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Resting-state fMRI reveals network disintegration during delirium.

Fri, 07/13/2018 - 16:20
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Resting-state fMRI reveals network disintegration during delirium.

Neuroimage Clin. 2018;20:35-41

Authors: van Montfort SJT, van Dellen E, van den Bosch AMR, Otte WM, Schutte MJL, Choi SH, Chung TS, Kyeong S, Slooter AJC, Kim JJ

Abstract
Delirium is characterized by inattention and other cognitive deficits, symptoms that have been associated with disturbed interactions between remote brain regions. Recent EEG studies confirm that disturbed global network topology may underlie the syndrome, but lack an anatomical basis. The aim of this study was to increase our understanding of the global organization of functional connectivity during delirium and to localize possible alterations. Resting-state fMRI data from 44 subjects were analyzed, and motion-free data were available in nine delirious patients, seven post delirium patients and thirteen non-delirious clinical controls. We focused on the functional network backbones using the minimum spanning tree, which allows unbiased network comparisons. During delirium a longer diameter (mean (M) = 0.30, standard deviation (SD) = 0.05, P = .024) and a lower leaf fraction (M = 0.32, SD = 0.03, P = .027) was found compared to the control group (M = 0.28, SD = 0.04 respectively M = 0.35, SD = 0.03), suggesting reduced functional network integration and efficiency. Delirium duration was strongly related to loss of network hierarchy (rho = -0.92, P = .001). Connectivity strength was decreased in the post delirium group (M = 0.16, SD = 0.01) compared to the delirium group (M = 0.17, SD = 0.03, P = .024) and the control group (M = 0.19, SD = 0.02, P = .001). Permutation tests revealed a decreased degree of the right posterior cingulate cortex during delirium and complex regional alterations after delirium. These findings indicate that delirium reflects disintegration of functional interactions between remote brain areas and suggest long-term impact after the syndrome resolves.

PMID: 29998059 [PubMed - in process]

Do Patients Thought to Lack Consciousness Retain the Capacity for Internal as Well as External Awareness?

Fri, 07/13/2018 - 16:20
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Do Patients Thought to Lack Consciousness Retain the Capacity for Internal as Well as External Awareness?

Front Neurol. 2018;9:492

Authors: Haugg A, Cusack R, Gonzalez-Lara LE, Sorger B, Owen AM, Naci L

Abstract
It is well established that some patients, who are deemed to have disorders of consciousness, remain entirely behaviorally non-responsive and are diagnosed as being in a vegetative state, yet can nevertheless demonstrate covert awareness of their external environment by modulating their brain activity, a phenomenon known as cognitive-motor dissociation. However, the extent to which these patients retain internal awareness remains unknown. To investigate the potential for internal and external awareness in patients with chronic disorders of consciousness (DoC), we asked whether the pattern of juxtaposition between the functional time-courses of the default mode (DMN) and fronto-parietal networks, shown in healthy individuals to mediate the naturally occurring dominance switching between internal and external aspects of consciousness, was present in these patients. We used a highly engaging movie by Alfred Hitchcock to drive the recruitment of the fronto-parietal networks, including the dorsal attention (DAN) and executive control (ECN) networks, and their maximal juxtaposition to the DMN in response to the complex stimulus, relative to rest and a scrambled, meaningless movie baseline condition. We tested a control group of healthy participants (N = 13/12) and two groups of patients with disorders of consciousness, one comprised of patients who demonstrated independent, neuroimaging-based evidence of covert external awareness (N = 8), and the other of those who did not (N = 8). Similarly to the healthy controls, only the group of patients with overt and, critically, covert external awareness showed significantly heightened differentiation between the DMN and the DAN in response to movie viewing relative to their resting state time-courses, which was driven by the movie's narrative. This result suggested the presence of functional integrity in the DMN and fronto-parietal networks and their relationship to one another in patients with covert external awareness. Similar to the effect in healthy controls, these networks became more strongly juxtaposed to one another in response to movie viewing relative to the baseline conditions, suggesting the potential for internal and external awareness during complex stimulus processing. Furthermore, our results suggest that naturalistic paradigms can dissociate between groups of DoC patients with and without covert awareness based on the functional integrity of brain networks.

