Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, using MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets in the ENIGMA consortium, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macro-structural asymmetry may reflect differences at the molecular, cytoarchitectonic or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia

Demonstrating the utility of Instrumented Gait Analysis in the treatment of children with cerebral palsy.

BackgroundInstrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone.DesignWe carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models' performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split.ResultsThe IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed.InterpretationThe results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators-including estimates of uncertainty-become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on

Management of hypertonia in cerebral palsy.

PURPOSE OF REVIEW: The review provides an update on the treatment of hypertonia in cerebral palsy, including physical management, pharmacotherapy, neurosurgical, and orthopedic procedures. RECENT FINDINGS: Serial casting potentiates the effect of Botulinum neurotoxin A injections for spasticity. Deep brain stimulation, intraventricular baclofen, and ventral and dorsal rhizotomy are emerging tools for the treatment of dystonia and/or mixed tone. The long-term results of selective dorsal rhizotomy and the timing of orthopedic surgery represent recent advances in the surgical management of hypertonia. SUMMARY: Management of hypertonia in cerebral palsy targets the functional goals of the patient and caregiver. Treatment options are conceptualized as surgical or nonsurgical, focal or generalized, and reversible or irreversible. The role of pharmacologic therapies is to improve function and mitigate adverse effects. Further investigation, including clinical trials, is required to determine the role of deep brain stimulation, intraventricular baclofen, orthopedic procedures for dystonia, and rhizotomy

Quantification of ultrasound emboli signals in patients with cardiac and carotid disease

Background and Purpose: The use of Doppler ultrasound to detect arterial emboli has major implications for the classification and treatment of stroke. Experimental studies indicate that embolic materials produce different ultrasound signals, depending on their acoustic properties. To examine the possibility of characterizing emboli of different sources in the clinical setting, we compared the emboli signals of patients with cardiac embolic sources with those of patients with signals of carotid embolic sources. Methods: Transcranial Doppler monitoring (30 minutes per patient) of the middle cerebral arteries was performed in 80 patients with prosthetic cardiac valves and 20 patients with internal carotid artery stenosis. The signal power of emboli was calculated in relation to the background Doppler signal. Results: In patients who were embolizing from prosthetic heart valves, the frequency of embolus signals was greater than in patients with carotid stenosis who were embolizing (mean +/- SEM: 58.2 +/- 11 versus 8.2 +/- 3 signals per hour; P < .0001, two-sample t test), and total signal power and duration also were higher (power, 2231 +/- 63 versus 455 +/- 109 power units; duration, 55.9 +/- 0.8 versus 29.9 +/- 1.4 milliseconds; both P < .001). The majority of emboli signals were seen during cardiac systole, especially in patients with carotid stenosis (89% in the first half of the cardiac cycle versus 72% in prosthetic valve patients). In 19 patients with prosthetic valves, embolus signals were also recorded from the anterior cerebral artery; the signal count was not significantly different from the middle cerebral artery (43.2 +/- 12.5 versus 64.3 +/- 16 per hour), but anterior cerebral artery signals were of higher power (3306 +/- 148 versus 2441 +/- 109 power units, P < .001). Conclusions: There is promise of being able to distinguish emboli on the basis of power measurements. Emboli of different sources (eg, carotid and cardiac) appear to have different ultrasonic characteristics, which are likely to be based on composition and size

Precision (CI) of CLI and IGA models.

A smaller value reflects a more precise prediction, and thus indicates superior performance.</p

S1 File -

BackgroundInstrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone.DesignWe carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models’ performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split.ResultsThe IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed.InterpretationThe results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators—including estimates of uncertainty—become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.</div

Better (almost) everywhere.

Note speed RMSE plotted at 100x to put it on similar scale as other outcomes.</p

Overall performance of the IGA and CLI models.

BackgroundInstrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone.DesignWe carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models’ performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split.ResultsThe IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed.InterpretationThe results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators—including estimates of uncertainty—become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.</div

Limb characteristics.

BackgroundInstrumented gait analysis (IGA) has been around for a long time but has never been shown to be useful for improving patient outcomes. In this study we demonstrate the potential utility of IGA by showing that machine learning models are better able to estimate treatment outcomes when they include both IGA and clinical (CLI) features compared to when they include CLI features alone.DesignWe carried out a retrospective analysis of data from ambulatory children diagnosed with cerebral palsy who were seen at least twice at our gait analysis center. Individuals underwent a variety of treatments (including no treatment) between sequential gait analyses. We fit Bayesian Additive Regression Tree (BART) models that estimated outcomes for mean stance foot progression to demonstrate the approach. We built two models: one using CLI features only, and one using CLI and IGA features. We then compared the models’ performance in detail. We performed similar, but less detailed, analyses for a number of other outcomes. All results were based on independent test data from a 70%/30% training/testing split.ResultsThe IGA model was more accurate than the CLI model for mean stance-phase foot progression outcomes (RMSEIGA = 11∘, RMSECLI = 13∘) and explained more than 1.5 × as much of the variance (R2IGA = .45, R2CLI = .28). The IGA model outperformed the CLI model for every level of treatment complexity, as measured by number of simultaneous surgeries. The IGA model also exhibited superior performance for estimating outcomes of mean stance-phase knee flexion, mean stance-phase ankle dorsiflexion, maximum swing-phase knee flexion, gait deviation index (GDI), and dimensionless speed.InterpretationThe results show that IGA has the potential to be useful in the treatment planning process for ambulatory children diagnosed with cerebral palsy. We propose that the results of machine learning outcome estimators—including estimates of uncertainty—become the primary IGA tool utilized in the clinical process, complementing the standard medical practice of conducting a through patient history and physical exam, eliciting patient goals, reviewing relevant imaging data, and so on.</div