New resting-state fMRI related studies at PubMed

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Behavioral and neural concordance in parent-child dyadic sleep patterns.

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Behavioral and neural concordance in parent-child dyadic sleep patterns.

Dev Cogn Neurosci. 2017 Jun 15;26:77-83

Authors: Lee TH, Miernicki ME, Telzer EH

Abstract
Sleep habits developed in adolescence shape long-term trajectories of psychological, educational, and physiological well-being. Adolescents' sleep behaviors are shaped by their parents' sleep at both the behavioral and biological levels. In the current study, we sought to examine how neural concordance in resting-state functional connectivity between parent-child dyads is associated with dyadic concordance in sleep duration and adolescents' sleep quality. To this end, we scanned both parents and their child (N=28 parent-child dyads; parent Mage=42.8years; adolescent Mage=14.9years; 14.3% father; 46.4% female adolescent) as they each underwent a resting-state scan. Using daily diaries, we also assessed dyadic concordance in sleep duration across two weeks. Our results show that greater daily concordance in sleep behavior is associated with greater neural concordance in default-mode network connectivity between parents and children. Moreover, greater neural and behavioral concordances in sleep associated with more optimal sleep quality in adolescents. The current findings expand our understanding of dyadic concordance by providing a neurobiological mechanism by which parents and children share daily sleep behaviors.

PMID: 28645041 [PubMed - as supplied by publisher]

Correlation-Weighted Sparse Group Representation for Brain Network Construction in MCI Classification.

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Correlation-Weighted Sparse Group Representation for Brain Network Construction in MCI Classification.

Med Image Comput Comput Assist Interv. 2016 Oct;9900:37-45

Authors: Yu R, Zhang H, An L, Chen X, Wei Z, Shen D

Abstract
Analysis of brain functional connectivity network (BFCN) has shown great potential in understanding brain functions and identifying biomarkers for neurological and psychiatric disorders, such as Alzheimer's disease and its early stage, mild cognitive impairment (MCI). In all these applications, the accurate construction of biologically meaningful brain network is critical. Due to the sparse nature of the brain network, sparse learning has been widely used for complex BFCN construction. However, the conventional l1-norm penalty in the sparse learning equally penalizes each edge (or link) of the brain network, which ignores the link strength and could remove strong links in the brain network. Besides, the conventional sparse regularization often overlooks group structure in the brain network, i.e., a set of links (or connections) sharing similar attribute. To address these issues, we propose to construct BFCN by integrating both link strength and group structure information. Specifically, a novel correlation-weighted sparse group constraint is devised to account for and balance among (1) sparsity, (2) link strength, and (3) group structure, in a unified framework. The proposed method is applied to MCI classification using the resting-state fMRI from ADNI-2 dataset. Experimental results show that our method is effective in modeling human brain connectomics, as demonstrated by superior MCI classification accuracy of 81.8%. Moreover, our method is promising for its capability in modeling more biologically meaningful sparse brain networks, which will benefit both basic and clinical neuroscience studies.

PMID: 28642938 [PubMed - in process]

Lower Functional Connectivity of the Periaqueductal Gray Is Related to Negative Affect and Clinical Manifestations of Fibromyalgia.

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Lower Functional Connectivity of the Periaqueductal Gray Is Related to Negative Affect and Clinical Manifestations of Fibromyalgia.

