Recommendations for the Use of Automated Gray Matter Segmentation Tools: Evidence from Huntington's Disease

Preliminary investigation of the interactive role of physiological arousal and insomnia complaints in gray matter volume alterations in chronic widespread pain

STUDY OBJECTIVES

Brain regions involved in insomnia and chronic pain are overlapping and diffuse. The interactive role of physiological arousal in associations between insomnia symptoms and neural regions is unknown. This preliminary study examined whether arousal interacted with sleep in associations with gray matter volume of frontal (dorsolateral prefrontal cortex, anterior cingulate cortex) and temporal (right/left hippocampus) regions in adults with chronic widespread pain and insomnia complaints.

METHODS

Forty-seven adults with chronic widespread pain and insomnia (mean age = 46.00, standard deviation = 13.88, 89% women) completed 14 daily diaries measuring sleep onset latency (SOL), wake time after sleep onset, and total sleep time (TST), as well as Holter monitor assessments of heart rate variability (measuring physiological arousal), and magnetic resonance imaging. Multiple regressions examined whether average SOL, wake time after sleep onset, or TST were independently or interactively (with arousal/heart rate variability) associated with dorsolateral prefrontal cortex, anterior cingulate cortex, and left/right hippocampus gray matter volumes.

RESULTS

Shorter TST was associated with lower right hippocampus volume. TST also interacted with arousal in its association with right hippocampal volume, Specifically, shorter TST was associated with lower volume at highest and average arousal levels. SOL interacted with arousal in its association with anterior cingulate cortex volume, such that, among individuals with lowest arousal, longer SOL was associated with lower volume.

CONCLUSIONS

Preliminary findings highlight the interactive roles of physiological arousal and insomnia symptoms in associations with neural structure in chronic widespread pain and insomnia. Individuals with the highest physiological arousal may be particularly vulnerable to the impact of shorter TST on hippocampal volume loss. Reducing SOL may only impact anterior cingulate cortex volume in those with lower physiological arousal.

CITATION

Curtis AF, Nair N, Hayse B, et al. Preliminary investigation of the interactive role of physiological arousal and insomnia complaints in gray matter volume alterations in chronic widespread pain. J Clin Sleep Med. 2024;20(2):293-302.

Freesurfer Software Update Significantly Impacts Striatal Volumes in the Huntington's Disease Young Adult Study and Will Influence HD-ISS Staging

Background

The Huntington's Disease Integrated Staging System (HD-ISS) defined disease onset using volumetric cut-offs for caudate and putamen derived from FreeSurfer 6 (FS6). The impact of the latest software update (FS7) on volumes remains unknown. The Huntington's Disease Young Adult Study (HD-YAS) is appropriately positioned to explore differences in FS bias when detecting early atrophy.

Objective

Explore the relationships and differences between raw caudate and putamen volumes, calculated total intracranial volumes (cTICV), and adjusted caudate and putamen volumes, derived from FS6 and FS7, in HD-YAS.

Methods

Images from 123 participants were segmented and quality controlled. Relationships and differences between volumes were explored using intraclass correlation (ICC) and Bland-Altman analysis.

Results

Across the whole cohort, ICC for raw caudate and putamen was 0.99, cTICV 0.93, adjusted caudate 0.87, and adjusted putamen 0.86 (all p < 0.0005). Compared to FS6, FS7 calculated: i) larger raw caudate (+0.8%, p < 0.00005) and putamen (+1.9%, p < 0.00005), with greater difference for larger volumes; and ii) smaller cTICV (-5.1%, p < 0.00005), with greater difference for smaller volumes. The systematic and proportional difference in cTICV was greater than raw volumes. When raw volumes were adjusted for cTICV, these effects compounded (adjusted caudate +7.0%, p < 0.00005; adjusted putamen +8.2%, p < 0.00005), with greater difference for larger volumes.

Conclusions

As new software is released, it is critical that biases are explored since differences have the potential to significantly alter the findings of HD trials. Until conversion factors are defined, the HD-ISS must be applied using FS6. This should be incorporated into the HD-ISS online calculator.

A systematic comparison of VBM pipelines and their application to age prediction

Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.

Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning

To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy-Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy-Richardson, 52 with a progressive supranuclear palsy-cortical variant (progressive supranuclear palsy-frontal, progressive supranuclear palsy-speech/language, or progressive supranuclear palsy-corticobasal), and 17 with a progressive supranuclear palsy-subcortical variant (progressive supranuclear palsy-parkinsonism or progressive supranuclear palsy-progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a 'subcortical' subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a 'cortical' subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy-subcortical cases and 81% of progressive supranuclear palsy-Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy-cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the 'subcortical' subtype was associated with worse clinical severity scores compared to the 'cortical subtype' (progressive supranuclear palsy rating scale and Unified Parkinson's Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.

Differential grey matter structure in women with premenstrual dysphoric disorder: evidence from brain morphometry and data-driven classification

Premenstrual dysphoric disorder (PMDD) is a female-specific condition classified in the Diagnostic and Statical Manual-5th edition under depressive disorders. Alterations in grey matter volume, cortical thickness and folding metrics have been associated with a number of mood disorders, though little is known regarding brain morphological alterations in PMDD. Here, women with PMDD and healthy controls underwent magnetic resonance imaging (MRI) during the luteal phase of the menstrual cycle. Differences in grey matter structure between the groups were investigated by use of voxel- and surface-based morphometry. Machine learning and multivariate pattern analysis were performed to test whether MRI data could distinguish women with PMDD from healthy controls. Compared to controls, women with PMDD had smaller grey matter volume in ventral posterior cortices and the cerebellum (Cohen's d = 0.45-0.76). Region-of-interest analyses further indicated smaller volume in the right amygdala and putamen of women with PMDD (Cohen's d = 0.34-0.55). Likewise, thinner cortex was observed in women with PMDD compared to controls, particularly in the left hemisphere (Cohen's d = 0.20-0.74). Classification analyses showed that women with PMDD can be distinguished from controls based on grey matter morphology, with an accuracy up to 74%. In line with the hypothesis of an impaired top-down inhibitory circuit involving limbic structures in PMDD, the present findings point to PMDD-specific grey matter anatomy in regions of corticolimbic networks. Furthermore, the results include widespread cortical and cerebellar regions, suggesting the involvement of distinct networks in PMDD pathophysiology.

Grey matter correlates of affective and somatic symptoms of premenstrual dysphoric disorder

Ovarian hormones fluctuations across the menstrual cycle are experienced by about 58% of women in their fertile age. Maladaptive brain sensitivity to these changes likely leads to the severe psychological, cognitive, and physical symptoms repeatedly experienced by women with Premenstrual Dysphoric Disorder (PMDD) during the late luteal phase of the menstrual cycle. However, the neuroanatomical correlates of these symptoms are unknown. The relationship between grey matter structure and PMDD symptom severity was delineated using structural magnetic resonance imaging during the late luteal phase of fifty-one women diagnosed with PMDD, combined with Voxel- and Surface-Based Morphometry, as well as subcortical volumetric analyses. A negative correlation was found between depression-related symptoms and grey matter volume of the bilateral amygdala. Moreover, the severity of affective and somatic PMDD symptoms correlated with cortical thickness, gyrification, sulcal depth, and complexity metrics, particularly in the prefrontal, cingulate, and parahippocampal gyri. The present findings provide the first evidence of grey matter morphological characteristics associated with PMDD symptomatology in brain regions expressing ovarian hormone receptors and of relevance to cognitive-affective functions, thus potentially having important implications for understanding how structural brain characteristics relate to PMDD symptomatology.

