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Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis.

Fri, 11/09/2018 - 20:07

Classification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity Analysis.

IEEE J Transl Eng Health Med. 2018;6:1801009

Authors: Wang Z, Zheng Y, Zhu DC, Bozoki AC, Li T

Abstract
This paper proposes a robust method for the Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal control subject classification under size limited fMRI data samples by exploiting the brain network connectivity pattern analysis. First, we select the regions of interest (ROIs) within the default mode network and calculate the correlation coefficients between all possible ROI pairs to form a feature vector for each subject. Second, we propose a regularized linear discriminant analysis (LDA) approach to reduce the noise effect due to the limited sample size. The feature vectors are then projected onto a one-dimensional axis using the proposed regularized LDA. Finally, an AdaBoost classifier is applied to carry out the classification task. The numerical analysis demonstrates that the purposed approach can increase the classification accuracy significantly. Our analysis confirms the previous findings that the hippocampus and the isthmus of the cingulate cortex are closely involved in the development of AD and MCI.

PMID: 30405975 [PubMed]

Multimodal Neuroimaging Approach to Variability of Functional Connectivity in Disorders of Consciousness: A PET/MRI Pilot Study.

Fri, 11/09/2018 - 20:07

Multimodal Neuroimaging Approach to Variability of Functional Connectivity in Disorders of Consciousness: A PET/MRI Pilot Study.

Front Neurol. 2018;9:861

Authors: Cavaliere C, Kandeepan S, Aiello M, Ribeiro de Paula D, Marchitelli R, Fiorenza S, Orsini M, Trojano L, Masotta O, St Lawrence K, Loreto V, Chronik BA, Nicolai E, Soddu A, Estraneo A

Abstract
Behavioral assessments could not suffice to provide accurate diagnostic information in individuals with disorders of consciousness (DoC). Multimodal neuroimaging markers have been developed to support clinical assessments of these patients. Here we present findings obtained by hybrid fludeoxyglucose (FDG-)PET/MR imaging in three severely brain-injured patients, one in an unresponsive wakefulness syndrome (UWS), one in a minimally conscious state (MCS), and one patient emerged from MCS (EMCS). Repeated behavioral assessment by means of Coma Recovery Scale-Revised and neurophysiological evaluation were performed in the two weeks before and after neuroimaging acquisition, to ascertain that clinical diagnosis was stable. The three patients underwent one imaging session, during which two resting-state fMRI (rs-fMRI) blocks were run with a temporal gap of about 30 min. rs-fMRI data were analyzed with a graph theory approach applied to nine independent networks. We also analyzed the benefits of concatenating the two acquisitions for each patient or to select for each network the graph strength map with a higher ratio of fitness. Finally, as for clinical assessment, we considered the best functional connectivity pattern for each network and correlated graph strength maps to FDG uptake. Functional connectivity analysis showed several differences between the two rs-fMRI acquisitions, affecting in a different way each network and with a different variability for the three patients, as assessed by ratio of fitness. Moreover, combined PET/fMRI analysis demonstrated a higher functional/metabolic correlation for patients in EMCS and MCS compared to UWS. In conclusion, we observed for the first time, through a test-retest approach, a variability in the appearance and temporal/spatial patterns of resting-state networks in severely brain-injured patients, proposing a new method to select the most informative connectivity pattern.

PMID: 30405513 [PubMed]

Increased Inhibition of the Amygdala by the mPFC may Reflect a Resilience Factor in Post-traumatic Stress Disorder: A Resting-State fMRI Granger Causality Analysis.

Fri, 11/09/2018 - 20:07

Increased Inhibition of the Amygdala by the mPFC may Reflect a Resilience Factor in Post-traumatic Stress Disorder: A Resting-State fMRI Granger Causality Analysis.

