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

MRI-based brain structural changes in adult-onset dentatorubral-pallidoluysian atrophy

BACKGROUND AND PURPOSE

Adult-onset dentatorubral-pallidoluysian atrophy (DRPLA) is a rare autosomal dominant spinocerebellar ataxia subtype, characterized by widespread brain neurodegeneration. However, comprehensive studies on whole-brain structural changes remain limited. This study aimed to quantify brain structural damage in adult-onset DRPLA and correlate these findings with clinical data.

METHODS

Twenty-four adult-onset DRPLA patients were recruited and underwent structural and diffusion MRI at 3.0 Tesla. Multimodal analyses were conducted to assess cortical thickness, the volumes of subcortical structures, the brainstem, cerebellum, and cerebral white matter (WM), as well as the microstructural integrity of WM. Results were compared with those from 30 age- and gender-matched healthy controls. The relationships between neuroimaging abnormalities and clinical data, including ataxia severity, cognitive performance, CAG repeat length, and disease duration, were explored.

RESULTS

In adult-onset DRPLA patients, reduced cortical thickness was observed in the bilateral frontal, parietal, temporal, and occipital lobes. Volumetric analysis revealed significant reductions in all subcortical structures, as well as in the brainstem, cerebellum, and cerebral WM substructures. Tract-based spatial statistics (TBSS) analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics demonstrated widespread WM abnormalities throughout the brain. Furthermore, several imaging measures demonstrated significant correlations with ataxia severity, cognitive function scores, CAG repeat length, and disease duration.

CONCLUSION

DRPLA should be considered a multisystem neurodegenerative disease, characterized by widespread and severe gray matter and WM atrophy throughout the brain. The advanced MRI techniques employed in this study expand our understanding of the underlying pathophysiological mechanisms.

Brain Volumetric Analysis Using Artificial Intelligence Software in Premanifest Huntington's Disease Individuals from a Colombian Caribbean Population

Background and objectives: The premanifest phase of Huntington's disease (HD) is characterized by the absence of motor symptoms and exhibits structural changes in imaging that precede clinical manifestation. This study aimed to analyze volumetric changes identified through brain magnetic resonance imaging (MRI) processed using artificial intelligence (AI) software in premanifest HD individuals, focusing on the relationship between CAG triplet expansion and structural biomarkers. Methods: The study included 36 individuals descending from families affected by HD in the Department of Atlántico. Sociodemographic data were collected, followed by peripheral blood sampling to extract genomic DNA for quantifying CAG trinucleotide repeats in the Huntingtin gene. Brain volumes were evaluated using AI software (Entelai/IMEXHS, v4.3.4) based on MRI volumetric images. Correlations between brain volumes and variables such as age, sex, and disease status were determined. All analyses were conducted using SPSS (v. IBM SPSS Statistics 26), with significance set at p < 0.05. Results: The analysis of brain volumes according to CAG repeat expansion shows that individuals with ≥40 repeats evidence significant increases in cerebrospinal fluid (CSF) volume and subcortical structures such as the amygdalae and left caudate nucleus, along with marked reductions in cerebral white matter, the cerebellum, brainstem, and left pallidum. In contrast, those with <40 repeats show minimal or moderate volumetric changes, primarily in white matter and CSF. Conclusions: These findings suggest that CAG expansion selectively impacts key brain regions, potentially influencing the progression of Huntington's disease, and that AI in neuroimaging could identify structural biomarkers long before clinical symptoms appear.

Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial

BACKGROUND

In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process.

OBJECTIVES

To improve stratification of Huntington's disease individuals for clinical trials.

METHODS

We used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement.

RESULTS

The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %).

CONCLUSIONS

This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

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

Previous observational investigations suggest that structural and diffusion imaging-derived phenotypes (IDPs) are associated with major neurodegenerative diseases; however, whether these associations are causal remains largely uncertain. Herein we conducted bidirectional two-sample Mendelian randomization analyses to infer the causal relationships between structural and diffusion IDPs and major neurodegenerative diseases using common genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables. Summary statistics of genome-wide association study (GWAS) for structural and diffusion IDPs were obtained from 33,224 individuals in the UK Biobank cohort. Summary statistics of GWAS for seven major neurodegenerative diseases were obtained from the largest GWAS for each disease to date. The forward MR analyses identified significant or suggestively statistical causal effects of genetically predicted three structural IDPs on Alzheimer's disease (AD), frontotemporal dementia (FTD), and multiple sclerosis. For example, the reduction in the surface area of the left superior temporal gyrus was associated with a higher risk of AD. The reverse MR analyses identified significantly or suggestively statistical causal effects of genetically predicted AD, Lewy body dementia (LBD), and FTD on nine structural and diffusion IDPs. For example, LBD was associated with increased mean diffusivity in the right superior longitudinal fasciculus and AD was associated with decreased gray matter volume in the right ventral striatum. Our findings might contribute to shedding light on the prediction and therapeutic intervention for the major neurodegenerative diseases at the neuroimaging level.

