Using smartphone sensors for ataxia trials: consensus guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers

Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers

Age Correction in Dementia – Matching to a Healthy Brain

In recent research, many univariate and multivariate approaches have been proposed to improve automatic classification of various dementia syndromes using imaging data. Some of these methods do not provide the possibility to integrate possible confounding variables like age into the statistical evaluation. A similar problem sometimes exists in clinical studies, as it is not always possible to match different clinical groups to each other in all confounding variables, like for example, early-onset (age<65 years) and late-onset (age≥65) patients with Alzheimer's disease (AD). Here, we propose a simple method to control for possible effects of confounding variables such as age prior to statistical evaluation of magnetic resonance imaging (MRI) data using support vector machine classification (SVM) or voxel-based morphometry (VBM). We compare SVM results for the classification of 80 AD patients and 79 healthy control subjects based on MRI data with and without prior age correction. Additionally, we compare VBM results for the comparison of three different groups of AD patients differing in age with the same group of control subjects obtained without including age as covariate, with age as covariate or with prior age correction using the proposed method. SVM classification using the proposed method resulted in higher between-group classification accuracy compared to uncorrected data. Further, applying the proposed age correction substantially improved univariate detection of disease-related grey matter atrophy using VBM in AD patients differing in age from control subjects. The results suggest that the approach proposed in this work is generally suited to control for confounding variables such as age in SVM or VBM analyses. Accordingly, the approach might improve and extend the application of these methods in clinical neurosciences

The Combination of DAT-SPECT, Structural and Diffusion MRI Predicts Clinical Progression in Parkinson’s Disease

There is an increasing interest in identifying non-invasive biomarkers of disease severity and prognosis in idiopathic Parkinson’s disease (PD). Dopamine-transporter SPECT (DAT-SPECT), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI) provide unique information about the brain’s neurotransmitter and microstructural properties. In this study, we evaluate the relative and combined capability of these imaging modalities to predict symptom severity and clinical progression in de novo PD patients. To this end, we used MRI, SPECT, and clinical data of de novo drug-naïve PD patients (n = 205, mean age 61 ± 10) and age-, sex-matched healthy controls (n = 105, mean age 58 ± 12) acquired at baseline. Moreover, we employed clinical data acquired at 1 year follow-up for PD patients with or without L-Dopa treatment in order to predict the progression symptoms severity. Voxel-based group comparisons and covariance analyses were applied to characterize baseline disease-related alterations for DAT-SPECT, DTI, and sMRI. Cortical and subcortical alterations in de novo PD patients were found in all evaluated imaging modalities, in line with previously reported midbrain-striato-cortical network alterations. The combination of these imaging alterations was reliably linked to clinical severity and disease progression at 1 year follow-up in this patient population, providing evidence for the potential use of these modalities as imaging biomarkers for disease severity and prognosis that can be integrated into clinical trials

Progressive decline in gray and white matter integrity in de novo Parkinson's disease: An analysis of longitudinal Parkinson progression markers initiative diffusion tensor imaging data

