Domain selection and familywise error rate for functional data: A unified framework.

Functional data are smooth, often continuous, random curves, which can be seen as an extreme case of multivariate data with infinite dimensionality. Just as componentwise inference for multivariate data naturally performs feature selection, subsetwise inference for functional data performs domain selection. In this paper, we present a unified testing framework for domain selection on populations of functional data. In detail, p-values of hypothesis tests performed on pointwise evaluations of functional data are suitably adjusted for providing control of the familywise error rate (FWER) over a family of subsets of the domain. We show that several state-of-the-art domain selection methods fit within this framework and differ from each other by the choice of the family over which the control of the FWER is provided. In the existing literature, these families are always defined a priori. In this work, we also propose a novel approach, coined thresholdwise testing, in which the family of subsets is instead built in a data-driven fashion. The method seamlessly generalizes to multidimensional domains in contrast to methods based on a priori defined families. We provide theoretical results with respect to consistency and control of the FWER for the methods within the unified framework. We illustrate the performance of the methods within the unified framework on simulated and real data examples and compare their performance with other existing methods.

Semi-supervised clustering of quaternion time series: Application to gait analysis in multiple sclerosis using motion sensor data.

Recent approaches in gait analysis involve the use of wearable motion sensors to extract spatio-temporal parameters that characterize multiple aspects of an individual's gait. In particular, the medical community could largely benefit from this type of devices as they could provide the clinicians with a valuable tool for assessing gait impairment. Motion sensor data are however complex and there is an urgent unmet need to develop sound statistical methods for analyzing such data and extracting clinically relevant information. In this article, we measure gait by following the hip rotation over time and the resulting statistical unit is a time series of unit quaternions. We explore the possibility to form groups of patients with similar walking impairment by taking into account their walking data and their global decease severity with semi-supervised clustering. We generalize a compromise-based method named hclustcompro to unit quaternion time series by combining it with the proper dissimilarity quaternion dynamic time warping. We apply this method on patients diagnosed with multiple sclerosis to form groups of patients with similar walking deficiencies while accounting for the clinical assessment of their overall disability. We also compare the compromise-based clustering approach with the method mergeTrees that falls into a sub-class of ensemble clustering named collaborative clustering. The results provide a first proof of both the interest of using wearable motion sensors for assessing gait impairment and the use of prior knowledge to guide the clustering process. It also demonstrates that compromise-based clustering is a more appropriate approach in this context.

Subtype Diagnosis of Sporadic Creutzfeldt-Jakob Disease with Diffusion Magnetic Resonance Imaging.

OBJECTIVE: Sporadic Creutzfeldt-Jakob disease (sCJD) comprises several subtypes as defined by genetic and prion protein characteristics, which are associated with distinct clinical and pathological phenotypes. To date, no clinical test can reliably diagnose the subtype. We established two procedures for the antemortem diagnosis of sCJD subtype using diffusion magnetic resonance imaging (MRI). METHODS: MRI of 1,458 patients referred to the National Prion Disease Pathology Surveillance Center were collected through its consultation service. One neuroradiologist blind to the diagnosis scored 12 brain regions and generated a lesion profile for each MRI scan. We selected 487 patients with autopsy-confirmed diagnosis of "pure" sCJD subtype and at least one positive diffusion MRI examination. We designed and tested two data-driven procedures for subtype diagnosis: the first procedure-prion subtype classification algorithm with MRI (PriSCA_MRI)-uses only MRI examinations; the second-PriSCA_MRI + Gen-includes knowledge of the prion protein codon 129 genotype, a major determinant of sCJD subtypes. Both procedures were tested on the first MRI and the last MRI follow-up. RESULTS: PriSCA_MRI classified the 3 most prevalent subtypes with 82% accuracy. PriSCA_MRI + Gen raised the accuracy to 89% and identified all subtypes. Individually, the 2 most prevalent sCJD subtypes, MM1 and VV2, were diagnosed with sensitivities up to 95 and 97%, respectively. The performances of both procedures did not change in 168 patients with longitudinal MRI studies when the last examination was used. INTERPRETATION: This study provides the first practical algorithms for antemortem diagnosis of sCJD subtypes. MRI diagnosis of subtype is likely to be attainable at early disease stages to prognosticate clinical course and design future therapeutic trials. ANN NEUROL 2021;89:560-572.

