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Functional neuroimaging in the acute phase of Takotsubo syndrome: volumetric and functional changes of the right insular cortex.

Sat, 02/01/2020 - 16:40
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Functional neuroimaging in the acute phase of Takotsubo syndrome: volumetric and functional changes of the right insular cortex.

Clin Res Cardiol. 2020 Jan 30;:

Authors: Dichtl W, Tuovinen N, Barbieri F, Adukauskaite A, Senoner T, Rubatscher A, Hintringer F, Siedentopf C, Bauer A, Gizewski ER, Steiger R

Abstract
BACKGROUND: A brain-heart interaction has been proposed in Takotsubo syndrome (TTS). Structural changes in the limbic system and hypoconnectivity between certain brain areas in the chronic phase of the disease have been reported, but little is known concerning functional neuroimaging in the acute phase. We hypothesizedh anatomical and functional changes in the central nervous system and investigated whole-brain volumetric and functional connectivity alterations in the acute phase TTS patients compared to controls.
METHODS: Anatomical and resting-state functional magnetic resonance imaging were performed in postmenopausal females: thirteen in the acute TTS phase and thirteen healthy controls without evidence of coronary artery disease. Voxel-based morphometry and graph theoretical analysis were applied to identify anatomical and functional differences between patients and controls.
RESULTS: Significantly lower gray matter volumes were found in TTS patients in the right middle frontal gyrus (p = 0.004) and right subcallosal cortex (p = 0.009) compared to healthy controls. When lower threshold was applied, volumetric changes were noted in the right insular cortex (p = 0.0113), the right paracingulate cortex (p = 0.012), left amygdala (p = 0.018), left central opercular cortex (p = 0.017), right (p = 0.013) and left thalamus (p = 0.017), and left cerebral cortex (p = 0.017). Graph analysis revealed significantly (p < 0.01) lower functional connectivity in TTS patients compared to healthy controls, particularly in the connections originating from the right insular cortex, temporal lobes, and precuneus.
CONCLUSION: In the acute phase of TTS volumetric changes in frontal regions and the central autonomic network (i.e. insula, anterior cingulate cortex, and amygdala) were noted. In particular, the right insula, associated with sympathetic autonomic tone, had both volumetric and functional changes.

PMID: 32002630 [PubMed - as supplied by publisher]

DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI.

Sat, 02/01/2020 - 16:40
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DeepFMRI: End-to-end deep learning for functional connectivity and classification of ADHD using fMRI.

J Neurosci Methods. 2020 Jan 27;:108506

Authors: Riaz A, Asad M, Alonso E, Slabaugh G

Abstract
Background Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behaviour analysis. New method In this paper, we propose an end-to-end deep learning architecture to diagnose ADHD. Our aim is to (1) automatically classify a subject as ADHD or healthy control, and (2) demonstrate the importance of functional connectivity to increase classification accuracy and provide interpretable results. The proposed method, called DeepFMRI, is comprised of three sequential networks, namely (1) a feature extractor, (2) a functional connectivity network, and (3) a classification network. The model takes fMRI pre-processed time-series signals as input and outputs a diagnosis, and is trained end-to-end using back-propagation. Results Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative method outperforms previous state-of-the-art. Different imaging sites contributed the data to the ADHD-200 dataset. For the New York University imaging site, our proposed method was able to achieve classification accuracy of 73.1% (specificity 91.6%, sensitivity 65.5%). Comparison with Existing Methods In this work, we propose a novel end-to-end deep learning method incorporating functional connectivity for the classification of ADHD. To the best of our knowledge, this has not been explored by existing studies. Conclusions The results suggest that the proposed end-to-end deep learning architecture achieves better performance as compared to the other state-of-the-art methods. The findings suggest that the frontal lobe contains the most discriminative power towards the classification of ADHD.

PMID: 32001294 [PubMed - as supplied by publisher]

Basolateral Amygdala Connectivity With Subgenual Anterior Cingulate Cortex Represents Enhanced Fear-Related Memory Encoding in Anxious Humans.

Sat, 02/01/2020 - 16:40
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Basolateral Amygdala Connectivity With Subgenual Anterior Cingulate Cortex Represents Enhanced Fear-Related Memory Encoding in Anxious Humans.