Background: Progressive neuronal loss in neurodegenerative diseases such as Parkinson's disease (PD) is associated with progressive degeneration of associated white matter tracts as measured by diffusion tensor imaging (DTI). These findings may have diagnostic and functional implications but their value in de novo PD remains unknown. Here we analyzed longitudinal DTI data from Parkinson's Progression Markers Initiative de novo PD patients for changes over time relative to healthy control (HC) participants. Methods: Baseline and 1-year follow-up DTI MRI data from 71 PD patients and 45 HC PPMI participants were included in the analyses. Whole-brain fractional anisotropy (FA) and mean diffusivity (MD) images were compared for baseline group differences and group-by-time interactions. Baseline and 1-year changes in DTI values were correlated with changes in DTI measures and symptom severity, respectively. Results: At baseline, PD patients showed significantly increased FA in brainstem, cerebellar, anterior corpus callosal, inferior frontal and inferior fronto-occipital white matter and increased MD in primary sensorimotor and supplementary motor regions. Over 1 year PD patients showed a significantly stronger decline in FA compared to HC in the optic radiation and corpus callosum and parietal, occipital, posterior temporal, posterior thalamic, and vermis gray matter. Significant increases in MD were observed in white matter of the midbrain, optic radiation and corpus callosum, while gray matter of prefrontal, insular and posterior thalamic regions. Baseline brainstem FA white matter (WM) values predicted 1-year changes in FA white matter and MD gray matter values. White but not gray matter changes in both FA and MD were significantly associated with changes in symptom severity. Conclusion: Significant gray and white matter DTI alterations are observable at the time of PD diagnosis and expand in the first year of de novo PD to other cortical and white matter regions. This pattern of DTI changes is in line with preclinical and neuroanatomical studies suggesting that the increased spatial spread of alpha-synuclein neuropathology is the key mechanism of PD progression. Taken together, these findings suggest that DTI may serve as a sensitive biomarker of disease progression in early-stage PD

Towards increasing the clinical applicability of machine learning biomarkers in psychiatry.

Due to a lack of objective biomarkers, psychiatric diagnoses still rely strongly on patient reporting and clinician judgement. The ensuing subjectivity negatively affects the definition and reliability of psychiatric diagnoses1,2. Recent research has suggested that a combination of advanced neuroimaging and machine learning may provide a solution to this predicament by establishing such objective biomarkers for psychiatric conditions, improving the diagnostic accuracy, prognosis and development of novel treatments3.These promises led to widespread interest in machine learning applications for mental health4, including a recent paper that reports a biological marker for one of the most difficult yet momentous questions in psychiatry—the assessment of suicidal behaviour5. Just et al. compared a group of 17 participants with suicidal ideation with 17 healthy controls, reporting high discrimination accuracy using task-based functional magnetic resonance imaging signatures of life- and death-related concepts3. The authors further reported high discrimination between nine ideators who had attempted suicide versus eight ideators who had not. While being a laudable effort into a difficult topic, this study unfortunately illustrates some common conceptual and technical issues in the field that limit translation into clinical practice and raise unrealistic hopes when the results are communicated to the general public.From a conceptual point of view, machine learning studies aimed at clinical applications need to carefully consider any decisions that might hamper the interpretation or generalizability of their results. Restrictiveness to an arbitrary setting may become detrimental for machine learning applications by providing overly optimistic results that are unlikely to generalize. As an example, Just et al. excluded more than half of the patients and healthy controls initially enrolled in the study from the main analysis due to missing desired functional magnetic resonance imaging effects (a rank accuracy of at least 0.6 based on all 30 concepts). This exclusion introduces a non-assessable bias to the interpretation of the results, in particular when considering that only six of the 30 concepts were selected for the final classification procedure. While Just et al. attempt to address this question by applying the trained classifier to the initially excluded 21 suicidal ideators, they explicitly omit the excluded 24 controls from this analysis, preventing any interpretation of the extent to which the classifier decision is dependent on this initial choice.From a technical point of view, machine learning-based predictions based on neuroimaging data in small samples are intrinsically highly variable, as stable accuracy estimates and high generalizability are only achieved with several hundreds of participants6,7. The study by Just et al. falls into this category of studies with a small sample size. To estimate the impact of uncertainty on the results by Just et al., we adapted a simulation approach with the code and data kindly provided by the authors, randomly permuting (800 times) the labels across the groups using their default settings and computing the accuracies. These results showed that the 95% confidence interval for classification accuracy obtained using this dataset is about 20%, leaving large uncertainty with respect to any potential findings.Special care is also required with respect to any subjective choices in feature and classifier settings or group selection. While ad-hoc selection of a specific setting is subjective, testing of different ones and outcome-based post-hoc justification of such leads to overfitting, thus limiting the generalizability of any classification. Such overfitting may occur when multiple models or parameter choices are tested with respect to their ability to predict the testing data and only those that perform best are reported. To illustrate this issue, we performed an additional analysis with the code and data kindly provided by Just et al. More specifically, in the code and the manuscript, we identified the following non-exhaustive number of prespecified settings: (1) removal of occipital cortex data; (2) subdivision of clusters larger than 11 mm; (3) selection of voxels with at least four contributing participants in each group; (4) selection of stable clusters containing at least five voxels; (5) selection of the 1,200 most stable features; and (6) manual copying and replacing of a cluster for one control participant. Importantly, according to the publication or code documentation, all of these parameters were chosen ad hoc and for none of these settings was a parameter search performed. We systematically evaluated the effect of each of these choices on the accuracy for differentiation between suicide ideators and controls in the original dataset provided by Just et al. As shown in Fig. 1, each of the six parameters represents an optimum choice for differentiation accuracy in this dataset, with any (even minor) change often resulting in substantially lower accuracy estimates. Similarly, data leakage may also contribute to optimistic results when information outside the training set is used to build a prediction model. More generally, whenever human interventions guide the development of machine learning models for the prediction of clinical conditions, a careful evaluation and reporting of any researcher’s degrees of freedom is essential to avoid data leakage and overfitting. Subsequent sharing of data processing and analysis pipelines, as well as collected data, is a further key step to increase reproducibility and facilitate replication of potential findings

