Forum of resting-state fMRI

Altered intrinsic regional activity and corresponding brain pathways reflect the symptom severity of functional dyspepsia.

Neurogastroenterol Motil. 2014 May;26(5):660-9

Authors: Nan J, Liu J, Zhang D, Yang Y, Yan X, Yin Q, Xiong S, von Deneen KM, Liang F, Gong Q, Qin W, Tian J, Zeng F

Abstract
BACKGROUND: Increasing evidence shows central abnormalities in functional dyspepsia (FD) patients, but whether the symptom severity is directly reflected in altered brain patterns remains unclear. The purpose of this study was to explore how FD affected the resting functional brain patterns for different degrees of symptom severity.
METHODS: Functional magnetic resonance imaging was carried out in 40 FD patients and 20 healthy controls. The resting-state brain changes in regional homogeneity (ReHo) and seed correlation analysis were investigated in patients relative to controls. To what degree the brain changes reflected the severity of the disease was assessed by a pattern classification technique.
KEY RESULTS: Altered ReHo values (p < 0.05, FDR corrected) were discovered in multiple brain areas in FD patients, and only the anterior cingulate cortex (ACC) and thalamus exhibited significant correlation with the severity of dyspepsia symptoms. Compared with controls, the neural signal changes of the thalamus were not found in the less severe FD patient group but in the relatively more severe group, while the ACC showed aberrations in both groups. Seed-based correlation analysis revealed ACC- and thalamus-related functional connectivity differences between FD patients and controls at a voxel-wise level, and the altered thalamic circuits provided the best performance in distinguishing FD patients with different levels of symptom severity.
CONCLUSIONS & INFERENCES: Our results indicated that the functional abnormalities of the ACC and thalamus may occur at different clinical courses in FD. This may help us better understand the progression of FD.

PMID: 24467632 [PubMed - indexed for MEDLINE]

The value of resting-state functional magnetic resonance imaging in stroke.

Stroke. 2014 Sep;45(9):2818-24

Authors: Ovadia-Caro S, Margulies DS, Villringer A

PMID: 25013022 [PubMed - indexed for MEDLINE]

Common intrinsic connectivity states among posteromedial cortex subdivisions: Insights from analysis of temporal dynamics.

Neuroimage. 2014 Jun;93 Pt 1:124-37

Authors: Yang Z, Craddock RC, Margulies DS, Yan CG, Milham MP

Abstract
Perspectives of human brain functional connectivity continue to evolve. Static representations of functional interactions between brain regions are rapidly giving way to dynamic perspectives, which emphasize non-random temporal variations in intrinsic functional connectivity (iFC) patterns. Here, we bring this dynamic perspective to our understanding of iFC patterns for posteromedial cortex (PMC), a cortical hub known for its functional diversity. Previous work has consistently differentiated iFC patterns among PMC subregions, though assumed static iFC over time. Here, we assessed iFC as a function of time utilizing a sliding-window correlation approach, and applied hierarchical clustering to detect representative iFC states from the windowed iFC. Across subregions, five iFC states were detected over time. Although with differing frequencies, each subregion was associated with each of the states, suggesting that these iFC states are "common" to PMC subregions. Importantly, each subregion possessed a unique preferred state(s) and distinct transition patterns, explaining previously observed iFC differentiations. These results resonate with task-based fMRI studies suggesting that large-scale functional networks can be flexibly reconfigured in response to changing task-demands. Additionally, we used retest scans (~1week later) to demonstrate the reproducibility of the iFC states identified, and establish moderate to high test-retest reliability for various metrics used to quantify switching behaviors. We also demonstrate the ability of dynamic properties in the visual PMC subregion to index inter-individual differences in a measure of concept formation and mental flexibility. These findings suggest functional relevance of dynamic iFC and its potential utility in biomarker identification over time, as d-iFC methodologies are refined and mature.

PMID: 24560717 [PubMed - indexed for MEDLINE]

Borders, map clusters, and supra-areal organization in visual cortex.

