New resting-state fMRI related studies at PubMed

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Resting-state functional magnetic resonance study of primary open-angle glaucoma based on voxelwise brain network degree centrality.

Fri, 09/27/2019 - 10:20

Resting-state functional magnetic resonance study of primary open-angle glaucoma based on voxelwise brain network degree centrality.

Neurosci Lett. 2019 Sep 23;:134500

Authors: Zhang Q, Shu Y, Li X, Xiong C, Li P, Pang Y, Ye W, Yang L, Zeng X, Zhang X

Abstract
OBJECTIVE: To investigate alterations in the functional brain networks of patients with primary open-angle glaucoma (POAG) by using the resting-state functional magnetic resonance imaging (fMRI) voxelwise degree centrality (DC) method.
MATERIALS AND METHODS: Thirteen patients with POAG and thirteen healthy subjects were recruited for this study, and each participant underwent a rs-fMRI scan. The voxelwise DC method was used to assess the features of spontaneous brain activity. The differences in the mean DC across brain regions between the POAG group and the healthy control group were analyzed, and the correlations between the DC values of altered brain regions and various clinical ophthalmic parameters were analyzed in the POAG group.
RESULTS: Compared with healthy controls, patients with POAG exhibited significantly decreased DC values of the left superior frontal gyrus and the left postcentral gyrus as well as significantly increased DC values of the left superior occipital gyrus. In POAG patients, the DC value of the left superior occipital gyrus was significantly positively correlated with age (r = 0.571, P = 0.042) and negatively correlated with the intraocular pressure of the right eye (r=-0.625, P = 0.022). The DC value of the left superior frontal gyrus was significantly positively correlated with the right eye average cup-to-disc ratio (r = 0.683, P = 0.010), vertical cup-to-disc ratio (r = 0.779, P = 0.002), and pattern standard deviation (r = 0.567, P = 0.043).
CONCLUSION: The results showed that altered DC values in three brain regions may reflect the underlying pathological mechanisms of POAG. Decreased DC values of the left superior occipital gyrus could be useful imaging markers for determining the extent of brain damage in POAG patients compared to healthy subjects.

PMID: 31557522 [PubMed - as supplied by publisher]

Psychological Resilience Enhances the Orbitofrontal Network in the Elderly With Mild Cognitive Impairment.

Fri, 09/27/2019 - 10:20
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Psychological Resilience Enhances the Orbitofrontal Network in the Elderly With Mild Cognitive Impairment.

Front Psychiatry. 2019;10:615

Authors: Son SJ, Park B, Choi JW, Roh HW, Kim NR, Sin JE, Kim H, Lim HK, Hong CH

Abstract
Background: It has been suggested that maintaining the efficient organization of the brain's functional connectivity (FC) supports neuroflexibility under neurogenerative stress. This study examined psychological resilience-related FC in 112 older adults with mild cognitive impairment (MCI). Methods: Using a resting-state functional magnetic resonance imaging (fMRI) approach, we investigated reorganization of the orbitofrontal gyrus (OFG)/amygdala (AMG)/hippocampus (HP)/parahippocampal gyrus (PHG) FC according to the different levels of resilience scale. Results: Compared with the low resilient group, the high resilient group had greater connectivity strengths between the left inferior OFG and right superior OFG (P < 0.05, Bonferroni corrected), between the right inferior OFG and left PHG (P < 0.05, Bonferroni corrected), and between the right middle OFG and left PHG (false discovery rate < 0.05). Conclusion: Psychological resilience may be associated with enhancement of the orbitofrontal network in the elderly with MCI.

PMID: 31555158 [PubMed]

Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.

Fri, 09/27/2019 - 10:20
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Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.

Front Psychiatry. 2019;10:572

Authors: Zhang T, Zhao Z, Zhang C, Zhang J, Jin Z, Li L

Abstract
Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01-0.08 Hz; slow-4: 0.027-0.08 Hz; slow-5: 0.01-0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI-EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer's disease at an early stage.

PMID: 31555157 [PubMed]

The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain.

Fri, 09/27/2019 - 10:20
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The Effect of Aging on Resting State Connectivity of Predefined Networks in the Brain.

