Changes in Brain Functional Network Connectivity in Adult Moyamoya Diseases

Impact of Anesthesia on Brain Functional Networks in Moyamoya Disease and Spinal Lesions

AIMS

To analyze the effects of intravenous propofol combined with remifentanil on whole-brain functional networks in patients with ischemic moyamoya disease (IMMD) and intraspinal space-occupying lesions (SOLs) using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Ten patients with IMMD and 10 sex- and age-matched patients with lumbar SOL (normal cerebrovascular findings on preoperative MRI) were recruited. General anesthesia was administered using propofol and remifentanil. rs-fMRI imaging was performed in both awake and anesthetized states. Whole-brain functional network in different states was constructed based on graph theory tools.

RESULTS

In awake patients with IMMD, significant reductions in nodal strength (NS) were observed in the default mode network (DMN), sensorimotor network, and frontoparietal control network (FPN), compared to patients with SOL. Nodal efficiency (NE) showed further significant network declines. Under anesthesia, patients with IMMD: (1) exhibited disease-specific decreases in NS and NE across several networks, potentially reflecting underlying cerebral pathology. (2) Propofol's effects also contributed to significant NS and NE reductions in several brain regions. Changes before and after anesthesia in patients with IMMD were significantly decreased in specific regions (discussed in detail) per analysis of NS versus NE. DMN connectivity correlated moderately with Montreal Cognitive Assessment scores.

CONCLUSIONS

Reduced whole-brain functional connectivity in patients with IMMD before anesthesia was similar to the alterations caused by systemic intravenous drugs administered after anesthesia.

TRIAL REGISTRATION

ChiCTR2300075268.

Machine learning-based algorithm of drug-resistant prediction in newly diagnosed patients with temporal lobe epilepsy

OBJECTIVES

To develop a predicted algorithm for drug-resistant epilepsy (DRE) in newly diagnosed temporal lobe epilepsy (TLE) patients.

METHODS

A total of 139 newly diagnosed TLE patients were prospectively enrolled, and long-term video EEG monitoring was recorded. Clinical evaluations, including seizure frequency and antiseizure medications (ASMs) usage, were collected and prospectively followed up for 24 months. Interictal EEG data were used for feature extraction, identifying 216 EEG network features. Traditional machine learning and ensemble learning techniques were employed to predict DRE outcomes.

RESULTS

Over two years, TLE patients with DRE exhibited significant EEG differences, particularly in frontotemporal θ-band networks, characterized by increased connectivity metrics such as phase lag index (P = 0.000), etc. The predictive algorithm based on EEG features achieved accuracies between 59.2 %-84.6 % (AUC: 0.60-0.87). When compared to the whole brain, EEG features of the frontotemporal network showed improved classification performance in Naïve Bayes (P = 0.032), Tree Bagger (P = 0.021), and Subspace Discriminant (P = 0.022) models. The ensemble learning technique (Tree Bagger) delivered the best prediction results, achieving 91.5 % accuracy, 97 % sensitivity, 81 % specificity, and AUC of 0.92.

CONCLUSIONS

Increased frontotemporal EEG connectivity was observed in TLE patients with 2-year DRE. A predictive model based on routine EEG provides an accessible method for forecasting ASMs efficacy.

SIGNIFICANCE

This study highlights the clinical utility of EEG-based algorithms in identifying DRE early, aiding personalized treatment strategies and improving patient outcomes.

Reorganization of gray matter networks in patients with Moyamoya disease

Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function, but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed. Compared to HCs, MMD patients exhibited decreased clustering coefficient (Cp) (P = 0.006) and local efficiency (Eloc) (P = 0.013). Ischemic patients showed decreased Eloc and increased characteristic path length (Lp) compared to asymptomatic and hemorrhagic patients (P < 0.001, Bonferroni corrected). MMD patients had significant regional abnormalities, including decreased degree centrality (DC) in the left medial orbital superior frontal gyrus, left orbital inferior frontal gyrus, and right calcarine fissure and surrounding cortex (P < 0.05, FDR corrected). Increased DC was found in bilateral olfactory regions, with higher betweenness centrality (BC) in the right median cingulate, paracingulate fusiform gyrus, and left pallidum (P < 0.05, FDR corrected). Ischemic patients had lower BC in the right hippocampus compared to hemorrhagic patients, while hemorrhagic patients had decreased DC in the right triangular part of the inferior frontal gyrus compared to asymptomatic patients (P < 0.05, Bonferroni corrected). Subnetworks related to MMD and white matter hyperintensity volume were identified. There is significant reorganization of gray matter networks in patients compared to HCs, and among different types of patients. Gray matter networks can effectively detect MMD-related brain structural changes.