PMID: 29997565 [PubMed]

[Resting-state functional MRI studies of amyotrophic lateral sclerosis patients with various levels of cognitive impairment].

Fri, 07/13/2018 - 16:20
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[Resting-state functional MRI studies of amyotrophic lateral sclerosis patients with various levels of cognitive impairment].

Zhonghua Yi Xue Za Zhi. 2018 Jul 03;98(25):2002-2006

Authors: Shen DC, Hou B, Cui B, Li XL, Peng P, Tai HF, Zhang K, Liu SW, Fu HH, Liu MS, Feng F, Cui LY

Abstract
Objective: To characterize the brain functional changes of amyotrophic lateral sclerosis (ALS) patients with various levels of cognitive impairment as measured by resting-state functional MRI (RS-fMRI). Methods: From September 2013 to March 2017, a total of 55 patients diagnosed with ALS in Peking Union Medical College Hospital and 20 healthy controls (HCs) were included in this study, and all participants underwent neuropsychological assessments and diffusion tensor imaging scans. According to their cognitive performance, ALS patients were further subclassified into ALS with normal cognition (ALS-Cn, n=27), those with cognitive impairment (ALS-Ci, n=17) and ALS-FTD (n=11). Comparisons of fractional amplitude of low frequency fluctuation (fALFF) value and regional homogeneity (ReHo) value were conducted among the 4 subgroups. Results: The fALFF showed significant differences in bilateral frontal lobe, left temporal lobe and cingulate gyrus, (P<0.001, uncorrected) and the ReHo showed significant differences in left frontal lobe, right temporal lobe and left cingulate gyrus (P<0.001, FDR corrected). The differences mainly stemmed from that patients with ALS-FTD showed decreased fALFF and ReHo in these areas when compared to the other three groups, especially in relation to HCs, mainly locating in left prefrontal lobe and anterior cingulate cortex. The whole-brain comparisons of fALFF and ReHo between ALS-Ci, ALS-Cn and HCs revealed no significant difference (P<0.001, uncorrected). Conclusion: Hypoactivities are detected in extramotor areas in patients with ALS-FTD. RS-fMRI is helpful in investigating the pathophysiologic mechanism of cognitive impairment in ALS.

PMID: 29996600 [PubMed - in process]

Modes of Resting Functional Brain Organization Differentiate Suicidal Thoughts and Actions: A Preliminary Study.

Thu, 07/12/2018 - 15:20
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Modes of Resting Functional Brain Organization Differentiate Suicidal Thoughts and Actions: A Preliminary Study.

J Clin Psychiatry. 2018 Jul 10;79(4):

Authors: Cáceda R, Bush K, James GA, Stowe ZN, Kilts CD

Abstract
OBJECTIVE: A major target in suicide prevention is interrupting the progression from suicidal thoughts to action. Use of complex algorithms in large samples has identified individuals at very high risk for suicide. We tested the ability of data-driven pattern classification analysis of brain functional connectivity to differentiate recent suicide attempters from patients with suicidal ideation.
METHODS: We performed a cross-sectional study using resting-state functional magnetic resonance imaging in depressed inpatients and outpatients of both sexes recruited from a university hospital between March 2014 and June 2016: recent suicide Attempters within 3 days of an attempt (n = 10), Suicidal Ideators (n = 9), Depressed Non-Suicidal Controls (n = 17), and Healthy Controls (n = 18). All depressed patients fulfilled DSM-IV-TR criteria for major depressive episode and either major depressive disorder, bipolar disorder, or depression not otherwise specified. A subset of suicide attempters (n = 7) were rescanned within 7 days. We used a support vector machine data-driven neural pattern classification analysis of resting-state functional connectivity to characterize recent suicide attempters and then tested the classifier's specificity.
RESULTS: A binary classifier trained to discriminate patterns of resting-state functional connectivity robustly differentiated Suicide Attempters from Suicidal Ideators (mean accuracy = 0.788, signed rank test: P = .002; null hypothesis: area under the curve = 0.5), with distinct functional connectivity between the default mode and the limbic, salience, and central executive networks. The classifier did not discriminate stable Suicide Attempters from Suicidal Ideators (mean accuracy = 0.58, P = .33) or presence from absence of lifetime suicidal behavior (mean accuracy = 0.543, P = .348) and was not improved by modeling clinical variables (mean accuracy = 0.736, P = .002).
CONCLUSIONS: Measures of intrinsic brain organization may have practical value as objective measures of suicide risk and its underlying mechanisms. Further incorporation of serum or cognitive markers and use of a prospective study design are needed to validate and refine the clinical relevance of this candidate biomarker of suicide risk.

