Forum of resting-state fMRI

Pediatric applications of functional magnetic resonance imaging.

Pediatr Radiol. 2015 Sep;45 Suppl 3:382-96

Authors: Altman NR, Bernal B

Abstract
Pediatric functional MRI has been used for the last 2 decades but is now gaining wide acceptance in the preoperative workup of children with brain tumors and medically refractory epilepsy. This review covers pediatrics-specific difficulties such as sedation and task paradigm selection according to the child's age and cognitive level. We also illustrate the increasing uses of functional MRI in the depiction of cognitive function, neuropsychiatric disorders and response to pharmacological agents. Finally, we review the uses of resting-state fMRI in the evaluation of children and in the detection of epileptogenic regions.

PMID: 26346144 [PubMed - in process]

Decoupled temporal variability and signal synchronization of spontaneous brain activity in loss of consciousness: An fMRI study in anesthesia.

Neuroimage. 2015 Sep 3;

Authors: Huang Z, Zhang J, Wu J, Qin P, Wu X, Wang Z, Dai R, Li Y, Liang W, Mao Y, Yang Z, Zhang J, Wolff A, Northoff G

Abstract
Two aspects of the low frequency fluctuations of spontaneous brain activity have been proposed which reflect the complex and dynamic features of resting-state activity, namely temporal variability and signal synchronization. The relationship between them, especially its role in consciousness, nevertheless remains unclear. Our study examined the temporal variability and signal synchronization of spontaneous brain activity, as well as their relationship during loss of consciousness. We applied an intra-subject design of resting-state functional magnetic resonance imaging (rs-fMRI) in two conditions: during wakefulness, and under anesthesia with clinical unconsciousness. In addition, an independent group of patients with disorders of consciousness (DOC) was included in order to test the reliability of our findings. We observed a global reduction in the temporal variability, local and distant brain signal synchronization for subjects during anesthesia. Importantly, we found a link between temporal variability and both local and distant signal synchronizations during wakefulness: the higher the degree of temporal variability, the higher its intra-regional homogeneity and inter-regional functional connectivity. In contrast, this link was broken down under anesthesia, implying a decoupling between temporal variability and signal synchronization; this decoupling was reproduced in patients with DOC. Our results suggest that there exist some as yet unclear physiological mechanisms of consciousness which "couple" the two mathematically independent measures, temporal variability and signal synchronization of spontaneous brain activity. Our findings not only extend our current knowledge of the neural correlates of anesthetic-induced unconsciousness, but have implications for both computational neural modeling and clinical practice, such as in the diagnosis of loss of consciousness in patients with DOC.

PMID: 26343319 [PubMed - as supplied by publisher]

Mapping the functional connectivity of the substantia nigra, red nucleus and dentate nucleus: A network analysis hypothesis associated with the extrapyramidal system.

Neurosci Lett. 2015 Sep 2;606:36-41

Authors: Zhang HY, Tang H, Chen WX, Ji GJ, Ye J, Wang N, Wu JT, Guan B

Abstract
This study aimed to examine the functional networks related to the extrapyramidal system using a temporal oscillation signal correlation analysis method based on critical nodes in the substantia nigra (SN), red nucleus (RN) and dentate nucleus (DN). Nineteen healthy subjects underwent resting-state fMRI and susceptibility weighted imaging (SWI). For the brain network analysis, the SN, RN and DN were positioned on susceptibility weighted images and used as seeds for temporal correlations analyzed via BOLD data. T-tests were performed for the correlation coefficients of each seed. We demonstrated that the SN, RN and DN were functionally connected to each other, and, in general, their connectivity maps overlapped in a series of subcortical extrapyramidal structures and regions of cerebral cortices. A Granger causality analysis indicated that the effective connectivity graphs within extrapyramidal structures mainly exhibited a spacial up-down pattern for the positive and negative influences, respectively. Our findings suggest that extensive regions involved in the extrapyramidal system constituted a relatively exclusive network via spatial-temporal correlation signals that analogously corresponded to the anatomical structures. The investigation of extrapyramidal system networks may have potential clinical implications.

