Alteration of multilayer network perspective on gray and white matter connectivity in obstructive sleep apnea.

PURPOSE: The objective of this research was to examine changes in the neural networks of both gray and white matter in individuals with obstructive sleep apnea (OSA) in comparison to those without the condition, employing a comprehensive multilayer network analysis. METHODS: Patients meeting the criteria for OSA were recruited through polysomnography, while a control group of healthy individuals matched for age and sex was also assembled. Utilizing T1-weighted imaging, a morphometric similarity network was crafted to represent gray matter, while diffusion tensor imaging provided structural connectivity for constructing a white matter network. A multilayer network analysis was then performed, employing graph theory methodologies. RESULTS: We included 40 individuals diagnosed with OSA and 40 healthy participants in our study. Analysis revealed significant differences in various global network metrics between the two groups. Specifically, patients with OSA exhibited higher average degree overlap and average multilayer clustering coefficient (28.081 vs. 23.407, p < 0.001; 0.459 vs. 0.412, p = 0.004), but lower multilayer modularity (0.150 vs. 0.175, p = 0.001) compared to healthy controls. However, no significant differences were observed in average multiplex participation, average overlapping strength, or average weighted multiplex participation between the patients with OSA and healthy controls. Moreover, several brain regions displayed notable differences in degree overlap at the nodal level between patients with OSA and healthy controls. CONCLUSION: Remarkable alterations in the multilayer network, indicating shifts in both gray and white matter, were detected in patients with OSA in contrast to their healthy counterparts. Further examination at the nodal level unveiled notable changes in regions associated with cognition, underscoring the effectiveness of multilayer network analysis in exploring interactions across brain layers.

Correlation of diffusion tensor tractography with obstructive sleep apnea severity.

INTRODUCTION: Using correlation tractography, this study aimed to find statistically significant correlations between white matter (WM) tracts in participants with obstructive sleep apnea (OSA) and OSA severity. We hypothesized that changes in certain WM tracts could be related to OSA severity. METHODS: We enrolled 40 participants with OSA who underwent diffusion tensor imaging (DTI) using a 3.0 Tesla MRI scanner. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and quantitative anisotropy (QA)-values were used in the connectometry analysis. The apnea-hypopnea index (AHI) is a representative measure of the severity of OSA. Diffusion MRI connectometry that was used to derive correlational tractography revealed changes in the values of FA, MD, AD, RD, and QA when correlated with the AHI. A false-discovery rate threshold of 0.05 was used to select tracts to conduct multiple corrections. RESULTS: Connectometry analysis revealed that the AHI in participants with OSA was negatively correlated with FA values in WM tracts that included the cingulum, corpus callosum, cerebellum, inferior longitudinal fasciculus, fornices, thalamic radiations, inferior fronto-occipital fasciculus, superior and posterior corticostriatal tracts, medial lemnisci, and arcuate fasciculus. However, there were no statistically significant results in the WM tracts, in which FA values were positively correlated with the AHI. In addition, connectometry analysis did not reveal statistically significant results in WM tracts, in which MD, AD, RD, and QA values were positively or negatively correlated with the AHI. CONCLUSION: Several WM tract changes were correlated with OSA severity. However, WM changes in OSA likely involve tissue edema and not neuronal changes, such as axonal loss. Connectometry analyses are valuable tools for detecting WM changes in sleep disorders.

Temporal Muscle Thickness and Sarcopenia Components in Healthy Adults, Validated through Allgeun Diagnostic Tool.

