New resting-state fMRI related studies at PubMed

Individual Uniqueness in the Neonatal Functional Connectome

Mon, 03/22/2021 - 10:00

Cereb Cortex. 2021 Mar 22:bhab041. doi: 10.1093/cercor/bhab041. Online ahead of print.


The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0-30 mm) and middle-range (30-60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

PMID:33749736 | DOI:10.1093/cercor/bhab041

Trading off spatio-temporal properties in 3D high-speed fMRI using interleaved stack-of-spirals trajectories

Mon, 03/22/2021 - 10:00

Magn Reson Med. 2021 Mar 10. doi: 10.1002/mrm.28742. Online ahead of print.


PURPOSE: Highly undersampled acquisitions have been proposed to push the limits of temporal resolution in functional MRI. This contribution is aimed at identifying parameter sets that let the user trade-off between ultra-high temporal resolution and spatial signal quality by varying the sampling densities. The proposed method maintains the synergies of a temporal resolution that enables direct filtering of physiological artifacts for highest statistical power, and 3D read-outs with optimal use of encoding capabilities of multi-coil arrays for efficient sampling and high signal-to-noise ratio (SNR).

METHODS: One- to four-shot interleaved spherical stack-of-spiral trajectories with repetition times from 96 to 352 ms at a nominal resolution of 3 mm using different sampling densities were compared for image quality and temporal SNR (tSNR). The one- and three-shot trajectories were employed in a resting state study for functional characterization.

RESULTS: Compared to a previously described single-shot trajectory, denser sampled trajectories of the same type are shown to be less prone to blurring and off-resonance vulnerability that appear in addition to the variable density artifacts of the point spread function. While the multi-shot trajectories lead to a decrease in tSNR efficiency, the high SNR due to the 3D read-out, combined with notable increases in image quality, leads to superior overall results of the three-shot interleaved stack of spirals. A resting state analysis of 15 subjects shows significantly improved functional sensitivity in areas of high off-resonance gradients.

CONCLUSION: Mild variable-density sampling leads to excellent tSNR behavior and no increased off-resonance vulnerability, and is suggested unless maximum temporal resolution is sought.

PMID:33749021 | DOI:10.1002/mrm.28742

The Impact of the BAILAMOS™ Dance Program on Brain Functional Connectivity and Cognition in Older Latino Adults: A Pilot Study

Mon, 03/22/2021 - 10:00

J Cogn Enhanc. 2021 Mar;5(1):1-14. doi: 10.1007/s41465-020-00185-1. Epub 2020 Aug 3.


Dance is a culturally salient form of physical activity (PA) for older Latinos. Resting-state functional connectivity (FC) is a putative biomarker for age-related cognitive decline. We aimed to investigate the impact of the BAILAMOS™ dance program on FC in three brain functional networks (Default Mode [DMN], Frontoparietal [FPN], and Salience [SAL] networks), and cognition. Ten cognitively healthy older Latinos participated in the four-month BAILAMOS™ dance program. We assessed PA levels (self-reported and device-assessed) and estimated cardiorespiratory fitness, cognition, and resting-state FC via functional magnetic resonance imaging at baseline and post-intervention. We performed paired t-tests and Pearson correlations. Given the pilot nature of the study, significance levels were set at p < 0.05 and effect sizes are reported. We observed a significant increase in self-reported moderate leisure-time PA from pre- to post-intervention (t(9) = 3.16, p = 0.011, d = 0.66). FC within-FPN regions of interest (ROIs) significantly increased pre- to post-intervention (t(9) = 2.35, p = 0.043, d = 0.70). DMN ROIs showed an increase, with a moderate effect size, in the integration with other networks' ROIs (t(9) = 1.96, p = 0.081, d = 0.64) post-intervention. Increases in moderate leisure-time PA at post-intervention were associated with increases in the FC within-FPN (R = 0.79, p = 0.006). Our results suggest that dance might be a promising approach for improving age-related disruption of FC within- and between-networks commonly associated with cognitive decline.

PMID:33748658 | PMC:PMC7968343 | DOI:10.1007/s41465-020-00185-1

4D Modeling of fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)

Mon, 03/22/2021 - 10:00

IEEE Trans Cogn Dev Syst. 2020 Sep;12(3):451-460. doi: 10.1109/tcds.2019.2916916. Epub 2019 May 14.


