Brain spontaneous fluctuations in sensorimotor regions were directly related to eyes open and eyes closed: evidences from a machine learning approach.
Front Hum Neurosci. 2014;8:645
Authors: Liang B, Zhang D, Wen X, Xu P, Peng X, Huang X, Liu M, Huang R
Previous studies have demonstrated that the difference between resting-state brain activations depends on whether the subject was eyes open (EO) or eyes closed (EC). However, whether the spontaneous fluctuations are directly related to these two different resting states are still largely unclear. In the present study, we acquired resting-state functional magnetic resonance imaging data from 24 healthy subjects (11 males, 20.17 ± 2.74 years) under the EO and EC states. The amplitude of the spontaneous brain activity in low-frequency band was subsequently investigated by using the metric of fractional amplitude of low frequency fluctuation (fALFF) for each subject under each state. A support vector machine (SVM) analysis was then applied to evaluate whether the category of resting states could be determined from the brain spontaneous fluctuations. We demonstrated that these two resting states could be decoded from the identified pattern of brain spontaneous fluctuations, predominantly based on fALFF in the sensorimotor module. Specifically, we observed prominent relationships between increased fALFF for EC and decreased fALFF for EO in sensorimotor regions. Overall, the present results indicate that a SVM performs well in the discrimination between the brain spontaneous fluctuations of distinct resting states and provide new insight into the neural substrate of the resting states during EC and EO.
PMID: 25191258 [PubMed]
Power spectral aspects of the default mode network in schizophrenia: an MEG study.
BMC Neurosci. 2014 Sep 5;15(1):104
Authors: Kim JS, Shin KS, Jung WH, Kim SN, Kwon JS, Chung CK
BACKGROUND: Symptoms of schizophrenia are related to deficits in self-monitoring function, which may be a consequence of irregularity in aspects of the default mode network (DMN). Schizophrenia can also be characterized by a functional abnormality of the brain activity that is reflected in the resting state. Oscillatory analysis provides an important understanding of resting brain activity. However, conventional methods using electroencephalography are restricted because of low spatial resolution, despite their excellent temporal resolution.The aim of this study was to investigate resting brain oscillation and the default mode network based on a source space in various frequency bands such as theta, alpha, beta, and gamma using magnetoencephalography. In addition, we investigated whether these resting and DMN activities could distinguish schizophrenia patients from normal controls. To do this, the power spectral density of each frequency band at rest was imaged and compared on a spatially normalized brain template in 20 patients and 20 controls.
RESULTS: The spatial distribution of DMN activity in the alpha band was similar to that found in previous fMRI studies. The posterior cingulate cortex (PCC) and lateral inferior parietal cortex were activated at rest, while the medial prefrontal cortex (MPFC) was deactivated at rest rather than during the task. Although the MPFC and PCC regions exhibited contrasting activation patterns, these two regions were significantly coherent at rest. The DMN and resting activities of the PCC were increased in schizophrenia patients, predominantly in the theta and alpha bands.
CONCLUSIONS: By using MEG to identify the DMN regions, predominantly in the alpha band, we found that both resting and DMN activities were augmented in the posterior cingulate in schizophrenia patients. Furthermore, schizophrenia patients exhibited decreased coherence between the PCC and MPFC in the gamma band at rest.
PMID: 25189680 [PubMed - as supplied by publisher]
Multimodal fMRI Resting-State Functional Connectivity in Granulin Mutations: The Case of Fronto-Parietal Dementia.
PLoS One. 2014;9(9):e106500
Authors: Premi E, Cauda F, Gasparotti R, Diano M, Archetti S, Padovani A, Borroni B
BACKGROUND: Monogenic dementias represent a great opportunity to trace disease progression from preclinical to symptomatic stages. Frontotemporal Dementia related to Granulin (GRN) mutations presents a specific framework of brain damage, involving fronto-temporal regions and long inter-hemispheric white matter bundles. Multimodal resting-state functional MRI (rs-fMRI) is a promising tool to carefully describe disease signature from the earliest disease phase.
OBJECTIVE: To define local connectivity alterations in GRN related pathology moving from the presymptomatic (asymptomatic GRN mutation carriers) to the clinical phase of the disease (GRN- related Frontotemporal Dementia).