Biol Psychiatry Cogn Neurosci Neuroimaging. 2019 Nov 27;:

Authors: Hakamata Y, Mizukami S, Izawa S, Moriguchi Y, Hori H, Kim Y, Hanakawa T, Inoue Y, Tagaya H

Abstract
BACKGROUND: The amygdala can enhance emotional memory encoding as well as anxiogenesis via corticotropin-releasing factor neurons. However, the amygdala's explicit role in emotional encoding remains unclarified in humans. We examined how functional connectivity (FC) of amygdala subnuclei affects emotional encoding, considering its mechanism in which anxiety, attention, and cortisol conceivably participate.
METHODS: A total of 65 healthy humans underwent resting-state functional magnetic resonance imaging scans and saliva collection at 10 points in time over 2 days. FC analysis was performed for basolateral amygdala subnucleus (BLA) and centromedial amygdala subnucleus. We assessed attentional control via an emotional Stroop task and assessed emotional encoding via a facial identification task that examines how strongly a neutral face is memorized when accompanied by an emotional face (fearful, sad, or happy). FC and task performance were compared between high-anxious and non-high-anxious groups classified by anxious personality scores.
RESULTS: BLA connected with subgenual anterior cingulate cortex (sgACC) in proportion to the strength of fear-related encoding, whereas centromedial subnucleus connected with caudate nucleus for happy-related encoding. The high-anxious group showed more enhanced fear-related encoding but impaired happy-related encoding compared with the non-high-anxious group. BLA-sgACC FC was more intensified in the high-anxious group than in the non-high-anxious group; however, centromedial-caudate FC did not differ between them. Although emotional encoding was uncorrelated with either attentional control or cortisol, BLA-sgACC was positively correlated with cortisol increase after awakening.
CONCLUSIONS: The study revealed that neural interactions of BLA, specifically with sgACC, might play a critical role in fear-related memory encoding, depending on the individual's level of anxiety. These findings aid in understanding the complicated mechanisms of emotional memory in anxiety disorders.

PMID: 32001192 [PubMed - as supplied by publisher]

Functional Connectivity of the Striatum as a Neural Correlate of Symptom Severity in Patient with Obsessive-Compulsive Disorder.

Sat, 02/01/2020 - 16:40
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Functional Connectivity of the Striatum as a Neural Correlate of Symptom Severity in Patient with Obsessive-Compulsive Disorder.

Psychiatry Investig. 2020 Feb 03;:

Authors: Park J, Kim T, Kim M, Lee TY, Kwon JS

Abstract
OBJECTIVE: It is well established that the cortico-striato-thalamo-cortical (CSTC) circuit is implicated in the pathophysiology of obsessive- compulsive disorder (OCD). However, reports on corticostriatal functional connectivity (FC) in OCD have been inconsistent due to the structural and functional heterogeneity of the striatum. Therefore, in the present study, we investigated corticostriatal FC using a fine 12-seed striatal parcellation to overcome this heterogeneity and discover the neural correlates of symptoms in OCD patients.
METHODS: We recruited 23 OCD patients and 23 healthy controls (HCs). Whole-brain FC based on striatal seeds was examined using resting-state functional magnetic resonance imaging data and compared across OCD patients and HCs. We conducted correlation analysis between FCs of striatal subregions with significant group differences and symptom severity scores on the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), Hamilton Rating Scale for Depression, and Hamilton Rating Scale for Anxiety (HAM-A).
RESULTS: Compared to HCs, patients demonstrated increased FC of the dorsal caudal putamen and ventral rostral putamen (VRP) with several cortical regions, such as the intracalcarine cortex, inferior frontal gyrus, supramarginal/angular gyrus (SMG/AG), and postcentral gyrus (PCG). Furthermore, FC between the VRP and SMG/AG and between the VRP and PCG was negatively correlated with scores on the Y-BOCS compulsive subscale and the HAM-A, respectively.
CONCLUSION: These findings suggest that striatal subregions have strengthened FC with extensive cortical regions, which may reflect neural correlates of compulsive and anxious symptoms in OCD patients. These results contribute to an improved understanding of OCD pathophysiology by complementing the current evidence regarding striatal FC.

PMID: 32000480 [PubMed - as supplied by publisher]

Structural connectivity prior to whole-body sensorimotor skill learning associates with changes in resting state functional connectivity.

Sat, 02/01/2020 - 16:40
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Structural connectivity prior to whole-body sensorimotor skill learning associates with changes in resting state functional connectivity.

