Development of an Automated MRI-Based Diagnostic Protocol for Amyotrophic Lateral Sclerosis Using Disease-Specific Pathognomonic Features: A Quantitative Disease-State Classification Study

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.

The involvement of language-associated networks, tracts, and cortical regions in frontotemporal dementia and amyotrophic lateral sclerosis: Structural and functional alterations

BACKGROUND

Language deficits are cardinal manifestations of some frontotemporal dementia (FTD) phenotypes and also increasingly recognized in sporadic and familial amyotrophic lateral sclerosis (ALS). They have considerable social and quality-of-life implications, and adaptive strategies are challenging to implement. While the neuropsychological profiles of ALS-FTD phenotypes are well characterized, the neuronal underpinnings of language deficits are less well studied.

METHODS

A multiparametric, quantitative neuroimaging study was conducted to characterize the involvement of language-associated networks, tracts, and cortical regions with a panel of structural, diffusivity, and functional magnetic resonance imaging (MRI) metrics. Seven study groups were evaluated along the ALS-FTD spectrum: healthy controls (HC), individuals with ALS without cognitive impairment (ALSnci), C9orf72-negative ALS-FTD, C9orf72-positive ALS-FTD, behavioral-variant FTD (bvFTD), nonfluent variant primary progressive aphasia (nfvPPA), and semantic variant PPA (svPPA). The integrity of the Broca's area, Wernicke's area, frontal aslant tract (FAT), arcuate fascicle (AF), inferior occipitofrontal fascicle (IFO), inferior longitudinal fascicle (ILF), superior longitudinal fascicle (SLF), and uncinate fascicle (UF) was quantitatively evaluated. The functional connectivity (FC) between Broca's and Wernicke' areas and FC along the FAT was also specifically assessed.

RESULTS

Patients with nfvPPA and svPPA exhibit distinctive patterns of gray and white matter degeneration in language-associated brain regions. Individuals with bvFTD exhibit Broca's area, right FAT, right IFO, and UF degeneration. The ALSnci group exhibits Broca's area atrophy and decreased FC along the FAT. Both ALS-FTD cohorts, irrespective of C9orf72 status, show bilateral FAT, AF, and IFO pathology. Interestingly, only C9orf72-negative ALS-FTD patients exhibit bilateral uncinate and right ILF involvement, while C9orf72-positive ALS-FTD patients do not.

CONCLUSIONS

Language-associated tracts and networks are not only affected in language-variant FTD phenotypes but also in ALS and bvFTD. Language domains should be routinely assessed in ALS irrespective of the genotype.

Presymptomatic grey matter alterations in ALS kindreds: a computational neuroimaging study of asymptomatic C9orf72 and SOD1 mutation carriers

BACKGROUND

The characterisation of presymptomatic disease-burden patterns in asymptomatic mutation carriers has a dual academic and clinical relevance. The understanding of disease propagation mechanisms is of considerable conceptual interests, and defining the optimal time of pharmacological intervention is essential for improved clinical trial outcomes.

METHODS

In a prospective, multimodal neuroimaging study, 22 asymptomatic C9orf72 GGGGCC hexanucleotide repeat carriers, 13 asymptomatic subjects with SOD1, and 54 "gene-negative" ALS kindreds were enrolled. Cortical and subcortical grey matter alterations were systematically appraised using volumetric, morphometric, vertex, and cortical thickness analyses. Using a Bayesian approach, the thalamus and amygdala were further parcellated into specific nuclei and the hippocampus was segmented into anatomically defined subfields.

RESULTS

Asymptomatic GGGGCC hexanucleotide repeat carriers in C9orf72 exhibited early subcortical changes with the preferential involvement of the pulvinar and mediodorsal regions of the thalamus, as well as the lateral aspect of the hippocampus. Volumetric approaches, morphometric methods, and vertex analyses were anatomically consistent in capturing focal subcortical changes in asymptomatic C9orf72 hexanucleotide repeat expansion carriers. SOD1 mutation carriers did not exhibit significant subcortical grey matter alterations. In our study, none of the two asymptomatic cohorts exhibited cortical grey matter alterations on either cortical thickness or morphometric analyses.

DISCUSSION

The presymptomatic radiological signature of C9orf72 is associated with selective thalamic and focal hippocampal degeneration which may be readily detectable before cortical grey matter changes ensue. Our findings confirm selective subcortical grey matter involvement early in the course of C9orf72-associated neurodegeneration.

Brainstem-cortex disconnection in amyotrophic lateral sclerosis: bulbar impairment, genotype associations, asymptomatic changes and biomarker opportunities

BACKGROUND

Bulbar dysfunction is a cardinal feature of ALS with important quality of life and management implications. The objective of this study is the longitudinal evaluation of a large panel imaging metrics pertaining to bulbar dysfunction, encompassing cortical measures, structural and functional cortico-medullary connectivity indices and brainstem metrics.

METHODS

A standardised, multimodal imaging protocol was implemented with clinical and genetic profiling to systematically appraise the biomarker potential of specific metrics. A total of 198 patients with ALS and 108 healthy controls were included.

RESULTS

Longitudinal analyses revealed progressive structural and functional disconnection between the motor cortex and the brainstem over time. Cortical thickness reduction was an early feature on cross-sectional analyses with limited further progression on longitudinal follow-up. Receiver operating characteristic analyses of the panel of MR metrics confirmed the discriminatory potential of bulbar imaging measures between patients and controls and area-under-the-curve values increased significantly on longitudinal follow-up. C9orf72 carriers exhibited lower brainstem volumes, lower cortico-medullary structural connectivity and faster cortical thinning. Sporadic patients without bulbar symptoms, already exhibit significant brainstem and cortico-medullary connectivity alterations.

DISCUSSION

Our results indicate that ALS is associated with multi-level integrity change from cortex to brainstem. The demonstration of significant corticobulbar alterations in patients without bulbar symptoms confirms considerable presymptomatic disease burden in sporadic ALS. The systematic assessment of radiological measures in a single-centre academic study helps to appraise the diagnostic and monitoring utility of specific measures for future clinical and clinical trial applications.

Hippocampal Metabolic Alterations in Amyotrophic Lateral Sclerosis: A Magnetic Resonance Spectroscopy Study

BACKGROUND

Magnetic resonance spectroscopy (MRS) in amyotrophic lateral sclerosis (ALS) has been overwhelmingly applied to motor regions to date and our understanding of frontotemporal metabolic signatures is relatively limited. The association between metabolic alterations and cognitive performance in also poorly characterised.

MATERIAL AND METHODS

In a multimodal, prospective pilot study, the structural, metabolic, and diffusivity profile of the hippocampus was systematically evaluated in patients with ALS. Patients underwent careful clinical and neurocognitive assessments. All patients were non-demented and exhibited normal memory performance. 1H-MRS spectra of the right and left hippocampi were acquired at 3.0T to determine the concentration of a panel of metabolites. The imaging protocol also included high-resolution T1-weighted structural imaging for subsequent hippocampal grey matter (GM) analyses and diffusion tensor imaging (DTI) for the tractographic evaluation of the integrity of the hippocampal perforant pathway zone (PPZ).

RESULTS

ALS patients exhibited higher hippocampal tNAA, tNAA/tCr and tCho bilaterally, despite the absence of volumetric and PPZ diffusivity differences between the two groups. Furthermore, superior memory performance was associated with higher hippocampal tNAA/tCr bilaterally. Both longer symptom duration and greater functional disability correlated with higher tCho levels.

CONCLUSION

Hippocampal 1H-MRS may not only contribute to a better academic understanding of extra-motor disease burden in ALS, but given its sensitive correlations with validated clinical metrics, it may serve as practical biomarker for future clinical and clinical trial applications. Neuroimaging protocols in ALS should incorporate MRS in addition to standard structural, functional, and diffusion sequences.

