New resting-state fMRI related studies at PubMed

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Functional plasticity of the dorsomedial prefrontal cortex in depression reorganized by electroconvulsive therapy: Validation in two independent samples.

Sat, 09/22/2018 - 19:20
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Functional plasticity of the dorsomedial prefrontal cortex in depression reorganized by electroconvulsive therapy: Validation in two independent samples.

Hum Brain Mapp. 2018 Sep 21;:

Authors: Bai T, Wei Q, Zu M, Xie W, Wang J, Gong-Jun J, Yu F, Tian Y, Wang K

Abstract
Previous studies have implied a key role for the prefrontal cortex in the antidepressive effect of electroconvulsive therapy (ECT). However, there is still ubiquitous inconsistency across these studies, partly due to several confounding effects induced by the use of different samples. Studies with independent samples are necessary for validations to minimize confounding effects. In the current study, resting-state magnetic resonance imaging of 84 participants was collected using two scanners and two types of scanning parameters. One sample consisted of 28 patients and 23 healthy controls, and the other sample consisted of 33 patients. The local activity (indexed by the amplitude of low-frequency fluctuations) and functional connectivity were used to examine functional plasticity in the two independent samples before and after ECT. Both samples showed increased local activity of the dorsomedial prefrontal cortex (DMPFC) and enhanced connectivity of the DMPFC with the posterior cingulate cortex (PCC) following ECT. The enhanced connectivity between the DMPFC and PCC was positively associated with clinical improvement for both samples. These findings provide relatively strong evidence to support the functional plasticity of the dorsomedial prefrontal cortex and reorganization by ECT. The functional plasticity of the DMPFC-PCC may underlie the antidepressive effect of ECT.

PMID: 30240504 [PubMed - as supplied by publisher]

Frequency-specific age-related decreased brain network diversity in cognitively healthy elderly: A whole-brain data-driven analysis.

Sat, 09/22/2018 - 19:20
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Frequency-specific age-related decreased brain network diversity in cognitively healthy elderly: A whole-brain data-driven analysis.

Hum Brain Mapp. 2018 Sep 21;:

Authors: Lou W, Wang D, Wong A, Chu WCW, Mok VCT, Shi L

Abstract
Age-related changes in functional brain network have been well documented. However, recent studies have suggested the nonstationary properties of the functional connectivity of the brain, and little is known about the changes of functional connectivity dynamics during aging. In this study, a two-step singular value decomposition was introduced to capture the dynamic patterns of the time-varying functional connectivity in different frequency intervals, and the whole-brain and regional brain diversity were quantified by using Shannon entropy. The relationships between age and functional connectivity dynamics were investigated in a relatively large sample cohort of cognitively healthy elderly (N = 188, ages 65-80). The results showed an age-related decreased diversity in the whole brain as well as in the right inferior frontal gyrus, right amygdala, right hippocampus, left parahippocampal, and left inferior parietal gyrus in the frequency interval of 0.06-0.12 Hz. In addition, the whole-brain diversity during resting state could also reflect the general mental flexibility. This study provided the first evidence of frequency-specific age effects on the functional connectivity dynamics in cognitively healthy elderly, and may shed new light on the dynamic functional connectivity analysis of aging and neurodegenerative diseases.

PMID: 30240493 [PubMed - as supplied by publisher]

Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data.

Sat, 09/22/2018 - 19:20
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Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data.

Neuroimage Clin. 2018 Sep 04;20:724-730

Authors: Zurita M, Montalba C, Labbé T, Cruz JP, Dalboni da Rocha J, Tejos C, Ciampi E, Cárcamo C, Sitaram R, Uribe S

Abstract
Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.

PMID: 30238916 [PubMed - as supplied by publisher]

Ketamine influences the locus coeruleus norepinephrine network, with a dependency on norepinephrine transporter genotype - a placebo controlled fMRI study.

Sat, 09/22/2018 - 19:20
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Ketamine influences the locus coeruleus norepinephrine network, with a dependency on norepinephrine transporter genotype - a placebo controlled fMRI study.