Neuroimage. 2019 08 15;197:191-199

Authors: Mizuguchi N, Maudrich T, Kenville R, Carius D, Maudrich D, Villringer A, Ragert P

Abstract
Changes in resting state functional connectivity are induced by sensorimotor training and assumed to be concomitant of motor learning, although a potential relationship between functional and structural connectivity associated with sensorimotor sequence learning remains elusive. To investigate whether initial structural connectivity relates to changes in functional connectivity, we evaluated resting state functional connectivity (rs-FC), white matter fibre density (FD), fibre-bundle cross-section (FC), and gray matter volume (GMV) in healthy human participants before and after two days of performing a complex whole-body serial reaction time task (CWB-SRTT). As CWB-SRTT was implicit, participants were not told about the presence of any sequence. Since the lateral prefrontal cortex (PFC) plays an important role in sequence learning, we hypothesized that structural connectivity within the PFC prior to learning is associated with changes in rs-FC involving the lateral PFC. Sequence specific improvements, as assessed by the time difference between the last random and the last sequence blocks, were observed for reaction times, suggesting that sensorimotor sequence memory was acquired. Rs-FC between the right lateral PFC and bilateral striatum increased significantly in the learning group, when compared to a control group who performed only random blocks. This indicated that rs-FC changes are related to sequence memory but not to exercise itself. In addition, changes in rs-FC between the right lateral PFC and the left striatum were correlated with sequence specific improvements in individual reaction times. Furthermore, changes in rs-FC between right lateral PFC and left striatum were positively correlated with FC in the right anterior corona radiata measured before the task. We did not find any structural changes or significant correlations in FD or GMV. These findings suggest that an early phase of sensorimotor sequence learning in complex whole-body movements is associated with an increase in rs-FC between prefrontal and subcortical regions. Furthermore, we provide novel evidence that CWB-SRTT-induced changes in rs-FC were correlated with FC within the PFC.

PMID: 31029869 [PubMed - indexed for MEDLINE]

Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data.

Sat, 02/01/2020 - 16:40
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Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data.

IEEE Trans Neural Syst Rehabil Eng. 2019 03;27(3):429-439

Authors: Sun S, Li X, Zhu J, Wang Y, La R, Zhang X, Wei L, Hu B

Abstract
Existing studies have shown functional brain networks in patients with major depressive disorder (MDD) have abnormal network topology structure. But the methods to construct brain network still exist some issues to be solved. This paper is to explore reliable and robust construction methods of functional brain network using different coupling methods and binarization approaches, based on high-density 128-channel resting state EEG recordings from 16 MDD patients and 16 normal controls (NC). It was found that the combination of imaginary part of coherence and cluster-span threshold outperformed other methods. Based on this combination, right hemisphere function deficiency, symmetry breaking and randomized network structure were found in MDD, which confirmed that MDD had aberrant cognitive processing. Furthermore, clustering coefficient in left central region in theta band and node betweenness centrality in right temporal region in alpha band were significantly negatively correlated with depressive level. And these network metrics had the ability to discriminate MDD from NC, which indicated that these network metrics might be served as the electrophysiological characteristics for probable MDD identification. Hence, this paper may provide reliable methods to construct functional brain network and offer potential biomarkers in MDD.

PMID: 30676968 [PubMed - indexed for MEDLINE]

Aberrant interhemispheric functional connectivity in first-episode, drug-naïve major depressive disorder.

Sat, 02/01/2020 - 16:40
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Aberrant interhemispheric functional connectivity in first-episode, drug-naïve major depressive disorder.

Brain Imaging Behav. 2019 Oct;13(5):1302-1310

Authors: Yang H, Wang C, Ji G, Feng Z, Duan J, Chen F, Zhou XJ, Shi Y, Xie H

Abstract
Many studies have indicated that depression is associated with impairment of the topological organization of the brain functional network, which may lead to disruption of mood and cognition in depressive patients. The abnormality of homotopic connectivity provides a basis for the clinical manifestations of depression, such as emotional and cognitive disorders. Several studies have investigated the abnormal imbalance of homotopic regions between the hemispheres in depressive patients. However, the reported findings are inconsistent. Additionally, the published studies have focused on only the grey matter when investigating functional connectivity abnormalities of the bilateral cerebral hemispheres in major depressive disorder (MDD). The aim of this study is to investigate functional connectivity abnormalities of the bilateral cerebral hemispheres in patients with first-episode, drug-naïve MDD using a voxel-mirrored homotopic connectivity (VMHC) method. Based on DSM-IV diagnostic criteria, 23 first-episode, drug-naïve MDD patients were recruited, together with 20 gender- and age-matched healthy normal controls. A Philips Achieva 3.0 T MRI scanner was used to acquire brain functional images at resting state as well as high-resolution structural images. The functional images were preprocessed by using Data Processing Assistant for Resting-State Functional MR Imaging toolkit and SPM8.VMHC between the bilateral hemispheres was computed and compared between the MDD and control groups. The correlation between the VMHC values of the abnormal homotopy function areas and the Hamilton Depression Rating Scale (HAMD) was evaluated in the MDD patients. Compared with the control group, the MDD patients showed significantly decreased VMHC values in the bilateral brain regions including the insular, putamen, and frontal white matter. The MDD patients did not exhibit increased VMHC values in any brain regions compared with the normal controls. In addition, a negative correlation was observed between the VMHC value in the frontal lobe white-matter and the HAMD in the MDD patients. Abnormalities in brain homotopic functional connectivity observed in this study may indicate abnormal neural circuits related to aberrant cognition and emotional processing in MDD. Although the physiological significance underlaying abnormal VMHC in white matter in the frontal lobe needs further research, our study new angle to investigate the role of white-matter abnormalities in MDD as well as other psychiatric disorders.