Neuroimage. 2014 Jun;93 Pt 2:292-7

Authors: Buckner RL, Yeo BT

Abstract
V1 is a canonical cortical area with clearly delineated architectonic boundaries and a continuous topographic representation of the visual hemifield. It thus serves as a touchstone for understanding what new mapping methods can tell us about cortical organization. By parcellating human cortex using local gradients in functional connectivity, Wig et al. (2014--in this issue) detected the V1 border with V2. By contrast, previously-published clustering methods that focus on global similarity in connectivity reveal a supra-areal organization that emphasizes eccentricity bands spanning V1 and its neighboring extrastriate areas; i.e. in the latter analysis, the V1 border is not evident. Thus the focus on local connectivity gradients emphasizes qualitatively different features of cortical organization than are captured by global similarity measures. What is intriguing to consider is that each kind of information might be telling us something unique about cortical organization. Global similarity measures may be detecting map clusters and other supra-areal arrangements that reflect a fundamental level of organization.

PMID: 24374078 [PubMed - indexed for MEDLINE]

Global resting-state functional magnetic resonance imaging analysis identifies frontal cortex, striatal, and cerebellar dysconnectivity in obsessive-compulsive disorder.

Biol Psychiatry. 2014 Apr 15;75(8):595-605

Authors: Anticevic A, Hu S, Zhang S, Savic A, Billingslea E, Wasylink S, Repovs G, Cole MW, Bednarski S, Krystal JH, Bloch MH, Li CS, Pittenger C

Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) is associated with regional hyperactivity in cortico-striatal circuits. However, the large-scale patterns of abnormal neural connectivity remain uncharacterized. Resting-state functional connectivity studies have shown altered connectivity within the implicated circuitry, but they have used seed-driven approaches wherein a circuit of interest is defined a priori. This limits their ability to identify network abnormalities beyond the prevailing framework. This limitation is particularly problematic within the prefrontal cortex (PFC), which is large and heterogeneous and where a priori specification of seeds is therefore difficult. A hypothesis-neutral, data-driven approach to the analysis of connectivity is vital.
METHODS: We analyzed resting-state functional connectivity data collected at 3T in 27 OCD patients and 66 matched control subjects with a recently developed data-driven global brain connectivity (GBC) method, both within the PFC and across the whole brain.
RESULTS: We found clusters of decreased connectivity in the left lateral PFC in both whole-brain and PFC-restricted analyses. Increased GBC was found in the right putamen and left cerebellar cortex. Within regions of interest in the basal ganglia and thalamus, we identified increased GBC in dorsal striatum and anterior thalamus, which was reduced in patients on medication. The ventral striatum/nucleus accumbens exhibited decreased global connectivity but increased connectivity specifically with the ventral anterior cingulate cortex in subjects with OCD.
CONCLUSIONS: These findings identify previously uncharacterized PFC and basal ganglia dysconnectivity in OCD and reveal differentially altered GBC in dorsal and ventral striatum. Results highlight complex disturbances in PFC networks, which could contribute to disrupted cortical-striatal-cerebellar circuits in OCD.

PMID: 24314349 [PubMed - indexed for MEDLINE]

The effects of pharmacological treatment on functional brain connectome in obsessive-compulsive disorder.

Biol Psychiatry. 2014 Apr 15;75(8):606-14

Authors: Shin DJ, Jung WH, He Y, Wang J, Shim G, Byun MS, Jang JH, Kim SN, Lee TY, Park HY, Kwon JS

Abstract
BACKGROUND: Previous neuroimaging studies of obsessive-compulsive disorder (OCD) have reported both baseline functional alterations and pharmacological changes in localized brain regions and connections; however, the effects of selective serotonin reuptake inhibitor (SSRI) treatment on the whole-brain functional network have not yet been elucidated.
METHODS: Twenty-five drug-free OCD patients underwent resting-state functional magnetic resonance imaging. After 16-weeks, seventeen patients who received SSRI treatment were rescanned. Twenty-three matched healthy control subjects were examined at baseline for comparison, and 21 of them were rescanned after 16 weeks. Topological properties of brain networks (including small-world, efficiency, modularity, and connectivity degree) were analyzed cross-sectionally and longitudinally with graph-theory approach.
RESULTS: At baseline, OCD patients relative to healthy control subjects showed decreased small-world efficiency (including local clustering coefficient, local efficiency, and small-worldness) and functional association between default-mode and frontoparietal modules as well as widespread altered connectivity degrees in many brain areas. We observed clinical improvement in OCD patients after 16 weeks of SSRI treatment, which was accompanied by significantly elevated small-world efficiency, modular organization, and connectivity degree. Improvement of obsessive-compulsive symptoms was significantly correlated with changes in connectivity degree in right ventral frontal cortex in OCD patients after treatment.
CONCLUSIONS: This is first study to use graph-theory approach for investigating valuable biomarkers for the effects of SSRI on neuronal circuitries of OCD patients. Our findings suggest that OCD phenomenology might be the outcome of disrupted optimal balance in the brain networks and that reinstating this balance after SSRI treatment accompanies significant symptom improvement.