METHODS: Thirty-one GRN Thr272fs mutation carriers (14 patients with Frontotemporal Dementia and 17 asymptomatic carriers) and 38 healthy controls were recruited. Local connectivity measures (Regional Homogeneity (ReHo), Fractional Amplitude of Low Frequency Fluctuation (fALFF) and Degree Centrality (DC)) were computed, considering age and gender as nuisance variables as well as the influence of voxel-level gray matter atrophy.
RESULTS: Asymptomatic GRN carriers had selective reduced ReHo in the left parietal region and increased ReHo in frontal regions compared to healthy controls. Considering Frontotemporal Dementia patients, all measures (ReHo, fALFF and DC) were reduced in inferior parietal, frontal lobes and posterior cingulate cortex. Considering GRN mutation carriers, an inverse correlation with age in the posterior cingulate cortex, inferior parietal lobule and orbitofrontal cortex was found.
CONCLUSIONS: GRN pathology is characterized by functional brain network alterations even decades before the clinical onset; they involve the parietal region primarily and then spread to the anterior regions of the brain, supporting the concept of molecular nexopathies.
PMID: 25188321 [PubMed - as supplied by publisher]
Voxel-Wise Motion Artifacts in Population-Level Whole-Brain Connectivity Analysis of Resting-State fMRI.
PLoS One. 2014;9(9):e104947
Authors: Spisák T, Jakab A, Kis SA, Opposits G, Aranyi C, Berényi E, Emri M
Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.
PMID: 25188284 [PubMed - as supplied by publisher]
Resting-state functional connectivity predicts the strength of hemispheric lateralization for language processing in temporal lobe epilepsy and normals.
Hum Brain Mapp. 2014 Sep 3;
Authors: Doucet GE, Pustina D, Skidmore C, Sharan A, Sperling MR, Tracy JI
In temporal lobe epilepsy (TLE), determining the hemispheric specialization for language before surgery is critical to preserving a patient's cognitive abilities post-surgery. To date, the major techniques utilized are limited by the capacity of patients to efficiently realize the task. We determined whether resting-state functional connectivity (rsFC) is a reliable predictor of language hemispheric dominance in right and left TLE patients, relative to controls. We chose three subregions of the inferior frontal cortex (pars orbitalis, pars triangularis, and pars opercularis) as the seed regions. All participants performed both a verb generation task and a resting-state fMRI procedure. Based on the language task, we computed a laterality index (LI) for the resulting network. This revealed that 96% of the participants were left-hemisphere dominant, although there remained a large degree of variability in the strength of left lateralization. We tested whether LI correlated with rsFC values emerging from each seed. We revealed a set of regions that was specific to each group. Unique correlations involving the epileptic mesial temporal lobe were revealed for the right and left TLE patients, but not for the controls. Importantly, for both TLE groups, the rsFC emerging from a contralateral seed was the most predictive of LI. Overall, our data depict the broad patterns of rsFC that support strong versus weak left hemisphere language laterality. This project provides the first evidence that rsFC data may potentially be used on its own to verify the strength of hemispheric dominance for language in impaired or pathologic populations. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 25187327 [PubMed - as supplied by publisher]
GABA Concentration in Posterior Cingulate Cortex Predicts Putamen Response during Resting State fMRI.
PLoS One. 2014;9(9):e106609
Authors: Arrubla J, Tse DH, Amkreutz C, Neuner I, Shah NJ
The role of neurotransmitters in the activity of resting state networks has been gaining attention and has become a field of research with magnetic resonance spectroscopy (MRS) being one of the key techniques. MRS permits the measurement of γ-aminobutyric acid (GABA) and glutamate levels, the central biochemical constituents of the excitation-inhibition balance in vivo. The inhibitory effects of GABA in the brain have been largely investigated in relation to the activity of resting state networks in functional magnetic resonance imaging (fMRI). In this study GABA concentration in the posterior cingulate cortex (PCC) was measured using single voxel spectra acquired with standard point resolved spectroscopy (PRESS) from 20 healthy male volunteers at 3 T. Resting state fMRI was consecutively measured and the values of GABA/Creatine+Phosphocreatine ratio (GABA ratio) were included in a general linear model matrix as a step of dual regression analysis in order to identify voxels whose neuroimaging metrics during rest were related to individual levels of the GABA ratio. Our data show that the connection strength of putamen to the default-mode network during resting state has a negative linear relationship with the GABA ratio measured in the PCC. These findings highlight the role of PCC and GABA in segregation of the motor input, which is an inherent condition that characterises resting state.
