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Updated: 4 hours 33 min ago

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity.

10 hours 34 min ago

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity.

PLoS Biol. 2020 Feb;18(2):e3000602

Authors: Orban C, Kong R, Li J, Chee MWL, Yeo BTT

Abstract
The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain's global signal (GS) amplitude (henceforth referred to as "fluctuation") increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

PMID: 32069275 [PubMed - in process]

Altered Dynamic Neural Activity in the Default Mode Network in Lung Cancer Patients After Chemotherapy.

10 hours 34 min ago

Altered Dynamic Neural Activity in the Default Mode Network in Lung Cancer Patients After Chemotherapy.

Med Sci Monit. 2020 Feb 18;26:e921700

Authors: You J, Hu L, Zhang Y, Chen F, Yin X, Jin M, Chen YC

Abstract
BACKGROUND Few studies have examined functional brain changes specifically associated with chemotherapy (CTx) in patients with lung cancer. This prospective longitudinal research aimed to explore the change in intrinsic brain activity by investigating patients with lung cancer after CTx. MATERIAL AND METHODS Sixteen patients and 20 healthy individuals were enrolled in this study. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), dynamic amplitude of low-frequency fluctuation (dALFF), and dynamic regional homogeneity (dReHo) were computed. The group differences in resting state functional magnetic resonance imaging (rs-fMRI) parameters were compared. Alterations in the rs-fMRI parameters from before CTx to after CTx were assessed using the paired t-test. We performed correlation analyses between rs-fMRI parameters and Montreal Cognitive Assessment (MoCA) scores. RESULTS We found statistically significant differences in MoCA scores before CTx and after CTx. Compared to the healthy group, rs-fMRI values decreased in the frontal regions as well as parietal regions compared to values before CTx. In addition, we found significantly decreased rs-fMRI values in the default-mode network (DMN) region of the brain before CTx compared to after CTx. We found no significant correlations between altered intrinsic activity values and MoCA scores. CONCLUSIONS The current study indicated that patients with lung cancer after CTx had decreased dynamic brain activity in the DMN region, and the DMN is vulnerable when patients undergoing CTx.

PMID: 32069270 [PubMed - in process]

Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder.

10 hours 34 min ago

Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder.

Prog Neuropsychopharmacol Biol Psychiatry. 2020 Feb 14;:109889

Authors: Lu F, Cui Q, Huang X, Li L, Duan X, Chen H, Pang Y, He Z, Sheng W, Han S, Chen Y, Yang Y, Luo W, Yu Y, Jia X, Tang Q, Li D, Xie A, Chen H

Abstract
OBJECTIVE: Major depressive disorder (MDD) is a ubiquitous mental illness with heterogeneous symptoms, however, the pathophysiology mechanisms are still not fully understood. Clinical and preclinical studies suggested that depression could cause disturbances in sensory perception systems, disruptions in auditory and visual functions may serve as an essential clinical features underlying MDD.
METHODS: The current study investigated the abnormal intrinsic connectivity within and between visual and auditory networks in 95 MDD patients and 97 age-, gender-, education level-matched healthy controls (HCs) by using resting-state functional magnetic resonance imaging (fMRI). One auditory network (AN) and three visual components including visual component 1 (VC1), VC2, and VC3 were identified by using independent component analysis method based on the fMRI networks during the resting state with the largest spatial correlations, combining with brain regions and specific network templates.
RESULTS: We found that MDD could be characterized by the following disrupted network model relative to HCs: (i) reduced within-network connectivity in the AN, VC2, and VC3; (ii) reduced between-network connectivity between the AN and the VC3. Furthermore, aberrant functional connectivity (FC) within the visual network was linked to the clinical symptoms.
CONCLUSIONS: Overall, our results demonstrated that abnormalities of FC in perception systems including intrinsic visual and auditory networks may explain neurobiological mechanisms underlying MDD and could serve as a potential effective biomarker.

PMID: 32067960 [PubMed - as supplied by publisher]

Effects of COMT rs4680 and BDNF rs6265 polymorphisms on brain degree centrality in Han Chinese adults who lost their only child.

