Cognitive state following mild stroke: A matter of hippocampal mean diffusivity.
Hippocampus. 2015 Jul 27;
Authors: Kliper E, Ben Assayag E, Korczyn AD, Auriel E, Shopin L, Hallevi H, Shenhar-Tsarfaty S, Mike A, Artzi M, Klovatch I, Bornstein NM, Ben Bashat D
Introduction The hippocampus is known to play a vital role in learning and memory and was demonstrated as an early imaging marker for Alzheimer's disease (AD). However, its role as a predictor for mild cognitive impairment and dementia following stroke is unclear. The main purpose of this study was to examine the associations between hippocampal volume, mean diffusivity (MD) and connectivity and cognitive state following stroke. Materials and Methods Eighty three consecutive first ever mild to moderate stroke or transient ischemic attack (TIA) survivors from our ongoing prospective TABASCO (Tel Aviv Brain Acute Stroke Cohort) study underwent magnetic resonance imaging scans within seven days of stroke onset. Hippocampal volume was measured from T1 weighted images, hippocampal mean diffusivity was calculated from diffusion tensor imaging and connectivity was calculated from resting state fMRI. Global cognitive assessments were evaluated during hospitalization and six and twelve months later using a computerized neuropsychological battery. Multiple linear regression analysis was used to test which of the hippocampi measurements best predict cognitive state. Results All three imaging parameters were significantly correlated to each other (|r's| >0.3, p's<0.005), and with cognitive state 6 and 12 months after the event. Multiple regression analyses demonstrated the predictive role of hippocampal mean diffusivity (β=-0.382, p=0.026) on cognitive state, above and beyond that of volume and connectivity of this structure. Discussion To our knowledge, the combination of hippocampal volume, mean diffusivity and connectivity in first ever post stroke or TIA patients has not yet been considered in relation to cognitive state. The results demonstrate the predictive role of hippocampal mean diffusivity, suggesting that these changes may precede and contribute to volumetric and connectivity changes in the hippocampi, potentially serving as a marker for early identification of patients at risk of developing cognitive impairment or dementia. This article is protected by copyright. All rights reserved.
PMID: 26222988 [PubMed - as supplied by publisher]
Measuring Asymmetric Interactions in Resting State Brain Networks.
Inf Process Med Imaging. 2015;24:399-410
Authors: Joshi AA, Salloum R, Bhushan C, Leahy RM
Directed graph representations of brain networks are increasingly being used to indicate the direction and level of influence among brain regions. Most of the existing techniques for directed graph representations are based on time series analysis and the concept of causality, and use time lag information in the brain signals. These time lag-based techniques can be inadequate for functional magnetic resonance imaging (fMRI) signal analysis due to the limited time resolution of fMRI as well as the low frequency hemodynamic response. The aim of this paper is to present a novel measure of necessity that uses asymmetry in the joint distribution of brain activations to infer the direction and level of interaction among brain regions. We present a mathematical formula for computing necessity and extend this measure to partial necessity, which can potentially distinguish between direct and indirect interactions. These measures do not depend on time lag for directed modeling of brain interactions and therefore are more suitable for fMRI signal analysis. The necessity measures were used to analyze resting state fMRI data to determine the presence of hierarchy and asymmetry of brain interactions during resting state. We performed ROI-wise analysis using the proposed necessity measures to study the default mode network. The empirical joint distribution of the fMRI signals was determined using kernel density estimation, and was used for computation of the necessity and partial necessity measures. The significance of these measures was determined using a one-sided Wilcoxon rank-sum test. Our results are consistent with the hypothesis that the posterior cingulate cortex plays a central role in the default mode network.
PMID: 26221690 [PubMed - in process]
Bootstrapped Permutation Test for Multiresponse Inference on Brain Behavior Associations.