Neuroimage Clin. 2018 Sep 04;20:715-723

Authors: Liebe T, Li M, Colic L, Munk MHJ, Sweeney-Reed CM, Woelfer M, Kretzschmar MA, Steiner J, von Düring F, Behnisch G, Schott BH, Walter M

Abstract
BACKGROUND: Ketamine is receiving increasing attention as a rapid-onset antidepressant in patients suffering from major depressive disorder (MDD) with treatment resistance or severe suicidal ideation. Ketamine modulates several neurotransmitter systems, including norepinephrine via the norepinephrine transporter (NET), both peripherally and centrally. The locus coeruleus (LC), which has high NET concentration, has been attributed to brain networks involved in depression. Thus we investigated the effects of single-dose of racemic ketamine on the LC using resting state functional MRI.
METHODS: Fifty-nine healthy participants (mean age 25.57 ± 4.72) were examined in a double-blind, randomized, placebo-controlled study with 7 Tesla MRI. We investigated the resting state functional connectivity (rs-fc) of the LC before and one hour after subanesthetic ketamine injection (0.5 mg/kg), as well as associations between its rs-fc and a common polymorphism in the NET gene (rs28386840).
RESULTS: A significant interaction of drug and time was revealed, and post hoc testing showed decreased rs-fc between LC and the thalamus after ketamine administration compared with baseline levels, including the mediodorsal, ventral anterior, ventral lateral, ventral posterolateral and centromedian nuclei. The rs-fc reduction was more pronounced in NET rs28386840 [AA] homozygous subjects than in [T] carriers.
CONCLUSIONS: We demonstrated acute rs-fc changes after ketamine administration in the central node of the norepinephrine pathway. These findings may contribute to understanding the antidepressant effect of ketamine at the system level, supporting modes of action on networks subserving aberrant arousal regulation in depression.

PMID: 30238915 [PubMed - as supplied by publisher]

Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations.

Sat, 09/22/2018 - 19:20
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Resting-State fMRI Dynamics and Null Models: Perspectives, Sampling Variability, and Simulations.

Front Neurosci. 2018;12:551

Authors: Miller RL, Abrol A, Adali T, Levin-Schwarz Y, Calhoun VD

Abstract
Studies of resting state functional MRI (rs-fRMI) are increasingly focused on "dynamics", or on those properties of brain activation that manifest and vary on timescales shorter than the scan's full duration. This shift in focus has led to a flurry of interest in developing hypothesis testing frameworks and null models applicable to the dynamical setting. Thus far however, these efforts have been weakened by a number of crucial shortcomings that are outlined and discussed in this article. We focus here on aspects of recently proposed null models that, we argue, are poorly formulated relative to the hypotheses they are designed to test, i.e., their potential role in separating functionally relevant BOLD signal dynamics from noise or intermittent background and maintenance type processes is limited by factors that are fundamental rather than merely quantitative or parametric. In this short position paper, we emphasize that (1) serious care must be exercised in building null models for rs-fMRI dynamics from distributionally stationary univariate or multivariate timeseries, i.e., timeseries whose values are each independently drawn from one pre-specified probability distribution; and (2) measures such as kurtosis that quantify over-concentration of observed values in the far tails of some reference distribution may not be particularly suitable for capturing signal features most plausibly contributing to functionally relevant brain dynamics. Other metrics targeted, for example, at capturing the type of epochal signal variation that is often viewed as a signature of brain responsiveness to stimuli or experimental tasks, could play a more scientifically clarifying role. As we learn more about the phenomenon of functionally relevant brain dynamics and its imaging correlates, scientifically meaningful null hypotheses and well-tuned null models will naturally emerge. We also revisit the important concept of distributional stationarity, discuss how it manifests within realizations vs. across multiple realizations, and provide guidance on the benefits and limitations of employing this type of stationarity in modeling the absence of functionally relevant temporal dynamics in resting state fMRI. We hope that the discussions herein are useful, and promote thoughtful consideration of these important issues.

PMID: 30237758 [PubMed]

In vivo mapping of brainstem nuclei functional connectivity disruption in Alzheimer's disease.