Front Psychiatry. 2018;9:516

Authors: Chen F, Ke J, Qi R, Xu Q, Zhong Y, Liu T, Li J, Zhang L, Lu G

Abstract
Purpose: To determine whether effective connectivity of the amygdala is altered in traumatized subjects with and without post-traumatic stress disorder (PTSD). Materials and Methods: Resting-state functional MRI data were obtained for 27 patients with typhoon-related PTSD, 33 trauma-exposed controls (TEC), and 30 healthy controls (HC). Effective connectivity of the bilateral amygdala was examined with Granger causality analysis and then compared between groups by conducting an analysis of variance. Results: Compared to the HC group, both the PTSD group and the TEC group showed increased effective connectivity from the amygdala to the medial prefrontal cortex (mPFC). The TEC group showed increased effective connectivity from the mPFC to the amygdala relative to the HC group. Compared to the TEC group, the PTSD group showed increased effective connectivity from the amygdala to the supplementary motor area (SMA), whereas decreased effective connectivity was detected from the SMA to the amygdala. Both the PTSD group and the TEC group showed decreased effective connectivity from the superior temporal gyrus (STG) to the amygdala relative to the HC group. Compared to the HC group, the TEC group showed increased effective connectivity from the amygdala to the dorsolateral prefrontal cortex (dlPFC), while both the PTSD group and the TEC group showed decreased effective connectivity from the dlPFC to the amygdala. The PTSD group showed decreased effective connectivity from the precuneus to the amygdala relative to both control groups, but increased effective connectivity from the amygdala to the precuneus relative to the HC group. Conclusion: Trauma leads to an increased down-top excitation from the amygdala to the mPFC and less regulation of the amygdala by the dlPFC. The results suggest that increased inhibition of the amygdala by the mPFC may reflect a resilience factor, and altered amygdala-SMA and amygdala-STG effective connectivity may reflect compensatory mechanisms of brain function. These data raise the possibility that insufficient inhibition of the amygdala by the mPFC might lead to PTSD in those who have been exposed to traumatic incidents, and may inform future therapeutic interventions.

PMID: 30405457 [PubMed]

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective.

Fri, 11/09/2018 - 20:07

Eyes-Open and Eyes-Closed Resting States With Opposite Brain Activity in Sensorimotor and Occipital Regions: Multidimensional Evidences From Machine Learning Perspective.

Front Hum Neurosci. 2018;12:422

Authors: Wei J, Chen T, Li C, Liu G, Qiu J, Wei D

Abstract
Studies have demonstrated that there are widespread significant differences in spontaneous brain activity between eyes-open (EO) and eyes-closed (EC) resting states. However, it remains largely unclear whether spontaneous brain activity is effectively related to EO and EC resting states. The amplitude, local functional concordance, inter-hemisphere functional synchronization, and network centrality of spontaneous brain activity were measured by the fraction amplitude of low frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC), respectively. Using the public Eyes-open/Eyes-closed dataset, we employed the support vector machine (SVM) and bootstrap technique to establish linking models for the fALFF, ReHo, VMHC and DC dimensions. The classification accuracies of linking models are 0.72 (0.59, 0.82), 0.88 (0.79, 0.97), 0.82 (0.74, 0.91) and 0.70 (0.62, 0.79), respectively. Specifically, we observed that brain activity in the EO condition is significantly greater in attentional system areas, including the fusiform gyrus, occipital and parietal cortex, but significantly lower in sensorimotor system areas, including the precentral/postcentral gyrus, paracentral lobule (PCL) and temporal cortex compared to the EC condition from the four dimensions. The results consistently indicated that spontaneous brain activity is effectively related to EO and EC resting states, and the two resting states are of opposite brain activity in sensorimotor and occipital regions. It may provide new insight into the neural substrate of the resting state and help computational neuroscientists or neuropsychologists to choose an appropriate resting state condition to investigate various mental disorders from the resting state functional magnetic resonance imaging (fMRI) technique.

PMID: 30405376 [PubMed]

Default Mode Network Complexity and Cognitive Decline in Mild Alzheimer's Disease.

Fri, 11/09/2018 - 20:07

Default Mode Network Complexity and Cognitive Decline in Mild Alzheimer's Disease.