Inf Process Med Imaging. 2015;24:113-24
Authors: Ng B, Poline JB, Thirion B, Greicius M, IMAGEN Consortium
Despite that diagnosis of neurological disorders commonly involves a collection of behavioral assessments, most neuroimaging studies investigating the associations between brain and behavior largely analyze each behavioral measure in isolation. To jointly model multiple behavioral scores, sparse multiresponse regression (SMR) is often used. However, directly applying SMR without statistically controlling for false positives could result in many spurious findings. For models, such as SMR, where the distribution of the model parameters is unknown, permutation test and stability selection are typically used to control for false positives. In this paper, we present another technique for inferring statistically significant features from models with unknown parameter distribution. We refer to this technique as bootstrapped permutation test (BPT), which uses Studentized statistics to exploit the intuition that the variability in parameter estimates associated with relevant features would likely be higher with responses permuted. On synthetic data, we show that BPT provides higher sensitivity in identifying relevant features from the SMR model than permutation test and stability selection, while retaining strong control on the false positive rate. We further apply BPT to study the associations between brain connectivity estimated from pseudo-rest fMRI data of 1139 fourteen year olds and behavioral measures related to ADHD. Significant connections are found between brain networks known to be implicated in the behavioral tasks involved. Moreover, we validate the identified connections by fitting a regression model on pseudo-rest data with only those connections and applying this model on resting state fMRI data of 337 left out subjects to predict their behavioral scores. The predicted scores significantly correlate with the actual scores, hence verifying the behavioral relevance of the found connections.
PMID: 26221670 [PubMed - in process]
Joint Spectral Decomposition for the Parcellation of the Human Cerebral Cortex Using Resting-State fMRI.
Inf Process Med Imaging. 2015;24:85-97
Authors: Arslan S, Parisot S, Rueckert D
Identification of functional connections within the human brain has gained a lot of attention due to its potential to reveal neural mechanisms. In a whole-brain connectivity analysis, a critical stage is the computation of a set of network nodes that can effectively represent cortical regions. To address this problem, we present a robust cerebral cortex parcellation method based on spectral graph theory and resting-state fMRI correlations that generates reliable parcellations at the single-subject level and across multiple subjects. Our method models the cortical surface in each hemisphere as a mesh graph represented in the spectral domain with its eigenvectors. We connect cortices of different subjects with each other based on the similarity of their connectivity profiles and construct a multi-layer graph, which effectively captures the fundamental properties of the whole group as well as preserves individual subject characteristics. Spectral decomposition of this joint graph is used to cluster each cortical vertex into a subregion in order to obtain whole-brain parcellations. Using rs-fMRI data collected from 40 healthy subjects, we show that our proposed algorithm computes highly reproducible parcellations across different groups of subjects and at varying levels of detail with an average Dice score of 0.78, achieving up to 9% better reproducibility compared to existing approaches. We also report that our group-wise parcellations are functionally more consistent, thus, can be reliably used to represent the population in network analyses.
PMID: 26221668 [PubMed - in process]
Repetitive speech elicits widespread deactivation in the human cortex: the "Mantra" effect?
Brain Behav. 2015 Jul;5(7):e00346
Authors: Berkovich-Ohana A, Wilf M, Kahana R, Arieli A, Malach R
BACKGROUND: Mantra (prolonged repetitive verbal utterance) is one of the most universal mental practices in human culture. However, the underlying neuronal mechanisms that may explain its powerful emotional and cognitive impact are unknown. In order to try to isolate the effect of silent repetitive speech, which is used in most commonly practiced Mantra meditative practices, on brain activity, we studied the neuronal correlates of simple repetitive speech in nonmeditators - that is, silent repetitive speech devoid of the wider context and spiritual orientations of commonly practiced meditation practices.
METHODS: We compared, using blood oxygenated level-dependent (BOLD) functional magnetic resonance imaging (fMRI), a simple task of covertly repeating a single word to resting state activity, in 23 subjects, none of which practiced meditation before.
RESULTS: We demonstrate that the repetitive speech was sufficient to induce a widespread reduction in BOLD signal compared to resting baseline. The reduction was centered mainly on the default mode network, associated with intrinsic, self-related processes. Importantly, contrary to most cognitive tasks, where cortical-reduced activation in one set of networks is typically complemented by positive BOLD activity of similar magnitude in other cortical networks, the repetitive speech practice resulted in unidirectional negative activity without significant concomitant positive BOLD. A subsequent behavioral study showed a significant reduction in intrinsic thought processes during the repetitive speech condition compared to rest.
