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Differential impact of reward and punishment on functional connectivity after skill learning.

Fri, 01/11/2019 - 22:34
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Differential impact of reward and punishment on functional connectivity after skill learning.

Neuroimage. 2019 Jan 07;:

Authors: Steel A, Silson EH, Stagg CJ, Baker CI

Abstract
Reward and punishment shape behavior, but the mechanisms underlying their effect on skill learning are not well understood. Here, we tested whether the functional connectivity of premotor cortex (PMC), a region known to be critical for learning of sequencing skills, is altered after training by reward or punishment given during training. Resting-state fMRI was collected in two experiments before and after participants trained on either a serial reaction time task (SRTT; n = 36) or force-tracking task (FTT; n = 36) with reward, punishment, or control feedback. In each experiment, training-related change in PMC functional connectivity was compared across feedback groups. In both tasks, reward and punishment differentially affected PMC functional connectivity. On the SRTT, participants trained with reward showed an increase in functional connectivity between PMC and cerebellum as well as PMC and striatum, while participants trained with punishment showed an increase in functional connectivity between PMC and medial temporal lobe connectivity. After training on the FTT, subjects trained with control and reward showed increases in PMC connectivity with parietal and temporal cortices after training, while subjects trained with punishment showed increased PMC connectivity with ventral striatum. While the results from the two experiments overlapped in some areas, including ventral pallidum, temporal lobe, and cerebellum, these regions showed diverging patterns of results across the two tasks for the different feedback conditions. These findings suggest that reward and punishment strongly influence spontaneous brain activity after training, and that the regions implicated depend on the task learned.

PMID: 30630080 [PubMed - as supplied by publisher]

Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning.

Fri, 01/11/2019 - 22:34
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Functional brain networks and neuroanatomy underpinning nausea severity can predict nausea susceptibility using machine learning.

J Physiol. 2019 Jan 10;:

Authors: Ruffle JK, Patel A, Giampietro V, Howard MA, Sanger G, Andrews PLR, Williams SCR, Aziz Q, Farmer AD

Abstract
KEY POINTS: Nausea is an adverse experience characterised by alterations in autonomic and cerebral function. Susceptibility to nausea is difficult to predict, but machine learning has yet to be applied to this field of study. The severity of nausea that individuals experience is related to the underlying morphology (shape) of the subcortex; namely of the amygdala, caudate and putamen. A functional brain network related to nausea severity was identified, which included the thalamus, cingulate cortices (anterior, mid and posterior), caudate nucleus and nucleus accumbens. Sympathetic nervous system function and sympathovagal balance was closely related to both this nausea-associated anatomical variation and functional connectivity network. Machine learning accurately predicted susceptibility or resistance to nausea. These novel anatomical and functional brain biomarkers for nausea severity may permit objective identification of individuals susceptible to nausea, using artificial intelligence/machine learning. Brain data may be useful to identify individuals more susceptible to nausea.
ABSTRACT: Objectives Nausea is a highly individual and variable experience. The central processing of nausea remains poorly understood, although numerous influential factors have been proposed including brain structure, function and autonomic nervous system (ANS) activity. We investigated the role of these factors in nausea severity and if susceptibility to nausea could be predicted using machine learning. Design 28 healthy participants (15 male; mean age 24 years) underwent quantification of resting sympathetic and parasympathetic nervous system activity. All were exposed to a 10-minute motion-sickness video during fMRI. Neuroanatomical shape differences of the subcortex and functional brain networks associated with the severity of nausea were investigated. A machine learning neural network was trained to predict nausea susceptibility, or resistance, using baseline ANS data and detected brain features. Results Increasing nausea scores positively correlated with shape variation of the left amygdala, right caudate and bilateral putamen (corrected-p = 0.05). A functional brain network active in participants reporting nausea was identified implicating the thalamus, anterior, middle and posterior cingulate cortices, caudate nucleus and nucleus accumbens (corrected-p = 0.043). Both neuroanatomical differences and the functional nausea-brain network were closely related to sympathetic nervous system activity. Using these data, a machine learning model predicted susceptibility to nausea with an overall accuracy of 82.1%. Conclusions Nausea severity relates to underlying subcortical morphology and a functional brain network in its experience; both measures are potential biomarkers in trials of anti-nausea therapies. The use of machine learning should be further investigated as an objective means to develop models predicting nausea susceptibility. This article is protected by copyright. All rights reserved.