PMID: 29995357 [PubMed - in process]

Dynamic Pain Connectome Functional Connectivity and Oscillations Reflect Multiple Sclerosis Pain.

Thu, 07/12/2018 - 15:20
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Dynamic Pain Connectome Functional Connectivity and Oscillations Reflect Multiple Sclerosis Pain.

Pain. 2018 Jul 02;:

Authors: Bosma RL, Kim JA, Cheng JC, Rogachov A, Hemington KS, Osborne NR, Oh J, Davis KD

Abstract
Pain is a prevalent and debilitating symptom of multiple sclerosis (MS), yet the mechanisms underlying this pain are unknown. Previous studies have found that the functional relationships between the salience network, specifically the right temporoparietal junction a salience node (SN), and other components of the dynamic pain connectome (default mode (DMN), ascending and descending pathways) are abnormal in many chronic pain conditions. Here we use resting state fMRI and measures of static and dynamic functional connectivity (sFC, dFC), and regional BOLD variability to test the hypothesis that MS patients have abnormal DMN-SN cross-network sFC, SN-ascending and SN-descending pathways dFC, and disrupted BOLD variability in the dynamic pain connectome that relate to pain inference and neuropathic pain. Thirty-one MS patients and 31 controls completed questionnaires to characterize pain and pain interference, and underwent a resting state fMRI scan from which measures of sFC, dFC, and BOLD variability were compared. We found that 1) ∼50% of our patients had neuropathic pain features, 2) abnormalities in SN-DMN sFC were driven by the mixed-neuropathic subgroup, 3) in patients with mixed-neuropathic pain, dFC measures showed that there was a striking change in how the SN was engaged with the ascending nociceptive pathway and descending modulation pathway, 4) BOLD variability was increased in the DMN, 5) the degrees of sFC and BOLD variability abnormalities were related to pain interference. We propose that abnormal SN-DMN cross-network FC and temporal dynamics within and between regions of the dynamic pain connectome reflect MS pain features.

PMID: 29994989 [PubMed - as supplied by publisher]

Identifying Resting-state Multi-Frequency Biomarkers via Tree-Guided Group Sparse Learning for Schizophrenia Classification.

Thu, 07/12/2018 - 15:20
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Identifying Resting-state Multi-Frequency Biomarkers via Tree-Guided Group Sparse Learning for Schizophrenia Classification.

IEEE J Biomed Health Inform. 2018 Jan 23;:

Authors: Huang J, Zhu Q, Hao X, Shi X, Gao S, Xu X, Zhang D

Abstract
The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional magnetic resonance imaging based schizophrenia diagnosis. However, previous studies usually measure the fALFF with specific bands from 0.01-0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. As we konow, fALFF data is intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multi-frequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multi-frequency bands (i.e., slow-5:0.01-0.027 Hz, slow-4:0.027-0.073 Hz, slow-3:0.073-0.198 Hz and slow-2:0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches, and select those significant patches related to schizophrenia using random forest-based importancescore. Moreover, we use tree-structured sparse learning method for feature selection with above patches spatial constraint. Finally, considering biomarkers from multi-frequency bands can reflect complementary information among multiple frequency bands, we adopt the multi-kernel learning (MKL) method to combine features of multi-frequency bands for classification. Our experimental results show that these biomarkers from multi-frequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating the multi-frequency bands analysis can better account for classification of schizophrenia.

PMID: 29994431 [PubMed - as supplied by publisher]

Tensor Based Temporal and Multi-layer Community Detection for Studying Brain Dynamics During Resting State fMRI.

Thu, 07/12/2018 - 15:20
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Tensor Based Temporal and Multi-layer Community Detection for Studying Brain Dynamics During Resting State fMRI.