A data-driven model of brain volume changes in progressive supranuclear palsy

The most common clinical phenotype of progressive supranuclear palsy is Richardson syndrome, characterized by levodopa unresponsive symmetric parkinsonism, with a vertical supranuclear gaze palsy, early falls and cognitive impairment. There is currently no detailed understanding of the full sequence of disease pathophysiology in progressive supranuclear palsy. Determining the sequence of brain atrophy in progressive supranuclear palsy could provide important insights into the mechanisms of disease progression, as well as guide patient stratification and monitoring for clinical trials. We used a probabilistic event-based model applied to cross-sectional structural MRI scans in a large international cohort, to determine the sequence of brain atrophy in clinically diagnosed progressive supranuclear palsy Richardson syndrome. A total of 341 people with Richardson syndrome (of whom 255 had 12-month follow-up imaging) and 260 controls were included in the study. We used a combination of 12-month follow-up MRI scans, and a validated clinical rating score (progressive supranuclear palsy rating scale) to demonstrate the longitudinal consistency and utility of the event-based model's staging system. The event-based model estimated that the earliest atrophy occurs in the brainstem and subcortical regions followed by progression caudally into the superior cerebellar peduncle and deep cerebellar nuclei, and rostrally to the cortex. The sequence of cortical atrophy progresses in an anterior to posterior direction, beginning in the insula and then the frontal lobe before spreading to the temporal, parietal and finally the occipital lobe. This in vivo ordering accords with the post-mortem neuropathological staging of progressive supranuclear palsy and was robust under cross-validation. Using longitudinal information from 12-month follow-up scans, we demonstrate that subjects consistently move to later stages over this time interval, supporting the validity of the model. In addition, both clinical severity (progressive supranuclear palsy rating scale) and disease duration were significantly correlated with the predicted subject event-based model stage (P < 0.01). Our results provide new insights into the sequence of atrophy progression in progressive supranuclear palsy and offer potential utility to stratify people with this disease on entry into clinical trials based on disease stage, as well as track disease progression.

A comparison of automated atrophy measures across the frontotemporal dementia spectrum: Implications for trials

Background

Frontotemporal dementia (FTD) is a common cause of young onset dementia, and whilst there are currently no treatments, there are several promising candidates in development and early phase trials. Comprehensive investigations of neuroimaging markers of disease progression across the full spectrum of FTD disorders are lacking and urgently needed to facilitate these trials.

Objective

To investigate the comparative performance of multiple automated segmentation and registration pipelines used to quantify longitudinal whole-brain atrophy across the clinical, genetic and pathological subgroups of FTD, in order to inform upcoming trials about suitable neuroimaging-based endpoints.

Methods

Seventeen fully automated techniques for extracting whole-brain atrophy measures were applied and directly compared in a cohort of 226 participants who had undergone longitudinal structural 3D T1-weighted imaging. Clinical diagnoses were behavioural variant FTD (n = 56) and primary progressive aphasia (PPA, n = 104), comprising semantic variant PPA (n = 38), non-fluent variant PPA (n = 42), logopenic variant PPA (n = 18), and PPA-not otherwise specified (n = 6). 49 of these patients had either a known pathogenic mutation or postmortem confirmation of their underlying pathology. 66 healthy controls were included for comparison. Sample size estimates to detect a 30% reduction in atrophy (80% power; 0.05 significance) were computed to explore the relative feasibility of these brain measures as surrogate markers of disease progression and their ability to detect putative disease-modifying treatment effects.

Results

Multiple automated techniques showed great promise, detecting significantly increased rates of whole-brain atrophy (p<0.001) and requiring sample sizes of substantially less than 100 patients per treatment arm. Across the different FTD subgroups, direct measures of volume change consistently outperformed their indirect counterparts, irrespective of the initial segmentation quality. Significant differences in performance were found between both techniques and patient subgroups, highlighting the importance of informed biomarker choice based on the patient population of interest.

Conclusion

This work expands current knowledge and builds on the limited longitudinal investigations currently available in FTD, as well as providing valuable information about the potential of fully automated neuroimaging biomarkers for sporadic and genetic FTD trials.

Neurobiological and behavioural outcomes of biofeedback-based training in autism: a randomized controlled trial

The human brain has demonstrated the power to structurally change as a result of movement-based interventions. However, it is unclear whether these structural brain changes differ in autistic individuals compared to non-autistic individuals. The purpose of the present study was to pilot a randomized controlled trial to investigate brain, balance, autism symptom severity and daily living skill changes that result from a biofeedback-based balance intervention in autistic adolescents (13-17 years old). Thirty-four autistic participants and 28 age-matched non-autistic participants underwent diagnostic testing and pre-training assessment (neuroimaging, cognitive, autism symptom severity and motor assessments) and were then randomly assigned to 6 weeks of a balance-training intervention or a sedentary-control condition. After the 6 weeks, neuroimaging, symptom severity and motor assessments were repeated. Results found that both the autistic and non-autistic participants demonstrated similar and significant increases in balance times with training. Furthermore, individuals in the balance-training condition showed significantly greater improvements in postural sway and reductions in autism symptom severity compared to individuals in the control condition. Daily living scores did not change with training, nor did we observe hypothesized changes to the microstructural properties of the corticospinal tract. However, follow-up voxel-based analyses found a wide range of balance-related structures that showed changes across the brain. Many of these brain changes were specific to the autistic participants compared to the non-autistic participants, suggesting distinct structural neuroplasticity in response to balance training in autistic participants. Altogether, these findings suggest that biofeedback-based balance training may target postural stability challenges, reduce core autism symptoms and influence neurobiological change. Future research is encouraged to examine the superior cerebellar peduncle in response to balance training and symptom severity changes in autistic individuals, as the current study produced overlapping findings in this brain region.