PMID: 25184505 [PubMed - as supplied by publisher]
Age-related Reorganizational Changes in Modularity and Functional Connectivity of Human Brain Networks.
Brain Connect. 2014 Sep 2;
Authors: Song J, Birn R, Boly M, Meier TB, Nair VA, Meyerand ME, Prabhakaran V
The human brain undergoes both morphological and functional modifications across the human lifespan. It is important to understand the aspects of brain reorganization that are critical in normal aging. To address this question, one approach is to investigate age-related topological changes of the brain. In this study, we developed a brain network model using graph theory methods applied to the resting-state functional MRI (fMRI) data acquired from two groups of normal healthy adults classified by age. We found that brain functional networks demonstrated modular organization in both groups with modularity decreased with aging, suggesting less distinct functional divisions across whole brain networks. Local efficiency was also decreased with aging but not global efficiency. Besides these brain-wide observations, we also observed consistent alterations of network properties at regional level in the elderly, particularly, in two major functional networks--the default mode and the sensorimotor network. Specifically, we found that measures of regional strength, local and global efficiency of functional connectivity were increased in the sensorimotor network while decreased in the default mode network with aging. These results indicate that global reorganization of brain functional networks may reflect overall topological changes with aging and that aging likely alters individual brain networks differently depending on the functional properties. Moreover, these findings highly correspond to the observation of decline in cognitive functions but maintenance of primary information processing in normal healthy aging, implying an underlying compensation mechanism evolving with aging to support higher level cognitive functioning.
PMID: 25183440 [PubMed - as supplied by publisher]
Bayesian networks for fMRI: a primer.
Neuroimage. 2014 Feb 1;86:573-82
Authors: Mumford JA, Ramsey JD
Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches are more attractive than other functional connectivity analyses in that they can often operate on larger sets of nodes and run searches over a wide range of candidate networks. An important study by Smith et al. (2011) illustrated that many Bayesian network approaches did not perform well in identifying the directionality of connections in simulated single-subject data. Since then, new Bayesian network approaches have been developed that have overcome the failures in the Smith work. Additionally, an important discovery was made that shows a preprocessing step used in the Smith data puts some of the Bayesian network methods at a disadvantage. This work provides a review of Bayesian network analyses, focusing on the methods used in the Smith work as well as methods developed since 2011 that have improved estimation performance. Importantly, only approaches that have been specifically designed for fMRI data perform well, as they have been tailored to meet the challenges of fMRI data. Although this work does not suggest a single best model, it describes the class of models that perform best and highlights the features of these models that allow them to perform well on fMRI data. Specifically, methods that rely on non-Gaussianity to direct causal relationships in the network perform well.
PMID: 24140939 [PubMed - indexed for MEDLINE]
Advances in functional magnetic resonance imaging of the human brainstem.
Neuroimage. 2014 Feb 1;86:91-8
Authors: Beissner F, Schumann A, Brunn F, Eisenträger D, Bär KJ
The brainstem is of tremendous importance for our daily survival, and yet the functional relationships between various nuclei, their projection targets, and afferent regulatory areas remain poorly characterized. The main reason for this lies in the sub-optimal performance of standard neuroimaging methods in this area. In particular, fMRI signals are much harder to detect in the brainstem region compared to cortical areas. Here we describe and validate a new approach to measure activation of brainstem nuclei in humans using standard fMRI sequences and widely available tools for statistical image processing. By spatially restricting an independent component analysis to an anatomically defined brainstem mask, we excluded those areas from the analysis that were strongly affected by physiological noise. This allowed us to identify for the first time intrinsic connectivity networks in the human brainstem and to map brainstem-cortical connectivity purely based on functionally defined regions of interest.
PMID: 23933038 [PubMed - indexed for MEDLINE]
Neuroimaging of the Philadelphia neurodevelopmental cohort.
Neuroimage. 2014 Feb 1;86:544-53
Authors: Satterthwaite TD, Elliott MA, Ruparel K, Loughead J, Prabhakaran K, Calkins ME, Hopson R, Jackson C, Keefe J, Riley M, Mentch FD, Sleiman P, Verma R, Davatzikos C, Hakonarson H, Gur RC, Gur RE
The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale, NIMH funded initiative to understand how brain maturation mediates cognitive development and vulnerability to psychiatric illness, and understand how genetics impacts this process. As part of this study, 1445 adolescents ages 8-21 at enrollment underwent multimodal neuroimaging. Here, we highlight the conceptual basis for the effort, the study design, and the measures available in the dataset. We focus on neuroimaging measures obtained, including T1-weighted structural neuroimaging, diffusion tensor imaging, perfusion neuroimaging using arterial spin labeling, functional imaging tasks of working memory and emotion identification, and resting state imaging of functional connectivity. Furthermore, we provide characteristics regarding the final sample acquired. Finally, we describe mechanisms in place for data sharing that will allow the PNC to become a freely available public resource to advance our understanding of normal and pathological brain development.