Thalamic pathology in frontotemporal dementia: Predilection for specific nuclei, phenotype-specific signatures, clinical correlates, and practical relevance

BACKGROUND

Frontotemporal dementia (FTD) phenotypes are classically associated with distinctive cortical atrophy patterns and regional hypometabolism. However, the spectrum of cognitive and behavioral manifestations in FTD arises from multisynaptic network dysfunction. The thalamus is a key hub of several corticobasal and corticocortical circuits. The main circuits relayed via the thalamic nuclei include the dorsolateral prefrontal circuit, the anterior cingulate circuit, and the orbitofrontal circuit.

METHODS

In this paper, we have reviewed evidence for thalamic pathology in FTD based on radiological and postmortem studies. Original research papers were systematically reviewed for preferential involvement of specific thalamic regions, for phenotype-associated thalamic disease burden patterns, characteristic longitudinal changes, and genotype-associated thalamic signatures. Moreover, evidence for presymptomatic thalamic pathology was also reviewed. Identified papers were systematically scrutinized for imaging methods, cohort sizes, clinical profiles, clinicoradiological associations, and main anatomical findings. The findings of individual research papers were amalgamated for consensus observations and their study designs further evaluated for stereotyped shortcomings. Based on the limitations of existing studies and conflicting reports in low-incidence FTD variants, we sought to outline future research directions and pressing research priorities.

RESULTS

FTD is associated with focal thalamic degeneration. Phenotype-specific thalamic traits mirror established cortical vulnerability patterns. Thalamic nuclei mediating behavioral and language functions are preferentially involved. Given the compelling evidence for considerable thalamic disease burden early in the course of most FTD subtypes, we also reflect on the practical relevance, diagnostic role, prognostic significance, and monitoring potential of thalamic metrics in FTD.

CONCLUSIONS

Cardinal manifestations of FTD phenotypes are likely to stem from thalamocortical circuitry dysfunction and are not exclusively driven by focal cortical changes.

Diffusion Tensor Imaging in Amyotrophic Lateral Sclerosis: Machine Learning for Biomarker Development

Diffusion tensor imaging (DTI) allows the in vivo imaging of pathological white matter alterations, either with unbiased voxel-wise or hypothesis-guided tract-based analysis. Alterations of diffusion metrics are indicative of the cerebral status of patients with amyotrophic lateral sclerosis (ALS) at the individual level. Using machine learning (ML) models to analyze complex and high-dimensional neuroimaging data sets, new opportunities for DTI-based biomarkers in ALS arise. This review aims to summarize how different ML models based on DTI parameters can be used for supervised diagnostic classifications and to provide individualized patient stratification with unsupervised approaches in ALS. To capture the whole spectrum of neuropathological signatures, DTI might be combined with additional modalities, such as structural T1w 3-D MRI in ML models. To further improve the power of ML in ALS and enable the application of deep learning models, standardized DTI protocols and multi-center collaborations are needed to validate multimodal DTI biomarkers. The application of ML models to multiparametric MRI/multimodal DTI-based data sets will enable a detailed assessment of neuropathological signatures in patients with ALS and the development of novel neuroimaging biomarkers that could be used in the clinical workup.

Cerebellar pathology in motor neuron disease: neuroplasticity and neurodegeneration

Amyotrophic lateral sclerosis is a relentlessly progressive multi-system condition. The clinical picture is dominated by upper and lower motor neuron degeneration, but extra-motor pathology is increasingly recognized, including cerebellar pathology. Post-mortem and neuroimaging studies primarily focus on the characterization of supratentorial disease, despite emerging evidence of cerebellar degeneration in amyotrophic lateral sclerosis. Cardinal clinical features of amyotrophic lateral sclerosis, such as dysarthria, dysphagia, cognitive and behavioral deficits, saccade abnormalities, gait impairment, respiratory weakness and pseudobulbar affect are likely to be exacerbated by co-existing cerebellar pathology. This review summarizes in vivo and post mortem evidence for cerebellar degeneration in amyotrophic lateral sclerosis. Structural imaging studies consistently capture cerebellar grey matter volume reductions, diffusivity studies readily detect both intra-cerebellar and cerebellar peduncle white matter alterations and functional imaging studies commonly report increased functional connectivity with supratentorial regions. Increased functional connectivity is commonly interpreted as evidence of neuroplasticity representing compensatory processes despite the lack of post-mortem validation. There is a scarcity of post-mortem studies focusing on cerebellar alterations, but these detect pTDP-43 in cerebellar nuclei. Cerebellar pathology is an overlooked facet of neurodegeneration in amyotrophic lateral sclerosis despite its contribution to a multitude of clinical symptoms, widespread connectivity to spinal and supratentorial regions and putative role in compensating for the degeneration of primary motor regions.

Neuroimaging in hereditary spastic paraplegias: from qualitative cues to precision biomarkers

INTRODUCTION

: Hereditary spastic paraplegias (HSP) include a clinically and genetically heterogeneous group of conditions. Novel imaging modalities have been increasingly applied to HSP cohorts which helps to quantitatively evaluate the integrity of specific anatomical structures and develop monitoring markers for both clinical care and future clinical trials.

AREAS COVERED

: Advances in HSP imaging are systematically reviewed with a focus on cohort sizes, imaging modalities, study design, clinical correlates, methodological approaches, and key findings.

EXPERT OPINION

: A wide range of imaging techniques have been recently applied to HSP cohorts. Common shortcomings of existing studies include the evaluation of genetically unconfirmed or admixed cohorts, limited sample sizes, unimodal imaging approaches, lack of postmortem validation, and a limited clinical battery, often exclusively focusing on motor aspects of the condition. A number of innovative methodological approaches have also be identified, such as robust longitudinal study designs, the implementation of multimodal imaging protocols, complementary cognitive assessments, and the comparison of HSP cohorts to MND cohorts. Collaborative multicentre initiatives may overcome sample limitations, and comprehensive clinical profiling with motor, extrapyramidal, cerebellar, and neuropsychological assessments would permit systematic clinico-radiological correlations. Academic achievements in HSP imaging have the potential to be developed into viable clinical applications to expedite the diagnosis and monitor disease progression.

Mapping cortical disease-burden at individual-level in frontotemporal dementia: implications for clinical care and pharmacological trials

Imaging studies of FTD typically present group-level statistics between large cohorts of genetically, molecularly or clinically stratified patients. Group-level statistics are indispensable to appraise unifying radiological traits and describe genotype-associated signatures in academic studies. However, in a clinical setting, the primary objective is the meaningful interpretation of imaging data from individual patients to assist diagnostic classification, inform prognosis, and enable the assessment of progressive changes compared to baseline scans. In an attempt to address the pragmatic demands of clinical imaging, a prospective computational neuroimaging study was undertaken in a cohort of patients across the spectrum of FTD phenotypes. Cortical changes were evaluated in a dual pipeline, using standard cortical thickness analyses and an individualised, z-score based approach to characterise subject-level disease burden. Phenotype-specific patterns of cortical atrophy were readily detected with both methodological approaches. Consistent with their clinical profiles, patients with bvFTD exhibited orbitofrontal, cingulate and dorsolateral prefrontal atrophy. Patients with ALS-FTD displayed precentral gyrus involvement, nfvPPA patients showed widespread cortical degeneration including insular and opercular regions and patients with svPPA exhibited relatively focal anterior temporal lobe atrophy. Cortical atrophy patterns were reliably detected in single individuals, and these maps were consistent with the clinical categorisation. Our preliminary data indicate that standard T1-weighted structural data from single patients may be utilised to generate maps of cortical atrophy. While the computational interpretation of single scans is challenging, it offers unrivalled insights compared to visual inspection. The quantitative evaluation of individual MRI data may aid diagnostic classification, clinical decision making, and assessing longitudinal changes.