Since the human brain functional mechanism has been enabled for investigation by the functional Magnetic Resonance Imaging (fMRI) technology, simultaneous modeling of both the spatial and temporal patterns of brain functional networks from 4D fMRI data has been a fundamental but still challenging research topic for neuroimaging and medical image analysis fields. Currently, general linear model (GLM), independent component analysis (ICA), sparse dictionary learning, and recently deep learning models, are major methods for fMRI data analysis in either spatial or temporal domains, but there are few joint spatial-temporal methods proposed, as far as we know. As a result, the 4D nature of fMRI data has not been effectively investigated due to this methodological gap. The recent success of deep learning applications for functional brain decoding and encoding greatly inspired us in this work to propose a novel framework called spatio-temporal convolutional neural network (ST-CNN) to extract both spatial and temporal characteristics from targeted networks jointly and automatically identify of functional networks. The identification of Default Mode Network (DMN) from fMRI data was used for evaluation of the proposed framework. Results show that only training the framework on one fMRI dataset is sufficiently generalizable to identify the DMN from different datasets of different cognitive tasks and resting state. Further investigation of the results shows that the joint-learning scheme can capture the intrinsic relationship between the spatial and temporal characteristics of DMN and thus it ensures the accurate identification of DMN from independent datasets. The ST-CNN model brings new tools and insights for fMRI analysis in cognitive and clinical neuroscience studies.

PMID:33748420 | PMC:PMC7978010 | DOI:10.1109/tcds.2019.2916916

A Machine Learning Enhanced Mechanistic Simulation Framework for Functional Deficit Prediction in TBI

Mon, 03/22/2021 - 10:00

Front Bioeng Biotechnol. 2021 Mar 3;9:587082. doi: 10.3389/fbioe.2021.587082. eCollection 2021.


Resting state functional magnetic resonance imaging (rsfMRI), and the underlying brain networks identified with it, have recently appeared as a promising avenue for the evaluation of functional deficits without the need for active patient participation. We hypothesize here that such alteration can be inferred from tissue damage within the network. From an engineering perspective, the numerical prediction of tissue mechanical damage following an impact remains computationally expensive. To this end, we propose a numerical framework aimed at predicting resting state network disruption for an arbitrary head impact, as described by the head velocity, location and angle of impact, and impactor shape. The proposed method uses a library of precalculated cases leveraged by a machine learning layer for efficient and quick prediction. The accuracy of the machine learning layer is illustrated with a dummy fall case, where the machine learning prediction is shown to closely match the full simulation results. The resulting framework is finally tested against the rsfMRI data of nine TBI patients scanned within 24 h of injury, for which paramedical information was used to reconstruct in silico the accident. While more clinical data are required for full validation, this approach opens the door to (i) on-the-fly prediction of rsfMRI alterations, readily measurable on clinical premises from paramedical data, and (ii) reverse-engineered accident reconstruction through rsfMRI measurements.

PMID:33748080 | PMC:PMC7965982 | DOI:10.3389/fbioe.2021.587082

Effect of Acupuncture Stimulation of Hegu (LI4) and Taichong (LR3) on the Resting-State Networks in Alzheimer's Disease: Beyond the Default Mode Network

Mon, 03/22/2021 - 10:00

Neural Plast. 2021 Mar 8;2021:8876873. doi: 10.1155/2021/8876873. eCollection 2021.


