New resting-state fMRI related studies at PubMed

Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses

Mon, 03/15/2021 - 10:00

Front Neurosci. 2021 Feb 24;15:574979. doi: 10.3389/fnins.2021.574979. eCollection 2021.

ABSTRACT

In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.

PMID:33716640 | PMC:PMC7943734 | DOI:10.3389/fnins.2021.574979

Incorporation of anatomical MRI knowledge for enhanced mapping of brain metabolism using functional PET

Mon, 03/15/2021 - 10:00

Neuroimage. 2021 Mar 11:117928. doi: 10.1016/j.neuroimage.2021.117928. Online ahead of print.

ABSTRACT

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional Gaussian filtering using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging.

PMID:33716154 | DOI:10.1016/j.neuroimage.2021.117928

Detection of Prenatal Alcohol Exposure Using Machine Learning Classification of Resting-State Functional Network Connectivity Data

Mon, 03/15/2021 - 10:00

Alcohol. 2021 Mar 11:S0741-8329(21)00031-8. doi: 10.1016/j.alcohol.2021.03.001. Online ahead of print.

ABSTRACT

Fetal Alcohol Spectrum Disorder (FASD), a wide range of physical and neurobehavioral abnormalities associated with prenatal alcohol exposure (PAE), is recognized as a significant public health concern. Advancements in the diagnosis of FASD have been hindered by a lack of consensus in diagnostic criteria and limited use of objective biomarkers. Previous research from our group utilized resting state functional magnetic resonance imaging (fMRI) to measure functional network connectivity (FNC) revealed several sex- and region-dependent alterations in FNC as a result of moderate PAE relative to controls. Considering that FNC is sensitive to moderate PAE, this study explored the use of FNC data and machine learning methods to detect PAE among a sample of rodents exposed to alcohol prenatally and controls. We utilized previously acquired resting state fMRI data collected from adult rats exposed to moderate levels of prenatal alcohol (PAE) or a saccharin control solution (SAC) to assess FNC of resting state networks extracted by spatial group independent component analysis (GICA). FNC data was subjected to binary classification using support vector machine (SVM)-based algorithms and leave-one-out-cross validation (LOOCV) in an aggregated sample of males and females (n=48; 12 male PAE, 12 female PAE, 12 male SAC, 12 female SAC), a males only sample (n=24; 12 PAE, 12 SAC), and a females only sample (n=24; 12 PAE, 12 SAC). Results revealed that a quadratic SVM (QSVM) kernel was significantly effective for PAE detection in females. QSVM-kernel-based classification resulted in accuracy rates of 62.5% for all animals, 58.3% for males, and 79.2% for females. Additionally, qualitative evaluation of QSVM weights implicate an overarching theme of several hippocampal and cortical networks in contributing to the formation of correct classification decisions by QSVM. Our results suggest that binary classification using QSVM and adult female FNC data is a potential candidate for the translational development of novel and non-invasive techniques for the identification of FASD.

PMID:33716098 | DOI:10.1016/j.alcohol.2021.03.001

Pathogenesis and brain functional imaging in nocturnal enuresis: A review

Mon, 03/15/2021 - 10:00

Exp Biol Med (Maywood). 2021 Mar 9:1535370221997363. doi: 10.1177/1535370221997363. Online ahead of print.