Front Neurosci. 2018;12:770

Authors: Grieder M, Wang DJJ, Dierks T, Wahlund LO, Jann K

Abstract
The human resting-state is characterized by spatially coherent brain activity at a low temporal frequency. The default mode network (DMN), one of so-called resting-state networks, has been associated with cognitive processes that are directed toward the self, such as introspection and autobiographic memory. The DMN's integrity appears to be crucial for mental health. For example, patients with Alzheimer's disease or other psychiatric conditions show disruptions of functional connectivity within the brain regions of the DMN. However, in prodromal or early stages of Alzheimer's disease, physiological alterations are sometimes elusive, despite manifested cognitive impairment. While functional connectivity assesses the signal correlation between brain areas, multi-scale entropy (MSE) measures the complexity of the blood-oxygen level dependent signal within an area and thus might show local changes before connectivity is affected. Hence, we investigated alterations of functional connectivity and MSE within the DMN in fifteen mild Alzheimer's disease patients as compared to fourteen controls. Potential associations of MSE with functional connectivity and cognitive abilities [i.e., mini-mental state examination (MMSE)] were assessed. A moderate decrease of DMN functional connectivity between posterior cingulate cortex and right hippocampus in Alzheimer's disease was found, whereas no differences were evident for whole-network functional connectivity. In contrast, the Alzheimer's disease group yielded lower global DMN-MSE than the control group. The most pronounced regional effects were localized in left and right hippocampi, and this was true for most scales. Moreover, MSE significantly correlated with functional connectivity, and DMN-MSE correlated positively with the MMSE in Alzheimer's disease. Most interestingly, the right hippocampal MSE was positively associated with semantic memory performance. Thus, our results suggested that cognitive decline in Alzheimer's disease is reflected by decreased signal complexity in DMN nodes, which might further lead to disrupted DMN functional connectivity. Additionally, altered entropy in Alzheimer's disease found in the majority of the scales indicated a disturbance of both local information processing and information transfer between distal areas. Conclusively, a loss of nodal signal complexity potentially impairs synchronization across nodes and thus preempts functional connectivity changes. MSE presents a putative functional marker for cognitive decline that might be more sensitive than functional connectivity alone.

PMID: 30405347 [PubMed]

Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach.

Fri, 11/09/2018 - 20:07

Connecting Openness and the Resting-State Brain Network: A Discover-Validate Approach.

Front Neurosci. 2018;12:762

Authors: Wang J, Hu Y, Li H, Ge L, Li J, Cheng L, Yang Z, Zuo X, Xu Y

Abstract
In personality neuroscience, the openness-brain association has been a topic of interest. Previous studies usually started from difference in openness trait and used it to infer brain functional activity characteristics, but no study has used a "brain-first" research strategy to explore that association based on more objective brain imaging data. In this study, we used a fully data-driven approach to discover and validate the association between openness and the resting-state brain network. We collected data of 120 subjects as a discovery sample and 56 subjects as a validation sample. The Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) was used to measure the personality characteristics of all the subjects. Using an exploratory approach based on independent component analysis of resting-state functional magnetic resonance imaging (fMRI) data, we identified a parietal network that consisted of the precuneus and inferior parietal lobe. The inter-subject similarity of the parietal memory network exhibited significant associations with openness trait, and this association was validated using the 56-subject independent sample. This finding connects the openness trait to the characteristics of a neural network and helps to understand the underlying biology of the openness trait.

PMID: 30405342 [PubMed]

Identification of Subclinical Language Deficit using Machine Learning Classification based on Post-stroke Functional Connectivity derived from Low Frequency Oscillations.

Thu, 11/08/2018 - 08:43
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Identification of Subclinical Language Deficit using Machine Learning Classification based on Post-stroke Functional Connectivity derived from Low Frequency Oscillations.

Brain Connect. 2018 Nov 06;:

Authors: Mohanty R, Nair VA, Tellapragada N, Wiliams LM, Kang TJ, Prabhakaran V

Abstract
Post-stroke neuropsychological evaluation can take a long time to assess impairments in subjects without overt clinical deficits. We utilized functional connectivity (FC) from ten-minute non-invasive resting-state functional MRI (rs-fMRI) to identify stroke subjects at risk for subclinical language deficit (SLD) using a machine learning classifier. Discriminative ability of FC derived from slow-4 (0.027-0.073 Hz), slow-5 (0.01-0.027 Hz) and low frequency oscillations (LFO; 0.01-0.1 Hz) were compared. Sixty clinically non-aphasic right-handed subjects were categorized into three groups based on stroke status and normalized verbal fluency score (VFS): 20 ischemic stroke subjects at a higher risk of SLD (LD+; mean VFS=-1.77), 20 ischemic stroke subjects with lower risk of SLD (LD-; mean VFS=-0.05), 20 healthy controls (HC; mean VFS=0.29). T1-weighted and rs-fMRI scans were acquired within 30 days of stroke onset. Blood-oxygen-level-dependent signal was extracted from regions in the language network and FC based on Pearson's correlation was evaluated. Selected features were used by a multiclass support vector machine to classify test subject into one of the subgroups. Classifier performance was assessed using a nested leave-one-out cross-validation. FC derived from slow-4 (70%) band provided the best accuracy in comparison to LFO (65%) and slow-5 (50%) , reasonably higher than random chance (33.33%). Based on subgroup-specific accuracy, classification was best realized within the slow-4 band for LD+ (81.6%) and LD- (78.3%) and slow-4 and LFO bands for HC (80%), i.e., early stage stroke subjects showed a slow-4 FC dominance whereas HC also indicated the normalized involvement of FC in LFO. While frontal FC differentiated between stroke and healthy, occipital FC differentiated between the two stroke groups. We demonstrated that stroke subjects at risk for SLD can be differentiated from control subjects using rs-fMRI with a classifier with reasonable accuracy in an expedited manner, which otherwise could take longer to identify via neuropsychological assessments.

