Suchergebnisse

International audience Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future. Over the past two decades, resting-state functional connectivity measured by functional magnetic resonance imaging (fMRI) has played an essential role in understanding brain functional networks in healthy and patient populations 1–5. Resting-state functional connectivity is measured by the temporal co-activation level of spontaneous fMRI signals between spatially distinct brain regions in the absence of a perceptual or behavioral task 6. Although the participants are not engaged in any particular task, there is increasing evidence that spontaneous thoughts (known as mind wandering, daydreaming, self-generated mental activity or task-unrelated thought) that are minimally constrained by external perception emerge during fMRI scans and may potentially affect resting-state fMRI data ...

Aims: A possible addiction mechanism has been represented by altered functional connectivity (FC) in the resting state. The aim of this study was to evaluate the FCs of the insula and nucleus accumbens among subjects with Internet gaming disorder (IGD). Methods: We recruited 30 males with IGD and 30 controls and evaluated their FC using functional magnetic imaging scanning under resting, a state with relaxation, closed eyes, with inducement to think of nothing systematically, become motionless, and instructed not to fall asleep. Results: Subjects with IGD had a lower FC with the left insula over the left dorsolateral prefrontal cortex (DLPFC) and orbital frontal lobe and a higher FC with the insula with the contralateral insula than controls. The inter-hemispheric insula connectivity positively correlated with impulsivity. Further, they had lower FC with the left nucleus accumbens over the left DLPFC and with the right nucleus accumbens over the left DLPFC, and insula and a higher FC with that over the right precuneus. Conclusion: The elevated inter-hemispheric insula FC is found to be associated with impulsivity and might explain why it is involved in IGD. The attenuated frontostriatal suggests that the emotion-driven gaming urge through nucleus accumbens could not be well regulated by the frontal lobe of subjects with IGD.

Alzheimer's disease (AD) is the most common dementia with dramatic consequences. The research in structural and functional neuroimaging showed altered brain connectivity in AD. In this study, we investigated the whole-brain resting state functional connectivity (FC) of the subjects with preclinical Alzheimer's disease (PAD), mild cognitive impairment due to AD (MCI) and mild dementia due to Alzheimer's disease (AD), the impact of APOE4 carriership, as well as in relation to variations in core AD CSF biomarkers. The synchronization in the whole-brain was monotonously decreasing during the course of the disease progression. Furthermore, in AD patients we found widespread significant decreases in functional connectivity (FC) strengths particularly in the brain regions with high global connectivity. We employed a whole-brain computational modeling approach to study the mechanisms underlying these alterations. To characterize the causal interactions between brain regions, we estimated the effective connectivity (EC) in the model. We found that the significant EC differences in AD were primarily located in left temporal lobe. Then, we systematically manipulated the underlying dynamics of the model to investigate simulated changes in FC based on the healthy control subjects. Furthermore, we found distinct patterns involving CSF biomarkers of amyloid-beta (Aβ1 − 42) total tau (t-tau) and phosphorylated tau (p-tau). CSF Aβ1 − 42 was associated to the contrast between healthy control subjects and clinical groups. Nevertheless, tau CSF biomarkers were associated to the variability in whole-brain synchronization and sensory integration regions. These associations were robust across clinical groups, unlike the associations that were found for CSF Aβ1 − 42. APOE4 carriership showed no significant correlations with the connectivity measures. ; GD is supported by the ERC Advanced Grant: DYSTRUCTURE (no. 295129), by the Spanish Research ProjectPSI2016-75688-P (AEI/FEDER) and by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 720270 (HBP SGA1).