IEEE Trans Biomed Eng. 2018 Jul 09;:

Authors: Al-Sharoa E, Al-Khassaweneh M, Aviyente S

Abstract
OBJECTIVE: In recent years, resting state fMRI has been widely utilized to understand the functional organization of the brain for healthy and disease populations. Recent studies show that functional connectivity during resting state is a dynamic process. Studying this temporal dynamics provides a better understanding of the human brain compared to static network analysis.
METHODS: In this paper, a new tensor based temporal and multi-layer community detection algorithm is introduced to identify and track the brain network community structure across time and subjects. The framework studies the temporal evolution of communities in fMRI connectivity networks constructed across different regions of interests (ROIs). The proposed approach relies on determining the subspace that best describes the community structure using Tucker decomposition of the tensor.
RESULTS: The brain dynamics are summarized into a set of functional connectivity states that are repeated over time and subjects. The dynamic behavior of the brain is evaluated in terms of consistency of different subnetworks during resting state. The results illustrate that some of the networks, such as the default mode, cognitive control and bilateral limbic networks, have low consistency over time indicating their dynamic behavior.
CONCLUSION: The results indicate that the functional connectivity of the brain is dynamic and the detected community structure experiences smooth temporal evolution.
SIGNIFICANCE: The work in this paper provides evidence for temporal brain dynamics during resting state through dynamic multi-layer community detection which enables us to better understand the behavior of different subnetworks.

PMID: 29993516 [PubMed - as supplied by publisher]

Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments?

Thu, 07/12/2018 - 15:20
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Altered functional network connectivity in preterm infants: antecedents of cognitive and motor impairments?

Brain Struct Funct. 2018 Jul 10;:

Authors: Gozdas E, Parikh NA, Merhar SL, Tkach JA, He L, Holland SK

Abstract
Very preterm infants (≤ 31 weeks gestational age) are at high risk for brain injury and delayed development. Applying functional connectivity and graph theory methods to resting state MRI data (fcMRI), we tested the hypothesis that preterm infants would demonstrate alterations in connectivity measures both globally and in specific networks related to motor, language and cognitive function, even when there is no anatomical imaging evidence of injury. Fifty-one healthy full-term controls and 24 very preterm infants without significant neonatal brain injury, were evaluated at term-equivalent age with fcMRI. Preterm subjects showed lower functional connectivity from regions associated with motor, cognitive, language and executive function, than term controls. Examining brain networks using graph theory measures of functional connectivity, very preterm infants also exhibited lower rich-club coefficient and assortativity but higher small-worldness and no significant difference in modularity when compared to term infants. The findings provide evidence that functional connectivity exhibits deficits soon after birth in very preterm infants in key brain networks responsible for motor, language and executive functions, even in the absence of anatomical lesions. These functional network measures could serve as prognostic biomarkers for later developmental disabilities and guide decisions about early interventions.

PMID: 29992470 [PubMed - as supplied by publisher]

Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.

Thu, 07/12/2018 - 15:20
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Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.

Brain Imaging Behav. 2018 Jul 11;:

Authors: Saccà V, Sarica A, Novellino F, Barone S, Tallarico T, Filippelli E, Granata A, Chiriaco C, Bruno Bossio R, Valentino P, Quattrone A

Abstract
Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis.

PMID: 29992392 [PubMed - as supplied by publisher]

Resting cerebral blood flow alterations specific to the comitant exophoria patients revealed by arterial spin labeling perfusion magnetic resonance imaging.

Thu, 07/12/2018 - 15:20
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Resting cerebral blood flow alterations specific to the comitant exophoria patients revealed by arterial spin labeling perfusion magnetic resonance imaging.

Microvasc Res. 2018 Jul 02;120:67-73

Authors: Huang X, Zhou S, Su T, Ye L, Zhu PW, Shi WQ, Min YL, Yuan Q, Yang QC, Zhou FQ, Shao Y

