scholarly journals Impacts of Acute Sucralose and Glucose on Brain Activity during Food Decisions in Humans

Nutrients ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3283
Author(s):  
Xiaobei Zhang ◽  
Shan Luo ◽  
Sabrina Jones ◽  
Eustace Hsu ◽  
Kathleen A. Page ◽  
...  
Keyword(s):  
Brain Activity ◽  
Cue Reactivity ◽  
A Priori ◽  
Region Of Interest ◽  
Functional Magnetic Resonance ◽  
Frontal Pole ◽  
Flavored Water ◽  
Food Decisions ◽  
Significant Attenuation ◽  
The Brain

It is not known how acute sucralose and glucose alter signaling within the brain when individuals make decisions about available food. Here we examine this using Food Bid Task in which participants bid on visually depicted food items, while simultaneously undergoing functional Magnetic Resonance Imaging. Twenty-eight participants completed three sessions after overnight fast, distinguished only by the consumption at the start of the session of 300 mL cherry flavored water with either 75 g glucose, 0.24 g sucralose, or no other ingredient. There was a marginally significant (p = 0.05) effect of condition on bids, with 13.0% lower bids after glucose and 16.6% lower bids after sucralose (both relative to water). Across conditions, greater activity within regions a priori linked to food cue reactivity predicted higher bids, as did greater activity within the medial orbitofrontal cortex and bilateral frontal pole. There was a significant attenuation within the a priori region of interest (ROI) after sucralose compared to water (p < 0.05). Activity after glucose did not differ significantly from either of the other conditions in the ROI, but an attenuation in signal was observed in the parietal cortex, relative to the water condition. Taken together, these data suggest attenuation of central nervous system (CNS) signaling associated with food valuation after glucose and sucralose.

scholarly journals Adaptive brain activity changes during tongue movement with palatal coverage from fMRI data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuka Inamochi ◽  
Kenji Fueki ◽  
Nobuo Usui ◽  
Masato Taira ◽  
Noriyuki Wakabayashi
Keyword(s):  
Spatial Information ◽  
Brain Activity ◽  
Motor Impairment ◽  
Angular Gyrus ◽  
Tongue Movement ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Oral Environment ◽  
Motor Dexterity ◽  
The Brain

AbstractSuccessful adaptation to wearing dentures with palatal coverage may be associated with cortical activity changes related to tongue motor control. The purpose was to investigate the brain activity changes during tongue movement in response to a new oral environment. Twenty-eight fully dentate subjects (mean age: 28.6-years-old) who had no experience with removable dentures wore experimental palatal plates for 7 days. We measured tongue motor dexterity, difficulty with tongue movement, and brain activity using functional magnetic resonance imaging during tongue movement at pre-insertion (Day 0), as well as immediately (Day 1), 3 days (Day 3), and 7 days (Day 7) post-insertion. Difficulty with tongue movement was significantly higher on Day 1 than on Days 0, 3, and 7. In the subtraction analysis of brain activity across each day, activations in the angular gyrus and right precuneus on Day 1 were significantly higher than on Day 7. Tongue motor impairment induced activation of the angular gyrus, which was associated with monitoring of the tongue’s spatial information, as well as the activation of the precuneus, which was associated with constructing the tongue motor imagery. As the tongue regained the smoothness in its motor functions, the activation of the angular gyrus and precuneus decreased.

Methods of Predicting the Brain Activity Based on Noun

2013 ◽  
Vol 347-350 ◽  
pp. 2516-2520
Author(s):  
Jian Hua Jiang ◽  
Xu Yu ◽  
Zhi Xing Huang
Keyword(s):  
Brain Activity ◽  
Past Research ◽  
Neural Representation ◽  
Fmri Data ◽  
Semantic Features ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Past ◽  
Machine Learning Methods ◽  
The Brain

Over the last decade, functional magnetic resonance imaging (fMRI) has become a primary tool to predict the brain activity.During the past research, researchers transfer the focus from the picture to the word.The results of these researches are relatively successful. In this paper, several typical methods which are machine learning methods are introduced. And most of the methods are by using fMRI data associated with words features. The semantic features (properties or factors) support words neural representation, and have a certain commonality in the people.The purpose of the application of these methods is used for prediction or classification.

