Neurodegeneration of brain networks in the amyotrophic lateral sclerosis-frontotemporal lobar degeneration (ALS-FTLD) continuum: evidence from MRI and MEG studies

The integrity of the corticospinal tract and corpus callosum, and the risk of ALS: univariable and multivariable Mendelian randomization

Studies suggest that amyotrophic lateral sclerosis (ALS) compromises the integrity of white matter fiber tracts, primarily affecting motor fibers. However, it remains uncertain whether the integrity of these fibers influences the risk of ALS. We performed bidirectional two-sample Mendelian randomization (MR) and multivariable MR analyses to evaluate the associative relationships between the integrity of fiber tracts [including the corticospinal tract (CST) and corpus callosum (CC)] and the risk of ALS. Genetic instrumental variables for specific fiber tracts were obtained from published genome-wide association studies (GWASs), including 33,292 European individuals from five diffusion magnetic resonance imaging (dMRI) datasets. Summary-level GWAS data for ALS were derived from 27,205 ALS patients and 110,881 controls. The MR results suggested that an increase in the first principal component (PC1) of fractional anisotropy (FA) in the genu of the CC (GCC) was correlated with an increased risk of ALS (PFDR = 0.001, odds ratio = 1.363, 95% confidence interval 1.178-1.577). Although other neuroimaging phenotypes [mean diffusivity in the CST, radial diffusivity (RD) in the CST, FA in the GCC, PC1 in the body of the CC (BCC), PC1 in the CST, and RD in the GCC] did not pass correction, they were also considered to have suggestive associations with the risk of ALS. No evidence revealed that ALS caused changes in the integrity of fiber tracts. In summary, the results of this study provide genetic support for the potential association between the integrity of specific fiber tracts and the risk of ALS. Greater fiber integrity in the GCC and BCC may be a risk factor for ALS, while greater fiber integrity in the CST may have a protective effect on ALS. This study provides insights into ALS development.

Limbic Network and Papez Circuit Involvement in ALS: Imaging and Clinical Profiles in GGGGCC Hexanucleotide Carriers in C9orf72 and C9orf72-Negative Patients

Background: While frontotemporal involvement is increasingly recognized in Amyotrophic lateral sclerosis (ALS), the degeneration of limbic networks remains poorly characterized, despite growing evidence of amnestic deficits, impaired emotional processing and deficits in social cognition. Methods: A prospective neuroimaging study was conducted with 204 individuals with ALS and 111 healthy controls. Patients were stratified for hexanucleotide expansion status in C9orf72. A deep-learning-based segmentation approach was implemented to segment the nucleus accumbens, hypothalamus, fornix, mammillary body, basal forebrain and septal nuclei. The cortical, subcortical and white matter components of the Papez circuit were also systematically evaluated. Results: Hexanucleotide repeat expansion carriers exhibited bilateral amygdala, hypothalamus and nucleus accumbens atrophy, and C9orf72 negative patients showed bilateral basal forebrain volume reductions compared to controls. Both patient groups showed left rostral anterior cingulate atrophy, left entorhinal cortex thinning and cingulum and fornix alterations, irrespective of the genotype. Fornix, cingulum, posterior cingulate, nucleus accumbens, amygdala and hypothalamus degeneration was more marked in C9orf72-positive ALS patients. Conclusions: Our results highlighted that mesial temporal and parasagittal subcortical degeneration is not unique to C9orf72 carriers. Our radiological findings were consistent with neuropsychological observations and highlighted the importance of comprehensive neuropsychological testing in ALS, irrespective of the underlying genotype.

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.

