New resting-state fMRI related studies at PubMed

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That's me in the spotlight: neural basis of individual differences in self-consciousness.

Fri, 05/25/2018 - 18:40
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That's me in the spotlight: neural basis of individual differences in self-consciousness.

Soc Cogn Affect Neurosci. 2017 Sep 01;12(9):1384-1393

Authors: de Caso I, Poerio G, Jefferies E, Smallwood J

Abstract
A long-standing literature implicates activity within the default mode network (DMN) to processes linked to the self. However, contemporary work suggests that other large-scale networks networks might also be involved. For instance, goal-directed autobiographical planning requires positive functional connectivity (FC) between DMN and frontoparietal control (FPCN) networks. The present study examined the inter-relationship between trait self-focus (measured via a self-consciousness scale; SCS), incidental memory in a self-reference paradigm, and resting state FC of large-scale networks. Behaviourally, we found that private SCS was linked to stronger incidental memory for self-relevant information. We also examined how patterns of FC differed according to levels of self-consciousness by using the SCS data to drive multiple regression analyses with seeds from the DMN, the FPCN and the limbic network. High levels of SCS was not linked to differences in the functional behaviour of the DMN, however, it was linked to stronger FC between FPCN and a cluster extending into the hippocampus, which meta analytic decoding using Neurosynth linked to episodic memory retrieval. Subsequent analysis demonstrated that trait variance in this pattern of FC was a moderator for the observed relationship between private SCS and enhanced memory for self-items. Together these findings suggest that interactions between the FPCN and hippocampus may support the memory advantage of self-relevant information associated with SCS and confirm theoretical positions that argue that that self-related processing does not simply depend upon the DMN, but instead relies on complex patterns of interactions between multiple large-scale networks.

PMID: 28575483 [PubMed - indexed for MEDLINE]

Endogenous GLP-1 alters postprandial functional connectivity between homeostatic and reward-related brain regions involved in the regulation of appetite in healthy lean males: a pilot study.

Thu, 05/24/2018 - 11:00
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Endogenous GLP-1 alters postprandial functional connectivity between homeostatic and reward-related brain regions involved in the regulation of appetite in healthy lean males: a pilot study.

Diabetes Obes Metab. 2018 May 22;:

Authors: Meyer-Gerspach AC, Ly HG, Borgwardt S, Dupont P, Beglinger C, Van Oudenhove L, Wölnerhanssen BK

Abstract
AIMS: Peripheral infusion of glucagon-like peptide-1 (GLP-1) can affect brain activity in areas involved in the regulation of appetite, including hypothalamic and reward-related brain regions. In contrast, the physiological role of endogenous GLP-1 in the central regulation of appetite has hardly been investigated.
MATERIALS AND METHODS: The study was performed as randomized, cross-over trial. Twelve healthy volunteers received an intragastric (ig) glucose (gluc) load with or without intravenous (iv) exendin9-39 (ex9-39; specific GLP-1 receptor antagonist). Functional magnetic resonance imaging was used to investigate the effect of endogenous GLP-1 on resting state functional connectivity (rsFC) between homeostatic and reward-related brain regions. Visual analogue scales were used to rate appetite-related sensations. Blood samples were collected for GI hormone measurements.
RESULTS: i) iv-ex9-39/ig-gluc induced a significantly higher rsFC relative to ig-gluc between the hypothalamus and the left lateral orbitofrontal cortex (OFC) as well as the left amygdala (p≤0.001, respectively); ii) iv-ex9-39/ig-gluc induced a significantly higher rsFC relative to ig-gluc between the right nucleus accumbens and the right lateral OFC (p<0.001); iii) iv-ex9-39/ig-gluc induced a significantly lower rsFC relative to ig-gluc between the midbrain and the right caudate nucleus (p=0.001); iv) ig-gluc significantly decreased prospective food consumption and increased fullness sensations compared to pre-infusion baseline (p=0.028 and p=0.019, respectively), these effects were not present in the iv-ex9-39/ig-gluc condition.
CONCLUSIONS: This pilot trial provides preliminary experimental evidence that glucose-induced endogenous GLP-1 affects central regulation of appetite by modulating rsFC in homeostatic and reward-related brain regions in healthy lean male subjects in a GLP-1 receptor-mediated fashion. This article is protected by copyright. All rights reserved.

