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Neural markers of depression risk predict the onset of depression.

Tue, 02/05/2019 - 21:01

Neural markers of depression risk predict the onset of depression.

Psychiatry Res Neuroimaging. 2019 Jan 25;285:31-39

Authors: Shapero BG, Chai XJ, Vangel M, Biederman J, Hoover CS, Whitfield-Gabrieli S, Gabrieli JDE, Hirshfeld-Becker DR

Abstract
Although research highlights neural correlates of Major Depressive Disorder (MDD), it is unclear whether these correlates reflect the state of depression or a pre-existing risk factor. The current study examined whether baseline differences in brain activations, resting-state connectivity, and brain structural differences between non-symptomatic children at high- and low-risk for MDD based on familial depression prospectively predict the onset of a depressive episode or increases in depressive symptomatology. We re-assessed 44 participants (28 high-risk; 16 low-risk) who had undergone neuroimaging in a previous study 3-4 years earlier (Mean age at follow-up = 14.3 years, SD = 1.9 years; 45% females; 70% Caucasian). We investigated whether baseline brain imaging data (including an emotional face match task fMRI, resting-state fMRI and structural MRI) that differentiated the risk groups also predicted the onset of depression. Resting-state functional connectivity abnormalities in the default mode and cognitive control network that differentiated high-risk from low-risk youth at baseline predicted the onset of MDD during adolescence, after taking risk status into account. Increased functional activation to both happy and fearful faces was associated with greater decreases in self-reported depression symptoms at follow-up. This preliminary evidence could be used to identify youth at-risk for depression and inform early intervention strategies.

PMID: 30716688 [PubMed - as supplied by publisher]

The inner fluctuations of the brain in presymptomatic Frontotemporal Dementia: The chronnectome fingerprint.

Tue, 02/05/2019 - 21:01

The inner fluctuations of the brain in presymptomatic Frontotemporal Dementia: The chronnectome fingerprint.

Neuroimage. 2019 Feb 01;:

Authors: Premi E, Calhoun VD, Diano M, Gazzina S, Cosseddu M, Alberici A, Archetti S, Paternicò D, Gasparotti R, van Swieten J, Galimberti D, Sanchez-Valle R, Laforce R, Moreno F, Synofzik M, Graff C, Masellis M, Tartaglia MC, Rowe J, Vandenberghe R, Finger E, Tagliavini F, de Mendonça A, Santana I, Butler C, Ducharme S, Gerhard A, Danek A, Levin J, Otto M, Frisoni G, Cappa S, Sorbi S, Padovani A, Rohrer JD, Borroni B, Genetic FTD Initiative, GENFI

Abstract
Frontotemporal Dementia (FTD) is preceded by a long period of subtle brain changes, occurring in the absence of overt cognitive symptoms, that need to be still fully characterized. Dynamic network analysis based on resting-state magnetic resonance imaging (rs-fMRI) is a potentially powerful tool for the study of preclinical FTD. In the present study, we employed a "chronnectome" approach (recurring, time-varying patterns of connectivity) to evaluate measures of dynamic connectivity in 472 at-risk FTD subjects from the Genetic Frontotemporal dementia research Initiative (GENFI) cohort. We considered 249 subjects with FTD-related pathogenetic mutations and 223 mutation non-carriers (HC). Dynamic connectivity was evaluated using independent component analysis and sliding-time window correlation to rs-fMRI data, and meta-state measures of global brain flexibility were extracted. Results show that presymptomatic FTD exhibits diminished dynamic fluidity, visiting less meta-states, shifting less often across them, and travelling through a narrowed meta-state distance, as compared to HC. Dynamic connectivity changes characterize preclinical FTD, arguing for the desynchronization of the inner fluctuations of the brain. These changes antedate clinical symptoms, and might represent an early signature of FTD to be used as a biomarker in clinical trials.

PMID: 30716457 [PubMed - as supplied by publisher]

Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals.

Tue, 02/05/2019 - 21:01

Reproducibility of functional brain alterations in major depressive disorder: Evidence from a multisite resting-state functional MRI study with 1,434 individuals.

