Most recent paper

Aberrant Resting-State Cerebellar-Cerebral Functional Connectivity in Methamphetamine-Dependent Individuals After Six Months Abstinence.

Front Psychiatry. 2020;11:191

Authors: Li X, Su H, Zhong N, Chen T, Du J, Xiao K, Xu D, Song W, Jiang H, Zhao M

Abstract
Background: Structural and functional alterations in the cerebellum have been consistently reported in addiction literatures. However, evidence implicating the resting-state cerebellar-cerebral functional connectivity in methamphetamine (MA) use disorder still remains limited.
Methods: Resting-state functional magnetic resonance imaging (fMRI) scans were obtained from 34 MA dependent individuals with about six months abstinence and 31 healthy controls (well matched for age, gender and education) in this study. Seed-based functional connectivity analysis was employed to investigate the differences in cerebellar-cerebral functional connectivity between two groups. The correlations between significant functional connectivity and each clinical characteristic were also explored.
Results: Compared to healthy controls, MA dependent individuals showed disrupted functional connectivity between the cerebellum and several cerebral functional networks, including the default-mode, affective-limbic, and sensorimotor networks. Within the MA group, functional connectivity of the right cerebellar lobule VI-precuneus coupling was negatively correlated with addiction severity.
Conclusion: The present study suggests that cerebellar dysfunction, in particular aberrant cerebellar-cerebral functional connectivity, might involve in neurobiological mechanism of MA dependence, which supply a potential target for therapeutic interventions in the future.

PMID: 32296352 [PubMed]

Distinct BOLD variability changes in the default mode and salience networks in Alzheimer's disease spectrum and associations with cognitive decline.

Sci Rep. 2020 Apr 15;10(1):6457

Authors: Zhang L, Zuo XN, Ng KK, Chong JSX, Shim HY, Ong MQW, Loke YM, Choo BL, Chong EJY, Wong ZX, Hilal S, Venketasubramanian N, Tan BY, Chen CL, Zhou JH

Abstract
Optimal levels of intrinsic Blood-Oxygenation-Level-Dependent (BOLD) signal variability (variability hereafter) are important for normative brain functioning. However, it remains largely unknown how network-specific and frequency-specific variability changes along the Alzheimer's disease (AD) spectrum and relates to cognitive decline. We hypothesized that cognitive impairment was related to distinct BOLD variability alterations in two brain networks with reciprocal relationship, i.e., the AD-specific default mode network (DMN) and the salience network (SN). We examined variability of resting-state fMRI data at two characteristic slow frequency-bands of slow4 (0.027-0.073 Hz) and slow5 (0.01-0.027 Hz) in 96 AD, 98 amnestic mild cognitive impairment (aMCI), and 48 age-matched healthy controls (HC) using two commonly used pre-processing pipelines. Cognition was measured with a neuropsychological assessment battery. Using both global signal regression (GSR) and independent component analysis (ICA), results generally showed a reciprocal DMN-SN variability balance in aMCI (vs. AD and/or HC), although there were distinct frequency-specific variability patterns in association with different pre-processing approaches. Importantly, lower slow4 posterior-DMN variability correlated with poorer baseline cognition/smaller hippocampus and predicted faster cognitive decline in all patients using both GSR and ICA. Altogether, our findings suggest that reciprocal DMN-SN variability balance in aMCI might represent an early signature in neurodegeneration and cognitive decline along the AD spectrum.

PMID: 32296093 [PubMed - in process]

Predicting Long-Term After-Effects of Theta-Burst Stimulation on Supplementary Motor Network Through One-Session Response.

