A new approach to digitized cognitive monitoring: validity of the SelfCog in Huntington's disease

Cognitive deficits represent a hallmark of neurodegenerative diseases, but evaluating their progression is complex. Most current evaluations involve lengthy paper-and-pencil tasks which are subject to learning effects dependent on the mode of response (motor or verbal), the countries’ language or the examiners. To address these limitations, we hypothesized that applying neuroscience principles may offer a fruitful alternative. We thus developed the SelfCog, a digitized battery that tests motor, executive, visuospatial, language and memory functions in 15 min. All cognitive functions are tested according to the same paradigm, and a randomization algorithm provides a new test at each assessment with a constant level of difficulty. Here, we assessed its validity, reliability and sensitivity to detect decline in early-stage Huntington’s disease in a prospective and international multilingual study (France, the UK and Germany). Fifty-one out of 85 participants with Huntington’s disease and 40 of 52 healthy controls included at baseline were followed up for 1 year. Assessments included a comprehensive clinical assessment battery including currently standard cognitive assessments alongside the SelfCog. We estimated associations between each of the clinical assessments and SelfCog using Spearman’s correlation and proneness to retest effects and sensitivity to decline through linear mixed models. Longitudinal effect sizes were estimated for each cognitive score. Voxel-based morphometry and tract-based spatial statistics analyses were conducted to assess the consistency between performance on the SelfCog and MRI 3D-T1 and diffusion-weighted imaging in a subgroup that underwent MRI at baseline and after 12 months. The SelfCog detected the decline of patients with Huntington’s disease in a 1-year follow-up period with satisfactory psychometric properties. Huntington’s disease patients are correctly differentiated from controls. The SelfCog showed larger effect sizes than the classical cognitive assessments. Its scores were associated with grey and white matter damage at baseline and over 1 year. Given its good performance in longitudinal analyses of the Huntington’s disease cohort, it should likely become a very useful tool for measuring cognition in Huntington’s disease in the future. It highlights the value of moving the field along the neuroscience principles and eventually applying them to the evaluation of all neurodegenerative diseases

Cognitive decline in Huntington's disease in the Digitalized Arithmetic Task (DAT)

Background Efficient cognitive tasks sensitive to longitudinal deterioration in small cohorts of Huntington’s disease (HD) patients are lacking in HD research. We thus developed and assessed the digitized arithmetic task (DAT), which combines inner language and executive functions in approximately 4 minutes. Methods We assessed the psychometric properties of DAT in three languages, across four European sites, in 77 early-stage HD patients (age: 52 ± 11 years; 27 females), and 57 controls (age: 50 ± 10, 31 females). Forty-eight HD patients and 34 controls were followed up to one year with 96 participants who underwent MRI brain imaging (HD patients = 46) at baseline and 50 participants (HD patients = 22) at one year. Linear mixed models and Pearson correlations were used to assess associations with clinical assessment. Results At baseline, HD patients were less accurate (p = 0.0002) with increased response time (p<0.0001) when compared to DAT in controls. Test-retest reliability in HD patients ranged from good to excellent for response time (range: 0.63–0.79) and from questionable to acceptable for accuracy (range: r = 0.52–0.69). Only DAT, the Mattis Dementia Rating Scale, the Symbol Digit Modalities Test, and Total Functional Capacity scores were able to detect a decline within a one-year follow-up in HD patients (all p< 0.05). In contrast with all the other cognitive tasks, DAT correlated with striatal atrophy over time (p = 0.037) but not with motor impairment. Conclusions DAT is fast, reliable, motor-free, applicable in several languages, and able to unmask cognitive decline correlated with striatal atrophy in small cohorts of HD patients. This likely makes it a useful endpoint in future trials for HD and other neurodegenerative diseases

Deep learning-assisted model-based off-resonance correction for non-Cartesian susceptibility weighted imaging

Purpose: Patient-induced inhomogeneities in the static magnetic field cause distortions and blurring (off-resonance artifacts) during acquisitions with long readouts such as in susceptibility-weighted imaging (SWI). Conventional versatile correction methods based on extended Fourier models are too slow for clinical practice in computationally demanding cases such as 3D high-resolution non-Cartesian multi-coil acquisitions.Theory: Most reconstruction methods can be accelerated when performing off-resonance correction, by reducing the number of iterations, com-pressed coils and correction components. Recent state-of-the-art deep learning architectures could help but are generally not adapted to corrupted measurements as they rely on the standard Fourier operator in the data consistency term. The combination of correction models and neural networks is therefore necessary to reduce reconstruction times.Methods: Hybrid pipelines using UNets were trained stack-by-stack over 99 SWI 3D SPARKLING 20-fold accelerated acquisitions at 0.6mm isotropic resolution using different off-resonance correction methods. Target images were obtained using slow model-based corrections based on self-estimated ∆B 0 field maps. The proposed strategies, tested over 11 volumes, are compared to model-only and network-only pipelines.Results: The proposed hybrid pipelines achieved scores competing with 2-3 times slower baseline methods, and neural networks were observedto contribute both as pre-conditioner and through inter-iteration memory by allowing more degrees of freedom over the model design.Conclusion: A combination of model-based and network-based off-resonance correction was proposed to significantly accelerate conventional methods. Different promising synergies were observed between acceleration factors (iterations, coils, correction) and model/network that could be expanded in the future

Deep learning‐assisted model‐based off‐resonance correction for non‐Cartesian SWI

