Orthopedic Conditions and Interplay with Functional Abilities and MECP2 Variant Subtype in Rett Syndrome Patients.

PURPOSE: Rett syndrome (RTT) is a rare multi-systemic disorder primarily linked to mutations in MECP2 gene. This study aims to describe the prevalence of orthopedic conditions in RTT patients, and examine their intricate interplay with functional capabilities, and MECP2 variant subtypes. METHODS: Conducted as a cross-sectional retrospective observational study, the research encompassed 55 patients meeting clinical RTT criteria and holding MECP2 mutations. A review of clinical records was performed to gather demographic data, mutation subtypes, orthopedic conditions, management strategies, and assessments of function. RESULTS: Mean age of the participants was 10.22 ± 4.64 years (range, 2.9-19.41). Prevalence rates of orthopedic conditions were as follows: kyphoscoliosis 63.6%, hip displacement 14.6%, knee problems 40%, and foot deformities 75.5%. Significant relationship emerged between spinal (p < 0.01) and knee deformities (p < 0.01) with reduced motor function across various domains. Hip displacement significantly affected sitting ability (p = 0.002), and foot deformities impacted standing and walking capabilities (p = 0.049). Mutation clusters analysis revealed significant correlations with spinal (p = 0.022) and knee deformities (p = 0.002). Linear models highlighted the critical importance of mutation clusters, spine deformities, age, and hip management concerning functional variables. CONCLUSIONS: In this study, foot deformities were the most frequent orthopedic manifestation, followed by spinal, knee, and hip deformities; and unveiled their relationships with functional status and groups of mutations in RTT patients. LEVEL OF EVIDENCE: Level IV, Case series.

Visually-Induced Motor Imagery Effects on Motor Adaptation to Reverse Steering Cycling. A Randomized Controlled Trial.

Purpose: First, testing an intervention of neuromodulation based on motor imagery and action observation as a promoter of motor adaptation of a complex motor task involving balance. Second, determining what prior balance factors can affect the motor adaptation task. Methods: A double-blind randomized controlled trial was performed. Forty-eight healthy subjects were recruited. The balance of all participants during gait and standing was assessed before adapting to the complex, multi-limb motor task of riding an inverse steering bicycle (ISB). Two interventions were carried out interleaved among trials of adaptation to the motor task: the experimental group (n = 24) was asked to perform neuromodulation (EN) by watching first-person ISB riding through immersive VR glasses and, simultaneously, mentally mimicking the movements. The control group (CG) was asked to watch a slideshow video of steady landscape images. Results: The results showed that the EN group did not improve the motor adaptation rate and induced higher adaptation times with respect to the CG. However, while the motor adaptation success showed a significant dependence on the prior proprioceptive participation in balance in the CG, the EN group did not present any relationship between the prior balance profile and motor adaptation outcome. Conclusions: Results point to a benefit of the visually guided neuromodulation for the motor adaptation of the subjects with low participation of proprioception in balance. Moreover, the results from the control group would allow to disclose prognostic factors about the success of the motor adaptation, and also prescription criteria for the proposed neuromodulation based on the balance profile.

Long-term Impact of Planovalgus Foot on Activities of Daily Living in Patients With Down Syndrome.

