scholarly journals The Noise-Resilient Brain: Resting-State Oscillatory Activity Predicts Words-In-Noise Recognition

2019 ◽  
Author(s):  
Thomas Houweling ◽  
Robert Becker ◽  
Alexis Hervais-Adelman
Keyword(s):  
Individual Differences ◽  
Phonological Processing ◽  
Resting State ◽  
Recognition Performance ◽  
Brain Activity ◽  
Superior Temporal Gyrus ◽  
Oscillatory Activity ◽  
List Type ◽  
Gamma Frequency ◽  
Human Connectome Project

AbstractThe role of neuronal oscillations in the processing of speech has recently come to prominence. Since resting-state (RS) brain activity has been shown to predict both task-related brain activation and behavioural performance, we set out to establish whether inter-individual differences in spectrally-resolved RS-MEG power are associated with variations in words-in-noise recognition in a sample of 88 participants made available by the Human Connectome Project. Positive associations with resilience to noise were observed with power in the range 21 and 29Hz in a number of areas along the left temporal gyrus and temporo-parietal association areas peaking in left posterior superior temporal gyrus (pSTG). Significant associations were also found in the right posterior superior temporal gyrus in the frequency range 30 to 40Hz. We propose that individual differences in words-in-noise performance are related to baseline excitability levels of the neural substrates of phonological processing.HighlightsPower of resting MEG activity predicts Words-In-Noise recognition performanceSignificant associations in higher beta and lower gamma frequency bandStrongest in left-lateralised perisylvian cluster peaking in posterior STGEffects are spectrally and spatially consistent with phoneme-level processing

scholarly journals Brain connectivity at rest predicts individual differences in normative activity during movie watching

2021 ◽  
Author(s):  
David C Gruskin ◽  
Gaurav H Patel
Keyword(s):  
Spatial Distribution ◽  
Individual Differences ◽  
Resting State ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Sensory Information ◽  
Resting State Functional Connectivity ◽  
Similar Region ◽  
Human Connectome Project

When multiple individuals are exposed to the same sensory event, some are bound to have less typical experiences than others. These atypical experiences are underpinned by atypical stimulus-evoked brain activity, the extent of which is often indexed by intersubject correlation (ISC). Previous research has attributed individual differences in ISC to variation in trait-like behavioral phenotypes. Here, we extend this line of work by showing that an individual's degree and spatial distribution of ISC are closely related to their brain's intrinsic functional architecture. Using resting state and movie watching fMRI data from 176 Human Connectome Project participants, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC with considerable accuracy. Similar region-level analyses demonstrate that the amount of ISC a brain region exhibits during movie watching is associated with its connectivity to others at rest, and that the nature of these connectivity-activity relationships varies as a function of the region's role in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings suggest that the brain's ability to process complex sensory information is tightly linked to its baseline functional organization and motivate a more comprehensive understanding of individual responses to naturalistic stimuli.

scholarly journals PCA-based source-space contrast maps reveal psychologically meaningful individual differences in continuous MEG activity

2019 ◽  
Author(s):  
Erkka Heinilä ◽  
Aapo Hyvärinen ◽  
Tapani Ristaniemi ◽  
Lauri Parkkonen ◽  
Tiina Parviainen
Keyword(s):  
Individual Differences ◽  
Brain Activity ◽  
Principal Component ◽  
Mind Wandering ◽  
Oscillatory Activity ◽  
Behavioral Characteristics ◽  
Focused Attention ◽  
Specific Method ◽  
List Type ◽  
The Brain

AbstractWithin the field of neuroimaging, there has been an increasing trend towards studying brain activity in naturalistic conditions, and it is possible to robustly estimate networks of on-going oscillatory activity in the brain. However, not many studies have focused on differences between individuals in on-going brain activity that would be associable to psychological or behavioral characteristics. Existing standard methods can perform well at single-participant level, but generalizing the methodology across many participants is challenging due to individual differences of brains. As an example of a clinically relevant, naturalistic condition we consider here mindfulness. Trait mindfulness, as well as a mindfulness-based intervention cultivating focused attention, is often associated with benefits for psychological health. Therefore, the manner in which the brain engages in focused attention vs. mind wandering is likely to associate with individual differences in psycho–behavioral tendencies.We recorded MEG from 29 participants both in a state of focused attention and in a state of simulated mind wandering. We used Principal Component Analysis to decompose spatial average activation maps of focused attention contrasted with two different mind wandering states. The first principal component, which reflected differential engagement of bilateral parietal areas during focused attention vs. mind wandering, was associated with behavioral characteristics of inhibition, anxiousness and depression, as measured by standard questionnaires. We demonstrated that such decomposition of time-averaged contrast maps can overcome some of the challenges in methods based on concatenated data, especially from the perspective of behaviorally and clinically relevant characteristics in the ongoing brain oscillatory activity.HighlightsWe present a specific method to analyse/establish associations between brain oscillations and behavioral characteristics.We found that activity levels in parietal areas during mind wandering compared to focused attention were associated with the behavioral trait of inhibition and anxiety.