It was reported that acupuncture could treat Alzheimer's disease (AD) with the potential mechanisms remaining unclear. The aim of the study is to explore the effect of the combination stimulus of Hegu (LI4) and Taichong (LR3) on the resting-state brain networks in AD, beyond the default network (DMN). Twenty-eight subjects including 14 AD patients and 14 healthy controls (HCs) matched by age, gender, and educational level were recruited in this study. After the baseline resting-state MRI scans, the manual acupuncture stimulation was performed for 3 minutes, and then, another 10 minutes of resting-state fMRI scans was acquired. In addition to the DMN, five other resting-state networks were identified by independent component analysis (ICA), including left frontal parietal network (lFPN), right frontal parietal network (rFPN), visual network (VN), sensorimotor network (SMN), and auditory network (AN). And the impaired connectivity in the lFPN, rFPN, SMN, and VN was found in AD patients compared with those in HCs. After acupuncture, significantly decreased connectivity in the right middle frontal gyrus (MFG) of rFPN (P = 0.007) was identified in AD patients. However, reduced connectivity in the right inferior frontal gyrus (IFG) (P = 0.047) and left superior frontal gyrus (SFG) (P = 0.041) of lFPN and some regions of the SMN (the left inferior parietal lobula (P = 0.004), left postcentral gyrus (PoCG) (P = 0.001), right PoCG (P = 0.032), and right MFG (P = 0.010)) and the right MOG of VN (P = 0.003) was indicated in HCs. In addition, after controlling for the effect of acupuncture on HCs, the functional connectivity of the right cerebellum crus I, left IFG, and left angular gyrus (AG) of lFPN showed to be decreased, while the left MFG of IFPN and the right lingual gyrus of VN increased in AD patients. These findings might have some reference values for the interpretation of the combination stimulus of Hegu (LI4) and Taichong (LR3) in AD patients, which could deepen our understanding of the potential mechanisms of acupuncture on AD.

PMID:33747074 | PMC:PMC7960059 | DOI:10.1155/2021/8876873

Neuroimaging Markers of Mal de Débarquement Syndrome

Mon, 03/22/2021 - 10:00

Front Neurol. 2021 Mar 4;12:636224. doi: 10.3389/fneur.2021.636224. eCollection 2021.


Mal de débarquement syndrome (MdDS) is a motion-induced disorder of oscillating vertigo that persists after the motion has ceased. The neuroimaging characteristics of the MdDS brain state have been investigated with studies on brain metabolism, structure, functional connectivity, and measurements of synchronicity. Baseline metabolism and resting-state functional connectivity studies indicate that a limbic focus in the left entorhinal cortex and amygdala may be important in the pathology of MdDS, as these structures are hypermetabolic in MdDS and exhibit increased functional connectivity to posterior sensory processing areas and reduced connectivity to the frontal and temporal cortices. Both structures are tunable with periodic stimulation, with neurons in the entorhinal cortex required for spatial navigation, acting as a critical efferent pathway to the hippocampus, and sending and receiving projections from much of the neocortex. Voxel-based morphometry measurements have revealed volume differences between MdDS and healthy controls in hubs of multiple resting-state networks including the default mode, salience, and executive control networks. In particular, volume in the bilateral anterior cingulate cortices decreases and volume in the bilateral inferior frontal gyri/anterior insulas increases with longer duration of illness. Paired with noninvasive neuromodulation interventions, functional neuroimaging with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and simultaneous fMRI-EEG have shown changes in resting-state functional connectivity that correlate with symptom modulation, particularly in the posterior default mode network. Reduced parieto-occipital connectivity with the entorhinal cortex and reduced long-range fronto-parieto-occipital connectivity correlate with symptom improvement. Though there is a general theme of desynchronization correlating with reduced MdDS symptoms, the prediction of optimal stimulation parameters for noninvasive brain stimulation in individuals with MdDS remains a challenge due to the large parameter space. However, the pairing of functional neuroimaging and noninvasive brain stimulation can serve as a probe into the biological underpinnings of MdDS and iteratively lead to optimal parameter space identification.

PMID:33746890 | PMC:PMC7970001 | DOI:10.3389/fneur.2021.636224

Increased Within-Network Functional Connectivity May Predict NEDA Status in Fingolimod-Treated MS Patients

Mon, 03/22/2021 - 10:00

Front Neurol. 2021 Mar 5;12:632917. doi: 10.3389/fneur.2021.632917. eCollection 2021.


