Structural Connectivity Alterations in Amyotrophic Lateral Sclerosis: A Graph Theory Based Imaging Study

Different patterns of structural network impairments in two amyotrophic lateral sclerosis subtypes driven by 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging

The structural network damages in amyotrophic lateral sclerosis patients are evident but contradictory due to the high heterogeneity of the disease. We hypothesized that patterns of structural network impairments would be different in amyotrophic lateral sclerosis subtypes by a data-driven method using 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid imaging. The data of positron emission tomography, structural MRI and diffusion tensor imaging in fifty patients with amyotrophic lateral sclerosis and 23 healthy controls were collected by a 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance hybrid. Two amyotrophic lateral sclerosis subtypes were identified as the optimal cluster based on grey matter volume and standardized uptake value ratio. Network metrics at the global, local and connection levels were compared to explore the impaired patterns of structural networks in the identified subtypes. Compared with healthy controls, the two amyotrophic lateral sclerosis subtypes displayed a pattern of a locally impaired structural network centralized in the sensorimotor network and a pattern of an extensively impaired structural network in the whole brain. When comparing the two amyotrophic lateral sclerosis subgroups by a support vector machine classifier based on the decreases in nodal efficiency of structural network, the individualized network scores were obtained in every amyotrophic lateral sclerosis patient and demonstrated a positive correlation with disease severity. We clustered two amyotrophic lateral sclerosis subtypes by a data-driven method, which encompassed different patterns of structural network impairments. Our results imply that amyotrophic lateral sclerosis may possess the intrinsic damaged pattern of white matter network and thus provide a latent direction for stratification in clinical research.

Biomarkers in amyotrophic lateral sclerosis: current status and future prospects

Disease heterogeneity in amyotrophic lateral sclerosis poses a substantial challenge in drug development. Categorization based on clinical features alone can help us predict the disease course and survival, but quantitative measures are also needed that can enhance the sensitivity of the clinical categorization. In this Review, we describe the emerging landscape of diagnostic, categorical and pharmacodynamic biomarkers in amyotrophic lateral sclerosis and their place in the rapidly evolving landscape of new therapeutics. Fluid-based markers from cerebrospinal fluid, blood and urine are emerging as useful diagnostic, pharmacodynamic and predictive biomarkers. Combinations of imaging measures have the potential to provide important diagnostic and prognostic information, and neurophysiological methods, including various electromyography-based measures and quantitative EEG-magnetoencephalography-evoked responses and corticomuscular coherence, are generating useful diagnostic, categorical and prognostic markers. Although none of these biomarker technologies has been fully incorporated into clinical practice or clinical trials as a primary outcome measure, strong evidence is accumulating to support their clinical utility.

Graph network measures reveal distinct white matter abnormalities in motor and extra-motor brain regions of two UMN-predominant ALS subtypes

BACKGROUND

Routine clinical magnetic resonance imaging (MRI) shows bilateral corticospinal tract (CST) hyperintensity in some patients with upper motor neuron (UMN)-predominant ALS (ALS-CST+) but not in others (ALS-CST-). Although, similar in their UMN features, the ALS-CST+ patient group is significantly younger in age, has faster disease progression and shorter survival than the ALS-CST- patient group. Reasons for the differences are unclear.

METHOD

In order to evaluate more objective MRI measures of these ALS subgroups, we used diffusion tensor images (DTI) obtained using single shot echo planar imaging sequence from 1.5 T Siemens MRI Scanner. We performed an exploratory whole brain white matter (WM) network analysis using graph theory approach on 45 ALS patients (ALS-CST+) (n = 21), and (ALS-CST-) (n = 24) and neurological controls (n = 14).

RESULTS

Significant (p < 0.05) differences in nodal degree measure between ALS patients and controls were observed in motor and extra motor regions, supplementary motor area, subcortical WM regions, cerebellum and vermis. Importantly, WM network abnormalities were significantly (p < 0.05) different between ALS-CST+ and ALS-CST- subgroups. Compared to neurologic controls, both ALS subgroups showed hubs in the right superior occipital gyrus and cuneus as well as significantly (p < 0.05) reduced small worldness supportive of WM network damage.

