Most recent paper

Forced conceptual thought induced by electrical stimulation of the left prefrontal gyrus involves widespread neural networks.

Epilepsy Behav. 2020 Jan 14;104(Pt A):106644

Authors: Liu A, Friedman D, Barron DS, Wang X, Thesen T, Dugan P

Abstract
BACKGROUND: Early accounts of forced thought were reported at the onset of a focal seizure, and characterized as vague, repetitive, and involuntary intellectual auras distinct from perceptual or psychic hallucinations or illusions. Here, we examine the neural underpinnings involved in conceptual thought by presenting a series of 3 patients with epilepsy reporting intrusive thoughts during electrical stimulation of the left lateral prefrontal cortex (PFC) during invasive surgical evaluation. We illustrate the widespread networks involved through two independent brain imaging modalities: resting state functional magnetic resonance imaging (fMRI) (rs-fMRI) and task-based meta-analytic connectivity modeling (MACM).
METHODS: We report the clinical and stimulation characteristics of three patients with left hemispheric language dominance who demonstrate forced thought with functional mapping. To examine the brain networks underlying this phenomenon, we used the regions of interest (ROI) centered at the active electrode pairs. We modeled functional networks using two approaches: (1) rs-fMRI functional connectivity analysis, representing 81 healthy controls and (2) meta-analytic connectivity modeling (MACM), representing 8260 healthy subjects. We also determined the overlapping regions between these three subjects' rs-fMRI and MACM networks through a conjunction analysis.
RESULTS: We identified that left PFC was associated with a large-scale functional network including frontal, temporal, and parietal regions, a network that has been associated with multiple cognitive functions including semantics, speech, attention, working memory, and explicit memory.
CONCLUSIONS: We illustrate the neural networks involved in conceptual thought through a unique patient population and argue that PFC supports this function through activation of a widespread network.

PMID: 31951969 [PubMed - as supplied by publisher]

Fetal Response to a Maternal Internal Auditory Stimulus.

J Magn Reson Imaging. 2020 Jan 17;:

Authors: Goldberg E, McKenzie CA, de Vrijer B, Eagleson R, de Ribaupierre S

Abstract
BACKGROUND: Functional MRI (fMRI) is a noninvasive method to investigate the neural correlates of brain development. Insight into the rapidly developing brain in utero is limited, and fetal fMRI can be used to gain a greater understanding of the developmental process. Fetal brain fMRI is typically limited to resting-state fMRI due to the difficulty to instruct or provide a stimulus to the fetus. Previous studies have employed auditory task fMRI with an external sound stimulus directly on the abdomen of the mother; however, this practice has since been deemed unsafe for the developing fetus.
PURPOSE: To investigate a reliable and safe paradigm to study the development of fetal brain networks, we postulated that an internal task, such as the mother's singing, as the auditory stimulus would result in activation in the fetal primary auditory cortex.
STUDY TYPE: Cohort.
POPULATION: Pregnant women with singleton pregnancies (n = 9; 33-38 weeks gestational age).
FIELD STRENGTH/SEQUENCE: All subjects underwent two task-based block design blood oxygen level-dependent (BOLD) at 1.5T or 3T.
ASSESSMENT: Each volume was assessed for fetal motion and manually reoriented and realigned to correct for fetal motion. Once the motion was corrected, a gestational age-matched parcellated atlas with regions of interest overlaid onto the activation map was used to determine which regions in the brain had activation during task phases.
STATISTICAL TESTS: First Level Analysis. MRI data were analyzed using SPM 12 as a task fMRI.
RESULTS: Eight subjects had activation on the right Heschl's gyrus; six fetuses demonstrated activation on the left when exposed to the internal acoustic stimulus. Additionally, activation was found on the right and left middle cingulate cortex (MCC) and the left putamen.
DATA CONCLUSION: Maternal singing can be used as an internal stimulus to activate the auditory network and Heschl's gyrus during fetal fMRI.
LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.

PMID: 31951084 [PubMed - as supplied by publisher]

Potential for Resting-State fMRI of the Amygdala in Elucidating Neurological Mechanisms of Adaptive Self-Regulatory Strategies: A Systematic Review.