PMID: 29790260 [PubMed - as supplied by publisher]

Correlated gene expression and anatomical communication support synchronized brain activity in the mouse functional connectome.

Thu, 05/24/2018 - 11:00
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Correlated gene expression and anatomical communication support synchronized brain activity in the mouse functional connectome.

J Neurosci. 2018 May 22;:

Authors: Mills BD, Grayson DS, Shunmugavel A, Miranda-Dominguez O, Feczko E, Earl E, Neve K, Fair DA

Abstract
Cognition and behavior depend on synchronized intrinsic brain activity that is organized into functional networks across the brain. Research has investigated how anatomical connectivity both shapes and is shaped by these networks, but not how anatomical connectivity interacts with intra-areal molecular properties to drive functional connectivity. Here, we present a novel linear model to explain functional connectivity by integrating systematically obtained measurements of axonal connectivity, gene expression, and resting state functional connectivity MRI in the mouse brain. The model suggests that functional connectivity arises from both anatomical links and inter-areal similarities in gene expression. By estimating these effects, we identify anatomical modules in which correlated gene expression and anatomical connectivity support functional connectivity. Along with providing evidence that not all genes equally contribute to functional connectivity, this research establishes new insights regarding the biological underpinnings of coordinated brain activity measured by BOLD fMRI.SIGNIFICANCE STATEMENTEfforts at characterizing the functional connectome with fMRI have risen exponentially over the last decade. Yet despite this rise, the biological underpinnings of these functional measurements are still largely unknown. The current report begins to fill this void by investigating the molecular underpinnings of the functional connectome through an integration of systematically obtained structural information and gene expression data throughout the rodent brain. We find that both white matter connectivity and similarity in regional gene expression relate to resting state functional connectivity. The current report furthers our understanding of the biological underpinnings of the functional connectome and provides a linear model that can be utilized to streamline preclinical animal studies of disease.

PMID: 29789379 [PubMed - as supplied by publisher]

Handedness-dependent functional organizational patterns within the bilateral vestibular cortical network revealed by fMRI connectivity based parcellation.

Wed, 05/23/2018 - 16:20

Handedness-dependent functional organizational patterns within the bilateral vestibular cortical network revealed by fMRI connectivity based parcellation.

Neuroimage. 2018 May 19;:

Authors: Kirsch V, Boegle R, Keeser D, Kierig E, Ertl-Wagner B, Brandt T, Dieterich M

Abstract
Current evidence points towards a vestibular cortex that involves a multisensory bilateral temporo-parietal-insular network with a handedness-dependent hemispheric lateralization. This study aimed to identify handedness-dependent organizational patterns of (lateralized and non-lateralized) functional subunits within the human vestibular cortex areas. 60 healthy volunteers (30 left-handed and 30 right-handed) were examined on a 3T MR scanner using resting state functional MRI (fMRI). The data was analyzed in four major steps using a functional connectivity based parcellation (fCBP) approach: (1) independent component analysis (ICA) on a whole brain level to identify different resting state networks (RSN); (2) creation of a vestibular informed mask from four whole brain ICs that included reference coordinates of the vestibular network extracted from meta-analyses of vestibular neuroimaging experiments; (3) Re-ICA confined to the vestibular informed mask; (4) cross-correlation of the activated voxels within the vestibular subunits (parcels) to each other (P-to-P) and to the whole-brain RSN (P-to-RSN). This approach disclosed handedness-dependency, inter-hemispheric symmetry, the scale of connectedness to major whole brain RSN and the grade of spatial overlap of voxels within parcels (common/unique) as meaningful discriminatory organizational categories within the vestibular cortex areas. This network consists of multiple inter-hemisphere symmetric (not lateralized), well-connected (many RSN-assignments) multisensory areas (or hubs; e.g., superior temporal gyrus, temporo-parietal intersection) organized around an asymmetric (lateralized, "dominant") and functionally more specialized (few RSN-assignments) core region in the parieto-insular cortex. The latter is in the middle, posterior and inferior insula. In conclusion, the bilateral cortical vestibular network contains not only a handedness-dependent lateralized central region concentrated in the right hemisphere in right-handers and left hemisphere in left-handers, but also surrounding inter-hemisphere symmetric multisensory vestibular areas that seem to be functionally influenced by their neighboring sensory systems (e.g., temporo-parietal intersection by the visual system). One may speculate that the development of an asymmetrical organized vestibular subsystem reflects a more recent phylogenetic evolution of various multisensory vestibular functions. The right hemispheric dominance of spatial orientation and its disorders, spatial neglect and pusher syndrome, may serve as examples.

