New resting-state fMRI related studies at PubMed

Quantitative Assessment of the Impact of Geometric Distortions and Their Correction on fMRI Data Analyses

Fri, 03/26/2021 - 10:00

Front Neurosci. 2021 Mar 9;15:642808. doi: 10.3389/fnins.2021.642808. eCollection 2021.


Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B0 field inhomogeneity. The component derived from in-plane spin dephasing induces pixel intensity variations and, more critically, geometric distortions. Despite the physical mechanisms underlying the susceptibility artifact being well established, a systematic investigation on the impact of the associated geometric distortions, and the direct comparison of different approaches to tackle them, on fMRI data analyses is missing. Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE). We first characterized the geometric distortions and the correction approaches based on the estimated ΔB0 field offset and voxel shift maps, and then conducted three types of analyses on the distorted and corrected fMRI data: (1) registration into structural data, (2) identification of resting-state networks (RSNs), and (3) mapping of task-related brain regions of interest. GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP. fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.

PMID:33767610 | PMC:PMC7985341 | DOI:10.3389/fnins.2021.642808

Recovery-associated resting-state activity and connectivity alterations in Anorexia nervosa

Fri, 03/26/2021 - 10:00

Biol Psychiatry Cogn Neurosci Neuroimaging. 2021 Mar 22:S2451-9022(21)00083-5. doi: 10.1016/j.bpsc.2021.03.006. Online ahead of print.


BACKGROUND: Previous studies provided controversial insight on the impact of starvation, disease status and underlying grey matter volume (GMV) changes on resting-state functional magnetic resonance imaging (rsfMRI) alterations in Anorexia nervosa (AN). Here we adapt a combined longitudinal and cross-sectional approach to disentangle the effects of these factors on resting-state alterations in AN.

METHODS: Overall, 87 female subjects were included in the study: adolescent patients with acute AN scanned at inpatient admission (N = 22, mean age 15.3 years) and at discharge (N = 21), 21 patients recovered from AN (22.3 years) and two groups of healthy age-matched controls (both N = 22, 16.0 and 22.5 years). Whole-brain measures of resting-state activity and functional connectivity were computed (Network Based Statistics, Global Correlation, Integrated Local Correlation, fractional Amplitude of Low Frequency Fluctuations) to assess rsfMRI alterations over the course of AN treatment before and after controlling for underlying GMV.

RESULTS: Patients with acute AN displayed strong and widespread prefrontal, sensorimotor, parietal, temporal, precuneal and insular reductions of resting-state connectivity and activity. All alterations were independent of GMV and were largely normalized in short- and absent in long-term recovered AN.

CONCLUSIONS: Resting-state fMRI alterations in AN constitute acute and GMV independent presumably starvation-related phenomena. The majority of alterations found here normalized over the course of recovery without evidence for possible preexisting trait- or remaining "scar"-effects.

PMID:33766777 | DOI:10.1016/j.bpsc.2021.03.006

Long-term ovarian hormone deprivation alters functional connectivity, brain neurochemical profile and white matter integrity in the Tg2576 amyloid mouse model of Alzheimer's disease

Thu, 03/25/2021 - 10:00

Neurobiol Aging. 2021 Feb 22;102:139-150. doi: 10.1016/j.neurobiolaging.2021.02.011. Online ahead of print.


Premenopausal bilateral ovariectomy is considered to be one of the risk factors of Alzheimer's disease (AD). However, the underlying mechanisms remain unclear. Here, we aimed to investigate long-term neurological consequences of ovariectomy in a rodent AD model, TG2576 (TG), and wild-type mice (WT) that underwent an ovariectomy or sham-operation, using in vivo MRI biomarkers. An increase in osmoregulation and energy metabolism biomarkers in the hypothalamus, a decrease in white matter integrity, and a decrease in the resting-state functional connectivity was observed in ovariectomized TG mice compared to sham-operated TG mice. In addition, we observed an increase in functional connectivity in ovariectomized WT mice compared to sham-operated WT mice. Furthermore, genotype (TG vs. WT) effects on imaging markers and GFAP immunoreactivity levels were observed, but there was no effect of interaction (Genotype × Surgery) on amyloid-beta-and GFAP immunoreactivity levels. Taken together, our results indicated that both genotype and ovariectomy alters imaging biomarkers associated with AD.

