Intact sensory-motor network structure and function in far from onset premanifest Huntington's disease

Diagnostic contribution and therapeutic perspectives of transcranial magnetic stimulation in dementia

Transcranial magnetic stimulation (TMS) is a powerful tool to probe in vivo brain circuits, as it allows to assess several cortical properties such asexcitability, plasticity and connectivity in humans. In the last 20 years, TMS has been applied to patients with dementia, enabling the identification of potential markers of thepathophysiology and predictors of cognitive decline; moreover, applied repetitively, TMS holds promise as a potential therapeutic intervention. The objective of this paper is to present a comprehensive review of studies that have employed TMS in dementia and to discuss potential clinical applications, from the diagnosis to the treatment. To provide a technical and theoretical framework, we first present an overview of the basic physiological mechanisms of the application of TMS to assess cortical excitability, excitation and inhibition balance, mechanisms of plasticity and cortico-cortical connectivity in the human brain. We then review the insights gained by TMS techniques into the pathophysiology and predictors of progression and response to treatment in dementias, including Alzheimer's disease (AD)-related dementias and secondary dementias. We show that while a single TMS measure offers low specificity, the use of a panel of measures and/or neurophysiological index can support the clinical diagnosis and predict progression. In the last part of the article, we discuss the therapeutic uses of TMS. So far, only repetitive TMS (rTMS) over the left dorsolateral prefrontal cortex and multisite rTMS associated with cognitive training have been shown to be, respectively, possibly (Level C of evidence) and probably (Level B of evidence) effective to improve cognition, apathy, memory, and language in AD patients, especially at a mild/early stage of the disease. The clinical use of this type of treatment warrants the combination of brain imaging techniques and/or electrophysiological tools to elucidate neurobiological effects of neurostimulation and to optimally tailor rTMS treatment protocols in individual patients or specific patient subgroups with dementia or mild cognitive impairment.

Use of random matrix theory in the discovery of resting state brain networks

Connectomics identifies brain networks in vivo in resting state functional MRI. However, the presence of noise produces spurious identification of brain networks, which have low test-retest reliability. A Network Based Statistics approach to network identification has been previously proposed that affords much better statistical power relative to Bonferroni method but nevertheless provides a sufficiently conservative, family-wise control for false positives. We propose the use of Random Matrix Theory (RMT) to discover brain networks and to associate those networks with demographic and clinical variables. We parcellated the brain into cortical and subcortical regions using either an anatomical or a functional brain atlas. We applied RMT to study functional connectivity across brain regions by first computing the correlation matrix for time courses in those brain regions and then identifying eigenvalues that deviate from the theoretical random distribution that RMT predicts, on the assumption that real brain networks would produce eigenvalues that differ significantly from the random distribution. We assessed the specificity and test-retest reliability of identified networks through application of this RMT-based approach to (1) synthetic data generated under the null-hypothesis, (2) resting state functional MRI data from 4 real-world cohorts of patients and healthy controls, and (3) synthetic data generated by the addition of increasing amounts of noise to real-world datasets. Our findings showed that RMT method was robust to the atlas used for parcellating the brain and did not discover a brain network in synthetic data when in fact a network was not present (i.e., specificity was high); RMT-identified networks in the real-world dataset had high test-retest reliability; and RMT-based method consistently discovered the same network in the presence of increasing noise in the real-world dataset.

Diffusion Tensor Imaging in Preclinical and Human Studies of Huntington's Disease: What Have we Learned so Far?

BACKGROUND

Huntington's Disease is an irreversible neurodegenerative disease characterized by the progressive deterioration of specific brain nerve cells. The current evaluation of cellular and physiological events in patients with HD relies on the development of transgenic animal models. To explore such events in vivo, diffusion tensor imaging has been developed to examine the early macro and microstructural changes in brain tissue. However, the gap in diffusion tensor imaging findings between animal models and clinical studies and the lack of microstructural confirmation by histological methods has questioned the validity of this method.

OBJECTIVE

This review explores white and grey matter ultrastructural changes associated to diffusion tensor imaging, as well as similarities and differences between preclinical and clinical Huntington's Disease studies.

METHODS

A comprehensive review of the literature using online-resources was performed (Pub- Med search).

RESULTS

Similar changes in fractional anisotropy as well as axial, radial and mean diffusivities were observed in white matter tracts across clinical and animal studies. However, comparative diffusion alterations in different grey matter structures were inconsistent between clinical and animal studies.

CONCLUSION

Diffusion tensor imaging can be related to specific structural anomalies in specific cellular populations. However, some differences between animal and clinical studies could derive from the contrasting neuroanatomy or connectivity across species. Such differences should be considered before generalizing preclinical results into the clinical practice. Moreover, current limitations of this technique to accurately represent complex multicellular events at the single micro scale are real. Future work applying complex diffusion models should be considered.

Diffusion imaging in Huntington's disease: comprehensive review

Huntington's disease (HD) is a monogenic disorder with 100% penetrance. With the advent of genetic testing in adults, disease-related, structural brain changes can be investigated from the earliest, premorbid stages of HD. While examining macrostructural change characterises global neuronal damage, investigating microstructural alterations provides information regarding brain organisation and its underlying biological properties. Diffusion MRI can be used to track the progression of microstructural anomalies in HD decades prior to clinical disease onset, providing a greater understanding of neurodegeneration. Multiple approaches, including voxelwise, region of interest and tractography, have been used in HD cohorts, showing a centrifugal pattern of white matter (WM) degeneration starting from deep brain areas, which is consistent with neuropathological studies. The corpus callosum, longer WM tracts and areas that are more densely connected, in particular the sensorimotor network, also tend to be affected early during premanifest stages. Recent evidence supports the routine inclusion of diffusion analyses within clinical trials principally as an additional measure to improve understanding of treatment effects, while the advent of novel techniques such as multitissue compartment models and connectomics can help characterise the underpinnings of progressive functional decline in HD.