Front Neurosci. 2020;14:237

Authors: Ji GJ, Sun J, Liu P, Wei J, Li D, Wu X, Zhang L, Yu F, Bai T, Zhu C, Tian Y, Wang K

Abstract
To understand the neural mechanism of repetitive transcranial magnetic stimulation (rTMS), the after-effects following one session or multiple days of stimulation have been widely investigated. However, the relation between the short-term effect (STE) and long-term effect (LTE) of rTMS is largely unknown. This study aims to explore whether the after-effects of 5-day rTMS on supplementary motor area (SMA) network could be predicted by one-session response. A primary cohort of 38 healthy participants underwent five daily sessions of real or sham continuous theta-burst stimulation (cTBS) on the left SMA. Resting-state functional magnetic resonance imaging (fMRI) data were acquired at the first (before and after the first stimulation) and sixth experimental day. The SMA connectivity changes after the first cTBS and after 5 days of stimulation were defined as STE and LTE, respectively. Compared to the baseline, significant STE and LTE were found in the bilateral paracentral gyrus (ParaCG) after real stimulation, suggesting shared neural correlates of short- and long-term stimulations. Region-of-interest analysis indicated that the resting-state functional connectivity between SMA and ParaCG increased after real stimulation, while no significant change was found after sham stimulation. Leave-one-out cross-validation indicated that the LTE in ParaCG could be predicted by the STE after real but not sham stimulations. In an independent cohort, the after-effects of rTMS on ParaCG and short- to long-term prediction were reproduced at the region-of-interest level. These imaging evidences indicate that one-session rTMS can aid to predict the regions responsive to long-term stimulation and the individualized response degree.

PMID: 32292326 [PubMed]

Deep Temporal Organization of fMRI Phase Synchrony Modes Promotes Large-Scale Disconnection in Schizophrenia.

Front Neurosci. 2020;14:214

Authors: Zarghami TS, Hossein-Zadeh GA, Bahrami F

Abstract
Itinerant dynamics of the brain generates transient and recurrent spatiotemporal patterns in neuroimaging data. Characterizing metastable functional connectivity (FC) - particularly at rest and using functional magnetic resonance imaging (fMRI) - has shaped the field of dynamic functional connectivity (DFC). Mainstream DFC research relies on (sliding window) correlations to identify recurrent FC patterns. Recently, functional relevance of the instantaneous phase synchrony (IPS) of fMRI signals has been revealed using imaging studies and computational models. In the present paper, we identify the repertoire of whole-brain inter-network IPS states at rest. Moreover, we uncover a hierarchy in the temporal organization of IPS modes. We hypothesize that connectivity disorder in schizophrenia (SZ) is related to the (deep) temporal arrangement of large-scale IPS modes. Hence, we analyze resting-state fMRI data from 68 healthy controls (HC) and 51 SZ patients. Seven resting-state networks (and their sub-components) are identified using spatial independent component analysis. IPS is computed between subject-specific network time courses, using analytic signals. The resultant phase coupling patterns, across time and subjects, are clustered into eight IPS states. Statistical tests show that the relative expression and mean lifetime of certain IPS states have been altered in SZ. Namely, patients spend (45%) less time in a globally coherent state and a subcortical-centered state, and (40%) more time in states reflecting anticoupling within the cognitive control network, compared to the HC. Moreover, the transition profile (between states) reveals a deep temporal structure, shaping two metastates with distinct phase synchrony profiles. A metastate is a collection of states such that within-metastate transitions are more probable than across. Remarkably, metastate occupation balance is altered in SZ, in favor of the less synchronous metastate that promotes disconnection across networks. Furthermore, the trajectory of IPS patterns is less efficient, less smooth, and more restricted in SZ subjects, compared to the HC. Finally, a regression analysis confirms the diagnostic value of the defined IPS measures for SZ identification, highlighting the distinctive role of metastate proportion. Our results suggest that the proposed IPS features may be used for classification studies and for characterizing phase synchrony modes in other (clinical) populations.

PMID: 32292324 [PubMed]

Alterations of Brain Signal Oscillations in Older Individuals with HIV Infection and Parkinson's Disease.