ABSTRACT

Nocturnal enuresis is a common and distressing developmental disease, which may cause various degrees of psychosocial stress and impairment to self-esteem in affected children as well as agitation to their parents or caregivers. Nevertheless, the etiology and pathogenesis of nocturnal enuresis are not understood. Currently, nocturnal enuresis is generally considered a multifactorial disease associated with a complex interaction of somatic, psychosocial, and environmental factors. A variety of postulations have been proposed to explain the occurrence and progression of nocturnal enuresis, including hereditary aberration, abnormal circadian rhythm of antidiuretic hormone secretion during sleep, bladder dysfunction, abnormal sleep, difficulties in arousal, neuropsychological disorders, and maturational delays of the brain. In recent decades, the introduction of functional neuroimaging technologies has provided new approaches for uncovering the mechanisms underlying nocturnal enuresis. The main neuroimaging modalities have included brain morphometry based on structural magnetic resonance imaging (MRI), task-based and event-related functional MRI (fMRI), and resting-state fMRI. The relevant studies have indicated that nocturnal enuresis is associated with functional and structural alterations of the brain. In this review, we briefly summarized the popular hypotheses regarding the pathogenesis of nocturnal enuresis and the current progress of functional neuroimaging studies in examining the underlying mechanisms thereof.

PMID:33715529 | DOI:10.1177/1535370221997363

Dysfunctional beliefs and attitudes about sleep are associated with regional homogeneity of left inferior occidental gyrus in primary insomnia patients: a preliminary resting state functional magnetic resonance imaging study

Sun, 03/14/2021 - 10:00

Sleep Med. 2021 Feb 24;81:188-193. doi: 10.1016/j.sleep.2021.02.039. Online ahead of print.

ABSTRACT

BACKGROUND: The neural mechanisms of sleep beliefs and attitudes in primary insomnia (PI) patients at resting state remain unclear. The aim of this study was to investigate the features of regional homogeneity (ReHo) in PI using resting-state functional magnetic resonance imaging (rsfMRI).

METHODS: Thirty-two PI patients and 34 normal controls (NC) underwent rsfMRI using a 3 T scanner at Tongde Hospital of Zhejiang Province. Participants were assessed with the Dysfunctional Beliefs and Attitudes about Sleep scale (DBAS-16) and Pittsburgh Sleep Quality Index (PSQI). Statistical analyses were performed to determine the regions in which ReHo differed between the two groups. Correlation analyses were performed between the ReHo index of each of these regions and DBAS-16 in PI patients.

RESULTS: PI patients showed increased ReHo values in the right superior frontal gyrus, and decreased ReHo values in the left cerebellar gyrus, left inferior occipital gyrus (IOG) and left amygdala compared with those of NC. ReHo values in the left IOG were negatively correlated with total DBAS-16 scores, and scores for "consequences of insomnia" and"worry/helplessness about sleep"in PI patients.

CONCLUSIONS: These results suggest that ReHo alterations in the left IOG may play an important role in the dysfunctional beliefs and attitudes about sleep in PI.

PMID:33714848 | DOI:10.1016/j.sleep.2021.02.039

Functional MRI in Parkinson's disease with freezing of gait: a systematic review of the literature

Sat, 03/13/2021 - 11:00

Neurol Sci. 2021 Mar 13. doi: 10.1007/s10072-021-05121-5. Online ahead of print.

ABSTRACT

BACKGROUND: Freezing of gait (FOG), a common and disabling symptom of Parkinson's disease (PD), is characterized by an episodic inability to generate effective stepping. Functional MRI (fMRI) has been used to evaluate abnormal brain connectivity patterns at rest and brain activation patterns during specific tasks in patients with PD-FOG. This review has examined the existing functional neuroimaging literature in PD-FOG, including those with treatment. Summarizing these articles provides an opportunity for a better understanding of the underlying pathophysiology in PD-FOG.

METHODS: According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we performed a literature review of studies using fMRI to investigate the underlying pathophysiological mechanisms of PD-FOG.

RESULTS: We initially identified 201 documents. After excluding the duplicates, reviews, and other irrelevant articles, 39 articles were finally identified, including 18 task-based fMRI studies and 21 resting-state fMRI studies.

CONCLUSIONS: Studies using fMRI techniques to evaluate PD-FOG have found dysfunctional connectivity in widespread cortical and subcortical regions. Standardized imaging protocols and detailed subtypes of PD-FOG are furthered required to elucidate current findings.

