Mining genetic, transcriptomic, and imaging data in Parkinson’s disease

Parkinson’s disease (PD) is a brain disorder that leads to shaking, stiffness and difficulties with walking, balance, and coordination. Affected people may also have mental and behavioral changes, sleep problems, depression, memory difficulties and fatigue. PD is an age-related disease, with an increased prevalence in populations of subjects over the age of 60. About 5 to 10% of PD patients have an "early-onset" variant and it is often, but not always, inherited. PD is characterized by the loss of groups of neurons involved in the control of voluntary movements. Here we present a novel imaging-genetics workflow on Parkinson’s disease aimed to discover some new potential candidate biomarkers for Parkinson’s disease onset, by interpolating genotyping, transcriptomic, functional (Dopamine Transporter Scan) and morphological (Magnetic Resonance Imaging) imaging data. The proposed tutorial has the aim to encourage and stimulate the attendees on the biomedical research with the advantage of integration of heterogenous data. In the last decade the use of images together with genetics data has become widespread among the bioinformatics researchers. This has allowed to inspect and investigate in detail different specific diseases, to better understand their origin and cause. While in recent years many imaging genetics analyses have been developed and successfully applied to characterize brain functioning and neurodegenerative diseases such as Alzheimer’s disease, to our knowledge, no standard imaging genetics workflow has been proposed for PD. The novelty of our workflow can be summarized as follows: • We propose a domain free and easy-to-use workflow, integrating heterogenous data, such as genotyping, transcriptomic, and imaging data. • The workflow addresses the complexity of integrating real multi-source data when a limited number of data are available by proposing three step-based method, where the first step integrates genotyping and imaging features considering each feature individually, the second step summarizes imaging features in a single measure, and the last step focuses on linking potential functional effects caused by the biomarkers found during the two previous phases. • We propose a validation of the method on genetic and imaging data related to PD, showing our new results. The data used for this tutorial were obtained from the Parkinson’s Progression Marker Initiative (PPMI) data portal. Currently, PPMI is the most complete and comprehensive collection of PD-related data. The dataset that will be used in the tutorial consists in a set of polymorphisms, more specifically insertions and deletions (indels) or Single Nucleotide Polymorphisms (SNPs), and transcriptomic data retrieved by RNA sequencing. In addition, DaTSCAN and MRI data are used, which have been shown to be effective in providing potential biomarkers for PD onset and progression. The attendees will acquire an experience on how to conduct a complete imaging-genetics workflow, in a specific case study of Parkinsonian subjects. After the tutorial session the attendees will be able to conduct themselves an imaging-genetics pipeline, which could also be applied to study other neurological diseases. The tutorial will introduce the partecipants to the biological background, especially with the notion of DNA, RNA, Single-nucleotide polymorphism (SNP) and Genome-Wide Association Study (GWAS). The participants will have the opportunity to get familiar with PLINK, a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyzes in a computationally efficient manner. It provides a large range of functionalities designed for data management, summary statistics, quality control, population stratification detection, association analysis, etc. for genotyping data analysis. The audience will also learn how to run code on the widely used R programming environment for statistical computing and graphics. They will also learn some notions about Python, especially how to deal efficiently, with genotyping data using Pandas library, which was designed for data manipulation and analysis. The tutorial code is wrapped in different Jupyter notebooks (formerly IPython Notebooks), that is a web-based and system-independent interactive computational environment for easy analysis reproducibility

Multi view based imaging genetics analysis on Parkinson disease

Longitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinson\u2019s Disease (PD) as a case study. Parkinson's disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases

The role of left fronto-parietal tracts in hand selection: evidence from neurosurgery

Strong right-hand preference on the population level is a uniquely human feature, although its neural basis is still not clearly defined. Recent behav

Столкновение геополитических интересов НАТО и России в Молдавии

<p>(A) H-reflex modulation recorded in the right FCR muscle of 14 right-handed observers, during observation of one cycle of a flexion-extension movement of the mover’s right hand (B, average movement trace performed by the mover in all different experiments (±SEM), when observers are explicitly instructed to report the mover’s hand position corresponding to the last time the LED light was flashed in each trial. In panel A the cumulative plot of the average data points from all subjects is fitted with a common sinusoid equation with the same period as that fitting the movement. Note the reduced scale of the ordinate compared to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177457#pone.0177457.g002" target="_blank">Fig 2A</a>. Δϕ: phase difference between reflex modulation in flexor muscle of the observer and hand oscillation of the mover. Flex = downward direction of the moving hand.</p