PMID:33765427 | DOI:10.1016/j.neurobiolaging.2021.02.011

Identifying emergent mesoscopic-macroscopic functional brain network dynamics in infants at term-equivalent age with electric source neuroimaging

Thu, 03/25/2021 - 10:00

Brain Connect. 2021 Mar 25. doi: 10.1089/brain.2020.0965. Online ahead of print.


AIM: To identify and characterise the functional brain networks at the time when the brain is yet to develop higher order functions in term-born and preterm infants at term-equivalent age.

INTRODUCTION: Although fMRI data have revealed the existence of spatially structured resting-state brain activity in infants, the temporal information of fMRI data limits the characterisation of fast time-scale brain oscillations. Here we use infants' high-density EEG to characterise spatiotemporal and spectral functional organisation of brain network dynamics.

METHODS: We used source-reconstructed EEG and graph theoretical analyses in 100 infants (84 preterm, 16 term born) to identify the rich-club topological organisation, temporal dynamics and spectral fingerprints of dynamic functional brain networks.

RESULTS: Five dynamic functional brain networks are identified, which have rich-club topological organisations, distinctive spectral fingerprints (in the delta and low-alpha frequency) and scale-invariant temporal dynamics (<0.1 Hz): The default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor and visual-limbic system. The temporal dynamics of these networks are correlated in a hierarchically leading-following organisation, showing that infant brain networks arise from long-range synchronisation of band-limited cortical oscillation based on interacting fast and slow coherent cortical oscillations.

CONCLUSION: Dynamic functional brain networks do not solely depend on the maturation of cognitive networks, instead, the brain network dynamics exist in infants at term-age well before the childhood and adulthood, and hence it offers a quantitative measurement of neurotypical development in infants. Impact Statement: Our work offers novel functional insights into the brain network characterisation in infants, providing a new functional basis for future deployable prognostication approaches.

PMID:33764807 | DOI:10.1089/brain.2020.0965

Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks

Thu, 03/25/2021 - 10:00

Eur J Neurosci. 2021 Mar 25. doi: 10.1111/ejn.15206. Online ahead of print.


Visual information processing requires an efficient visual attention system. The neural theory of visual attention (TVA) proposes that visual processing speed depends on the coordinated activity between frontoparietal and occipital brain areas. Previous research has shown that the coordinated activity between (i.e., functional connectivity, 'inter-FC') cingulo-opercular (COn) and right-frontoparietal (RFPn) networks is linked to visual processing speed. However, how inter-FC of COn and RFPn with visual networks links to visual processing speed has not been directly addressed yet. Forty-eight healthy adult participants (27 females) underwent resting-state (rs-)fMRI and performed a whole-report psychophysical task. To obtain inter-FC, we analyzed the entire frequency range available in our rs-fMRI data (i.e., 0.01-0.4 Hz) to avoid discarding neural information. Following previous approaches, we analyzed the data across frequency bins (Hz): Slow-5 (0.01-0.027), Slow-4 (0.027-0.073), Slow-3 (0.073-0.198), and Slow-2 (0.198-0.4). We used the mathematical TVA framework to estimate an individual, latent-level visual processing speed parameter. We found that visual processing speed was negatively associated with inter-FC between RFPn and visual networks in Slow-5 and Slow-2, with no corresponding significant association for inter-FC between COn and visual networks. These results provide the first empirical evidence that links inter-FC between RFPn and visual networks with the visual processing speed parameter. These findings suggest that direct connectivity between occipital and right frontoparietal, but not frontoinsular, regions support visual processing speed.