INTRODUCTION: Planovalgus foot (PVF) is the most common orthopaedic abnormality in children with Down syndrome (DS), and as a result these patients rarely develop an adequate plantar arch in adulthood. The present study aims to evaluate the impact of PVF on activities of daily living and participation in sports among young adults with DS and determine whether this impact is related to the degree of foot deformity based on clinical and imaging studies. METHODS: Observational analytical study examining a database of 649 patients with DS from a pediatric referral center, identifying those individuals over age 20 years at the time of the study with a childhood diagnosis of PVF. Finally, 51 patients (102 feet) were evaluated based on clinical and imaging studies, and function was assessed using the The Foot and Ankle Outcome Score (FAOS) and the Visual Analogue Scale (VAS) pain scale. A correlation analysis was performed to determine the clinical and radiographic variables associated with functional outcomes. Linear regression models were obtained to quantify the impact of these variables on function. RESULTS: Patients had a mean age of 26.14±3.88 years and body mass index of 24.51±4.57. Clinically, 63.65% presented grade 3 or 4 PVF, and most were flexible. Radiographically, midfoot flattening was mild-moderate in 92.16%, 58.82% had medial talo-navicular uncoverage, and 30.39% had an increased hallux valgus (HV) angle. Mean scores for all FAOS subscales were between 65 and 71% and the mean VAS score was 1.45±1.96. An association analysis revealed a tendency toward lower scores on all FAOS subscales and greater pain according to the VAS scale in more severe PVF and in cases of moderate HV with asymmetry between feet. Linear regression models showed that major contributors to functional scores were radiographic evidence of hindfoot valgus, midfoot abduction, and flattening, and HV. CONCLUSIONS: Young adults with DS who are diagnosed with PVF in childhood have acceptable functional scores and low pain. Alteration of radiographic parameters toward flatter, more valgus and abducted feet and greater and asymmetric HV tend to be associated with worse long-term functional scores in activities of daily living and sports participation and increased pain. Therefore, non-operative management of these patients is justified, although individualized treatment is recommended. LEVEL OF EVIDENCE: Level IV, Case series.

Is There a Relationship Between the Functional Level of Juvenile and Adolescent Patients With Down Syndrome and Hip Dysplasia?

BACKGROUND: The prevalence of hip dysplasia among patients with Down syndrome (DS) is higher than in the general population. We hypothesize that a relationship may exist between functional level and hip dysplasia in DS, but this has not been studied to date. The aim of this study is to evaluate whether there is a relationship between functional level and radiographic parameters of hip dysplasia or other measures. METHODS: Retrospective cross-sectional comparative study of 652 patients with DS from a pediatric referral center database. Patients over 8 years of age with an anteroposterior pelvis radiograph and with no exclusion criteria were selected, totaling 132 patients (264 hips; 54.55% females; mean age 12.96 ± 2.87 y). Several radiographic parameters of the acetabulum [Sharp angle (SA), Tönnis angle (TA), Wiberg center-edge angle (W-CEA), extrusion index (EI), and acetabular retroversion signs], the proximal femur [neck shaft angle (NSA)], and joint congruence [Shenton line (SL)] were assessed. Patients were classified into 2 levels based on functional skills. A multivariate association analysis was performed between radiographic parameters and functional level. RESULTS: Sixty-one patients were compatible with a functional level I and 71 with a level II. Forty-six hips were dysplastic and 60 were borderline according to the W-CEA. A statistically significant relationship was found between the categorical distribution of certain radiographic measurements of hip dysplasia (EI, SA, TA, W-CEA, SL, and classification by functional level ( P < 0.0005). A significant receiver operating characteristic curve was obtained for W-CEA with a cutt-off point at 26.4 degrees for level I (area under the curve = 0.763; P < 0.005; sensitivity = 0.800 and specificity = 0.644). There was a fairly high correlation between EI and TA (0.749; P < 0.0005), EI and W-CEA (-0.817; P < 0.0005), and TA and W-CEA (-0.748; P < 0.0005). Numerous hips showed signs of acetabular retroversion, with no significant differences found between functional levels or association with hip dysplasia measures. CONCLUSIONS: The present study reveals a relationship between an increased risk of hip dysplasia and reduced functional levels in DS children older than 8 years. These findings may guide individualized clinical follow-up of hip development in DS children considering their functional level. LEVEL OF EVIDENCE: Level III, retrospective comparative study.

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.

BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19. OBJECTIVE: The objective of this observational longitudinal study was to identify nonoverlapping severity subgroups (ie, clusters) among patients with COVID-19, based exclusively on clinical data and standard laboratory tests obtained during patient assessment in the emergency department. METHODS: We applied unsupervised machine learning to a data set of 853 patients with COVID-19 from the HM group of hospitals (HM Hospitales) in Madrid, Spain. Age and sex were not considered while building the clusters, as these variables could introduce biases in machine learning algorithms and raise ethical implications or enable discrimination in triage protocols. RESULTS: From 850 clinical and laboratory variables, four tests-the serum levels of aspartate transaminase (AST), lactate dehydrogenase (LDH), C-reactive protein (CRP), and the number of neutrophils-were enough to segregate the entire patient pool into three separate clusters. Further, the percentage of monocytes and lymphocytes and the levels of alanine transaminase (ALT) distinguished cluster 3 patients from the other two clusters. The highest proportion of deceased patients; the highest levels of AST, ALT, LDH, and CRP; the highest number of neutrophils; and the lowest percentages of monocytes and lymphocytes characterized cluster 1. Cluster 2 included a lower proportion of deceased patients and intermediate levels of the previous laboratory tests. The lowest proportion of deceased patients; the lowest levels of AST, ALT, LDH, and CRP; the lowest number of neutrophils; and the highest percentages of monocytes and lymphocytes characterized cluster 3. CONCLUSIONS: A few standard laboratory tests, deemed available in all emergency departments, have shown good discriminative power for the characterization of severity subgroups among patients with COVID-19.

Prior cortical activity differences during an action observation plus motor imagery task related to motor adaptation performance of a coordinated multi-limb complex task.

Motor adaptation is the ability to develop new motor skills that makes performing a consolidated motor task under different psychophysical conditions possible. There exists a proven relationship between prior brain activity at rest and motor adaptation. However, the brain activity at rest is highly variable both between and within subjects. Here we hypothesize that the cortical activity during the original task to be later adapted is a more reliable and stronger determinant of motor adaptation. Consequently, we present a study to find cortical areas whose activity, both at rest and during first-person virtual reality simulation of bicycle riding, characterizes the subjects who did and did not adapt to ride a reverse steering bicycle, a complex motor adaptation task involving all limbs and balance. The results showed that cortical activity differences during the simulated task were higher, more significant, spatially larger, and spectrally wider than at rest for good performers. In this sense, the activity of the left anterior insula, left dorsolateral and ventrolateral inferior prefrontal areas, and left inferior premotor cortex (action understanding hub of the mirror neuron circuit) during simulated bicycle riding are the areas with the most descriptive power for the ability of adapting the motor task. Trials registration Trial was registered with the NIH Clinical Trials Registry (clinicaltrials.gov), with the registration number NCT02999516 (21/12/2016).

A data mining approach for classification of orthostatic and essential tremor based on MRI-derived brain volume and cortical thickness.

OBJECTIVE: Orthostatic tremor (OT) is an extremely rare, misdiagnosed, and underdiagnosed disorder affecting adults in midlife. There is debate as to whether it is a different condition or a variant of essential tremor (ET), or even, if both conditions coexist. Our objective was to use data mining classification methods, using magnetic resonance imaging (MRI)-derived brain volume and cortical thickness data, to identify morphometric measures that help to discriminate OT patients from those with ET. METHODS: MRI-derived brain volume and cortical thickness were obtained from 14 OT patients and 15 age-, sex-, and education-matched ET patients. Feature selection and machine learning methods were subsequently applied. RESULTS: Four MRI features alone distinguished the two, OT from ET, with 100% diagnostic accuracy. More specifically, left thalamus proper volume (normalized by the total intracranial volume), right superior parietal volume, right superior parietal thickness, and right inferior parietal roughness (i.e., the standard deviation of cortical thickness) were shown to play a key role in OT and ET characterization. Finally, the left caudal anterior cingulate thickness and the left caudal middle frontal roughness allowed us to separate with 100% diagnostic accuracy subgroups of OT patients (primary and those with mild parkinsonian signs). CONCLUSIONS: A data mining approach applied to MRI-derived brain volume and cortical thickness data may differentiate between these two types of tremor with an accuracy of 100%. Our results suggest that OT and ET are distinct conditions.

Smartwatch for the analysis of rest tremor in patients with Parkinson's disease.

