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Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia.

Thu, 11/15/2018 - 11:20
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Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia.

Front Neuroinform. 2018;12:71

Authors: Moghimi P, Lim KO, Netoff TI

Abstract
Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classification performance compared to single cross validation. The anatomical atlas resulted in higher classification results. Prewhitening resulted in a 10% boost in classification performance. Overall, a classification performance of 80% was obtained with a double-cross validation scheme using prewhitened time series and an anatomical brain atlas. However, reproducibility of classification within subjects across scans was surprisingly low and comparable to across subject classification rates, indicating that subject state during the short scan significantly influences the estimated features and classification performance.

PMID: 30425631 [PubMed]

Abnormal intrinsic brain activities in stable patients with COPD: a resting-state functional MRI study.

Thu, 11/15/2018 - 11:20
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Abnormal intrinsic brain activities in stable patients with COPD: a resting-state functional MRI study.

Neuropsychiatr Dis Treat. 2018;14:2763-2772

Authors: Wang W, Li H, Peng D, Luo J, Xin H, Yu H, Yu J

Abstract
Objective: The majority of previous neuroimaging studies have reported both structural and functional changes in COPD, whereas the intrinsic low-frequency oscillations changes and the relationship between the abnormal brain regions and the clinical performances remain unknown. The present study was conducted with the aim of evaluating the intrinsic brain activity in COPD patients using the amplitude of low-frequency fluctuation (ALFF) method.
Methods: All participants, including 19 stable patients with COPD and 20 normal controls (NCs) matched in age, sex, and education, underwent resting-state functional MRI scans and performed cognitive function tests and respiratory functions tests. The local spontaneous brain activity was examined using the voxel-wise ALFF. Pearson's correlation analysis was used to investigate the relationships between the brain regions with altered ALFF signal values and the clinical features in COPD patients.
Results: Compared with the NCs, COPD patients showed significantly lower cognitive function scores. Also, lower ALFF areas in the cluster of the posterior cingulate cortex (PCC) and precuneus, as well as a higher ALFF area in the brainstem were also found in COPD patients. The mean ALFF values in the PCC, precuneus, and brainstem showed high sensitivity and specificity in operating characteristic curves analysis, which might have the ability to distinguish COPD from NCs. Meanwhile, the mean signal values of the lower ALFF cluster displayed significant positive correlations with FEV1/FVC proportion and significant negative correlation with PaCO2; the higher ALFF cluster showed significant positive correlation with FEV1 proportion in COPD.
Conclusion: According to the results of the present study, the COPD patients showed abnormal intrinsic brain activities in the precuneus, PCC, and brainstem, which might provide useful information to better understand the underlying pathophysiology of cognitive impairment.

PMID: 30425494 [PubMed]

Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.

Thu, 11/15/2018 - 11:20
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Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network.

Jpn J Radiol. 2018 Sep;36(9):566-574

Authors: Jiang D, Dou W, Vosters L, Xu X, Sun Y, Tan T

Abstract
PURPOSE: To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly.
MATERIALS AND METHODS: Multi-channel DnCNN (MCDnCNN) method with two training strategies was developed to denoise MR images with and without a specific noise level, respectively. To evaluate our method, three datasets from two public data sources of IXI dataset and Brainweb, including T1 weighted MR images acquired at 1.5 and 3 T as well as MR images simulated with a widely used MR simulator, were randomly selected and artificially added with different noise levels ranging from 1 to 15%. For comparison, four other state-of-the-art denoising methods were also tested using these datasets.
RESULTS: In terms of the highest peak-signal-to-noise-ratio and global of structure similarity index, our proposed MCDnCNN model for a specific noise level showed the most robust denoising performance in all three datasets. Next to that, our general noise-applicable model also performed better than the rest four methods in two datasets. Furthermore, our training model showed good general applicability.
CONCLUSION: Our proposed MCDnCNN model has been demonstrated to robustly denoise three dimensional MR images with Rician noise.

PMID: 29982919 [PubMed - indexed for MEDLINE]

Lithium Monotherapy Associated Longitudinal Effects on Resting State Brain Networks in Clinical Treatment of Bipolar Disorder.