PMID:33713258 | DOI:10.1007/s10072-021-05121-5

Molecular Mechanisms of Adenosine Stress T1 Mapping

Fri, 03/12/2021 - 11:00

Circ Cardiovasc Imaging. 2021 Mar;14(3):e011774. doi: 10.1161/CIRCIMAGING.120.011774. Epub 2021 Mar 12.

ABSTRACT

BACKGROUND: Adenosine stress T1 mapping is an emerging magnetic resonance imaging method to investigate coronary vascular function and myocardial ischemia without application of a contrast agent. Using gene-modified mice and 2 vasodilators, we elucidated and compared the mechanisms of adenosine myocardial perfusion imaging and adenosine T1 mapping.

METHODS: Wild-type (WT), A2AAR-/- (adenosine A2A receptor knockout), A2BAR-/- (adenosine A2B receptor knockout), A3AR-/- (adenosine A3 receptor knockout), and eNOS-/- (endothelial nitric oxide synthase knockout) mice underwent rest and stress perfusion magnetic resonance imaging (n=8) and T1 mapping (n=10) using either adenosine, regadenoson (a selective A2AAR agonist), or saline. Myocardial blood flow and T1 were computed from perfusion imaging and T1 mapping, respectively, at rest and stress to assess myocardial perfusion reserve and T1 reactivity (ΔT1). Changes in heart rate for each stress agent were also calculated. Two-way ANOVA was used to detect differences in each parameter between the different groups of mice.

RESULTS: Myocardial perfusion reserve was significantly reduced only in A2AAR-/- compared to WT mice using adenosine (1.06±0.16 versus 2.03±0.52, P<0.05) and regadenoson (0.98±026 versus 2.13±0.75, P<0.05). In contrast, adenosine ΔT1 was reduced compared with WT mice (3.88±1.58) in both A2AAR-/- (1.63±1.32, P<0.05) and A2BAR-/- (1.55±1.35, P<0.05). Furthermore, adenosine ΔT1 was halved in eNOS-/- (1.76±1.46, P<0.05) versus WT mice. Regadenoson ΔT1 was approximately half of adenosine ΔT1 in WT mice (1.97±1.50, P<0.05), and additionally, it was significantly reduced in eNOS-/- mice (-0.22±1.46, P<0.05). Lastly, changes in heart rate was 2× greater using regadenoson versus adenosine in all groups except A2AAR-/-, where heart rate remained constant.

CONCLUSIONS: The major findings are that (1) although adenosine myocardial perfusion reserve is mediated through the A2A receptor, adenosine ΔT1 is mediated through the A2A and A2B receptors, (2) adenosine myocardial perfusion reserve is endothelial independent while adenosine ΔT1 is partially endothelial dependent, and (3) ΔT1 mediated through the A2A receptor is endothelial dependent while ΔT1 mediated through the A2B receptor is endothelial independent.

PMID:33706537 | PMC:PMC7969455 | DOI:10.1161/CIRCIMAGING.120.011774

Neural correlates of improved inductive reasoning ability in abacus-trained children: A resting state fMRI study

Fri, 03/12/2021 - 11:00

Psych J. 2021 Mar 11. doi: 10.1002/pchj.439. Online ahead of print.

ABSTRACT

The ability to perform inductive reasoning is critical to human intelligence. Abacus-based mental calculation (AMC) training may improve mathematics-related abilities and the transfer to cognitive ability. Thus, it was hypothesized that inductive reasoning abilities can be improved by AMC training. The aim of the current study was to examine the underlying neurobiological mechanisms of this possible adaption by resting-state functional magnetic resonance imaging (rs-fMRI). Sixty-three children were randomly assigned to either the AMC-trained or the nontrained group. The AMC-trained group was required to perform abacus training for 2 hr per week for 5 years whereas the nontrained group was not required to perform any abacus training. Each participant's rs-fMRI data were collected after abacus training, and regional homogeneity (ReHo) analysis was performed to determine the neural activity differences between groups. The participants' posttraining mathematical ability, intelligence quotients, and inductive reasoning ability were recorded and evaluated. The results revealed that AMC-trained children exhibited a significantly higher mathematical ability and inductive reasoning performance and higher ReHo in the rostrolateral prefrontal cortex (RLPFC) compared to the nontrained group. In particular, the increased ReHo in the RLPFC was found to be positively correlated with improved inductive reasoning performance. Our findings suggest that rs-fMRI can reflect the modulation of training in task-related networks.