Brain Connect. 2020 Jan 17;:

Authors: Warren SM, Chou YH, Steklis HD

Abstract
Evolutionary-developmental theories consider the evolved mechanisms underlying adaptive behavioral strategies shaped in response to early environmental cues. Identifying neurological mechanisms mediating processes of conditional adaptation in humans is an active area of research. Resting-state functional magnetic resonance imaging (RS-fMRI) captures functional connectivity theorized to represent the underlying functional architecture of the brain. This allows for investigating how underlying functional brain connections are related to early experiences during development, as well as current traits and behaviors. This review explores the potential of RS-fMRI of the amygdala for advancing research on the neurological mechanisms underlying adaptive strategies developed in early adverse environments. RS-fMRI studies of early life stress and amygdala functional connectivity within the frame of evolutionary theories are reviewed, specifically regarding the development of self-regulatory strategies. The potential of RS-fMRI for investigating the effects of early life stress on developmental trajectories of self-regulation is discussed.

PMID: 31950847 [PubMed - as supplied by publisher]

Sleep/Wake Regularity Associated with Default Mode Network Structure among Healthy Adolescents and Young Adults.

Sci Rep. 2020 Jan 16;10(1):509

Authors: Lunsford-Avery JR, Damme KSF, Engelhard MM, Kollins SH, Mittal VA

Abstract
Sleep deprivation and disorders are linked to reduced DMN connectivity. Less is known about how naturalistic sleep patterns - specifically sleep irregularity - relate to the DMN, particularly among adolescents and young adults. Additionally, no studies have utilized graph theory analysis to clarify whether sleep-related decreases in connectivity reflect global or local DMN changes. Twenty-five healthy adolescents and young adults (age range = 12-22; mean = 18.08; SD = 2.64, 56% female) completed 7 days of actigraphy and resting-state fMRI. Sleep regularity was captured by the Sleep Regularity Index (SRI) and the relationship between the SRI and DMN was examined using graph theory analysis. Analogous analyses explored relationships between the SRI and additional resting-state networks. Greater sleep regularity related to decreased path length (increased network connectivity) in DMN regions, particularly the right and left lateral parietal lobule, and the Language Network, including the left inferior frontal gyrus and the left posterior superior frontal gyrus. Findings were robust to covariates including sex and age. Sleep and DMN function may be tightly linked during adolescence and young adulthood, and reduced DMN connectivity may reflect local changes within the network. Future studies should assess how this relationship impacts cognitive development and neuropsychiatric outcomes in this age group.

PMID: 31949189 [PubMed - in process]

Effect of connectivity measures on the identification of brain functional core network at rest.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:6426-6429

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

Abstract
Magneto/Electro-encephalography (M/EEG) source connectivity is an emergent tool to identify brain networks with high time/space resolution. Here, we aim to identify the brain core network (s-core decomposition) using dense-EEG. We also evaluate the effect of the functional connectivity methods used and more precisely the effect of the correction for the so-called source leakage problem. Two connectivity measures were evaluated: the phase locking value (PLV) and phase lag index (PLI) that supposed to deal with the leakage problem by removing the zero-lag connections. Both methods were evaluated on resting state dense-EEG signals recorded from 19 healthy participants. Core networks obtained by each method was compared to those computed using fMRI from 487 healthy participants at rest (from the Human Connectome Project - HCP). The correlation between networks obtained by EEG and fMRI was used as performance criterion. Results show that PLV networks are closer to fMRI networks with significantly higher correlation values with fMRI networks, than PLI networks. Results suggest caution when selecting the functional connectivity methods and mainly methods that remove the zero-lag connections as it can severely affect the network characteristics. The choice of functional connectivity measure is indeed crucial not only in cognitive neuroscience but also in clinical neuroscience.

PMID: 31947313 [PubMed - in process]

Disruption of brain network organization in primary open angle glaucoma.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:4338-4341

Authors: Minosse S, Floris R, Nucci C, Toschi N, Garaci F, Martucci A, Lanzafame S, Di Giuliano F, Picchi E, Cesareo M, Mancino R, Guerrisi M

Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) is commonly employed to study changes in functional brain connectivity. Recently, the hypothesis of a brain involvement in primary open angle glaucoma has sprung interest for neuroimaging studies in this pathology. The purpose of this study is to evaluate a putative reorganization of brain networks in glaucomatous patients through graph-theoretical measures of integration, segregation and centrality by exploiting a multivariate networks association measure and a recently introduced global and local brain network disruption index. Nineteen glaucoma patients and sixteen healthy control subjects (age: 50 - 76, mean 61 years) underwent rs-fMRI examination at 3T. After preprocessing, rs-fMRI time series were parcellated into 116 regions (AAL atlas), adjacency matrices were computed based on partial correlations and graph-theoretical measures of integration, segregation and centrality as well as group-wise and subject-wise disruption index estimates were generated for all subjects. We found that the group-wise disruption index was negative and statistically different from 0 in for all graph theoretical metrics. Additionally, statistically significant group-wise differences in subject-wise disruption indexes were found in all local metrics. The differences in local network measures highlight cerebral reorganization of brain networks in glaucoma patients, supporting the interpretation of glaucoma as central nervous system disease, likely part of the heterogeneous group of recently described disconnection syndromes.

PMID: 31946828 [PubMed - in process]

Resting-State Functional Connectivity in Popular Targets for Deep Brain Stimulation in the Treatment of Major Depression: An Application of a Graph Theory.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:4334-4337

Authors: Amiri S, Arbabi M, Kazemi K, Parvaresh-Rizi M, Mirbagheri MM

Abstract
We examined the functional connectivity of subcallosal cingulate gyrus (SCG), nucleus accumbens (NAc), and ventral caudate (VCa), the main target areas for the treatment of major depression disorder (MDD), using deep brain stimulation (DBS). MDD is one of the most common diseases in the world, and approximately 30% of MDD patients do not respond to common therapies, including psychotherapy and antidepressant medications. Alternatively, DBS has been recently used to treat MDD. Resting state fMRI was obtained from seventeen healthy subjects and seven MDD patients. The functional connectivity network of the brain was constructed for all subjects and measured by the `degree' value for each SCG, NAc, and VCa regions using the graph theory analysis. The results show that the degree values of VCa and the left SCG are higher in the MDD group than the healthy group. Furthermore, the patterns of the degree values were different for the right and left hemispheres in MDD patients. Our findings suggest that degree values and their patterns have a potential to be used as diagnosis tools to detect the brain areas with abnormal functional connectivity.

PMID: 31946827 [PubMed - in process]

Resting State Neural Correlates of Cardiac Sympathetic Dynamics in Healthy Subjects.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:4330-4333

Authors: Valenza G, Duggento A, Passamonti L, Toschi N, Barbieri R

Abstract
Recent advances in functional Magnetic Resonance Imaging (fMRI) research have uncovered the existence of the central autonomic network (CAN), which comprises brain regions whose activity correlates with autonomic nervous system dynamics. By exploiting the spectral paradigm of heartbeat dynamics, cortical and sub-cortical areas functionally linked to vagal activity have been identified. However, due to methodological limitations, functional neural correlates of cardiac sympathetic dynamics remain uncharacterized. To this extent, we exploit the high spatiotemporal resolution of fMRI data from the Human Connectome Project to study the CAN activity by correlating a recently proposed instantaneous characterization of sympathetic activity (the sympathetic activity index - SAI) from heartbeat dynamics. SAI estimates are embedded into the probabilistic modeling of inhomogeneous point-processes, and are derived from a combination of disentangling coefficients linked to the orthonormal Laguerre functions. By analyzing resting state recordings from 34 young healthy people, we obtain positive correlations between instantaneous SAI estimates and a number of brain regions including frontal pole, insular cortex, frontal and temporal gyri, lateral occipital cortex, paracingulate and cingulate gyri, precuneus and temporal fusiform cortices, as well as thalamus, caudate nucleus, putamen, brain-stem, hippocampus, amygdala, and nucleus accumbens. Our findings significantly extend current knowledge on the CAN, opening new avenues in the characterization of healthy and pathological states in humans.