CONCLUSIONS

Significant differences between ALS-CST+ and ALS-CST- subgroups of WM network abnormalities, age of onset, symptom duration prior to MRI, and progression rate suggest these patients represent distinct clinical phenotypes and possibly pathophysiologic mechanisms of ALS.

Modeling Neurodegenerative Diseases Using In Vitro Compartmentalized Microfluidic Devices

The human brain is a complex organ composed of many different types of cells interconnected to create an organized system able to efficiently process information. Dysregulation of this delicately balanced system can lead to the development of neurological disorders, such as neurodegenerative diseases (NDD). To investigate the functionality of human brain physiology and pathophysiology, the scientific community has been generated various research models, from genetically modified animals to two- and three-dimensional cell culture for several decades. These models have, however, certain limitations that impede the precise study of pathophysiological features of neurodegeneration, thus hindering therapeutical research and drug development. Compartmentalized microfluidic devices provide in vitro minimalistic environments to accurately reproduce neural circuits allowing the characterization of the human central nervous system. Brain-on-chip (BoC) is allowing our capability to improve neurodegeneration models on the molecular and cellular mechanism aspects behind the progression of these troubles. This review aims to summarize and discuss the latest advancements of microfluidic models for the investigations of common neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis.

Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia

Frontotemporal dementia associated with granulin (GRN) mutations presents asymmetric brain atrophy. We applied a Minimum Spanning Tree plus an Efficiency Cost Optimization approach to cortical thickness data in order to test whether graph theory measures could identify global or local impairment of connectivity in the presymptomatic phase of pathology, where other techniques failed in demonstrating changes. We included 52 symptomatic GRN mutation carriers (SC), 161 presymptomatic GRN mutation carriers (PSC) and 341 non-carriers relatives from the Genetic Frontotemporal dementia research Initiative cohort. Group differences of global, nodal and edge connectivity in (Minimum Spanning Tree plus an Efficiency Cost Optimization) graph were tested via Structural Equation Models. Global graph perturbation was selectively impaired in SC compared to non-carriers, with no changes in PSC. At the local level, only SC exhibited perturbation of frontotemporal nodes, but edge connectivity revealed a characteristic pattern of interhemispheric disconnection, involving homologous parietal regions, in PSC. Our results suggest that GRN-related frontotemporal dementia resembles a disconnection syndrome, with interhemispheric disconnection between parietal regions in presymptomatic phases that progresses to frontotemporal areas as symptoms emerge.

A Comparative Analysis of White Matter Structural Networks on SCLC Patients After Chemotherapy

Previous sMRI, DTI and rs-fMRI studies in small cell lung cancer (SCLC) patients have reported that patients after chemotherapy had gray and white matter structural alterations along with functional deficits. Nonetheless, few are known regarding the potential alterations in the topological organization of the WM structural network in SCLC patients after chemotherapy. In this context, the scope of the present study is to evaluate the WM structural network of 20 SCLC patients after chemotherapy and to 14 healthy controls, by applying a combination of DTI with graph theory. The results revealed that both SCLC and healthy controls groups demonstrated small world properties. The SCLC patients had decreased values in the clustering coefficient, local efficiency and degree metrics as well as increased shortest path length when compared to the healthy controls. Moreover, the two groups reported different topological reorganization of hub distribution. Lastly, the SCLC patients exhibited significantly decreased structural connectivity in comparison to the healthy group. These results underline that the topological organization of the WM structural network in SCLC patients was disrupted and hence constitute new vital information regarding the effects that chemotherapy and cancer may have in the patients' brain at network level.

