High transition frequencies of dynamic functional connectivity states in the creative brain.
Sci Rep. 2017 Apr 06;7:46072
Authors: Li J, Zhang D, Liang A, Liang B, Wang Z, Cai Y, Gao M, Gao Z, Chang S, Jiao B, Huang R, Liu M
Creativity is thought to require the flexible reconfiguration of multiple brain regions that interact in transient and complex communication patterns. In contrast to prior emphases on searching for specific regions or networks associated with creative performance, we focused on exploring the association between the reconfiguration of dynamic functional connectivity states and creative ability. We hypothesized that a high frequency of dynamic functional connectivity state transitions will be associated with creative ability. To test this hypothesis, we recruited a high-creative group (HCG) and a low-creative group (LCG) of participants and collected resting-state fMRI (R-fMRI) data and Torrance Tests of Creative Thinking (TTCT) scores from each participant. By combining an independent component analysis with a dynamic network analysis approach, we discovered the HCG had more frequent transitions between dynamic functional connectivity (dFC) states than the LCG. Moreover, a confirmatory analysis using multiplication of temporal derivatives also indicated that there were more frequent dFC state transitions in the HCG. Taken together, these results provided empirical evidence for a linkage between the flexible reconfiguration of dynamic functional connectivity states and creative ability. These findings have the potential to provide new insights into the neural basis of creativity.
PMID: 28383052 [PubMed - in process]
Association between serotonin denervation and resting-state functional connectivity in mild cognitive impairment.
Hum Brain Mapp. 2017 Apr 05;:
Authors: Barrett FS, Workman CI, Sair HI, Savonenko AV, Kraut MA, Sodums DJ, Joo JJ, Nassery N, Marano CM, Munro CA, Brandt J, Zhou Y, Wong DF, Smith GS
Resting-state functional connectivity alterations have been demonstrated in Alzheimer's disease (AD) and mild cognitive impairment (MCI) before the observation of AD neuropathology, but mechanisms driving these changes are not well understood. Serotonin neurodegeneration has been observed in MCI and AD and is associated with cognitive deficits and neuropsychiatric symptoms, but the role of the serotonin system in relation to brain network dysfunction has not been a major focus of investigation. The current study investigated the relationship between serotonin transporter availability (SERT; measured using positron emission tomography) and brain network functional connectivity (measured using resting-state functional MRI) in 20 participants with MCI and 21 healthy controls. Two SERT regions of interest were selected for the analysis: the Dorsal Raphe Nuclei (DRN) and the precuneus which represent the cell bodies of origin and a cortical target of projections of the serotonin system, respectively. Both regions show decreased SERT in MCI compared to controls and are the site of early AD pathology. Average resting-state functional connectivity did not differ between MCI and controls. Decreased SERT in DRN was associated with lower hippocampal resting-state connectivity in MCI participants compared to controls. Decreased SERT in the right precuneus was also associated with lower resting-state connectivity of the retrosplenial cortex to the dorsal lateral prefrontal cortex and higher resting-state connectivity of the retrosplenial cortex to the posterior cingulate and in patients with MCI but not in controls. These results suggest that a serotonergic mechanism may underlie changes in brain functional connectivity in MCI. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
PMID: 28379618 [PubMed - as supplied by publisher]
Latent and Abnormal Functional Connectivity Circuits in Autism Spectrum Disorder.
Front Neurosci. 2017;11:125
Authors: Chen S, Xing Y, Kang J
Autism spectrum disorder (ASD) is associated with disrupted brain networks. Neuroimaging techniques provide noninvasive methods of investigating abnormal connectivity patterns in ASD. In the present study, we compare functional connectivity networks in people with ASD with those in typical controls, using neuroimaging data from the Autism Brain Imaging Data Exchange (ABIDE) project. Specifically, we focus on the characteristics of intrinsic functional connectivity based on data collected by resting-state functional magnetic resonance imaging (rs-fMRI). Our aim was to identify disrupted brain connectivity patterns across all networks, instead of in individual edges, by using advanced statistical methods. Unlike many brain connectome studies, in which networks are prespecified before the edge connectivity in each network is compared between clinical groups, we detected the latent differentially expressed networks automatically. Our network-level analysis identified abnormal connectome networks that (i) included a high proportion of edges that were differentially expressed between people with ASD and typical controls; and (ii) showed highly-organized graph topology. These findings provide new insight into the study of the underlying neuropsychiatric mechanism of ASD.
