impacts significantly on brain structure, cognitive function and motor performance.

Diffusion models are advantageous for examining brain microstructure non-invasively and their validation is important for transference into the clinical domain. Neurite Orientation Dispersion and Density Imaging (NODDI) is a promising model for estimating multiple diffusion compartments from MRI data acquired in a clinically feasible time. As a relatively new model, it is necessary to examine NODDI under certain experimental conditions, such as change in magnetic field-strength, and assess it in relation to diffusion tensor imaging (DTI), an established model that is largely understood by the neuroimaging community. NODDI measures (intracellular volume fraction, ν , and orientation distribution, OD) were compared with DTI at 1.5 and 3 T data in healthy adults in whole-brain tissue masks and regions of white- and deep grey-matter. Within-session reproducibility and between-subject differences of NODDI with field-strength were also investigated. Field-strength had a significant effect on NODDI measures, suggesting careful interpretation of results from data acquired at 1.5 and 3 T. It was demonstrated that NODDI is feasible at 1.5 T, but with lower ν in white-matter regions compared with 3 T. Furthermore, the advantages of NODDI over DTI in a region of complex microstructure were shown. Specifically, in the centrum-semiovale where FA is typically as low as in grey-matter, ν was comparable to other white-matter regions yet accompanied by an OD similar to deep grey-matter. In terms of reproducibility, NODDI measures varied more than DTI. It may be that NODDI is more susceptible to noisier parameter estimates when compared with DTI, conversely it may have greater sensitivity to true within- and between-subject heterogeneity. Hum Brain Mapp 37:4550-4565, 2016. © 2016 Wiley Periodicals, Inc.

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White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human wholebrain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Increasing evidence is emerging for sexual dimorphism in the trajectory of white matter development in children assessed using volumetric magnetic resonance imaging (MRI) and more recently diffusion MRI. Recent studies using diffusion MRI have examined cohorts with a wide age range (typically between 5 and 30 years) showing focal regions of differential diffusivity and fractional anisotropy (FA) and have implicated puberty as a possible contributory factor. To further investigate possible dimorphic trajectories in a young cohort, presumably closer to the expected onset of puberty, we used tract-based spatial statistics to investigate diffusion metrics. The cohort consisted of 23 males and 30 females between the ages of 8 and 16 years. Differences in diffusion metrics were corrected for age, total brain volume, and full scale IQ. In contrast to previous studies showing focal differences between males and females, widespread sexually dimorphic trajectories in structural white matter development were observed. These differences were characterized by more advanced development in females compared to males indicated by lower mean diffusivity, radial and axial diffusivity, and higher FA in females. This difference appeared to be larger at lower ages (8–9 years) with diffusion measures from males and females tending to converge between 10 and 14 years of age. Males showed a steeper slope for age-diffusion metric correlations compared to females, who either did not correlate with age or correlated in fewer regions. Further studies are now warranted to determine the role of hormones on the observed differences, particularly in 8–9-year-old children.