PMID: 29787866 [PubMed - as supplied by publisher]

Altered cerebro-cerebellum resting-state functional connectivity in HIV-infected male patients.

Wed, 05/23/2018 - 16:20
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Altered cerebro-cerebellum resting-state functional connectivity in HIV-infected male patients.

J Neurovirol. 2018 May 21;:

Authors: Wang H, Li R, Zhou Y, Wang Y, Cui J, Nguchu BA, Qiu B, Wang X, Li H

Abstract
In addition to the role of planning and executing movement, the cerebellum greatly contributes to cognitive process. Numerous studies have reported structural and functional abnormalities in the cerebellum for HIV-infected patients, but little is known about the altered functional connectivity of particular cerebellar subregions and the cerebrum. Therefore, this study aimed to explore the resting-state functional connectivity (rsFC) changes of the cerebellum and further analyze the relationship between the rsFC changes and the neuropsychological evaluation. The experiment involved 26 HIV-infected men with asymptomatic neurocognitive impairment (ANI) and 28 healthy controls (HC). We selected bilateral hemispheric lobule VI and lobule IX as seed regions and mapped the whole-brain rsFC for each subregion. Results revealed that right lobule VI showed significant increased rsFC with the anterior cingulate cortex (ACC) in HIV-infected subjects. In addition, the correlation analysis on HIV-infected subjects illustrated the increased rsFC was negatively correlated with the attention/working memory score. Moreover, significantly increased cerebellar rsFCs were also observed in HIV-infected patients related to right inferior frontal gyrus (IFG) and right superior medial gyrus (SMG) while decreased rsFC was just found between right lobule VI and the left hippocampus (HIP). These findings suggested that, abnormalities of cerebro-cerebellar functional connectivity might be associated with cognitive dysfunction in HIV-infected men, particularly working memory impairment. It could also be the underlying mechanism of ANI, providing further evidence for early injury in the neural substrate of HIV-infected patients.

PMID: 29785582 [PubMed - as supplied by publisher]

A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models.

Wed, 05/23/2018 - 16:20
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A guide to using functional magnetic resonance imaging to study Alzheimer's disease in animal models.

Dis Model Mech. 2018 May 18;11(5):

Authors: Asaad M, Lee JH

Abstract
Alzheimer's disease is a leading healthcare challenge facing our society today. Functional magnetic resonance imaging (fMRI) of the brain has played an important role in our efforts to understand how Alzheimer's disease alters brain function. Using fMRI in animal models of Alzheimer's disease has the potential to provide us with a more comprehensive understanding of the observations made in human clinical fMRI studies. However, using fMRI in animal models of Alzheimer's disease presents some unique challenges. Here, we highlight some of these challenges and discuss potential solutions for researchers interested in performing fMRI in animal models. First, we briefly summarize our current understanding of Alzheimer's disease from a mechanistic standpoint. We then overview the wide array of animal models available for studying this disease and how to choose the most appropriate model to study, depending on which aspects of the condition researchers seek to investigate. Finally, we discuss the contributions of fMRI to our understanding of Alzheimer's disease and the issues to consider when designing fMRI studies for animal models, such as differences in brain activity based on anesthetic choice and ways to interrogate more specific questions in rodents beyond those that can be addressed in humans. The goal of this article is to provide information on the utility of fMRI, and approaches to consider when using fMRI, for studies of Alzheimer's disease in animal models.

PMID: 29784664 [PubMed - in process]

Disrupted reward and cognitive control networks contribute to anhedonia in depression.

Wed, 05/23/2018 - 03:40
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Disrupted reward and cognitive control networks contribute to anhedonia in depression.