scholarly journals Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

2021 ◽  
Author(s):  
Ying-Qiu Zheng ◽  
Seyedeh-Rezvan Farahibozorg ◽  
Weikang Gong ◽  
Hossein Rafipoor ◽  
Saad Jbabdi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Predictive Accuracy ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Human Connectome Project ◽  
Wide Range ◽  
Dual Regression ◽  
Project Data ◽  
Test Retest Reliability

Modelling and predicting individual differences in task-evoked FMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble leaner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of tfMRI scans, suggesting that it has potential to supplement traditional task localisers.

scholarly journals Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Noxious Stimulus ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

scholarly journals Intrinsic sensory disinhibition contributes to intrusive re-experiencing in combat veterans

2019 ◽  
Author(s):  
Kevin J. Clancy ◽  
Alejandro Albizu ◽  
Norman B. Schmidt ◽  
Wen Li
Keyword(s):  
Resting State ◽  
Emotional Response ◽  
Combat Veterans ◽  
Strong Association ◽  
Cortical Inhibition ◽  
Oscillatory Activity ◽  
List Type ◽  
Trauma Memory ◽  
Novel Target ◽  
The Relationship

ABSTRACTIntrusive re-experiencing of traumatic events is a hallmark symptom of posttraumatic stress disorder (PTSD). In contrast to abstract, verbal intrusions in other affective disorders, intrusive re-experiencing in PTSD is characterized by vivid sensory details as “flashbacks”. While prevailing PTSD models largely focus on dysregulated emotional processes, we hypothesize that deficient sensory inhibition in PTSD could drive overactivation of sensory representations of trauma memories, precipitating sensory-rich intrusions of trauma. In 86 combat veterans, we examined resting-state alpha (8-12 Hz) oscillatory activity (in both power and posterior→frontal connectivity), given its key role in sensory cortical inhibition, in association with intrusive re-experiencing symptoms. A subset (N = 35) of veterans further participated in an odor task (including both combat and non-combat odors) to assess olfactory trauma memory and emotional response. We observed a strong association between intrusive re-experiencing symptoms and attenuated resting-state posterior→frontal alpha connectivity, which were both correlated with olfactory trauma memory (but not emotional response). Importantly, olfactory trauma memory was further identified as a full mediator of the relationship between alpha connectivity and intrusive re-experiencing in these veterans, suggesting that deficits in intrinsic sensory inhibition can contribute to intrusive re-experiencing of trauma via heightened trauma memory. Therefore, by permitting unfiltered sensory cues to enter information processing and spontaneously activating sensory representations of trauma, impaired sensory inhibition can constitute a sensory mechanism of intrusive re-experiencing in PTSD.HIGHLIGHTSAlpha oscillations (indexing sensory inhibition) measured in 86 combat veteransRe-experiencing symptom severity was associated with attenuated alpha connectivityTrauma memory for, not emotional response to, odors mediated this relationshipTrauma memories may arise via disinhibited activation of sensory representationsSensory systems may be novel target for intrusive re-experiencing symptom treatment

scholarly journals A distributed brain network predicts general intelligence from resting-state human neuroimaging data

2018 ◽  
Vol 373 (1756) ◽  
pp. 20170284 ◽  
Author(s):  
Julien Dubois ◽  
Paola Galdi ◽  
Lynn K. Paul ◽  
Ralph Adolphs
Keyword(s):  
Individual Differences ◽  
Resting State ◽  
Brain Volume ◽  
Resting State Fmri ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Reliable Estimate ◽  
General Intelligence ◽  
Total Brain Volume ◽  
Human Connectome Project

Individual people differ in their ability to reason, solve problems, think abstractly, plan and learn. A reliable measure of this general ability, also known as intelligence, can be derived from scores across a diverse set of cognitive tasks. There is great interest in understanding the neural underpinnings of individual differences in intelligence, because it is the single best predictor of long-term life success. The most replicated neural correlate of human intelligence to date is total brain volume; however, this coarse morphometric correlate says little about function. Here, we ask whether measurements of the activity of the resting brain (resting-state fMRI) might also carry information about intelligence. We used the final release of the Young Adult Human Connectome Project (N= 884 subjects after exclusions), providing a full hour of resting-state fMRI per subject; controlled for gender, age and brain volume; and derived a reliable estimate of general intelligence from scores on multiple cognitive tasks. Using a cross-validated predictive framework, we predicted 20% of the variance in general intelligence in the sampled population from their resting-state connectivity matrices. Interestingly, no single anatomical structure or network was responsible or necessary for this prediction, which instead relied on redundant information distributed across the brain.This article is part of the theme issue ‘Causes and consequences of individual differences in cognitive abilities’.

