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]
Resting-State and Task-Based Functional Brain Connectivity in Developmental Dyslexia.
Cereb Cortex. 2014 Aug 28;
Authors: Schurz M, Wimmer H, Richlan F, Ludersdorfer P, Klackl J, Kronbichler M
Reading requires the interaction between multiple cognitive processes situated in distant brain areas. This makes the study of functional brain connectivity highly relevant for understanding developmental dyslexia. We used seed-voxel correlation mapping to analyse connectivity in a left-hemispheric network for task-based and resting-state fMRI data. Our main finding was reduced connectivity in dyslexic readers between left posterior temporal areas (fusiform, inferior temporal, middle temporal, superior temporal) and the left inferior frontal gyrus. Reduced connectivity in these networks was consistently present for 2 reading-related tasks and for the resting state, showing a permanent disruption which is also present in the absence of explicit task demands and potential group differences in performance. Furthermore, we found that connectivity between multiple reading-related areas and areas of the default mode network, in particular the precuneus, was stronger in dyslexic compared with nonimpaired readers.
PMID: 25169986 [PubMed - as supplied by publisher]
k-t FASTER: Acceleration of functional MRI data acquisition using low rank constraints.
Magn Reson Med. 2014 Aug 28;
Authors: Chiew M, Smith SM, Koopmans PJ, Graedel NN, Blumensath T, Miller KL
PURPOSE: In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via Truncation of Effective Rank), which exploits the low-rank structure of fMRI data.
THEORY AND METHODS: Using matrix completion, 4.27× retrospectively and prospectively under-sampled data were reconstructed (coil-independently) using an iterative nonlinear algorithm, and compared with several different reconstruction strategies. Matrix reconstruction error was evaluated; a dual regression analysis was performed to determine fidelity of recovered fMRI resting state networks (RSNs).
RESULTS: The retrospective sampling data showed that k-t FASTER produced the lowest error, approximately 3-4%, and the highest quality RSNs. These results were validated in prospectively under-sampled experiments, with k-t FASTER producing better identification of RSNs than fully sampled acquisitions of the same duration.
CONCLUSION: With k-t FASTER, incoherently under-sampled fMRI data can be robustly recovered using only rank constraints. This technique can be used to improve the speed of fMRI sampling, particularly for multivariate analyses such as temporal independent component analysis. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 25168207 [PubMed - as supplied by publisher]
Characterization of thalamo-cortical association using amplitude and connectivity of functional MRI in mild traumatic brain injury.
J Magn Reson Imaging. 2014 Jun;39(6):spcone
Authors: Zhou Y, Lui YW, Zuo XN, Milham MP, Reaume J, Grossman RI, Ge Y
PURPOSE: To examine thalamic and cortical injuries using fractional amplitude of low-frequency fluctuations (fALFFs) and functional connectivity MRI (fcMRI) based on resting state (RS) and task-related fMRI in patients with mild traumatic brain injury (MTBI).
MATERIALS AND METHODS: Twenty-seven patients and 27 age-matched controls were recruited. The 3 Tesla fMRI at RS and finger tapping task were used to assess fALFF and fcMRI patterns. fALFFs were computed with filtering (0.01-0.08 Hz) and scaling after preprocessing. fcMRI was performed using a standard seed-based correlation method, and delayed fcMRI (coherence) in frequency domain were also performed between thalamus and cortex.
RESULTS: In comparison with controls, MTBI patients exhibited significantly decreased fALFFs in the thalamus (and frontal/temporal subsegments) and cortical frontal and temporal lobes; as well as decreased thalamo-thalamo and thalamo-frontal/ thalamo-temporal fcMRI at rest based on RS-fMRI (corrected P < 0.05). This thalamic and cortical disruption also existed at task-related condition in patients.
CONCLUSION: The decreased fALFFs (i.e., lower neuronal activity) in the thalamus and its segments provide additional evidence of thalamic injury in patients with MTBI. Our findings of fALFFs and fcMRI changes during motor task and resting state may offer insights into the underlying cause and primary location of disrupted thalamo-cortical networks after MTBI. J. Magn. Reson. Imaging 2014;39:1558-1568. © 2013 Wiley Periodicals, Inc.
PMID: 25167964 [PubMed - as supplied by publisher]
Increasing the reliability of data analysis of functional magnetic resonance imaging by applying a new blockwise permutation method.