J Psychiatr Res. 2018 May 12;103:61-68

Authors: Gong L, He C, Zhang H, Zhang H, Zhang Z, Xie C

Abstract
Neuroimaging studies have identified that anhedonia, a core feature of major depressive disorder (MDD), is associated with dysfunction in reward and cognitive control processing. However, it is still not clear how the reward network (β-network) and the cognitive control network (δ-network) are linked to biased anhedonia in MDD patients. Sixty-eight MDD patients and 64 cognitively normal (CN) subjects underwent a resting-state functional magnetic resonance imaging scan. A 2*2 ANCOVA analysis was used to explore the differences in the nucleus accumbens-based, voxelwise functional connectivity (FC) between the groups. Then, the β- and δ-networks were constructed, and the FC intensities were compared within and between theβ- and δ-networks across all subjects. Multiple linear regression analyses were also employed to investigate the relationships between the neural features of the β- and δ-networks and anhedonia in MDD patients. Compared to the CN subjects, the MDD patients showed synergistic functional decoupling in both the β- and δ-networks, as well as decreased FC intensities in the intra- and inter- β- and δ-networks. In addition, the FC in both the β- and δ-networks was significantly correlated with anhedonia severity in the MDD patients. Importantly, the integrated neural features of the β- and δ-networks could more precisely predict anhedonic symptoms. These findings initially demonstrated that the imbalance between β- and δ-network activity successfully predicted anhedonia severity and suggested that the neural features of both the β- and δ-networks could represent a fundamental mechanism that underlies anhedonia in MDD patients.

PMID: 29783076 [PubMed - as supplied by publisher]

Brain Correlates of Continuous Pain in Rheumatoid Arthritis as Measured by Pulsed Arterial Spin Labeling.

Wed, 05/23/2018 - 03:40
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Brain Correlates of Continuous Pain in Rheumatoid Arthritis as Measured by Pulsed Arterial Spin Labeling.

Arthritis Care Res (Hoboken). 2018 May 21;:

Authors: Lee YC, Fine A, Protsenko E, Massarotti E, Edwards RR, Mawla I, Napadow V, Loggia ML

Abstract
OBJECTIVE: Central nervous system pathways involving pain modulation shape the pain experience in patients with chronic pain. Our objectives were to understand the mechanisms underlying pain in rheumatoid arthritis (RA) and identify brain signals that may serve as imaging markers for developing targeted treatments for RA pain.
METHODS: Subjects with RA and matched controls underwent functional magnetic resonance imaging, using pulsed arterial spin labeling (pASL). The imaging conditions included: 1) resting state, 2) low intensity stimulus and 3) high intensity stimulus. Stimuli consisted of mechanical pressure applied to metacarpophalangeal (MCP) joints with an automated cuff inflator. The low intensity stimulus was 30 mmHg. The high intensity stimulus was the amount of pressure required to achieve 40/100 pain intensity for each RA patient, with the same amount of pressure given to the matched control.
RESULTS: Among RA patients, regional cerebral blood flow (rCBF) in medial frontal cortex (MFC) and dorsolateral prefrontal cortex increased during both low and high pressure stimuli. No rCBF changes were noted for pain-free controls. In region of interest analyses among RA patients, baseline rCBF in MFC was negatively correlated with pressure required for the high intensity stimulus (p<0.01) and positively correlated with pain induced by the low intensity stimulus (p<0.05). Baseline rCBF also marginally correlated with disease activity (p=0.05). rCBF during high pain was positively correlated with pain severity and interference (p's<0.05).
CONCLUSION: In response to clinically-relevant joint pain evoked by MCP pressure, neural processing in MFC increases and is directly associated with clinical pain in RA. This article is protected by copyright. All rights reserved.

PMID: 29781581 [PubMed - as supplied by publisher]

Generation of Individual Whole-Brain Atlases With Resting-State fMRI Data Using Simultaneous Graph Computation and Parcellation.

Wed, 05/23/2018 - 03:40
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Generation of Individual Whole-Brain Atlases With Resting-State fMRI Data Using Simultaneous Graph Computation and Parcellation.

Front Hum Neurosci. 2018;12:166

Authors: Wang J, Hao Z, Wang H

Abstract
The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI) data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC). The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.

