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Reduced Interhemispheric Functional Connectivity in Obsessive-Compulsive Disorder Patients.

Sun, 06/30/2019 - 21:13
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Reduced Interhemispheric Functional Connectivity in Obsessive-Compulsive Disorder Patients.

Front Psychiatry. 2019;10:418

Authors: Deng K, Qi T, Xu J, Jiang L, Zhang F, Dai N, Cheng Y, Xu X

Abstract
Background: Neuroimaging studies have shown that the high synchrony of spontaneous neural activity in the homotopic regions between hemispheres is an important functional structural feature of normal human brains, and this feature is abnormal in the patients with various mental disorders. However, little is known about this feature in obsessive-compulsive disorder (OCD). This study aimed to further analyze the underlying neural mechanisms of OCD and to explore whether clinical characteristics are correlated with the alerted homotopic connectivity in patients with OCD. Methods: Using voxel-mirrored homotopic connectivity (VMHC) during resting state, we compared 46 OCD patients and 46 healthy controls (HCs) matched for age, gender, and education level. A partial correlation analysis was used to investigate the relationship between altered VMHC and clinical characteristics in patients with OCD. Results: Patients with OCD showed lower VMHC than HCs in fusiform gyrus/inferior occipital gyrus, lingual gyrus, postcentral gyrus/precentral gyrus, putamen, and orbital frontal gyrus. A significant positive correlation was observed between altered VMHC in the angular gyrus/middle occipital gyrus and illness duration in patients. Conclusions: Interhemispheric functional imbalance may be an essential aspect of the pathophysiological mechanism of OCD, which is reflected not only in the cortico-striato-thalamo-cortical (CSTC) loop but also elsewhere in the brain.

PMID: 31249539 [PubMed]

Localized Connectivity in Obsessive-Compulsive Disorder: An Investigation Combining Univariate and Multivariate Pattern Analyses.

Sun, 06/30/2019 - 21:13
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Localized Connectivity in Obsessive-Compulsive Disorder: An Investigation Combining Univariate and Multivariate Pattern Analyses.

Front Behav Neurosci. 2019;13:122

Authors: Hu X, Zhang L, Bu X, Li H, Li B, Tang W, Lu L, Hu X, Tang S, Gao Y, Yang Y, Roberts N, Gong Q, Huang X

Abstract
Recent developments in psychoradiological researches have highlighted the disrupted organization of large-scale functional brain networks in obsessive-compulsive disorder (OCD). However, whether abnormal activation of localized brain areas would affect network dysfunction remains to be fully characterized. We applied both univariate analysis and multivariate pattern analysis (MVPA) approaches to investigate the abnormalities of regional homogeneity (ReHo), an index to measure the localized connectivity, in 88 medication-free patients with OCD and 88 healthy control subjects (HCS). Resting-state functional magnetic resonance imaging (RS-fMRI) data of all the participants were acquired in a 3.0-T scanner. First, we adopted a traditional univariate analysis to explore ReHo alterations between the patient group and the control group. Subsequently, we utilized a support vector machine (SVM) to examine whether ReHo could be further used to differentiate patients with OCD from HCS at the individual level. Relative to HCS, OCD patients showed lower ReHo in the bilateral cerebellum and higher ReHo in the bilateral superior frontal gyri (SFG), right inferior parietal gyrus (IPG), and precuneus [P < 0.05, family-wise error (FWE) correction]. ReHo value in the left SFG positively correlated with Yale-Brown Obsessive Compulsive Scale (Y-BOCS) total score (r = 0 0.241, P = 0.024) and obsessive subscale (r = 0.224, P = 0.036). The SVM classification regarding ReHo yielded an accuracy of 78.98% (sensitivity = 78.41%, specificity = 79.55%) with P < 0.001 after permutation testing. The most discriminative regions contributing to the SVM classification were mainly located in the frontal, temporal, and parietal regions as well as in the cerebellum while the right orbital frontal cortex was identified with the highest discriminative power. Our findings not only suggested that the localized activation disequilibrium between the prefrontal cortex (PFC) and the cerebellum appeared to be associated with the pathophysiology of OCD but also indicated the translational role of the localized connectivity as a potential discriminative pattern to detect OCD at the individual level.

PMID: 31249515 [PubMed]

Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review.