PMID: 31946826 [PubMed - in process]

Predicting Male vs. Female from Task-fMRI Brain Connectivity.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:4089-4092

Authors: Sen B, Parhi KK

Abstract
A number of behavioral and cognitive functions of brain differ between male and female. Occurrences of psychiatric disorders, e.g., attention deficit hyperactivity disorder, autism, depression and schizophrenia also vary from male to female. Understanding the unique cognitive expressions in gender-specific brain functions may lead to insights into the risks and associated responses for a certain external simulation or medications. Previously resting-state functional magnetic resonance imaging (r-fMRI) has been used extensively to understand gender differences using functional network connectivity analysis. However, how the brain functional network changes during a cognitive task for different genders is relatively unknown. This paper makes use of a large data set to test whether task-fMRI functional connectivity can be utilized to predict male vs. female. In addition, it also identifies functional connectivity features that are most predictive of gender. The cognitive task-fMRI data consisting 475 healthy controls is taken from the Human Connectome Project (HCP) database. Pearson correlation coefficients are extracted using mean time-series from anatomical brain regions. Partial least squares (PLS) regression with feature selection on the correlation coefficients achieves a classification accuracy of 0.88 for classifying male vs. female using emotion task data. In addition it is found that inter hemispheric connectivity is most important for predicting gender from task-fMRI.

PMID: 31946770 [PubMed - in process]

Classification of Major Depressive Disorder from Resting-State fMRI.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:3511-3514

Authors: Sen B, Mueller B, Klimes-Dougan B, Cullen K, Parhi KK

Abstract
Major Depressive Disorder (MDD) is a very serious mental illness that can affect the daily lives of patients. Accurate diagnosis of this disorder is necessary for planning individualized treatment. However, diagnosing MDD requires the clinicians to personally interview the subjects and rate the symptoms based on Diagnostic and Statistical Manual of Mental Disorders (DSM), which can be very time consuming. Discovering quantifiable signals and biomarkers associated with MDD using functional magnetic resonance imaging (fMRI) scans of patients have the potential to assist the clinicians in their assessment. This paper explores the use of resting-state functional connectivity and network features to classify MDD vs. healthy subjects. For each subject, mean time-series are extracted from 85 brain regions and they are decomposed to 4-frequency bands. Mean time-series for each of the frequency bands are utilized to compute the Pearson correlation and network characteristics. Features are selected separately from correlation and network characteristics using Minimum Redundancy Maximum Relevance (mRMR) to create the final classifier. The proposed scheme achieves 79% accuracy (65 out of 82 subjects classified correctly) with 86% sensitivity (42 out of 49 MDD subjects identified correctly) and 70% specificity (23 out of 33 controls identified correctly) using leave-one-out classification with in-fold feature selection. Pearson correlation had the highest discrimination in band 0.015-0.03 Hz and network based features had the highest discrimination in band 0.03-0.06 Hz for distinguishing MDD vs. healthy subjects.

PMID: 31946635 [PubMed - in process]

Reconstructing Cortical Intrinsic Connectivity Networks Using a Regression Method Combining EEG Data from Sensor and Source Levels.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:1698-1701

Authors: Shou G, Ding L

Abstract
Intrinsic connectivity networks (ICNs) have been widely studied using functional magnetic resonance imaging (fMRI) data and electrophysiological data (e.g., electroencephalography (EEG) or magnetoencephalography (MEG)). Two major methods, i.e., seed-based correlation analysis (SBCA) and independent component analysis (ICA), are widely used to extract ICNs. Among them, ICA usually involves a dual regression analysis in order to obtain final spatial definitions of ICNs. Recently, we proposed a framework that includes cortical source imaging, source-level ICA, and statistical correlation analysis, to extract cortical ICNs from resting-state EEG data. In the present study, we proposed an alternative framework that uses sensor-level ICA and regression analysis instead of source-level ICA and correlation analysis, considering the well-studied characteristics of sensor-level ICs in differentiating neural activities from artifacts and the benefit of regression in accommodating multivariate analysis over correlation. In the present study, we mainly investigated the performance of the proposed procedure in extracting cortical ICNs. Meanwhile, we also investigated different variants of the regressors sampled at different frequencies to formulate the regression model. The results demonstrated that cortical ICNs corresponding to major ICNs identified in literature could be obtained by the proposed framework. In general, spatial patterns of cortical ICNs obtained via both correlation and regression analyses show statistically significant similarity. However, the cortical ICNs reconstructed using the regression analysis exhibit more focal and more superficial spatial patterns, in general, that the cortical ICNs from the correlation analysis. The different variants of regressors at the same sampling frequency do not produce obvious impacts on spatial patterns of cortical ICNs, while the different sampling frequencies show large effects on extracted spatial patterns of cortical ICNs. In summary, it is suggested that the proposed framework with the regression analysis is promising in reconstructing cortical ICNs from EEG, while the sampling frequency used in the formulation process of regressors may have large impacts on reconstructed cortical ICN patterns.