PMID: 29780309 [PubMed]

Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects.

Wed, 05/23/2018 - 03:40
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Resting-State Connectivity Biomarkers of Cognitive Performance and Social Function in Individuals With Schizophrenia Spectrum Disorder and Healthy Control Subjects.

Biol Psychiatry. 2018 Apr 13;:

Authors: Viviano JD, Buchanan RW, Calarco N, Gold JM, Foussias G, Bhagwat N, Stefanik L, Hawco C, DeRosse P, Argyelan M, Turner J, Chavez S, Kochunov P, Kingsley P, Zhou X, Malhotra AK, Voineskos AN, Social Processes Initiative in Neurobiology of the Schizophrenia(s) Group

Abstract
BACKGROUND: Deficits in neurocognition and social cognition are drivers of reduced functioning in schizophrenia spectrum disorders, with potentially shared neurobiological underpinnings. Many studies have sought to identify brain-based biomarkers of these clinical variables using a priori dichotomies (e.g., good vs. poor cognition, deficit vs. nondeficit syndrome).
METHODS: We evaluated a fully data-driven approach to do the same by building and validating a brain connectivity-based biomarker of social cognitive and neurocognitive performance in a sample using resting-state and task-based functional magnetic resonance imaging (n = 74 healthy control participants, n = 114 persons with schizophrenia spectrum disorder, 188 total). We used canonical correlation analysis followed by clustering to identify a functional connectivity signature of normal and poor social cognitive and neurocognitive performance.
RESULTS: Persons with poor social cognitive and neurocognitive performance were differentiated from those with normal performance by greater resting-state connectivity in the mirror neuron and mentalizing systems. We validated our findings by showing that poor performers also scored lower on functional outcome measures not included in the original analysis and by demonstrating neuroanatomical differences between the normal and poorly performing groups. We used a support vector machine classifier to demonstrate that functional connectivity alone is enough to distinguish normal and poorly performing participants, and we replicated our findings in an independent sample (n = 75).
CONCLUSIONS: A brief functional magnetic resonance imaging scan may ultimately be useful in future studies aimed at characterizing long-term illness trajectories and treatments that target specific brain circuitry in those with impaired cognition and function.

PMID: 29779671 [PubMed - as supplied by publisher]

A Connectome-wide Functional Signature of Transdiagnostic Risk for Mental Illness.

Wed, 05/23/2018 - 03:40
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A Connectome-wide Functional Signature of Transdiagnostic Risk for Mental Illness.

Biol Psychiatry. 2018 Apr 10;:

Authors: Elliott ML, Romer A, Knodt AR, Hariri AR

Abstract
BACKGROUND: High rates of comorbidity, shared risk, and overlapping therapeutic mechanisms have led psychopathology research toward transdiagnostic dimensional investigations of clustered symptoms. One influential framework accounts for these transdiagnostic phenomena through a single general factor, sometimes referred to as the "p" factor, associated with risk for all common forms of mental illness.
METHODS: We build on previous research identifying unique structural neural correlates of the p factor by conducting a data-driven analysis of connectome-wide intrinsic functional connectivity (n = 605).
RESULTS: We demonstrate that higher p factor scores and associated risk for common mental illness maps onto hyperconnectivity between visual association cortex and both frontoparietal and default mode networks.
CONCLUSIONS: These results provide initial evidence that the transdiagnostic risk for common forms of mental illness is associated with patterns of inefficient connectome-wide intrinsic connectivity between visual association cortex and networks supporting executive control and self-referential processes, networks that are often impaired across categorical disorders.

PMID: 29779670 [PubMed - as supplied by publisher]

Body Topography Parcellates Human Sensory and Motor Cortex.

Wed, 05/23/2018 - 03:40
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Body Topography Parcellates Human Sensory and Motor Cortex.