PMID:33764572 | DOI:10.1111/ejn.15206

Resting-State Network Plasticity Induced by Music Therapy after Traumatic Brain Injury

Thu, 03/25/2021 - 10:00

Neural Plast. 2021 Mar 8;2021:6682471. doi: 10.1155/2021/6682471. eCollection 2021.


Traumatic brain injury (TBI) is characterized by a complex pattern of abnormalities in resting-state functional connectivity (rsFC) and network dysfunction, which can potentially be ameliorated by rehabilitation. In our previous randomized controlled trial, we found that a 3-month neurological music therapy intervention enhanced executive function (EF) and increased grey matter volume in the right inferior frontal gyrus (IFG) in patients with moderate-to-severe TBI (N = 40). Extending this study, we performed longitudinal rsFC analyses of resting-state fMRI data using a ROI-to-ROI approach assessing within-network and between-network rsFC in the frontoparietal (FPN), dorsal attention (DAN), default mode (DMN), and salience (SAL) networks, which all have been associated with cognitive impairment after TBI. We also performed a seed-based connectivity analysis between the right IFG and whole-brain rsFC. The results showed that neurological music therapy increased the coupling between the FPN and DAN as well as between these networks and primary sensory networks. By contrast, the DMN was less connected with sensory networks after the intervention. Similarly, there was a shift towards a less connected state within the FPN and SAL networks, which are typically hyperconnected following TBI. Improvements in EF were correlated with rsFC within the FPN and between the DMN and sensorimotor networks. Finally, in the seed-based connectivity analysis, the right IFG showed increased rsFC with the right inferior parietal and left frontoparietal (Rolandic operculum) regions. Together, these results indicate that the rehabilitative effects of neurological music therapy after TBI are underpinned by a pattern of within- and between-network connectivity changes in cognitive networks as well as increased connectivity between frontal and parietal regions associated with music processing.

PMID:33763126 | PMC:PMC7964116 | DOI:10.1155/2021/6682471

Isoflurane affects brain functional connectivity in rats 1 month after exposure

Thu, 03/25/2021 - 10:00

Neuroimage. 2021 Mar 21:117987. doi: 10.1016/j.neuroimage.2021.117987. Online ahead of print.


Isoflurane, the most commonly used preclinical anesthetic, induces brain plasticity and long-term cellular and molecular changes leading to behavioral and/or cognitive consequences. These changes are most likely associated with network-level changes in brain function. To elucidate the mechanisms underlying long-term effects of isoflurane, we investigated the influence of a single isoflurane exposure on functional connectivity, brain electrical activity, and gene expression. Male Wistar rats (n=22) were exposed to 1.8% isoflurane for 3 h. Control rats (n=22) spent 3 h in the same room without exposure to anesthesia. After 1 month, functional connectivity was evaluated with resting-state functional magnetic resonance imaging (fMRI; n=6+6) and local field potential measurements (n=6+6) in anesthetized animals. A whole genome expression analysis (n=10+10) was also conducted with mRNA-sequencing from cortical and hippocampal tissue samples. Isoflurane treatment strengthened thalamo-cortical and hippocampal-cortical functional connectivity. Cortical low-frequency fMRI power was also significantly increased in response to the isoflurane treatment. The local field potential results indicating strengthened hippocampal-cortical alpha and beta coherence were in good agreement with the fMRI findings. Furthermore, altered expression was found in 20 cortical genes, several of which are involved in neuronal signal transmission, but no gene expression changes were noted in the hippocampus. Isoflurane induced prolonged changes in thalamo-cortical and hippocampal-cortical function and expression of genes contributing to signal transmission in the cortex. Further studies are required to investigate whether these changes are associated with the postoperative behavioral and cognitive symptoms commonly observed in patients and animals.