Sarcopenia, characterized by progressive muscle loss and functional decline, poses significant risks, including falls, impaired daily activities, and increased mortality. We developed Allgeun, a novel device that measures handgrip strength, muscle mass, and physical performance. This study aimed to investigate whether temporal muscle thickness (TMT) could be used as a sarcopenia marker and to evaluate the usability of Allgeun. This prospective study enrolled 28 participants without medical or neurological disorders. They underwent three-dimensional T1-weighted imaging using a 3 Tesla magnetic resonance imaging scanner. TMT was measured based on T1-weighted images by a board-certified neuroradiologist. Allgeun was used to measure the following three key components of sarcopenia: muscle strength (handgrip strength), muscle mass (calf and thigh circumference), and physical performance (five times the chair stand test). Correlation analysis was conducted between TMT and the results of the handgrip strength, calf and thigh circumferences, and chair stand tests. There were moderate positive correlations between TMT and calf circumference (r = 0.413, p = 0.029), thigh circumference (r = 0.486, p = 0.008), and handgrip strength (r = 0.444, p = 0.018). However, no significant correlation was observed between TMT and physical performance (r = -0.000, p = 0.998). Our findings underscore TMT's potential as an indicator of sarcopenia, particularly regarding muscle mass and strength. Additionally, we demonstrated that the new device, Allgeun, is useful for screening and diagnosing the severity of sarcopenia.

Structural connectivity as a predictive factor for perampanel response in patients with epilepsy.

OBJECTIVES: This study aimed to identify clinical characteristics that could predict the response to perampanel (PER) and determine whether structural connectivity is a predictive factor. METHODS: We enrolled patients with epilepsy who received PER and were followed-up for a minimum of 12 months. Good PER responders, who were seizure-free or presented with more than 50 % seizure reduction, were classified separately from poor PER responders who had seizure reduction of less than 50 % or non-responders. A graph theoretical analysis was conducted based on diffusion tensor imaging to calculate network measures of structural connectivity among patients with epilepsy. RESULTS: 106 patients with epilepsy were enrolled, including 26 good PER responders and 80 poor PER responders. Good PER responders used fewer anti-seizure medications before PER administration compared to those by poor PER responders (3 vs. 4; p = 0.042). Early PER treatment was more common in good PER responders than poor PER responders (46.2 vs. 21.3 %, p = 0.014). Regarding cortical structural connectivity, the global efficiency was higher and characteristic path length was lower in good PER responders than in poor PER responders (0.647 vs. 0.635, p = 0.006; 1.726 vs. 1,759, p = 0.008, respectively). For subcortical structural connectivity, the mean clustering coefficient and small-worldness index were higher in good PER responders than in poor PER responders (0.821 vs. 0.791, p = 0.009; 0.597 vs. 0.560, p = 0.009, respectively). CONCLUSION: This study demonstrated that early PER administration can predict a good PER response in patients with epilepsy, and structural connectivity could potentially offer clinical utility in predicting PER response.

Functional Connectivity Alterations in Patients with Post-stroke Epilepsy Based on Source-level EEG and Graph Theory.

We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE.

Sarcopenia and anti-seizure medication response in juvenile myoclonic epilepsy.

INTRODUCTION: This study aimed to investigate the presence of sarcopenia in patients with juvenile myoclonic epilepsy (JME) and the association between sarcopenia and response to anti-seizure medication (ASM) in patients with JME. METHODS: We enrolled 42 patients with JME and 42 healthy controls who underwent brain magnetic resonance imaging with three-dimensional T1-weighted imaging. We measured the temporal muscle thickness (TMT), a radiographic marker for sarcopenia, using T1-weighted imaging. We compared the TMT between patients with JME and healthy controls and analyzed it according to the ASM response in patients with JME. We also performed a receiver operating characteristic (ROC) curve analysis to evaluate how well the TMT differentiated the groups. RESULTS: The TMT in patients with JME did not differ from that in healthy controls (9.630 vs. 9.956 mm, p = .306); however, ASM poor responders had a lower TMT than ASM good responders (9.109 vs. 10.104 mm, p = .023). ROC curve analysis revealed that the TMT exhibited a poor performance in differentiating patients with JME from healthy controls, with an area under the ROC curve of .570 (p = .270), but good performance in differentiating between ASM good and poor responders, with an area under the ROC curve of .700 (p = .015). CONCLUSION: The TMT did not differ between patients with JME and healthy controls; however, it was reduced in ASM poor responders compared to ASM good responders, suggesting a link between ASM response and sarcopenia in patients with JME. TMT can be used to investigate sarcopenia in various neurological disorders.

Brain connectivity in status epilepticus as a predictor of outcome: A diffusion tensor imaging study.