Brain anomalies in early psychosis: From secondary to primary psychosis

Brain anomalies are frequently found in early psychoses. Although they may remain undetected for many years, their interpretation is critical for differential diagnosis. In secondary psychoses, their identification may allow specific management. They may also shed light on various pathophysiological aspects of primary psychoses. Here we reviewed cases of secondary psychoses associated with brain anomalies, reported over a 20-year period in adolescents and young adults aged 13-30 years old. We considered age at first psychotic symptoms, relevant medical history, the nature of psychiatric symptoms, clinical red flags, the nature of the brain anomaly reported, and the underlying disease. We discuss the relevance of each brain area in light of normal brain function, recent case-control studies, and postulated pathophysiology. We show that anomalies in all regions, whether diffuse, multifocal, or highly localized, may lead to psychosis, without necessarily being associated with non-psychiatric symptoms. This underlines the interest of neuroimaging in the initial workup, and supports the hypothesis of psychosis as a global network dysfunction that involves many different regions.

Differential tractography as a dynamic imaging biomarker: A methodological pilot study for Huntington's disease

Huntington's disease (HD) is a neurodegenerative disorder characterized by motor, psychiatric, and cognitive symptoms. Due to its diverse manifestations, the scientific community has long recognized the need for sensitive, objective, individualized, and dynamic disease assessment tools. We examined the feasibility of Differential Tractography as a biomarker to evaluate correlation of symptom severity and of HD progression at the individual level. Differential tractography is a novel tractography modality that maps pathways with axonal injury characterized by a decrease of anisotropic diffusion pattern. We recruited sixteen patients scanned at 0-, 6-, and 12-month intervals by diffusion MRI scans for differential tractography assessment and correlated its volumetric findings with the Unified Huntington's Disease Rating Scale (UHDRS). Deterministic fiber tracking algorithm was applied. Longitudinal data was modeled using the generalized estimating equation (GEE) model and correlated with UHDRS scores, in addition to Spearman correlation for cross-sectional data. Our results show that volumes of affected pathways revealed by differential tractography significantly correlated with UHDRS scores in longitudinal data (p-value < 0.001), and chronological changes in differential tractography also correlated with the changes in UHDRS (p-value < 0.001). This technique opens new clinical avenues as a clinical translational tool to evaluate presymptomatic and symptomatic gene positive individuals. Our results provide support that differential tractography has the potential to be used as a dynamic imaging biomarker to assess at the individual level in a non-invasive manner, disease progression in HD. Critically important, differential tractography proves to be a quantitative tool for following degeneration in presymptomatic patients, with potential applications in clinical trials.

Huntington's disease mouse models: unraveling the pathology caused by CAG repeat expansion

Huntington's disease (HD) is a neurodegenerative disease that results in motor and cognitive dysfunction, leading to early death. HD is caused by an expansion of CAG repeats in the huntingtin gene (HTT). Here, we review the mouse models of HD. They have been used extensively to better understand the molecular and cellular basis of disease pathogenesis as well as to provide non-human subjects to test the efficacy of potential therapeutics. The first and best-studied in vivo rodent model of HD is the R6/2 mouse, in which a transgene containing the promoter and exon 1 fragment of human HTT with 150 CAG repeats was inserted into the mouse genome. R6/2 mice express rapid, robust behavioral pathologies and display a number of degenerative abnormalities in neuronal populations most vulnerable in HD. The first conditional full-length mutant huntingtin (mHTT) mouse model of HD was the bacterial artificial chromosome (BAC) transgenic mouse model of HD (BACHD), which expresses human full-length mHTT with a mixture of 97 CAG-CAA repeats under the control of endogenous HTT regulatory machinery. It has been useful in identifying the role of mHTT in specific neuronal populations in degenerative processes. In the knock-in (KI) model of HD, the expanded human CAG repeats and human exon 1 are inserted into the mouse Htt locus, so a chimera of the full-length mouse protein with the N-terminal human portion is expressed. Many of aspects of the pathology and behavioral deficits in the KI model better mimic disease characteristics found in HD patients than other models. Accordingly, some have proposed that these mice may be preferable models of the disease over others. Indeed, as our understanding of HD advances, so will the design of animal models to test and develop HD therapies.