An Exploratory Pilot Study of Brain Activation and Functional Connectivity Induced by the “Goldberg” Variations 276 years after their Commission

2019 ◽  
Vol 34 (4) ◽  
pp. 191-197
Author(s):  
Christof Karmonik ◽  
Makiko Hirata ◽  
Saba Elias ◽  
J Todd Frazier
Keyword(s):  
Pilot Study ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Brain Activation ◽  
Johann Sebastian Bach ◽  
Technical Development ◽  
The Subject ◽  
Goldberg Variations ◽  
The Brain

Around 1741, composer Johann Sebastian Bach published a long and complicated keyboard piece, calling it Aria with diverse variations for a harpsichord with two manuals. It was the capstone of a publication project called German Clavier-Übung (Keyboard Practice) where Bach wanted to show what was possible at the keyboard in terms of technical development, virtuosic finesse and compositional sophistication. The music is meticulously patterned, beginning with a highly ornamented Aria, the bass line of which fuels the 30 variations that follow. The piece is clearly divided into two parts with the second half beginning with an overture with a fanfare opening, in variation 16. The piece ends as it begins, with the return of the Aria. Here, we present an investigation into activation and connectivity in the brain of a pianist, who listened to her own recording of the “Goldberg” variation while undergoing a fMRI examination. Similarity of brain connectivity is quantified and compared with the subjective scores provided by the subject.

scholarly journals Algebra dissociates from arithmetic in the brain semantic network

2022 ◽  
Vol 18 (1) ◽  
Author(s):  
Dazhi Cheng ◽  
Mengyi Li ◽  
Jiaxin Cui ◽  
Li Wang ◽  
Naiyi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Supplementary Motor Area ◽  
Semantic Network ◽  
Brain Activation ◽  
Motor Area ◽  
Angular Gyrus ◽  
Single Trial ◽  
Positive Functional ◽  
Brain Behavior ◽  
The Brain

Abstract Background Mathematical expressions mainly include arithmetic (such as 8 − (1 + 3)) and algebra (such as a − (b + c)). Previous studies have shown that both algebraic processing and arithmetic involved the bilateral parietal brain regions. Although previous studies have revealed that algebra was dissociated from arithmetic, the neural bases of the dissociation between algebraic processing and arithmetic is still unclear. The present study uses functional magnetic resonance imaging (fMRI) to identify the specific brain networks for algebraic and arithmetic processing. Methods Using fMRI, this study scanned 30 undergraduates and directly compared the brain activation during algebra and arithmetic. Brain activations, single-trial (item-wise) interindividual correlation and mean-trial interindividual correlation related to algebra processing were compared with those related to arithmetic. The functional connectivity was analyzed by a seed-based region of interest (ROI)-to-ROI analysis. Results Brain activation analyses showed that algebra elicited greater activation in the angular gyrus and arithmetic elicited greater activation in the bilateral supplementary motor area, left insula, and left inferior parietal lobule. Interindividual single-trial brain-behavior correlation revealed significant brain-behavior correlations in the semantic network, including the middle temporal gyri, inferior frontal gyri, dorsomedial prefrontal cortices, and left angular gyrus, for algebra. For arithmetic, the significant brain-behavior correlations were located in the phonological network, including the precentral gyrus and supplementary motor area, and in the visuospatial network, including the bilateral superior parietal lobules. For algebra, significant positive functional connectivity was observed between the visuospatial network and semantic network, whereas for arithmetic, significant positive functional connectivity was observed only between the visuospatial network and phonological network. Conclusion These findings suggest that algebra relies on the semantic network and conversely, arithmetic relies on the phonological and visuospatial networks.

scholarly journals Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Dongya Wu ◽  
Xin Li ◽  
Jun Feng
Keyword(s):  
Neural Network ◽  
Functional Connectivity ◽  
Brain Connectivity ◽  
Face Area ◽  
The Face ◽  
The Individual ◽  
The Right ◽  
And Function ◽  
The Relationship ◽  
The Brain

