New resting-state fMRI related studies at PubMed

Subscribe to New resting-state fMRI related studies at PubMed feed New resting-state fMRI related studies at PubMed
NCBI: db=pubmed; Term=resting state fMRI
Updated: 5 hours 22 min ago

On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease.

Wed, 10/17/2018 - 16:20
Related Articles

On the Extraction and Analysis of Graphs From Resting-State fMRI to Support a Correct and Robust Diagnostic Tool for Alzheimer's Disease.

Front Neurosci. 2018;12:528

Authors: Bachmann C, Jacobs HIL, Porta Mana P, Dillen K, Richter N, von Reutern B, Dronse J, Onur OA, Langen KJ, Fink GR, Kukolja J, Morrison A

Abstract
The diagnosis of Alzheimer's disease (AD), especially in the early stage, is still not very reliable and the development of new diagnosis tools is desirable. A diagnosis based on functional magnetic resonance imaging (fMRI) is a suitable candidate, since fMRI is non-invasive, readily available, and indirectly measures synaptic dysfunction, which can be observed even at the earliest stages of AD. However, the results of previous attempts to analyze graph properties of resting state fMRI data are contradictory, presumably caused by methodological differences in graph construction. This comprises two steps: clustering the voxels of the functional image to define the nodes of the graph, and calculating the graph's edge weights based on a functional connectivity measure of the average cluster activities. A variety of methods are available for each step, but the robustness of results to method choice, and the suitability of the methods to support a diagnostic tool, are largely unknown. To address this issue, we employ a range of commonly and rarely used clustering and edge definition methods and analyze their graph theoretic measures (graph weight, shortest path length, clustering coefficient, and weighted degree distribution and modularity) on a small data set of 26 healthy controls, 16 subjects with mild cognitive impairment (MCI) and 14 with Alzheimer's disease. We examine the results with respect to statistical significance of the mean difference in graph properties, the sensitivity of the results to model and parameter choices, and relative diagnostic power based on both a statistical model and support vector machines. We find that different combinations of graph construction techniques yield contradicting, but statistically significant, relations of graph properties between health conditions, explaining the discrepancy across previous studies, but casting doubt on such analyses as a method to gain insight into disease effects. The production of significant differences in mean graph properties turns out not to be a good predictor of future diagnostic capacity. Highest predictive power, expressed by largest negative surprise values, are achieved for both atlas-driven and data-driven clustering (Ward clustering), as long as graphs are small and clusters large, in combination with edge definitions based on correlations and mutual information transfer.

PMID: 30323734 [PubMed]

Modulation of prefrontal connectivity in postherpetic neuralgia patients with chronic pain: a resting-state functional magnetic resonance-imaging study.

Wed, 10/17/2018 - 16:20
Related Articles

Modulation of prefrontal connectivity in postherpetic neuralgia patients with chronic pain: a resting-state functional magnetic resonance-imaging study.

J Pain Res. 2018;11:2131-2144

Authors: Li J, Huang X, Sang K, Bodner M, Ma K, Dong XW

Abstract
Background: Although the interaction between pain and cognition has been recognized for decades, the neural substrates underlying their association remain unclear. The prefrontal cortex (PFC) is known as a critical brain area for higher cognitive functions, as well as for pain perception and modulation. The objective of the present study was to explore the role of the PFC in the interaction between chronic pain and cognitive functions by examining the relationship between spontaneous activity in the frontal lobe and pain intensity reported by postherpetic neuralgia (PHN) patients.
Methods: Resting-state functional magnetic resonance imaging data from 16 PHN patients were collected, and regional homogeneity and related functional connectivity were analyzed.
Results: The results showed negative correlations between patients' pain scores and regional homogeneity values in several prefrontal areas, including the left lateral PFC, left medial PFC, and right lateral orbitofrontal cortex (P<0.05, AlphaSim-corrected). Further analysis revealed that the functional connectivity of some of these prefrontal areas with other cortical regions was also modulated by pain intensity. Therefore, functional connections of the left lateral PFC with both the left parietal cortex and the left occipital cortex were correlated with patients' pain ratings (P<0.05, AlphaSim-corrected). Similarly, functional connectivity between the right lateral orbitofrontal cortex and bilateral postcentral/precentral gyri was also correlated with pain intensity in the patients (P<0.05, AlphaSim-corrected).
Conclusion: Our findings indicate that activity in the PFC is modulated by chronic pain in PHN patients. The pain-related modulation of prefrontal activity may serve as the neural basis for interactions between chronic pain and cognitive functions, which may link to cognitive impairments observed in chronic pain patients.

PMID: 30323648 [PubMed]

Body composition-related functions: a problem-oriented approach to phenotyping.

Wed, 10/17/2018 - 16:20
Related Articles

Body composition-related functions: a problem-oriented approach to phenotyping.