Front Aging Neurosci. 2019;11:234

Authors: Varangis E, Habeck CG, Razlighi QR, Stern Y

Abstract
Recent studies have found a deleterious effect of age on a wide variety of measures of functional connectivity, and some hints at a relationship between connectivity at rest and cognitive functioning. However, few studies have combined multiple functional connectivity methods, or examined them over a wide range of adult ages, to try to uncover which metrics and networks seem to be particularly sensitive to age-related decline across the adult lifespan. The present study utilized multiple resting state functional connectivity methods in a sample of adults from 20-80 years old to gain a more complete understanding of the effect of aging on network function and integrity. Whole-brain results showed that aging results in weakening average within-network connectivity, lower system segregation and local efficiency, and higher participation coefficient. Network-level results suggested that nearly every primary sensory and cognitive network faces some degree of age-related decline, including reduced within-network connectivity, higher network-based participation coefficient, and reduced network-level local efficiency. Further, some of these connectivity metrics showed relationships with cognitive performance. Thus, these results suggest that a multi-method analysis of functional connectivity data may be critical to capture the full effect of aging on the health of brain networks.

PMID: 31555124 [PubMed]

Brain intrinsic network connectivity in individuals with frequent tanning behavior.

Fri, 09/27/2019 - 10:20
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Brain intrinsic network connectivity in individuals with frequent tanning behavior.

Am J Drug Alcohol Abuse. 2018;44(6):668-677

Authors: Ketcherside A, Filbey FM, Aubert PM, Seibyl JP, Price JL, Adinoff B

Abstract
BACKGROUND: Emergent studies suggest a bidirectional relationship between brain functioning and the skin. This neurocutaneous connection may be responsible for the reward response to tanning and, thus, may contribute to excessive tanning behavior. To date, however, this association has not yet been examined.
OBJECTIVES: To explore whether intrinsic brain functional connectivity within the default mode network (DMN) is related to indoor tanning behavior.
METHODS: Resting state functional connectivity (rsFC) was obtained in twenty adults (16 females) with a history of indoor tanning. Using a seed-based [(posterior cingulate cortex (PCC)] approach, the relationship between tanning severity and FC strength was assessed. Tanning severity was measured with symptom count from the Structured Clinical Interview for Tanning Abuse and Dependence (SITAD) and tanning intensity (lifetime indoor tanning episodes/years tanning).
RESULTS: rsFC strength between the PCC and other DMN regions (left globus pallidus, left medial frontal gyrus, left superior frontal gyrus) is positively correlated with tanning symptom count. rsFC strength between the PCC and salience network regions (right anterior cingulate cortex, left inferior parietal lobe, left inferior temporal gyrus) is correlated with tanning intensity.
CONCLUSION: Greater connectivity between tanning severity and DMN and salience network connectivity suggests that heightened self-awareness of salient stimuli may be a mechanism that underlies frequent tanning behavior. These findings add to the growing evidence of brain-skin connection and reflect dysregulation in the reward processing networks in those with frequent tanning.

PMID: 29714526 [PubMed - indexed for MEDLINE]

Amygdala sub-regional functional connectivity predicts anxiety in children with reading disorder.

Fri, 09/27/2019 - 10:20
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Amygdala sub-regional functional connectivity predicts anxiety in children with reading disorder.

Dev Sci. 2018 09;21(5):e12631

Authors: Davis K, Margolis AE, Thomas L, Huo Z, Marsh R

Abstract
Pediatric reading disorder (RD) is associated with an increased risk of anxiety symptoms, yet understudied are the neurobiological factors that might underlie anxiety in children with RD. Given the role of the amygdala in anxiety, we assessed resting state functional connectivity of amygdalar subregions in children with RD to identify functional correlates of anxiety and reading impairment. We collected resting state functional MRI data from 22 children with RD and 21 typically developing (TD) children, ages 7 to 13 years. We assessed group differences in resting state functional connectivity (RSFC) from amygdalar subregions. Associations of amygdalar RSFC and volume with reading impairment, reading fluency scores, and anxiety symptoms were explored. Relative to TD children, those with RD showed increased RSFC from amygdalar nuclei to medial prefrontal cortex. Across all subjects, RSFC from right centromedial amygdala to left medial prefrontal cortex positively predicted both reading impairment and self-reported anxiety, and anxiety mediated the relationship between RSFC and reading impairment. These findings are consistent with amygdalar functional abnormalities in pediatric anxiety disorders, suggesting a common neurobiological mechanism underlying anxiety and reading impairment in children. Thus, aberrant patterns of RSFC from amygdalar subregions may serve as potential targets for the treatment of anxiety symptoms that typically co-occur with RD. Our dimensional approach to studying anxiety in RD revealed how amygdalar connectivity underlies anxiety and reading impairment across a continuum from normal to abnormal.

