New resting-state fMRI related studies at PubMed

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

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