PMID: 30145713 [PubMed - indexed for MEDLINE]

Abnormalities of thalamus volume and resting state functional connectivity in primary insomnia patients.

Sat, 02/01/2020 - 16:40
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Abnormalities of thalamus volume and resting state functional connectivity in primary insomnia patients.

Brain Imaging Behav. 2019 Oct;13(5):1193-1201

Authors: Li M, Wang R, Zhao M, Zhai J, Liu B, Yu D, Yuan K

Abstract
Primary insomnia (PI) is associated with deteriorating attention, memory, physical and mood complaints. Based on the extensive literature demonstrating the critical roles of the thalamus in sleep regulation, we hypothesized that insomnia would be associated with functional and structural changes of the thalamus. This information is needed to better understand the neural mechanisms of insomnia, and would be useful for informing future attempts to alleviate or treat insomnia symptoms. Twenty-seven PI patients and 39 matched healthy controls were included in the present study. Subcortical volume and resting state functional connectivity (RSFC) of thalamus were compared between groups, and the relationships between neuroimaging differences and clinical features, including the Pittsburgh Sleep Quality Index (PSQI), the Insomnia Severity Index Scale (ISI), the Self-Rating Anxiety Scale (SAS) and the Self-Rating Depression Scale (SDS), also be explored. Compared with the control group, the PI group showed significantly reduced volume of thalamus. In addition, several brain regions showed reduced RSFC with thalamus in PI patients, such as anterior cingulate cortex (ACC), orbitofrontal cortex, hippocampus, caudate and putamen. Correlation analyses revealed that, several of these RSFC patterns were negatively correlated with PSQI score among PI patients, including thalamic connections with the putamen, caudate, hippocampus. Negative correlation was also observed between the RSFC strength of right thalamus-right ACC and SDS score in PI patients. This work demonstrates the structural and functional abnormalities of the thalamus in PI patients that were associated with key clinical features of insomnia. These data further highlight the important role of the thalamus in sleep and PI.

PMID: 30091019 [PubMed - indexed for MEDLINE]

Mapping correlations of psychological and structural connectome properties of the dataset of the human connectome project with the maximum spanning tree method.

Sat, 02/01/2020 - 16:40
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Mapping correlations of psychological and structural connectome properties of the dataset of the human connectome project with the maximum spanning tree method.

Brain Imaging Behav. 2019 Oct;13(5):1185-1192

Authors: Szalkai B, Varga B, Grolmusz V

Abstract
Genome-wide association studies (GWAS) opened new horizons in genomics and medicine by discovering novel genetic factors in numerous health conditions. The analogous analysis of the correlations of large quantities of psychological and brain imaging measures may yield similarly striking results in the brain science. Smith et al. (Nat Neurosci. 18(11): 1565-1567, 2015) presented a study of the associations between MRI-detected resting-state functional connectomes and behavioral data, based on the Human Connectome Project's (HCP) data release. Here we analyze the pairwise correlations between 717 psychological-, anatomical- and structural connectome-properties, based also on the Human Connectome Project's 500-subject dataset. For the connectome properties, we have focused on the structural (or anatomical) connectomes, instead of the functional connectomes. For the structural connectome analysis we have computed and publicly deposited structural braingraphs at the site http://braingraph.org . Numerous non-trivial and hard-to-compute graph-theoretical parameters (like minimum bisection width, minimum vertex cover, eigenvalue gap, maximum matching number, maximum fractional matching number) were computed for braingraphs of each subject, gained from the left- and right hemispheres and the whole brain. The correlations of these parameters, as well as other anatomical and behavioral measures were detected and analyzed. For discovering and visualizing the most interesting correlations in the 717 x 717 matrix, we have applied the maximum spanning tree method. Apart from numerous natural correlations, which describe parameters computable or approximable from one another, we have found several significant, novel correlations in the dataset, e.g., between the score of the NIH Toolbox 9-hole Pegboard Dexterity Test and the maximum weight graph theoretical matching in the left hemisphere. We also have found correlations described very recently and independently from the HCP-dataset: e.g., between gambling behavior and the number of the connections leaving the insula: these already known findings independently validate the power of our method.