Front Neuroinform. 2014;8:72
Authors: Adolf D, Weston S, Baecke S, Luchtmann M, Bernarding J, Kropf S
A recent paper by Eklund et al. (2012) showed that up to 70% false positive results may occur when analyzing functional magnetic resonance imaging (fMRI) data using the statistical parametric mapping (SPM) software, which may mainly be caused by insufficient compensation for the temporal correlation between successive scans. Here, we show that a blockwise permutation method can be an effective alternative to the standard correction method for the correlated residuals in the general linear model, assuming an AR(1)-model as used in SPM for analyzing fMRI data. The blockwise permutation approach including a random shift developed by our group (Adolf et al., 2011) accounts for the temporal correlation structure of the data without having to provide a specific definition of the underlying autocorrelation model. 1465 publicly accessible resting-state data sets were re-analyzed, and the results were compared with those of Eklund et al. (2012). It was found that with the new permutation method the nominal familywise error rate for the detection of activated voxels could be maintained approximately under even the most critical conditions in which Eklund et al. found the largest deviations from the nominal error level. Thus, the method presented here can serve as a tool to ameliorate the quality and reliability of fMRI data analyses.
PMID: 25165444 [PubMed]
Cognitive impairment and resting-state network connectivity in Parkinson's disease.
Hum Brain Mapp. 2014 Aug 28;
Authors: Baggio HC, Segura B, Sala-Llonch R, Marti MJ, Valldeoriola F, Compta Y, Tolosa E, Junqué C
The purpose of this work was to evaluate changes in the connectivity patterns of a set of cognitively relevant, dynamically interrelated brain networks in association with cognitive deficits in Parkinson's disease (PD) using resting-state functional MRI. Sixty-five nondemented PD patients and 36 matched healthy controls were included. Thirty-four percent of PD patients were classified as having mild cognitive impairment (MCI) based on performance in attention/executive, visuospatial/visuoperceptual (VS/VP) and memory functions. A data-driven approach using independent component analysis (ICA) was used to identify the default-mode network (DMN), the dorsal attention network (DAN) and the bilateral frontoparietal networks (FPN), which were compared between groups using a dual-regression approach controlling for gray matter atrophy. Additional seed-based analyses using a priori defined regions of interest were used to characterize local changes in intranetwork and internetwork connectivity. Structural group comparisons through voxel-based morphometry and cortical thickness were additionally performed to assess associated gray matter atrophy. ICA results revealed reduced connectivity between the DAN and right frontoinsular regions in MCI patients, associated with worse performance in attention/executive functions. The DMN displayed increased connectivity with medial and lateral occipito-parietal regions in MCI patients, associated with worse VS/VP performance, and with occipital reductions in cortical thickness. In line with data-driven results, seed-based analyses mainly revealed reduced within-DAN, within-DMN and DAN-FPN connectivity, as well as loss of normal DAN-DMN anticorrelation in MCI patients. Our findings demonstrate differential connectivity changes affecting the networks evaluated, which we hypothesize to be related to the pathophysiological bases of different types of cognitive impairment in PD. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 25164875 [PubMed - as supplied by publisher]
Behavioral relevance of the dynamics of functional brain connectome.
Brain Connect. 2014 Aug 27;
Authors: Jia H, Hu X, Deshpande G
While many previous studies assumed the functional connectivity (FC) between brain regions to be stationary, recent studies have demonstrated that FC dynamically varies across time. However, two challenges have limited the interpretability of dynamic FC information. First, a principled framework for selecting the temporal extent of the window used to examine the dynamics is lacking and this has resulted in ad-hoc selections of window lengths and subsequent divergent results. Second, it is unclear whether there is any behavioral relevance to the dynamics of the functional connectome in addition to that obtained from conventional static FC. In this work, we address these challenges by first proposing a principled framework for selecting the extent of the temporal windows in a dynamic and data-driven fashion based on statistical tests of the stationarity of time series. Further, we propose a method involving three levels of clustering - across space, time and subjects - which allow for group-level inferences of the dynamics. Next, using a large resting state fMRI and behavioral dataset from the Human Connectome Project, we demonstrate that metrics derived from dynamic FC can explain more than twice the variance in 75 behaviors across different domains (alertness, cognition, emotion and personality traits) as compared to static FC in healthy individuals. Further, we found that individuals with brain networks exhibiting greater dynamics performed more favorably in behavioral tasks. This indicates that the ease with which brain regions engage or disengage may provide potential biomarkers for disorders involving altered neural circuitry.
PMID: 25163490 [PubMed - as supplied by publisher]
An eight month randomized controlled exercise intervention alters resting state synchrony in overweight children.
Neuroscience. 2014 Jan 3;256:445-55
Authors: Krafft CE, Pierce JE, Schwarz NF, Chi L, Weinberger AL, Schaeffer DJ, Rodrigue AL, Camchong J, Allison JD, Yanasak NE, Liu T, Davis CL, McDowell JE
Children with low aerobic fitness have altered brain function compared to higher-fit children. This study examined the effect of an 8-month exercise intervention on resting state synchrony. Twenty-two sedentary, overweight (body mass index ≥85th percentile) children 8-11 years old were randomly assigned to one of two after-school programs: aerobic exercise (n=13) or sedentary attention control (n=9). Before and after the 8-month programs, all subjects participated in resting state functional magnetic resonance imaging scans. Independent components analysis identified several networks, with four chosen for between-group analysis: salience, default mode, cognitive control, and motor networks. The default mode, cognitive control, and motor networks showed more spatial refinement over time in the exercise group compared to controls. The motor network showed increased synchrony in the exercise group with the right medial frontal gyrus compared to controls. Exercise behavior may enhance brain development in children.