Recent advances in the diagnosis and prognosis of amyotrophic lateral sclerosis

The diagnosis of amyotrophic lateral sclerosis can be challenging due to its heterogeneity in clinical presentation and overlap with other neurological disorders. Diagnosis early in the disease course can improve outcomes as timely interventions can slow disease progression. An evolving awareness of disease genotypes and phenotypes and new diagnostic criteria, such as the recent Gold Coast criteria, could expedite diagnosis. Improved prognosis, such as that achieved with the survival model from the European Network for the Cure of ALS, could inform the patient and their family about disease course and improve end-of-life planning. Novel staging and scoring systems can help monitor disease progression and might potentially serve as clinical trial outcomes. Lastly, new tools, such as fluid biomarkers, imaging modalities, and neuromuscular electrophysiological measurements, might increase diagnostic and prognostic accuracy.

Clusters of anatomical disease-burden patterns in ALS: a data-driven approach confirms radiological subtypes

Amyotrophic lateral sclerosis (ALS) is associated with considerable clinical heterogeneity spanning from diverse disability profiles, differences in UMN/LMN involvement, divergent progression rates, to variability in frontotemporal dysfunction. A multitude of classification frameworks and staging systems have been proposed based on clinical and neuropsychological characteristics, but disease subtypes are seldom defined based on anatomical patterns of disease burden without a prior clinical stratification. A prospective research study was conducted with a uniform imaging protocol to ascertain disease subtypes based on preferential cerebral involvement. Fifteen brain regions were systematically evaluated in each participant based on a comprehensive panel of cortical, subcortical and white matter integrity metrics. Using min–max scaled composite regional integrity scores, a two-step cluster analysis was conducted. Two radiological clusters were identified; 35.5% of patients belonging to ‘Cluster 1’ and 64.5% of patients segregating to ‘Cluster 2’. Subjects in Cluster 1 exhibited marked frontotemporal change. Predictor ranking revealed the following hierarchy of anatomical regions in decreasing importance: superior lateral temporal, inferior frontal, superior frontal, parietal, limbic, mesial inferior temporal, peri-Sylvian, subcortical, long association fibres, commissural, occipital, ‘sensory’, ‘motor’, cerebellum, and brainstem. While the majority of imaging studies first stratify patients based on clinical criteria or genetic profiles to describe phenotype- and genotype-associated imaging signatures, a data-driven approach may identify distinct disease subtypes without a priori patient categorisation. Our study illustrates that large radiology datasets may be potentially utilised to uncover disease subtypes associated with unique genetic, clinical or prognostic profiles.

The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation

INTRODUCTION

While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterised based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely on complex computational models and large training datasets to categorise individual patients into diagnostic subgroups based on distinguishing imaging features. Reliable individual subject data interpretation is hugely important in the clinical setting to expedite the diagnosis and classify individuals into relevant prognostic categories.

AREAS COVERED

This article reviews (1) the neuroimaging studies that propose single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) potential practical implications and (3) the limitations of current single-subject data interpretation models.

EXPERT OPINION

Classification studies in FTLD have demonstrated the feasibility of categorising individual subjects into diagnostic groups based on multiparametric imaging data. Preliminary data indicate that pre-symptomatic FTLD mutation carriers may also be reliably distinguished from controls. Despite momentous advances in the field, significant further improvements are needed before these models can be developed into viable clinical applications.

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.

White matter microstructure alterations in frontotemporal dementia: Phenotype-associated signatures and single-subject interpretation

BACKGROUND

Frontotemporal dementias (FTD) include a genetically heterogeneous group of conditions with distinctive molecular, radiological and clinical features. The majority of radiology studies in FTD compare FTD subgroups to healthy controls to describe phenotype- or genotype-associated imaging signatures. While the characterization of group-specific imaging traits is academically important, the priority of clinical imaging is the meaningful interpretation of individual datasets.

METHODS

To demonstrate the feasibility of single-subject magnetic resonance imaging (MRI) interpretation, we have evaluated the white matter profile of 60 patients across the clinical spectrum of FTD. A z-score-based approach was implemented, where the diffusivity metrics of individual patients were appraised with reference to demographically matched healthy controls. Fifty white matter tracts were systematically evaluated in each subject with reference to normative data.

RESULTS

The z-score-based approach successfully detected white matter pathology in single subjects, and group-level inferences were analogous to the outputs of standard track-based spatial statistics.

CONCLUSIONS

Our findings suggest that it is possible to meaningfully evaluate the diffusion profile of single FTD patients if large normative datasets are available. In contrast to the visual review of FLAIR and T2-weighted images, computational imaging offers objective, quantitative insights into white matter integrity changes even at single-subject level.

Phenotypic categorisation of individual subjects with motor neuron disease based on radiological disease burden patterns: A machine-learning approach

Motor neuron disease is an umbrella term encompassing a multitude of clinically heterogeneous phenotypes. The early and accurate categorisation of patients is hugely important, as MND phenotypes are associated with markedly different prognoses, progression rates, care needs and benefit from divergent management strategies. The categorisation of patients shortly after symptom onset is challenging, and often lengthy clinical monitoring is needed to assign patients to the appropriate phenotypic subgroup. In this study, a multi-class machine-learning strategy was implemented to classify 300 patients based on their radiological profile into diagnostic labels along the UMN-LMN spectrum. A comprehensive panel of cortical thickness measures, subcortical grey matter variables, and white matter integrity metrics were evaluated in a multilayer perceptron (MLP) model. Additional exploratory analyses were also carried out using discriminant function analyses (DFA). Excellent classification accuracy was achieved for amyotrophic lateral sclerosis in the testing cohort (93.7%) using the MLP model, but poor diagnostic accuracy was detected for primary lateral sclerosis (43.8%) and poliomyelitis survivors (60%). Feature importance analyses highlighted the relevance of white matter diffusivity metrics and the evaluation of cerebellar indices, cingulate measures and thalamic radiation variables to discriminate MND phenotypes. Our data suggest that radiological data from single patients may be meaningfully interpreted if large training data sets are available and the provision of diagnostic probability outcomes may be clinically useful in patients with short symptom duration. The computational interpretation of multimodal radiology datasets herald viable diagnostic, prognostic and clinical trial applications.

Multiparametric Microstructural MRI and Machine Learning Classification Yields High Diagnostic Accuracy in Amyotrophic Lateral Sclerosis: Proof of Concept

The potential of multiparametric quantitative neuroimaging has been extensively discussed as a diagnostic tool in amyotrophic lateral sclerosis (ALS). In the past, the integration of multimodal, quantitative data into a useful diagnostic classifier was a major challenge. With recent advances in the field, machine learning in a data driven approach is a potential solution: neuroimaging biomarkers in ALS are mainly observed in the cerebral microstructure, with diffusion tensor imaging (DTI) and texture analysis as promising approaches. We set out to combine these neuroimaging markers as age-corrected features in a machine learning model with a cohort of 502 subjects, divided into 404 patients with ALS and 98 healthy controls. We calculated a linear support vector classifier (SVC) which is a very robust model and then verified the results with a multilayer perceptron (MLP)/neural network. Both classifiers were able to separate ALS patients from controls with receiver operating characteristic (ROC) curves showing an area under the curve (AUC) of 0.87-0.88 ("good") for the SVC and 0.88-0.91 ("good" to "excellent") for the MLP. Among the coefficients of the SVC, texture data contributed the most to a correct classification. We consider these results as a proof of concept that demonstrated the power of machine learning in the application of multiparametric quantitative neuroimaging data to 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.

