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Elucidating the putative link between prefrontal neurotransmission, functional connectivity, and affective symptoms in irritable bowel syndrome.

Sat, 09/21/2019 - 23:02
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Elucidating the putative link between prefrontal neurotransmission, functional connectivity, and affective symptoms in irritable bowel syndrome.

Sci Rep. 2019 Sep 19;9(1):13590

Authors: Icenhour A, Tapper S, Bednarska O, Witt ST, Tisell A, Lundberg P, Elsenbruch S, Walter S

Abstract
Altered neural mechanisms are well-acknowledged in irritable bowel syndrome (IBS), a disorder of brain-gut-communication highly comorbid with anxiety and depression. As a key hub in corticolimbic inhibition, medial prefrontal cortex (mPFC) may be involved in disturbed emotion regulation in IBS. However, aberrant mPFC excitatory and inhibitory neurotransmission potentially contributing to psychological symptoms in IBS remains unknown. Using quantitative magnetic resonance spectroscopy (qMRS), we compared mPFC glutamate + glutamine (Glx) and γ-aminobutyric acid (GABA+) concentrations in 64 women with IBS and 32 age-matched healthy women (HCs) and investigated their association with anxiety and depression in correlational and subgroup analyses. Applying functional magnetic resonance imaging (fMRI), we explored whether altered neurotransmission was paralleled by aberrant mPFC resting-state functional connectivity (FC). IBS patients did not differ from HCs with respect to mPFC GABA+ or Glx levels. Anxiety was positively associated with mPFC GABA+ concentrations in IBS, whereas Glx was unrelated to psychological or gastrointestinal symptoms. Subgroup comparisons of patients with high or low anxiety symptom severity and HCs revealed increased GABA+ in patients with high symptom severity, and lower mPFC FC with adjacent anterior cingulate cortex (ACC), a crucial region of emotion modulation. Our findings provide novel evidence that altered prefrontal inhibitory neurotransmission may be linked to anxiety in IBS.

PMID: 31537890 [PubMed - in process]

Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods.

Fri, 09/20/2019 - 20:01
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Dynamic up- and down-regulation of the default (DMN) and extrinsic (EMN) mode networks during alternating task-on and task-off periods.

PLoS One. 2019;14(9):e0218358

Authors: Hugdahl K, Kazimierczak K, Beresniewicz J, Kompus K, Westerhausen R, Ersland L, Grüner R, Specht K

Abstract
Using fMRI, Hugdahl et al. (2015) reported the existence of a general-domain cortical network during active task-processing which was non-specific to the cognitive task being processed. They labelled this network the extrinsic mode network (EMN). The EMN would be predicted to be negatively, or anti-correlated with the classic default mode network (DMN), typically observed during periods of rest, such that while the EMN should be down-regulated and the DMN up-regulated in the absence of demands for task-processing, the reverse should occur when demands change from resting to task-processing. This would require alternating periods of task-processing and resting and analyzing data continuously when demands change from active to passive periods and vice versa. We were particularly interested in how the networks interact in the critical transition points between conditions. For this purpose, we used an auditory task with multiple cognitive demands in a standard fMRI block-design. Task-present (ON) blocks were alternated with an equal number of task-absent, or rest (OFF) blocks to capture network dynamics across time and changing environmental demands. To achieve this, we specified the onset of each block, and used a finite-impulse response function (FIR) as basis function for estimation of the fMRI-BOLD response. During active (ON) blocks, the results showed an initial rapid onset of activity in the EMN network, which remained throughout the period, and faded away during the first scan of the OFF-block. During OFF blocks, activity in the DMN network showed an initial time-lag where neither the EMN nor the DMN was active, after which the DMN was up-regulated. Studying network dynamics in alternating passive and active periods may provide new insights into brain network interaction and regulation.

PMID: 31536496 [PubMed - in process]

A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia from EEG Connectivity Patterns.

Fri, 09/20/2019 - 20:01
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A Multi-Domain Connectome Convolutional Neural Network for Identifying Schizophrenia from EEG Connectivity Patterns.

