Altered spontaneous neural activity in first-episode, unmedicated patients with major depressive disorder.
Neuroreport. 2014 Sep 16;
Authors: Shen T, Qiu M, Li C, Zhang J, Wu Z, Wang B, Jiang K, Peng D
Abnormal brain function is presumed to be a pathophysiological aspect of major depressive disorder (MDD). However, the underlying patterns of spontaneous neural activity have been poorly characterized and replicated to date. In this study, we applied a novel approach of fractional amplitude of low-frequency fluctuation (fALFF) to investigate the alteration of spontaneous neural activity in MDD. Sixteen first-episode, unmedicated patients with MDD and 16 healthy controls were recruited and subjected to resting-state fMRI scans to measure the fALFF across the whole brain. Compared with healthy controls, MDD patients exhibited decreased fALFF in the right angular gyrus, left middle temporal gyrus, left superior temporal gyrus, right putamen, right precuneus, and the right superior temporal gyrus. Differences in fALFF between MDD patients and controls indicated that altered spontaneous neural activity was distributed across a number of specific brain regions among MDD patients. These atypical functional regions may help explain some of the neural processes underlying the clinical symptoms accompanying MDD.
PMID: 25229945 [PubMed - as supplied by publisher]
Corrigendum: The quest for EEG power band correlation with ICA derived fMRI resting state networks.
Front Hum Neurosci. 2014;8:539
Authors: Meyer MC, Janssen RJ, Van Oort ES, Beckmann CF, Barth M
[This corrects the article on p. 315 in vol. 7, PMID: 23805098.].
PMID: 25228866 [PubMed - as supplied by publisher]
Resting state fMRI feature-based cerebral glioma grading by support vector machine.
Int J Comput Assist Radiol Surg. 2014 Sep 17;
Authors: Wu J, Qian Z, Tao L, Yin J, Ding S, Zhang Y, Yu Z
PURPOSE : Tumor grading plays an essential role in the optimal selection of solid tumor treatment. Noninvasive methods are needed for clinical grading of tumors. This study aimed to extract parameters of resting state blood oxygenation level-dependent functional magnetic resonance imaging (RS-fMRI) in the region of glioma and use the extracted features for tumor grading. METHODS : Tumor segmentation was performed with both conventional MRI and RS-fMRI. Four typical parameters, signal intensity difference ratio, signal intensity correlation (SIC), fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo), were defined to analyze tumor regions. Mann-Whitney [Formula: see text] test was employed to identify statistical difference of these four parameters between low-grade glioma (LGG) and high-grade glioma (HGG), respectively. Support vector machine (SVM) was employed to assess the diagnostic contributions of these parameters. RESULTS : Compared with LGG, HGG had more complex anatomical morphology and BOLD-fMRI features in the tumor region. SIC [Formula: see text], fALFF ([Formula: see text]) and ReHo ([Formula: see text]) were selected as features for classification according to the test [Formula: see text] value. The accuracy, sensitivity and specificity of SVM classification were better than 80, where SIC had the best classification accuracy (89). CONCLUSION : Parameters of RS-fMRI are effective to classify the tumor grade in glioma cases. The results indicate that this technique has clinical potential to serve as a complementary diagnostic tool.
PMID: 25227532 [PubMed - as supplied by publisher]
Insights into the mechanisms of absence seizure generation provided by EEG with functional MRI.