Front Neuroanat. 2017;11:47

Authors: Coulombe MA, Lawrence KS, Moulin DE, Morley-Forster P, Shokouhi M, Nielson WR, Davis KD

Abstract
Fibromyalgia (FM) syndrome is characterized by chronic widespread pain, muscle tenderness and emotional distress. Previous studies found reduced endogenous pain modulation in FM. This deficiency of pain modulation may be related to the attributes of chronic pain and other clinical symptoms experienced in patients with FM. Thus, we tested whether there is a link between the clinical symptoms of FM and functional connectivity (FC) of the periaqueductal gray (PAG), a key node of pain modulation. We acquired resting state 3T functional MRI (rsfMRI) data from 23 female patients with FM and 16 age- and sex- matched healthy controls (HC) and assessed FM symptoms with the Brief Pain Inventory (BPI), Fibromyalgia Impact Questionnaire (FIQ), Hospital Anxiety and Depression Scale (HADS) and Pain Catastrophizing Scale (PCS). We found that patients with FM exhibit statistically significant disruptions in PAG FC, particularly with brain regions implicated in negative affect, self-awareness and saliency. Specifically, we found that, compared to HCs, the FM patients had stronger PAG FC with the lingual gyrus and hippocampus but weaker PAG FC with regions associated with motor/executive functions, the salience (SN) and default mode networks (DMN). The attenuated PAG FC was also negatively correlated with FIQ scores, and positively correlated with the magnification subscale of the PCS. These alterations were correlated with emotional and behavioral symptoms of FM. Our study implicates the PAG as a site of dysfunction contributing to the clinical manifestations and pain in FM.

PMID: 28642688 [PubMed - in process]

Common and distinct brain networks underlying panic and social anxiety disorders.

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Common and distinct brain networks underlying panic and social anxiety disorders.

Prog Neuropsychopharmacol Biol Psychiatry. 2017 Jun 19;:

Authors: Kim YK, Yoon HK

Abstract
Although panic disorder (PD) and phobic disorders are independent anxiety disorders with distinct sets of diagnostic criteria, there is a high level of overlap between them in terms of pathogenesis and neural underpinnings. Functional connectivity research using resting-state functional magnetic resonance imaging (rsfMRI) shows great potential in identifying the similarities and differences between PD and phobias. Understanding common and distinct networks between PD and phobic disorders is critical for identifying both specific and general neural characteristics of these disorders. We review recent rsfMRI studies and explore the clinical relevance of resting-state functional connectivity (rsFC) in PD and phobias. Although findings differ between studies, there are some meaningful, consistent findings. Social anxiety disorder (SAD) and PD share common default mode network alterations. Alterations within the sensorimotor network are observed primarily in PD. Increased connectivity in the salience network is consistently reported in SAD. This review supports hypotheses that PD and phobic disorders share common rsFC abnormalities and that the different clinical phenotypes between the disorders come from distinct brain functional network alterations.

PMID: 28642079 [PubMed - as supplied by publisher]

Abnormal amplitude of low-frequency fluctuations and functional connectivity of resting-state functional magnetic resonance imaging in patients with leukoaraiosis.

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Abnormal amplitude of low-frequency fluctuations and functional connectivity of resting-state functional magnetic resonance imaging in patients with leukoaraiosis.

Brain Behav. 2017 Jun;7(6):e00714

Authors: Cheng R, Qi H, Liu Y, Zhao S, Li C, Liu C, Zheng J

Abstract
INTRODUCTION: This study aimed to investigate the cerebral function deficits in patients with leukoaraiosis (LA) and the correlation with white matter hyperintensity (WMH) using functional MRI (fMRI) technology.
MATERIALS AND METHODS: Twenty-eight patients with LA and 30 volunteers were enrolled in this study. All patients underwent structural MRI and resting-state functional MRI (rs-fMRI) scanning. The amplitude of low-frequency fluctuations (ALFF) of rs-fMRI signals for the two groups was compared using two-sample t tests. A one-sample t test was performed on the individual z-value maps to identify the functional connectivity of each group. The z values were compared between the two groups using a two-sample t test. Partial correlations between ALFF values and functional connectivity of the brain regions that showed group differences and Fazekas scores of the WMH were analyzed.
RESULTS: Compared with the control group, the LA group showed a significant decrease in the ALFF in the left parahippocampal gyrus (PHG) and an increased ALFF in the left inferior semi-lunar lobule and right superior orbital frontal gyrus (SOFG). The patients with LA showed an increased functional connectivity between the right insular region and the right SOFG and between the right calcarine cortex and the left PHG. After the effects of age, gender, and years of education were corrected as covariates, the functional connectivity strength of the right insular and the right SOFG showed close correlations with the Fazekas scores.
CONCLUSION: Our results enhance the understanding of the pathomechanism of LA. Leukoaraiosis is associated with widespread cerebral function deficits, which show a close correlation with WMH and can be measured by rs-fMRI.