PMID: 28377688 [PubMed - in process]
Short- and long-range functional connectivity density alterations in adolescents with pure conduct disorder at resting-state.
Neuroscience. 2017 Apr 01;:
Authors: Lu FM, Zhou JS, Wang XP, Xiang YT, Yuan Z
Conduct disorder (CD) is a developmental disorder defined by a repetitive and persistent display of antisocial and aggressive behaviors that violates the rights of others or basic social rules. Recently, resting-state functional magnetic resonance imaging (rsfMRI) has been widely adopted to investigate the altered intrinsic neural activities and the disrupted endogenous brain connectivity of CD. In this study, functional connectivity density (FCD) mapping, a newly developed ultrafast voxel-wise method based on rsfMRI, was applied for the first time to examine the changes in the brain functional connectivity in CD at the voxel level. We assessed the differences in FCD between eighteen male adolescents with CD and eighteen typically-developing (TD) individuals. Then, the identified brain regions in which CD patients and healthy controls exhibited significant difference in FCD were extracted to calculate the correlations between measures of FCD values and clinical data. We discovered that compared to healthy controls, CD patients showed increased short-range FCD in the default-mode network including the bilateral posterior cingulate cortex (PCC) and the bilateral precuneus (PCUN). More importantly, increased short-range FCD values in the bilateral PCC, the bilateral PCUN, and increased long-range FCD values in the left MCC showed significant correlations with the impulsivity. Overall, these results suggested that the FCD abnormalities in CD patients occurred in brain regions known to be involved in cognition, emotion and visual perception.
PMID: 28377176 [PubMed - as supplied by publisher]
Effect of smoking on resting-state functional connectivity in smokers: An fMRI study.
Respirology. 2017 Apr 04;:
Authors: Zhou S, Xiao D, Peng P, Wang SK, Liu Z, Qin HY, Li SS, Wang C
BACKGROUND AND OBJECTIVE: Smoking is a leading cause of death in the world. Aberrant brain function has been repeatedly linked to tobacco smoking. However, little is known about insula-based resting-state functional connectivity (rsFC) in non-deprived tobacco-dependent smokers. This study characterized the correlation between insula-based rsFC and tobacco dependence severity in non-deprived smokers.
METHODS: A total of 37 male smokers and 37 age-matched male non-smokers completed resting-state functional MRI (fMRI) scans. The insula-based rsFC differences between smokers and controls were investigated and the correlation between insula-based rsFC and FTND (Fagerström Test for Nicotine Dependence) scores were then assessed.
RESULTS: Compared with controls, smokers showed significantly lower rsFC between orbitofrontal cortex, superior frontal gyrus, temporal lobe and insula. The rsFC between orbitofrontal cortex, temporal lobe, inferior parietal cortex, occipital lobe and insula was positively correlated with FTND. However, the rsFC between anterior cingulate cortex and insula was negatively correlated with FTND.
CONCLUSION: Our findings suggest differences in brain functional connectivity between smokers and non-smokers. This study sheds new insights into the neural mechanisms of tobacco dependence.
PMID: 28374936 [PubMed - as supplied by publisher]
Restructuring Reward Mechanisms in Nicotine Addiction: A Pilot fMRI Study of Mindfulness-Oriented Recovery Enhancement for Cigarette Smokers.