Propagation patterns in motor neuron diseases: Individual and phenotype-associated disease-burden trajectories across the UMN-LMN spectrum of MNDs

Motor neuron diseases encompass a divergent group of conditions with considerable differences in clinical manifestations, survival, and genetic vulnerability. One of the key aspects of clinical heterogeneity is the preferential involvement of upper (UMN) and lower motor neurons (LMN). While longitudinal imaging patters are relatively well characterized in ALS, progressive cortical changes in UMN,- and LMN-predominant conditions are seldom evaluated. Accordingly, the objective of this study is the juxtaposition of longitudinal trajectories in 3 motor neuron phenotypes; a UMN-predominant syndrome (PLS), a mixed UMN-LMN condition (ALS), and a lower motor neuron condition (poliomyelitis survivors). A standardized imaging protocol was implemented in a prospective, multi-timepoint longitudinal study with a uniform follow-up interval of 4 months. Forty-five poliomyelitis survivors, 61 patients with amyotrophic lateral sclerosis (ALS), and 23 patients with primary lateral sclerosis (PLS) were included. Cortical thickness alterations were evaluated in a dual analysis pipeline, using standard cortical thickness analyses, and a z-score-based individualized approach. Our results indicate that PLS patients exhibit rapidly progressive cortical thinning primarily in motor regions; ALS patients show cortical atrophy in both motor and extra-motor regions, while poliomyelitis survivors exhibit cortical thickness gains in a number of cerebral regions. Our findings suggest that dynamic cortical changes in motor neuron diseases may depend on relative UMN and/or LMN involvement, and increased cortical thickness in LMN-predominant conditions may represent compensatory, adaptive processes.

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.

Evaluation and categorisation of individual patients based on white matter profiles: Single-patient diffusion data interpretation in neurodegeneration

The majority of radiology studies in neurodegenerative conditions infer group-level imaging traits from group comparisons. While this strategy is helpful to define phenotype-specific imaging signatures for academic use, the meaningful interpretation of single scans of individual subjects is more important in everyday clinical practice. Accordingly, we present a computational method to evaluate individual subject diffusion tensor data to highlight white matter integrity alterations. Fifty white matter tracts were quantitatively evaluated in 132 patients with amyotrophic lateral sclerosis (ALS) with respect to normative values from 100 healthy subjects. Fractional anisotropy and radial diffusivity alterations were assessed individually in each patient. The approach was validated against standard tract-based spatial statistics and further scrutinised by the assessment of 78 additional data sets with a blinded diagnosis. Our z-score-based approach readily detected white matter degeneration in individual ALS patients and helped to categorise single subjects with a 'blinded diagnosis' as likely 'ALS' or 'control'. The group-level inferences from the z-score-based approach were analogous to the standard TBSS output maps. The benefit of the z-score-based strategy is that it enables the interpretation of single DTI datasets as well as the comparison of study groups. Outputs can be summarised either visually by highlighting the affected tracts, or, listing the affected tracts in a text file with reference to normative data, making it particularly useful for clinical applications. While individual diffusion data cannot be visually appraised, our approach provides a viable framework for single-subject imaging data interpretation.

Primary Lateral Sclerosis: Clinical, radiological and molecular features

Primary Lateral Sclerosis (PLS) is an uncommon motor neuron disorder. Despite the well-recognisable constellation of clinical manifestations, the initial diagnosis can be challenging and therapeutic options are currently limited. There have been no recent clinical trials of disease-modifying therapies dedicated to this patient cohort and awareness of recent research developments is limited. The recent consensus diagnostic criteria introduced the category 'probable' PLS which is likely to curtail the diagnostic journey of patients. Extra-motor clinical manifestations are increasingly recognised, challenging the view of PLS as a 'pure' upper motor neuron condition. The post mortem literature of PLS has been expanded by seminal TDP-43 reports and recent PLS studies increasingly avail of meticulous genetic profiling. Research in PLS has gained unprecedented momentum in recent years generating novel academic insights, which may have important clinical ramifications.

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review

INTRODUCTION

The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackling of rare diseases. In this scenario, amyotrophic lateral sclerosis (ALS) involves complexities that are yet not demystified. In ALS, the biomedical signals present themselves as potential biomarkers that, when used in tandem with smart algorithms, can be useful to applications within the context of the disease.

METHODS

This Systematic Literature Review (SLR) consists of searching for and investigating primary studies that use ML techniques and biomedical signals related to ALS. Following the definition and execution of the SLR protocol, 18 articles met the inclusion, exclusion, and quality assessment criteria, and answered the SLR research questions.

DISCUSSIONS

Based on the results, we identified three classes of ML applications combined with biomedical signals in the context of ALS: diagnosis (72.22%), communication (22.22%), and survival prediction (5.56%).

CONCLUSIONS

Distinct algorithmic models and biomedical signals have been reported and present promising approaches, regardless of their classes. In summary, this SLR provides an overview of the primary studies analyzed as well as directions for the construction and evolution of technology-based research within the scope of ALS.

Extra-motor manifestations in post-polio syndrome (PPS): fatigue, cognitive symptoms and radiological features

BACKGROUND

There is a paucity of cerebral neuroimaging studies in post-polio syndrome (PPS), despite the severity of neurological and neuropsychological sequelae associated with the condition. Fatigue, poor concentration, limited exercise tolerance, paraesthesia and progressive weakness are frequently reported, but the radiological underpinnings of these symptoms are poorly characterised.

OBJECTIVE

The aim of this study is to evaluate cortical and subcortical alterations in a cohort of adult polio survivors to explore the anatomical substrate of extra-motor manifestations.

METHODS

Thirty-six patients with post-polio syndrome, a disease-control group with amyotrophic lateral sclerosis patients and a cohort of healthy individuals were included in a prospective neuroimaging study with a standardised clinical and radiological protocol. Validated clinical instruments were utilised to assess mood, cognitive and behavioural domains and specific aspects of fatigue. Cortical thickness analyses, subcortical volumetry, brainstem segmentation and region-of-interest (ROI) white matter analyses were undertaken to assess regional grey and white matter alterations.

RESULTS

A high proportion of PPS patients exhibited apathy, verbal fluency deficits and reported self-perceived fatigue. On ROI analyses, cortical atrophy was limited to the cingulate gyrus, and the temporal pole and subcortical atrophy were only detected in the left nucleus accumbens. No FA reductions were noted to indicate white matter degeneration in any of the lobes.

CONCLUSIONS

Despite the high incidence of extra-motor manifestations in PPS, only limited cortical, subcortical and white matter degeneration was identified. Our findings suggest that non-structural causes, such as polypharmacy and poor sleep, may contribute to the complex symptomatology of post-polio syndrome.

