New resting-state fMRI related studies at PubMed

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Can Resting-State Functional MRI Predict Response to Cholinesterase Inhibitors in Individuals with Cognitive Impairment?

Tue, 07/30/2019 - 10:20
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Can Resting-State Functional MRI Predict Response to Cholinesterase Inhibitors in Individuals with Cognitive Impairment?

Radiology. 2018 12;289(3):786-787

Authors: Chiang GC

PMID: 30204074 [PubMed - indexed for MEDLINE]

Alterations in Regional Homogeneity Assessed by fMRI in Patients with Migraine Without Aura.

Mon, 07/29/2019 - 15:40
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Alterations in Regional Homogeneity Assessed by fMRI in Patients with Migraine Without Aura.

J Med Syst. 2019 Jul 27;43(9):298

Authors: Chen C, Yan M, Yu Y, Ke J, Xu C, Guo X, Lu H, Wang X, Hu L, Wang J, Ni J, Zhao H

Abstract
The aim of this study was to investigate the alterations in regional homogeneity assessed by fMRI in patients with migraine without aura (MWoA). Fifty-six eligible MWoA patients and 32 matched healthy volunteers were enrolled in this study. MWoA patients were divided into three groups according to the headache days per month within 3 months: infrequent episodic migraine (IEM) group, frequent episodic migraine (FEM) group, and chronic migraine (CM) group. Data collection and rest-state fMRI examination were performed in all cases. The ReHo method was used to analyze the blood oxygen level dependent (BLOD) signals of the adjacent voxels in the brain regions of each patient, and the consistency of their fluctuations in the sequences of same time. Compared with normal controls, ReHo values of bilateral thalami, right insula and right middle temporal gyrus increased and both precentral gyri decreased in the IEM group; ReHo values of bilateral thalami and the right middle temporal gyrus increased; ReHo values of both anterior cingulate cortex, precentral gyri and putamen decreased in the FEM group. Compared with control group, ReHo values of left olfactory cortex, right hippocampus, parahippocampal gyrus, suboccipital gyrus and precuneus increased, both precentral gyri, precuneus, putamen and anterior cingulate cortex decreased in the CM group. Compared with IEM group, ReHo values of both putamen, left middle frontal gyrus, right superior frontal gyrus increased, and the left precuneus decreased in the FEM group. Compared with FEM group, ReHo values of left olfactory and left precuneus increased, and the right superior frontal gyrus, insula, middle temporal gyrus, thalami, both superior temporal gyri decreased in the CM group. In the IEM group, the changes of function focus on the regions associated with coding, conduction and regulation of pain signals. In the FEM group, functional alterations mainly concentrated on the regions associated with pain regulation and emotion cognition. In the CM group, the changes focus on the regions related to spatial attention and cognition, affective disorders and pain feedback, which may be associated with migraine production, development and chronification.

PMID: 31352647 [PubMed - in process]

The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding.

Mon, 07/29/2019 - 15:40
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The superior longitudinal fasciculus and its functional triple-network mechanisms in brooding.

Neuroimage Clin. 2019 Jul 19;24:101935

Authors: Pisner DA, Shumake J, Beevers CG, Schnyer DM

Abstract
Brooding, which refers to a repetitive focus on one's distress, is associated with functional connectivity within Default-Mode, Salience, and Executive-Control networks (DMN; SN; ECN), comprising the so-called "triple-network" of attention. Individual differences in brain structure that might perseverate dysfunctional connectivity of brain networks associated with brooding are less clear, however. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between brooding severity, white-matter microstructure, and resting-state functional connectivity in depressed adults (N = 32-44), and then examined whether findings directly replicated in a demographically-similar, independent sample (N = 36-45). Among the fully-replicated results, three core findings emerged. First, brooding severity is associated with functional integration and segregation of the triple-network, particularly with a Precuneal subnetwork of the DMN. Second, microstructural asymmetry of the Superior Longitudinal Fasciculus (SLF) provides a robust structural connectivity basis for brooding and may account for over 20% of its severity (Discovery: adj. R2 = 0.18; Replication: adj. R2 = 0.22; MSE = 0.06, Predictive R2 = 0.22). Finally, microstructure of the right SLF and auxiliary white-matter is associated with the functional connectivity correlates of brooding, both within and between components of the triple-network (Discovery: adj. R2 = 0.21; Replication: adj. R2 = 0.18; MSE = 0.03, Predictive R2 = 0.21-0.22). By cross-validating multimodal discovery with replication, the present findings help to reproducibly unify disparate perspectives of brooding etiology. Based on that synthesis, our study reformulates brooding as a microstructural-functional connectivity neurophenotype.