Neuroimage. 2019 Feb 01;:

Authors: Xia M, Si T, Sun X, Ma Q, Liu B, Wang L, Meng J, Chang M, Huang X, Chen Z, Tang Y, Xu K, Gong Q, Wang F, Qiu J, Xie P, Li L, He Y, DIDA-Major Depressive Disorder Working Group

Abstract
Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P < .05 and a height threshold of P < .001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder.

PMID: 30716456 [PubMed - as supplied by publisher]

Distinctive Interaction Between Cognitive Networks and the Visual Cortex in Early Blind Individuals.

Tue, 02/05/2019 - 21:01

Distinctive Interaction Between Cognitive Networks and the Visual Cortex in Early Blind Individuals.

Cereb Cortex. 2019 Jan 31;:

Authors: Abboud S, Cohen L

Abstract
In early blind individuals, brain activation by a variety of nonperceptual cognitive tasks extends to the visual cortex, while in the sighted it is restricted to supramodal association areas. We hypothesized that such activation results from the integration of different sectors of the visual cortex into typical task-dependent networks. We tested this hypothesis with fMRI in blind and sighted subjects using tasks assessing speech comprehension, incidental long-term memory and both verbal and nonverbal executive control, in addition to collecting resting-state data. All tasks activated the visual cortex in blind relative to sighted subjects, which enabled its segmentation according to task sensitivity. We then assessed the unique brain-scale functional connectivity of the segmented areas during resting state. Language-related seeds were preferentially connected to frontal and temporal language areas; the seed derived from the executive task was connected to the right dorsal frontoparietal executive network; and the memory-related seed was uniquely connected to mesial frontoparietal areas involved in episodic memory retrieval. Thus, using a broad set of language, executive, and memory tasks in the same subjects, combined with resting state connectivity, we demonstrate the selective integration of different patches of the visual cortex into brain-scale networks with distinct localization, lateralization, and functional roles.

PMID: 30715236 [PubMed - as supplied by publisher]

rfDemons: Resting fMRI-based Cortical Surface Registration using the BrainSync Transform.

Tue, 02/05/2019 - 21:01
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rfDemons: Resting fMRI-based Cortical Surface Registration using the BrainSync Transform.

Med Image Comput Comput Assist Interv. 2018 Sep;11072:198-205

Authors: Joshi AA, Li J, Chong M, Akrami H, Leahy RM

Abstract
Cross subject functional studies of cerebral cortex require cortical registration that aligns functional brain regions. While cortical folding patterns are approximate indicators of the underlying cytoarchitecture, coregistration based on these features alone does not accurately align functional regions in cerebral cortex. This paper presents a method for cortical surface registration (rfDemons) based on resting fMRI (rfMRI) data that uses curvature-based anatomical registration as an initialization. In contrast to existing techniques that use connectivity-based features derived from rfMRI, the proposed method uses 'synchronized' resting rfMRI time series directly. The synchronization of rfMRI data is performed using the BrainSync transform which applies an orthogonal transform to the rfMRI time series to temporally align them across subjects. The rfDemons method was applied to rfMRI from the Human Connectome Project and evaluated using task fMRI data to explore the impact of cortical registration performed using resting fMRI data on functional alignment of the cerebral cortex.

PMID: 30714047 [PubMed - in process]

Editorial for the special issue "Resting-state fMRI and cognition" in Brain and Cognition.

Tue, 02/05/2019 - 21:01
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Editorial for the special issue "Resting-state fMRI and cognition" in Brain and Cognition.

Brain Cogn. 2019 Jan 31;:

Authors: Lotze M, Langner R

PMID: 30712965 [PubMed - as supplied by publisher]

Interactive effect of 5-HTTLPR and BDNF polymorphisms on amygdala intrinsic functional connectivity and anxiety.

Tue, 02/05/2019 - 03:00

Interactive effect of 5-HTTLPR and BDNF polymorphisms on amygdala intrinsic functional connectivity and anxiety.