IEEE J Biomed Health Inform. 2019 Sep 13;:

Authors: Phang CR, Noman FM, Hussain H, Ting CM, Ombao H

Abstract
OBJECTIVE: We exploit altered patterns in brain functional connectivity as features for automatic discriminative analysis of neuropsychiatric patients. Deep learning methods have been introduced to functional network classification only very recently for fMRI, and the proposed architectures essentially focused on a single type of connectivity measure.
METHODS: We propose a deep convolutional neural network (CNN) framework for classification of electroencephalogram (EEG)-derived brain connectome in schizophrenia (SZ). To capture complementary aspects of disrupted connectivity in SZ, we explore combination of various connectivity features consisting of time and frequency-domain metrics of effective connectivity based on vector autoregressive model and partial directed coherence, and complex network measures of network topology. We design a novel multi-domain connectome CNN (MDC-CNN) based on a parallel ensemble of 1D and 2D CNNs to integrate the features from various domains and dimensions using different fusion strategies. We also consider an extension to dynamic brain connectivity using the recurrent neural networks.
RESULTS: Hierarchical latent representations learned by the multiple convolutional layers from EEG connectivity reveal apparent group differences between SZ and healthy controls (HC). Results on a large resting-state EEG dataset show that the proposed CNNs significantly outperform traditional support vector machine classifiers. The MDC-CNN with combined connectivity features further improves performance over single-domain CNNs using individual features, achieving remarkable accuracy of 91.69% with a decision-level fusion.
CONCLUSION: The proposed MDC-CNN by integrating information from diverse brain connectivity descriptors is able to accurately discriminate SZ from HC.
SIGNIFICANCE: The new framework is potentially useful for developing diagnostic tools for SZ and other disorders.

PMID: 31536026 [PubMed - as supplied by publisher]

The reorganization of resting-state brain networks associated with motor imagery training in chronic stroke patients.

Fri, 09/20/2019 - 20:01
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The reorganization of resting-state brain networks associated with motor imagery training in chronic stroke patients.

IEEE Trans Neural Syst Rehabil Eng. 2019 Sep 13;:

Authors: Wang H, Xu G, Wang X, Sun C, Zhu B, Fan M, Jia J, Guo X, Sun L

Abstract
A number of studies have suggested that motor imagery training (MIT) has a positive influence on the upper extremity motor recovery in stroke patients, but little is known about its neural basis. To investigate the cortical motor network plasticity after MIT, 34 chronic hemiplegic subjects with subcortical stroke were recruited and randomly allocated to either the conventional rehabilitation therapy (CRT) or the CRT+MIT. The patients were assessed with the upper limb section of Fugl-Meyer assessment Scale (FM-UL) and resting-state fMRI before and after the 4 weeks of treatment. Seed-based functional connectivity (FC) of the ipsilesional primary motor cortex (M1) and graph-theory based analysis were used to explore the relationships between the motor recovery and reorganization of motor networks. We found that the patients in the MIT group showed more improvement in the FM-UL scores compared with the CRT group. Both groups presented increased inter-hemispheric and decreased intra-hemispheric FC of the ipsilesional M1 after intervention. However, the MIT group showed increased FC of the ipsilesional M1 with the ipsilesional precentral and postcentral gyri, middle cingulate gyrus and supramarginal gyrus after intervention, while the CRT group showed decreased FC in these regions. In addition, the clustering coefficient was significantly increased in the MIT group but not in the CRT group, and the increment of clustering coefficient was significantly positively correlated with improvement of FM-UL scores. Therefore, MIT might contribute to the motor recovery in stroke patients through the following network reorganization, i.e., promoting the efficiency of regional neuronal communication and the reorganization of intrinsic FC of the ipsilesional M1 involving widely distributed motor network in both hemispheres.

PMID: 31536007 [PubMed - as supplied by publisher]

Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review.

Thu, 09/19/2019 - 23:00
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Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review.

Hum Brain Mapp. 2019 Sep 18;:

Authors: Pini L, Jacquemot C, Cagnin A, Meneghello F, Semenza C, Mantini D, Vallesi A

Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to shed light on the pathophysiological mechanisms of Huntington's disease (HD), paving the way to new therapeutic interventions. A systematic literature review was conducted in three online databases according to PRISMA guidelines, using keywords for HD, functional connectivity, and rs-fMRI. We included studies investigating connectivity in presymptomatic (pre-HD) and manifest HD gene carriers compared to healthy controls, implementing seed-based connectivity, independent component analysis, regional property, and graph analysis approaches. Visual network showed reduced connectivity in manifest HD, while network/areas underpinning motor functions were consistently altered in both manifest HD and pre-HD, showing disease stage-dependent changes. Cognitive networks underlying executive and attentional functions showed divergent anterior-posterior alterations, possibly reflecting compensatory mechanisms. The involvement of these networks in pre-HD is still unclear. In conclusion, aberrant connectivity of the sensory-motor network is observed in the early stage of HD while, as pathology spreads, other networks might be affected, such as the visual and executive/attentional networks. Moreover, sensory-motor and executive networks exhibit hyper- and hypo-connectivity patterns following different spatiotemporal trajectories. These findings could potentially help to implement future huntingtin-lowering interventions.