PMID: 24099506 [PubMed - indexed for MEDLINE]

Are you listening? Brain activation associated with sustained nonspatial auditory attention in the presence and absence of stimulation.

Hum Brain Mapp. 2014 May;35(5):2233-52

Authors: Seydell-Greenwald A, Greenberg AS, Rauschecker JP

Abstract
Neuroimaging studies investigating the voluntary (top-down) control of attention largely agree that this process recruits several frontal and parietal brain regions. Since most studies used attention tasks requiring several higher-order cognitive functions (e.g. working memory, semantic processing, temporal integration, spatial orienting) as well as different attentional mechanisms (attention shifting, distractor filtering), it is unclear what exactly the observed frontoparietal activations reflect. The present functional magnetic resonance imaging study investigated, within the same participants, signal changes in (1) a "Simple Attention" task in which participants attended to a single melody, (2) a "Selective Attention" task in which they simultaneously ignored another melody, and (3) a "Beep Monitoring" task in which participants listened in silence for a faint beep. Compared to resting conditions with identical stimulation, all tasks produced robust activation increases in auditory cortex, cross-modal inhibition in visual and somatosensory cortex, and decreases in the default mode network, indicating that participants were indeed focusing their attention on the auditory domain. However, signal increases in frontal and parietal brain areas were only observed for tasks 1 and 2, but completely absent for task 3. These results lead to the following conclusions: under most conditions, frontoparietal activations are crucial for attention since they subserve higher-order cognitive functions inherently related to attention. However, under circumstances that minimize other demands, nonspatial auditory attention in the absence of stimulation can be maintained without concurrent frontal or parietal activations.

PMID: 23913818 [PubMed - indexed for MEDLINE]

The self and its resting state in consciousness: an investigation of the vegetative state.

Hum Brain Mapp. 2014 May;35(5):1997-2008

Authors: Huang Z, Dai R, Wu X, Yang Z, Liu D, Hu J, Gao L, Tang W, Mao Y, Jin Y, Wu X, Liu B, Zhang Y, Lu L, Laureys S, Weng X, Northoff G

Abstract
Recent studies have demonstrated resting-state abnormalities in midline regions in vegetative state/unresponsive wakefulness syndrome and minimally conscious state patients. However, the functional implications of these resting-state abnormalities remain unclear. Recent findings in healthy subjects have revealed a close overlap between the neural substrate of self-referential processing and the resting-state activity in cortical midline regions. As such, we investigated task-related neural activity during active self-referential processing and various measures of resting-state activity in 11 patients with disorders of consciousness (DOC) and 12 healthy control subjects. Overall, the results revealed that DOC patients exhibited task-specific signal changes in anterior and posterior midline regions, including the perigenual anterior cingulate cortex (PACC) and posterior cingulate cortex (PCC). However, the degree of signal change was significantly lower in DOC patients compared with that in healthy subjects. Moreover, reduced signal differentiation in the PACC predicted the degree of consciousness in DOC patients. Importantly, the same midline regions (PACC and PCC) in DOC patients also exhibited severe abnormalities in the measures of resting-state activity, that is functional connectivity and the amplitude of low-frequency fluctuations. Taken together, our results provide the first evidence of neural abnormalities in both the self-referential processing and the resting state in midline regions in DOC patients. This novel finding has important implications for clinical utility and general understanding of the relationship between the self, the resting state, and consciousness.