PMID: 29143475 [PubMed - indexed for MEDLINE]

Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

Thu, 09/26/2019 - 15:40
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Alteration of functional connectivity in patients with Alzheimer's disease revealed by resting-state functional magnetic resonance imaging.

Neural Regen Res. 2020 Feb;15(2):285-292

Authors: Zhao J, Du YH, Ding XT, Wang XH, Men GZ

Abstract
The main symptom of patients with Alzheimer's disease is cognitive dysfunction. Alzheimer's disease is mainly diagnosed based on changes in brain structure. Functional connectivity reflects the synchrony of functional activities between non-adjacent brain regions, and changes in functional connectivity appear earlier than those in brain structure. In this study, we detected resting-state functional connectivity changes in patients with Alzheimer's disease to provide reference evidence for disease prediction. Functional magnetic resonance imaging data from patients with Alzheimer's disease were used to show whether particular white and gray matter areas had certain functional connectivity patterns and if these patterns changed with disease severity. In nine white and corresponding gray matter regions, correlations of normal cognition, early mild cognitive impairment, and late mild cognitive impairment with blood oxygen level-dependent signal time series were detected. Average correlation coefficient analysis indicated functional connectivity patterns between white and gray matter in the resting state of patients with Alzheimer's disease. Functional connectivity pattern variation correlated with disease severity, with some regions having relatively strong or weak correlations. We found that the correlation coefficients of five regions were 0.3-0.5 in patients with normal cognition and 0-0.2 in those developing Alzheimer's disease. Moreover, in the other four regions, the range increased to 0.45-0.7 with increasing cognitive impairment. In some white and gray matter areas, there were specific connectivity patterns. Changes in regional white and gray matter connectivity patterns may be used to predict Alzheimer's disease; however, detailed information on specific connectivity patterns is needed. All study data were obtained from the Alzheimer's Disease Neuroimaging Initiative Library of the Image and Data Archive Database.

PMID: 31552901 [PubMed]

Effects of Naltrexone on Large-Scale Network Interactions in Methamphetamine Use Disorder.

Thu, 09/26/2019 - 15:40
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Effects of Naltrexone on Large-Scale Network Interactions in Methamphetamine Use Disorder.

Front Psychiatry. 2019;10:603

Authors: Kohno M, Morales AM, Dennis LE, McCready H, Hoffman WF, Korthuis PT

Abstract
Naltrexone attenuates craving, and the subjective effects of methamphetamine and extended-release naltrexone (XR-NTX) reduces functional connectivity between regions of the striatum and limbic cortex. Naltrexone modulates neural activity at dopaminergic synapses; however, it is unclear whether naltrexone has an effect on large-scale brain networks. Functional networks interact to coordinate behavior, and as substance-use disorders are associated with an imbalance between reward and cognitive control networks, treatment approaches that target interactive brain systems underlying addiction may be a useful adjunct for behavioral therapies. The objective of this study was to examine the effect of XR-NTX on large-scale brain networks and to determine whether changes in network relationships attenuate drug use, craving, and addiction severity. Thirty-nine participants in or seeking treatment for methamphetamine-use disorder were enrolled in a clinical trial of XR-NTX between May 2013 and March 2015 (Clinicaltrials.gov NCT01822132). Functional magnetic resonance imaging (fMRI) and questionnaires were conducted before and after double-blinded randomization to a 4-week injection of XR-NTX or placebo. In the XR-NTX group, methamphetamine use was reduced along with a decrease in the coupling between executive control (ECN) and default mode (DMN) networks. As decoupling of ECN and DMN networks was associated with change in the severity of dependence, the results suggest that XR-NTX may modulate and enhance ECN attentional resources and suppress DMN self-referential and emotional processing. This study identifies the effect of naltrexone on changes in the intrinsic functional coupling of large-scale brain networks and provides a more systematic understanding of how large-scale networks interact to promote behavioral change in methamphetamine-use disorder.

