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

Fri, 07/26/2019 - 21:39
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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]

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

Thu, 07/25/2019 - 21:38
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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 - 21:38
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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 - 03:37
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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 - 03:37
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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]

Abnormal intra-network architecture in extra-striate cortices in amblyopia: a resting state fMRI study.

Thu, 07/25/2019 - 03:37
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Abnormal intra-network architecture in extra-striate cortices in amblyopia: a resting state fMRI study.

Eye Vis (Lond). 2019;6:20

Authors: Lu Z, Huang Y, Lu Q, Feng L, Nguchu BA, Wang Y, Wang H, Li G, Zhou Y, Qiu B, Zhou J, Wang X

Abstract
Background: Amblyopia (lazy eye) is one of the most common causes of monocular visual impairment. Intensive investigation has shown that amblyopes suffer from a range of deficits not only in the primary visual cortex but also the extra-striate visual cortex. However, amblyopic brain processing deficits in large-scale information networks especially in the visual network remain unclear.
Methods: Through resting state functional magnetic resonance imaging (rs-fMRI), we studied the functional connectivity and efficiency of the brain visual processing networks in 18 anisometropic amblyopic patients and 18 healthy controls (HCs).
Results: We found a loss of functional correlation within the higher visual network (HVN) and the visuospatial network (VSN) in amblyopes. Additionally, compared with HCs, amblyopic patients exhibited disruptions in local efficiency in the V3v (third visual cortex, ventral part) and V4 (fourth visual cortex) of the HVN, as well as in the PFt, hIP3 (human intraparietal area 3), and BA7p (Brodmann area 7 posterior) of the VSN. No significant alterations were found in the primary visual network (PVN).
Conclusion: Our results indicate that amblyopia results in an intrinsic decrease of both network functional correlations and local efficiencies in the extra-striate visual networks.

PMID: 31334295 [PubMed]

Chemotherapy Potentially Facilitates the Occurrence of Radiation Encephalopathy in Patients With Nasopharyngeal Carcinoma Following Radiotherapy: A Multiparametric Magnetic Resonance Imaging Study.

Thu, 07/25/2019 - 03:37
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Chemotherapy Potentially Facilitates the Occurrence of Radiation Encephalopathy in Patients With Nasopharyngeal Carcinoma Following Radiotherapy: A Multiparametric Magnetic Resonance Imaging Study.

Front Oncol. 2019;9:567

Authors: Zhang Y, Yi X, Gao J, Li L, Liu L, Qiu T, Zhang J, Zhang Y, Liao W

Abstract
Radiation encephalopathy (RE) is deemed to be a disease induced only by radiotherapy (RT), with the effects of chemotherapeutic agents on the brains of nasopharyngeal carcinoma (NPC) patients being largely overlooked. In this study, we investigated structural and functional brain alterations in NPC patients following RT with or without chemotherapy. Fifty-six pre-RT, 37 post-RT, and 108 post-CCRT (concomitant chemo-radiotherapy) NPC patients were enrolled in this study. A surface-based local gyrification index (LGI) was obtained from high resolution MRI and was used to evaluate between-group differences in cortical folding. Seed-based functional connectivity (FC) analysis of resting-state fMRI data was also conducted to investigate the functional significance of the cortical folding alterations. Compared with the Pre-RT group, patients in the Post-CCRT group showed LGI reductions in widespread brain regions including the bilateral temporal lobes, insula, frontal lobes, and parietal lobes. Compared with the Post-RT group, patients in the Post-CCRT group showed LGI reductions in the right insula, which extended to the adjacent frontal lobe. Seed-based FC analysis showed that patients in the Post-CCRT group had lower FC between the insula and the left middle frontal gyrus than patients in the Pre-RT group. The follow-up results showed that patients in the Post-CCRT group had a much higher RE incidence rate (20.4%) than patients in the Post-RT group (2.7%; P = 0.01). These findings indicate that chemotherapy potentially facilitated the occurrence of RE in NPC patients who underwent radiotherapy.

PMID: 31334108 [PubMed]

Understanding the neural mechanisms of lisdexamfetamine dimesylate (LDX) pharmacotherapy in Binge Eating Disorder (BED): a study protocol.

Thu, 07/25/2019 - 03:37
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Understanding the neural mechanisms of lisdexamfetamine dimesylate (LDX) pharmacotherapy in Binge Eating Disorder (BED): a study protocol.

J Eat Disord. 2019;7:23

Authors: Griffiths KR, Yang J, Touyz SW, Hay PJ, Clarke SD, Korgaonkar MS, Gomes L, Anderson G, Foster S, Kohn MR

