scholarly journals Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down syndrome: A network science EEG study

2020 ◽  
pp. 1-21
Author(s):  
Alexandra Anagnostopoulou ◽  
Charis Styliadis ◽  
Panagiotis Kartsidis ◽  
Evangelia Romanopoulou ◽  
Vasiliki Zilidou ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Functional Connectivity ◽  
Cognitive Training ◽  
Resting State ◽  
Network Architecture ◽  
Right Hemisphere ◽  
Random Network ◽  
Brain Activity ◽  
Hierarchical Organization ◽  
The Brain

Understanding the neuroplastic capacity of people with Down syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a 10-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional connectivity in conjunction with psychosomatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left-to-right hemisphere, as well as increased physical and cognitive performance. Our findings reveal a strong adaptive neuroplastic reorganization as a result of the training that leads to a less-random network with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful relationships to underlying Down syndrome processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.

scholarly journals Computerized physical and cognitive training improves the functional architecture of the brain in adults with Down Syndrome: a longitudinal network science EEG study

2020 ◽  
Author(s):  
Alexandra Anagnostopoulou ◽  
Charis Styliadis ◽  
Panagiotis Kartsidis ◽  
Evangelia Romanopoulou ◽  
Vasiliki Zilidou ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Cognitive Training ◽  
Resting State ◽  
Network Architecture ◽  
Network Science ◽  
Right Hemisphere ◽  
Brain Activity ◽  
Hierarchical Organization ◽  
The Brain ◽  
Training Protocol

AbstractUnderstanding the neuroplastic capacity of people with Down Syndrome (PwDS) can potentially reveal the causal relationship between aberrant brain organization and phenotypic characteristics. We used resting-state EEG recordings to identify how a neuroplasticity-triggering training protocol relates to changes in the functional connectivity of the brain’s intrinsic cortical networks. Brain activity of 12 PwDS before and after a ten-week protocol of combined physical and cognitive training was statistically compared to quantify changes in directed functional in conjunction with psycho-somatometric assessments. PwDS showed increased connectivity within the left hemisphere and from left to right hemisphere, as well as increased physical and cognitive performance. Our results reveal a strong adaptive neuroplastic reorganization, as a result of the training that leads to a more complex and less-random network, with a more pronounced hierarchical organization. Our results go beyond previous findings by indicating a transition to a healthier, more efficient, and flexible network architecture, with improved integration and segregation abilities in the brain of PwDS. Resting-state electrophysiological brain activity is used here for the first time to display meaningful longitudinal relationships to underlying DS processes and outcomes of importance in a translational inquiry. This trial is registered with ClinicalTrials.gov Identifier NCT04390321.Author SummaryThe effects of cognitive and physical training on the neuroplasticity attributes of people with and without cognitive impairment have been well documented via neurophysiological evaluations and network science indices. However, there is still insufficient evidence for people with Down Syndrome (PwDS). We investigated the effects of a combinational training protocol on the brain network organization of 12 adult PwDS using EEG and network indices coupled with tests assessing their cognitive and physical capacity. We report evidence of adaptational neuroplastic effects, pointing to a transitional state towards a healthier organization with an increased ability to integrate and segregate information. Our findings underline the ability of the DS brain to respond to the cognitive demands of external stimuli, reflecting the possibility of developing independent- living skills.

scholarly journals Brain activity fluctuations propagate as waves traversing the cortical hierarchy

2020 ◽  
Author(s):  
Yameng Gu ◽  
Lucas E. Sainburg ◽  
Sizhe Kuang ◽  
Feng Han ◽  
Jack W. Williams ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Hierarchical Organization ◽  
Subcortical Structures ◽  
Cortical Hierarchy ◽  
Neural Origin ◽  
The Brain

AbstractThe brain exhibits highly organized patterns of spontaneous activity as measured by resting-state fMRI fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, though this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

scholarly journals Brain Activity Fluctuations Propagate as Waves Traversing the Cortical Hierarchy