PMID: 26342496 [PubMed - as supplied by publisher]

Structural and functional neural correlates of self-reported attachment in healthy adults: evidence for an amygdalar involvement.

Brain Imaging Behav. 2015 Sep 3;

Authors: Rigon A, Duff MC, Voss MW

Abstract
The concept of attachment in long-term interpersonal relationships has been linked to relationship outcome and social-emotional health. To date, no relationship between the structural properties of the human amygdala and attachment in romantic relationships (measured through self-reported attachment related anxiety and avoidance) has been described. The aim of the current study was to investigate the relationship between amygdala structure as well as amygdala structural and functional connectivity and attachment anxiety and avoidance. To this end, we collected self-report attachment data on a sample of female young adults. We then examined associations between attachment and mean diffusivity, fractional anisotropy and resting state functional connectivity MRI (rs-FC) of the amygdala and its white matter connections with the prefrontal cortex. We found that lower integrity of the left amygdala was linked with attachment avoidance (e.g., being less comfortable in seeking proximity with others and depending on others) and that greater structural integrity of the uncinate fasciculus was positively associated with avoidance. Lastly, we found that stronger rs-FC between the bilateral amygdala and medial prefrontal regions was linked with greater avoidance. Our findings are compatible with and expand previous results reported by studies that have taken a task-related fMRI approach, furthering our understanding of the neurobiological mechanisms of attachment, and in particular implicating the system formed by amygdala and prefrontal areas in the patterns of behavior that regulate emotional proximity in romantic relationships. These findings have the potential to further our understanding of the affective mechanisms underlying attachment behavior.

PMID: 26334650 [PubMed - as supplied by publisher]

Identifying Shared Brain Networks in Individuals by Decoupling Functional and Anatomical Variability.

Cereb Cortex. 2015 Sep 1;

Authors: Langs G, Wang D, Golland P, Mueller S, Pan R, Sabuncu MR, Sun W, Li K, Liu H

Abstract
The connectivity architecture of the human brain varies across individuals. Mapping functional anatomy at the individual level is challenging, but critical for basic neuroscience research and clinical intervention. Using resting-state functional connectivity, we parcellated functional systems in an "embedding space" based on functional characteristics common across the population, while simultaneously accounting for individual variability in the cortical distribution of functional units. The functional connectivity patterns observed in resting-state data were mapped in the embedding space and the maps were aligned across individuals. A clustering algorithm was performed on the aligned embedding maps and the resulting clusters were transformed back to the unique anatomical space of each individual. This novel approach identified functional systems that were reproducible within subjects, but were distributed across different anatomical locations in different subjects. Using this approach for intersubject alignment improved the predictability of individual differences in language laterality when compared with anatomical alignment alone. Our results further revealed that the strength of association between function and macroanatomy varied across the cortex, which was strong in unimodal sensorimotor networks, but weak in association networks.

PMID: 26334050 [PubMed - as supplied by publisher]

Characterizations of resting-state modulatory interactions in human brain.

J Neurophysiol. 2015 Sep 2;:jn.00893.2014

Authors: Di X, Biswal BB

Abstract
Functional connectivity between two brain regions measured using functional MRI (fMRI) have been shown to be modulated by other regions even in resting-state, i.e. without performing specific tasks. We aimed to characterize large scale modulatory interactions by performing ROI-based (region of interest) physiophysiological interaction (PPI) analysis on resting-state fMRI data. Modulatory interactions were calculated for every possible combination of three ROIs among 160 ROIs sampling the whole brain. Firstly, among all the significant modulatory interactions, there were considerably more negative than positive effects, i.e. in more cases an increase of activity in one region was associated with decreased functional connectivity between two other regions. Next, modulatory interactions were categorized as whether the three ROIs were from one single network module, two modules, or three different modules (defined by a modularity analysis on their functional connectivity). Positive modulatory interactions were more represented than expected in cases that the three ROIs were from a single module, suggesting increased within module processing efficiency through positive modulatory interactions. In contrast, negative modulatory interactions were more represented than expected in cases that the three ROIs were from two modules, suggesting a tendency of between modules segregation through negative modulatory interactions. Regions that were more likely to have modulatory interactions were then identified. The numbers of significant modulatory interactions for different regions were correlated with the regions' connectivity strengths and connection degrees. These results demonstrate whole brain characteristics of modulatory interactions, and may provide guidance for future studies of connectivity dynamics in both resting-state and task-state.