Wearable technology used in Parkinson's disease (PD) research has become an increasing focus of interest in this field. Our group assessed the feasibility, clinical correlation, reliability, and acceptance of smartwatches in order to quantify arm resting tremors in PD patients. An Android application on a smartwatch was used to obtain raw data from the smartwatch's gyroscopes. Twenty-two PD patients were consecutively recruited and followed for 1 year. Arm rest tremors were video filmed and scored by two independent raters using the motor subscale of the Unified Parkinson's Disease Rating Scale (UPDRS-III). The tremor intensity parameter was defined by the root mean square of the angular speed measured by the smartwatch at the wrist. Sixty-four smartwatch evaluations were completed. The Spearman coefficient among the mean of the resting tremor (UPDRS-III) scores and smartwatch measurements for tremor intensity was 0.81 (p < .001); smartwatch reliability to quantify tremors was checked by intraclass reliability coefficient with a resting tremor = 0.89, minimum detectable change = 59.03%. Good acceptance of the system was shown. Smartwatch use for PD tremor analysis is possible, reliable, well-correlated with clinical scores, and well-accepted by patients for clinical follow-up. The results from these experiments suggest that this commodity hardware has the potential to quantify PD patients' tremors objectively in a consulting-room.

EEG Microstates Change in Response to Increase in Dopaminergic Stimulation in Typical Parkinson's Disease Patients.

Objectives: Characterizing pharmacological response in Parkinson's Disease (PD) patients may be a challenge in early stages but gives valuable clues for diagnosis. Neurotropic drugs may modulate Electroencephalography (EEG) microstates (MS). We investigated EEG-MS default-mode network changes in response to dopaminergic stimulation in PD. Methods: Fourteen PD subjects in HY stage III or less were included, and twenty-one healthy controls. All patients were receiving dopaminergic stimulation with levodopa or dopaminergic agonists. Resting EEG activity was recorded before the first daily PD medication dose and 1 h after drug intake resting EEG activity was again recorded. Time and frequency variables for each MS were calculated. Results: Parkinson's disease subjects MS A duration decreases after levodopa intake, MS B appears more often than before levodopa intake. MS E was not present, but MS G was. There were no significant differences between control subjects and patients after medication intake. Conclusion: Clinical response to dopaminergic drugs in PD is characterized by clear changes in MS profile. Significance: This work demonstrates that there are clear EEG MS markers of PD dopaminergic stimulation state. The characterization of the disease and its response to dopaminergic medication may be of help for early therapeutic diagnosis.

Tremor severity in Parkinson's disease and cortical changes of areas controlling movement sequencing: A preliminary study.

There remains much to learn about the changes in cortical anatomy that are associated with tremor severity in Parkinson's disease (PD). For this reason, we used a combination of structural neuroimaging to measure cortical thickness and neurophysiological studies to analyze whether PD tremor was associated with cortex integrity. Magnetic resonance imaging and neurophysiological assessment were performed in 13 nondemented PD patients (9 women, 69.2%) with a clearly tremor-dominant phenotype. Cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis software. Assessment of tremor was performed by means of high-density surface electromyography (hdEMG) and inertial measurement units (IMUs). Individual motor unit discharge patterns were identified from surface hdEMG and tremor metrics quantifying motor unit synchronization from IMUs. Increased motor unit synchronization (i.e., more severe tremor) was associated with cortical changes (i.e., atrophy) in wide-spread cortical areas, including caudal middle frontal regions bilaterally (dorsal premotor cortices), left inferior parietal lobe (posterior parietal cortex), left lateral orbitofrontal cortex, cingulate cortex bilaterally, left posterior and transverse temporal cortex, and left occipital lobe, as well as reduced left middle temporal volume. Given that the majority of these areas are involved in controlling movement sequencing, our results support Albert's classic hypothesis that PD tremor may be the result of an involuntary activation of a program of motor behavior used in the genesis of rapid voluntary alternating movements.

Essential tremor quantification based on the combined use of a smartphone and a smartwatch: The NetMD study.

