Can Gait Characteristics Be Represented by Physical Activity Measured with Wrist-Worn Accelerometers?

Wearable accelerometers allow for continuous monitoring of function and behaviors in the participant's naturalistic environment. Devices are typically worn in different body locations depending on the concept of interest and endpoint under investigation. The lumbar and wrist are commonly used locations: devices placed at the lumbar region enable the derivation of spatio-temporal characteristics of gait, while wrist-worn devices provide measurements of overall physical activity (PA). Deploying multiple devices in clinical trial settings leads to higher patient burden negatively impacting compliance and data quality and increases the operational complexity of the trial. In this work, we evaluated the joint information shared by features derived from the lumbar and wrist devices to assess whether gait characteristics can be adequately represented by PA measured with wrist-worn devices. Data collected at the Pfizer Innovation Research (PfIRe) Lab were used as a real data example, which had around 7 days of continuous at-home data from wrist- and lumbar-worn devices (GENEActiv) obtained from a group of healthy participants. The relationship between wrist- and lumbar-derived features was estimated using multiple statistical methods, including penalized regression, principal component regression, partial least square regression, and joint and individual variation explained (JIVE). By considering multilevel models, both between- and within-subject effects were taken into account. This work demonstrated that selected gait features, which are typically measured with lumbar-worn devices, can be represented by PA features measured with wrist-worn devices, which provides preliminary evidence to reduce the number of devices needed in clinical trials and to increase patients' comfort. Moreover, the statistical methods used in this work provided an analytic framework to compare repeated measures collected from multiple data modalities.

Total activation: fMRI deconvolution through spatio-temporal regularization.

Confirmatory approaches to fMRI data analysis look for evidence for the presence of pre-defined regressors modeling contributions to the voxel time series, including the BOLD response following neuronal activation. As more complicated questions arise about brain function, such as spontaneous and resting-state activity, new methodologies are required. We propose total activation (TA) as a novel fMRI data analysis method to explore the underlying activity-inducing signal of the BOLD signal without any timing information that is based on sparse spatio-temporal priors and characterization of the hemodynamic system. Within a variational framework, we formulate a convex cost function-including spatial and temporal regularization terms-that is solved by fast iterative shrinkage algorithms. The temporal regularization expresses that the activity-inducing signal is block-type without restrictions on the timing nor duration. The spatial regularization favors coherent activation patterns in anatomically-defined brain regions. TA is evaluated using a software phantom and an event-related fMRI experiment with prolonged resting state periods disturbed by visual stimuli. The results illustrate that both block-type and spike-type activities can be recovered successfully without prior knowledge of the experimental paradigm. Further processing using hierarchical clustering shows that the activity-inducing signals revealed by TA contain information about meaningful task-related and resting-state networks, demonstrating good abilities for the study of non-stationary dynamics of brain activity.

Patient Willingness to Use Digital Health Technologies: A Quantitative and Qualitative Survey in Patients with Cancer Cachexia.

Purpose: The objective of this study was to gain insights into the patients' perspectives on the impact of cancer cachexia on physical activity and their willingness to wear digital health technology (DHT) devices in clinical trials. Patients and Methods: We administered a quantitative 20-minute online survey on aspects of physical activity (on a 0-100 scale) to 50 patients with cancer cachexia recruited through Rare Patient Voice, LLC. A subset of 10 patients took part in qualitative 45-minute web-based interviews with a demonstration of DHT devices. Survey questions related to the impact of weight loss (a key characteristic in Fearon's cachexia definition) on physical activity, patients' expectations regarding desired improvements and their level of meaningful activities, as well as preferences for DHT. Results: Seventy-eight percent of patients reported that their physical activity was impacted by cachexia, and for 77% of them, such impact was consistent over time. Patients perceived most impact of weight loss on walking distance, time and speed, and on level of activity during the day. Sleep, activity level, walking quality and distance were identified as the most meaningful activities to improve. Patients would like to see a moderate improvement of activity levels and consider it meaningful to perform physical activity of moderate intensity (eg, walk at normal pace) on a regular basis. The wrist was the preferred location for wearing a DHT device, followed by arm, ankle, and waist. Conclusion: Most patients reported physical activity limitations since the occurrence of weight loss compatible with cancer-associated cachexia. Walking distance, sleep and quality of walk were the most meaningful activities to moderately improve, and patients consider moderate physical activity as meaningful. Finally, this study population found the proposed wear of DHT devices on the wrist and around the waist acceptable for the duration of clinical studies.

Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks.

Spontaneous fluctuations in the blood oxygenation level dependent signal measured through resting-state functional magnetic resonance imaging have been corroborated to aggregate into multiple functional networks. Abnormal resting brain activity is observed in mood disorder patients, however with inconsistent results. How do such alterations relate to clinical symptoms; e.g., level of depression and rumination tendencies? Here we recovered spatially and temporally overlapping functional networks from 31 mood disorder patients and healthy controls during rest, by applying novel methods that identify transient changes in spontaneous brain activity. Our unique approach disentangles the dynamic engagement of resting-state networks unconstrained by the slow hemodynamic response. This time-varying characterization provides moment-to-moment information about functional networks in terms of their durations and dynamic coupling, and offers novel evidence for selective contributionsto particular clinical symptoms. Patients showed increased duration of default-mode network (DMN), increased duration and occurrence of posterior DMN as well as insula- and amygdala-centered networks, but decreased occurrence of visual and anterior salience networks. Coupling between limbic (insula and amygdala) networks was also reduced. Depression level modulated DMN duration, whereas intrusive thoughts correlated with occurrence of insula and posterior DMN. Anatomical network organization was similar to controls. In sum, altered brain dynamics in mood disorder patients appear to mediate distinct clinical dimensions including increased self-processing, and decreased attention to external world.

Deconvolution of Sustained Neural Activity From Large-Scale Calcium Imaging Data.

Recent technological advances in light-sheet microscopy make it possible to perform whole-brain functional imaging at the cellular level with the use of Ca2+ indicators. The outstanding spatial extent and resolution of this type of data open unique opportunities for understanding the complex organization of neuronal circuits across the brain. However, the analysis of this data remains challenging because the observed variations in fluorescence are, in fact, noisy indirect measures of the neuronal activity. Moreover, measuring over large field-of-view negatively impact temporal resolution and signal-to-noise ratio, which further impedes conventional spike inference. Here we argue that meaningful information can be extracted from large-scale functional imaging data by deconvolving with the calcium response and by modeling moments of sustained neuronal activity instead of individual spikes. Specifically, we characterize the calcium response by a linear system of which the inverse is a differential operator. This operator is then included in a regularization term promoting sparsity of activity transients through generalized total variation. Our results illustrate the numerical performance of the algorithm on simulated signals; i.e., we show the firing rate phase transition at which our model outperforms spike inference. Finally, we apply the proposed algorithm to experimental data from zebrafish larvæ. In particular, we show that, when applied to a specific group of neurons, the algorithm retrieves neural activation that matches the locomotor behavior unknown to the method.

White matter atrophy in cerebral amyloid angiopathy.

OBJECTIVE: We postulated that cerebral amyloid angiopathy (CAA) is associated with white matter atrophy (WMA) and that WMA can be related to cognitive changes in CAA. METHODS: White matter volume expressed as percent of intracranial volume (pWMV) of prospectively enrolled patients without dementia diagnosed with probable CAA was compared to age-matched healthy controls (HC) and patients with Alzheimer disease (AD). Cognitive scores were also sought to understand the potential effects of WMA on cognitive function. RESULTS: Patients with CAA (n = 72) had significantly lower pWMV (27.97% ± 2.63) when compared to age-matched HC (n = 72; mean difference [MD], 2.38%; p < 0.0001) and patients with AD (n = 72; MD, 1.57%; p < 0.0001). Differences were most pronounced in the posterior occipital regions in both comparisons. When comparisons were restricted to groups of patients with CAA but no intracerebral hemorrhage (n = 32) or hypertension (n = 32), and age-matched HC and AD, the significant differences were unaltered. Within the CAA cohort, higher age, lobar microbleed counts, and presence of hypertension were associated with lower pWMV (p = 0.0007, p = 0.031, and p = 0.003, respectively). All associations remained independent in multivariable analyses. Within the CAA cohort, higher pWMV independently correlated with better scores of executive function. CONCLUSIONS: Patients with CAA show WMA when compared to age-matched HC and patients with AD. WMA independently correlates with the number of lobar microbleeds, a marker of CAA severity. Consistent spatial patterns of WMA especially in posterior regions might be related to CAA. The association between WMA and measures of executive function suggests that WMA might represent an important mediator of CAA-related neurologic dysfunction.