PMID:33762218 | DOI:10.1016/j.neuroimage.2021.117987

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

Thu, 03/25/2021 - 10:00

Curr Alzheimer Res. 2021 Mar 24. doi: 10.2174/1567205018666210324130502. Online ahead of print.


BACKGROUND: As a potential brain imaging biomarker, amplitude of low frequency fluc-tuation (ALFF) has been used as a feature to distinguish patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease.

METHODS: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM).

RESULTS: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups.

CONCLUSION: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

PMID:33761855 | DOI:10.2174/1567205018666210324130502

Distinct patterns of resting-state connectivity in U.S. service members with mild traumatic brain injury versus posttraumatic stress disorder

Wed, 03/24/2021 - 10:00

Brain Imaging Behav. 2021 Mar 23. doi: 10.1007/s11682-021-00464-1. Online ahead of print.


Mild traumatic brain injury (mTBI) is highly prevalent in military populations, with many service members suffering from long-term symptoms. Posttraumatic stress disorder (PTSD) often co-occurs with mTBI and predicts worse clinical outcomes. Functional neuroimaging research suggests there are both overlapping and distinct patterns of resting-state functional connectivity (rsFC) in mTBI versus PTSD. However, few studies have directly compared rsFC of cortical networks in military service members with these two conditions. In the present study, U.S. service members (n = 137; ages 19-59; 120 male) underwent resting-state fMRI scans. Participants were divided into three study groups: mTBI only, PTSD only, and orthopedically injured (OI) controls. Analyses investigated group differences in rsFC for cortical networks: default mode (DMN), frontoparietal (FPN), salience, somatosensory, motor, auditory, and visual. Analyses were family-wise error (FWE) cluster-corrected and Bonferroni-corrected for number of network seeds regions at the whole brain level (pFWE < 0.002). Both mTBI and PTSD groups had reduced rsFC for DMN and FPN regions compared with OI controls. These group differences were largely driven by diminished connectivity in the PTSD group. rsFC with the middle frontal gyrus of the FPN was increased in mTBI, but decreased in PTSD. Overall, these results suggest that PTSD symptoms may have a more consistent signal than mTBI. Our novel findings of opposite patterns of connectivity with lateral prefrontal cortex highlight a potential biomarker that could be used to differentiate between these conditions.

PMID:33759113 | DOI:10.1007/s11682-021-00464-1

Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex

Wed, 03/24/2021 - 10:00

Eur Radiol. 2021 Mar 23. doi: 10.1007/s00330-021-07825-w. Online ahead of print.


OBJECTIVE: The study aimed to evaluate the predictive validity of the neural network (NN) method for presurgical mapping of motor areas using resting-state functional MRI (rs-fMRI) data of patients with brain tumor located in the perirolandic cortex (PRC).

METHODS: A total of 109 patients with brain tumors occupying PRC underwent rs-fMRI and hand movement task-based fMRI (tb-fMRI) scans. Using a NN model trained on fMRI data of 47 healthy controls, individual task activation maps were predicted from their rs-fMRI data. NN-predicted maps were compared with task activation and independent component analysis (ICA)-derived maps. Spatial Pearson's correlation coefficients (CC) matrices and Dice coefficients (DC) between task activation and predicted activation using NN (DCNN_Act) and ICA (DCICA_Act) were calculated and compared using non-parametric tests. The effects of tumor types and head motion on predicted maps were demonstrated.

RESULTS: The CC matrix of NN-predicted maps showed higher diagonal values compared with ICA-derived maps (p < 0.001). DCNN_Act were higher than DCICA_Act (p < 0.001) for patients with or without motor deficits. Lower DCs were found in subjects with head motion greater than one voxel. DCs were higher on the nontumor side than on the tumor side (p < 0.001), especially in the glioma group compared with meningioma and metastatic groups.

CONCLUSIONS: This study indicated that the NN approach could predict individual motor activation using rs-fMRI data and could have promising clinical applications in brain tumor patients with anatomical and functional reorganizations.