Evid Based Complement Alternat Med. 2017;2017:7018014
Authors: Froeliger B, Mathew AR, McConnell PA, Eichberg C, Saladin ME, Carpenter MJ, Garland EL
The primary goal of this pilot feasibility study was to examine the effects of Mindfulness-Oriented Recovery Enhancement (MORE), a behavioral treatment grounded in dual-process models derived from cognitive science, on frontostriatal reward processes among cigarette smokers. Healthy adult (N = 13; mean (SD) age 49 ± 12.2) smokers provided informed consent to participate in a 10-week study testing MORE versus a comparison group (CG). All participants underwent two fMRI scans: pre-tx and after 8-weeks of MORE. Emotion regulation (ER), smoking cue reactivity (CR), and resting-state functional connectivity (rsFC) were assessed at each fMRI visit; smoking and mood were assessed throughout. As compared to the CG, MORE significantly reduced smoking (d = 2.06) and increased positive affect (d = 2.02). MORE participants evidenced decreased CR-BOLD response in ventral striatum (VS; d = 1.57) and ventral prefrontal cortex (vPFC; d = 1.7) and increased positive ER-BOLD in VS (dVS = 2.13) and vPFC (dvmPFC = 2.66). Importantly, ER was correlated with smoking reduction (r's = .68 to .91) and increased positive affect (r's = .52 to .61). These findings provide preliminary evidence that MORE may facilitate the restructuring of reward processes and play a role in treating the pathophysiology of nicotine addiction.
PMID: 28373890 [PubMed - in process]
Can brain state be manipulated to emphasize individual differences in functional connectivity?
Neuroimage. 2017 Mar 31;:
Authors: Finn ES, Scheinost D, Finn DM, Shen X, Papademetris X, Constable RT
While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within- to between-subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within- and between-subject variability across conditions using data from the Human Connectome Project, and outline questions for future study.
PMID: 28373122 [PubMed - as supplied by publisher]
Higher serum cholesterol is associated with intensified age-related neural network decoupling and cognitive decline in early- to mid-life.
Hum Brain Mapp. 2017 Mar 31;:
Authors: Spielberg JM, Sadeh N, Leritz EC, McGlinchey RE, Milberg WP, Hayes JP, Salat DH
Mounting evidence indicates that serum cholesterol and other risk factors for cardiovascular disease intensify normative trajectories of age-related cognitive decline. However, the neural mechanisms by which this occurs remain largely unknown. To understand the impact of cholesterol on brain networks, we applied graph theory to resting-state fMRI in a large sample of early- to mid-life Veterans (N = 206, Meanage = 32). A network emerged (centered on the banks of the superior temporal sulcus) that evidenced age-related decoupling (i.e., decreased network connectivity with age), but only in participants with clinically-elevated total cholesterol (≥180 mg/dL). Crucially, decoupling in this network corresponded to greater day-to-day disability and mediated age-related declines in psychomotor speed. Finally, examination of network organization revealed a pattern of age-related dedifferentiation for the banks of the superior temporal sulcus, again present only with higher cholesterol. More specifically, age was related to decreasing within-module communication (indexed by Within-Module Degree Z-Score) and increasing between-module communication (indexed by Participation Coefficient), but only in participants with clinically-elevated cholesterol. Follow-up analyses indicated that all findings were driven by low-density lipoprotein (LDL) levels, rather than high-density lipoprotein (HDL) or triglycerides, which is interesting as LDL levels have been linked to increased risk for cardiovascular disease, whereas HDL levels appear inversely related to such disease. These findings provide novel insight into the deleterious effects of cholesterol on brain health and suggest that cholesterol accelerates the impact of age on neural trajectories by disrupting connectivity in circuits implicated in integrative processes and behavioral control. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
PMID: 28370780 [PubMed - as supplied by publisher]
Tired and misconnected: A breakdown of brain modularity following sleep deprivation.
