scholarly journals Regular cannabis and alcohol use is associated with resting-state time course power spectra in incarcerated adolescents

2017 ◽  
Vol 178 ◽  
pp. 492-500 ◽  
Author(s):  
Sandra Thijssen ◽  
Barnaly Rashid ◽  
Shruti Gopal ◽  
Prashanth Nyalakanti ◽  
Vince D. Calhoun ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Resting State ◽  
Time Course ◽  
Power Spectra ◽  
State Time ◽  
Incarcerated Adolescents

scholarly journals Power spectra reveal distinct BOLD resting‐state time courses in white matter

2021 ◽  
Author(s):  
Muwei Li ◽  
Yurui Gao ◽  
Zhaohua Ding ◽  
John C. Gore
Keyword(s):  
White Matter ◽  
Resting State ◽  
Functional Integration ◽  
Power Spectra ◽  
Spectral Measurements ◽  
State Time ◽  
Power Spectral ◽  
Time Courses ◽  
Two Peaks

AbstractAccurate characterization of the time courses of BOLD signal changes is crucial for the analysis and interpretation of functional MRI data. While several studies have shown that white matter (WM) exhibits distinct BOLD responses evoked by tasks, there have been no comprehensive investigations into the time courses of spontaneous signal fluctuations in WM. We measured the power spectra of the resting‐state time courses in a set of regions within WM identified as showing synchronous signals using independent components analysis. In each component, a clear separation between voxels into two categories was evident, based on their power spectra: one group exhibited a single peak, the other had an additional peak at a higher frequency. Their groupings are location‐specific, and their distributions reflect unique neurovascular and anatomical configurations. Importantly, the two categories of voxels differed in their engagement in functional integration, revealed by differences in the number of inter‐ regional connections based on the two categories separately. Moreover, the power spectral measurements in voxels with two peaks in specific components predict specific human behaviors. Taken together, these findings suggest WM signals are heterogeneous in nature and depend on local structural‐vascular‐functional associations.

scholarly journals Power spectra reveal distinct BOLD resting-state time courses in white matter

2021 ◽  
Vol 118 (44) ◽  
pp. e2103104118
Author(s):  
Muwei Li ◽  
Yurui Gao ◽  
Zhaohua Ding ◽  
John C. Gore
Keyword(s):  
White Matter ◽  
Resting State ◽  
Functional Integration ◽  
Power Spectra ◽  
Blood Oxygen Level Dependent ◽  
Blood Oxygen Level ◽  
State Time ◽  
Components Analysis ◽  
Time Courses

Accurate characterization of the time courses of blood-oxygen-level–dependent (BOLD) signal changes is crucial for the analysis and interpretation of functional MRI data. While several studies have shown that white matter (WM) exhibits distinct BOLD responses evoked by tasks, there have been no comprehensive investigations into the time courses of spontaneous signal fluctuations in WM. We measured the power spectra of the resting-state time courses in a set of regions within WM identified as showing synchronous signals using independent components analysis. In each component, a clear separation between voxels into two categories was evident, based on their power spectra: one group exhibited a single peak, and the other had an additional peak at a higher frequency. Their groupings are location specific, and their distributions reflect unique neurovascular and anatomical configurations. Importantly, the two categories of voxels differed in their engagement in functional integration, revealed by differences in the number of interregional connections based on the two categories separately. Taken together, these findings suggest WM signals are heterogeneous in nature and depend on local structural-vascular-functional associations.

scholarly journals Age-related differences in resting state functional connectivity in pediatric migraine

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tiffany Bell ◽  
Akashroop Khaira ◽  
Mehak Stokoe ◽  
Megan Webb ◽  
Melanie Noel ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Power Spectra ◽  
Interaction Effects ◽  
Resting State Functional Connectivity ◽  
Default Mode ◽  
Pediatric Migraine ◽  
Age Related ◽  
Status Interaction ◽  
Underlying Mechanisms