Wed, 11/14/2018 - 16:40
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Lithium Monotherapy Associated Longitudinal Effects on Resting State Brain Networks in Clinical Treatment of Bipolar Disorder.

Bipolar Disord. 2018 Nov 12;:

Authors: Spielberg JM, Matyi MA, Karne H, Anand A

Abstract
OBJECTIVES: Lithium is one of the most effective and specific treatments for bipolar disorder (BP), but the neural mechanisms by which lithium impacts symptoms remain unclear. Past research has been limited by a reliance on cross-sectional designs, which does not allow for identification of within-person changes due to lithium and has not examined communication between brain regions (i.e., networks). In the present study, we prospectively investigated the lithium monotherapy associated effects in vivo on the brain connectome in medication-free BP patients. In particular, we examined the within-person impact of lithium treatment on connectome indices previously linked to mania and depression in bipolar disorder.
METHODS: Thirty-nine medication-free subjects - 26 BP (13 (hypo)manic and 13 depressed) and 13 closely matched health controls (HC) - were included. fMRI data was obtained at 3 timepoints: baseline, after 2 weeks, and after 8 weeks (total of 117 scans: 78 BP and 39 HC scans). BP subjects were clinically treated with lithium for 8 weeks while HC were scanned at the same time points but not treated. Graph theory metrics and repeated measures GLM were used to analyze lithium treatment associated effects.
RESULTS: Consistent with hypotheses, lithium treatment was associated with a normalizing effect on mania-related connectome indices. Furthermore, shifts in both mania- and depression-related connectome indices were proportional to symptom change. Finally, lithium treatment-associated impact on amygdala function differed depending on baseline mood.
CONCLUSIONS: Present findings provide deeper insight into the therapeutic neural mechanisms associated with lithium treatment. This article is protected by copyright. All rights reserved.

PMID: 30421491 [PubMed - as supplied by publisher]

Independent Component Analysis and Graph Theoretical Analysis in Patients with Narcolepsy.

Wed, 11/14/2018 - 16:40
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Independent Component Analysis and Graph Theoretical Analysis in Patients with Narcolepsy.

Neurosci Bull. 2018 Nov 13;:

Authors: Xiao F, Lu C, Zhao D, Zou Q, Xu L, Li J, Zhang J, Han F

Abstract
The present study was aimed to evaluate resting-state functional connectivity and topological properties of brain networks in narcolepsy patients compared with healthy controls. Resting-state fMRI was performed in 26 adult narcolepsy patients and 30 matched healthy controls. MRI data were first analyzed by group independent component analysis, then a graph theoretical method was applied to evaluate the topological properties in the whole brain. Small-world network parameters and nodal topological properties were measured. Altered topological properties in brain areas between groups were selected as region-of-interest seeds, then the functional connectivity among these seeds was compared between groups. Partial correlation analysis was performed to evaluate the relationship between the severity of sleepiness and functional connectivity or topological properties in the narcolepsy patients. Twenty-one independent components out of 48 were obtained. Compared with healthy controls, the narcolepsy patients exhibited significantly decreased functional connectivity within the executive and salience networks, along with increased functional connectivity in the bilateral frontal lobes within the executive network. There were no differences in small-world network properties between patients and controls. The altered brain areas in nodal topological properties between groups were mainly in the inferior frontal cortex, basal ganglia, anterior cingulate, sensory cortex, supplementary motor cortex, and visual cortex. In the partial correlation analysis, nodal topological properties in the putamen, anterior cingulate, and sensory cortex as well as functional connectivity between these regions were correlated with the severity of sleepiness (sleep latency, REM sleep latency, and Epworth sleepiness score) among narcolepsy patients. Altered connectivity within the executive and salience networks was found in narcolepsy patients. Functional connection changes between the left frontal cortex and left caudate nucleus may be one of the parameters describing the severity of narcolepsy. Changes in the nodal topological properties in the left putamen and left posterior cingulate, changes in functional connectivity between the left supplementary motor area and right occipital as well as in functional connectivity between the left anterior cingulate gyrus and bilateral postcentral gyrus can be considered as a specific indicator for evaluating the severity of narcolepsy.