Hum Brain Mapp. 2017 Apr 03;:
Authors: Ben Simon E, Maron-Katz A, Lahav N, Shamir R, Hendler T
Sleep deprivation (SD) critically affects a range of cognitive and affective functions, typically assessed during task performance. Whether such impairments stem from changes to the brain's intrinsic functional connectivity remain largely unknown. To examine this hypothesis, we applied graph theoretical analysis on resting-state fMRI data derived from 18 healthy participants, acquired during both sleep-rested and sleep-deprived states. We hypothesized that parameters indicative of graph connectivity, such as modularity, will be impaired by sleep deprivation and that these changes will correlate with behavioral outcomes elicited by sleep loss. As expected, our findings point to a profound reduction in network modularity without sleep, evident in the limbic, default-mode, salience and executive modules. These changes were further associated with behavioral impairments elicited by SD: a decrease in salience module density was associated with worse task performance, an increase in limbic module density was predictive of stronger amygdala activation in a subsequent emotional-distraction task and a shift in frontal hub lateralization (from left to right) was associated with increased negative mood. Altogether, these results portray a loss of functional segregation within the brain and a shift towards a more random-like network without sleep, already detected in the spontaneous activity of the sleep-deprived brain. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.
PMID: 28370703 [PubMed - as supplied by publisher]
PTSD and cognitive symptoms relate to inhibition-related prefrontal activation and functional connectivity.
Depress Anxiety. 2017 Mar 29;:
Authors: Clausen AN, Francisco AJ, Thelen J, Bruce J, Martin LE, McDowd J, Simmons WK, Aupperle RL
BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with reduced executive functioning and verbal memory performance, as well as abnormal task-specific activity in prefrontal cortex (PFC) and anterior cingulate cortices (ACC). The current study examined how PTSD symptoms and neuropsychological performance in combat veterans relates to (1) medial PFC and ACC activity during cognitive inhibition, and (2) task-independent PFC functional connectivity.
METHODS: Thirty-nine male combat veterans with varying levels of PTSD symptoms completed the multisource interference task during functional magnetic resonance imaging. Robust regression analyses were used to assess relationships between percent signal change (PSC: incongruent-congruent) and both PTSD severity and neuropsychological performance. Analyses were conducted voxel-wise and for PSC extracted from medial PFC and ACC regions of interest. Resting-state scans were available for veterans with PTSD. Regions identified via task-based analyses were used as seeds for resting-state connectivity analyses.
RESULTS: Worse PTSD severity and neuropsychological performance related to less medial PFC and rostral ACC activity during interference processing, driven partly by increased activation to congruent trials. Worse PTSD severity related to reduced functional connectivity between these regions and bilateral, lateral PFC (Brodmann area 10). Worse neuropsychological performance related to reduced functional connectivity between these regions and the inferior frontal gyrus.
CONCLUSIONS: PTSD and associated neuropsychological deficits may result from difficulties regulating medial PFC regions associated with "default mode," or self-referential processing. Further clarification of functional coupling deficits between default mode and executive control networks in PTSD may enhance understanding of neuropsychological and emotional symptoms and provide novel treatment targets.
PMID: 28370684 [PubMed - as supplied by publisher]
The role of high-field magnetic resonance imaging in parkinsonian disorders: Pushing the boundaries forward.
Mov Disord. 2017 Mar 28;:
Authors: Lehericy S, Vaillancourt DE, Seppi K, Monchi O, Rektorova I, Antonini A, McKeown MJ, Masellis M, Berg D, Rowe JB, Lewis SJ, Williams-Gray CH, Tessitore A, Siebner HR, International Parkinson and Movement Disorder Society (IPMDS)-Neuroimaging Study Group
Historically, magnetic resonance imaging (MRI) has contributed little to the study of Parkinson's disease (PD), but modern MRI approaches have unveiled several complementary markers that are useful for research and clinical applications. Iron- and neuromelanin-sensitive MRI detect qualitative changes in the substantia nigra. Quantitative MRI markers can be derived from diffusion weighted and iron-sensitive imaging or volumetry. Functional brain alterations at rest or during task performance have been captured with functional and arterial spin labeling perfusion MRI. These markers are useful for the diagnosis of PD and atypical parkinsonism, to track disease progression from the premotor stages of these diseases and to better understand the neurobiological basis of clinical deficits. A current research goal using MRI is to generate time-dependent models of the evolution of PD biomarkers that can help understand neurodegeneration and provide reliable markers for therapeutic trials. This article reviews recent advances in MRI biomarker research at high-field (3T) and ultra high field-imaging (7T) in PD and atypical parkinsonism. © 2017 International Parkinson and Movement Disorder Society.