BACKGROUND: The use of wearable technology is an emerging field of research in movement disorders. This paper introduces a clinical study to evaluate the feasibility, clinical correlation and reliability of using a system based in smartwatches to quantify tremor in essential tremor (ET) patients and check its acceptance as clinical monitoring tool. NEW METHOD: The system is based on a commercial smartwatch and an Android smartphone. An investigational Android application controls the process of recording raw data from the smartwatch three-dimensional gyroscopes. Thirty-four ET patients were consecutively enrolled in the experiments and assessed along one year. Arm tremor was videofilmed and scored using the Fahn-Tolosa-Marin Tremor Rating Scale (FTM-TRS). Tremor intensity was quantified with the root mean square of angular velocity measured in the patients' wrists. RESULTS: Eighty-two assessments with smartwatches were performed. Spearman's correlation coefficients (ρ) between clinical tremor (FTM-TRS) scores and smartwatch measures for tremor intensity were 0.590 at rest; ρ = 0.738 in steady posture; ρ = 0.189 in finger-to-nose maneuvers; and ρ = 0.652 in pouring water task. Smartwatch reliability was checked by intraclass realiability coefficients: 0.85, 0.95, 0.91, 0.95 respectively. Most of patients showed good acceptance of the system. COMPARISON WITH EXISTING METHOD(S): This commodity hardware contributes to quantify tremor objectively in a consulting-room by customized Android smart devices as clinical monitoring tool. CONCLUSIONS: The NetMD system for tremor analysis is feasible, well-correlated with clinical scores, reliable and well-accepted by patients to tremor follow-up. Therefore, it could be an option to objectively quantify tremor in ET patients during their regular follow-up.

Corrigendum: Low Latency Estimation of Motor Intentions to Assist Reaching Movements along Multiple Sessions in Chronic Stroke Patients: A Feasibility Study.

[This corrects the article on p. 126 in vol. 11, PMID: 28367109.].

A data mining approach using cortical thickness for diagnosis and characterization of essential tremor.

Essential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects. We found that cortical thickness features alone distinguished the two, ET from controls, with 81% diagnostic accuracy. More specifically, roughness (i.e., the standard deviation of cortical thickness) of the right inferior parietal and right fusiform areas was shown to play a key role in ET characterization. Moreover, these features allowed us to identify subgroups of ET patients as well as healthy subjects at risk for ET. Since treatment of tremors is disease specific, accurate and early diagnosis plays an important role in tremor management. Supporting the clinical diagnosis with novel computer approaches based on the objective evaluation of neuroimage data, like the one presented here, may represent a significant step in this direction.

A methodology for the characterization and diagnosis of cognitive impairments-Application to specific language impairment.

OBJECTIVES: The diagnosis of mental disorders is in most cases very difficult because of the high heterogeneity and overlap between associated cognitive impairments. Furthermore, early and individualized diagnosis is crucial. In this paper, we propose a methodology to support the individualized characterization and diagnosis of cognitive impairments. The methodology can also be used as a test platform for existing theories on the causes of the impairments. We use computational cognitive modeling to gather information on the cognitive mechanisms underlying normal and impaired behavior. We then use this information to feed machine-learning algorithms to individually characterize the impairment and to differentiate between normal and impaired behavior. We apply the methodology to the particular case of specific language impairment (SLI) in Spanish-speaking children. METHODS AND MATERIALS: The proposed methodology begins by defining a task in which normal and individuals with impairment present behavioral differences. Next we build a computational cognitive model of that task and individualize it: we build a cognitive model for each participant and optimize its parameter values to fit the behavior of each participant. Finally, we use the optimized parameter values to feed different machine learning algorithms. The methodology was applied to an existing database of 48 Spanish-speaking children (24 normal and 24 SLI children) using clustering techniques for the characterization, and different classifier techniques for the diagnosis. RESULTS: The characterization results show three well-differentiated groups that can be associated with the three main theories on SLI. Using a leave-one-subject-out testing methodology, all the classifiers except the DT produced sensitivity, specificity and area under curve values above 90%, reaching 100% in some cases. CONCLUSIONS: The results show that our methodology is able to find relevant information on the underlying cognitive mechanisms and to use it appropriately to provide better diagnosis than existing techniques. It is also worth noting that the individualized characterization obtained using our methodology could be extremely helpful in designing individualized therapies. Moreover, the proposed methodology could be easily extended to other languages and even to other cognitive impairments not necessarily related to language.

Computational cognitive modeling for the diagnosis of Specific Language Impairment.

Specific Language Impairment (SLI), as many other cognitive deficits, is difficult to diagnose given its heterogeneous profile and its overlap with other impairments. Existing techniques are based on different criteria using behavioral variables on different tasks. In this paper we propose a methodology for the diagnosis of SLI that uses computational cognitive modeling in order to capture the internal mechanisms of the normal and impaired brain. We show that machine learning techniques that use the information of these models perform better than those that only use behavioral variables.