PMID: 30629751 [PubMed - as supplied by publisher]

Correlation between changes in functional connectivity in the dorsal attention network and the after-effects induced by prism adaptation in healthy humans: A dataset of resting-state fMRI and pointing after prism adaptation.

Fri, 01/11/2019 - 22:34
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Correlation between changes in functional connectivity in the dorsal attention network and the after-effects induced by prism adaptation in healthy humans: A dataset of resting-state fMRI and pointing after prism adaptation.

Data Brief. 2019 Feb;22:583-589

Authors: Tsujimoto K, Mizuno K, Nishida D, Tahara M, Yamada E, Shindo S, Watanabe Y, Kasuga S, Liu M

Abstract
It has been reported that it is possible to observe transient changes in resting-state functional connectivity (FC) in the attention networks of healthy adults during treatment with prism adaptation. by using functional magnetic resonance imaging (fMRI) (see "Prism adaptation changes resting-state functional connectivity in the dorsal stream of visual attention networks in healthy adults: A fMRI study" (Tsujimoto et al., 2018) [1]. Recent neuroimaging and neurophysiological studies support the idea that prism adaptation (PA) affects the visual attention and sensorimotor networks, which include the parietal cortex and cerebellum. These data demonstrate the effect of PA on resting-state functional connectivity between the primary motor cortex and cerebellum. Additionally, it evaluates changes of resting-state FC before and after PA in healthy individuals using fMRI. Analyses focus on FC between the primary motor cortex and cerebellum, and the correlation between changes in FC and its after-effects following a single PA session. Here, we show data that demonstrate the change in resting-state FC between the primary motor cortex and cerebellum, as well as a correlation between the change ratio of FC and the amplitude of the after-effect.

PMID: 30627613 [PubMed]

Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.

Fri, 01/11/2019 - 22:34
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Identifying neuropsychiatric disorders using unsupervised clustering methods: Data and code.

Data Brief. 2019 Feb;22:570-573

Authors: Zhao X, Rangaprakash D, Denney TS, Katz JS, Dretsch MN, Deshpande G

Abstract
This article provides data for five different neuropsychiatric disorders-Attention Deficit Hyperactivity Disorder, Alzheimer's Disease, Autism Spectrum Disorder, Post-Traumatic Stress Disorder, and Post-Concussion Syndrome-along with healthy controls. The data includes clinical diagnostic labels, phenotypic variables, and resting-state functional magnetic resonance imaging connectivity features obtained from individuals. In addition, it provides the source MATLAB codes used for data analyses. Three existing clustering methods have been incorporated into the provided code, which do not require a priori specification of the number of clusters. A genetic algorithm based feature selection method has also been included to find the relevant subset of features and clustering the subset of data simultaneously. Findings from this data set and further detailed interpretations are available in our recent research study (Zhao et al., 2017) [1]. This contribution is a valuable asset for performing unsupervised machine learning on fMRI data to investigate the correspondence of clinical diagnostic grouping with the underlying neurobiological/phenotypic clusters.

PMID: 30627610 [PubMed]

Learning Pairwise-Similarity Guided Sparse Functional Connectivity Network for MCI Classification.

Fri, 01/11/2019 - 22:34
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Learning Pairwise-Similarity Guided Sparse Functional Connectivity Network for MCI Classification.

Asian Conf Pattern Recognit. 2018 Nov;2017:917-922

Authors: Chen X, Zhang H, Zhang Y, Yang J, Shen D

Abstract
Learning functional connectivity (FC) network from resting-state function magnetic resonance imaging (RS-fMRI) data via sparse representation (SR) or weighted SR (WSR) has been proved to be promising for the diagnosis of Alzheimer's disease and its prodromal stage, mild cognitive impairment (MCI). However, traditional SR/WSR based approaches learn the representation of each brain region independently, without fully taking into account the possible relationship between brain regions. To remedy this limitation, we propose a novel FC modeling approach by considering two types of possible relationship between different brain regions which are incorporated into SR/WSR approaches in the form of regularization. In this way, the representations of all brain regions can be jointly learned. Furthermore, an efficient alternating optimization algorithm is also developed to solve the resulting model. Experimental results show that our proposed method not only outperforms SR and WSR in the diagnosis of MCI subjects, but also leads to the brain FC network with better modularity structure.

PMID: 30627592 [PubMed]

Differentiation of Transformed Bipolar Disorder From Unipolar Depression by Resting-State Functional Connectivity Within Reward Circuit.