PMID: 28370449 [PubMed - as supplied by publisher]
Insula and amygdala resting-state functional connectivity differentiate bipolar from unipolar depression.
Acta Psychiatr Scand. 2017 Mar 28;:
Authors: Ambrosi E, Arciniegas DB, Madan A, Curtis KN, Patriquin MA, Jorge RE, Spalletta G, Fowler JC, Frueh BC, Salas R
OBJECTIVE: Distinguishing depressive episodes due to bipolar disorder (BD) or major depressive disorder (MDD) solely on clinical grounds is challenging. We aimed at comparing resting-state functional connectivity (rsFC) of regions subserving emotional regulation in similarly depressed BD and MDD.
METHOD: We enrolled 76 in-patients (BD, n = 36; MDD, n = 40) and 40 healthy controls (HC). A seed-based approach was used to identify regions showing different rsFC with the insula and the amygdala. Insular and amygdalar parcellations were then performed along with diagnostic accuracy of the main findings.
RESULTS: Lower rsFC between the left insula and the left mid-dorsolateral prefrontal cortex and between bilateral insula and right frontopolar prefrontal cortex (FPPFC) was observed in BD compared to MDD and HC. These results were driven by the dorsal anterior and posterior insula (PI). Lower rsFC between the right amygdala and the left anterior hippocampus was observed in MDD compared to BD and HC. These results were driven by the centromedial and laterobasal amygdala. Left PI/right FPPC rsFC showed 78% accuracy differentiating BD and MDD.
CONCLUSION: rsFC of amygdala and insula distinguished between depressed BD and MDD. The observed differences suggest the possibility of differential pathophysiological mechanisms of emotional dysfunction in bipolar and unipolar depression.
PMID: 28369737 [PubMed - as supplied by publisher]
The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions.
Gigascience. 2017 Feb 01;6(2):1-14
Authors: O'Connor D, Potler NV, Kovacs M, Xu T, Ai L, Pellman J, Vanderwal T, Parra LC, Cohen S, Ghosh S, Escalera J, Grant-Villegas N, Osman Y, Bui A, Craddock RC, Milham MP
Background: Although typically measured during the resting state, a growing literature is illustrating the ability to map intrinsic connectivity with functional MRI during task and naturalistic viewing conditions. These paradigms are drawing excitement due to their greater tolerability in clinical and developing populations and because they enable a wider range of analyses (e.g., inter-subject correlations). To be clinically useful, the test-retest reliability of connectivity measured during these paradigms needs to be established. This resource provides data for evaluating test-retest reliability for full-brain connectivity patterns detected during each of four scan conditions that differ with respect to level of engagement (rest, abstract animations, movie clips, flanker task). Data are provided for 13 participants, each scanned in 12 sessions with 10 minutes for each scan of the four conditions. Diffusion kurtosis imaging data was also obtained at each session.
Findings: Technical validation and demonstrative reliability analyses were carried out at the connection-level using the Intraclass Correlation Coefficient and at network-level representations of the data using the Image Intraclass Correlation Coefficient. Variation in intrinsic functional connectivity across sessions was generally found to be greater than that attributable to scan condition. Between-condition reliability was generally high, particularly for the frontoparietal and default networks. Between-session reliabilities obtained separately for the different scan conditions were comparable, though notably lower than between-condition reliabilities.
Conclusions: This resource provides a test-bed for quantifying the reliability of connectivity indices across subjects, conditions and time. The resource can be used to compare and optimize different frameworks for measuring connectivity and data collection parameters such as scan length. Additionally, investigators can explore the unique perspectives of the brain's functional architecture offered by each of the scan conditions.
