Brain networks in patients with isolated or recurrent transient global amnesia

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.

Involvement of the default mode network in patients with transient global amnesia: multilayer network

INTRODUCTION

We aimed to investigate the alterations in the multilayer network in patients with transient global amnesia (TGA).

METHODS

We enrolled 124 patients with TGA and 80 healthy controls. Both patients with TGA and healthy controls underwent a three-teslar brain magnetic resonance imaging (MRI). A gray matter layer matrix was created using a morphometric similarity network derived from the T1-weighted imaging, and a white matter layer matrix was constructed using structural connectivity based on the diffusion tensor imaging. A multilayer network analysis was performed by applying graph theoretical analysis.

RESULTS

There were no significant differences in global network measures between the groups. However, several regions, related to the default mode network, showed significant differences in nodal network measures between the groups. Multi-richness in the left pars opercularis, multi-rich-club degree in the right posterior cingulate gyrus, and weighted multiplex participation in the right posterior cingulate gyrus were higher in patients with TGA compared with healthy controls (15.47 vs. 12.26, p = 0.0005; 41.68 vs. 37.16, p = 0.0005; 0.90 vs. 0.80, p = 0.0005; respectively). The multiplex core-periphery in the left precuneus was higher (0.96 vs. 0.84, p = 0.0005), whereas that in the transverse temporal gyrus was lower in patients with TGA compared with healthy controls (0.00 vs. 0.02, p = 0.0005).

CONCLUSION

We newly find the alterations in the multilayer network in patients with TGA compared with healthy controls, which shows the involvement of the default mode network. These changes may be related to the pathophysiology of TGA.

Guideline "Transient Global Amnesia (TGA)" of the German Society of Neurology (Deutsche Gesellschaft für Neurologie): S1-guideline

INTRODUCTION

In 2022 the DGN (Deutsche Gesellschaft für Neurologie) published an updated Transient Global Amnesia (TGA) guideline. TGA is characterized by a sudden onset of retrograde and anterograde amnesia for a period of one to a maximum of 24 h (with an average of 6 to 8 h). The incidence is estimated between 3 and 8 per 100,000 population/year. TGA is a disorder that occurs predominantly between 50 and 70 years.

RECOMMENDATIONS

The diagnosis of TGA should be made clinically. In case of an atypical clinical presentation or suspicion of a possible differential diagnosis, further diagnostics should be performed immediately. The detection of typical unilateral or bilateral punctate DWI/T2 lesions in the hippocampus (especially the CA1 region) in a proportion of patients proves TGA. The sensitivity of MRI is considered higher when performed between 24 and 72 h after onset. If additional DWI changes occur outside the hippocampus, a vascular etiology should be considered, and prompt sonographic and cardiac diagnostics should be performed EEG may help to differentiate TGA from rare amnestic epileptic attacks, especially in recurrent amnestic attacks. TGA in patients < 50 years of age is a rarity, therefore it is mandatory to rapidly search for other causes in particular in younger patients. The cause of TGA is still unknown. Numerous findings in recent years point to a multifactorial genesis. Because the pathomechanism of TGA is not yet clearly known, no evidence-based therapeutic or prophylactic recommendations can be made.

CONCLUSIONS

There is no evidence for chronic sequelae of TGA with respect to cerebral ischemia, chronic memory impairment, or the onset of dementia-related syndromes.

Can Artificial Intelligence Diagnose Transient Global Amnesia Using Electroencephalography Data?

BACKGROUND AND PURPOSE

This study aimed to determine the ability of deep learning using convolutional neural networks (CNNs) to diagnose transient global amnesia (TGA) based on electroencephalography (EEG) data, and to differentiate between patients with recurrent TGA events and those with a single TGA event.

METHODS

We retrospectively enrolled newly diagnosed patients with TGA and healthy controls. All patients with TGA and the healthy controls underwent EEG. The EEG signals were converted into images using time-frequency analysis with short-time Fourier transforms. We employed two CNN models (AlexNet and VGG19) to classify the patients with TGA and the healthy controls, and for further classification of patients with recurrent TGA events and those with a single TGA event.

RESULTS

We enrolled 171 patients with TGA and 68 healthy controls. The accuracy and area under the curve (AUC) of the AlexNet and VGG19 models in classifying patients with TGA and healthy controls were 70.4% and 71.8%, and 0.718 and 0.743, respectively. In addition, the accuracy and AUC of the AlexNet and VGG19 models in classifying patients with recurrent TGA events and those with a single TGA event were 71.1% and 88.4%, and 0.773 and 0.873, respectively.

CONCLUSIONS

We have successfully demonstrated the feasibility of deep learning in diagnosing TGA based on EEG data, and used two different CNN models to distinguish between patients with recurrent TGA events and those with a single TGA event.

Limbic covariance network alterations in patients with transient global amnesia

BACKGROUND

We compared limbic structure volumes and graph theory parameters of the limbic covariance network between patients with transient global amnesia (TGA) and healthy controls, and between patients with single and recurrent TGA events.

METHODS

We retrospectively enrolled 122 patients with TGA (single event, n = 107; recurrent events, n = 15) and 50 healthy controls who underwent three-dimensional T1-weighted MRI imaging of the brain. Volumetric analysis of the subcortical limbic structures, including the hippocampus, amygdala, thalamus, mammillary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens, was performed. We examined the limbic covariance network using a graph theory.

RESULTS

Limbic structure volumes did not differ between patients with TGA and healthy controls, and between patients with a single event and those with recurrent events. However, the radius of the limbic covariance network was significantly greater in patients with TGA than in healthy controls (6.595 vs. 4.564, p = 0.040). Furthermore, the radius, diameter, eccentricity, and characteristics path length were greater (4.066 vs. 2.000, p = 0.009; 7.062 vs. 3.645, p = 0.029; 5.633 vs. 2.774, p = 0.013; 3.373 vs. 1.688, p = 0.004; respectively), whereas the average strength, global efficiency, local efficiency, mean clustering coefficient, transitivity, and small-worldness index were lower (5.595 vs. 10.831, p = 0.004; 0.350 vs. 0.642, p = 0.002; 0.531 vs. 1.724, p = 0.004; 0.304 vs. 0.624, p = 0.006; 0.456 vs. 0.935, p = 0.003; 0.913 vs. 0.993, p = 0.017; respectively), in patients with recurrent events than in those with a single event.

CONCLUSION

The limbic covariance network shows significant alterations in patients with TGA, as well as differences between patients with recurrent events and those with a single event. These findings suggest that changes in the limbic covariance network could be related to the pathogenesis of TGA.