scholarly journals Distributed affective space represents multiple emotion categories across the brain

2017 ◽  
Author(s):  
Heini Saarimäki ◽  
Lara Farzaneh Ejtehadian ◽  
Enrico Glerean ◽  
liro P. Jääskeläinen ◽  
Patrik Vuilleumier ◽  
...  
Keyword(s):  
Functional Organization ◽  
Brain Activity ◽  
Premotor Cortex ◽  
Subjective Feeling ◽  
Neural Activation ◽  
Functional Magnetic Resonance ◽  
Activation Patterns ◽  
Discrete Emotion ◽  
Subcortical Regions ◽  
The Brain

The functional organization of human emotion systems as well as their neuroanatomical basis and segregation in the brain remains unresolved. Here we used pattern classification and hierarchical clustering to reveal and characterize the organization of discrete emotion categories in the human brain. We induced 14 emotions (6 “basic”, such as fear and anger; and 8 “non-basic”, such as shame and gratitude) and a neutral state in participants using guided mental imagery while their brain activity was measured with functional magnetic resonance imaging (fMRI). Twelve out of 14 emotions could be reliably classified from the fMRI signals. All emotions engaged a multitude of brain areas, primarily in midline cortices including anterior and posterior cingulate and precuneus, in subcortical regions, and in motor regions including cerebellum and premotor cortex. Similarity of subjective emotional experiences was associated with similarity of the corresponding neural activation patterns. We conclude that the emotions included in the study have discrete neural bases characterized by specific, distributed activation patterns in widespread cortical and subcortical circuits, and highlight both overlaps and differences in the locations of these for each emotion. Locally differentiated engagement of these globally shared circuits defines the unique neural fingerprint activity pattern and the corresponding subjective feeling associated with each emotion.

scholarly journals GPT-2’s activations predict the degree of semantic comprehension in the human brain

2021 ◽  
Author(s):  
Charlotte Caucheteux ◽  
Alexandre Gramfort ◽  
Jean-Rémi King
Keyword(s):  
Brain Activity ◽  
Planum Temporale ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Responses ◽  
The Brain ◽  
Semantic Comprehension ◽  
Empirical Framework ◽  
Superior Frontal Cortex ◽  
Language Network

Language transformers, like GPT-2, have demonstrated remarkable abilities to process text, and now constitute the backbone of deep translation, summarization and dialogue algorithms. However, whether these models actually understand language is highly controversial. Here, we show that the representations of GPT-2 not only map onto the brain responses to spoken stories, but also predict the extent to which subjects understand the narratives. To this end, we analyze 101 subjects recorded with functional Magnetic Resonance Imaging while listening to 70 min of short stories. We then fit a linear model to predict brain activity from GPT-2 activations, and correlate this mapping with subjects’ comprehension scores as assessed for each story. The results show that GPT-2’s brain predictions significantly correlate with semantic comprehension. These effects are bilaterally distributed in the language network and peak with a correlation above 30% in the infero-frontal and medio-temporal gyri as well as in the superior frontal cortex, the planum temporale and the precuneus. Overall, this study provides an empirical framework to probe and dissect semantic comprehension in brains and deep learning algorithms.

scholarly journals Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity

2021 ◽  
Author(s):  
Lynn Le ◽  
Luca Ambrogioni ◽  
Katja Seeliger ◽  
Yağmur Güçlütürk ◽  
Marcel van Gerven ◽  
...  
Keyword(s):  
Visual Field ◽  
Spatial Information ◽  
Brain Activity ◽  
Region Of Interest ◽  
Magnetic Resonance Data ◽  
Recent Success ◽  
Machine Learning Applications ◽  
Video Reconstruction ◽  
The Brain ◽  
Image Network

AbstractReconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.