PMID: 30088220 [PubMed - indexed for MEDLINE]

On the acquisition of the water signal during water suppression: High-speed MR spectroscopic imaging with water referencing and concurrent functional MRI.

Fri, 01/31/2020 - 16:00
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On the acquisition of the water signal during water suppression: High-speed MR spectroscopic imaging with water referencing and concurrent functional MRI.

NMR Biomed. 2020 Jan 30;:e4261

Authors: Posse S, Sa De La Rocque Guimaraes B, Hutchins-Delgado T, Vakamudi K, Fotso Tagne K, Moeller S, Dager SR

Abstract
This study evaluated the utility of concurrent water signal acquisition as part of the water suppression in MR spectroscopic imaging (MRSI), to allow simultaneous water referencing for metabolite quantification, and to concurrently acquire functional MRI (fMRI) data. We integrated a spatial-spectral binomial water excitation RF pulse and a short spatial-spectral echo-planar readout into the water suppression module of 2D and 3D proton-echo-planar-spectroscopic-imaging (PEPSI) with a voxel size as small as 4 x 4 x 6 mm3 . Metabolite quantification in reference to tissue water was validated in healthy controls for different prelocalization methods (spin-echo, PRESS and semi-LASER) and the clinical feasibility of a 3-minute 3D semi-Laser PEPSI scan (TR/TE: 1250/32 ms) with water referencing in patients with brain tumors was demonstrated. Spectral quality, SNR, Cramer-Rao-lower-bounds and water suppression efficiency were comparable with conventional PEPSI. Metabolite concentration values in reference to tissue water, using custom LCModel-based spectral fitting with relaxation correction, were in the range of previous studies and independent of the prelocalization method used. Next, we added a phase-encoding undersampled echo-volumar imaging (EVI) module during water suppression to concurrently acquire metabolite maps with water referencing and fMRI data during task execution and resting state in healthy controls. Integration of multimodal signal acquisition prolongated minimum TR by less than 50 ms on average. Visual and motor activation in concurrent fMRI/MRSI (TR: 1250-1500 ms, voxel size: 4 x 4 x 6 mm3 ) was readily detectable in single-task blocks with percent signal change comparable with conventional fMRI. Resting-state connectivity in sensory and motor networks was detectable in 4 minutes. This hybrid water suppression approach for multimodal imaging has the potential to significantly reduce scan time and extend neuroscience research and clinical applications through concurrent quantitative MRSI and fMRI acquisitions.

PMID: 31999397 [PubMed - as supplied by publisher]

Predictive Pattern Classification Can Distinguish Gender Identity Subtypes from Behavior and Brain Imaging.

Fri, 01/31/2020 - 16:00
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Predictive Pattern Classification Can Distinguish Gender Identity Subtypes from Behavior and Brain Imaging.

Cereb Cortex. 2020 Jan 29;:

Authors: Clemens B, Derntl B, Smith E, Junger J, Neulen J, Mingoia G, Schneider F, Abel T, Bzdok D, Habel U

Abstract
The exact neurobiological underpinnings of gender identity (i.e., the subjective perception of oneself belonging to a certain gender) still remain unknown. Combining both resting-state functional connectivity and behavioral data, we examined gender identity in cisgender and transgender persons using a data-driven machine learning strategy. Intrinsic functional connectivity and questionnaire data were obtained from cisgender (men/women) and transgender (trans men/trans women) individuals. Machine learning algorithms reliably detected gender identity with high prediction accuracy in each of the four groups based on connectivity signatures alone. The four normative gender groups were classified with accuracies ranging from 48% to 62% (exceeding chance level at 25%). These connectivity-based classification accuracies exceeded those obtained from a widely established behavioral instrument for gender identity. Using canonical correlation analyses, functional brain measurements and questionnaire data were then integrated to delineate nine canonical vectors (i.e., brain-gender axes), providing a multilevel window into the conventional sex dichotomy. Our dimensional gender perspective captures four distinguishable brain phenotypes for gender identity, advocating a biologically grounded reconceptualization of gender dimorphism. We hope to pave the way towards objective, data-driven diagnostic markers for gender identity and transgender, taking into account neurobiological and behavioral differences in an integrative modeling approach.

PMID: 31999324 [PubMed - as supplied by publisher]

Reduced Global-Brain Functional Connectivity and Its Relationship With Symptomatic Severity in Cervical Dystonia.

Fri, 01/31/2020 - 16:00
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Reduced Global-Brain Functional Connectivity and Its Relationship With Symptomatic Severity in Cervical Dystonia.