2021 ◽  
Author(s):  
Yameng Gu ◽  
Lucas E Sainburg ◽  
Sizhe Kuang ◽  
Feng Han ◽  
Jack W Williams ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Spontaneous Activity ◽  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Hierarchical Organization ◽  
Subcortical Structures ◽  
Cortical Hierarchy ◽  
Neural Origin ◽  
The Brain

Abstract The brain exhibits highly organized patterns of spontaneous activity as measured by resting-state functional magnetic resonance imaging (fMRI) fluctuations that are being widely used to assess the brain’s functional connectivity. Some evidence suggests that spatiotemporally coherent waves are a core feature of spontaneous activity that shapes functional connectivity, although this has been difficult to establish using fMRI given the temporal constraints of the hemodynamic signal. Here, we investigated the structure of spontaneous waves in human fMRI and monkey electrocorticography. In both species, we found clear, repeatable, and directionally constrained activity waves coursed along a spatial axis approximately representing cortical hierarchical organization. These cortical propagations were closely associated with activity changes in distinct subcortical structures, particularly those related to arousal regulation, and modulated across different states of vigilance. The findings demonstrate a neural origin of spatiotemporal fMRI wave propagation at rest and link it to the principal gradient of resting-state fMRI connectivity.

scholarly journals Effects of Cognitive Training in Mild Cognitive Impairmentmeasured by Resting State Functional Imaging

2020 ◽  
Vol 10 (11) ◽  
pp. 175
Author(s):  
Seungho Kim ◽  
Eunhee Park ◽  
Hyunsil Cha ◽  
Jae-Chang Jung ◽  
Tae-Du Jung ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Training Program ◽  
Cognitive Training ◽  
Resting State ◽  
Cognitive Abilities ◽  
Motor Coordination ◽  
Network Connectivity ◽  
Functional Magnetic Resonance ◽  
Function Test ◽  
The Brain

Mild cognitive impairment (MCI) is defined as an intermediate state of cognitive alteration between normal aging and dementia. In this study, we performed a functional network connectivity analysis using resting-state functional magnetic resonance imaging to investigate the association between changes in functional connectivity in the brain and the improvement in cognitive abilities after cognitive training. A computerized cognitive training program was used to improve the abilities of fifteen participants with MCI. The cognitive training program (Comcog), which consists of three weekly sessions totaling 90 min, was conducted with all participants over six weeks. The cognitive abilities before (pre-Comcog) and after (post-Comcog) the cognitive training process were measured using a neurocognitive function test. After the Comcog, the participants enhanced their visual and verbal memories, attention, and visuo-motor coordination. The functional connectivity between cingulo-opercular (CON) and default mode (DMN) showed significant improvements after Comcog training. Therefore, our study suggests that cognitive training may improve the cognitive abilities of participants. This improvement was associated with an increase in the functional connectivity between DMN and CON. The increase in functional connectivity after cognitive training was specifically associated with overall cognitive functions, including executive, memory, decision-making, and motivational functions.

scholarly journals Multifractal and entropy analysis of resting-state electroencephalography reveals spatial organization in local dynamic functional connectivity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Frigyes Samuel Racz ◽  
Orestis Stylianou ◽  
Peter Mukli ◽  
Andras Eke
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Spatial Organization ◽  
Brain Activity ◽  
Surrogate Data ◽  
Brain Regions ◽  
Local Dynamic ◽  
Dynamic Functional Connectivity ◽  
Multifractal Scaling ◽  
The Brain