scholarly journals Altered resting-state network dynamics in Schizophrenia

2020 ◽  
Author(s):  
Ana Rita Lopes ◽  
Anna Sardinha Letournel ◽  
Joana Cabral
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Phase Locking ◽  
Bold Signal ◽  
Functional Magnetic Resonance ◽  
Probability Of Occurrence ◽  
Magnetic Resonance Data ◽  
Resonance Data ◽  
The Brain ◽  
Time Point

Schizophrenia remains a poorly understood disease, hence the interest in assessing and indirectly characterizing brain activity and connectivity. This paper aims to search for potential biomarkers in schizophrenia with functional magnetic resonance data, between subjects in the resting state. Firstly, we used fMRI from an open database, SchizConnect, of 48 subjects, in which 27 were control subjects, with no apparent disease and the others 21 were patients with schizophrenia. With the SPM tool, we proceeded to manually pre-process the images obtained, at the risk of having influenced the final results. Then, with the AAL atlas as a reference, we divided the brain into 116 areas. Then, brain activity in these areas were analysed, using the LEiDA method, which aims to characterize brain activity at each time point t by phase locking patterns of the BOLD signal. After the application of LEiDA, brain activity was evaluated based on trajectories and bar graphs of functional connectivity states in which the probability of occurrence and their dwell time were calculated for each state. It was also found that the visual cortex was the subsystem that showed significantly more probability of occurrence in schizophrenia patients to be assessed, and may correspond to symptoms of hallucinations by the patients with schizophrenia.

scholarly journals Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function

2019 ◽  
Vol 26 (2) ◽  
pp. 117-133 ◽  
Author(s):  
Corey Horien ◽  
Abigail S. Greene ◽  
R. Todd Constable ◽  
Dustin Scheinost
Keyword(s):  
Functional Organization ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Organization ◽  
Neural Processes ◽  
Relative Utility ◽  
And Function ◽  
Brain Behavior ◽  
The Brain

Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.

scholarly journals INTRODUCTION TO THE SPECIAL ISSUE OF ECONOMICS AND PHILOSOPHY ON NEUROECONOMICS

2008 ◽  
Vol 24 (3) ◽  
pp. 301-302 ◽  
Author(s):  
Giacomo Bonanno ◽  
Christian List ◽  
Bertil Tungodden ◽  
Peter Vallentyne
Keyword(s):  
Decision Making ◽  
Imaging Techniques ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Special Issue ◽  
Individual Decision ◽  
The Past ◽  
Economic Choices ◽  
The Brain

The past fifteen years or so have witnessed considerable progress in our understanding of how the human brain works. One of the objectives of the fast-growing field of neuroscience is to deepen our knowledge of how the brain perceives and interacts with the external world. Advances in this direction have been made possible by progress in brain imaging techniques and by clinical data obtained from patients with localized brain lesions. A relatively new field within neuroscience is neuroeconomics, which focuses on individual decision making and aims to systematically classify and map the brain activity that correlates with decision-making that pertains to economic choices. Neuroeconomic studies rely heavily on functional magnetic resonance imaging (fMRI), which measures the haemodynamic response (that is, changes in the blood flow) related to neural activity in the brain.

scholarly journals What part of the brain is involved in graphic design thinking in landscape architecture?

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0258413
Author(s):  
Yu-Ping Tsai ◽  
Shih-Han Hung ◽  
Tsung-Ren Huang ◽  
William C. Sullivan ◽  
Shih-An Tang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Design Process ◽  
Graphic Design ◽  
Landscape Architecture ◽  
Design Thinking ◽  
Brain Activity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Specific Activities ◽  
The Brain

Graphic design thinking is a key skill for landscape architects, but little is known about the links between the design process and brain activity. Based on Goel’s frontal lobe lateralization hypothesis (FLLH), we used functional magnetic resonance imaging (fMRI) to scan the brain activity of 24 designers engaging in four design processes—viewing, copy drawing, preliminary ideas, and refinement—during graphic design thinking. The captured scans produced evidence of dramatic differences between brain activity when copying an existing graphic and when engaging in graphic design thinking. The results confirm that designs involving more graphic design thinking exhibit significantly more activity in the left prefrontal cortex. These findings illuminate the design process and suggest the possibility of developing specific activities or exercises to promote graphic design thinking in landscape architecture.

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