PMID: 28638719 [PubMed - in process]

Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders.

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Stochastic Signatures of Involuntary Head Micro-movements Can Be Used to Classify Females of ABIDE into Different Subtypes of Neurodevelopmental Disorders.

Front Integr Neurosci. 2017;11:10

Authors: Torres EB, Mistry S, Caballero C, Whyatt CP

Abstract
Background: The approximate 5:1 male to female ratio in clinical detection of Autism Spectrum Disorder (ASD) prevents research from characterizing the female phenotype. Current open access repositories [such as those in the Autism Brain Imaging Data Exchange (ABIDE I-II)] contain large numbers of females to help begin providing a new characterization of females on the autistic spectrum. Here we introduce new methods to integrate data in a scale-free manner from continuous biophysical rhythms of the nervous systems and discrete (ordinal) observational scores. Methods: New data-types derived from image-based involuntary head motions and personalized statistical platform were combined with a data-driven approach to unveil sub-groups within the female cohort. Further, to help refine the clinical DSM-based ASD vs. Asperger's Syndrome (AS) criteria, distributional analyses of ordinal score data from Autism Diagnostic Observation Schedule (ADOS)-based criteria were used on both the female and male phenotypes. Results: Separate clusters were automatically uncovered in the female cohort corresponding to differential levels of severity. Specifically, the AS-subgroup emerged as the most severely affected with an excess level of noise and randomness in the involuntary head micro-movements. Extending the methods to characterize males of ABIDE revealed ASD-males to be more affected than AS-males. A thorough study of ADOS-2 and ADOS-G scores provided confounding results regarding the ASD vs. AS male comparison, whereby the ADOS-2 rendered the AS-phenotype worse off than the ASD-phenotype, while ADOS-G flipped the results. Females with AS scored higher on severity than ASD-females in all ADOS test versions and their scores provided evidence for significantly higher severity than males. However, the statistical landscapes underlying female and male scores appeared disparate. As such, further interpretation of the ADOS data seems problematic, rather suggesting the critical need to develop an entirely new metric to measure social behavior in females. Conclusions: According to the outcome of objective, data-driven analyses and subjective clinical observation, these results support the proposition that the female phenotype is different. Consequently the "social behavioral male ruler" will continue to mask the female autistic phenotype. It is our proposition that new observational behavioral tests ought to contain normative scales, be statistically sound and combined with objective data-driven approaches to better characterize the females across the human lifespan.

PMID: 28638324 [PubMed - in process]

Intrinsic functional connectivity alteration of dorsal and rostral anterior cingulate cortex in obsessive-compulsive disorder: a resting fMRI study.

Thu, 06/22/2017 - 14:40
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Intrinsic functional connectivity alteration of dorsal and rostral anterior cingulate cortex in obsessive-compulsive disorder: a resting fMRI study.

Neurosci Lett. 2017 Jun 18;:

Authors: Zhang Z, Fan Q, Zhu Y, Tan L, Chen Y, Gao R, Zhang H, Li Y, Xiao Z

Abstract
Cortico-striato-thalamo-cortical (CSTC) circuit has been implicated in OCD pathophysiology by converging neuroimaging findings. The anterior cingulate cortex (ACC), as an important part within CSTC circuit, plays a critical role in OCD etiology. The ACC can be divided into dorsal and rostral parts anatomically, which are involved in cognitive process and emotional function, respectively. However, the diverse function of intrinsic signals from dorsal and rostral ACC regions remains unclear in OCD study. In this work, we applied resting-state functional magnetic resonance imaging (rs-fMRI) technology to investigate and differentiate the functional connectivity (FC) characteristics between dACC and rACC in unmedicated OCD patients. Also, the correlation between the altered FC and clinical symptom severity was analyzed. Decreased FC of rACC-DLPFC and increased FC between dACC and caudate were found. Moreover, the altered dACC-caudate FC was positively correlated with total Y-BOCS and compulsion score in OCD patients. Our findings implied the crossed function of dorsal and rostral circuits in the pathophysiologic mechanism of OCD. The dorsal cingulate-striatum functional pathway served as a potential biomarker for OCD symptomatology and merits further investigations.

PMID: 28636929 [PubMed - as supplied by publisher]

Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning.

Thu, 06/22/2017 - 14:40
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Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning.

World J Biol Psychiatry. 2017 Feb 08;:1-11

Authors: Sato JR, Biazoli CE, Salum GA, Gadelha A, Crossley N, Vieira G, Zugman A, Picon FA, Pan PM, Hoexter MQ, Amaro E, Anés M, Moura LM, Del'Aquilla MAG, Mcguire P, Rohde LA, Miguel EC, Jackowski AP, Bressan RA

Abstract
OBJECTIVES: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. Many of these disorders affect brain development and its connectivity. In this study, we propose a novel method for assessing brain networks based on the combination of a graph theory measure (eigenvector centrality) and a one-class support vector machine (OC-SVM).
METHODS: We applied this approach to resting-state fMRI data from 622 children and adolescents. Eigenvector centrality (EVC) of nodes from positive- and negative-task networks were extracted from each subject and used as input to an OC-SVM to label individual brain networks as typical or atypical. We hypothesised that classification of these subjects regarding the pattern of brain connectivity would predict the level of psychopathology.
RESULTS: Subjects with atypical brain network organisation had higher levels of psychopathology (p < 0.001). There was a greater EVC in the typical group at the bilateral posterior cingulate and bilateral posterior temporal cortices; and significant decreases in EVC at left temporal pole.
CONCLUSIONS: The combination of graph theory methods and an OC-SVM is a promising method to characterise neurodevelopment, and may be useful to understand the deviations leading to mental disorders.

PMID: 28635541 [PubMed - as supplied by publisher]

Abnormal Resting-State Functional Connectivity in Progressive Supranuclear Palsy and Corticobasal Syndrome.

Thu, 06/22/2017 - 14:40
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Abnormal Resting-State Functional Connectivity in Progressive Supranuclear Palsy and Corticobasal Syndrome.

Front Neurol. 2017;8:248

Authors: Bharti K, Bologna M, Upadhyay N, Piattella MC, Suppa A, Petsas N, Giannì C, Tona F, Berardelli A, Pantano P

Abstract
BACKGROUND: Pathological and MRI-based evidence suggests that multiple brain structures are likely to be involved in functional disconnection between brain areas. Few studies have investigated resting-state functional connectivity (rsFC) in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). In this study, we investigated within- and between-network rsFC abnormalities in these two conditions.
METHODS: Twenty patients with PSP, 11 patients with CBS, and 16 healthy subjects (HS) underwent a resting-state fMRI study. Resting-state networks (RSNs) were extracted to evaluate within- and between-network rsFC using the Melodic and FSLNets software packages.
RESULTS: Increased within-network rsFC was observed in both PSP and CBS patients, with a larger number of RSNs being involved in CBS. Within-network cerebellar rsFC positively correlated with mini-mental state examination scores in patients with PSP. Compared to healthy volunteers, PSP and CBS patients exhibit reduced functional connectivity between the lateral visual and auditory RSNs, with PSP patients additionally showing lower functional connectivity between the cerebellar and insular RSNs. Moreover, rsFC between the salience and executive-control RSNs was increased in patients with CBS compared to HS.
CONCLUSION: This study provides evidence of functional brain reorganization in both PSP and CBS. Increased within-network rsFC could represent a higher degree of synchronization in damaged brain areas, while between-network rsFC abnormalities may mainly reflect degeneration of long-range white matter fibers.