PMID: 30421271 [PubMed - as supplied by publisher]

Functional hierarchy of oculomotor and visual motion subnetworks within the human cortical optokinetic system.

Wed, 11/14/2018 - 16:40
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Functional hierarchy of oculomotor and visual motion subnetworks within the human cortical optokinetic system.

Brain Struct Funct. 2018 Nov 13;:

Authors: Ruehl RM, Hoffstaedter F, Reid A, Eickhoff S, Zu Eulenburg P

Abstract
Optokinetic look nystagmus (look OKN) is known to engage cortical visual motion and oculomotor hubs. Their functional network hierarchy, however, and the role of the cingulate eye field (CEF) and the dorsolateral prefrontal cortex (DLPFC) in particular have not been investigated. We used look OKN in fMRI to identify all cortical visual motion and oculomotor hubs involved. Using these activations as seed regions, we employed hierarchical clustering in two differing resting state conditions from a separate public data set. Robust activations in the CEF highlight its functional role in OKN and involvement in higher order oculomotor control. Deactivation patterns indicate a decreased modulatory involvement of the DLPFC. The hierarchical clustering revealed a changeable organization of the eye fields, hMT, V3A, and V6 depending on the resting state condition, segregating executive from higher order visual subnetworks. Overall, hierarchical clustering seems to allow for a robust delineation of physiological cortical networks.

PMID: 30421037 [PubMed - as supplied by publisher]

Aberrances of Cortex Excitability and Connectivity Underlying Motor Deficit in Acute Stroke.

Wed, 11/14/2018 - 16:40
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Aberrances of Cortex Excitability and Connectivity Underlying Motor Deficit in Acute Stroke.

Neural Plast. 2018;2018:1318093

Authors: Du J, Hu J, Hu J, Xu Q, Zhang Q, Liu L, Ma M, Xu G, Zhang Y, Liu X, Lu G, Zhang Z, Yang F

Abstract
Purpose: This study was aimed at evaluating the motor cortical excitability and connectivity underlying the neural mechanism of motor deficit in acute stroke by the combination of functional magnetic resonance imaging (fMRI) and electrophysiological measures.
Methods: Twenty-five patients with motor deficit after acute ischemic stroke were involved. General linear model and dynamic causal model analyses were applied to fMRI data for detecting motor-related activation and effective connectivity of the motor cortices. Motor cortical excitability was determined as a resting motor threshold (RMT) of motor evoked potential detected by transcranial magnetic stimulation (TMS). fMRI results were correlated with cortical excitability and upper extremity Fugl-Meyer assessment scores, respectively.
Results: Greater fMRI activation likelihood and motor cortical excitability in the ipsilesional primary motor area (M1) region were associated with better motor performance. During hand movements, the inhibitory connectivity from the contralesional to the ipsilesional M1 was correlated with the degree of motor impairment. Furthermore, ipsilesional motor cortex excitability was correlated with an enhancement of promoting connectivity in ipsilesional M1 or a reduction of interhemispheric inhibition in contralesional M1.
Conclusions: The study suggested that a dysfunction of the ipsilesional M1 and abnormal interhemispheric interactions might underlie the motor disability in acute ischemic stroke. Modifying the excitability of the motor cortex and correcting the abnormal motor network connectivity associated with the motor deficit might be the therapeutic target in early neurorehabilitation for stroke patients.

PMID: 30420876 [PubMed - in process]

Disrupted Functional Connectivity of Cornu Ammonis Subregions in Amnestic Mild Cognitive Impairment: A Longitudinal Resting-State fMRI Study.

Wed, 11/14/2018 - 16:40
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Disrupted Functional Connectivity of Cornu Ammonis Subregions in Amnestic Mild Cognitive Impairment: A Longitudinal Resting-State fMRI Study.