Abstract Background Migraine affects roughly 10% of youth aged 5–15 years, however the underlying mechanisms of migraine in youth are poorly understood. Multiple structural and functional alterations have been shown in the brains of adult migraine sufferers. This study aims to investigate the effects of migraine on resting-state functional connectivity during the period of transition from childhood to adolescence, a critical period of brain development and the time when rates of pediatric chronic pain spikes. Methods Using independent component analysis, we compared resting state network spatial maps and power spectra between youth with migraine aged 7–15 and age-matched controls. Statistical comparisons were conducted using a MANCOVA analysis. Results We show (1) group by age interaction effects on connectivity in the visual and salience networks, group by sex interaction effects on connectivity in the default mode network and group by pubertal status interaction effects on connectivity in visual and frontal parietal networks, and (2) relationships between connectivity in the visual networks and the migraine cycle, and age by cycle interaction effects on connectivity in the visual, default mode and sensorimotor networks. Conclusions We demonstrate that brain alterations begin early in youth with migraine and are modulated by development. This highlights the need for further study into the neural mechanisms of migraine in youth specifically, to aid in the development of more effective treatments.

scholarly journals Time Course Based Artifact Identification for Independent Components of Resting-State fMRI

2013 ◽  
Vol 7 ◽  
Author(s):  
Christian Rummel ◽  
Rajeev Kumar Verma ◽  
Veronika Schöpf ◽  
Eugenio Abela ◽  
Martinus Hauf ◽  
...  
Keyword(s):  
Resting State ◽  
Time Course ◽  
Resting State Fmri ◽  
Independent Components ◽  
Artifact Identification

Processing of Natural Time Series of Intensities in the Early Visual System of the Blowfly

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 24-24 ◽  
Author(s):  
J H van Hateren
Keyword(s):  
Time Series ◽  
Visual System ◽  
Visual Processing ◽  
Time Course ◽  
Temporal Frequency ◽  
Power Spectra ◽  
Calliphora Vicina ◽  
Intensity Measurements ◽  
Natural Time ◽  
Early Visual System

The first steps of processing in the visual system of the blowfly are well suited for studying the relationship between the properties of the environment and the function of visual processing (eg Srinivasan et al, 1982 Proceedings of the Royal Society, London B216 427; van Hateren, 1992 Journal of Comparative Physiology A171 157). Although the early visual system appears to be linear to some extent, there are also reports on functionally significant nonlinearities (Laughlin, 1981 Zeitschrift für Naturforschung36c 910). Recent theories using information theory for understanding the early visual system perform reasonably well, but not quite as well as the real visual system when confronted with natural stimuli [eg van Hateren, 1992 Nature (London)360 68]. The main problem seems to be that they lack a component that adapts with the right time course to changes in stimulus statistics (eg the local average light intensity). In order to study this problem of adaptation with a relatively simple, yet realistic, stimulus I recorded time series of natural intensities, and played them back via a high-brightness LED to the visual system of the blowfly ( Calliphora vicina). The power spectra of the intensity measurements and photoreceptor responses behave approximately as 1/ f, with f the temporal frequency, whilst those of second-order neurons (LMCs) are almost flat. The probability distributions of the responses of LMCs are almost gaussian and largely independent of the input contrast, unlike the distributions of photoreceptor responses and intensity measurements. These results suggest that LMCs are in effect executing a form of contrast normalisation in the time domain.

scholarly journals State-Dependent Effective Connectivity in Resting-State fMRI

2021 ◽  
Vol 15 ◽  
Author(s):  
Hae-Jeong Park ◽  
Jinseok Eo ◽  
Chongwon Pae ◽  
Junho Son ◽  
Sung Min Park ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Empirical Bayes ◽  
Time Course ◽  
Effective Connectivity ◽  
The State ◽  
Causal Modeling ◽  
Occupation Times ◽  
State Dependent ◽  
Mental Diseases

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

scholarly journals Increased resting-state functional connectivity within theta and alpha frequency bands in dysphoria: Towards a novel measure of depression risk

2020 ◽  
Author(s):  
Carola Dell'Acqua ◽  
Shadi Ghiasi ◽  
Simone Messerotti ◽  
Alberto Greco ◽  
Claudio Gentili ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Power Spectra ◽  
Female Gender ◽  
Phase Coherence ◽  
Resting State Functional Connectivity ◽  
Alpha Asymmetry ◽  
Connectivity Patterns ◽  
Eeg Power ◽  
Eeg Functional Connectivity

Background: The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown. Methods: 32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence. Results: While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally. Limitations: All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas. Conclusions: An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.

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