PMID: 23921101 [PubMed - indexed for MEDLINE]
Preserved Modular Network Organization in the Sedated Rat Brain.
PLoS One. 2014;9(9):e106156
Authors: D'Souza DV, Jonckers E, Bruns A, Künnecke B, von Kienlin M, Van der Linden A, Mueggler T, Verhoye M
Translation of resting-state functional connectivity (FC) magnetic resonance imaging (rs-fMRI) applications from human to rodents has experienced growing interest, and bears a great potential in pre-clinical imaging as it enables assessing non-invasively the topological organization of complex FC networks (FCNs) in rodent models under normal and various pathophysiological conditions. However, to date, little is known about the organizational architecture of FCNs in rodents in a mentally healthy state, although an understanding of the same is of paramount importance before investigating networks under compromised states. In this study, we characterized the properties of resting-state FCN in an extensive number of Sprague-Dawley rats (n = 40) under medetomidine sedation by evaluating its modular organization and centrality of brain regions and tested for reproducibility. Fully-connected large-scale complex networks of positively and negatively weighted connections were constructed based on Pearson partial correlation analysis between the time courses of 36 brain regions encompassing almost the entire brain. Applying recently proposed complex network analysis measures, we show that the rat FCN exhibits a modular architecture, comprising six modules with a high between subject reproducibility. In addition, we identified network hubs with strong connections to diverse brain regions. Overall our results obtained under a straight medetomidine protocol show for the first time that the community structure of the rat brain is preserved under pharmacologically induced sedation with a network modularity contrasting from the one reported for deep anesthesia but closely resembles the organization described for the rat in conscious state.
PMID: 25181007 [PubMed - as supplied by publisher]
Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture.
Philos Trans R Soc Lond B Biol Sci. 2014 Oct 5;369(1653)
Authors: Krienen FM, Yeo BT, Buckner RL
Functional coupling across distributed brain regions varies across task contexts, yet there are stable features. To better understand the range and central tendencies of network configurations, coupling patterns were explored using functional MRI (fMRI) across 14 distinct continuously performed task states ranging from passive fixation to increasingly demanding classification tasks. Mean global correlation profiles across the cortex ranged from 0.69 to 0.82 between task states. Network configurations from both passive fixation and classification tasks similarly predicted task coactivation patterns estimated from meta-analysis of the literature. Thus, even across markedly different task states, central tendencies dominate the coupling configurations. Beyond these shared components, distinct task states displayed significant differences in coupling patterns in response to their varied demands. One possibility is that anatomical connectivity provides constraints that act as attractors pulling network configurations towards a limited number of robust states. Reconfigurable coupling modes emerge as significant modifications to a core functional architecture.
PMID: 25180304 [PubMed - in process]
Voxel-wise resting-state MEG source magnitude imaging study reveals neurocircuitry abnormality in active-duty service members and veterans with PTSD.
Neuroimage Clin. 2014;5:408-419
Authors: Huang MX, Yurgil KA, Robb A, Angeles A, Diwakar M, Risbrough VB, Nichols SL, McLay R, Theilmann RJ, Song T, Huang CW, Lee RR, Baker DG
Post-traumatic stress disorder (PTSD) is a leading cause of sustained impairment, distress, and poor quality of life in military personnel, veterans, and civilians. Indirect functional neuroimaging studies using PET or fMRI with fear-related stimuli support a PTSD neurocircuitry model that includes amygdala, hippocampus, and ventromedial prefrontal cortex (vmPFC). However, it is not clear if this model can fully account for PTSD abnormalities detected directly by electromagnetic-based source imaging techniques in resting-state. The present study examined resting-state magnetoencephalography (MEG) signals in 25 active-duty service members and veterans with PTSD and 30 healthy volunteers. In contrast to the healthy volunteers, individuals with PTSD showed: 1) hyperactivity from amygdala, hippocampus, posterolateral orbitofrontal cortex (OFC), dorsomedial prefrontal cortex (dmPFC), and insular cortex in high-frequency (i.e., beta, gamma, and high-gamma) bands; 2) hypoactivity from vmPFC, Frontal Pole (FP), and dorsolateral prefrontal cortex (dlPFC) in high-frequency bands; 3) extensive hypoactivity from dlPFC, FP, anterior temporal lobes, precuneous cortex, and sensorimotor cortex in alpha and low-frequency bands; and 4) in individuals with PTSD, MEG activity in the left amygdala and posterolateral OFC correlated positively with PTSD symptom scores, whereas MEG activity in vmPFC and precuneous correlated negatively with symptom score. The present study showed that MEG source imaging technique revealed new abnormalities in the resting-state electromagnetic signals from the PTSD neurocircuitry. Particularly, posterolateral OFC and precuneous may play important roles in the PTSD neurocircuitry model.