BACKGROUND AND PURPOSE: We aimed to explore structural connectivity in status epilepticus. METHODS: We enrolled participants who underwent diffusion tensor imaging. We applied graph theory to investigate structural connectivity. We compared the structural connectivity measures between patients and healthy controls and between patients with poor (modified Rankin Scale [mRS] >3) and good (mRS ≤3) admission outcomes. RESULTS: We enrolled 28 patients and 31 healthy controls (age 65.5 vs.62.0 years, p = .438). Of these patients, 16 and 12 showed poor and good admission outcome (age 65.5 vs.62.0 years, p = .438). The assortative coefficient (-0.113 vs. -0.121, p = .021), mean clustering coefficient (0.007 vs.0.006, p = .009), global efficiency (0.023 vs.0.020, p = .009), transitivity (0.007 vs.0.006, p = .009), and small-worldness index (0.006 vs.0.005, p = .021) were higher in patients with status epilepticus than in healthy controls. The assortative coefficient (-0.108 vs. -0.119, p = .042), mean clustering coefficient (0.007 vs.0.006, p = .042), and transitivity (0.008 vs.0.007, p = .042) were higher in patients with poor admission outcome than in those with good admission outcome. MRS score was positively correlated with structural connectivity measures, including the assortative coefficient (r = 0.615, p = .003), mean clustering coefficient (r = 0.544, p = .005), global efficiency (r = 0.515, p = .007), transitivity (r = 0.547, p = .007), and small-worldness index (r = 0.435, p = .024). CONCLUSION: We revealed alterations in structural connectivity, showing increased integration and segregation in status epilepticus, which might be related with neuronal synchronization. This effect was more pronounced in patients with a poor admission outcome, potentially reshaping our understanding for comprehension of status epilepticus mechanisms and the development of more targeted treatments.

Identification of amnestic mild cognitive impairment by structural and functional MRI using a machine-learning approach.

OBJECTIVE: The importance of early treatment for mild cognitive impairment (MCI) has been extensively shown. However, classifying patients presenting with memory complaints in clinical practice as having MCI vs normal results is difficult. Herein, we assessed the feasibility of applying a machine learning approach based on structural volumes and functional connectomic profiles to classify the cognitive levels of cognitively unimpaired (CU) and amnestic MCI (aMCI) groups. We further applied the same method to distinguish aMCI patients with a single memory impairment from those with multiple memory impairments. METHODS: Fifty patients with aMCI were enrolled and classified as having either verbal or visual-aMCI (verbal or visual memory impairment), or both aMCI (verbal and visual memory impairments) based on memory test results. In addition, 26 CU patients were enrolled in the control group. All patients underwent structural T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. We obtained structural volumes and functional connectomic profiles from structural and functional MRI, respectively, using graph theory. A support vector machine (SVM) algorithm was employed, and k-fold cross-validation was performed to discriminate between groups. RESULTS: The SVM classifier based on structural volumes revealed an accuracy of 88.9% at classifying the cognitive levels of patients with CU and aMCI. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 92.9%. In the classification of verbal or visual-aMCI (n = 22) versus both aMCI (n = 28), the SVM classifier based on structural volumes revealed a low accuracy of 36.7%. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 53.1%. CONCLUSION: Structural volumes and functional connectomic profiles obtained using a machine learning approach can be used to classify cognitive levels to distinguish between aMCI and CU patients. In addition, combining the functional connectomic profiles with structural volumes results in a better classification performance than the use of structural volumes alone for identifying both "aMCI versus CU" and "verbal- or visual-aMCI versus both aMCI" patients.

Alterations in the multilayer network in patients with rapid eye movement sleep behaviour disorder.