Front Neurol. 2014;5:162
Authors: Carney PW, Jackson GD
Absence seizures (AS) are brief epileptic events characterized by loss of awareness with subtle motor features. They may be very frequent, and impact on attention, learning, and memory. A number of pathophysiological models have been developed to explain the mechanism of absence seizure generation, which relies heavily on observations from animal studies. Studying the structural and functional relationships between large-scale brain networks in humans is only practical with non-invasive whole brain techniques. EEG with functional MRI (EEG-fMRI) is one such technique that provides an opportunity to explore the interactions between brain structures involved in AS generation. A number of fMRI techniques including event-related analysis, time-course analysis, and functional connectivity (FC) have identified a common network of structures involved in AS. This network comprises the thalamus, midline, and lateral parietal cortex [the default mode network (DMN)], caudate nuclei, and the reticular structures of the pons. The main component displaying an increase in blood oxygen level dependent (BOLD) signal relative to the resting state, in group studies, is the thalamus while the most consistent cortical change is reduced BOLD signal in the DMN. Time-course analysis shows that, rather than some structures being activated or inactivated during AS, there appears to be increase in activity across components of the network preceding or following the electro-clinical onset of the seizure. The earliest change in BOLD signal occurs in the DMN, prior to the onset of epileptiform events. This region also shows altered FC in patients with AS. Hence, it appears that engagement of this network is central to AS. In this review, we will explore the insights of EEG-fMRI studies into the mechanisms of AS and consider how the DMN is likely to be the major large-scale brain network central to both seizure generation and seizure manifestations.
PMID: 25225491 [PubMed]
Denoising the Speaking Brain: Toward a Robust Technique for Correcting Artifact-Contaminated fMRI Data under Severe Motion.
Neuroimage. 2014 Sep 12;
Authors: Xu Y, Tong Y, Liu S, Chow HM, AbdulSabur NY, Mattay GS, Braun AR
A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity.
PMID: 25225001 [PubMed - as supplied by publisher]
Understanding Human Original Actions Directed at Real-Worls Goals: The Role of the Lateral Prefrontal Cortex.
Neuroimage. 2014 Sep 12;
Authors: Sitnikova T, Rosen BR, Lord LD, Caroline West W
Adaptive, original actions, which can succeed in multiple contextual situations, require understanding of what is relevant to a goal. Recognizing what is relevant may also help in predicting kinematics of observed, original actions. During action observation, comparisons between sensory input and expected action kinematics have been argued critical to accurate goal inference. Experimental studies with laboratory tasks, both in humans and nonhuman primates, demonstrated that the lateral prefrontal cortex (LPFC) can learn, hierarchically organize, and use goal-relevant information. To determine whether this LPFC capacity is generalizable to real-world cognition, we recorded functional magnetic resonance imaging (fMRI) data in the human brain during comprehension of original and usual object-directed actions embedded in video-depictions of real-life behaviors. We hypothesized that LPFC will contribute to forming goal-relevant representations necessary for kinematic predictions of original actions. Additionally, resting-state fMRI was employed to examine functional connectivity between the brain regions delineated in the video fMRI experiment. According to behavioral data, original videos could be understood by identifying elements relevant to real-life goals at different levels of abstraction. Patterns of enhanced activity in four regions in the left LPFC, evoked by original, relative to usual, video scenes, were consistent with previous neuroimaging findings on representing abstract and concrete stimuli dimensions relevant to laboratory goals. In the anterior left LPFC, the activity increased selectively when representations of broad classes of objects and actions, which could achieve the perceived overall behavioral goal, were likely to bias kinematic predictions of original actions. In contrast, in the more posterior regions, the activity increased even when concrete properties of the target object were more likely to bias the kinematic prediction. Functional connectivity was observed between contiguous regions along the rostro-caudal LPFC axis, but not between the regions that were not immediately adjacent. These findings generalize the representational hierarchy account of LPFC function to diverse core principles that can govern both production and comprehension of flexible real-life behavior.
PMID: 25224997 [PubMed - as supplied by publisher]
Optimizing affinity measures for parcellating brain structures based on resting state fMRI data: a validation on medial superior frontal cortex.
J Neurosci Methods. 2014 Sep 12;
Authors: Cheng H, Wu H, Fan Y
BACKGROUND: Parcellating brain structures into functionally homogeneous subregions based on resting state fMRI data could be achieved by grouping image voxels using clustering algorithms, such as normalized cut. The affinity between brain voxels adopted in the clustering algorithms is typically characterized by a combination of the similarity of their functional signals and their spatial distance with parameters empirically specified. However, improper parameter setting of the affinity measure may result in parcellation results biased to spatial smoothness.