PMID: 31551824 [PubMed]

Metastable Resting State Brain Dynamics.

Thu, 09/26/2019 - 15:40
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Metastable Resting State Brain Dynamics.

Front Comput Neurosci. 2019;13:62

Authors: Beim Graben P, Jimenez-Marin A, Diez I, Cortes JM, Desroches M, Rodrigues S

Abstract
Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation-BOLD-signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.

PMID: 31551744 [PubMed]

Intrinsic Frequencies of the Resting-State fMRI Signal: The Frequency Dependence of Functional Connectivity and the Effect of Mode Mixing.

Thu, 09/26/2019 - 15:40
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Intrinsic Frequencies of the Resting-State fMRI Signal: The Frequency Dependence of Functional Connectivity and the Effect of Mode Mixing.

Front Neurosci. 2019;13:900

Authors: Yuen NH, Osachoff N, Chen JJ

Abstract
The frequency characteristics of the resting-state BOLD fMRI (rs-fMRI) signal are of increasing scientific interest, as we discover more frequency-specific biological interpretations. In this work, we use variational mode decomposition (VMD) to precisely decompose the rs-fMRI time series into its intrinsic mode functions (IMFs) in a data-driven manner. The accuracy of the VMD decomposition of constituent IMFs is verified through simulations, with higher reconstruction accuracy and much-reduced mode mixing relative to previous methods. Furthermore, we examine the relative contribution of the VMD-derived modes (frequencies) to the rs-fMRI signal as well as functional connectivity measurements. Our primary findings are: (1) The rs-fMRI signal within the 0.01-0.25 Hz range can be consistently characterized by four intrinsic frequency clusters, centered at 0.028 Hz (IMF4), 0.080 Hz (IMF3), 0.15 Hz (IMF2) and 0.22 Hz (IMF1); (2) these frequency clusters were highly reproducible, and independent of rs-fMRI data sampling rate; (3) not all frequencies were associated with equivalent network topology, in contrast to previous findings. In fact, while IMF4 is most likely associated with physiological fluctuations due to respiration and pulse, IMF3 is most likely associated with metabolic processes, and IMF2 with vasomotor activity. Both IMF3 and IMF4 could produce the brain-network topology typically observed in fMRI, whereas IMF1 and IMF2 could not. These findings provide initial evidence of feasibility in decomposing the rs-fMRI signal into its intrinsic oscillatory frequencies in a reproducible manner.

PMID: 31551676 [PubMed]

Functional connectivity of the default mode network is associated with prospection in schizophrenia patients and individuals with social anhedonia.

Thu, 09/26/2019 - 15:40
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Functional connectivity of the default mode network is associated with prospection in schizophrenia patients and individuals with social anhedonia.

Prog Neuropsychopharmacol Biol Psychiatry. 2019 06 08;92:412-420

Authors: Yang ZY, Zhang RT, Li Y, Wang Y, Wang YM, Wang SK, Öngür D, Cheung EFC, Chan RCK