PMID: 25180160 [PubMed - as supplied by publisher]
Regional functional connectivity predicts distinct cognitive impairments in Alzheimer's disease spectrum.
Neuroimage Clin. 2014;5:385-395
Authors: Ranasinghe KG, Hinkley LB, Beagle AJ, Mizuiri D, Dowling AF, Honma SM, Finucane MM, Scherling C, Miller BL, Nagarajan SS, Vossel KA
Understanding neural network dysfunction in neurodegenerative disease is imperative to effectively develop network-modulating therapies. In Alzheimer's disease (AD), cognitive decline associates with deficits in resting-state functional connectivity of diffuse brain networks. The goal of the current study was to test whether specific cognitive impairments in AD spectrum correlate with reduced functional connectivity of distinct brain regions. We recorded resting-state functional connectivity of alpha-band activity in 27 patients with AD spectrum - 22 patients with probable AD (5 logopenic variant primary progressive aphasia, 7 posterior cortical atrophy, and 10 early-onset amnestic/dysexecutive AD) and 5 patients with mild cognitive impairment due to AD. We used magnetoencephalographic imaging (MEGI) to perform an unbiased search for regions where patterns of functional connectivity correlated with disease severity and cognitive performance. Functional connectivity measured the strength of coherence between a given region and the rest of the brain. Decreased neural connectivity of multiple brain regions including the right posterior perisylvian region and left middle frontal cortex correlated with a higher degree of disease severity. Deficits in executive control and episodic memory correlated with reduced functional connectivity of the left frontal cortex, whereas visuospatial impairments correlated with reduced functional connectivity of the left inferior parietal cortex. Our findings indicate that reductions in region-specific alpha-band resting-state functional connectivity are strongly correlated with, and might contribute to, specific cognitive deficits in AD spectrum. In the future, MEGI functional connectivity could be an important biomarker to map and follow defective networks in the early stages of AD.
PMID: 25180158 [PubMed - as supplied by publisher]
Resting-sate functional reorganization of the rat limbic system following neuropathic injury.
Sci Rep. 2014;4:6186
Authors: Baliki MN, Chang PC, Baria AT, Centeno MV, Apkarian AV
Human brain imaging studies from various clinical cohorts show that chronic pain is associated with large-scale brain functional and morphological reorganization. However, how the rat whole-brain network is topologically reorganized to support persistent pain-like behavior following neuropathic injury remains unknown. Here we compare resting state fMRI functional connectivity-based whole-brain network properties between rats receiving spared nerve injury (SNI) vs. sham injury, at 5 days (n = 11 SNI; n = 12 sham) and 28 days (n = 11 SNI; n = 12 sham) post-injury. Similar to the human, the rat brain topological properties exhibited small world features and did not differ between SNI and sham. Local neural networks in SNI animals showed minimal disruption at day 5, and more extensive reorganization at day 28 post-injury. Twenty-eight days after SNI, functional connection changes were localized mainly to within the limbic system, as well as between the limbic and nociceptive systems. No connectivity changes were observed within the nociceptive network. Furthermore, these changes were lateralized and in proportion to the tactile allodynia exhibited by SNI animals. The findings establish that SNI is primarily associated with altered information transfer of limbic regions and provides a novel translational framework for understanding brain functional reorganization in response to a persistent neuropathic injury.
PMID: 25178478 [PubMed - as supplied by publisher]
Increased resting state functional connectivity in the default mode network in recovered anorexia nervosa.