10 hours 34 min ago
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Effects of COMT rs4680 and BDNF rs6265 polymorphisms on brain degree centrality in Han Chinese adults who lost their only child.

Transl Psychiatry. 2020 Jan 30;10(1):46

Authors: Qi R, Luo Y, Zhang L, Weng Y, Surento W, Li L, Cao Z, Lu GM

Abstract
Losing one's only child is a major traumatic life event that may lead to posttraumatic stress disorder (PTSD); however, not all parents who experience this trauma develop PTSD. Genetic variants are associated with the risk of developing PTSD. Catechol-O-methyltransferase (COMT) rs4680 and brain-derived neurotrophic factor (BDNF) rs6265 are two most well-described single-nucleotide polymorphisms that relate to stress response; however, the neural mechanism underlying their effects on adults who lost an only child remains poorly understood. Two hundred and ten Han Chinese adults who had lost their only child (55 with PTSD and 155 without PTSD) were included in this imaging genetics study. Participants were divided into subgroups according to their COMT rs4680 and BDNF rs6265 genotypes. Degree Centrality (DC)-a resting-state fMRI index reflecting the brain network communication-was compared with a three-way (PTSD diagnosis, COMT, and BDNF polymorphisms) analysis of covariance. Diagnosis state had a significant effect on DC in bilateral inferior parietal lobules and right middle frontal gyrus (MFG), where PTSD adults showed weaker DC. BDNF × diagnosis interaction effect was found in the right MFG and hippocampus, and these two regions were reversely modulated. Also, there was a significant COMT × BDNF interaction effect in left cuneus, middle temporal gyrus, right inferior occipital gyrus, and bilateral putamen, independent of PTSD diagnosis. These findings suggest that the modulatory effect of BDNF polymorphism on the MFG and hippocampus may contribute to PTSD development in bereaved adults. Interactions of COMT × BDNF polymorphisms modulate some cortices and basal ganglia, irrespective of PTSD development.

PMID: 32066722 [PubMed - in process]

Altered lateral geniculate nucleus functional connectivity in migraine without aura: a resting-state functional MRI study.

10 hours 34 min ago
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Altered lateral geniculate nucleus functional connectivity in migraine without aura: a resting-state functional MRI study.

J Headache Pain. 2020 Feb 17;21(1):17

Authors: Zhang D, Huang X, Su W, Chen Y, Wang P, Mao C, Miao Z, Liu C, Xu C, Yin X, Wu X

Abstract
OBJECTIVES: To investigate the structural and functional connectivity changes of lateral geniculate nucleus (LGN) and their relationships with clinical characteristics in patients without aura.
METHODS: Conventional MRI, 3D structure images and resting state functional MRI were performed in 30 migraine patients without aura (MwoA) and 22 healthy controls (HC). The lateral geniculate nucleus volumes and the functional connectivity (FC) of bilateral lateral geniculate nucleus were computed and compared between groups.
RESULTS: The lateral geniculate nucleus volumes in patient groups did not differ from the controls. The brain regions with increased FC of the left LGN mainly located in the left cerebellum and right lingual gyrus in MwoA compared with HC. The increased FC of right LGN located in left inferior frontal gyrus in MwoA compared with HC. The correlation analysis showed a positive correlation between VLSQ-8 score and the increased FC of left cerebellum and right lingual gyrus.
CONCLUSIONS: Photophobia in MwoA could be mediated by abnormal resting state functional connectivity in visual processing regions, the pain perception regulatory network and emotion regulation network. This result is valuable to further understanding about the clinical manifestation and pathogenesis of migraine.

PMID: 32066379 [PubMed - in process]

Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI.

Wed, 02/19/2020 - 08:43

Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI.

Neuroimage Clin. 2020 Feb 06;26:102208

Authors: Sen B, Bernstein GA, Mueller BA, Cullen KR, Parhi KK

Abstract
This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.

PMID: 32065968 [PubMed - as supplied by publisher]

The functional connectivity profile of tics and obsessive-compulsive symptoms in Tourette Syndrome.

Wed, 02/19/2020 - 08:43

The functional connectivity profile of tics and obsessive-compulsive symptoms in Tourette Syndrome.