Sun, 06/30/2019 - 21:13
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Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review.

Front Neurosci. 2019;13:585

Authors: Farahani FV, Karwowski W, Lighthall NR

Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.

PMID: 31249501 [PubMed]

Sequential replay of nonspatial task states in the human hippocampus.

Sun, 06/30/2019 - 21:13
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Sequential replay of nonspatial task states in the human hippocampus.

Science. 2019 06 28;364(6447):

Authors: Schuck NW, Niv Y

Abstract
Sequential neural activity patterns related to spatial experiences are "replayed" in the hippocampus of rodents during rest. We investigated whether replay of nonspatial sequences can be detected noninvasively in the human hippocampus. Participants underwent functional magnetic resonance imaging (fMRI) while resting after performing a decision-making task with sequential structure. Hippocampal fMRI patterns recorded at rest reflected sequentiality of previously experienced task states, with consecutive patterns corresponding to nearby states. Hippocampal sequentiality correlated with the fidelity of task representations recorded in the orbitofrontal cortex during decision-making, which were themselves related to better task performance. Our findings suggest that hippocampal replay may be important for building representations of complex, abstract tasks elsewhere in the brain and establish feasibility of investigating fast replay signals with fMRI.

PMID: 31249030 [PubMed - in process]

Aberrant topological organization of the default mode network in temporal lobe epilepsy revealed by graph-theoretical analysis.

Fri, 06/28/2019 - 18:08

Aberrant topological organization of the default mode network in temporal lobe epilepsy revealed by graph-theoretical analysis.

Neurosci Lett. 2019 Jun 24;:134351

Authors: Zhou X, Zhang Z, Liu J, Qin L, Zheng J

Abstract
Numerous neuroimaging studies have reported aberrant functional activities of the default mode network (DMN) in temporal lobe epilepsy (TLE). However, the alteration on topological organization within the DMN has not been clearly illuminated. In this study, we aimed to investigate the topological abnormalities within the DMN from a larger-scale perspective. Twenty three patients with TLE and 23 age, gender, and education matched healthy controls (HCs) were recruited in this study. All participants underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scanning and completed the Attention Network Test (ANT) for executive function assessment. Specific subregions in the DMN were extracted for network construction according to the automated anatomical labelling atlas. Network properties, including global properties, nodal properties and edge analyses, were assessed using graph theory. Correlation analyses were performed between significantly different topological properties and clinical factors in patients. The ANT tests showed that executive function was impaired in the patients with TLE compared with the HCs. Furthermore, the TLE group showed decreased nodal strength in the left medial orbital superior frontal gyrus and increased nodal strength in the right inferior parietal gyrus in the DMN. Significantly increased functional connectivities between subregions in the DMN were primarily located in the bilateral superior frontal gyrus, inferior parietal gyrus, precuneus, and posterior cingulate gyrus. Moreover, the degree centrality of the right inferior parietal gyrus was positively correlated with disease duration. In conclusion, this study indicated that there existed a disrupted topological reorganization within the DMN in patients with TLE, which may further contribute to executive deficits and, to some extent, play a compensatory role.

PMID: 31247225 [PubMed - as supplied by publisher]

Volume of Hypothalamus as a Diagnostic Biomarker of Chronic Migraine.

Fri, 06/28/2019 - 18:08

Volume of Hypothalamus as a Diagnostic Biomarker of Chronic Migraine.