The cortical neurophysiological signature of amyotrophic lateral sclerosis

The progressive loss of motor function characteristic of amyotrophic lateral sclerosis is associated with widespread cortical pathology extending beyond primary motor regions. Increasing muscle weakness reflects a dynamic, variably compensated brain network disorder. In the quest for biomarkers to accelerate therapeutic assessment, the high temporal resolution of magnetoencephalography is uniquely able to non-invasively capture micro-magnetic fields generated by neuronal activity across the entire cortex simultaneously. This study examined task-free magnetoencephalography to characterize the cortical oscillatory signature of amyotrophic lateral sclerosis for having potential as a pharmacodynamic biomarker. Eight to ten minutes of magnetoencephalography in the task-free, eyes-open state was recorded in amyotrophic lateral sclerosis (n = 36) and healthy age-matched controls (n = 51), followed by a structural MRI scan for co-registration. Extracted magnetoencephalography metrics from the delta, theta, alpha, beta, low-gamma, high-gamma frequency bands included oscillatory power (regional activity), 1/f exponent (complexity) and amplitude envelope correlation (connectivity). Groups were compared using a permutation-based general linear model with correction for multiple comparisons and confounders. To test whether the extracted metrics could predict disease severity, a random forest regression model was trained and evaluated using nested leave-one-out cross-validation. Amyotrophic lateral sclerosis was characterized by reduced sensorimotor beta band and increased high-gamma band power. Within the premotor cortex, increased disability was associated with a reduced 1/f exponent. Increased disability was more widely associated with increased global connectivity in the delta, theta and high-gamma bands. Intra-hemispherically, increased disability scores were particularly associated with increases in temporal connectivity and inter-hemispherically with increases in frontal and occipital connectivity. The random forest model achieved a coefficient of determination (R2) of 0.24. The combined reduction in cortical sensorimotor beta and rise in gamma power is compatible with the established hypothesis of loss of inhibitory, GABAergic interneuronal circuits in pathogenesis. A lower 1/f exponent potentially reflects a more excitable cortex and a pathology unique to amyotrophic lateral sclerosis when considered with the findings published in other neurodegenerative disorders. Power and complexity changes corroborate with the results from paired-pulse transcranial magnetic stimulation. Increased magnetoencephalography connectivity in worsening disability is thought to represent compensatory responses to a failing motor system. Restoration of cortical beta and gamma band power has significant potential to be tested in an experimental medicine setting. Magnetoencephalography-based measures have potential as sensitive outcome measures of therapeutic benefit in drug trials and may have a wider diagnostic value with further study, including as predictive markers in asymptomatic carriers of disease-causing genetic variants.

Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme

BACKGROUND

Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.

METHOD

11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.

RESULT

The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies.

CONCLUSION

Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.

Functional alterations in large-scale resting-state networks of amyotrophic lateral sclerosis: A multi-site study across Canada and the United States

Amyotrophic lateral sclerosis (ALS) is a multisystem neurodegenerative disorder characterized by progressive degeneration of upper motor neurons and lower motor neurons, and frontotemporal regions resulting in impaired bulbar, limb, and cognitive function. Magnetic resonance imaging studies have reported cortical and subcortical brain involvement in the pathophysiology of ALS. The present study investigates the functional integrity of resting-state networks (RSNs) and their importance in ALS. Intra- and inter-network resting-state functional connectivity (Rs-FC) was examined using an independent component analysis approach in a large multi-center cohort. A total of 235 subjects (120 ALS patients; 115 healthy controls (HC) were recruited across North America through the Canadian ALS Neuroimaging Consortium (CALSNIC). Intra-network and inter-network Rs-FC was evaluated by the FSL-MELODIC and FSLNets software packages. As compared to HC, ALS patients displayed higher intra-network Rs-FC in the sensorimotor, default mode, right and left fronto-parietal, and orbitofrontal RSNs, and in previously undescribed networks including auditory, dorsal attention, basal ganglia, medial temporal, ventral streams, and cerebellum which negatively correlated with disease severity. Furthermore, ALS patients displayed higher inter-network Rs-FC between the orbitofrontal and basal ganglia RSNs which negatively correlated with cognitive impairment. In summary, in ALS there is an increase in intra- and inter-network functional connectivity of RSNs underpinning both motor and cognitive impairment. Moreover, the large multi-center CALSNIC dataset permitted the exploration of RSNs in unprecedented detail, revealing previously undescribed network involvement in ALS.

A review of MEG dynamic brain network research

The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.

Cortical and subcortical changes in resting-state neuronal activity and connectivity in early symptomatic ALS and advanced frontotemporal dementia

The objective of this study was to examine if patterns of resting-state brain activity and functional connectivity in cortical and subcortical regions in patients with early symptomatic amyotrophic lateral sclerosis (ALS) resemble those of behavioural variant frontotemporal dementia (bvFTD). In a cross-sectional design, eyes-closed resting-state magnetoencephalography (MEG) data of 34 ALS patients, 18 bvFTD patients and 18 age- and gender-matched healthy controls (HCs) were projected to source-space using an atlas-based beamformer. Group differences in peak frequency, band-specific oscillatory activity and functional connectivity (corrected amplitude envelope correlation) in 78 cortical regions and 12 subcortical regions were determined. False discovery rate was used to correct for multiple comparisons. BvFTD patients, as compared to ALS and HCs, showed lower relative beta power in parietal, occipital, temporal and nearly all subcortical regions. Compared to HCs, patients with ALS and patients with bvFTD had a higher delta (0.5-4 Hz) and gamma (30-48 Hz) band resting-state functional connectivity in a high number of overlapping regions in the frontal lobe and in limbic and subcortical regions. Higher delta band connectivity was widespread in the bvFTD patients compared to HCs. ALS showed a more widespread higher gamma band functional connectivity compared to bvFTD. In conclusion, MEG in early symptomatic ALS patients shows resting-state functional connectivity changes in frontal, limbic and subcortical regions that overlap considerably with bvFTD. The findings show the potential of MEG to detect brain changes in early symptomatic phases of ALS and contribute to our understanding of the disease spectrum, with ALS and bvFTD at the two extreme ends.