PMID: 28369458 [PubMed - in process]
Financial Exploitation Is Associated With Structural and Functional Brain Differences in Healthy Older Adults.
J Gerontol A Biol Sci Med Sci. 2017 Mar 28;:
Authors: Spreng RN, Cassidy BN, Darboh BS, DuPre E, Lockrow AW, Setton R, Turner GR
Background: Age-related brain changes leading to altered socioemotional functioning may increase vulnerability to financial exploitation. If confirmed, this would suggest a novel mechanism leading to heightened financial exploitation risk in older adults. Development of predictive neural markers could facilitate increased vigilance and prevention. In this preliminary study, we sought to identify structural and functional brain differences associated with financial exploitation in older adults.
Methods: Financially exploited older adults (n = 13, 7 female) and a matched cohort of older adults who had been exposed to, but avoided, a potentially exploitative situation (n = 13, 7 female) were evaluated. Using magnetic resonance imaging, we examined cortical thickness and resting state functional connectivity. Behavioral data were collected using standardized cognitive assessments, self-report measures of mood and social functioning.
Results: The exploited group showed cortical thinning in anterior insula and posterior superior temporal cortices, regions associated with processing affective and social information, respectively. Functional connectivity encompassing these regions, within default and salience networks, was reduced, while between network connectivity was increased. Self-reported anger and hostility was higher for the exploited group.
Conclusions: We observed financial exploitation associated with brain differences in regions involved in socioemotional functioning. These exploratory and preliminary findings suggest that alterations in brain regions implicated in socioemotional functioning may be a marker of financial exploitation risk. Large-scale, prospective studies are necessary to validate this neural mechanism, and develop predictive markers for use in clinical practice.
PMID: 28369260 [PubMed - as supplied by publisher]
Alterations of Intrinsic Brain Connectivity Patterns in Depression and Bipolar Disorders: A Critical Assessment of Magnetoencephalography-Based Evidence.
Front Psychiatry. 2017;8:41
Authors: Alamian G, Hincapié AS, Combrisson E, Thiery T, Martel V, Althukov D, Jerbi K
Despite being the object of a thriving field of clinical research, the investigation of intrinsic brain network alterations in psychiatric illnesses is still in its early days. Because the pathological alterations are predominantly probed using functional magnetic resonance imaging (fMRI), many questions about the electrophysiological bases of resting-state alterations in psychiatric disorders, particularly among mood disorder patients, remain unanswered. Alongside important research using electroencephalography (EEG), the specific recent contributions and future promise of magnetoencephalography (MEG) in this field are not fully recognized and valued. Here, we provide a critical review of recent findings from MEG resting-state connectivity within major depressive disorder (MDD) and bipolar disorder (BD). The clinical MEG resting-state results are compared with those previously reported with fMRI and EEG. Taken together, MEG appears to be a promising but still critically underexploited technique to unravel the neurophysiological mechanisms that mediate abnormal (both hyper- and hypo-) connectivity patterns involved in MDD and BD. In particular, a major strength of MEG is its ability to provide source-space estimations of neuromagnetic long-range rhythmic synchronization at various frequencies (i.e., oscillatory coupling). The reviewed literature highlights the relevance of probing local and interregional rhythmic synchronization to explore the pathophysiological underpinnings of each disorder. However, before we can fully take advantage of MEG connectivity analyses in psychiatry, several limitations inherent to MEG connectivity analyses need to be understood and taken into account. Thus, we also discuss current methodological challenges and outline paths for future research. MEG resting-state studies provide an important window onto perturbed spontaneous oscillatory brain networks and hence supply an important complement to fMRI-based resting-state measurements in psychiatric populations.
PMID: 28367127 [PubMed - in process]
Small-world bias of correlation networks: From brain to climate.