PMID: 23818102 [PubMed - indexed for MEDLINE]

Complexity of low-frequency blood oxygen level-dependent fluctuations covaries with local connectivity.

Hum Brain Mapp. 2014 Apr;35(4):1273-83

Authors: Anderson JS, Zielinski BA, Nielsen JA, Ferguson MA

Abstract
Very low-frequency blood oxygen level-dependent (BOLD) fluctuations have emerged as a valuable tool for describing brain anatomy, neuropathology, and development. Such fluctuations exhibit power law frequency dynamics, with largest amplitude at lowest frequencies. The biophysical mechanisms generating such fluctuations are poorly understood. Using publicly available data from 1,019 subjects of age 7-30, we show that BOLD fluctuations exhibit temporal complexity that is linearly related to local connectivity (regional homogeneity), consistently and significantly covarying across subjects and across gray matter regions. This relationship persisted independently of covariance with gray matter density or standard deviation of BOLD signal. During late neurodevelopment, BOLD fluctuations were unchanged with age in association cortex while becoming more random throughout the rest of the brain. These data suggest that local interconnectivity may play a key role in establishing the complexity of low-frequency BOLD fluctuations underlying functional magnetic resonance imaging connectivity. Stable low-frequency power dynamics may emerge through segmentation and integration of connectivity during development of distributed large-scale brain networks.

PMID: 23417795 [PubMed - indexed for MEDLINE]

Overlapping and segregated resting-state functional connectivity in patients with major depressive disorder with and without childhood neglect.

Hum Brain Mapp. 2014 Apr;35(4):1154-66

Authors: Wang L, Dai Z, Peng H, Tan L, Ding Y, He Z, Zhang Y, Xia M, Li Z, Li W, Cai Y, Lu S, Liao M, Zhang L, Wu W, He Y, Li L

Abstract
Many studies have suggested that childhood maltreatment increase risk of adulthood major depressive disorder (MDD) and predict its unfavorable treatment outcome, yet the neural underpinnings associated with childhood maltreatment in MDD remain poorly understood. Here, we seek to investigate the whole-brain functional connectivity patterns in MDD patients with childhood maltreatment. Resting-state functional magnetic resonance imaging was used to explore intrinsic or spontaneous functional connectivity networks of 18 MDD patients with childhood neglect, 20 MDD patients without childhood neglect, and 20 healthy controls. Whole-brain functional networks were constructed by measuring the temporal correlations of every pairs of brain voxels and were further analyzed by using graph-theory approaches. Relative to the healthy control group, the two MDD patient groups showed overlapping reduced functional connectivity strength in bilateral ventral medial prefrontal cortex/ventral anterior cingulate cortex. However, compared with MDD patients without a history of childhood maltreatment, those patients with such a history displayed widespread reduction of functional connectivity strength primarily in brain regions within the prefrontal-limbic-thalamic-cerebellar circuitry, and these reductions significantly correlated with measures of childhood neglect. Together, we showed that the MDD groups with and without childhood neglect exhibited overlapping and segregated functional connectivity patterns in the whole-brain networks, providing empirical evidence for the contribution of early life stress to the pathophysiology of MDD.

PMID: 23408420 [PubMed - indexed for MEDLINE]

Testing group differences in brain functional connectivity: using correlations or partial correlations?

Brain Connect. 2014 Dec 10;

Authors: Kim J, Wozniak J, Pan W, Mueller BA

Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) allows one to study brain functional connectivity, partly motivated by evidence that complex disorders, such as Alzheimer's disease, may have altered functional brain connectivity patterns as compared to healthy subjects. A functional connectivity network describes statistical associations of the neural activities among distinct and distant brain regions. Recently, there is a major interest in group-level functional network analysis, however, there is a relative lack of studies on statistical inference, such as significance testing for group comparisons. In particular, it is still debatable which statistic should be used to measure pairwise associations as the connectivity weights. Many functional connectivity studies have used either (full or marginal) correlations or partial correlations for pairwise associations. This paper investigates the performance of using either correlations or partial correlations for testing group differences in brain connectivity, and {how sparsity levels and topological structures of the connectivity} would influence statistical power to detect group differences. Our results suggest that, in general, testing group differences in networks deviates from estimating networks. For example, high regularization on both covariance matrices and precision matrices may lead to higher statistical power; in particular, optimally selected regularization (e.g. by cross-validation or even at the true sparsity level) on the precision matrices with small estimation errors may have low power. Most importantly, and perhaps surprisingly, using either correlations or partial correlations may give very different testing results, depending on which of the covariance matrices and the precision matrices are sparse. Specifically, if the precision matrices are sparse, presumably and arguably a reasonable assumption, then using correlations often yields much higher powered and more stable testing results than using partial correlations; the conclusion is reversed if the covariance matrices, not the precision matrices, are sparse. These results may have useful implications to future studies on testing functional connectivity differences.

