Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

International audienceThe January 2013 Special Issue of IEEE transactions on medical imaging discusses papers on medical imaging and image computing in computational physiology. Aslanid and co-researchers present an experimental technique based on stained micro computed tomography (CT) images to construct very detailed atrial models of the canine heart. The paper by Sebastian proposes a model of the cardiac conduction system (CCS) based on structural information derived from stained calf tissue. Ho, Mithraratne and Hunter present a numerical simulation of detailed cerebral venous flow. The third category of papers deals with computational methods for simulating medical imagery and incorporate knowledge of imaging physics and physiology/biophysics. The work by Morales showed how the combination of device modeling and virtual deployment, in addition to patient-specific image-based anatomical modeling, can help to carry out patient-specific treatment plans and assess alternative therapeutic strategies

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness

Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being employed in this fast-growing field. We identify the gaps that exist between the current state-of-the-art in statistical and biophysical computational modelling in human neuroscience, and the aspirational goal of a mature field of modelling disorders of consciousness; which might drive improved treatments and outcomes in the clinic. Finally, we make several recommendations for how the field as a whole can work together to address these challenges

Towards multimodal nonlinear microscopy in clinics

Multimodal nonlinear microscopy combining two photon excited fluorescence (TPEF), second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) represents a promising and powerful tool for biomedical diagnostics. The method enables label-free visualization of morphology and chemical composition of complex tissues as well as disease related changes and is as such as detailed as staining histologic methods. In this work a compact microscope utilizing novel fiber laser sources and a new approach for data analysis based on colocalization have been developed and tested for detecting various disease patterns, e.g., atherosclerosis and brain tumors.Mit Hilfe der nichtlinearen Multikontrast-Mikroskopie basierend auf den Prozessen Zweiphotonenfluoreszenz (TPEF), Frequenzverdopplung (SHG) und kohärente anti-Stokes Raman-Streuung (CARS), können Morphologie, chemische Zusammensetzung sowie krankheitsbedingte Veränderungen komplexer Gewebe label-frei analog zu histologischen Färbungen dargestellt werden. Potentiell eignet sich die Methode daher für die in vivo Bildgebung und könnte die medizinische Diagnostik entscheidend verbessern. Im Rahmen dieser Arbeit wurde ein kompaktes TPEF/SHG/CARS-Forschungsmikroskop unter Verwendung neuer Faserlaserquellen speziell für die Verwendung in der Klinik entwickelt. Dabei wurde erforscht, wie sich der Bildkontrast durch nahinfrarote Laser sowie eine hohe spektrale Auflösung verbessern lässt. Zusätzlich wurde an Methoden der Datenanalyse multispektraler CARS-Daten gearbeitet, um mittels der Kolokalisationsanalyse die Verteilung verschiedener molekularer Marker in komplexen Geweben zu visualisieren. Das Potential für klinische Anwendungen wurde an verschiedenen Krankheitsbildern wie Arteriosklerose und Tumoren des Hirns demonstriert

Blueprint for mechanistic, data-rich early phase clinical pharmacology studies in dermatology

Drug Delivery Technolog

CRISPR-Cas9システムを用いた味覚受容体発現調節物質のスクリーニング系の開発

Taste recognition mediated by taste receptors is critical for the survival of animals in nature and is an important determinant of nutritional status and quality of life in humans. However, many factors including aging, diabetes, zinc deficiency, infection with influenza or cold viruses, and chemotherapy can trigger dysgeusia, for which a standard treatment has not been established. We here established an engineered strain of medaka (Oryzias latipes) that expresses green fluorescent protein (GFP) from the endogenous taste 1 receptor 3 (T1R3) gene locus with the use of the CRISPR-Cas9 system. This T1R3-GFP knock-in (KI) strain allows direct visualization of expression from this locus by monitoring of GFP fluorescence. The pattern of GFP expression in the T1R3-GFP KI fish thus mimicked that of endogenous T1R3 gene expression. Furthermore, exposure of T1R3-GFP KI medaka to water containing monosodium glutamate or the anticancer agent 5-fluorouracil resulted in an increase or decrease, respectively, in GFP fluorescence intensity, effects that also recapitulated those on T1R3 mRNA abundance. Finally, screening for agents that affect GFP fluorescence intensity in T1R3-GFP KI medaka identified tryptophan as an amino acid that increases T1R3 gene expression. The establishment of this screening system for taste receptor expression in medaka provides a new tool for the development of potential therapeutic agents for dysgeusia

Lead-OR: A multimodal platform for deep brain stimulation surgery

Background: Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced. Methods: Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform. Results: We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool. Conclusions: This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages

Measuring cortical connectivity in Alzheimer's disease as a brain neural network pathology: Toward clinical applications

Objectives: The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer’s disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. Methods: We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). Results: Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior–posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. Conclusions: Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD. (JINS, 2016, 22, 138–163