PMID: 30398379 [PubMed - as supplied by publisher]

Commute Time as a Method to Explore Brain Functional Connectomes.

Thu, 11/08/2018 - 08:43
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Commute Time as a Method to Explore Brain Functional Connectomes.

Brain Connect. 2018 Nov 06;:

Authors: Sato JR, Sato CM, Silva MC, Biazoli CE

Abstract
Graph theory has been extensively applied to investigate the brain complex networks in current neuroscience research. Many metrics derived from graph theory, such as local and global efficiencies, are based on the path length between nodes. These approaches are commonly used in the analyses of brain networks assessed by resting-state fMRI, though relying on the strong assumption that information flow throughout the network is restricted to the shortest paths. In this study, we propose the utilization of the commute time as a tool to investigate regional centrality on the functional Connectome. Our initial hypothesis was that an alternative approach that considers alternative routes (such as the commute time) could provide further information into the organization of functional networks. However, our empirical findings on the ADHD-200 database suggest that, at the group level, the commute time and shortest path are highly correlated. In contrast, at the subject level, we discovered that the commute time is much less susceptible to head motion artifacts when compared to metric based on shortest paths. Given the overall similarity between the measures, we argue that commute time might be advantageous particularly for connectomic studies in populations where motion artifacts are a major issue.

PMID: 30398376 [PubMed - as supplied by publisher]

Effective Connectivity Within the Default Mode Network In Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project.

Thu, 11/08/2018 - 08:43
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Effective Connectivity Within the Default Mode Network In Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project.

Brain Connect. 2018 Nov 06;:

Authors: Cook CJ, Hwang G, Mathis J, Nair VA, Conant L, Allen L, Almane DN, Birn R, DeYoe E, Felton E, Forseth C, Humphries C, Kraegel P, Nencka A, Nwoke O, Raghavan M, Rivera-Bonet C, Rozman M, Tellapragada N, Ustine C, Ward D, Struck A, Maganti R, Hermann B, Prabhakaran V, Binder J, Meyerand ME

Abstract
The Epilepsy Connectome Project examines the differences in connectomes between temporal lobe epilepsy (TLE) patients and healthy controls. Using this data, the effective connectivity of the default mode network in patients with left TLE compared to healthy controls was investigated using spectral dynamic causal modeling of resting state functional magnetic resonance imaging data. Group comparisons were made using two parametric empirical Bayes (PEB) models. The first level of each PEB model consisted of each participant's spectral dynamic causal modeling. Two different second level models were constructed: the first comparing effective connectivity of the groups directly and the second using the Rey Auditory Verbal Learning Test (RAVLT) delayed free recall index as a covariate at the second level in order to assess effective connectivity controlling for the poor memory performance of left TLE patients. After an automated search over the nested parameter space and thresholding parameters at 95% posterior probability, both models revealed numerous connections in the DMN which lead to inhibition of the left hippocampal formation. Left hippocampal formation inhibition may be an inherent result of the left temporal epileptogenic focus as memory differences were controlled for in one model and the same connections remained. An excitatory connection from the posterior cingulate cortex to the medial prefrontal cortex was found to be concomitant with left hippocampal formation inhibition in TLE patients when including RAVLT delayed free recall at the second level.

PMID: 30398367 [PubMed - as supplied by publisher]

Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data.

Thu, 11/08/2018 - 08:43
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Characterizing directed functional pathways in the visual system by multivariate nonlinear coherence of fMRI data.

