State fMRI Research Articles

Functional connectivity of sensorimotor network is enhanced in spastic diplegic cerebral palsy: A multimodal study using fMRI and MEG

ObjectiveTo assess the effects to functional connectivity (FC) caused by lesions related to spastic diplegic cerebral palsy (CP) in children and adolescents using multiple imaging modalities. MethodsWe used resting state magnetoencephalography (MEG) envelope signals in alpha, beta and gamma ranges and resting state functional magnetic resonance imaging (fMRI) signals to quantify FC between selected sensorimotor regions of interest (ROIs) in 11 adolescents with spastic diplegic cerebral palsy and 24 typically developing controls. Motor performance of the hands was quantified with gross motor, fine motor and kinesthesia tests. ResultsIn fMRI, participants with CP showed enhanced FC within posterior parietal regions; in MEG, they showed enhanced interhemispheric FC between sensorimotor regions and posterior parietal regions both in alpha and lower beta bands. There was a correlation between the kinesthesia score and fronto-parietal connectivity in the control population. ConclusionsCP is associated with enhanced FC in sensorimotor network. This difference is not correlated with hand coordination performance. The effect of the lesion is likely not fully captured by temporal correlation of ROI signals. SignificanceBrain lesions can show as increased temporal correlation of activity between remote brain areas. We suggest this effect is likely separate from typical physiological correlates of functional connectivity.

＜Editors' Choice＞ Pattern of THK 5351 retention in normal aging involves core regions of resting state networks associated with higher cognitive function.

We aimed to elucidate the distribution pattern of the positron emission tomography probe [18F]THK 5351, a marker for astrogliosis and tau accumulation, in healthy aging. We also assessed the relationship between THK5351 retention and resting state networks. We enrolled 62 healthy participants in this study. All participants underwent magnetic resonance imaging/positron emission tomography scanning consisting of T1-weighted images, resting state functional magnetic resonance imaging, Pittsburgh Compound-B and THK positron emission tomography. The preprocessed THK images were entered into a scaled subprofile modeling/principal component analysis to extract THK distribution patterns. Using the most significant THK pattern, we generated regions of interest, and performed seed-based functional connectivity analyses. We also evaluated the functional connectivity overlap ratio to identify regions with high between-network connectivity. The most significant THK distributions were observed in the medial prefrontal cortex and bilateral putamen. The seed regions of interest in the medial prefrontal cortex had a functional connectivity map that significantly overlapped with regions of the dorsal default mode network. The seed regions of interest in the putamen showed strong overlap with the basal ganglia and anterior salience networks. The functional connectivity overlap ratio also showed that three peak regions had the characteristics of connector hubs. We have identified an age-related spatial distribution of THK in the medial prefrontal cortex and basal ganglia in normal aging. Interestingly, the distribution's peaks are located in regions of connector hubs that are strongly connected to large-scale resting state networks associated with higher cognitive function.

79 Continuous Theta Burst Stimulation (cTBS) over the Inferior Parietal Cortex Decreases Default Mode Connectivity and Improves Overnight Sleep in People with Insomnia