CONCLUSIONS: Our results are compatible with a global gating model that can exert a widespread induction of negative BOLD in the absence of a corresponding positive activation. The triggering of a global inhibition by the minimally demanding repetitive speech may account for the long-established psychological calming effect associated with commonly practiced Mantra-related meditative practices.
PMID: 26221571 [PubMed - in process]
The therapeutic effect of Xueshuan Xinmai tablets on memory injury and brain activity in post-stroke patients: a pilot placebo controlled fMRI study.
Int J Clin Exp Med. 2015;8(5):7507-16
Authors: Wei D, Lv C, Zhang J, Peng D, Hu L, Zhang Z, Wang Y
OBJECTIVE: The purpose of this study was to explore the effects of Xueshuan Xinmai tablets (XXMT) for the treatment of cognition, brain activation in the rehabilitation period of ischemic stroke patients.
METHODS: 28 adults patients, aged 50-80 years, in the rehabilitation period of ischemic stroke were divided into XXMT treatment group and placebo control group. Patients received 3 months treatment (oral 0.8 g, 3 times per day). Before and after treatment, all patients were evaluated by a series of neuropsychological tests followed by resting-state functional magnetic resonance imaging (fMRI).
RESULTS: In the XXMT treatment group, the patients' episodic memory showed significant improvement. The resting-state fMRI analysis indicated that a significant decline in the fractional amplitude of low-frequency fluctuation value was observed in the bilateral middle cingulate gyrus.
CONCLUSIONS: Yiqi Huoxue effect under XXMT administration has a favorable mediation on episodic memory, consequently suppresses the activation of the cingulate gyrus in the rehabilitation period of ischemic stroke patients.
PMID: 26221294 [PubMed]
A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: Application to schizophrenia, bipolar, and schizoaffective disorders.
Neuroimage. 2015 Jul 24;
Authors: Du Y, Pearlson GD, Liu J, Sui J, Yu Q, He H, Castro E, Calhoun VD
Schizophrenia (SZ), bipolar disorder (BP) and schizoaffective disorder (SAD) share some common symptoms, and there is a debate about whether SAD is an independent category. To the best of our knowledge, no study has been done to differentiate these three disorders or to investigate the distinction of SAD as an independent category using fMRI data. The present study is aimed to explore biomarkers from resting-state fMRI networks for differentiating these disorders and investigate the relationship among these disorders based on fMRI networks with an emphasis on SAD. Firstly, a novel group ICA method, group information guided independent component analysis (GIG-ICA), was applied to extract subject-specific brain networks from fMRI data of 20 healthy controls (HC), 20 SZ patients, 20 BP patients, 20 patients suffering SAD with manic episodes (SADM), and 13 patients suffering SAD with depressive episodes exclusively (SADD). Then, five-level one-way analysis of covariance and multiclass support vector machine recursive feature elimination were employed to identify discriminative regions from the networks. Subsequently, the t-distributed stochastic neighbor embedding (t-SNE) projection and the hierarchical clustering methods were implemented to investigate the relationship among those groups. Finally, to evaluate the generalization ability, 16 new subjects were classified based on the found regions and the trained model using original 93 subjects. Results show that the discriminative regions mainly include frontal, parietal, precuneus, cingulate, supplementary motor, cerebellar, insula and supramarginal cortices, which performed well in distinguishing different groups. SADM and SADD were the most similar to each other, although SADD had greater similarity to SZ compared to other groups, which indicates SAD may be an independent category. BP was closer to HC compared with other psychotic disorders. In summary, resting-state fMRI brain networks extracted via GIG-ICA provide a promising potential to differentiate SZ, BP, and SAD.
PMID: 26216278 [PubMed - as supplied by publisher]
Earlier adolescent substance use onset predicts stronger connectivity between reward and cognitive control brain networks.