Sci Rep. 2018 Nov 05;8(1):16362

Authors: Goelman G, Dan R, Keadan T

Abstract
A multivariate measure of directed functional connectivity is used with resting-state fMRI data of 40 healthy subjects to identify directed pathways of signal progression in the human visual system. The method utilizes 4-nodes networks of mutual interacted BOLD signals to obtains their temporal hierarchy and functional connectivity. Patterns of signal progression were defined at frequency windows by appealing to a hierarchy based upon phase differences, and their significance was assessed by permutation testing. Assuming consistent phase relationship between neuronal and fMRI signals and unidirectional coupling, we were able to characterize directed pathways in the visual system. The ventral and dorsal systems were found to have different functional organizations. The dorsal system, particularly of the left hemisphere, had numerous feedforward pathways connecting the striate and extrastriate cortices with non-visual regions. The ventral system had fewer pathways primarily of two types: (1) feedback pathways initiated in the fusiform gyrus that were either confined to the striate and the extrastriate cortices or connected to the temporal cortex, (2) feedforward pathways initiated in V2, excluded the striate cortex, and connected to non-visual regions. The multivariate measure demonstrated higher specificity than bivariate (pairwise) measure. The analysis can be applied to other neuroimaging and electrophysiological data.

PMID: 30397245 [PubMed - in process]

Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer's Disease.

Wed, 11/07/2018 - 14:09
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Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer's Disease.

Bioinformatics. 2018 Nov 05;:

Authors: Wang M, Hao X, Huang J, Shao W, Zhang D

Abstract
Motivation: Neuroimaging genetics is an emerging field to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. However, most of the current studies only focus on the associations between brain structure imaging and genetic variants, while neglecting the connectivity information between brain regions. In addition, the brain itself is a complex network, and the higher-order interaction may contain useful information for the mechanistic understanding of diseases (i.e., Alzheimer's disease (AD)).
Results: A general framework is proposed to exploit network voxel information and network connectivity information as intermediate traits that bridge genetic risk factors and disease status. Specifically, we first use the sparse representation (SR) model to build hyper-network to express the connectivity features of the brain. The network voxel node features and network connectivity edge features are extracted from the structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (fMRI), respectively. Secondly, a diagnosis-aligned multi-modality regression method is adopted to fully explore the relationships among modalities of different subjects, which can help further mine the relation between the risk genetics and brain network features. In experiments, all methods are tested on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results not only verify the effectiveness of our proposed framework but also discover some brain regions and connectivity features that are highly related to diseases.
Availability: The Matlab code is available at http://ibrain.nuaa.edu.cn/2018/list.htm.

PMID: 30395195 [PubMed - as supplied by publisher]

Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity with Machine Learning.

Wed, 11/07/2018 - 14:09
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Resting-state Functional Connectivity and Deception: Exploring Individualized Deceptive Propensity with Machine Learning.

Neuroscience. 2018 Oct 28;:

Authors: Tang H, Lu X, Cui Z, Feng C, Lin Q, Cui X, Su S, Liu C

Abstract
Individuals show a great heterogeneity in determining to be honest or deceptive in daily life. A large number of studies have investigated the neural substrates of deception; however, the brain networks contributed to the individual difference in deception still remain unclear. The current study tried to address this issue by employing a machine-learning approach to predict individuals' deceptive propensity with topological properties of whole-brain resting-state functional connectivity (RSFC). Participants finished the resting-state functional MRI (fMRI) data acquisition, and then played as proposers in a modified ultimatum game in which they spontaneously chose to be honest or deceptive one week later. A linear relevance vector regression (RVR) model was trained and validated to examine the relationship between topological properties of networks of RSFC and actual deceptive behaviors. Results showed that the machine-learning model sufficiently decoded individual differences of deception using three brain networks based on RSFC, including the executive controlling network (DLPFC, MFC and OFC), the social and mentalizing network (the temporal lobe, TPJ and IPL), and the reward network (putamen and thalamus). These networks have been found to form a signaling cognitive framework of deception by coding mental states of others and the reward or values of deception or honesty, and integrating this information to make final deceptive or honest decisions. These findings suggest the potentiality in using RSFC as a task-independent neural trait to predict deceptive propensity, and shed light on using machine-learning approaches in deception detection.

PMID: 30394323 [PubMed - as supplied by publisher]

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning.

Wed, 11/07/2018 - 14:09
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Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning.

Front Appl Math Stat. 2018;4:

Authors: Zhao X, Rangaprakash D, Yuan B, Denney TS, Katz JS, Dretsch MN, Deshpande G

Abstract
Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them-ADNI, ADHD-200, and ABIDE-which are publicly available, and a fourth one-PTSD and PCS-which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git.