PMID: 31532053 [PubMed - as supplied by publisher]

Ketamine effects on default mode network activity and vigilance: A randomized, placebo-controlled crossover simultaneous fMRI/EEG study.

Thu, 09/19/2019 - 23:00
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Ketamine effects on default mode network activity and vigilance: A randomized, placebo-controlled crossover simultaneous fMRI/EEG study.

Hum Brain Mapp. 2019 Sep 18;:

Authors: Zacharias N, Musso F, Müller F, Lammers F, Saleh A, London M, de Boer P, Winterer G

Abstract
In resting-state functional connectivity experiments, a steady state (of consciousness) is commonly supposed. However, recent research has shown that the resting state is a rather dynamic than a steady state. In particular, changes of vigilance appear to play a prominent role. Accordingly, it is critical to assess the state of vigilance when conducting pharmacodynamic studies with resting-state functional magnetic resonance imaging (fMRI) using drugs that are known to affect vigilance such as (subanesthetic) ketamine. In this study, we sought to clarify whether the previously described ketamine-induced prefrontal decrease of functional connectivity is related to diminished vigilance as assessed by electroencephalography (EEG). We conducted a randomized, double-blind, placebo-controlled crossover study with subanesthetic S-Ketamine in N = 24 healthy, young subjects by simultaneous acquisition of resting-state fMRI and EEG data. We conducted seed-based default mode network functional connectivity and EEG power spectrum analyses. After ketamine administration, decreased functional connectivity was found in medial prefrontal cortex whereas increased connectivities were observed in intraparietal cortices. In EEG, a shift of energy to slow (delta, theta) and fast (gamma) wave frequencies was seen in the ketamine condition. Frontal connectivity is negatively related to EEG gamma and theta activity while a positive relationship is found for parietal connectivity and EEG delta power. Our results suggest a direct relationship between ketamine-induced functional connectivity changes and the concomitant decrease of vigilance in EEG. The observed functional changes after ketamine administration may serve as surrogate end points and provide a neurophysiological framework, for example, for the antidepressant action of ketamine (trial name: 29JN1556, EudraCT Number: 2009-012399-28).

PMID: 31532029 [PubMed - as supplied by publisher]

Perturbations of language network connectivity in primary progressive aphasia.

Thu, 09/19/2019 - 23:00
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Perturbations of language network connectivity in primary progressive aphasia.

Cortex. 2019 Sep 09;:

Authors: Bonakdarpour B, Hurley RS, Wang AR, Fereira HR, Basu A, Chatrathi A, Guillaume K, Rogalski EJ, Mesulam MM

Abstract
Aphasias are caused by disruption in structural integrity and interconnectivity within a large-scale distributed language network. We investigated the distribution and behavioral consequences of altered functional connectivity in three variants of primary progressive aphasia (PPA). The goal was to clarify relationships among atrophy, resting connectivity, and the resulting behavioral changes in 73 PPA and 33 control participants. Three core regions of the left perisylvian language network: the inferior frontal gyrus (IFG), middle temporal gyrus (MTG), and anterior temporal lobe (ATL) were evaluated in agrammatic (PPA-G), logopenic (PPA-L), and semantic (PPA-S) PPA variants. All PPA groups showed decreased connectivity between IFG and MTG. The PPA-S group also showed additional loss of connectivity strength between ATL and the other language regions. Decreased connectivity between the IFG and MTG nodes in PPA-G remained significant even when controlled for the effect of atrophy. In the PPA group as a whole, IFG-MTG connectivity strength correlated with repetition and grammar scores, whereas MTG-ATL connectivity correlated with picture naming and single-word comprehension. There was no significant change in the connectivity of homologous regions in the right hemisphere. These results show that language impairments in PPA are associated with perturbations of functional connectivity within behaviorally concordant components of the language network. Altered connectivity in PPA may reflect not only the irreversible loss of cortical components indexed by atrophy, but also the dysfunction of remaining neurons.