Eur J Clin Nutr. 2018 Oct 15;:

Authors: Müller MJ, Geisler C, Hübers M, Pourhassan M, Bosy-Westphal A

Abstract
AIM: The objective of this study is to generate metabolic phenotypes based on structure-function relationships.
METHODS: In 459 healthy adults (54% females, 18 and 40 years old), we analyzed body composition by air-displacement densitometry (to assess fat mass, (FM) and fat-free mass (FFM)) and whole-body magnetic resonance imaging (to assess skeletal muscle mass (SMM) and masses of brain, heart, liver, kidneys, and subcutaneous (SAT) and visceral adipose tissue (VAT)), resting energy expenditure (REE) by indirect calorimetry, and plasma concentrations of insulin (Ins) and leptin (Lep).
RESULTS: Three "functional body composition-derived phenotypes" (FBCPs) were derived: (1) REE on FFM-FBCP, (2) Lep on FM-FBCP, and (3) Ins on VAT-FBCP. Assuming that being within the ± 5% range of the respective regression lines reflects a "normal" structure-function relationship, three "normal" FBCPs were generated with prevalences of 9.0%, 5.1%, and 6.8%, respectively, of the study population. The three "FBCPs" did not overlap and were independent from each other. When compared with the two other FBCPs, the "Lep on FM-FBCP" was leanest, whereas the "REE on FFM-FBCP" had the highest BMI and SAT. Taking into account FFM composition, a hierarchical multi-level model is proposed with brain at level 1, the liver at level 2, and SMM and FM at level 3 with insulin coordinating the interplay between level 1 and 2, whereas variance in plasma insulin levels impacts energy and substrate metabolism in SMM and AT.
CONCLUSION: Structure-function relationships can be used to generate FBCPs. Different FBCPs reflect different dimensions of normality (or health). This is evidence for the idea that there is no across the board "normal" state.

PMID: 30323173 [PubMed - as supplied by publisher]

Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding.

Wed, 10/17/2018 - 16:20
Related Articles

Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding.

Proc Natl Acad Sci U S A. 2018 10 02;115(40):10154-10159

Authors: Vos de Wael R, Larivière S, Caldairou B, Hong SJ, Margulies DS, Jefferies E, Bernasconi A, Smallwood J, Bernasconi N, Bernhardt BC

Abstract
The hippocampus plays key roles in cognition and affect and serves as a model system for structure/function studies in animals. So far, its complex anatomy has challenged investigations targeting its substructural organization in humans. State-of-the-art MRI offers the resolution and versatility to identify hippocampal subfields, assess its microstructure, and study topographical principles of its connectivity in vivo. We developed an approach to unfold the human hippocampus and examine spatial variations of intrinsic functional connectivity in a large cohort of healthy adults. In addition to mapping common and unique connections across subfields, we identified two main axes of subregional connectivity transitions. An anterior/posterior gradient followed long-axis landmarks and metaanalytical findings from task-based functional MRI, while a medial/lateral gradient followed hippocampal infolding and correlated with proxies of cortical myelin. Findings were consistent in an independent sample and highly stable across resting-state scans. Our results provide robust evidence for long-axis specialization in the resting human hippocampus and suggest an intriguing interplay between connectivity and microstructure.

PMID: 30249658 [PubMed - indexed for MEDLINE]

Lower cognitive control network connectivity in stroke participants with depressive features.

Wed, 10/17/2018 - 16:20
Related Articles

Lower cognitive control network connectivity in stroke participants with depressive features.

Transl Psychiatry. 2018 03 09;7(11):4

Authors: Egorova N, Cumming T, Shirbin C, Veldsman M, Werden E, Brodtmann A

Abstract
Around one-third of people develop depression following ischaemic stroke, yet the underlying mechanisms are poorly understood. Post-stroke depression has been linked to frontal infarcts, mainly lesions in the left dorsolateral prefrontal cortex (DLPFC). But depression is a network disorder that cannot be fully characterised through lesion-symptom mapping. Researchers of depression in non-stroke populations have successfully tapped into the cognitive control network (CCN) using the bilateral DLPFC as a seed, and found that CCN resting-state connectivity is reduced in even mildly depressed subjects, compared to healthy controls. Hence, we aimed to investigate the association between post-stroke depressive features and the CCN resting-state connectivity in a stroke population. We analysed DLPFC resting-state connectivity in 64 stroke participants, 20 of whom showed depressive features assessed with the Patient Health Questionnaire (PHQ-9) at 3 months after stroke. We directly compared groups showing symptoms of depression with those who did not, and performed a regression with PHQ-9 scores in all participants, controlling for age, gender, lesion volume and stroke severity. Post-stroke depression was associated with lower connectivity between the left DLPFC and the right supramarginal gyrus (SMG) in both group and regression analyses. Neither the seed nor the results overlapped with stroke lesions. These findings confirm an important role of the left DLPFC in post-stroke depression, but now show that large-scale network disruptions following stroke associated with depressive features occur without lesions in the DLPFC.

