Improving the signal detection accuracy of functional Magnetic Resonance Imaging

Janssen, Niels; Hernández‐Cabrera, Juan Andrés; Ezama, Laura

doi:10.1016/j.neuroimage.2018.01.076

Cited by 6 publications

(3 citation statements)

References 47 publications

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“…MRI can provide objective and reliable imaging biomarkers that are necessary to help diagnose or identify MHE (Zhang et al 2014 ). One major drawback of MRI concerns the lack of detection accuracy of the measured signal, but with technical advances, a solution to that problem is imminent (Janssen et al 2018 ).…”

Section: Diagnosis Of Mhe: Cognitive Evaluationmentioning

confidence: 99%

Cognition-tracking-based strategies for diagnosis and treatment of minimal hepatic encephalopathy

et al. 2020

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Minimal hepatic encephalopathy (MHE), which shows mild cognitive impairment, is a subtle complication of cirrhosis that has been shown to affect daily functioning and quality of life. However, until 2014, relevant guidelines do not give much attention to the diagnosis and treatment of MHE, resulting in patients being ignored and denied the benefits of treatment. In this review, we summarize recent cognition-based research about (1) alteration of nerve cells, including astrocytes, microglial cells and neurons, in mild cognitive impairment in MHE; (2) comparison of methods in detecting cognitive impairment in MHE; and (3) comparison of methods for therapy of cognitive impairment in MHE. We hope to provide information about diagnosis and treatment of cognitive impairment in patients with MHE.

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Section: Diagnosis Of Mhe: Cognitive Evaluationmentioning

confidence: 99%

Cognition-tracking-based strategies for diagnosis and treatment of minimal hepatic encephalopathy

et al. 2020

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“…This method was used to predict early damage to the brain and its neural growth. In another work, a new framework for improving the fMRI detection accuracy was used [13]. To increase the detection rate, the signal was extracted by converting the large volume of the brain into specific stimulus portions.…”

Section: 34mentioning

confidence: 99%

“…In another job, Cecotti et al Using Eqs. (13) to (16), they classify the set of images: the human face (target) and others (non -objective) [34]. In their work, a CNN is embedded with a space filter.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Study of Interactive Systems Based on Brain and Computer Interfaces

Ghadiri¹

2019

Preprint

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Scientists have always been looking for ways to create an effective relationship between humans and the machine, so that this relationship is as close as possible to human relationships, since even the most sophisticated machines do not have any particular effect without human intervention. This association results from brain-generated neural responses due to motor activity or cognitive activity. Communication methods include muscle and non-muscle activities that create brain activity or brainwaves and lead to a hardware device to perform a specific task. BCI was originally designed as a communication tool for patients with neuromuscular disorders, but due to recent advances in BCI devices such as passive electrodes, wireless headset, adaptive software, and cost reduction, it has been used to link the rest of the body. The BCI is a bridge between the signals generated by thoughts in our brain and the machines. BCI has been a successful invention in the field of brain imaging, which can be used in a variety of areas, including helping motor activity, vision, hearing, and any damage that the body sustains. The BCI device records brain responses using invasive, semi-invasive and non-invasive methods including Electroencephalography (EEG), Magnetizhenophyllography (MEG), and Magnetic Resonance Imaging (MRI). Brain response using pattern recognition methods to control any translation application. In this article, a review of various techniques for extracting features and classification algorithms has been presented on brain data. A significant comparative analysis of existing BCI techniques is provided.

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The Dynamics of Speech Motor Control Revealed with Time-Resolved fMRI

Janssen

Mendieta

2019

Cerebral Cortex

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Holding a conversation means that speech must be started, maintained, and stopped continuously. The brain networks that underlie these aspects of speech motor control remain poorly understood. Here we collected functional magnetic resonance imaging (fMRI) data while participants produced normal and fast rate speech in response to sequences of visually presented objects. We took a non-conventional approach to fMRI data analysis that allowed us to study speech motor behavior as it unfolded over time. To this end, whole-brain fMRI signals were extracted in stimulus-locked epochs using slice-based fMRI. These data were then subjected to group independent component analysis to discover spatially independent networks that were associated with different temporal activation profiles. The results revealed two basic brain networks with different temporal dynamics: a cortical network that was activated continuously during speech production, and a second cortico-subcortical network that increased in activity during the initiation and suppression of speech production. Additional analyses explored whether key areas involved in motor suppression such as the right inferior frontal gyrus, sub-thalamic nucleus and pre-supplementary motor area provide first-order signals to stop speech. The results reveal for the first time the brain networks associated with the initiation, maintenance, and suppression of speech motor behavior.

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Improving the signal detection accuracy of functional Magnetic Resonance Imaging

Cited by 6 publications

References 47 publications

Cognition-tracking-based strategies for diagnosis and treatment of minimal hepatic encephalopathy

Cognition-tracking-based strategies for diagnosis and treatment of minimal hepatic encephalopathy

Study of Interactive Systems Based on Brain and Computer Interfaces

The Dynamics of Speech Motor Control Revealed with Time-Resolved fMRI

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