PMID: 28634465 [PubMed - in process]

Inconsistency in Abnormal Brain Activity across Cohorts of ADHD-200 in Children with Attention Deficit Hyperactivity Disorder.

Thu, 06/22/2017 - 14:40
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Inconsistency in Abnormal Brain Activity across Cohorts of ADHD-200 in Children with Attention Deficit Hyperactivity Disorder.

Front Neurosci. 2017;11:320

Authors: Wang JB, Zheng LJ, Cao QJ, Wang YF, Sun L, Zang YF, Zhang H

Abstract
Many papers have shown results from the multi-site dataset of resting-state fMRI (rs-fMRI) in attention deficit hyperactivity disorder (ADHD), a data-sharing project named ADHD-200. However, few studies have illustrated that to what extent the pooled findings were consistent across cohorts. The present study analyzed three voxel-wise whole-brain metrics, i.e., amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) based on the pooled dataset as well as individual cohort of ADHD-200. In addition to the conventional frequency band of 0.01-0.08 Hz, sub-frequency bands of 0-0.01, 0.01-0.027, 0.027-0.073, 0.073-0.198, and 0.198-0.25 Hz, were assessed. While the pooled dataset showed abnormal activity in some brain regions, e.g., the bilateral sensorimotor cortices, bilateral cerebellum, and the bilateral lingual gyrus, these results were highly inconsistent across cohorts, even across the three cohorts from the same research center. The standardized effect size was rather small. These findings suggested a high heterogeneity of spontaneous brain activity in ADHD. Future studies based on multi-site large-sample dataset should be performed on pooled data and single cohort data, respectively and the effect size must be shown.

PMID: 28634439 [PubMed - in process]

Fine Subdivisions of the Semantic Network Supporting Social and Sensory-Motor Semantic Processing.

Thu, 06/22/2017 - 14:40
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Fine Subdivisions of the Semantic Network Supporting Social and Sensory-Motor Semantic Processing.

Cereb Cortex. 2017 Jun 15;:1-12

Authors: Lin N, Wang X, Xu Y, Wang X, Hua H, Zhao Y, Li X

Abstract
Neuroimaging studies have consistently indicated that semantic processing involves a brain network consisting of multimodal cortical regions distributed in the frontal, parietal, and temporal lobes. However, little is known about how semantic information is organized and processed within the network. Some recent studies have indicated that sensory-motor semantic information modulates the activation of this network. Other studies have indicated that this network responds more to social semantic information than to other information. Using fMRI, we collectively investigated the brain activations evoked by social and sensory-motor semantic information by manipulating the sociality and imageability of verbs in a word comprehension task. We detected 2 subgroups of brain regions within the network showing sociality and imageability effects, respectively. The 2 subgroups of regions are distinct but overlap in bilateral angular gyri and adjacent middle temporal gyri. A follow-up analysis of resting-state functional connectivity showed that dissociation of the 2 subgroups of regions is partially associated with their intrinsic functional connectivity differences. Additionally, an interaction effect of sociality and imageability was observed in the left anterior temporal lobe. Our findings indicate that the multimodal cortical semantic network has fine subdivisions that process and integrate social and sensory-motor semantic information.

PMID: 28633369 [PubMed - as supplied by publisher]

Identifying the epileptogenic zone in interictal resting-state MEG source-space networks.

Thu, 06/22/2017 - 14:40
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Identifying the epileptogenic zone in interictal resting-state MEG source-space networks.