PMID: 29520018 [PubMed - indexed for MEDLINE]

Resting-state network connectivity in cognitively unimpaired drug-naïve patients with rigidity-dominant Parkinson's disease.

Tue, 10/16/2018 - 15:20
Related Articles

Resting-state network connectivity in cognitively unimpaired drug-naïve patients with rigidity-dominant Parkinson's disease.

J Neurol Sci. 2018 Oct 03;395:147-152

Authors: Hou Y, Yang J, Luo C, Ou R, Zou Y, Song W, Gong Q, Shang H

Abstract
BACKGROUND: Parkinson's disease (PD) could be classified into akinetic-rigidity (PDAR), tremor-dominant (PDTD) and mixed subtypes. PDAR patients are more prone to develop cognitive deficits. The default mode network (DMN), fronto-parietal network (FPN) and dorsal attention network (DAN) play important roles in cognitive processing. Our aim was to evaluate changes in connectivity patterns of the DMN, and its interrelation with the FPN and DAN in cognitively unimpaired drug-naïve PDAR patients.
METHOD: Resting-state functional MRI (rs-fMRI) data was collected in 20 cognitively unimpaired early-stage drug-naïve PDAR patients and 20 age-, gender- and cognition- matched healthy controls (HCs). Group-level independent component analysis (ICA) was used to investigate changes in functional connectivity (FC) within the DMN between PDAR and HCs groups, and relationships between the DMN and FPN/DAN were evaluated by seed-based approach.
RESULTS: In PDAR patients, a significantly decreased FC, as compared with HCs, was observed in the left inferior parietal lobule (IPL) within the DMN. And the left IPL had a reduced FC with the left anterior cingulate cortex (ACC), left superior frontal gyrus (SFG), and left precuneus. However, no differences were detected in the FC between the left IPL and FPN/DAN. In addition, cognitive scores on the brief visuospatial memory test revised (BVMT-R), representing for cognitive memory domain, were positively correlated with the FC of the left IPL with bilateral SFG.
CONCLUSIONS: Our study mainly revealed altered within-DMN connectivity in cognitively unimpaired PDAR patients, which could provide further insights into the mechanism underlying cognitive decline evolution in the PD subtype.

PMID: 30321795 [PubMed - as supplied by publisher]

Cognitive control neuroimaging measures differentiate between those with and without future recurrence of depression.

Tue, 10/16/2018 - 15:20
Related Articles

Cognitive control neuroimaging measures differentiate between those with and without future recurrence of depression.

Neuroimage Clin. 2018 Oct 04;20:1001-1009

Authors: Langenecker SA, Jenkins LM, Stange JP, Chang YS, DelDonno SR, Bessette KL, Passarotti AM, Bhaumik R, Ajilore O, Jacobs RH

Abstract
BACKGROUND: Major Depressive Disorder (MDD) is a prevalent, disruptive illness. A majority of those with MDD are at high risk for recurrence and increased risk for morbidity and mortality. This study examined whether multimodal baseline (and retest) Cognitive Control performance and neuroimaging markers (task activation and neural connectivity between key brain nodes) could differentiate between those with and without future recurrence of a major depressive (MD) episode within one year. We hypothesized that performance and neuroimaging measures of Cognitive Control would identify markers that differ between these two groups.
METHODS: A prospective cohort study of young adults (ages 18-23) with history (h) of early-onset MDD (N = 60), now remitted, and healthy young adults (N = 49). Baseline Cognitive Control measures of performance, task fMRI and resting state connectivity (and reliability retest 4-12 weeks later) were used to compare those with future recurrence of MDD (N = 21) relative to those without future recurrence of MDD (N = 34 with resilience). The measures tested were (1) Parametric Go/No-Go (PGNG) performance, and task activation for (2) PGNG Correct Rejections, (3) PGNG Commission errors, and (4 & 5), resting state connectivity analyses of Cognitive Control Network to and from subgenual anterior cingulate.
RESULTS: Relative to other groups at baseline, the group with MDD Recurrence had less bilateral middle frontal gyrus activation during commission errors. MDD Recurrence exhibited greater connectivity of right middle frontal gyrus to subgenual anterior cingulate (SGAC). SGAC connectivity was also elevated in this group to numerous regions in the Cognitive Control Network. Moderate to strong ICCs were present from test to retest, and highest for rs-fMRI markers. There were modest, significant correlations between task, connectivity and behavioral markers that distinguished between groups.
CONCLUSION: Markers of Cognitive Control function could identify those with early course MD who are at risk for depression recurrence. Those at high risk for recurrence would benefit from maintenance or preventative treatments. Future studies could test and validate these markers as potential predictors, accounting for sample selection and bias in feature detection.