PMID: 26334022 [PubMed - as supplied by publisher]

Alterations of Regional Cerebral Blood Flow in Tinnitus Patients as Assessed Using Single-Photon Emission Computed Tomography.

PLoS One. 2015;10(9):e0137291

Authors: Ueyama T, Donishi T, Ukai S, Yamamoto Y, Ishida T, Tamagawa S, Hotomi M, Shinosaki K, Yamanaka N, Kaneoke Y

Abstract
Tinnitus is the perception of phantom sound without an external auditory stimulus. Using neuroimaging techniques, such as positron emission tomography, electroencephalography, magnetoencephalography, and functional magnetic resonance imaging (fMRI), many studies have demonstrated that abnormal functions of the central nervous system are closely associated with tinnitus. In our previous research, we reported using resting-state fMRI that several brain regions, including the rectus gyrus, cingulate gyrus, thalamus, hippocampus, caudate, inferior temporal gyrus, cerebellar hemisphere, and medial superior frontal gyrus, were associated with tinnitus distress and loudness. To reconfirm these results and probe target regions for repetitive transcranial magnetic stimulation (rTMS), we investigated the regional cerebral blood flow (rCBF) between younger tinnitus patients (<60 years old) and the age-matched controls using single-photon emission computed tomography and easy Z-score imaging system. Compared with that of controls, the rCBF of tinnitus patients was significantly lower in the bilateral medial superior frontal gyri, left middle occipital gyrus and significantly higher in the bilateral cerebellar hemispheres and vermis, bilateral middle temporal gyri, right fusiform gyrus. No clear differences were observed between tinnitus patients with normal and impaired hearing. Regardless of the assessment modality, similar brain regions were identified as characteristic in tinnitus patients. These regions are potentially involved in the pathophysiology of chronic subjective tinnitus.

PMID: 26332128 [PubMed - in process]

Role of local and distant functional connectivity density in the development of minimal hepatic encephalopathy.

Sci Rep. 2015;5:13720

Authors: Qi R, Zhang LJ, Chen HJ, Zhong J, Luo S, Ke J, Xu Q, Kong X, Liu C, Lu GM

Abstract
The progression of functional connectivity (FC) patterns from non-hepatic encephalopathy (non-HE) to minimal HE (MHE) is not well known. This resting-state functional magnetic resonance imaging (rs-fMRI) study investigated the evolution of intrinsic FC patterns from non-HE to MHE. A total of 103 cirrhotic patients (MHE, n = 34 and non-HE, n = 69) and 103 healthy controls underwent rs-fMRI scanning. Maps of distant and local FC density (dFCD and lFCD, respectively) were compared among MHE, non-HE, and healthy control groups. Decreased lFCD in anterior cingulate cortex, pre- and postcentral gyri, cuneus, lingual gyrus, and putamen was observed in both MHE and non-HE patients relative to controls. There was no difference in lFCD between MHE and non-HE groups. The latter showed decreased dFCD in inferior parietal lobule, cuneus, and medial frontal cortex relative to controls; however, MHE patients showed decreased dFCD in frontal and parietal cortices as well as increased dFCD in thalamus and caudate head relative to control and non-HE groups. Abnormal FCD values in some regions correlated with MHE patients' neuropsychological performance. In conclusion, lFCD and dFCD were perturbed in MHE. Impaired dFCD in regions within the cortico-striato-thalamic circuit may be more closely associated with the development of MHE.

PMID: 26329994 [PubMed - in process]

Changes of Functional Brain Networks in Major Depressive Disorder: A Graph Theoretical Analysis of Resting-State fMRI.