Chaos. 2017 Mar;27(3):035812
Authors: Hlinka J, Hartman D, Jajcay N, Tomeček D, Tintěra J, Paluš M
Complex systems are commonly characterized by the properties of their graph representation. Dynamical complex systems are then typically represented by a graph of temporal dependencies between time series of state variables of their subunits. It has been shown recently that graphs constructed in this way tend to have relatively clustered structure, potentially leading to spurious detection of small-world properties even in the case of systems with no or randomly distributed true interactions. However, the strength of this bias depends heavily on a range of parameters and its relevance for real-world data has not yet been established. In this work, we assess the relevance of the bias using two examples of multivariate time series recorded in natural complex systems. The first is the time series of local brain activity as measured by functional magnetic resonance imaging in resting healthy human subjects, and the second is the time series of average monthly surface air temperature coming from a large reanalysis of climatological data over the period 1948-2012. In both cases, the clustering in the thresholded correlation graph is substantially higher compared with a realization of a density-matched random graph, while the shortest paths are relatively short, showing thus distinguishing features of small-world structure. However, comparable or even stronger small-world properties were reproduced in correlation graphs of model processes with randomly scrambled interconnections. This suggests that the small-world properties of the correlation matrices of these real-world systems indeed do not reflect genuinely the properties of the underlying interaction structure, but rather result from the inherent properties of correlation matrix.
PMID: 28364746 [PubMed - in process]
Presurgical Brain Mapping of the Ventral Somatomotor Network in Patients with Brain Tumors Using Resting-State fMRI.
AJNR Am J Neuroradiol. 2017 Mar 31;:
Authors: Yahyavi-Firouz-Abadi N, Pillai JJ, Lindquist MA, Calhoun VD, Agarwal S, Airan RD, Caffo B, Gujar B, Sair HI
BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor.
MATERIALS AND METHODS: We identified 26 surgically na|fkve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated.
RESULTS: The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups.
CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.
PMID: 28364005 [PubMed - as supplied by publisher]
Multi-Echo fMRI: A Review of Applications in fMRI Denoising and Analysis of BOLD Signals.
Neuroimage. 2017 Mar 28;:
Authors: Kundu P, Voon V, Balchandani P, Lombardo MV, Poser BA, Bandettini P
In recent years the field of fMRI research has enjoyed expanded technical abilities related to resolution, as well as use across many fields of brain research. At the same time, the field has also dealt with uncertainty related to many known and unknown effects of artifact in fMRI data. In this review we discuss an emerging fMRI technology, called multi-echo (ME)-fMRI, which focuses on improving the fidelity and interpretability of fMRI. Where the essential problem of standard single-echo fMRI is the indeterminacy of sources of signals, whether BOLD or artifact, this is not the case for ME-fMRI. By acquiring multiple echo images per slice, the ME approach allows T2⁎ decay to be modeled at every voxel at every time point. Since BOLD signals arise by changes in T2⁎ over time, an fMRI experiment sampling the T2⁎ signal decay can be analyzed to distinguish BOLD from artifact signal constituents. While the ME approach has a long history of use in theoretical and validation studies, modern MRI systems enable whole-brain multi-echo fMRI at high resolution. This review covers recent multi-echo fMRI acquisition methods, and the analysis steps for this data to make fMRI at once more principled, straightforward, and powerful. After a brief overview of history and theory, T2⁎ modeling and applications will be discussed. These applications include T2⁎ mapping and combining echoes from ME data to increase BOLD contrast and mitigate dropout artifacts. Next, the modeling of fMRI signal changes to detect signal origins in BOLD-related T2⁎ versus artifact-related S0 changes will be reviewed. A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA). After describing how ME-fMRI and ME-ICA lead to a general model for analysis of fMRI signals, applications in animal and human imaging will be discussed. Applications include removing motion artifacts in resting state data at subject and group level. New imaging methods such as multi-band multi-echo fMRI and imaging at 7T are demonstrated throughout the review, and a practical analysis pipeline is described. The review culminates with evidence from recent studies of major boosts in statistical power from using multi-echo fMRI for detecting activation and connectivity in healthy individuals and patients with neuropsychiatric disease. In conclusion, the review shows evidence that the multi-echo approach expands the range of experiments that is practicable using fMRI. These findings suggest a compelling future role of the multi-echo approach in subject-level and clinical fMRI.