PMID: 31352219 [PubMed - as supplied by publisher]

Uncovering multi-site identifiability based on resting-state functional connectomes.

Mon, 07/29/2019 - 15:40
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Uncovering multi-site identifiability based on resting-state functional connectomes.

Neuroimage. 2019 Jul 25;:

Authors: Bari S, Amico E, Vike N, Talavage TM, Goñi J

Abstract
Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together-activities which are otherwise limited by the availability of subjects or funds at a single site. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity "fingerprints", and to improve identifiability of functional connectomes. The individual fingerprinting of functional connectivity profiles is promising due to its potential as a robust neuroimaging biomarker with which to draw single-subject inferences. We evaluated, on two independent multi-site datasets, individual fingerprints in test-retest visit pairs within and across two sites and present a generalized framework based on principal component analysis to improve identifiability. Those principal components that maximized differential identifiability of a training dataset were used as an orthogonal connectivity basis to reconstruct the individual functional connectomes of training and validation sets. The optimally reconstructed functional connectomes showed a substantial improvement in individual fingerprinting of the subjects within and across the two sites and test-retest visit pairs relative to the original data. A notable increase in ICC values for functional edges and resting-state networks were also observed for reconstructed functional connectomes. Improvements in identifiability were not found to be affected by global signal regression. Post-hoc analyses assessed the effect of the number of fMRI volumes on identifiability and showed that multi-site differential identifiability was for all cases maximized after optimal reconstruction. Finally, the generalizability of the optimal set of orthogonal basis of each dataset was evaluated through a leave-one-out procedure. Overall, results demonstrate that the data-driven framework presented in this study systematically improves identifiability in resting-state functional connectomes in multi-site studies.

PMID: 31352124 [PubMed - as supplied by publisher]

In search of multimodal brain alterations in Alzheimer's and Binswanger's disease.

Mon, 07/29/2019 - 15:40
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In search of multimodal brain alterations in Alzheimer's and Binswanger's disease.

Neuroimage Clin. 2019 Jul 15;:101937

Authors: Fu Z, Iraji A, Caprihan A, Adair JC, Sui J, Rosenberg GA, Calhoun VD

Abstract
Structural and functional brain abnormalities have been widely identified in dementia, but with variable replicability and significant overlap. Alzheimer's disease (AD) and Binswanger's disease (BD) share similar symptoms and common brain changes that can confound diagnosis. In this study, we aimed to investigate correlated structural and functional brain changes in AD and BD by combining resting-state functional magnetic resonance imaging (fMRI) and diffusion MRI. A group independent component analysis was first performed on the fMRI data to extract 49 intrinsic connectivity networks (ICNs). Then we conducted a multi-set canonical correlation analysis on three features, functional network connectivity (FNC) between ICNs, fractional anisotropy (FA) and mean diffusivity (MD). Two inter-correlated components show significant group differences. The first component demonstrates distinct brain changes between AD and BD. AD shows increased cerebellar FNC but decreased thalamic and hippocampal FNC. Such FNC alterations are linked to the decreased corpus callosum FA. AD also has increased MD in the frontal and temporal cortex, but BD shows opposite alterations. The second component demonstrates specific brain changes in BD. Increased FNC is mainly between default mode and sensory regions, while decreased FNC is mainly within the default mode domain and related to auditory regions. The FNC changes are associated with FA changes in posterior/middle cingulum cortex and visual cortex and increased MD in thalamus and hippocampus. Our findings provide evidence of linked functional and structural deficits in dementia and suggest that AD and BD have both common and distinct changes in white matter integrity and functional connectivity.

PMID: 31351845 [PubMed - as supplied by publisher]

Discovering common change-point patterns in functional connectivity across subjects.