PLoS One. 2015;10(9):e0133775

Authors: Ye M, Yang T, Qing P, Lei X, Qiu J, Liu G

Abstract
Recent developments in graph theory have heightened the need for investigating the disruptions in the topological structure of functional brain network in major depressive disorder (MDD). In this study, we employed resting-state functional magnetic resonance imaging (fMRI) and graph theory to examine the whole-brain functional networks among 42 MDD patients and 42 healthy controls. Our results showed that compared with healthy controls, MDD patients showed higher local efficiency and modularity. Furthermore, MDD patients showed altered nodal centralities of many brain regions, including hippocampus, temporal cortex, anterior cingulate gyrus and dorsolateral prefrontal gyrus, mainly located in default mode network and cognitive control network. Together, our results suggested that MDD was associated with disruptions in the topological structure of functional brain networks, and provided new insights concerning the pathophysiological mechanisms of MDD.

PMID: 26327292 [PubMed - in process]

Differences in the resting-state fMRI global signal amplitude between the eyes open and eyes closed states are related to changes in EEG vigilance.

Neuroimage. 2015 Aug 29;

Authors: Wong CW, DeYoung PN, Liu TT

Abstract
In resting-state functional connectivity magnetic resonance imaging (fcMRI) studies, measures of functional connectivity are often calculated after the removal of a global mean signal component. While the application of the global signal regression approach has been shown to reduce the influence of physiological artifacts and enhance the detection of functional networks, there is considerable controversy regarding its use as the method can lead to significant bias in the resultant connectivity measures. In addition, evidence from recent studies suggests that the global signal is linked to neural activity and may carry clinically relevant information. For instance, in a prior study we found that the amplitude of the global signal was negatively correlated with EEG measures of vigilance across subjects and experimental runs. Furthermore, caffeine-related decreases in global signal amplitude were associated with increases in EEG vigilance. In this study, we extend the prior work by examining measures of global signal amplitude and EEG vigilance under eyes-closed (EC) and eyes-open (EO) resting-state conditions. We show that changes (EO minus EC) in the global signal amplitude are negatively correlated with the associated changes in EEG vigilance. The slope of this EO-EC relation is comparable with the slope of the previously reported relation between caffeine-related changes in the global signal amplitude and EEG vigilance. Our findings provide further support for a basic relationship between global signal amplitude and EEG vigilance.

PMID: 26327245 [PubMed - as supplied by publisher]

Towards a standardized structural-functional group connectome in MNI space.

Neuroimage. 2015 Aug 29;

Authors: Horn A, Blankenburg F

Abstract
The analysis of the structural architecture of the human brain in terms of connectivity between its sub-regions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute - Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. As a second experiment, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to prior literature findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) to perform fiber tracking directly within MNI space. Hence, it may be used for various purposes such as the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step towards a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level.

PMID: 26327244 [PubMed - as supplied by publisher]

Two distinct scene processing networks connecting vision and memory.

J Vis. 2015 Sep 1;15(12):571

Authors: Baldassano C, Esteva A, Beck D, Fei-Fei L

Abstract
Research on visual scene understanding has identified a number of regions involved in processing natural scenes, but has lacked a unifying framework for understanding how these different regions are organized and interact. We propose a new organizational principle, in which scene processing relies on two distinct networks at the edge of visual cortex. The first network consists of the Transverse Occipital Sulcus (TOS, or the Occipital Place Area) and the posterior portion of the Parahippocampal Place Area (PPA). These regions have a well-defined retinotopic organization and do not show strong memory or context effects, suggesting that this network primarily processes visual features from the current view of a scene. The second network consists of the caudal Inferior Parietal Lobule (cIPL), Retrosplenial Cortex (RSC), and the anterior portion of the PPA. These regions are involved in a wide range of both visual and non-visual tasks involving episodic memory, navigation, imagination, and default mode processing, and connect information about a current scene view with a much broader temporal and spatial context. We provide evidence for this division from a diverse set of sources. Using a data-driven approach to parcellate resting-state fMRI data, we identify coherent functional regions corresponding to scene-processing areas. We then show that a network clustering analysis separates these scene-related regions into two adjacent networks, which exhibit sharp changes in connectivity properties across their narrow border. Additionally, we argue that the cIPL has been previously overlooked as a critical region for full scene understanding, based on a meta-analysis of previous functional studies as well as diffusion tractography results showing that cIPL is well-positioned to connect visual cortex with many other cortical systems. This new framework for understanding the neural substrates of scene processing bridges results from many lines of research, and makes specific predictions about functional properties of these regions. Meeting abstract presented at VSS 2015.