A Novel Walking Activity Recognition Model for Rotation Time Series Collected by a Wearable Sensor in a Free-Living Environment.

Solutions to assess walking deficiencies are widespread and largely used in healthcare. Wearable sensors are particularly appealing, as they offer the possibility to monitor gait in everyday life, outside a facility in which the context of evaluation biases the measure. While some wearable sensors are powerful enough to integrate complex walking activity recognition models, non-invasive lightweight sensors do not always have the computing or memory capacity to run them. In this paper, we propose a walking activity recognition model that offers a viable solution to this problem for any wearable sensors that measure rotational motion of body parts. Specifically, the model was trained and tuned using data collected by a motion sensor in the form of a unit quaternion time series recording the hip rotation over time. This time series was then transformed into a real-valued time series of geodesic distances between consecutive quaternions. Moving average and moving standard deviation versions of this time series were fed to standard machine learning classification algorithms. To compare the different models, we used metrics to assess classification performance (precision and accuracy) while maintaining the detection prevalence at the level of the prevalence of walking activities in the data, as well as metrics to assess change point detection capability and computation time. Our results suggest that the walking activity recognition model with a decision tree classifier yields the best compromise in terms of precision and computation time. The sensor that was used had purposely low computing and memory capacity so that reported performances can be thought of as the lower bounds of what can be achieved. Walking activity recognition is performed online, i.e., on-the-fly, which further extends the range of applicability of our model to sensors with very low memory capacity.

Corpus Callosum White Matter Diffusivity Reflects Cumulative Neurological Comorbidity in Tuberous Sclerosis Complex.

INTRODUCTION: Neurological manifestations in Tuberous Sclerosis Complex (TSC) are highly variable. Diffusion tensor imaging (DTI) may reflect the neurological disease burden. We analyzed the association of autism spectrum disorder (ASD), intellectual disability (ID) and epilepsy with callosal DTI metrics in subjects with and without TSC. METHODS: 186 children underwent 3T MRI DTI: 51 with TSC (19 with concurrent ASD), 46 with non-syndromic ASD and 89 healthy controls (HC). Subgroups were based on presence of TSC, ASD, ID, and epilepsy. Density-weighted DTI metrics obtained from tractography of the corpus callosum were fitted using a 2-parameter growth model. We estimated distributions using bootstrapping and calculated half-life and asymptote of the fitted curves. RESULTS: TSC was associated with a lower callosal fractional anisotropy (FA) than ASD, and ASD with a lower FA than HC. ID, epilepsy and ASD diagnosis were each associated with lower FA values, demonstrating additive effects. In TSC, the largest change in FA was related to a comorbid diagnosis of ASD. Mean diffusivity (MD) showed an inverse relationship to FA. Some subgroups were too small for reliable data fitting. CONCLUSIONS: Using a cross-disorder approach, this study demonstrates cumulative abnormality of callosal white matter diffusion with increasing neurological comorbidity.

Bone age assessment practices in infants and older children among Society for Pediatric Radiology members.