PMID: 31946224 [PubMed - in process]

Characterizing Brain Network Topology in Cervical Dystonia Patients and Unaffected Relatives via Graph Theory.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:1694-1697

Authors: Narasimham S, Sundarajan V, McGovern E, Quinlivan B, Killian O, O'Riordan S, Hutchinson M, Reilly RB

Abstract
Cervical Dystonia (CD) is a neurological movement disorder characterized by intermittent muscle contractions in the head and neck. The pathophysiology and neural networks underpinning this condition are incompletely understood. There is increasing evidence that isolated focal dystonias are due to network-wide functional alterations. An abnormal temporal discrimination threshold (TDT) is believed to be a mediational endophenotype due to its prevalence in unaffected first-degree relatives as well as patients. However the neural correlates linking abnormal TDT and CD remain poorly understood. Probing changes in large-scale network topology via graph theory with resting state fMRI data from relatives and patients may provide further insight into the pathophysiology of CD. In this study, resting state fMRI data were acquired and analyzed from 16 CD patients with abnormal TDT, 32 unaffected first degree relatives (16 with normal TDT and 16 with abnormal TDT) and 16 healthy controls. Graph theory metrics demonstrating network topology were extracted. The results indicate large-scale functional reorganization of networks in relatives (with abnormal TDT) along with a manifestation of topological aberrations similar to patients.

PMID: 31946223 [PubMed - in process]

Mild Cognitive Impairment Diagnosis Using Extreme Learning Machine Combined With Multivoxel Pattern Analysis on Multi-Biomarker Resting-State FMRI.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:882-885

Authors: Duc NT, Ryu S, Choi M, Iqbal Qureshi MN, Lee B

Abstract
This paper proposed a classification framework that integrates hybrid multivoxel pattern analyses (MVPA) and extreme learning machine (ELM) for automated Mild Cognitive Impairment (MCI) diagnosis applied on concatenations of multi-biomarker resting-state fMRI. Given three-dimensional (3D) regional coherences and functional connectivity patterns measured during resting state, we performed 3D univariate t-tests to obtain initial univariate features which show the significant changes. To enhance discriminative patterns, we employed multivariate feature reductions using recursive feature elimination in combination with univariate t-test. The maximal amount of information changes were achieved by concatenations of multiple functional metrics. The classifications were performed by an ELM, and its efficiency was compared to SVMs. This study reported mean accuracies using 10-fold cross-validation, followed by permutation tests to assess the statistical significance of discriminative results. In diagnosis of MCI, the proposed method achieved a maximal accuracy of 97.86% (p<; 0.001) in ADNI2 cohort and thus has potentials to assist the clinicians in MCI diagnosis.

PMID: 31946035 [PubMed - in process]

Investigations on the Functional connectivity disruptive patterns of progressive neurodegenerative disorders.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:800-803

Authors: A K, Prakash SS, P S, Carshia S A

Abstract
Alzheimer's Disease (AD) and Parkinson's Disease (PD) are neurodegenerative diseases of the brain that affects the memory and motor regions respectively. Neurological disorders are the result of alterations at molecular level due to complex mechanisms between genetic and environmental factors. Classical approaches are focused on determining how disruptions in functional connectivity occur in the memory regions of AD and motor regions of PD. There have been studies stating that in addition to dementia, motor abnormalities may also be observed in Alzheimer's patients and on the other hand, dementia may occur in Parkinson's patients a year or more after the onset of motor symptoms. In this work, to substantiate this hypothesis the brain connectivity patterns and functional topology of motor and memory regions in AD and PD patients were analyzed and compared. Resting state functional connectivity (rs-fMRI) has been found to demonstrate the brain networks in both AD and PD. Graph theoretical modelling is being significantly used in studying the topology of the brain networks. The results show the disruption of connectivity in motor regions in later stages of AD in addition to memory regions and conversely in PD the memory regions were found to have disrupted connectivity in addition to the motor regions. Further, the Z scores of intra and inter hemispheric regions in AD and PD also indicate the disruption in connectivity as the disease progresses.