Sat, 09/22/2018 - 19:20
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In vivo mapping of brainstem nuclei functional connectivity disruption in Alzheimer's disease.

Neurobiol Aging. 2018 Aug 23;72:72-82

Authors: Serra L, D'Amelio M, Di Domenico C, Dipasquale O, Marra C, Mercuri NB, Caltagirone C, Cercignani M, Bozzali M

Abstract
We assessed here functional connectivity changes in the locus coeruleus (LC) and ventral tegmental area (VTA) of patients with Alzheimer's disease (AD). We recruited 169 patients with either AD or amnestic mild cognitive impairment due to AD and 37 elderly controls who underwent cognitive and neuropsychiatric assessments and resting-state functional magnetic resonance imaging at 3T. Connectivity was assessed between LC and VTA and the rest of the brain. In amnestic mild cognitive impairment patients, VTA disconnection was predominant with parietal regions, while in AD patients, it involved the posterior nodes of the default-mode network. We also looked at the association between neuropsychiatric symptoms (assessed by the neuropsychiatric inventory) and VTA connectivity. Symptoms such as agitation, irritability, and disinhibition were associated with VTA connectivity with the parahippocampal gyrus and cerebellar vermis, while sleep and eating disorders were associated with VTA connectivity to the striatum and the insular cortex. This suggests a contribution of VTA degeneration to AD pathophysiology and to the occurrence of neuropsychiatric symptoms. We did not find evidence of LC disconnection, but this could be explained by the size of this nucleus, which makes it difficult to isolate. These results are consistent with animal findings and have potential implications for AD prognosis and therapies.

PMID: 30237073 [PubMed - as supplied by publisher]

Human Connectome Project-style resting-state functional MRI at 7 Tesla using radiofrequency parallel transmission.

Sat, 09/22/2018 - 19:20
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Human Connectome Project-style resting-state functional MRI at 7 Tesla using radiofrequency parallel transmission.

Neuroimage. 2018 Sep 17;:

Authors: Wu X, Auerbach EJ, Vu AT, Moeller S, Van de Moortele PF, Yacoub E, Uğurbil K

Abstract
We investigate the utility of RF parallel transmission (pTx) for whole-brain resting-state functional MRI (rfMRI) acquisition at 7 Tesla (7T). To this end, Human Connectome Project (HCP)-style data acquisitions were chosen as a showcase example. Five healthy subjects were scanned in pTx and single-channel transmit (1Tx) modes. The pTx data were acquired using a prototype 16-channel transmit system and a commercially available Nova 8-channel transmit 32-channel receive RF head coil. Additionally, pTx single-spoke multiband (MB) pulses were designed to image sagittal slices. HCP-style 7T rfMRI data (1.6-mm isotropic resolution, 5-fold slice and 2-fold in-plane acceleration, 3600 vol and ∼ 1-h scan) were acquired with pTx and the results were compared to those acquired with the original 7T HCP rfMRI protocol. The use of pTx significantly improved flip-angle uniformity across the brain, with coefficient of variation (i.e., std/mean) of whole-brain flip-angle distribution reduced on average by ∼39%. This in turn yielded ∼17% increase in group temporal SNR (tSNR) as averaged across the entire brain and ∼10% increase in group functional contrast-to-noise ratio (fCNR) as averaged across the grayordinate space (including cortical surfaces and subcortical voxels). Furthermore, when placing a seed in either the posterior parietal lobe or putamen estimate seed-based dense connectome, the increase in fCNR was observed to translate into stronger correlation of the seed with the rest of the grayordinate space. We have demonstrated the utility of pTx for slice-accelerated high-resolution whole-brain rfMRI at 7T; as compared to current state-of-the-art, the use of pTx improves flip-angle uniformity, increases tSNR, enhances fCNR and strengthens functional connectivity estimation.

PMID: 30237033 [PubMed - as supplied by publisher]

Electrophysiological Signatures of the Resting-state fMRI Global Signal: A Simultaneous EEG-fMRI Study.

Sat, 09/22/2018 - 19:20
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Electrophysiological Signatures of the Resting-state fMRI Global Signal: A Simultaneous EEG-fMRI Study.