Hum Brain Mapp. 2014 Feb;35(2):483-91
Authors: Cowdrey FA, Filippini N, Park RJ, Smith SM, McCabe C
Functional brain imaging studies have shown abnormal neural activity in individuals recovered from anorexia nervosa (AN) during both cognitive and emotional task paradigms. It has been suggested that this abnormal activity which persists into recovery might underpin the neurobiology of the disorder and constitute a neural biomarker for AN. However, no study to date has assessed functional changes in neural networks in the absence of task-induced activity in those recovered from AN. Therefore, the aim of this study was to investigate whole brain resting state functional connectivity in nonmedicated women recovered from anorexia nervosa. Functional magnetic resonance imaging scans were obtained from 16 nonmedicated participants recovered from anorexia nervosa and 15 healthy control participants. Independent component analysis revealed functionally relevant resting state networks. Dual regression analysis revealed increased temporal correlation (coherence) in the default mode network (DMN) which is thought to be involved in self-referential processing. Specifically, compared to healthy control participants the recovered anorexia nervosa participants showed increased temporal coherence between the DMN and the precuneus and the dorsolateral prefrontal cortex/inferior frontal gyrus. The findings support the view that dysfunction in resting state functional connectivity in regions involved in self-referential processing and cognitive control might be a vulnerability marker for the development of anorexia nervosa.
PMID: 23033154 [PubMed - indexed for MEDLINE]
A single session of exercise increases connectivity in sensorimotor-related brain networks: a resting-state fMRI study in young healthy adults.
Front Hum Neurosci. 2014;8:625
Authors: Rajab AS, Crane DE, Middleton LE, Robertson AD, Hampson M, MacIntosh BJ
Habitual long term physical activity is known to have beneficial cognitive, structural, and neuro-protective brain effects, but to date there is limited knowledge on whether a single session of exercise can alter the brain's functional connectivity, as assessed by resting-state functional magnetic resonance imaging (rs-fMRI). The primary objective of this study was to characterize potential session effects in resting-state networks (RSNs). We examined the acute effects of exercise on the functional connectivity of young healthy adults (N = 15) by collecting rs-fMRI before and after 20 min of moderate intensity aerobic exercise and compared this with a no-exercise control group (N = 15). Data were analyzed using independent component analysis, denoising and dual regression procedures. Regions of interest-based group session effect statistics were calculated in RSNs of interest using voxel-wise permutation testing and Cohen's D effect size. Group analysis in the exercising group data set revealed a session effect in sub-regions of three sensorimotor related areas: the pre and/or postcentral gyri, secondary somatosensory area and thalamus, characterized by increased co-activation after exercise (corrected p < 0.05). Cohen's D analysis also showed a significant effect of session in these three RSNs (p< 0.05), corroborating the voxel-wise findings. Analyses of the no-exercise dataset produced no significant results, thereby providing support for the exercise findings and establishing the inherent test-retest reliability of the analysis pipeline on the RSNs of interest. This study establishes the feasibility of rs-fMRI to localize brain regions that are associated with acute exercise, as well as an analysis consideration to improve sensitivity to a session effect.
PMID: 25177284 [PubMed]
Optimization of anesthesia protocol for resting-state fMRI in mice based on differential effects of anesthetics on functional connectivity patterns.
Neuroimage. 2014 Aug 28;
Authors: Grandjean J, Schroeter A, Batata I, Rudin M
Resting state-fMRI (rs-fMRI) in mice allows studying mechanisms underlying functional connectivity (FC) as well as alterations of FC occurring in murine models of neurological diseases. Mouse fMRI experiments are typically carried out under anesthesia to minimize animal movement and potential distress during examination. Yet, anesthesia inevitably affects FC patterns. Such effects have to be understood for proper interpretation of data. We have compared the influence of four commonly used anesthetics on rs-fMRI. Rs-fMRI data acquired under isoflurane, propofol, and urethane presented similar patterns when accounting for anesthesia depth. FC maps displayed bilateral correlation with respect to cortical seeds, but no significant inter-hemispheric striatal connectivity. In contrast, for medetomidine we detected bilateral striatal, but compromised inter-hemispheric cortical connectivity. The spatiotemporal patterns of the rs-fMRI signal have been rationalized considering anesthesia depth and pharmacodynamic properties of the anesthetics. Our results bridge the results from different studies from the burgeoning field of mouse rs-fMRI and offer a framework for understanding the influences of anesthetics on FC patterns. Utilizing this information we suggest the combined use of medetomidine and isoflurane representing the two proposed classes of anesthetics; the combination of low doses of the two anesthetics retained strong correlations both within cortical and subcortical structures, without the potential seizure-inducing effects of medetomidine, rendering this regimen an attractive anesthesia for rs-fMRI in mice.