Distinct Role of Striatal Functional Connectivity and Dopaminergic Loss in Parkinson’s Symptoms

Degeneration of dopaminergic neurons is a hallmark of Parkinson’s disease. However, its link to Parkinson’s disease symptoms remains unclear. Striatal resting state functional connectivity differentiates between Parkinson’s disease patients and healthy controls and might be a potential mediator of the effects of striatal dopaminergic degeneration onto Parkinson’s disease symptoms. Here, we evaluated the relationship between dopaminergic deficits, striatal functional connectivity (SFC) at rest and different Parkinson’s disease clinical symptoms in the largest currently established cohort of de novo Parkinson’s disease patients. We show that SFC is an independent predictor of symptom severity in Parkinson’s disease in addition to striatal dopaminergic deficits. Furthermore, we find that distinct SFC networks are associated with symptoms reflecting the ability to perform daily routine automatized motor tasks and clinician-rated Parkinson’s disease motor symptoms. We find that reduced SFC is a major and independent predictor of Parkinson’s disease symptoms going beyond the mere reflection of striatal dopaminergic input loss. These findings indicate the high value of SFC as a clinically relevant biomarker in Parkinson’s disease

Bidirectional gray matter changes after complex motor skill learning

Long-term motor skill learning has been consistently shown to result in functional as well as structural changes in the adult human brain. However, the effect of short learning periods on brain structure is not well understood. In the present study, subjects performed a sequential pinch force task (SPFT) for 20 min on 5 consecutive days. Changes in brain structure were evaluated with anatomical magnetic resonance imaging (MRI) scans acquired on the first and last day of motor skill learning. Behaviorally, the SPFT resulted in sequence-specific learning with the trained (right) hand. Structural gray matter (GM) alterations in left M1, right ventral premotor cortex (PMC) and right dorsolateral prefrontal cortex (DLPFC) correlated with performance improvements in the SPFT. More specifically we found that subjects with strong sequence-specific performance improvements in the SPFT also had larger increases in GM volume in the respective brain areas. On the other hand, subjects with small behavioral gains either showed no change or even a decrease in GM volume during the time course of learning. Furthermore, cerebellar GM volume before motor skill learning predicted (A) individual learning-related changes in the SPFT and (B) the amount of structural changes in left M1, right ventral PMC and DLPFC. In summary, we provide novel evidence that short-term motor skill learning is associated with learning-related structural brain alterations. Additionally, we showed that practicing a motor skill is not exclusively accompanied by increased GM volume. Instead, bidirectional structural alterations explained the variability of the individual learning success

Combined Evaluation of FDG-PET and MRI Improves Detection and Differentiation of Dementia