Only a few studies have evaluated the brain functional changes associated with disease-modifying therapies (DMTs) in multiple sclerosis (MS), though none used a composite measure of clinical and MRI outcomes to evaluate DMT-related brain functional connectivity (FC) measures predictive of short-term outcome. Therefore, we investigated the following: (1) baseline FC differences between patients who showed evidence of disease activity after a specific DMT and those who did not; (2) DMT-related effects on FC, and; (3) possible relationships between DMT-related FC changes and changes in performance. We used a previously analyzed dataset of 30 relapsing MS patients who underwent fingolimod treatment for 6 months and applied the "no evidence of disease activity" (NEDA-3) status as a clinical response indicator of treatment efficacy. Resting-state fMRI data were analyzed to obtain within- and between-network FC measures. After therapy, 14 patients achieved NEDA-3 status (hereinafter NEDA), while 16 did not (EDA). The two groups significantly differed at baseline, with the NEDA group having higher within-network FC in the anterior and posterior default mode, auditory, orbitofrontal, and right frontoparietal networks than the EDA. After therapy, NEDA showed significantly reduced within-network FC in the posterior default mode and left frontoparietal networks and increased between-network FC in the posterior default mode/orbitofrontal networks; they also showed PASAT improvement, which was correlated with greater within-network FC decrease in the posterior default mode network and with greater between-network FC increase. No significant longitudinal FC changes were found in the EDA. Taken together, these findings suggest that NEDA status after fingolimod is related to higher within-network FC at baseline and to a consistent functional reorganization after therapy.

PMID:33746887 | PMC:PMC7973271 | DOI:10.3389/fneur.2021.632917

Reduced Inter-hemispheric Resting State Functional Connectivity and Its Association With Social Deficits in Autism

Mon, 03/22/2021 - 10:00

Front Psychiatry. 2021 Mar 4;12:629870. doi: 10.3389/fpsyt.2021.629870. eCollection 2021.


Autism spectrum disorder (ASD) is an early onset developmental disorder which persists throughout life and is increasing in prevalence over the last few decades. Given its early onset and variable cognitive and emotional functional impairments, it is generally challenging to assess ASD individuals using task-based behavioral and functional MRI paradigms. Consequently, resting state functional MRI (rs-fMRI) has become a key approach for examining ASD-associated neural alterations and revealed functional alterations in large-scale brain networks relative to typically developing (TD) individuals, particularly those involved in social-cognitive and affective processes. Recent progress suggests that alterations in inter-hemispheric resting state functional connectivity (rsFC) between regions in the 2 brain hemispheres, particularly homotopic ones, may be of great importance. Here we have reviewed neuroimaging studies examining inter-hemispheric rsFC abnormities in ASD and its associations with symptom severity. As an index of inter-hemispheric functional connectivity, we have additionally reviewed previous studies on corpus callosum (CC) volumetric and fiber changes in ASD. There are converging findings on reduced inter-hemispheric (including homotopic) rsFC in large-scale brain networks particularly in posterior hubs of the default mode network, reduced volumes in the anterior and posterior CC, and on decreased FA and increased MD or RD across CC subregions. Associations between the strength of inter-hemispheric rsFC and social impairments in ASD together with their classification performance in distinguishing ASD subjects from TD controls across ages suggest that the strength of inter-hemispheric rsFC may be a more promising biomarker for assisting in ASD diagnosis than abnormalities in either brain wide rsFC or brain structure.

PMID:33746796 | PMC:PMC7969641 | DOI:10.3389/fpsyt.2021.629870

Treatment Response Prediction and Individualized Identification of Short-Term Abstinence Methamphetamine Dependence Using Brain Graph Metrics

Mon, 03/22/2021 - 10:00

Front Psychiatry. 2021 Mar 3;12:583950. doi: 10.3389/fpsyt.2021.583950. eCollection 2021.


Background: The abuse of methamphetamine (MA) worldwide has gained international attention as the most rapidly growing illicit drug problem. The classification and treatment response prediction of MA addicts are thereby paramount, in order for effective treatments to be more targeted to individuals. However, there has been limited progress. Methods: In the present study, 43 MA-dependent participants and 38 age- and gender-matched healthy controls were enrolled, and their resting-state functional magnetic resonance imaging data were collected. MA-dependent participants who showed 50% reduction in craving were defined as responders to treatment. The present study used the machine learning method, which is a support vector machine (SVM), to detect the most relevant features for discriminating and predicting the treatment response for MA-dependent participants based on the features extracted from the functional graph metrics. Results: A classifier was able to differentiate MA-dependent subjects from normal controls, with a cross-validated prediction accuracy, sensitivity, and specificity of 73.2% [95% confidence interval (CI) = 71.23-74.17%), 66.05% (95% CI = 63.06-69.04%), and 80.35% (95% CI = 77.77-82.93%), respectively, at the individual level. The most accurate combination of classifier features included the nodal efficiency in the right middle temporal gyrus and the community index in the left precentral gyrus and cuneus. Between these two, the community index in the left precentral gyrus had the highest importance. In addition, the classification performance of the other classifier used to predict the treatment response of MA-dependent subjects had an accuracy, sensitivity, and specificity of 71.2% (95% CI = 69.28-73.12%), 86.75% (95% CI = 84.48-88.92%), and 55.65% (95% CI = 52.61-58.79%), respectively, at the individual level. Furthermore, the most accurate combination of classifier features included the nodal clustering coefficient in the right orbital part of the superior frontal gyrus, the nodal local efficiency in the right orbital part of the superior frontal gyrus, and the right triangular part of the inferior frontal gyrus and right temporal pole of middle temporal gyrus. Among these, the nodal local efficiency in the right temporal pole of the middle temporal gyrus had the highest feature importance. Conclusion: The present study identified the most relevant features of MA addiction and treatment based on SVMs and the features extracted from the graph metrics and provided possible biomarkers to differentiate and predict the treatment response for MA-dependent patients. The brain regions involved in the best combinations should be given close attention during the treatment of MA.