Abstract
BACKGROUND: Prospection, which is closely related to negative symptoms in patients with schizophrenia, is mainly associated with the Default Mode Network (DMN). Although abnormalities of the DMN have been reported in schizophrenia patients and at-risk individuals, little is known about the relationship between functional connectivity of the DMN and prospection in these clinical and subclinical populations.
METHOD: Study 1 recruited 40 schizophrenia patients and 29 healthy controls, while 31 individuals with social anhedonia (SocAhn) and 28 controls participated in Study 2. Participants in both studies were asked to complete a prospection task and underwent resting-state functional MRI scans. Eleven regions of interest (ROIs) in the DMN were defined. Functional connectivity between each ROI and whole brain voxels were calculated and compared between groups (schizophrenia vs. control and SocAhn vs. control). Correlation analysis was conducted between altered functional connectivity and prospection variables in the schizophrenia and SocAhn groups.
RESULTS: Schizophrenia patients showed both hyper-connectivity and hypo-connectivity at the medial temporal lobe (MTL) subsystem of the DMN. Decreased connectivity between the ventral medial prefrontal cortex (vMPFC) and the right superior temporal gyrus (rSTG) was correlated with poor thought/emotion details in prospection. In individuals with SocAhn, decreased connectivity between the retrosplenial cortex (Rsp), a region of the MTL subsystem, and the right fusiform gyrus, was found and this was correlated with their prospection performance.
CONCLUSION: Altered functional connectivity of the key nodes of the MTL subsystem was found in both patients with schizophrenia and individuals with SocAhn. Moreover, hypo-connectivity of the vMPFC was found to be correlated with prospection impairments in schizophrenia patients.

PMID: 30822447 [PubMed - indexed for MEDLINE]

Cold Water Pressor Test Differentially Modulates Functional Network Connectivity in Fibromyalgia Patients Compared with Healthy Controls.

Thu, 09/26/2019 - 15:40
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Cold Water Pressor Test Differentially Modulates Functional Network Connectivity in Fibromyalgia Patients Compared with Healthy Controls.

Conf Proc IEEE Eng Med Biol Soc. 2018 07;2018:578-582

Authors: Jarrahi B, Martucci KT, Nilakantan AS, Mackey S

Abstract
Fibromyalgia is a multifaceted chronic pain condition of unknown etiology. Conditioned pain modulation (CPM) such as cold water pressor test of the foot, is widely documented as being disrupted in patients with fibromyalgia. To date, the mechanisms underlying such dysregulation of the descending control of pain in fibromyalgia remain poorly understood. In this study, we used ICA-based network analysis to comprehensively compare differences in functional network connectivity among relevant (nonartifactual) intrinsic connectivity brain networks during the resting state before and after cold pressor test in patients with fibromyalgia and healthy controls. The results revealed significant differences in functional connectivity between the two groups that included the networks that integrate cognitive control and attention systems with memory, emotion and brainstem regions. Specifically, functional connectivity involving central executive network was absent in patients with fibromyalgia compared with controls. Patients showed significant functional connectivity changes involving subcortical and brainstem networks with the sensorimotor and dorsal attention networks. Accordingly, aberrant CPM in patients with fibromyalgia may be due to the differences in functional connectivity involving the subcortical/brainstem regions, and is facilitated by the recruitment of the dorsal attention network in lieu of the central executive network. Future research replicating the present findings with larger sample size can shed more light on neurobiology of endogenous pain modulation in fibromyalgia.

PMID: 30440463 [PubMed - indexed for MEDLINE]

Altered task-related modulation of long-range connectivity in children with autism.

Thu, 09/26/2019 - 15:40
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Altered task-related modulation of long-range connectivity in children with autism.

Autism Res. 2018 02;11(2):245-257

Authors: Pillai AS, McAuliffe D, Lakshmanan BM, Mostofsky SH, Crone NE, Ewen JB

Abstract
Functional connectivity differences between children with autism spectrum disorder (ASD) and typically developing children have been described in multiple datasets. However, few studies examine the task-related changes in connectivity in disorder-relevant behavioral paradigms. In this paper, we examined the task-related changes in functional connectivity using EEG and a movement-based paradigm that has behavioral relevance to ASD. Resting-state studies motivated our hypothesis that children with ASD would show a decreased magnitude of functional connectivity during the performance of a motor-control task. Contrary to our initial hypothesis, however, we observed that task-related modulation of functional connectivity in children with ASD was in the direction opposite to that of TDs. The task-related connectivity changes were correlated with clinical symptom scores. Our results suggest that children with ASD may have differences in cortical segregation/integration during the performance of a task, and that part of the differences in connectivity modulation may serve as a compensatory mechanism. Autism Res 2018, 11: 245-257. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.
LAY SUMMARY: Decreased connectivity between brain regions is thought to cause the symptoms of autism. Because most of our knowledge comes from data in which children are at rest, we do not know how connectivity changes directly lead to autistic behaviors, such as impaired gestures. When typically developing children produced complex movements, connectivity decreased between brain regions. In children with autism, connectivity increased. It may be that behavior-related changes in brain connectivity are more important than absolute differences in connectivity in autism.