Dev Cogn Neurosci. 2015 Jul 17;
Authors: Weissman DG, Schriber RA, Fassbender C, Atherton O, Krafft C, Robins RW, Hastings PD, Guyer AE
BACKGROUND: Early adolescent onset of substance use is a robust predictor of future substance use disorders. We examined the relation between age of substance use initiation and resting state functional connectivity (RSFC) of the core reward processing (nucleus accumbens; NAcc) to cognitive control (prefrontal cortex; PFC) brain networks.
METHOD: Adolescents in a longitudinal study of Mexican-origin youth reported their substance use annually from ages 10 to 16 years. At age 16, 69 adolescents participated in a resting state functional magnetic resonance imaging scan. Seed-based correlational analyses were conducted using regions of interest in bilateral NAcc.
RESULTS: The earlier that adolescents initiated substance use, the stronger the connectivity between bilateral NAcc and right dorsolateral PFC, right dorsomedial PFC, right pre-supplementary motor area, right inferior parietal lobule, and left medial temporal gyrus.
DISCUSSION: The regions that demonstrated significant positive linear relationships between the number of adolescent years using substances and connectivity with NAcc are nodes in the right frontoparietal network, which is central to cognitive control. The coupling of reward and cognitive control networks may be a mechanism through which earlier onset of substance use is related to brain function over time, a trajectory that may be implicated in subsequent substance use disorders.
PMID: 26215473 [PubMed - as supplied by publisher]
Integration of multimodal neuroimaging methods: a rationale for clinical applications of simultaneous EEG-fMRI.
Funct Neurol. 2015 Jan-Mar;30(1):9-20
Authors: Vitali P, Di Perri C, Vaudano AE, Meletti S, Villani F
Functional magnetic resonance imaging (fMRI), which has high spatial resolution, is increasingly used to evaluate cerebral functions in neurological and psychiatric diseases. The main limitation of fMRI is that it detects neural activity indirectly, through the associated slow hemodynamic variations. Because neurovascular coupling can be regionally altered by pathological conditions or drugs, fMRI responses may not truly reflect neural activity. Electroencephalography (EEG) recordings, which directly detect neural activity with optimal temporal resolution, can now be obtained during fMRI data acquisition. Therefore, there is a growing interest in combining the techniques to obtain simultaneous EEG-fMRI recordings. The EEG-fMRI approach has several promising clinical applications. The first is the detection of cortical areas involved in interictal and ictal epileptic activity. Second, combining evoked potentials with fMRI could be an accurate way to study eloquent cortical areas for the planning of neurosurgery or rehabilitation, circumventing the above-mentioned limitation of fMRI. Finally, the use of this approach to evaluate the functional connectivity of resting-state networks would extend the applications of EEG-fMRI to uncooperative or unconscious patients. Integration of multimodal neuroimaging methods: a rationale for clinical applications of simultaneous EEG-fMRI.
PMID: 26214023 [PubMed - as supplied by publisher]
Time-frequency analysis of resting state and evoked EEG data recorded at higher magnetic fields up to 9.4 T.
J Neurosci Methods. 2015 Jul 23;
Authors: Abbasi O, Dammers J, Arrubla J, Warbrick T, Butz M, Neuner I, Shah NJ
BACKGROUND: Combining both high temporal and spatial resolution by means of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is of relevance to neuroscientists. This combination, however, leads to a distortion of the EEG signal by the so-called cardio-ballistic artefacts. The aim of the present study was developing an approach to restore meaningful physiological EEG data from recordings at different magnetic fields.
NEW METHODS: The distortions introduced by the magnetic field were corrected using a combination of concepts from independent component analysis (ICA) and mutual information (MI). Thus, the components were classified as either related to the cardio-ballistic artefacts or to the signals of interest. EEG data from two experimental paradigms recorded at different magnetic field strengths up to 9.4T were analyzed: (i) spontaneous activity using an eyes-open/eyes-closed alternation, and (ii) responses to auditory stimuli, i.e. auditory evoked potentials.
RESULTS: Even at ultra-high magnetic fields up to 9.4T the proposed artefact rejection approach restored the physiological time-frequency information contained in the signal of interest and the data were suitable for subsequent analyses.