Case Report: Event-Related Desynchronization Observed During Volitional Swallow by Electroencephalography Recordings in ALS Patients With Dysphagia

Dysphagia is a severe disability affecting daily life in patients with amyotrophic lateral sclerosis (ALS). It is caused by degeneration of both the bulbar motor neurons and cortical motoneurons projecting to the oropharyngeal areas. A previous report showed decreased event-related desynchronization (ERD) in the medial sensorimotor areas in ALS dysphagic patients. In the process of degeneration, brain reorganization may also be induced in other areas than the sensorimotor cortices. Furthermore, ALS patients with dysphagia often show a longer duration of swallowing. However, there have been no reports on brain activity in other cortical areas and the time course of brain activity during prolonged swallowing in these patients. In this case report, we investigated the distribution and the time course of ERD and corticomuscular coherence (CMC) in the beta (15–25 Hz) frequency band during volitional swallow using electroencephalography (EEG) in two patients with ALS. Case 1 (a 71-year-old man) was diagnosed 2 years before the evaluation. His first symptom was muscle weakness in the right hand; 5 months later, dysphagia developed and exacerbated. Since his dietary intake decreased, he was given an implantable venous access port. Case 2 (a 64-year-old woman) was diagnosed 1 year before the evaluation. Her first symptom was open-nasal voice and dysarthria; 3 months later, dysphagia developed and exacerbated. She was given a percutaneous endoscopic gastrostomy. EEG recordings were performed during volitional swallowing, and the ERD was calculated. The average swallow durations were 7.6 ± 3.0 s in Case 1 and 8.3 ± 2.9 s in Case 2. The significant ERD was localized in the prefrontal and premotor areas and lasted from a few seconds after the initiation of swallowing to the end in Case 1. The ERD was localized in the lateral sensorimotor areas only at the initiation of swallowing in Case 2. CMC was not observed in either case. These results suggest that compensatory processes for cortical motor outputs might depend on individual patients and that a new therapeutic approach using ERD should be developed according to the individuality of ALS patients with dysphagia.

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.

Disruption of the white matter structural network and its correlation with baseline progression rate in patients with sporadic amyotrophic lateral sclerosis

OBJECTIVE

There is increasing evidence that amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease impacting large-scale brain networks. However, it is still unclear which structural networks are associated with the disease and whether the network connectomics are associated with disease progression. This study was aimed to characterize the network abnormalities in ALS and to identify the network-based biomarkers that predict the ALS baseline progression rate.

METHODS

Magnetic resonance imaging was performed on 73 patients with sporadic ALS and 100 healthy participants to acquire diffusion-weighted magnetic resonance images and construct white matter (WM) networks using tractography methods. The global and regional network properties were compared between ALS and healthy subjects. The single-subject WM network matrices of patients were used to predict the ALS baseline progression rate using machine learning algorithms.

RESULTS

Compared with the healthy participants, the patients with ALS showed significantly decreased clustering coefficient Cp (P = 0.0034, t = 2.98), normalized clustering coefficient γ (P = 0.039, t = 2.08), and small-worldness σ (P = 0.038, t = 2.10) at the global network level. The patients also showed decreased regional centralities in motor and non-motor systems including the frontal, temporal and subcortical regions. Using the single-subject structural connection matrix, our classification model could distinguish patients with fast versus slow progression rate with an average accuracy of 85%.

CONCLUSION

Disruption of the WM structural networks in ALS is indicated by weaker small-worldness and disturbances in regions outside of the motor systems, extending the classical pathophysiological understanding of ALS as a motor disorder. The individual WM structural network matrices of ALS patients are potential neuroimaging biomarkers for the baseline disease progression in clinical practice.

White matter degeneration in remote brain areas of stroke patients with motor impairment due to basal ganglia lesions

Diffusion tensor imaging (DTI) studies have revealed distinct white matter (WM) characteristics of the brain following diseases. Beyond the lesion‐symptom maps, stroke is characterized by extensive structural and functional alterations of brain areas remote to local lesions. Here, we further investigated the structural changes over a global level by using DTI data of 10 ischemic stroke patients showing motor impairment due to basal ganglia lesions and 11 healthy controls. DTI data were processed to obtain fractional anisotropy (FA) maps, and multivariate pattern analysis was used to explore brain regions that play an important role in classification based on FA maps. The WM structural network was constructed by the deterministic fiber‐tracking approach. In comparison with the controls, the stroke patients showed FA reductions in the perilesional basal ganglia, brainstem, and bilateral frontal lobes. Using network‐based statistics, we found a significant reduction in the WM subnetwork in stroke patients. We identified the patterns of WM degeneration affecting brain areas remote to the lesions, revealing the abnormal organization of the structural network in stroke patients, which may be helpful in understanding of the neural mechanisms underlying hemiplegia.