KEY POINTS: • The neural network machine learning approach successfully predicted hand motor activation in patients with a tumor in the perirolandic cortex, despite space-occupying effects and possible functional reorganization. • Compared to the conventional independent component analysis, the neural network approach utilizing resting-state fMRI data yielded a higher correlation to the active task hand activation data. • The Dice coefficient of machine learning-predicted activation vs. task fMRI activation was different between tumor and nontumor side, also between tumor types, which might indicate different effects of possible neurovascular uncoupling on resting-state and task fMRI.

PMID:33758954 | DOI:10.1007/s00330-021-07825-w

Altered spontaneous brain activity in patients with diabetic optic neuropathy: A resting-state functional magnetic resonance imaging study using regional homogeneity

Wed, 03/24/2021 - 10:00

World J Diabetes. 2021 Mar 15;12(3):278-291. doi: 10.4239/wjd.v12.i3.278.


BACKGROUND: Diabetes is a common chronic disease. Given the increasing incidence of diabetes, more individuals are affected by diabetic optic neuropathy (DON), which results in decreased vision. Whether DON leads to abnormalities of other visual systems, including the eye, the visual cortex, and other brain regions, remains unknown.

AIM: To investigate the local characteristics of spontaneous brain activity using regional homogeneity (ReHo) in patients with DON.

METHODS: We matched 22 patients with DON with 22 healthy controls (HCs). All subjects underwent resting-state functional magnetic resonance imaging. The ReHo technique was used to record spontaneous changes in brain activity. Receiver operating characteristic (ROC) curves were applied to differentiate between ReHo values for patients with DON and HCs. We also assessed the correlation between Hospital Anxiety and Depression Scale scores and ReHo values in DON patients using Pearson correlation analysis.

RESULTS: ReHo values of the right middle frontal gyrus (RMFG), left anterior cingulate (LAC), and superior frontal gyrus (SFG)/left frontal superior orbital gyrus (LFSO) were significantly lower in DON patients compared to HCs. Among these, the greatest difference was observed in the RMFG. The result of the ROC curves suggest that ReHo values in altered brain regions may help diagnose DON, and the RMFG and LAC ReHo values are more clinically relevant than SFG/LFSO. We also found that anxiety and depression scores of the DON group were extremely negatively correlated with the LAC ReHo values (r = -0.9336, P < 0.0001 and r = -0.8453, P < 0.0001, respectively).

CONCLUSION: Three different brain regions show ReHo changes in DON patients, and these changes could serve as diagnostic and/or prognostic biomarkers to further guide the prevention and treatment of DON patients.

PMID:33758647 | PMC:PMC7958477 | DOI:10.4239/wjd.v12.i3.278

Speech Motor Function and Auditory Perception in Succinic Semialdehyde Dehydrogenase Deficiency: Toward Pre-Supplementary Motor Area (SMA) and SMA-Proper Dysfunctions

Wed, 03/24/2021 - 10:00

J Child Neurol. 2021 Mar 23:8830738211001210. doi: 10.1177/08830738211001210. Online ahead of print.


This study reviews the fundamental roles of pre-supplementary motor area (SMA) and SMA-proper responsible for speech-motor functions and auditory perception in succinic semialdehyde dehydrogenase (SSADH) deficiency. We comprehensively searched the databases of PubMed, Google Scholar, and the electronic journals Springer, PreQuest, and Science Direct associated with keywords SSADHD, SMA, auditory perception, speech, and motor with AND operator. Transcranial magnetic stimulation emerged for assessing excitability/inhibitory M1 functions, but its role in pre-SMA and SMA proper dysfunction remains unknown. There was a lack of data on resting-state and task-based functional magnetic resonance imaging (MRI), with a focus on passive and active tasks for both speech and music, in terms of analysis of SMA-related cortex and its connections. Children with SSADH deficiency likely experience a dysfunction in connectivity between SMA portions with cortical and subcortical areas contributing to disabilities in speech-motor functions and auditory perception. Early diagnosis of auditory-motor disabilities in children with SSADH deficiency by neuroimaging techniques invites opportunities for utilizing sensory-motor integration as future interventional strategies.