Front Neurol. 2019;10:606

Authors: Chen Z, Chen X, Liu M, Ma L, Yu S

Abstract
It is believed than hypothalamus (HTH) might be involved in generation of migraine, and evidence from high resolution fMRI reported that the more anterior part of HTH seemed to play an important role in migraine chronification. The current study was aimed to identify the alteration of morphology and resting-state functional connectivity (FC) of the hypothalamus (HTH) in interictal episodic migraine (EM) and chronic migraine (CM). High-resolution structural and resting-state functional magnetic resonance images were acquired in 18 EM patients, 16 CM patients, and 21 normal controls (NC). The volume of HTH was calculated and voxel-based morphometry (VBM) was performed over the whole HTH. Receiver operating characteristics (ROC) curve analysis was applied to evaluate the diagnostic efficacy of HTH volume. Correlation analyses with clinical variables were performed and FC maps were generated for positive HTH regions according to VBM comparison. The volume of the HTH significantly decreased in both EM and CM patients compared with NC. The cut-off volume of HTH as 1.429 ml had a good diagnostic accuracy for CM with sensitivity of 81.25% and specificity of 100%. VBM analyses identified volume reduction of posterior HTH in EM vs. NC which was negatively correlated with headache frequency. The posterior HTH presented decreased FC with the left inferior temporal gyrus (Brodmann area 20) in EM. Decreased volume of anterior HTH was identified in CM vs. NC and CM vs. EM which was positively correlated with headache frequency in CM. The anterior HTH presented increased FC with the right anterior orbital gyrus (AOrG) (Brodmann area 11) in CM compared with NC and increased FC with the right medial orbital gyrus (MOrG) (Brodmann area 11) in CM compared with EM. Our study provided evidence of structural plasticity and FC changes of HTH in the pathogensis of migraine generation and chronification, supporting potential therapeutic target toward the HTH and its peptide.

PMID: 31244765 [PubMed]

Functional Correlates of Resting-State Connectivity in the Default Mode Network of Heroin Users on Methadone Treatment and Medication-Free Therapeutic Community Program.

Fri, 06/28/2019 - 18:08

Functional Correlates of Resting-State Connectivity in the Default Mode Network of Heroin Users on Methadone Treatment and Medication-Free Therapeutic Community Program.

Front Psychiatry. 2019;10:381

Authors: Kuo LW, Lin PS, Lin SY, Liu MF, Jan H, Lee HC, Wang SC

Abstract
The treatment of heroin addiction is a complex process involving changes in addictive behavior and brain functioning. The goal of this study was to explore the brain default mode network (DMN) functional connectivity using resting-state functional magnetic resonance imaging (rs-fMRI) and decision-making performance based on the Cambridge gambling task in heroin-dependent individuals undergoing methadone treatment (MT, n = 11) and medication-free faith-based therapeutic community program (TC, n = 11). The DMN involved the medial prefrontal cortex (mPFC), left inferior parietal lobe (IPLL), right inferior parietal lobe (IPLR), and posterior cingulate cortex (PCC) subregions for all participants in both the MT and TC groups. Compared with MT, TC had an increased functional connectivity in IPLL-IPLR and IPLR-PCC and decreased functional connectivity in mPFC-IPLL and IPLL-PCC. Both groups exhibited no significant difference in the regional rs-fMRI metric [i.e., amplitude of low-frequency fluctuation (ALFF)]. In the analysis of the neural correlates for decision-making performance, risk adjustment was positively associated with ALFF in IPLL for all participants considering the group effects. The involvement of IPL in decision-making performance and treatment response among heroin-dependent patients warrants further investigation.

PMID: 31244690 [PubMed]

Connectome-Based Biomarkers Predict Subclinical Depression and Identify Abnormal Brain Connections With the Lateral Habenula and Thalamus.

Fri, 06/28/2019 - 18:08

Connectome-Based Biomarkers Predict Subclinical Depression and Identify Abnormal Brain Connections With the Lateral Habenula and Thalamus.

Front Psychiatry. 2019;10:371

Authors: Zhu Y, Qi S, Zhang B, He D, Teng Y, Hu J, Wei X

Abstract
Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification of its etiological origin are urgent. Bursts within the lateral habenula (LHb) drive depression in rats, but whether dysfunctional LHb is associated with SD in human is unknown. Here we develop connectome-based biomarkers which predict SD and identify dysfunctional brain regions and connections. T1 weighted images and resting-state functional MRI (fMRI) data were collected from 34 subjects with SD and 40 healthy controls (HCs). After the brain is parcellated into 48 brain regions (246 subregions) using the human Brainnetome Atlas, the functional network of each participant is constructed by calculating the correlation coefficient for the time series of fMRI signals of each pair of subregions. Initial candidates of abnormal connections are identified by the two-sample t-test and input into Support Vector Machine models as features. A total of 24 anatomical-region-based models, 231 sliding-window-based models, and 100 random-selection-based models are built. The performance of these models is estimated through leave-one-out cross-validation and evaluated by measures of accuracy, sensitivity, confusion matrix, receiver operating characteristic curve, and the area under the curve (AUC). After confirming the region with the highest accuracy, subregions within the thalamus and connections associated with subregions of LHb are compared. It is found that five prediction models using connections of the thalamus, posterior superior temporal sulcus, cingulate gyrus, superior parietal lobule, and superior frontal gyrus achieve an accuracy >0.9 and an AUC >0.93. Among 90 abnormal connections associated with the thalamus, the subregion of the right posterior parietal thalamus where LHb is located has the most connections (n = 18), the left subregion only has 3 connections. In SD group, 10 subregions in the thalamus have significantly different node degrees with those in the HC group, while 8 subregions have lower degrees ( p < 0.01), including the one with LHb. These results implicate abnormal brain connections associated with the thalamus and LHb to be associated with SD. Integration of these connections by machine learning can provide connectome-based biomarkers to accurately diagnose SD.