PMID: 31530376 [PubMed - as supplied by publisher]

Maternal immune activation during pregnancy impacts on brain structure and function in the adult offspring.

Thu, 09/19/2019 - 23:00
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Maternal immune activation during pregnancy impacts on brain structure and function in the adult offspring.

Brain Behav Immun. 2019 Sep 14;:

Authors: Kreitz S, Zambon A, Ronovsky M, Budinsky L, Helbich TH, Sideromenos S, Ivan C, Konerth L, Wank I, Berger A, Pollak A, Hess A, Pollak DD

Abstract
Gestational infection constitutes a risk factor for the occurrence of psychiatric disorders in the offspring. Activation of the maternal immune system (MIA) with subsequent impact on the development of the fetal brain is considered to form the neurobiological basis for aberrant neural wiring and the psychiatric manifestations later in offspring life. The examination of validated animal models constitutes a premier resource. Here we used a mouse model of MIA based upon systemic treatment of pregnant mice with Poly(I:C) (polyriboinosinic-polyribocytidilic acid), for the unbiased and comprehensive analysis of the impact of MIA on adult offspring brain activity, morphometry, connectivity and function by a magnetic resonance imaging (MRI) approach. Overall lower neural activity, smaller brain regions and less effective fiber structure were observed for Poly(I:C) offspring compared to the control group. The corpus callosum was significantly smaller and presented with a disruption in myelin/ fiber structure in the MIA progeny. Subsequent resting-state functional MRI experiments demonstrated a paralleling dysfunctional interhemispheric connectivity. Additionally, while the overall flow of information was intact, cortico-limbic connectivity was hampered and limbic circuits revealed hyperconnectivity in Poly(I:C) offspring. Our study sheds new light on the impact of maternal infection during pregnancy on the offspring brain and identifies aberrant resting-state functional connectivity patterns as possible correlates of the behavioral phenotype with relevance for psychiatric disorders.

PMID: 31526827 [PubMed - as supplied by publisher]

Estimating repetitive spatiotemporal patterns from many subjects' resting-state fMRIs.

Tue, 09/17/2019 - 19:58

Estimating repetitive spatiotemporal patterns from many subjects' resting-state fMRIs.

Neuroimage. 2019 Sep 13;:116182

Authors: Takeda Y, Itahashi T, Sato MA, Yamashita O

Abstract
Recently, we proposed a method to estimate repetitive spatiotemporal patterns from resting-state brain activity data (SpatioTemporal Pattern estimation, STeP) (Takeda et al., 2016). From such resting-state data as functional MRI (fMRI), STeP can estimate several spatiotemporal patterns and their onsets even if they are overlapping. Nowadays, a growing number of resting-state data are publicly available from such databases as the Autism Brain Imaging Data Exchange (ABIDE), which promote a better understanding of resting-state brain activities. In this study, we extend STeP to make it applicable to such big databases, thus proposing the method we call BigSTeP. From many subjects' resting-state data, BigSTeP estimates spatiotemporal patterns that are common across subjects (common spatiotemporal patterns) as well as the corresponding spatiotemporal patterns in each subject (subject-specific spatiotemporal patterns). After verifying the performance of BigSTeP by simulation tests, we applied it to over 1,000 subjects' resting-state fMRIs (rsfMRIs) obtained from ABIDE I. This revealed two common spatiotemporal patterns and the corresponding subject-specific spatiotemporal patterns. The common spatiotemporal patterns included spatial patterns resembling the default mode (DMN), sensorimotor, auditory, and visual networks, suggesting that these networks are time-locked with each other. We compared the subject-specific spatiotemporal patterns between autism spectrum disorder (ASD) and typically developed (TD) groups. As a result, significant differences were concentrated at a specific time in a pattern, when the DMN exhibited large positive activity. This suggests that the differences are context-dependent, that is, the differences in fMRI activities between ASDs and TDs do not always occur during the resting state but tend to occur when the DMN exhibits large positive activity. All of these results demonstrate the usefulness of BigSTeP in extracting inspiring hypotheses from big databases in a data-driven way.

PMID: 31525496 [PubMed - as supplied by publisher]

Capturing the Forest But Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics.

Tue, 09/17/2019 - 19:58

Capturing the Forest But Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics.