Regional prefrontal cortical atrophy predicts specific cognitive-behavioral symptoms in ALS-FTD

Amyotrophic Lateral Sclerosis-Frontotemporal Dementia (ALS-FTD) may present typical behavioral variant FTD symptoms. This study aims to determine whether profile and severity of cognitive-behavioral symptoms in ALS/ALS-FTD are predicted by regional cortical atrophy. The hypothesis is that executive dysfunction can be predicted by dorsolateral prefrontal cortical (dlPFC) atrophy, apathy by dorsomedial PFC (dmPFC) and anterior cingulate cortical (ACC) atrophy, disinhibition by orbitofrontal cortical (OFC) atrophy. 3.0 Tesla MRI scans were acquired from 22 people with ALS or ALS-FTD. Quantitative cortical thickness analysis was performed with FreeSurfer. A priori-defined regions of interest (ROI) were used to measure cortical thickness in each participant and calculate magnitude of atrophy in comparison to 115 healthy controls. Spearman correlations were used to evaluate associations between frontal ROI cortical thickness and cognitive-behavioral symptoms, measured by Neuropsychiatric Inventory Questionnaire (NPI-Q) and Clinical Dementia Rating (CDR) scale. ALS-FTD participants exhibited variable degrees of apathy (NPI-Q/apathy: 1.6 ± 1.2), disinhibition (NPI-Q/disinhibition: 1.2 ± 1.2), executive dysfunction (CDR/judgment-problem solving: 1.7 ± 0.8). Within the ALS-FTD group, executive dysfunction correlated with dlPFC atrophy (ρ:-0.65;p < 0.05); similar trends were seen for apathy with ACC (ρ:-0.53;p < 0.10) and dmPFC (ρ:-0.47;p < 0.10) atrophy, for disinhibition with OFC atrophy (ρ:-0.51;p < 0.10). Compared to people with ALS, those with ALS-FTD showed more diffuse atrophy involving precentral gyrus, prefrontal, temporal regions. Profile and severity of cognitive-behavioral symptoms in ALS-FTD are predicted by regional prefrontal atrophy. These findings are consistent with established brain-behavior models and support the role of quantitative MRI in diagnosis, management, counseling, monitoring and prognostication for a neurodegenerative disorder with diverse phenotypes.

Extra-motor cerebral changes and manifestations in primary lateral sclerosis

Primary lateral sclerosis (PLS) is classically considered a 'pure' upper motor neuron disorder. Motor cortex atrophy and pyramidal tract degeneration are thought to be pathognomonic of PLS, but extra-motor cerebral changes are poorly characterized. In a prospective neuroimaging study, forty PLS patients were systematically evaluated with a standardised imaging, genetic and clinical protocol. Patients were screened for ALS and HSP associated mutations, as well as C9orf72 hexanucleotide repeats. Clinical assessment included composite reflex scores, spasticity scales, functional rating scales, and screening for cognitive and behavioural deficits. The neuroimaging protocol evaluated cortical atrophy patterns, subcortical grey matter changes and white matter alterations in whole-brain and region-of-interest analyses. PLS patients tested negative for known ALS- and HSP-associated mutations and C9orf72 repeat expansions. Voxel-wise analyses revealed anterior cingulate, dorsolateral prefrontal, insular, opercular, orbitofrontal and bilateral mesial temporal grey matter changes and white matter alterations in the fornix, brainstem, temporal lobes, and cerebellum. Significant thalamus, caudate, hippocampus, putamen and accumbens nucleus volume reductions were also identified. Extra-motor clinical manifestations were dominated by verbal fluency deficits, language deficits, apathy and pseudobulbar affect. Our clinical and radiological evaluation confirms considerable extra-motor changes in a population-based cohort of PLS patients. Our data suggest that PLS should no longer be considered a neurodegenerative disorder selectively affecting the pyramidal system. PLS is associated with widespread extra-motor changes and manifestations which should be carefully considered in the multidisciplinary management of this low-incidence condition.

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.

Neuroimaging in primary lateral sclerosis

Increased interest in the underlying pathogenesis of primary lateral sclerosis (PLS) and its relationship to amyotrophic lateral sclerosis (ALS) has corresponded to a growing number of CNS imaging studies, especially in the past decade. Both its rarity and uncertainty of definite diagnosis prior to 4 years from symptom onset have resulted in PLS being less studied than ALS. In this review, we highlight most relevant papers applying magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and positron emission tomography (PET) to analyzing CNS changes in PLS, often in relation to ALS. In patients with PLS, mostly brain, but also spinal cord has been evaluated since significant neurodegeneration is essentially restricted to upper motor neuron (UMN) structures and related pathways. Abnormalities of cortex and subcortical white matter tracts have been identified by structural and functional MRI and MRS studies, while metabolic and cell-specific changes in PLS brain have been revealed using various PET radiotracers. Future neuroimaging studies will continue to explore the interface between the PLS-ALS continuum, identify more changes unique to PLS, apply novel MRI and MRS sequences showing greater structural and neurochemical detail, as well as expand the repertoire of PET radiotracers that reveal various cellular pathologies. Neuroimaging has the potential to play an important role in the evaluation of novel therapies for patients with PLS.

The presymptomatic phase of amyotrophic lateral sclerosis: are we merely scratching the surface?

Presymptomatic studies in ALS have consistently captured considerable disease burden long before symptom manifestation and contributed important academic insights. With the emergence of genotype-specific therapies, however, there is a pressing need to address practical objectives such as the estimation of age of symptom onset, phenotypic prediction, informing the optimal timing of pharmacological intervention, and identifying a core panel of biomarkers which may detect response to therapy. Existing presymptomatic studies in ALS have adopted striking different study designs, relied on a variety of control groups, used divergent imaging and electrophysiology methods, and focused on different genotypes and demographic groups. We have performed a systematic review of existing presymptomatic studies in ALS to identify common themes, stereotyped shortcomings, and key learning points for future studies. Existing presymptomatic studies in ALS often suffer from sample size limitations, lack of disease controls and rarely follow their cohort until symptom manifestation. As the characterisation of presymptomatic processes in ALS serves a multitude of academic and clinical purposes, the careful review of existing studies offers important lessons for future initiatives.

MRI data confirm the selective involvement of thalamic and amygdalar nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis

A standardised imaging protocol was implemented to evaluate disease burden in specific thalamic and amygdalar nuclei in 133 carefully phenotyped and genotyped motor neuron disease patients. “Switchboard malfunction in motor neuron diseases: selective pathology of thalamic nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis” [1] “Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis” [2] Raw volumetric data, group comparisons, effect sizes and percentage change are presented. Both ALS and PLS patients exhibited focal thalamus atrophy in ventral lateral and ventral anterior regions revealing extrapyramidal motor degeneration. Reduced accessory basal nucleus and cortical nucleus volumes were noted in the amygdala of C9orf72 negative ALS patients compared to healthy controls. ALS patients carrying the GGGGCC hexanucleotide repeats in C9orf72 exhibited preferential pathology in the mediodorsal-paratenial-reuniens thalamic nuclei and in the lateral nucleus and cortico-amygdaloid transition area of the amygdala. Considerable thalamic atrophy was observed in the sensory nuclei and lateral geniculate region of PLS patients. Our data demonstrate genotype-specific patterns of thalamus and amygdala involvement in ALS and a distinct disease-burden pattern in PLS. The dataset may be utilised for validation purposes, meta-analyses and the interpretation of thalamic and amygdalar profiles from other ALS genotypes.

Imaging and clinical data indicate considerable disease burden in 'probable' PLS: Patients with UMN symptoms for 2-4 years

Primary lateral sclerosis (PLS) is an adult-onset upper motor neuron disease manifesting in progressive spasticity and gradually resulting in considerably motor disability. In the absence of early disease-specific diagnostic indicators, the majority of patients with PLS face a circuitous diagnostic journey. Until the recent publication of consensus diagnostic criteria, 4-year symptom duration was required to establish the diagnosis. The new diagnostic criteria introduced the category of ‘probable PLS’ for patients with a symptom duration of 2–4 years. “Evolving diagnostic criteria in primary lateral sclerosis: The clinical and radiological basis of "probable PLS" [1]. This dataset provides radiological metrics in a cohort of ‘probable PLS’ patients, ‘definite PLS’ patients and age-matched healthy controls. Region-of-interest radiological data include diffusivity metrics in the corticospinal tracts and corpus callosum as well as mean cortical thickness values in the pre- and para-central gyri in each hemisphere. Our data indicate considerable grey matter and relatively limited white matter involvement in ‘probable PLS’ which supports the rationale for this diagnostic category as a clinically useful entity. The introduction of this diagnostic category will likely facilitate the timely recruitment of PLS patients into research studies and pharmacological trials before widespread neurodegenerative change ensues.