International audiencePurpose Patient‐induced inhomogeneities in the static magnetic field cause distortions and blurring (off‐resonance artifacts) during acquisitions with long readouts such as in SWI. Conventional versatile correction methods based on extended Fourier models are too slow for clinical practice in computationally demanding cases such as 3D high‐resolution non‐Cartesian multi‐coil acquisitions. Theory Most reconstruction methods can be accelerated when performing off‐resonance correction by reducing the number of iterations, compressed coils, and correction components. Recent state‐of‐the‐art unrolled deep learning architectures could help but are generally not adapted to corrupted measurements as they rely on the standard Fourier operator in the data consistency term. The combination of correction models and neural networks is therefore necessary to reduce reconstruction times. Methods Hybrid pipelines using UNets were trained stack‐by‐stack over 99 SWI 3D SPARKLING 20‐fold accelerated acquisitions at 0.6 mm isotropic resolution using different off‐resonance correction methods. Target images were obtained using slow model‐based corrections based on self‐estimated field maps. The proposed strategies, tested over 11 volumes, are compared to model‐only and network‐only pipelines. Results The proposed hybrid pipelines achieved scores competing with two to three times slower baseline methods, and neural networks were observed to contribute both as pre‐conditioner and through inter‐iteration memory by allowing more degrees of freedom over the model design. Conclusion A combination of model‐based and network‐based off‐resonance correction was proposed to significantly accelerate conventional methods. Different promising synergies were observed between acceleration factors (iterations, coils, correction) and model/network that could be expanded in the future

The specific role of the striatum in interval timing: The Huntington’s disease model

International audienceTime processing over intervals of hundreds of milliseconds to minutes, also known as interval timing, is associated with the striatum. Huntington's disease patients (HD) with striatal degeneration have impaired interval timing, but the extent and specificity of these deficits remain unclear. Are they specific to the temporal domain, or do they extend to the spatial domain too? Do they extend to both the perception and production of interval timing? Do they appear before motor symptoms in Huntington's disease (Pre-HD)? We addressed these issues by assessing both temporal abilities (in the seconds range) and spatial abilities (in the cm range) in 20 Pre-HD, 25 HD patients, and 25 healthy Controls, in discrimination, bisection and production paradigms. In addition, all participants completed a questionnaire assessing temporal and spatial disorientation in daily life, and the gene carriers (i.e., HD and Pre-HD participants) underwent structural brain MRI. Overall, HD patients were more impaired in the temporal than in the spatial domain in the behavioral tasks, and expressed a greater disorientation in the temporal domain in the daily life questionnaire. In contrast, Pre-HD participants showed no sign of a specific temporal deficit. Furthermore, MRI analyses indicated that performances in the temporal discrimination task were associated with a larger striatal grey matter volume in the striatum in gene carriers. Altogether, behavioral, brain imaging and questionnaire data support the hypothesis that the striatum is a specific component of interval timing processes. Evaluations of temporal disorientation and interval timing processing could be used as clinical tools for HD patients

Deep learning-assisted model-based off-resonance correction for non-Cartesian susceptibility weighted imaging

Purpose: Patient-induced inhomogeneities in the static magnetic field cause distortions and blurring (off-resonance artifacts) during acquisitions with long readouts such as in susceptibility-weighted imaging (SWI). Conventional versatile correction methods based on extended Fourier models are too slow for clinical practice in computationally demanding cases such as 3D high-resolution non-Cartesian multi-coil acquisitions.Theory: Most reconstruction methods can be accelerated when performing off-resonance correction, by reducing the number of iterations, com-pressed coils and correction components. Recent state-of-the-art deep learning architectures could help but are generally not adapted to corrupted measurements as they rely on the standard Fourier operator in the data consistency term. The combination of correction models and neural networks is therefore necessary to reduce reconstruction times.Methods: Hybrid pipelines using UNets were trained stack-by-stack over 99 SWI 3D SPARKLING 20-fold accelerated acquisitions at 0.6mm isotropic resolution using different off-resonance correction methods. Target images were obtained using slow model-based corrections based on self-estimated ∆B 0 field maps. The proposed strategies, tested over 11 volumes, are compared to model-only and network-only pipelines.Results: The proposed hybrid pipelines achieved scores competing with 2-3 times slower baseline methods, and neural networks were observedto contribute both as pre-conditioner and through inter-iteration memory by allowing more degrees of freedom over the model design.Conclusion: A combination of model-based and network-based off-resonance correction was proposed to significantly accelerate conventional methods. Different promising synergies were observed between acceleration factors (iterations, coils, correction) and model/network that could be expanded in the future

Post-contrast 3D T1-weighted TSE MR sequences (SPACE, CUBE, VISTA/BRAINVIEW, isoFSE, 3D MVOX): Technical aspects and clinical applications

International audiencePost-contrast three-dimensional T1-weighted imaging of the brain is widely used for a broad range of vascular, inflammatory or tumoral diseases. The variable flip angle 3D TSE sequence is now available from several manufacturers (CUBE, General Electric; SPACE, Siemens; VISTA/BRAINVIEW, Philips; isoFSE, Itachi; 3D MVOX, Canon). Compared to gradient-echo (GRE) techniques, 3D TSE offers the advantages of useful image contrasts and reduction of artifacts from static field inhomogeneity. However, the respective role of 3D TSE and GRE MR sequences remains to be elucidated, particularly in the setting of post-contrast imaging. The purpose of this review was (1) to describe the technical aspects of 3D TSE sequences, (2) to illustrate the main clinical applications of the post-contrast 3D T1-w TSE sequence through clinical cases, (3) to discuss the respective role of post-contrast 3D TSE and GRE imaging in the field of neuroimaging