Neural Comput. 2019 Sep 16;:1-35

Authors: Shaw SB, Dhindsa K, Reilly JP, Becker S

Abstract
The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be "atoms of thought," involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

PMID: 31525310 [PubMed - as supplied by publisher]

3D-CNN based discrimination of schizophrenia using resting-state fMRI.

Mon, 09/16/2019 - 22:56
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3D-CNN based discrimination of schizophrenia using resting-state fMRI.

Artif Intell Med. 2019 Jul;98:10-17

Authors: Qureshi MNI, Oh J, Lee B

Abstract
MOTIVATION: This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain. Resting-state functional MRI data from a total of 144 subjects (72 patients with schizophrenia and 72 healthy controls) was obtained from a publicly available dataset using a three-dimensional convolution neural network 3D-CNN based deep learning classification framework and ICA based features.
RESULTS: We achieved 98.09 ± 1.01% ten-fold cross-validated classification accuracy with a p-value < 0.001 and an area under the curve (AUC) of 0.9982 ± 0.015. In addition, differences in functional connectivity between the two groups were statistically analyzed across multiple resting-state networks. The disconnection between the visual and frontal network was prominent in patients, while they showed higher connectivity between the default mode network and other task-positive/ cerebellar networks. These ICA functional network maps served as highly discriminative three-dimensional imaging features for the discrimination of schizophrenia in this study.
CONCLUSION: Due to the very high AUC, this research with more validation on the cross diagnosis and publicly available dataset, may be translated in future as an adjunct tool to assist clinicians in the initial screening of schizophrenia.

PMID: 31521248 [PubMed - in process]

Alterations in basal ganglia-cerebello-thalamo-cortical connectivity and whole brain functional network topology in Tourette's syndrome.

Sat, 09/14/2019 - 19:54
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Alterations in basal ganglia-cerebello-thalamo-cortical connectivity and whole brain functional network topology in Tourette's syndrome.

Neuroimage Clin. 2019 Sep 03;24:101998

Authors: Ramkiran S, Heidemeyer L, Gaebler A, Shah NJ, Neuner I

Abstract
Tourette Syndrome (TS) is a neuropsychiatric disorder characterized by the presence of motor and vocal tics. Major pathophysiological theories posit a dysfunction of the cortico-striato-thalamo-cortical circuits as being a representative hallmark of the disease. Recent evidence suggests a more widespread dysfunction of brain networks in TS including the cerebellum and going even beyond classic motor pathways. In order to characterize brain network dysfunction in TS, in this study we investigated functional and effective-like connectivity as well as topological changes of basal ganglia-thalamo-cortical and cortico-cerebellar brain networks. We collected resting-state fMRI data from 28 TS patients (age: 32 ± 11 years) and 28 age-matched, healthy controls (age: 31 ± 9 years). Region of interest based (ROI-ROI) bivariate correlation and ROI-ROI bivariate regression were employed as measures of functional and effective-like connectivity, respectively. Graph theoretical measures of centrality (degree, cost, betweenness centrality), functional segregation (clustering coefficient, local efficiency) and functional integration (average path length, global efficiency) were used to assess topological brain network changes. In this study, TS patients exhibited increased basal ganglia-cortical and thalamo-cortical connectivity, reduced cortico-cerebellar connectivity, and an increase in parallel communication through the basal ganglia, thalamus and cerebellum (increased global efficiency). Additionally, we observed a reduction in serial information transfer (reduction in average path length) within the default mode and the salience network. In summary, our findings show that TS is characterized by increased connectivity and functional integration of multiple basal ganglia-thalamo-cortical circuits, suggesting a predominance of excitatory neurotransmission and a lack of brain maturation. Moreover, topological changes of cortico-cerebellar and brain networks involved in interoception may be underestimated neural correlates of tics and the crucial premonitory urge feeling.

PMID: 31518769 [PubMed - as supplied by publisher]

Segregated precuneus network and default mode network in naturalistic imaging.

Sat, 09/14/2019 - 19:54
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Segregated precuneus network and default mode network in naturalistic imaging.