PMID: 28898569 [PubMed - indexed for MEDLINE]

Measurement of active motor threshold using a dynamometer during navigated transcranial magnetic stimulation in a postoperative brain tumor patient: technical note.

Wed, 09/25/2019 - 14:20

Measurement of active motor threshold using a dynamometer during navigated transcranial magnetic stimulation in a postoperative brain tumor patient: technical note.

World Neurosurg. 2019 Sep 21;:

Authors: Henrique da Costa Ferreira Pinto P, Nigri F, Caparelli-Daquer EM, Dutra do Souto AA, Miranda Chaves Christiani M

Abstract
BACKGROUND: Navigated transcranial magnetic stimulation (nTMS) is being used for different purposes in patients with brain tumors. However, the procedure requires a positive electrophysiological response. For patients with negative response in rest conditions, active motor threshold (AMT) may be used. However, sometimes it is difficult to obtain AMT measures due to inability of the patient to sustain steady muscle contraction. Herein, we describe a simple method by using a hand dynamometer to obtain AMT measures during nTMS session.
CASE DESCRIPTION: A 68-year-old woman underwent total removal of a right frontal lobe oligodendroglioma World Health Organization grade II, 15 years ago. Cranial magnetic resonance imaging during follow-up revealed local recurrence. In the postoperative period, she developed left upper limb paresis. A postoperative nTMS session was performed for motor electrophysiological evaluation. However, using the standard technique for AMT measurement, the patient was unable to perform sustained muscle contraction as required. A hand dynamometer was used. It allowed sustained muscle contraction for AMT measurement. A counter force for the index finger flexion, the hand support to stabilize hand joints and a numerical screen serving for both the examiner and the patient as a feedback parameter may explain the success obtained with this simple device.
CONCLUSION: Although more studies are necessary to validate the method, the hand dynamometer should be considered for patients unable to sustain muscle contraction during AMT measurement.

PMID: 31550542 [PubMed - as supplied by publisher]

Development of the functional connectivity of the frontoparietal mirror neuron network in preschool Children: An investigation under resting state.

Wed, 09/25/2019 - 14:20
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Development of the functional connectivity of the frontoparietal mirror neuron network in preschool Children: An investigation under resting state.

J Clin Neurosci. 2019 Sep 20;:

Authors: Dai J, Li C, Zhai H

Abstract
Previous task-related imaging studies in adults have demonstrated that there is a frontoparietal mirror neuron system (MNS) that preferentially engages in self-recognition. However, the development of the MNS during preschool (age 3-5 years) has not been thoroughly examined. In this study, we investigated the development of the MNS by examining the correlations in spontaneous fluctuations of the functional magnetic resonance imaging (fMRI) blood oxygen level-dependent (BOLD) signal in healthy, 3-5-year-old preschool children (n = 30, 15 in each group). Using a ROI-based (inferior frontal gyrus) functional connectivity analysis, we identified a right lateralized MNS during rest in both groups with a positive correlation between the inferior frontal gyrus and inferior parietal lobule. A significant increase in the functional connectivity of the MNS was observed in the older group. Our results suggest that the spontaneous functional connectivity of the MNS is shaped at as early as 3 years of age and undergoes age-related development within the preschool period.

PMID: 31548088 [PubMed - as supplied by publisher]

Axial variation of deoxyhemoglobin density as a source of the low-frequency time lag structure in blood oxygenation level-dependent signals.

Tue, 09/24/2019 - 13:40
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Axial variation of deoxyhemoglobin density as a source of the low-frequency time lag structure in blood oxygenation level-dependent signals.