scholarly journals Functional and diffusion MRI reveal the functional and structural basis of infants’ noxious-evoked brain activity

2021 ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Structural Basis ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

Abstract Understanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-evoked brain activity. We demonstrate that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n=18). Applying this prediction model to independent Developing Human Connectome Project data (n=215), we identify negative associations between predicted noxious-evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. This study in healthy neonates demonstrates that noxious-evoked brain activity is tightly coupled to both resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

scholarly journals Linking alpha oscillations, attention and inhibitory control in adult ADHD with EEG neurofeedback

2019 ◽  
Author(s):  
Marie-Pierre Deiber ◽  
Roland Hasler ◽  
Julien Colin ◽  
Alexandre Dayer ◽  
Jean-Michel Aubry ◽  
...  
Keyword(s):  
Inhibitory Control ◽  
Resting State ◽  
Adult Adhd ◽  
Alpha Rhythm ◽  
Oscillatory Activity ◽  
Supporting Evidence ◽  
Alpha Power ◽  
Adhd Group ◽  
List Type ◽  
Alpha Oscillations

AbstractAbnormal patterns of electrical oscillatory activity have been repeatedly described in adult ADHD. In particular, the alpha rhythm (8-12 Hz), known to be modulated during attention, has previously been considered as candidate biomarker for ADHD. In the present study, we asked adult ADHD patients to self-regulate their own alpha rhythm using neurofeedback (NFB), in order to examine the modulation of alpha oscillations on attentional performance and brain plasticity. Twenty-five adult ADHD patients and 22 healthy controls underwent a 64-channel EEG-recording at resting-state and during a Go/NoGo task, before and after a 30 min-NFB session designed to reduce (desynchronize) the power of the alpha rhythm. Alpha power was compared across conditions and groups, and the effects of NFB were statistically assessed by comparing behavioral and EEG measures pre-to-post NFB. Firstly, we found that relative alpha power was attenuated in our ADHD cohort compared to control subjects at baseline and across experimental conditions, suggesting a signature of cortical hyper-activation. Both groups demonstrated a significant and targeted reduction of alpha power during NFB. Interestingly, we observed a post-NFB increase in resting-state alpha (i.e. rebound) in the ADHD group, which restored alpha power towards levels of the normal population. Importantly, the degree of post-NFB alpha normalisation during the Go/NoGo task correlated with individual improvements in motor inhibition (i.e. reduced commission errors and slower reaction times in NoGo trials) only in the ADHD group. Overall, our findings offer novel supporting evidence implicating alpha oscillations in inhibitory control, as well as their potential role in the homeostatic regulation of cortical excitatory/inhibitory balance.HighlightsResting alpha power is reduced in adult ADHD suggesting cortical hyper-activationAdult ADHD patients successfully reduce alpha power during neurofeedbackA post-neurofeedback rebound normalizes alpha power in adult ADHDAlpha power rebound correlates with improvement of inhibitory control in adult ADHD

scholarly journals EEG correlates of working memory performance in females

2017 ◽  
Author(s):  
Yuri G. Pavlov ◽  
Boris Kotchoubey
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
High Performance ◽  
Brain Activity ◽  
Memory Performance ◽  
Two Dimensions ◽  
Oscillatory Activity ◽  
Theta Power ◽  
Low Performance ◽  
Oscillatory Brain Activity

AbstractBackgroundThe study investigates oscillatory brain activity during working memory (WM) tasks. The tasks employed varied in two dimensions. First, they differed in complexity from average to highly demanding. Second, we used two types of tasks, which required either only retention of stimulus set or retention and manipulation of the content. We expected to reveal EEG correlates of temporary storage and central executive components of WM and to assess their contribution to individual differences.ResultsGenerally, as compared with the retention condition, manipulation of stimuli in WM was associated with distributed suppression of alpha1 activity and with the increase of the midline theta activity. Load and task dependent decrement of beta1 power was found during task performance. Beta2 power increased with the increasing WM load and did not significantly depend on the type of the task.At the level of individual differences, we found that the high performance (HP) group was characterized by higher alpha rhythm power. The HP group demonstrated task-related increment of theta power in the left anterior area and a gradual increase of theta power at midline area. In contrast, the low performance (LP) group exhibited a drop of theta power in the most challenging condition. HP group was also characterized by stronger desynchronization of beta1 rhythm over the left posterior area in the manipulation condition. In this condition, beta2 power increased in the HP group over anterior areas, but in the LP group over posterior areas.ConclusionsWM performance is accompanied by changes in EEG in a broad frequency range from theta to higher beta bands. The most pronounced differences in oscillatory activity between individuals with high and low WM performance can be observed in the most challenging WM task.

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.

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