Reliability and availability of granger causality density in localization of Rolandic focus in BECTS

Disrupted Balance of Gray Matter Volume and Directed Functional Connectivity in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Alterations in functional connectivity have been demonstrated in Alzheimer's disease (AD), an age-progressive neurodegenerative disorder that affects cognitive function; however, directional information flow has never been analyzed.

OBJECTIVE

This study aimed to determine changes in resting-state directional functional connectivity measured using a novel approach, granger causality density (GCD), in patients with AD, and mild cognitive impairment (MCI) and explore novel neuroimaging biomarkers for cognitive decline detection.

METHODS

In this study, structural MRI, resting-state functional magnetic resonance imaging, and neuropsychological data of 48 Alzheimer's Disease Neuroimaging Initiative participants were analyzed, comprising 16 patients with AD, 16 with MCI, and 16 normal controls. Volume-based morphometry (VBM) and GCD were used to calculate the voxel-based gray matter (GM) volumes and directed functional connectivity of the brain. We made full use of voxel-based between-group comparisons of VBM and GCD values to identify specific regions with significant alterations. In addition, Pearson's correlation analysis was conducted between directed functional connectivity and several clinical variables. Furthermore, receiver operating characteristic (ROC) analysis related to classification was performed in combination with VBM and GCD.

RESULTS

In patients with cognitive decline, abnormal VBM and GCD (involving inflow and outflow of GCD) were noted in default mode network (DMN)-related areas and the cerebellum. GCD in the DMN midline core system, hippocampus, and cerebellum was closely correlated with the Mini- Mental State Examination and Functional Activities Questionnaire scores. In the ROC analysis combining VBM with GCD, the neuroimaging biomarker in the cerebellum was optimal for the early detection of MCI, whereas the precuneus was the best in predicting cognitive decline progression and AD diagnosis.

CONCLUSION

Changes in GM volume and directed functional connectivity may reflect the mechanism of cognitive decline. This discovery could improve our understanding of the pathology of AD and MCI and provide available neuroimaging markers for the early detection, progression, and diagnosis of AD and MCI.

Interictal epileptiform discharges changed epilepsy-related brain network architecture in BECTS

To investigate directed information flow of epileptiform activity in benign epilepsy with centrotemporal spikes (BECTS) during ictal epileptiform discharges (IEDs) and non-IEDs periods. In this multi-center study, a total of 188 subjects, including 50 BECTS and 138 normal children's controls (NCs) from three different centers (Center 1: females/males, 38/55; mean age, 9.33 ± 2.6 years; Center 2: females/males,7/10; mean age, 8.59 ± 2.32 years; Center 3: females/males, 14/14; mean age, 13 ± 3.42 years) were recruited. The BECTS were classified into IEDs (females/males, 12/15; mean age, 8.15 ± 1.68 years) and non-IEDs (females/males, 10/13; mean age, 9.09 ± 1.98 years) subgroups depending on presence of central-temporal spikes from an EEG-fMRI examination. Three new methods, structural equation parametric modeling, dynamic causal modeling and granger causality density (GCD) were used to determine optimal network architectures for BECTS. Three multicentric NCs determined a reliable and consistent network architecture by structural equation parametric modeling method. Further analyses were used for IEDs and non-IEDs to determine the brain network architecture by structural equation parametric modeling, dynamic causal modeling and GCD, respectively. The brain network architecture of IEDs substate, non-IEDs substate and NCs are different. IEDs promoted the driving effect of the Rolandic areas with more output information flows, and increased the targeted effect of the top of pre-/post-central gyrus with more input information flows. The information flow arises from the Rolandic areas, and subsequently propagates to the top of pre-/post-central gyrus and thalamus. From non-IEDs status to IEDs status, the thalamus load may play an important role in the modulation and regulation of epileptiform activity. These findings shed new light on pathophysiological mechanism of directed localization of epileptiform activity in BECTS.

Brain network excitatory/inhibitory imbalance is a biomarker for drug-naive Rolandic epilepsy: A radiomics strategy

OBJECTIVE

Seizure occurs when the balance between excitatory and inhibitory (E/I) inputs to neurons is perturbed, resulting in abnormal electrical activity. This study investigated whether an existing E/I imbalance in neural networks is a useful diagnostic biomarker for Rolandic epilepsy by a resting-state dynamic causal modeling-based support vector machine (rs-DCM-SVM) algorithm.

METHODS

This multicenter study enrolled a discovery cohort (76 children with Rolandic epilepsy and 76 normal controls [NCs]) and a replication cohort (59 children with Rolandic epilepsy and 60 NCs). Spatial independent component analysis was used to seven canonical neural networks, and a total of 25 regions of interest were selected from these networks. The rs-DCM-SVM classifier was used for individual classification, consensus feature selection, and feature ranking.

RESULTS

The rs-DCM-SVM classifier showed that the E/I imbalance in brain networks is a useful neuroimaging biomarker for Rolandic epilepsy, with an accuracy of 88.2% and 81.5% and an area under curve of .92 and .83 in the discovery and the replication cohorts, respectively. Consensus brain regions with the highest contributions to the classification were located within the epilepsy-related networks, indicating that this classifier was suitable. Consensus functional connection pairs with the highest contributions to the classification were associated with an excitation network loop and an inhibition network loop. The excitation loop mediated the integration of advanced cognitive networks (subcortex, dorsal attention, default mode, executive control, and salience networks), whereas the inhibition loop was involved in the segregation of sensorimotor and language networks. The two loops showed functional segregation.

SIGNIFICANCE

Brain E/I imbalance has potential to serve as a biomarker for individual classification in children with Rolandic epilepsy, and might be an important mechanism for causing seizures and cognitive impairment in children with Rolandic epilepsy.