This study aimed to reveal the pathophysiology of isolated rapid eye movement sleep behaviour disorder (RBD) in patients using multilayer network analysis. Participants eligible for isolated RBD were included and verified via polysomnography. Both iRBD patients and healthy controls underwent brain MRI, including T1-weighted imaging and diffusion tensor imaging. Grey matter matrix was derived from T1-weighted images using a morphometric similarity network. White matter matrix was formed from diffusion tensor imaging-based structural connectivity. Multilayer network analysis of grey and white matter was performed using graph theory. We studied 29 isolated RBD patients and 30 healthy controls. Patients exhibited a higher average overlap degree (27.921 vs. 23.734, p = 0.002) and average multilayer clustering coefficient (0.474 vs. 0.413, p = 0.002) compared with controls. Additionally, several regions showed significant differences in the degree of overlap and multilayer clustering coefficient between patients with isolated RBD and healthy controls at the nodal level. The degree of overlap in the left medial orbitofrontal, left posterior cingulate, and right paracentral nodes and the multilayer clustering coefficients in the left lateral occipital, left rostral middle frontal, right fusiform, right inferior posterior parietal, and right parahippocampal nodes were higher in patients with isolated RBD than in healthy controls. We found alterations in the multilayer network at the global and nodal levels in patients with isolated RBD, and these changes may be associated with the pathophysiology of isolated RBD. Multilayer network analysis can be used widely to explore the mechanisms underlying various neurological disorders.

Brain network reconstruction of abnormal functional connectivity in Lennox-Gastaut syndrome according to drug responsiveness: A retrospective study.

OBJECTIVE: Functional network effects of resective or palliative epilepsy surgery in Lennox-Gastaut syndrome (LGS) patients are different according to the seizure outcome. This study aimed to clarify whether the response to antiseizure medications (ASM) can affect to alteration of brain network connectivity. METHODS: In this retrospective study, 37 patients with LGS who underwent 1st electroencephalography (EEG) and 40 healthy controls were enrolled. Among them, 24 LGS patients had follow-up EEG data and were classified as drug responders and non-responders according to the ASM response. Graphical theoretical analysis was used to assess functional connectivity using resting-state EEG. RESULTS: The 1st EEG showed a decreased radius in patients with LGS compared with that in healthy controls (3.987 vs. 4.279, P = 0.003). Follow-up EEG data of patients with LGS revealed significant differences in functional connectivity depending on the ASM response. On follow-up EEG, non-responders (n = 11) demonstrated significant increases in global network parameters, whereas responders (n = 13) showed no significant difference in functional connectivity compared with healthy controls. CONCLUSIONS: The functional connectivity patterns in patients with LGS differed from those in healthy controls. Functional connectivity in drug-responsive patients with LGS tended to preserve the network of brain connections in a pattern similar to that in healthy controls, whereas non-responders showed more disrupted functional connectivity.

Increased Thalamic Connectivity in Juvenile Myoclonic Epilepsy Based on Electroencephalography Source-Level Analysis.

Background: This study investigated alterations in the intrinsic thalamic network of patients with juvenile myoclonic epilepsy (JME) based on an electroencephalography (EEG) source-level analysis. Materials and Methods: We enrolled patients newly diagnosed with JME as well as healthy controls. The assessments were conducted in the resting state. We computed sources based on the scalp electrical potentials using a minimum-norm imaging method and a standardized, low-resolution, brain electromagnetic tomography approach. To create a functional connectivity matrix, we used the Talairach atlas to define thalamic nodes and applied the coherence method to measure brain synchronization as edges. We then calculated the intrinsic thalamic network using graph theory. We compared the intrinsic thalamic network of patients with JME with those of healthy controls. Results: This study included 67 patients with JME and 66 healthy controls. EEG source-level analysis revealed significant differences in the intrinsic thalamic networks between patients with JME and healthy controls. The measures of functional connectivity (radius, diameter, and characteristic path length) were significantly lower in patients with JME than in healthy controls (radius: 2.769 vs. 3.544, p = 0.015; diameter: 4.464 vs. 5.443, p = 0.024; and characteristic path length: 2.248 vs. 2.616, p = 0.046). Conclusions: We demonstrated alterations in the intrinsic thalamic network in patients with JME compared with those in healthy controls based on the EEG source-level analysis. These findings indicated increased thalamic connectivity in the JME group. These intrinsic thalamic network changes may be related to the pathophysiology of JME.

Sarcopenia in patients with isolated rapid eye movement sleep behavior disorder.