COMPARISON WITH EXISTING METHODS: Blind source separation (BSS) has been used to retrieve information from EEG data recorded inside the MR scanner in previous studies. After applying the presented method on EEG data recorded at 4T, 7T, and 9.4T, we could retrieve more information than from data cleaned with the BSS method.
CONCLUSIONS: The present work demonstrates that EEG data recorded at ultra-high magnetic fields can be used for studying neuroscientific research question related to oscillatory activity.
PMID: 26213220 [PubMed - as supplied by publisher]
AICHA: An atlas of intrinsic connectivity of homotopic areas.
J Neurosci Methods. 2015 Jul 23;
Authors: Joliot M, Jobard G, Naveau M, Delcroix N, Petit L, Zago L, Crivello F, Mellet E, Mazoyer B, Tzourio-Mazoyer N
BACKGROUND: Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has an homologue in the other hemisphere.
NEW METHOD: We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1- homogeneity of its constituting voxels intrinsic activity, and 2- a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling.
RESULTS: AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 grey nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities.
COMPARISON WITH EXISTING METHOD: ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy.
CONCLUSION: AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization.
PMID: 26213217 [PubMed - as supplied by publisher]
Functional System and Areal Organization of a Highly Sampled Individual Human Brain.
Neuron. 2015 Jul 22;
Authors: Laumann TO, Gordon EM, Adeyemo B, Snyder AZ, Joo SJ, Chen MY, Gilmore AW, McDermott KB, Nelson SM, Dosenbach NU, Schlaggar BL, Mumford JA, Poldrack RA, Petersen SE
Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT.
PMID: 26212711 [PubMed - as supplied by publisher]
Test-Retest Reliability of Graph Metrics in High-resolution Functional Connectomics: A Resting-State Functional MRI Study.
CNS Neurosci Ther. 2015 Jul 27;
Authors: Du HX, Liao XH, Lin QX, Li GS, Chi YZ, Liu X, Yang HZ, Wang Y, Xia MR
BACKGROUND: The combination of resting-state functional MRI (R-fMRI) technique and graph theoretical approaches has emerged as a promising tool for characterizing the topological organization of brain networks, that is, functional connectomics. In particular, the construction and analysis of high-resolution brain connectomics at a voxel scale are important because they do not require prior regional parcellations and provide finer spatial information about brain connectivity. However, the test-retest reliability of voxel-based functional connectomics remains largely unclear.
AIMS: This study tended to investigate both short-term (∼20 min apart) and long-term (6 weeks apart) test-retest (TRT) reliability of graph metrics of voxel-based brain networks.
METHODS: Based on graph theoretical approaches, we analyzed R-fMRI data from 53 young healthy adults who completed two scanning sessions (session 1 included two scans 20 min apart; session 2 included one scan that was performed after an interval of ∼6 weeks).
RESULTS: The high-resolution networks exhibited prominent small-world and modular properties and included functional hubs mainly located at the default-mode, salience, and executive control systems. Further analysis revealed that test-retest reliabilities of network metrics were sensitive to the scanning orders and intervals, with fair to excellent long-term reliability between Scan 1 and Scan 3 and lower reliability involving Scan 2. In the long-term case (Scan 1 and Scan 3), most network metrics were generally test-retest reliable, with the highest reliability in global metrics in the clustering coefficient and in the nodal metrics in nodal degree and efficiency.
CONCLUSION: We showed high test-retest reliability for graph properties in the high-resolution functional connectomics, which provides important guidance for choosing reliable network metrics and analysis strategies in future studies.
PMID: 26212146 [PubMed - as supplied by publisher]
Susceptibility to everyday cognitive failure is reflected in functional network interactions in the resting brain.