Front Hum Neurosci. 2018;12:413

Authors: Li H, Jia X, Qi Z, Fan X, Ma T, Pang R, Ni H, Li CR, Lu J, Li K

Abstract
Background: The cornu ammonis (CA), as part of the hippocampal formation, represents a primary target region of neural degeneration in amnestic mild cognitive impairment (aMCI). Previous studies have revealed subtle structural deficits of the CA subregions (CA1-CA3, bilateral) in aMCI; however, it is not clear how the network function is impacted by aMCI. The present study examined longitudinal changes in resting state functional connectivity (FC) of each CA subregion and how these changes relate to neuropsychological profiles in aMCI. Methods: Twenty aMCI and 20 healthy control (HC) participants underwent longitudinal cognitive assessment and resting state functional MRI scans at baseline and 15 months afterward. Imaging data were processed with published routines in SPM8 and CONN software. Two-way analysis of covariance was performed with covariates of age, gender, education level, follow up interval, gray matter volume, mean FD, as well as global correlation (GCOR). Pearson's correlation was conducted to evaluate the relationship between the longitudinal changes in CA subregional FC and neuropsychological performance in aMCI subjects. Results: Resting state FC between the right CA1 and right middle temporal gyrus (MTG) as well as between the left CA2 and bilateral cuneal cortex (CC) were decreased in aMCI subjects as compared to HC. Longitudinal decrease in FC between the right CA1 and right MTG was correlated with reduced capacity of episodic memory in aMCI subjects. Conclusion: The current findings suggest functional alterations in the CA subregions. CA1 connectivity with the middle temporal cortex may represent an important neural marker of memory dysfunction in aMCI.

PMID: 30420801 [PubMed]

Psychotic symptoms influence the development of anterior cingulate BOLD variability in 22q11.2 deletion syndrome.

Wed, 11/14/2018 - 16:40
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Psychotic symptoms influence the development of anterior cingulate BOLD variability in 22q11.2 deletion syndrome.

Schizophr Res. 2018 03;193:319-328

Authors: Zöller D, Padula MC, Sandini C, Schneider M, Scariati E, Van De Ville D, Schaer M, Eliez S

Abstract
Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder associated with a broad phenotype of clinical, cognitive and psychiatric features. Due to the very high prevalence of schizophrenia (30-40%), the investigation of psychotic symptoms in the syndrome is promising to reveal biomarkers for the development of psychosis, also in the general population. Since schizophrenia is seen as a disorder of the dynamic interactions between brain networks, we here investigated brain dynamics, assessed by the variability of blood oxygenation level dependent (BOLD) signals, in patients with psychotic symptoms. We included 28 patients with 22q11DS presenting higher positive psychotic symptoms, 29 patients with lower positive psychotic symptoms and 69 healthy controls between 10 and 30years old. To overcome limitations of mass-univariate approaches, we employed multivariate analysis, namely partial least squares correlation, combined with proper statistical testing, to analyze resting-state BOLD signal variability and its age-relationship in patients with positive psychotic symptoms. Our results revealed a missing positive age-relationship in the dorsal anterior cingulate cortex (dACC) in patients with higher positive psychotic symptoms, leading to globally lower variability in the dACC in those patients. Patients without positive psychotic symptoms and healthy controls had the same developmental trajectory in this region. Alterations of brain structure and function in the ACC have been previously reported in 22q11DS and linked to psychotic symptoms. The present results support the implication of this region in the development of psychotic symptoms and suggest aberrant BOLD signal variability development as a potential biomarker for psychosis.

PMID: 28803847 [PubMed - indexed for MEDLINE]

Intrinsic functional clustering of anterior cingulate cortex in the common marmoset.

Tue, 11/13/2018 - 15:20

Intrinsic functional clustering of anterior cingulate cortex in the common marmoset.

Neuroimage. 2018 Nov 09;:

Authors: Schaeffer DJ, Gilbert KM, Ghahremani M, Gati JS, Menon RS, Everling S

Abstract
The common marmoset (Callithrix jacchus) has garnered recent attention as a potentially powerful preclinical model and complement to other canonical mammalian models of human brain diseases (e.g., rodents and Old World non-human primates). With a granular frontal cortex and the advent of transgenic modifications, marmosets are well positioned to serve as neuropsychiatric models of prefrontal cortex dysfunction. A critical step in the development of marmosets for such models is to characterize functional network topologies of frontal cortex in healthy, normally functioning marmosets. Here, we sought to characterize the intrinsic functional connectivity of anterior cingulate cortex (ACC) in marmosets using resting state functional magnetic resonance imaging (RS-fMRI). Seven lightly anesthetized marmosets were imaged at ultra-high field (9.4 T) and hierarchical clustering was employed to extract functional clusters of ACC from the RS-fMRI data. The data demonstrated three functionally discrete clusters within ACC. The functional connectivity between these clusters with the rest of the brain was also found to be distinct, supporting the hypothesis that ACC subregions serve different circuits and their concomitant functions. In a separate seed-based analysis, we also sought to delineate finer-grained patterns of ACC connectivity between marmoset primary motor area 4 ab and putative eye movement areas (8aD and 8 aV). This analysis demonstrated distinct patterns of ACC functional connectivity between motor and eye movement regions that overlapped well with what has been shown in humans and macaques. Overall, these results demonstrate that marmosets have a network topology of ACC that resembles that of Old World primates, giving further credence to the use of marmosets for preclinical studies of intractable human brain diseases.

PMID: 30419289 [PubMed - as supplied by publisher]

Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data.

Tue, 11/13/2018 - 15:20

Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data.

PLoS One. 2018;13(11):e0207385

Authors: Rasero J, Aerts H, Ontivero Ortega M, Cortes JM, Stramaglia S, Marinazzo D

Abstract
Intrinsic Connectivity Networks, patterns of correlated activity emerging from "resting-state" BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

PMID: 30419063 [PubMed - in process]

Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso.

Tue, 11/13/2018 - 15:20

Capturing Dynamic Connectivity from Resting State fMRI using Time-Varying Graphical Lasso.

IEEE Trans Biomed Eng. 2018 Nov 09;:

Authors: Cai B, Zhang G, Zhang A, Stephen JM, Wilson TW, Calhoun VD, Wang Y

Abstract
Functional connectivity (FC) within the human brain evaluated through functional magnetic resonance imaging (fMRI) data has attracted increasing attention and has been employed to study the development of the brain or health conditions of the brain. Many different approaches have been proposed to estimate FC from fMRI data, whereas many of them rely on an implicit assumption that functional connectivity should be static throughout the fMRI scan session. Recently, the fMRI community has realized the limitation of assuming static connectivity and dynamic approaches are more prominent in the resting state fMRI (rs-fMRI) analysis. The sliding window technique has been widely used in many studies to capture network dynamics, but has a number of limitations. In this study, we apply a time-varying graphical lasso (TVGL) model, an extension from the traditional graphical lasso, to address the challenge, which can greatly improve the estimation of FC. The performance of estimating dynamic FC is evaluated with the TVGL through both simulated experiments and real rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) project. Improved performance is achieved over the sliding window technique. In particular, group differences and transition behaviours between young adults and children are investigated using the estimated dynamic connectivity networks, which help us to better unveil the mechanisms underlying the evolution of the brain over time.

PMID: 30418876 [PubMed - as supplied by publisher]

Sensitive Period for Cognitive Repurposing of Human Visual Cortex.

Tue, 11/13/2018 - 15:20

Sensitive Period for Cognitive Repurposing of Human Visual Cortex.

Cereb Cortex. 2018 Nov 12;:

Authors: Kanjlia S, Pant R, Bedny M

Abstract
Studies of sensory loss are a model for understanding the functional flexibility of human cortex. In congenital blindness, subsets of visual cortex are recruited during higher-cognitive tasks, such as language and math tasks. Is such dramatic functional repurposing possible throughout the lifespan or restricted to sensitive periods in development? We compared visual cortex function in individuals who lost their vision as adults (after age 17) to congenitally blind and sighted blindfolded adults. Participants took part in resting-state and task-based fMRI scans during which they solved math equations of varying difficulty and judged the meanings of sentences. Blindness at any age caused "visual" cortices to synchronize with specific frontoparietal networks at rest. However, in task-based data, visual cortices showed regional specialization for math and language and load-dependent activity only in congenital blindness. Thus, despite the presence of long-range functional connectivity, cognitive repurposing of human cortex is limited by sensitive periods.

