Search results for "MRi"
showing 10 items of 733 documents
Distinct Patterns of Functional Connectivity During the Comprehension of Natural, Narrative Speech.
2020
Recent continuous task studies, such as narrative speech comprehension, show that fluctuations in brain functional connectivity (FC) are altered and enhanced compared to the resting state. Here, we characterized the fluctuations in FC during comprehension of speech and time-reversed speech conditions. The correlations of Hilbert envelope of source-level EEG data were used to quantify FC between spatially separate brain regions. A symmetric multivariate leakage correction was applied to address the signal leakage issue before calculating FC. The dynamic FC was estimated based on a sliding time window. Then, principal component analysis (PCA) was performed on individually concatenated and te…
The Brain Electrophysiological recording & STimulation (BEST) toolbox
2021
Abstract Non-invasive brain stimulation (NIBS) experiments involve many recurring procedures that are not sufficiently standardized in the community. Given the diversity in experimental design and experience of the investigators, automated but yet flexible data collection and analysis tools are needed to increase objectivity, reliability, and reproducibility of NIBS experiments. The B rain E lectrophysiological recording and ST imulation (BEST) Toolbox is a MATLAB-based, open-source software with graphical user interface that allows users to design, run, and share freely configurable multi-protocol, multi-session NIBS studies, including transcranial magnetic, electric, and ultrasound stimul…
Theory of Heterogeneous Circuits With Stochastic Memristive Devices
2022
We introduce an approach based on the Chapman-Kolmogorov equation to model heterogeneous stochastic circuits, namely, the circuits combining binary or multi-state stochastic memristive devices and continuum reactive components (capacitors and/or inductors). Such circuits are described in terms of occupation probabilities of memristive states that are functions of reactive variables. As an illustrative example, the series circuit of a binary memristor and capacitor is considered in detail. Some analytical solutions are found. Our work offers a novel analytical/numerical tool for modeling complex stochastic networks, which may find a broad range of applications.
Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging
2021
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardwar…
Minimally Invasive Assessment of Mental Stress based on Wearable Wireless Physiological Sensors and Multivariate Biosignal Processing
2019
The development of connected health technologies for the continuous monitoring of the psychophysical state of individuals performing daily life activities requires the aggregation of non-intrusive sensors and the availability of methods and algorithms for extracting the relevant physiological information. The present study proposes an integrated approach for the objective assessment of mental stress which combines wirelessly connected low invasive biosensors with multivariate physiological time series analysis. In a group of 18 healthy subjects monitored in a relaxed resting state and during two experimental conditions inducing mental stress and sustained attention (respectively, mental ari…
Discovering Aberrant Patterns of Human Connectome in Alzheimer's Disease via Subgraph Mining
2012
Alzheimer's disease (AD) is the most common cause of age-related dementia, which prominently affects the human connectome. Diffusion weighted imaging (DWI) provides a promising way to explore the organization of white matter fiber tracts in the human brain in a non-invasive way. However, the immense amount of data from millions of voxels of a raw diffusion map prevent an easy way to utilizable knowledge. In this paper, we focus on the question how we can identify disrupted spatial patterns of the human connectome in AD based on a data mining framework. Using diffusion tractography, the human connectomes for each individual subject were constructed based on two diffusion derived attributes: …
Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project
2021
ABSTRACTDiffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project - a large collaborative open-source project which …
The representation of segmental information: an fMRI investigation of the consonant-vowel distinction
2004
Harvard University, Cambridge, MA, USAAvailable online 23 July 2004IntroductionRecent studies suggest that consonants and vowels are repre-sented separately in cognitive/neural space. Much of the evidencecomes from research on dysgraphia (for review, see Miceli & Cap-asso, submitted). In the first place, letter substitution errors preservethe consonant/vowel (CV) status of the target (e.g., cinema fi ciremaor cinoma, but not cintma). Second, there are reports of selectiveimpairment for consonants or vowels. Additional evidence comesfrom disorders of phonology, demonstrating the dissociability be-tween consonants and vowels (Caramazza, Chialant, Capasso, Mthe ISI was variable (mean 6.75 s). Th…
Eine Kombination niedrig und hochauflösender dynamischer T1-gewichteter Sequenzen zur besseren Beurteilung der Morphologie Kontrastmittel aufnehmende…
2002
Purpose: Presentation of a new protocol for simultaneous acquisition of both low and high resolution T 1 -weighted images of breast lesions for dynamic contrast-enhanced MR mammography. Demonstration of possible diagnostic improvement with representative measurements in patients with suspected breast cancer by adding morphologic parameters from high resolution sequences to the analysis of the signal-time curve. Materials and Methods: Dynamic MR imaging was performed with a 1.5 T system (Magnetom SONATA, Siemens Medical Systems, Germany) and the manufacturer's double-breast coil. Coronal T 1 -weighted 3D FLASH sequences (spatial resolution 1.25 ×1.25 mm 2 ; slice thickness 1.7 mm) were acqui…
How is gravity integrated into motor planning : behavioural and fMRI approaches
2016
Gravity is immutable, ubiquitous and affects the dynamic of our daily movements. The gravitational attraction (9.81 m / s2) which varies less than 1% of the earth's surface, is an actress of the evolution of all living species. Thanks to an efficient sensorimotor system, the dynamical consequences of the effects of gravity on our movements are stored as internal representations. To circumvent the time delays of the afferent signals coming from the sensorimotor system (too long to plan quick movements), the Central Nervous System (CNS) acts in a proactive fashion by using suitable internal models developed during our past experiences. These models are mainly used during the motor planning to…