J Psychiatr Res. 2020 Jan 30;123:128-135

Authors: Bhikram T, Arnold P, Crawley A, Abi-Jaoude E, Sandor P

Abstract
Tourette Syndrome (TS) is characterized by the presence of tics and sensory phenomena, such as premonitory urges, and is often accompanied by significant obsessive-compulsive symptoms (OCS). The goal of this exploratory study was to determine the association between functional connectivity and the different symptom domains of TS, as little is currently known about how they differ. Resting-state functional magnetic resonance imaging was performed in 39 patients with TS and 20 matched healthy controls. Seed-based functional connectivity of the supplementary motor area (SMA), orbitofrontal cortex (OFC), insula, caudate and putamen were compared between the groups, and correlated with clinical measures within the patient group. When compared to controls, patients with TS exhibited greater connectivity between the temporal gyri, insula and putamen, and between the OFC and cingulate cortex. Tic severity was associated with greater connectivity between the putamen and the sensorimotor cortex; OCS severity was associated with less connectivity between the SMA and thalamus and between the caudate and precuneus; and premonitory urge severity was associated with less connectivity between the OFC and sensorimotor cortex and between the inferior frontal gyrus and the putamen and insula seeds. Functional connectivity within sensorimotor processing regions were associated with all of the investigated symptom domains, including OCS, suggesting dysfunctions in the sensorimotor system may explain most of the observed symptoms in TS, and not just tics.

PMID: 32065948 [PubMed - as supplied by publisher]

A graph theory study of resting-state functional connectivity in children with Tourette syndrome.

Wed, 02/19/2020 - 08:43
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A graph theory study of resting-state functional connectivity in children with Tourette syndrome.

Cortex. 2020 Jan 28;126:63-72

Authors: Openneer TJC, Marsman JC, van der Meer D, Forde NJ, Akkermans SEA, Naaijen J, Buitelaar JK, Dietrich A, Hoekstra PJ

Abstract
Little is known about the brain's functional organization during resting-state in children with Tourette syndrome (TS). We aimed to investigate this with a specific focus on the role of comorbid attention-deficit/hyperactivity disorder (ADHD). We applied graph theoretical analysis to resting-state functional magnetic resonance imaging data of 109 8-to-12-year-old children with TS (n = 46), ADHD without tics (n = 23), and healthy controls (n = 40). First, we compared these three groups, and in a second comparison four groups, distinguishing TS with (TS + ADHD, n = 19) and without comorbid ADHD (TS-ADHD, n = 27). Weighted brain graphs were constructed for both comparisons to investigate global efficiency, local efficiency, and clustering coefficient per acquired network. Local efficiency and clustering coefficient were significantly lower in children with TS-ADHD in the default mode network compared with healthy controls, and in the frontoparietal network compared with ADHD; we also found associations with higher tic severity. Our study supports a different functional brain network organization in children with TS-ADHD, compared with healthy controls and children with ADHD.

PMID: 32062470 [PubMed - as supplied by publisher]

Structural and functional correlates of smartphone addiction.

Wed, 02/19/2020 - 08:43
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Structural and functional correlates of smartphone addiction.

Addict Behav. 2020 Feb 01;105:106334

Authors: Horvath J, Mundinger C, Schmitgen MM, Wolf ND, Sambataro F, Hirjak D, Kubera KM, Koenig J, Christian Wolf R

Abstract
Popularity and availability of smartphones have dramatically increased in the past years. This trend is accompanied by increased concerns regarding potentially adverse effects of excessive smartphone use, particularly with respect to physical and mental health. Recently, the term "smartphone addiction" (SPA) has been introduced to describe smartphone-related addictive behavior and associated physical and psychosocial impairment. Here, we used structural and functional magnetic resonance imaging (MRI) at 3 T to investigate gray matter volume (GMV) and intrinsic neural activity in individuals with SPA (n = 22) compared to a control group (n = 26). SPA was assessed using the Smartphone Addiction Inventory (SPAI), GMV was investigated by means of voxel-based morphometry, and intrinsic neural activity was measured by the amplitude of low frequency fluctuations (ALFF). Compared to controls, individuals with SPA showed lower GMV in left anterior insula, inferior temporal and parahippocampal cortex (p < 0.001, uncorrected for height, followed by correction for spatial extent). Lower intrinsic activity in SPA was found in the right anterior cingulate cortex (ACC). A significant negative association was found between SPAI and both ACC volume and activity. In addition, a significant negative association between SPAI scores and left orbitofrontal GMV was found. This study provides first evidence for distinct structural and functional correlates of behavioral addiction in individuals meeting psychometric criteria for SPA. Given their widespread use and increasing popularity, the present study questions the harmlessness of smartphones, at least in individuals that may be at increased risk for developing smartphone-related addictive behaviors.