Brain connectivity plays an important role in determining the brain region’s function. Previous researchers proposed that the brain region’s function is characterized by that region’s input and output connectivity profiles. Following this proposal, numerous studies have investigated the relationship between connectivity and function. However, this proposal only utilizes direct connectivity profiles and thus is deficient in explaining individual differences in the brain region’s function. To overcome this problem, we proposed that a brain region’s function is characterized by that region’s multi-hops connectivity profile. To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that the prediction performance is essentially improved. Results also indicated that the two-layer graph neural network is the best in characterizing rFFA’s face activation and revealed a hierarchical network for the face processing of rFFA.

scholarly journals The effect of fatigue on brain connectivity networks

2020 ◽  
Vol 6 (2) ◽  
pp. 120-131
Author(s):  
Shangen Zhang ◽  
Jingnan Sun ◽  
Xiaorong Gao
Keyword(s):  
Functional Connectivity ◽  
Normal State ◽  
Brain Connectivity ◽  
Memory Task ◽  
Visual Presentation ◽  
Mental States ◽  
Parietal Region ◽  
Brain Response ◽  
Brain Functional Connectivity ◽  
The Brain

In the fatigue state, the neural response characteristics of the brain might be different from those in the normal state. Brain functional connectivity analysis is an effective tool for distinguishing between different brain states. For example, comparative studies on the brain functional connectivity have the potential to reveal the functional differences in different mental states. The purpose of this study was to explore the relationship between human mental states and brain control abilities by analyzing the effect of fatigue on the brain response connectivity. In particular, the phase‐scrambling method was used to generate images with two noise levels, while the N‐back working memory task was used to induce the fatigue state in subjects. The paradigm of rapid serial visual presentation (RSVP) was used to present visual stimuli. The analysis of brain connections in the normal and fatigue states was conducted using the open‐source eConnectome toolbox. The results demonstrated that the control areas of neural responses were mainly distributed in the parietal region in both the normal and fatigue states. Compared to the normal state, the brain connectivity power in the parietal region was significantly weakened under the fatigue state, which indicates that the control ability of the brain is reduced in the fatigue state.

scholarly journals Study of the Functional Brain Connectivity and Lower-Limb Motor Imagery Performance After Transcranial Direct Current Stimulation

2020 ◽  
Vol 30 (08) ◽  
pp. 2050038
Author(s):  
Mario Ortiz ◽  
Eduardo Iáñez ◽  
Jorge A. Gaxiola-Tirado ◽  
David Gutiérrez ◽  
José M. Azorín
Keyword(s):  
Motor Imagery ◽  
Direct Current ◽  
Transcranial Direct Current Stimulation ◽  
Brain Connectivity ◽  
Direct Current Stimulation ◽  
Learning Tasks ◽  
The Subject ◽  
The Relationship ◽  
The Brain ◽  
Accuracy Performance

The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain–computer interface (BCI) based on electroencephalography is simulated in offline analysis to study the effect that tDCS has over different electrode configurations for the BCI. This way, the BCI performance is used as a validation index of the effect of the tDCS setup by the analysis of the classifier accuracy of the experimental sessions. In addition, the relationship between the brain connectivity and the BCI accuracy performance is analyzed. Results indicate that tDCS group, in comparison to the placebo sham group, shows a higher significant number of connectivity interactions in the motor electrodes during MI tasks and an increasing BCI accuracy over the days. However, the asymmetric tDCS setup does not improve the BCI performance of the electrodes in the intended hemisphere.

scholarly journals Brain functional connectivity upon awakening from sleep predicts interindividual differences in dream recall frequency

SLEEP ◽  
2020 ◽  
Vol 43 (12) ◽  
Author(s):  
Raphael Vallat ◽  
Alain Nicolas ◽  
Perrine Ruby
Keyword(s):  
Functional Connectivity ◽  
Short Term Memory ◽  
Brain Connectivity ◽  
Dream Recall ◽  
Mental Calculation ◽  
Interindividual Differences ◽  
Sleep Inertia ◽  
Early Afternoon ◽  
Brain Functional Connectivity ◽  
The Brain