PMID: 30321791 [PubMed - as supplied by publisher]

Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI.

Tue, 10/16/2018 - 15:20
Related Articles

Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI.

Med Image Comput Comput Assist Interv. 2018 Sep;11072:232-239

Authors: Li H, Fan Y

Abstract
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes. Besides sliding window based approaches, a variety of methods have been developed to automatically split the entire functional MRI scan into segments by detecting change points of functional signals to facilitate better characterization of temporally dynamic functional connectivity patterns. However, these methods are based on certain assumptions for the functional signals, such as Gaussian distribution, which are not necessarily suitable for the fMRI data. In this study, we develop a deep learning based framework for adaptively detecting temporally dynamic functional state transitions in a data-driven way without any explicit modeling assumptions, by leveraging recent advances in recurrent neural networks (RNNs) for sequence modeling. Particularly, we solve this problem in an anomaly detection framework with an assumption that the functional profile of one single time point could be reliably predicted based on its preceding profiles within a stable functional state, while large prediction errors would occur around change points of functional states. We evaluate the proposed method using both task and resting-state fMRI data obtained from the human connectome project and experimental results have demonstrated that the proposed change point detection method could effectively identify change points between different task events and split the resting-state fMRI into segments with distinct functional connectivity patterns.

PMID: 30320310 [PubMed - in process]

Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism.

Tue, 10/16/2018 - 15:20
Related Articles

Resting-state brain network features associated with short-term skill learning ability in humans and the influence of N-methyl-d-aspartate receptor antagonism.

Netw Neurosci. 2018;2(4):464-480

Authors: Zang Z, Geiger LS, Braun U, Cao H, Zangl M, Schäfer A, Moessnang C, Ruf M, Reis J, Schweiger JI, Dixson L, Moscicki A, Schwarz E, Meyer-Lindenberg A, Tost H

Abstract
Graph theoretical functional magnetic resonance imaging (fMRI) studies have demonstrated that brain networks reorganize significantly during motor skill acquisition, yet the associations between motor learning ability, brain network features, and the underlying biological mechanisms remain unclear. In the current study, we applied a visually guided sequential pinch force learning task and graph theoretical analyses to investigate the associations between short-term motor learning ability and resting-state brain network metrics in 60 healthy subjects. We further probed the test-retest reliability (n = 26) and potential effects of the N-methyl-d-aspartate (NMDA) antagonist ketamine (n = 19) in independent healthy volunteers. Our results show that the improvement of motor performance after short-term training was positively correlated with small-worldness (p = 0.032) and global efficiency (p = 0.025), whereas negatively correlated with characteristic path length (p = 0.014) and transitivity (p = 0.025). In addition, using network-based statistics (NBS), we identified a learning ability-associated (p = 0.037) and ketamine-susceptible (p = 0.027) cerebellar-cortical network with fair to good reliability (intraclass correlation coefficient [ICC] > 0.7) and higher functional connectivity in better learners. Our results provide new evidence for the association of intrinsic brain network features with motor learning and suggest a role of NMDA-related glutamatergic processes in learning-associated subnetworks.

PMID: 30320294 [PubMed]

Apolipoprotein E ε4 Specifically Modulates the Hippocampus Functional Connectivity Network in Patients With Amnestic Mild Cognitive Impairment.

Tue, 10/16/2018 - 15:20
Related Articles

Apolipoprotein E ε4 Specifically Modulates the Hippocampus Functional Connectivity Network in Patients With Amnestic Mild Cognitive Impairment.

Front Aging Neurosci. 2018;10:289

Authors: Zhu L, Shu H, Liu D, Guo Q, Wang Z, Zhang Z

Abstract
The presence of both apolipoprotein E (APOE) ε4 allele and amnestic mild cognitive impairment (aMCI) are considered to be risk factors for Alzheimer's disease (AD). Numerous neuroimaging studies have suggested that the modulation of APOE ε4 affects intrinsic functional brain networks, both in healthy populations and in AD patients. However, it remains largely unclear whether and how ε4 allele modulates the brain's functional network architecture in subjects with aMCI. Using resting-state functional magnetic resonance imaging (fMRI) and graph-theory approaches-functional connectivity strength (FCS), we investigate the topological organization of the whole-brain functional network in 28 aMCI ε4 carriers and 38 aMCI ε3ε3 carriers. In the present study, we first observe that ε4-related FCS increases in the right hippocampus/parahippocampal gyrus (HIP/PHG). Subsequent seed-based resting-state functional connectivity (RSFC) analysis revealed that, compared with the ε3ε3 carriers, the ε4 carriers had lower or higher RSFCs between the right HIP/PHG seed and the bilateral medial prefrontal cortex (MPFC) or the occipital cortex, respectively. Further correlation analyses have revealed that the FCS values in the right HIP/PHG and lower HIP/PHG-RSFCs with the bilateral MPFC were significantly correlated with the impairment of episodic memory and executive function in the aMCI ε4 carriers. Importantly, the logistic regression analysis showed that the HIP/PHG-RSFC with the bilateral MPFC predicted aMCI-conversion to AD. These findings suggest that the APOE ε4 allele may modulate the large-scale brain network in aMCI subjects, facilitating our understanding of how the entire assembly of the brain network reorganizes in response to APOE variants in aMCI. Further longitudinal studies need to be conducted, in order to examine whether these network measures could serve as primary predictors of conversion from aMCI ε4 carriers to AD.