Psychiatry Res Neuroimaging. 2019 Jan 29;285:1-8

Authors: Loewenstern J, You X, Merchant J, Gordon EM, Stollstorff M, Devaney J, Vaidya CJ

Abstract
The serotonin transporter (5-HTTLPR) and brain-derived neurotrophic factor (BDNF) gene polymorphisms have been associated with risk for affective disorders and functional variability of the amygdala. We examined whether the two genotypes interactively influence intrinsic functional connectivity (FC) of the amygdala and whether FC mediates the genetic association with anxiety. Eighty genotyped healthy adults underwent resting state fMRI and completed the self-reported State-Trait Anxiety Inventory. Interactive genetic association with anxiety was observed such that effects of 5-HTTLPR depended on the BDNF Val66Met polymorphism (rs6265 variant), with higher anxiety scores in short and Met carriers compared to the other allelic groups. Voxel-wise FC with left and right amygdala seeds identified regions that were sensitive to variability in anxiety scores. A significant moderated mediation model demonstrated that the effect of 5-HTTLPR genotype on anxiety, moderated by BDNF Val66Met genotype, was fully mediated by FC between the left amygdala and the right dorsolateral prefrontal cortex, a cognitive control-related region, during a task-free state. FC was highest in carriers of the 5-HTTLPR short allele and BDNF Met allele. These findings establish intrinsic amygdala-prefrontal functional connectivity as a potential intermediate phenotype for anxiety, an important step toward identification of causal pathways for vulnerability to affective disorders.

PMID: 30711709 [PubMed - as supplied by publisher]

Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project.

Tue, 02/05/2019 - 03:00

Altered Connectivity Between Cerebellum, Visual, and Sensory-Motor Networks in Autism Spectrum Disorder: Results from the EU-AIMS Longitudinal European Autism Project.

Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Dec 05;:

Authors: Oldehinkel M, Mennes M, Marquand A, Charman T, Tillmann J, Ecker C, Dell'Acqua F, Brandeis D, Banaschewski T, Baumeister S, Moessnang C, Baron-Cohen S, Holt R, Bölte S, Durston S, Kundu P, Lombardo MV, Spooren W, Loth E, Murphy DGM, Beckmann CF, Buitelaar JK, EU-AIMS LEAP group

Abstract
BACKGROUND: Resting-state functional magnetic resonance imaging-based studies on functional connectivity in autism spectrum disorder (ASD) have generated inconsistent results. Interpretation of findings is further hampered by small samples and a focus on a limited number of networks, with networks underlying sensory processing being largely underexamined. We aimed to comprehensively characterize ASD-related alterations within and between 20 well-characterized resting-state networks using baseline data from the EU-AIMS (European Autism Interventions-A Multicentre Study for Developing New Medications) Longitudinal European Autism Project.
METHODS: Resting-state functional magnetic resonance imaging data was available for 265 individuals with ASD (7.5-30.3 years; 73.2% male) and 218 typically developing individuals (6.9-29.8 years; 64.2% male), all with IQ > 70. We compared functional connectivity within 20 networks-obtained using independent component analysis-between the ASD and typically developing groups, and related functional connectivity within these networks to continuous (overall) autism trait severity scores derived from the Social Responsiveness Scale Second Edition across all participants. Furthermore, we investigated case-control differences and autism trait-related alterations in between-network connectivity.
RESULTS: Higher autism traits were associated with increased connectivity within salience, medial motor, and orbitofrontal networks. However, we did not replicate previously reported case-control differences within these networks. The between-network analysis did reveal case-control differences showing on average 1) decreased connectivity of the visual association network with somatosensory, medial, and lateral motor networks, and 2) increased connectivity of the cerebellum with these sensory and motor networks in ASD compared with typically developing subjects.
CONCLUSIONS: We demonstrate ASD-related alterations in within- and between-network connectivity. The between-network alterations broadly affect connectivity between cerebellum, visual, and sensory-motor networks, potentially underlying impairments in multisensory and visual-motor integration frequently observed in ASD.

