A peek inside the Huntington's brain: will functional imaging take us one step closer in solving the puzzle?

Brain compensation during response inhibition in premanifest Huntington's disease

Premanifest Huntington's disease (pre-HD) individuals typically show increased task-related functional magnetic resonance imaging (fMRI), suggested to reflect compensatory strategies. Despite the evidence, no study has attempted to understand the compensatory process in light of 'formal' models of compensation. We used a quantitative model of compensation - the Compensation-Related Utilization of Neural Circuits Hypothesis (CRUNCH), to characterise compensation in pre-HD using fMRI. Pre-HD individuals (n = 15) and controls (n = 15) performed a modified stop-signal task that incremented in four levels of stop difficulty. Our results did not support the critical assumption of the CRUNCH model - controls did not show increased fMRI activity with increased level of stop difficulty; however, controls showed decreased fMRI activity with increased stop difficulty in right inferior frontal gyrus and right caudate nucleus. Relative to controls, pre-HD individuals showed increased fMRI activity in right inferior frontal gyrus and in right caudate nucleus at higher levels of stop difficulty, which is the opposite effect to that predicted by the model. Our findings suggest a compensatory process of the response inhibition network in pre-HD; however, the pattern of fMRI activity was not in the manner expected by CRUNCH.

Functional connectivity modeling of consistent cortico-striatal degeneration in Huntington's disease

Huntington's disease (HD) is a progressive neurodegenerative disorder characterized by a complex neuropsychiatric phenotype. In a recent meta-analysis we identified core regions of consistent neurodegeneration in premanifest HD in the striatum and middle occipital gyrus (MOG). For early manifest HD convergent evidence of atrophy was most prominent in the striatum, motor cortex (M1) and inferior frontal junction (IFJ). The aim of the present study was to functionally characterize this topography of brain atrophy and to investigate differential connectivity patterns formed by consistent cortico-striatal atrophy regions in HD. Using areas of striatal and cortical atrophy at different disease stages as seeds, we performed task-free resting-state and task-based meta-analytic connectivity modeling (MACM). MACM utilizes the large data source of the BrainMap database and identifies significant areas of above-chance co-activation with the seed-region via the activation-likelihood-estimation approach. In order to delineate functional networks formed by cortical as well as striatal atrophy regions we computed the conjunction between the co-activation profiles of striatal and cortical seeds in the premanifest and manifest stages of HD, respectively. Functional characterization of the seeds was obtained using the behavioral meta-data of BrainMap. Cortico-striatal atrophy seeds of the premanifest stage of HD showed common co-activation with a rather cognitive network including the striatum, anterior insula, lateral prefrontal, premotor, supplementary motor and parietal regions. A similar but more pronounced co-activation pattern, additionally including the medial prefrontal cortex and thalamic nuclei was found with striatal and IFJ seeds at the manifest HD stage. The striatum and M1 were functionally connected mainly to premotor and sensorimotor areas, posterior insula, putamen and thalamus. Behavioral characterization of the seeds confirmed that experiments activating the MOG or IFJ in conjunction with the striatum were associated with cognitive functions, while the network formed by M1 and the striatum was driven by motor-related tasks. Thus, based on morphological changes in HD, we identified functionally distinct cortico-striatal networks resembling a cognitive and motor loop, which may be prone to early disruptions in different stages of the disease and underlie HD-related cognitive and motor symptom profiles. Our findings provide an important link between morphometrically defined seed-regions and corresponding functional circuits highlighting the functional and ensuing clinical relevance of structural damage in HD.

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Pre-HD atrophy seeds showed common functional co-activation with a cognitive network.

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Modeling of manifest-HD seeds delineated a segregation of a cognitive and motor loop.

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Behavioral decoding of atrophy seeds confirmed functional segregation of networks.

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Based on morphometric changes in HD distinct corticostriatal networks were identified.

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Findings depict functional and ensuing clinical relevance of structural damage in HD.

JF, Carron SP, Ando A, Churchyard A, Chua P, Bohanna I, Dymowski AR, Poudel G, Egan GF. Functional magnetic resonance imaging of working memory in Huntington's disease: cross-sectional data from the IMAGE-HD study

We used functional magnetic resonance imaging (fMRI) to investigate spatial working memory (WM) in an N-BACK task (0, 1, and 2-BACK) in premanifest Huntington's disease (pre-HD, n = 35), early symptomatic Huntington's disease (symp-HD, n = 23), and control (n = 32) individuals. Overall, both WM conditions (1-BACK and 2-BACK) activated a large network of regions throughout the brain, common to all groups. However, voxel-wise and time-course analyses revealed significant functional group differences, despite no significant behavioral performance differences. During 1-BACK, voxel-wise blood-oxygen-level-dependent (BOLD) signal activity was significantly reduced in a number of regions from the WM network (inferior frontal gyrus, anterior insula, caudate, putamen, and cerebellum) in pre-HD and symp-HD groups, compared with controls; however, time-course analysis of the BOLD response in the dorsolateral prefrontal cortex (DLPFC) showed increased activation in symp-HD, compared with pre-HD and controls. The pattern of reduced voxel-wise BOLD activity in pre-HD and symp-HD, relative to controls, became more pervasive during 2-BACK affecting the same structures as in 1-BACK, but also incorporated further WM regions (anterior cingulate gyrus, parietal lobe and thalamus). The DLPFC BOLD time-course for 2-BACK showed a reversed pattern to that observed in 1-BACK, with a significantly diminished signal in symp-HD, relative to pre-HD and controls. Our findings provide support for functional brain reorganisation in cortical and subcortical regions in both pre-HD and symp-HD, which are modulated by task difficulty. Moreover, the lack of a robust striatal BOLD signal in pre-HD may represent a very early signature of change observed up to 15 years prior to clinical diagnosis.