PMID: 31244688 [PubMed]

Exploring the Neural Correlates in Adopting a Realistic View: A Neural Structural and Functional Connectivity Study With Female Nurses.

Fri, 06/28/2019 - 18:08

Exploring the Neural Correlates in Adopting a Realistic View: A Neural Structural and Functional Connectivity Study With Female Nurses.

Front Hum Neurosci. 2019;13:197

Authors: Ogino Y, Kawamichi H, Kakeda T, Saito S

Abstract
Empathizing leads to positive and negative consequences. To avoid empathy-induced distress, adopting a realistic view (dealing with a situation practically and efficiently independent of one's emotional state) is important. We hypothesized that empathy-demanding professions (e.g., nursing) may require individuals to adopt a realistic view, which may demonstrate modulated neural structure and functional connectivity. We confirmed that female nurses showed a higher tendency, compared to controls, to adopt a realistic view, using the Fantasy subscale of the Interpersonal Reactivity Index (IRI; inverse scale of the realistic view). We then employed voxel-based morphometry (VBM) and resting-state functional magnetic resonance imaging (rs-fMRI) to explore the neural underpinnings related to realistic view adoption. Nurses exhibited significantly lower gray-matter volume (GMV) in the right striatum. In multiple regression analysis, only the Fantasy subscale score showed a significant positive correlation with GMV within the striatum cluster. Moreover, nurses exhibited lower functional connectivity between the right striatum and the right lateral prefrontal cortex (PFC), representing emotional regulation. These findings show that structural differences in the striatum correlated with the realistic view. Furthermore, lower functional connectivity between the striatum and lateral PFC suggests that nurses may use efficient coping strategies that may lessen the recruitment of effortful emotional regulation.

PMID: 31244632 [PubMed]

SNCA rs11931074 polymorphism correlates with spontaneous brain activity and motor symptoms in Chinese patients with Parkinson's disease.

Fri, 06/28/2019 - 18:08
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SNCA rs11931074 polymorphism correlates with spontaneous brain activity and motor symptoms in Chinese patients with Parkinson's disease.

J Neural Transm (Vienna). 2019 Jun 26;:

Authors: Si QQ, Yuan YS, Zhi Y, Wang M, Wang JW, Shen YT, Wang LN, Li JY, Wang XX, Zhang KZ

Abstract
The α-synuclein (SNCA) gene is thought to be involved in levels of α-synuclein and influence the susceptibility for the development of Parkinson's disease (PD). The aim of the present study is to explore the association among SNCA rs1193074 polymorphism, spontaneous brain activity and clinical symptoms in PD patients. 62 PD patients and 47 healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans. Also blood sample of each participant was genotyped for rs11931074 polymorphism (PD: TT = 19, GT = 32, GG = 11; HC: TT = 10, GT = 25, GG = 12) and then examined to ascertain the influence of different genotypes on regional brain activity with amplitude low-frequency fluctuation analysis (ALFF). Furthermore, we evaluated the relationship among genotypes, interactive brain region and clinical symptoms in PD. Compared with HC subjects, PD patients showed decreased ALFF values in right lingual gyrus and increased ALFF values in right cerebellum posterior lobe. Significant interaction of ''groups × genotypes'' was found in the right angular gyrus, where there were higher ALFF values in TT genotype than in GT or GG genotype in the PD group and there was a contrary trend in the HC group. And further Spearman's correlative analyses revealed that ALFF values in right angular gyrus were negatively associated with unified Parkinson's disease rating scale (UPDRS) III score in PD-TT genotype. Our study shows for the first time that SNCA rs11931074 polymorphism might modulate brain functional alterations and correlate with motor symptoms in Chinese PD patients.