PMID: 30319395 [PubMed]

Fronto-Temporal Circuits in Musical Hallucinations: A PET-MR Case Study.

Tue, 10/16/2018 - 15:20
Related Articles

Fronto-Temporal Circuits in Musical Hallucinations: A PET-MR Case Study.

Front Hum Neurosci. 2018;12:385

Authors: Cavaliere C, Longarzo M, Orsini M, Aiello M, Grossi D

Abstract
The aim of the study is to investigate morphofunctional circuits underlying musical hallucinations (MH) in a 72-years old female that underwent a simultaneous 18fluoredeoxyglucose positron emission tomography (PET) and advanced magnetic resonance (MR) exam. This represents a particular case of MH occurred in an healthy subject, not displaying neurological or psychopathological disorders, and studied simultaneously with a multimodal approach. For the resting-state fMRI analysis a seed to seed approach was chosen. For the task-based fMRI, 4 different auditory stimuli were presented. Imaging findings were compared with data obtained by ten healthy controls matched for age and sex. Neuropsychological evaluation and questionnaires investigating depression and anxiety were also administered. PET findings showed hypermetabolism of: superior temporal gyri, anterior cingulate, left orbital frontal, and medial temporal cortices. Structural MRI did not show macroscopical lesions except for gliotic spots along the uncinate fascicle pathways with an increased cortical thickness for the right orbitofrontal cortex (p = 0.003). DTI showed increased fractional anisotropy values in the left uncinate fascicle, when compared to controls (p = 0.04). Resting-state fMRI showed increased functional connectivity between the left inferior frontal gyrus and the left temporal fusiform cortex (p = 0.01). Task-based fMRI confirmed PET findings showing an increased activation of the superior temporal gyrus in all the auditory tasks except for the monotone stimulus, with a significant activation of the left orbital frontal cortex only during the song in foreign language, object of MH. Results on cognitive test did not show cognitive impairment, excepting for the performance on Frontal Assessment Battery where the patient fails in the cognitive domains of conceptualization, sensitive to interference, and inhibitory control. The subject did not show depressive or anxiety symptoms. Summarizing, multimodal imaging analyses in the MH case showed a microstructural alteration of the left uncinate fascicle paralleled by an increased metabolism and functional connectivity of cortical regions that receive left uncinate projections (orbital frontal cortex, and medial temporal cortex). This alteration of fronto-hyppocampal circuits could be responsible of retrieval of known songs even in the absence of real stimuli.

PMID: 30319380 [PubMed]

Inside the Developing Brain to Understand Teen Behavior From Rat Models: Metabolic, Structural, and Functional-Connectivity Alterations Among Limbic Structures Across Three Pre-adolescent Stages.

Tue, 10/16/2018 - 15:20
Related Articles

Inside the Developing Brain to Understand Teen Behavior From Rat Models: Metabolic, Structural, and Functional-Connectivity Alterations Among Limbic Structures Across Three Pre-adolescent Stages.