PMID: 30711508 [PubMed - as supplied by publisher]

Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior.

Tue, 02/05/2019 - 03:00

Individual-specific fMRI-Subspaces improve functional connectivity prediction of behavior.

Neuroimage. 2019 Jan 31;:

Authors: Kashyap R, Kong R, Bhattacharjee S, Li J, Zhou J, Yeo T

Abstract
There is significant interest in using resting-state functional connectivity (RSFC) to predict human behavior. Good behavioral prediction should in theory require RSFC to be sufficiently distinct across participants; if RSFC were the same across participants, then behavioral prediction would obviously be poor. Therefore, we hypothesize that removing common resting-state functional magnetic resonance imaging (rs-fMRI) signals that are shared across participants would improve behavioral prediction. Here, we considered 803 participants from the human connectome project (HCP) with four rs-fMRI runs. We applied the common and orthogonal basis extraction (COBE) technique to decompose each HCP run into two subspaces: a common (group-level) subspace shared across all participants and a subject-specific subspace. We found that the first common COBE component of the first HCP run was localized to the visual cortex and was unique to the run. On the other hand, the second common COBE component of the first HCP run and the first common COBE component of the remaining HCP runs were highly similar and localized to regions within the default network, including the posterior cingulate cortex and precuneus. Overall, this suggests the presence of run-specific (state-specific) effects that were shared across participants. By removing the first and second common COBE components from the first HCP run, and the first common COBE component from the remaining HCP runs, the resulting RSFC improves behavioral prediction by an average of 11.7% across 58 behavioral measures spanning cognition, emotion and personality.

PMID: 30711467 [PubMed - as supplied by publisher]

Computerized cognitive training for Chinese mild cognitive impairment patients: A neuropsychological and fMRI study.

Sun, 02/03/2019 - 08:59

Computerized cognitive training for Chinese mild cognitive impairment patients: A neuropsychological and fMRI study.

Neuroimage Clin. 2019 Jan 26;22:101691

Authors: Li BY, He NY, Qiao Y, Xu HM, Lu YZ, Cui PJ, Ling HW, Yan FH, Tang HD, Chen SD

Abstract
BACKGROUND: Computerized multi-model training has been widely studied for its effect on delaying cognitive decline. In this study, we designed the first Chinese-version computer-based multi-model cognitive training for mild cognitive impairment (MCI) patients. Neuropsychological effects and neural activity changes assessed by functional MRI were both evaluated.
METHOD: MCI patients in the training group were asked to take training 3-4 times per week for 6 months. Neuropsychological and resting-state fMRI assessment were performed at baseline and at 6 months. Patients in both groups were continuously followed up for another 12 months and assessed by neuropsychological tests again.
RESULTS: 78 patients in the training group and 63 patients in the control group accomplished 6-month follow-up. Training group improved 0.23 standard deviation (SD) of mini-mental state examination, while control group had 0.5 SD decline. Addenbrooke's cognitive examination-revised scores in attention (p = 0.002) and memory (p = 0.006), as well as stroop color-word test interference index (p = 0.038) and complex figure test-copy score (p = 0.035) were also in favor of the training effect. Difference between the changes of two groups after training was not statistically significant. The fMRI showed increased regional activity at bilateral temporal poles, insular cortices and hippocampus. However, difference between the changes of two groups after another 12 months was not statistically significant.
CONCLUSIONS: Multi-model cognitive training help MCI patients to gained cognition benefit, especially in memory, attention and executive function. Functional neuroimaging provided consistent neural activation evidence. Nevertheless, after one-year follow up after last training, training effects were not significant. The study provided new evidence of beneficial effect of multi-model cognitive training.

PMID: 30708349 [PubMed - as supplied by publisher]

Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders.

Sun, 02/03/2019 - 08:59

Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders.