Brain Struct Funct. 2019 Sep 12;:

Authors: Deng Z, Wu J, Gao J, Hu Y, Zhang Y, Wang Y, Dong H, Yang Z, Zuo X

Abstract
A resting-state network centered at the precuneus has been recently proposed as a precuneus network (PCUN) or "parietal memory network". Due to its spatial adjacency and overlapping with the default mode network (DMN), it is still not consensus to consider PCUN and DMN separately. Whether considering PCUN and DMN as different networks is a critical question that influences our understanding of brain functions and impairments. Previous resting-state studies using multiple methodologies have demonstrated a robust separation of the two networks. However, since there is no gold standard in justifying the functional difference between the networks in resting-state, we still lack of biological evidence to directly support the separation of the two networks. This study compared the responses and functional couplings of PCUN and DMN when participants were watching a movie and examined how the continuity of the movie context modulated the response of the networks. We identified PCUN and DMN in resting-state fMRI of 48 healthy subjects. The networks' response to a context-rich video and its context-shuffled version was characterized using the variance of temporal fluctuations and functional connectivity metrics. The results showed that (1) scrambling the contextual information altered the fluctuation level of DMN and PCUN in reversed ways; (2) compared to DMN, the FC within PCUN showed significantly higher sensitivity to the contextual continuity; (3) PCUN exhibited a significantly stronger functional network connectivity with the primary visual regions than DMN. These findings provide evidence for the distinct functional roles of PCUN and DMN in processing context-rich information and call for separately considering the functions and impairments of these networks in resting-state studies.

PMID: 31515678 [PubMed - as supplied by publisher]

Propagations of spontaneous brain activity in awake rats.

Fri, 09/13/2019 - 22:53
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Propagations of spontaneous brain activity in awake rats.

Neuroimage. 2019 Sep 09;:116176

Authors: Liu Y, Zhang N

Abstract
Slow propagations of spontaneous brain activity have been reported in multiple species. However, systematical investigation of the organization of such brain activity is still lacking. In this study, we analyzed propagations of spontaneous brain activity using a reference library of characteristic resting-state functional connectivity (RSFC) patterns in awake rodents. We found that transitions through multiple distinct RSFC patterns were reproducible not only in transition sequences but also in transition time delays. In addition, the organization of these transitions and their spatiotemporal dynamic patterns were revealed using a graphical model. We further identified prominent brain regions involved in these transitions. These results provide a comprehensive framework of brainwide propagations of spontaneous activity in awake rats. This study also offers a new tool to study the spatiotemporal dynamics of activity in the resting brain.

PMID: 31513942 [PubMed - as supplied by publisher]

Functional Connectivity Changes of the Visual Cortex in the Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study.

Fri, 09/13/2019 - 22:53
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Functional Connectivity Changes of the Visual Cortex in the Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study.

Spine (Phila Pa 1976). 2019 Sep 10;:

Authors: Chen Z, Zhao R, Wang Q, Yu C, Li F, Liang M, Zong Y, Zhao Y, Xiong W, Su Z, Xue Y

Abstract
: Study Design. Cross-sectional study.
OBJECTIVE: To analyze altered functional connectivity (FC)in the visual cortex of cervical spondylotic myelopathy (CSM) patients using resting-state fMRI.
SUMMARY OF BACKGROUND DATA: We previously showed changes in visual cortex neural activity in CSM patients.
METHODS: Thirty CSM patients and 20 healthy controls were recruited. MR data were collected using a 3.0 T MR. FC of the regions of interest (ROI) (Brodmann's areas (BA) 17/18/19/7) were calculated in a voxel-wise manner and compared between groups. Correlation analyses were performed between preoperative Japanese Orthopaedic Association (JOA) scores and altered FC, as well as between preoperative best corrected visual acuity (BCVA) and altered FC. Furthermore, the FC where was compared between the pre-operative and the postoperative CSM patients in an ROI-wise manner.
RESULTS: Increased FC was found between BA19 and the cerebellum inferior lobe; between the left BA7 and bilateral calcarine, right lingual, right fusiform gyrus, and left precuneus (BA17);between the left BA7 and right fusiform gyrus and right inferior occipital gyrus (right BA19); and between the right BA7 and right superior lobe of cerebellum (right BA19)in CSM patients (P < 0.05). A negative correlation was found between JOA score and FC of the left and right BA19, and a positive correlation was found between the BCVA and FC of the left and right BA7 (P < 0.05). ROI analysis demonstrated statistically significant FC differences in between the pre-operative and the postoperative CSM patients (P < 0.05).
CONCLUSION: FC changes were present in the visual cortex of CSM patients, which negatively correlated with preoperative JOA scores and positively correlated with preoperative BCVA. Significant recovery of FC in the visual cortex was detected in CSM patients postoperatively.
LEVEL OF EVIDENCE: 4.