PMID:33757330 | DOI:10.1177/08830738211001210

Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

Tue, 03/23/2021 - 10:00

Brain Imaging Behav. 2021 Mar 23. doi: 10.1007/s11682-021-00468-x. Online ahead of print.


To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n = 30; noHE, n = 32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary, DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

PMID:33755921 | DOI:10.1007/s11682-021-00468-x

Reduced network integration in default mode and executive networks is associated with social and personal optimism biases

Tue, 03/23/2021 - 10:00

Hum Brain Mapp. 2021 Mar 23. doi: 10.1002/hbm.25411. Online ahead of print.


An optimism bias refers to the belief in good things happening to oneself in the future with a higher likelihood than is justified. Social optimism biases extend this concept to groups that one identifies with. Previous literature has found that both personal and social optimism biases are linked to brain structure and task-related brain function. Less is known about whether optimism biases are also expressed in resting-state functional connectivity (RSFC). Forty-two participants completed questionnaires on dispositional personal optimism (which is not necessarily unjustified) and comparative optimism (i.e., whether we see our own future as being rosier than a comparison person's future) and underwent a resting-state functional magnetic resonance imaging scan. They further undertook an imaginative soccer task in order to assess both their personal and social optimism bias. We tested associations of these data with RSFC within and between 13 networks, using sparse canonical correlation analyses (sCCAs). We found that the primary sCCA component was positively connected to personal and social optimism bias and negatively connected to dispositional personal pessimism. This component was associated with (a) reduced integration of the default mode network, (b) reduced integration of the central executive and salience networks, and (c) reduced segregation between the default mode network and the central executive network. Our finding that optimism biases are linked to RSFC indicates that they may be rooted in neurobiology that exists outside of concurrent tasks. This poses questions as to what the limits of the malleability of such biases may be.

PMID:33755272 | DOI:10.1002/hbm.25411

Cingulo-opercular control network and disused motor circuits joined in standby mode

Tue, 03/23/2021 - 10:00

Proc Natl Acad Sci U S A. 2021 Mar 30;118(13):e2019128118. doi: 10.1073/pnas.2019128118.


Whole-brain resting-state functional MRI (rs-fMRI) during 2 wk of upper-limb casting revealed that disused motor regions became more strongly connected to the cingulo-opercular network (CON), an executive control network that includes regions of the dorsal anterior cingulate cortex (dACC) and insula. Disuse-driven increases in functional connectivity (FC) were specific to the CON and somatomotor networks and did not involve any other networks, such as the salience, frontoparietal, or default mode networks. Censoring and modeling analyses showed that FC increases during casting were mediated by large, spontaneous activity pulses that appeared in the disused motor regions and CON control regions. During limb constraint, disused motor circuits appear to enter a standby mode characterized by spontaneous activity pulses and strengthened connectivity to CON executive control regions.

PMID:33753484 | DOI:10.1073/pnas.2019128118

Network Functional Connectivity Underlying Dissociable Cognitive and Affective Components of Empathy in Adolescence

Tue, 03/23/2021 - 10:00

Neuropsychologia. 2021 Mar 19:107832. doi: 10.1016/j.neuropsychologia.2021.107832. Online ahead of print.