Abnormal synchrony of resting state networks in premanifest and symptomatic Huntington disease: the IMAGE-HD study

BACKGROUND

Functional neural impairments have been documented in people with symptomatic Huntington disease (symp-HD) and in premanifest gene carriers (pre-HD). This study aimed to characterize synchrony in resting state cerebral networks in both pre-HD and symp-HD populations and to determine its association with disease burden and neurocognitive functions.

METHODS

We acquired functional magnetic resonance imaging (fMRI) data from pre-HD, symp-HD and healthy control participants. The fMRI data were analyzed using multisubject independent component analysis and dual regression. We compared networks of interest among the groups using a nonparametric permutation method and correcting for multiple comparisons.

RESULTS

Our study included 25 people in the pre-HD, 23 in the symp-HD and 18 in the healthy control groups. Compared with the control group, the pre-HD group showed decreased synchrony in the sensorimotor and dorsal attention networks; decreased level of synchrony in the sensorimotor network was associated with poorer motor performance. Compared with the control group, the symp-HD group showed widespread reduction in synchrony in the dorsal attention network, which was associated with poorer cognitive performance. The posterior putamen and superior parietal cortex were functionally disconnected from the frontal executive network in the symp-HD compared with control and pre-HD groups. Furthermore, the left frontoparietal network showed areas of increased synchrony in the symp-HD compared with the pre-HD group.

LIMITATIONS

We could not directly correct for influence of autonomic changes (e.g., heart rate) and respiration on resting state synchronization.

CONCLUSION

Our findings suggest that aberrant synchrony in the sensorimotor and dorsal attention networks may serve as an early signature of neural change in pre-HD individuals. The altered synchrony in dorsal attention, frontoparietal and corticostriatal networks may contribute to the development of clinical symptoms in people with Huntington disease.

Multi-modal neuroimaging in premanifest and early Huntington's disease: 18 month longitudinal data from the IMAGE-HD study

IMAGE-HD is an Australian based multi-modal longitudinal magnetic resonance imaging (MRI) study in premanifest and early symptomatic Huntington's disease (pre-HD and symp-HD, respectively). In this investigation we sought to determine the sensitivity of imaging methods to detect macrostructural (volume) and microstructural (diffusivity) longitudinal change in HD. We used a 3T MRI scanner to acquire T1 and diffusion weighted images at baseline and 18 months in 31 pre-HD, 31 symp-HD and 29 controls. Volume was measured across the whole brain, and volume and diffusion measures were ascertained for caudate and putamen. We observed a range of significant volumetric and, for the first time, diffusion changes over 18 months in both pre-HD and symp-HD, relative to controls, detectable at the brain-wide level (volume change in grey and white matter) and in caudate and putamen (volume and diffusivity change). Importantly, longitudinal volume change in the caudate was the only measure that discriminated between groups across all stages of disease: far from diagnosis (>15 years), close to diagnosis (<15 years) and after diagnosis. Of the two diffusion metrics (mean diffusivity, MD; fractional anisotropy, FA), only longitudinal FA change was sensitive to group differences, but only after diagnosis. These findings further confirm caudate atrophy as one of the most sensitive and early biomarkers of neurodegeneration in HD. They also highlight that different tissue properties have varying schedules in their ability to discriminate between groups along disease progression and may therefore inform biomarker selection for future therapeutic interventions.

Altered resting-state connectivity in Huntington's disease

Huntington's disease (HD) is an autosomal dominantly inherited neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms. Using resting-state fMRI (rs-fMRI) we investigated the functional integrity of resting-state networks (RSN) in HD. 17 HD and 19 matched control participants were examined at a 3 Tesla MR scanner. After controlling for structural degeneration by means of voxel-based morphometry, task-free rs-fMRI data were analyzed using Independent Component Analysis (ICA) and a dual-regression approach in the context of genetic and clinical parameters. Further, we evaluated HD-related differences in interregional connectivity between networks. RSN analysis showed a significant increase in intrinsic functional connectivity in the HD sample compared with controls, including the thalamus, striatum, prefrontal, premotor, and parietal maps. A subset of the Default Mode Network (DMN) was also affected. In the HD cohort, motor impairment correlated with higher network connectivity in mainly motor and parietal cortices. Deteriorating total functional capacity was additionally associated with higher connectivity in the striatum, thalamus, insular and frontal areas. This pattern of increased activity in intrinsic functional networks might suggest a reduced ability of intra-network differentiation with clinical disease progression in HD. Finally, results showed reduced long-range connectivity between parietal ICA components in HD compared to controls, indicating impaired functional coupling between interregional networks in HD. Our data demonstrates that functional connectivity is profoundly altered in HD, both within and between RSN. Rs-fMRI analysis may provide additional valuable insights into neuronal dysfunctions beyond HD-related structural degeneration and disruptions of functional circuits in HD.