Evolving diagnostic criteria in primary lateral sclerosis: The clinical and radiological basis of "probable PLS"

INTRODUCTION

Primary lateral sclerosis is a rare neurodegenerative disorder of the upper motor neurons. Diagnostic criteria have changed considerably over the years, and the recent consensus criteria introduced 'probable PLS' for patients with a symptom duration of 2-4 years. The objective of this study is the systematic evaluation of clinical and neuroimaging characteristics in early PLS by studying a group of 'probable PLS patients' in comparison to a cohort of established PLS patients.

METHODS

In a prospective neuroimaging study, thirty-nine patients were stratified by the new consensus criteria into 'probable' (symptom duration 2-4 years) or 'definite' PLS (symptom duration >4 years). Patients were evaluated with a standardised battery of clinical instruments (ALSFRS-r, Penn upper motor neuron score, the modified Ashworth spasticity scale), whole genome sequencing, and underwent structural and diffusion MRI. The imaging profile of the two PLS cohorts were contrasted to a dataset of 100 healthy controls. All 'probable PLS' patients subsequently fulfilled criteria for 'definite' PLS on longitudinal follow-up and none transitioned to develop ALS.

RESULTS

PLS patients tested negative for known ALS- or HSP-associated mutations on whole genome sequencing. Despite their shorter symptom duration, 'probable PLS' patients already exhibited considerable functional disability, upper motor neuron disease burden and the majority of them required walking aids for safe ambulation. Their ALSFRS-r, UMN and modified Ashworth score means were 83%, 98% and 85% of the 'definite' group respectively. Motor cortex thickness was significantly reduced in both PLS groups in comparison to controls, but cortical changes were less widespread in 'probable' PLS on morphometric analyses. Corticospinal tract and corpus callosum metrics were relatively well preserved in the 'probable' group in contrast to the widespread white matter degeneration observed in the 'definite' group.

CONCLUSIONS

Our clinical and radiological analyses support the recent introduction of the 'probable' PLS category, as this cohort already exhibits considerable disability and cerebral changes consistent with established PLS. Before the publication of the new consensus criteria, these patients would have not been diagnosed with PLS on the basis of their symptom duration despite their significant functional impairment and motor cortex atrophy. The introduction of this new category will facilitate earlier recruitment into clinical trials, and shorten the protracted diagnostic uncertainty the majority of PLS patients face.

Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis

Temporal lobe studies in motor neuron disease overwhelmingly focus on white matter alterations and cortical grey matter atrophy. Reports on amygdala involvement are conflicting and the amygdala is typically evaluated as single structure despite consisting of several functionally and cytologically distinct nuclei. A prospective, single-centre, neuroimaging study was undertaken to comprehensively characterise amygdala pathology in 100 genetically-stratified ALS patients, 33 patients with PLS and 117 healthy controls. The amygdala was segmented into groups of nuclei using a Bayesian parcellation algorithm based on a probabilistic atlas and shape deformations were additionally assessed by vertex analyses. The accessory basal nucleus (p = .021) and the cortical nucleus (p = .022) showed significant volume reductions in C9orf72 negative ALS patients compared to controls. The lateral nucleus (p = .043) and the cortico-amygdaloid transition (p = .024) were preferentially affected in C9orf72 hexanucleotide carriers. A trend of total volume reduction was identified in C9orf72 positive ALS patients (p = .055) which was also captured in inferior-medial shape deformations on vertex analyses. Our findings highlight that the amygdala is affected in ALS and our study demonstrates the selective involvement of specific nuclei as opposed to global atrophy. The genotype-specific patterns of amygdala involvement identified by this study are consistent with the growing literature of extra-motor clinical features. Mesial temporal lobe pathology in ALS is not limited to hippocampal pathology but, as a key hub of the limbic system, the amygdala is also affected in ALS.

"Switchboard" malfunction in motor neuron diseases: Selective pathology of thalamic nuclei in amyotrophic lateral sclerosis and primary lateral sclerosis

The thalamus is a key cerebral hub relaying a multitude of corticoefferent and corticoafferent connections and mediating distinct extrapyramidal, sensory, cognitive and behavioural functions. While the thalamus consists of dozens of anatomically well-defined nuclei with distinctive physiological roles, existing imaging studies in motor neuron diseases typically evaluate the thalamus as a single structure. Based on the unique cortical signatures observed in ALS and PLS, we hypothesised that similarly focal thalamic involvement may be observed if the nuclei are individually evaluated. A prospective imaging study was undertaken with 100 patients with ALS, 33 patients with PLS and 117 healthy controls to characterise the integrity of thalamic nuclei. ALS patients were further stratified for the presence of GGGGCC hexanucleotide repeat expansions in C9orf72. The thalamus was segmented into individual nuclei to examine their volumetric profile. Additionally, thalamic shape deformations were evaluated by vertex analyses and focal density alterations were examined by region-of-interest morphometry. Our data indicate that C9orf72 negative ALS patients and PLS patients exhibit ventral lateral and ventral anterior involvement, consistent with the ‘motor’ thalamus. Degeneration of the sensory nuclei was also detected in C9orf72 negative ALS and PLS. Both ALS groups and the PLS cohort showed focal changes in the mediodorsal-paratenial-reuniens nuclei, which mediate memory and executive functions. PLS patients exhibited distinctive thalamic changes with marked pulvinar and lateral geniculate atrophy compared to both controls and C9orf72 negative ALS. The considerable ventral lateral and ventral anterior pathology detected in both ALS and PLS support the emerging literature of extrapyramidal dysfunction in MND. The involvement of sensory nuclei is consistent with sporadic reports of sensory impairment in MND. The unique thalamic signature of PLS is in line with the distinctive clinical features of the phenotype. Our data confirm phenotype-specific patterns of thalamus involvement in motor neuron diseases with the preferential involvement of nuclei mediating motor and cognitive functions. Given the selective involvement of thalamic nuclei in ALS and PLS, future biomarker and natural history studies in MND should evaluate individual thalamic regions instead overall thalamic changes.

Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis

OBJECTIVE

Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression.

METHODS

Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets.

RESULTS

We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients.

INTERPRETATION

Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725-738.

The French national protocol for Kennedy's disease (SBMA): consensus diagnostic and management recommendations

Background

Kennedy’s disease (KD), also known as spinal and bulbar muscular atrophy (SBMA), is a rare, adult-onset, X-linked recessive neuromuscular disease caused by CAG expansions in exon 1 of the androgen receptor gene (AR). The objective of the French national diagnostic and management protocol is to provide evidence-based best practice recommendations and outline an optimised care pathway for patients with KD, based on a systematic literature review and consensus multidisciplinary observations.

Results

The initial evaluation, confirmation of the diagnosis, and management should ideally take place in a tertiary referral centre for motor neuron diseases, and involve an experienced multidisciplinary team of neurologists, endocrinologists, cardiologists and allied healthcare professionals. The diagnosis should be suspected in an adult male presenting with slowly progressive lower motor neuron symptoms, typically affecting the lower limbs at onset. Bulbar involvement (dysarthria and dysphagia) is often a later manifestation of the disease. Gynecomastia is not a constant feature, but is suggestive of a suspected diagnosis, which is further supported by electromyography showing diffuse motor neuron involvement often with asymptomatic sensory changes. A suspected diagnosis is confirmed by genetic testing. The multidisciplinary assessment should ascertain extra-neurological involvement such as cardiac repolarisation abnormalities (Brugada syndrome), signs of androgen resistance, genitourinary abnormalities, endocrine and metabolic changes (glucose intolerance, hyperlipidemia). In the absence of effective disease modifying therapies, the mainstay of management is symptomatic support using rehabilitation strategies (physiotherapy and speech therapy). Nutritional evaluation by an expert dietician is essential, and enteral nutrition (gastrostomy) may be required. Respiratory management centres on the detection and treatment of bronchial obstructions, as well as screening for aspiration pneumonia (chest physiotherapy, drainage, positioning, breath stacking, mechanical insufflation-exsufflation, cough assist machnie, antibiotics). Non-invasive mechanical ventilation is seldom needed. Symptomatic pharmaceutical therapy includes pain management, endocrine and metabolic interventions. There is no evidence for androgen substitution therapy.

