scholarly journals fMRI Dependent Components Analysis Reveals Dynamic Relations Between Functional Large Scale Cortical Networks

2016 ◽  
Author(s):  
Uri Hertz ◽  
Daniel Zoran ◽  
Yair Weiss ◽  
Amir Amedi
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Group Analysis ◽  
Mental States ◽  
Cortical Networks ◽  
Experimental Conditions ◽  
Dependency Structure ◽  
Cortical Areas ◽  
Novel Approach ◽  
Components Analysis

AbstractOne of the major advantages of whole brain fMRI is the detection of large scale cortical networks. Dependent Components Analysis (DCA) is a novel approach designed to extract both cortical networks and their dependency structure. DCA is fundamentally different from prevalent data driven approaches, i.e. spatial ICA, in that instead of maximizing the independence of components it optimizes their dependency (in a tree graph structure, tDCA) depicting cortical areas as part of multiple cortical networks. Here tDCA was shown to reliably detect large scale functional networks in single subjects and in group analysis, by clustering non-noisy components on one branch of the tree structure. We used tDCA in three fMRI experiments in which identical auditory and visual stimuli were presented, but novelty information and task relevance were modified. tDCA components tended to include two anticorrelated networks, which were detected in two separate ICA components, or belonged in one component in seed functional connectivity. Although sensory components remained the same across experiments, other components changed as a function of the experimental conditions. These changes were either within component, where it encompassed other cortical areas, or between components, where the pattern of anticorrelated networks and their statistical dependency changed. Thus tDCA may prove to be a useful, robust tool that provides a rich description of the statistical structure underlying brain activity and its relationships to changes in experimental conditions. This tool may prove effective in detection and description of mental states, neural disorders and their dynamics.

Opioidergic System and Functional Architecture of Intrinsic Brain Activity: Implications for Psychiatric Disorders

2020 ◽  
Vol 26 (4) ◽  
pp. 343-358
Author(s):  
Giulio Rocchi ◽  
Bruno Sterlini ◽  
Samuele Tardito ◽  
Matilde Inglese ◽  
Anna Corradi ◽  
...  
Keyword(s):  
Psychiatric Disorders ◽  
Opioid Receptors ◽  
Large Scale ◽  
Brain Activity ◽  
Subcortical Structures ◽  
Opioidergic System ◽  
Sensorimotor Network ◽  
Cortical Areas ◽  
High Doses ◽  
Intrinsic Brain Activity

The opioidergic system and intrinsic brain activity, as organized in large-scale networks such as the salience network (SN), sensorimotor network (SMN), and default-mode network (DMN), play core roles in healthy behavior and psychiatric disorders. This work aimed to investigate how opioidergic signaling affects intrinsic brain activity in healthy individuals by reviewing relevant neuroanatomical, molecular, functional, and pharmacological magnetic resonance imaging studies in order to clarify their physiological links and changes in psychiatric disorders. The SN shows dense opioidergic innervations of subcortical structures and high expression levels of opioid receptors in subcortical-cortical areas, with enhanced or reduced activity with low or very high doses of opioids, respectively. The SMN shows high levels of opioid receptors in subcortical areas and functional disconnection caused by opioids. The DMN shows low levels of opioid receptors in cortical areas and inhibited or enhanced activity with low or high doses of opioids, respectively. Finally, we proposed a working model. Opioidergic signaling enhances SN and suppresses SMN (and DMN) activity, resulting in affective excitation with psychomotor inhibition; stronger increases in opioidergic signaling attenuate the SN and SMN while disinhibiting the DMN, dissociating affective and psychomotor functions from the internal states; the opposite occurs with a deficit of opioidergic signaling.

scholarly journals Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior

2021 ◽  
Author(s):  
Hadas Benisty ◽  
Andrew H Moberly ◽  
Sweyta Lohani ◽  
Daniel Barson ◽  
Ronald R Coifman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Neural Activity ◽  
Large Scale ◽  
Analytical Framework ◽  
Network Activity ◽  
Wide Field ◽  
Time Varying ◽  
Cortical Networks ◽  
Novel Approach ◽  
Spontaneous Behavior

Experimental work across a variety of species has demonstrated that spontaneously generated behaviors are robustly coupled to variation in neural activity within the cerebral cortex. Indeed, functional magnetic resonance imaging (fMRI) data suggest that functional connectivity in cortical networks varies across distinct behavioral states, providing for the dynamic reorganization of patterned activity. However, these studies generally lack the temporal resolution to establish links between cortical signals and the continuously varying fluctuations in spontaneous behavior typically observed in awake animals. Here, we took advantage of recent developments in wide-field, mesoscopic calcium imaging to monitor neural activity across the neocortex of awake mice. Applying a novel approach to quantifying time-varying functional connectivity, we show that spontaneous behaviors are more accurately represented by fast changes in the correlational structure versus the magnitude of large-scale network activity. Moreover, dynamic functional connectivity reveals subnetworks that are not predicted by traditional anatomical atlas-based parcellation of the cortex. These results provide insight into how behavioral information is represented across the mammalian neocortex and demonstrate a new analytical framework for investigating time-varying functional connectivity in neural networks.