PMID: 32062336 [PubMed - as supplied by publisher]

Optimising network modelling methods for fMRI.

Wed, 02/19/2020 - 08:43
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Optimising network modelling methods for fMRI.

Neuroimage. 2020 Feb 13;:116604

Authors: Pervaiz U, Vidaurre D, Woolrich MW, Smith SM

Abstract
A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.

PMID: 32062083 [PubMed - as supplied by publisher]

Enhanced Positive Functional Connectivity Strength in Left-sided Chronic Subcortical Stroke.

Wed, 02/19/2020 - 08:43
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Enhanced Positive Functional Connectivity Strength in Left-sided Chronic Subcortical Stroke.

Brain Res. 2020 Feb 13;:146727

Authors: Diao Q, Liu J, Zhang X

Abstract
Patients with stroke often exhibit evidence of abnormal functional connectivity (FC). However, whether and how anatomical distance affects FC at rest remains unclear in patients with chronic subcortical stroke. Eighty-six patients with chronic (more than six months post-onset) subcortical stroke (44 left-sided patients and 42 right-sided patients) with different degrees of functional recovery, and 75 matched healthy controls underwent resting-state functional magnetic resonance imaging scanning. Positive functional connectivity strength (FCS) was computed for each voxel in the brain using a data-driven whole-brain resting state FCS method, which was further divided into short- and long-range FCS. Compared with healthy controls, patients with left-sided infarctions exhibited stronger global- and long-range FCS in the left sensorimotor cortex (SMC), and no significant intergroup difference was found for short-range FCS. No significant differences were found between the patients with right-sided infarctions and healthy controls for global, long- and short-range FCS. These findings suggested that the positive FCS alteration was connection-distance dependent within patients with left-sided chronic subcortical stroke. Also, a positive correlation was found between the FCS in the left SMC and the accuracy of the Flanker test, reflecting a compensatory FCS alteration for altered attention and executive function abilities exhibited by those with left-sided stroke.

PMID: 32061738 [PubMed - as supplied by publisher]

The Neural Dynamics of Individual Differences in Episodic Autobiographical Memory.

Sun, 02/16/2020 - 20:41
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The Neural Dynamics of Individual Differences in Episodic Autobiographical Memory.

eNeuro. 2020 Feb 10;:

Authors: Petrican R, Palombo DJ, Sheldon S, Levine B

Abstract
The ability to mentally travel to specific events from one's past, dubbed episodic autobiographical memory (E-AM), contributes to adaptive functioning. Nonetheless, the mechanisms underlying its typical interindividual variation remain poorly understood. To address this issue, we capitalize on existing evidence that successful performance on E-AM tasks draws on the ability to visualize past episodes and reinstate their unique spatiotemporal context. Hence, here, we test whether features of the brain's functional architecture relevant to perceptual versus conceptual processes shape individual differences in both self-rated E-AM and laboratory-based episodic memory for random visual scene sequences (visual EM). We propose that superior subjective E-AM and visual EM are associated with greater similarity in static neural organization patterns, potentially indicating greater efficiency in switching, between rest and mental states relevant to encoding perceptual information. Complementarily, we postulate that impoverished subjective E-AM and visual EM are linked to dynamic brain organization patterns implying a predisposition towards semanticizing novel perceptual information. Analyses were conducted on resting state and task-based fMRI data from 329 participants (160 women) in the Human Connectome Project who completed visual and verbal EM assessments, and an independent gender diverse sample (N = 59) who self-rated their E-AM. Interindividual differences in subjective E-AM were linked to the same neural mechanisms underlying visual, but not verbal, EM, in general agreement with the hypothesized static and dynamic brain organization patterns. Our results suggest that higher E-AM entails more efficient processing of temporally extended information sequences, whereas lower E-AM entails more efficient semantic or gist-based processing.Significance Statement The ability to revisit specific events from one's past is key to identity formation and optimal interpersonal functioning. Nonetheless, the mechanisms underlying its typical interindividual variation are yet to be fully characterized. Here, we provide novel evidence that, among younger adults, dispositional variations in subjective mental time travel draw on the same dynamic and static features of the brain's architecture that are uniquely implicated in memory for spatiotemporal contexts. Specifically, the subjective sense of being able to revisit one's past relates to neural mechanisms supporting serial mental operations, whereas difficulties in accessing past experiences may be traced back to a predisposition towards gist-based processing of incoming information.