Abstract Functional connectivity of the brain fluctuates even in resting-state condition. It has been reported recently that fluctuations of global functional network topology and those of individual connections between brain regions expressed multifractal scaling. To expand on these findings, in this study we investigated if multifractality was indeed an inherent property of dynamic functional connectivity (DFC) on the regional level as well. Furthermore, we explored if local DFC showed region-specific differences in its multifractal and entropy-related features. DFC analyses were performed on 62-channel, resting-state electroencephalography recordings of twelve young, healthy subjects. Surrogate data testing verified the true multifractal nature of regional DFC that could be attributed to the presumed nonlinear nature of the underlying processes. Moreover, we found a characteristic spatial distribution of local connectivity dynamics, in that frontal and occipital regions showed stronger long-range correlation and higher degree of multifractality, whereas the highest values of entropy were found over the central and temporal regions. The revealed topology reflected well the underlying resting-state network organization of the brain. The presented results and the proposed analysis framework could improve our understanding on how resting-state brain activity is spatio-temporally organized and may provide potential biomarkers for future clinical research.

scholarly journals Source Space Analysis of Event-Related Dynamic Reorganization of Brain Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Andreas A. Ioannides ◽  
Stavros I. Dimitriadis ◽  
George A. Saridis ◽  
Marotesa Voultsidou ◽  
Vahe Poghosyan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Activity ◽  
Clinical Applications ◽  
Brain Areas ◽  
Connectivity Patterns ◽  
The Brain ◽  
Typical Subject ◽  
Different Parts ◽  
Neuroimaging Techniques

How the brain works is nowadays synonymous with how different parts of the brain work together and the derivation of mathematical descriptions for the functional connectivity patterns that can be objectively derived from data of different neuroimaging techniques. In most cases static networks are studied, often relying on resting state recordings. Here, we present a quantitative study of dynamic reconfiguration of connectivity for event-related experiments. Our motivation is the development of a methodology that can be used for personalized monitoring of brain activity. In line with this motivation, we use data with visual stimuli from a typical subject that participated in different experiments that were previously analyzed with traditional methods. The earlier studies identified well-defined changes in specific brain areas at specific latencies related to attention, properties of stimuli, and tasks demands. Using a recently introduced methodology, we track the event-related changes in network organization, at source space level, thus providing a more global and complete view of the stages of processing associated with the regional changes in activity. The results suggest the time evolving modularity as an additional brain code that is accessible with noninvasive means and hence available for personalized monitoring and clinical applications.

scholarly journals Latent functional connectivity underlying multiple brain states

2021 ◽  
Author(s):  
Ethan M McCormick ◽  
Katelyn L Arnemann ◽  
Takuya Ito ◽  
Stephen Jose Hanson ◽  
Michael W Cole
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Network Architecture ◽  
Brain Activity ◽  
Brain Network ◽  
Mathematical Framework ◽  
Human Connectome Project ◽  
Brain States ◽  
Task Conditions ◽  
Psychometric G

Functional connectivity (FC) studies have predominantly focused on resting state, where ongoing dynamics are thought to primarily reflect the brain's intrinsic network architecture, which is thought to be broadly relevant to brain function because it persists across brain states. However, it is unknown whether resting state is the optimal state for measuring intrinsic FC. We propose that latent FC, reflecting patterns of connectivity shared across many brain states, may better capture intrinsic FC relative to measures derived from resting state alone. We estimated latent FC in relation to 7 highly distinct task states (24 task conditions) and resting state using fMRI data from 352 participants from the Human Connectome Project. Latent FC was estimated independently for each connection by applying leave-one-task-out factor analysis on the state FC estimates. Compared to resting-state connectivity, we found that latent connectivity improves generalization to held-out brain states, better explaining patterns of both connectivity and task-evoked brain activity. We also found that latent connectivity improved prediction of behavior, measured by the general intelligence factor psychometric g. Our results suggest that patterns of FC shared across many brain states, rather than just resting state, better reflects general, state-independent connectivity. This affirms the notion of "intrinsic" brain network architecture as a set of connectivity properties persistent across brain states, providing an updated conceptual and mathematical framework of intrinsic connectivity as a latent factor.