Neuroimage. 2015 Jul 22;
Authors: Bey K, Montag C, Reuter M, Weber B, Markett S
The proneness to minor errors and slips in everyday life as assessed by the Cognitive Failures Questionnaire (CFQ) constitutes a trait characteristic and is reflected in stable features of brain structure and function. It is unclear, however, how dynamic interactions of large-scale brain networks contribute to this disposition. To address this question, we performed a high model order independent component analysis (ICA) with subsequent dual regression on resting-state fMRI data from 71 subjects to extract temporal time courses describing the dynamics of 17 resting-state networks (RSN). Dynamic network interactions between all 17 RSN were assessed by linear correlations between networks' time courses. On this basis, we investigated the relationship between subject-level RSN interactions and the susceptibility to everyday cognitive failure. We found that CFQ scores were significantly correlated with the interplay of the cingulo-opercular network (CON) and a posterior parietal network which unifies clusters in the posterior cingulate, precuneus, intraparietal lobules and middle temporal regions. Specifically, a higher positive functional connectivity between these two RSN was indicative of higher proneness to cognitive failure. Both the CON and posterior parietal network are implicated in cognitive functions, such as tonic alertness and executive control. Results indicate that proper checks and balances between the two networks are needed to protect against cognitive failure. Furthermore, we demonstrate that the study of temporal network dynamics in the resting state is a feasible tool to investigate individual differences in cognitive ability and performance.
PMID: 26210814 [PubMed - as supplied by publisher]
Local and global resting-state activity in the noradrenergic and dopaminergic pathway modulated by reboxetine and amisulpride in healthy subjects.
Int J Neuropsychopharmacol. 2015 Jul 25;
Authors: Metzger CD, Wiegers M, Walter M, Abler B, Graf H
BACKGROUND: Various psychiatric populations are currently investigated with resting-state fMRI aiming to individualize diagnostics, treatment options and to improve treatment outcome. Many of these studies are conducted in large naturalistic samples providing rich insights regarding disease-related neural alterations but with the common psychopharmacological medication limiting interpretations of the results. We therefore investigated effects of common noradrenergic and anti-dopaminergic medication on local and global resting-state (rs) activity in healthy volunteers to further the understanding of the respective effects independent from disease-related alterations.
METHODS: Within a randomized, double-blind, placebo-controlled cross-over design, we investigated 19 healthy male subjects by resting-state fMRI after the intake of reboxetine (4mg/d), amisulpride (200mg/d) and placebo for 7 days each. Treatment-related differences in local and global rs-activity were measured by the fractional amplitude of low frequency fluctuations (fALFF) and resting-state functional connectivity (rs-FC).
RESULTS: fALFF revealed alterations of local rs-activity within regions of the core noradrenergic pathway including the locus coeruleus under reboxetine correlated with its plasma levels. Moreover, reboxetine led to increased rs-FC between regions within this pathway, i.e. locus coeruleus, tectum, thalamus and amygdala. Amisulpride modulated local rs-activity of regions within the dopaminergic pathway with the altered signal in the putamen correlating with amisulpride plasma levels. Correspondingly, amisulpride increased rs-FC between regions of the dopaminergic pathway comprising substantia nigra and putamen.
CONCLUSION: Our data provide evidence how psychopharmacological agents alter local and global rs-activity within the respective neuroanatomical pathways in healthy subjects that may help with interpretating 'big-data' in psychiatric populations.
PMID: 26209860 [PubMed - as supplied by publisher]
Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.
Neuroimage. 2015 Jul 21;
Authors: Brier MR, Mitra A, McCarthy JE, Ances BM, Snyder AZ
Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity.
PMID: 26208872 [PubMed - as supplied by publisher]
Significant feed-forward connectivity revealed by high frequency components of BOLD fMRI signals.
Neuroimage. 2015 Jul 21;
Authors: Lin FH, Chu YH, Hsu YC, Lin JF, Tsai KW, Tsai SY, Kuo WJ
Granger causality analysis has been suggested as a method of estimating causal modulation without specifying the direction of information flow a priori. Using BOLD-contrast functional MRI (fMRI) data, such analysis has been typically implemented in the time domain. In this study, we used magnetic resonance inverse imaging, a method of fast fMRI enabled by massively parallel detection allowing up to 10Hz sampling rate, to investigate the causal modulation at different frequencies up to 5Hz. Using a visuomotor two-choice reaction-time task, both the spectral decomposition of Granger causality and isolated effective coherence revealed that the BOLD signal at frequency up to 3Hz can still be used to estimate significant dominant directions of information flow consistent with results from the time-domain Granger causality analysis. We showed the specificity of estimated dominant directions of information flow at high frequencies by contrasting causality estimates using data collected during the visuomotor task and resting state. Our data suggest that hemodynamic responses carry physiological information related to inter-regional modulation at frequency higher than what has been commonly considered.