NEW METHOD: To obtain a functionally homogeneous and spatially contiguous brain parcellation result, we propose to optimize the affinity measure of image voxels using a constrained bi-level programming optimization method. Particularly, we first identify the space of all possible parameters that are able to generate spatially contiguous brain parcellation results. Then, within the constrained parameter space we search those leading to the brain parcellation results with optimal functional homogeneity and spatial smoothness.
RESULTS AND COMPARISON WITH EXISTING METHODS: The method has successfully parcellated medial superior frontal cortex into supplementary motor area (SMA) and pre-SMA for 106 subjects based on their resting state fMRI data. These results have been validated through functional connectivity analysis and meta-analysis of existing functional imaging studies and compared with those obtained by state-of-the-art brain parcellation methods.
CONCLUSIONS: The validation results have demonstrated that our method could obtain brain parcellation results consistent with the existing functional anatomy knowledge, and the comparison results have further demonstrated that optimizing affinity measure could improve the brain parcellation's robustness and functional homogeneity.
PMID: 25224735 [PubMed - as supplied by publisher]
Exploring variations in functional connectivity of the resting state default mode network in mild traumatic brain injury.
Brain Connect. 2014 Sep 15;
Authors: Nathan DE, Yeh PH, French LM, Harper JF, Liu W, Wolfowitz RD, Wang BQ, Graner JL, Oakes T, Riedy G
A definitive diagnosis of mTBI is difficult due to the absence of biomarkers in standard clinical imaging. The brain is a complex network of interconnected neurons and subtle changes can modulate key networks of cognitive function. The resting state default mode network (DMN) has been shown to be sensitive to changes induced by pathology. This study seeks to determine if quantitative measures of the DMN are sensitive in distinguishing mTBI subjects. Resting state fMRI data were obtained for healthy (N=12) and mTBI subjects (N=15). DMN maps were computed using dual-regression independent component analysis (ICA). A goodness-of-fit index (GOF) was calculated to assess the degree of spatial specificity and sensitivity between healthy controls and mTBI subjects. DMN regions and neuropsychological assessments were examined to identify potential relationships. The resting state DMN maps indicate an increase in spatial co-activity in mTBI subjects within key regions of the DMN. Significant co-activity within the cerebellum and supplementary motor areas of mTBI subjects were also observed. This has not been previously reported in seed-based resting state network analysis. The GOF suggested the presence of high variability within the mTBI subject group, with poor sensitivity and specificity. The neuropsychological data showed correlations between areas of co-activity within the resting state network in the brain with a number of measures of emotion and cognitive functioning. The poor performance of the GOF highlights the key challenge associated with mTBI injury: the high variability in injury mechanisms and subsequent recovery. However, the quantification of the DMN using dual regression ICA has potential to distinguish mTBI from healthy subjects, and provide information on the relationship of aspects of cognitive and emotional functioning with their potential neural correlates.
PMID: 25222050 [PubMed - as supplied by publisher]
Microstructure, length, and connection of limbic tracts in normal human brain development.
Front Aging Neurosci. 2014;6:228
Authors: Yu Q, Peng Y, Mishra V, Ouyang A, Li H, Zhang H, Chen M, Liu S, Huang H
The cingulum and fornix play an important role in memory, attention, spatial orientation, and feeling functions. Both microstructure and length of these limbic tracts can be affected by mental disorders such as Alzheimer's disease, depression, autism, anxiety, and schizophrenia. To date, there has been little systematic characterization of their microstructure, length, and functional connectivity in normally developing brains. In this study, diffusion tensor imaging (DTI) and resting state functional MRI (rs-fMRI) data from 65 normally developing right-handed subjects from birth to young adulthood was acquired. After cingulate gyrus part of the cingulum (cgc), hippocampal part of the cingulum (cgh) and fornix (fx) were traced with DTI tractography, absolute and normalized tract lengths and DTI-derived metrics including fractional anisotropy, mean, axial, and radial diffusivity were measured for traced limbic tracts. Free water elimination (FWE) algorithm was adopted to improve accuracy of the measurements of DTI-derived metrics. The role of these limbic tracts in the functional network at birth and adulthood was explored. We found a logarithmic age-dependent trajectory for FWE-corrected DTI metric changes with fast increase of microstructural integrity from birth to 2 years old followed by a slow increase to 25 years old. Normalized tract length of cgc increases with age, while no significant relationship with age was found for normalized tract lengths of cgh and fx. Stronger microstructural integrity on the left side compared to that of the right side was found. With integrated DTI and rs-fMRI, the key connectional role of cgc and cgh in the default mode network was confirmed as early as birth. Systematic characterization of length and DTI metrics after FWE correction of limbic tracts offers insight into their morphological and microstructural developmental trajectories. These trajectories may serve as a normal reference for pediatric patients with mental disorders.