Effects of repetitive transcranial magnetic stimulation combined with cognitive training on resting-state brain activity in Alzheimer’s disease

2022 ◽  
pp. 197140092110674
Author(s):  
Yuanyuan Qin ◽  
Fengxia Zhang ◽  
Min Zhang ◽  
Wenzhen Zhu
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Functional Connectivity ◽  
Cognitive Training ◽  
Resting State ◽  
Magnetic Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Brain Activity ◽  
Low Frequency ◽  
Frequency Fluctuation ◽  
Real Group

Objectives Repetitive transcranial magnetic stimulation (rTMS) is a promising tool to modulate brain plasticity, but the neural basis has been little addressed. The purpose was to investigate the effects of rTMS on resting-state brain activity in patients with Alzheimer’s disease (AD). Methods Seventeen patients with mild or moderate AD were enrolled and randomly divided into one of the two intervention groups: (1) real rTMS combined with cognitive training (real group, n = 9); (2) sham rTMS with cognitive training (sham group, n = 8). 10 Hz rTMS was used to stimulate the left dorsolateral prefrontal cortex and then the left lateral temporal lobe for 20 min each day for 4 weeks. Each patient underwent neuropsychological assessment and resting-state functional magnetic resonance imaging (rsfMRI) before and after treatment. The fractional amplitude of low frequency fluctuation (fALFF) of rsfMRI data in real group were: (1) compared to sham; (2) correlated with rTMS-induced cognitive alterations. Results Significantly increased fALFF in right cerebellum/declive, left lingual/cuneus and left cingulate gyrus, as well as decreased fALFF in left middle frontal gyrus were found after 10 Hz rTMS, but not after sham stimulation. Using these suprathreshold regions, we found that rTMS increased functional connectivity between the right cerebellum/declive and left precentral/postcentral gyrus. The fALFF increase in left lingual/cuneus and right cerebellum/declive was associated with significant improvement in cognitive function. Conclusions rTMS combined with cognitive training induced increased low frequency fluctuation neural oscillations and functional connectivity in brain regions subserving cognition, suggesting a possible neuronal mechanism of the beneficial effects of rTMS.

scholarly journals Learning differentially reorganizes brain activity and connectivity

2020 ◽  
Author(s):  
Maxwell A. Bertolero ◽  
Azeez Adebimpe ◽  
Ankit N. Khambhati ◽  
Marcelo G. Mattar ◽  
Daniel Romer ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Learning Performance ◽  
Functional Architecture ◽  
Time Space ◽  
Novel Objects ◽  
Value Learning ◽  
The Brain

Human learning is a complex process in which future behavior is altered via the reorganization of brain activity and connectivity. It remains unknown whether activity and connectivity differentially reorganize during learning, and, if so, how that differential reorganization tracks stages of learning across distinct brain areas. Here, we address this gap in knowledge by measuring brain activity and functional connectivity in a longitudinal fMRI experiment in which healthy adult human participants learn the values of novel objects over the course of four days. An increasing similarity in activity or functional connectivity across subjects during learning reflects reorganization toward a common functional architecture. We assessed the presence of reorganization in activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic reorganization of brain connectivity and activity—as a function of time, space, and performance—that occurs while subjects learn. Spatially localized brain activity reorganizes across the brain to a common functional architecture early in learning, and this reorganization tracks early learning performance. In contrast, spatially distributed connectivity reorganizes across the brain to a common functional architecture as training progresses, and this reorganization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of reorganization in activity and connectivity at different phases of value learning, which inform the ongoing study of learning processes more generally.

scholarly journals Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

2021 ◽  
Vol 15 ◽  
Author(s):  
Nikki Leeuwis ◽  
Sue Yoon ◽  
Maryam Alimardani
Keyword(s):  
Functional Connectivity ◽  
Motor Imagery ◽  
Resting State ◽  
Right Hemisphere ◽  
Brain Activity ◽  
Brain Regions ◽  
High Rate ◽  
Imagery Task ◽  
Mu Suppression ◽  
Promising Tool

Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.

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