Epilepsia. 2017 Jan;58(1):137-148

Authors: Nissen IA, Stam CJ, Reijneveld JC, van Straaten IE, Hendriks EJ, Baayen JC, De Witt Hamer PC, Idema S, Hillebrand A

Abstract
OBJECTIVE: In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the epileptogenic zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the epileptogenic zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks.
METHODS: Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 cortical and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas.
RESULTS: Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure-free patients (79% sensitivity).
SIGNIFICANCE: Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the epileptogenic zone in patients without interictal abnormalities.

PMID: 27888520 [PubMed - indexed for MEDLINE]

Affective traits and history of depression are related to ventral striatum connectivity.

Thu, 06/22/2017 - 02:00

Affective traits and history of depression are related to ventral striatum connectivity.

J Affect Disord. 2017 Jun 15;221:72-80

Authors: DelDonno SR, Jenkins LM, Crane NA, Nusslock R, Ryan KA, Shankman SA, Phan KL, Langenecker SA

Abstract
INTRODUCTION: Studying remitted Major Depressive Disorder (rMDD) facilitates a better understanding of neural mechanisms for risk, given that confounding effects of active symptoms are removed. Disrupted functional connectivity has been reported in multiple networks in MDD. However, no study to date of rMDD has specifically examined connectivity of the ventral striatum (VS), a region highly implicated in reward and motivation. We investigated functional connectivity of the VS in individuals with and without a history of MDD, and in relation to affective personality traits.
METHODS: Forty-two individuals with rMDD and 28 healthy controls across two sites completed resting-state fMRI and the Behavioral Inhibition System/Behavioral Activation System Scale. Voxel-wise, whole-brain comparisons were conducted across and between groups for four seeds: left and right inferior VS (VSi), left and right superior VS (VSs).
RESULTS: VSs connectivity to temporal and subcortical regions including the putamen and amygdala was positive and greater in HCs compared to rMDD individuals. Across groups, VSi connectivity was positively correlated with trait reward-responsiveness in somatomotor regions. Across groups, VSs connectivity was positively correlated with trait drive, particularly in the putamen, parahippocampal, and inferior temporal gyrus, and was negatively associated with trait behavioral inhibition in the anterior cingulate, frontal gyri, and insula.
LIMITATIONS: Limitations include scanning at two sites and using multiple comparisons.
DISCUSSION: Group connectivity differences emerged from the VSs rather than VSi. VSs showed associations with trait drive and behavioral inhibition, whereas VSi corrrelated with reward-responsiveness. Depression history and affective traits contribute meaningful and specific information about VS connectivity in understanding risk for MDD.

PMID: 28633048 [PubMed - as supplied by publisher]

Detecting large-scale networks in the human brain using high-density electroencephalography.

Thu, 06/22/2017 - 02:00
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Detecting large-scale networks in the human brain using high-density electroencephalography.

Hum Brain Mapp. 2017 Jun 20;:

Authors: Liu Q, Farahibozorg S, Porcaro C, Wenderoth N, Mantini D

Abstract
High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

PMID: 28631281 [PubMed - as supplied by publisher]

The motor cortical representation of a muscle is not homogeneous in brain connectivity.

Thu, 06/22/2017 - 02:00
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The motor cortical representation of a muscle is not homogeneous in brain connectivity.

Exp Brain Res. 2017 Jun 19;:

Authors: Smith JA, Albishi A, Babikian S, Asavasopon S, Fisher BE, Kutch JJ

Abstract
Functional connectivity patterns of the motor cortical representational area of single muscles have not been extensively mapped in humans, particularly for the axial musculature. Functional connectivity may provide a neural substrate for adaptation of muscle activity in axial muscles that have both voluntary and postural functions. The purpose of this study was to combine brain stimulation and neuroimaging to both map the cortical representation of the external oblique (EO) in primary motor cortex (M1) and supplementary motor area (SMA), and to establish the resting-state functional connectivity associated with this representation. Motor-evoked potentials were elicited from the EO muscle in stimulation locations encompassing M1 and SMA. The coordinates of locations with the largest motor-evoked potentials were confirmed with task-based fMRI imaging during EO activation. The M1 and SMA components of the EO representation demonstrated significantly different resting-state functional connectivity with other brain regions: the SMA representation of the EO muscle was significantly more connected to the putamen and cerebellum, and the M1 representation of the EO muscle was significantly more connected to somatosensory cortex and the superior parietal lobule. This study confirms the representation of a human axial muscle in M1 and SMA, and demonstrates for the first time that different parts of the cortical representation of a human axial muscle have resting-state functional connectivity with distinct brain regions. Future studies can use the brain regions of interest we have identified here to test the association between resting-state functional connectivity and control of the axial muscles.