White Matter Structural Network Analysis to Differentiate Alzheimer's Disease and Subcortical Ischemic Vascular Dementia

To explore the evaluation of white matter structural network analysis in the differentiation of Alzheimer's disease (AD) and subcortical ischemic vascular dementia (SIVD), 67 participants [31 AD patients, 19 SIVD patients, and 19 normal control (NC)] were enrolled in this study. Each participant underwent 3.0T MRI scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters [gamma, sigma, lambda, global shortest path length (Lp), global efficiency (Eg), and local efficiency (Eloc)] and nodal parameters [betweenness centrality (BC)] were obtained. Network-based statistical analysis (NBS) was employed to analyze the group differences of structural connections. The diagnosis efficiency of nodal BC in identifying different types of dementia was assessed by receiver operating characteristic (ROC) analysis. There were no significant differences of gender and years of education among the groups. There were no significant differences of sigma and gamma in AD vs. NC and SIVD vs. NC, whereas the Eg values of AD and SIVD were statistically decreased, and the lambda values were increased. The BC of the frontal cortex, left superior parietal gyrus, and left precuneus in AD patients were obviously reduced, while the BC of the prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients had decreased structural connections in the frontal, prefrontal, and subcortical regions, while AD patients had decreased structural connections in the temporal and occipital regions and increased structural connections in the frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in the right putamen for AD vs. SIVD. White matter structural network analysis may be a potential and promising method, and the topological changes of the network, especially the BC change in the right putamen, were valuable in differentiating AD and SIVD patients.

Neuroanatomical Alterations in Patients With Tinnitus Before and After Sound Therapy: A Combined VBM and SCN Study

Many neuroanatomical alterations have been detected in patients with tinnitus in previous studies. However, little is known about the morphological and structural covariance network (SCN) changes before and after long-term sound therapy. This study aimed to explore alterations in brain anatomical and SCN changes in patients with idiopathic tinnitus using voxel-based morphometry (VBM) analysis 24 weeks before and after sound therapy. Thirty-three tinnitus patients underwent magnetic resonance imaging scans at baseline and after 24 weeks of sound therapy. Twenty-six age- and sex-matched healthy control (HC) individuals also underwent two scans over a 24-week interval; 3.0T MRI and high-resolution 3D structural images were acquired with a 3D-BRAVO pulse sequence. Structural image data preprocessing was performed using the VBM8 toolbox. The Tinnitus Handicap Inventory (THI) score was assessed for the severity of tinnitus before and after treatment. Two-way mixed model analysis of variance (ANOVA) and post hoc analyses were performed to determine differences between the two groups (patients and HCs) and between the two scans (at baseline and on the 24th week). Student-Newman-Keuls (SNK) tests were used in the post hoc analysis. Interaction effects between the two groups and the two scans demonstrated significantly different gray matter (GM) volume in the right parahippocampus gyrus, right caudate, left superior temporal gyrus, left cuneus gyrus, and right calcarine gyrus; we found significantly decreased GM volume in the above five brain regions among the tinnitus patients before sound therapy (baseline) compared to that in the HC group. The 24-week sound therapy group demonstrated significantly greater brain volume compared with the baseline group among these brain regions. We did not find significant differences in brain regions between the 24-week sound therapy and HC groups. The SCN results showed that the left superior temporal gyrus and left rolandic operculum were significantly different in nodal efficiency, nodal degree centrality, and nodal betweenness centrality after FDR correction. This study characterized the effect of sound therapy on brain GM volume, especially in the left superior temporal lobe. Notably, sound therapy had a normalizing effect on tinnitus patients.