PMID: 31513096 [PubMed - as supplied by publisher]

Intrinsic neural circuitry of depression in adolescent females.

Fri, 09/13/2019 - 22:53
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Intrinsic neural circuitry of depression in adolescent females.

J Child Psychol Psychiatry. 2019 Sep 12;:

Authors: Jin J, Van Snellenberg JX, Perlman G, DeLorenzo C, Klein DN, Kotov R, Mohanty A

Abstract
BACKGROUND: Adolescence is characterized by affective and cognitive changes that increase vulnerability to depression, especially in females. Neurodevelopmental models attribute adolescent depression to abnormal responses in amygdala, striatum, and prefrontal cortex (PFC). We examined whether the strength of functional brain networks involving these regions predicts depression symptoms in adolescent females.
METHODS: In this longitudinal study, we recorded resting-state functional connectivity (RSFC) in 174 adolescent females. Using a cross-validation strategy, we related RSFC profiles that included (a) a network consisting of amygdala, striatum, and PFC (within-circuit model), (b) connectivity of this network to the whole brain (extended-circuit model), and (c) a network consisting of the entire brain (whole-brain model) to depression symptoms assessed concurrently and 18 months later.
RESULTS: In testing subsets, the within-circuit RSFC profiles were associated with depression symptoms concurrently and 18 months later, while the extended-circuit and whole-brain model did not explain any additional variance in depression symptoms. Connectivity related to anterior cingulate and ventromedial prefrontal cortex contributed most to the association.
CONCLUSIONS: Our results demonstrate that RSFC-based brain networks that include amygdala, striatum, and PFC are stable neural signatures of concurrent and future depression symptoms, representing a significant step toward identifying the neural mechanism of depression in adolescence.

PMID: 31512744 [PubMed - as supplied by publisher]

Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification.

Fri, 09/13/2019 - 22:53
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Functional connectivity between white matter and gray matter based on fMRI for Alzheimer's disease classification.

Brain Behav. 2019 Sep 11;:e01407

Authors: Zhao J, Ding X, Du Y, Wang X, Men G

Abstract
INTRODUCTION: Alzheimer's disease (AD) is a chronic neurodegenerative disease that generally starts slowly and leads to deterioration over time. Finding biomarkers more effective to predict AD transition is important for clinical medicine. And current research indicated that the lesion regions occur in both gray matter (GM) and white matter (WM).
METHODS: This paper extracted BOLD time series from WM and GM, combined WM and GM together for analysis, constructed functional connectivity (FC) of static (sWGFC) and dynamic (dWGFC) between WM and GM, as well as static (sGFC) and dynamic (dGFC) FC within GM in order to evaluate the methods and areas most useful as feature sets for distinguishing NC from AD. These features will be evaluated using support vector machine (SVM) classifiers.
RESULTS: The FC constructed by WM BOLD time series based on fMRI showed widely differences between the AD group and NC group. In terms of the results of the classification, the performance of feature subsets selected from sWGFC was better than sGFC, and the performance of feature subsets selected from dWGFC was better than dGFC. Overall, the feature subsets selected from dWGFC was the best.
CONCLUSION: These results indicated that there is a wide range of disconnection between WM and GM in AD, and association between WM and GM based on fMRI only is an effective strategy, and the FC between WM and GM could be a potential biomarker in the process of cognitive impairment and AD.

PMID: 31512413 [PubMed - as supplied by publisher]

Differential resting state connectivity responses to glycemic state in type 1 diabetes.

Fri, 09/13/2019 - 22:53
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Differential resting state connectivity responses to glycemic state in type 1 diabetes.

J Clin Endocrinol Metab. 2019 Sep 12;:

Authors: Parikh L, Seo D, Lacadie C, Belfort-DeAguiar R, Groskreutz D, Hamza M, Dai F, Scheinost D, Sinha R, Constable RT, Sherwin R, Hwang JJ

Abstract
CONTEXT: Individuals with type 1 diabetes (T1DM) have alterations in brain activity which have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain's resting state activity remains unclear.
OBJECTIVE: To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware).
DESIGN: Observational study.
SETTING: Academic medical center.
PARTICIPANTS: 27 individuals with T1DM and 12 healthy control volunteers participated in the study.
INTERVENTION: All participants underwent BOLD resting state fMRI brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dl)-hypoglycemic (60mg/dl) clamp.
OUTCOME: Changes in resting state functional connectivity.
RESULTS: Using two separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia amongst HC, T1DM-Aware and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016).
CONCLUSION: These findings provide evidence that individuals with T1DM have changes in the brain's resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes amongst individuals with T1DM.