Objective:Chronic insomnia is a highly prevalent disorder affecting approximately one-in-three Americans. Insomnia is associated with increased cognitive and brain arousal. Compared to healthy individuals, those with insomnia tend to show greater activation/connectivity within the default mode network (DMN) of the brain, consistent with the hyperarousal theory. We investigated whether it would be possible to suppress activation of the DMN to improve sleep using a type of repetitive transcranial magnetic stimulation (rTMS) known as continuous theta burst stimulation (cTBS).Participants and Methods:Participants (n=9, 6 female; age=25.4, SD=5.9 years) meeting criteria for insomnia/sleep disorder on standardized scales completed a counterbalanced sham-controlled crossover design in which they served as their own controls on two separate nights of laboratory monitored sleep on separate weeks. Each session included two resting state functional magnetic resonance imaging (fMRI) sessions separated by a brief rTMS session. Stimulation involved a 40 second cTBS stimulation train applied over an easily accessible cortical surface node of the DMN located at the left inferior parietal lobe. After scanning/stimulation, the participant was escorted to an isolated sleep laboratory bedroom, fitted with polysomnography (PSG) electrodes, and allowed an 8-hour sleep opportunity from 2300 to 0700. PSG was monitored continuously and scored for standard outcomes, including total sleep time (TST), percentage of time various sleep stages, and number of arousals.Results:Consistent with our hypothesis, a single session of active cTBS produced a significant reduction of functional connectivity (p < .05, FDR corrected) within the DMN. In contrast, the sham condition produced no changes in functional connectivity from pre- to post-treatment. Furthermore, after controlling for age, we also found that the active treatment was associated with meaningful trends toward greater overnight improvements in sleep compared to the sham condition. First, the active cTBS condition was associated with significantly greater TST compared to sham (F(1,7)=14.19, p=.007, partial eta-squared=.67). Overall, individuals obtained 26.5 minutes more sleep on the nights that they received the active cTBS compared to the sham condition. Moreover, the active cTBS condition was associated with a significant increase in the percentage of time in rapid eye movement (REM%) sleep compared to the sham condition (F(1,7)=7.05, p=.033, partial eta-squared=.50), which was significant after controlling for age. Overall, active treatment was associated with an increase of 6.76% more of total sleep time in REM compared to sham treatment. Finally, active cTBS was associated with fewer arousals from sleep (t(8) = -1.84, p = .051, d = .61), with an average of 15.1 fewer arousals throughout the night than sham.Conclusions:Overall, these findings suggest that this simple and brief cTBS approach can alter DMN brain functioning in the expected direction and was associated with trends toward improved objectively measured sleep, including increased TST and REM% and fewer arousals during the night following stimulation. These findings emerged after only a single 40-second treatment, and it remains to be seen whether multiple treatments over several days or weeks can sustain or even improve upon these outcomes.

6 Graph Analysis of Resting State Functional Brain Networks and Associations with Cognitive Outcomes in Survivors on Pediatric Brain Tumor

Objective:Adolescent and young adult survivors of pediatric brain tumors often live with long-term neuropsychological deficits, which have been found to be related to functional and structural brain changes related to the presence of the tumor itself as well as treatments such as radiation therapy. The importance of brain networks has become a central focus of research over recent decades across neurological populations. Graph theory is one way of analyzing network properties that can describe the integration, segregation, and other aspects of network organization. The existing literature using graph theory with survivors of brain tumor is small and inconsistent; therefore, more work is needed, particularly in survivors of pediatric brain tumors. The present study used graph theory to determine whether functional network properties in this population differ from healthy controls; whether graph metrics relate to core cognitive skills: attention, working memory, and processing speed; and whether they relate to a cumulative measure of neurological risk.Participants and Methods:31 survivors and 31 matched controls completed neuropsychological testing including measures of attention, working memory, and processing speed. They also underwent resting state functional magnetic resonance imaging. Resting state data were preprocessed and spatially constrained independent component analysis was completed to construct connectivity matrices. Finally, graph metrics were calculated utilizing an area under the curve method, including global efficiency, clustering coefficient, betweenness centrality, and small-worldness. Group differences and associations between graph metrics, cognitive outcomes, and neurological risk were analyzed using SPSS version 28.0.Results:Results revealed a significant difference such that brain tumor survivors exhibited less small-world properties in their functional brain networks. This was found to be related to working memory, such that less smallworldness in the network was related to poorer performance. There were no significant relationships with neurological risk, but there were nonsignificant correlations of small-moderate effect size such that lower global efficiency and clustering coefficient were associated with greater neurological risk. Comparisons to structural network analysis from a similar sample and additional post-hoc analyses are also discussed.Conclusions:These findings reveal that survivors of pediatric brain tumor indeed display significant differences in functional brain networks that are quantifiable by graph theory. It is also possible that, with further work, we might better understand how metrics such as smallworldness can be used to predict long-term cognitive outcomes and functional independence in adulthood.

