scholarly journals Dynamic changes in large-scale functional connectivity predict performance in a multisensory task

2019 ◽  
Author(s):  
Karl J. Hollensteiner ◽  
Edgar Galindo-Leon ◽  
Florian Pieper ◽  
Gerhard Engler ◽  
Guido Nolte ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Long Range ◽  
Large Scale ◽  
Principal Component ◽  
Stimulus Onset ◽  
Functional Coupling ◽  
Phase Coupling ◽  
Network State ◽  
Task Period ◽  
Response Onset

AbstractComplex and variable behavior requires fast changes of functional connectivity in large-scale cortical networks. Here, we report on the cortical dynamics of functional coupling across visual, auditory and parietal areas during a lateralized detection task in the ferret. We hypothesized that fluctuations in coupling, indicative of dynamic variations in the network state, might predict the animals’ performance. While power for hit and miss trials showed significant differences only around stimulus and response onset, phase coupling already differed before stimulus onset. Principal component analysis of directed coupling at the single-trial level during this period revealed subnetworks that most strongly related to behavior. While higher global phase coupling of visual and auditory regions to parietal cortex was predictive of task performance, a second component showed that a reduction in coupling between subnetworks of sensory modalities was also necessary, probably to allow a better detection of the unimodal signals. Furthermore, we observed that long-range coupling became more predominant during the task period compared to the pre-stimulus baseline. Taken together, these results suggest that fluctuations in the network state, particular with respect to long-range connectivity, are key determinants of the animals’ behavior.

scholarly journals Long-range functional coupling predicts performance: oscillatory EEG networks in multisensory processing

2015 ◽  
Author(s):  
Peng Wang ◽  
Florian Göschl ◽  
Uwe Friese ◽  
Peter König ◽  
Andreas K. Engel
Keyword(s):  
Long Range ◽  
Large Scale ◽  
Multisensory Processing ◽  
Data Driven ◽  
Matching Task ◽  
Functional Coupling ◽  
Phase Coupling ◽  
Brain Areas ◽  
Phase Dynamics ◽  
Brain Functions

AbstractThe integration of sensory signals from different modalities requires flexible interaction of remote brain areas. One candidate mechanism to establish communication in the brain is transient synchronization of oscillatory neural signals. Although there is abundant evidence for the involvement of cortical oscillations in brain functions based on the analysis of local power, assessment of the phase dynamics among spatially distributed neuronal populations and their relevance for behavior is still sparse. In the present study, we investigated the interaction between remote brain areas by analyzing high-density electroencephalogram (EEG) data obtained from human participants engaged in a visuotactile pattern matching task. We deployed an approach for purely data-driven clustering of neuronal phase coupling in source space, which allowed imaging of large-scale functional networks in space, time and frequency without defining a priori constraints. Based on the phase coupling results, we further explored how brain areas interacted across frequencies by computing phase-amplitude coupling. Several networks of interacting sources were identified with our approach, synchronizing their activity within and across the theta (~5 Hz), alpha (~10 Hz), and beta (~ 20 Hz) frequency bands and involving multiple brain areas that have previously been associated with attention and motor control. We demonstrate the functional relevance of these networks by showing that phase delays – in contrast to spectral power – were predictive of task performance. The data-driven analysis approach employed in the current study allowed an unbiased examination of functional brain networks based on EEG source level connectivity data. Showcased for multisensory processing, our results provide evidence that large-scale neuronal coupling is vital to long-range communication in the human brain and relevant for the behavioral outcome in a cognitive task.

scholarly journals Can fMRI functional connectivity index dynamic neural communication?

2021 ◽  
Author(s):  
Sonsoles Alonso Martinez ◽  
Alberto Llera Arenas ◽  
Gert T Ter Horst ◽  
Diego Vidaurre
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Necessary Conditions ◽  
Principal Component ◽  
Temporal Complexity ◽  
Neural Communication ◽  
Connectivity Model ◽  
Neural Populations ◽  
Changing Patterns ◽  
Brain Fmri