BACKGROUND: Numerous bone age estimation techniques exist, but little is known about what methods radiologists use in clinical practice. OBJECTIVE: To determine which methods pediatric radiologists use to assess bone age in children, and their confidence in these methods. MATERIALS AND METHODS: Society for Pediatric Radiology (SPR) members were invited to complete an online survey regarding bone age assessment. Respondents were asked to identify the methods used and their confidence with their technique for the following groups: Infants (<1 year old), 1- to 3-year-olds and 3- to 18-year-olds. RESULTS: Of the 937 SPR members invited, 441 responded (47%). For infants, 70% of respondents use the hand/wrist method of Greulich and Pyle, 27% use a hemiskeleton method (e.g., Sontag or Elgenmark), and 14.4% use the knee method of Pyle and Hoerr. Of these respondents, 34% were not confident with their technique. For 1- to 3-year-olds, 86% used Greulich and Pyle, and 19% used a hemiskeleton method; 21% were not confident with their technique in this age group. For 3- to 18-year-olds, 97% used Greulich and Pyle, and only 6% of respondents were not confident with their technique in this category. A logistic regression analysis demonstrated that the chronological age of the patient had the greatest impact on reader confidence, with the odds ratios for confidence being 4 times greater in the 3- to 18-year-olds category compared to the younger groups. CONCLUSION: For children older than 3 years, the majority of pediatric radiologists are very confident in their use of Greulich and Pyle for bone age assessment. However a variety of methodologies are used when assessing bone age in infants and younger children, and pediatric radiologists are less confident assessing bone age in these children. This survey highlights the need for a consensus protocol on bone age assessment of younger children and infants that provides readers with a higher degree of confidence.

Systematic combined noncontrast CT-CT angiography in the management of unexplained nontraumatic coma.

BACKGROUND: Nontraumatic impaired consciousness is a common issue in emergency departments with a serious but widely variable prognosis. STUDY OBJECTIVES: The aim of this prospective study was to evaluate the ability of systematic combined noncontrast computed tomography (NCCT)/computed tomography angiography (CTA) imaging, firstly to provide a neurologic prognosis and secondly to ensure early detection of basilar artery occlusion (BAO), in unexplained nontraumatic impaired consciousness management. METHODS: Combined NCCT/CTA imaging was performed on 65 patients with impaired consciousness and no history of trauma prospectively over 14 months in a single center. Images were assessed based on visual and quantitative criteria. Clinical outcome was assessed using the modified Rankin Scale at 3 months. Statistical analysis aimed to identify the prognostic value of combined NCCT/CTA imaging and its ability for early BAO detection. RESULTS: This study shows that combined NCCT/CTA imaging was a significant predictor of poor neurological outcome, with a positive predictive value of 94.6%. The combination was also crucial for early detection of BAO, given that 42.8% of cases were misdiagnosed with NCCT alone. Basilar artery occlusion represented 10.8% of all unexplained nontraumatic impaired consciousness. CONCLUSIONS: Systematic combined NCCT/CTA imaging is an efficient tool for predicting poor neurologic prognosis in cases of unexplained nontraumatic impaired consciousness and is also essential for detecting BAO.

Prognostic factors for outcomes after mechanical thrombectomy with solitaire stent.

BACKGROUND AND PURPOSE: Endovascular mechanical thrombectomy is emerging as a promising therapeutic approach for acute ischemic stroke. This study was aimed at identifying factors influencing outcomes after thrombectomy with a Solitaire stent device. MATERIALS AND METHODS: Forty-five consecutive patients treated with thrombectomy using Solitaire FR were retrospectively included. Clinical, imaging and logistic variables were analyzed. A multivariate logistic regression analysis was used to identify variables influencing clinical outcome, based on discharge NIHSS score change and mRS at 3 months. RESULTS: Patient mean age and initial NIHSS score was 58 years (range 24-88) and 17 (range 6-32), respectively. An MRI was performed for 80% of patients, showing severe DWI lesion for 28% of patients and associated FLAIR hyperintensity for 58% of patients. Mean time from symptom onset to recanalization was 299min for the 32 ACO and 473min for the 13 PCO. Angiographic efficacy (TICI 2b-3) was achieved for 93% of patients and good clinical outcomes at discharge and at 3 months (mRS≤2) were achieved for 49% and 58% of patients, respectively. Independent prognostic factors for predicting good clinical outcomes at discharge were a short time to recanalization and FLAIR negativity. At 3 months, they were a short time to recanalization and patient age. DWI lesion severity was an associated prognostic factor. CONCLUSION: Two main prognostic factors for predicting a good clinical outcome after thrombectomy at 3 months were short time from symptom onset to recanalization and patient age.

