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

Magnetic resonance imaging allows acquiring functional and structural connectivity data from which high-density whole-brain networks can be derived to carry out connectome-wide analyses in normal and clinical populations. Graph theory has been widely applied to investigate the modular structure of brain connections by using centrality measures to identify the “hub” of human connectomes, and community detection methods to delineate subnetworks associated with diverse cognitive and sensorimotor functions. These analyses typically rely on a preprocessing step (pruning) to reduce computational complexity and remove the weakest edges that are most likely affected by experimental noise. However, weak links may contain relevant information about brain connectivity, therefore, the identification of the optimal trade-off between retained and discarded edges is a subject of active research. We introduce a pruning algorithm to identify edges that carry the highest information content. The algorithm selects both strong edges (i.e. edges belonging to shortest paths) and weak edges that are topologically relevant in weakly connected subnetworks. The newly developed “strong–weak” pruning (SWP) algorithm was validated on simulated networks that mimic the structure of human brain networks. It was then applied for the analysis of a real dataset of subjects affected by amyotrophic lateral sclerosis (ALS), both at the early (ALS2) and late (ALS3) stage of the disease, and of healthy control subjects. SWP preprocessing allowed identifying statistically significant differences in the path length of networks between patients and healthy subjects. ALS patients showed a decrease of connectivity between frontal cortex to temporal cortex and parietal cortex and between temporal and occipital cortex. Moreover, degree of centrality measures revealed significantly different hub and centrality scores between patient subgroups. These findings suggest a widespread alteration of network topology in ALS associated with disease progression.

Download Full-text

Automated Acoustic Analysis of Oral Diadochokinesis to Assess Bulbar Motor Involvement in Amyotrophic Lateral Sclerosis

Journal of Speech Language and Hearing Research ◽

10.1044/2019_jslhr-19-00178 ◽

2020 ◽

Vol 63 (1) ◽

pp. 59-73 ◽

Cited By ~ 3

Author(s):

Panying Rong

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Acoustic Analysis ◽

Speaking Rate ◽

Disease Stage ◽

Healthy Controls ◽

Tongue Movement ◽

Major Barrier ◽

Syllable Repetition ◽

Oral Diadochokinesis ◽

Lateral Sclerosis

Purpose The purpose of this article was to validate a novel acoustic analysis of oral diadochokinesis (DDK) in assessing bulbar motor involvement in amyotrophic lateral sclerosis (ALS). Method An automated acoustic DDK analysis was developed, which filtered out the voice features and extracted the envelope of the acoustic waveform reflecting the temporal pattern of syllable repetitions during an oral DDK task (i.e., repetitions of /tɑ/ at the maximum rate on 1 breath). Cycle-to-cycle temporal variability (cTV) of envelope fluctuations and syllable repetition rate (sylRate) were derived from the envelope and validated against 2 kinematic measures, which are tongue movement jitter (movJitter) and alternating tongue movement rate (AMR) during the DDK task, in 16 individuals with bulbar ALS and 18 healthy controls. After the validation, cTV, sylRate, movJitter, and AMR, along with an established clinical speech measure, that is, speaking rate (SR), were compared in their ability to (a) differentiate individuals with ALS from healthy controls and (b) detect early-stage bulbar declines in ALS. Results cTV and sylRate were significantly correlated with movJitter and AMR, respectively, across individuals with ALS and healthy controls, confirming the validity of the acoustic DDK analysis in extracting the temporal DDK pattern. Among all the acoustic and kinematic DDK measures, cTV showed the highest diagnostic accuracy (i.e., 0.87) with 80% sensitivity and 94% specificity in differentiating individuals with ALS from healthy controls, which outperformed the SR measure. Moreover, cTV showed a large increase during the early disease stage, which preceded the decline of SR. Conclusions This study provided preliminary validation of a novel automated acoustic DDK analysis in extracting a useful measure, namely, cTV, for early detection of bulbar ALS. This analysis overcame a major barrier in the existing acoustic DDK analysis, which is continuous voicing between syllables that interferes with syllable structures. This approach has potential clinical applications as a novel bulbar assessment.

Download Full-text

Intra- and Inter-Modular Connectivity Alterations in the Brain Structural Network of Spinocerebellar Ataxia Type 3

Entropy ◽

10.3390/e21030317 ◽

2019 ◽

Vol 21 (3) ◽

pp. 317 ◽

Cited By ~ 1

Author(s):

Chi-Wen Jao ◽

Bing-Wen Soong ◽

Tzu-Yun Wang ◽

Hsiu-Mei Wu ◽

Chia-Feng Lu ◽

...