Sun, 07/28/2019 - 14:40
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Discovering common change-point patterns in functional connectivity across subjects.

Med Image Anal. 2019 Jul 22;58:101532

Authors: Dai M, Zhang Z, Srivastava A

Abstract
This paper studies change-points in human brain functional connectivity (FC) and seeks patterns that are common across multiple subjects under identical external stimulus. FC relates to the similarity of fMRI responses across different brain regions when the brain is simply resting or performing a task. While the dynamic nature of FC is well accepted, this paper develops a formal statistical test for finding change-points in times series associated with FC. It represents short-term connectivity by a symmetric positive-definite matrix, and uses a Riemannian metric on this space to develop a graphical method for detecting change-points in a time series of such matrices. It also provides a graphical representation of estimated FC for stationary subintervals in between the detected change-points. Furthermore, it uses a temporal alignment of the test statistic, viewed as a real-valued function over time, to remove inter-subject variability and to discover common change-point patterns across subjects. This method is illustrated using data from Human Connectome Project (HCP) database for multiple subjects and tasks.

PMID: 31351229 [PubMed - as supplied by publisher]

Resting-state connectivity reveals a role for sensorimotor systems in vocal emotional processing in children.

Sun, 07/28/2019 - 14:40
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Resting-state connectivity reveals a role for sensorimotor systems in vocal emotional processing in children.

Neuroimage. 2019 Jul 24;:116052

Authors: Correia AI, Branco P, Martins M, Reis AM, Martins N, Castro SL, Lima CF

Abstract
Voices are a primary source of emotional information in everyday interactions. Being able to process non-verbal vocal emotional cues, namely those embedded in speech prosody, impacts on our behaviour and communication. Extant research has delineated the role of temporal and inferior frontal brain regions for vocal emotional processing. A growing number of studies also suggest the involvement of the motor system, but little is known about such potential involvement. Using resting-state fMRI, we ask if the patterns of motor system intrinsic connectivity play a role in emotional prosody recognition in children. Fifty-five 8-year-old children completed an emotional prosody recognition task and a resting-state scan. Better performance in emotion recognition was predicted by a stronger connectivity between the inferior frontal gyrus (IFG) and motor regions including primary motor, lateral premotor and supplementary motor sites. This is mostly driven by the IFG pars triangularis and cannot be explained by differences in domain-general cognitive abilities. These findings indicate that individual differences in the engagement of sensorimotor systems, and in its coupling with inferior frontal regions, underpin variation in children's emotional speech perception skills. They suggest that sensorimotor and higher-order evaluative processes interact to aid emotion recognition, and have implications for models of vocal emotional communication.

PMID: 31351162 [PubMed - as supplied by publisher]

A graph representation of functional diversity of brain regions.

Sun, 07/28/2019 - 14:40
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A graph representation of functional diversity of brain regions.

Brain Behav. 2019 Jul 27;:e01358

Authors: Yin D, Chen X, Zeljic K, Zhan Y, Shen X, Yan G, Wang Z

Abstract
INTRODUCTION: Modern network science techniques are popularly used to characterize the functional organization of the brain. A major challenge in network neuroscience is to understand how functional characteristics and topological architecture are related in the brain. Previous task-based functional neuroimaging studies have uncovered a core set of brain regions (e.g., frontal and parietal) supporting diverse cognitive tasks. However, the graph representation of functional diversity of brain regions remains to be understood.
METHODS: Here, we present a novel graph measure, the neighbor dispersion index, to test the hypothesis that the functional diversity of a brain region is embodied by the topological dissimilarity of its immediate neighbors in the large-scale functional brain network.
RESULTS: We consistently identified in two independent and publicly accessible resting-state functional magnetic resonance imaging datasets that brain regions in the frontoparietal and salience networks showed higher neighbor dispersion index, whereas those in the visual, auditory, and sensorimotor networks showed lower neighbor dispersion index. Moreover, we observed that human fluid intelligence was associated with the neighbor dispersion index of dorsolateral prefrontal cortex, while no such association for the other metrics commonly used for characterizing network hubs was noticed even with an uncorrected p < .05.
CONCLUSIONS: This newly developed graph theoretical method offers fresh insight into the topological organization of functional brain networks and also sheds light on individual differences in human intelligence.