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.

Moving Toward Patient-Tailored Treatment in ALS and FTD: The Potential of Genomic Assessment as a Tool for Biological Discovery and Trial Recruitment

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are two devastating and intertwined neurodegenerative diseases. Historically, ALS and FTD were considered distinct disorders given differences in presenting clinical symptoms, disease duration, and predicted risk of developing each disease. However, research over recent years has highlighted the considerable clinical, pathological, and genetic overlap of ALS and FTD, and these two syndromes are now thought to represent different manifestations of the same neuropathological disease spectrum. In this review, we discuss the need to shift our focus from studying ALS and FTD in isolation to identifying the biological mechanisms that drive these diseases-both common and distinct-to improve treatment discovery and therapeutic development success. We also emphasize the importance of genomic data to facilitate a "precision medicine" approach for treating ALS and FTD.

Cortical progression patterns in individual ALS patients across multiple timepoints: a mosaic-based approach for clinical use

INTRODUCTION

The majority of imaging studies in ALS infer group-level imaging signatures from group comparisons, as opposed to estimating disease burden in individual patients. In a condition with considerable clinical heterogeneity, the characterisation of individual patterns of pathology is hugely relevant. In this study, we evaluate a strategy to track progressive cortical involvement in single patients by using subject-specific reference cohorts.

METHODS

We have interrogated a multi-timepoint longitudinal dataset of 61 ALS patients to demonstrate the utility of estimating cortical disease burden and the expansion of cerebral atrophy over time. We contrast our strategy to the gold-standard approach to gauge the advantages and drawbacks of our method. We modelled the evolution of cortical integrity in a conditional growth model, in which we accounted for age, gender, disability, symptom duration, education and handedness. We hypothesised that the variance associated with demographic variables will be successfully eliminated in our approach.

RESULTS

In our model, the only covariate which modulated the expansion of atrophy was motor disability as measured by the ALSFRS-r (t(153) = - 2.533, p = 0.0123). Using the standard approach, age also significantly influenced progression of CT change (t(153) = - 2.151, p = 0.033) demonstrating the validity and potential clinical utility of our approach.

CONCLUSION

Our strategy of estimating the extent of cortical atrophy in individual patients with ALS successfully corrects for demographic factors and captures relevant cortical changes associated with clinical disability. Our approach provides a framework to interpret single T1-weighted images in ALS and offers an opportunity to track cortical propagation patterns both at individual subject level and at cohort level.

The imaging signature of C9orf72 hexanucleotide repeat expansions: implications for clinical trials and therapy development

While C9orf72-specific imaging signatures have been proposed by both ALS and FTD research groups and considerable presymptomatic alterations have also been confirmed in young mutation carriers, considerable inconsistencies exist in the literature. Accordingly, a systematic review of C9orf72-imaging studies has been performed to identify consensus findings, stereotyped shortcomings, and unique contributions to outline future directions. A formal literature review was conducted according to the STROBE guidelines. All identified papers were individually reviewed for sample size, choice of controls, study design, imaging modalities, statistical models, clinical profiling, and identified genotype-associated pathological patterns. A total of 74 imaging papers were systematically reviewed. ALS patients with GGGGCC repeat expansions exhibit relatively limited motor cortex involvement and widespread extra-motor pathology. C9orf72 positive FTD patients often show preferential posterior involvement. Reports of thalamic involvement are relatively consistent across the various phenotypes. Asymptomatic hexanucleotide repeat carriers often exhibit structural and functional changes decades prior to symptom onset. Common shortcomings included sample size limitations, lack of disease-controls, limited clinical profiling, lack of genetic testing in healthy controls, and absence of post mortem validation. There is a striking paucity of longitudinal studies and existing presymptomatic studies have not evaluated the predictive value of radiological changes with regard to age of onset and phenoconversion. With the advent of antisense oligonucleotide therapies, the meticulous characterisation of C9orf72-associated changes has gained practical relevance. Neuroimaging offers non-invasive biomarkers for future clinical trials, presymptomatic ascertainment, diagnostic and prognostic applications.