Thu, 01/10/2019 - 21:36
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Differentiation of Transformed Bipolar Disorder From Unipolar Depression by Resting-State Functional Connectivity Within Reward Circuit.

Front Psychol. 2018;9:2586

Authors: Shi J, Geng J, Yan R, Liu X, Chen Y, Zhu R, Wang X, Shao J, Bi K, Xiao M, Yao Z, Lu Q

Abstract
Previous studies have found that neural functional abnormalities detected by functional magnetic resonance imaging (fMRI) in brain regions implicated in reward processing during reward tasks show promise to distinguish bipolar from unipolar depression (UD), but little is known regarding resting-state functional connectivity (rsFC) within the reward circuit. In this study, we investigated neurobiomarkers for early recognition of bipolar disorder (BD) by retrospectively comparing rsFC within the reward circuit between UD and depressed BD. Sixty-six depressed patients were enrolled, none of whom had ever experienced any manic/hypomanic episodes before baseline. Simultaneously, 40 matched healthy controls (HC) were also recruited. Neuroimaging data of each participant were obtained from resting-state fMRI scans. Some patients began to manifest bipolar disorder (tBD) during the follow-up period. All patients were retrospectively divided into two groups (33 tBD and 33 UD) according to the presence or absence of mania/hypomania in the follow-up. rsFC between key regions of the reward circuit was calculated and compared among groups. Results showed decreased rsFC between the left ventral tegmental area (VTA) and left ventral striatum (VS) in the tBD group compared with the UD group, which showed good accuracy in predicting diagnosis (tBD vs. UD) according to receiver operating characteristic (ROC) analysis. No significant different rsFC was found within the reward circuit between any patient group and HC. Our preliminary findings indicated that bipolar disorder, in early depressive stages before onset of mania/hypomania attacks, already differs from UD in the reward circuit of VTA-VS functional synchronicity at the resting state.

PMID: 30622492 [PubMed]

Effect of Increasing Age on Brain Dysfunction in Cirrhosis.

Thu, 01/10/2019 - 00:35
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Effect of Increasing Age on Brain Dysfunction in Cirrhosis.

Hepatol Commun. 2019 Jan;3(1):63-73

Authors: Liu R, Ahluwalia V, Kang JD, Ghosh SS, Zhou H, Li Y, Zhao D, Gurley E, Li X, White MB, Fagan A, Lippman HR, Wade JB, Hylemon PB, Bajaj JS

Abstract
Patients with cirrhosis are growing older, which could have an impact on brain dysfunction beyond hepatic encephalopathy. Our aim was to study the effect of concomitant aging and cirrhosis on brain inflammation and degeneration using human and animal experiments. For the human study, age-matched patients with cirrhosis and controls between 65 and 85 years underwent cognitive testing, quality of life (QOL) assessment, and brain magnetic resonance (MR) spectroscopy and resting state functional MR imaging (rs-fMRI) analysis. Data were compared between groups. For the animal study, young (10-12 weeks) and old (1.5 years) C57BL/6 mice were given either CCl4 gavage to develop cirrhosis or a vehicle control and were followed for 12 weeks. Cortical messenger RNA (mRNA) expression of inflammatory mediators (interleukin [IL]-6, IL-1β, transforming growth factor β [TGF-β], and monocyte chemoattractant protein 1), sirtuin-1, and gamma-aminobutyric acid (GABA)-ergic synaptic plasticity (neuroligin-2 [NLG2], discs large homolog 4 [DLG4], GABA receptor, subunit gamma 1/subunit B1 [GABRG1/B1]) were analyzed and compared between younger/older control and cirrhotic mice. The human study included 46 subjects (23/group). Patients with cirrhosis had worse QOL and cognition. On MR spectroscopy, patients with cirrhosis had worse changes related to ammonia and lower N-acetyl aspartate, whereas rs-fMRI analysis revealed that these patients demonstrated functional connectivity changes in the frontoparietal cortical region compared to controls. Results of the animal study showed that older mice required lower CCl4 to reach cirrhosis. Older mice, especially with cirrhosis, demonstrated higher cortical inflammatory mRNA expression of IL-6, IL-1β, and TGF-β; higher glial and microglial activation; and lower sirtuin-1 expression compared to younger mice. Older mice also had lower expression of DLG4, an excitatory synaptic organizer, and higher NLG2 and GABRG1/B1 receptor expression, indicating a predominantly inhibitory synaptic organization. Conclusion: Aging modulates brain changes in cirrhosis; this can affect QOL, cognition, and brain connectivity. Cortical inflammation, microglial activation, and altered GABA-ergic synaptic plasticity could be contributory.