What you see is what you get: motor resonance in peripheral vision

Observation of others' actions evokes a subliminal motor resonant response, which reflects the motor program encoding observed actions. The possibility that actions located in the peripheral field of vision may also activate motor resonant responses has not been investigated. We examine the excitability modulation of motor pathways in response to grasping actions viewed in near peripheral vision; results are directly compared to responses to the same actions viewed in central vision (Borroni et al. in Eur J Neurosci 34:662-669, 2011. doi:10.1111/j.1460-9568.2011.07779.x). We hypothesize that actions observed in peripheral vision are effective in modulating the excitability of motor pathways, but that responses have a low kinematic specificity. While the neural resources of central vision provide the most accurate perception of biological motion, the decreased visual acuity in periphery may be sufficient to discriminate only general aspects of movement and perhaps to recognize the gist of visual scenes. Right-handed subjects observed a video of two grasping actions at 10A degrees eccentricity in the horizontal plane. Motor-evoked potentials were elicited in the right OP and ADM muscles by TMS of the left primary motor cortex at different delays during the observed actions. Results show that actions viewed in near peripheral vision are effective in modulating the subliminal activation of motor circuits, but that responses are rough and inaccurate, and do not reflect the motor program encoding the observed action or its goal. We suggest that due to their limited kinematic accuracy, these subliminal motor responses may provide information about the general aspects of observed actions, rather than their specific execution

The role of attention in human motor resonance

<div><p>Observation of others' actions evokes in primary motor cortex and spinal circuits of observers a subliminal motor resonance response, which reflects the motor program encoding observed actions. We investigated the role of attention in human motor resonance with four experimental conditions, explored in different subject groups: in the first <i>explicit</i> condition, subjects were asked to observe a rhythmic hand flexion-extension movement performed live in front of them. In two other conditions subjects had to monitor the activity of a LED light mounted on the oscillating hand. The hand was clearly visible but it was not the focus of subjects’ attention: in the <i>semi-implicit</i> condition hand movement was relevant to task completion, while in the <i>implicit</i> condition it was irrelevant. In a fourth, <i>baseline</i>, condition subjects observed the rhythmic oscillation of a metal platform. Motor resonance was measured with the H-reflex technique as the excitability modulation of cortico-spinal motorneurons driving a hand flexor muscle. As expected, a normal resonant response developed in the <i>explicit</i> condition, and no resonant response in the <i>baseline</i> condition. Resonant responses also developed in both <i>semi-implicit</i> and <i>implicit</i> conditions and, surprisingly, were not different from each other, indicating that viewing an action is, <i>per se</i>, a powerful stimulus for the action observation network, even when it is not the primary focus of subjects’ attention and even when irrelevant to the task. However, the amplitude of these responses was much reduced compared to the <i>explicit</i> condition, and the phase-lock between the time courses of observed movement and resonant motor program was lost. In conclusion, different parameters of the response were differently affected by subtraction of attentional resources with respect to the <i>explicit</i> condition: time course and muscle selection were preserved while the activation of motor circuits resulted in much reduced amplitude and lost its kinematic specificity.</p></div

Phase differences between observed movement and H-reflex modulation.

<p>Derived in each subject from the sinewave function fitting the subject’s average data points in the <i>explicit</i>, <i>semi-implicit</i> and <i>implicit</i> observation conditions. Note that in the <i>explicit</i> condition phases are always in advance of the observed movement (as in the actual execution of the same movement), whereas in the <i>semi-implicit</i> and <i>implicit</i> conditions they are scattered across the entire possible range (-180° to +180°).</p

Data acquisition and experimental protocol.

<p>(A) Average traces (μV ±SEM) of 25 H-reflexes recorded from a single subject, in a single trial of movement observation in the <i>explicit</i> condition. (B) Average sinusoidal time course of 25 flexion-extension hand movements. Black dots on the sinewave indicate the 5 different delays during the hand flexion-extension cycle in which reflexes were recorded (d1 = 0, d2 = 200, d3 = 400, d4 = 600, d5 = 800 ms) corresponding to 5 different hand angular positions, dividing the 1Hz oscillation cycle in five equal parts. Note the motor resonant response, i.e. the modulation of the reflex amplitude matches the cyclic time course of the observed movement, with smaller reflexes recorded during observation of the extension phase (e.g. d1 and d5) and larger ones during the observation of the flexion phase (e.g. d3 and d4).</p

Sinusoidal time-course of reflex amplitude modulation.

<p>Average correlation coefficients of the circular-linear analysis (±SEM) obtained in the <i>explicit</i>, <i>semi-implicit</i> and <i>implicit</i> conditions are significantly different from the R coefficients obtained in the <i>baseline</i> condition (** p≤0.01, ***p≤0.001).</p