PMID: 31243602 [PubMed - as supplied by publisher]

Left frontal connectivity attenuates the adverse effect of entorhinal tau pathology on memory.

Thu, 06/27/2019 - 21:06
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Left frontal connectivity attenuates the adverse effect of entorhinal tau pathology on memory.

Neurology. 2019 Jun 24;:

Authors: Neitzel J, Franzmeier N, Rubinski A, Ewers M, Alzheimer's Disease Neuroimaging Initiative (ADNI)

Abstract
OBJECTIVE: To investigate whether higher global left frontal cortex (gLFC) connectivity, a putative neural substrate of cognitive reserve, attenuates the effect of entorhinal tau PET levels on episodic memory in older adults.
METHODS: Cross-sectional 18F-AV-1451 PET (to assess tau pathology), 18F-AV-45 or 18F-BAY94-9172 PET (to assess β-amyloid [Aβ]), and resting-state fMRI were obtained in 125 elderly participants from the Alzheimer's Neuroimaging Initiative, including 82 cognitively normal participants (amyloid PET-positive [Aβ+], n = 27) and 43 patients with amnestic mild cognitive impairment (Aβ+ = 15). Resting-state fMRI gLFC connectivity was computed for each participant as the average functional connectivity between the left frontal cortex (LFC) (seed) and each remaining voxel in the gray matter. As a measure of tau pathology, we assessed the mean tau PET uptake in the entorhinal cortex. In linear mixed-effects regression analysis, we tested the interaction term gLFC connectivity × entorhinal tau PET on delayed free recall performance. In addition, we assessed whether higher connectivity of the whole frontoparietal control network (FPCN), of which the LFC is a major hub, is associated with reserve.
RESULTS: Higher entorhinal tau PET was strongly associated with poorer delayed free recall performance (β/SE = -0.49/0.07, p < 0.001). A significant gLFC connectivity × entorhinal tau PET interaction was found (β/SE = 0.19/0.06, p = 0.003), such that at higher levels of gLFC connectivity, the decrease in memory score per unit of entorhinal tau PET was attenuated. The FPCN connectivity × tau interaction was also significant (β/SE = 0.10/0.04, p = 0.012).
CONCLUSION: Both gLFC and FPCN connectivity are associated with higher resilience against the adverse effect of early-stage entorhinal tau pathology on memory performance.

PMID: 31235661 [PubMed - as supplied by publisher]

Prism adaptation enhances decoupling between the default mode network and the attentional networks.

Tue, 06/25/2019 - 21:03
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Prism adaptation enhances decoupling between the default mode network and the attentional networks.

Neuroimage. 2019 Jun 21;:

Authors: Wilf M, Serino A, Clarke S, Crottaz-Herbette S

Abstract
Prism adaptation (PA) is a procedure used for studying visuomotor plasticity in healthy individuals, as well as for alleviating spatial neglect in patients. The adaptation is achieved by performing goal-directed movements while wearing prismatic lenses that induce a lateral displacement of visual information. This results in an initial movement error that is compensated by a recalibration of sensory-motor coordinates; consequently, a lateral bias in both motor and perceptual measurements occurs after prism removal, i.e., after effects. Neuroimaging studies have shown that a brief exposure to rightward prism changes the activations in the inferior parietal lobule (IPL) and modulates interhemispheric balance during attention tasks. However, it is yet unknown how PA changes global interplay between cortical networks as evident from task-free resting state connectivity. Thus we compared resting state functional connectivity patterns before ('Pre') and after ('Post') participants performed session of pointing movements with rightward-shifting prism (N = 14) or with neutral lenses (as a control condition; N = 12). Global connectivity analysis revealed significant decreases in functional connectivity following PA in two nodes of the Default Mode Network (DMN), and the left anterior insula. Further analyses of these regions showed specific connectivity decrease between either of the DMN nodes and areas within the attentional networks, including the inferior frontal gyrus, the anterior insula and the right superior temporal sulcus. On the other hand, the anterior insula decreased its connectivity to a large set of areas, all within the boundaries of the DMN. These results demonstrate that a brief exposure to PA enhances the decoupling between the DMN and the attention networks. The change in interplay between those pre-existing networks might be the basis of the rapid and wide-ranged behavioural changes induce by PA in healthy individuals.