PMID: 30619995 [PubMed]

Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures.

Thu, 01/10/2019 - 00:35
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Multimodal Data and Machine Learning for Detecting Specific Biomarkers in Pediatric Epilepsy Patients With Generalized Tonic-Clonic Seizures.

Front Neurol. 2018;9:1038

Authors: Wang J, Li Y, Wang Y, Huang W

Abstract
Previous neuroimaging studies of epilepsy with generalized tonic-clonic seizures (GTCS) focus mainly on adults. However, the neural mechanisms that underline this type of epilepsy remain unclear, especially for children. The aim of the present study was to detect the effect of epilepsy on brains of children with GTCS and to investigate whether the changes in the brain can be used to discriminate between epileptic children and healthy children at the level of the individual. To achieve this purpose, we measured gray matter (GM) volume and fractional amplitude of low-frequency fluctuation (fALFF) differences on multimodel magnetic resonance imaging in 14 children with GTCS and 30 age- and gender-matched healthy controls. The patients showed GM volume reduction and a fALFF increase in the thalamus, hippocampus, temporal and other deep nuclei. A significant decrease of fALFF was mainly found in the default mode network (DMN). In addition, epileptic duration was significantly negatively related to the GM volumes and significantly positively related to the fALFF value of right thalamus. A support vector machine (SVM) applied to the GM volume of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 74.42% (P < 0.002). A SVM applied to the fALFF of the right thalamus correctly identified epileptic children with a statistically significant accuracy of 83.72% (P < 0.002). The consistent neuroimaging results indicated that the right thalamus plays an important role in reflecting the chronic damaging effect of GTCS epilepsy in children. The length of time of a child's epileptic history was correlated with greater GM volume reduction and a fALFF increase in the right thalamus. GM volumes and fALFF values in the right thalamus can identify children with GTCS from the healthy controls with high accuracy and at an individual subject level. These results are likely to be valuable in explaining the clinical problems and understanding the brain abnormalities underlying this disorder.

PMID: 30619025 [PubMed]

Abnormal Default Mode Network Homogeneity in Treatment-Naive Patients With First-Episode Depression.

Thu, 01/10/2019 - 00:35
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Abnormal Default Mode Network Homogeneity in Treatment-Naive Patients With First-Episode Depression.

Front Psychiatry. 2018;9:697

Authors: Gao Y, Wang M, Yu R, Li Y, Yang Y, Cui X, Zheng J

Abstract
Background and Objective: The default mode network (DMN) may be an important component involved in the broad-scale cognitive problems seen in patients with first-episode treatment-naive depression. Nevertheless, information is scarce regarding the changes in network homogeneity (NH) found in the DMN of these patients. Therefore, in this study, we explored the NH of the DMN in patients with first-episode treatment-naive depression. Methods: The study included 66 patients and 74 control participants matched by age, gender, educational level and health status who underwent resting-state functional magnetic resonance imaging (rs-fMRI) and the attentional network test (ANT). To assess data, the study utilizes NH and independent component analysis (ICA). Additionally, Spearman's rank correlation analysis is performed among significantly abnormal NH in depression patients and clinical measurements and executive control reaction time (ECRT). Results: In comparison with the control group, patients with first-episode treatment-naive depression showed lower NH in the bilateral angular gyrus (AG), as well as increased NH in the bilateral precuneus (PCu) and posterior cingulate cortex (PCC). Likewise, patients with first-episode treatment-naive depression had longer ECRT. No significant relation was found between abnormal NH values and the measured clinical variables. Conclusions: Our results suggest patients with first-episode treatment-naive depression have abnormal NH values in the DMN. This highlights the significance of DMN in the pathophysiology of cognitive problems in depression. Our study also found alterations in executive functions in patients with first-episode treatment-naive depression.

PMID: 30618871 [PubMed]

Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis.

Thu, 01/10/2019 - 00:35
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Multivariate Deep Learning Classification of Alzheimer's Disease Based on Hierarchical Partner Matching Independent Component Analysis.

Front Aging Neurosci. 2018;10:417

Authors: Qiao J, Lv Y, Cao C, Wang Z, Li A

Abstract
Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer's disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.

