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

The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making

While some model-informed drug development frameworks are well recognized as enabling clinical trials, the value of disease progression modeling (DPM) in impacting medical product development has yet to be fully realized. The Clinical Trials Transformation Initiative assembled a diverse project team from across the patient, academic, regulatory, and industry sectors of practice to advance the use of DPM for decision making in clinical trials and medical product development. This team conducted a scoping review to explore current applications of DPM and convened a multi-stakeholder expert meeting to discuss its value in medical product development. In this article, we present the scoping review and expert meeting output and propose key questions that medical product developers and regulators may use to inform clinical development strategy, appreciate the therapeutic context and endpoint selection, and optimize trial design with disease progression models. By expanding awareness of the unique value of DPM, this article does not aim to be technical in nature but rather aims to highlight the potential of DPM to improve the quality and efficiency of medical product development.

Brain Age as a New Measure of Disease Stratification in Huntington's Disease

BACKGROUND

Despite advancements in understanding Huntington's disease (HD) over the past two decades, absence of disease-modifying treatments remains a challenge. Accurately characterizing progression states is crucial for developing effective therapeutic interventions. Various factors contribute to this challenge, including the need for precise methods that can account for the complex nature of HD progression.

OBJECTIVE

This study aims to address this gap by leveraging the concept of the brain's biological age as a foundation for a data-driven clustering method to delineate various states of progression. Brain-predicted age, influenced by somatic expansion and its impact on brain volumes, offers a promising avenue for stratification by stratifying subgroups and determining the optimal timing for interventions.

METHODS

To achieve this, data from 953 participants across diverse cohorts, including PREDICT-HD, TRACK-HD, and IMAGE-HD, were meticulously analyzed. Brain-predicted age was computed using sophisticated algorithms, and participants were categorized into four groups based on CAG and age product score. Unsupervised k-means clustering with brain-predicted age difference (brain-PAD) was then employed to identify distinct progression states.

RESULTS

The analysis revealed significant disparities in brain-predicted age between HD participants and controls, with these differences becoming more pronounced as the disease progressed. Brain-PAD demonstrated a correlation with disease severity, effectively identifying five distinct progression states characterized by significant longitudinal disparities.

CONCLUSIONS

These findings highlight the potential of brain-PAD in capturing HD progression states, thereby enhancing prognostic methodologies and providing valuable insights for future clinical trial designs and interventions. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Neurochemical changes in the progression of Huntington's disease: A meta-analysis of in vivo1H-MRS studies

Proton magnetic resonance spectroscopy (1H-MRS) allows measuring specific brain metabolic alterations in Huntington's disease (HD), and these metabolite profiles may serve as non-invasive biomarkers associated with disease progression. Despite this potential, previous findings are inconsistent. Accordingly, we performed a meta-analysis on available in vivo1H-MRS studies in premanifest (Pre-HD) and symptomatic HD stages (Symp-HD), and quantified neurometabolic changes relative to controls in 9 Pre-HD studies (227 controls and 188 mutation carriers) and 14 Symp-HD studies (326 controls and 306 patients). Our results indicated decreased N-acetylaspartate and creatine in the basal ganglia in both Pre-HD and Symp-HD. The overall level of myo-inositol was decreased in Pre-HD while increased in Symp-HD. Besides, Symp-HD patients showed more severe metabolism disruption than Pre-HD patients. Taken together, 1H-MRS is important for elucidating progressive metabolite changes from Pre-HD to clinical conversion; N-acetylaspartate and creatine in the basal ganglia are already sensitive at the preclinical stage and are promising biomarkers for tracking disease progression; overall myo-inositol is a possible characteristic metabolite for distinguishing HD stages.

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.

Unraveling progression subtypes in people with Huntington's disease

Background

Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care.

Methods

Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits.

Results

Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only.

Conclusion

In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00368-2.

Real-world experience with Deutetrabenazine management in patients with Huntington's disease using video-based telemedicine

BACKGROUND

Huntington's disease (HD) is a rare progressive neurological disorder, and telemedicine has the potential to improve the quality of care for patients with HD. Deutetrabenazine (DTBZ) can reduce chorea symptoms in HD; however, there is limited experience with this medication in Asian countries.

METHODS

Retrospective and prospective studies were employed to explore the feasibility and reliability of a video-based telemedicine system for HD patient care. Reliability was demonstrated through consistency between selected-item scores (SIS) and total motor scores (TMS) and the agreement of scores obtained from hospital and home videos. Finally, a single-centre real-world DTBZ management study was conducted based on the telemedicine system to explore the efficacy of DTBZ in patients with HD.