Mitochondrial biology and the identification of biomarkers of Huntington's disease

Apart from finding novel compounds for treating Huntington's disease (HD) an important challenge at present consists in finding reliable read-outs or biomarkers that reflect key biological processes involved in HD pathogenesis. The core elements of HD biology, for example, HTT RNA levels or protein species can serve as biomarker, as could measures from biological systems or pathways in which Huntingtin plays an important role. Here we review the evidence for the involvement of mitochondrial biology in HD. The most consistent findings pertain to mitochondrial quality control, for example, fission/fusion. However, a convincing mitochondrial signature with biomarker potential is yet to emerge. This requires more research including in peripheral sources of human material, such as blood, or skeletal muscle.

Resting-state connectivity stratifies premanifest Huntington's disease by longitudinal cognitive decline rate

Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. In Huntington’s disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRI (rs-fMRI) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding TRACK-HD study when available), as well as functional connectivity density (FCD) maps in single rs-fMRI visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast- from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast- from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. These results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.

Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has the potential to shed light on the pathophysiological mechanisms of Huntington's disease (HD), paving the way to new therapeutic interventions. A systematic literature review was conducted in three online databases according to PRISMA guidelines, using keywords for HD, functional connectivity, and rs‐fMRI. We included studies investigating connectivity in presymptomatic (pre‐HD) and manifest HD gene carriers compared to healthy controls, implementing seed‐based connectivity, independent component analysis, regional property, and graph analysis approaches. Visual network showed reduced connectivity in manifest HD, while network/areas underpinning motor functions were consistently altered in both manifest HD and pre‐HD, showing disease stage‐dependent changes. Cognitive networks underlying executive and attentional functions showed divergent anterior–posterior alterations, possibly reflecting compensatory mechanisms. The involvement of these networks in pre‐HD is still unclear. In conclusion, aberrant connectivity of the sensory‐motor network is observed in the early stage of HD while, as pathology spreads, other networks might be affected, such as the visual and executive/attentional networks. Moreover, sensory‐motor and executive networks exhibit hyper‐ and hypo‐connectivity patterns following different spatiotemporal trajectories. These findings could potentially help to implement future huntingtin‐lowering interventions.

Cortical neurodevelopment in pre-manifest Huntington's disease

Background

The expression of the HTT CAG repeat expansion mutation causes neurodegeneration in Huntington's disease (HD). Objectives: In light of the – mainly in-vitro – evidence suggesting an additional role of huntingtin in neurodevelopment we used 3T MRI to test the hypothesis that in CAG-expanded individuals without clinical signs of HD (preHD) there is evidence for neurodevelopmental abnormalities.

Methods

We specifically investigated the complexity of cortical folding, a measure of cortical neurodevelopment, employing a novel method to quantify local fractal dimension (FD) measures that uses spherical harmonic reconstructions.

Results

The complexity of cortical folding differed at a group level between preHD (n = 57) and healthy volunteers (n = 57) in areas of the motor and visual system as well as temporal cortical areas. However, there was no association between the complexity of cortical folding and the loss in putamen volume that was clearly evident in preHD.

Conclusions

Our results suggest that HTT CAG repeat length may have an influence on cortical folding without evidence that this leads to developmental pathology or was clinically meaningful. This suggests that the HTT CAG-repeat expansion mutation may influence the processes governing cortical neurodevelopment; however, that influence seems independent of the events that lead to neurodegeneration.

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Measures of cortical neurodevelopment in preclinical Huntington's disease (HD) gene carriers differ from healthy volunteers

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The influence on cortical folding of the HD gene was not associated with developmental pathology or clinically meaningful

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The influence of the HD gene on cortical neurodevelopment may differ from that on neurodegeneration

Structural and Functional Brain Mapping Correlates of Impaired Eye Movement Control in Parkinsonian Syndromes: A Systems-Based Concept

The investigation of the human oculomotor system by eye movement recordings provides an approach to behavior and its alterations in disease. The neurodegenerative process underlying parkinsonian syndromes, including Parkinson’s disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA) changes structural and functional brain organization, and thus affects eye movement control in a characteristic manner. Video-oculography has been established as a non-invasive recording device for eye movements, and systematic investigations of eye movement control in a clinical framework have emerged as a functional diagnostic tool in neurodegenerative parkinsonism. Disease-specific brain atrophy in parkinsonian syndromes has been reported for decades, these findings were refined by studies utilizing diffusion tensor imaging (DTI) and task-based/task-free functional MRI—both MRI techniques revealed disease-specific patterns of altered structural and functional brain organization. Here, characteristic disturbances of eye movement control in parkinsonian syndromes and their correlations with the structural and functional brain network alterations are reviewed. On this basis, we discuss the growing field of graph-based network analysis of the structural and functional connectome as a promising candidate for explaining abnormal phenotypes of eye movement control at the network level, both in health and in disease.