PMID: 30618723 [PubMed]

Dissociable Effects of Aging on Salience Subnetwork Connectivity Mediate Age-Related Changes in Executive Function and Affect.

Thu, 01/10/2019 - 00:35
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Dissociable Effects of Aging on Salience Subnetwork Connectivity Mediate Age-Related Changes in Executive Function and Affect.

Front Aging Neurosci. 2018;10:410

Authors: Touroutoglou A, Zhang J, Andreano JM, Dickerson BC, Barrett LF

Abstract
Aging is associated with both changes in affective experience and attention. An intrinsic brain network subserving these functions, the salience network, has not shown clear evidence of a corresponding age-related change. We propose a solution to this discrepancy: that aging differentially affects the connectivity of two dissociated subsystems of the salience network identified in our prior research (Touroutoglou et al., 2012). We examined the age-related changes in intrinsic connectivity between a dorsal and a ventral salience subsystem in a sample of 111 participants ranging in age from 18 years to 81 years old. We predicted that connectivity within the ventral subsystem is relatively preserved with age, while connectivity in the dorsal subsystem declines. Our findings showed that the connectivity within the ventral subsystem was not only preserved but it actually increased with age, whereas the connectivity within the dorsal subsystem decreased with age. Furthermore, age-related increase in arousal experience was partially mediated by age-related increases in ventral salience subsystem, whereas age-related decline in executive function was fully mediated by age-related decreases in dorsal salience subsystem connectivity. These findings explain previously conflicting results on age-related changes in the salience network, and suggest a mechanism for relatively preserved affective function in the elderly.

PMID: 30618717 [PubMed]

Language Network Function in Young Children Born Very Preterm.

Thu, 01/10/2019 - 00:35
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Language Network Function in Young Children Born Very Preterm.

Front Hum Neurosci. 2018;12:512

Authors: Choi EJ, Vandewouw MM, Young JM, Taylor MJ

Abstract
Language deficits are reported in preterm born children across development. Recent neuroimaging studies have found functional alterations in large-scale brain networks underlying these language deficits, but the early childhood development of the language network has not been investigated. Here, we compared intrinsic language network connectivity in 4-year-old children born VPT and term-born controls, using defined language regions (Broca's area, Wernicke's areas, and their homologues in the right hemisphere). Resting-state functional magnetic resonance imaging (fMRI) was obtained, and the group differences in whole-brain connectivity were examined from each seed as well as correlations with language outcomes. We found significantly decreased functional connectivity in almost all language regions in children born VPT compared to their term controls. Notably, Broca's area homologue in the right hemisphere emerged as a functional hub of decreased connectivity in VPT group, specifically to bilateral inferior frontal and supramarginal gyri; connectivity strength between Broca's area homologue with the right supramarginal and the left inferior frontal gyri was associated with better language outcomes at 4 years of age. Wernicke's area and its homologue also showed decreased inter-hemispheric connections to bilateral supramarginal gyri in the VPT group. Decreased intra- and inter-hemispheric connectivity among primary language regions suggests immature and altered function in the language network in children born VPT.

PMID: 30618688 [PubMed]

Neural Correlates of Non-clinical Internet Use in the Motivation Network and Its Modulation by Subclinical Autistic Traits.

Thu, 01/10/2019 - 00:35
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Neural Correlates of Non-clinical Internet Use in the Motivation Network and Its Modulation by Subclinical Autistic Traits.