J Neuroimmune Pharmacol. 2020 Apr 14;:

Authors: Müller-Oehring EM, Hong JY, Hughes RL, Kwon D, Brontë-Stewart HM, Poston KL, Schulte T

Abstract
More than 30 years after the human immunodeficiency virus (HIV) epidemic, HIV patients are now aging due to the advances of antiretroviral therapy. With immunosenescence and the susceptibility of dopamine-rich basal ganglia regions to HIV-related injury, older HIV patients may show neurofunctional deficits similar to patients with Parkinson's disease (PD). We examined the amplitudes of low frequency fluctuations (ALFF) across different frequency bands of the BOLD signal in 30 older HIV-infected individuals, 33 older healthy controls, and 36 PD patients. Participants underwent resting-state fMRI, neuropsychological testing, and a clinical motor exam. HIV patients mainly showed abnormalities in cortical ALFF with reduced prefrontal amplitudes and enhanced sensorimotor and inferior temporal amplitudes. Frontal hypoactivation was overlapping for HIV and PD groups and different from controls. PD patients further exhibited reduced pallidum amplitudes compared to the other groups. In the HIV group, lower pallidum amplitudes were associated with lower CD4+ nadir and CD4+ T cell counts. Abnormalities in ALFF dynamics were largely associated with cognitive and motor functioning in HIV and PD groups. The disruption of neurofunctional frequency dynamics in subcortical-cortical circuits could contribute to the development of cognitive and motor dysfunction and serve as a biomarker for monitoring disease progression with immunosenescence. Graphical Abstract.

PMID: 32291601 [PubMed - as supplied by publisher]

Auditory and tactile frequency representations are co-embedded in modality-defined cortical sensory systems.

Neuroimage. 2020 Apr 11;:116837

Authors: Rahman MS, Barnes KA, Crommett LE, Tommerdahl M, Yau JM

Abstract
Sensory information is represented and elaborated in hierarchical cortical systems that are thought to be dedicated to individual sensory modalities. This traditional view of sensory cortex organization has been challenged by recent evidence of multimodal responses in primary and association sensory areas. Although it is indisputable that sensory areas respond to multiple modalities, it remains unclear whether these multimodal responses reflect selective information processing for particular stimulus features. Here, we used fMRI adaptation to identify brain regions that are sensitive to the temporal frequency information contained in auditory, tactile, and audiotactile stimulus sequences. A number of brain regions distributed over the parietal and temporal lobes exhibited frequency-selective temporal response modulation for both auditory and tactile stimulus events, as indexed by repetition suppression effects. A smaller set of regions responded to crossmodal adaptation sequences in a frequency-dependent manner. Despite an extensive overlap of multimodal frequency-selective responses across the parietal and temporal lobes, representational similarity analysis revealed a cortical "regional landscape" that clearly reflected distinct somatosensory and auditory processing systems that converged on modality-invariant areas. These structured relationships between brain regions were also evident in spontaneous signal fluctuation patterns measured at rest. Our results reveal that multimodal processing in human cortex can be feature-specific and that multimodal frequency representations are embedded in the intrinsically hierarchical organization of cortical sensory systems.

PMID: 32289461 [PubMed - as supplied by publisher]

Sensation-seeking is related to functional connectivities of the medial orbitofrontal cortex with the anterior cingulate cortex.