Conclusion

The French national Kennedy’s disease protocol provides management recommendations for patients with KD. In a low-incidence condition, sharing and integrating regional expertise, multidisciplinary experience and defining consensus best-practice recommendations is particularly important. Well-coordinated collaborative efforts will ultimately pave the way to the development of evidence-based international guidelines.

Progressive brainstem pathology in motor neuron diseases: Imaging data from amyotrophic lateral sclerosis and primary lateral sclerosis

A standardised, single-centre, longitudinal imaging protocol was used to evaluate longitudinal brainstem alterations in 100 patients with amyotrophic lateral sclerosis (ALS) with reference to 33 patients with primary lateral sclerosis (PLS), 30 patients with frontotemporal dementia (FTD) and 100 healthy controls. “Brainstem pathology in amyotrophic lateral sclerosis and primary lateral sclerosis: A longitudinal neuroimaging study” [1] ALS patients were scanned twice; 4 months apart. T1-weighted imaging data were acquired on a 3 T Philips Achieva MRI system, using a 3D Inversion Recovery prepared Spoiled Gradient Recalled echo (IR-SPGR) sequence. Raw MRI data underwent meticulous quality control before pre-processing. A Bayesian segmentation algorithm was utilised to parcellate the brainstem into the medulla oblongata, pons and mesencephalon before estimating the volume of each segment. Vertex-based shape analyses were carried out to characterise anatomical patterns of atrophy. Brainstem volume loss in ALS was dominated by medulla oblongata atrophy, but significant pontine pathology was also detected. Brainstem volume reductions were more significant in PLS than in ALS after correcting for demographic variables and total intracranial volume. Shape analyses revealed bilateral ‘flattening’ of the medullary pyramids in ALS compared to healthy controls. Our data demonstrate that computational neuroimaging readily detects brainstem pathology in vivo in both amyotrophic lateral sclerosis and primary lateral sclerosis.

Thalamic, hippocampal and basal ganglia pathology in primary lateral sclerosis and amyotrophic lateral sclerosis: Evidence from quantitative imaging data

Primary lateral sclerosis and amyotrophic lateral sclerosis are primarily associated with motor cortex and corticospinal tract pathology. A standardised, prospective, single-centre neuroimaging protocol was used to characterise thalamic, hippocampal and basal ganglia involvement in 33 patients with primary lateral sclerosis (PLS), 100 patients with amyotrophic lateral sclerosis (ALS), and 117 healthy controls. “Widespread subcortical grey matter degeneration in primary lateral sclerosis: a multimodal imaging study with genetic profiling” [1] Imaging data were acquired on a 3 T MRI system using a 3D Inversion Recovery prepared Spoiled Gradient Recalled echo sequence. Model based segmentation was used to estimate the volumes of the thalamus, hippocampus, amygdala, caudate, pallidum, putamen and accumbens nucleus in each hemisphere. The hippocampus was further parcellated into cytologically-defined subfields. Total intracranial volume (TIV) was estimated for each participant to aid the interpretation of subcortical volume alterations. Group comparisons were corrected for age, gender, TIV, education and symptom duration. Considerable thalamic, hippocampal and accumbens nucleus atrophy was detected in PLS compared to healthy controls and selective dentate, molecular layer, CA1, CA3, and CA4 hippocampal pathology was also identified. In ALS, additional volume reductions were noted in the amygdala, left caudate and the hippocampal-amygdala transition area of the hippocampus. Our imaging data provide evidence of extensive and phenotype-specific patterns of subcortical degeneration in PLS.

Texture classification of MR images of the brain in ALS using M-CoHOG: A multi-center study

Gradient-based texture analysis methods have become popular in computer vision and image processing and has many applications including medical image analysis. This motivates us to develop a texture feature extraction method to discriminate Amyotrophic Lateral Sclerosis (ALS) patients from controls. But, the lack of data in ALS research is a major constraint and can be mitigated by using data from multiple centers. However, multi-center data gives some other challenges such as differing scanner parameters and variation in intensity of the medical images, which motivate the development of the proposed method. To investigate these challenges, we propose a gradient-based texture feature extraction method called Modified Co-occurrence Histograms of Oriented Gradients (M-CoHOG) to extract texture features from 2D Magnetic Resonance Images (MRI). We also propose a new feature-normalization technique before feeding the normalized M-CoHOG features into an ensemble of classifiers, which can accommodate for variation of data from different centers. ALS datasets from four different centers are used in the experiments. We analyze the classification accuracy of single center data as well as that arising from multiple centers. It is observed that the extracted texture features from downsampled images are more significant in distinguishing between patients and controls. Moreover, using an ensemble of classifiers shows improvement in classification accuracy over a single classifier in multi-center data. The proposed method outperforms the state-of-the-art methods by a significant margin.

Widespread subcortical grey matter degeneration in primary lateral sclerosis: a multimodal imaging study with genetic profiling

BACKGROUND

Primary lateral sclerosis (PLS) is a low incidence motor neuron disease which carries a markedly better prognosis than amyotrophic lateral sclerosis (ALS). Despite sporadic reports of extra-motor symptoms, PLS is widely regarded as a pure upper motor neuron disorder. The post mortem literature of PLS is strikingly sparse and very little is known of subcortical grey matter pathology in this condition.

METHODS

A prospective imaging study was undertaken with 33 PLS patients, 117 healthy controls and 100 ALS patients to specifically assess the integrity of subcortical grey matter structures and determine whether PLS and ALS have divergent thalamic, hippocampal and basal ganglia signatures. Volumetric, morphometric, segmentation and vertex-wise analyses were carried out in the three study groups to evaluate the integrity of thalamus, hippocampus, caudate, amygdala, pallidum, putamen and accumbens nucleus in each hemisphere. The hippocampus was further parcellated to characterise the involvement of specific subfields.

RESULTS

Considerable thalamic, caudate, and hippocampal atrophy was detected in PLS based on both volumetric and vertex analyses. Significant volume reductions were also detected in the accumbens nuclei. Hippocampal atrophy in PLS was dominated by dentate gyrus, hippocampal tail and CA4 subfield volume reductions. The morphometric comparison of ALS and PLS cohorts revealed preferential medial bi-thalamic pathology in PLS compared to the predominant putaminal degeneration detected in ALS. Another distinguishing feature between ALS and PLS was the preferential atrophy of the amygdala in ALS.

CONCLUSIONS

PLS is associated with considerable subcortical grey matter degeneration and due to the extensive extra-motor involvement, it should no longer be regarded a pure upper motor neuron disorder. Given its unique pathological features and a clinical course which differs considerably from ALS, dedicated research studies and disease-specific therapeutic strategies are urgently required in PLS.

Brainstem pathology in amyotrophic lateral sclerosis and primary lateral sclerosis: A longitudinal neuroimaging study

•

Computational neuroimaging captures focal brainstem pathology in motor neuron diseases in contrast to both healthy- and disease controls.