PMID: 30418533 [PubMed - as supplied by publisher]

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

Tue, 11/13/2018 - 15:20

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

Magn Reson Med. 2018 Nov 12;:

Authors: Chen VC, Lin TY, Yeh DC, Chai JW, Weng JC

Abstract
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response to chemo drugs, it is termed chemo-brain. In this study, we aimed to construct machine-learning models to detect the subtle alternations of the brain in postchemotherapy BC patients.
METHODS: Nineteen BC patients undergoing chemotherapy and 20 healthy controls (HCs) were recruited for this study. Both groups underwent resting-state functional MRI and generalized q-sampling imaging (GQI).
RESULTS: Logistic regression (LR) with GQI indices in standardized voxel-wise analysis, LR with mean regional homogeneity in regional summation analysis, decision tree classifier (CART) with generalized fractional anisotropy in voxel-wise analysis, and XGBoost (XGB) with normalized quantitative anisotropy had formidable performances in classifying subjects into a chemo-brain group or an HC group. Classifying the brain MRIs of HC and postchemotherapy patients by conducting leave-one-out cross-validation resulted in the highest accuracy of 84%, which was attained by LR, CART, and XGB with multiple feature sets.
CONCLUSIONS: In our study, we constructed the machine-learning models that were able to identify chemo-brains from normal brains. We are hopeful that these results will be helpful in clinically tracking chemo-brains in the future.

PMID: 30417933 [PubMed - as supplied by publisher]

Altered small-world, functional brain networks in patients with lower back pain.

Tue, 11/13/2018 - 15:20

Altered small-world, functional brain networks in patients with lower back pain.

Sci China Life Sci. 2018 Nov 02;:

Authors: Liu J, Zhang F, Liu X, Zhuo Z, Wei J, Du M, Chan Q, Wang X, Wang D

Abstract
In this study, we aimed to investigate the functional network changes that occur in patients with lower back pain (LBP). We also investigated the link between LBP and the small-world properties of functional networks within the brain. Functional MRI (fMRI) was performed on 20 individuals with LBP and 17 age and gender-matched normal controls during the resting state. The severity of the pain in the individuals with LBP ranged from 5 to 8 on a 0-10 scale, with 0 indicating no pain. Network-based statistics were performed to investigate the differences between the brain networks of individuals with LBP and those of normal controls. Several small-world parameters of brain networks were calculated, including the clustering coefficient, characteristic path length, local efficiency, and global efficiency. These criteria reflect the overall network efficiency. The brain networks in the individuals with LBP due to herniation of a lumbar disc demonstrated a significantly longer characteristic path length as well as a lower clustering coefficient, global efficiency, and local efficiency compared to those in control subjects. We found that LBP patients tended to have unstable and inefficient brain networks when compared with healthy controls. In addition, LBP individuals showed significantly decreased functional connectivity in the anterior cingulate cortex, middle cingulate cortex, post cingulate cortex, inferior frontal gyrus, middle temporal gyrus, occipital gyrus, postcentral gyrus, precentral gyrus, supplementary motor area, thalamus, fusiform, caudate, and cerebellum. We believe that these regions may be involved in the pathophysiology of lower back pain.

PMID: 30417246 [PubMed - as supplied by publisher]

Grading of Frequency Spectral Centroid Across Resting-State Networks.

Tue, 11/13/2018 - 15:20

Grading of Frequency Spectral Centroid Across Resting-State Networks.

Front Hum Neurosci. 2018;12:436

Authors: Ries A, Chang C, Glim S, Meng C, Sorg C, Wohlschläger A

Abstract
Ongoing, slowly fluctuating brain activity is organized in resting-state networks (RSNs) of spatially coherent fluctuations. Beyond spatial coherence, RSN activity is governed in a frequency-specific manner. The more detailed architecture of frequency spectra across RSNs is, however, poorly understood. Here we propose a novel measure-the Spectral Centroid (SC)-which represents the center of gravity of the full power spectrum of RSN signal fluctuations. We examine whether spectral underpinnings of network fluctuations are distinct across RSNs. We hypothesize that spectral content differs across networks in a consistent way, thus, the aggregate representation-SC-systematically differs across RSNs. We therefore test for a significant grading (i.e., ordering) of SC across RSNs in healthy subjects. Moreover, we hypothesize that such grading is biologically significant by demonstrating its RSN-specific change through brain disease, namely major depressive disorder. Our results yield a highly organized grading of SC across RSNs in 820 healthy subjects. This ordering was largely replicated in an independent dataset of 25 healthy subjects, pointing toward the validity and consistency of found SC grading across RSNs. Furthermore, we demonstrated the biological relevance of SC grading, as the SC of the salience network-a RSN well known to be implicated in depression-was specifically increased in patients compared to healthy controls. In summary, results provide evidence for a distinct grading of spectra across RSNs, which is sensitive to major depression.