Normal aging causes disruptions in the brain that can lead to cognitive decline. Resting-state functional magnetic resonance imaging studies have found significant age-related alterations in functional connectivity across various networks. Nevertheless, most of the studies have focused mainly on static functional connectivity. Studying the dynamics of resting-state brain activity across the whole-brain functional network can provide a better characterization of age-related changes. Here, we employed two data-driven whole-brain approaches based on the phase synchronization of blood-oxygen-level-dependent signals to analyze resting-state fMRI data from 620 subjects divided into two groups (middle-age group (n = 310); age range, 50–64 years versus older group (n = 310); age range, 65–91 years). Applying the intrinsic-ignition framework to assess the effect of spontaneous local activation events on local–global integration, we found that the older group showed higher intrinsic ignition across the whole-brain functional network, but lower metastability. Using Leading Eigenvector Dynamics Analysis, we found that the older group showed reduced ability to access a metastable substate that closely overlaps with the so-called rich club. These findings suggest that functional whole-brain dynamics are altered in aging, probably due to a deficiency in a metastable substate that is key for efficient global communication in the brain. ; A.E. was supported by the Catalan project Imagenoma de L'Envelliment (Aging Imageomics Study). G.D. was supported by the Spanish Ministry of Economy and Competitiveness, Spain (grant agreement number PSI2016- 75688-P, MINECO/AEI/FEDER-EU); European Union's Horizon 2020 FET Flagship Human Brain Project (grant agreement number 785907, HBP SGA2); the Catalan Research Support, Spain (grant agreement number 2017 SGR 1545) and La Marató TV3 2017 (grant agreement 201725.33).

Neurobiological models to explain vulnerability of major depressive disorder (MDD) are scarce and previous functional magnetic resonance imaging studies mostly examined "static" functional connectivity (FC). Knowing that FC constantly evolves over time, it becomes important to assess how FC dynamically differs in remitted‐MDD patients vulnerable for new depressive episodes. Using a recently developed method to examine dynamic FC, we characterized re‐emerging FC states during rest in 51 antidepressant‐free MDD patients at high risk of recurrence (≥2 previous episodes), and 35 healthy controls. We examined differences in occurrence, duration, and switching profiles of FC states after neutral and sad mood induction. Remitted MDD patients showed a decreased probability of an FC state (p < 0.005) consisting of an extensive network connecting frontal areas—important for cognitive control—with default mode network, striatum, and salience areas, involved in emotional and self‐referential processing. Even when this FC state was observed in patients, it lasted shorter (p < 0.005) and was less likely to switch to a smaller prefrontal–striatum network (p < 0.005). Differences between patients and controls decreased after sad mood induction. Further, the duration of this FC state increased in remitted patients after sad mood induction but not in controls (p < 0.05). Our findings suggest reduced ability of remitted‐MDD patients, in neutral mood, to access a clinically relevant control network involved in the interplay between externally and internally oriented attention. When recovering from sad mood, remitted recurrent MDD appears to employ a compensatory mechanism to access this FC state. This study provides a novel neurobiological profile of MDD vulnerability. ; This study is supported by unrestricted personal grants from the Academic Medical Centre to C.A.F. (AMC MD‐PhD Scholarship) and Dr. R.J.M. (AMC PhD Scholarship) and a dedicated grant from the Dutch Brain Foundation (Hersenstichting The Netherlands: 2009(2)‐72). Dr. J.C. was supported under the project NORTE‐01‐0145‐FEDER‐000023, by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Dr. G.D. is supported by the Spanish Research Project PSI2016‐75688‐P (AEI/FEDER) and by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). Dr. M.L.K. is supported by the ERC Consolidator Grant: CAREGIVING (no. 615539) and Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). Dr. H.G.R. is supported by a NWO/ZonMW VENI‐Grant #016.126.059. Dr. PE acknowledges support from the EPSRC award EP/N014529/1 funding the EPSRC Centre for Mathematics of Precision Healthcare at Imperial.

Abstract
Background and Objectives It remains to be determined whether noninvasive functional imaging techniques can rival the clinical potential of direct electrocortical stimulation (DES). In this study, we compared the results of resting-state functional magnetic resonance imaging (rs-fMRI) to those of DES for language mapping. Our goals were twofold: (1) to replicate a previous study that demonstrated that resting-state connectivity (RSC) was significantly larger between positive DES language sites than between negative DES language sites and (2) to compare the spatial resolution of rs-fMRI to that of DES.
Methods We conducted a retrospective study of nine low-grade glioma patients. Language sites were identified by intraoperative DES. We compared RSC values between and within groups of DES-positive and DES-negative regions of interest (ROIs). Both close-negative sites (i.e., DES-negative sites <1 cm apart from and on the same gyrus as DES-positive sites) and far-negative sites (i.e., purely randomly chosen sites not in the vicinity of the tumor or of the DES-positive sites but on the same lobe) were included. Receiver operating characteristics were used to quantify comparisons.
Results Functional connectivity between all positive language sites was on average significantly higher than between all close-negative sites and between all far-negative sites. The functional connectivity between the positive language ROIs and their respective close-negative control sites was not smaller than between all positive language sites.
Conclusion rs-fMRI likely reflects similar neural information as detected with DES, but in its current form does not reach the spatial resolution of DES.

Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation—BOLD—signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively. ; SR would like to acknowledge Ikerbasque (The Basque Foundation for Science) and moreover, this research is supported by the Basque Government through the BERC 2018-2021 program and by the Spanish State Research Agency through BCAM Severo Ochoa excellence accreditation SEV2017-0718 and through project RTI2018-093860-B- C21 funded by (AEI/FEDER, UE) and acronym MathNEURO. JC acknowledges financial support from Ikerbasque, Ministerio Economia, Industria y Competitividad (Spain) and FEDER (grant DPI2016-79874-R) and the Department of Economical Development and Infrastructure of the Basque Country (Elkartek Program, KK-2018/00032). Finally, PG acknowledges BCAM's hospitality during a visiting fellowship in fall 2017.

Abstract

Aims
Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain's salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse.


Methods
Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30 days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures.


Results
Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: β = .337, p-FDR = .016; left LPFC: β = .291, p-FDR = .028) but not heavy drinking (p-FDR &gt; .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms—right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r = .25, p = .03).


Conclusions
These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.

Abstract Visual information processing requires an efficient visual attention system. The neural theory of visual attention (TVA) proposes that visual processing speed depends on the coordinated activity between frontoparietal and occipital brain areas. Previous research has shown that the coordinated activity between (i.e., functional connectivity and "inter-FC") cingulo-opercular (COn) and right-frontoparietal (RFPn) networks is linked to visual processing speed. However, how inter-FC of COn and RFPn with visual networks links to visual processing speed has not been directly addressed yet. Forty-eight healthy adult participants (27 females) underwent resting-state (rs-)fMRI and performed a whole-report psychophysical task. To obtain inter-FC, we analyzed the entire frequency range available in our rs-fMRI data (i.e., 0.01–0.4 Hz) to avoid discarding neural information. Following previous approaches, we analyzed the data across frequency bins (Hz): Slow-5 (0.01–0.027), Slow-4 (0.027–0.073), Slow-3 (0.073–0.198), and Slow-2 (0.198–0.4). We used the mathematical TVA framework to estimate an individual, latent-level visual processing speed parameter. We found that visual processing speed was negatively associated with inter-FC between RFPn and visual networks in Slow-5 and Slow-2, with no corresponding significant association for inter-FC between COn and visual networks. These results provide the first empirical evidence that links inter-FC between RFPn and visual networks with the visual processing speed parameter. These findings suggest that direct connectivity between occipital and right frontoparietal, but not frontoinsular, regions support visual processing speed. ; This work was supported by grants from the German Forschungsgemeinschaft to K. F. (grant number FI 1424/2e1) and C. S. (grant number SO 1336/1e1); the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014– 2020) under the Marie Skłodowska- Curie Grant Agreements No. 859890 (SmartAge) and No. 754388 ...

The amygdala is a major structure that orchestrates defensive reactions to environmental threats and is implicated in hypervigilance and symptoms of heightened arousal in posttraumatic stress disorder (PTSD). The basolateral and centromedial amygdala (CMA) complexes are functionally heterogeneous, with distinct roles in learning and expressing fear behaviors. PTSD differences in amygdala-complex function and functional connectivity with cortical and subcortical structures remain unclear. Recent military veterans with PTSD (n=20) and matched trauma-exposed controls (n=22) underwent a resting-state fMRI scan to measure task-free synchronous blood-oxygen level dependent activity. Whole-brain voxel-wise functional connectivity of basolateral and CMA seeds was compared between groups. The PTSD group had stronger functional connectivity of the basolateral amygdala (BLA) complex with the pregenual anterior cingulate cortex (ACC), dorsomedial prefrontal cortex, and dorsal ACC than the trauma-exposed control group (p<0.05; corrected). The trauma-exposed control group had stronger functional connectivity of the BLA complex with the left inferior frontal gyrus than the PTSD group (p<0.05; corrected). The CMA complex lacked connectivity differences between groups. We found PTSD modulates BLA complex connectivity with prefrontal cortical targets implicated in cognitive control of emotional information, which are central to explanations of core PTSD symptoms. PTSD differences in resting-state connectivity of BLA complex could be biasing processes in target regions that support behaviors central to prevailing laboratory models of PTSD such as associative fear learning. Further research is needed to investigate how differences in functional connectivity of amygdala complexes affect target regions that govern behavior, cognition, and affect in PTSD.