•

ALS patients exhibit progressive medulla oblongata, pontine and mesencephalic volume loss over time.

•

Brainstem atrophy in ALS and PLS is dominated by medulla oblongata volume reductions.

•

Vertex analyses of ALS patients reveal flattening of the medullary pyramids bilaterally.

•

Morphometric analyses in ALS detect density reductions in the mesencephalic crura consistent with corticospinal tract degeneration.

Background

Brainstem pathology is a hallmark feature of ALS, yet most imaging studies focus on cortical grey matter alterations and internal capsule white matter pathology. Brainstem imaging in ALS provides a unique opportunity to appraise descending motor tract degeneration and bulbar lower motor neuron involvement.

Methods

A prospective longitudinal imaging study has been undertaken with 100 patients with ALS, 33 patients with PLS, 30 patients with FTD and 100 healthy controls. Volumetric, vertex and morphometric analyses were conducted correcting for demographic factors to characterise disease-specific patterns of brainstem pathology. Using a Bayesian segmentation algorithm, the brainstem was segmented into the medulla, pons and mesencephalon to measure regional volume reductions, shape analyses were performed to ascertain the atrophy profile of each study group and region-of-interest morphometry was used to evaluate focal density alterations.

Results

ALS and PLS patients exhibit considerable brainstem atrophy compared to both disease- and healthy controls. Volume reductions in ALS and PLS are dominated by medulla oblongata pathology, but pontine atrophy can also be detected. In ALS, vertex analyses confirm the flattening of the medullary pyramids bilaterally in comparison to healthy controls and widespread pontine shape deformations in contrast to PLS. The ALS cohort exhibit bilateral density reductions in the mesencephalic crura in contrast to healthy controls, central pontine atrophy compared to disease controls, peri-aqueduct mesencephalic and posterior pontine changes in comparison to PLS patients.

Conclus

ions: Computational brainstem imaging captures the degeneration of both white and grey matter components in ALS. Our longitudinal data indicate progressive brainstem atrophy over time, underlining the biomarker potential of quantitative brainstem measures in ALS. At a time when a multitude of clinical trials are underway worldwide, there is an unprecedented need for accurate biomarkers to monitor disease progression and detect response to therapy. Brainstem imaging is a promising addition to candidate biomarkers of ALS and PLS.

Adaptive functional reorganization in amyotrophic lateral sclerosis: coexisting degenerative and compensatory changes

BACKGROUND AND PURPOSE

Considerable functional reorganization takes place in amyotrophic lateral sclerosis (ALS) in face of relentless structural degeneration. This study evaluates functional adaptation in ALS patients with lower motor neuron predominant (LMNp) and upper motor neuron predominant (UMNp) dysfunction.

METHODS

Seventeen LMNp ALS patients, 14 UMNp ALS patients and 14 controls participated in a functional magnetic resonance imaging study. Study-group-specific activation patterns were evaluated during preparation for a motor task. Connectivity analyses were carried out using the supplementary motor area (SMA), cerebellum and striatum as seed regions and correlations were explored with clinical measures.

RESULTS

Increased cerebellar, decreased dorsolateral prefrontal cortex and decreased SMA activation were detected in UMNp patients compared to controls. Increased cerebellar activation was also detected in UMNp patients compared to LMNp patients. UMNp patients exhibit increased effective connectivity between the cerebellum and caudate, and decreased connectivity between the SMA and caudate and between the SMA and cerebellum when performing self-initiated movement. In UMNp patients, a positive correlation was detected between clinical variables and striato-cerebellar connectivity.

CONCLUSIONS

Our findings indicate that, despite the dysfunction of SMA-striatal and SMA-cerebellar networks, cerebello-striatal connectivity increases in ALS indicative of compensatory processes. The coexistence of circuits with decreased and increased connectivity suggests concomitant neurodegenerative and adaptive changes in ALS.

Diffusion kurtosis and quantitative susceptibility mapping MRI are sensitive to structural abnormalities in amyotrophic lateral sclerosis

Objective

To construct a clinical diagnostic biomarker using state-of-the-art microstructural MRI in the motor cortex of people with amyotrophic lateral sclerosis (ALS).

Methods

Clinical and MRI data were obtained from 21 ALS patients (aged 54 ± 14 years, 33% female) and 63 age- and gender-matched controls (aged 48 ± 18 years, 43% female). MRI was acquired at 3T and included T1-weighted scan (for volumetrics), arterial spin labelling (for cerebral blood flow), susceptibility-weighted angiography (for iron deposition) and multiband diffusion kurtosis imaging (for tissue microstructure). Group differences in imaging measures in the motor cortex were tested by general linear model and relationships to clinical variables by linear regression.

Results

The ALS group had mild-to-moderate impairment (disease duration: 1.8 ± 0.8 years; ALS functional rating scale 40.2 ± 6.0; forced vital capacity 83% ± 22%). No age or gender differences were present between groups. We found significant group differences in diffusion kurtosis metrics (apparent, mean, radial and axial kurtosis: p < .01) and iron deposition in the motor cortex (p = .03). Within the ALS group, we found significant relationships between motor cortex volume, apparent diffusion and disease duration (adjusted R² = 0.27, p = .011); and between the apparent and radial kurtosis metrics and ALS functional rating scale (adjusted R² = 0.25, p = .033). A composite imaging biomarker comprising kurtosis and iron deposition measures yielded a maximal diagnostic accuracy of 83% (81% sensitivity, 85% specificity) and an area-under-the-curve of 0.86.

Conclusion

Diffusion kurtosis is sensitive to early changes present in the motor region in ALS. We propose a composite imaging biomarker reflecting tissue microstructural changes in early ALS that may provide clinically valuable diagnostic information.

•

A biomarker based on diffusion kurtosis imaging achieved an accuracy of 83%.

•

Kurtosis-based measures were more abnormal in ALS than tensor-based measures.

•

Motor cortex in the symptomatic hemisphere was smaller and had greater iron concentration.

•

There was a 1 mL volume loss per year in ALS motor cortex.

The clinical and radiological profile of primary lateral sclerosis: a population-based study

BACKGROUND

Primary lateral sclerosis is a progressive upper-motor-neuron disorder associated with markedly longer survival than ALS. In contrast to ALS, the genetic susceptibility, histopathological profile and imaging signature of PLS are poorly characterised. Suspected PLS patients often face considerable diagnostic delay and prognostic uncertainty.

OBJECTIVE

To characterise the distinguishing clinical, genetic and imaging features of PLS in contrast to ALS and healthy controls.

METHODS

A prospective population-based study was conducted with 49 PLS patients, 100 ALS patients and 100 healthy controls using genetic profiling, standardised clinical assessments and neuroimaging. Whole-brain and region-of-interest analyses were undertaken to evaluate patterns of grey and white matter degeneration.

RESULTS

In PLS, disease burden in the motor cortex is more medial than in ALS consistent with its lower limb symptom-predominance. PLS is associated with considerable cerebellar white and grey matter degeneration and the extra-motor profile of PLS includes marked insular, inferior frontal and left pars opercularis pathology. Contrary to ALS, PLS spares the postcentral gyrus. The body and splenium of the corpus callosum are preferentially affected in PLS, in contrast to the genu involvement observed in ALS. Clinical measures show anatomically meaningful correlations with imaging metrics in a somatotopic distribution. PLS patients tested negative for C9orf72 repeat expansions, known ALS and HSP-associated genes.

CONCLUSIONS

Multiparametric imaging in PLS highlights disease-specific motor and extra-motor involvement distinct from ALS. In a condition where limited post-mortem data are available, imaging offers invaluable pathological insights. Anatomical correlations with clinical metrics confirm the biomarker potential of quantitative neuroimaging in PLS.