INTRODUCTION: Various biomarkers have been reported in recent literature regarding imaging abnormalities in different types of dementia. These biomarkers have helped to significantly improve early detection and also differentiation of various dementia syndromes. In this study, we systematically applied whole-brain and region-of-interest (ROI) based support vector machine classification separately and on combined information from different imaging modalities to improve the detection and differentiation of different types of dementia. METHODS: Patients with clinically diagnosed Alzheimer's disease (AD: n = 21), with frontotemporal lobar degeneration (FTLD: n = 14) and control subjects (n = 13) underwent both [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) scanning and magnetic resonance imaging (MRI), together with clinical and behavioral assessment. FDG-PET and MRI data were commonly processed to get a precise overlap of all regions in both modalities. Support vector machine classification was applied with varying parameters separately for both modalities and to combined information obtained from MR and FDG-PET images. ROIs were extracted from comprehensive systematic and quantitative meta-analyses investigating both disorders. RESULTS: Using single-modality whole-brain and ROI information FDG-PET provided highest accuracy rates for both, detection and differentiation of AD and FTLD compared to structural information from MRI. The ROI-based multimodal classification, combining FDG-PET and MRI information, was highly superior to the unimodal approach and to the whole-brain pattern classification. With this method, accuracy rate of up to 92% for the differentiation of the three groups and an accuracy of 94% for the differentiation of AD and FTLD patients was obtained. CONCLUSION: Accuracy rate obtained using combined information from both imaging modalities is the highest reported up to now for differentiation of both types of dementia. Our results indicate a substantial gain in accuracy using combined FDG-PET and MRI information and suggest the incorporation of such approaches to clinical diagnosis and to differential diagnostic procedures of neurodegenerative disorders

Relating constructs of attention and working memory to social withdrawal in Alzheimer's disease and schizophrenia: issues regarding paradigm selection

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Progressive Decline in Gray and White Matter Integrity in de novo Parkinson’s Disease: An Analysis of Longitudinal Parkinson Progression Markers Initiative Diffusion Tensor Imaging Data

Background: Progressive neuronal loss in neurodegenerative diseases such as Parkinson’s disease (PD) is associated with progressive degeneration of associated white matter tracts as measured by diffusion tensor imaging (DTI). These findings may have diagnostic and functional implications but their value in de novo PD remains unknown. Here we analyzed longitudinal DTI data from Parkinson’s Progression Markers Initiative de novo PD patients for changes over time relative to healthy control (HC) participants.Methods: Baseline and 1-year follow-up DTI MRI data from 71 PD patients and 45 HC PPMI participants were included in the analyses. Whole-brain fractional anisotropy (FA) and mean diffusivity (MD) images were compared for baseline group differences and group–by–time interactions. Baseline and 1-year changes in DTI values were correlated with changes in DTI measures and symptom severity, respectively.Results: At baseline, PD patients showed significantly increased FA in brainstem, cerebellar, anterior corpus callosal, inferior frontal and inferior fronto-occipital white matter and increased MD in primary sensorimotor and supplementary motor regions. Over 1 year PD patients showed a significantly stronger decline in FA compared to HC in the optic radiation and corpus callosum and parietal, occipital, posterior temporal, posterior thalamic, and vermis gray matter. Significant increases in MD were observed in white matter of the midbrain, optic radiation and corpus callosum, while gray matter of prefrontal, insular and posterior thalamic regions. Baseline brainstem FA white matter (WM) values predicted 1-year changes in FA white matter and MD gray matter values. White but not gray matter changes in both FA and MD were significantly associated with changes in symptom severity.Conclusion: Significant gray and white matter DTI alterations are observable at the time of PD diagnosis and expand in the first year of de novo PD to other cortical and white matter regions. This pattern of DTI changes is in line with preclinical and neuroanatomical studies suggesting that the increased spatial spread of alpha-synuclein neuropathology is the key mechanism of PD progression. Taken together, these findings suggest that DTI may serve as a sensitive biomarker of disease progression in early-stage PD