J Neurosci Methods. 2018 Sep 17;:

Authors: Huang X, Long Z, Yao D, Lei X

Abstract
BACKGROUND: The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined.
NEW METHODS: We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects.
RESULTS: We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30-45 Hz) and a negative correlation in the low-frequency band (4-20 Hz). Comparison with existing method(s): Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity.
CONCLUSIONS: These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.

PMID: 30236777 [PubMed - as supplied by publisher]

Thought experiment: Decoding cognitive processes from the fMRI data of one individual.

Fri, 09/21/2018 - 12:40

Thought experiment: Decoding cognitive processes from the fMRI data of one individual.

PLoS One. 2018;13(9):e0204338

Authors: Wegrzyn M, Aust J, Barnstorf L, Gippert M, Harms M, Hautum A, Heidel S, Herold F, Hommel SM, Knigge AK, Neu D, Peters D, Schaefer M, Schneider J, Vormbrock R, Zimmer SM, Woermann FG, Labudda K

Abstract
Cognitive processes, such as the generation of language, can be mapped onto the brain using fMRI. These maps can in turn be used for decoding the respective processes from the brain activation patterns. Given individual variations in brain anatomy and organization, analyzes on the level of the single person are important to improve our understanding of how cognitive processes correspond to patterns of brain activity. They also allow to advance clinical applications of fMRI, because in the clinical setting making diagnoses for single cases is imperative. In the present study, we used mental imagery tasks to investigate language production, motor functions, visuo-spatial memory, face processing, and resting-state activity in a single person. Analysis methods were based on similarity metrics, including correlations between training and test data, as well as correlations with maps from the NeuroSynth meta-analysis. The goal was to make accurate predictions regarding the cognitive domain (e.g. language) and the specific content (e.g. animal names) of single 30-second blocks. Four teams used the dataset, each blinded regarding the true labels of the test data. Results showed that the similarity metrics allowed to reach the highest degrees of accuracy when predicting the cognitive domain of a block. Overall, 23 of the 25 test blocks could be correctly predicted by three of the four teams. Excluding the unspecific rest condition, up to 10 out of 20 blocks could be successfully decoded regarding their specific content. The study shows how the information contained in a single fMRI session and in each of its single blocks can allow to draw inferences about the cognitive processes an individual engaged in. Simple methods like correlations between blocks of fMRI data can serve as highly reliable approaches for cognitive decoding. We discuss the implications of our results in the context of clinical fMRI applications, with a focus on how decoding can support functional localization.

PMID: 30235321 [PubMed - in process]

The Relationship Between Resting State Network Connectivity and Individual Differences in Executive Functions.

Fri, 09/21/2018 - 12:40

The Relationship Between Resting State Network Connectivity and Individual Differences in Executive Functions.

Front Psychol. 2018;9:1600

Authors: Reineberg AE, Gustavson DE, Benca C, Banich MT, Friedman NP

Abstract
The brain is organized into a number of large networks based on shared function, for example, high-level cognitive functions (frontoparietal network), attentional capabilities (dorsal and ventral attention networks), and internal mentation (default network). The correlations of these networks during resting-state fMRI scans varies across individuals and is an indicator of individual differences in ability. Prior work shows higher cognitive functioning (as measured by working memory and attention tasks) is associated with stronger negative correlations between frontoparietal/attention and default networks, suggesting that increased ability may depend upon the diverging activation of networks with contrasting function. However, these prior studies lack specificity with regard to the higher-level cognitive functions involved, particularly with regards to separable components of executive function (EF). Here we decompose EF into three factors from the unity/diversity model of EFs: Common EF, Shifting-specific EF, and Updating-specific EF, measuring each via factor scores derived from a battery of behavioral tasks completed by 250 adult participants (age 28) at the time of a resting-state scan. We found the hypothesized segregated pattern only for Shifting-specific EF. Specifically, after accounting for one's general EF ability (Common EF), individuals better able to fluidly switch between task sets have a stronger negative correlation between the ventral attention network and the default network. We also report non-predicted novel findings in that individuals with higher Shifting-specific abilities exhibited more positive connectivity between frontoparietal and visual networks, while those individuals with higher Common EF exhibited increased connectivity between sensory and default networks. Overall, these results reveal a new degree of specificity with regard to connectivity/EF relationships.