PMID: 31946016 [PubMed - in process]

A New Mutual Information Measure to Estimate Functional Connectivity: Preliminary Study.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:640-643

Authors: Sayed Hussein Jomaa ME, Colominas MA, Jrad N, Bogaert PV, Humeau-Heurtier A

Abstract
Functional Connectivity (FC) is a powerful tool to investigate brain networks both in rest and while performing tasks. Functional magnetic resonance imaging (fMRI) gave good spatial estimation of FC but lacked the temporal resolution. Electroencephalography (EEG) allows estimating FC with good temporal resolution. In this study we introduce a new method based on Mutual Information and Multivariate Improved Weighted Multi-scale Permutation Entropy to estimate FC of brain using EEG. We applied this method on resting-state EEG signals from healthy children. Using network measures of nodes and Wilcoxon signed-rank test, we identified the most important nodes in the estimated networks. Comparing the localization of those outstanding nodes with the regions involved in resting-state networks (RSNs) estimated from fMRI showed that our proposal is efficient in the identification of nodes belonging to RSNs and could be used as a general estimator for FC without having to band-pass the signals into frequency bands.

PMID: 31945979 [PubMed - in process]

Hybridizing EMD with cICA for fMRI Analysis of Patient Groups.

Conf Proc IEEE Eng Med Biol Soc. 2019 Jul;2019:194-197

Authors: Wein S, Tome AM, Goldhacker M, Greenlee MW, Lang EW

Abstract
Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.

PMID: 31945876 [PubMed - in process]

Overlapping attentional networks yield divergent behavioral predictions across tasks: Neuromarkers for diffuse and focused attention?

Neuroimage. 2020 Jan 12;:116535

Authors: Wu EXW, Liaw GJ, Goh RZ, Chia TTY, Chee AMJ, Obana T, Rosenberg MD, Yeo BTT, Asplund CL

Abstract
Attention is a critical cognitive function, allowing humans to select, enhance, and sustain focus on information of behavioral relevance. Attention contains dissociable neural and psychological components. Nevertheless, some brain networks support multiple attentional functions. In this study, we used the visual attentional blink (VAB) as a test of the functional generalizability of the brain's attentional networks. In a VAB task, attention devoted to a target often causes a subsequent item to be missed. Although frequently attributed to limitations in attentional capacity or selection, VAB deficits attenuate when participants are distracted or deploy attention diffusely. The VAB is also behaviorally and theoretically dissociable from other attention tasks. Here we used Connectome-based Predictive Models (CPMs), which associate individual differences in task performance with functional connectivity patterns, to test their ability to predict performance for multiple attentional tasks. We constructed visual attentional blink (VAB) CPMs, and then used them and a sustained attention network model (saCPM; Rosenberg et al., 2016a,b) to predict performance. The latter model had been previously shown to successfully predict performance across tasks involving selective attention, inhibitory control, and even reading recall. Participants (n = 73; 24 males) underwent fMRI while performing the VAB task and while resting. Outside the scanner, they completed other cognitive tasks over several days. A vabCPM constructed from VAB task data (behavior and fMRI) successfully predicted VAB performance. Strikingly, the network edges that predicted better VAB performance (positive edges) predicted worse performance for selective and sustained attention tasks, and vice versa. Predictions from applying the saCPM to the data mirrored these results, with the network's negative edges predicting better VAB performance. The vabCPM's positive edges partially yet significantly overlapped with the saCPM's negative edges, and vice versa. Many positive edges from the vabCPM involved the default mode network, whereas many negative edges involved the salience/ventral attention network. We conclude that the vabCPM and saCPM networks reflect general attentional functions that influence performance on many tasks. The networks may indicate an individual's propensity to deploy attention in a more diffuse or a more focused manner.

PMID: 31940476 [PubMed - as supplied by publisher]

Alterations of grey matter volumes and network-level functions in patients with stable chronic obstructive pulmonary disease.