PMID: 28631147 [PubMed - as supplied by publisher]

Functional connectivity disturbances of the ascending reticular activating system in temporal lobe epilepsy.

Thu, 06/22/2017 - 02:00
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Functional connectivity disturbances of the ascending reticular activating system in temporal lobe epilepsy.

J Neurol Neurosurg Psychiatry. 2017 Jun 19;:

Authors: Englot DJ, D'Haese PF, Konrad PE, Jacobs ML, Gore JC, Abou-Khalil BW, Morgan VL

Abstract
OBJECTIVE: Seizures in temporal lobe epilepsy (TLE) disturb brain networks and lead to connectivity disturbances. We previously hypothesised that recurrent seizures in TLE may lead to abnormal connections involving subcortical activating structures including the ascending reticular activating system (ARAS), contributing to neocortical dysfunction and neurocognitive impairments. However, no studies of ARAS connectivity have been previously reported in patients with epilepsy.
METHODS: We used resting-state functional MRI recordings in 27 patients with TLE (67% right sided) and 27 matched controls to examine functional connectivity (partial correlation) between eight brainstem ARAS structures and 105 cortical/subcortical regions. ARAS nuclei included: cuneiform/subcuneiform, dorsal raphe, locus coeruleus, median raphe, parabrachial complex, pontine oralis, pedunculopontine and ventral tegmental area. Connectivity patterns were related to disease and neuropsychological parameters.
RESULTS: In control subjects, regions showing highest connectivity to ARAS structures included limbic structures, thalamus and certain neocortical areas, which is consistent with prior studies of ARAS projections. Overall, ARAS connectivity was significantly lower in patients with TLE than controls (p<0.05, paired t-test), particularly to neocortical regions including insular, lateral frontal, posterior temporal and opercular cortex. Diminished ARAS connectivity to these regions was related to increased frequency of consciousness-impairing seizures (p<0.01, Pearson's correlation) and was associated with impairments in verbal IQ, attention, executive function, language and visuospatial memory on neuropsychological evaluation (p<0.05, Spearman's rho or Kendell's tau-b).
CONCLUSIONS: Recurrent seizures in TLE are associated with disturbances in ARAS connectivity, which are part of the widespread network dysfunction that may be related to neurocognitive problems in this devastating disorder.

PMID: 28630376 [PubMed - as supplied by publisher]

Exploring Connectivity with Large-Scale Granger Causality on Resting-State Functional MRI.

Thu, 06/22/2017 - 02:00
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Exploring Connectivity with Large-Scale Granger Causality on Resting-State Functional MRI.

J Neurosci Methods. 2017 Jun 16;:

Authors: DSouza AM, Abidin AZ, Leistritz L, Wismüller A

Abstract
BACKGROUND: Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction.
NEW METHOD: We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters.
RESULTS: Results indicate that lsGC reliably recovers underlying network structure with Area Under receiver operator characteristic Curve (AUC) of 0.93 at TR=1.5s for a 10-minute session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86).
COMPARISON WITH EXISTING METHOD(S): Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem.
CONCLUSIONS: Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution.

PMID: 28629720 [PubMed - as supplied by publisher]

Multi-modal neuroimaging of adolescents with non-suicidal self-injury: Amygdala functional connectivity.