Front Hum Neurosci. 2018;12:493

Authors: Fujiwara H, Yoshimura S, Kobayashi K, Ueno T, Oishi N, Murai T

Abstract
Background: Increasing evidence regarding the neural correlates of excessive or pathological internet use (IU) has accumulated in recent years, and comorbidity with depression and autism has been reported in multiple studies. However, psychological and neural correlates of non-clinical IU in healthy individuals remain unclear. Objectives: The aim of the current study was to investigate the relationships between non-clinical IU and functional connectivity (FC), focusing on the brain's motivation network. We sought to clarify the influence of depression and autistic traits on these relationships in healthy individuals. Methods: Resting-state functional magnetic resonance imaging (fMRI) was performed in 119 healthy volunteers. IU, depression, and autistic traits were assessed using the Generalized Problematic Internet Use Scale 2 (GPIUS2), Beck Depression Inventory-II (BDI-II), and the autism spectrum quotient (AQ) scale, respectively. Correlational analyses were performed using CONN-software within the motivation-related network, which consisted of 22 brain regions defined by a previous response-conflict task-based fMRI study with a reward cue. We also performed mediation analyses via the bootstrap method. Results: Total GPIUS2 scores were positively correlated with FC between the (a) left middle frontal gyrus (MFG) and bilateral medial prefrontal cortex; (b) left MFG and right supplementary motor area (SMA); (c) left MFG and right anterior insula, and (d) right MFG and right insula. The "Mood Regulation" subscale of the GPIUS2 was positively correlated with FC between left MFG and right SMA. The "Deficient Self-Regulation" subscale was positively correlated with FC between right MFG and right anterior insula (statistical thresholds, FDR < 0.05). Among these significant correlations, those between GPIUS2 (total and "Mood Regulation" subscale) scores and FC became stronger after controlling for AQ scores (total and "Attention Switching" subscale), indicating significant mediation by AQ (95% CI < 0.05). In contrast, BDI-II had no mediating effect. Conclusion: Positive correlations between IU and FC in the motivation network may indicate health-promoting effects of non-clinical IU. However, this favorable association is attenuated in individuals with subclinical autistic traits, suggesting the importance of a personalized educational approach for these individuals in terms of adequate IU.

PMID: 30618678 [PubMed]

Altered Brain Structure and Functional Connectivity of Primary Visual Cortex in Optic Neuritis.

Thu, 01/10/2019 - 00:35
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Altered Brain Structure and Functional Connectivity of Primary Visual Cortex in Optic Neuritis.

Front Hum Neurosci. 2018;12:473

Authors: Huang J, Duan Y, Liu S, Liang P, Ren Z, Gao Y, Liu Y, Zhang X, Lu J, Li K

Abstract
Previous studies have revealed brain adaptations to injury that occurs in optic neuritis (ON); however, the mechanisms underlying the functional connectivity (FC) and gray matter volume (GMV) changes in ON have not been clarified. Here, 51 single attack ON patients and 45 recurrent attacks ON patients were examined using structural MRI and resting-state functional MRI (RS-fMRI), and compared to 49 age- and gender-matched healthy controls (HC). FC analysis with a seed in primary visual cortex (V1 area) was used to assess the differences among three groups. Whole brain GMV was assessed using voxel-based morphometry (VBM). Correlation analyses were performed between FC results, structural MRI and clinical variables. We found positive correlations between the Paced Auditory Serial Addition Test (PASAT) score and FC in V1 area with bilateral middle frontal gyrus. Disease duration is significantly negatively related to FC in V1 area with the left inferior parietal lobule. Compared to the HC, single attack ON patients were found to have decreased FC values in the frontal, temporal lobes, right inferior occipital gyrus, right insula, right inferior parietal lobule, and significant increased FC values in the left thalamus. Recurrent attacks ON patients had the same pattern with single attack ON. No significant differences were found in brain GMV among three groups. This study provides the imaging evidence that impairment and compensation of V1 area connectivity coexist in ON patients, and provides important insights into the underlying neural mechanisms of ON.

PMID: 30618673 [PubMed]

Multi-Granularity Whole-Brain Segmentation Based Functional Network Analysis Using Resting-State fMRI.

Thu, 01/10/2019 - 00:35
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Multi-Granularity Whole-Brain Segmentation Based Functional Network Analysis Using Resting-State fMRI.

Front Neurosci. 2018;12:942

Authors: Gong Y, Wu H, Li J, Wang N, Liu H, Tang X

Abstract
In this work, we systematically analyzed the effects of various nodal definitions, as determined by a multi-granularity whole-brain segmentation scheme, upon the topological architecture of the human brain functional network using the resting-state functional magnetic resonance imaging data of 19 healthy, young subjects. A number of functional networks were created with their nodes defined according to two types of anatomical definitions (Type I and Type II) each of which consists of five granularity levels of whole brain segmentations with each level linked through ontology-based, hierarchical, structural relationships. Topological properties were computed for each network and then compared across levels within the same segmentation type as well as between Type I and Type II. Certain network architecture patterns were observed in our study: (1) As the granularity changes, the absolute values of each node's nodal degree and nodal betweenness change accordingly but the relative values within a single network do not change considerably; (2) The average nodal degree is generally affected by the sparsity level of the network whereas the other topological properties are more specifically affected by the nodal definitions; (3) Within the same ontology relationship type, as the granularity decreases, the network becomes more efficient at information propagation; (4) The small-worldness that we observe is an intrinsic property of the brain's resting-state functional network, independent of the ontology type and the granularity level. Furthermore, we validated the aforementioned conclusions and measured the reproducibility of this multi-granularity network analysis pipeline using another dataset of 49 healthy young subjects that had been scanned twice.

