Longitudinal Structural MRI in Neurologically Healthy Adults

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.

Chronic Hyponatremia and Brain Structure and Function Before and After Treatment

RATIONALE & OBJECTIVE

Hyponatremia is the most common electrolyte disorder and is associated with significant morbidity and mortality. This study investigated neurocognitive impairment, brain volume, and alterations in magnetic resonance imaging (MRI)-based measures of cerebral function in patients before and after treatment for hyponatremia.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

Patients with presumed chronic hyponatremia without signs of hypo- or hypervolemia treated in the emergency department of a German tertiary-care hospital.

EXPOSURE

Hyponatremia (ie, plasma sodium concentration [Na+]<125mmol/L) before and after treatment leading to [Na+]>130mmol/L.

OUTCOMES

Standardized neuropsychological testing (Mini-Mental State Examination, DemTect, Trail Making Test A/B, Beck Depression Inventory, Timed Up and Go) and resting-state MRI were performed before and after treatment of hyponatremia to assess total brain and white and gray matter volumes as well as neuronal activity and its synchronization.

ANALYTICAL APPROACH

Changes in outcomes after treatment for hyponatremia assessed using bootstrapped confidence intervals and Cohen d statistic. Associations between parameters were assessed using correlation analyses.

RESULTS

During a 3.7-year period, 26 patients were enrolled. Complete data were available for 21 patients. Mean [Na+]s were 118.4mmol/L before treatment and 135.5mmol/L after treatment. Most measures of cognition improved significantly. Comparison of MRI studies showed a decrease in brain tissue volumes, neuronal activity, and synchronization across all gray matter after normalization of [Na+]. Volume effects were particularly prominent in the hippocampus. During hyponatremia, synchronization of neuronal activity was negatively correlated with [Na+] (r=-0.836; 95% CI, -0.979 to-0.446) and cognitive function (Mini-Mental State Examination, r=-0.523; 95% CI, -0.805 to-0.069; DemTect, r=-0.744; 95% CI, -0.951 to-0.385; and Trail Making Test A, r=0.692; 95% CI, 0.255-0.922).

LIMITATIONS

Small sample size, insufficient quality of several MRI scans as a result of motion artifact.

CONCLUSIONS

Resolution of hyponatremia was associated with improved cognition and reductions in brain volumes and neuronal activity. Impaired cognition during hyponatremia is closely linked to increased neuronal activity rather than to tissue volumes. Furthermore, the hippocampus appears to be particularly susceptible to hyponatremia, exhibiting pronounced changes in tissue volume.

PLAIN-LANGUAGE SUMMARY

Hyponatremia is a common clinical problem, and patients often present with neurologic symptoms that are at least partially reversible. This study used neuropsychological testing and magnetic resonance imaging to examine patients during and after correction of hyponatremia. Treatment led to an improvement in patients' cognition as well as a decrease in their brain volumes, spontaneous neuronal activity, and synchronized neuronal activity between remote brain regions. Volume effects were particularly prominent in the hippocampus, an area of the brain that is important for the modulation of memory. During hyponatremia, patients with the lowest sodium concentrations had the highest levels of synchronized neuronal activity and the poorest cognitive test results.

Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group

 Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.

Impact of follow ups, time interval and study duration in diffusion & myelin MRI clinical study in MS

It is currently unknown how quantitative diffusion and myelin MRI designs affect the results of a longitudinal study. We used two independent datasets containing 6 monthly MRI measurements from 20 healthy controls and 20 relapsing-remitting multiple sclerosis (RR-MS) patients. Six designs were tested, including 3 MRI acquisitions, either over 6 months or over a shorter study duration, with balanced (same interval) or unbalanced (different interval) time intervals between MRI acquisitions. First, we show that in RR-MS patients, the brain changes over time obtained with 3 MRI acquisitions were similar to those observed with 5 MRI acquisitions and that designs with an unbalanced time interval showed the highest similarity, regardless of study duration. No significant brain changes were found in the healthy controls over the same periods. Second, the study duration affects the sample size in the RR-MS dataset; a longer study requires more subjects and vice versa. Third, the number of follow-up acquisitions and study duration affect the sensitivity and specificity of the associations with clinical parameters, and these depend on the white matter bundle and MRI measure considered. Together, this suggests that the optimal design depends on the assumption of the dynamics of change in the target population and the accuracy required to capture these dynamics. Thus, this work provides a better understanding of key factors to consider in a longitudinal study and provides clues for better strategies in clinical trial design.

Interindividual differences in intergenerational sustainable behavior are associated with cortical thickness of the dorsomedial and dorsolateral prefrontal cortex

Intergenerational sustainability requires people of the present generation to make sacrifices today to benefit others of future generations (e.g. mitigating climate change, reducing public debt). Individuals vary greatly in their intergenerational sustainability, and the cognitive and neural sources of these interindividual differences are not yet well understood. We here combined neuroscientific and behavioral methods by assessing interindividual differences in cortical thickness and by using a common-pool resource paradigm with intergenerational contingencies. This enabled us to look for objective, stable, and trait-like neural markers of interindividual differences in consequential intergenerational behavior. We found that individuals behaving sustainably (vs. unsustainably) were marked by greater cortical thickness of the dorsomedial and dorsolateral prefrontal cortex. Given that these brain areas are involved in perspective-taking and self-control and supported by mediation analyses, we speculate that greater cortical thickness of these brain areas better enable individuals to take the perspective of future generations and to resist temptations to maximize personal benefits that incur costs for future generations. By meeting recent calls for the contribution of neuroscience to sustainability research, it is our hope that the present study advances the transdisciplinary understanding of interindividual differences in intergenerational sustainability.

Tracking Huntington's Disease Progression Using Motor, Functional, Cognitive, and Imaging Markers

BACKGROUND

Potential therapeutic targets and clinical trials for Huntington's disease have grown immensely in the last decade. However, to improve clinical trial outcomes, there is a need to better characterize profiles of signs and symptoms across different epochs of the disease to improve selection of participants.

OBJECTIVE

The objective of the present study was to best distinguish longitudinal trajectories across different Huntington's disease progression groups.

METHODS

Clinical and morphometric imaging data from 1082 participants across IMAGE-HD, TRACK-HD, and PREDICT-HD studies were combined, with longitudinal times ranging between 1 and 10 years. Participants were classified into 4 groups using CAG and age product. Using multivariate linear mixed modeling, 63 combinations of markers were tested for their sensitivity in differentiating CAG and age product groups. Next, multivariate linear mixed modeling was applied to define the best combination of markers to track progression across individual CAG and age product groups.

RESULTS

Putamen and caudate volumes, individually and/or combined, were identified as the best variables to both differentiate CAG and age product groups and track progression within them. The model using only caudate volume best described advanced disease progression in the combined data set. Contrary to expectations, combining clinical markers and volumetric measures did not improve tracking longitudinal progression.

CONCLUSIONS

Monitoring volumetric changes throughout a trial (alongside primary and secondary clinical end points) may provide a more comprehensive understanding of improvements in functional outcomes and help to improve the design of clinical trials. Alternatively, our results suggest that imaging deserves consideration as an end point in clinical trials because of the prospect of greater sensitivity. © 2021 International Parkinson and Movement Disorder Society.

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.