Neuroimage. 2020 Apr 11;:116845

Authors: Wan Z, Rolls ET, Cheng W, Feng J

Abstract
Sensation-seeking is a multifaceted personality trait with components that include experience-seeking, thrill and adventure seeking, disinhibition, and susceptibility to boredom, and is an aspect of impulsiveness. We analysed brain regions involved in sensation-seeking in a large-scale study with 414 participants and showed that the sensation-seeking score could be optimally predicted from the functional connectivity with typically (in different participants) 18 links between brain areas (measured in the resting state with fMRI) with correlation r=0.34 (p=7.3×10-13) between the predicted and actual sensation-seeking score across all participants. Interestingly, 8 of the 11 links that were common for all participants were between the medial orbitofrontal cortex and the anterior cingulate cortex and yielded a prediction accuracy r=0.30 (p=4.8×10-10). We propose that this important aspect of personality, sensation-seeking, reflects a strong effect of reward (in which the medial orbitofrontal cortex is implicated) on promoting actions to obtain rewards (in which the anterior cingulate cortex is implicated). Risk-taking was found to have a moderate correlation with sensation-seeking (r=0.49, p=3.9×10-26), and three of these functional connectivities were significantly correlated (p<0.05) with the overall risk-taking score. This discovery helps to show how the medial orbitofrontal and anterior cingulate cortices influence behaviour and personality, and indicate that sensation-seeking can involve in part the medial orbitofrontal cortex reward system, which can thereby become associated with risk-taking and a type of impulsiveness.

PMID: 32289458 [PubMed - as supplied by publisher]

Reducing Inter-Site Variability for Fluctuation Amplitude Metrics in Multisite Resting State BOLD-fMRI Data.

Neuroinformatics. 2020 Apr 13;:

Authors: Wang X, Wang Q, Zhang P, Qian S, Liu S, Liu DQ

Abstract
It has been reported that resting state fluctuation amplitude (RSFA) exhibits extremely large inter-site variability, which limits its application in multisite studies. Although global normalization (GN) based approaches are efficient in reducing the site effects, they may cause spurious results. In this study, our purpose was to find alternative strategies to minimize the substantial site effects for RSFA, without the risk of introducing artificial findings. We firstly modified the ALFF algorithm so that it is conceptually validated and insensitive to data length, then found that (a) global mean amplitude of low-frequency fluctuation (ALFF) covaried only with BOLD signal intensity, while global mean fractional ALFF (fALFF) was significantly correlated with TRs across different sites; (b) The inter-site variations in raw RSFA values were significant across the entire brain and exhibited similar trends between gray matter and white matter; (c) For ALFF, signal intensity rescaling could dramatically reduce inter-site variability by several orders, but could not fully removed the globally distributed inter-site variability. For fALFF, the global site effects could be completely removed by TR controlling; (d) Meanwhile, the magnitude of the inter-site variability of fALFF could also be reduced to an acceptable level, as indicated by the detection power of fALFF in multisite data quite close to that in monosite data. Thus our findings suggest GN based harmonization methods could be replaced with only controlling for confounding factors including signal scaling, TR and full-band power.

PMID: 32285299 [PubMed - as supplied by publisher]

Internal jugular vein compression applied during competitive female soccer season preserves functional and structural connectome organization.

Brain Connect. 2020 Apr 13;:

Authors: Dudley J, Yuan W, Diekfuss J, Barber Foss KD, DiCesare CA, Altaye M, Logan K, Leach J, Myer G

Abstract
Characterization of, and evaluation of strategies to mitigate, the effects of sub-concussive impacts (SCI) on brain structure and function are crucial to understand potential long-term neurological risks associated with sports participation. In this study, we applied a graph theoretical framework to resting state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) data to evaluate the efficacy of a jugular vein compression collar for preserving functional and structural measures of brain network organization in a cohort of female high school soccer players throughout a season of competitive play. Athletes were assigned to a collar (N = 72) or non-collar (N = 56) group before engaging in a season of play, during which head impact data were recorded via accelerometer for every practice and competition. Participants completed neuroimaging sessions before and following the season. Non-collar-wearing athletes exhibited significantly increased rs-fMRI-derived global clustering coefficients (p = 0.032) and DTI-derived modularity (p = 0.042), compared to collar-wearing athletes. No longitudinal changes in any graph measures were observed for the collar group (p > 0.05). The observed increase in graph measures in the non-collar group is congruent with previous studies of SCI and is similar to graph theoretical studies of traumatic brain injury. The absence of alterations in graph metrics in the collar group indicates a potential ameliorating effect of the collar device against network reorganization, in line with previous literature.

