Most recent paper

Subscribe to Most recent paper feed Most recent paper
NCBI: db=pubmed; Term="resting"[All Fields] AND "fMRI"[All Fields]
Updated: 46 min 13 sec ago

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

Sun, 07/28/2019 - 21:41
Related Articles

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 - 21:41
Related Articles

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]

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

Sun, 07/28/2019 - 21:41
Related Articles

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 - 21:41
Related Articles

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 - 21:41
Related Articles

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]

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

Fri, 07/26/2019 - 21:39
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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
Related Articles

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]

Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.

Wed, 07/24/2019 - 00:36
Related Articles

Deep Learning of Static and Dynamic Brain Functional Networks for Early MCI Detection.

IEEE Trans Med Imaging. 2019 Jul 17;:

Authors: Kam TE, Zhang H, Jiao Z, Shen D

Abstract
While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain functional networks (BFNs). We propose a novel CNN framework to simultaneously learn embedded features from BFNs for brain disease diagnosis. Since BFNs can be built by considering both static and dynamic functional connectivity (FC), we first decompose rs-fMRI into multiple static BFNs with modified independent component analysis. Then, voxel-wise variability in dynamic FC is used to quantify BFN dynamics. A set of paired 3D images representing static/dynamic BFNs can be fed into 3D CNNs, from which we can hierarchically and simultaneously learn static/dynamic BFN features. As a result, dynamic BFN features can complement static BFN features and, at meantime, different BFNs can help each other towards a joint and better classification. We validate our method with a publicly accessible, large cohort of rs-fMRI dataset in early-stage mild cognitive impairment (eMCI) diagnosis, which is one of the most challenging problems to the clinicians. By comparing with a conventional method, our method shows significant diagnostic performance improvement by almost 10%. This result demonstrates the effectiveness of deep learning in preclinical Alzheimer's disease diagnosis, based on the complex and high-dimensional voxel-wise spatiotemporal patterns of the resting-state brain functional connectomics. The framework provides a new but intuitive way to fully exploit deeply embedded diagnostic features from rs-fMRI for better individualized diagnosis of various neurological diseases.

PMID: 31329111 [PubMed - as supplied by publisher]

State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits.

Tue, 07/23/2019 - 00:35
Related Articles

State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits.

Neuroimage. 2019 Jul 18;:116036

Authors: Takagi Y, Hirayama JI, Tanaka SC

Abstract
An increasing number of functional magnetic resonance imaging (fMRI) studies have revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have inherently focused on state-specific characterizations of brain networks and their functions, several recent studies reported on the potential state-unspecific nature of functional brain networks, such as global similarities across different experimental conditions or states, including both task and resting states. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variations in whole-brain functional connectivity patterns, called "Common Neural Modes" (CNMs), from a large-scale fMRI database including eight task/resting states. Furthermore, we tested how CNMs accounted for variability in individual cognitive measures. The results revealed that three CNMs were robustly extracted under various dimensions of features used. Each of these CNMs was preferentially correlated with different aspects of representative cognitive measures, reflecting stable individual traits. Importantly, the association between CNMs and cognitive measures emerged from brain connectivity data alone ("unsupervised"), whereas previous related studies have explicitly used both connectivity and cognitive measures to build their prediction models ("supervised"). The three CNMs were also able to predict several life outcomes, including income and life satisfaction, and achieved the highest level of performance when combined with a conventional cognitive measure. Our findings highlight the importance of state-unspecific brain networks in characterizing fundamental individual variation.

PMID: 31326571 [PubMed - as supplied by publisher]

Comment on "resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis".

Mon, 07/22/2019 - 03:35
Related Articles

Comment on "resting-state fMRI in Parkinson's disease patients with cognitive impairment: A meta-analysis".

Parkinsonism Relat Disord. 2019 Jul 12;:

Authors: Wang H, Wang W, Wang X, Hu J, Yao L

PMID: 31324553 [PubMed - as supplied by publisher]

A systematic review of brain functional connectivity patterns involved in episodic and semantic memory.

Sat, 07/20/2019 - 18:34

A systematic review of brain functional connectivity patterns involved in episodic and semantic memory.

Rev Neurosci. 2019 Jul 19;:

Authors: Palacio N, Cardenas F

Abstract
The study of functional connectivity and declarative memory has lately been focused on finding biomarkers of neuropsychological diseases. However, little is known about its patterns in healthy brains. Thus, in this systematic review we analyze and integrate the findings of 81 publications regarding functional connectivity (measured by fMRI during both task and resting-state) and semantic and episodic memory in healthy adults. Moreover, we discriminate and analyze the main areas and links found in specific memory phases (encoding, storage or retrieval) based on several criteria, such as time length, depth of processing, rewarding value of the information, vividness and amount or kind of details retrieved. There is a certain degree of overlap between the networks of episodic and semantic memory and between the encoding and retrieval stages. Although several differences are pointed out during the article, this calls to attention the need for further empirical studies that actively compare both types of memory, particularly using other baseline conditions apart from the traditional resting state. Indeed, the active involvement of the default mode network in both declarative memory and resting condition suggests the possibility that during rest there is an on-going memory processing. We find support for the 'attention to memory' hypothesis, the memory differentiation model and the appropriate transfer hypothesis, but some evidence is inconsistent with the traditional hub-and-spoke model.

PMID: 31323012 [PubMed - as supplied by publisher]