RESULTS

There were 77 patients included in the retrospective study, and a strong correlation was found between SIS and TMS (r = 0.911, P < 0.0001), indicating good representativeness. There were 32 patients enrolled in the prospective study. The reliability was further confirmed, indicated by correlations between SIS and TMS (r = 0.964, P < 0.0001) and consistency of SIS derived from the in-person and virtual visits (r = 0.969, P < 0.0001). There were 17 patients included in the DTBZ study with a mean 1.41 (95% confidence interval, 0.37-2.46) improvement in chorea score and reported treatment success.

CONCLUSIONS

A video-based telemedicine system is a feasible and reliable option for HD patient care. It may also be used for drug management as a supplementary tool for clinical visits.

Making Sense of Censored Covariates: Statistical Methods for Studies of Huntington's Disease

The landscape of survival analysis is constantly being revolutionized to answer biomedical challenges, most recently the statistical challenge of censored covariates rather than outcomes. There are many promising strategies to tackle censored covariates, including weighting, imputation, maximum likelihood, and Bayesian methods. Still, this is a relatively fresh area of research, different from the areas of censored outcomes (i.e., survival analysis) or missing covariates. In this review, we discuss the unique statistical challenges encountered when handling censored covariates and provide an in-depth review of existing methods designed to address those challenges. We emphasize each method's relative strengths and weaknesses, providing recommendations to help investigators pinpoint the best approach to handling censored covariates in their data.

Underestimated olfactory domains in Huntington's disease: odour discrimination and threshold

BACKGROUND

Olfaction has recently found clinical value in prediction, discrimination and prognosis of some neurodegenerative disorders. However, data originating from standard tests on olfactory dysfunction in Huntington's disease are limited to odour identification, which is only one domain of olfactory perceptual space.

METHOD

Twenty-five patients and 25 age- and gender-matched controls were evaluated by the Sniffin' Sticks test in three domains of odour threshold, odour discrimination, odour identification and the sum score of them. Patients' motor function was assessed based on the Unified Huntington's Disease Rating Scale.

RESULTS

Compared with controls, patients' scores of all olfactory domains and their sum were significantly lower. Besides, our patients' odour threshold and odour discrimination impairments were more frequently impaired than odour identification impairment (86 per cent and 81 per cent vs 34 per cent, respectively).

CONCLUSION

Olfactory impairment is a common finding in patients with Huntington's disease; it is not limited to odour identification but is more pronounced in odour discrimination and odour threshold.

From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology

With the inexorable aging of the global populace, neurodegenerative diseases (NDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) pose escalating challenges, which are underscored by their socioeconomic repercussions. A pivotal aspect in addressing these challenges lies in the elucidation and application of biomarkers for timely diagnosis, vigilant monitoring, and effective treatment modalities. This review delineates the quintessence of biomarkers in the realm of NDs, elucidating various classifications and their indispensable roles. Particularly, the quest for novel biomarkers in AD, transcending traditional markers in PD, and the frontier of biomarker research in ALS are scrutinized. Emergent susceptibility and trait markers herald a new era of personalized medicine, promising enhanced treatment initiation especially in cases of SOD1-ALS. The discourse extends to diagnostic and state markers, revolutionizing early detection and monitoring, alongside progression markers that unveil the trajectory of NDs, propelling forward the potential for tailored interventions. The synergy between burgeoning technologies and innovative techniques like -omics, histologic assessments, and imaging is spotlighted, underscoring their pivotal roles in biomarker discovery. Reflecting on the progress hitherto, the review underscores the exigent need for multidisciplinary collaborations to surmount the challenges ahead, accelerate biomarker discovery, and herald a new epoch of understanding and managing NDs. Through a panoramic lens, this article endeavors to provide a comprehensive insight into the burgeoning field of biomarkers in NDs, spotlighting the promise they hold in transforming the diagnostic landscape, enhancing disease management, and illuminating the pathway toward efficacious therapeutic interventions.

Measuring cognitive impairment and monitoring cognitive decline in Huntington's disease: a comparison of assessment instruments

BACKGROUND

Progressive cognitive decline is an inevitable feature of Huntington's disease (HD) but specific criteria and instruments are still insufficiently developed to reliably classify patients into categories of cognitive severity and to monitor the progression of cognitive impairment.