PMID: 31233909 [PubMed - as supplied by publisher]

New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia.

Tue, 06/25/2019 - 21:03
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New graph-theoretical-multimodal approach using temporal and structural correlations reveal disruption in the thalamo-cortical network in patients with schizophrenia.

Brain Connect. 2019 Jun 22;:

Authors: Forlim CG, Finotelli P, Dulio P, Klock L, Pini A, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Gallinat J, Kühn S

Abstract
Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modelled independently: functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural and topological information of MRI measurements (structural and resting state imaging) to patients with schizophrenia(N=35) and matched healthy individuals(N=41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole brain connectivity including functional, structural and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting state fMRI, and can be applied to EEG, MEG etc.

PMID: 31232080 [PubMed - as supplied by publisher]

Alterations of cerebral perfusion and functional brain connectivity in medication-naïve male adults with attention-deficit/hyperactivity disorder.

Tue, 06/25/2019 - 21:03
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Alterations of cerebral perfusion and functional brain connectivity in medication-naïve male adults with attention-deficit/hyperactivity disorder.

CNS Neurosci Ther. 2019 Jun 23;:

Authors: Tan YW, Liu L, Wang YF, Li HM, Pan MR, Zhao MJ, Huang F, Wang YF, He Y, Liao XH, Qian QJ

Abstract
AIMS: Functional brain abnormalities, including altered cerebral perfusion and functional connectivities, have been illustrated in adults with attention-deficit/hyperactivity disorder (aADHD). The present study attempted to explore the alterations of cerebral blood flow (CBF) and resting-state functional connectivity (RSFC) simultaneously to understand the neural mechanisms for adults with ADHD comprehensively.
METHODS: Resting-state arterial spin labeling (ASL) and blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) data were acquired for 69 male aADHD and 69 matched healthy controls (HCs). The altered CBFs associated with aADHD were explored based on both categorical (aADHD vs HCs) and dimensional (correlation with aADHD core symptoms) perspectives. Then, the seed-based RSFC analyses were developed for the regions showing significant alterations of CBF.
RESULTS: Significantly decreased CBF in the large-scale resting-state networks regions (eg, ventral attentional network, somatomotor network, limbic network) and subcortical regions was indicated in aADHD compared with HCs. The correlation analyses indicated that the hypoperfusion in left putamen/global pallidum and left amygdala/hippocampus was correlated with ADHD inattentive and total symptoms, respectively. Further, weaker negative functional connectivity between left amygdala and bilateral supplementary motor area, bilateral superior frontal gyrus, and left medial frontal gyrus was found in adults with ADHD.
CONCLUSION: The present findings suggested alterations of both cerebral perfusion and functional connectivity for the left amygdala in aADHD. The combination of CBF and RSFCs may help to interpret the neuropathogenesis of ADHD more comprehensively.

PMID: 31231983 [PubMed - as supplied by publisher]

Clinical Personal Connectomics Using Hybrid PET/MRI.

Tue, 06/25/2019 - 21:03
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Clinical Personal Connectomics Using Hybrid PET/MRI.

Nucl Med Mol Imaging. 2019 Jun;53(3):153-163

Authors: Lee DS

Abstract
Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield personal interregional brain connectivity based on perfusion signal on MRI on individual subject bases and FDG PET produces the topography of glucose metabolism. Assuming metabolism perfusion coupling and disregarding the slight difference of representing time of metabolism (before image acquisition) and representing time of perfusion (during image acquisition), topography of brain metabolism on FDG PET and topologically analyzed brain connectivity on resting-state fMRI might be related to yield personal connectomics of individual subjects and even individual patients. The work of association of FDG PET/resting-state fMRI is yet to be warranted; however, the statistics behind the group comparison of connectivity on FDG PET or resting-state MRI was already developed. Before going further into the connectomics construction using directed weighted brain graphs of FDG PET or resting-state fMRI, I detailed in this review the plausibility of using hybrid PET/MRI to enable the interpretation of personal connectomics which can lead to the clinical use of brain connectivity in the near future.