Smoothing method for unit quaternion time series in a classification problem: an application to motion data.

Smoothing orientation data is a fundamental task in different fields of research. Different methods of smoothing time series in quaternion algebras have been described in the literature, but their application is still an open point. This paper develops a smoothing approach for smoothing quaternion time series to obtain good performance in classification problems. Starting from an existing method which involves an angular velocity transformation of unit quaternion time series, a new method which employ the logarithm function to transform the quaternion time series to a real three-dimensional time series is proposed. Empirical evidences achieved on real data set and artificially noisy data sets confirm the effectiveness of the proposed method compared with the classical approach based on angular velocity transformation. The R functions developed for this paper will be provided in a Github repository.

Functional arterial spin labeling: Optimal sequence duration for motor activation mapping in clinical practice.

PURPOSE: To determine the minimal optimal functional arterial spin labeling (fASL) sequence duration allowing steady and reproducible motor activation mapping. MATERIALS AND METHODS: Three magnetic resonance imaging (MRI) sessions including fASL and blood oxygenation level-dependent (BOLD) functional MRI (fMRI) sequences were performed on 12 healthy subjects at 3T with a 32-channel coil. The raw 7-minute fASL sequence was truncated to obtain six fASL sequences with durations ranging from 1-6 minutes. All the resulting fASL activations were compared between themselves and with both the 7-minute fASL and BOLD activations. Quantitative parameters assessed activation location (activated volume, barycenter, and distance between barycenters), activation quantification (activation-related cerebral blood flow), and intraindividual reproducibility across fMRI sessions. The statistical analysis was based on analysis of variance (ANOVA) and Tukey's multiple comparisons. RESULTS: Four-minute fASL achieved steady location and quantification of activation with the activated volume corresponding to 81% of the 7-minute fASL volume and a barycenter located 1.2 mm from the 7-minute fASL barycenter and 3.0 mm from the BOLD fMRI barycenter. Four-minute fASL reproducibility was high and statistically equivalent to 7-minute values. CONCLUSION: A 4-minute fASL sequence is thus a reliable tool for motor activation mapping and suitable for use in clinical practice.

Arterial spin labeling for motor activation mapping at 3T with a 32-channel coil: reproducibility and spatial accuracy in comparison with BOLD fMRI.

Functional arterial spin labeling (fASL) is an innovative biomarker of neuronal activation that allows direct and absolute quantification of activation-related CBF and is less sensitive to venous contamination than BOLD fMRI. This study evaluated fASL for motor activation mapping in comparison with BOLD fMRI in terms of involved anatomical area localization, intra-individual reproducibility of location, quantification of neuronal activation, and spatial accuracy. Imaging was performed at 3T with a 32-channel coil and dedicated post-processing tools were used. Twelve healthy right-handed subjects underwent fASL and BOLD fMRI while performing a right hand motor activation task. Three sessions were performed 7days apart in similar physiological conditions. Our results showed an activation in the left primary hand motor area for all 36 sessions in both fASL and BOLD fMRI. The individual functional maps for fASL demonstrated activation in ipsilateral secondary motor areas more often than the BOLD fMRI maps. This finding was corroborated by the group maps. In terms of activation location, fASL reproducibility was comparable to BOLD fMRI, with a distance between activated volumes of 2.1mm and an overlap ratio for activated volumes of 0.76, over the 3 sessions. In terms of activation quantification, fASL reproducibility was higher, although not significantly, with a CVintra of 11.6% and an ICC value of 0.75. Functional ASL detected smaller activation volumes than BOLD fMRI but the areas had a high degree of co-localization. In terms of spatial accuracy in detecting activation in the hand motor area, fASL had a higher specificity (43.5%) and a higher positive predictive value (69.8%) than BOLD fMRI while maintaining high sensitivity (90.7%). The high intra-individual reproducibility and spatial accuracy of fASL revealed in the present study will subsequently be applied to pathological subjects.