scholarly journals Capturing the spatiotemporal dynamics of task-initiated thoughts with combined EEG and fMRI

2018 ◽  
Author(s):  
Lucie Bréchet ◽  
Denis Brunet ◽  
Gwénaël Birot ◽  
Rolf Gruetter ◽  
Christoph M. Michel ◽  
...  
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Mental Arithmetic ◽  
Brain Activity ◽  
Activity Patterns ◽  
Mental States ◽  
Spatiotemporal Dynamics ◽  
Source Analysis ◽  
Eeg Source Analysis ◽  
Large Scale Networks

AbstractWhen at rest, our mind wanders from thought to thought in distinct mental states. Despite the marked importance of ongoing mental processes, it is challenging to capture and relate these states to specific cognitive contents. In this work, we employed ultra-high field functional magnetic resonance imaging (fMRI) and high-density electroencephalography (EEG) to study the ongoing thoughts of participants instructed to retrieve self-relevant past episodes for periods of 20s. These task-initiated, participant-driven activity patterns were compared to a distinct condition where participants performed serial mental arithmetic operations, thereby shifting from self-related to self-unrelated thoughts. BOLD activity mapping revealed selective activity changes in temporal, parietal and occipital areas (“posterior hot zone”), evincing their role in integrating the re-experienced past events into conscious representations during memory retrieval. Functional connectivity analysis showed that these regions were organized in two major subparts of the default mode network, previously associated to “scene-reconstruction” and “self-experience” subsystems. EEG microstate analysis allowed studying these participant-driven thoughts in the millisecond range by determining the temporal dynamics of brief periods of stable scalp potential fields. This analysis revealed selective modulation of occurrence and duration of specific microstates in both conditions. EEG source analysis revealed similar spatial distributions between the sources of these microstates and the regions identified with fMRI. These findings support growing evidence that specific fMRI networks can be captured with EEG as repeatedly occurring, integrated brief periods of synchronized neuronal activity, lasting only fractions of seconds.SignificanceWe investigated the spatiotemporal dynamics of large-scale brain networks related to specific conscious thoughts. We demonstrate here that instructing participants to direct their thoughts to either episodic autobiographic memory or to mental arithmetic modulates distinct networks both in terms of highly spatially-specific BOLD signal oscillations as well as fast sub-second dynamics of EEG microstates. The combined findings from the two modalities evince a clear link between hemodynamic and electrophysiological signatures of spontaneous brain activity by the occurrence of thoughts that last for fractions of seconds, repeatedly appearing over time as integrated coherent activities of specific large-scale networks.

scholarly journals Contemplative Neuroscience as an Approach to Volitional Consciousness

2017 ◽  
Author(s):  
Evan Thompson
Keyword(s):  
Cognitive Neuroscience ◽  
Large Scale ◽  
Brain Activity ◽  
Methodological Approach ◽  
Self Regulation ◽  
Mental States ◽  
Neural Correlates ◽  
Coordination Dynamics ◽  
Intentional Causation ◽  
Contemplative Neuroscience

This chapter presents a methodological approach to volitional consciousness for cognitive neuroscience based on studying the voluntary self-generation and self-regulation of mental states in meditation. Called contemplative neuroscience, this approach views attention, awareness, and emotion regulation as flexible and trainable skills, and works with experimental participants who have undergone training in contemplative practices designed to hone these skills. Drawing from research on the dynamical neural correlates of contemplative mental states and theories of large-scale neural coordination dynamics, I argue for the importance of global system causation in brain activity and present an “interventionist” approach to intentional causation.

scholarly journals Thresholded Partial Least Squares: Fast Construction of Interpretable Whole-brain Decoders

2021 ◽  
Author(s):  
Sangil Lee ◽  
Eric T. Bradlow ◽  
Joseph W. Kable
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Brain Activity ◽  
Mental States ◽  
Computationally Efficient ◽  
Activity Data ◽  
Data Set ◽  
Whole Brain ◽  
Novel Approach ◽  
Brain Data

AbstractRecent neuroimaging research has shown that it is possible to decode mental states and predict future consumer behavior from brain activity data (a time-series of images). However, the unique characteristics (and high dimensionality) of neuroimaging data, coupled with a need for neuroscientifically interpretable models, has largely discouraged the use of the entire brain’s data as predictors. Instead, most neuroscientific research uses “regionalized” (partial-brain) data to reduce the computational burden and to improve interpretability (i.e., localizability of signal), at the cost of losing potential information. Here we propose a novel approach that can build whole-brain neural decoders (using the entire data set and capitalizing on the full correlational structure) that are both interpretable and computationally efficient. We exploit analytical properties of the partial least squares algorithm to build a regularized regression model with variable selection that boasts (in contrast to most statistical methods) a unique ‘fit-once-tune-later’ approach where users need to fit the model only once and can choose the best tuning parameters post-hoc. We demonstrate its efficacy in a large neuroimaging dataset against off-the-shelf prediction methods and show that our new method scales exceptionally with increasing data size, yields more interpretable results, and uses less computational memory, while retaining high predictive power.