Regional homogeneity in patients with obsessive–compulsive disorder and depression: A resting state functional magnetic resonance imaging study

ObjectiveTo explore the brain functional impairment of patients with obsessive–compulsive disorder (OCD) with and without depressive symptoms and analyze the correlation between the degree of impairment and the severity of symptoms. MethodFourteen patients with OCD who met the ICD-10 diagnostic criteria for OCD were included. The group having OCD with depression (OCDd) consisted of 15 patients, and 17 healthy controls (HC) matched for age and education were also included. The Yale–Brown OCD Scale (Y-BOCS) and the 24-item Hamilton Assessment of Depression Scale (HAMD) were administered to the OCD and OCDd groups. Resting-state functional brain magnetic resonance imaging was performed in the three groups of participants. ResultThe OCDd group had lower scores on the HAMD, Y-BOCS, and obsessive–compulsive thinking subscales compared with the OCD group (P < 0.05). The scores on the OCDd subscale were negatively correlated with the HAMD scores (R = − 0.568, P = 0.027). The OCDd group had higher regional homogeneity (ReHo) values in the lingual gyrus than the OCD group. The OCDd group had higher ReHo values in the lingual gyrus than the HC group, and the OCDd group had higher ReHo values than the HC group. These differences were statistically significant (P < 0.05). After correction for multiple comparisons, significant difference was observed between the OCDd and HC groups (P＜0.05). In the OCD group, the ReHo value of the lingual gyrus was negatively correlated with the Y-BOCS total score and the compulsive behavior subscale score (R = − 0.609, −0.552; P = 0.016, 0.033). ConclusionAbnormal ReHo values in the lingual gyrus and right medial superior frontal gyrus were found in the patients with OCDd. In the OCDd group, the ReHo values of the lingual gyrus were negatively correlated with the scores on the Y-BOCS total and obsessive–compulsive subscales, suggesting that abnormal local coherence of the lingual gyrus may be related to the severity of OCD.

A - 149 Association between Brain Network Organization and Symptom Severity from Baseline to Post-Concussion.

Concussions have been associated with functional connectivity changes that impact physical and psychiatric health. Previous research has focused on the relationship between average network functional connectivity changes following concussion and symptom severity, but this approach may be insensitive to subtle nodal changes that still result in the disruption of within network organization, and therefore, impact symptom severity. We hypothesized that individuals who exhibit more disruptions in brain network organization post-concussion (~48hrs post-diagnosis) would self-report a higher symptom load acutely post-injury. Resting state functional magnetic resonance imaging data were obtained for collegiate football players (n = 37; Mage = 19.8, SDage = 1.85) at baseline and acute post-concussion. Functional connectivity matrices were constructed using 264 regions (Power et al., 2011). We calculated normalized betweenness centrality (BCnorm) for all DMN nodes at both time-points. Then similarity of pre-post patterns for BCnorm in the DMN were calculated. Symptom load was baseline-post concussion difference severity score from Post-Concussion Symptom Scale clusters (affective, cognitive, sleep, somatic) (Collins et al., 2012). Finally, correlations were calculated of DMN-BCnorm similarity with symptom cluster changes. Athletes who have more disruptions to brain network organization in the DMN from baseline to post-concussion report more somatic symptoms acutely post-concussion. We did not find any relationships between disruption of brain network properties in the DMN and changes in other symptom clusters. Athletes who exhibit more disruptions in the DMN report more somatic symptoms following concussion. These results suggest that examining patterns of brain network organization may be more sensitive than network average when relating brain changes to symptom change.

Abnormal network connections to early visual cortex in posterior cortical atrophy

IntroductionPosterior Cortical Atrophy (PCA), a visual variant of Alzheimer's disease, initially manifests with higher-order visual disorders and parieto/temporo-occipital atrophy. Recent studies have shown remote functional impairment in both distant brain networks and along the calcarine sulcus (V1). Functional alteration in the calcarine differs along its length, reflecting center to periphery visual space differences. Herein, we aim to connect between these two sets of findings by looking at the retinotopic patterns of functional connectivity between large-scale brain networks and V1, comparing patients with normally sighted subjects. MethodsResting state functional magnetic resonance imaging (fMRI) and T1 anatomical scans were obtained from 11 PCA patients and 17 age-matched healthy volunteers. Default mode network (DMN) and fronto parietal network (FPN) were defined and differences between the networks in patients and healthy controls were evaluated at the whole brain level, specifically their connectivity to V1. ResultsConnectivity patterns within the DMN and the FPN were similar between the groups, although differences were found in regions within and beyond the networks. Focusing on V1, in the control group we identified the expected pattern of a distributed connectivity along eccentricity, with foveal regions showing stronger connectivity to the FPN and peripheral regions showing stronger connectivity to the DMN. However, in PCA patients we could not identify a clear difference in connectivity along the eccentricities. ConclusionLost specialization of function along the calcarine in PCA patients may have further implications on large-scale networks or vice versa. This impairment, distant from the core pathology, might explain patients' visual disabilities.

Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis.

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.

Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics

Many studies have analyzed resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data to elucidate the effects of neurological and neuropsychiatric disorders upon the interactions of brain regions over time. Existing studies often use either machine learning classification or clustering algorithms. Additionally, several studies have used clustering algorithms to extract features related to brain states trajectories that can be used to train interpretable classifiers. However, the combination of explainable dFNC classifiers followed by clustering algorithms is highly underutilized. In this study, we show how such an approach can be used to study the effects of schizophrenia (SZ) upon brain activity. Specifically, we train an explainable deep learning model to classify between individuals with SZ and healthy controls. We then cluster the resulting explanations, identifying discriminatory states of dFNC. We lastly apply several novel measures to quantify aspects of the classifier explanations and obtain additional insights into the effects of SZ upon brain network dynamics. Specifically, we uncover effects of schizophrenia upon subcortical, sensory, and cerebellar network interactions. We also find that individuals with SZ likely have reduced variability in overall brain activity and that the effects of SZ may be temporally localized. In addition to uncovering effects of SZ upon brain network dynamics, our approach could provide novel insights into a variety of neurological and neuropsychiatric disorders in future dFNC studies.

Changes of brain network in patients with insomnia following radiotherapy for cervical cancer and their correlation with IT-TMS treatment efficacy: a graph-theory analysis

To analyze the changes of brain small-world and node function network properties in patients with insomnia following radiotherapy for cervical cancer based on graph theory analysis and explore the correlation between functional networks and the clinical efficacy of individual-target transcranial magnetic stimulation (IT-TMS) for treatment of insomnia. The resting state functional magnetic resonance imaging (rs-fMRI) data were collected from 30 patients with insomnia following radiotherapy for cervical cancer and 30 matched healthy individuals. All the patients received accelerated intelligent neuromodulation TMS therapy. Using graph theory analysis and GRETNA software, the functional connectivity matrices were constructed and the attribute features were extracted. The scores on the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS) of the participants were collected before and after IT-TMS, and the correlation between improvement in insomnia and the functional network was investigated. The two groups matched for age, gender, and education level (P>0.05) showed significant differences in PSQI, ISI, SAS and SDS scores (P<0.05). Both groups showed attributes of the small-world network. Compared with the healthy individuals, the patients showed significantly decreased σ, EI, Cp and Lp (P<0.05) and increased Eg (P<0.05) at baseline, which, along with insomnia symptoms, were all improved after IT-TMS treatment. The patients showed reduced functional connections of the node network at follow-up compared with the baseline, and the low functional connectivity between the right insula and left superior frontal gyrus was correlated with the improvement of ISI scores. The patients with insomnia following radiotherapy for cervical cancer have impaired information integration ability of the brain network, IT-TMS can significantly improve insomnia symptoms by reducing the hyperconnectivity between the default mode network and the salience network.

MRgFUS of the nucleus ventralis intermedius in essential tremor modulates functional connectivity within the classical tremor network and beyond

BackgroundMagnetic resonance-guided focused ultrasound (MRgFUS) of the thalamic ventral intermediate nucleus is an incisionless lesional treatment for essential tremor. ObjectiveTo examine relationships between tremor severity and functional connectivity in patients with essential tremor and to assess long-term changes in the tremor network after sonication of the ventral intermediate nucleus. MethodsTwenty-one patients with essential tremor (70.33 ± 11.32 years) were included in the final analysis and underwent resting state functional magnetic resonance imaging at 3 T before and 6 months after treatment. Tremor severity (Fahn-Tolosa-Marin Clinical Rating Scale) was evaluated and functional connectivity was investigated using independent component analysis. ResultsMRgFUS of the thalamic ventral intermediate nucleus reduced contralateral tremor effectively. Multiple regression analysis revealed exclusively negative correlations between FC and tremor severity, notably in the right cerebellar lobe VI and the left cerebellar lobe VIIIa (cerebellar network), in the left occipital fusiform gyrus (lateral visual network), the anterior division of the left superior temporal gyrus (fronto-parieto-temporal network), and in the posterior division of the left parahippocampal gyrus and the bilateral lingual gyri (default mode network). Six months after treatment, increased functional connectivity was observed in almost all tremor-associated clusters, except the cluster localized in the left cerebellum. ConclusionsOur findings suggest that tremor-related activity in essential tremor extends beyond the classical cerebellar network, additionally involving areas related to visual processing. Functional restoration of network activity after sonication of the ventral intermediate nucleus is observed within the classical tremor network (cerebellum) and notably also in visual processing areas.