Cereb Cortex. 2017 Jul 01;27(7):3790-3805

Authors: Kuehn E, Dinse J, Jakobsen E, Long X, Schäfer A, Bazin PL, Villringer A, Sereno MI, Margulies DS

Abstract
The cytoarchitectonic map as proposed by Brodmann currently dominates models of human sensorimotor cortical structure, function, and plasticity. According to this model, primary motor cortex, area 4, and primary somatosensory cortex, area 3b, are homogenous areas, with the major division lying between the two. Accumulating empirical and theoretical evidence, however, has begun to question the validity of the Brodmann map for various cortical areas. Here, we combined in vivo cortical myelin mapping with functional connectivity analyses and topographic mapping techniques to reassess the validity of the Brodmann map in human primary sensorimotor cortex. We provide empirical evidence that area 4 and area 3b are not homogenous, but are subdivided into distinct cortical fields, each representing a major body part (the hand and the face). Myelin reductions at the hand-face borders are cortical layer-specific, and coincide with intrinsic functional connectivity borders as defined using large-scale resting state analyses. Our data extend the Brodmann model in human sensorimotor cortex and suggest that body parts are an important organizing principle, similar to the distinction between sensory and motor processing.

PMID: 28184419 [PubMed - indexed for MEDLINE]

On characterizing population commonalities and subject variations in brain networks.

Wed, 05/23/2018 - 03:40
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On characterizing population commonalities and subject variations in brain networks.

Med Image Anal. 2017 May;38:215-229

Authors: Ghanbari Y, Bloy L, Tunc B, Shankar V, Roberts TPL, Edgar JC, Schultz RT, Verma R

Abstract
Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs.

PMID: 26674972 [PubMed - indexed for MEDLINE]

Longitudinal Observations Using Simultaneous fMRI, Multiple Channel Electrophysiology Recording, and Chemical Microiontophoresis in The Rat Brain.

Mon, 05/21/2018 - 12:00
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Longitudinal Observations Using Simultaneous fMRI, Multiple Channel Electrophysiology Recording, and Chemical Microiontophoresis in The Rat Brain.

J Neurosci Methods. 2018 May 17;:

Authors: Jaime S, Cavazos JE, Yang Y, Lu H

Abstract
BACKGROUND: fMRI blood oxygenation level-dependent (BOLD) signal has been widely used as a surrogate for neural activity. However, interpreting differences in BOLD fMRI based on underlying neuronal activity remains a challenge. Concurrent rsMRI data collection and electrophysiological recording in combination with microiontophoretically injected modulatory chemicals allows for improved understanding of the relationship between resting state BOLD and neuronal activity.
NEW METHODS: Simultaneous fMRI, multi-channel intracortical electrophysiology and focal pharmacological manipulation data to be acquired longitudinally in rats for up to 2 months. Our artifact replacing technique is optimized for combined LFP and rsMRI data collection.
RESULTS: Intracortical implantation of a multichannel microelectrode array resulted in minimal distortion and signal loss in fMRI images inside a 9.4 T MRI scanner. rsMRI-induced electrophysiology artifacts were replaced using an in-house developed algorithm. Microinjection of AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) enhanced dopaminergic neuronal activity in the ventral tegmental area (VTA) and altered LFP signal and fMRI functional connectivity in the striatum.
COMPARISONS WITH EXISTING METHOD(S): Nanomanufacturing advances permit the production of MRI-compatible microelectrode arrays (with 16 or more channels), extending research beyond conventional methods limited to fewer channels. Our method permits longitudinal data collection of LFP and rsMRI and our algorithm effectively detects and replaces fMRI-induced electrophysiological noise, permitting rsMRI data collection concomitant with LFP recordings.
CONCLUSIONS: Our model consists of longitudinal concurrent fMRI and multichannel intracortical electrophysiological recording during microinjection of pharmacological agents to modulate neural activity in the rat brain. We used commercial micro-electrodes and recording system and can be readily generalized to other labs.

PMID: 29778509 [PubMed - as supplied by publisher]

Mutual connectivity analysis of resting-state functional MRI data with local models.

Mon, 05/21/2018 - 05:40
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Mutual connectivity analysis of resting-state functional MRI data with local models.