PMID:33709543 | DOI:10.1002/pchj.439

The Association Between Insular Subdivisions Functional Connectivity and Suicide Attempt in Adolescents and Young Adults with Major Depressive Disorder

Fri, 03/12/2021 - 11:00

Brain Topogr. 2021 Mar 11. doi: 10.1007/s10548-021-00830-8. Online ahead of print.

ABSTRACT

Previous studies demonstrated the possible involvement of insula in suicide owing to depression. However, the function of insula in young depressed patients with suicide attempt (SA) remains to be revealed. This study aimed to explore the association between resting-state functional connectivity (FC) of insula and SA in young depressed patients. Fifty-eight adolescents and young adults with major depressive disorder, including 22 with a history of at least one SA (SA group) and 36 without a history of SA (NSA group) were scanned with a 3.0T functional magnetic resonance imaging system, and the resting-state functional magnetic resonance imaging data was extracted. Whole brain resting-state FC of insular subdivisions were compared between the two groups. Significantly increased FC of the left posterior insula with the orbital part of left inferior frontal gyrus, the right supplementary motor area and the bilateral paracentral lobule extending to the bilateral middle cingulate cortex was observed in the SA group compared with the NSA group. In addition, the orbital part of left superior frontal gyrus in the SA group exhibited significantly increased FC with the right posterior insula compared with the NSA group. However, no significant correlation was found between the insular subdivisions FC and different clinical variables in two groups. The present study highlighted the disruptions of the resting-state FC of the posterior insula with the orbitofrontal cortex and a series of motor cortices, and added incremental value to the knowledge of the neural mechanism underlying SA in young depressed patients.

PMID:33709259 | DOI:10.1007/s10548-021-00830-8

ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

Fri, 03/12/2021 - 11:00

AIMS Neurosci. 2021 Feb 19;8(2):295-321. doi: 10.3934/Neuroscience.2021016. eCollection 2021.

ABSTRACT

We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan-Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme (consisting of an outer and an inner loop of "Leave one out" cross-validation (LOOCV)). The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus.

PMID:33709030 | PMC:PMC7940114 | DOI:10.3934/Neuroscience.2021016

Functional plasticity abnormalities over the lifespan of first-episode patients with major depressive disorder: a resting state fMRI study

Fri, 03/12/2021 - 11:00

Ann Transl Med. 2021 Feb;9(4):349. doi: 10.21037/atm-21-367.

ABSTRACT

BACKGROUND: Neurodevelopmental and neurodegenerative theories of depression suggest that patients with major depressive disorder (MDD) may follow abnormal developmental, maturational, and aging processes. However, a lack of lifespan studies has precluded verification of these theories. Herein, we analyzed functional magnetic resonance imaging (fMRI) data to comprehensively characterize age-related functional trajectories, as measured by the fractional amplitude of low frequency fluctuations (fALFF), over the course of MDD.

METHODS: In total, 235 MDD patients with age-differentiated onsets and 235 age- and sex-matched healthy controls (HC) were included in this study. We determined the pattern of age-related fALFF changes by cross-sectionally establishing the general linear model (GLM) between fALFF and age over a lifespan. Furthermore, the subjects were divided into four age groups to assess age-related neural changes in detail. Inter-group fALFF comparison (MDD vs. HC) was conducted in each age group and Granger causal analysis (GCA) was applied to investigate effective connectivity between regions.