Cognitive deficits and altered functional brain network organization in pediatric brain tumor patients.

Survivors of pediatric brain tumors experience significant cognitive deficits from their diagnosis and treatment. The exact mechanisms of cognitive injury are poorly understood, and validated predictors of long-term cognitive outcome are lacking. Resting state functional magnetic resonance imaging allows for the study of the spontaneous fluctuations in bulk neural activity, providing insight into brain organization and function. Here, we evaluated cognitive performance and functional network architecture in pediatric brain tumor patients. Forty-nine patients (7-18 years old) with a primary brain tumor diagnosis underwent resting state imaging during regularly scheduled clinical visits. All patients were tested with a battery of cognitive assessments. Extant data from 139 typically developing children were used as controls. We found that obtaining high-quality imaging data during routine clinical scanning was feasible. Functional network organization was significantly altered in patients, with the largest disruptions observed in patients who received propofol sedation. Awake patients demonstrated significant decreases in association network segregation compared to controls. Interestingly, there was no difference in the segregation of sensorimotor networks. With a median follow-up of 3.1 years, patients demonstrated cognitive deficits in multiple domains of executive function. Finally, there was a weak correlation between decreased default mode network segregation and poor picture vocabulary score. Future work with longer follow-up, longitudinal analyses, and a larger cohort will provide further insight into this potential predictor.

Brain Microstructure and Brain Function Changes in Space Headache by Head-Down-Tilted Bed Rest.

INTRODUCTION: Several astronauts have experienced severe headaches during spaceflight, but no studies have examined the associated brain microstructure and functional changes. Head-down-tilted bed rest (HDBR) is a well-established method for studying the physical effects of microgravity on the ground. In this study, we analyzed the changes in brain microstructure and function during headache caused by HDBR using diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (R-fMRI).METHODS: We imaged 28 healthy subjects with DTI and R-fMRI in the horizontal supine position and HDBR. Using Tract-Based Spatial Statistics, fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity were compared between the headache and non-headache groups. Additionally, an analysis of functional connectivity (FC) was performed, followed by a correlation analysis between FC and numerical rating scale.RESULTS: HDBR caused headaches in 21 of 28 subjects. DTI analysis showed no significant change in fractional anisotropy after HDBR, whereas axial diffusivity, radial diffusivity, and mean diffusivity increased significantly. R-fMRI analysis showed a significant decrease in FC in several areas after HDBR. The headache group showed significantly higher FC before HDBR, and both groups showed higher FC after HDBR. Correlation analysis showed a positive correlation between FC and numerical rating scale before HDBR but negative after HDBR.DISCUSSION: We demonstrated the image change in the acute phase of space headache by HDBR using DTI and R-fMRI. Changes in brain microstructure and function specific to patients developing headaches may be evaluated by imaging.Goto M, Shibata Y, Ishiyama S, Matsumaru Y, Ishikawa E. Brain microstructure and brain function changes in space headache by head-down-tilted bed rest. Aerosp Med Hum Perform. 2023; 94(9):678-685.

Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data

IntroductionFunctional connectivity in the brain is often studied with blood oxygenation level dependent (BOLD) resting state functional magnetic resonance imaging (rsfMRI), but the BOLD signal is several steps removed from neuronal activity. Arterial spin labeling (ASL), particularly pulsed ASL (PASL), has also the capacity to measure the blood-flow changes in response to activity. In this paper, we investigated the feasibility of extracting major brain networks from PASL data, in contrast with rsfMRI analsyis. Materials and methodsIn this retrospective study, we analyzed a cohort dataset that consists of 21 mild traumatic brain injury (mTBI) patients and 29 healthy controls, which was collected in a previous study. By extracting 10 major brain networks from the data of both PASL and rsfMRI, we contrasted their similarities and differences in the 10 networks extracted from both modalities. ResultsOur data demonstrated that PASL could be used to extract all 10 major brain networks. Eight out of 10 networks demonstrated over 60 ​% similarity to rsfMRI data. Meanwhile, there are similar but not identical changes in networks detected between mTBI patients and healthy controls with both modalities. Notably, the PASL-extracted default mode network (DMN), other than the rsfMRI-extracted DMN, includes some regions known to be associated with the DMN in other studies. It demonstrated that PASL data can be analyzed to identify resting state networks with reasonable reliability, even without rsfMRI data. ConclusionOur analysis provides an opportunity to extract functional connectivity information in heritage datasets in which ASL but not BOLD was collected.

Topological analysis of functional connectivity in Parkinson's disease.

Parkinson's disease (PD) is a clinically heterogeneous disorder, which mainly affects patients' motor and non-motor function. Functional connectivity was preliminary explored and studied through resting state functional magnetic resonance imaging (rsfMRI). Through the topological analysis of 54 PD scans and 31 age-matched normal controls (NC) in the Neurocon dataset, leveraging on rsfMRI data, the brain functional connection and the Vietoris-Rips (VR) complex were constructed. The barcodes of the complex were calculated to reflect the changes of functional connectivity neural circuits (FCNC) in brain network. The 0-dimensional Betti number β0 means the number of connected branches in VR complex. The average number of connected branches in PD group was greater than that in NC group when the threshold δ ≤ 0.7. Two-sample Mann-Whitney U test and false discovery rate (FDR) correction were used for statistical analysis to investigate the FCNC changes between PD and NC groups. In PD group, under threshold of 0.7, the number of FCNC involved was significantly differences and these brain regions include the Cuneus_R, Lingual_R, Fusiform_R and Heschl_R. There are also significant differences in brain regions in the Frontal_Inf_Orb_R and Pallidum_R, when the threshold increased to 0.8 and 0.9 (p < 0.05). In addition, when the length of FCNC was medium, there was a significant statistical difference between the PD group and the NC group in the Neurocon dataset and the Parkinson's Progression Markers Initiative (PPMI) dataset. Topological analysis based on rsfMRI data may provide comprehensive information about the changes of FCNC and may provide an alternative for clinical differential diagnosis.

The restoration ability of a short nap after sleep deprivation on the brain cognitive function: A dynamic functional connectivity analysis.

The brain function impairment induced by sleep deprivation (SD) is temporary and can be fully reversed with sufficient sleep. However, in many cases, long-duration recovery sleep is not feasible. Thus, this study aimed to investigate whether a short nap after SD is sufficient to restore brain function. The data of 38 subjects, including resting state functional magnetic resonance imaging data collected at three timepoints (before SD, after 30 h of SD, and after a short nap following SD) and psychomotor vigilance task (PVT) data, were collected. Dynamic functional connectivity (DFC) analysis was used to evaluate changes in brain states among three timepoints, and four DFC states were distinguished across the three timepoints. Before SD, state 2 (a resting-like FC matrix) was dominant (48.26%). However, after 30 h SD, the proportion of state 2 dramatically decreased, and state 3 (still resting-like, but FCs were weakened) became dominant (40.92%). The increased proportion of state 3 positively correlated with a larger PVT "lapse" time. After a nap, the proportions of states 2 and 3 significantly increased and decreased, respectively, and the change in proportion of state 2 negatively correlated with the change in PVT "lapse" time. Taken together, the results indicated that, after a nap, the cognitive function impairment caused by SD may be reversed to some extent. Additionally, DFC differed among timepoints, which was also associated with the extent of cognitive function impairment after SD (state 3) and the extent of recovery therefrom after a nap (state 2).