PMID:33746790 | PMC:PMC7965948 | DOI:10.3389/fpsyt.2021.583950

The Altered Functional Connectivity With Pain Features Integration and Interaction in Migraine Without Aura

Mon, 03/22/2021 - 10:00

Front Neurosci. 2021 Mar 4;15:646538. doi: 10.3389/fnins.2021.646538. eCollection 2021.


INTRODUCTION: Migraine without aura (MwoA) is a primary type of migraine, a common disabling disorder, and a disabling neurological condition. The headache is a complex experience, a common form of pain, in which multiple sensory information dimensions are combined to provide a unified conscious event. Migraine ictal have unique neuroimage biomarkers, but the brain is also affected during the inter-ictal phase. According to the current studies, a hypothesis was constructed that the altered integration of pain spatial and intensity information impacts headache intensity in the inter-ictal period.

METHODS: In this study, we applied theory-based region-to-region functional connectivity (FC) analyses to compare the differences in resting-state FC between MwoA participants and healthy controls with the pain integration hypothesis. After the correlation matrices between FC edges and clinical symptoms were constructed, the moderating effect and simple slope tests were investigated to explain whether and how the dysfunction of pain features discrimination affects the clinical symptoms.

RESULTS: Functional connectivity analyses showed significantly decreased FC edges between the left dorsolateral superior frontal gyrus (SFGdor) and left insula, and an increased FC edge between the left SFGdor and bilateral angular gyrus. The correlation matrix showed no significant correlation between significantly altered FC edge and headache duration, frequency, Zung self-rating anxiety scale, and Zung self-rating depression scale. Only one significantly altered edge in the MwoA condition was significantly correlated with headache intensity. Moderating Module 1 and 2 manifested the moderator variable (altered rs-FC edge) moderated the link between the normal edges and headache intensity.

CONCLUSION: The pain features integration processes in migraineurs vary from HCs, related to the clinical symptoms during a migraine attack. Moreover, the clinical symptoms will be affected by one or more discrimination modules. And the spatial or intensity discrimination modules have a higher impact when combined with another module on clinical symptoms than the single module.

PMID:33746709 | PMC:PMC7969893 | DOI:10.3389/fnins.2021.646538

Prefrontal network dysfunctions in rapid eye movement sleep behavior disorder

Sun, 03/21/2021 - 10:00

Parkinsonism Relat Disord. 2021 Mar 13;85:72-77. doi: 10.1016/j.parkreldis.2021.03.005. Online ahead of print.


INTRODUCTION: Resting-state functional connectivity magnetic resonance imaging (rsfcMRI) of rapid eye movement (REM) sleep behavior disorder (RBD) may provide an early biomarker of α-synucleinopathy. However, few rsfcMRI studies have examined cognitive networks. To elucidate brain network changes in RBD, we performed rsfcMRI in patients with polysomnography-confirmed RBD and healthy controls (HCs), with a sufficiently large sample size in each group.

METHODS: We analyzed rsfcMRI data from 50 RBD patients and 70 age-matched HCs. Although RBD patients showed no motor signs, some exhibited autonomic and cognitive problems. Several resting-state functional networks were extracted by group independent component analysis from HCs, including the executive-control (ECN), default-mode (DMN), basal ganglia (BGN), and sensory-motor (SMN) networks. Functional connectivity (FC) was compared between groups using dual regression analysis. In the RBD group, correlation analysis was performed between FC and clinical/cognitive scales.