PMID: 31350830 [PubMed - as supplied by publisher]

Differential effects of Down's syndrome and Alzheimer's neuropathology on default mode connectivity.

Sun, 07/28/2019 - 14:40
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Differential effects of Down's syndrome and Alzheimer's neuropathology on default mode connectivity.

Hum Brain Mapp. 2019 Jul 26;:

Authors: Wilson LR, Vatansever D, Annus T, Williams GB, Hong YT, Fryer TD, Nestor PJ, Holland AJ, Zaman SH

Abstract
Down's syndrome is a chromosomal disorder that invariably results in both intellectual disability and Alzheimer's disease neuropathology. However, only a limited number of studies to date have investigated intrinsic brain network organisation in people with Down's syndrome, none of which addressed the links between functional connectivity and Alzheimer's disease. In this cross-sectional study, we employed 11 C-Pittsburgh Compound-B (PiB) positron emission tomography in order to group participants with Down's syndrome based on the presence of fibrillar beta-amyloid neuropathology. We also acquired resting state functional magnetic resonance imaging data to interrogate the connectivity of the default mode network; a large-scale system with demonstrated links to Alzheimer's disease. The results revealed widespread positive connectivity of the default mode network in people with Down's syndrome (n = 34, ages 30-55, median age = 43.5) and a stark lack of anti-correlation. However, in contrast to typically developing controls (n = 20, ages 30-55, median age = 43.5), the Down's syndrome group also showed significantly weaker connections in localised frontal and posterior brain regions. Notably, while a comparison of the PiB-negative Down's syndrome group (n = 19, ages 30-48, median age = 41.0) to controls suggested that alterations in default mode connectivity to frontal brain regions are related to atypical development, a comparison of the PiB-positive (n = 15, ages 39-55, median age = 48.0) and PiB-negative Down's syndrome groups indicated that aberrant connectivity in posterior cortices is associated with the presence of Alzheimer's disease neuropathology. Such distinct profiles of altered connectivity not only further our understanding of the brain physiology that underlies these two inherently linked conditions but may also potentially provide a biomarker for future studies of neurodegeneration in people with Down's syndrome.

PMID: 31350817 [PubMed - as supplied by publisher]

Sino Longitudinal Study on Cognitive Decline (SILCODE): protocol for a Chinese longitudinal observational study to develop risk prediction models of conversion to mild cognitive impairment in individuals with subjective cognitive decline.

Sun, 07/28/2019 - 14:40
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Sino Longitudinal Study on Cognitive Decline (SILCODE): protocol for a Chinese longitudinal observational study to develop risk prediction models of conversion to mild cognitive impairment in individuals with subjective cognitive decline.

BMJ Open. 2019 Jul 26;9(7):e028188

Authors: Li X, Wang X, Su L, Hu X, Han Y

Abstract
INTRODUCTION: Understanding the biological mechanism of subjective cognitive decline (SCD) in preclinical Alzheimer's disease (AD) and identifying those who will soon convert to mild cognitive impairment (MCI) are critical for developing appropriate strategies for early diagnosis and intervention of AD. We present the study protocol of the Sino Longitudinal Study on Cognitive Decline (SILCODE), a longitudinal observational study focusing on SCD in the context of AD.
METHODS AND ANALYSIS: Within SILCODE, approximately 800 subjects with SCD who are between 50 and 79 years old will be recruited through standardised public advertisements or memory clinics. They will undergo extensive assessment, including clinical and neuropsychological assessments, blood sample collection for plasma beta-amyloid and ApoE genotype, urine samples collection for AD7c-NTP, and multimodal MRI scans (structural MRI, diffusion tensor imaging, resting-state functional MRI and optional task-based functional MRI) as well as optional glucose metabolism and amyloid positron emission tomography. Subjects will be contacted by telephone every 3 months and interviewed, on average, every 15 months for 5 years. The study endpoint is the development of mild cognitive impairment or dementia. Jak & Bondi's actuarial neuropsychological method will be used for diagnosis of MCI. The least absolute shrinkage and selection operator logistic regression model followed by the sub-distribution hazard function model with death as a competing risk will be constructed to establish risk prediction models.
ETHICS AND DISSEMINATION: The ethics committee of the Xuanwu Hospital of Capital Medical University has approved this study protocol (ID: [2017]046). The results will be published in peer-reviewed journals and presented at national and international scientific conferences.
TRIAL REGISTRATION NUMBER: NCT03370744; Pre-results.