PMID: 25221509 [PubMed]
Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands.
Front Neurosci. 2014;8:258
Authors: Deligianni F, Centeno M, Carmichael DW, Clayden JD
Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.
PMID: 25221467 [PubMed]
Sy20-4virtual reality therapy for internet gaming disorder.
Alcohol Alcohol. 2014 Sep;49 Suppl 1:i19
Authors: Kim SM, Han DH
INTRODUCTION: Studies using functional magnetic resonance imaging (fMRI) have demonstrated dysfunction in the cortico-limbic circuit in individuals with Internet gaming disorder (IGD). We hypothesized that virtual reality therapy (VRT) for IGD would improve the functional connectivity of the cortico-limbic circuit.
METHODS: In the Chung-Ang University Hospital, 24 adults with IGD and 12 casual game users were recruited. IGD group was randomly assigned into the cognitive behavior therapy (CBT) group (N = 12) and VRT group (N = 12). The severity of IGD was evaluated with the Young's Internet Addiction Scale (YIAS) before and after the treatment period. Using resting-state fMRI, functional connectivity from posterior cingulate (PCC) seed to other brain areas was investigated.
RESULTS: During the treatment period, both CBT and VRT groups showed significant reductions on the YIAS scores. At baseline, IGD group showed a reduced connectivity in cortico-striatal-limbic circuit. In the CBT group, the connectivity from PCC seed to bilateral lenticular nucleus and cerebellum increased during 8-session CBT. In the VRT group, the connectivity from PCC seed to left thalamus-frontal lobe-cerebellum increased during 8-session VRT.
CONCLUSION: Treatment of IGD using VRT seemed to improve the severity of IGD, which showed similar effectiveness to CBT, and enhance the balance of the cortico-striatal-limbic circuit.
PMID: 25221038 [PubMed - in process]
Sy08-2neurophysiological and neuroimaging aspects between internet gaming disorder and alcohol use disorder.
Alcohol Alcohol. 2014 Sep;49 Suppl 1:i10
Authors: Choi JS
INTRODUCTION: Internet Gaming Disorder (IGD) causes significant public mental health problems worldwide, especially in Korea. It is important to compare characteristics of IGD with those of substance addiction in order to elucidate the pathophysiology of IGD. In this study, we explored the neurophysiological and neuroimaging features among patients with IGD and those with Alcohol Use Disorder (AUD).
METHOD: First, we performed resting-state EEG in male patients with IGD (N = 20) and compared the results with those of male patients with AUD (N = 20) and healthy controls (N = 20). All patients were seeking treatment at our clinics due to their excessive Internet game use or alcohol drinking. Second, we performed resting-state functional MRI study in same subjects. However, some subjects were excluded in the analysis due to their motion artifacts. Sixteen male patients with IGD, 14 male patients with AUD, and 15 healthy male controls were included in the final analysis.
RESULT: Patients with IGD showed decreased beta activity compared with those with healthy controls, whereas patients with AUD showed increased beta activity compared with those with healthy controls. In addition, both clinical groups showed decreased delta activity compared with those with healthy controls. In the resting-state fMRI, IGD group showed a significant regional homogeneity (ReHo) decrease in the right superior temporal gyrus (STG) and increase in the posterior cingulate cortex (PCC) compared with healthy controls. AUD group showed significant decrease in the anterior cingulate cortex (ACC) and increase in the PCC compared with healthy controls.