Neuropathology of Speech Network Distinguishes Bulbar From Nonbulbar Amyotrophic Lateral Sclerosis

Bulbar amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative subtype affecting speech and swallowing motor functions as well as associated with the burden of cognitive deficits. The neuroanatomical underpinnings of bulbar ALS are not well understood. The aim of this study was to compare neuropathology of the speech network (SpN) between 3 cases of bulbar-onset ALS (bALS), 3 cases of spinal-onset ALS (sALS) with antemortem bulbar ALS (sALSwB) against 3 sALS without antemortem bulbar ALS (sALSnoB) and 3 controls. Regional distribution and severity of neuronal loss, TDP-43 (transactive response DNA-binding protein of 43 kDa), and tau proteinopathy were examined. All 3 bALS cases showed marked neuronal loss and severe proteinopathy across most SpN regions; sALSwB cases showed no neuronal loss but mild and variable TDP-43 pathology in focal regions; sALSnoB cases demonstrated an absence of pathology. Two bALS cases had coexisting tauopathy in SpN regions, which was not noted in any sALS cases. The findings suggested that bALS may have a distinct neuropathological signature characterized by marked neuronal loss and polypathology in the SpN. Milder TDP-43 pathology in the SpN for sALSwB cases suggested a link between severity of bulbar ALS and SpN damage. Findings support a clinicopathologic link between bulbar symptoms and pathology in the SpN.

Mindfulness Meditation Is Related to Long-Lasting Changes in Hippocampal Functional Topology during Resting State: A Magnetoencephalography Study

It has been suggested that the practice of meditation is associated to neuroplasticity phenomena, reducing age-related brain degeneration and improving cognitive functions. Neuroimaging studies have shown that the brain connectivity changes in meditators. In the present work, we aim to describe the possible long-term effects of meditation on the brain networks. To this aim, we used magnetoencephalography to study functional resting-state brain networks in Vipassana meditators. We observed topological modifications in the brain network in meditators compared to controls. More specifically, in the theta band, the meditators showed statistically significant (p corrected = 0.009) higher degree (a centrality index that represents the number of connections incident upon a given node) in the right hippocampus as compared to controls. Taking into account the role of the hippocampus in memory processes, and in the pathophysiology of Alzheimer's disease, meditation might have a potential role in a panel of preventive strategies.

Amnestic Mild Cognitive Impairment Is Associated With Frequency-Specific Brain Network Alterations in Temporal Poles

There is general agreement that the neuropathological processes leading to Alzheimer’s disease (AD) begin decades before the clinical onset. In order to detect early topological changes, we applied functional connectivity and network analysis to magnetoencephalographic (MEG) data obtained from 16 patients with amnestic Mild Cognitive Impairment (aMCI), a prodromal stage of AD, and 16 matched healthy control (HCs). Significant differences between the two groups were found in the theta band, which is associated with memory processes, in both temporal poles (TPs). In aMCI, the degree and betweenness centrality (BC) were lower in the left superior TP, whereas in the right middle TP the BC was higher. A statistically significant negative linear correlation was found between the BC of the left superior TP and a delayed recall score, a sensitive marker of the “hippocampal memory” deficit in early AD. Our results suggest that the TPs, which are involved early in AD pathology and belong to the memory circuitry, have an altered role in the functional network in aMCI.

Brain functional networks become more connected as amyotrophic lateral sclerosis progresses: a source level magnetoencephalographic study

This study hypothesizes that the brain shows hyper connectedness as amyotrophic lateral sclerosis (ALS) progresses. 54 patients (classified as "early stage" or "advanced stage") and 25 controls underwent magnetoencephalography and MRI recordings. The activity of the brain areas was reconstructed, and the synchronization between them was estimated in the classical frequency bands using the phase lag index. Brain topological metrics such as the leaf fraction (number of nodes with degree of 1), the degree divergence (a measure of the scale-freeness) and the degree correlation (a measure of disassortativity) were estimated. Betweenness centrality was used to estimate the centrality of the brain areas. In all frequency bands, it was evident that, the more advanced the disease, the more connected, scale-free and disassortative the brain networks. No differences were evident in specific brain areas. Such modified brain topology is sub-optimal as compared to controls. Within this framework, our study shows that brain networks become more connected according to disease staging in ALS patients.