Kynurenine pathway metabolites in cerebrospinal fluid and blood as potential biomarkers in Huntington's disease

Converging lines of evidence from several models, and post-mortem human brain tissue studies, support the involvement of the kynurenine pathway (KP) in Huntington's disease (HD) pathogenesis. Quantifying KP metabolites in HD biofluids is desirable, both to study pathobiology and as a potential source of biomarkers to quantify pathway dysfunction and evaluate the biochemical impact of therapeutic interventions targeting its components. In a prospective single-site controlled cohort study with standardised collection of cerebrospinal fluid (CSF), blood, phenotypic and imaging data, we used high-performance liquid-chromatography to measure the levels of KP metabolites-tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, anthranilic acid and quinolinic acid-in CSF and plasma of 80 participants (20 healthy controls, 20 premanifest HD and 40 manifest HD). We investigated short-term stability, intergroup differences, associations with clinical and imaging measures and derived sample-size calculation for future studies. Overall, KP metabolites in CSF and plasma were stable over 6 weeks, displayed no significant group differences and were not associated with clinical or imaging measures. We conclude that the studied metabolites are readily and reliably quantifiable in both biofluids in controls and HD gene expansion carriers. However, we found little evidence to support a substantial derangement of the KP in HD, at least to the extent that it is reflected by the levels of the metabolites in patient-derived biofluids.

Progressive Neurodegeneration Across Chronic Stages of Severe Traumatic Brain Injury

OBJECTIVE

To examine the trajectory of structural gray matter changes across 2 chronic periods of recovery in individuals who have sustained severe traumatic brain injury (TBI), adding to the growing literature indicating that neurodegenerative processes occur in the months to years postinjury.

PARTICIPANTS

Patients who experienced posttraumatic amnesia of 1 hour or more, and/or scored 12 or less on the Glasgow Coma Scale at the emergency department or the scene of the accident, and/or had positive brain imaging findings were recruited while receiving inpatient care, resulting in 51 patients with severe TBI.

METHODS

Secondary analyses of gray matter changes across approximately 5 months, 1 year, and 2.5 years postinjury were undertaken, using an automated segmentation protocol with improved accuracy in populations with morphological anomalies. We compared patients and matched controls on regions implicated in poorer long-term clinical outcome (accumbens, amygdala, brainstem, hippocampus, thalamus). To model brain-wide patterns of change, we then conducted an exploratory principal component analysis (PCA) on the linear slopes of all regional volumes across the 3 time points. Finally, we assessed nonlinear trends across earlier (5 months-1 year) versus later (1-2.5 years) time-windows with PCA to compare degeneration rates across time. Chronic degeneration was predicted cortically and subcortically brain-wide, and within specific regions of interest.

RESULTS

(1) From 5 months to 1 year, patients showed significant degeneration in the accumbens, and marginal degeneration in the amygdala, brainstem, thalamus, and the left hippocampus when examined unilaterally, compared with controls. (2) PCA components representing subcortical and temporal regions, and regions from the basal ganglia, significantly differed from controls in the first time-window. (3) Progression occurred at the same rate across both time-windows, suggesting neither escalation nor attenuation of degeneration across time.

CONCLUSION

Localized yet progressive decline emphasizes the necessity of developing interventions to offset degeneration and improve long-term functioning.

Dynamics of Cortical Degeneration Over a Decade in Huntington's Disease

BACKGROUND

Characterizing changing brain structure in neurodegeneration is fundamental to understanding long-term effects of pathology and ultimately providing therapeutic targets. It is well established that Huntington's disease (HD) gene carriers undergo progressive brain changes during the course of disease, yet the long-term trajectory of cortical atrophy is not well defined. Given that genetic therapies currently tested in HD are primarily expected to target the cortex, understanding atrophy across this region is essential.

