Measuring hand use in the home after cervical spinal cord injury using egocentric video

Background: Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its ability to detect functional use in the home environment. Objective: To develop and validate a wearable vision-based system for measuring hand use in the home among individuals living with tetraplegia. Methods: Several deep learning algorithms for detecting functional hand-object interactions were developed and compared. The most accurate algorithm was used to extract measures of hand function from 65 hours of unscripted video recorded at home by 20 participants with tetraplegia. These measures were: the percentage of interaction time over total recording time (Perc); the average duration of individual interactions (Dur); the number of interactions per hour (Num). To demonstrate the clinical validity of the technology, egocentric measures were correlated with validated clinical assessments of hand function and independence (Graded Redefined Assessment of Strength, Sensibility and Prehension - GRASSP, Upper Extremity Motor Score - UEMS, and Spinal Cord Independent Measure - SCIM). Results: Hand-object interactions were automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results demonstrated that higher UEMS and better prehension were related to greater time spent interacting, whereas higher SCIM and better hand sensation resulted in a higher number of interactions performed during the egocentric video recordings. Conclusions: For the first time, measures of hand function automatically estimated in an unconstrained environment in individuals with tetraplegia have been validated against internationally accepted measures of hand function. Future work will necessitate a formal evaluation of the reliability and responsiveness of the egocentric-based performance measures for hand use

Large-scale information processing during spontaneous brain activity revealed by cross-frequency coupling

Amplitude-amplitude (AAC), phase-phase (PPC), and phase-amplitude (PAC) coupling of brain activity are mechanisms that shape the information flow across multiple spatiotemporal scales; however, it is unclear how they are related. We used source-space projected resting-state magnetoencephalography data and empirical mode decomposition to obtain AAC-, PPC-, and PAC-based functional connectivity matrices. We found that specific PAC interactions are highly variable across subjects, but the global topological properties of the network are consistent. PPC and AAC were consistent at both the local and global scales. Additionally, the higher the spatial complexity of PAC is, the stronger its correlation with AAC and PPC will be. Finally, direct and indirect functional connections are differently correlated to the properties of the underlying anatomical scaffold. Together, our results suggest that PPC of high frequencies facilitates the integration of information, AAC of low frequencies facilitates the segregation of information, and PAC is the mechanism binding these two information streams

A wearable vision-based system for detecting hand-object interactions in individuals with cervical spinal cord injury: First results in the home environment

: Cervical spinal cord injury (cSCI) causes the paralysis of upper and lower limbs and trunk, significantly reducing quality of life and community participation of the affected individuals. The functional use of the upper limbs is the top recovery priority of people with cSCI and wearable vision-based systems have recently been proposed to extract objective outcome measures that reflect hand function in a natural context. However, previous studies were conducted in a controlled environment and may not be indicative of the actual hand use of people with cSCI living in the community. Thus, we propose a deep learning algorithm for automatically detecting hand-object interactions in egocentric videos recorded by participants with cSCI during their daily activities at home. The proposed approach is able to detect hand-object interactions with good accuracy (F1-score up to 0.82), demonstrating the feasibility of this system in uncontrolled situations (e.g., unscripted activities and variable illumination). This result paves the way for the development of an automated tool for measuring hand function in people with cSCI living in the community

Measuring Hand Use in the Home after Cervical Spinal Cord Injury Using Egocentric Video

: Egocentric video has recently emerged as a potential solution for monitoring hand function in individuals living with tetraplegia in the community, especially for its ability to detect functional use in the home environment. The aim of this study was to develop and validate a wearable vision-based system for measuring hand use in the home among individuals living with tetraplegia. Several deep learning algorithms for detecting functional hand-object interactions were developed and compared. The most accurate algorithm was used to extract measures of hand function from 65 h of unscripted video recorded at home by 20 participants with tetraplegia. These measures were: the percentage of interaction time over total recording time (Perc); the average duration of individual interactions (Dur); and the number of interactions per hour (Num). To demonstrate the clinical validity of the technology, egocentric measures were correlated with validated clinical assessments of hand function and independence (Graded Redefined Assessment of Strength, Sensibility and Prehension [GRASSP], Upper Extremity Motor Score [UEMS], and Spinal Cord Independent Measure [SCIM]). Hand-object interactions were automatically detected with a median F1-score of 0.80 (0.67-0.87). Our results demonstrated that higher UEMS and better prehension were related to greater time spent interacting, whereas higher SCIM and better hand sensation resulted in a higher number of interactions performed during the egocentric video recordings. For the first time, measures of hand function automatically estimated in an unconstrained environment in individuals with tetraplegia have been validated against internationally accepted measures of hand function. Future work will necessitate a formal evaluation of the reliability and responsiveness of the egocentric-based performance measures for hand use

Design of optimal nonlinear network controllers for Alzheimer's disease.