RESULTS: Compared with the HC, no significant quadratic or linear age effects were found in MDD over the entire lifespan, suggesting that depression affects the normal developmental, maturational, and degenerative process. Inter-group differences in fALFF values varied significantly at different ages of onset. This implies that MDD may impact brain functions in a highly dynamic way, with different patterns of alterations at different stages of life. Moreover, the GCA analysis results indicated that MDD followed a distinct pattern of effective connectivity relative to HC, and this may be the neural basis of MDD with age-differentiated onsets.

CONCLUSIONS: Our findings provide evidence that normal developmental, maturational, and ageing processes were affected by MDD. Most strikingly, functional plasticity changes in MDD with different ages of onset involved dynamic interactions between neuropathological processes in a tract-specific manner.

PMID:33708976 | PMC:PMC7944321 | DOI:10.21037/atm-21-367

Alteration of spatial patterns at the network-level in facial synkinesis: an independent component and connectome analysis

Fri, 03/12/2021 - 11:00

Ann Transl Med. 2021 Feb;9(3):240. doi: 10.21037/atm-20-4865.

ABSTRACT

BACKGROUND: The treatment of post-facial palsy synkinesis (PFPS) remains inadequate. Previous studies have confirmed that brain plasticity is involved in the process of functional restoration. Isolated activation has been well studied, however, the brain works as an integrity of several isolated regions. This study aimed to assess the alteration of the brain network topology with overall and local characteristics of information dissemination. Understanding the neural mechanisms of PFPS could help to improve therapy options and prognosis.

METHODS: Patients with facial synkinesis and healthy controls (HCs) were estimated using functional magnetic resonance imaging (fMRI) of resting-state. Subsequently, an independent component analysis (ICA) was used to extract four subnets from the whole brain. Then we used the measurements of graph theory and calculated in the whole-brain network and each sub-network.

RESULTS: We found no significant difference between the patient group and the HCs on the whole-brain scale. Then we identified four subnetworks from the resting-state data. In the sub-network property analysis, patients' locally distributed properties in the sensorimotor network (SMN) and ventral default mode network (vDMN) were reduced. It revealed that γ (10,000 permutations, P=0.048) and S (10,000 permutations, P=0.022) within the SMN progressively decreased in patients with PFPS. For the analysis of vDMN, significant differences were found in γ (10,000 permutations, P=0.019), Elocal (10,000 permutations, P=0.008), and β (10,000 permutations, P=0.011) between the groups.

CONCLUSIONS: Our results demonstrated a reduction in local network processing efficiency in patients with PFPS. Therefore, we speculate that decreased characteristics in the intra-vDMN and intra-SMN, rather than the whole-brain network, may serve distinct symptoms such as facial nerve damage or more synkinetic movements. This finding of the alteration of network properties is a small step forward to help uncover the underlying mechanism.

PMID:33708867 | PMC:PMC7940883 | DOI:10.21037/atm-20-4865

Dynamic Structural and Functional Reorganizations Following Motor Stroke

Fri, 03/12/2021 - 11:00

Med Sci Monit. 2021 Mar 11;27:e929092. doi: 10.12659/MSM.929092.