PMID: 30233455 [PubMed]

Subregions of the Anterior Cingulate Cortex Form Distinct Functional Connectivity Patterns in Young Males With Internet Gaming Disorder With Comorbid Depression.

Fri, 09/21/2018 - 12:40

Subregions of the Anterior Cingulate Cortex Form Distinct Functional Connectivity Patterns in Young Males With Internet Gaming Disorder With Comorbid Depression.

Front Psychiatry. 2018;9:380

Authors: Lee D, Lee J, Namkoong K, Jung YC

Abstract
Depression is one of the most common comorbid conditions in Internet Gaming Disorder (IGD). Although there have been many studies on the pathophysiology of IGD, the neurobiological basis underlying the close association between depression and IGD has not been fully clarified. Previous neuroimaging studies have demonstrated functional and structural abnormalities in the anterior cingulate cortex (ACC) in IGD patients. In this study, we explored functional connectivity (FC) abnormalities involving subregions of the ACC in IGD subjects with comorbid depression. We performed a resting state seed-based FC analysis of 21 male young adults with IGD with comorbid depression (IGDdep+ group, 23.6 ± 2.4 years), 22 male young adults without IGD with comorbid depression (IGDdep- group, 24.0 ± 1.6 years), and 20 male age-matched healthy controls (24.0 ± 2.2 years). ACC-seeded FC was evaluated using the CONN-fMRI FC toolbox. The dorsal ACC (dACC), the pregenual ACC (pgACC), and the subgenual ACC (sgACC) were selected as seed regions. Both IGD groups had stronger pgACC FC with the right precuneus, the posterior cingulate cortex, and the left inferior frontal gyrus/insula than the control group. The IGDdep+ group had stronger dACC FC with the left precuneus and the right cerebellar lobule IX than the control and IGDdep- groups. The IGDdep+ group also had weaker pgACC FC with the right dorsomedial prefrontal cortex and the right supplementary motor area and had weaker sgACC FC with the left precuneus, the left lingual gyrus, and the left postcentral gyrus than the other groups. The strength of the connectivity between the sgACC and the left precuneus correlated positively with a higher omission error rate in the continuous performance test in the IGDdep+ group. In addition, the IGDdep- group had stronger sgACC FC with the left dorsolateral prefrontal cortex than the other groups. Our findings suggest that young males with IGD comorbid with depression have FC alterations of the default mode network and diminished FC with the prefrontal cortex. This altered FC pattern may be involved in the close association of IGD and depression.

PMID: 30233421 [PubMed]

BRANT: A Versatile and Extendable Resting-State fMRI Toolkit.

Fri, 09/21/2018 - 12:40

BRANT: A Versatile and Extendable Resting-State fMRI Toolkit.

Front Neuroinform. 2018;12:52

Authors: Xu K, Liu Y, Zhan Y, Ren J, Jiang T

Abstract
Data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us with a variety of functions and user friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs, we have developed a versatile and extendable MATLAB-based toolbox, BRANT (BRAinNetome fmri Toolkit), with a wide range of rs-fMRI data processing functions and code-generated GUIs. During the implementation, we have also empowered the toolbox with parallel computing techniques, efficient file handling methods for compressed file format, and one-line scripting. In BRANT, users can find rs-fMRI batch processing functions for preprocessing, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization, while developers can quickly publish scripts with code-generated GUIs.

PMID: 30233348 [PubMed]

Corrigendum: Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance.

Fri, 09/21/2018 - 12:40

Corrigendum: Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance.

Front Hum Neurosci. 2018;12:345

Authors: Ramos-Nuñez AI, Fischer-Baum S, Martin RC, Yue Q, Ye F, Deem MW

Abstract
[This corrects the article DOI: 10.3389/fnhum.2017.00420.].