Reproducibility of Brain MRI Segmentation Algorithms: Empirical Comparison of Local MAP PSTAPLE, FreeSurfer, and FSL-FIRST.

BACKGROUND AND PURPOSE: Segmentation of human brain structures is crucial for the volumetric quantification of brain disease. Advances in algorithmic approaches have led to automated techniques that save time compared to interactive methods. Recently, the utility and accuracy of template library fusion algorithms, such as Local MAP PSTAPLE (PSTAPLE), have been demonstrated but there is little guidance regarding its reproducibility compared to single template-based algorithms such as FreeSurfer and FSL-FIRST. METHODS: Eight repeated magnetic resonance imagings of 20 subjects were segmented using FreeSurfer, FSL-FIRST, and PSTAPLE. We reported the reproducibility of segmentation-derived volume measurements for brain structures and calculated sample size estimates for detecting hypothetical rates of tissue atrophy given the observed variances. RESULTS: PSTAPLE had the most reproducible volume measurements for hippocampus, putamen, thalamus, caudate, pallidum, amygdala, Accumbens area, and cortical regions. FreeSurfer was most reproducible for brainstem. PSTAPLE was the most accurate algorithm in terms of several metrics include Dice's coefficient. The sample size estimates showed that a study utilizing PSTAPLE would require tens to hundreds less subjects than the other algorithms for detecting atrophy rates typically observed in brain disease. CONCLUSIONS: PSTAPLE is a useful tool for automatic human brain segmentation due to its precision and accuracy, which enable the detection of the size of the effect typically reported for neurological disorders with a substantially reduced sample size, in comparison to the other tools we assessed. This enables randomized controlled trials to be executed with reduced cost and duration, in turn, facilitating the assessment of new therapeutic interventions.

Analytic quantification of bias and variance of coil sensitivity profile estimators for improved image reconstruction in MRI.

Magnetic resonance (MR) imaging provides a unique in-vivo capability of visualizing tissue in the human brain non-invasively, which has tremendously improved patient care over the past decades. However, there are still prominent artifacts, such as intensity inhomogeneities due to the use of an array of receiving coils (RC) to measure the MR signal or noise amplification due to accelerated imaging strategies. It is critical to mitigate these artifacts for both visual inspection and quantitative analysis. The cornerstone to address this issue pertains to the knowledge of coil sensitivity profiles (CSP) of the RCs, which describe how the measured complex signal decays with the distance to the RC. Existing methods for CSP estimation share a number of limitations: (i) they primarily focus on CSP magnitude, while it is known that the solution to the MR image reconstruction problem involves complex CSPs and (ii) they only provide point estimates of the CSPs, which makes the task of optimizing the parameters and acquisition protocol for their estimation difficult. In this paper, we propose a novel statistical framework for estimating complex-valued CSPs. We define a CSP estimator that uses spatial smoothing and additional body coil data for phase normalization. The main contribution is to provide detailed information on the statistical distribution of the CSP estimator, which yields automatic determination of the optimal degree of smoothing for ensuring minimal bias and provides guidelines to the optimal acquisition strategy.

Tubers are neither static nor discrete: Evidence from serial diffusion tensor imaging.

OBJECTIVE: To assess the extent and evolution of tissue abnormality of tubers, perituber tissue, and normal-appearing white matter (NAWM) in patients with tuberous sclerosis complex using serial diffusion tensor imaging. METHODS: We applied automatic segmentation based on a combined global-local intensity mixture model of 3T structural and 35 direction diffusion tensor MRIs (diffusion tensor imaging) to define 3 regions: tuber tissue, an equal volume perituber rim, and the remaining NAWM. For each patient, scan, lobe, and tissue type, we analyzed the averages of mean diffusivity (MD) and fractional anisotropy (FA) in a generalized additive mixed model. RESULTS: Twenty-five patients (mean age 5.9 years; range 0.5-24.5 years) underwent 2 to 6 scans each, totaling 70 scans. Average time between scans was 1.2 years (range 0.4-2.9). Patient scans were compared with those of 73 healthy controls. FA values were lowest, and MD values were highest in tubers, next in perituber tissue, then in NAWM. Longitudinal analysis showed a positive (FA) and negative (MD) correlation with age in tubers, perituber tissue, and NAWM. All 3 tissue types followed a biexponential developmental trajectory, similar to the white matter of controls. An additional qualitative analysis showed a gradual transition of diffusion values across the tissue type boundaries. CONCLUSIONS: Similar to NAWM, tuber and perituber tissues in tuberous sclerosis complex undergo microstructural evolution with age. The extent of diffusion abnormality decreases with distance to the tuber, in line with known extension of histologic, immunohistochemical, and molecular abnormalities beyond tuber pathology.