TdCCA with Dual-Modal Signal Fusion: Degenerated Occipital and Frontal Connectivity of Adult Moyamoya Disease for Early Identification

Cognitive impairment in patients with moyamoya disease (MMD) manifests earlier than clinical symptoms. Early identification of brain connectivity changes is essential for uncovering the pathogenesis of cognitive impairment in MMD. We proposed a temporally driven canonical correlation analysis (TdCCA) method to achieve dual-modal synchronous information fusion from electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) for exploring the differences in brain connectivity between MMD and normal control groups. The dual-modal fusion features were extracted based on the imaginary part of coherence of the EEG signal (EEG iCOH) and the Pearson correlation coefficients of the fNIRS signal (fNIRS COR) in the resting and working memory state. The machine learning model showed that the accuracy of TdCCA method reached 97%, far higher than single-modal features and feature-level fusion CCA method. Brain connectivity analysis revealed a significant reduction in the strength of the connections between the right occipital lobe and frontal lobes (EEG iOCH: p = 0.022, fNIRS COR p = 0.011) in MMD. These differences reflected the impaired transient memory and executive function in MMD patients. This study contributes to the understanding of the neurophysiological nature of cognitive impairment in MMD and provides a potential adjuvant early identification method for individuals with chronic cerebral ischemia.

Applications of Functional Magnetic Resonance Imaging to the Study of Functional Connectivity and Activation in Neurological Disease: A Scoping Review of the Literature

BACKGROUND

Functional magnetic resonance imaging (fMRI) has transformed our understanding of brain's functional architecture, providing critical insights into neurological diseases. This scoping review synthesizes the current landscape of fMRI applications across various neurological domains, elucidating the evolving role of both task-based and resting-state fMRI in different settings.

METHODS

We conducted a comprehensive scoping review following the Preferred Reporting Items for Systematic Review and Meta-Analyses Extension for Scoping Reviews guidelines. Extensive searches in Medline/PubMed, Embase, and Web of Science were performed, focusing on studies published between 2003 and 2023 that utilized fMRI to explore functional connectivity and regional activation in adult patients with neurological conditions. Studies were selected based on predefined inclusion and exclusion criteria, with data extracted.

RESULTS

We identified 211 studies, covering a broad spectrum of neurological disorders including mental health, movement disorders, epilepsy, neurodegeneration, traumatic brain injury, cerebrovascular accidents, vascular abnormalities, neurorehabilitation, neuro-critical care, and brain tumors. The majority of studies utilized resting-state fMRI, underscoring its prominence in identifying disease-specific connectivity patterns. Results highlight the potential of fMRI to reveal the underlying pathophysiological mechanisms of various neurological conditions, facilitate diagnostic processes, and potentially guide therapeutic interventions.

CONCLUSIONS

fMRI serves as a powerful tool for elucidating complex neural dynamics and pathologies associated with neurological diseases. Despite the breadth of applications, further research is required to standardize fMRI protocols, improve interpretative methodologies, and enhance the translation of imaging findings to clinical practice. Advances in fMRI technology and analytics hold promise for improving the precision of neurological assessments and interventions.

Preoperative Brain Functional Connectivity Improve Predictive Accuracy of Outcomes After Revascularization in Moyamoya Disease

BACKGROUND

In patients with moyamoya disease (MMD), focal impairments in cerebral hemodynamics are often inconsistent with patients' clinical prognoses. Evaluation of entire brain functional networks may enable predicting MMD outcomes after revascularization.

OBJECTIVE

To investigate whether preoperative brain functional connectivity could predict outcomes after revascularization in MMD.

METHODS

We included 34 patients with MMD who underwent preoperative MRI scanning and combined revascularization surgery. We used region of interest analyses to explore the differences in functional connectivity for 90 paired brain regions between patients who had favorable outcomes 1 year after surgery (no recurrent stroke, with improved preoperative symptoms, or modified Rankin Scale [mRS]) and those who had unimproved outcomes (recurrent stroke, persistent symptoms, or declined mRS). Variables, including age, body mass index, mRS at admission, Suzuki stage, posterior cerebral artery involvement, and functional connectivity with significant differences between the groups, were included in the discriminant function analysis to predict patient outcomes.