Robust Recovery of Temporal Overlap Between Network Activity Using Transient-Informed Spatio-Temporal Regression.

Functional magnetic resonance imaging is a non-invasive tomographic imaging modality that has provided insights into system-level brain function. New analysis methods are emerging to study the dynamic behavior of brain activity. The innovation-driven co-activation pattern (iCAP) approach is one such approach that relies on the detection of timepoints with a significant transient activity to subsequently retrieve spatially and temporally overlapping large-scale brain networks. To recover temporal profiles of the iCAPs for further time-resolved analysis, spatial patterns are fitted back to the activity-inducing signals. In this crucial step, spatial dependences can hinder the recovery of temporal overlapping activity. To overcome this effect, we propose a novel back-projection method that optimally fits activity-inducing signals given a set of transient timepoints and spatial maps of iCAPs, thus taking into account both spatial and temporal constraints. Validation on simulated data shows that transient-based constraints improve the quality of fitted time courses. Further evaluation on experimental data demonstrates that overfitting and underfitting are prevented by the use of optimized spatio-temporal constraints. Spatial and temporal properties of resulting iCAPs support that brain activity is characterized by the recurrent co-activation and co-deactivation of spatially overlapping large-scale brain networks. This new approach opens new avenues to explore the brain's dynamic core.

Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome.

BACKGROUND: Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers. METHODS: We retrieved and examined dynamic properties of large-scale functional brain networks using innovation-driven coactivation patterns. The study included resting-state functional magnetic resonance scans from 78 patients with 22q11DS and 85 healthy control subjects. After group comparison of temporal brain network activation properties, functional signatures of prodromal psychotic symptoms and anxiety were extracted using multivariate partial least squares correlation. RESULTS: Patients with 22q11DS had shorter activation in cognitive brain networks, longer activation in emotion processing networks, and generally increased segregation between brain networks. The functional signature of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Further, the functional signature of anxiety uncovered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral subdivisions of the anterior cingulate and medial prefrontal cortices. Coupling of amygdala with the dorsal anterior cingulate and medial prefrontal cortices was promoting anxiety, whereas coupling with the ventral anterior cingulate and medial prefrontal cortices had a protective function. CONCLUSIONS: Using innovation-driven coactivation patterns for dynamic large-scale brain network analysis, we uncovered patterns of brain network activation duration and coupling that are relevant in clinical risk factors for psychosis in 22q11DS. Our results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.

Increased Thalamocortical Connectivity in Schizophrenia Correlates With Sleep Spindle Deficits: Evidence for a Common Pathophysiology.

BACKGROUND: Converging evidence implicates abnormal thalamocortical interactions in the pathophysiology of schizophrenia. This evidence includes consistent findings of increased resting-state functional connectivity of the thalamus with somatosensory and motor cortex during wake and reduced spindle activity during sleep. We hypothesized that these abnormalities would be correlated, reflecting a common mechanism: reduced inhibition of thalamocortical neurons by the thalamic reticular nucleus (TRN). The TRN is the major inhibitory nucleus of the thalamus and is abnormal in schizophrenia. Reduced TRN inhibition would be expected to lead to increased and less filtered thalamic relay of sensory and motor information to the cortex during wake and reduced burst firing necessary for spindle initiation during sleep. METHODS: Overnight polysomnography and resting-state functional connectivity magnetic resonance imaging were performed in 26 outpatients with schizophrenia and 30 demographically matched healthy individuals. We examined the relations of sleep spindle density during stage 2 non-rapid eye movement sleep with connectivity of the thalamus to the cortex during wakeful rest. RESULTS: As in prior studies, patients with schizophrenia exhibited increased functional connectivity of the thalamus with bilateral somatosensory and motor cortex and reduced sleep spindle density. Spindle density inversely correlated with thalamocortical connectivity, including in somotosensory and motor cortex, regardless of diagnosis. CONCLUSIONS: These findings link two biomarkers of schizophrenia-the sleep spindle density deficit and abnormally increased thalamocortical functional connectivity-and point to deficient TRN inhibition as a plausible mechanism. If TRN-mediated thalamocortical dysfunction increases risk for schizophrenia and contributes to its manifestations, understanding its mechanism could guide the development of targeted interventions.