PMID: 25492804 [PubMed - as supplied by publisher]

Brain graph topology changes associated with anti-epileptic drug use.

Brain Connect. 2014 Dec 10;

Authors: Haneef Z, Levin HS, Chiang S

Abstract
Neuroimaging studies of functional connectivity (FC) using graph theory have furthered our understanding of the network structure in temporal lobe epilepsy (TLE). Brain network effects of anti-epileptic drugs could influence such studies, but have not been systematically studied. Resting state fMRI was analyzed in 25 patients with temporal lobe epilepsy using graph theory analysis. Patients were divided into two groups based on anti-epileptic medication use: those taking carbamazepine/oxcarbazepine (n=9) and those not taking carbamazepine/oxcarbazepine (n=16) as part of their medication regimen. The following graph topology metrics were analyzed: global efficiency, betweenness centrality, clustering coefficient, and small-world index. Multiple linear regression was used to examine the association of carbamazepine/oxcarbazepine with graph topology. The two groups did not differ from each other based on epilepsy characteristics. Use of carbamazepine/oxcarbazepine was associated with a lower betweenness centrality. Longer epilepsy duration was also associated with a lower BC. These findings can inform graph theory based studies in patients with TLE. The changes observed are discussed in relation to the anti-epileptic mechanism of action and adverse effects of carbamazepine/oxcarbazepine.

PMID: 25492633 [PubMed - as supplied by publisher]

Findings in resting-state fMRI by differences from K-means clustering.

Stud Health Technol Inform. 2014;207:300-10

Authors: Chyzhyk D, Graña M

Abstract
Resting state fMRI has growing number of studies with diverse aims, always centered on some kind of functional connectivity biomarker obtained from correlation regarding seed regions, or by analytical decomposition of the signal towards the localization of the spatial distribution of functional connectivity patterns. In general, studies are computationally costly and very sensitive to noise and preprocessing of data. In this paper we consider clustering by K-means as a exploratory procedure which can provide some results with little computational effort, due to efficient implementations that are readily available. We demonstrate the approach on a dataset of schizophrenia patients, finding differences between patients with and without auditory hallucinations.

PMID: 25488236 [PubMed - in process]

Mutually temporally independent connectivity patterns: A new framework to study resting state brain dynamics with application to explain group difference based on gender.

Neuroimage. 2014 Dec 5;

Authors: Yaesoubi M, Miller RL, Calhoun VD

Abstract
Functional connectivity analysis of the human brain is an active area in fMRI research. It focuses on identifying meaningful brain networks that have coherent activity either during a task or in the resting state. These networks are generally identified either as collections of voxels whose time series correlate strongly with a pre-selected region or voxel, or using data-driven methodologies such as independent component analysis (ICA) that compute sets of maximally spatially independent voxel weightings (component spatial maps (SMs)), each associated with a single time course (TC). Studies have shown that regardless of the way these networks are defined, the activity coherence among them has a dynamic nature which is hard to estimate with global coherence analysis such as correlation or mutual information. Sliding window analyses in which functional network connectivity (FNC) is estimated separately at each time window is one of the more widely employed approaches to studying the dynamic nature of functional network connectivity (dFNC). Observed FNC patterns are summarized and replaced with a smaller set of prototype connectivity patterns ("states" or "components"), and then a dynamical analysis is applied to the resulting sequences of prototype states. In this work we are looking for a small set of connectivity patterns whose weighted contributions to the dynamically changing dFNCs are independent of each other in time. We discuss our motivation for this work and how it differs from existing approaches. Also, in a group analysis based on gender we show that males significantly differ from females by occupying significantly more combinations of these connectivity patterns over the course of the scan.