Front Behav Neurosci. 2018;12:208

Authors: Zoratto F, Altabella L, Tistarelli N, Laviola G, Adriani W, Canese R

Abstract
Adolescence is an age of transition when most brain structures undergo drastic modifications, becoming progressively more interconnected and undergoing several changes from a metabolic and structural viewpoint. In the present study, three MR techniques are used in rats to investigate how metabolites, structures and patterns of connectivity do change. We focused in particular on areas belonging to the limbic system, across three post-weaning developmental stages: from "early" (PND 21-25) to "mid" (i.e., a juvenile transition, PND 28-32) and then to "late" (i.e., the adolescent transition, PND 35-39). The rs-fMRI data, with comparison between early and mid (juvenile transition) age-stage rats, highlights patterns of enhanced connectivity from both Striata to both Hippocampi and from there to (left-sided) Nucleus accumbens (NAcc) and Orbitofrontal Cortex (OFC). Also, during this week there is a maturation of pathways from right Striatum to ipsilateral NAcc, from right OFC to ipsilateral NAcc and vice versa, from left Prefrontal Cortex to ipsilateral OFC and eventually from left Striatum, NAcc and Prefrontal Cortex to contralateral OFC. After only 1 week, in late age-stage rats entering into adolescence, the first pathway mentioned above keeps on growing while other patterns appear: both NAcc are reached from contralateral Striatum, right Hippocampus from both Amygdalae, and left NAcc -further- from right Hippocampus. It's interesting to notice the fact that, independently from the age when these connections develop, Striata of both hemispheres send axons to both Hippocampi and both NAcc sides, both Hippocampi reach left NAcc and OFC and finally both NAcc sides reach right OFC. Intriguingly, the Striatum only indirectly reaches the OFC by passing through Hippocampus and NAcc. Data obtained with DTI highlight how adolescents' neurite density may be affected within sub-cortical gray matter, especially for NAcc and OFC at "late" age-stage (adolescence). Finally, levels of metabolites were investigated by 1H-MRS in the anterior part of the hippocampus: we put into evidence an increase in myo-inositol during juvenile transition and a taurine reduction plus a total choline increase during adolescent transition. In this paper, the aforementioned pattern guides the formulation of hypotheses concerning the correlation between the establishment of novel brain connections and the emergence of behavioral traits that are typical of adolescence.

PMID: 30319367 [PubMed]

The Relation Between White Matter Microstructure and Network Complexity: Implications for Processing Efficiency.

Tue, 10/16/2018 - 15:20
Related Articles

The Relation Between White Matter Microstructure and Network Complexity: Implications for Processing Efficiency.

Front Integr Neurosci. 2018;12:43

Authors: McDonough IM, Siegel JT

Abstract
Brain structure has been proposed to facilitate as well as constrain functional interactions within brain networks. Simulation models suggest that integrity of white matter (WM) microstructure should be positively related to the complexity of BOLD signal - a measure of network interactions. Using 121 young adults from the Human Connectome Project, we empirically tested whether greater WM integrity would be associated with greater complexity of the BOLD signal during rest via multiscale entropy. Multiscale entropy measures the lack of predictability within a given time series across varying time scales, thus being able to estimate fluctuating signal dynamics within brain networks. Using multivariate analysis techniques (Partial Least Squares), we found that greater WM integrity was associated with greater network complexity at fast time scales, but less network complexity at slower time scales. These findings implicate two separate pathways through which WM integrity affects brain function in the prefrontal cortex - an executive-prefrontal pathway and a perceptuo-occipital pathway. In two additional samples, the main patterns of WM and network complexity were replicated. These findings support simulation models of WM integrity and network complexity and provide new insights into brain structure-function relationships.

PMID: 30319365 [PubMed]

The Changes of Functional Connectivity Strength in Electroconvulsive Therapy for Depression: A Longitudinal Study.

Tue, 10/16/2018 - 15:20
Related Articles

The Changes of Functional Connectivity Strength in Electroconvulsive Therapy for Depression: A Longitudinal Study.

Front Neurosci. 2018;12:661

Authors: Wei Q, Bai T, Chen Y, Ji G, Hu X, Xie W, Xiong Z, Zhu D, Wei L, Hu P, Yu Y, Wang K, Tian Y

Abstract
Electroconvulsive therapy (ECT) is an effective treatment for depression, but the mechanism of ECT for depression is still unclear. Recently, neuroimaging studies have reported that the prefrontal cortex, hippocampus, angular gyrus, insular and other brain regions are involved in the mechanism of ECT for depression, and these regions are highly overlapped with the location of brain hubs. Here, we try to explore the effects of ECT on the functional connectivity of brain hubs in depression patients. In current study, depression patients were assessed at three time points: prior to ECT, at the completion of ECT and about 1 month after the completion of ECT. At each time point, resting-state functional magnetic resonance imaging, assessment of clinical symptoms and cognition function were performed respectively, which was compared with 20 normal controls. Functional connectivity strength (FCS) was used to identify brain hubs. The results showed that FCS of left angular gyrus in depression patients significantly increased after ECT, accompanied by improved mood. The changed FCS in depression patients recovered obviously at 1 month after the completion of ECT. It suggested that ECT could modulate functional connectivity of left angular gyrus in depression patients.

PMID: 30319341 [PubMed]

Dynamic fMRI Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity.

Tue, 10/16/2018 - 15:20
Related Articles

Dynamic fMRI Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity.