Abstract Why do some individuals recall dreams every day while others hardly ever recall one? We hypothesized that sleep inertia—the transient period following awakening associated with brain and cognitive alterations—could be a key mechanism to explain interindividual differences in dream recall at awakening. To test this hypothesis, we measured the brain functional connectivity (combined electroencephalography–functional magnetic resonance imaging) and cognition (memory and mental calculation) of high dream recallers (HR, n = 20) and low dream recallers (LR, n = 18) in the minutes following awakening from an early-afternoon nap. Resting-state scans were acquired just after or before a 2 min mental calculation task, before the nap, 5 min after awakening from the nap, and 25 min after awakening. A comic was presented to the participants before the nap with no explicit instructions to memorize it. Dream(s) and comic recall were collected after the first post-awakening scan. As expected, between-group contrasts of the functional connectivity at 5 min post-awakening revealed a pattern of enhanced connectivity in HR within the default mode network (DMN) and between regions of the DMN and regions involved in memory processes. At the behavioral level, a between-group difference was observed in dream recall, but not comic recall. Our results provide the first evidence that brain functional connectivity right after awakening is associated with interindividual trait differences in dream recall and suggest that the brain connectivity of HR at awakening facilitates the maintenance of the short-term memory of the dream during the sleep–wake transition.

scholarly journals Longitudinal variations of brain functional connectivity: A case report study based on a mouse model of epilepsy

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 144 ◽  
Author(s):  
A. Erramuzpe ◽  
J. M. Encinas ◽  
A. Sierra ◽  
M. Maletic-Savatic ◽  
A.L. Brewster ◽  
...  
Keyword(s):  
Case Report ◽  
Status Epilepticus ◽  
Functional Connectivity ◽  
Mouse Model ◽  
Latent Period ◽  
Brain Connectivity ◽  
Low Frequency ◽  
Longitudinal Variations ◽  
Brain Functional Connectivity ◽  
The Brain

Brain Functional Connectivity (FC) quantifies statistical dependencies between areas of the brain.FC has been widely used to address altered function of brain circuits in control conditions compared to different pathological states, including epilepsy, a major neurological disorder. However, FC also has the as yet unexplored potential to help us understand the pathological transformation of the brain circuitry.Our hypothesis is that FC can differentiate global brain interactions across a time-scale of days. To this end, we present a case report study based on a mouse model for epilepsy and analyze longitudinal intracranial electroencephalography data of epilepsy to calculate FC across three stages:  1, the initial insult (status epilepticus); 2, the latent period, when epileptogenic networks emerge; and 3, chronic epilepsy, when unprovoked seizures occur as spontaneous events.We found that the overall network FC at low frequency bands decreased immediately after status epilepticus was provoked, and increased monotonously later on during the latent period. Overall, our results demonstrate the capacity  of FC to address longitudinal variations of brain connectivity across the establishment of pathological states.

scholarly journals Neural and social correlates of attitudinal brokerage: using the complete social networks of two entire villages

2021 ◽  
Vol 288 (1944) ◽  
pp. 20202866
Author(s):  
Yoosik Youm ◽  
Junsol Kim ◽  
Seyul Kwak ◽  
Jeanyung Chey
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Functional Connectivity ◽  
Cross Validation ◽  
Brain Connectivity ◽  
External Validation ◽  
Gender Role Attitudes ◽  
Brain Regions ◽  
Individual Level ◽  
The Brain

To avoid polarization and maintain small-worldness in society, people who act as attitudinal brokers are critical. These people maintain social ties with people who have dissimilar and even incompatible attitudes. Based on resting-state functional magnetic resonance imaging ( n = 139) and the complete social networks from two Korean villages ( n = 1508), we investigated the individual-level neural capacity and social-level structural opportunity for attitudinal brokerage regarding gender role attitudes. First, using a connectome-based predictive model, we successfully identified the brain functional connectivity that predicts attitudinal diversity of respondents' social network members. Brain regions that contributed most to the prediction included mentalizing regions known to be recruited in reading and understanding others’ belief states. This result was corroborated by leave-one-out cross-validation, fivefold cross-validation and external validation where the brain connectivity identified in one village was used to predict the attitudinal diversity in another independent village. Second, the association between functional connectivity and attitudinal diversity of social network members was contingent on a specific position in a social network, namely, the structural brokerage position where people have ties with two people who are not otherwise connected.