PMID: 31511880 [PubMed - as supplied by publisher]

Differential resting state connectivity responses to glycemic state in type 1 diabetes.

Fri, 09/13/2019 - 22:53
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Differential resting state connectivity responses to glycemic state in type 1 diabetes.

J Clin Endocrinol Metab. 2019 Sep 12;:

Authors: Parikh L, Seo D, Lacadie C, Belfort-DeAguiar R, Groskreutz D, Hamza M, Dai F, Scheinost D, Sinha R, Constable RT, Sherwin R, Hwang JJ

Abstract
CONTEXT: Individuals with type 1 diabetes (T1DM) have alterations in brain activity which have been postulated to contribute to the adverse neurocognitive consequences of T1DM; however, the impact of T1DM and hypoglycemic unawareness on the brain's resting state activity remains unclear.
OBJECTIVE: To determine whether individuals with T1DM and hypoglycemia unawareness (T1DM-Unaware) had changes in the brain resting state functional connectivity compared to healthy controls (HC) and those with T1DM and hypoglycemia awareness (T1DM-Aware).
DESIGN: Observational study.
SETTING: Academic medical center.
PARTICIPANTS: 27 individuals with T1DM and 12 healthy control volunteers participated in the study.
INTERVENTION: All participants underwent BOLD resting state fMRI brain imaging during a 2-step hyperinsulinemic euglycemic (90 mg/dl)-hypoglycemic (60mg/dl) clamp.
OUTCOME: Changes in resting state functional connectivity.
RESULTS: Using two separate methods of functional connectivity analysis, we identified distinct differences in the resting state brain responses to mild hypoglycemia amongst HC, T1DM-Aware and T1DM-Unaware participants, particularly in the angular gyrus, an integral component of the default mode network (DMN). Furthermore, changes in angular gyrus connectivity also correlated with greater symptoms of hypoglycemia (r = 0.461, P = 0.003) as well as higher scores of perceived stress (r = 0.531, P = 0.016).
CONCLUSION: These findings provide evidence that individuals with T1DM have changes in the brain's resting state connectivity patterns, which may be further associated with differences in awareness to hypoglycemia. These changes in connectivity may be associated with alterations in functional outcomes amongst individuals with T1DM.

PMID: 31511876 [PubMed - as supplied by publisher]

Parcellation of the Hippocampus Using Resting Functional Connectivity in Temporal Lobe Epilepsy.

Thu, 09/12/2019 - 19:52
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Parcellation of the Hippocampus Using Resting Functional Connectivity in Temporal Lobe Epilepsy.

Front Neurol. 2019;10:920

Authors: Barnett AJ, Man V, McAndrews MP

Abstract
We have previously shown that the connectivity of the hippocampus to other regions of the default mode network (DMN) is a strong indicator of memory ability in people with temporal lobe epilepsy (TLE). Recent work in the cognitive neuroscience literature has suggested that the anterior and posterior aspects of the hippocampus have distinct connections to the rest of the DMN and may support different memory operations. Further, structural analysis of epileptogenic hippocampi has found greater atrophy, characterized by mesial temporal sclerosis, in the anterior region of the hippocampus. Here, we used resting state FMRI data to parcellate the hippocampus according to its functional connectivity to the rest of the brain in people with left lateralized TLE (LTLE) and right lateralized TLE (RTLE), and in a group of neurologically healthy controls. We found similar anterior and posterior compartments in all groups. However, there was weaker connectivity of the epileptogenic hippocampus to multiple regions of the DMN. Both TLE groups showed reduced connectivity of the posterior hippocampus to key hubs of the DMN, the posterior cingulate cortex (PCC) and the medial pre-frontal cortex (mPFC). In the LTLE group, the anterior hippocampus also showed reduced connectivity to the DMN, and this effect was influenced by the presence of mesial temporal sclerosis. When we explored brain-behavior relationships, we found that reduced connectivity of the left anterior hippocampus to the DMN hubs related to poorer verbal memory ability in people with LTLE, and reduced connectivity of the right posterior hippocampus to the PCC related to poorer visual memory ability in those with RTLE. These findings may inform models regarding functional distinctions of the hippocampal anteroposterior axis.

PMID: 31507522 [PubMed]