Empathy, the capacity to understand and share others' emotions, can occur through cognitive and affective components. These components are different conceptually, behaviorally, and in the brain. Neuroimaging task-based research in adolescents and adults document that cognitive empathy associates with the default mode and frontoparietal networks, whereas regions of the salience network underlie affective empathy. However, cognitive empathy is slower to mature than affective empathy and the extant literature reveals considerable developmental differences between adolescent and adult brains within and between these three networks. We extend previous work by examining empathy's association with functional connectivity within and between these networks in adolescents. Participants (n=84, aged 13-17; 46.4% female) underwent resting state fMRI and completed self-report measures (Interpersonal Reactivity Index) for empathy as part of a larger Nathan-Kline Institute study. Regression analyses revealed adolescents reporting higher cognitive empathy had higher within DMN connectivity. Post hoc analysis revealed cognitive empathy's association within DMN connectivity is independent of affective empathy or empathy in general; and this association is driven by positive pairwise connections between the bilateral angular gyri and medial prefrontal cortex. These results suggest introspective cognitive processes related to the DMN are specifically important for cognitive empathy in adolescence.

PMID:33753085 | DOI:10.1016/j.neuropsychologia.2021.107832

"Unrest while Resting"? Brain Entropy in Autism Spectrum Disorder

Tue, 03/23/2021 - 10:00

Brain Res. 2021 Mar 19:147435. doi: 10.1016/j.brainres.2021.147435. Online ahead of print.


Biological systems typically exhibit complex behavior with nonlinear dynamic properties. Nonlinear signal processing techniques such as sample entropy is a novel approach to characterize the temporal dynamics of brain connectivity. Estimating entropy is especially important in clinical populations such as autism spectrum disorder (ASD) as differences in entropy may signal functional alterations in the brain. Considering the models of disrupted brain network connectivity in ASD, sample entropy would provide a novel direction to understand brain organization. Resting state fMRI data from 45 high-functioning children with ASD and 45 age-and-IQ-matched typically developing (TD) children were obtained from the Autism Brain Imaging Data Exchange (ABIDE-II) database. Data were preprocessed using the CONN toolbox. Sample entropy was then calculated using the complexity toolbox, in a whole-brain voxelwise manner as well as in regions of interests (ROIs) based methods. ASD participants demonstrated significantly increased entropy in left angular gyrus, superior parietal lobule, and right inferior temporal gyrus; and reduced sample entropy in superior frontal gyrus compared to TD participants. Positive correlations of average entropy in clusters of significant group differences scores across all subjects were found. Finally, ROI analysis revealed a main effect of lobes. Differences in entropy between the ASD and TD groups suggests that entropy may provide another important index of brain dysfunction in clinical populations like ASD. Further, the relationship between increased entropy and ASD symptoms in our study underscores the role of optimal brain synchronization in cognitive and behavioral functions.

PMID:33753068 | DOI:10.1016/j.brainres.2021.147435

Human biventricular electromechanical simulations on the progression of electrocardiographic and mechanical abnormalities in post-myocardial infarction

Mon, 03/22/2021 - 10:00

Europace. 2021 Mar 4;23(Supplement_1):i143-i152. doi: 10.1093/europace/euaa405.


AIMS: Develop, calibrate and evaluate with clinical data a human electromechanical modelling and simulation framework for multiscale, mechanistic investigations in healthy and post-myocardial infarction (MI) conditions, from ionic to clinical biomarkers.

METHODS AND RESULTS: Human healthy and post-MI electromechanical simulations were conducted with a novel biventricular model, calibrated and evaluated with experimental and clinical data, including torso/biventricular anatomy from clinical magnetic resonance, state-of-the-art human-based membrane kinetics, excitation-contraction and active tension models, and orthotropic electromechanical coupling. Electromechanical remodelling of the infarct/ischaemic region and the border zone were simulated for ischaemic, acute, and chronic states in a fully transmural anterior infarct and a subendocardial anterior infarct. The results were compared with clinical electrocardiogram and left ventricular ejection fraction (LVEF) data at similar states. Healthy model simulations show LVEF 63%, with 11% peak systolic wall thickening, QRS duration and QT interval of 100 ms and 330 ms. LVEF in ischaemic, acute, and chronic post-MI states were 56%, 51%, and 52%, respectively. In linking the three post-MI simulations, it was apparent that elevated resting potential due to hyperkalaemia in the infarcted region led to ST-segment elevation, while a large repolarization gradient corresponded to T-wave inversion. Mechanically, the chronic stiffening of the infarct region had the benefit of improving systolic function by reducing infarct bulging at the expense of reducing diastolic function by inhibiting inflation.