scholarly journals Large-scale intrinsic connectivity is consistent across varying task demands

2018 ◽  
Author(s):  
Paulina Kieliba ◽  
Sasidhar Madugula ◽  
Nicola Filippini ◽  
Eugene P. Duff ◽  
Tamar R. Makin
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Large Scale ◽  
Brain Activation ◽  
Whole Brain ◽  
Activation Patterns ◽  
Intrinsic Connectivity ◽  
Connectivity Patterns ◽  
Brain Functional Connectivity ◽  
The Brain

AbstractMeasuring whole-brain functional connectivity patterns based on task-free (‘restingstate’) spontaneous fluctuations in the functional MRI (fMRI) signal is a standard approach to probing habitual brain states, independent of task-specific context. This view is supported by spatial correspondence between task- and rest-derived connectivity networks. Yet, it remains unclear whether intrinsic connectivity observed in a resting-state acquisitions is persistent during task. Here, we sought to determine how changes in ongoing brain activation, elicited by task performance, impact the integrity of whole-brain functional connectivity patterns. We employed a ‘steadystates’ paradigm, in which participants continuously executed a specific task (without baseline periods). Participants underwent separate task-based (visual, motor and visuomotor) or task-free (resting) steady-state scans, each performed over a 5-minute period. This unique design allowed us to apply a set of traditional resting-state analyses to various task-states. In addition, a classical fMRI block-design was employed to identify individualized brain activation patterns for each task, allowing to characterize how differing activation patterns across the steady-states impact whole-brain intrinsic connectivity patterns. By examining correlations across segregated brain regions (nodes) and the whole brain (using independent component analysis), we show that the whole-brain network architecture characteristic of the resting-state is robustly preserved across different steady-task states, despite striking inter-task changes in brain activation (signal amplitude). Subtler changes in functional connectivity were detected locally, within the active networks. Together, we show that intrinsic connectivity underlying the canonical resting-state networks is relatively stable even when participants are engaged in different tasks and is not limited to the resting-state.New and NoteworthyDoes intrinsic functional connectivity (FC) reflect the canonical or transient state of the brain? We tested the consistency of the intrinsic connectivity networks across different task-conditions. We show that despite local changes in connectivity, at the whole-brain level there is little modulation in FC patterns, despite profound and large-scale activation changes. We therefore conclude that intrinsic FC largely reflects the a priori habitual state of the brain, independent of the specific cognitive context.

scholarly journals A Comparative Study of the Effect of Weighted or Binary Functional Brain Networks in fMRI Data Analysis

2020 ◽  
Author(s):  
Hesam Ahmadi ◽  
Emad Fatemizadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Correlation Coefficients ◽  
Clustering Coefficient ◽  
Fmri Data ◽  
Threshold Method ◽  
P Values ◽  
Fixed Threshold ◽  
Brain Functional Connectivity ◽  
The Brain

Purpose: Graph theory is a widely used and reliable tool to quantify brain connectivity. Brain functional connectivity is modeled as graph edges employing correlation coefficients. The correlation coefficients can be used as the weight that shows the power of connectivity between two nodes or can be binarized to show the existence of a connection regardless of its strength. To binarize the brain graph two approaches, namely fixed threshold and fixed density are often used. Materials and Methods: This paper aims to investigate the difference between weighted or binarized graphs in brain functional connectivity analysis. To achieve this goal, the brain connectivity matrices are generated employing the functional Magnetic Resonance Imaging (fMRI) data of Alzheimer's Disease (AD). After preprocessing the data, weighted and binarized connectivity matrices are constructed using a fixed threshold and fixed density techniques. Graph global features are extracted and a non-parametric statistical test is performed to analyze the performance of the methods. Results: Results show that all three methods are powerful in distinguishing the healthy group from AD subjects. The P-Values of the weighted graph is close to the fixed threshold method. Conclusion: Also, it is worthwhile mentioning that the fixed threshold method is robust in changing the threshold while the fixed density method is very sensitive. On the other hand, graph global measures such as clustering coefficient and transitivity, regardless of the method, show significant differences between the control and AD groups. Furthermore, the P-Values of modularity measure are very varied according to the method and the selected threshold.

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