Abstract
PURPOSE: It has been shown in many previous studies that there were significant changes of the brain anatomy and function in strabismus. However, the significance of the alterations of resting cerebral blood flow (CBF) in comitant exophoria (CE) remains obscure. Arterial spin labeling (ASL) MRI, which is a noninvasive method, could be applied to detect the cerebral blood flow quantitatively. Our study aimed to compare the resting CBF between the comitant exophoria and health controls using pseudo-continuous arterial spin labeling (pCASL) perfusion MRI method.
METHODS: 32 patients (25 males and 7 females) with CE (study group), and 32 (25 males and 7 females) healthy individuals with matched age and sex status (control group) underwent a whole-brain pCASL magnetic resonance (MR) examination at the resting state. The resting CBF were voxel-wise compared between the two groups using an analysis of variance designed in a statistical parametric mapping program. The CE patients were distinguishable from the healthy controls (HCs) by receiver operating characteristic (ROC) curves.
RESULTS: Compared with the control group, the CE group showed significantly increased resting CBF values in the right parahippocampal regions, bilateral medial frontal gyrus/anterior cingulate cortex, left inferior frontal gyrus, right inferior frontal gyrus, left superior frontal gyrus, bilateral medial cingulate cortex, right middle frontal gyrus, and right paracentral lobule.
CONCLUSION: Comitant exophoria showed increased resting CBF in eye movement-related brain areas including supplementary eye field, cingulate eye field and frontal eye field, which could be an explanation of the brain function compensation for the ocular motility disorders in the CE patients.

PMID: 29991447 [PubMed - as supplied by publisher]

Brain network topology influences response to intensive comprehensive aphasia treatment.

Thu, 07/12/2018 - 15:20
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Brain network topology influences response to intensive comprehensive aphasia treatment.

NeuroRehabilitation. 2018 Jul 04;:

Authors: Baliki MN, Babbitt EM, Cherney LR

Abstract
BACKGROUND: Recent imaging studies indicate that aphasia is associated with large-scale reorganization of brain networks. Today, neuroimaging studies show that various brain connectivity properties, as measured by resting state fMRI, can partially explain different behavioral symptoms in and across various patient groups. Despite these observations, the neural networks underlying the progress and recovery of aphasia following intensive treatment remains relatively obscure.
OBJECTIVE: To examine the role of brain network properties in determining recovery of aphasia following intensive therapy in stroke patients.
METHODS: We studied eight patients with left hemispheric lesions who completed an intensive comprehensive aphasia program (ICAP). Language and cognition were assessed before and after four weeks of intensive treatment. In addition, all patients underwent resting state fMRI prior to and after treatment. We used graph theory analysis to evaluate relationships of baseline brain network properties, such as efficiency, modularity, and connectivity to clinical improvements.
RESULTS: We found global properties such as efficiency and interhemispheric connectivity could partially explain recovery. More importantly, we identified two unique brain networks that are significantly associated with improvement in language and attention related behavior.
CONCLUSIONS: These results suggest baseline brain functional properties play a key role in determining responsiveness of patients with aphasia to intensive comprehensive aphasia treatment. Furthermore, these results indicate that brain mechanisms underlying language comprehension and processes are different from those involved in spatial attention.

PMID: 29991147 [PubMed - as supplied by publisher]

Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.

Wed, 07/11/2018 - 14:00
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Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.

Neuroimage. 2018 Jul 07;:

Authors: Zhang H, Shen D, Lin W

Abstract
Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project, it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.

PMID: 29990581 [PubMed - as supplied by publisher]

Correction: Reduced Topological Efficiency in Cortical-Basal Ganglia Motor Network of Parkinson's Disease: A Resting State fMRI Study.

Wed, 07/11/2018 - 14:00
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Correction: Reduced Topological Efficiency in Cortical-Basal Ganglia Motor Network of Parkinson's Disease: A Resting State fMRI Study.

PLoS One. 2018;13(7):e0200623

Authors: Wei L, Zhang J, Long Z, Wu GR, Hu X, Zhang Y, Wang J

Abstract
[This corrects the article DOI: 10.1371/journal.pone.0108124.].

PMID: 29990373 [PubMed - in process]

Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning.

Wed, 07/11/2018 - 14:00
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Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning.

IEEE/ACM Trans Comput Biol Bioinform. 2017 Nov 23;:

Authors: Ju R, Hu C, Zhou P, Li Q

Abstract
Computerized healthcare has undergone rapid development thanks to the advances in medical imaging and machine learning technologies. Especially, recent progress on deep learning opens a new era for multimedia based clinical decision support. In this paper, we use deep learning with brain network and clinical relevant text information to make early diagnosis of Alzheimer's Disease (AD). The clinical relevant text information includes age, gender and ApoE gene of the subject. The brain network is constructed by computing functional connectivity of brain regions using resting-state functional magnetic resonance imaging (R-fMRI) data. A targeted autoencoder network is built to distinguish normal aging from mild cognitive impairment, an early stage of AD. The proposed method reveals discriminative brain network features effectively and provides a reliable classifier for AD detection. Compared to traditional classifiers based on R-fMRI time series data, 31.21% improvement of the prediction accuracy on average is achieved by the proposed deep learning method, and standard deviation reduces by 51.23% on average that means our prediction model is more stable and reliable compared to traditional methods. Our work excavates deep learning's advantages of classifying high-dimensional multimedia data in medical services, and could help predict and prevent AD at an early stage.