CONCLUSION: These results showed neurobiological similarity and disparity of resting-state EEG and fMRI features among IGD, AUD and healthy controls. These findings may contribute to elucidate the pathogenesis and neurobiological underpinning of IGD.
PMID: 25220988 [PubMed - in process]
Positive and negative affective processing exhibit dissociable functional hubs during the viewing of affective pictures.
Hum Brain Mapp. 2014 Sep 12;
Authors: Zhang W, Li H, Pan X
Recent resting-state functional magnetic resonance imaging (fMRI) studies using graph theory metrics have revealed that the functional network of the human brain possesses small-world characteristics and comprises several functional hub regions. However, it is unclear how the affective functional network is organized in the brain during the processing of affective information. In this study, the fMRI data were collected from 25 healthy college students as they viewed a total of 81 positive, neutral, and negative pictures. The results indicated that affective functional networks exhibit weaker small-worldness properties with higher local efficiency, implying that local connections increase during viewing affective pictures. Moreover, positive and negative emotional processing exhibit dissociable functional hubs, emerging mainly in task-positive regions. These functional hubs, which are the centers of information processing, have nodal betweenness centrality values that are at least 1.5 times larger than the average betweenness centrality of the network. Positive affect scores correlated with the betweenness values of the right orbital frontal cortex (OFC) and the right putamen in the positive emotional network; negative affect scores correlated with the betweenness values of the left OFC and the left amygdala in the negative emotional network. The local efficiencies in the left superior and inferior parietal lobe correlated with subsequent arousal ratings of positive and negative pictures, respectively. These observations provide important evidence for the organizational principles of the human brain functional connectome during the processing of affective information. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 25220389 [PubMed - as supplied by publisher]
Alterations in effective connectivity anchored on the insula in major depressive disorder.
Eur Neuropsychopharmacol. 2014 Aug 19;
Authors: Iwabuchi SJ, Peng D, Fang Y, Jiang K, Liddle EB, Liddle PF, Palaniyappan L
Recent work has identified disruption of several brain networks involving limbic and cortical regions that contribute to the generation of diverse symptoms of major depressive disorder (MDD). Of particular interest are the networks anchored on the right anterior insula, which binds the cortical and limbic regions to enable key functions that integrate bottom-up and top-down information in emotional and cognitive processing. Emotional appraisal has been linked to a presumed hierarchy of processing, from sensory percepts to affective states. But it is unclear whether the network level dysfunction seen in depression relates to a breakdown of this presumed hierarchical processing system from sensory to higher cognitive regions, mediated by core limbic regions (e.g. insula). In 16 patients with current MDD, and 16 healthy controls, we investigated differences in directional influences between anterior insula and the rest of the brain using resting-state functional magnetic resonance imaging (fMRI) and Granger-causal analysis (GCA), using anterior insula as a seed region. Results showed a failure of reciprocal influence between insula and higher frontal regions (dorsomedial prefrontal cortex) in addition to a weakening of influences from sensory regions (pulvinar and visual cortex) to the insula. This suggests dysfunction of both sensory and putative self-processing regulatory loops centered around the insula in MDD. For the first time, we demonstrate a network-level processing defect extending from sensory to frontal regions through insula in depression. Within limitations of inferences drawn from GCA of resting fMRI, we offer a novel framework to advance targeted network modulation approaches to treat depression.