ABSTRACT

BACKGROUND The combined effects of bilateral corticospinal tract (CST) reorganization and interhemispheric functional connectivity (FC) reorganization on motor recovery of upper and lower limbs after stroke remain unknown. MATERIAL AND METHODS A total of 34 patients underwent magnetic resonance imaging (MRI) examination at weeks 1, 4, and 12 after stroke, with a control group of 34 healthy subjects receiving 1 MRI examination. Interhemispheric FC in the somatomotor network (SMN) was calculated using the resting-state functional MRI (rs-fMRI). Fractional anisotropy (FA) of bilateral CST was recorded as a measure of reorganization obtained from diffusion tensor imaging (DTI). After intergroup comparisons, multiple linear regression analysis was used to explore the effects of altered FA and interhemispheric FC on motor recovery. RESULTS Interhemispheric FC restoration mostly occurred within 4 weeks after stroke, and FA in ipsilesional remained CST consistently elevated within 12 weeks. Multivariate linear regression analysis showed that the increase in both interhemispheric FC and ipsilesional CST-FA were significantly correlated with greater motor recovery from week 1 to week 4 following stroke. Moreover, only increased FA of ipsilesional CST was significantly correlated with greater motor recovery during weeks 4 to 12 after stroke compared to interhemispheric FC. CONCLUSIONS Our results show dynamic structural and functional reorganizations following motor stroke, and structure reorganization may be more related to motor recovery at the late subacute phase. These results may play a role in guiding neurological rehabilitation.

PMID:33707406 | DOI:10.12659/MSM.929092

The effect of effort-reward imbalance on brain structure and resting-state functional connectivity in individuals with high levels of schizotypal traits

Fri, 03/12/2021 - 11:00

Cogn Neuropsychiatry. 2021 Mar 11:1-17. doi: 10.1080/13546805.2021.1899906. Online ahead of print.

ABSTRACT

INTRODUCTION: Effort-reward imbalance (ERI) is a typical psychosocial stress. Schizotypal traits are attenuated features of schizophrenia in the general population. According to the diathesis-stress model, schizotypal traits and psychosocial stress contribute to the onset of schizophrenia. However, few studies examined the effects of these factors on brain alterations. This study aimed to examine relationships between ERI, schizotypal traits and brain structures and functions.

METHODS: We recruited 37 (13 male, 24 female) participants with high levels of schizotypal traits and 36 (12 male, 24 female) participants with low levels of schizotypal traits by the Schizotypal Personality Questionnaire (SPQ). The Chinese school version of the effort-reward imbalance questionnaire (C-ERI-S) was used to measure ERI. We conducted the voxel-based morphometry (VBM) and whole brain resting-state functional connectivity (rsFC) analysis using reward or stress-related regions as seeds.

RESULTS: Participants with high levels of schizotypal traits were more likely to perceive ERI. The severity of ERI was correlated with grey matter volume (GMV) reduction of the left pallidum and altered rsFC among the prefrontal, striatum and cerebellum in participants with high levels of schizotypal traits.

CONCLUSION: ERI is associated with GMV reduction and altered rsFC in individuals with high levels of schizotypal traits.

PMID:33706673 | DOI:10.1080/13546805.2021.1899906

Gender-related differences in frontal-parietal modular segregation and altered effective connectivity in internet gaming disorder

Thu, 03/11/2021 - 11:00

J Behav Addict. 2021 Mar 10. doi: 10.1556/2006.2021.00015. Online ahead of print.

ABSTRACT

BACKGROUND: Although previous studies have revealed gender-related differences in executive function in internet gaming disorder (IGD), neural mechanisms underlying these processes remain unclear, especially in terms of brain networks.

METHODS: Resting-state fMRI data were collected from 78 subjects with IGD (39 males, 20.8 ± 2.16 years old) and 72 with recreational game use (RGU) (39 males, 21.5 ± 2.56 years old). By utilizing graph theory, we calculated participation coefficients among brain network modules for all participants and analyzed the diagnostic-group-by-gender interactions. We further explored possible causal relationships between networks through spectral dynamic causal modeling (spDCM) to assess differences in between-network connections.

RESULTS: Compared to males with RGU, males with IGD demonstrated reduced modular segregation of the frontal-parietal network (FPN). Male IGD subjects also showed increased connections between the FPN and cingulo-opercular network (CON); however, these differences were not found in female subjects. Further spDCM analysis indicated that the causal influence from CON to FPN in male IGD subjects was enhanced relative to that of RGU males, while this influence was relatively reduced in females with IGD.