PMID: 30393630 [PubMed]

Modulation of resting-state network connectivity by verbal divergent thinking training.

Wed, 11/07/2018 - 14:09
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Modulation of resting-state network connectivity by verbal divergent thinking training.

Brain Cogn. 2018 Oct 25;128:1-6

Authors: Fink A, Benedek M, Koschutnig K, Papousek I, Weiss EM, Bagga D, Schöpf V

Abstract
A growing body of evidence suggests that creativity is associated with functional connectivity across widespread neural networks, including regions associated with executive processes and cognitive control, along with regions linked to the default mode network (DMN) of the brain. This study investigated whether a three-week verbal divergent thinking training modulates functional connectivity in networks that have been related to creativity. In a task-based functional imaging study (Fink et al., 2015), the employed creativity training was found to modulate brain activity in regions closely associated with semantic memory demands. Hence, the specific aim of this study was to assess whether the observed task-related brain changes relate to changes in functional connectivity patterns of the brain at rest, as assessed by independent component analysis. The participants were tested at three time points with an inter-test interval of four weeks each, and randomly assigned to two groups which received the verbal creativity training time-delayed. Findings revealed that successful training of verbal creativity was mirrored by functional connectivity changes in the DMN, sensorimotor and auditory network, and the attention network. These rather global changes in resting-state functional connectivity supplement the findings of task-based fMRI, where changes in more task specific brain regions were found.

PMID: 30393122 [PubMed - as supplied by publisher]

A principled multivariate intersubject analysis of Generalized Partial Directed Coherence with Dirichlet Regression: application to healthy aging in areas exhibiting cortical thinning.

Wed, 11/07/2018 - 14:09
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A principled multivariate intersubject analysis of Generalized Partial Directed Coherence with Dirichlet Regression: application to healthy aging in areas exhibiting cortical thinning.

J Neurosci Methods. 2018 Oct 27;:

Authors: Vieira BH, Garrido Salmon CE

Abstract
BACKGROUND: Generalized Partial Directed Coherence (GPDC) is a multivariate measure of predictability between functional timeseries defined in the frequency domain. However, analysis has often been constrained by its compositional nature. Specifically, the squared GPDC from a node region to all nodes in any given frequency must sum to one.
NEW METHOD: When analyzing GPDC spectra, it is imperative to consider that squared GPDC from a source timeseries sums to one over its target timeseries. Dirichlet Regression allows the modeling of compositional data and, therefore, becomes a principled choice for the multivariate analysis of GPDC on arbitrary subject-level variables.
RESULTS: Eleven resting-state fMRI connections underwent age-related alterations, with two decreases in squared GPDC from a region to itself in two frequencies, signaling increased integration with the rest, and nine increases in squared GPDC, one involving different regions. All frequencies had at least one alteration due to age.
COMPARISON WITH EXISTING METHOD(S): Our methodology identifies alterations in GPDC in more connections than a naïve approach based on linear regression and centered log-ratio analysis. We also studied alternative connectivity indices between the same ROIs, uncovering no effect of age on the time-domain predictive-causality metrics for any connection, while for Pearson correlation five connections displayed significant effects of age, with parallels to the results pertaining to GPDC.
CONCLUSIONS: Dirichlet Regression allows the study of continuous or discrete variables as predictors for the analysis of GPDC, enabling a wider adoption of this measure of connectivity.

PMID: 30392951 [PubMed - as supplied by publisher]

Functional connectivity in ASD: Atypical pathways in brain networks supporting action observation and joint attention.

Wed, 11/07/2018 - 14:09
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Functional connectivity in ASD: Atypical pathways in brain networks supporting action observation and joint attention.