PMID: 25219936 [PubMed - as supplied by publisher]
[Research progress of brain functional magnetic resonance imaging in post-traumatic stress disorder].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2014 Jun;31(3):691-7
Authors: Wang T, Zhang J, Huang H, Gong Q
Post-traumatic stress disorder (PTSD) is a mental disorder causing great distress to individuals, families and even society, and there is not yet effective way of unified prevention and treatment up till now. Lots of neuroimaging techniques, however, such as the magnetic resonance imaging, are widely used to the study of the pathogenesis of PTSD with the development of medical imaging. Functional magnetic resonance imaging (fMRI) can be applied to detect the abnormalities not only of the brain morphology but also of the function of various cerebral areas and neural circuit, and plays an important role in studying the pathogenesis of psychiatric diseases. In this paper, we mainly review the task-related and resting-state functional magnetic resonance imaging studies of the PTSD, and finally suggest possible directions for future research.
PMID: 25219259 [PubMed - in process]
Unravelling the Intrinsic Functional Organization of the Human Striatum: A Parcellation and Connectivity Study Based on Resting-State fMRI.
PLoS One. 2014;9(9):e106768
Authors: Jung WH, Jang JH, Park JW, Kim E, Goo EH, Im OS, Kwon JS
As the main input hub of the basal ganglia, the striatum receives projections from the cerebral cortex. Many studies have provided evidence for multiple parallel corticostriatal loops based on the structural and functional connectivity profiles of the human striatum. A recent resting-state fMRI study revealed the topography of striatum by assigning each voxel in the striatum to its most strongly correlated cortical network among the cognitive, affective, and motor networks. However, it remains unclear what patterns of striatal parcellation would result from performing the clustering without subsequent assignment to cortical networks. Thus, we applied unsupervised clustering algorithms to parcellate the human striatum based on its functional connectivity patterns to other brain regions without any anatomically or functionally defined cortical targets. Functional connectivity maps of striatal subdivisions, identified through clustering analyses, were also computed. Our findings were consistent with recent accounts of the functional distinctions of the striatum as well as with recent studies about its functional and anatomical connectivity. For example, we found functional connections between dorsal and ventral striatal clusters and the areas involved in cognitive and affective processes, respectively, and between rostral and caudal putamen clusters and the areas involved in cognitive and motor processes, respectively. This study confirms prior findings, showing similar striatal parcellation patterns between the present and prior studies. Given such striking similarity, it is suggested that striatal subregions are functionally linked to cortical networks involving specific functions rather than discrete portions of cortical regions. Our findings also demonstrate that the clustering of functional connectivity patterns is a reliable feature in parcellating the striatum into anatomically and functionally meaningful subdivisions. The striatal subdivisions identified here may have important implications for understanding the relationship between corticostriatal dysfunction and various neurodegenerative and psychiatric disorders.
PMID: 25203441 [PubMed - as supplied by publisher]
Designing hyperbolic secant excitation pulses to reduce signal dropout in gradient-echo echo-planar imaging.
Magn Reson Med. 2014 Sep 9;
Authors: Wastling SJ, Barker GJ
PURPOSE: To design hyperbolic secant (HS) excitation pulses to reduce signal dropout in the orbitofrontal and inferior temporal regions in gradient-echo echo-planar imaging (GE-EPI) for functional MRI (fMRI) applications.
METHODS: An algorithm based on Bloch simulations optimizes the HS pulse parameters needed to give the desired signal response across the range of susceptibility gradients observed in the human head (approximately ±250 μT·m(-1) ). The impact of the HS pulse on the signal, temporal signal-to-noise ratio, blood oxygen level-dependent (BOLD) sensitivity, and ability to detect resting state BOLD signal changes was assessed in six healthy male volunteers at 3T.
RESULTS: The optimized HS pulse (μ = 4.25, β = 3040 Hz, A0 = 12.3 μT, Δf = 4598 Hz) had a near uniform signal response for through-plane susceptibility gradients in the range ±250 μT·m(-1) . Signal, temporal signal-to-noise ratio, BOLD sensitivity, and the detectability of resting state networks were all partially recovered in the orbitofrontal and inferior temporal regions; however, there were signal losses of up to 50% in regions of homogeneous field (and signal loss from in-plane susceptibility gradients remained).
CONCLUSION: The HS pulse reduced signal dropout and could be used to acquire task and resting state fMRI data without loss of spatial coverage or temporal resolution. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 25203420 [PubMed - as supplied by publisher]
What we talk about when we talk about the default mode network.
