Strong-Weak Pruning for Brain Network Identification in Connectome-Wide Neuroimaging: Application to Amyotrophic Lateral Sclerosis Disease Stage Characterization

Synaptic Dysfunction and Plasticity in Amyotrophic Lateral Sclerosis

Recent evidence has supported the hypothesis that amyotrophic lateral sclerosis (ALS) is a multi-step disease, as the onset of symptoms occurs after sequential exposure to a defined number of risk factors. Despite the lack of precise identification of these disease determinants, it is known that genetic mutations may contribute to one or more of the steps leading to ALS onset, the remaining being linked to environmental factors and lifestyle. It also appears evident that compensatory plastic changes taking place at all levels of the nervous system during ALS etiopathogenesis may likely counteract the functional effects of neurodegeneration and affect the timing of disease onset and progression. Functional and structural events of synaptic plasticity probably represent the main mechanisms underlying this adaptive capability, causing a significant, although partial and transient, resiliency of the nervous system affected by a neurodegenerative disease. On the other hand, the failure of synaptic functions and plasticity may be part of the pathological process. The aim of this review was to summarize what it is known today about the controversial involvement of synapses in ALS etiopathogenesis, and an analysis of the literature, although not exhaustive, confirmed that synaptic dysfunction is an early pathogenetic process in ALS. Moreover, it appears that adequate modulation of structural and functional synaptic plasticity may likely support function sparing and delay disease progression.

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics

Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of 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.

Reduced Occipital Cortex Excitability in Amyotrophic Lateral Sclerosis

PURPOSE

In addition to motor cortex involvement, sensory abnormalities have been demonstrated in amyotrophic lateral sclerosis (ALS), including structural and metabolic alterations in the occipital cortex. The aim of this study was to examine occipital excitability changes in ALS.

METHODS

Twenty-one patients with ALS and 16 healthy subjects were enrolled into the study. Phosphene experience and phosphene threshold were studied to assess occipital excitability. Cognitive function was evaluated in both groups by means of Montreal Cognitive Assessment and Addenbrooke's Cognitive Examination-Revised visuospatial score tests.

RESULTS

Phosphene was experienced in 13 (81.3%) healthy subjects and 9 (42.9%) patients with ALS (P = 0.04). The mean phosphene threshold was not significantly different between the two groups. No correlation existed between phosphene threshold and motor cortical excitability parameters, ALS Functional Rating Scale Revised, Montreal Cognitive Assessment, and Addenbrooke's Cognitive Examination-Revised scores.

CONCLUSIONS

Visual cortex is affected, and the occipital excitability is reduced in ALS, without any relation to motor cortical excitability changes, providing another clue suggestive of sensory involvement in ALS.

Advanced neuroimaging approaches in amyotrophic lateral sclerosis: refining the clinical diagnosis

Introduction: In the last decade, multiparametric magnetic resonance imaging (MRI) has achieved tremendous advances in applications to amyotrophic lateral sclerosis (ALS) to increase the understanding of the associated pathophysiology. The aim of this review is to summarize recent progress in the development of MRI-based techniques aiming to support the clinical diagnosis in ALS.Areas covered: The review of structural and functional MRI applications to ALS and its variants (restricted phenotypes) is focused on the potential of MRI techniques which contribute to the diagnostic work-up of patients with the clinical presentation of a motor neuron disease. The potential of specific MRI methods for patient diagnosis and monitoring is discussed, and the future design of clinical MRI applications to ALS is conceptualized.Expert opinion: Current multiparametric MRI allows for the use as a clinical biological marker and a technical instrument in the clinical diagnosis of patients with ALS and also of patients with ALS variants. Composite neuroimaging indices of specific anatomical areas derived from different MRI techniques might guide in the diagnostic applications to ALS. Such a development of ALS-specific MRI-based composite scores with sufficient discriminative power versus ALS mimics at an individual level requires standardized advanced protocols and comprehensive analysis approaches.