Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity

Altered spreading of fast aperiodic brain waves relates to disease duration in Amyotrophic Lateral Sclerosis

OBJECTIVE

To test the hypothesis that patients affected by Amyotrophic Lateral Sclerosis (ALS) show an altered spatio-temporal spreading of neuronal avalanches in the brain, and that this may related to the clinical picture.

METHODS

We obtained the source-reconstructed magnetoencephalography (MEG) signals from thirty-six ALS patients and forty-two healthy controls. Then, we used the construct of the avalanche transition matrix (ATM) and the corresponding network parameter nodal strength to quantify the changes in each region, since this parameter provides key information about which brain regions are mostly involved in the spreading avalanches.

RESULTS

ALS patients presented higher values of the nodal strength in both cortical and sub-cortical brain areas. This parameter correlated directly with disease duration.

CONCLUSIONS

In this work, we provide a deeper characterization of neuronal avalanches propagation in ALS, describing their spatio-temporal trajectories and identifying the brain regions most likely to be involved in the process. This makes it possible to recognize the brain areas that take part in the pathogenic mechanisms of ALS. Furthermore, the nodal strength of the involved regions correlates directly with disease duration.

SIGNIFICANCE

Our results corroborate the clinical relevance of aperiodic, fast large-scale brain activity as a biomarker of microscopic changes induced by neurophysiological processes.

Abnormal brain functional network dynamics in amyotrophic lateral sclerosis patients with depression

Since depression is common in amyotrophic lateral sclerosis (ALS) patients, we aimed to explore the specific brain functional network dynamics in ALS patients with depression (ALS-D) compared with healthy controls (HCs) and ALS patients without depressive symptoms (ALS-ND). According to the DSM-V, 32 ALS-D patients were selected from a large and newly diagnosed ALS cohort. Then, 32 demographic- and cognitive-matched ALS-ND patients were also selected, and 64 HCs were recruited. These participants underwent resting-state fMRI scans, and functional connectivity state analysis and dynamic graph theory were applied to evaluate brain functional network dynamics. Moreover, the Hamilton Depression Rating Scale (HDRS) was used to quantify depressive symptoms in the ALS-D patients. Four distinct states were identified in the ALS-D patients and controls. Compared with that in HCs, the fraction rate (FR) in state 2 was significantly decreased in ALS-D patients, and the FR in state 4 was significantly increased in ALS-D patients. Compared with that of HCs, the dwell time in state 4 was significantly increased in the ALS-D patients. Moreover, compared with that in the ALS-D patients, the FR in state 3 was significantly decreased in the ALS-ND patients. Among the ALS-D patients, there was the suggestion of a positive association between HDRS scores and dwell time of state 4, but this association did not reach statistical significance (r = 0.354; p = 0.055). Depression is an important feature of ALS patients, and we found a special pattern of brain functional network dynamics in ALS-D patients. Our findings may play an important role in understanding the mechanism underlying depression in ALS patients and help develop therapeutic interventions for depressed ALS patients.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.

Altered dynamic brain activity and its association with memory decline after night shift-related sleep deprivation in nurses

AIMS AND OBJECTIVES

To investigate, for the first time, aberrant time-varying local brain activity in nurses following night shift-related sleep deprivation (SD) and its association with memory decline.

BACKGROUND

Prior studies have elucidated alterations in static local brain activity resulting from SD in the occupations outside medical profession.

DESIGN

A longitudinal study followed the STROBE recommendations.

METHODS

Twenty female nurses underwent resting-state functional magnetic resonance imaging and memory function assessment (by Complex Figure Test (CFT) and the California Verbal Learning Test, Second Edition (CVLT-II)) twice, once in a rested wakefulness (RW) state and another after SD. By combining the sliding-window approach and amplitude of low-frequency fluctuation (ALFF) analysis, the dynamic ALFF (dALFF) variability was calculated to reflect the characteristics of dynamic local brain activity.

RESULTS

Poor performance on the CFT and CVLT-II was observed in nurses with night shift-related SD. Reduced dALFF variability was found in a set of cognition-related brain regions (including the medial/middle/superior frontal gyrus, anterior/posterior cingulate gyrus, precuneus, angular gyrus, orbitofrontal and subgenual areas, and posterior cerebellum lobe), while increased dALFF variability was observed in the somatosensory-related, visual and auditory regions. SD-related dALFF variability alterations correlated with changes in subjects' performance on the CFT and CVLT-II.