Front Neurol. 2019;10:1358

Authors: Pan P, Wei S, Ou Y, Jiang W, Li W, Lei Y, Liu F, Guo W, Luo S

Abstract
Background: Altered functional connectivity (FC) is related to pathophysiology of patients with cervical dystonia (CD). However, inconsistent results may be obtained due to different selected regions of interest. We explored voxel-wise brain-wide FC changes in patients with CD at rest in an unbiased manner and analyzed their correlations with symptomatic severity using the Tsui scale. Method: A total of 19 patients with CD and 21 sex- and age-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Global-brain FC (GFC) was applied to analyze the images. Support vector machine was used to distinguish the patients from the controls. Results: Patients with CD exhibited decreased GFC in the right precentral gyrus and right supplementary motor area (SMA) that belonged to the M1-SMA motor network. Significantly negative correlation was observed between GFC values in the right precentral gyrus and symptomatic severity in the patients (r = -0.476, p = 0.039, uncorrected). Decreased GFC values in these two brain regions could be utilized to differentiate the patients from the controls with good accuracies, sensitivities and specificities (83.33, 85.71, and 80.95% in the right precentral gyrus; and 87.59, 89.49, and 85.71% in the right SMA). Conclusions: Our investigation suggests that patients with CD show reduced GFC in brain regions of the M1-SMA motor network and provides further insights into the pathophysiology of CD. GFC values in the right precentral gyrus and right SMA may be used as potential biomarkers to recognize the patients from the controls.

PMID: 31998218 [PubMed]

Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database.

Fri, 01/31/2020 - 16:00
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Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database.

Front Hum Neurosci. 2019;13:457

Authors: Kurashige H, Kaneko J, Yamashita Y, Osu R, Otaka Y, Hanakawa T, Honda M, Kawabata H

Abstract
To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as 'emotion,' 'attention,' 'episodic memory,' etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.

PMID: 31998102 [PubMed]

Corrigendum: Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study.

Fri, 01/31/2020 - 16:00
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Corrigendum: Topological Modification of Brain Networks Organization in Children With High Intelligence Quotient: A Resting-State fMRI Study.

Front Hum Neurosci. 2019;13:450

Authors: Suprano I, Delon-Martin C, Kocevar G, Stamile C, Hannoun S, Achard S, Badhwar A, Fourneret P, Revol O, Nusbaum F, Sappey-Marinier D

Abstract
[This corrects the article DOI: 10.3389/fnhum.2019.00241.].

PMID: 31998099 [PubMed - in process]

Healthy Subjects With Extreme Patterns of Performance Differ in Functional Network Topology and Benefits From Nicotine.

Fri, 01/31/2020 - 16:00
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Healthy Subjects With Extreme Patterns of Performance Differ in Functional Network Topology and Benefits From Nicotine.

Front Syst Neurosci. 2019;13:83

Authors: Gießing C, Ahrens S, Thiel CM

Abstract
Do subjects with atypical patterns in attentional and executive behaviour show different brain network topology and react differently towards nicotine administration? The efficacy of pro-cognitive drugs like nicotine considerably varies between subjects and previous theoretical and empirical evidence suggest stronger behavioural nicotine effects in subjects with low performance. One problem is, however, how to best define low performance, especially if several cognitive functions are assessed for subject characterisation. We here present a method that used a multivariate, robust outlier detection algorithm to identify subjects with suspicious patterns of performance in attentional and executive functioning. In contrast to univariate approaches, this method is sensitive towards extreme positions within the multidimensional space that do not have to be extreme values in the individual behavioural distributions. The method was applied to a dataset of healthy, non-smoking subjects (n = 34) who were behaviorally characterised by an attention and executive function test on which N = 12 volunteers were classified as outliers. All subjects then underwent a resting-state functional magnetic resonance imaging (fMRI) scan to characterise brain network topology and an experimental behavioural paradigm under placebo and nicotine (7 mg patch) that gauged aspects of attention and executive function. Our results indicate that subjects with an atypical multivariate pattern in attention and executive functioning showed significant differences in nodal brain network integration in visual association and pre-motor brain regions during resting state. These differences in brain network topology significantly predicted larger individual nicotine effects on attentional processing. In summary, the current approach successfully identified a subgroup of healthy volunteers with low behavioural performance who differ in brain network topology and attentional benefit from nicotine.

PMID: 31998085 [PubMed]

Abnormal brain activity in adolescents with Internet addiction who attempt suicide: an assessment using functional magnetic resonance imaging.

Fri, 01/31/2020 - 16:00
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Abnormal brain activity in adolescents with Internet addiction who attempt suicide: an assessment using functional magnetic resonance imaging.