PMID: 32283941 [PubMed - as supplied by publisher]

A proof-of-concept study comparing tinnitus and neural connectivity changes following multisensory perceptual training with and without a low-dose of fluoxetine.

Int J Neurosci. 2020 Apr 13;:1-12

Authors: Searchfield GD, Spiegel DP, Poppe TNER, Durai M, Jensen M, Kobayashi K, Park J, Russell BR, Shekhawat GS, Sundram F, Thompson BB, Wise KJ

Abstract
Background. This proof-of-concept study investigated a method of multisensory perceptual training for tinnitus, and whether a short, low-dose administration of fluoxetine enhanced training effects and changed neural connectivity.Methods. A double-blind, randomized placebo controlled design with 20 participants (17 male, 3 female, mean age = 57.1 years) involved 30 min daily computer-based, multisensory training (matching visual, auditory and tactile stimuli to perception of tinnitus) for 20 days, and random allocation to take 20 mg fluoxetine or placebo daily. Behavioral measures of tinnitus and correlations between pairs of a priori regions of interest (ROIs), obtained using resting-state functional magnetic resonance imaging (rs-fMRI), were performed before and after the training.Results. Significant changes in ratings of tinnitus loudness, annoyance, and problem were observed with training. No statistically significant changes in Tinnitus Functional Index, Tinnitus Handicap Inventory or Depression Anxiety Stress Scales were found with training. Fluoxetine did not alter any of the behavioural outcomes of training compared to placebo. Significant changes in connectivity between ROIs were identified with training; sensory and attention neural network ROI changes correlated with significant tinnitus rating changes. Rs-fMRI results suggested that the direction of functional connectivity changes between auditory and non-auditory networks, with training and fluoxetine, were opposite to the direction of those changes with multisensory training and placebo.Conclusions. Improvements in tinnitus measures were correlated with changes in sensory and attention networks. The results provide preliminary evidence for changes in rs-fMRI accompanying a multisensory training method in persons with tinnitus.

PMID: 32281466 [PubMed - as supplied by publisher]

Functional anomaly mapping reveals local and distant dysfunction caused by brain lesions.

Neuroimage. 2020 Apr 09;:116806

Authors: DeMarco AT, Turkeltaub PE

Abstract
The lesion method has been important for understanding brain-behavior relationships in humans, but has previously used maps based on structural damage. Lesion measurement based on structural damage may label partly damaged but functional tissue as abnormal, and moreover, ignores distant dysfunction in structurally intact tissue caused by deafferentation, diaschisis, and other processes. A reliable method to map functional integrity of tissue throughout the brain would provide a valuable new approach to measuring lesions. Here, we use machine learning on four dimensional resting state fMRI data obtained from left-hemisphere stroke survivors in the chronic period of recovery and control subjects to generate graded maps of functional anomaly throughout the brain in individual patients. These functional anomaly maps identify areas of obvious structural lesions and are stable across multiple measurements taken months and even years apart. Moreover, the maps identify functionally anomalous regions in structurally intact tissue, providing a direct measure of remote effects of lesions on the function of distant brain structures. Multivariate lesion-behavior mapping using functional anomaly maps replicates classic behavioral localization, identifying inferior frontal regions related to speech fluency, lateral temporal regions related to auditory comprehension, parietal regions related to phonology, and the hand area of motor cortex and descending corticospinal pathways for hand motor function. Further, this approach identifies relationships between tissue function and behavior distant from the structural lesions, including right premotor dysfunction related to ipsilateral hand movement, and right cerebellar regions known to contribute to speech fluency. Brain-wide maps of the functional effects of focal lesions could have wide implications for lesion-behavior association studies and studies of recovery after brain injury.

PMID: 32278896 [PubMed - as supplied by publisher]

Cerebrovascular reactivity mapping using intermittent breath modulation.