PMID: 25175535 [PubMed - as supplied by publisher]
Classification algorithms using multiple MRI features in mild traumatic brain injury.
Neurology. 2014 Aug 29;
Authors: Lui YW, Xue Y, Kenul D, Ge Y, Grossman RI, Wang Y
OBJECTIVE: The purpose of this study was to develop an algorithm incorporating MRI metrics to classify patients with mild traumatic brain injury (mTBI) and controls.
METHODS: This was an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant prospective study. We recruited patients with mTBI and healthy controls through the emergency department and general population. We acquired data on a 3.0T Siemens Trio magnet including conventional brain imaging, resting-state fMRI, diffusion-weighted imaging, and magnetic field correlation (MFC), and performed multifeature analysis using the following MRI metrics: mean kurtosis (MK) of thalamus, MFC of thalamus and frontal white matter, thalamocortical resting-state networks, and 5 regional gray matter and white matter volumes including the anterior cingulum and left frontal and temporal poles. Feature selection was performed using minimal-redundancy maximal-relevance. We used classifiers including support vector machine, naive Bayesian, Bayesian network, radial basis network, and multilayer perceptron to test maximal accuracy.
RESULTS: We studied 24 patients with mTBI and 26 controls. Best single-feature classification uses thalamic MK yielding 74% accuracy. Multifeature analysis yields 80% accuracy using the full feature set, and up to 86% accuracy using minimal-redundancy maximal-relevance feature selection (MK thalamus, right anterior cingulate volume, thalamic thickness, thalamocortical resting-state network, thalamic microscopic MFC, and sex).
CONCLUSION: Multifeature analysis using diffusion-weighted imaging, MFC, fMRI, and volumetrics may aid in the classification of patients with mTBI compared with controls based on optimal feature selection and classification methods.
CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that classification algorithms using multiple MRI features accurately identifies patients with mTBI as defined by American Congress of Rehabilitation Medicine criteria compared with healthy controls.
PMID: 25171930 [PubMed - as supplied by publisher]
Abnormal Causal Connectivity by Structural Deficits in First-Episode, Drug-Naive Schizophrenia at Rest.
Schizophr Bull. 2014 Aug 28;
Authors: Guo W, Liu F, Liu J, Yu L, Zhang J, Zhang Z, Xiao C, Zhai J, Zhao J
Anatomical deficits and resting-state functional connectivity (FC) alterations in prefrontal-thalamic-cerebellar circuit have been implicated in the neurobiology of schizophrenia. However, the effect of structural deficits in schizophrenia on causal connectivity of this circuit remains unclear. This study was conducted to examine the causal connectivity biased by structural deficits in first-episode, drug-naive schizophrenia patients. Structural and resting-state functional magnetic resonance imaging (fMRI) data were obtained from 49 first-episode, drug-naive schizophrenia patients and 50 healthy controls. Data were analyzed by voxel-based morphometry and Granger causality analysis. The causal connectivity of the integrated prefrontal-thalamic (limbic)-cerebellar (sensorimotor) circuit was partly affected by structural deficits in first-episode, drug-naive schizophrenia as follows: (1) unilateral prefrontal-sensorimotor connectivity abnormalities (increased driving effect from the left medial prefrontal cortex [MPFC] to the sensorimotor regions); (2) bilateral limbic-sensorimotor connectivity abnormalities (increased driving effect from the right anterior cingulate cortex [ACC] to the sensorimotor regions and decreased feedback from the sensorimotor regions to the right ACC); and (3) bilateral increased and decreased causal connectivities among the sensorimotor regions. Some correlations between the gray matter volume of the seeds, along with their causal effects and clinical variables (duration of untreated psychosis and symptom severity), were also observed in the patients. The findings indicated the partial effects of structural deficits in first-episode, drug-naive schizophrenia on the prefrontal-thalamic (limbic)-cerebellar (sensorimotor) circuit. Schizophrenia may reinforce the driving connectivities from the left MPFC or right ACC to the sensorimotor regions and may disrupt bilateral causal connectivities among the sensorimotor regions.
PMID: 25170032 [PubMed - as supplied by publisher]