PMID: 31350244 [PubMed - in process]

Identifying inter-individual differences in pain threshold using brain connectome: a test-retest reproducible study.

Sun, 07/28/2019 - 14:40
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Identifying inter-individual differences in pain threshold using brain connectome: a test-retest reproducible study.

Neuroimage. 2019 Jul 23;:116049

Authors: Tu Y, Zhang B, Cao J, Wilson G, Zhang Z, Kong J

Abstract
Individuals are unique in terms of brain and behavior. Some are very sensitive to pain, while others have a high tolerance. However, how inter-individual intrinsic differences in the brain are related to pain is unknown. Here, we performed longitudinal test-retest analyses to investigate pain threshold variability among individuals using a resting-state fMRI brain connectome. Twenty-four healthy subjects who received four MRI sessions separated by at least 7 days were included in the data analysis. Subjects' pain thresholds were measured using two modalities of experimental pain (heat and pressure) on two different locations (heat pain: leg and arm; pressure pain: leg and thumbnail). Behavioral results showed strong inter-individual variability and strong within-individual stability in pain threshold. Resting state fMRI data analyses showed that functional connectivity profiles can accurately identify subjects across four sessions, indicating that an individual's connectivity profile may be intrinsic and unique. By using multivariate pattern analyses, we found that connectivity profiles could be used to predict an individual's pain threshold at both within-session and between-session levels, with the most predictive contribution from medial-frontal and frontal-parietal networks. These results demonstrate the potential of using a resting-state fMRI brain connectome to build a 'neural trait' for characterizing an individual's pain-related behavior, and such a 'neural trait' may eventually be used to personalize clinical assessments.

PMID: 31349067 [PubMed - as supplied by publisher]

Dissociating individual connectome traits using low-rank learning.

Sun, 07/28/2019 - 14:40
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Dissociating individual connectome traits using low-rank learning.

Brain Res. 2019 Jul 23;:146348

Authors: Qin J, Shen H, Zeng LL, Gao K, Luo Z, Hu D

Abstract
Intrinsic functional connectivity (FC) exhibits high variability across individuals, which may account for the diversity of cognitive and behavioural ability. This variability in connectivity could be attributed to individual-specific trait and inter-session state differences (intra-subject differences), as well as a small amount of noise. However, it is still a challenge to perform accurate identification of connectivity traits from FC. Here, we introduced a novel low-rank learning model to solve this problem with a new constraint item that could reduce intra-subject differences. The model could dissociate FC into a substrate (substrate) that delineates functional characteristics common across the population and connectivity traits that are expected to account for individual behavioural differences. Subsequently, we performed a sparse dictionary learning algorithm on the extracted connectivity traits and obtained a dictionary matrix, named connectivity dictionary. We could then predict cognitive behaviours, including fluid intelligence, oral reading recognition, grip strength and anger-aggression, more accurately using the connectivity dictionary than the original FC. The results reflect that we captured individual connectivity traits that more effectively represent cognitive behaviour. Moreover, we found that the functional substrate is significantly correlated with large-scale anatomical brain architecture, and individual differences in connectivity traits are constrained by the connectivity substrate. Our findings may advance our understanding of the relationships among anatomy, function, and behaviour.

PMID: 31348912 [PubMed - as supplied by publisher]

Brachial plexus injury and resting-state fMRI: Need for consensus.

Sun, 07/28/2019 - 14:40
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Brachial plexus injury and resting-state fMRI: Need for consensus.