METHODS

Capitalizing on a unique longitudinal dataset with a minimum of 3 and maximum of 7 brain scans from 49 HD gene carriers and 49 age-matched control subjects, we implemented a novel dynamical systems approach to infer patterns of regional neurodegeneration over 10 years. We use Bayesian hierarchical modeling to map participant- and group-level trajectories of atrophy spatially and temporally, additionally relating atrophy to the genetic marker of HD (CAG-repeat length) and motor and cognitive symptoms.

RESULTS

We show, for the first time, that neurodegenerative changes exhibit complex temporal dynamics with substantial regional variation around the point of clinical diagnosis. Although widespread group differences were seen across the cortex, the occipital and parietal regions undergo the greatest rate of cortical atrophy. We have established links between atrophy and genetic markers of HD while demonstrating that specific cortical changes predict decline in motor and cognitive performance.

CONCLUSIONS

HD gene carriers display regional variability in the spatial pattern of cortical atrophy, which relates to genetic factors and motor and cognitive symptoms. Our findings indicate a complex pattern of neuronal loss, which enables greater characterization of HD progression.

Validating Automated Segmentation Tools in the Assessment of Caudate Atrophy in Huntington's Disease

Background: Neuroimaging shows considerable promise in generating sensitive and objective outcome measures for therapeutic trials across a range of neurodegenerative conditions. For volumetric measures the current gold standard is manual delineation, which is unfeasible for samples sizes required for large clinical trials. Methods: Using a cohort of early Huntington's disease (HD) patients (n = 46) and controls (n = 35), we compared the performance of four automated segmentation tools (FIRST, FreeSurfer, STEPS, MALP-EM) with manual delineation for generating cross-sectional caudate volume, a region known to be vulnerable in HD. We then examined the effect of each of these baseline regions on the ability to detect change over 15 months using the established longitudinal Caudate Boundary Shift Integral (cBSI) method, an automated longitudinal pipeline requiring a baseline caudate region as an input. Results: All tools, except Freesurfer, generated significantly smaller caudate volumes than the manually derived regions. Jaccard indices showed poorer levels of overlap between each automated segmentation and manual delineation in the HD patients compared with controls. Nevertheless, each method was able to demonstrate significant group differences in volume (p < 0.001). STEPS performed best qualitatively as well as quantitively in the baseline analysis. Caudate atrophy measures generated by the cBSI using automated baseline regions were largely consistent with those derived from a manually segmented baseline, with STEPS providing the most robust cBSI values across both control and HD groups. Conclusions: Atrophy measures from the cBSI were relatively robust to differences in baseline segmentation technique, suggesting that fully automated pipelines could be used to generate outcome measures for clinical trials.

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

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.

SeSBAT: Single Subject Brain Analysis Toolbox. Application to Huntington's Disease as a Preliminary Study

Magnetic resonance imaging (MRI) biomarkers require complex processing routines that are time-consuming and labor-intensive for clinical users. The Single Subject Brain Analysis Toolbox (SeSBAT) is a fully automated MATLAB toolbox with a graphical user interface (GUI) that offers standardized and optimized protocols for the pre-processing and analysis of anatomical MRI data at the single-subject level. In this study, the two-fold strategy provided by SeSBAT is illustrated through its application on a cohort of 42 patients with Huntington’s disease (HD), in pre-manifest and early manifest stages, as a suitable model of neurodegenerative processes. On the one hand, hypothesis-driven analysis can be used to extract biomarkers of neurodegeneration in specific brain regions of interest (ROI-based analysis). On the other hand, an exploratory voxel-based morphometry (VBM) approach can detect volume changes due to neurodegeneration throughout the whole brain (whole-brain analysis). That illustration reveals the potential of SeSBAT in providing potential prognostic biomarkers in neurodegenerative processes in clinics, which could be critical to overcoming the limitations of current qualitative evaluation strategies, and thus improve the diagnosis and monitoring of neurodegenerative disorders. Furthermore, the importance of the availability of tools for characterization at the single-subject level has been emphasized, as there is high interindividual variability in the pattern of neurodegeneration. Thus, tools like SeSBAT could pave the way towards more effective and personalized medicine.