Brain Res. 2018 Oct 27;:

Authors: Delbruck E, Yang M, Yassine A, Grossman ED

Abstract
Autism Spectrum Disorder (ASD) is a developmental disorder characterized by impaired social communication, including attending to and interpreting social cues, initiating and responding to joint attention, and engaging in abstract social cognitive reasoning. Current studies emphasize a underconnectivity in ASD, particularly for brain systems that support abstract social reasoning and introspective thought. Here, we evaluate intrinsic connectivity in children with ASD, targeting brain systems that support the developmental precursors to social reasoning, namely perception of social cues and joint attention. Using resting state fMRI made available through the Autism Brain Imaging Data Exchange (ABIDE), we compute functional connectivity within and between nodes in the action observation, attention and social cognitive networks in children and adolescents with ASD. We also compare connectivity strength to observational assessments that explicitly evaluate severity of ASD on two distinct subdomains using the ADOS-Revised schedule: social affective (SA) and restricted, repetitive behaviors (RRB). Compared to age-matched controls, children with ASD have decreased functional connectivity in a number of connections in the action observation network, particularly in the lateral occipital cortex (LOTC) and fusiform gyrus (FG). Distinct patterns of connections were also correlated with symptom severity on the two subdomains of the ADOS. ADOS-SA severity most strongly correlated with connectivity to the left TPJ, while ADOS-RRB severity correlated with connectivity to the dMPFC. We conclude that atypical connectivity in the action observation system may underlie some of the more complex deficits in social cognitive systems in ASD.

PMID: 30392771 [PubMed - as supplied by publisher]

Associations between children's family environment, spontaneous brain oscillations, and emotional and behavioral problems.

Wed, 11/07/2018 - 14:09
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Associations between children's family environment, spontaneous brain oscillations, and emotional and behavioral problems.

Eur Child Adolesc Psychiatry. 2018 Nov 03;:

Authors: Sato JR, Biazoli CE, Salum GA, Gadelha A, Crossley N, Vieira G, Zugman A, Picon FA, Pan PM, Hoexter MQ, Amaro E, Anés M, Moura LM, Del'Aquilla MAG, Mcguire P, Rohde LA, Miguel EC, Bressan RA, Jackowski AP

Abstract
The family environment in childhood has a strong effect on mental health outcomes throughout life. This effect is thought to depend at least in part on modifications of neurodevelopment trajectories. In this exploratory study, we sought to investigate whether a feasible resting-state fMRI metric of local spontaneous oscillatory neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), is associated with the levels of children's family coherence and conflict. Moreover, we sought to further explore whether spontaneous activity in the brain areas influenced by family environment would also be associated with a mental health outcome, namely the incidence of behavioral and emotional problems. Resting-state fMRI data from 655 children and adolescents (6-15 years old) were examined. The quality of the family environment was found to be positively correlated with fALFF in the left temporal pole and negatively correlated with fALFF in the right orbitofrontal cortex. Remarkably, increased fALFF in the temporal pole was associated with a lower incidence of behavioral and emotional problems, whereas increased fALFF in the orbitofrontal cortex was correlated with a higher incidence.

PMID: 30392120 [PubMed - as supplied by publisher]

Changes in resting-state functional brain activity are associated with waning cognitive functions in HIV-infected children.

Wed, 11/07/2018 - 14:09
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Changes in resting-state functional brain activity are associated with waning cognitive functions in HIV-infected children.

Neuroimage Clin. 2018 Oct 29;20:1204-1210

Authors: Yadav SK, Gupta RK, Hashem S, Bhat AA, Garg RK, Venkatesh V, Gupta PK, Singh AK, Chaturvedi S, Ahmed SN, Azeem MW, Haris M

Abstract
Delayed brain development in perinatally HIV-infected children may affect the functional brain activity and subsequently cognitive function. The current study evaluated the functional brain activity in HIV-infected children by quantifying the amplitude of low frequency fluctuations (ALFF) and functional connectivity (FC). Additionally, correlation of ALFF and FC with cognitive measures was performed. Twenty-six HIV-infected children and 20 control children underwent neuropsychological (NP) assessment and resting-state functional magnetic resonance imaging (rs-fMRI). ALFF and FC maps were generated and group differences were analyzed using two-sample t-test. Furthermore, ALFF and FC showing significant group differences were correlated with NP scores using Pearson's correlation. Significantly lower ALFF in the left middle temporal gyrus, precentral and post central gyrus was observed in HIV-infected children compared to controls. FC was significantly reduced in the right inferior parietal, vermis, middle temporal and left postcentral regions, and significantly increased in the right precuneus, superior parietal and left middle frontal regions in HIV-infected children as compared to control. HIV-infected children showed significantly lower NP scores in various domains including closure, exclusion, memory, verbal meaning, quantity and hidden figure than controls. These waning cognitive functions were significantly associated with changes in ALFF and FC in HIV-infected children. The findings suggest that abnormal ALFF and FC may responsible for cognitive deficits in HIV-infected children. ALFF and FC in association with cognitive evaluation may provide a clinical biomarker to evaluate functional brain activity and to plan neurocognitive intervention in HIV-infected children undergoing standard treatment.