PMID: 29989989 [PubMed - as supplied by publisher]

Distinct neural correlates of trait resilience within core neurocognitive networks in at-risk children and adolescents.

Wed, 07/11/2018 - 14:00
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Distinct neural correlates of trait resilience within core neurocognitive networks in at-risk children and adolescents.

Neuroimage Clin. 2018;20:24-34

Authors: Iadipaolo AS, Marusak HA, Paulisin SM, Sala-Hamrick K, Crespo LM, Elrahal F, Peters C, Brown S, Rabinak CA

Abstract
Background: Most children who are exposed to threat-related adversity (e.g., violence, abuse, neglect) are resilient - that is, they show stable trajectories of healthy psychological development. Despite this, most research on neurodevelopmental changes following adversity has focused on the neural correlates of negative outcomes, such as psychopathology. The neural correlates of trait resilience in pediatric populations are unknown, and it is unclear whether they are distinct from those related to adversity exposure and the absence of negative outcomes (e.g., depressive symptomology).
Methods: This functional magnetic resonance imaging (fMRI) study reports on a diverse sample of 55 children and adolescents (ages 6-17 years) recruited from a range of stressful environments (e.g., lower income, threat-related adversity exposure). Participants completed a multi-echo multi-band resting-state fMRI scan and self-report measures of trait resilience and emotion-related symptomology (e.g., depressive symptoms). Resting-state data were submitted to an independent component analysis (ICA) to identify core neurocognitive networks (salience and emotion network [SEN], default mode network [DMN], central executive network [CEN]). We tested for links among trait resilience and dynamic (i.e., time-varying) as well as conventional static (i.e., averaged across the entire session) resting-state functional connectivity (rsFC) of core neurocognitive networks.
Results: Youth with higher trait resilience spent a lower fraction of time in a particular dynamic rsFC state, characterized by heightened rsFC between the anterior DMN and right CEN. Within this state, trait resilience was associated with lower rsFC of the SEN with the right CEN and anterior DMN. There were no associations among trait resilience and conventional static rsFC. Importantly, although more resilient youth reported lower depressive symptoms, the effects of resilience on rsFC were independent of depressive symptoms and adversity exposure.
Conclusions: The present study is the first to report on the neural correlates of trait resilience in youth, and offers initial insight into potential adaptive patterns of brain organization in the context of environmental stressors. Understanding the neural dynamics underlying positive adaptation to early adversity will aid in the development of interventions that focus on strengthening resilience rather than mitigating already-present psychological problems.

PMID: 29988970 [PubMed - in process]

Abnormal degree centrality in chronic users of codeine-containing cough syrups: A resting-state functional magnetic resonance imaging study.

Wed, 07/11/2018 - 14:00
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Abnormal degree centrality in chronic users of codeine-containing cough syrups: A resting-state functional magnetic resonance imaging study.

Neuroimage Clin. 2018;19:775-781

Authors: Hua K, Wang T, Li C, Li S, Ma X, Li C, Li M, Fu S, Yin Y, Wu Y, Liu M, Yu K, Fang J, Wang P, Jiang G