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery.

BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) tractography reconstruction of white matter pathways can help guide brain tumor resection. However, DTI tracts are complex mathematical objects and the validity of tractography-derived information in clinical settings has yet to be fully established. To address this issue, we initiated the DTI Challenge, an international working group of clinicians and scientists whose goal was to provide standardized evaluation of tractography methods for neurosurgery. The purpose of this empirical study was to evaluate different tractography techniques in the first DTI Challenge workshop. METHODS: Eight international teams from leading institutions reconstructed the pyramidal tract in four neurosurgical cases presenting with a glioma near the motor cortex. Tractography methods included deterministic, probabilistic, filtered, and global approaches. Standardized evaluation of the tracts consisted in the qualitative review of the pyramidal pathways by a panel of neurosurgeons and DTI experts and the quantitative evaluation of the degree of agreement among methods. RESULTS: The evaluation of tractography reconstructions showed a great interalgorithm variability. Although most methods found projections of the pyramidal tract from the medial portion of the motor strip, only a few algorithms could trace the lateral projections from the hand, face, and tongue area. In addition, the structure of disagreement among methods was similar across hemispheres despite the anatomical distortions caused by pathological tissues. CONCLUSIONS: The DTI Challenge provides a benchmark for the standardized evaluation of tractography methods on neurosurgical data. This study suggests that there are still limitations to the clinical use of tractography for neurosurgical decision making.

Adaptive multi-modal particle filtering for probabilistic white matter tractography.

Particle filtering has recently been introduced to perform probabilistic tractography in conjunction with DTI and Q-Ball models to estimate the diffusion information. Particle filters are particularly well adapted to the tractography problem as they offer a way to approximate a probability distribution over all paths originated from a specified voxel, given the diffusion information. In practice however, they often fail at consistently capturing the multi-modality of the target distribution. For brain white matter tractography, this means that multiple fiber pathways are unlikely to be tracked over extended volumes. We propose to remedy this issue by formulating the filtering distribution as an adaptive M-component non-parametric mixture model. Such a formulation preserves all the properties of a classical particle filter while improving multi-modality capture. We apply this multi-modal particle filter to both DTI and Q-Ball models and propose to estimate dynamically the number of modes of the filtering distribution. We show on synthetic and real data how this algorithm outperforms the previous versions proposed in the literature.

Non-local robust detection of DTI white matter differences with small databases.

Diffusion imaging, through the study of water diffusion, allows for the characterization of brain white matter, both at the population and individual level. In recent years, it has been employed to detect brain abnormalities in patients suffering from a disease, e.g., from multiple sclerosis (MS). State-of-the-art methods usually utilize a database of matched (age, sex, ...) controls, registered onto a template, to test for differences in the patient white matter. Such approaches however suffer from two main drawbacks. First, registration algorithms are prone to local errors, thereby degrading the comparison results. Second, the database needs to be large enough to obtain reliable results. However, in medical imaging, such large databases are hardly available. In this paper, we propose a new method that addresses these two issues. It relies on the search for samples in a local neighborhood of each pixel to increase the size of the database. Then, we propose a new test based on these samples to perform a voxelwise comparison of a patient image with respect to a population of controls. We demonstrate on simulated and real MS patient data how such a framework allows for an improve detection power and a better robustness and reproducibility, even with a small database.