Neuroimage Clin. 2019 Jan 17;21:101678

Authors: Dajani DR, Burrows CA, Odriozola P, Baez A, Nebel MB, Mostofsky SH, Uddin LQ

Abstract
BACKGROUND: Current diagnostic systems for neurodevelopmental disorders do not have clear links to underlying neurobiology, limiting their utility in identifying targeted treatments for individuals. Here, we aimed to investigate differences in functional brain network integrity between traditional diagnostic categories (autism spectrum disorder [ASD], attention-deficit/hyperactivity disorder [ADHD], typically developing [TD]) and carefully consider the impact of comorbid ASD and ADHD on functional brain network integrity in a sample adequately powered to detect large effects. We also assess the neurobiological separability of a novel, potential alternative categorical scheme based on behavioral measures of executive function.
METHOD: Five-minute resting-state fMRI data were obtained from 168 children (128 boys, 40 girls) with ASD, ADHD, comorbid ASD and ADHD, and TD children. Independent component analysis and dual regression were used to compute within- and between-network functional connectivity metrics at the individual level.
RESULTS: No significant group differences in within- or between-network functional connectivity were observed between traditional diagnostic categories (ASD, ADHD, TD) even when stratified by comorbidity (ASD + ADHD, ASD, ADHD, TD). Similarly, subgroups classified by executive functioning levels showed no group differences.
CONCLUSIONS: Using clinical diagnosis and behavioral measures of executive function, no differences in functional connectivity were observed among the categories examined. Despite our limited ability to detect small- to medium-sized differences between groups, this work contributes to a growing literature suggesting that traditional diagnostic categories do not define neurobiologically separable groups. Future work is necessary to ascertain the validity of the executive function-based nosology, but current results suggest that nosologies reliant on behavioral data alone may not lead to discovery of neurobiologically distinct categories.

PMID: 30708240 [PubMed - as supplied by publisher]

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks.

Sun, 02/03/2019 - 08:59

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks.

Neuroimage. 2019 Jan 29;:

Authors: Elliott ML, Knodt AR, Cooke M, Kim MJ, Melzer TR, Keenan R, Ireland D, Ramrakha S, Poulton R, Caspi A, Moffitt TE, Hariri AR

Abstract
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

PMID: 30708106 [PubMed - as supplied by publisher]

Functional connectivity changes in core resting state networks are associated with cognitive performance in Systemic Lupus Erythematosus.

Sun, 02/03/2019 - 08:59

Functional connectivity changes in core resting state networks are associated with cognitive performance in Systemic Lupus Erythematosus.

J Comp Neurol. 2019 Feb 01;:

Authors: Nystedt J, Mannfolk P, Jönsen A, Nilsson P, Strandberg TO, Sundgren PC

Abstract
To investigate core resting state networks in SLE patients with and without neuropsychiatric symptoms by examining functional connectivity changes correlating with results of cognitive testing. Structural MRI and resting state-fMRI (rs-fMRI) were performed in 61 female SLE patients (mean age: 36.8 years, range 18.2-52.0 years) and 20 healthy controls (HC) (mean age 36.2 years, range 23.3-52.2 years) in conjunction with clinical examination and cognitive testing. Alterations in core resting state networks, not found in our healthy controls sample, correlated with cognitive performance gauged by neuropsychological tests in non-neuropsychiatric SLE (nNP) as well as in neuropsychiatric SLE patients (NP). The observed pattern of increased functional connectivity in core resting state networks correlated with reduced cognitive performance on all cognitive domains tested and with a heavily focus on DM, CE and DM-CE in the NP subgroup. Furthermore, we found that the observed alterations in memory and psychomotor speed correlated with disease duration. In SLE patients both with and without clinically overt neuropsychiatric manifestations, we found changes in the functional connectivity of core resting state networks essential to cognitive functions. These findings may represent a rewiring of functional architecture in response to neuronal damage and could indicate suboptimal compensatory mechanisms at play. This article is protected by copyright. All rights reserved.

PMID: 30707449 [PubMed - as supplied by publisher]

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.

Sun, 02/03/2019 - 08:59
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Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.