Abstract
Background: The efficacy and safety of Lisdexamfetamine dimesylate (LDX) in the treatment of moderate to severe binge eating disorder (BED) has been demonstrated in multiple randomised clinical trials. Despite this, little is known about how LDX acts to improve binge eating symptoms. This study aims to provide a comprehensive understanding of the neural mechanisms by which LDX improves symptoms of BED. We hypothesise that LDX will act by normalising connectivity within neural circuits responsible for reward and impulse control, and that this normalisation will correlate with reduced binge eating episodes.
Methods: This is an open-label Phase 4 clinical trial of LDX in adults with moderate to severe BED. Enrolment will include 40 adults with moderate to severe BED aged 18-40 years and Body Mass Index (BMI) of 20-45 kg/m2, and 22 healthy controls matched for age, gender and BMI. Clinical interview and validated scales are used to confirm diagnosis and screen for exclusion criteria, which include comorbid anorexia nervosa or bulimia nervosa, use of psychostimulants within the past 6 months, and current use of antipsychotics or noradrenaline reuptake inhibitors. Baseline assessments include clinical symptoms, multimodal neuroimaging, cognitive assessment of reward sensitivity and behavioural inhibition, and an (optional) genetic sample. A subset of these assessments are repeated after eight weeks of treatment with LDX titrated to either 50 or 70 mg. The primary outcome measures are resting-state intrinsic connectivity and the number of binge eating episodes. Analyses will be applied to resting-state fMRI data to characterise pharmacological effects across the functional connectome, and assess correlations with symptom measure changes. Comparison of neural measures between controls and those with BED post-treatment will also be performed to determine whether LDX normalises brain function.
Discussion: First enrolment was in May 2018, and is ongoing. This study is the first comprehensive investigation of the neurobiological changes that occur with LDX treatment in adults with moderate to severe BED.
Trial registration: ACTRN12618000623291, Australian and New Zealand Clinical Trials Registry URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=374913&isReview=true. Date of Registration: 20 April 2018.

PMID: 31333843 [PubMed]

A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data.

Thu, 07/25/2019 - 03:37
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A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data.

Front Psychiatry. 2019;10:392

Authors: Dekhil O, Ali M, El-Nakieb Y, Shalaby A, Soliman A, Switala A, Mahmoud A, Ghazal M, Hajjdiab H, Casanova MF, Elmaghraby A, Keynton R, El-Baz A, Barnes G

Abstract
Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.

PMID: 31333507 [PubMed]

Baseline Functional Connectivity Features of Neural Network Nodes Can Predict Improvement After Sound Therapy Through Adjusted Narrow Band Noise in Tinnitus Patients.

Thu, 07/25/2019 - 03:37
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Baseline Functional Connectivity Features of Neural Network Nodes Can Predict Improvement After Sound Therapy Through Adjusted Narrow Band Noise in Tinnitus Patients.

Front Neurosci. 2019;13:614

Authors: Han L, Na Z, Chunli L, Yuchen C, Pengfei Z, Hao W, Xu C, Peng Z, Zheng W, Zhenghan Y, Shusheng G, Zhenchang W

Abstract
Previous resting-state functional magnetic resonance imaging (fMRI) studies have shown neural connectivity alterations after the treatment of tinnitus. We aim to study the value of the baseline functional connectivity features of neural network nodes to predict outcomes of sound therapy through adjusted narrow band noise. The fMRI data of 27 untreated tinnitus patients and 27 matched healthy controls were analyzed. We calculated the graph-theoretical metric degree centrality (DC) to characterize the functional connectivity of the neural network nodes. Therapeutic outcomes are determined by the changes in the Tinnitus Handicap Inventory (THI) score after a 12-week intervention. The connectivity of 10 brain nodes in tinnitus patients was significantly increased at baseline. The functional connectivity of right insula, inferior parietal lobule (IPL), bilateral thalami, and left middle temporal gyrus was significantly modified with the sound therapy, and such changes correlated with THI changes in tinnitus patients. Receiver operating characteristic curve analyses revealed that the measurements from the five brain regions were effective at classifying improvement after therapy. After age, gender, and education correction, the adjusted area under the curve (AUC) values for the bilateral thalami were the highest (left, 0.745; right, 0.708). Our study further supported the involvement of the fronto-parietal-cingulate network in tinnitus and found that the connectivity of the thalamus at baseline is an object neuroimaging-based indicator to predict clinical outcome of sound therapy through adjusted narrow band noise.

PMID: 31333394 [PubMed]

Resting-state functional connectivity in treatment response and resistance in schizophrenia: A systematic review.

Thu, 07/25/2019 - 03:37
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Resting-state functional connectivity in treatment response and resistance in schizophrenia: A systematic review.

Schizophr Res. 2019 Jul 19;:

Authors: Chan NK, Kim J, Shah P, Brown EE, Plitman E, Carravaggio F, Iwata Y, Gerretsen P, Graff-Guerrero A

Abstract
BACKGROUND: Treatment-resistant schizophrenia (TRS) and treatment-responsive schizophrenia may exhibit distinct pathophysiology. Several functional magnetic resonance imaging (fMRI) studies have used resting-state functional connectivity analyses (rs-FC) in TRS patients to identify markers of treatment resistance. However, to date, existing findings have not been systematically evaluated.
METHODS: A systematic literature search using Embase, MEDLINE, PsycINFO, ProQuest, PUBMED, and Scopus was performed. The query sought fMRI articles investigating rs-FC in treatment response or resistance in patients with schizophrenia. Only studies that examined treatment response, operationalized as the explicit categorization of patients by their response to antipsychotic medication, were considered eligible. Pairwise comparisons between patient groups and controls were extracted from each study.
RESULTS: The search query identified 159 records. Ten studies met inclusion criteria. Five studies examined not TRS (NTRS), and 8 studies examined TRS. Differences in rs-FC analysis methodology precluded direct comparisons between studies. However, disruptions in areas involved in visual and auditory information processing were implicated in both patients with TRS and NTRS. Changes in connectivity with sensorimotor network areas tended to appear in the context of TRS but not NTRS. Moreover, there was some indication that this connectivity could be affected by clozapine.
CONCLUSIONS: Functional connectivity may provide clinically meaningful biomarkers of treatment response and resistance in schizophrenia. Studies generally identified similar areas of disruption, though methodological differences largely precluded direct comparison between disruption effects. Implementing data sharing as standard practice will allow future reviews and meta-analyses to identify rs-FC correlates of TRS.

PMID: 31331784 [PubMed - as supplied by publisher]