RESULTS

Functional connectivity between posterior cingulate cortex and paracentral lobule within the right hemisphere, and interhemispheric connection between superior parietal gyrus and middle frontal gyrus, precuneus and middle cingulate cortex, cuneus and precuneus, differed significantly between the groups (P < .001, false discovery rate corrected) and had the greatest discriminant function in the prediction model. Although clinical characteristics of patients with MMD showed great accuracy in predicting outcomes (64.7%), adding information on functional connections improved accuracy to 91.2%.

CONCLUSION

Preoperative functional connectivity derived from rs-fMRI may be an early hallmark for predicting patients' prognosis after revascularization surgery for MMD.

Frontotemporal phase lag index correlates with seizure severity in patients with temporal lobe epilepsy

Objectives

To find the brain network indicators correlated with the seizure severity in temporal lobe epilepsy (TLE) by graph theory analysis.

Methods

We enrolled 151 patients with TLE and 36 age- and sex-matched controls with video-EEG monitoring. The 90-s interictal EEG data were acquired. We adopted a network analyzing pipeline based on graph theory to quantify and localize their functional networks, including weighted classical network, minimum spanning tree, community structure, and LORETA. The seizure severities were evaluated using the seizure frequency, drug-resistant epilepsy (DRE), and VA-2 scores.

Results

Our network analysis pipeline showed ipsilateral frontotemporal activation in patients with TLE. The frontotemporal phase lag index (PLI) values increased in the theta band (4-7 Hz), which were elevated in patients with higher seizure severities (P < 0.05). Multivariate linear regression analysis showed that the VA-2 scores were independently correlated with frontotemporal PLI values in the theta band (β = 0.259, P = 0.001) and age of onset (β = -0.215, P = 0.007).

Significance

This study illustrated that the frontotemporal PLI in the theta band independently correlated with seizure severity in patients with TLE. Our network analysis provided an accessible approach to guide the treatment strategy in routine clinical practice.

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.

Mental Calculation Drives Reliable and Weak Distant Connectivity While Music Listening Induces Dense Local Connectivity

Mathematical calculation usually requires sustained attention to manipulate numbers in the mind, while listening to light music has a relaxing effect on the brain. The differences in the corresponding brain functional network topologies underlying these behaviors remain rarely known. Here, we systematically examined the brain dynamics of four behaviors (resting with eyes closed and eyes open, tasks of music listening and mental calculation) using 64-channel electroencephalogram (EEG) recordings and graph theory analysis. We developed static and dynamic minimum spanning tree (MST) analysis method and demonstrated that the brain network topology under mental calculation is a more line-like structure with less tree hierarchy and leaf fraction; however, the hub regions, which are mainly located in the frontal, temporal and parietal regions, grow more stable over time. In contrast, music-listening drives the brain to exhibit a highly rich network of star structure, and the hub regions are mainly located in the posterior regions. We then adopted the dynamic dissimilarity of different MSTs over time based on the graph Laplacian and revealed low dissimilarity during mental calculation. These results suggest that the human brain functional connectivity of individuals has unique dynamic diversity and flexibility under various behaviors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-021-00027-w.

Sparsely Wiring Connectivity in the Upper Beta Band Characterizes the Brains of Top Swimming Athletes

Human brains are extremely energy costly in neural connections and activities. However, it is unknown what is the difference in the brain connectivity between top athletes with long-term professional trainings and age-matched controls. Here we ask whether long-term training can lower brain-wiring cost while have better performance. Since elite swimming requires athletes to move their arms and legs at different tempos in time with high coordination skills, we selected an eye-hand-foot complex reaction (CR) task to examine the relations between the task performance and the brain connections and activities, as well as to explore the energy cost-efficiency of top athletes. Twenty-one master-level professional swimmers and 23 age-matched non-professional swimmers as controls were recruited to perform the CR task with concurrent 8-channel EEG recordings. Reaction time and accuracy of the CR task were recorded. Topological network analysis of various frequency bands was performed using the phase lag index (PLI) technique to avoid volume conduction effects. The wiring number of connections and mean frequency were calculated to reflect the wiring and activity cost, respectively. Results showed that professional athletes demonstrated better eye-hand-foot coordination than controls when performing the CR task, indexing by faster reaction time and higher accuracy. Comparing to controls, athletes' brain demonstrated significantly less connections and weaker correlations in upper beta frequency band between the frontal and parietal regions, while demonstrated stronger connectivity in the low theta frequency band between sites of F3 and Cz/C4. Additionally, athletes showed highly stable and low eye-blinking rates across different reaction performance, while controls had high blinking frequency with high variance. Elite athletes' brain may be characterized with energy efficient sparsely wiring connections in support of superior motor performance and better cognitive performance in the eye-hand-foot complex reaction task.