Neurosci Lett. 2020 Jan 11;:134748

Authors: Wang W, Wang P, Peng Z, Wang X, Wang G, Li Q, Hou J, Fan L, Liu S

Abstract
OBJECTIVE: The purpose of this study was to investigate structural and functional alterations of the brain in the patients with stable chronic obstructive pulmonary disorder (COPD) and further investigate how these alterations correlated to parameters of pulmonary function test, cognitive function and disease duration in patients with COPD.
METHOD: Forty-five patients with stable COPD and forty age- and gender-matched healthy controls were enrolled into this study. Both resting-state fMRI and structural MRI were acquired for each participant. Voxel-based morphology was utilized to analyze alterations of the grey matter volume (GMV), and the seed-based resting-state functional connectivity (FC) was used to evaluate the network-level functional alterations.
RESULTS: Compared to healthy controls, patients with stable COPD showed decreased GMV in the left supramarginal gyrus/precentral gyrus (SMG/PreCG), bilateral posterior midcingulate cortex (pMCC), right middle occipital gyrus (MOG) and right SMG. Furthermore, COPD patients mainly showed decreased FC within the visual network, frontoparietal network and other brain regions. Subsequent correlational analyses revealed that the decreased FC within visual network, frontoparietal network were positively correlated with the Montreal Cognitive Assessment score, language-domain score, attention-domain score and disease duration in patients with COPD.
CONCLUSION: Our findings provided evidence that COPD patients showed decreased GMV and regional and network-level functional alterations within the visual network, frontoparietal network and other networks. We speculated that atrophic GMV and FC of visual network and frontoparietal network are involved in the neural mechanism of mild cognitive impairment in stable COPD patients.

PMID: 31935432 [PubMed - as supplied by publisher]

Default Mode Network Connectivity and Social Dysfunction in Major Depressive Disorder.

Sci Rep. 2020 Jan 13;10(1):194

Authors: Saris IMJ, Penninx BWJH, Dinga R, van Tol MJ, Veltman DJ, van der Wee NJA, Aghajani M

Abstract
Though social functioning is often hampered in Major Depressive Disorder (MDD), we lack a complete and integrated understanding of the underlying neurobiology. Connectional disturbances in the brain's Default Mode Network (DMN) might be an associated factor, as they could relate to suboptimal social processing. DMN connectional integrity, however, has not been explicitly studied in relation to social dysfunctioning in MDD patients. Applying Independent Component Analysis and Dual Regression on resting-state fMRI data, we explored DMN intrinsic functional connectivity in relation to social dysfunctioning (i.e. composite of loneliness, social disability, small social network) among 74 MDD patients (66.2% female, Mean age = 36.9, SD = 11.9). Categorical analyses examined whether DMN connectivity differs between high and low social dysfunctioning MDD groups, dimensional analyses studied linear associations between social dysfunction and DMN connectivity across MDD patients. Threshold-free cluster enhancement (TFCE) with family-wise error (FWE) correction was used for statistical thresholding and multiple comparisons correction (P < 0.05). The analyses cautiously linked greater social dysfunctioning among MDD patients to diminished DMN connectivity, specifically within the rostromedial prefrontal cortex and posterior superior frontal gyrus. These preliminary findings pinpoint DMN connectional alterations as potentially germane to social dysfunction in MDD, and may as such improve our understanding of the underlying neurobiology.

PMID: 31932627 [PubMed - in process]

Abnormal dynamic functional network connectivity of the mirror neuron system network and the mentalizing network in patients with adolescent-onset, first-episode, drug-naïve schizophrenia.

Neurosci Res. 2020 Jan 10;:

Authors: Sun F, Zhao Z, Lan M, Xu Y, Huang M, Xu D

Abstract
Previous studies based on an assumption of connectivity stationarity reported disconnections in mirror neuron system (MNS) and mentalizing networks of schizophrenic brains with social cognitive disruptions. However, recent studies demonstrated that functional brain connections are dynamic, and static connectivity metrics fail to capture time-varying properties of functional connections. The present study used a dynamic functional connectivity (dFC) method to test whether alterations of functional connectivity in the two networks are time-varying in adolescent-onset schizophrenia (AOS) patients. We collected resting-state fMRI data from 28 patients with AOS patients and 22 matched healthy controls. Static functional connectivity and dFC were used to explore the connectivity difference in the MNS and mentalizing networks between the two groups, respectively. Then a Pearson's correlation analysis between the connectivity showing intergroup differences and clinical scores was conducted in the AOS group. Compared with static functional connectivity analyses, dFC revealed state-specific connectivity decreases within the MNS network in the AOS group. Additionally, the dFC between the left middle temporal gyrus and left V5 was negatively correlated with the item2 of PANSS negative scores across all the AOS patients. Our findings suggest that social dysfunctions in AOS patients may be associated with the altered integrity and interaction of the MNS and mentalizing networks, and the functional impairments in the MNS are dynamic over time.

PMID: 31931027 [PubMed - as supplied by publisher]