Sci Rep. 2019 Jan 31;9(1):1103

Authors: Nie D, Lu J, Zhang H, Adeli E, Wang J, Yu Z, Liu L, Wang Q, Wu J, Shen D

Abstract
High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image features from magnetic resonance images (MRI). However, feature engineering is usually time consuming, laborious and subjective. Most importantly, the engineered features cannot effectively encode other predictive but implicit information provided by multi-modal neuroimages. We propose a two-stage learning-based method to predict the overall survival (OS) time of high-grade gliomas patient. At the first stage, we adopt deep learning, a recently dominant technique of artificial intelligence, to automatically extract implicit and high-level features from multi-modal, multi-channel preoperative MRI such that the features are competent of predicting survival time. Specifically, we utilize not only contrast-enhanced T1 MRI, but also diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI), for computing multiple metric maps (including various diffusivity metric maps derived from DTI, and also the frequency-specific brain fluctuation amplitude maps and local functional connectivity anisotropy-related metric maps derived from rs-fMRI) from 68 high-grade glioma patients with different survival time. We propose a multi-channel architecture of 3D convolutional neural networks (CNNs) for deep learning upon those metric maps, from which high-level predictive features are extracted for each individual patch of these maps. At the second stage, those deeply learned features along with the pivotal limited demographic and tumor-related features (such as age, tumor size and histological type) are fed into a support vector machine (SVM) to generate the final prediction result (i.e., long or short overall survival time). The experimental results demonstrate that this multi-model, multi-channel deep survival prediction framework achieves an accuracy of 90.66%, outperforming all the competing methods. This study indicates highly demanded effectiveness on prognosis of deep learning technique in neuro-oncological applications for better individualized treatment planning towards precision medicine.

PMID: 30705340 [PubMed - in process]

Neurofeedback of core language network nodes modulates connectivity with the default-mode network: A double-blind fMRI neurofeedback study on auditory verbal hallucinations.

Fri, 02/01/2019 - 23:58
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Neurofeedback of core language network nodes modulates connectivity with the default-mode network: A double-blind fMRI neurofeedback study on auditory verbal hallucinations.

Neuroimage. 2019 Jan 28;:

Authors: Zweerings J, Hummel B, Keller M, Zvyagintsev M, Schneider F, Klasen M, Mathiak K

Abstract
BACKGROUND: The experience of auditory verbal hallucinations in schizophrenia is associated with changes in brain network function. In particular, studies indicate altered functional coupling between nodes of the language and default mode networks. Neurofeedback based on real-time functional magnetic resonance imaging (rtfMRI) can be used to modulate such aberrant network connectivity.
METHODS: We investigated resting-state connectivity changes after neurofeedback (NF) in 21 patients with schizophrenia and 35 healthy individuals. All participants underwent two days of neurofeedback training of important nodes of the left-hemispheric language network including the inferior frontal gyrus (IFG) and posterior superior temporal gyrus (pSTG). In a double-blind randomized cross-over design, participants learned to down- and up-regulate their brain activation in the designated target regions based on NF. Prior to and after each training day, a resting state measurement took place.
RESULTS: Coupling between nodes of the language and the default mode network (DMN) selectively increased after down-as compared to up-regulation NF. Network analyses revealed more pronounced increases in functional connectivity between nodes of the language network and the DMN in patients compared to healthy individuals. In particular, down-regulation NF led to increased coupling between nodes of the language network and bilateral inferior parietal lobe (IPL) as well as posterior cingulate cortex (PCC)/precuneus in patients. Up-regulation strengthened connectivity with the medial prefrontal cortex (mPFC). Improved well-being four weeks after the training predicted increased functional coupling between the left IFG and left IPL.
CONCLUSION: Modulatory effects emerged as increased internetwork communication, indicating that down-regulation NF selectively enhances coupling between language and DM network nodes in patients with AVH. RtfMRI NF may thus be used to modulate brain network function that is relevant to the phenomenology of AVH. Specific effects of self-regulation on symptom improvement have to be explored in therapeutic interventions.

PMID: 30703519 [PubMed - as supplied by publisher]

Acute stress alters the 'default' brain processing.