Dynamic functional connectivity reveals hyper-connected pattern and abnormal variability in freezing of gait of Parkinson's disease

BackgroundFreezing of gait (FOG) is an intractable and paroxysmal gait disorder that seriously affects the quality of life of Parkinson's disease (PD) patients. Emerging studies have reported abnormal brain activity of distributed networks in FOG patients, whereas ignoring the intrinsic dynamic fluctuations of functional connectivity. The purpose of this study was to examine the dynamic functional network connectivity (dFNC) of PD-FOG. MethodsIn total, 52 PD patients with FOG (PD-FOG), 73 without FOG (PD-NFOG) and 38 healthy controls (HCs) received resting state functional magnetic resonance imaging (rs-fMRI). Sliding window method, k-means clustering and graph theory analysis were employed to retrieve dynamic characteristics of PD-FOG. Partial correlation analysis was conducted to verify whether the dFNC was related to freezing gait severity. ResultsSeven brain networks were identified and configured into seven states. Compared to PD-NFOG, significant spatial pattern was identified for state 2 in freezers, showing increased functional coupling between default mode network (DMN) and basal ganglia network (BG), as a concrete manifestation of increased precuneus-caudate coupling. The mean dwell time and fractional window of state 2 had a positive correlation with FOG severity. Furthermore, PD-FOG group exhibited lower variance in nodal efficiency of independent components (IC) 7 (left precuneus). ConclusionsOur study suggested that aberrant coupling of precuneus-caudate and disrupted variability of precuneus efficiency might be associated to the neural mechanisms of FOG.

Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder.

Suicide is one of the leading causes of death across different age groups. The persistence of suicidal ideation and the progression of suicidal ideations to action could be related to impulsivity, the tendency to act on urges with low temporal latency, and little forethought. Quantifying impulsivity could thus help suicidality estimation and risk assessments in ideation-to-action suicidality frameworks. To model suicidality with impulsivity quantification, we obtained questionnaires, behavioral tests, heart rate variability (HRV), and resting state functional magnetic resonance imaging measurements from 34 participants with mood disorders. The participants were categorized into three suicidality groups based on their Mini-International Neuropsychiatric Interview: none, low, and moderate to severe. Questionnaire and HRV-based impulsivity measures were significantly different between the suicidality groups with higher subscales of impulsivity associated with higher suicidality. A multimodal system to characterize impulsivity objectively resulted in a classification accuracy of 96.77% in the three-class suicidality group prediction task. This study elucidates the relative sensitivity of various impulsivity measures in differentiating participants with suicidality and demonstrates suicidality prediction with high accuracy using a multimodal objective impulsivity characterization in participants with mood disorders.

Subject-specific whole-brain parcellations of nodes and boundaries are modulated differently under 10 Hz rTMS

Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain’s functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.

Resting state functional connectivity as a predictor of brief intervention response in adults with alcohol use disorder: Apreliminary study.

Brief interventions for alcohol use disorder (AUD) are generally efficacious, albeit with variability in response. Resting state functional connectivity (rsFC) may characterize neurobiological indicators that predict the response to brief interventions and is the focus of the current investigation. Forty-six individuals with AUD (65.2% female) completed a resting state functional magnetic resonance imaging (fMRI) scan immediately followed by a brief intervention aimed at reducing alcohol consumption. Positive clinical response was defined as a reduction in alcohol consumption by at least one World Health Organization (WHO) risk drinking level at 3-month follow-up. rsFC was analyzed using seed-to-voxel analysis with seed regions from four networks: salience network, reward network, frontoparietal network, and default mode network. At baseline, responders had greater rsFC between the following seed regions in relation to voxel-based clusters than non-responders: (i) anterior cingulate cortex (ACC) in relation to left postcentral gyrus and right supramarginal gyrus (salience network); (ii) right posterior parietal cortex in relation to right ventral ACC (salience network); (iii) right interior frontal gyrus (IFG) pars opercularis in relation to right cerebellum and right occipital fusiform gyrus (frontoparietal); and (iv) right primary motor cortex in relation to left thalamus (default mode). Lower rsFC in responders vs. nonresponders was seen between the (i) right rostral prefrontal cortex in relation to left IFG pars triangularis (frontoparietal); (ii) right IFG pars triangularis in relation to right cerebellum (frontoparietal); (iii) right IFG pars triangularis in relation to right frontal eye fields and right angular gyrus (frontoparietal); and (iv) right nucleus accumbens in relation to right orbital frontal cortex and right insula (reward). Resting state functional connectivity in the frontoparietal, salience, and reward networks predicts the response to a brief intervention in individuals with AUD and could reflect greater receptivity or motivation for behavior change.