RESULTS: Patients with RBD showed reduced striatal-prefrontal FC in ECN, consistent with executive dysfunctions. No abnormalities were found in DMN. In the motor networks, we identified reduced midbrain-pallidum FC in BGN and reduced motor and somatosensory cortex FC in SMN.

CONCLUSION: We found abnormal ECN and normal DMN as a possible hallmark of cognitive dysfunctions in early α-synucleinopathies. We replicated abnormalities in BGN and SMN corresponding to subclinical movement disorder of RBD. RsfcMRI may provide an early biomarker of both cognitive and motor network dysfunctions of α-synucleinopathies.

PMID:33744693 | DOI:10.1016/j.parkreldis.2021.03.005

The acts of opening and closing the eyes are of importance for congenital blindness: Evidence from resting-state fMRI

Sun, 03/21/2021 - 10:00

Neuroimage. 2021 Mar 17:117966. doi: 10.1016/j.neuroimage.2021.117966. Online ahead of print.


Volitional eye closure is observed only in conscious and awake humans, and is rare in animals. It is believed that eye closure can focus one's attention inward and facilitate activities such as meditation and mental imagery. Congenital blind individuals are also required to close their eyes for these activities. Resting-state functional magnetic resonance imaging (RS-fMRI) studies have found robust differences between the eyes-closed (EC) and eyes-open (EO) conditions in some brain regions in the sighted. This study analyzed data from 21 congenital blind individuals and 21 sighted controls by using amplitude of low-frequency fluctuation (ALFF) of RS-fMRI. The blind group and the sighted group shared similar pattern of differences between the EC and EO condition: ALFF was higher in the EC condition than the EO condition in the bilateral primary sensorimotor cortex, bilateral supplementary motor area, and inferior occipital cortex, while ALFF was lower in the EC condition than the EO condition in the medial prefrontal cortex, highlighting the "nature" effect on the difference between the EC and EO conditions. The results of other matrices such as fractional ALFF (fALFF) and regional homogeneity (ReHo) showed similar patterns to that of ALFF. Moreover, no significant difference was observed between the EC-EO pattern of the two subgroups of congenital blind (i.e., with and without light perception), suggesting that the EC-EO difference is irrespective to residual light perception which reinforced the "nature" effect. We also found between-group differences, i.e., more probably "nurture effect", in the posterior insula and fusiform. Our results suggest that the acts of closing and opening the eyes are of importance for the congenital blind, and that these actions and their differences might be inherent in the nature of humans.

PMID:33744460 | DOI:10.1016/j.neuroimage.2021.117966

Which multiband factor should you choose for your resting-state fMRI study?

Sun, 03/21/2021 - 10:00

Neuroimage. 2021 Mar 17:117965. doi: 10.1016/j.neuroimage.2021.117965. Online ahead of print.


Multiband acquisition, also called simultaneous multislice, has become a popular technique in resting-state functional connectivity studies. Multiband (MB) acceleration leads to a higher temporal resolution but also leads to spatially heterogeneous noise amplification, suggesting the costs may be greater in areas such as the subcortex. We evaluate MB factors of 2, 3, 4, 6, 8, 9, and 12 with 2 mm isotropic voxels, and additionally 2 mm and 3.3 mm single-band acquisitions, on a 32-channel head coil. Noise amplification was greater in deeper brain regions, including subcortical regions. Correlations were attenuated by noise amplification, which resulted in spatially varying biases that were more severe at higher MB factors. Temporal filtering decreased spatial biases in correlations due to noise amplification, but also tended to decrease effect sizes. In seed-based correlation maps, left-right putamen connectivity and thalamo-motor connectivity were highest in the single-band 3.3 mm protocol. In correlation matrices, MB 4, 6, and 8 had a greater number of significant correlations than the other acquisitions (both with and without temporal filtering). We recommend single-band 3.3 mm for seed-based subcortical analyses, and MB 4 provides a reasonable balance for studies analyzing both seed-based correlation maps and connectivity matrices. In multiband studies including secondary analyses of large-scale datasets, we recommend reporting effect sizes or test statistics instead of correlations. If correlations are reported, temporal filtering (or another method for thermal noise removal) should be used. The Emory Multiband Dataset is available on OpenNeuro.