In order to continuously respond to a changing environment and support self-generating cognition and behaviour, neural communication must be highly flexible and dynamic at the same time than hierarchically organized. While whole-brain fMRI measures have revealed robust yet changing patterns of statistical dependencies between regions, it is not clear whether these statistical patterns (referred to as functional connectivity) can reflect dynamic large-scale communication in a way that is relevant to cognition. For functional connectivity to reflect actual communication, we propose three necessary conditions: it must span sufficient temporal complexity to support the needs of cognition while still being highly organized so that the system behaves reliably; it must be able to adapt to the current behavioural context; and it must exhibit fluctuations at sufficiently short timescales. In this paper, we introduce principal components of connectivity analysis (PCCA), an approach based on running principal component analysis on multiple runs of a time-varying functional connectivity model to show that functional connectivity follows low- yet multi-dimensional trajectories that can be reliably measured, and that these trajectories meet the aforementioned criteria to index flexible communication between neural populations and support moment-to-moment cognition.

scholarly journals Topographic Shifts in Functional Connectivity and Reduced Lateralization in 16p11.2 Deletion Carriers: A Genetics-First Approach to Understanding Autism

2020 ◽  
Author(s):  
ABID Y QURESHI ◽  
Jared A. Nielsen ◽  
Jorge Sepulcre
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Language Ability ◽  
Hemispheric Lateralization ◽  
Functional Coupling ◽  
Resting State Functional Connectivity ◽  
Hierarchical Processing ◽  
Large Scale Networks

Abstract Background: The study of autism has been confounded by genetic and etiologic heterogeneity. The current study utilized a genetics-first approach to investigate the underlying neurobiology of autism by studying individuals with copy number variation at 16p11.2. Our aim was to investigate the prevailing theories of brain connectivity in autism – specifically, in regard to (1) distributed brain networks, (2) local and distant connectivity, and (3) functional lateralization. Methods: We analyzed resting-state functional connectivity MRI acquired in 26 carriers of a 16p11.2 deletion and 42 age-matched control participants. We also compared the functional connectivity metrics with measures of language ability, IQ, and social behavior.Results: We do not find widespread disruption of canonical large-scale networks in the deletion carriers. Nor do we find quantitative differences in the degree of local and distant connections. Instead, we discover unique connections in 16p11.2 deletion carriers that are not present in the control participants. Specifically, functional coupling between auditory cortex and regions of the default network is present only in the deletion carriers. In addition to the topographic shifts in functional connectivity, we observe reduced right hemispheric lateralization in the deletion carriers and less left hemispheric lateralization in individuals with poorer language ability.Conclusions: Links or connections between primary sensory areas and higher-order association areas violate fundamental large-scale circuit properties by functionally connecting brain regions specialized in local (hierarchical) processing with those that specialize in distant (parallel) processing. Aberrant hemispheric lateralization and connections between auditory and default networks may underlie difficulties with language and social interactions.

scholarly journals Modulation of large-scale cortical coupling by transcranial alternating current stimulation

2018 ◽  
Author(s):  
Bettina C. Schwab ◽  
Jonas Misselhorn ◽  
Andreas K. Engel
Keyword(s):  
Functional Connectivity ◽  
Long Range ◽  
Electric Fields ◽  
Large Scale ◽  
Alternating Current ◽  
Total Power ◽  
Maximum Effect ◽  
High Definition ◽  
Transcranial Alternating Current Stimulation ◽  
Eeg Connectivity

AbstractBackgroundLong-range functional connectivity in the brain is considered fundamental for cognition and is known to be altered in many neuropsychiatric disorders. To modify such coupling independent of sensory input, noninvasive brain stimulation could be of utmost value.ObjectiveFirst, we tested if transcranial alternating current stimulation (tACS) is able to influence functional connectivity in the human brain. Second, we investigated the specificity of effects in frequency and space.MethodsEEG aftereffects of bifocal high-definition tACS were analyzed systematically in sensor and source space. Participants were stimulated transcranially in counterbalanced order (1) in-phase, with identical electric fields in both hemispheres, (2) anti-phase, with phase-reversed electric fields in the two hemispheres, and (3) jittered-phase, generated by subtle frequency shifts continuously changing the phase relation between the two fields.ResultsWhile total power and spatial distribution of the fields were comparable between conditions, global pre-post stimulation changes in EEG connectivity were larger after in-phase stimulation than after anti-phase or jittered-phase stimulation. Those differences in connectivity were restricted to the stimulated frequency band and decayed within the first 120 s after stimulation offset. Source reconstruction localized the maximum effect between the stimulated occipitoparietal areas.ConclusionThe relative phase of bifocal alpha-tACS modulated alpha-band connectivity between the targeted regions. As side effects did not differ between stimulation conditions, we conclude that neural activity was phase-specifically influenced by the electric fields. We thus suggest bifocal high-definition tACS as a tool to manipulate long-range cortico-cortical coupling which outlasts the stimulation period.