Neural Regen Res. 2020 Aug;15(8):1554-1559

Authors: Huang Y, Xu L, Kuang L, Wang W, Cao J, Xiao MN

Abstract
Internet addiction is associated with an increased risk of suicidal behavior and can lead to brain dysfunction among adolescents. However, whether brain dysfunction occurs in adolescents with Internet addiction who attempt suicide remains unknown. This observational cross-sectional study enrolled 41 young Internet addicts, aged from 15 to 20 years, from the Department of Psychiatry, the First Affiliated Hospital of Chongqing Medical University, China from January to May 2018. The participants included 21 individuals who attempted suicide and 20 individuals with Internet addiction without a suicidal attempt history. Brain images in the resting state were obtained by a 3.0 T magnetic resonance imaging scanner. The results showed that activity in the gyrus frontalis inferior of the right pars triangularis and the right pars opercularis was significantly increased in the suicidal attempt group compared with the non-suicidal attempt group. In the resting state, the prefrontal lobe of adolescents who had attempted suicide because of Internet addiction exhibited functional abnormalities, which may provide a new basis for studying suicide pathogenesis in Internet addicts. The study was authorized by the Ethics Committee of Chongqing Medical University, China (approval No. 2017 Scientific Research Ethics (2017-157)) on December 11, 2017.

PMID: 31997822 [PubMed]

Multivariate Classification of Earthquake Survivors with Posttraumatic Stress Disorder Based on Large-scale Brain Networks.

Fri, 01/31/2020 - 16:00
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Multivariate Classification of Earthquake Survivors with Posttraumatic Stress Disorder Based on Large-scale Brain Networks.

Acta Psychiatr Scand. 2020 Jan 29;:

Authors: Zhu H, Yuan M, Qiu C, Ren Z, Li Y, Wang J, Huang X, Lui S, Gong Q, Zhang W, Zhang Y

Abstract
OBJECTIVE: The identification of posttraumatic stress disorder (PTSD) among natural disaster survivors is remarkably challenging, and there are no reliable objective signatures that can be used to assist clinical diagnosis and optimize treatment. The current study aimed to establish a neurobiological signature of PTSD from the connectivity of large-scale brain networks and clarify the brain network mechanisms of PTSD.
METHODS: We examined fifty-seven unmedicated survivors with chronic PTSD and 59 matched trauma-exposed healthy controls (TEHCs) using resting-state functional magnetic resonance imaging (rs-fMRI). We extracted the node-to-network connectivity and obtained a feature vector with a dimensionality of 864 (108 nodes× 8 networks) to represent each subject's functional connectivity (FC) profile. Multivariate pattern analysis with a relevance vector machine was then used to distinguish PTSD patients from TEHCs.
RESULTS: We achieved a promising diagnostic accuracy of 89.2% in distinguishing PTSD patients from TEHCs. The most heavily weighted connections for PTSD classification were among the default mode network (DMN), visual network (VIS), somatomotor network, limbic network, and dorsal attention network (DAN). The strength of the anticorrelation of FC between the ventral medial prefrontal cortex (vMPFC) in DMN and the VIS and DAN was associated with the severity of PTSD.
CONCLUSIONS: This study achieved relatively high accuracy in classifying PTSD patients versus TEHCs at the individual level. This performance demonstrates that rs-fMRI-derived multivariate classification based on large-scale brain networks can provide potential signatures both to facilitate clinical diagnosis and to clarify the underlying brain network mechanisms of PTSD caused by natural disasters.

PMID: 31997301 [PubMed - as supplied by publisher]

Exploring the Correlation Between M/EEG Source-Space and fMRI Networks at Rest.

Fri, 01/31/2020 - 16:00
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Exploring the Correlation Between M/EEG Source-Space and fMRI Networks at Rest.

Brain Topogr. 2020 Jan 29;:

Authors: Rizkallah J, Amoud H, Fraschini M, Wendling F, Hassan M

Abstract
Magneto/electro-encephalography (M/EEG) source connectivity is an emerging approach to estimate brain networks with high temporal and spatial resolutions. Here, we aim to evaluate the effect of functional connectivity (FC) methods on the correlation between M/EEG source-space and fMRI networks at rest. Two main FC families are tested: (i) FC methods that do not remove zero-lag connectivity including Phase Locking Value (PLV) and Amplitude Envelope Correlation (AEC) and (ii) FC methods that remove zero-lag connections such as Phase Lag Index (PLI) and two orthogonalisation approaches combined with PLV (PLVCol, PLVPas) and AEC (AECCol, AECPas). Methods are evaluated on resting state M/EEG signals recorded from healthy participants at rest (N = 74). Networks obtained by each FC method are compared with fMRI networks (obtained from the Human Connectome Project). Results show low correlations for all FC methods, however PLV and AEC networks are significantly correlated with fMRI networks (ρ = 0.12, p = 1.93 × 10-8 and ρ = 0.06, p = 0.007, respectively), while other methods are not. These observations are consistent for all M/EEG frequency bands and for different FC matrices threshold. Our main message is to be careful in selecting FC methods when comparing or combining M/EEG with fMRI. We consider that more comparative studies based on simulation and real data and at different levels (node, module or sub networks) are still needed in order to improve our understanding on the relationships between M/EEG source-space networks and fMRI networks at rest.