PMID:33744454 | DOI:10.1016/j.neuroimage.2021.117965

Action observation treatment-based exoskeleton (AOT-EXO) for upper extremity after stroke: study protocol for a randomized controlled trial

Sun, 03/21/2021 - 10:00

Trials. 2021 Mar 20;22(1):222. doi: 10.1186/s13063-021-05176-x.


BACKGROUND: Stroke produces multiple symptoms, including sensory, motor, cognitive and psychological dysfunctions, among which motor deficit is the most common and is widely recognized as a major contributor to long-term functional disability. Robot-assisted training is effective in promoting upper extremity muscle strength and motor impairment recovery after stroke. Additionally, action observation treatment can enhance the effects of physical and occupational therapy by increasing neural activation. The AOT-EXO trial aims to investigate whether action observation treatment coupled with robot-assisted training could enhance motor circuit activation and improve upper extremity motor outcomes.

METHODS: The AOT-EXO trial is a multicentre, prospective, three-group randomized controlled trial (RCT). We will screen and enrol 132 eligible patients in the trial implemented in the Department of Rehabilitation Medicine of Tongji Hospital, Optical Valley Branch of Tongji Hospital and Hubei Province Hospital of Integrated Chinese & Western Medicine in Wuhan, China. Prior to study participation, written informed consent will be obtained from eligible patients in accordance with the Declaration of Helsinki. The enrolled stroke patients will be randomized to three groups: the CT group (conventional therapy); EXO group (exoskeleton therapy) and AOT-EXO group (action observation treatment-based exoskeleton therapy). The patients will undergo blinded assessments at baseline, post-intervention (after 4 weeks) and follow-up (after 12 weeks). The primary outcome will be the Fugl-Meyer Assessment for Upper Extremity (FMA-UE). Secondary outcomes will include the Action Research Arm Test (ARAT), modified Barthel Index (MBI), kinematic metrics assessed by inertial measurement unit (IMU), resting motor threshold (rMT), motor evoked potentials (MEP), functional magnetic resonance imaging (fMRI) and safety outcomes.

DISCUSSION: This trial will provide evidence regarding the feasibility and efficacy of the action observation treatment-based exoskeleton (AOT-EXO) for post-stroke upper extremity rehabilitation and elucidate the potential underlying kinematic and neurological mechanisms.

TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900026656 . Registered on 17 October 2019.

PMID:33743788 | DOI:10.1186/s13063-021-05176-x

Abnormal white matter functional connectivity density in antipsychotic-naive adolescents with schizophrenia

Sat, 03/20/2021 - 10:00

Clin Neurophysiol. 2021 Feb 20;132(5):1025-1032. doi: 10.1016/j.clinph.2020.12.031. Online ahead of print.


OBJECTIVES: This study aimed to assess the white matter (WM) functional hubs and abnormal functional connectivity pattern in adolescents with schizophrenia (AOS) and to explore the potential mechanisms.

METHODS: Based on resting-state fMRI data, we measured the WM functional connectivity density (FCD) at local- and long- ranges in 39 AOS and 31 healthy controls (HCs). Group comparison was conducted between the two groups. Spearman rank correlation analysis between the altered WM FCD and clinical PANSS scores was performed.

RESULTS: In the local scale, the functional hubs of the WM were mainly located in the corona radiata and cerebellum. Compared with HCs, AOS patients exhibited decreased FCD in the superior corona radiata. In the long-range, the functional hubs of the WM were mainly located in the external capsule and pons. AOS patients exhibited increased FCD in the cingulum but decreased FCD in the right dorsal raphe nuclei (DR). Furthermore, the aberrant long-range FCD in the right DR was inversely proportional to the clinical symptoms.

CONCLUSION: These findings indicated that the pathophysiology of schizophrenia may also lie in WM functional dysconnectivity.

SIGNIFICANCE: The current results provided initial evidence for the hypothesis of abnormal WM functional connectivity in schizophrenia.

PMID:33743297 | DOI:10.1016/j.clinph.2020.12.031

Brain dynamics: Synchronous peaks, functional connectivity, and its temporal variability

Sat, 03/20/2021 - 10:00

Hum Brain Mapp. 2021 Mar 20. doi: 10.1002/hbm.25404. Online ahead of print.