PMID: 32060035 [PubMed - as supplied by publisher]

Frequency-Specific Changes of Resting Brain Activity in Parkinson's Disease: A Machine Learning Approach.

Sun, 02/16/2020 - 20:41
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Frequency-Specific Changes of Resting Brain Activity in Parkinson's Disease: A Machine Learning Approach.

Neuroscience. 2020 Feb 11;:

Authors: Tian ZY, Qian L, Fang L, Peng XH, Zhu XH, Wu M, Wang WZ, Zhang WH, Zhu BQ, Wan M, Hu X, Shao J

Abstract
The application of Resting State functional MRI (RS-fMRI) in Parkinson's disease was widely performed using standard statistical tests, however, the machine learning approach has not yet been investigated in PD using RS-fMRI. In current study, we utilized the mean regional amplitude values as the features in patients with PD (n = 72) and in healthy controls (HC, n = 89). The t-test and linear support vector machine were employed to select the features and make prediction, respectively. Three frequency bins (Slow-5: 0.0107 - 0.0286 Hz; Slow-4: 0.0286 - 0.0821 Hz; Conventional: 0.01 - 0.08 Hz) were analyzed. Our results showed that the Slow-4 may provide important information than Slow-5 in PD, and it had almost identical classification performance compared with the Combined (Slow-5 and Slow-4) and Conventional frequency bands. Similar with previous neuroimaging studies in PD, the discriminative regions were mainly included the disrupted motor system, aberrant visual cortex, dysfunction of paralimbic/limbic and basal ganglia networks. The lateral parietal lobe, such as right IPL and SMG, was detected as the discriminative features exclusively in Slow-4. Our findings, at the first time, indicated that the machine learning approach is a promising choice for detecting abnormal regions in PD, and a multi-frequency scheme would provide us more specific information.

PMID: 32059985 [PubMed - as supplied by publisher]

Toward identifying reproducible brain signatures of obsessive-compulsive profiles: rationale and methods for a new global initiative.

Sun, 02/16/2020 - 20:41
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Toward identifying reproducible brain signatures of obsessive-compulsive profiles: rationale and methods for a new global initiative.

BMC Psychiatry. 2020 Feb 14;20(1):68

Authors: Simpson HB, van den Heuvel OA, Miguel EC, Reddy YCJ, Stein DJ, Lewis-Fernández R, Shavitt RG, Lochner C, Pouwels PJW, Narayanawamy JC, Venkatasubramanian G, Hezel DM, Vriend C, Batistuzzo MC, Hoexter MQ, de Joode NT, Costa DL, de Mathis MA, Sheshachala K, Narayan M, van Balkom AJLM, Batelaan NM, Venkataram S, Cherian A, Marincowitz C, Pannekoek N, Stovezky YR, Mare K, Liu F, Otaduy MCG, Pastorello B, Rao R, Katechis M, Van Meter P, Wall M