PLoS One. 2019;14(9):e0222787

Authors: Aso T, Urayama S, Fukuyama H, Murai T

Abstract
Perfusion-related information is reportedly embedded in the low-frequency component of a blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal. The blood-propagation pattern through the cerebral vascular tree is detected as an interregional lag variation of spontaneous low-frequency oscillations (sLFOs). Mapping of this lag, or phase, has been implicitly treated as a projection of the vascular tree structure onto real space. While accumulating evidence supports the biological significance of this signal component, the physiological basis of the "perfusion lag structure," a requirement for an integrative resting-state fMRI-signal model, is lacking. In this study, we conducted analyses furthering the hypothesis that the sLFO is not only largely of systemic origin, but also essentially intrinsic to blood, and hence behaves as a virtual tracer. By summing the small fluctuations of instantaneous phase differences between adjacent vascular regions, a velocity response to respiratory challenges was detected. Regarding the relationship to neurovascular coupling, the removal of the whole lag structure, which can be considered as an optimized global-signal regression, resulted in a reduction of inter-individual variance while preserving the fMRI response. Examination of the T2* and S0, or non-BOLD, components of the fMRI signal revealed that the lag structure is deoxyhemoglobin dependent, while paradoxically presenting a signal-magnitude reduction in the venous side of the cerebral vasculature. These findings provide insight into the origin of BOLD sLFOs, suggesting that they are highly intrinsic to the circulating blood.

PMID: 31545839 [PubMed - in process]

Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI.

Tue, 09/24/2019 - 13:40
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Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI.

Front Neurol. 2019;10:904

Authors: Hojjati SH, Ebrahimzadeh A, Babajani-Feremi A

Abstract
Accurate prediction of the early stage of Alzheimer's disease (AD) is important but very challenging. The goal of this study was to utilize predictors for diagnosis conversion to AD based on integrating resting-state functional MRI (rs-fMRI) connectivity analysis and structural MRI (sMRI). We included 177 subjects in this study and aimed at identifying patients with mild cognitive impairment (MCI) who progress to AD, MCI converter (MCI-C), patients with MCI who do not progress to AD, MCI non-converter (MCI-NC), patients with AD, and healthy controls (HC). The graph theory was used to characterize different aspects of the rs-fMRI brain network by calculating measures of integration and segregation. The cortical and subcortical measurements, e.g., cortical thickness, were extracted from sMRI data. The rs-fMRI graph measures were combined with the sMRI measures to construct input features of a support vector machine (SVM) and classify different groups of subjects. Two feature selection algorithms [i.e., the discriminant correlation analysis (DCA) and sequential feature collection (SFC)] were used for feature reduction and selecting a subset of optimal features. Maximum accuracy of 67 and 56% for three-group ("AD, MCI-C, and MCI-NC" or "MCI-C, MCI-NC, and HC") and four-group ("AD, MCI-C, MCI-NC, and HC") classification, respectively, were obtained with the SFC feature selection algorithm. We also identified hub nodes in the rs-fMRI brain network which were associated with the early stage of AD. Our results demonstrated the potential of the proposed method based on integration of the functional and structural MRI for identification of the early stage of AD.

PMID: 31543860 [PubMed]

Brain Function Network and Young Adult Smokers: A Graph Theory Analysis Study.

Tue, 09/24/2019 - 13:40
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Brain Function Network and Young Adult Smokers: A Graph Theory Analysis Study.

Front Psychiatry. 2019;10:590

Authors: Tan Y, Chen J, Liao W, Qian Z

Abstract
Cigarette smoking is associated with abnormalities in the widespread inter-regional functional connectivity of the brain. However, few studies focused on the abnormalities in the topological organization of brain functional networks in young smokers. In the current study, resting-state functional magnetic resonance images were acquired from 30 young male smokers and 32 age-, gender-, and education-matched healthy male nonsmokers. A functional network was constructed by calculating the Pearson correlation coefficients among 246 subregions in the human Brainnetome Atlas. The topological parameters were compared between smokers and nonsmokers. The results showed that the functional network of both young smokers and nonsmokers had small-world topology. Compared to nonsmokers, young smokers exhibited a decreased clustering coefficient (Cp) and local network efficiency (Elocal). Cp and Elocal were negatively correlated with the duration of cigarette use. In addition, increased nodal efficiency (Enodal) was mainly located in the prefrontal cortex (PFC), cingulate gyrus, insula, and caudate. Decreased connectivities among the PFC, cingulate gyrus, insula, basal ganglia (of specific node), and thalamus were also observed. In sum, we revealed the abnormal topological organization of brain functional networks in young smokers, which may improve our understanding of the neural mechanism of young smokers from a brain functional network topological organization perspective.