We describe advances in the understanding of brain dynamics that are important for understanding the operation of the cerebral cortex in health and disease. Peaks in the resting state fMRI BOLD signal in many different brain areas can become synchronized. In data from 1,017 participants from the Human Connectome Project, we show that early visual and connected areas have the highest probability of synchronized peaks. We show that these cortical areas also have low temporal variability of their functional connectivity. We show that there is an approximately reciprocal relation between the probability that a brain region will be involved in synchronized peaks and the temporal variability of the connectivity of a brain region. We show that a high probability of synchronized peaks and a low temporal variability of the connectivity of cortical areas are related to high mean functional connectivity, and provide an account of how these dynamics with some of the properties of avalanches arise. These discoveries help to advance our understanding of cortical operation in health, and in some mental disorders including schizophrenia.

PMID:33742498 | DOI:10.1002/hbm.25404

Effects of Moderate Alcohol Levels on Default Mode Network Connectivity in Heavy Drinkers

Sat, 03/20/2021 - 10:00

Alcohol Clin Exp Res. 2021 Mar 20. doi: 10.1111/acer.14602. Online ahead of print.


BACKGROUND: It is well established that even moderate levels of alcohol affect cognitive functions such as memory, self-related information processing and response inhibition. Nevertheless, the neural mechanisms underlying these alcohol-induced changes are still unclear, especially on the network level. The default mode network (DMN) plays an important role in memory and self-initiated mental activities, hence studying functional interactions of the DMN may provide new insights into the neural mechanisms underlying alcohol-related changes.

METHODS: We investigated resting-state functional connectivity (rsFC) of the DMN in a cohort of 37 heavy drinkers at a breath alcohol concentration of 0.8 g/kg. Alcohol and saline were infused in a single-blind crossover design.

RESULTS: Intra-network connectivity analyses revealed that participants showed significantly decreased rsFC of the right hippocampus and right middle temporal gyrus under acute alcohol exposure. Moreover, follow-up analyses revealed that these rsFC decreases were more pronounced in participants who reported stronger craving for alcohol. Exploratory inter-network connectivity analyses of the DMN with other resting-state networks showed no significant alcohol-induced changes, but suffered from low statistical power.

CONCLUSIONS: Our results indicate that acute alcohol exposure affects rsFC within the DMN. Functionally, this finding may be associated with impairments in memory encoding and self-referential processes commonly observed during alcohol intoxication. Future resting-state functional magnetic resonance imaging studies might therefore also investigate memory function and test whether DMN-related connectivity changes are associated with alcohol-induced impairments or craving.

PMID:33742481 | DOI:10.1111/acer.14602

The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

Sat, 03/20/2021 - 10:00

Sci Data. 2021 Mar 19;8(1):85. doi: 10.1038/s41597-021-00870-6.


We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.

PMID:33741990 | DOI:10.1038/s41597-021-00870-6

Prediction of stimulus-independent and task-unrelated thought from functional brain networks

Sat, 03/20/2021 - 10:00

Nat Commun. 2021 Mar 19;12(1):1793. doi: 10.1038/s41467-021-22027-0.


Neural substrates of "mind wandering" have been widely reported, yet experiments have varied in their contexts and their definitions of this psychological phenomenon, limiting generalizability. We aimed to develop and test the generalizability, specificity, and clinical relevance of a functional brain network-based marker for a well-defined feature of mind wandering-stimulus-independent, task-unrelated thought (SITUT). Combining functional MRI (fMRI) with online experience sampling in healthy adults, we defined a connectome-wide model of inter-regional coupling-dominated by default-frontoparietal control subnetwork interactions-that predicted trial-by-trial SITUT fluctuations within novel individuals. Model predictions generalized in an independent sample of adults with attention-deficit/hyperactivity disorder (ADHD). In three additional resting-state fMRI studies (total n = 1115), including healthy individuals and individuals with ADHD, we demonstrated further prediction of SITUT (at modest effect sizes) defined using multiple trait-level and in-scanner measures. Our findings suggest that SITUT is represented within a common pattern of brain network interactions across time scales and contexts.

PMID:33741956 | DOI:10.1038/s41467-021-22027-0