Neurol India. 2019 May-Jun;67(3):679-683

Authors: Thaploo D, Bhat DI, Kulkarni MV, Devi BI

Abstract
Objective: The purpose of the study is to conduct the systematic review of literature available on resting-state functional MRI (fMRI) and brachial plexus injury.
Methods: We reviewed all the literature that are available on PubMed; keywords used were resting state, brachial plexus injury, and functional imaging. The reference papers listed were also reviewed. The research items were restricted to publications in English. Some papers have also incorporated studies such as task-based fMRI and transcranial magnetic stimulation (TMS), but only resting-state studies were included for this review.
Results: A total of 13 papers were identified, and only 10 were reviewed based on the criteria. The reviewed papers were further categorized on the basis of whether or not any surgical intervention was done. Seven papers have surgical management such as contralateral cervical 7 (CC7) neurotisation or intercostal nerve (ICN) musculocutaneous nerve (MCN) neurotisation.
Conclusion: There is conclusive evidence showing that there is cortical reorganisation following brachial plexus injury in both birth and traumatic cases. The changes are restricted to some of the resting-state networks only (default mode network, sensorimotor network, in particular). However, no study till date has focused on a far more longitudinal approach at studying these changes. It will be interesting to see the exact time and effect of these changes.

PMID: 31347534 [PubMed - in process]

Synchronization and variability imbalance underlie cognitive impairment in primary-progressive multiple sclerosis.

Sun, 07/28/2019 - 14:40
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Synchronization and variability imbalance underlie cognitive impairment in primary-progressive multiple sclerosis.

Sci Rep. 2017 04 21;7:46411

Authors: Petracca M, Saiote C, Bender HA, Arias F, Farrell C, Magioncalda P, Martino M, Miller A, Northoff G, Lublin F, Inglese M

Abstract
We aimed to investigate functional connectivity and variability across multiple frequency bands in brain networks underlying cognitive deficits in primary-progressive multiple sclerosis (PP-MS) and to explore how they are affected by the presence of cortical lesions (CLs). We analyzed functional connectivity and variability (measured as the standard deviation of BOLD signal amplitude) in resting state networks (RSNs) associated with cognitive deficits in different frequency bands in 25 PP-MS patients (12 M, mean age 50.9 ± 10.5 years) and 20 healthy subjects (9 M, mean age 51.0 ± 9.8 years). We confirmed the presence of a widespread cognitive deterioration in PP-MS patients, with main involvement of visuo-spatial and executive domains. Cognitively impaired patients showed increased variability, reduced synchronicity between networks involved in the control of cognitive macro-domains and hyper-synchronicity limited to the connections between networks functionally more segregated. CL volume was higher in patients with cognitive impairment and was correlated with functional connectivity and variability. We demonstrate, for the first time, that a functional reorganization characterized by hypo-synchronicity of functionally-related/hyper-synchronicity of functionally-segregated large scale networks and an abnormal pattern of neural activity underlie cognitive dysfunction in PP-MS, and that CLs possibly play a role in variability and functional connectivity abnormalities.

PMID: 28429774 [PubMed - indexed for MEDLINE]

Novel relative relevance score for estimating Brain Connectivity from fMRI data using an explainable neural network approach.

Fri, 07/26/2019 - 10:20

Novel relative relevance score for estimating Brain Connectivity from fMRI data using an explainable neural network approach.

J Neurosci Methods. 2019 Jul 22;:108371

Authors: Dang S, Chaudhury S

Abstract
BACKGROUND: Functional integration or connectivity in brain is directional, non-linear as well as variable in time-lagged dependence. Deep neural networks (DNN) have become an indispensable tool everywhere, by learning higher levels of abstract and complex patterns from raw data. However, in neuroscientific community they generally work as black-boxes, leading to the explanation of results difficult and less intuitive. We aim to propose a brain-connectivity measure based on an explainable NN (xNN) approach.
NEW METHOD: We build a NN-based predictor for regression problem. Since we aim to determine the contribution/relevance of past data-point from one region i in the prediction of current data-point from another region j, i.e. the higher-order connectivity between two brain-regions, we employ layer-wise relevance propagation (Bach et al., 2015) (LRP, a method for explaining DNN predictions), which has not been done before to the best of our knowledge. Specifically, we propose a novel score depending on weights as a quantitative measure of connectivity, called as relative relevance score (xNN-RRS). The RRS is an intuitive and transparent score. We provide an interpretation of the trained NN weights with-respect-to the brain-connectivity.
RESULTS: Face validity of our approach is demonstrated with experiments on simulated data, over existing methods. We also demonstrate construct validity of xNN-RRS in a resting-state fMRI experiment.
COMPARISON: Our approach shows superior performance, in terms of accuracy and computational complexity, over existing state-of-the-art methods for brain-connectivity estimation.
CONCLUSION: The proposed method is promising to serve as a first post-hoc explainable NN-approach for brain-connectivity analysis in clinical applications.

