Objective and personalized longitudinal assessment of a pregnant patient with post severe brain trauma

Abstract

Background: Following severe trauma to the brain (whether internally generated by seizures, tumors or externally caused by collision with or penetration of objects) individuals may experience initial coma state followed by slow recovery and rehabilitation treatment. At present there is no objective biometric to track the daily progression of the person for extended periods of time.Objective: We introduce new analytical techniques to process data from physically wearable sensors and help track the longitudinal progression of motions and physiological states upon the brain trauma.Setting and Participant: The data used to illustrate the methods were collected at the hospital settings from a pregnant patient in coma state. The patient had brain trauma from a large debilitating seizure due to a large tumor in the right pre-frontal lobe.Main Measures: We registered the wrist motions and the surface-skin-temperature across several daily sessions in four consecutive months. A new statistical technique is introduced for personalized analyses of the rates of change of the stochastic signatures of these patterns.Results: We detected asymmetries in the wrists’ data that identified in the dominant limb critical points of change in physiological and motor control states. These patterns could blindly identify the time preceding the baby’s delivery by C-section when the patient systematically brought her hand to her abdominal area. Changes in temperature were sharp and accompanied by systematic changes in the statistics of the motions that rendered her dominant wrist’s micro-movements more systematically reliable and predictable than those of the non-dominant writst.Conclusions: The new analytics paired with wearable sensing technology may help track the day-by-day individual progression of a patient with post brain trauma in clinical settings and in the home environment.

Aging with Autism Departs Greatly from Typical Aging

“…the model also proposes that studying autism across these levels may help us stratify the heterogeneity of autism across aging. Over years of peer reviewed work, using this model has served to reveal several stochastic features of voluntary [2,14], spontaneous [15], involuntary [15][16][17][18] and autonomic [19,20] motions, offering classification power in autism and other neuropsychiatric and neurological conditions (e.g., schizophrenia [21], Parkinson's disease [22][23][24][25], neuropathies [26] and impairments of the nervous systems due to traumatic brain injury inducing coma [27] or stroke [28]). [29], with permission from Elsevier).…”

Motor noise is rich signal in autism research and pharmacological treatments

“…To model them, we build on previous research6 whereby the amplitudes and inter-spike interval times are modeled as independent and identically distributed (iid) random variables following a Gamma distribution. This framework has proven amenable to computational tractability, facilitating both inference462324 and interpretation of the results. As such, the Gamma process is used here in combination with a waveform (coined “micro-movements”) representing the fluctuations in amplitude and timing of the spike trains derived from the rate of change in head positions and orientations.…”

Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion

“…In Figure 5 D, we show the estimated Gamma family of probability distribution functions (PDF) color coded by quadrant location (whereby each PDF curve corresponds to each point in Figure 5 A,B). We also mark one point as one curve and localize it according to the Kantarovich/Wasserstein distance (K/W distance) relative to a theoretical Gaussian (best) or theoretical Exponential (worst) value in Figure 5 E. As noted before, these criteria were empirically determined from examining thousands of cases across multiple pathologies of the nervous system [ 4 , 7 , 29 , 42 , 43 ], also including the evolution of motor noise in neonates [ 41 ] and a comatose patient [ 44 ] to classify deliberate volition in the precise sense of physical realization of mental intent vs. spontaneous random noise.…”