OBJECTIVES: Evaluating of sarcopenia is important for promoting healthy aging, preventing functional decline, reducing the risk of falls and fractures, and improving overall quality of life. This study aimed to investigate sarcopenia in patients with isolated rapid eye movement sleep behavior disorder (RBD) using temporal muscle thickness (TMT) measurement. METHODS: This investigation was retrospectively conducted at a single tertiary hospital. We recruited patients diagnosed with isolated RBD confirmed by polysomnography and clinical history and healthy participants as controls. Patients with isolated RBD and healthy controls underwent brain MRI scans, including three-dimensional T1-weighted imaging. We measured TMT, a radiographic marker of sarcopenia, based on the T1-weighted imaging. We compared the TMT between the groups and performed receiver operating characteristic (ROC) curve analysis to evaluate how well the TMT differentiated patients with isolated RBD from healthy controls. We also conducted a correlation analysis between the TMT and clinical factors. RESULTS: Our study included 28 patients with isolated RBD and 30 healthy controls. There was a significant difference in the TMT of both groups. The TMT was reduced in patients with isolated RBD than in healthy controls (11.843 vs. 10.420 mm, p = 0.002). In the ROC curve analysis, the TMT exhibited good performance in differentiating patients with isolated RBD from healthy controls, with an area under the curve of 0.708. Furthermore, age was negatively correlated with TMT in patients with isolated RBD (r = -0.453, p = 0.015). CONCLUSION: We demonstrate that TMT is reduced in patients with isolated RBD compared with healthy controls, confirming sarcopenia in patients with isolated RBD. The result suggests an association between neurodegeneration and sarcopenia. TMT can be used to evaluate sarcopenia in sleep disorders.

Cerebellar volumes and the intrinsic cerebellar network in patients with obstructive sleep apnea.

PURPOSE: This research aimed to explore changes in both cerebellar volume and the intrinsic cerebellar network in patients with obstructive sleep apnea (OSA). METHODS: Newly diagnosed OSA patients and healthy controls were included in the study. All participants underwent three-dimensional T1-weighted imaging using a 3-T MRI scanner. Cerebellar volumes, both overall and subdivided, were quantified using the ACAPULCO program. The intrinsic cerebellar network was assessed using the BRAPH program, which applied graph theory to the cerebellar volume subdivision. Comparisons were drawn between the patients with OSA and healthy controls. RESULTS: The study revealed that the 26 patients with OSA exhibited a notably lower total cerebellar volume compared to the 28 healthy controls (8.330 vs. 9.068%, p < 0.001). The volume of the left lobule VIIB was reduced in patients with OSA compared to healthy controls (0.339 vs. 0.407%, p = 0.001). Among patients with OSA, there was a negative correlation between the volume of the left lobule X and apnea-hypopnea index during non-rapid eye movement sleep (r = - 0.536, p = 0.005). However, no significant differences were observed in the intrinsic cerebellar network between patients and healthy controls. CONCLUSION: This study established that patients with OSA exhibited decreased total cerebellar volumes and particularly reduced volumes in subdivisions such as the left lobule VIIB compared to healthy controls. These findings suggest potential involvement of the cerebellum in the underlying mechanisms of OSA.

Structural connectivity as a predictive factor for responsiveness to levetiracetam treatment in epilepsy.

PURPOSE: To investigate whether structural connectivity or glymphatic system function is a potential predictive factor for levetiracetam (LEV) response in patients with newly diagnosed epilepsy. METHODS: We enrolled patients with newly diagnosed epilepsy who were administered LEV as initial monotherapy and underwent diffusion tensor imaging (DTI) at diagnosis. We categorized the patients into drug response. We used graph theory to calculate the network measures for structural connectivity based on the DTI scans in patients with epilepsy. Additionally, we evaluated glymphatic system function by calculating the DTI analysis along the perivascular space (DTI-ALPS) index based on DTI scans. RESULTS: We enrolled 84 patients with epilepsy. The clinical factors and DTI-ALPS index did not differ between the groups. However, some of the structural connectivity measures significantly differ between the groups. The poor responders exhibited a higher mean clustering coefficient, global efficiency, and small-worldness index than the good responders (p = 0.003, p = 0.048, and p = 0.038, respectively). In the receiver operating characteristic curve analysis, the mean clustering coefficient exhibited the highest performance in predicting the responsiveness to LEV (area under the curve of 0.677). In the multiple logistic regression analysis, the mean clustering coefficient of the structural connectivity measures was the only significant predictor of LEV response (p = 0.014). Furthermore, in the survival analysis, the mean clustering coefficient was the only significant predictor of LEV response (p = 0.026). CONCLUSION: We demonstrated that structural connectivity is a potential predictive factor for responsiveness to LEV treatment in patients with newly diagnosed epilepsy.