Fri, 02/01/2019 - 23:58
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Acute stress alters the 'default' brain processing.

Neuroimage. 2019 Jan 28;:

Authors: Zhang W, Hashemi MM, Kaldewaij R, Koch SBJ, Beckmann C, Klumpers F, Roelofs K

Abstract
Active adaptation to acute stress is essential for coping with daily life challenges. The stress hormone cortisol, as well as large scale re-allocations of brain resources have been implicated in this adaptation. Stress-induced shifts between large-scale brain networks, including salience (SN), central executive (CEN) and default mode networks (DMN), have however been demonstrated mainly under task-conditions. It remains unclear whether such network shifts also occur in the absence of ongoing task-demands, and most critically, whether these network shifts are predictive of individual variation in the magnitude of cortisol stress-responses. In a sample of 335 healthy participants, we investigated stress-induced functional connectivity changes (delta-FC) of the SN, CEN and DMN, using resting-state fMRI data acquired before and after a socially evaluated cold-pressor test and a mental arithmetic task. To investigate which network changes are associated with acute stress, we evaluated the association between cortisol increase and delta-FC of each network. Stress-induced cortisol increase was associated with increased connectivity within the SN, but with decreased coupling of DMN at both local (within network) and global (synchronization with brain regions also outside the network) levels. These findings indicate that acute stress prompts immediate connectivity changes in large-scale resting-state networks, including the SN and DMN in the absence of explicit ongoing task-demands. Most interestingly, this brain reorganization is coupled with individuals' cortisol stress-responsiveness. These results suggest that the observed stress-induced network reorganization might function as a neural mechanism determining individual stress reactivity and, therefore, it could serve as a promising marker for future studies on stress resilience and vulnerability.

PMID: 30703518 [PubMed - as supplied by publisher]

Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis.

Fri, 02/01/2019 - 23:58
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Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis.

IEEE Trans Biomed Eng. 2019 Jan 28;:

Authors: Matsubara T, Tashiro T, Uehara K

Abstract
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Many studies have been conducted on machine learning techniques that seek brain imaging data for specific biomarkers of disorders. These studies have encountered the following dilemma: An end-to-end classification overfits to a small number of high-dimensional samples but unsupervised feature-extraction has the risk of extracting a signal of no interest. In addition, such studies often provided only diagnoses for patients without presenting the reasons for these diagnoses. This study proposed a deep neural generative model of resting-state functional magnetic resonance imaging (fMRI) data. The proposed model is conditioned by the assumption of the subject's state and estimates the posterior probability of the subject's state given the imaging data, using Bayes' rule. This study applied the proposed model to diagnose schizophrenia and bipolar disorders. Diagnostic accuracy was improved by a large margin over competitive approaches, namely classifications of functional connectivity and discriminative/generative models of region-wise signals with or without unsupervised feature-extractors. The proposed model visualizes brain regions largely related to the disorders, thus motivating further biological investigation.

PMID: 30703004 [PubMed - as supplied by publisher]

Sex differences in the relationship between cardiorespiratory fitness and brain function in older adulthood.

Fri, 02/01/2019 - 23:58
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Sex differences in the relationship between cardiorespiratory fitness and brain function in older adulthood.

J Appl Physiol (1985). 2019 Jan 31;:

Authors: Dimech CJ, Anderson JAE, Lockrow AW, Spreng RN, Turner GR

Abstract
We investigated sex differences in the association between a measure of physical health, cardiorespiratory fitness (CRF), and brain function using resting state functional connectivity fMRI. We examined these sex differences in the default, frontoparietal control, and cingulo-opercular networks, assemblies of functionally connected brain regions known to be impacted by both age and fitness level. Forty-nine healthy older adults (29 female) were scanned to obtain measures of intrinsic connectivity within and across these three networks. We calculated global efficiency (a measure of network integration), and local efficiency (a measure of network specialization) using graph theoretical methods. Across all three networks combined local efficiency was positively associated with CRF, and this was more robust in male versus female older adults. Further, global efficiency was negatively associated with CRF, but only in males. Our findings suggest that in older adults, associations between brain network integrity and physical health are sex-dependent. These results underscore the importance of considering sex differences when examining associations between fitness and brain function in older adulthood.