Brain Connect. 2018 Oct 15;:

Authors: Mokhtari F, Laurienti PJ, Rejeski WJ, Ballard G

Abstract
As brain network organization likely fluctuates over time to react to internal and external stimuli, the validity of conventional static brain connectivity models are being questioned. Thus, there is a growing interest in using so-called dynamic network analyses. Brain network analyses yield complex network data that is difficult to analyze and interpret. To deal with the complex structures, data reduction techniques that simplify the data are often used. For dynamic network analyses, data simplification is even of greater importance, as dynamic connectivity analyses result in a time series of complex networks. Decomposition/factorization methods that identifying the main components underlying the data are gaining popularity for dynamic brain network simplification. A new challenge that must be faced when using these data reduction techniques is how to interpret the resulting network components. Thus, the primary goal of this paper is to specifically address this challenge and discuss issues that must be considered when interpreting the network components. Based on simulated and real fMRI data analysis, we argue that the network components cannot be interpreted in the same manner as the original functional networks, e.g. the connections in the components represent complex relationships between nodes not simple associations between the time series. We also demonstrate the associated weight that varies across time and across participants in a study population that accompanies the network components. The network components should always be interpreted in conjunction with these weights that denote how any one component contributes to overall brain network connectivity at given time or in any given participant.

PMID: 30318906 [PubMed - as supplied by publisher]

ABCA7 Risk Variant in Healthy Older African Americans is Associated with a Functionally Isolated Entorhinal Cortex Mediating Deficient Generalization of Prior Discrimination Training.

Tue, 10/16/2018 - 15:20
Related Articles

ABCA7 Risk Variant in Healthy Older African Americans is Associated with a Functionally Isolated Entorhinal Cortex Mediating Deficient Generalization of Prior Discrimination Training.

Hippocampus. 2018 Oct 14;:

Authors: Sinha N, Reagh ZM, Tustison NJ, Berg CN, Shaw A, Myers CE, Hill D, Yassa MA, Gluck MA

Abstract
Using high-resolution resting state fMRI, the present study tested the hypothesis that ABCA7 genetic risk differentially affects intra-medial temporal lobe (MTL) functional connectivity between MTL subfields, versus internetwork connectivity of the MTL with the medial prefrontal cortex (mPFC), in non-demented older African Americans. Although the association of ABCA7 risk variants with Alzheimer's disease (AD) has been confirmed worldwide, its effect size on the relative odds of being diagnosed with AD is significantly higher in African Americans. However, little is known about the neural correlates of cognitive function in older African Americans and how they relate to AD risk conferred by ABCA7. In a case-control fMRI study of 36 healthy African Americans, we observed ABCA7 related impairments in behavioral generalization that was mediated by dissociation in entorhinal cortex (EC) resting state functional connectivity. Specifically, ABCA7 risk variant was associated with EC-hippocampus hyper-synchronization and EC-mPFC hypo-synchronization. Carriers of the risk genotype also had a significantly smaller anterolateral EC (alEC), despite our finding no group differences on standardized neuropsychological tests. Our findings suggest a model where impaired cortical connectivity leads to a more functionally isolated EC at rest, which translates into aberrant EC-hippocampus hyper-synchronization resulting in generalization deficits. While we cannot identify the exact mechanism underlying the observed alterations in EC structure and network function, considering the relevance of Aβ in ABCA7 related AD pathogenesis, the results of our study may reflect the synergistic reinforcement between amyloid and tau pathology in the EC, which significantly increases tau-induced neuronal loss and accelerates synaptic alterations. Finally, our results add to a growing literature suggesting that generalization of learning may be a useful tool for assessing the mild cognitive deficits seen in the earliest phases of prodromal AD, even before the more commonly reported deficits in episodic memory arise. This article is protected by copyright. All rights reserved.

PMID: 30318785 [PubMed - as supplied by publisher]

Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates.

Tue, 10/16/2018 - 15:20
Related Articles

Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates.

Hum Brain Mapp. 2018 Oct 15;:

Authors: Eklund A, Knutsson H, Nichols TE

Abstract
Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event-related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one-sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two-sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.

PMID: 30318709 [PubMed - as supplied by publisher]

Detecting perfusion deficit in Alzheimer's disease and mild cognitive impairment patients by resting-state fMRI.

Tue, 10/16/2018 - 15:20
Related Articles

Detecting perfusion deficit in Alzheimer's disease and mild cognitive impairment patients by resting-state fMRI.