Anti-seizure medication response and the glymphatic system in patients with focal epilepsy.

BACKGROUND AND PURPOSE: We aimed to evaluate (i) glymphatic system function in patients with focal epilepsy in comparison with healthy controls, and (ii) the association between anti-seizure medication (ASM) response and glymphatic system function by using diffusion tensor image analysis along the perivascular space (DTI-ALPS). METHODS: We retrospectively enrolled 100 patients with focal epilepsy who had normal brain magnetic resonance imaging (MRI) findings, and classified them as "poor" or "good" ASM responders according to their seizure control at the time of brain MRI. We also included 79 age- and sex-matched healthy controls. All patients and healthy controls underwent conventional brain MRI and diffusion tensor imaging. The DTI-ALPS index was calculated using the DSI studio program. RESULTS: Of the 100 patients with focal epilepsy, 38 and 62 were poor and good ASM responders, respectively. The DTI-ALPS index differed significantly between patients with focal epilepsy and healthy controls and was significantly lower in patients with focal epilepsy (1.55 vs. 1.70; p < 0.001). The DTI-ALPS index also differed significantly according to ASM response and was lower in poor ASM responders (1.48 vs. 1.59; p = 0.047). Furthermore, the DTI-ALPS index was negatively correlated with age (r = -0.234, p = 0.019) and duration of epilepsy (r = -0.240, p = 0.016) in patients with focal epilepsy. CONCLUSION: Our study is the first to identify, in focal epilepsy patients, a greater reduction in glymphatic system function among poor ASM responders compared to good responders. To confirm our results, further prospective multicenter studies with large sample sizes are needed.

Neuroimaging correlation with EEG in status epilepticus.

PURPOSE: This study was to correlate EEG patterns with peri­ictal perfusion CT abnormality (PCA) or peri­ictal MR abnormality (PMA) in patients with status epilepticus (SE). METHODS: This is a retrospective study done with SE patients from January 2016 to December 2021. We defined the PCA as single or multi-territorial cortical and/or thalamic hyper-perfusion. The PMA was defined as increased signal intensity in multiple brain regions including the cortex and subcortical regions. EEG patterns were categorized into electrographic seizure (ESz)/electroclinical seizure (ECSz), ictal-interictal continuum (IIC), and lateralized periodic discharges (LPDs) per the American Clinical Neurophysiology Society's guideline. We analyzed the association between the patterns of EEG and the presence of PCA or PMA. RESULTS: Among 73 patients, 26 % (19/73) showed PCA and 25 % (18/73) demonstrated PMA. The patterns of EEG were as follows; ESz/ECSz in 25 % (18/73), IIC in 34 % (25/73), and LPD in 12 % (9/73). There was a significant correlation between the patterns of EEG and the presence of PMA, but not PCA. 48 % (12/25) had both PMA and PCA whereas 52 % (13/25) showed either PMA (6/25) or PCA (7/25). CONCLUSION: Although PCA did not reveal an electro-radiographical correlation, PMA was strongly linked to ESz, ECSz, IIC, and LPD.

Altered cerebellar volumes and intrinsic cerebellar networks in patients with transient global amnesia.