Neuroimage. 2020 Apr 08;:116787

Authors: Liu P, Xu C, Lin Z, Sur S, Li Y, Yasar S, Rosenberg P, Albert M, Lu H

Abstract
Cerebrovascular reactivity (CVR), an index of brain vessel's dilatory capacity, is typically measured using hypercapnic gas inhalation or breath-holding as a vasoactive challenge. However, these methods require considerable subject cooperation and could be challenging in clinical studies. More recently, there have been attempts to use resting-state BOLD data to map CVR by utilizing spontaneous changes in breathing pattern. However, in subjects who have small fluctuations in their spontaneous breathing pattern, the CVR results could be noisy and unreliable. In this study, we aim to develop a new method for CVR mapping that does not require gas-inhalation yet provides substantially higher sensitivity than resting-state CVR mapping. This new method is largely based on resting-state scan, but introduces intermittent modulation of breathing pattern in the subject to enhance fluctuations in their end-tidal CO2 (EtCO2) level. Here we examined the comfort level, sensitivity, and accuracy of this method in two studies. First, in 8 healthy young subjects, we developed the intermittent breath-modulation method using two different modulation frequencies, 6 seconds per breath and 12 seconds per breath, respectively, and compared the results to three existing CVR methods, specifically hypercapnic gas inhalation, breath-holding, and resting-state. Our results showed that the comfort level of the 6-second breath-modulation method was significantly higher than breath-holding (p=0.007) and CO2-inhalation (p=0.015) methods, while not different from the resting-state, i.e. free breathing method (p=0.52). When comparing the sensitivity of CVR methods, the breath-modulation methods revealed higher Z-statistics compared to the resting-state scan (p<0.008) and was comparable to breath-holding results. Next, we tested the feasibility of breath-modulation CVR mapping (6 seconds per breath) in 21 cognitively normal elderly participants and compared quantitative CVR values to that obtained with the CO2-inhalation method. Whole-brain CVR was found to be 0.150±0.055 and 0.154±0.032 %ΔBOLD/mmHg for the breath-modulation and CO2-inhalation method, respectively, with a significant correlation between them (y=0.97x, p=0.007). CVR mapping with intermittent breath modulation may be a useful method that combines the advantages of resting-state and CO2-inhalation based approaches.

PMID: 32278094 [PubMed - as supplied by publisher]

Towards HCP-Style Macaque Connectomes: 24-Channel 3T Multi-Array Coil, MRI Sequences and Preprocessing.

Neuroimage. 2020 Apr 07;:116800

Authors: Autio JA, Glasser MF, Ose T, Donahue CJ, Bastiani M, Ohno M, Kawabata Y, Urushibata Y, Murata K, Nishigori K, Yamaguchi M, Hori Y, Yoshida A, Go Y, Coalson TS, Jbabdi S, Sotiropoulos SN, Kennedy H, Smith S, Van Essen DC, Hayashi T

Abstract
Macaque monkeys are an important animal model where invasive investigations can lead to a better understanding of the cortical organization of primates including humans. However, the tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the smaller macaque brain, high spatial, temporal, and angular resolutions combined with high signal-to-noise ratio are required to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enables adaptation of the Human Connectome Project (HCP) image acquisition protocols to the in-vivo macaque brain. In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available in studies of the human brain.

PMID: 32276072 [PubMed - as supplied by publisher]

Transient spectral events in resting state MEG predict individual task responses.

Neuroimage. 2020 Apr 07;:116818

Authors: Becker R, Vidaurre D, Quinn AJ, Abeysuriya RG, Parker Jones O, Jbabdi S, Woolrich MW

Abstract
Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual's brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N = 89) that we can predict the spatial and spectral content of an individual's task response using features estimated from the individual's resting MEG data. This works by learning when transient spectral 'bursts' or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.

PMID: 32276062 [PubMed - as supplied by publisher]

EEG microstates are correlated with brain functional networks during slow-wave sleep.