PMID: 31344374 [PubMed - as supplied by publisher]

Resting-state connectivity within and across neural circuits in anorexia nervosa.

Fri, 07/26/2019 - 10:20
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Resting-state connectivity within and across neural circuits in anorexia nervosa.

Brain Behav. 2019 01;9(1):e01205

Authors: Uniacke B, Wang Y, Biezonski D, Sussman T, Lee S, Posner J, Steinglass J

Abstract
INTRODUCTION: Obsessional thoughts and ritualized eating behaviors are characteristic of Anorexia Nervosa (AN), leading to the common suggestion that the illness shares neurobiology with obsessive-compulsive disorder (OCD). Resting-state functional connectivity MRI (rs-fcMRI) is a measure of functional neural architecture. This longitudinal study examined functional connectivity in AN within the limbic cortico-striato-thalamo-cortical (CSTC) loop, as well as in the salience network, the default mode network, and the executive control network (components of the triple network model of psychopathology).
METHODS: Resting-state functional connectivity MRI scans were collected in unmedicated female inpatients with AN (n = 25) and healthy controls (HC; n = 24). Individuals with AN were scanned before and after weight restoration and followed for one month after hospital discharge. HC were scanned twice over the same timeframe.
RESULTS: Using a seed-based correlation approach, individuals with AN had increased connectivity within the limbic CSTC loop when underweight, only. There was no significant association between limbic CSTC connectivity and obsessive-compulsive symptoms or prognosis. Exploratory analyses of functional network connectivity within the triple network model showed reduced connectivity between the salience network and left executive control network among AN relative to HC. These abnormalities persisted following weight restoration.
CONCLUSIONS: The CSTC findings suggest that the neural underpinnings of obsessive-compulsive symptoms may differ from those of OCD. The inter-network abnormalities warrant examination in relation to illness-specific behaviors, namely abnormal eating behavior. This longitudinal study highlights the complexity of the neural underpinnings of AN.

PMID: 30590873 [PubMed - indexed for MEDLINE]

Sex-Dependent Associations among Maternal Depressive Symptoms, Child Reward Network, and Behaviors in Early Childhood.

Thu, 07/25/2019 - 15:37
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Sex-Dependent Associations among Maternal Depressive Symptoms, Child Reward Network, and Behaviors in Early Childhood.

Cereb Cortex. 2019 Jul 24;:

Authors: Wang Q, Zhang H, Poh JS, Pecheva D, Broekman BFP, Chong YS, Shek LP, Gluckman PD, Fortier MV, Meaney MJ, Qiu A

Abstract
Maternal depression is associated with disrupted neurodevelopment in offspring. This study examined relationships among postnatal maternal depressive symptoms, the functional reward network and behavioral problems in 4.5-year-old boys (57) and girls (65). We employed canonical correlation analysis to evaluate whether the resting-state functional connectivity within a reward network, identified through an activation likelihood estimation (ALE) meta-analysis of fMRI studies, was associated with postnatal maternal depressive symptoms and child behaviors. The functional reward network consisted of three subnetworks, that is, the mesolimbic, mesocortical, and amygdala-hippocampus reward subnetworks. Postnatal maternal depressive symptoms were associated with the functional connectivity of the mesocortical subnetwork with the mesolimbic and amygdala-hippocampus complex subnetworks in girls and with the functional connectivity within the mesocortical subnetwork in boys. The functional connectivity of the amygdala-hippocampus subnetwork with the mesocortical and mesolimbic subnetworks was associated with both internalizing and externalizing problems in girls, while in boys, the functional connectivity of the mesocortical subnetwork with the amygdala-hippocampus complex and the mesolimbic subnetworks was associated with the internalizing and externalizing problems, respectively. Our findings suggest that the functional reward network might be a promising neural phenotype for effects of maternal depression and potential intervention to nurture child behavioral development.

