Cortical morphometry and neural dysfunction in Huntington’s disease: a review

Abstract

Numerous neuroimaging techniques have been used to identify biomarkers of disease progression in Huntington's disease (HD). To date, the earliest and most sensitive of these is caudate volume; however, it is becoming increasingly evident that numerous changes to cortical structures, and their interconnected networks, occur throughout the course of the disease. The mechanisms by which atrophy spreads from the caudate to these cortical regions remains unknown. In this review, the neuroimaging literature specific to T1‐weighted and diffusion‐weighted magnetic resonance imaging is summarized and new strategies for the investigation of cortical morphometry and the network spread of degeneration in HD are proposed. This new avenue of research may enable further characterization of disease pathology and could add to a suite of biomarker/s of disease progression for patient stratification that will help guide future clinical trials.

Brain anomalies in early psychosis: From secondary to primary psychosis

“…Notably in this last case, no caudate nucleus atrophy was reported, although it is a common finding not only in adult-but also in juvenile-onset Huntington's disease (Arraj et al 2021). Given that diagnosis is often guided by motor symptoms, which may correlate with caudate atrophy (Tan et al 2021), early caudate preservation in the context of diffuse cortical atrophy may be a hypothesis to explain why this patient initially presented with psychotic features, three years prior to motor dysfunction. Finally, genetical secondary psychoses with diffuse white matter atrophy have also been described in cases of adult-onset vanishing white matter leukoencephalopathy (Denier 2007) and X-linked adrenoleukodystrophy (Ramos-Ríos, Berdullas, and Santos-García 2009).…”

From Pathophysiology to Treatment of Huntington's Disease

“…Mohan et al recently used machine learning to develop a new disease progression model with nine disease states of increasing severity, based on clinical data only [224]. Another focus in HD-research, requiring artificial intelligence, is the use of network models to explain the pathology of the disease progression and disease phenotypes [158,225]. However, machine learning algorithms require large amounts of data, before they start to provide useful results, especially when it comes to neural networks.…”

Bidirectional two-sample Mendelian randomization analyses support causal relationships between structural and diffusion imaging-derived phenotypes and the risk of major neurodegenerative diseases

“…A large number of case-control investigations have revealed that individuals diagnosed with major neurodegenerative diseases have structural changes in a wide range of brain regions, including many heritable imaging-derived phenotypes (IDPs) that can be measured by non-invasive magnetic resonance imaging (MRI) technique. For instance, atrophy of the entorhinal cortex and hippocampus is a key neuropathological feature of AD patients, however, the atrophy of the hippocampus could also be observed in patients with LBD, PD, and HD, although the atrophy level was less than that in patients with AD [ 9 – 12 ]. Investigations have also identified atrophy of the cingulate cortex in multiple neurodegenerative diseases, such as AD, MS, and FTD [ 9 , 13 , 14 ].…”