PMID: 30233342 [PubMed - in process]

Assessing functional connectivity of brain network in children with nocturnal enuresis using resting state functional magnetic resonance imaging.

Fri, 09/21/2018 - 12:40

Assessing functional connectivity of brain network in children with nocturnal enuresis using resting state functional magnetic resonance imaging.

Neurol India. 2018 Sep-Oct;66(5):1367-1369

Authors: Vadapalli R

PMID: 30233005 [PubMed - in process]

Degree centrality and voxel-mirrored homotopic connectivity in children with nocturnal enuresis: A functional MRI study.

Fri, 09/21/2018 - 12:40

Degree centrality and voxel-mirrored homotopic connectivity in children with nocturnal enuresis: A functional MRI study.

Neurol India. 2018 Sep-Oct;66(5):1359-1364

Authors: Jiang K, Ding L, Li H, Shen H, Zheng A, Zhao F, Gao M, Dong X, Yu S

Abstract
Aim: To determine the characteristics of brain development in children with nocturnal enuresis, we investigated the intensity of functional connectivity both among the nodes in the brain network and between the two hemispheres of the brain.
Materials and Methods: Twenty-three children with nocturnal enuresis (NE) and an equal number of normal children were examined using resting-state functional magnetic resonance imaging (fMRI) scans. Data analysis was done via the degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) approaches. Moreover, we compared the children's psychological status by utilizing the self-concept scale.
Results: In four areas of the brain, the the DC values of the NE group were obviously lower than that of the normal controls. These four areas were the posterior cerebellar lobe, anterior cingulate cortex (ACC), medial frontal gyrus, and superior left temporal gyrus (P < 0.05, after correction). We also found two brain areas where the VMHC values of the NE group were obviously lower than that of the normal controls. The two groups were the cerebellar lobe and the anterior cingulate cortex (ACC) [P < 0.05, after correction]. A psychological comparison between the children with NE and that in the normal group on the self-concept scale was also performed. The scores of the children with NE were lower than normal controls regarding behavior, appearance and property, anxiety, gregariousness, happiness, and satisfaction (P < 0.05).
Conclusions: These findings provide evidence of the deficit of urination control in children with NE. Furthermore, through the methods of DC and VMHC, which are based on functional connectivity, it was also possible to explain why children with NE often have the concomitant symptoms of attention, control, and memory problems. The analysis of the self-concept scale suggests that children with NE lack self-confidence.

PMID: 30233003 [PubMed - in process]

Salience network coupling is linked to both tobacco smoking and symptoms of attention deficit hyperactivity disorder (ADHD).

Fri, 09/21/2018 - 12:40
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Salience network coupling is linked to both tobacco smoking and symptoms of attention deficit hyperactivity disorder (ADHD).

Drug Alcohol Depend. 2018 01 01;182:93-97

Authors: Janes AC, Gilman JM, Frederick BB, Radoman M, Pachas G, Fava M, Evins AE

Abstract
INTRODUCTION: Attention deficit hyperactivity disorder (ADHD) symptoms, even those below diagnostic threshold, enhance the likelihood of nicotine dependence, suggesting a neurobiological link between disorders. Of particular interest is the salience network (SN), which mediates attention to salient internal/external stimuli to guide behavior and is anchored by the dorsal anterior cingulate cortex (dACC) and bilateral anterior insula (AI). Disrupted interactions between the SN and the default mode (DMN) and central executive networks (CEN) have been noted in both ADHD and nicotine dependence. Further, enhanced intra-SN coupling between the dACC-AI influences aspects of nicotine dependence such as reactivity to smoking cues.
METHODS: To identify links between SN functional connectivity and ADHD symptoms in nicotine dependence, we compared 21 nicotine dependent individuals with 17 non-smokers on ADHD symptoms as measured by the ADHD self-report scale (ASRS) and resting state intra and inter-SN functional connectivity.
RESULTS: Relative to healthy controls, nicotine dependent individuals had significantly higher ASRS scores and greater dACC-AI coupling. No group differences were noted on inter-SN network coupling. A significant association was found between ASRS and dACC-AI coupling both in the entire cohort and specifically when evaluating nicotine dependent individuals alone.
CONCLUSIONS: The greater ASRS scores in nicotine dependent individuals is in line with existent literature and the stronger dACC-AI coupling in smokers further supports the role of this network in nicotine dependence. The significant association between dACC-AI coupling and ASRS suggests that intra-SN coupling strength may impact neurocognitive functioning associated with both ADHD symptoms and nicotine dependence.