PMID: 26326259 [PubMed - as supplied by publisher]

Cerebellar Contributions to Visual Attention and Visual Working Memory Revealed by Functional MRI and Intrinsic Functional Connectivity.

J Vis. 2015 Sep 1;15(12):232

Authors: Brissenden J, Levin E, Osher D, Rosen M, Halko M, Somers D

Abstract
The study of cerebellum function has been traditionally limited to the motor domain. Recent research, however, has begun to characterize the cerebellum's role in cognition (see Schmahmann, 2010) and has demonstrated intrinsic functional connectivity between cerebral cortical networks and distinct cerebellar regions (Buckner et al., 2011). Here, in two separate fMRI experiments, we investigated whether cerebro-cerebellar connectivity of dorsal attention network (DAN) predicts cerebellar activation during visual attention and visual working memory (VWM) task performance. In experiment 1 (N=8), subjects performed a multiple-object tracking task. In experiment 2 (N=9), subjects performed a VWM change detection task using oriented bars. Memory load was varied across blocks (set size: SS0, SS1, or SS4). Both experiments employed resting-state functional connectivity analysis using cortical network seeds (Yeo et al., 2011) to parcellate cerebro-cerebellar networks in individual subjects. In experiment 1, a region-of-interest analysis revealed a robust attentional effect within cerebellar regions functionally connected to the cortical DAN (p&lt; .01). Conversely, cerebellar regions functionally connected to the cortical default mode network (DMN) showed reliable deactivation (p&lt; .001). In experiment 2, contrasting SS4 with SS0 and SS1 resulted in a similar pattern of competitive interaction between cerebellar nodes of the DAN and DMN. Load-dependent activation spatially corresponded with cerebellar DAN nodes (SS4-SS0: p&lt; .005; SS4-SS1: p&lt; .0001) and load-dependent deactivation was observed within cerebellar DMN nodes (SS4-SS0: p&lt; .005; SS4-SS1: p&lt; .0005). Across both experiments the strength of intrinsic functional connectivity, with either the cortical DAN or the cortical DMN, significantly predicted the response of individual cerebellar voxels (Experiment 1: rDAN =.67, rDMN =-.71; Experiment 2: rDAN =.60, rDMN =-.56). Our results indicate that cerebellar nodes of the DAN contribute to network function across a diverse range of attentive and working memory conditions. Meeting abstract presented at VSS 2015.

PMID: 26325920 [PubMed - as supplied by publisher]

Structural and functional connectivity of visual and auditory attentional networks: insights from the Human Connectome Project.

J Vis. 2015 Sep 1;15(12):223

Authors: Osher D, Tobyne S, Congden K, Michalka S, Somers D

Abstract
Recent work in our laboratory has suggested that human caudal lateral frontal cortex contains four interleaved regions in each hemisphere that exhibit strong sensory-specific biases in attention tasks (Michalka et al, 2014). Two visually-biased attention regions, superior and inferior pre-central sulcus (sPCS, iPCS), anatomically alternate with two auditory-biased attention regions, caudal inferior frontal sulcus (cIFS) and the transverse gyrus intersection the precentral sulcus (tgPCS). These small regions were identified in fMRI studies in a small number of individual subjects. Here, we have investigated these regions and their putative networks by mining the WashU-Minn Human Connectome Project (HCP) dataset. We used data from the 482 HCP participants with both diffusion-weighted imaging and resting-state fMRI. We defined seed regions from our individual subject data in a task that contrasted auditory and visual spatial attention. Probabilistic activation maps were constructed and thresholded to generate ROIs. These ROIs served as seed regions for resting state and tractography analyses of the HCP dataset. Stronger functional connectivity was observed for the sPCS and iPCS than for tgPCS and cIFS with superior parietal lobule visual attention regions, and conversely stronger connectivity was observed for the tgPCS and cIFS than for sPCS and iPCS with superior temporal lobe auditory attention regions. A similar pattern was observed with tractography for all ROIs, except for tgPCS. We next analyzed the whole-brain connectivity patterns of these ROIs using a multivariate approach; we found that the modality of sensory-bias can be predicted well above chance in both hemispheres at a voxelwise scale (L:71%, R:80%), using only the connectivity pattern of an individual voxel. A long-term goal of this analysis is to develop reliable methods for identifying fine-scale brain networks in large population datasets, which could have important clinical applications. Our preliminary results reveal both successes and challenges of these efforts. Meeting abstract presented at VSS 2015.