PMID: 31543831 [PubMed]

Cerebellar Transcranial Magnetic Stimulation Improves Ataxia in Minamata Disease.

Tue, 09/24/2019 - 13:40
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Cerebellar Transcranial Magnetic Stimulation Improves Ataxia in Minamata Disease.

Case Rep Neurol. 2019 May-Aug;11(2):167-172

Authors: Nakamura M, Bekki M, Miura Y, Itatani M, Jie LX

Abstract
Minamata disease (MD) is a form of intoxication involving the central nervous system and is caused by ingesting seafood from methylmercury-contaminated areas in Japan. In MD, cerebellar ataxia is a cardinal feature observed in approximately 80% of MD patients. Although cerebellar transcranial magnetic stimulation (TMS) has recently been used for treating cerebellar ataxia, the optimal stimulation conditions remain unclear. Here, we report the first case of cerebellar ataxia in an MD patient that was significantly improved after high-frequency cerebellar TMS. To determine the optimal stimulation conditions, we examined the excitability of the primary motor cortex (M1) using resting-state functional magnetic resonance imaging (rs-fMRI). rs-fMRI revealed M1 hyperconnectivity, which was indicative of activation of the dentato-thalamo-cortical (DTC) pathway. Thus, high-frequency cerebellar TMS was applied to inhibit the DTC pathway. Improvement of cerebellar ataxia was only observed after real TMS, not sham stimulation. As this effect was consistent with inhibition of hyperconnectivity of M1, the effectiveness of high-frequency cerebellar TMS for cerebellar ataxia was thought to be caused by inhibition of the DTC pathway. Therefore, we suggest that the evaluation of M1 excitability using rs-fMRI can be effective for determining the optimal TMS stimulation conditions for cerebellar ataxia.

PMID: 31543798 [PubMed]

Machine Learning Models Identify Multimodal Measurements Highly Predictive of Transdiagnostic Symptom Severity for Mood, Anhedonia, and Anxiety.

Tue, 09/24/2019 - 13:40
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Machine Learning Models Identify Multimodal Measurements Highly Predictive of Transdiagnostic Symptom Severity for Mood, Anhedonia, and Anxiety.

Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Jul 30;:

Authors: Mellem MS, Liu Y, Gonzalez H, Kollada M, Martin WJ, Ahammad P

Abstract
BACKGROUND: Insights from neuroimaging-based biomarker research have not yet translated into clinical practice. This translational gap may stem from a focus on diagnostic classification, rather than on prediction of transdiagnostic psychiatric symptom severity. Currently, no transdiagnostic, multimodal predictive models of symptom severity that include neurobiological characteristics have emerged.
METHODS: We built predictive models of 3 common symptoms in psychiatric disorders (dysregulated mood, anhedonia, and anxiety) from the Consortium for Neuropsychiatric Phenomics dataset (N = 272), which includes clinical scale assessments, resting-state functional magnetic resonance imaging (MRI), and structural MRI measures from patients with schizophrenia, bipolar disorder, and attention-deficit/hyperactivity disorder and healthy control subjects. We used an efficient, data-driven feature selection approach to identify the most predictive features from these high-dimensional data.
RESULTS: This approach optimized modeling and explained 65% to 90% of variance across the 3 symptom domains, compared to 22% without using the feature selection approach. The top performing multimodal models retained a high level of interpretability that enabled several clinical and scientific insights. First, to our surprise, structural features did not substantially contribute to the predictive strength of these models. Second, the Temperament and Character Inventory scale emerged as a highly important predictor of symptom variation across diagnoses. Third, predictive resting-state functional MRI connectivity features were widely distributed across many intrinsic resting-state networks.
CONCLUSIONS: Combining resting-state functional MRI with select questions from clinical scales enabled high prediction of symptom severity across diagnostically distinct patient groups and revealed that connectivity measures beyond a few intrinsic resting-state networks may carry relevant information for symptom severity.

PMID: 31543457 [PubMed - as supplied by publisher]

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