PMID: 29175464 [PubMed - indexed for MEDLINE]

Hippocampal-subregion functional alterations associated with antidepressant effects and cognitive impairments of electroconvulsive therapy.

Thu, 09/20/2018 - 11:40

Hippocampal-subregion functional alterations associated with antidepressant effects and cognitive impairments of electroconvulsive therapy.

Psychol Med. 2018 Sep 19;:1-8

Authors: Bai T, Wei Q, Xie W, Wang A, Wang J, Ji GJ, Wang K, Tian Y

Abstract
BACKGROUND: Electroconvulsive therapy (ECT), an effective antidepressive treatment, is frequently accompanied by cognitive impairment (predominantly memory), usually transient and self-limited. The hippocampus is a key region involved in memory and emotion processing, and in particular, the anterior-posterior hippocampal subregions has been shown to be associated with emotion and memory. However, less is known about the relationship between hippocampal-subregion alterations following ECT and antidepressant effects or cognitive impairments.
METHODS: Resting-state functional connectivity (RSFC) based on the seeds of hippocampal subregions were investigated in 45 pre- and post-ECT depressed patients. Structural connectivity between hippocampal subregions and corresponding functionally abnormal regions was also conducted using probabilistic tractography. Antidepressant effects and cognitive impairments were measured by the Hamilton Depressive Rating Scale (HDRS) and the Category Verbal Fluency Test (CVFT), respectively. Their relationships with hippocampal-subregions alterations were examined.
RESULTS: After ECT, patients showed increased RSFC in the hippocampal emotional subregion (HIPe) with the left middle occipital gyrus (LMOG) and right medial temporal gyrus (RMTG). Decreased HDRS was associated with increased HIPe-RMTG RSFC (r = -0.316, p = 0.035) significantly and increased HIPe-LMOG RSFC at trend level (r = -0.283, p = 0.060). In contrast, the hippocampal cognitive subregion showed decreased RSFC with the bilateral angular gyrus, and was correlated with decreased CVFT (r = 0.418, p = 0.015 for left; r = 0.356, p = 0.042 for right). No significant changes were found in structural connectivity.
CONCLUSION: The hippocampal-subregions functional alterations may be specially associated with the antidepressant and cognitive effects of ECT.

PMID: 30229715 [PubMed - as supplied by publisher]

Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer's disease.

Thu, 09/20/2018 - 11:40
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Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer's disease.

Alzheimers Dement (N Y). 2018;4:395-413

Authors: Bayram E, Caldwell JZK, Banks SJ

Abstract
Alzheimer's disease (AD) is caused by a cascade of changes to brain integrity. Neuroimaging biomarkers are important in diagnosis and monitoring the effects of interventions. As memory impairments are among the first symptoms of AD, the relationship between imaging findings and memory deficits is important in biomarker research. The most established magnetic resonance imaging (MRI) finding is hippocampal atrophy, which is related to memory decline and currently used as a diagnostic criterion for AD. While the medial temporal lobes are impacted early by the spread of neurofibrillary tangles, other networks and regional changes can be found quite early in the progression. Atrophy in several frontal and parietal regions, cortical thinning, and white matter alterations correlate with memory deficits in early AD. Changes in activation and connectivity have been detected by functional MRI (fMRI). Task-based fMRI studies have revealed medial temporal lobe hypoactivation, parietal hyperactivation, and frontal hyperactivation in AD during memory tasks, and activation patterns of these regions are also altered in preclinical and prodromal AD. Resting state fMRI has revealed alterations in default mode network activity related to memory in early AD. These studies are limited in part due to the historic inclusion of patients who had suspected AD but likely did not have the disorder. Modern biomarkers allow for more diagnostic certainty, allowing better understanding of neuroimaging markers in true AD, even in the preclinical stage. Larger patient cohorts, comparison of candidate imaging biomarkers to more established biomarkers, and inclusion of more detailed neuropsychological batteries to assess multiple aspects of memory are needed to better understand the memory deficit in AD and help develop new biomarkers. This article reviews MRI findings related to episodic memory impairments in AD and introduces a new study with multimodal imaging and comprehensive neuropsychiatric evaluation to overcome current limitations.