PMID: 31339998 [PubMed - as supplied by publisher]

Tracking the Main States of Dynamic Functional Connectivity in Resting State.

Thu, 07/25/2019 - 15:37
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Tracking the Main States of Dynamic Functional Connectivity in Resting State.

Front Neurosci. 2019;13:685

Authors: Zhou Q, Zhang L, Feng J, Lo CZ

Abstract
Dynamical changes have recently been tracked in functional connectivity (FC) calculated from resting-state functional magnetic resonance imaging (R-fMRI), when a person is conscious but not carrying out a directed task during scanning. Diverse dynamical FC states (dFC) are believed to represent different internal states of the brain, in terms of brain-regional interactions. In this paper, we propose a novel protocol, the signed community clustering with the optimized modularity by two-step procedures, to track dynamical whole brain functional connectivity (dWFC) states. This protocol is assumption free without a priori threshold for the number of clusters. By applying our method on sliding window based dWFC's with automated anatomical labeling 2 (AAL2), three main dWFC states were extracted from R-fMRI datasets in Human Connectome Project, that are independent on window size. Through extracting the FC features of these states, we found the functional links in state 1 (WFC-C1) mainly involved visual, somatomotor, attention and cerebellar (posterior lobe) modules. State 2 (WFC-C2) was similar to WFC-C1, but more FC's linking limbic, default mode, and frontoparietal modules and less linking the cerebellum, sensory and attention modules. State 3 had more FC's linking default mode, limbic, and cerebellum, compared to WFC-C1 and WFC-C2. With tests of robustness and stability, our work provides a solid, hypothesis-free tool to detect dWFC states for the possibility of tracking rapid dynamical change in FCs among large data sets.

PMID: 31338016 [PubMed]

Combining multiple connectomes improves predictive modeling of phenotypic measures.

Thu, 07/25/2019 - 15:37
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Combining multiple connectomes improves predictive modeling of phenotypic measures.

Neuroimage. 2019 Jul 20;:116038

Authors: Gao S, Greene AS, Constable RT, Scheinost D

Abstract
Resting-state and task-based functional connectivity matrices, or connectomes, are powerful predictors of individual differences in phenotypic measures. However, most of the current state-of-the-art algorithms only build predictive models based on a single connectome for each individual. This approach neglects the complementary information contained in connectomes from different sources and reduces prediction performance. In order to combine different task connectomes into a single predictive model in a principled way, we propose a novel prediction framework, termed multidimensional connectome-based predictive modeling. Two specific algorithms are developed and implemented under this framework. Using two large open-source datasets with multiple tasks-the Human Connectome Project and the Philadelphia Neurodevelopmental Cohort, we validate and compare our framework against performing connectome-based predictive modeling (CPM) on each task connectome independently, CPM on a general functional connectivity matrix created by averaging together all task connectomes for an individual, and CPM with a naïve extension to multiple connectomes where each edge for each task is selected independently. Our framework exhibits superior performance in prediction compared with the other competing methods. We found that different tasks contribute differentially to the final predictive model, suggesting that the battery of tasks used in prediction is an important consideration. This work makes two major contributions: First, two methods for combining multiple connectomes from different task conditions in one predictive model are demonstrated; Second, we show that these models outperform a previously validated single connectome-based predictive model approach.

PMID: 31336188 [PubMed - as supplied by publisher]

Brain Dynamics: Global Pulse and Brain State Switching.

Thu, 07/25/2019 - 15:37
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Brain Dynamics: Global Pulse and Brain State Switching.

Curr Biol. 2019 Jul 22;29(14):R690-R692

Authors: Ville DV

Abstract
A major challenge in systems-level neuroscience is to understand the dynamic formation and succession of brain states. A new study has extracted reproducible brain states from mouse resting-state fMRI data, revealing interactions between occurrences of these states and the phase of global signal fluctuations and alterations of the states in a mouse model of autism.

PMID: 31336086 [PubMed - in process]

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