Abstract
BACKGROUND: Obsessive-compulsive disorder (OCD) has a lifetime prevalence of 2-3% and is a leading cause of global disability. Brain circuit abnormalities in individuals with OCD have been identified, but important knowledge gaps remain. The goal of the new global initiative described in this paper is to identify robust and reproducible brain signatures of measurable behaviors and clinical symptoms that are common in individuals with OCD. A global approach was chosen to accelerate discovery, to increase rigor and transparency, and to ensure generalizability of results.
METHODS: We will study 250 medication-free adults with OCD, 100 unaffected adult siblings of individuals with OCD, and 250 healthy control subjects at five expert research sites across five countries (Brazil, India, Netherlands, South Africa, and the U.S.). All participants will receive clinical evaluation, neurocognitive assessment, and magnetic resonance imaging (MRI). The imaging will examine multiple brain circuits hypothesized to underlie OCD behaviors, focusing on morphometry (T1-weighted MRI), structural connectivity (Diffusion Tensor Imaging), and functional connectivity (resting-state fMRI). In addition to analyzing each imaging modality separately, we will also use multi-modal fusion with machine learning statistical methods in an attempt to derive imaging signatures that distinguish individuals with OCD from unaffected siblings and healthy controls (Aim #1). Then we will examine how these imaging signatures link to behavioral performance on neurocognitive tasks that probe these same circuits as well as to clinical profiles (Aim #2). Finally, we will explore how specific environmental features (childhood trauma, socioeconomic status, and religiosity) moderate these brain-behavior associations.
DISCUSSION: Using harmonized methods for data collection and analysis, we will conduct the largest neurocognitive and multimodal-imaging study in medication-free subjects with OCD to date. By recruiting a large, ethno-culturally diverse sample, we will test whether there are robust biosignatures of core OCD features that transcend countries and cultures. If so, future studies can use these brain signatures to reveal trans-diagnostic disease dimensions, chart when these signatures arise during development, and identify treatments that target these circuit abnormalities directly. The long-term goal of this research is to change not only how we conceptualize OCD but also how we diagnose and treat it.

PMID: 32059696 [PubMed - in process]

Multi-modal normalization of resting-state using local physiology reduces changes in functional connectivity patterns observed in mTBI patients.

Sat, 02/15/2020 - 20:40

Multi-modal normalization of resting-state using local physiology reduces changes in functional connectivity patterns observed in mTBI patients.

Neuroimage Clin. 2020 Feb 04;26:102204

Authors: Champagne AA, Coverdale NS, Ross A, Chen Y, Murray CI, Dubowitz D, Cook DJ

Abstract
Blood oxygenation level dependent (BOLD) resting-state functional magnetic resonance imaging (rs-fMRI) may serve as a sensitive marker to identify possible changes in the architecture of large-scale networks following mild traumatic brain injury (mTBI). Differences in functional connectivity (FC) measurements derived from BOLD rs-fMRI may however be confounded by changes in local cerebrovascular physiology and neurovascular coupling mechanisms, without changes in the underlying neuronally driven connectivity of networks. In this study, multi-modal neuroimaging data including BOLD rs-fMRI, baseline cerebral blood flow (CBF0) and cerebrovascular reactivity (CVR; acquired using a hypercapnic gas breathing challenge) were collected in 23 subjects with reported mTBI (14.6±14.9 months post-injury) and 27 age-matched healthy controls. Despite no group differences in CVR within the networks of interest (P > 0.05, corrected), significantly higher CBF0 was documented in the mTBI subjects (P < 0.05, corrected), relative to the controls. A normalization method designed to account for differences in CBF0 post-mTBI was introduced to evaluate the effects of such an approach on reported group differences in network connectivity. Inclusion of regional perfusion measurements in the computation of correlation coefficients within and across large-scale networks narrowed the differences in FC between the groups, suggesting that this approach may elucidate unique changes in connectivity post-mTBI while accounting for shared variance with CBF0. Altogether, our results provide a strong paradigm supporting the need to account for changes in physiological modulators of BOLD in order to expand our understanding of the effects of brain injury on large-scale FC of cortical networks.

PMID: 32058317 [PubMed - as supplied by publisher]

Altered topological organization of functional brain networks in drug-naive patients with paroxysmal kinesigenic dyskinesia.

Sat, 02/15/2020 - 20:40

Altered topological organization of functional brain networks in drug-naive patients with paroxysmal kinesigenic dyskinesia.