Abstract
Codeine-containing cough syrups (CCS) have become one of the most popular drugs of abuse in young population worldwide. However, the neurobiological mechanisms underlying CCS-dependence are yet ill-defined. Therefore, understanding the brain abnormalities in chronic users of CCS is crucial for developing effective interventions. The present study depicted the intrinsic dysconnectivity pattern of whole-brain functional networks at the voxel level in chronic users of CCS. In addition, the degree centrality (DC) changes were correlated to the Barratt Impulsiveness Scale (BIS-11) total score, dose, duration of CCS use, and the age at first use of cough syrups. The current study included 38 chronic CCS users and 34 matched control subjects. All patients were evaluated using the BIS-11. Next, resting-state functional magnetic resonance imaging (rs-fMRI) datasets were acquired from these CCS users and controls. Whole-brain connectivity was analyzed using a graph theory approach: degree centrality (DC). CCS-dependent individuals exhibited low DC values in the left inferior parietal lobule and the left middle temporal gyrus, while high DC values were noted in the right pallidum and the right hippocampus (P < 0.01, AlphaSim corrected). Also, significant correlations were established between average DC value in the left inferior parietal lobule and attentional impulsivity scores and the age at first CCS use. The rs-fMRI study suggested that the abnormal intrinsic dysconnectivity pattern of whole-brain functional networks may provide an insight into the neural substrates of abnormalities in the cognitive control circuit, the reward circuit, and the learning and memory circuit in CCS-dependent individuals.

PMID: 29988765 [PubMed - in process]

Abnormal Regional Homogeneity and Functional Connectivity of Baseline Brain Activity in Hepatitis B Virus-Related Cirrhosis With and Without Minimal Hepatic Encephalopathy.

Wed, 07/11/2018 - 14:00
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Abnormal Regional Homogeneity and Functional Connectivity of Baseline Brain Activity in Hepatitis B Virus-Related Cirrhosis With and Without Minimal Hepatic Encephalopathy.

Front Hum Neurosci. 2018;12:245

Authors: Sun Q, Fan W, Ye J, Han P

Abstract
Background and Aims: Abnormalities in neural activity have been reported in cirrhosis with minimal hepatic encephalopathy (MHE). However, little is known about the neurophysiological mechanisms in this disorder. We aimed to investigate the altered patterns of regional synchronization and functional connections in hepatitis B virus-related cirrhosis (HBV-RC) patients with and without MHE using both regional homogeneity (ReHo) and region of interest (ROI)-based functional connectivity (FC) computational methods. Methods: Data of magnetic resonance imaging scans were collected from 30 HBV-RC patients with MHE, 32 HBV-RC patients without MHE (NMHE) and 64 well-matched controls. Several regions showing differences in ReHo after one-way analysis of variance (ANOVA) were defined as ROIs for FC analysis. Next, post hoc t-tests were applied to calculate the group differences in ReHo and FC (false discovery rate (FDR) correction, p < 0.05). Correlations between clinical variables and the altered ReHo and FC were then assessed in patient groups. Results: Across three groups, significant ReHo differences were found in nine ROI regions mainly within the visual network (VN), dorsal attention network (DAN), somatomotor network (SMN), fronto parietal control (FPC) network and thalamus. Compared with healthy controls (HC), the MHE group exhibited abnormal FC mainly between the right calcarine (CAL.R) and middle frontal gyrus (MFG.L)/right thalamus. The MHE patients showed increased FC between the MFG.L and CAL.R compared to NMHE patients. Disease duration of MHE patients was positively correlated with increased mean ReHo values in the right fusiform gyrus (FFG); psychometric hepatic encephalopathy score (PHES) test scores were negatively correlated with increased FC between MFG.L and CAL.R and positively correlated with reduced FC between the CAL.R and THA.R. For NMHE patients, the mean ReHo values in the right frontal pole were positively correlated with disease duration and positively correlated with the PHES scores. Conclusion: Our results exhibited that the functional brain modifications in patients with and without MHE are characterized by compound alterations in local coherence and functional connections in the VN, SMN, DAN, FPC networks and thalamus by using a combination of ReHo and ROI-based FC analysis. These functional imaging changes are correlated with disease duration/PHES. This study helped us gain a better understanding of the features of brain network modifications in cirrhosis.

PMID: 29988437 [PubMed]

Resting state fMRI and Probabilistic DTI demonstrate that the Greatest Functional and Structural Connectivity in the Hand Motor Homunculus Occurs in the area of the Thumb.

Wed, 07/11/2018 - 14:00
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Resting state fMRI and Probabilistic DTI demonstrate that the Greatest Functional and Structural Connectivity in the Hand Motor Homunculus Occurs in the area of the Thumb.