CONCLUSION: Our human-based multiscale modelling and simulation framework enables mechanistic investigations into patho-physiological electrophysiological and mechanical behaviour and can serve as testbed to guide the optimization of pharmacological and electrical therapies.

PMID:33751088 | DOI:10.1093/europace/euaa405

Functional connectivity and upper limb function in patients after pediatric arterial ischemic stroke with contralateral corticospinal tract wiring

Mon, 03/22/2021 - 10:00

Sci Rep. 2021 Mar 9;11(1):5490. doi: 10.1038/s41598-021-84671-2.


To develop individualized motor rehabilitation, knowledge of the relationship between neuroplastic reorganization and motor recovery after pediatric arterial ischemic stroke (AIS) is crucial. Thus, we investigated functional connectivity in patients after AIS with good motor outcome and in patients with hemiparesis compared with typically developing peers. We included 18 patients (n = 9 with hemiparesis, n = 9 with good motor outcome) with pediatric AIS in the chronic phase (≥ 2 years after diagnosis, diagnosed > 16 years) and 18 peers matched by age and gender. Participants underwent a standardized motor assessment, single-pulse transcranial magnetic stimulation to determine the type of corticospinal tract wiring, and resting-state functional magnetic resonance imaging to examine motor network connectivity. Corticospinal tract wiring was contralateral in all participants. Patients with hemiparesis had lower interhemispheric connectivity strength compared with patients with good clinical outcome and peers. Patients with good clinical outcome had higher intrahemispheric connectivity strength compared with peers. Further, higher intrahemispheric connectivity was related to better motor outcome in patients. Our findings suggest that better motor outcome after pediatric AIS is related to higher motor network connectivity strength. Thus, resting-state functional connectivity might be predictive for motor recovery after pediatric AIS.

PMID:33750854 | DOI:10.1038/s41598-021-84671-2

Detecting risk gene and pathogenic brain region in EMCI using a novel GERF algorithm based on brain imaging and genetic data

Mon, 03/22/2021 - 10:00

IEEE J Biomed Health Inform. 2021 Mar 22;PP. doi: 10.1109/JBHI.2021.3067798. Online ahead of print.


Fusion analysis of disease-related multi-modal data is becoming increasingly important to illuminate the pathogenesis of complex brain diseases. However, owing to the small amount and high dimension of multi-modal data, current machine learning methods do not fully achieve the high veracity and reliability of fusion feature selection. In this paper, we propose a genetic-evolutionary random forest (GERF) algorithm to discover the risk genes and disease-related brain regions of early mild cognitive impairment (EMCI) based on the genetic data and resting-state functional magnetic resonance imaging (rs-fMRI) data. Classical correlation analysis method is used to explore the association between brain regions and genes, and fusion features are constructed. The genetic-evolutionary idea is introduced to enhance the classification performance, and to extract the optimal features effectively. The proposed GERF algorithm is evaluated by the public Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and the results show that the algorithm achieves satisfactory classification accuracy in small sample learning. Moreover, we compare the GERF algorithm with other methods to prove its superiority. Furthermore, we propose the overall framework of detecting pathogenic factors, which can be accurately and efficiently applied to the multi-modal data analysis of EMCI and be able to extend to other diseases. This work provides a novel insight for early diagnosis and clinicopathologic analysis of EMCI, which facilitates clinical medicine to control further deterioration of diseases and is good for the accurate electric shock using transcranial magnetic stimulation.

PMID:33750717 | DOI:10.1109/JBHI.2021.3067798