Automatic Quantification of Abnormal Lung Parenchymal Attenuation on Chest Computed Tomography Images Using Densitometry and Texture-based Analysis.

Purpose: To compare texture-based analysis using convolutional neural networks (CNNs) against lung densitometry in detecting chest computed tomography (CT) image abnormalities.

Material And Methods: A U-NET was used for lung segmentation, and an ensemble of 7 CNN architectures was trained for the classification of low-attenuation areas (LAAs; emphysema, cysts), normal-attenuation areas (NAAs; normal parenchyma), and high-attenuation areas (HAAs; ground-glass opacities, crazy paving/linear opacity, consolidation). Lung densitometry also computes (LAAs, ≤-950 HU), NAAs (-949 to -700 HU), and HAAs (-699 to -250 HU).

Natural Language Processing for the Identification of Incidental Lung Nodules in Computed Tomography Reports: A Quality Control Tool.

Purpose: To evaluate the diagnostic performance of a natural language processing (NLP) model in detecting incidental lung nodules (ILNs) in unstructured chest computed tomography (CT) reports.

Methods: All unstructured consecutive reports of chest CT scans performed at a tertiary hospital between 2020 and 2021 were retrospectively reviewed (n = 21,542) to train the NLP tool. Internal validation was performed using reference readings by two radiologists of both CT scans and reports, using a different external cohort of 300 chest CT scans.

Self-blame in major depression: a randomised pilot trial comparing fMRI neurofeedback with self-guided psychological strategies.

Background: Overgeneralised self-blame and worthlessness are key symptoms of major depressive disorder (MDD) and have previously been associated with self-blame-selective changes in connectivity between right superior anterior temporal lobe (rSATL) and subgenual frontal cortices. Another study showed that remitted MDD patients were able to modulate this neural signature using functional magnetic resonance imaging (fMRI) neurofeedback training, thereby increasing their self-esteem. The feasibility and potential of using this approach in symptomatic MDD were unknown.

Striatal and septo-hypothalamic responses to anticipation and outcome of affiliative rewards.

Humans are intrinsically motivated to bond with others. The ability to experience affiliative emotions (such as affection/tenderness, sexual attraction, and admiration/awe) may incentivize and promote these affiliative bonds. Here, we interrogate the role of the critical reward circuitry, especially the Nucleus Accumbens (NAcc) and the septo-hypothalamic region, in the anticipation of and response to affiliative rewards using a novel incentive delay task.

Blame-rebalance fMRI neurofeedback in major depressive disorder: A randomised proof-of-concept trial.

Previously, using fMRI, we demonstrated lower connectivity between right anterior superior temporal (ATL) and anterior subgenual cingulate (SCC) regions while patients with major depressive disorder (MDD) experience guilt. This neural signature was detected despite symptomatic remission which suggested a putative role in vulnerability. This randomised controlled double-blind parallel group clinical trial investigated whether patients with MDD are able to voluntarily modulate this neural signature.

Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback.

Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure.

Altruistic decisions following penetrating traumatic brain injury.

The cerebral correlates of altruistic decisions have increasingly attracted the interest of neuroscientists. To date, investigations on the neural underpinnings of altruistic decisions have primarily been conducted in healthy adults undergoing functional neuroimaging as they engaged in decisions to punish third parties. The chief purpose of the present study was to investigate altruistic decisions following focal brain damage with a novel altruistic decision task.

Identifying musical pieces from fMRI data using encoding and decoding models.

Encoding models can reveal and decode neural representations in the visual and semantic domains. However, a thorough understanding of how distributed information in auditory cortices and temporal evolution of music contribute to model performance is still lacking in the musical domain. We measured fMRI responses during naturalistic music listening and constructed a two-stage approach that first mapped musical features in auditory cortices and then decoded novel musical pieces.

Enhancing Motor Network Activity Using Real-Time Functional MRI Neurofeedback of Left Premotor Cortex.

Neurofeedback by functional magnetic resonance imaging (fMRI) is a technique of potential therapeutic relevance that allows individuals to be aware of their own neurophysiological responses and to voluntarily modulate the activity of specific brain regions, such as the premotor cortex (PMC), important for motor recovery after brain injury. We investigated (i) whether healthy human volunteers are able to up-regulate the activity of the left PMC during a right hand finger tapping motor imagery (MI) task while receiving continuous fMRI-neurofeedback, and (ii) whether successful modulation of brain activity influenced non-targeted motor control regions. During the MI task, participants of the neurofeedback group (NFB) received ongoing visual feedback representing the level of fMRI responses within their left PMC.

FRIEND Engine Framework: a real time neurofeedback client-server system for neuroimaging studies.

In this methods article, we present a new implementation of a recently reported FSL-integrated neurofeedback tool, the standalone version of "Functional Real-time Interactive Endogenous Neuromodulation and Decoding" (FRIEND). We will refer to this new implementation as the FRIEND Engine Framework. The framework comprises a client-server cross-platform solution for real time fMRI and fMRI/EEG neurofeedback studies, enabling flexible customization or integration of graphical interfaces, devices, and data processing.

Functional dissociation of ventral frontal and dorsomedial default mode network components during resting state and emotional autobiographical recall.

Humans spend a substantial share of their lives mind-wandering. This spontaneous thinking activity usually comprises autobiographical recall, emotional, and self-referential components. While neuroimaging studies have demonstrated that a specific brain "default mode network" (DMN) is consistently engaged by the "resting state" of the mind, the relative contribution of key cognitive components to DMN activity is still poorly understood.

Voluntary enhancement of neural signatures of affiliative emotion using FMRI neurofeedback.

In Ridley Scott's film "Blade Runner", empathy-detection devices are employed to measure affiliative emotions. Despite recent neurocomputational advances, it is unknown whether brain signatures of affiliative emotions, such as tenderness/affection, can be decoded and voluntarily modulated. Here, we employed multivariate voxel pattern analysis and real-time fMRI to address this question.

Real-time fMRI pattern decoding and neurofeedback using FRIEND: an FSL-integrated BCI toolbox.

The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.

Identification of psychopathic individuals using pattern classification of MRI images.

Background: Psychopathy is a disorder of personality characterized by severe impairments of social conduct, emotional experience, and interpersonal behavior. Psychopaths consistently violate social norms and bring considerable financial, emotional, or physical harm to others and to society as a whole. Recent developments in analysis methods of magnetic resonance imaging (MRI), such as voxel-based-morphometry (VBM), have become major tools to understand the anatomical correlates of this disorder.