PMID: 30229130 [PubMed]

Effects of Semax on the Default Mode Network of the Brain.

Wed, 09/19/2018 - 10:40
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Effects of Semax on the Default Mode Network of the Brain.

Bull Exp Biol Med. 2018 Sep 17;:

Authors: Lebedeva IS, Panikratova YR, Sokolov OY, Kupriyanov DA, Rumshiskaya AD, Kost NV, Myasoedov NF

Abstract
The effects of nootropic drug Semax on the neuronal network of the brain were studied by the resting state functional magnetic-resonance imaging (resting state fMRI). The study was carried out on two groups of healthy volunteers (11 men and 13 women aged 43.9±9.5 years). Resting state fMRI was carried out 3 times: directly before and 5 and 20 min after intranasal 1% Semax (14 subjects) or placebo (10 subjects). The topography of the resting state default mode network was studied. A greater volume of the default mode network rostral (medial frontal cortex) subcomponent was detected in the Semax group in comparison with controls. Resting state fMRI confirmed Semax effects on the neuronal network of the brain and demonstrated topography of these effects.

PMID: 30225715 [PubMed - as supplied by publisher]

Resting-state functional brain connectivity best predicts the personality dimension of openness to experience.

Wed, 09/19/2018 - 10:40
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Resting-state functional brain connectivity best predicts the personality dimension of openness to experience.

Personal Neurosci. 2018 Jul 05;1:

Authors: Dubois J, Galdi P, Han Y, Paul LK, Adolphs R

Abstract
Personality neuroscience aims to find associations between brain measures and personality traits. Findings to date have been severely limited by a number of factors, including small sample size and omission of out-of-sample prediction. We capitalized on the recent availability of a large database, together with the emergence of specific criteria for best practices in neuroimaging studies of individual differences. We analyzed resting-state functional magnetic resonance imaging data from 884 young healthy adults in the Human Connectome Project (HCP) database. We attempted to predict personality traits from the "Big Five", as assessed with the NEO-FFI test, using individual functional connectivity matrices. After regressing out potential confounds (such as age, sex, handedness and fluid intelligence), we used a cross-validated framework, together with test-retest replication (across two sessions of resting-state fMRI for each subject), to quantify how well the neuroimaging data could predict each of the five personality factors. We tested three different (published) denoising strategies for the fMRI data, two inter-subject alignment and brain parcellation schemes, and three different linear models for prediction. As measurement noise is known to moderate statistical relationships, we performed final prediction analyses using average connectivity across both imaging sessions (1 h of data), with the analysis pipeline that yielded the highest predictability overall. Across all results (test/retest; 3 denoising strategies; 2 alignment schemes; 3 models), Openness to experience emerged as the only reliably predicted personality factor. Using the full hour of resting-state data and the best pipeline, we could predict Openness to experience (NEOFAC_O: r=0.24, R2=0.024) almost as well as we could predict the score on a 24-item intelligence test (PMAT24_A_CR: r=0.26, R2=0.044). Other factors (Extraversion, Neuroticism, Agreeableness and Conscientiousness) yielded weaker predictions across results that were not statistically significant under permutation testing. We also derived two superordinate personality factors ("α" and "β") from a principal components analysis of the NEO-FFI factor scores, thereby reducing noise and enhancing the precision of these measures of personality. We could account for 5% of the variance in the β superordinate factor (r=0.27, R2=0.050), which loads highly on Openness to experience. We conclude with a discussion of the potential for predicting personality from neuroimaging data and make specific recommendations for the field.

PMID: 30225394 [PubMed]

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