Front Hum Neurosci. 2014;8:619
Authors: Callard F, Margulies DS
The default mode network (DMN) has been widely defined as a set of brain regions that are engaged when people are in a "resting state" (left to themselves in a scanner, with no explicit task instruction). The network emerged as a scientific object in the early twenty-first century, and in just over a decade has become the focus of intense empirical and conceptual neuroscientific inquiry. In this Perspective, we contribute to the work of critical neuroscience by providing brief reflections on the birth, working life, and future of the DMN. We consider: how the DMN emerged through the convergence of distinct lines of scientific investigation; controversies surrounding the definition, function and localization of the DMN; and the lines of interdisciplinary investigation that the DMN has helped to enable. We conclude by arguing that one of the most pressing issues in the field in 2014 is to understand how the mechanisms of thought are related to the function of brain dynamics. While the DMN has been central in allowing the field to reach this point, it is not inevitable that the DMN itself will remain at the heart of future investigations of this complex problem.
PMID: 25202250 [PubMed]
Neuroplasticity to a Single-episode Traumatic Stress Revealed by Resting-state fMRI in Awake Rats.
Neuroimage. 2014 Sep 2;
Authors: Liang Z, King J, Zhang N
Substantial evidence has suggested that the brain structures of the medial prefrontal cortex (mPFC) and amygdala (AMYG) are implicated in the pathophysiology of stress-related disorders. However, little is known with respect to the system-level adaptation of their neural circuitries to the perturbations of traumatic stressors. By utilizing behavioral tests and an awake animal imaging approach, in the present study we non-invasively investigated the impact of single-episode predator odor exposure in an inescapable environment on behaviors and neural circuits in rodents. We found that predator odor exposure significantly increased the freezing behavior. In addition, animals exhibited heightened anxiety levels seven days after the exposure. Intriguingly, we also found that the intrinsic functional connectivity within the AMYG-mPFC circuit was considerably compromised seven days after the traumatic event. Our data provide neuroimaging evidence suggesting that prolonged neuroadaptation induced by a single episode of traumatic stress can be non-invasively detected in rodents. These results also support the face validity and construction validity of using the paradigm of single trauma exposure in an inescapable environment as an animal model for post-traumatic stress disorder. Taken together, the present study has opened a new avenue to investigating animal models of stress-related mental disorders by going beyond static neuroanatomy, and ultimately bridging the gap between basic biomedical and human imaging research.
PMID: 25193500 [PubMed - as supplied by publisher]
Brain spontaneous fluctuations in sensorimotor regions were directly related to eyes open and eyes closed: evidences from a machine learning approach.
Front Hum Neurosci. 2014;8:645
Authors: Liang B, Zhang D, Wen X, Xu P, Peng X, Huang X, Liu M, Huang R
Previous studies have demonstrated that the difference between resting-state brain activations depends on whether the subject was eyes open (EO) or eyes closed (EC). However, whether the spontaneous fluctuations are directly related to these two different resting states are still largely unclear. In the present study, we acquired resting-state functional magnetic resonance imaging data from 24 healthy subjects (11 males, 20.17 ± 2.74 years) under the EO and EC states. The amplitude of the spontaneous brain activity in low-frequency band was subsequently investigated by using the metric of fractional amplitude of low frequency fluctuation (fALFF) for each subject under each state. A support vector machine (SVM) analysis was then applied to evaluate whether the category of resting states could be determined from the brain spontaneous fluctuations. We demonstrated that these two resting states could be decoded from the identified pattern of brain spontaneous fluctuations, predominantly based on fALFF in the sensorimotor module. Specifically, we observed prominent relationships between increased fALFF for EC and decreased fALFF for EO in sensorimotor regions. Overall, the present results indicate that a SVM performs well in the discrimination between the brain spontaneous fluctuations of distinct resting states and provide new insight into the neural substrate of the resting states during EC and EO.
PMID: 25191258 [PubMed]