PMID: 31997058 [PubMed - as supplied by publisher]

Influence of 4-week multi-strain probiotic administration on resting-state functional connectivity in healthy volunteers.

Fri, 01/31/2020 - 16:00
Related Articles

Influence of 4-week multi-strain probiotic administration on resting-state functional connectivity in healthy volunteers.

Eur J Nutr. 2019 Aug;58(5):1821-1827

Authors: Bagga D, Aigner CS, Reichert JL, Cecchetto C, Fischmeister FPS, Holzer P, Moissl-Eichinger C, Schöpf V

Abstract
PURPOSE: Experimental investigations in rodents have contributed significantly to our current understanding of the potential importance of the gut microbiome and brain interactions for neurotransmitter expression, neurodevelopment, and behaviour. However, clinical evidence to support such interactions is still scarce. The present study used a double-blind, randomized, pre- and post-intervention assessment design to investigate the effects of a 4-week multi-strain probiotic administration on whole-brain functional and structural connectivity in healthy volunteers.
METHODS: Forty-five healthy volunteers were recruited for this study and were divided equally into three groups (PRP: probiotic, PLP: placebo, and CON: control). All the participants underwent resting-state functional MRI and diffusion MRI brain scans twice during the course of study, at the beginning (time point 1) and after 4 weeks (time point 2). MRI data were acquired using a 3T whole-body MR system (Magnetom Skyra, Siemens, Germany).
RESULTS: Functional connectivity (FC) changes were observed in the default mode network (DMN), salience network (SN), and middle and superior frontal gyrus network (MFGN) in the PRP group as compared to the PLP and CON groups. PRP group showed a significant decrease in FC in MFGN (in frontal pole and frontal medial cortex) and in DMN (in frontal lobe) as compared to CON and PLP groups, respectively. Further, significant increase in FC in SN (in cingulate gyrus and precuneus cortex) was also observed in PRP group as compared to CON group. The significance threshold was set to p < 0.05 FWE corrected. No significant structural differences were observed between the three groups.
CONCLUSIONS: This work provides new insights into the role of a multi-strain probiotic administration in modulating the behaviour, which is reflected as changes in the FC in healthy volunteers. This study motivates future investigations into the role of probiotics in context of major depression and stress disorders.

PMID: 29850990 [PubMed - indexed for MEDLINE]

Evaluating global brain connectivity as an imaging marker for depression: influence of preprocessing strategies and placebo-controlled ketamine treatment.

Thu, 01/30/2020 - 14:40
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Evaluating global brain connectivity as an imaging marker for depression: influence of preprocessing strategies and placebo-controlled ketamine treatment.

Neuropsychopharmacology. 2020 Jan 29;:

Authors: Kraus C, Mkrtchian A, Kadriu B, Nugent AC, Zarate CA, Evans JW

Abstract
Major depressive disorder (MDD) is associated with altered global brain connectivity (GBC), as assessed via resting-state functional magnetic resonance imaging (rsfMRI). Previous studies found that antidepressant treatment with ketamine normalized aberrant GBC changes in the prefrontal and cingulate cortices, warranting further investigations of GBC as a putative imaging marker. These results were obtained via global signal regression (GSR). This study is an independent replication of that analysis using a separate dataset. GBC was analyzed in 28 individuals with MDD and 22 healthy controls (HCs) at baseline, post placebo, and post ketamine. To investigate the effects of preprocessing, three distinct pipelines were used: (1) regression of white matter (WM)/cerebrospinal fluid (CSF) signals only (BASE); (2) WM/CSF + GSR (GSR); and (3) WM/CSF + physiological parameter regression (PHYSIO). Reduced GBC was observed in individuals with MDD only at baseline in the anterior and medial cingulate cortices, as well as in the prefrontal cortex only after regressing the global signal. Ketamine had no effect compared to baseline or placebo in either group in any pipeline. PHYSIO did not resemble GBC preprocessed with GSR. These results concur with several studies that used GSR to study GBC. Further investigations are warranted into disease-specific components of global fMRI signals that may drive these results and of GBCr as a potential imaging marker in MDD.

PMID: 31995812 [PubMed - as supplied by publisher]

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