CONCLUSIONS: These results suggest poor modular segmentation of the FPN and abnormal FPN/CON connections in males with IGD, suggesting a mechanism for male vulnerability to IGD. An increased "bottom-up" effect from the CON to FPN in male IGD subjects could reflect dysfunction between the brain networks. Different mechanisms may underlie in IGD, suggesting that different interventions may be optimal in males and females with IGD.

PMID:33704084 | DOI:10.1556/2006.2021.00015

Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

Thu, 03/11/2021 - 11:00

Front Neurol. 2021 Feb 22;12:642241. doi: 10.3389/fneur.2021.642241. eCollection 2021.

ABSTRACT

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62-0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

PMID:33692747 | PMC:PMC7937731 | DOI:10.3389/fneur.2021.642241

Altered Regional Cerebral Blood Flow and Brain Function Across the Alzheimer's Disease Spectrum: A Potential Biomarker

Thu, 03/11/2021 - 11:00

Front Aging Neurosci. 2021 Feb 22;13:630382. doi: 10.3389/fnagi.2021.630382. eCollection 2021.

ABSTRACT

Objective: To investigate variation in the characteristics of regional cerebral blood flow (rCBF), brain activity, and intrinsic functional connectivity (FC) across the Alzheimer's disease spectrum (ADS). Methods: The study recruited 20 individuals in each of the following categories: Alzheimer's disease (AD), mild cognitive impairment (MCI), subjective cognitive decline (SCD), and healthy control (HC). All participants completed the 3.0T resting-state functional MRI (rs-fMRI) and arterial spin labeling scans in addition to neuropsychological tests. Additionally, the normalized CBF, regional homogeneity (ReHo), and amplitude of low-frequency fluctuation (ALFF) of individual subjects were compared in the ADS. Moreover, the changes in intrinsic FC were investigated across the ADS using the abnormal rCBF regions as seeds and behavioral correlations. Finally, a support-vector classifier model of machine learning was used to distinguish individuals with ADS from HC. Results: Compared to the HC subjects, patients with AD showed the poorest level of rCBF in the left precuneus (LPCUN) and right middle frontal gyrus (RMFG) among all participants. In addition, there was a significant decrease in the ALFF in the bilateral posterior cingulate cortex (PCC) and ReHo in the right PCC. Moreover, RMFG- and LPCUN-based FC analysis revealed that the altered FCs were primarily located in the posterior brain regions. Finally, a combination of altered rCBF, ALFF, and ReHo in posterior cingulate cortex/precuneus (PCC/PCUN) showed a better ability to differentiate ADS from HC, AD from SCD and MCI, but not MCI from SCD. Conclusions: The study demonstrated the significance of an altered rCBF and brain activity in the early stages of ADS. These findings, therefore, present a potential diagnostic neuroimaging-based biomarker in ADS. Additionally, the study provides a better understanding of the pathophysiology of AD.

PMID:33692680 | PMC:PMC7937726 | DOI:10.3389/fnagi.2021.630382

Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T

Thu, 03/11/2021 - 11:00

Front Neurosci. 2021 Feb 22;15:625309. doi: 10.3389/fnins.2021.625309. eCollection 2021.

ABSTRACT

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised. Furthermore, by using Bayesian CF modeling it is possible to estimate the co-variability of the parameter estimates and therefore, apply CF modeling to single cases. However, no previous studies evaluated the (Bayesian) CF model using 3T resting-state fMRI data. This is important since 3T scanners are much more abundant and more often used in clinical research compared to 7T scanners. Therefore in this study, we investigate whether it is possible to obtain meaningful CF estimates from 3T resting state (RS) fMRI data. To do so, we applied the standard and Bayesian CF modeling approaches on two RS scans, which were separated by the acquisition of visual field mapping data in 12 healthy participants. Our results show good agreement between RS- and visual field (VF)- based maps using either the standard or Bayesian CF approach. In addition to quantify the uncertainty associated with each estimate in both RS and VF data, we applied our Bayesian CF framework to provide the underlying marginal distribution of the CF parameters. Finally, we show how an additional CF parameter, beta, can be used as a data-driven threshold on the RS data to further improve CF estimates. We conclude that Bayesian CF modeling can characterize local functional connectivity between visual cortical areas from RS data at 3T. Moreover, observations obtained using 3T scanners were qualitatively similar to those reported for 7T. In particular, we expect the ability to assess parameter uncertainty in individual participants will be important for future clinical studies.

