The variability inherently present in biophysical data is partly contributed by disparate sampling resolutions across instrumentations. This poses a potential problem for statistical inference using pooled data in open access repositories. Such repositories combine data collected from multiple research sites using variable sampling resolutions. One example is the Autism Brain Imaging Data Exchange repository containing thousands of imaging and demographic records from participants in the spectrum of autism and age-matched neurotypical controls. Further, statistical analyses of groups from different diagnoses and demographics may be challenging, owing to the disparate number of participants across different clinical subgroups. In this paper, we examine the noise signatures of head motion data extracted from resting state fMRI data harnessed under different sampling resolutions. We characterize the quality of the noise in the variability of the raw linear and angular speeds for different clinical phenotypes in relation to age-matched controls. Further, we use bootstrapping methods to ensure compatible group sizes for statistical comparison and report the ranges of physical involuntary head excursions of these groups. We conclude that different sampling rates do affect the quality of noise in the variability of head motion data and, consequently, the type of random process appropriate to characterize the time series data. Further, given a qualitative range of noise, from pink to brown noise, it is possible to characterize different clinical subtypes and distinguish them in relation to ranges of neurotypical controls. These results may be of relevance to the pre-processing stages of the pipeline of analyses of resting state fMRI data, whereby head motion enters the criteria to clean imaging data from motion artifacts.
As Parkinson's disease (PD) is a heterogeneous disorder, personalized medicine is truly required to optimize care. In their current form, standard scores from paper and pencil symptom-measures traditionally used to track disease progression are too coarse (discrete) to capture the granularity of the clinical phenomena under consideration, in the face of tremendous symptom diversity. For this reason, sensors, wearables, and mobile devices are increasingly incorporated into PD research and routine care. These digital measures, while more precise, yield data that are less standardized and interpretable than traditional measures, and consequently, the two types of data remain largely siloed. Both of these issues present barriers to the broad clinical application of the field's most precise assessment tools. This protocol addresses both problems. Using traditional tasks to measure cognition and motor control, we test the participant, while co-registering biophysical signals unobtrusively using wearables. We then integrate the scores from traditional paper-and-pencil methods with the digital data that we continuously register. We offer a new standardized data type and unifying statistical platform that enables the dynamic tracking of change in the person's stochastic signatures under different conditions that probe different functional levels of neuromotor control, ranging from voluntary to autonomic. The protocol and standardized statistical framework offer dynamic digital biomarkers of physical and cognitive function in PD that correspond to validated clinical scales, while significantly improving their precision. Video Link The video component of this article can be found at https://www.jove.com/video/59827/ 4. Among those are the disparity in the data that is acquired, namely discrete scores from clinical pencil-and-paper methods guided by observation, and continuous biophysical data physically acquired from the nervous systems output (e.g., using biosensors). The data from clinical scores tend to assume a one-size-fits all static model that enforces a single (theoretical) probability distribution function (PDF). This a priori assumption is imposed on the data without proper empirical validation, because normative data has not been acquired and characterized in the first place. As such, there is no proper similarity-metric-based criteria describing the neurotypical maturational states of the human nervous systems, as the healthy person ages and the probability spaces used to cast these parameter variations shift at some rate. Without normative data and proper similarity metrics, it is not possible to measure departures from typical states as they dynamically change across the person's life. It is also not possible to predict the sensory consequences of the upcoming changes.
Neurodevelopmental disorders are on the rise worldwide, with diagnoses that detect derailment from typical milestones by 3 to 4.5 years of age. By then, the circuitry in the brain has already reached some level of maturation that inevitably takes neurodevelopment through a different course. There is a critical need then to develop analytical methods that detect problems much earlier and identify targets for treatment. We integrate data from multiple sources, including neonatal auditory brainstem responses (ABR), clinical criteria detecting autism years later in those neonates, and similar ABR information for young infants and children who also received a diagnosis of autism spectrum disorders, to produce the earliest known digital screening biomarker to flag neurodevelopmental derailment in neonates. This work also defines concrete targets for treatment and offers a new statistical approach to aid in guiding a personalized course of maturation in line with the highly nonlinear, accelerated neurodevelopmental rates of change in early infancy.
Sensory transduction and transmission delays operate and propagate along different time scales. From microseconds in the auditory domain, to hundreds of milliseconds in the visual, and kinesthetic domains, the brain must successfully align disparate delays arising from endogenously self-generated streams of motor and visceral sensorial information, with exogenous sensory inputs. To produce a cohesive response to environmental goals, constantly explore, adapt, and develop a sense of simultaneity, the brain must resolve this major feat and compensate for excessive delays in any sensory modality. Disruption in these processes may lead to altered perception of the self and others, and inadvertently affect social interactions. But how early such issues may emerge and be reliably detectable, remains a challenge. Here we assess in neonates, the transmission latencies of a sound wave that travels from the cochlear nerve to the brainstem on its way to the primary auditory cortex. Already at birth, we find systematic and cumulative delays in the propagation of this wave in neonates that later received a diagnosis of autism. Furthermore, we discover that the distributions of such temporal delays have far narrower bandwidth than those from neonates who did not receive the autism diagnosis. We identify associated codependent genes' networks and define a reliable marker of neurodevelopment derail, detectable at birth. Under the precision autism model, we propose that the brainstem contains an endogenous clock anchoring and aligning disparate timescales critical for the emergence and maintenance of congruent percepts of the self and others.
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