PMID: 30702974 [PubMed - as supplied by publisher]

Enhanced functional connectivity of the default mode network (DMN) in patients with spleen deficiency syndrome: A resting-state fMRI study.

Fri, 02/01/2019 - 23:58
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Enhanced functional connectivity of the default mode network (DMN) in patients with spleen deficiency syndrome: A resting-state fMRI study.

Medicine (Baltimore). 2019 Feb;98(5):e14372

Authors: Ning YZ, Wu FZ, Xue S, Yin DQ, Zhu H, Liu J, Jia HX

Abstract
Numerous studies had investigated the biological basis of spleen deficiency syndrome on gastrointestinal dysfunctions. However, little was known about neuropsychological mechanism of spleen deficiency syndrome. The default model network (DMN) plays an important role in cognitive processing. Our aim is to investigate the change of neuropsychological tests and DMN in patients with spleen deficiency syndrome.Sixteen patients and 12 healthy subjects underwent functional magnetic resonance imaging examination, and 15 patients with spleen deficiency syndrome and 6 healthy subjects take part in the two neuropsychological tests.Compared with healthy subjects, patients with spleen deficiency syndrome revealed significantly increased functional connectivity within DMN, and significantly higher in the scores of 2-FT (P = .002) and 3-FT (P = .014).Our findings suggest that patients with spleen deficiency syndrome are associated with abnormal functional connectivity of DMN and part of neuropsychological tests, which provide new evidence in neuroimaging to support the notion of TCM that the spleen stores Yi and domains thoughts.

PMID: 30702629 [PubMed - in process]

Intrinsic brain abnormalities in drug-naive patients with obsessive-compulsive disorder: A resting-state functional MRI study.

Fri, 02/01/2019 - 23:58
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Intrinsic brain abnormalities in drug-naive patients with obsessive-compulsive disorder: A resting-state functional MRI study.

J Affect Disord. 2019 Feb 15;245:861-868

Authors: Yang X, Hu X, Tang W, Li B, Yang Y, Gong Q, Huang X

Abstract
BACKGROUND: Using the resting-state functional magnetic resonance imaging (rs-fMRI) in a relatively large sample of drug-naive patients with obsessive-compulsive disorder (OCD), the current study aims to explore alterations in regional and network-level neural function and to determine the association between these alterations in intrinsic neural activity and symptom severity in OCD.
METHODS: A total of 68 drug-naive OCD patients and 68 healthy control subjects (HCS) were examined using rs-fMRI. Regional cerebral function was evaluated by measuring the fraction of amplitude of low-frequency fluctuation (fALFF). Regions with fALFF alterations were used as seeds in whole-brain functional connectivity (FC) analysis. Statistical analyses of fALFF and FC differences between OCD patients with HCS were performed voxel-by-voxel using a two-sample t-test in Statistical Parametric Mapping version 8 (SPM8). Whole brain correlation analyses were performed to identify the association between functional neural correlates and OCD symptom severity evaluated using Yale-Brown Obsessive Compulsive scale (Y-BOCS) and subscale scores.
RESULTS: Relative to HCS, OCD patients showed higher fALFF in the right putamen and right superior frontal gyrus (P < 0.05, corrected for AlphaSim); higher FC in the limbic-striatal circuit and lower FC in the fronto-temporal and fronto-striato-thalamic networks (P < 0.05, corrected for AlphaSim). FC in striato-thalamic junction was negatively correlated with the Y-BOCS total score (r = -0.493, P < 0.001).
CONCLUSION: These findings of focal spontaneous hyperfunction confirmed the prevailing frontal-striatal model of OCD, and altered brain connectivity in large-scale resting-state networks indicated a connectivity-based pathophysiological process in OCD.

PMID: 30699871 [PubMed - in process]