PMID: 26325911 [PubMed - as supplied by publisher]

Abnormal Resting-State Connectivity at Functional MRI in Women with Premenstrual Syndrome.

PLoS One. 2015;10(9):e0136029

Authors: Liu Q, Li R, Zhou R, Li J, Gu Q

Abstract
OBJECTIVES: Premenstrual syndrome (PMS) refers to a series of cycling and relapsing physical, emotion and behavior syndromes that occur in the luteal phase and resolve soon after the onset of menses. Although PMS is widely recognized, its neural mechanism is still unclear.
DESIGN: To address this question, we measured brain activity for women with PMS and women without PMS (control group) using resting-state functional magnetic resonance imaging (rs-fMRI). In addition, the participants should complete the emotion scales (Beck Anxiety Inventory, BAI; Beck Depression Inventory, BDI, before the scanning) as well as the stress perception scale (Visual analog scale for stress, VAS, before and after the scanning).
RESULTS: The results showed that compared with the control group, the PMS group had decreased connectivity in the middle frontal gyrus (MFG) and theparahippocampalgyrus (PHG), as well as increased connectivity in the left medial/superior temporal gyri (MTG/STG) and precentralgyrus within the default mode network (DMN); in addition, the PMS group had higher anxiety and depression scale scores, together with lower stress perception scores. Finally, there were significantly positive correlations between the stress perception scores and functional connectivity in the MFG and cuneus. The BDI scores in the PMS group were correlated negatively with the functional connectivity in the MFG and precuneus and correlated positively with the functional connectivity in the MTG.
CONCLUSION: These findings suggest that compared with normal women, women with PMS displayed abnormal stress sensitivity, which was reflected in the decreased and increased functional connectivity within the DMN, blunted stress perception and higher depression.

PMID: 26325510 [PubMed - as supplied by publisher]

Making Large-Scale Networks from fMRI Data.

PLoS One. 2015;10(9):e0129074

Authors: Schmittmann VD, Jahfari S, Borsboom D, Savi AO, Waldorp LJ

Abstract
Pairwise correlations are currently a popular way to estimate a large-scale network (> 1000 nodes) from functional magnetic resonance imaging data. However, this approach generally results in a poor representation of the true underlying network. The reason is that pairwise correlations cannot distinguish between direct and indirect connectivity. As a result, pairwise correlation networks can lead to fallacious conclusions; for example, one may conclude that a network is a small-world when it is not. In a simulation study and an application to resting-state fMRI data, we compare the performance of pairwise correlations in large-scale networks (2000 nodes) against three other methods that are designed to filter out indirect connections. Recovery methods are evaluated in four simulated network topologies (small world or not, scale-free or not) in scenarios where the number of observations is very small compared to the number of nodes. Simulations clearly show that pairwise correlation networks are fragmented into separate unconnected components with excessive connectedness within components. This often leads to erroneous estimates of network metrics, like small-world structures or low betweenness centrality, and produces too many low-degree nodes. We conclude that using partial correlations, informed by a sparseness penalty, results in more accurate networks and corresponding metrics than pairwise correlation networks. However, even with these methods, the presence of hubs in the generating network can be problematic if the number of observations is too small. Additionally, we show for resting-state fMRI that partial correlations are more robust than correlations to different parcellation sets and to different lengths of time-series.