scholarly journals Speech-evoked brain activity is more robust to competing speech when it is spoken by someone familiar

2020 ◽  
Author(s):  
Emma Holmes ◽  
Ingrid S. Johnsrude
Keyword(s):  
Brain Activity ◽  
Primary Auditory Cortex ◽  
Target Sentence ◽  
Complex Signal ◽  
Middle Temporal ◽  
Cortical Areas ◽  
Novel Approach ◽  
Familiar Voice ◽  
Competing Speech ◽  
The Brain

AbstractPeople are much better at understanding speech when it is spoken by a familiar talker—such as a friend or partner—than when the interlocutor is unfamiliar. This provides an opportunity to examine the substrates of intelligibility and familiarity, independent of acoustics. Is the familiarity effect evident as early as primary auditory cortex, or only at later processing stages? Here, we presented sentences spoken by naturally familiar talkers (the participant’s friend or partner) and unfamiliar talkers (the friends or partners of other participants). We compared multivariate activity in speech-sensitive regions of cortex between conditions in which target sentences were presented alone and conditions in which the same target sentences were presented at the same time as a competing sentence. Using representational similarity analysis (RSA), we demonstrate that the pattern of activity evoked by a spoken sentence is less degraded by the presence of a competing sentence when it is spoken by a friend or partner than by someone unfamiliar; the results cannot be explained by acoustic differences since familiar and unfamiliar talkers were nearly identical across the group. This familiar-voice advantage is most prominent in nonprimary auditory cortical areas, along the posterior superior and middle temporal gyri. Across participants, the magnitude of the familiar-unfamiliar RSA difference correlates with the familiar-voice benefit to intelligibility. Overall, our results demonstrate that experience-driven improvements in intelligibility are associated with enhanced patterns of neural activity in nonprimary auditory cortical areas.Significance statementSpeech is a complex signal, and we do not yet fully understand how the content of a spoken sentence is encoded in cortex. Here, we used a novel approach based on analysing multivariate activity: we compared activity evoked by highly intelligible sentences presented alone and by the same sentences presented with a competing masker. The distributed pattern of activity in speech-sensitive regions of the brain was more similar between the alone and masker conditions when the target sentence was spoken by someone familiar—the participant’s friend or partner—than someone unfamiliar. This metric correlated with the intelligibility of the familiar voice. These results imply that the spatial pattern of activity in speech-sensitive regions reflects the intelligibility of a spoken sentence.

Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Antibacterial Activity ◽  
Large Scale ◽  
Research Work ◽  
Silver Sulfide ◽  
Morphological Properties ◽  
Effective Diameter ◽  
Novel Approach ◽  
Transmission Electron ◽  
Green Route ◽  
Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

Intraoperative localization of spatially and spectrally distinct resting-state networks in Parkinson’s disease

2020 ◽  
Vol 132 (4) ◽  
pp. 1234-1242 ◽  
Author(s):  
Paolo Belardinelli ◽  
Ramin Azodi-Avval ◽  
Erick Ortiz ◽  
Georgios Naros ◽  
Florian Grimm ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Network Effects ◽  
Brain Activity ◽  
Dynamic Imaging ◽  
Oscillatory Activity ◽  
Network Information ◽  
Novel Approach ◽  
Electrophysiological Recordings ◽  
Intraoperative Localization

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for symptomatic Parkinson’s disease (PD); the clinical benefit may not only mirror modulation of local STN activity but also reflect consecutive network effects on cortical oscillatory activity. Moreover, STN-DBS selectively suppresses spatially and spectrally distinct patterns of synchronous oscillatory activity within cortical-subcortical loops. These STN-cortical circuits have been described in PD patients using magnetoencephalography after surgery. This network information, however, is currently not available during surgery to inform the implantation strategy.The authors recorded spontaneous brain activity in 3 awake patients with PD (mean age 67 ± 14 years; mean disease duration 13 ± 7 years) during implantation of DBS electrodes into the STN after overnight withdrawal of dopaminergic medication. Intraoperative propofol was discontinued at least 30 minutes prior to the electrophysiological recordings. The authors used a novel approach for performing simultaneous recordings of STN local field potentials (LFPs) and multichannel electroencephalography (EEG) at rest. Coherent oscillations between LFP and EEG sensors were computed, and subsequent dynamic imaging of coherent sources was performed.The authors identified coherent activity in the upper beta range (21–35 Hz) between the STN and the ipsilateral mesial (pre)motor area. Coherence in the theta range (4–6 Hz) was detected in the ipsilateral prefrontal area.These findings demonstrate the feasibility of detecting frequency-specific and spatially distinct synchronization between the STN and cortex during DBS surgery. Mapping the STN with this technique may disentangle different functional loops relevant for refined targeting during DBS implantation.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach
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