Evaluation of mutant huntingtin and neurofilament proteins as potential markers in Huntington's disease

Huntington's disease (HD) is a genetic progressive neurodegenerative disorder, caused by a mutation in the HTT gene, for which there is currently no cure. The identification of sensitive indicators of disease progression and therapeutic outcome could help the development of effective strategies for treating HD. We assessed mutant huntingtin (mHTT) and neurofilament light (NfL) protein concentrations in cerebrospinal fluid (CSF) and blood in parallel with clinical evaluation and magnetic resonance imaging in premanifest and manifest HD mutation carriers. Among HD mutation carriers, NfL concentrations in plasma and CSF correlated with all nonbiofluid measures more closely than did CSF mHTT concentration. Longitudinal analysis over 4 to 8 weeks showed that CSF mHTT, CSF NfL, and plasma NfL concentrations were highly stable within individuals. In our cohort, concentration of CSF mHTT accurately distinguished between controls and HD mutation carriers, whereas NfL concentration, in both CSF and plasma, was able to segregate premanifest from manifest HD. In silico modeling indicated that mHTT and NfL concentrations in biofluids might be among the earliest detectable alterations in HD, and sample size prediction suggested that low participant numbers would be needed to incorporate these measures into clinical trials. These findings provide evidence that biofluid concentrations of mHTT and NfL have potential for early and sensitive detection of alterations in HD and could be integrated into both clinical trials and the clinic.

Evidence for peri-ictal blood-brain barrier dysfunction in patients with epilepsy

Epilepsy has been associated with a dysfunction of the blood-brain barrier. While there is ample evidence that a dysfunction of the blood-brain barrier contributes to epileptogenesis, blood-brain barrier dysfunction as a consequence of single epileptic seizures has not been systematically investigated. We hypothesized that blood-brain barrier dysfunction is temporally and anatomically associated with epileptic seizures in patients and used a newly-established quantitative MRI protocol to test our hypothesis. Twenty-three patients with epilepsy undergoing inpatient monitoring as part of their presurgical evaluation were included in this study (10 females, mean age ± standard deviation: 28.78 ± 8.45). For each patient, we acquired quantitative T1 relaxation time maps (qT1) after both ictal and interictal injection of gadolinium-based contrast agent. The postictal enhancement of contrast agent was quantified by subtracting postictal qT1 from interictal qT1 and the resulting ΔqT1 was used as a surrogate imaging marker of peri-ictal blood-brain barrier dysfunction. Additionally, the serum concentrations of MMP9 and S100, both considered biomarkers of blood-brain barrier dysfunction, were assessed in serum samples obtained prior to and after the index seizure. Fifteen patients exhibited secondarily generalized tonic-clonic seizures and eight patients exhibited focal seizures at ictal injection of contrast agent. By comparing ΔqT1 of the generalized tonic-clonic seizures and focal seizures groups, the anatomical association between ictal epileptic activity and postictal enhancement of contrast agent could be probed. The generalized tonic-clonic seizures group showed significantly higher ΔqT1 in the whole brain as compared to the focal seizures group. Specific analysis of scans acquired later than 3 h after the onset of the seizure revealed higher ΔqT1 in the generalized tonic-clonic seizures group as compared to the focal seizures group, which was strictly lateralized to the hemisphere of seizure onset. Both MMP9 and S100 showed a significantly increased postictal concentration. The current study provides evidence for the occurrence of a blood-brain barrier dysfunction, which is temporally and anatomically associated with epileptic seizures. qT1 after ictal contrast agent injection is rendered as valuable imaging marker of seizure-associated blood-brain barrier dysfunction and may be measured hours after the seizure. The observation of the strong anatomical association of peri-ictal blood-brain barrier dysfunction may spark the development of new functional imaging modalities for the post hoc visualization of brain areas affected by the seizure.