METHODS

We collected data from a cohort of 180 positive gene-carriers: 33 with premanifest HD and 147 with manifest HD. Using a specifically developed gold-standard for cognitive status we classified participants into those with normal cognition, those with mild cognitive impairment, and those with dementia. We administered the Parkinson's Disease-Cognitive Rating Scale (PD-CRS), the MMSE and the UHDRS cogscore at baseline, and at 6-month and 12-month follow-up visits. Cutoff scores discriminating between the three cognitive categories were calculated for each instrument. For each cognitive group and instrument we addressed cognitive progression, sensitivity to change, and the minimally clinical important difference corresponding to conversion from one category to another.

RESULTS

The PD-CRS cutoff scores for MCI and dementia showed excellent sensitivity and specificity ratios that were not achieved with the other instruments. Throughout follow-up, in all cognitive groups, PD-CRS captured the rate of conversion from one cognitive category to another and also the different patterns in terms of cognitive trajectories.

CONCLUSION

The PD-CRS is a valid and reliable instrument to capture MCI and dementia syndromes in HD. It captures the different trajectories of cognitive progression as a function of cognitive status and shows sensitivity to change in MCI and dementia.

Factors influencing cognitive function in patients with Huntington's disease from China: A cross-sectional clinical study

BACKGROUND AND AIM

Huntington's disease (HD) is an autosomal dominant inherited neurodegenerative disorder caused by CAG repeats expansion. Cognitive decline contributes to the loss of daily activity in manifest HD. We aimed to examine the cognition status in a Chinese HD cohort and explore factors influencing the diverse cognitive domains.

METHODS

A total of 205 participants were recruited in the study with the assessment by neuropsychological batteries, including the mini-mental state examination (MMSE), Stroop test, symbol digit modalities test (SDMT), trail making test (TMT), verbal fluency test (VFT), and Hopkins verbal learning test-revised, as well as motor and psychiatric assessment. Pearson correlation and multiple linear regression models were applied to investigate the correlation.

RESULTS

Only 41.46% of patients had normal global function first come to our center. There was a significantly difference in MMSE, Stroop test, SDMT, TMT, and VFT across each stage of HD patients (p < .05). Apathy of PBA-s was correlated to MMSE, animal VFT and Stroop-interference tests performance. Severity of motor symptoms, functional capacity, age, and age of motor symptom onset were correlated to all neuropsychological scores, whereas education attainment and diagnostic delay were correlated to most neuropsychological scores except TMT. Severity of motor symptoms, functional capacity, and education attainment showed independent predicting effect (p < .05) in diverse cognitive domains.

CONCLUSION

Cognitive impairment was very common in Chinese HD patients at the first visit and worse in the patients in advanced phase. The severity of motor symptoms and functional capacity were correlated to the diverse cognitive domains.

The shape of things to come. Mapping spatiotemporal progression of striatal morphology in Huntington disease: The IMAGE-HD study

Mapping the spatiotemporal progression of neuroanatomical change in Huntington's Disease (HD) is fundamental to the development of bio-measures for prognostication. Statistical shape analysis to measure the striatum has been performed in HD, however there have been a limited number of longitudinal studies. To address these limitations, we utilised the Spherical Harmonic Point Distribution Method (SPHARM-PDM) to generate point distribution models of the striatum in individuals, and used linear mixed models to test for localised shape change over time in pre-manifest HD (pre-HD), symp-HD (symp-HD) and control individuals. Longitudinal MRI scans from the IMAGE-HD study were used (baseline, 18 and 30 months). We found significant differences in the shape of the striatum between groups. Significant group-by-time interaction was observed for the putamen bilaterally, but not for caudate. A differential rate of shape change between groups over time was observed, with more significant deflation in the symp-HD group in comparison with the pre-HD and control groups. CAG repeats were correlated with bilateral striatal shape in pre-HD and symp-HD. Robust statistical analysis of the correlates of striatal shape change in HD has confirmed the suitability of striatal morphology as a potential biomarker correlated with CAG-repeat length, and potentially, an endophenotype.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

Longitudinal imaging highlights preferential basal ganglia circuit atrophy in Huntington's disease