PMID: 25485713 [PubMed - as supplied by publisher]

Evaluating structural symmetry of weighted brain networks via graph matching.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):733-40

Authors: Hu C, El Fakhri G, Li Q

Abstract
We study the symmetry of weighted brain networks to understand the roles of individual brain areas and the redundancy of the brain connectivity. We quantify the structural symmetry of every node pair in the network by isomorphism of the residual graphs of those nodes. The efficacy of the symmetry measure is evaluated on both simulated networks and real data sets. In the resting state fMRI (rs-fMRI) data, we discover that subjects with inattentive type of Attention Deficit Hyperactivity Disorder (ADHD) demonstrate a higher level of network symmetry in contrast to the typically development group, consistent with former findings. Moreover, by comparing the average functional networks of normal subjects during resting state and activation, we obtain a higher symmetry level in the rs-fMRI network when applying median thresholds to the networks. But the symmetry levels of the networks are almost the same when larger thresholds are used, which may imply the invariance of the prominent network symmetry for ordinary people.

PMID: 25485445 [PubMed - in process]

Brain connectivity hyper-network for MCI classification.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):724-32

Authors: Jie B, Shen D, Zhang D

Abstract
Brain connectivity network has been used for diagnosis and classification of neurodegenerative diseases, such as Alzheimer's disease (AD) as well as its early stage, i.e., mild cognitive impairment (MCI). However, conventional connectivity network is usually constructed based on the pairwise correlation among brain regions and thus ignores the higher-order relationship among them. Such information loss is unexpected because the brain itself is a complex network and the higher-order interaction may contain useful information for classification. Accordingly, in this paper, we propose a new brain connectivity hyper-network based method for MCI classification. Here, the connectivity hyper-network denotes a network where an edge can connect more than two brain regions, which can be naturally represented with a hyper-graph. Specifically, we first construct connectivity hyper-networks from the resting-state fMRI time series using sparse representation modeling. Then, we extract three sets of the brain-region specific features from the connectivity hyper-networks, and exploit a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Finally, we use multi-kernel support vector machine (SVM) for classification. The experimental results demonstrate the efficacy of our proposed method for MCI classification with comparison to the conventional connectivity network based methods.

PMID: 25485444 [PubMed - in process]

Group-wise functional community detection through joint Laplacian diagonalization.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):708-15

Authors: Dodero L, Gozzi A, Liska A, Murino V, Sona D

Abstract
There is a growing conviction that the understanding of the brain function can come through a deeper knowledge of the network connectivity between different brain areas. Resting state Functional Magnetic Resonance Imaging (rs-fMRI) is becoming one of the most important imaging modality widely used to understand network functionality. However, due to the variability at subject scale, mapping common networks across individuals is by now a real challenge. In this work we present a novel approach to group-wise community detection, i.e. identification of functional coherent sub-graphs across multiple subjects. This approach is based on a joint diagonalization of two or more graph Laplacians, aiming at finding a common eigenspace across individuals, over which clustering in fewer dimension can then be applied. This allows to identify common sub-networks across different graphs. We applied our method to rs-fMRI dataset of mouse brain finding most important sub-networks recently described in literature.

PMID: 25485442 [PubMed - in process]

Altered intrinsic regional brain spontaneous activity and subjective sleep quality in patients with chronic primary insomnia: a resting-state fMRI study.