PMID:33692669 | PMC:PMC7937930 | DOI:10.3389/fnins.2021.625309

Hippocampal functional connectivity development during the first two years indexes 4-year working memory performance

Wed, 03/10/2021 - 11:00

Cortex. 2021 Feb 17;138:165-177. doi: 10.1016/j.cortex.2021.02.005. Online ahead of print.

ABSTRACT

The hippocampus is a key limbic region involved in higher-order cognitive processes including learning and memory. Although both typical and atypical functional connectivity patterns of the hippocampus have been well-studied in adults, the developmental trajectory of hippocampal connectivity during infancy and how it relates to later working memory performance remains to be elucidated. Here we used resting state fMRI (rsfMRI) during natural sleep to examine the longitudinal development of hippocampal functional connectivity using a large cohort (N = 202) of infants at 3 weeks (neonate), 1 year, and 2 years of age. Next, we used multivariate modeling to investigate the relationship between both cross-sectional and longitudinal growth in hippocampal connectivity and 4-year working memory outcome. Results showed robust local functional connectivity of the hippocampus in neonates with nearby limbic and subcortical regions, with dramatic maturation and increasing connectivity with key default mode network (DMN) regions resulting in adult-like topology of the hippocampal functional connectivity by the end of the first year. This pattern was stabilized and further consolidated by 2 years of age. Importantly, cross-sectional and longitudinal measures of hippocampal connectivity in the first year predicted subsequent behavioral measures of working memory at 4 years of age. Taken together, our findings provide insight into the development of hippocampal functional circuits underlying working memory during this early critical period.

PMID:33691225 | DOI:10.1016/j.cortex.2021.02.005

Common and Distinct Neural Connectivity in Attention Deficit/Hyperactivity Disorder and Alcohol Use Disorder: A Study Using Resting-State Functional Magnetic Resonance Imaging

Wed, 03/10/2021 - 11:00

Alcohol Clin Exp Res. 2021 Mar 9. doi: 10.1111/acer.14593. Online ahead of print.

ABSTRACT

BACKGROUND: The relation between Attention Deficit/Hyperactivity Disorder (ADHD) and Alcohol Use Disorder (AUD) has been widely demonstrated. In this study, we aimed to investigate the connectivity traits that would help to understand the strong link between both disorders using a neuroimaging perspective.

METHODS: The study included an AUD group (N = 18), an ADHD group (N = 17), a group with AUD+ADHD comorbidity individuals (N = 12) and a control group (N = 18). We used resting-state functional connectivity in a seed-based approach in the Default Mode Networks, the Dorsal Attention Network and the Salience Network.

RESULTS: Within the Default Mode Networks, all groups shared increased connectivity towards the Temporal Gyrus when compared to the control group. Regarding the Dorsal Attention Network, the Brodmann Area 6 presented increased connectivity for each disorder group in comparison with the control group, displaying the strongest aberrations in the AUD+ADHD group. In the Salience Network, the Prefrontal Cortex showed decreased connectivity in every disorder group compared to the control group.

CONCLUSIONS: Despite the small and unequal sample size, our study suggests common neurobiological alterations in AUD and ADHD, supporting the hypothesis that ADHD might be a risk factor for the development of an AUD. The results highlight the importance of an early ADHD diagnosis and treatment to reduce this risk for a subsequent AUD.

PMID:33690916 | DOI:10.1111/acer.14593

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