PMID: 30416439 [PubMed]

The regulation of positive and negative emotions through instructed causal attributions in lifetime depression - A functional magnetic resonance imaging study.

Mon, 11/12/2018 - 14:00

The regulation of positive and negative emotions through instructed causal attributions in lifetime depression - A functional magnetic resonance imaging study.

Neuroimage Clin. 2018 Oct 25;20:1233-1245

Authors: Loeffler LAK, Radke S, Habel U, Ciric R, Satterthwaite TD, Schneider F, Derntl B

Abstract
Adequate emotional control is essential for mental health. Deficiencies in emotion regulation are evident in many psychiatric disorders, including depression. Patients with depression show, for instance, disrupted neural emotion regulation in cognitive regulation regions such as lateral and medial prefrontal cortices. Since depressed individuals tend to attribute positive events to external circumstances and negative events to themselves, modifying this non-self-serving attributional style may represent a promising regulation strategy. Spontaneous causal attributions are generally processed in medial brain structures, particularly the precuneus. However, so far no study has investigated neural correlates of instructed causal attributions (e.g. instructing a person to intentionally relate positive events to the self) and their potential to regulate emotions. The current study therefore aimed to examine how instructed causal attributions of positive and negative events affect the emotional experience of depressed individuals as well as its neural bases. For this purpose pictures of sad and happy faces were presented to 26 patients with a lifetime major depression (MDD) and 26 healthy controls (HC) during fMRI. Participants should respond naturally ("view") or imagine that the person on the picture was sad/happy because of them ("internal attribution") or because something else happened ("external attribution"). Trait attributional style and depressive symptoms were assessed with questionnaires to examine potential influential factors on emotion regulation ability. Results revealed that patients compared to controls show a non-self-serving trait attributional style (i.e. more external attributions of positive events and more internal attributions of negative events). Intriguingly, when instructed to apply specific causal attributions during the emotion regulation task, patients and controls were similarly able to regulate positive and negative emotions. Regulating emotions through instructed attributions (internal/external attribution>view) generally engaged the precuneus, which was correlated with patients' trait attributional style (i.e. more precuneus activation during external>view was linked to a general tendency to relate positive events to external sources). Up-regulating happiness through internal (compared to external) attributions recruited the parahippocampal gyrus only in controls. The down-regulation of sadness (external>internal attribution), in contrast, engaged the superior frontal gyrus only in patients. Superior frontal gyrus activation thereby correlated with depression severity, which implies a greater need of cognitive resources for a successful regulation in more severely depressed. Patients and controls did not differ in activation in brain regions related to cognitive emotion regulation or attribution. However, results point to a disturbed processing of positive emotions in depression. Interestingly, increased precuneus resting-state connectivity with emotion regulation brain regions (inferior parietal lobule, middle frontal gyrus) was linked to healthier attributions (i.e. external attributions of negative events) in patients and controls. Adequate neural communication between these regions therefore seem to facilitate an adaptive trait attributional style. Findings of this study emphasize that despite patients' dysfunctional trait attributional style, explicitly applying causal attributions effectively regulates emotions. Future research should examine the efficacy of instructed attributions in reducing negative affect and anhedonia in depressed patients, for instance by means of attribution trainings during psychotherapy.

PMID: 30414987 [PubMed - as supplied by publisher]

Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants.

Mon, 11/12/2018 - 14:00

Optimising neonatal fMRI data analysis: Design and validation of an extended dHCP preprocessing pipeline to characterise noxious-evoked brain activity in infants.