PMID: 26208871 [PubMed - as supplied by publisher]
Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox.
Authors: Ribeiro AS, Lacerda LM, Ferreira HA
Aim. In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity. Materials and Methods. The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19-73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also. Results. It was observed both a high inter-hemispheric symmetry and an intra-hemispheric modularity associated with structural data, whilst functional data presented lower inter-hemispheric symmetry and a high inter-hemispheric modularity. Furthermore, when testing for differences between two subgroups (<40 and >40 years old adults) we observed a significant reduction in the volume and thickness, and an increase in the mean diffusivity of most of the subcortical/cortical regions. Conclusion. While bridging the gap between the high numbers of packages and tools widely available for the neuroimaging community in one toolbox, MIBCA also offers different possibilities for combining, analysing and visualising data in novel ways, enabling a better understanding of the human brain.
PMID: 26207191 [PubMed]
The impact of eye closure on somatosensory perception in the elderly.
Behav Brain Res. 2015 Jul 20;
Authors: Brodoehl S, Klingner C, Stieglitz K, Witte OW
Visual dominance over other senses is a well-known phenomenon. Closing the eyes, even in complete darkness, can improve somatosensory perception by switching off various aspects of visual dominance. How and if this mechanism is affected by aging remains unknown. We performed detailed neurophysiological and functional MR-imaging on healthy young and elderly participants under the conditions of opened and closed eyes. We found an improved perception threshold in both groups when the eyes were closed, but the improvement was significantly less pronounced in the elderly. fMRI data revealed increased resting activity in the somatosensory cortex with closed eyes, and the stimulus-induced activity of the secondary somatosensory cortex decreased in the young but not in the elderly. This study demonstrates that a switch towards unisensory processing via eye closure is preserved but significantly reduced in the aging brain. We suggest that the decreased ability for unisensory processing is a general phenomenon in the aging brain resulting in a shift toward multisensory integration.
PMID: 26205825 [PubMed - as supplied by publisher]
The Functional Connectome of Speech Control.
PLoS Biol. 2015 Jul;13(7):e1002209
Authors: Fuertinger S, Horwitz B, Simonyan K
In the past few years, several studies have been directed to understanding the complexity of functional interactions between different brain regions during various human behaviors. Among these, neuroimaging research installed the notion that speech and language require an orchestration of brain regions for comprehension, planning, and integration of a heard sound with a spoken word. However, these studies have been largely limited to mapping the neural correlates of separate speech elements and examining distinct cortical or subcortical circuits involved in different aspects of speech control. As a result, the complexity of the brain network machinery controlling speech and language remained largely unknown. Using graph theoretical analysis of functional MRI (fMRI) data in healthy subjects, we quantified the large-scale speech network topology by constructing functional brain networks of increasing hierarchy from the resting state to motor output of meaningless syllables to complex production of real-life speech as well as compared to non-speech-related sequential finger tapping and pure tone discrimination networks. We identified a segregated network of highly connected local neural communities (hubs) in the primary sensorimotor and parietal regions, which formed a commonly shared core hub network across the examined conditions, with the left area 4p playing an important role in speech network organization. These sensorimotor core hubs exhibited features of flexible hubs based on their participation in several functional domains across different networks and ability to adaptively switch long-range functional connectivity depending on task content, resulting in a distinct community structure of each examined network. Specifically, compared to other tasks, speech production was characterized by the formation of six distinct neural communities with specialized recruitment of the prefrontal cortex, insula, putamen, and thalamus, which collectively forged the formation of the functional speech connectome. In addition, the observed capacity of the primary sensorimotor cortex to exhibit operational heterogeneity challenged the established concept of unimodality of this region.
PMID: 26204475 [PubMed - as supplied by publisher]