Neuroimage. 2018 May 16;:

Authors: DSouza AM, Abidin AZ, Chockanathan U, Schifitto G, Wismüller A

Abstract
Functional connectivity analysis of functional MRI (fMRI) can represent brain networks and reveal insights into interactions amongst different brain regions. However, most connectivity analysis approaches adopted in practice are linear and non-directional. In this paper, we demonstrate the advantage of a data-driven, directed connectivity analysis approach called Mutual Connectivity Analysis using Local Models (MCA-LM) that approximates connectivity by modeling nonlinear dependencies of signal interaction, over more conventionally used approaches, such as Pearson's and partial correlation, Patel's conditional dependence measures, etcetera. We demonstrate on realistic simulations of fMRI data that, at long sampling intervals, i.e. high repetition time (TR) of fMRI signals, MCA-LM performs better than or comparable to correlation-based methods and Patel's measures. However, at fast image acquisition rates corresponding to low TR, MCA-LM significantly outperforms these methods. This insight is particularly useful in the light of recent advances in fast fMRI acquisition techniques. Methods that can capture the complex dynamics of brain activity, such as MCA-LM, should be adopted to extract as much information as possible from the improved representation. Furthermore, MCA-LM works very well for simulations generated at weak neuronal interaction strengths, and simulations modeling inhibitory and excitatory connections as it disentangles the two opposing effects between pairs of regions/voxels. Additionally, we demonstrate that MCA-LM is capable of capturing meaningful directed connectivity on experimental fMRI data. Such results suggest that it introduces sufficient complexity into modeling fMRI time-series interactions that simple, linear approaches cannot, while being data-driven, computationally practical and easy to use. In conclusion, MCA-LM can provide valuable insights towards better understanding brain activity.

PMID: 29777828 [PubMed - as supplied by publisher]

Brain network segregation and integration during an epoch-related working memory fMRI experiment.

Mon, 05/21/2018 - 05:40
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Brain network segregation and integration during an epoch-related working memory fMRI experiment.

Neuroimage. 2018 May 16;:

Authors: Fransson P, Schiffler BC, Thompson WH

Abstract
The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual, sensorimotor and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, several subnetworks, including the visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

PMID: 29777824 [PubMed - as supplied by publisher]

Effects of global signal regression and subtraction methods on resting-state functional connectivity using arterial spin labeling data.

Mon, 05/21/2018 - 05:40
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Effects of global signal regression and subtraction methods on resting-state functional connectivity using arterial spin labeling data.

Magn Reson Imaging. 2018 May 16;:

Authors: Silva JPS, da Mata Mônaco L, Paschoal AM, de Oliveira ÍAF, Leoni RF

Abstract
BACKGROUND: Arterial spin labeling (ASL) is an established magnetic resonance imaging (MRI) technique that is finding broader applications in functional studies of the healthy and diseased brain. To promote improvement in cerebral blood flow (CBF) signal specificity, many algorithms and imaging procedures, such as subtraction methods, were proposed to eliminate or, at least, minimize noise sources. Therefore, this study addressed the main considerations of how CBF functional connectivity (FC) is changed, regarding resting brain network (RBN) identification and correlations between regions of interest (ROI), by different subtraction methods and removal of residual motion artifacts and global signal fluctuations (RMAGSF).
METHODS: Twenty young healthy participants (13 M/7F, mean age = 25 ± 3 years) underwent an MRI protocol with a pseudo-continuous ASL (pCASL) sequence. Perfusion-based images were obtained using simple, sinc and running subtraction. RMAGSF removal was applied to all CBF time series. Independent Component Analysis (ICA) was used for RBN identification, while Pearson' correlation was performed for ROI-based FC analysis.
RESULTS: Temporal signal-to-noise ratio (tSNR) was higher in CBF maps obtained by sinc subtraction, although RMAGSF removal had a significant effect on maps obtained with simple and running subtractions. Neither the subtraction method nor the RMAGSF removal directly affected the identification of RBNs. However, the number of correlated and anti-correlated voxels varied for different subtraction and filtering methods. In an ROI-to-ROI level, changes were prominent in FC values and their statistical significance.
CONCLUSIONS: Our study showed that both RMAGSF filtering and subtraction method might influence resting-state FC results, especially in an ROI level, consequently affecting FC analysis and its interpretation. Taking our results and the whole discussion together, we understand that for an exploratory assessment of the brain, one could avoid removing RMAGSF to not bias FC measures, but could use sinc subtraction to minimize low-frequency contamination. However, CBF signal specificity and frequency range for filtering purposes still need to be assessed in future studies.