We examined the genetic architecture of functional brain connectivity measures in resting state electroencephalographic (EEG) recordings. Previous studies in Dutch twins have suggested that genetic factors are a main source of variance in functional brain connectivity derived from EEG recordings. In addition, qualitative descriptors of the brain network derived from graph analysis — network clustering and average path length — are also heritable traits. Here we replicated previous findings for connectivity, quantified by the synchronization likelihood, and the graph theoretical parameters cluster coefficient and path length in an Australian sample of 16-year-old twins (879) and their siblings (93). Modeling of monozygotic and dizygotic twins and sibling resemblance indicated heritability estimates of the synchronization likelihood (27–74%) and cluster coefficient and path length in the alpha and theta band (40–44% and 23–40% respectively) and path length in the beta band frequency (41%). This corroborates synchronization likelihood and its graph theoretical derivatives cluster coefficient and path length as potential endophenotypes for behavioral traits and neurological disorders.

Resting-state fMRI (RS-fMRI) has become a useful tool to investigate the connectivity structure of mental health disorders. In the case of major depressive disorder (MDD), recent studies regarding the RS-fMRI have found abnormal connectivity in several regions of the brain, particularly in the default mode network (DMN). Thus, the relevance of the DMN to self-referential thoughts and ruminations has made the use of the resting-state approach particularly important for MDD. The majority of such research has relied on the grand averaged functional connectivity measures based on the temporal correlations between the BOLD time series of various brain regions. We, in our study, investigated the variations in the functional connectivity over time at global and local level using RS-fMRI BOLD time series of 27 MDD patients and 27 healthy control subjects. We found that global synchronization and temporal stability were significantly increased in the MDD patients. Furthermore, the participants with MDD showed significantly increased overall average (static) functional connectivity (sFC) but decreased variability of functional connectivity (vFC) within specific networks. Static FC increased to predominance among the regions pertaining to the default mode network (DMN), while the decreased variability of FC was observed in the connections between the DMN and the frontoparietal network. ; ERC Advanced Grant: DYSTRUCTURE (to G.D.); Contract grant number: 295129; Contract grant sponsor:/nSpanish Research Project; Contract grant number: PSI2013-42091- P; Contract grant sponsor: European Union Seventh Framework Programme (FP7-ICT Human Brain Project; Contract grant number: 604102; Contract grant sponsor: del Carlos III Health Institute (to C.S.M.); Contract grant number: CP10/00604

Abstract
The hemodynamic response function (HRF) characterizes temporal variations of blood oxygenation level-dependent (BOLD) signals. Although a variety of HRF models have been proposed for gray matter responses to functional demands, few studies have investigated HRF profiles in white matter particularly under resting conditions. In the present work we quantified the nature of the HRFs that are embedded in resting state BOLD signals in white matter, and which modulate the temporal fluctuations of baseline signals. We demonstrate that resting state HRFs in white matter could be derived by referencing to intrinsic avalanches in gray matter activities, and the derived white matter HRFs had reduced peak amplitudes and delayed peak times as compared with those in gray matter. Distributions of the time delays and correlation profiles in white matter depend on gray matter activities as well as white matter tract distributions, indicating that resting state BOLD signals in white matter encode neural activities associated with those of gray matter. This is the first investigation of derivations and characterizations of resting state HRFs in white matter and their relations to gray matter activities. Findings from this work have important implications for analysis of BOLD signals in the brain.