CONCLUSIONS

Night shift-related SD disturbed dynamic brain activity in high cognitive regions and induced compensatory reactions in primary perceptual cortex. Identifying dALFF variability abnormalities may broaden our understanding of neural substrates underlying SD-related cognitive alterations, especially memory dysfunction.

RELEVANCE TO CLINICAL PRACTICE

Night shift-related SD is as an important occupational hazard affecting brain function in nurses. The effective countermeasure addressing the adverse outcomes of SD should be advocated for nurses.

PATIENT OR PUBLIC CONTRIBUTION

Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.

Dynamic Alterations in Functional Connectivity Density in Amyotrophic Lateral Sclerosis: A Resting-State Functional Magnetic Resonance Imaging Study

Background and Aims

Current knowledge on the temporal dynamics of the brain functional organization in amyotrophic lateral sclerosis (ALS) is limited. This is the first study on alterations in the patterns of dynamic functional connection density (dFCD) involving ALS.

Methods

We obtained resting-state functional magnetic resonance imaging (fMRI) data from 50 individuals diagnosed with ALS and 55 healthy controls (HCs). We calculated the functional connectivity (FC) between a given voxel and all other voxels within the entire brain and yield the functional connection density (FCD) value per voxel. dFCD was assessed by sliding window correlation method. In addition, the standard deviation (SD) of dFCD across the windows was computed voxel-wisely to measure dFCD variability. The difference in dFCD variability between the two groups was compared using a two-sample t-test following a voxel-wise manner. The receiver operating characteristic (ROC) curve was used to assess the between-group recognition performance of the dFCD variability index.

Results

The dFCD variability was significantly reduced in the bilateral precentral and postcentral gyrus compared with the HC group, whereas a marked increase was observed in the left middle frontal gyrus of ALS patients. dFCD variability exhibited moderate potential (areas under ROC curve = 0.753–0.837, all P < 0.001) in distinguishing two groups.

Conclusion

ALS patients exhibit aberrant dynamic property in brain functional architecture. The dFCD evaluation improves our understanding of the pathological mechanisms underlying ALS and may assist in its diagnosis.

Brain Connectivity and Network Analysis in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with no effective treatment or cure. ALS is characterized by the death of lower motor neurons (LMNs) in the spinal cord and upper motor neurons (UMNs) in the brain and their networks. Since the lower motor neurons are under the control of UMN and the networks, cortical degeneration may play a vital role in the pathophysiology of ALS. These changes that are not apparent on routine imaging with CT scans or MRI brain can be identified using modalities such as diffusion tensor imaging, functional MRI, arterial spin labelling (ASL), electroencephalogram (EEG), magnetoencephalogram (MEG), functional near-infrared spectroscopy (fNIRS), and positron emission tomography (PET) scan. They can help us generate a representation of brain networks and connectivity that can be visualized and parsed out to characterize and quantify the underlying pathophysiology in ALS. In addition, network analysis using graph measures provides a novel way of understanding the complex network changes occurring in the brain. These have the potential to become biomarker for the diagnosis and treatment of ALS. This article is a systematic review and overview of the various connectivity and network-based studies in ALS.

Feature selection from magnetic resonance imaging data in ALS: a systematic review

Background:

With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection.

Methods:

We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported.

Results:

Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS.

Conclusions:

These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.

Abnormal Stability of Dynamic Functional Architecture in Amyotrophic Lateral Sclerosis: A Preliminary Resting-State fMRI Study

Purpose: Static and dynamic analyses for identifying functional connectivity (FC) have demonstrated brain dysfunctions in amyotrophic lateral sclerosis (ALS). However, few studies on the stability of dynamic FC have been conducted among ALS patients. This study explored the change of functional stability in ALS and how it correlates with disease severity.

Methods: We gathered resting-state functional magnetic resonance data from 20 patients with ALS and 22 healthy controls (HCs). The disease severity was assessed with the Revised ALS Functional Rating Scale (ALSFRS-R). We used a sliding window correlation approach to identify dynamic FC and measured the concordance of dynamic FC over time to obtain the functional stability of each voxel. We assessed the between-group difference in functional stability by voxel-wise two-sample t-test. The correlation between the functional stability index and ALSFRS-R in ALS patients was evaluated using Spearman's correlation analysis.

Results: Compared with the HC group, the ALS group had significantly increased functional stability in the left pre-central and post-central gyrus and right temporal pole while decreased functional stability in the right middle and inferior frontal gyrus. The results revealed a significant correlation between ALSFRS-R and the mean functional stability in the right temporal pole (r = −0.452 and P = 0.046) in the ALS patients.

Conclusions: ALS patients have abnormal stability of brain functional architecture, which is associated with the severity of the disease.