scholarly journals Modeling of Large-Scale Functional Brain Networks Based on Structural Connectivity from DTI: Comparison with EEG Derived Phase Coupling Networks and Evaluation of Alternative Methods along the Modeling Path

2016 ◽  
Author(s):  
Holger Finger ◽  
Marlene Bönstrup ◽  
Bastian Cheng ◽  
Arnaud Messé ◽  
Claus Hilgetag ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Large Scale ◽  
Structural Connectivity ◽  
Model Fit ◽  
Alternative Methods ◽  
Connectivity Matrix ◽  
Functional Coupling ◽  
Phase Lag ◽  
Modeling Framework ◽  
Zero Phase

Here we use computational modeling of fast neural dynamics to explore the relationship between structural and functional coupling in a population of healthy subjects. We use DTI data to estimate structural connectivity and subsequently model phase couplings from band-limited oscillatory signals derived from multichannel EEG data. Our results show that about 23.4% of the variance in empirical networks of resting-state fast oscillations is explained by the underlying white matter architecture. By simulating functional connectivity using a simple reference model, the match between simulated and empirical functional connectivity further increases to 45.4%. In a second step, we use our modeling framework to explore several technical alternatives along the modeling path. First, we find that an augmentation of homotopic connections in the structural connectivity matrix improves the link to functional connectivity while a correction for fiber distance slightly decreases the performance of the model. Second, a more complex computational model based on Kuramoto oscillators leads to a slight improvement of the model fit. Third, we show that the comparison of modeled and empirical functional connectivity at source level is much more specific for the underlying structural connectivity. However, different source reconstruction algorithms gave comparable results. Of note, as the fourth finding, the model fit was much better if zero-phase lag components were preserved in the empirical functional connectome, indicating a considerable amount of functionally relevant synchrony taking place with near zero or zero-phase lag. The combination of the best performing alternatives at each stage in the pipeline results in a model that explains 54.4% of the variance in the empirical EEG functional connectivity. Our study shows that large-scale brain circuits of fast neural network synchrony strongly rely upon the structural connectome and simple computational models of neural activity can explain missing links in the structure-function relationship.

scholarly journals Content-Based Estimation of Brain MRI Tilt in Three Orthogonal Directions

2021 ◽  
Author(s):  
Pooja Prabhu ◽  
A. K. Karunakar ◽  
Sanjib Sinha ◽  
N. Mariyappa ◽  
G. K. Bhargava ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Mri ◽  
Similarity Measures ◽  
Principal Component ◽  
Brain Anatomy ◽  
Morphological Operations ◽  
Mr Images ◽  
Tilt Correction ◽  
Brain Mr Images ◽  
The Brain

AbstractIn a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

scholarly journals Principal component analysis tomography in near-infrared integral field spectroscopy of young stellar objects – I. Revisiting the high-mass protostar W33A

2021 ◽  
Vol 503 (1) ◽  
pp. 270-291
Author(s):  
F Navarete ◽  
A Damineli ◽  
J E Steiner ◽  
R D Blum
Keyword(s):  
Large Scale ◽  
Near Infrared ◽  
Rotation Axis ◽  
Principal Component ◽  
Young Stellar Objects ◽  
High Mass ◽  
Continuum Emission ◽  
Rotating Disc ◽  
Stellar Objects ◽  
K Band