Keyword(s):

Spinocerebellar Ataxia ◽

Brain Networks ◽

Structural Connectivity ◽

Three Dimensional ◽

Brain Network ◽

Spinocerebellar Ataxia Type ◽

Compensatory Mechanisms ◽

Spinocerebellar Ataxia Type 3 ◽

Type 3 ◽

The Brain

In addition to cerebellar degeneration symptoms, patients with spinocerebellar ataxia type 3 (SCA3) exhibit extensive involvements with damage in the prefrontal cortex. A network model has been proposed for investigating the structural organization and functional mechanisms of clinical brain disorders. For neural degenerative diseases, a cortical feature-based structural connectivity network can locate cortical atrophied regions and indicate how their connectivity and functions may change. The brain network of SCA3 has been minimally explored. In this study, we investigated this network by enrolling 48 patients with SCA3 and 48 healthy subjects. A novel three-dimensional fractal dimension-based network was proposed to detect differences in network parameters between the groups. Copula correlations and modular analysis were then employed to categorize and construct the structural networks. Patients with SCA3 exhibited significant lateralized atrophy in the left supratentorial regions and significantly lower modularity values. Their cerebellar regions were dissociated from higher-level brain networks, and demonstrated decreased intra-modular connectivity in all lobes, but increased inter-modular connectivity in the frontal and parietal lobes. Our results suggest that the brain networks of patients with SCA3 may be reorganized in these regions, with the introduction of certain compensatory mechanisms in the cerebral cortex to minimize their cognitive impairment syndrome.

Download Full-text

Reduced structural connectivity within a prefrontal-motor-subcortical network in amyotrophic lateral sclerosis

Journal of Magnetic Resonance Imaging ◽

10.1002/jmri.24695 ◽

2014 ◽

Vol 41 (5) ◽

pp. 1342-1352 ◽

Cited By ~ 23

Author(s):

Colin R. Buchanan ◽

Lewis D. Pettit ◽

Amos J. Storkey ◽

Sharon Abrahams ◽

Mark E. Bastin

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Structural Connectivity ◽

Lateral Sclerosis

Download Full-text

Changes of Resting State Brain Networks in Amyotrophic Lateral Sclerosis

NeuroImage ◽

10.1016/s1053-8119(09)70061-8 ◽

2009 ◽

Vol 47 ◽

pp. S46

Author(s):

B Mohammadi ◽

K Kollewe ◽

A Samii ◽

K Krampfl ◽

R Dengler ◽

...

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Resting State ◽

Brain Networks ◽

Lateral Sclerosis

Download Full-text

Heritability of hierarchical structural brain network

10.1101/209635 ◽

2017 ◽

Author(s):

Moo K. Chung ◽

Zhan Luo ◽

Nagesh Adluru ◽

Andrew L. Alexander ◽

Davidson J. Richard ◽

...

Keyword(s):

Diffusion Weighted Imaging ◽

Brain Networks ◽

Structural Connectivity ◽

Imaging Study ◽

Brain Network ◽

Genetic Contribution ◽

Whole Brain ◽

Diffusion Weighted ◽

Structural Brain Network ◽

Structural Brain

ABSTRACTWe present a new structural brain network parcellation scheme that can subdivide existing parcellations into smaller subregions in a hierarchically nested fashion. The hierarchical parcellation was used to build multilayer convolutional structural brain networks that preserve topology across different network scales. As an application, we applied the method to diffusion weighted imaging study of 111 twin pairs. The genetic contribution of the whole brain structural connectivity was determined. We showed that the overall heritability is consistent across different network scales.

Download Full-text

Amyotrophic lateral sclerosis: disease stage related changes of biochemical markers (tau protein, creatine kinase, sCD14) in CSF and serum

Aktuelle Neurologie ◽

10.1055/s-2005-919638 ◽

2005 ◽

Vol 32 (S 4) ◽

Author(s):

H Tumani ◽

S.D Süssmuth ◽

J Brettschneider ◽

A Hinz ◽

A.C Ludolph

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Creatine Kinase ◽

Tau Protein ◽

Biochemical Markers ◽

Disease Stage ◽

Lateral Sclerosis

Download Full-text

Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

Frontiers in Neuroscience ◽

10.3389/fnins.2020.613990 ◽

2020 ◽

Vol 14 ◽

Author(s):

Seyyed Bahram Borgheai ◽

John McLinden ◽

Kunal Mankodiya ◽

Yalda Shahriari

Keyword(s):

Amyotrophic Lateral Sclerosis ◽

Prefrontal Cortex ◽

Neurodegenerative Diseases ◽

Spanning Tree ◽

Minimum Spanning Tree ◽

Brain Network ◽

Healthy Controls ◽

Topological Properties ◽

Network Disruption ◽

Lateral Sclerosis

Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS’ brain network and could hypothetically extend to applications in other neurodegenerative diseases.

Download Full-text