PMID: 30618571 [PubMed]

Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures.

Thu, 01/10/2019 - 00:35
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Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures.

Hum Brain Mapp. 2019 Jan 07;:

Authors: Espinoza FA, Liu J, Ciarochi J, Turner JA, Vergara VM, Caprihan A, Misiura M, Johnson HJ, Long JD, Bockholt JH, Paulsen JS, Calhoun VD

Abstract
Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.

PMID: 30618191 [PubMed - as supplied by publisher]

Training endogenous pain modulation: a preliminary investigation of neural adaptation following repeated exposure to clinically-relevant pain.

Thu, 01/10/2019 - 00:35
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Training endogenous pain modulation: a preliminary investigation of neural adaptation following repeated exposure to clinically-relevant pain.

Brain Imaging Behav. 2019 Jan 08;:

Authors: Sevel L, Boissoneault J, Alappattu M, Bishop M, Robinson M

Abstract
Analgesic treatments that aim to eliminate pain display marginal success in relieving chronic pain and may increase pain vulnerability. Repeated exposure to pain may result in increased pain modulation via engagement of anti-nociceptive brain regions. It was hypothesized that repeated exposure to delayed onset muscle soreness (DOMS) would result in increased pain modulatory capacity (PMC) via functional neural adaptation. 23 healthy participants completed Baseline and Follow Up resting-state fMRI and quantitative sensory testing (QST) visits 40 days apart. Participants were randomized to two groups: A Repeated DOMS Group (RD Group) that received four, weekly DOMS inductions and a Control Group that received one baseline induction. Daily pain ratings were collected for seven days post-induction, as were quantitative sensory testing (QST) metrics at baseline and Follow Up. Regional functional connectivity (FC) was estimated among areas involved in pain modulation. Seed and network FC was estimated among areas involved in pain modulation and sensory processing. Changes in FC were compared between groups. The RD Group displayed significant reductions in post-DOMS pain ratings and significant changes in thermal QST measures. RD Group participants displayed greater adaptation in nucleus accumbens-medial prefrontal cortex (NAc-mPFC) FC and in sensorimotor network (SMN) connectivity with the dorsomedial, ventromedial, and rostromedial prefrontal cortices. Changes in SMN-PFC connectivity correlated with reductions in post-DOMS affective distress. Results suggest that repeated exposure to clinically-relevant pain results in adaptations among brain regions involved in pain modulation. Repeated exposure to clinically-relevant pain may serve as a mechanism to increase PMC via inhibition of emotional valuation of painful stimuli.

PMID: 30617786 [PubMed - as supplied by publisher]

Chemotherapy-induced functional changes of the default mode network in patients with lung cancer.

Thu, 01/10/2019 - 00:35
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Chemotherapy-induced functional changes of the default mode network in patients with lung cancer.

Brain Imaging Behav. 2019 Jan 07;:

Authors: Zhang Y, Chen YC, Hu L, You J, Gu W, Li Q, Chen H, Mao C, Yin X

Abstract
Previous studies have demonstrated that cognitive impairment is associated with neurophysiological changes in lung cancer following chemotherapy. This study aimed to investigate the intrinsic functional connectivity (FC) pattern within the default mode network (DMN) and its associations with cognitive impairment in patients with lung cancer revealed by resting-state functional magnetic resonance imaging (fMRI). Resting-state fMRI scans were acquired from 21 post-chemotherapy and 27 non-chemotherapy lung cancer patients and 30 healthy controls. All groups were age, gender and education-matched. The posterior cingulate cortex (PCC) was chosen as the seed region to detect the FC patterns and then determine whether these changes were related with specific cognitive performance. Compared with non-chemotherapy lung cancer patients, chemotherapy patients revealed decreased FC between the PCC and the right anterior cingulate cortex (ACC), left inferior parietal lobule (IPL), and left medial prefrontal cortex (mPFC), as well as increased FC with the left postcentral gyrus (PoCG). Relative to healthy controls, post-chemotherapy patients exhibited reduced FC between the PCC and the left ACC and left temporal lobe, as well as increased FC with the right PoCG. Moreover, the decreased FC of the PCC to bilateral ACC in post-chemotherapy patients was positively associated with reduced MoCA scores (left: r = 0.529, p = 0.029; right: r = 0.577, p = 0.015). The current study mainly demonstrated reduced resting-state FC pattern within the DMN regions that was linked with impaired cognitive function in lung cancer patients after chemotherapy. These findings illustrated the potential role of the DMN in lung cancer patients that will provide novel insight into the underlying neuropathological mechanisms in chemotherapy-induced cognitive impairment.