PMID: 31231434 [PubMed]

Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri).

Tue, 06/25/2019 - 21:03
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Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri).

Front Neuroinform. 2019;13:42

Authors: Pallast N, Diedenhofen M, Blaschke S, Wieters F, Wiedermann D, Hoehn M, Fink GR, Aswendt M

Abstract
Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology. In vivo MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive in vivo high-resolution microscopy and ex vivo molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

PMID: 31231202 [PubMed]

Rostral-Caudal Hippocampal Functional Convergence Is Reduced Across the Alzheimer's Disease Spectrum.

Mon, 06/24/2019 - 21:02

Rostral-Caudal Hippocampal Functional Convergence Is Reduced Across the Alzheimer's Disease Spectrum.

Mol Neurobiol. 2019 Jun 22;:

Authors: Therriault J, Wang S, Mathotaarachchi S, Pascoal TA, Parent M, Beaudry T, Shin M, Al B, Kang MS, Ng KP, Dansereau C, Park MTM, Fonov V, Carbonell F, Zimmer E, Chakravarty MM, Bellec P, Gauthier S, Rosa-Neto P, Alzheimer’s Disease Neuroimaging Initiative

Abstract
Beginning in the early stages of Alzheimer's disease (AD), the hippocampus reduces its functional connections to other cortical regions due to synaptic depletion. However, little is known regarding connectivity abnormalities within the hippocampus. Here, we describe rostral-caudal hippocampal convergence (rcHC), a metric of the overlap between the rostral and caudal hippocampal functional networks, across the clinical spectrum of AD. We predicted a decline in rostral-caudal hippocampal convergence in the early stages of the disease. Using fMRI, we generated resting-state hippocampal functional networks across 56 controls, 48 early MCI (EMCI), 35 late MCI (LMCI), and 31 AD patients from the Alzheimer's Disease Neuroimaging Initiative cohort. For each diagnostic group, we performed a conjunction analysis and compared the rostral and caudal hippocampal network changes using a mixed effects linear model to estimate the convergence and differences between these networks, respectively. The conjunction analysis showed a reduction of rostral-caudal hippocampal convergence strength from early MCI to AD, independent of hippocampal atrophy. Our results demonstrate a parallel between the functional convergence within the hippocampus and disease stage, which is independent of brain atrophy. These findings support the concept that network convergence might contribute as a biomarker for connectivity dysfunction in early stages of AD.

PMID: 31230260 [PubMed - as supplied by publisher]

The anterior insula channels prefrontal expectancy signals during affective processing.

Mon, 06/24/2019 - 21:02

The anterior insula channels prefrontal expectancy signals during affective processing.

Neuroimage. 2019 Jun 20;:

Authors: Teckentrup V, van der Meer JN, Borchardt V, Fan Y, Neuser MP, Tempelmann C, Herrmann L, Walter M, Kroemer NB

Abstract
Expectancy shapes our perception of impending events. Although such an interplay between cognitive and affective processes is often impaired in mental disorders, it is not well understood how top-down expectancy signals modulate future affect. We therefore track the information flow in the brain during cognitive and affective processing segregated in time using task-specific cross-correlations. Participants in two independent fMRI studies (N1 = 37 & N2 = 55) were instructed to imagine a situation with affective content as indicated by a cue, which was then followed by an emotional picture congruent with expectancy. To correct for intrinsic covariance of brain function, we calculate resting-state cross-correlations analogous to the task. First, using factorial modeling of delta cross-correlations (task-rest) of the first study, we find that the magnitude of expectancy signals in the anterior insula cortex (AIC) modulates the BOLD response to emotional pictures in the anterior cingulate and dorsomedial prefrontal cortex in opposite directions. Second, using hierarchical linear modeling of lagged connectivity, we demonstrate that expectancy signals in the AIC indeed foreshadow this opposing pattern in the prefrontal cortex. Third, we replicate the results in the second study using a higher temporal resolution, showing that our task-specific cross-correlation approach robustly uncovers the dynamics of information flow. We conclude that the AIC arbitrates the recruitment of distinct prefrontal networks during cued picture processing according to triggered expectations. Taken together, our study provides new insights into neuronal pathways channeling cognition and affect within well-defined brain networks. Better understanding of such dynamics could lead to new applications tracking aberrant information processing in mental disorders.