ABSTRACT W33A is a well-known example of a high-mass young stellar object showing evidence of a circumstellar disc. We revisited the K-band NIFS/Gemini North observations of the W33A protostar using principal components analysis tomography and additional post-processing routines. Our results indicate the presence of a compact rotating disc based on the kinematics of the CO absorption features. The position–velocity diagram shows that the disc exhibits a rotation curve with velocities that rapidly decrease for radii larger than 0.1 arcsec (∼250 au) from the central source, suggesting a structure about four times more compact than previously reported. We derived a dynamical mass of 10.0$^{+4.1}_{-2.2}$ $\rm {M}_\odot$ for the ‘disc + protostar’ system, about ∼33 per cent smaller than previously reported, but still compatible with high-mass protostar status. A relatively compact H2 wind was identified at the base of the large-scale outflow of W33A, with a mean visual extinction of ∼63 mag. By taking advantage of supplementary near-infrared maps, we identified at least two other point-like objects driving extended structures in the vicinity of W33A, suggesting that multiple active protostars are located within the cloud. The closest object (Source B) was also identified in the NIFS field of view as a faint point-like object at a projected distance of ∼7000 au from W33A, powering extended K-band continuum emission detected in the same field. Another source (Source C) is driving a bipolar $\rm {H}_2$ jet aligned perpendicular to the rotation axis of W33A.

scholarly journals Farmers’ Perceptions of Commercial Insect-Based Feed for Sustainable Livestock Production in Kenya

2021 ◽  
Vol 13 (10) ◽  
pp. 5359
Author(s):  
Afrika Onguko Okello ◽  
Jonathan Makau Nzuma ◽  
David Jakinda Otieno ◽  
Michael Kidoido ◽  
Chrysantus Mbi Tanga
Keyword(s):  
Food Systems ◽  
Large Scale ◽  
Principal Component ◽  
Farm Income ◽  
Purchase Decisions ◽  
Wealth Status ◽  
Feed Formulation ◽  
Alternative Protein ◽  
Livestock Products ◽  
The Impact

The utilization of insect-based feeds (IBF) as an alternative protein source is increasingly gaining momentum worldwide owing to recent concerns over the impact of food systems on the environment. However, its large-scale adoption will depend on farmers’ acceptance of its key qualities. This study evaluates farmer’s perceptions of commercial IBF products and assesses the factors that would influence its adoption. It employs principal component analysis (PCA) to develop perception indices that are subsequently used in multiple regression analysis of survey data collected from a sample of 310 farmers. Over 90% of the farmers were ready and willing to use IBF. The PCA identified feed performance, social acceptability of the use of insects in feed formulation, feed versatility and marketability of livestock products reared on IBF as the key attributes that would inform farmers’ purchase decisions. Awareness of IBF attributes, group membership, off-farm income, wealth status and education significantly influenced farmers’ perceptions of IBF. Interventions such as experimental demonstrations that increase farmers’ technical knowledge on the productivity of livestock fed on IBF are crucial to reducing farmers’ uncertainties towards acceptability of IBF. Public partnerships with resource-endowed farmers and farmer groups are recommended to improve knowledge sharing on IBF.

scholarly journals Large-Scale Precipitation Variability over Northwest China Inferred from Tree Rings

2011 ◽  
Vol 24 (13) ◽  
pp. 3457-3468 ◽  
Author(s):  
Keyan Fang ◽  
Xiaohua Gou ◽  
Fahu Chen ◽  
Edward Cook ◽  
Jinbao Li ◽  
...  
Keyword(s):  
Tree Ring ◽  
Large Scale ◽  
Principal Component ◽  
Northwest China ◽  
Poor Quality ◽  
Contour Method ◽  
Precipitation Reconstruction ◽  
Nw China ◽  
Climate Station ◽  
Instrumental Records

Abstract A preliminary study of a point-by-point spatial precipitation reconstruction for northwestern (NW) China is explored, based on a tree-ring network of 132 chronologies. Precipitation variations during the past ~200–400 yr (the common reconstruction period is from 1802 to 1990) are reconstructed for 26 stations in NW China from a nationwide 160-station dataset. The authors introduce a “search spatial correlation contour” method to locate candidate tree-ring predictors for the reconstruction data of a given climate station. Calibration and verification results indicate that most precipitation reconstruction models are acceptable, except for a few reconstructions (stations Hetian, Hami, Jiuquan, and Wuwei) with degraded quality. Additionally, the authors compare four spatial precipitation factors in the instrumental records and reconstructions derived from a rotated principal component analysis (RPCA). The northern and southern Xinjiang factors from the instrumental and reconstructed data agree well with each other. However, differences in spatial patterns between the instrumentation and reconstruction data are also found for the other two factors, which probably result from the relatively poor quality of a few stations. Major drought events documented in previous studies—for example, from the 1920s through the 1930s for the eastern part of NW China—are reconstructed in this study.

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