Brain Connect. 2018 Jul 10;:

Authors: Hamidian S, Vachha B, Jenabi M, Karimi S, Young RJ, Holodny AI, Peck KK

Abstract
BACKGROUND AND PURPOSE: The primary hand motor region is classically believed to be in the "hand knob" area in the precentral gyrus (PCG). However, hand motor task-based activation is often localized outside this area. The purpose of this study is to investigate the structural and functional connectivity driven by different seed locations corresponding to the little, index, and thumb in the PCG using probabilistic diffusion tractography (PDT) and resting state fMRI (rfMRI).
METHODS: Twelve healthy subjects had three ROIs placed the left PCG: lateral to the hand knob (thumb area), within the hand knob (index finger area), and medial to the hand knob (little finger area). Connectivity maps were generated using PDT and rfMRI. Individual and group level analyses were performed.
RESULT: Results show that the greatest hand motor connectivity between both hemispheres was obtained using the ROI positioned just lateral to the hand knob in the PCG (the thumb area). The number of connected voxels in the PCG between the two hemispheres was greatest in the lateral-most ROI (the thumb area): 279 compared to 13 for the medial-most ROI and 9 for and the central hand knob ROI. Similarly, the highest white matter connectivity between the two hemispheres resulted from the ROI placed in the lateral portion of PCG (p<0.003).
CONCLUSIONS: The maximal functional and structural connectivity of the hand motor area between hemispheres occurs in the thumb area, located laterally at the "hand knob". Thus, this location appears optimal for rfMRI and PDT seeding of the motor area.

PMID: 29987948 [PubMed - as supplied by publisher]

Trait Emotional Empathy and Resting State Functional Connectivity in Default Mode, Salience, and Central Executive Networks.

Wed, 07/11/2018 - 14:00
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Trait Emotional Empathy and Resting State Functional Connectivity in Default Mode, Salience, and Central Executive Networks.

Brain Sci. 2018 Jul 06;8(7):

Authors: Bilevicius E, Kolesar TA, Smith SD, Trapnell PD, Kornelsen J

Abstract
Emotional empathy is the ability to experience and/or share another person&rsquo;s emotional states and responses. Although some research has examined the neural correlates of emotional empathy, there has been little research investigating whether this component of empathy is related to the functional connectivity of resting state networks in the brain. In the current study, 32 participants answered a trait emotional empathy questionnaire in a session previous to their functional magnetic resonance imaging scan. Results indicate that emotional empathy scores were correlated with different patterns of functional connectivity in the default mode network (DMN), salience network (SN), and left and right central executive networks. For example, within the DMN, emotional empathy scores positively correlated with connectivity in the premotor cortex. Within the SN, empathy scores were positively correlated with the fusiform gyrus and cuneus. These findings demonstrate that emotional empathy is associated with unique patterns of functional connectivity in four of the brain&rsquo;s resting state networks.

PMID: 29986390 [PubMed]

Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis.

Wed, 07/11/2018 - 14:00
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Disrupted topological organization of structural and functional brain connectomes in clinically isolated syndrome and multiple sclerosis.

Sci Rep. 2016 07 12;6:29383

Authors: Shu N, Duan Y, Xia M, Schoonheim MM, Huang J, Ren Z, Sun Z, Ye J, Dong H, Shi FD, Barkhof F, Li K, Liu Y

Abstract
The brain connectome of multiple sclerosis (MS) has been investigated by several previous studies; however, it is still unknown how the network changes in clinically isolated syndrome (CIS), the earliest stage of MS, and how network alterations on a functional level relate to the structural level in MS disease. Here, we investigated the topological alterations of both the structural and functional connectomes in 41 CIS and 32 MS patients, compared to 35 healthy controls, by combining diffusion tensor imaging and resting-state functional MRI with graph analysis approaches. We found that the structural connectome showed a deviation from the optimal pattern as early as the CIS stage, while the functional connectome only showed local changes in MS patients, not in CIS. When comparing two patient groups, the changes appear more severe in MS. Importantly, the disruptions of structural and functional connectomes in patients occurred in the same direction and locally correlated in sensorimotor component. Finally, the extent of structural network changes was correlated with several clinical variables in MS patients. Together, the results suggested early disruption of the structural brain connectome in CIS patients and provided a new perspective for investigating the relationship of the structural and functional alterations in MS.

PMID: 27403924 [PubMed - indexed for MEDLINE]

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