PMID: 31229657 [PubMed - as supplied by publisher]

Detecting resting-state brain activity using OEF-weighted imaging.

Mon, 06/24/2019 - 00:01
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Detecting resting-state brain activity using OEF-weighted imaging.

Neuroimage. 2019 Jun 19;:

Authors: Yang Y, Yin Y, Lu J, Zou Q, Gao JH

Abstract
Traditional resting-state functional magnetic resonance imaging (fMRI) is mainly based on the blood oxygenation level-dependent (BOLD) contrast. The oxygen extraction fraction (OEF) represents an important parameter of brain metabolism and is a key biomarker of tissue viability, detecting the ratio of oxygen utilization to oxygen delivery. Investigating spontaneous fluctuations in the OEF-weighted signal is crucial for understanding the underlying mechanism of brain activity because of the immense energy budget during the resting state. However, due to the poor temporal resolution of OEF mapping, no studies have reported using OEF contrast to assess resting-state brain activity. In this fMRI study, we recorded brain OEF-weighted fluctuations for 10 min in healthy volunteers across two scanning visits, using our recently developed pulse sequence that can acquire whole-brain voxel-wise OEF-weighted signals with a temporal resolution of 3 s. Using both group-independent component analysis and seed-based functional connectivity analysis, we robustly identified intrinsic brain networks, including the medial visual, lateral visual, auditory, default mode and bilateral executive control networks, using OEF contrast. Furthermore, we investigated the resting-state local characteristics of brain activity based on OEF-weighted signals using regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuations (fALFF). We demonstrated that the gray matter regions of the brain, especially those in the default mode network, showed higher ReHo and fALFF values with the OEF contrast. Moreover, voxel-wise test-retest reliability comparisons across the whole brain demonstrated that the reliability of resting-state brain activity based on the OEF contrast was moderate for the network indices and high for the local activity indices, especially for ReHo. Although the reliabilities of the OEF-based indices were generally lower than those based on BOLD, the reliability of OEF-ReHo was slightly higher than that of BOLD-ReHo, with a small effect size, which indicated that OEF-ReHo could be used as a reliable index for characterizing resting-state local brain activity as a complement to BOLD. In conclusion, OEF can be used as an effective contrast to study resting-state brain activity with a medium to high test-retest reliability.

PMID: 31228637 [PubMed - as supplied by publisher]

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm.

Mon, 06/24/2019 - 00:01
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A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm.

Hum Brain Mapp. 2019 Jun 22;:

Authors: Shang J, Fisher P, Bäuml JG, Daamen M, Baumann N, Zimmer C, Bartmann P, Boecker H, Wolke D, Sorg C, Koutsouleris N, Dwyer DB

Abstract
Imaging studies have characterized functional and structural brain abnormalities in adults after premature birth, but these investigations have mostly used univariate methods that do not account for hypothesized interdependencies between brain regions or quantify accuracy in identifying individuals. To overcome these limitations, we used multivariate machine learning to identify gray matter volume (GMV) and amplitude of low frequency fluctuations (ALFF) brain patterns that best classify young adults born very preterm/very low birth weight (VP/VLBW; n = 94) from those born full-term (FT; n = 92). We then compared the spatial maps of the structural and functional brain signatures and validated them by assessing associations with clinical birth history and basic cognitive variables. Premature birth could be predicted with a balanced accuracy of 80.7% using GMV and 77.4% using ALFF. GMV predictions were mediated by a pattern of subcortical and middle temporal reductions and volumetric increases of the lateral prefrontal, medial prefrontal, and superior temporal gyrus regions. ALFF predictions were characterized by a pattern including increases in the thalamus, pre- and post-central gyri, and parietal lobes, in addition to decreases in the superior temporal gyri bilaterally. Decision scores from each classification, assessing the degree to which an individual was classified as a VP/VLBW case, were predicted by the number of days in neonatal hospitalization and birth weight. ALFF decision scores also contributed to the prediction of general IQ, which highlighted their potential clinical significance. Combined, the results clarified previous research and suggested that primary subcortical and temporal damage may be accompanied by disrupted neurodevelopment of the cortex.

PMID: 31228329 [PubMed - as supplied by publisher]