J Magn Reson Imaging. 2018 Oct 14;:

Authors: Yan S, Qi Z, An Y, Zhang M, Qian T, Lu J

Abstract
BACKGROUND: Vascular factors contributing to cerebral hypoperfusion are implicated in the risk of developing Alzheimer's disease (AD).
PURPOSE: To investigate the time-shift mapping created time-shift value of the brain by resting-state functional magnetic resonance imaging (rs-fMRI), and to determine the differences in time-shift value among AD, mild cognitive impairment (MCI), and normal control (NC) groups to better understand the disease.
STUDY TYPE: Prospective.
SUBJECTS: Twenty-four AD, 24 MCI, and 24 age-matched NC participants.
FIELD STRENGTH/SEQUENCE: T2 *-weighted single-shot echo-planar imaging sequence was performed at 3T. In addition, a T1 -weighted fast spoiled gradient-echo sequence was acquired for coregistration.
ASSESSMENT: The brain time-shift value was determined from rs-fMRI-based blood oxygenation level-dependent (BOLD) signal in the three groups by time-shift mapping. The perfusion patterns were also investigated in the NC group.
STATISTICAL TESTS: One-way analysis of variance and chi-squared tests were used to compare demographic information. The normalized time-shift maps were analyzed in a second-level test using SPM8. All analyses were evaluated with a significance level of P < 0.05 after false discovery rate (FDR) correction.
RESULTS: The time-shift maps obtained from rs-fMRI are consistent with the cerebral blood supply atlas. Compared with NC, both MCI and AD groups had less early perfusion arrival areas among the whole brain. In the delayed time-shift value for the AD group, the areas were located in the bilateral precuneus, the sensory-motor cortex in the left hemisphere, and the bilateral calcarine sulcus, which were different from the MCI group (both P < 0.05, FDR corrected).
DATA CONCLUSION: The time-shift mapping method could detect perfusion deficits in AD and MCI noninvasively. The perfusion deficits detected by rs-fMRI may provide new insight for understanding the mechanism of neurodegeneration. Level of Evidence 2 Technical Efficacy Stage 3 J. Magn. Reson. Imaging 2018.

PMID: 30318645 [PubMed - as supplied by publisher]

Default-mode network and deep gray-matter analysis in neuromyelitis optica patients.

Tue, 10/16/2018 - 15:20
Related Articles

Default-mode network and deep gray-matter analysis in neuromyelitis optica patients.

J Neuroradiol. 2018 Jul;45(4):256-260

Authors: Rueda-Lopes FC, Pessôa FMC, Tukamoto G, Malfetano FR, Scherpenhuijzen SB, Alves-Leon S, Gasparetto EL

Abstract
OBJECTIVE: The aim of our study was to detect functional changes in default-mode network of neuromyelitis optica (NMO) patients using resting-state functional magnetic resonance images and the evaluation of subcortical gray-matter structures volumes.
MATERIALS AND METHODS: NMO patients (n=28) and controls patients (n=19) were enrolled. We used the integrated registration and segmentation tool, part of FMRIB's Software Library (FSL) to segment subcortical structures including the thalamus, caudate nucleus, putamen, hippocampus and amygdalae. Resting-state functional magnetic resonance images were post-processed using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components, also part of FSL. Average Z-values extracted from the default-mode network were compared between patients and controls using t-tests (P values <0.05 were considered statistically significant).
RESULTS: There were areas of increased synchronization in the default-mode network of patients compared to controls, notably in the precuneus and right hippocampus (corrected P<0.01). The frontal area had decreased synchronization in patients compared to controls (corrected P<0.01). There were no observed differences between patients and controls in subcortical volumes or average Z-values values for default-mode network.
CONCLUSION: The hyperactivity of certain default-mode network areas may reflect cortical compensation for subtle structural damage in NMO patients.

PMID: 29470996 [PubMed - indexed for MEDLINE]

Alerted default mode: functional connectivity changes in the aftermath of social stress.

Tue, 10/16/2018 - 15:20
Related Articles

Alerted default mode: functional connectivity changes in the aftermath of social stress.

Sci Rep. 2017 01 05;7:40180

Authors: Clemens B, Wagels L, Bauchmüller M, Bergs R, Habel U, Kohn N

Abstract
Stress affects the brain at a network level: the salience network is supposedly upregulated, while at the same time the executive control network is downregulated. While theoretically described, the effects in the aftermath of stress have thus far not been tested empirically. Here, we compared for the first time resting-state functional connectivity in a large sample of healthy volunteers before and after a mild social stressor. Following the theoretical prediction, we focused on connectivity of the salience network (SN), the executive control network (ECN) and the default mode network (DMN). The DMN exhibited increased resting-state functional connectivity following the cyberball task to the key nodes of the SN, namely the dorsal anterior cingulate cortex (dACC) and the anterior insula, as well as sensorimotor regions and higher-order visual areas. We conclude that this increased connectivity of the DMN with key nodes of the SN and regions responsible for preparatory motor activity and visual motion processing indicates a shift towards an 'alerted default mode' in the aftermath of stress. This brain response may be triggered or aggravated by (social) stress induced by the cyberball task, enabling individuals to better reorient attention, detect salient external stimuli, and deal with the emotional and affective consequences of stress.

PMID: 28054651 [PubMed - indexed for MEDLINE]

Pages