J Neurol Sci. 2020 Jan 22;411:116702

Authors: Zhang Y, Ren J, Qin Y, Yang C, Zhang T, Gong Q, Yang T, Zhou D

Abstract
BACKGROUND: Previous neuroimaging studies have revealed aberrant basal ganglia-thalamocortical circuit in patients with paroxysmal kinesigenic dyskinesia (PKD) with drug treatment. This study aims to investigate the topological organization of functional networks in drug-naive PKD.
METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 24 drug-naive PKD patients and 24 age, gender and mean framewise displacement (FD)-matched healthy controls (HCs). The network topological properties (including global and nodal measures) were analyzed between two groups by using graph-based theoretical approaches. Pearson's correlation analysis was performed between significant metrics and duration of disease and the age of onset of patients with PKD.
RESULTS: Compare to HCs, the drug-naïve PKD patients showed increased nodal centralities mainly in left precentral gyrus, basal ganglia and limbic regions and decreased nodal centralities in the temporal pole. Our results showed that drug-naïve PKD patients presented the small-world topology and at the global level no significant differences were found between PKD and HCs. In the correlation analysis, the increased nodal efficiency in the left pallidum was positively correlated with the onset of age.
CONCLUSIONS: Our findings supported the previous observation of the disruptive cortical-basal ganglia circuitry in PKD patients, but difference in that the prominent change of precentral area and temporal pole were also observed in our study when the potential impact of drug was excluded. These findings may provide a novel insight into further delineation of the pathophysiological genesis and possible target for PKD.

PMID: 32058179 [PubMed - as supplied by publisher]

Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure.

Sat, 02/15/2020 - 20:40

Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure.

Neuroimage. 2020 Feb 10;:116611

Authors: Pavlović DM, Guillaume BRL, Towlson EK, Kuek NMY, Afyouni S, Vértes PE, Thomas Yeo BT, Bullmore ET, Nichols TE

Abstract
There is great interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of an average group network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two new extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects on cluster structure of individual differences on subject-level covariates. Multi-subject Stochastic Blockmodels (MS-SBM) can flexibly account for between-subject variability in terms of a homogenous or heterogeneous effect on connectivity of covariates such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on Wald, likelihood ratio and Monte Carlo permutation tests. We show that multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition. Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting-state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; N=268 brain regions), we show that the Heterogeneous Stochastic Blockmodel identifies a range of network topologies simultaneously, including modular and core-periphery structure.

PMID: 32058004 [PubMed - as supplied by publisher]

TbCAPs: A ToolBox for Co-Activation pattern analysis.

Sat, 02/15/2020 - 20:40

TbCAPs: A ToolBox for Co-Activation pattern analysis.

Neuroimage. 2020 Feb 10;:116621

Authors: Bolton TAW, Tuleasca C, Wotruba D, Rey G, Dhanis H, Gauthier B, Delavari F, Morgenroth E, Gaviria J, Blondiaux E, Smigielski L, Van De Ville D

Abstract
Functional magnetic resonance imaging provides rich spatio-temporal data of human brain activity during task and rest. Many recent efforts have focussed on characterising dynamics of brain activity. One notable instance is co-activation pattern (CAP) analysis, a frame-wise analytical approach that disentangles the different functional brain networks interacting with a user-defined seed region. While promising applications in various clinical settings have been demonstrated, there is not yet any centralised, publicly accessible resource to facilitate the deployment of the technique. Here, we release a working version of TbCAPs, a new toolbox for CAP analysis, which includes all steps of the analytical pipeline, introduces new methodological developments that build on already existing concepts, and enables a facilitated inspection of CAPs and resulting metrics of brain dynamics.The toolbox is available on a public academic repository at https://c4science.ch/source/CAP_Toolbox.git. In addition, to illustrate the feasibility and usefulness of our pipeline, we describe an application to the study of human cognition. CAPs are constructed from resting-state fMRI using as seed the right dorsolateral prefrontal cortex, and, in a separate sample, we successfully predict a behavioural measure of continuous attentional performance from the metrics of CAP dynamics (R = 0.59).