Brain stimulation can modulate the activity of neural circuits impaired by Alzheimer's disease (AD), having promising clinical benefit. However, all individuals with the same condition currently receive identical brain stimulation, with limited theoretical basis for this generic approach. In this study, we introduce a control theory framework for obtaining exogenous signals that revert pathological electroencephalographic activity in AD at a minimal energetic cost, while reflecting patients' biological variability. We used anatomical networks obtained from diffusion magnetic resonance images acquired by the Alzheimer's Disease Neuroimaging Initiative (ADNI) as mediators for the interaction between Duffing oscillators. The nonlinear nature of the brain dynamics is preserved, given that we extend the so-called state-dependent Riccati equation control to reflect the stimulation objective in the high-dimensional neural system. By considering nonlinearities in our model, we identified regions for which control inputs fail to correct abnormal activity. There are changes to the way stimulated regions are ranked in terms of the energetic cost of controlling the entire network, from a linear to a nonlinear approach. We also found that limbic system and basal ganglia structures constitute the top target locations for stimulation in AD. Patients with highly integrated anatomical networks-namely, networks having low average shortest path length, high global efficiency-are the most suitable candidates for the propagation of stimuli and consequent success on the control task. Other diseases associated with alterations in brain dynamics and the self-control mechanisms of the brain can be addressed through our framework

Optimal nonlinear network control of Alzheimer’s.

<p><b>a</b>) Anatomical connection density matrices (<b><i>W</i></b>) for the interaction of 78 predefined brain regions were obtained for each of the patients in the study. The color code and size of the edges represent the weight of the connections. <b>b</b>) Duffing oscillators describe the activity in each brain region <i>i</i>, and are coupled through <b><i>W</i></b>. The parameter <i>γ</i> characterizes the nonlinearity of the system. By tuning <i>α</i> and the initial conditions, <b><i>z</i></b>₀ = [<b><i>x</i></b>₀,<b><i>y</i></b>₀]^<i>T</i>, ‘pathological EEG activity’ (high-amplitude theta-band oscillations, <i>f</i> ≈ 6.4 <i>Hz</i>) and ‘healthy EEG activity’ (low-amplitude alpha-band oscillations, <i>f</i> ≈ 8.0 <i>Hz</i>) are obtained. <b>c</b>) A hypothetical ‘controller’ is moved over all the regions. The controller applies the optimal (least energy-consuming) signal that steers the activity to the healthy state, and guarantees the shift of the EEG spectrum towards higher frequencies. Each stimulus depends on the region and patient receiving it through the dynamical system that is solved.</p

Ranking brain regions according to the mean inverse of the cost of controlling the network.

<p><b>a</b>) Order corresponding to the linear case. Given is the mean ± SEM of N = 41 subjects. Inputs entering regions in the leftmost part of the order control Alzheimer’s activity at a lowest cost. <b>b</b>) Graphical representation with the approximated location of the brain regions. The size of the spheres is directly proportional to the mean values in panel (<b>a</b>). Panels (<b>c</b>,<b>d</b>) are analogous to (<b>a</b>,<b>b</b>) except that the strength of the nonlinearity has been set to <i>γ</i> = 200 <i>s</i>⁻²<i>mV</i>⁻² and a new ranking is obtained. The red sphere represents the right postcentral gyrus, which yielded uncontrollable nonlinear systems for all the subjects in the sample.</p

The effect of the local topological measures on the performance of the controllers (nonlinear case).

<p>Relationship between the mean inverse of the cost across the sample and the mean node strength (<b>a</b>) (linear regression: F(1,76) = 55.95, P < 0.001), eccentricity (<b>b</b>) (linear regression: F(1,76) = 29.61, P < 0.001), closeness centrality (<b>c</b>) (linear regression: F(1,76) = 36.94, P < 0.001), betweenness centrality (<b>d</b>) (linear regression: F(1,76) = 20.90, P < 0.001), clustering coefficient (<b>e</b>) (linear regression: F(1,76) = 11.36, P = 0.001) and communicability (<b>f</b>) (linear regression: F(1,76) = 10.87, P = 0.002); N = 78 regions, in all cases. The Pearson correlation coefficients, <i>r</i>, are inserted. The strength of the nonlinearity was set to <i>γ</i> = 200 <i>s</i>⁻²<i>mV</i>⁻². See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006136#pcbi.1006136.s002" target="_blank">S2 Fig</a> for equivalent results obtained over linear systems.</p

Nonlinearity-related changes to the average brain regions’ ranking.

<p><b>a</b>) Rank correlations between the orders corresponding to different nonlinearities (paired t-test: large-sample approximation, P < 0.001 in all the cases, N = 78 regions). As the nonlinearity increases, the Spearman’s rho coefficients for the correlation between a ranking and both, the order corresponding to the previous nonlinearity and to the linear case, decrease. <b>b</b>) The rankings for the nonlinearities <i>γ</i> = 0 <i>s</i>⁻²<i>mV</i>⁻² and <i>γ</i> = 200 <i>s</i>⁻²<i>mV</i>⁻² are compared. These orders are similar in their top and bottom-most parts (inserted ellipses) and dissimilar in between.</p