PMID: 24096138 [PubMed - indexed for MEDLINE]
Functional brain-imaging correlates of negative affectivity and the onset of first-episode depression.
Psychol Med. 2014 Aug 27;:1-9
Authors: Davey CG, Whittle S, Harrison BJ, Simmons JG, Byrne ML, Schwartz OS, Allen NB
Background. The amygdala and subgenual anterior cingulate cortex (sACC) are key brain regions for the generation of negative affect. In this longitudinal fMRI study of adolescents we investigated how amygdala-sACC connectivity was correlated with negative affectivity (NA) both cross-sectionally and longitudinally, and examined its relationship to the onset of first-episode depression. Method. Fifty-six adolescents who were part of a larger longitudinal study of adolescent development were included. They had no history of mental illness at the time of their baseline scan (mean age 16.5 years) and had a follow-up scan 2 years later (mean age 18.8 years). We used resting-state functional-connectivity MRI to investigate whether cross-sectional and change measures of amygdala-sACC connectivity were (i) correlated with NA and its change over time, and (ii) related to the onset of first-episode depression. Results. The magnitude of amygdala connectivity with sACC showed significant positive correlation with NA at both time-points. Further analysis confirmed that change in amygdala-sACC connectivity between assessments was correlated with change in NA. Eight participants developed a first episode of depression between the baseline and follow-up assessments: they showed increased amygdala-sACC connectivity at follow-up. Conclusions. Amygdala-sACC connectivity is associated with NA in adolescence, with change in connectivity between these regions showing positive correlation with change in NA. Our observation that the onset of depression was associated with an increase in connectivity between the regions provides support for the neurobiological 'scar' hypothesis of depression.
PMID: 25162634 [PubMed - as supplied by publisher]
Insights from neuroenergetics into the interpretation of functional neuroimaging: an alternative empirical model for studying the brain's support of behavior.
J Cereb Blood Flow Metab. 2014 Aug 27;
Authors: Shulman RG, Hyder F, Rothman DL
Functional neuroimaging measures quantitative changes in neurophysiological parameters coupled to neuronal activity during observable behavior. These results have usually been interpreted by assuming that mental causation of behavior arises from the simultaneous actions of distinct psychological mechanisms or modules. However, reproducible localization of these modules in the brain using functional magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging has been elusive other than for sensory systems. In this paper, we show that neuroenergetic studies using PET, calibrated functional magnetic resonance imaging (fMRI), (13)C magnetic resonance spectroscopy, and electrical recordings do not support the standard approach, which identifies the location of mental modules from changes in brain activity. Of importance in reaching this conclusion is that changes in neuronal activities underlying the fMRI signal are many times smaller than the high ubiquitous, baseline neuronal activity, or energy in resting, awake humans. Furthermore, the incremental signal depends on the baseline activity contradicting theoretical assumptions about linearity and insertion of mental modules. To avoid these problems, while making use of these valuable results, we propose that neuroimaging should be used to identify observable brain activities that are necessary for a person's observable behavior rather than being used to seek hypothesized mental processes.Journal of Cerebral Blood Flow & Metabolism advance online publication, 27 August 2014; doi:10.1038/jcbfm.2014.145.
PMID: 25160670 [PubMed - as supplied by publisher]
Robust brain parcellation using sparse representation on resting-state fMRI.
Brain Struct Funct. 2014 Aug 26;
Authors: Zhang Y, Caspers S, Fan L, Fan Y, Song M, Liu C, Mo Y, Roski C, Eickhoff S, Amunts K, Jiang T
Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns. Many parcellation methods have been proposed for brain parcellation using rs-fMRI, but their results have been somewhat inconsistent, potentially due to various types of noise. In this study, we provide a robust parcellation method for rs-fMRI-based brain parcellation, which constructs a sparse similarity graph based on the sparse representation coefficients of each seed voxel and then uses spectral clustering to identify distinct modules. Both the local time-varying BOLD signals and whole-brain connectivity patterns may be used as features and yield similar parcellation results. The robustness of our method was tested on both simulated and real rs-fMRI datasets. In particular, on simulated rs-fMRI data, sparse representation achieved good performance across different noise levels, including high accuracy of parcellation and high robustness to noise. On real rs-fMRI data, stable parcellation of the medial frontal cortex (MFC) and parietal operculum (OP) were achieved on three different datasets, with high reproducibility within each dataset and high consistency across these results. Besides, the parcellation of MFC was little influenced by the degrees of spatial smoothing. Furthermore, the consistent parcellation of OP was also well corresponding to cytoarchitectonic subdivisions and known somatotopic organizations. Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation.