Neuropsychiatr Dis Treat. 2014;10:2163-2175

Authors: Dai XJ, Peng DC, Gong HH, Wan AL, Nie X, Li HJ, Wang YX

Abstract
STUDY OBJECTIVE: To prospectively explore the underlying regional homogeneity (ReHo) brain-activity deficit in patients with chronic primary insomnia (PCPIs) and its relationship with clinical features.
DESIGN: The ReHo method and Statistical Parametric Mapping 8 software were used to evaluate whether resting-state localized brain activity was modulated between PCPIs and good sleepers (GSs), and correlation analysis between altered regional brain areas and clinical features was calculated.
PATIENTS AND PARTICIPANTS: Twenty-four PCPIs (17 females, seven males) and 24 (12 females, 12 males) age-, sex-, and education-matched GSs.
MEASUREMENTS AND RESULTS: PCPIs disturbed subjective sleep quality, split positive mood, and exacerbated negative moods. Compared with GSs, PCPIs showed higher ReHo in left fusiform gyrus, and lower ReHo in bilateral cingulate gyrus and right cerebellum anterior lobe. Compared with female GSs, female PCPIs showed higher ReHo in the left fusiform gyrus and right posterior cingulate, and lower ReHo in the left cerebellum anterior lobe and left superior frontal gyrus. Compared with male GSs, male PCPIs showed higher ReHo in the right temporal lobe and lower ReHo in the bilateral frontal lobe. The fusiform gyrus showed strong positive correlations and the frontal lobe showed negative correlations with the clinical measurements.
CONCLUSION: The ReHo analysis is a useful noninvasive imaging tool for the detection of cerebral changes and the indexing of clinical features. The abnormal spontaneous activity areas provided important information on the neural mechanisms underlying emotion and sleep-quality impairment in PCPIs.

PMID: 25484585 [PubMed - as supplied by publisher]

Frontoparietal networks involved in categorization and item working memory.

Neuroimage. 2014 Dec 4;

Authors: Braunlich K, Gomez-Lavin J, Seger CA

Abstract
Categorization and memory for specific items are fundamental processes that allow us to apply knowledge to novel stimuli. This study directly compares categorization and memory using delay match to category (DMC) and delay match to sample (DMS) tasks. In DMC participants view and categorize a stimulus, maintain the category across a delay, and at the probe phase view another stimulus and indicate whether it is in the same category or not. In DMS, a standard item working memory task, participants encode and maintain a specific individual item, and at probe decide if the stimulus is an exact match or not. Constrained Principal Components Analysis was used to identify and compare activity within neural networks associated with these tasks, and we relate these networks to those that have been identified with resting state-fMRI. We found that two frontoparietal networks of particular interest. The first network included regions associated with the dorsal attention network and frontoparietal salience network; this network showed patterns of activity consistent with a role in rapid orienting to and processing of complex stimuli. The second uniquely involved regions of the frontoparietal central-executive network; this network responded more slowly following each stimulus and showed a pattern of activity consistent with a general role in role in decision-making across tasks. Additional components were identified that were associated with visual, somatomotor and default mode networks.

PMID: 25482265 [PubMed - as supplied by publisher]

Adaptive Motor Imagery: A Multimodal Study of Immobilization-Induced Brain Plasticity.

Cereb Cortex. 2014 Dec 4;

Authors: Burianová H, Sowman PF, Marstaller L, Rich AN, Williams MA, Savage G, Al-Janabi S, de Lissa P, Johnson BW

Abstract
The consequences of losing the ability to move a limb are traumatic. One approach that examines the impact of pathological limb nonuse on the brain involves temporary immobilization of a healthy limb. Here, we investigated immobilization-induced plasticity in the motor imagery (MI) circuitry during hand immobilization. We assessed these changes with a multimodal paradigm, using functional magnetic resonance imaging (fMRI) to measure neural activation, magnetoencephalography (MEG) to track neuronal oscillatory dynamics, and transcranial magnetic stimulation (TMS) to assess corticospinal excitability. fMRI results show a significant decrease in neural activation for MI of the constrained hand, localized to sensorimotor areas contralateral to the immobilized hand. MEG results show a significant decrease in beta desynchronization and faster resynchronization in sensorimotor areas contralateral to the immobilized hand. TMS results show a significant increase in resting motor threshold in motor cortex contralateral to the constrained hand, suggesting a decrease in corticospinal excitability in the projections to the constrained hand. These results demonstrate a direct and rapid effect of immobilization on MI processes of the constrained hand, suggesting that limb nonuse may not only affect motor execution, as evidenced by previous studies, but also MI. These findings have important implications for the effectiveness of therapeutic approaches that use MI as a rehabilitation tool to ameliorate the negative effects of limb nonuse.

PMID: 25477368 [PubMed - as supplied by publisher]