PMID: 30391858 [PubMed - as supplied by publisher]

Reward and executive control network resting-state functional connectivity is associated with impulsivity during reward-based decision making for cocaine users.

Wed, 11/07/2018 - 14:09
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Reward and executive control network resting-state functional connectivity is associated with impulsivity during reward-based decision making for cocaine users.

Drug Alcohol Depend. 2018 Oct 24;194:32-39

Authors: Hobkirk AL, Bell RP, Utevsky AV, Huettel S, Meade CS

Abstract
BACKGROUND: Cocaine addiction is related to impulsive decision making that is mediated by brain circuitry involved in reward processing and executive functions, such as cognitive control and attentional salience. Resting-state functional connectivity between reward and executive control circuitry is altered among cocaine users, with concomitant deficits in impulsivity and learning. Prior research has examined how select brain regions interact to influence impulsive decision making for drug users; however, research examining interactions between large-scale brain networks and impulsive behavior is limited.
METHODS: The current study compared reward and executive control network resting-state functional connectivity and its relationship to impulsive decision making between cocaine users (n = 37) and non-cocaine using control participants (n = 35). Participants completed computerized decision-making tasks and a separate resting-state functional magnetic resonance imaging scan. Data underwent independent component, dual regression, and linear regression moderation analyses.
RESULTS: Higher impulsivity on the Balloon Analogue Risk Task (BART) was associated with inverse resting-state connectivity between the left cognitive control and subgenual anterior cingulate extended reward networks for cocaine users, while the opposite was found for controls. Less impulsivity on the monetary choice questionnaire was associated with stronger positive resting-state connectivity between the attentional salience and striatal core reward networks for controls, while cocaine users showed no association between impulsivity and resting-state connectivity of these networks.
CONCLUSIONS: Cocaine users show aberrant associations between reward-executive control resting-state network coupling and impulsive decision making. The findings support the conclusion that an imbalance between reward and executive control circuitry contributes to impulsivity in drug use.

PMID: 30391836 [PubMed - as supplied by publisher]

Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs.

Wed, 11/07/2018 - 14:09
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Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs.

Neuroimage. 2018 Oct 31;:

Authors: Valente G, Kaas A, Formisano E, Goebel R

Abstract
Functional Magnetic Resonance Imaging (fMRI) has been successfully used for Brain Computer Interfacing (BCI) to classify (imagined) movements of different limbs. However, reliable classification of more subtle signals originating from co-localized neural networks in the sensorimotor cortex, e.g. individual movements of fingers of the same hand, has proved to be more challenging, especially when taking into account the requirement for high single trial reliability in the BCI context. In recent years, Multi Voxel Pattern Analysis (MVPA) has gained momentum as a suitable method to disclose such weak, distributed activation patterns. Much attention has been devoted to developing and validating data analysis strategies, but relatively little guidance is available on the choice of experimental design, even less so in the context of BCI-MVPA. When applicable, block designs are considered the safest choice, but the expectations, strategies and adaptation induced by blocking of similar trials can make it a sub-optimal strategy. Fast event-related designs, in contrast, require a more complicated analysis and show stronger dependence on linearity assumptions but allow for randomly alternating trials. However, they lack resting intervals that enable the BCI participant to process feedback. In this proof-of-concept paper a hybrid blocked fast-event related design is introduced that is novel in the context of MVPA and BCI experiments, and that might overcome these issues by combining the rest periods of the block design with the shorter and randomly alternating trial characteristics of a rapid event-related design. A well-established button-press experiment was used to perform a within-subject comparison of the proposed design with a block and a slow event-related design. The proposed hybrid blocked fast-event related design showed a decoding accuracy that was close to that of the block design, which showed highest accuracy. It allowed for across-design decoding, i.e. reliable prediction of examples obtained with another design. Finally, it also showed the most stable incremental decoding results, obtaining good performance with relatively few blocks. Our findings suggest that the blocked fast event-related design could be a viable alternative to block designs in the context of BCI-MVPA, when expectations, strategies and adaptation make blocking of trials of the same type a sub-optimal strategy. Additionally, the blocked fast event-related design is also suitable for applications in which fast incremental decoding is desired, and enables the use of a slow or block design during the test phase.

PMID: 30391345 [PubMed - as supplied by publisher]