Auf dem Weg zur automatisierten Programmierung der tiefen Hirnstimulation

Background: Deep Brain Stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment option for patients with Parkinson’s Disease (PD). To maximize treatment benefit, stimulation parameters need to be adjusted individually. Currently, this is performed following a trial-and-error approach, which is time-consuming, costly, and challenging for both patients and medical personnel. The recent introduction of directional electrodes has aggravated those difficulties, highlighting the need for more elaborate procedures to tailor DBS parameter selection to the individual patient. Recent studies suggested that the anatomical location of DBS electrodes could be used to predict beneficial stimulation parameters and guide DBS programming procedures. Methods: We developed StimFit, a software to automatically suggest optimal stimulation parameters in PD patients treated with STN-DBS based on reconstructed electrode locations. The software was trained on a dataset of 612 stimulation settings (applied in 31 patients) to predict motor improvement and side-effect probabilities with respect to electrode location and stimulation parameters. Model performance was retrospectively validated within the training cohort and tested on an independent dataset of 19 PD patients. The predictive models were then embedded in a non-linear optimization algorithm to find parameter combinations which would maximize predicted therapeutic benefit. A graphical user interface was designed to allow for a streamlined use of StimFit and the software was made publicly available. Next, StimFit was prospectively applied in 35 PD patients in a double-blind, cross-over trial to assess whether motor benefit of StimFit stimulation parameters would be non-inferior to patients’ standard of care treatment (SoC). Motor performance was evaluated according to the MDS-UPDRS-III under StimFit and SoC stimulation, randomizing the sequence of both conditions in a 1:1 ratio. Results: Motor outcome predictions of the data-driven model integrated in StimFit correlated well with observed outcome within the training cohort (R = 0.57, p < 0.001) as well as in the retrospective test cohort (R = 0.53, p < 0.001). In our prospective clinical trial StimFit and SoC stimulation resulted in clinically significant average motor improvement of 43 and 48 %, respectively. Mean absolute difference of motor outcome between both conditions was -1.6 ± 7.1 (95% CI: [-4.0, 0.9]) establishing non-inferiority of StimFit at the pre-defined margin of -5 points (p = 0.004).Conclusion: Beneficial stimulation parameters can be automatically derived from electrode location using data-driven approaches. Our results hold promise for more efficient and streamlined DBS programming procedures, but additional prospective studies are required to assess the effects of image-based DBS programming on non-motor domains and long-term quality of life.Hintergrund: Die Tiefe Hirnstimulation (THS) des Nucleus subthalamicus (STN) ist eine effektive Therapieoption zur Behandlung des idiopathischen Parkinson-Syndroms (IPS). Hierbei müssen die Stimulationsparameter individuell angepasst werden, was derzeit durch zeit- und ressourcenintensives Austesten erfolgt. Jüngste Studienergebnisse legen nahe, dass Informationen über die anatomische Lage der THS-Elektroden dafür genutzt werden könnten, vorteilhafte Stimulationseinstellungen zu identifizieren und somit die THS-Programmierung zu erleichtern. Methoden: Wir entwickelten eine Software (StimFit), durch welche optimale Stimulationseinstellungen für Patient*innen mit STN-THS auf Basis ihrer individuellen Elektrodenlagen vorgeschlagen werden können. Hierbei wurde ein Trainingsdatensatz von 612 Stimulationseinstellungen (31 Patient*innen) genutzt, um THS-Effekte in Abhängigkeit von Elektrodenlage und Stimulationsparametern zu prädizieren. Vorhersagegenauigkeiten wurden retrospektiv innerhalb des Trainingsdatensatzes, sowie in einer unabhängigen Testkohorte von 19 Patient*innen quantifiziert. Die validierten Vorhersagemodelle wurden dann in einen Optimierungsalgorithmus integriert, um Stimulationseinstellungen mit maximalem (prädizierten) therapeutischen Benefit zu ermitteln. Der Algorithmus wurde in eine grafische Benutzeroberfläche eingebettet und öffentlich zugänglich gemacht. In einer doppelblinden cross-over Studie wurde StimFit dann prospektiv an 35 Patient*innen mit STN-THS angewandt. Hierbei wurden sowohl die von StimFit vorgeschlagenen, als auch die durch traditionelle Optimierungsverfahren ermittelten („Standard of Care“, SoC) Stimulationseinstellungen in randomisierter Reihenfolge eingestellt. Die therapeutischen Effekte der StimFit-Einstellungen wurden mittels des MDS-UPDRS-III quantifiziert und diesbezüglich auf Nicht-Unterlegenheit gegenüber dem SoC untersucht. Ergebnisse: Die durch StimFit prädizierten motorischen Effekte korrelierten mit den empirischen Effekten innerhalb der Trainingskohorte (R = 0,57; p < 0,001) sowie in der retrospektiven Testkohorte (R = 0,53; p < 0,001). In der prospektiven Studie verbesserten sich die motorischen Symptome sowohl unter StimFit- als auch unter SoC-Stimulation (43 und 48 %). Der Summenscore des MDS-UPDRS-III unterschied sich statistisch nicht signifikant um -1,6 ± 7,1 (95% CI: [-4,0; 0,9]) zwischen beiden Stimulationskonditionen. Die Nicht-Unterlegenheit von StimFit konnte bei einer vordefinierten Grenze von -5 Punkten gezeigt werden (p = 0,004). Schlussfolgerungen: Effektive Stimulationseinstellungen können anhand der Elektrodenpositionen durch automatisierte datengetriebene Algorithmen abgeleitet werden und somit die Optimierung der THS-Parameter erleichtern. Weitere prospektive Studien sind notwendig, um Langzeiteffekte und den Einfluss datengetriebener THS-Programmierungsmethoden auf nicht-motorische Domänen und die Lebensqualität der Patient*innen zu ermitteln