Multimodal Wearable Sensors to Measure Gait and Voice.

BACKGROUND: Traditional measurement systems utilized in clinical trials are limited because they are episodic and thus cannot capture the day-to-day fluctuations and longitudinal changes that frequently affect patients across different therapeutic areas. OBJECTIVES: The aim of this study was to collect and evaluate data from multiple devices, including wearable sensors, and compare them to standard lab-based instruments across multiple domains of daily tasks. METHODS: Healthy volunteers aged 18-65 years were recruited for a 1-h study to collect and assess data from wearable sensors. They performed walking tasks on a gait mat while instrumented with a watch, phone, and sensor insoles as well as several speech tasks on multiple recording devices. RESULTS: Step count and temporal gait metrics derived from a single lumbar accelerometer are highly precise; spatial gait metrics are consistently 20% shorter than gait mat measurements. The insole's algorithm only captures about 72% of steps but does have precision in measuring temporal gait metrics. Mobile device voice recordings provide similar results to traditional recorders for average signal pitch and sufficient signal-to-noise ratio for analysis when hand-held. Lossless compression techniques are advised for signal processing. CONCLUSIONS: Gait metrics from a single lumbar accelerometer sensor are in reasonable concordance with standard measurements, with some variation between devices and across individual metrics. Finally, participants in this study were familiar with mobile devices and had high acceptance of potential future continuous wear for clinical trials.

Diffusion-weighted imaging evidence of altered white matter development from late childhood to early adulthood in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is thought to reflect disrupted development of brain connectivity characterized by white matter abnormalities and dyscoordination of activity across brain regions that give rise to core features. But there is little consensus about the nature, timing and location of white matter abnormalities as quantified with diffusion-weighted MRI. Inconsistent findings likely reflect small sample sizes, motion confounds and sample heterogeneity, particularly different age ranges across studies. We examined the microstructural integrity of major white matter tracts in relation to age in 38 high functioning ASD and 35 typically developing (TD) participants, aged 8-25, whose diffusion-weighted scans met strict data-quality criteria and survived group matching for motion. While there were no overall group differences in diffusion measures, the groups showed different relations with age. Only the TD group showed the expected positive correlations of fractional anisotropy with age. In parallel, axial diffusivity was unrelated to age in TD, but showed inverse correlations with age in ASD. Younger participants with ASD tended to have higher fractional anisotropy and axial diffusivity than their TD peers, while the opposite was true for older participants. Most of the affected tracts - cingulum bundle, inferior and superior longitudinal fasciculi - are association bundles related to cognitive, social and emotional functions that are abnormal in ASD. The manifestations of abnormal white matter development in ASD as measured by diffusion-weighted MRI depend on age and this may contribute to inconsistent findings across studies. We conclude that ASD is characterized by altered white matter development from childhood to early adulthood that may underlie abnormal brain function and contribute to core features.

Transient brain activity disentangles fMRI resting-state dynamics in terms of spatially and temporally overlapping networks.

Dynamics of resting-state functional magnetic resonance imaging (fMRI) provide a new window onto the organizational principles of brain function. Using state-of-the-art signal processing techniques, we extract innovation-driven co-activation patterns (iCAPs) from resting-state fMRI. The iCAPs' maps are spatially overlapping and their sustained-activity signals temporally overlapping. Decomposing resting-state fMRI using iCAPs reveals the rich spatiotemporal structure of functional components that dynamically assemble known resting-state networks. The temporal overlap between iCAPs is substantial; typically, three to four iCAPs occur simultaneously in combinations that are consistent with their behaviour profiles. In contrast to conventional connectivity analysis, which suggests a negative correlation between fluctuations in the default-mode network (DMN) and task-positive networks, we instead find evidence for two DMN-related iCAPs consisting the posterior cingulate cortex that differentially interact with the attention network. These findings demonstrate how the fMRI resting state can be functionally decomposed into spatially and temporally overlapping building blocks using iCAPs.