Tue, 06/20/2017 - 12:20
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Multi-modal neuroimaging of adolescents with non-suicidal self-injury: Amygdala functional connectivity.

J Affect Disord. 2017 Jun 13;221:47-55

Authors: Westlund Schreiner M, Klimes-Dougan B, Mueller BA, Eberly LE, Reigstad KM, Carstedt PA, Thomas KM, Hunt RH, Lim KO, Cullen KR

Abstract
BACKGROUND: Non-suicidal self-injury (NSSI) is a significant mental health problem among adolescents. Research is needed to clarify the neurobiology of NSSI and identify candidate neurobiological targets for interventions. Based on prior research implicating heightened negative affect and amygdala hyperactivity in NSSI, we pursued a systems approach to characterize amygdala functional connectivity networks during rest (resting-state functional connectivity [RSFC)]) and a task (task functional connectivity [TFC]) in adolescents with NSSI.
METHOD: We examined amygdala networks in female adolescents with NSSI and healthy controls (n = 45) using resting-state fMRI and a negative emotion face-matching fMRI task designed to activate the amygdala. Connectivity analyses included amygdala RSFC, amygdala TFC, and psychophysiological interactions (PPI) between amygdala connectivity and task conditions.
RESULTS: Compared to healthy controls, adolescents with NSSI showed atypical amygdala-frontal connectivity during rest and task; greater amygdala RSFC in supplementary motor area (SMA) and dorsal anterior cingulate; and differential amygdala-occipital connectivity between rest and task. After correcting for depression symptoms, amygdala-SMA RSFC abnormalities, among others, remained significant.
LIMITATIONS: This study's limitations include its cross-sectional design and its absence of a psychiatric control group.
CONCLUSIONS: Using a multi-modal approach, we identified widespread amygdala circuitry anomalies in adolescents with NSSI. While deficits in amygdala-frontal connectivity (driven by depression symptoms) replicates prior work in depression, hyperconnectivity between amygdala and SMA (independent of depression symptoms) has not been previously reported. This circuit may represent an important mechanism underlying the link between negative affect and habitual behaviors. These abnormalities may represent intervention targets for adolescents with NSSI.

PMID: 28628767 [PubMed - as supplied by publisher]

Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation.

Tue, 06/20/2017 - 12:20
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Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation.

Hum Brain Mapp. 2017 Jun 19;:

Authors: Shen H, Xu H, Wang L, Lei Y, Yang L, Zhang P, Qin J, Zeng LL, Zhou Z, Yang Z, Hu D

Abstract
Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage. Our analysis based on group-level sparse representation revealed that multiple functional networks involved in memory, emotion, attention, and vigilance processing were impaired by SD. Of particular interest, the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited. These results not only further elucidate the impact of SD on brain function but also demonstrate the ability of the proposed approach to provide new insights into the functional organization of the resting-state brain by permitting spatial overlap between networks and facilitating the description of the varied relationships of the overlapping regions with other regions of the brain in the context of different functional systems. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

PMID: 28627049 [PubMed - as supplied by publisher]

Aging and the Resting State: Is Cognition Obsolete?

Tue, 06/20/2017 - 12:20
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Aging and the Resting State: Is Cognition Obsolete?

Lang Cogn Neurosci. 2017;32(6):661-668

Authors: Campbell KL, Schacter DL

Abstract
Recent years have seen the rise in popularity of the resting state approach to neurocognitive aging, with many studies examining age differences in functional connectivity at rest and relating these differences to cognitive performance outside the scanner. There are many advantages to the resting state that likely contribute to its popularity and indeed, many insights have been gained from this work. However, there are also several limitations of the resting state approach that restrict its ability to contribute to the study of neurocognitive aging. In this opinion piece, we consider some of those limitations and argue that task-based studies are still essential to developing a mechanistic understanding of how age affects the brain in a cognitively relevant manner - a fundamental goal of neuroscientific research into aging.

PMID: 28626776 [PubMed - in process]

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