PMID: 29777822 [PubMed - as supplied by publisher]

Effect of phase-encoding direction on group analysis of resting-state functional magnetic resonance imaging.

Sat, 05/19/2018 - 14:40
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Effect of phase-encoding direction on group analysis of resting-state functional magnetic resonance imaging.

Psychiatry Clin Neurosci. 2018 May 17;:

Authors: Mori Y, Miyata J, Isobe M, Son S, Yoshihara Y, Aso T, Kouchiyama T, Murai T, Takahashi H

Abstract
AIM: Echo-planar imaging is a common technique used in functional magnetic resonance imaging (fMRI), however it suffers from image distortion and signal loss because of large susceptibility effects that are related to the phase-encoding direction of the scan. Despite this relationship, the majority of neuroimaging studies have not considered the influence of phase-encoding direction. Here, we aimed to clarify how phase-encoding direction can affect the outcome of an fMRI connectivity study of schizophrenia.
METHODS: Resting-state fMRI using anterior to posterior (A-P) and posterior to anterior (P-A) directions was used to examine 25 patients with schizophrenia (SC) and 37 matched healthy controls (HC). We conducted a functional connectivity analysis using independent component analysis and performed three group comparisons: A-P vs. P-A (all participants), SC vs. HC for the A-P and P-A datasets, and the interaction between phase-encoding direction and participant group.
RESULTS: The estimated functional connectivity differed between the two phase-encoding directions in areas that were more extensive than those where signal loss has been reported. Although functional connectivity in the SC group was lower than that in the HC group for both directions, the A-P and P-A conditions did not exhibit the same specific pattern of differences. Further, we observed an interaction between participant group and the phase-encoding direction in the left temporo-parietal junction and left fusiform gyrus.
CONCLUSION: Phase-encoding direction can influence the results of functional connectivity studies. Thus, appropriate selection and documentation of phase-encoding direction will be important in future resting-state fMRI studies. This article is protected by copyright. All rights reserved.

PMID: 29774625 [PubMed - as supplied by publisher]

Thalamocortical dysconnectivity in premenstrual syndrome.

Sat, 05/19/2018 - 14:40
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Thalamocortical dysconnectivity in premenstrual syndrome.

Brain Imaging Behav. 2018 May 17;:

Authors: Liu P, Wei Y, Liao H, Fan Y, Li R, Feng N, Duan G, Deng D, Qin W

Abstract
Premenstrual syndrome (PMS) is a menstrual cycle-related disorder. Although the precise pathophysiology is not fully understood, it is increasingly believed that the central nervous system plays a vital role in the development of PMS. The aim of this study is to elucidate specific functional connectivity between the thalamus and cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 20 PMS patients and 21 healthy controls (HCs). Seed-based functional connectivity between the thalamus and six cortical regions of interest, including the prefrontal cortex (PFC), posterior parietal cortex, somatosensory cortex, motor cortex/supplementary motor area, temporal and occipital lobe, was adopted to identify specific thalamocortical connectivity in the two groups. Correlation analysis was then used to examine relationships between the neuroimaging findings and clinical symptoms. Activity in distinct cortical regions correlated with specific sub-regions of the thalamus in the two groups. Comparison between groups exhibited decreased prefrontal-thalamic connectivity and increased posterior parietal-thalamic connectivity in the PMS patients. Within the PMS group, the daily record of severity of problems (DRSP) score negatively correlated with the prefrontal-thalamic connectivity. Our findings may provide preliminary evidence for abnormal thalamocortical connectivity in PMS patients and may contribute to a better understanding of the pathophysiology of PMS.

PMID: 29774500 [PubMed - as supplied by publisher]

Classification and characterisation of brain network changes in chronic back pain: A multicenter study.

Sat, 05/19/2018 - 14:40
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Classification and characterisation of brain network changes in chronic back pain: A multicenter study.

Wellcome Open Res. 2018;3:19

Authors: Mano H, Kotecha G, Leibnitz K, Matsubara T, Nakae A, Shenker N, Shibata M, Voon V, Yoshida W, Lee M, Yanagida T, Kawato M, Rosa MJ, Seymour B

Abstract
Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain. Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state. Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

PMID: 29774244 [PubMed]

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