Neural correlates of impaired emotion processing in manifest Huntington's disease

The complex phenotype of Huntington's disease (HD) encompasses motor, psychiatric and cognitive dysfunctions, including early impairments in emotion recognition. In this first functional magnetic resonance imaging study, we investigated emotion-processing deficits in 14 manifest HD patients and matched controls. An emotion recognition task comprised short video clips displaying one of six basic facial expressions (sadness, happiness, disgust, fear, anger and neutral). Structural changes between patients and controls were assessed by means of voxel-based morphometry. Along with deficient recognition of negative emotions, patients exhibited predominantly lower neural response to stimuli of negative valences in the amygdala, hippocampus, striatum, insula, cingulate and prefrontal cortices, as well as in sensorimotor, temporal and visual areas. Most of the observed reduced activity patterns could not be explained merely by regional volume loss. Reduced activity in the thalamus during fear correlated with lower thalamic volumes. During the processing of sadness, patients exhibited enhanced amygdala and hippocampal activity along with reduced recruitment of the medial prefrontal cortex. Higher amygdala activity was related to more pronounced amygdala atrophy and disease burden. Overall, the observed emotion-related dysfunctions in the context of structural neurodegeneration suggest both disruptions of striatal-thalamo-cortical loops and potential compensation mechanism with greater disease severity in manifest HD.

Structural MRI in Huntington's disease and recommendations for its potential use in clinical trials

Huntington's disease (HD) results in progressive impairment of motor and cognitive function and neuropsychiatric disturbance. There are no disease-modifying treatments available, but HD research is entering a critical phase where promising disease-specific therapies are on the horizon. Thus, a pressing need exists for biomarkers capable of monitoring progression and ultimately determining drug efficacy. Neuroimaging provides a powerful tool for assessing disease progression. However, in order to be accepted as biomarkers for clinical trials, imaging measures must be reproducible, robust to scanner differences, sensitive to disease-related change and demonstrate a relationship to clinically meaningful measures. We provide a review of the current structural imaging literature in HD and highlight inconsistencies between studies. We make recommendations for the standardisation of reporting for future studies, such as appropriate cohort characterisation and documentation of methodologies to facilitate comparisons and inform trial design. We also argue for an intensified effort to consider issues highlighted here so that we have the best chance of assessing the efficacy of the therapeutic benefit in forestalling this devastating disease.

Development of biomarkers for Huntington's disease

Huntington's disease is an autosomal dominant, progressive neurodegenerative disorder, for which there is no disease-modifying treatment. By use of predictive genetic testing, it is possible to identify individuals who carry the gene defect before the onset of symptoms, providing a window of opportunity for intervention aimed at preventing or delaying disease onset. However, without robust and practical measures of disease progression (ie, biomarkers), the efficacy of therapeutic interventions in this premanifest Huntington's disease population cannot be readily assessed. Current progress in the development of biomarkers might enable evaluation of disease progression in individuals at the premanifest stage of the disease; these biomarkers could be useful in defining endpoints in clinical trials in this population. Clinical, cognitive, neuroimaging, and biochemical biomarkers are being investigated for their potential in clinical use and their value in the development of future treatments for patients with Huntington's disease.

Evaluating imaging biomarkers for neurodegeneration in pre-symptomatic Huntington's disease using machine learning techniques

The development of MRI measures as biomarkers for neurodegenerative disease could prove extremely valuable for the assessment of neuroprotective therapies. Much current research is aimed at developing such biomarkers for use in people who are gene-positive for Huntington's disease yet exhibit few or no clinical symptoms of the disease (pre-HD). We acquired structural (T1), diffusion weighted and functional MRI (fMRI) data from 39 pre-HD volunteers and 25 age-matched controls. To determine whether it was possible to decode information about disease state from neuroimaging data, we applied multivariate pattern analysis techniques to several derived voxel-based and segmented region-based datasets. We found that different measures of structural, diffusion weighted, and functional MRI could successfully classify pre-HD and controls using support vector machines (SVM) and linear discriminant analysis (LDA) with up to 76% accuracy. The model producing the highest classification accuracy used LDA with a set of six volume measures from the basal ganglia. Furthermore, using support vector regression (SVR) and linear regression models, we were able to generate quantitative measures of disease progression that were significantly correlated with established measures of disease progression (estimated years to clinical onset, derived from age and genetic information) from several different neuroimaging measures. The best performing regression models used SVR with neuroimaging data from regions within the grey matter (caudate), white matter (corticospinal tract), and fMRI (insular cortex). These results highlight the utility of machine learning analyses in addition to conventional ones. We have shown that several neuroimaging measures contain multivariate patterns of information that are useful for the development of disease-state biomarkers for HD.