PMID: 30617783 [PubMed - as supplied by publisher]

Disruption of functional connectivity among subcortical arousal system and cortical networks in temporal lobe epilepsy.

Thu, 01/10/2019 - 00:35
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Disruption of functional connectivity among subcortical arousal system and cortical networks in temporal lobe epilepsy.

Brain Imaging Behav. 2019 Jan 07;:

Authors: Li R, Hu C, Wang L, Liu D, Liu D, Liao W, Xiao B, Chen H, Feng L

Abstract
Growing evidence has demonstrated widespread brain network alterations in temporal lobe epilepsy (TLE). However, the relatively accurate portrait of the subcortical-cortical relationship for impaired consciousness in TLE remains unclear. We proposed that consciousness-impairing seizures may invade subcortical arousal system and corresponding cortical regions, resulting in functional abnormalities and information flow disturbances between subcortical and cortical networks. We performed resting-state fMRI in 26 patients with TLE and 30 matched healthy controls. All included patients were diagnosed with impaired awareness during focal temporal lobe seizures. Functional connectivity density was adopted to determine whether local or distant network alterations occurred in TLE, and Granger causality analysis (GCA) was utilized to assess the direction and magnitude of causal influence among these altered brain networks further. Patients showed increased local functional connectivity in several arousal structures, such as the midbrain, thalamus, and cortical regions including bilateral prefrontal cortex (PFC), left superior temporal pole, left posterior insula, and cerebellum (P < 0.05, FDR corrected). GCA analysis revealed that the casual effects among these regions in patients were significantly sparser than those in controls (P < 0.05, uncorrected), including decreased excitatory and inhibitory effects among the midbrain, thalamus and PFC, and decreased inhibitory effect from the cerebellum to PFC. These findings suggested that consciousness-impairing seizures in TLE are associated with functional alterations and disruption of information process between the subcortical arousal system and cortical network. Understanding the functional networks and innervation pathway involved in TLE can provide insights into the mechanism underlying seizure-related loss of consciousness.

PMID: 30617780 [PubMed - as supplied by publisher]

Intrinsic thalamocortical connectivity varies in the age of onset subtypes in major depressive disorder.

Tue, 01/08/2019 - 21:35
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Intrinsic thalamocortical connectivity varies in the age of onset subtypes in major depressive disorder.

Neuropsychiatr Dis Treat. 2019;15:75-82

Authors: Brown EC, Clark DL, Hassel S, MacQueen G, Ramasubbu R

Abstract
Background: Differences in the thalamocortical system have been shown in patients with major depressive disorder (MDD). Given prior evidence of phenotypic heterogeneity by the age of onset in MDD, we examined whether differences in thalamocortical connectivity could identify biological subtypes of MDD defined by the age of illness onset.
Methods: A total of 94 subjects including 20 early-onset (EO) MDD (onset, 18 years), 34 adult-onset (AO) MDD, and 40 healthy controls (HCs) underwent resting-state functional MRI. Blood-oxygen-level-dependent time courses were extracted from six cortical regions of interest (ROIs) consisting of frontal, temporal, parietal, and occipital lobes and precentral and postcentral gyri. Each ROI's time course was then correlated with each voxel in thalamus, while covarying out signal from every other ROI.
Results: The analysis of variance results showed significant main effects of group in frontal and temporal connectivity with thalamus. Group contrasts showed a right fronto-thalamic hypo-connectivity only in AO-MDD, but not in EO-MDD, when compared to HCs. However, direct comparison between EO-MDD and AO-MDD showed no differences. Furthermore, there was a right temporal-thalamic hyperconnectivity in both EO-MDD and AO-MDD patients relative to HCs. These results were not accounted for by sex, age, or illness burden.
Conclusion: The age of illness onset may be a source of heterogeneity in fronto-thalamic intrinsic connectivity in MDD.

PMID: 30613149 [PubMed]