PMID: 25156576 [PubMed - as supplied by publisher]
Network analysis of the default mode network using functional connectivity MRI in temporal lobe epilepsy.
J Vis Exp. 2014;(90)
Authors: Haneef Z, Lenartowicz A, Yeh HJ, Engel J, Stern JM
Functional connectivity MRI (fcMRI) is an fMRI method that examines the connectivity of different brain areas based on the correlation of BOLD signal fluctuations over time. Temporal Lobe Epilepsy (TLE) is the most common type of adult epilepsy and involves multiple brain networks. The default mode network (DMN) is involved in conscious, resting state cognition and is thought to be affected in TLE where seizures cause impairment of consciousness. The DMN in epilepsy was examined using seed based fcMRI. The anterior and posterior hubs of the DMN were used as seeds in this analysis. The results show a disconnection between the anterior and posterior hubs of the DMN in TLE during the basal state. In addition, increased DMN connectivity to other brain regions in left TLE along with decreased connectivity in right TLE is revealed. The analysis demonstrates how seed-based fcMRI can be used to probe cerebral networks in brain disorders such as TLE.
PMID: 25146174 [PubMed - in process]
Network based statistical analysis detects changes induced by continuous theta-burst stimulation on brain activity at rest.
Front Psychiatry. 2014;5:97
Authors: Mastropasqua C, Bozzali M, Ponzo V, Giulietti G, Caltagirone C, Cercignani M, Koch G
We combined continuous theta-burst stimulation (cTBS) and resting state (RS)-fMRI approaches to investigate changes in functional connectivity (FC) induced by right dorsolateral prefrontal cortex (DLPFC)-cTBS at rest in a group of healthy subjects. Seed-based fMRI analysis revealed a specific pattern of correlation between the right prefrontal cortex and several brain regions: based on these results, we defined a 29-node network to assess changes in each network connection before and after, respectively, DLPFC-cTBS and sham sessions. A decrease of correlation between the right prefrontal cortex and right parietal cortex (Brodmann areas 46 and 40, respectively) was detected after cTBS, while no significant result was found when analyzing sham-session data. To our knowledge, this is the first study that demonstrates within-subject changes in FC induced by cTBS applied on prefrontal area. The possibility to induce selective changes in a specific region without interfering with functionally correlated area could have several implications for the study of functional properties of the brain, and for the emerging therapeutic strategies based on transcranial stimulation.
PMID: 25140158 [PubMed]
Longitudinal resting state fMRI analysis in healthy controls and premanifest Huntington's disease gene carriers: A three-year follow-up study.
Hum Brain Mapp. 2014 Aug 19;
Authors: Odish OF, van den Berg-Huysmans AA, van den Bogaard SJ, Dumas EM, Hart EP, Rombouts SA, van der Grond J, Roos RA, on behalf of the TRACK-HD Investigator Group
Background: We previously demonstrated that in the premanifest stage of Huntington's disease (preHD), a reduced functional connectivity exists compared to healthy controls. In the current study, we look at possible changes in functional connectivity occurring longitudinally over a period of 3 years, with the aim of assessing the potential usefulness of this technique as a biomarker for disease progression in preHD. Methods: Twenty-two preHD and 17 healthy control subjects completed resting state functional magnetic resonance imaging (fMRI) scans in two visits with 3 years in between. Differences in resting state connectivity were examined for eight networks of interest using FSL with three different analysis types: a dual regression method, region of interest approach, and an independent component analysis. To evaluate a possible combined effect of gray matter volume change and the change in blood oxygenation level dependent signal, the analysis was performed with and without voxel-wise correction for gray matter volume. To evaluate possible correlations between functional connectivity change and the predicted time to disease onset, the preHD group was classed as preHD-A if ≥10.9 years and preHD-B if <10.9 years from predicted disease onset. Possible correlations between burden of pathology score and functional connectivity change in preHD were also assessed. Finally, longitudinal change in whole brain and striatal volumetric measures was assessed in the studied cohort. Results: Longitudinal analysis of the resting state-fMRI (RS-fMRI) data revealed no differences in the degree of connectivity change between the groups over a period of 3 years, though a significantly higher rate of striatal atrophy was found in the preHD group compared to controls in the same period. Discussion: Based on the results found in this study, the provisional conclusion is that RS-fMRI lacks sensitivity in detecting changes in functional connectivity in HD gene carriers prior to disease manifestation over a 3-year follow-up period. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
PMID: 25139578 [PubMed - as supplied by publisher]