Neuroimage. 2020 Apr 07;:116786

Authors: Xu J, Pan Y, Zhou S, Zou G, Liu J, Su Z, Zou Q, Gao JH

Abstract
Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the "atoms of thought". Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.

PMID: 32276057 [PubMed - as supplied by publisher]

Two-week rTMS-induced neuroimaging changes measured with fMRI in depression.

J Affect Disord. 2020 Mar 20;270:15-21

Authors: Zheng A, Yu R, Du W, Liu H, Zhang Z, Xu Z, Xiang Y, Du L

Abstract
OBJECTIVE: To study the neuroimaging mechanisms of repetitive transcranial magnetic stimulation (rTMS) in treating major depressive disorder (MDD).
METHODS: Twenty-seven treatment-naive patients with major depressive disorder (MDD) and 27 controls were enrolled. All of them were scanned with resting-state functional magnetic resonance imaging (fMRI) at baseline, and 15 patients were rescanned after two-week rTMS. The amplitude of low frequency fluctuation (ALFF) and functional connection degree (FCD), based on voxels and 3 brain networks (default mode network [DMN], central executive network [CEN], salience network[SN]),were used as imaging indicators to analyze. The correlations of brain imaging changes after rTMS with clinical efficacy were calculated.
RESULTS: At baseline, patients groups showed increased ALFF in the right orbital frontal cortex (OFC) and decreased ALFF in the left striatal cortex and medial prefrontal cortex (PFC), while increased FCD in the right dorsal anterior cingulate cortex and OFC and decreased FCD in the right inferior parietal lobe and in the CEN. After rTMS, patients showed increased ALFF in the left dorsolateral prefrontal cortex (DLPFC)and superior frontal gyrus, FCD in the right dorsal anterior cingulate cortex, superior temporal gyrus and CEN, as well as decreased FCD in the bilateral lingual gyrus than pre-rTMS . These rTMS induced neuroimaging changes did not significantly correlated with clinical effecacy.
CONCLUSIONS: This study indicated that rTMS resulted in changes of ALFF and FCD in some brain regions and CEN. But we could not conclude this is the neuroimaging mechanism of rTMS according to the correlation analysis.

PMID: 32275215 [PubMed - as supplied by publisher]

Presurgical Localization of the Primary Sensorimotor Cortex in Gliomas : When is Resting State FMRI Beneficial and Sufficient?

Clin Neuroradiol. 2020 Apr 09;:

Authors: Voets NL, Plaha P, Parker Jones O, Pretorius P, Bartsch A

Abstract
PURPOSE: Functional magnetic resonance imaging (fMRI) has an established role in neurosurgical planning; however, ambiguity surrounds the comparative value of resting and task-based fMRI relative to anatomical localization of the sensorimotor cortex. This study was carried out to determine: 1) how often fMRI adds to prediction of motor risks beyond expert neuroradiological review, 2) success rates of presurgical resting and task-based sensorimotor mapping, and 3) the impact of accelerated resting fMRI acquisitions on network detectability.
METHODS: Data were collected at 2 centers from 71 patients with a primary brain tumor (31 women; mean age 41.9 ± 13.9 years) and 14 healthy individuals (6 women; mean age 37.9 ± 12.7 years). Preoperative 3T MRI included anatomical scans and resting fMRI using unaccelerated (TR = 3.5 s), intermediate (TR = 1.56 s) or high temporal resolution (TR = 0.72 s) sequences. Task fMRI finger tapping data were acquired in 45 patients. Group differences in fMRI reproducibility, spatial overlap and success frequencies were assessed with t‑tests and χ2-tests.
RESULTS: Radiological review identified the central sulcus in 98.6% (70/71) patients. Task-fMRI succeeded in 100% (45/45). Resting fMRI failed to identify a sensorimotor network in up to 10 patients; it succeeded in 97.9% (47/48) of accelerated fMRIs, compared to only 60.9% (14/23) of unaccelerated fMRIs ([Formula: see text](2) = 17.84, p < 0.001). Of the patients 12 experienced postoperative deterioration, largely predicted by anatomical proximity to the central sulcus.
CONCLUSION: The use of fMRI in patients with residual or intact presurgical motor function added value to uncertain anatomical localization in just a single peri-Rolandic glioma case. Resting fMRI showed high correspondence to task localization when acquired with accelerated sequences but offered limited success at standard acquisitions.