PMID: 32058000 [PubMed - as supplied by publisher]

Brain network interactions in transgender individuals with gender incongruence.

Sat, 02/15/2020 - 20:40

Brain network interactions in transgender individuals with gender incongruence.

Neuroimage. 2020 Feb 10;:116613

Authors: Uribe C, Junque C, Gómez-Gil E, Abos A, Mueller SC, Guillamon A

Abstract
Functional brain organization in transgender persons remains unclear. Our aims were to investigate global and regional connectivity differences within functional networks in transwomen and transmen with early-in-life onset gender incongruence; and to test the consistency of two available hypotheses that attempted to explain gender variants: (i) a neurodevelopmental cortical hypothesis that suggests the existence of different brain phenotypes based on structural MRI data and genes polymorphisms of sex hormone receptors; (ii) a functional-based hypothesis in relation to regions involved in the own body perception. T2*-weighted images in a 3-T MRI were obtained from 29 transmen and 17 transwomen as well as 22 cisgender women and 19 cisgender men. Resting-state independent component analysis, seed-to-seed functional network and graph theory analyses were performed. Transmen, transwomen, and cisgender women had decreased connectivity compared with cisgender men in superior parietal regions, as part of the salience (SN) and the executive control (ECN) networks. Transmen also had weaker connectivity compared with cisgender men between intra-SN regions and weaker inter-network connectivity between regions of the SN, the default mode network (DMN), the ECN and the sensorimotor network. Transwomen had lower small-worldness, modularity and clustering coefficient than cisgender men. There were no differences among transmen, transwomen, and ciswomen. Together these results underline the importance of the SN interacting with DMN, ECN, and sensorimotor networks in transmen, involving regions of the entire brain with a frontal predominance. Reduced global connectivity graph-theoretical measures were a characteristic of transwomen. It is proposed that the interaction between networks is a keystone in building a gendered self. Finally, our findings suggest that both proposed hypotheses are complementary in explaining brain differences between gender variants.

PMID: 32057995 [PubMed - as supplied by publisher]

Thalamocortical connectivity in electroconvulsive therapy for major depressive disorder.

Sat, 02/15/2020 - 20:40

Thalamocortical connectivity in electroconvulsive therapy for major depressive disorder.

J Affect Disord. 2020 Mar 01;264:163-171

Authors: Wei Q, Bai T, Brown EC, Xie W, Chen Y, Ji G, Ramasubbu R, Tian Y, Wang K

Abstract
BACKGROUND: Electroconvulsive therapy (ECT) can lead to rapid and effective responses in major depressive disorder (MDD). However, the precise neural mechanisms of ECT for MDD are still unclear. Previous work has confirmed that thalamocortical circuits play an important role in emotion and cognition. However, the relationship between mechanisms of ECT for MDD and thalamocortical connectivity has not yet been investigated.
METHOD: Thalamocortical functional connectivity analysis was performed on resting-state functional magnetic resonance imaging (fMRI) data collected from 28 MDD patients both pre- and post-ECT treatment, as well as 20 healthy controls. The cortex was parceled into six regions of interest (ROIs), which were used as seeds to assess the functional connectivity between the cortex and each voxel in the thalamus. Then, functional connectivity between the identified thalamic subregions and the rest of the brain was quantified to better localize thalamocortical connectivity related to ECT. Structural connectivity among the functionally abnormal regions was also determined using probabilistic tractography from diffusion tensor imaging (DTI) data.
RESULTS: There was decreased parietal cortex-left pulvinar and left pulvinar-bilateral precuneus functional connectivity in post-ECT MDD patients, compared to pre-ECT MDD patients. Furthermore, functional connectivity strength of parietal cortex-left pulvinar and left pulvinar-bilateral precuneus was negative correlation with verbal fluency test scores in post-ECT MDD patients. No significant change was found in structural connectivity analysis.
LIMITATIONS: The sample size of our study was not large.
CONCLUSION: Our findings implicate that the specific abnormalities in thalamocortical circuit may be associated with cognitive impairment induced by ECT.

PMID: 32056746 [PubMed - in process]