Neuroimage. 2018 Nov 08;:

Authors: Baxter L, Fitzgibbon S, Moultrie F, Goksan S, Jenkinson M, Smith S, Andersson J, Duff E, Slater R

Abstract
The infant brain is unlike the adult brain, with considerable differences in morphological, neurodynamic, and haemodynamic features. As the majority of current MRI analysis tools were designed for use in adults, a primary objective of the Developing Human Connectome Project (dHCP) is to develop optimised methodological pipelines for the analysis of neonatal structural, resting state, and diffusion MRI data. Here, in an independent neonatal dataset we have extended and optimised the dHCP fMRI preprocessing pipeline for the analysis of stimulus-response fMRI data. We describe and validate this extended dHCP fMRI preprocessing pipeline to analyse changes in brain activity evoked following an acute noxious stimulus applied to the infant's foot. We compare the results obtained from this extended dHCP pipeline to results obtained from a typical FSL FEAT-based analysis pipeline, evaluating the pipelines' outputs using a wide range of tests. We demonstrate that a substantial increase in spatial specificity and sensitivity to signal can be attained with a bespoke neonatal preprocessing pipeline through optimised motion and distortion correction, ICA-based denoising, and haemodynamic modelling. The improved sensitivity and specificity, made possible with this extended dHCP pipeline, will be paramount in making further progress in our understanding of the development of sensory processing in the infant brain.

PMID: 30414984 [PubMed - as supplied by publisher]

Altered resting-state dynamics in autism spectrum disorder: Causal to the social impairment?

Sun, 11/11/2018 - 13:00
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Altered resting-state dynamics in autism spectrum disorder: Causal to the social impairment?

Prog Neuropsychopharmacol Biol Psychiatry. 2018 Nov 07;:

Authors: Gotts SJ, Ramot M, Jasmin K, Martin A

Abstract
Autism spectrum disorder (ASD) is characterized by profound impairments in social abilities and by restricted interests and repetitive behaviors. Much work in the past decade has been dedicated to understanding the brain-bases of ASD, and in the context of resting-state functional connectivity fMRI in high-functioning adolescents and adults, the field has established a set of reliable findings: decreased cortico-cortical interactions among brain regions thought to be engaged in social processing, along with a simultaneous increase in thalamo-cortical and striato-cortical interactions. However, few studies have attempted to manipulate these altered patterns, leading to the question of whether such patterns are actually causally involved in producing the corresponding behavioral impairments. We discuss a few such recent attempts in the domains of fMRI neurofeedback and overt social interaction during scanning, and we conclude that the evidence of causal involvement is somewhat mixed. We highlight the potential role of the thalamus and striatum in ASD and emphasize the need for studies that directly compare scanning during multiple cognitive states in addition to the resting-state.

PMID: 30414457 [PubMed - as supplied by publisher]

Default Mode Network Lateralization and Memory in Healthy Aging and Alzheimer's Disease.

Sat, 11/10/2018 - 11:40

Default Mode Network Lateralization and Memory in Healthy Aging and Alzheimer's Disease.

J Alzheimers Dis. 2018 Nov 03;:

Authors: Banks SJ, Zhuang X, Bayram E, Bird C, Cordes D, Caldwell JZK, Cummings JL, Alzheimer’s Disease Neuroimaging Initiative

Abstract
Lateralization of default mode network (DMN) functioning has been shown to change with age. Similarly, lateralization of frontal lobe function has been shown to decline in age. The impact of amyloid pathology and the progression of Alzheimer's disease (AD) on resting state lateralization has not been investigated. Due to the preferential involvement of the left hemisphere in verbal tasks, there may be a benefit to higher levels of left-lateralization in the performance of verbal memory tasks. Here we compared functional lateralization of the anterior and posterior DMN between four groups of participants: amyloid negative (Aβ-) and amyloid positive (Aβ+) groups with normal cognition (NC), and Aβ+ groups with mild cognitive impairment (Aβ+MCI) or dementia (Aβ+AD). Differences were evident between groups in posterior DMN; the Aβ-NC group was more left-lateralized than both cognitively impaired Aβ+ groups. There was no difference in anterior DMN. No differences in overall network connectivity between groups were observed, suggesting that the functional lateralization finding is not secondary to general changes in connectivity. Left-lateralization of both networks was associated with better verbal recall performance. Older subjects, overall, had less left functional lateralization of the anterior DMN.

PMID: 30412488 [PubMed - as supplied by publisher]

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