PMID: 32274518 [PubMed - as supplied by publisher]

Cerebral regional and network characteristics in asthma patients: a resting-state fMRI study.

Front Med. 2020 Apr 08;:

Authors: Li S, Lv P, He M, Zhang W, Liu J, Gong Y, Wang T, Gong Q, Ji Y, Lui S

Abstract
Asthma is a serious health problem that involves not only the respiratory system but also the central nervous system. Previous studies identified either regional or network alterations in patients with asthma, but inconsistent results were obtained. A key question remains unclear: are the regional and neural network deficits related or are they two independent characteristics in asthma? Answering this question is the aim of this study. By collecting resting-state functional magnetic resonance imaging from 39 patients with asthma and 40 matched health controls, brain functional measures including regional activity (amplitude of low-frequency fluctuations) and neural network function (degree centrality (DC) and functional connectivity) were calculated to systematically characterize the functional alterations. Patients exhibited regional abnormities in the left angular gyrus, right precuneus, and inferior temporal gyrus within the default mode network. Network abnormalities involved both the sensorimotor network and visual network with key regions including the superior frontal gyrus and occipital lobes. Altered DC in the lingual gyrus was correlated with the degree of airway obstruction. This study elucidated different patterns of regional and network changes, thereby suggesting that the two parameters reflect different brain characteristics of asthma. These findings provide evidence for further understanding the potential cerebral alterations in the pathophysiology of asthma.

PMID: 32270434 [PubMed - as supplied by publisher]

Fully Integrated PET/MR Imaging for the Assessment of the Relationship Between Functional Connectivity and Glucose Metabolic Rate.

Front Neurosci. 2020;14:252

Authors: Shiyam Sundar LK, Baajour S, Beyer T, Lanzenberger R, Traub-Weidinger T, Rausch I, Pataraia E, Hahn A, Rischka L, Hienert M, Klebermass EM, Muzik O

Abstract
In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity.
METHODS: We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated.
RESULTS: The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks.
DISCUSSION: Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.

PMID: 32269510 [PubMed]

Resting-state networks of the neonate brain identified using independent component analysis.

Dev Neurobiol. 2020 Apr 08;:

Authors: Rajasilta O, Tuulari JJ, Björnsdotter M, Scheinin NM, Lehtola SJ, Saunavaara J, Häkkinen S, Merisaari H, Parkkola R, Lähdesmäki T, Karlsson L, Karlsson H

Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully used to probe the intrinsic functional organization of the brain and to study brain development. Here, we implemented a combination of individual and group independent component analysis (ICA) of FSL on a 6-minute resting-state data set acquired from 21 naturally sleeping term-born (age 26±6.7 d), healthy neonates to investigate the emerging functional resting state networks (RSNs). In line with previous literature, we found evidence of sensorimotor, auditory/language, visual, cerebellar, thalamic, parietal, prefrontal, anterior cingulate as well as dorsal and ventral aspects of the default-mode-network. Additionally, we identified RSNs in frontal, parietal and temporal regions that have not been previously described in this age group and correspond to the canonical RSNs established in adults. Importantly, we found that careful ICA-based denoising of fMRI data increased the number of networks identified with group ICA, whereas the degree of spatial smoothing did not change the amount of identified networks. Our results show that the infant brain has an established set of RSNs soon after birth.

PMID: 32267069 [PubMed - as supplied by publisher]