Huntington's disease is caused by a CAG repeat expansion in the Huntingtin gene (HTT), coding for polyglutamine in the Huntingtin protein, with longer CAG repeats causing earlier age of onset. The variable 'Age' × ('CAG'-L), where 'Age' is the current age of the individual, 'CAG' is the repeat length and L is a constant (reflecting an approximation of the threshold), termed the 'CAG Age Product' (CAP) enables the consideration of many individuals with different CAG repeat expansions at the same time for analysis of any variable and graphing using the CAG Age Product score as the X axis. Structural MRI studies have showed that progressive striatal atrophy begins many years prior to the onset of diagnosable motor Huntington's disease, confirmed by longitudinal multicentre studies on three continents, including PREDICT-HD, TRACK-HD and IMAGE-HD. However, previous studies have not clarified the relationship between striatal atrophy, atrophy of other basal ganglia structures, and atrophy of other brain regions. The present study has analysed all three longitudinal datasets together using a single image segmentation algorithm and combining data from a large number of subjects across a range of CAG Age Product score. In addition, we have used a strategy of normalizing regional atrophy to atrophy of the whole brain, in order to determine which regions may undergo preferential degeneration. This made possible the detailed characterization of regional brain atrophy in relation to CAG Age Product score. There is dramatic selective atrophy of regions involved in the basal ganglia circuit-caudate, putamen, nucleus accumbens, globus pallidus and substantia nigra. Most other regions of the brain appear to have slower but steady degeneration. These results support (but certainly do not prove) the hypothesis of circuit-based spread of pathology in Huntington's disease, possibly due to spread of mutant Htt protein, though other connection-based mechanisms are possible. Therapeutic targets related to prion-like spread of pathology or other mechanisms may be suggested. In addition, they have implications for current neurosurgical therapeutic approaches, since delivery of therapeutic agents solely to the caudate and putamen may miss other structures affected early, such as nucleus accumbens and output nuclei of the striatum, the substantia nigra and the globus pallidus.

Predictive Modeling of Huntington's Disease Unfolds Thalamic and Caudate Atrophy Dissociation

BACKGROUND

Atrophy in the striatum is a hallmark of Huntington's disease (HD), including the period before clinical motor diagnosis (before-CMD), but it extends to other subcortical structures. The study of the covariation of these structures could improve the detection of disease-related longitudinal progression before-CMD, provide mechanistic insights of the disease, and potentially be used to obtain accurate prospective estimates of atrophy before-CMD and early after-CMD.

METHODS

We analyzed data from 337 before-CMD individuals, 236 healthy control subjects, and 95 early after-CMD individuals from three studies, and we used nine subcortical regions volumes in two analyses. First, we discriminated before-CMD from healthy control trajectories by integrating volume changes from these regions. Second, we estimated prospective atrophy before-CMD and early after-CMD by considering the influence of a region's present volume over the future volume of another one.

RESULTS

Before-CMD progression was robustly detected across studies. Indeed, detection of before-CMD progression improved when multiple structures were integrated, as opposed to analyzing the striatum alone, likely because of the reduced partial correlation between caudate and thalamic volume change before-CMD. Our multivariate atrophy prediction model found a thalamus-caudate association that is consistent with this pattern, which yields an improved caudate atrophy prediction in early after-CMD.

CONCLUSIONS

This study is the first attempt to validate before-CMD multivariate subcortical change detection across studies and to do multivariate prospective atrophy prediction in HD. These models achieve improved performance by detecting a dissociation between caudate and thalamic atrophy trajectories, and they provide a possible mechanistic understanding of the dynamics of HD. © 2022 International Parkinson and Movement Disorder Society.

Neuroimaging Methods to Map In Vivo Changes of OXPHOS and Oxidative Stress in Neurodegenerative Disorders

Mitochondrial dysfunction is a pathophysiological hallmark of most neurodegenerative diseases. Several clinical trials targeting mitochondrial dysfunction have been performed with conflicting results. Reliable biomarkers of mitochondrial dysfunction in vivo are thus needed to optimize future clinical trial designs. This narrative review highlights various neuroimaging methods to probe mitochondrial dysfunction. We provide a general overview of the current biological understanding of mitochondrial dysfunction in degenerative brain disorders and how distinct neuroimaging methods can be employed to map disease-related changes. The reviewed methodological spectrum includes positron emission tomography, magnetic resonance, magnetic resonance spectroscopy, and near-infrared spectroscopy imaging, and how these methods can be applied to study alterations in oxidative phosphorylation and oxidative stress. We highlight the advantages and shortcomings of the different neuroimaging methods and discuss the necessary steps to use these for future research. This review stresses the importance of neuroimaging methods to gain deepened insights into mitochondrial dysfunction in vivo, its role as a critical disease mechanism in neurodegenerative diseases, the applicability for patient stratification in interventional trials, and the quantification of individual treatment responses. The in vivo assessment of mitochondrial dysfunction is a crucial prerequisite for providing individualized treatments for neurodegenerative disorders.