This study aimed to investigate the differences in cerebellar volumes and intrinsic cerebellar networks between patients with transient global amnesia (TGA) and healthy controls. We retrospectively enrolled patients with TGA and age- and sex-matched healthy controls. We used three-dimensional T1-weighted imaging at the time of TGA diagnosis to obtain cerebellar volumes, and the intrinsic cerebellar network was calculated by applying graph theory based on cerebellar volumes. The nodes were defined as individual cerebellar volumes, and edges as partial correlations, controlling for the effects of age and sex. The cerebellar volumes and intrinsic cerebellar networks were compared between the two groups. We enrolled 44 patients with TGA and 47 healthy controls. The volume of the left cerebellar white matter in patients with TGA was significantly lower than that in healthy controls (1.0328 vs. 1.0753%, p = 0.0094). In addition, there were significant differences in intrinsic cerebellar networks between the two groups. The small-worldness index in patients with TGA was higher than that in the healthy controls (0.951 vs. 0.880, p = 0.038). In the correlation analysis, the volumes of the right cerebellar cortex and lobules VIIIB were significantly correlated with age in patients with TGA (r = -0.323, p = 0.033; r = -0.313, p = 0.038, respectively). Patients with TGA exhibit alterations in cerebellar volumes and intrinsic cerebellar networks compared with healthy controls. These findings may contribute to a better understanding of the pathophysiology of the TGA.

Multilayer network changes in patients with migraine.

INTRODUCTION: To investigate changes in the multilayer network in patients with migraine compared to healthy controls. METHODS: This study enrolled 82 patients with newly diagnosed migraine without aura and 53 healthy controls. Brain magnetic resonance imaging (MRI) was conducted using a 3-tesla MRI scanner, including three-dimensional T1-weighted and diffusion tensor imaging (DTI). A gray matter layer matrix was created with a morphometric similarity network using T1-weighted imaging and the FreeSurfer program. A white matter layer matrix was also created with structural connectivity using the DTI studio (DSI) program. A multilayer network analysis was then performed by applying graph theory using the BRAPH program. RESULTS: Significant changes were observed in the multilayer network at the global level in patients with migraines compared to the healthy controls. The multilayer modularity (0.177 vs. 0.160, p = .0005) and average multiplex participation (0.934 vs. 0.924, p = .002) were higher in patients with migraines than in the healthy controls. In contrast, the average multilayer clustering coefficient (0.406 vs. 0.461, p = .0005), average overlapping strength (56.061 vs. 61.676, p = .0005), and average weighted multiplex participation (0.847 vs. 0.878, p = .0005) were lower in patients with migraine than in the healthy controls. In addition, several regions showed significant changes in the multilayer network at the nodal level, including multiplex participation, multilayer clustering coefficients, overlapping strengths, and weighted multiplex participation. CONCLUSION: This study demonstrated significant changes in the multilayer network in patients with migraines compared to healthy controls. This could aid an understanding of the complex brain network in patients with migraine and may be associated with the pathophysiology of migraines. Patients with migraine show multilayer network changes in widespreading brain regions compared to healthy controls, and specific brain areas seem to play a hub role for pathophysiology of the migraine.

Structural and functional multilayer network analysis in restless legs syndrome patients.

The combination of brain structural and functional connectivity offers complementary insights into its organisation. Multilayer network analysis explores various relationships across different layers within a single system. We aimed to investigate changes in the structural and functional multilayer network in 69 patients with primary restless legs syndrome (RLS) compared with 50 healthy controls. Participants underwent diffusion tensor imaging (DTI) and resting state-functional magnetic resonance imaging (rs-fMRI) using a three-tesla MRI scanner. We constructed a structural connectivity matrix derived from DTI using a DSI program and made a functional connectivity matrix based on rs-fMRI using an SPM program and CONN toolbox. A multilayer network analysis, using BRAPH program, was then conducted to assess the connectivity patterns in both groups. At the global level, significant differences there were between the patients with RLS and healthy controls. The average multiplex participation was lower in patients with RLS than in healthy controls (0.804 vs. 0.821, p = 0.042). Additionally, several regions showed significant differences in the nodal level in multiplex participation between patients with RLS and healthy controls, particularly the frontal and temporal lobes. The regions affected included the inferior frontal gyrus, medial orbital gyrus, precentral gyrus, rectus gyrus, insula, superior and inferior temporal gyrus, medial and lateral occipitotemporal gyrus, and temporal pole. These results represent evidence of diversity in interactions between structural and functional connectivity in patients with RLS, providing a more comprehensive understanding of the brain network in RLS. This may contribute to a precise diagnosis of RLS, and aid the development of a biomarker to track treatment effectiveness.