PMID: 28363836 [PubMed - as supplied by publisher]
A new method for independent component analysis with priori information based on multi-objective optimization.
J Neurosci Methods. 2017 Mar 28;:
Authors: Shi Y, Zeng W, Wang N, Zhao L
BACKGROUND: Currently the problem of incorporating priori information into an independent component analysis (ICA) model is often solved under the framework of constrained ICA, which utilizes the priori information as a reference signal to form a constraint condition and then introduce it into classical ICA. However, it is difficult to pre-determine a suitable threshold parameter to constrain the closeness between the output signal and the reference signal in the constraint condition.
NEW METHOD: In this paper, a new model of ICA with priori information as a reference signal is established on the framework of multi-objective optimization, where an adaptive weighted summation method is introduced to solve this multi-objective optimization problem with a new fixed-point learning algorithm.
RESULTS: The experimental results of fMRI hybrid data and task-related data on the single-subject level have demonstrated that the proposed method has a better overall performance on the recover abilities of both spatial source and time course.
COMPARISON WITH EXISTING METHODS: At the same time, compared with traditional ICA with reference methods and classical ICA method, the experimental results of resting-state fMRI data on the group-level have showed that the group independent component calculated by the proposed method has a higher correlation with the corresponding independent component of each subject through T-test.
CONCLUSIONS: The proposed method does not need us to select a threshold parameter to constrain the closeness between the output signal and the reference signal. In addition, the performance of functional connectivity detection has a great improvement in comparison with traditional methods.
PMID: 28363450 [PubMed - as supplied by publisher]
The resting state fMRI regional homogeneity (ReHo) metrics KCC-ReHo & Cohe-ReHo are valid indicators of tumor-related neurovascular uncoupling.
Brain Connect. 2017 Mar 31;:
Authors: Agarwal S, Sair HI, Pillai JJ
AIM: To determine whether regional homogeneity (ReHo) of resting state BOLD fMRI (rsfMRI) data based on Kendall's coefficient of concordance (KCC-ReHo) & Coherence (Cohe-ReHo) metrics may allow detection of brain tumor-induced NVU in the sensorimotor network similar to findings in standard motor task-based BOLD fMRI (tbfMRI) activation.
METHODS: Twelve de novo brain tumor patients undergoing clinical fMRI exams (tbfMRI and rsfMRI) were included in this IRB-approved study. Each patient displayed decreased/absent tbfMRI activation in the primary ipsilesional sensorimotor cortex in the absence of corresponding motor deficit or suboptimal task performance, consistent with NVU. Z-score maps for the motor tasks were obtained from the general linear model (GLM) analysis (reflecting motor activation vs. rest). KCC-ReHo and Cohe-ReHo maps were calculated from rsfMRI data. Precentral and postcentral gyri in contralesional (CL) and ipsilesional (IL) hemispheres were parcellated using an Automated Anatomical Labeling (AAL) template for each patient. Similar region of Interest (ROI) analysis was performed on the tbfMRI, KCC-ReHo & Cohe-ReHo maps to allow direct comparison of results.
RESULTS: Voxel values in CL & IL ROIs of each map were divided by the corresponding global mean of KCC-ReHo & Cohe-ReHo in bihemispheric cortical brain tissue. Group analysis revealed significantly decreased IL mean KCC-ReHo (p=0.02) and Cohe-ReHo (p=0.04) metrics compared to respective values in the CL ROIs, consistent with similar findings of significantly decreased ipsilesional BOLD signal for tbfMRI (p=0.0005).
CONCLUSION: Ipsilesional abnormalities in ReHo derived from rsfMRI may serve as a potential indicator of NVU in patients with brain tumors and other resectable brain lesions; as such, ReHo findings may complement findings on tbfMRI used for presurgical planning.
PMID: 28363248 [PubMed - as supplied by publisher]