PMID: 26325185 [PubMed - as supplied by publisher]

Default mode network alterations during implicit emotional faces processing in first-episode, treatment-naive major depression patients.

Front Psychol. 2015;6:1198

Authors: Shi H, Wang X, Yi J, Zhu X, Zhang X, Yang J, Yao S

Abstract
Previous studies have focused on resting-state default mode network (DMN) alterations in the development and maintenance of depression; however, only a few studies have addressed DMN changes during task-related processing and their results are inconsistent. Therefore, we explored DMN patterns in young adult patients with first-episode, treatment-naïve major depressive disorder (MDD) performing an implicit emotional processing task. Patients with MDD (N = 29) and healthy controls (N = 33) were subjected to functional magnetic resonance imaging (fMRI) at rest and while performing a gender judgment task. Group independent component analysis (ICA) was used to identify DMN component under task state for both groups. The DMN of participants with MDD had decreased functional connectivity in bilateral prefrontal areas compared to controls. Right prefrontal gyrus connectivity for MDD patients correlated negatively with scores on maladaptive scales of the Cognitive Emotion Regulation Questionnaire (CERQ). Our findings suggest that depressed people have altered DMN patterns during implicit emotional processing, which might be related to impaired internal monitoring and emotional regulation ability.

PMID: 26322003 [PubMed]

Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease.

Front Hum Neurosci. 2015;9:449

Authors: Griffanti L, Dipasquale O, Laganà MM, Nemni R, Clerici M, Smith SM, Baselli G, Baglio F

Abstract
Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest-crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier-FIX-cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment.

PMID: 26321937 [PubMed]

Detection of short-term activity avalanches in human brain default mode network with ultrafast MR encephalography.

Front Hum Neurosci. 2015;9:448

Authors: Rajna Z, Kananen J, Keskinarkaus A, Seppänen T, Kiviniemi V

Abstract
Recent studies pinpoint visually cued networks of avalanches with MEG/EEG data. Co-activation pattern (CAP) analysis can be used to detect single brain volume activity profiles and hemodynamic fingerprints of neuronal avalanches as sudden high signal activity peaks in classical fMRI data. In this study, we aimed to detect dynamic patterns of brain activity spreads with the use of ultrafast MR encephalography (MREG). MREG achieves 10 Hz whole brain sampling, allowing the estimation of spatial spread of an avalanche, even with the inherent hemodynamic delay of the BOLD signal. We developed a novel computational method to separate avalanche type fast activity spreads from motion artifacts, vasomotor fluctuations, and cardio-respiratory noise in human brain default mode network (DMN). Reproducible and classical DMN sources were identified using spatial ICA prior to advanced noise removal in order to assure that ICA converges to reproducible networks. Brain activity peaks were identified from parts of the DMN, and normalized MREG data around each peak were extracted individually to show dynamic avalanche type spreads as video clips within the DMN. Individual activity spread video clips of specific parts of the DMN were then averaged over the group of subjects. The experiments show that the high BOLD values around the peaks are mostly spreading along the spatial pattern of the particular DMN segment detected with ICA. With also the spread size and lifetime resembling the expected power law distributions, this indicates that the detected peaks are parts of activity avalanches, starting from (or crossing) the DMN. Furthermore, the split, one-sided sub-networks of the DMN show different spread directions within the same DMN framework. The results open possibilities to follow up brain activity avalanches in the hope to understand more about the system wide properties of diseases related to DMN dysfunction.

PMID: 26321936 [PubMed]

Corrigendum: Prevalence of increases in functional connectivity in visual, somatosensory and language areas in congenital blindness.

Front Neuroanat. 2015;9:106

Authors: Heine L, Bahri MA, Cavaliere C, Soddu A, Reislev NL, Laureys S, Ptito M, Kupers R

Abstract
[This corrects the article on p. 86 in vol. 9, PMID: 26190978.].

PMID: 26321919 [PubMed - as supplied by publisher]