Fluid and imaging biomarkers for Huntington's disease

Huntington's disease is a chronic progressive neurodegenerative condition for which there is no disease-modifying treatment. The known genetic cause of Huntington's disease makes it possible to identify individuals destined to develop the disease and instigate treatments before the onset of symptoms. Multiple trials are already underway that target the cause of HD, yet clinical measures are often insensitive to change over typical clinical trial duration. Robust biomarkers of drug target engagement, disease severity and progression are required to evaluate the efficacy of treatments and concerted efforts are underway to achieve this. Biofluid biomarkers have potential advantages of direct quantification of biological processes at the molecular level, whilst imaging biomarkers can quantify related changes at a structural level in the brain. The most robust biofluid and imaging biomarkers can offer complementary information, providing a more comprehensive evaluation of disease stage and progression to inform clinical trial design and endpoints.

Huntington's disease: Brain imaging in Huntington's disease

Huntington's disease (HD) gene-carriers show prominent neuronal loss by end-stage disease, and the use of magnetic resonance imaging (MRI) has been increasingly used to quantify brain changes during earlier stages of the disease. MRI offers an in vivo method of measuring structural and functional brain change. The images collected via MRI are processed to measure different anatomical features, such as brain volume, macro- and microstructural changes within white matter and functional brain activity. Structural imaging has demonstrated significant volume loss across multiple white and gray matter regions in HD, particularly within subcortical structures. There also appears to be increasing disorganization of white matter tracts and between-region connectivity with increasing disease progression. Finally, functional changes are thought to represent changes in brain activity underlying compensatory mechanisms in HD. This chapter will provide an overview of the principles of MRI and practicalities associated with using MRI in HD studies, and summarize findings from MRI studies investigating brain structure and function in HD.

A semi-automated algorithm for hypothalamus volumetry in 3 Tesla magnetic resonance images

The hypothalamus, a small diencephalic gray matter structure, is part of the limbic system. Volumetric changes of this structure occur in psychiatric diseases, therefore there is increasing interest in precise volumetry. Based on our detailed volumetry algorithm for 7 Tesla magnetic resonance imaging (MRI), we developed a method for 3 Tesla MRI, adopting anatomical landmarks and work in triplanar view. We overlaid T1-weighted MR images with gray matter-tissue probability maps to combine anatomical information with tissue class segmentation. Then, we outlined regions of interest (ROIs) that covered potential hypothalamus voxels. Within these ROIs, seed growing technique helped define the hypothalamic volume using gray matter probabilities from the tissue probability maps. This yielded a semi-automated method with short processing times of 20-40 min per hypothalamus. In the MRIs of ten subjects, reliabilities were determined as intraclass correlations (ICC) and volume overlaps in percent. Three raters achieved very good intra-rater reliabilities (ICC 0.82-0.97) and good inter-rater reliabilities (ICC 0.78 and 0.82). Overlaps of intra- and inter-rater runs were very good (≥ 89.7%). We present a fast, semi-automated method for in vivo hypothalamus volumetry in 3 Tesla MRI.

Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database

Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.

An image-based model of brain volume biomarker changes in Huntington's disease

Objective

Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine‐grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.

Methods

We employ a probabilistic event‐based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track‐HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides.

Results

The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross‐validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow‐up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers.

Interpretation

We used a data‐driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event‐based model, to provide new insight into Huntington's disease progression and to support fine‐grained patient stratification for future precision medicine in Huntington's disease.

Cerebrospinal fluid neurogranin and TREM2 in Huntington's disease

Biomarkers of Huntington’s disease (HD) in cerebrospinal fluid (CSF) could be of value in elucidating the biology of this genetic neurodegenerative disease, as well as in the development of novel therapeutics. Deranged synaptic and immune function have been reported in HD, and concentrations of the synaptic protein neurogranin and the microglial protein TREM2 are increased in other neurodegenerative diseases. We therefore used ELISAs to quantify neurogranin and TREM2 in CSF samples from HD mutation carriers and controls. CSF neurogranin concentration was not significantly altered in HD compared to controls, nor was it significantly associated with disease burden score, total functional capacity or motor score. An apparent increase in CSF TREM2 in manifest HD was determined to be due to increasing TREM2 with age. After age adjustment, there was no significant alteration of TREM2 in either HD group, nor any association with motor, functional or cognitive score, or brain volume quantified by MRI. Both analyses were well-powered, and sample size calculations indicated that several thousand samples per group would be needed to prove that disease-associated alterations do in fact exist. We conclude that neither neurogranin nor TREM2 is a useful biofluid biomarker for disease processes in Huntington’s disease.

Predicting age from cortical structure across the lifespan

Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.