Background Primary lateral sclerosis (PLS) is traditionally solely associated with progressive upper motor neuron dysfunction manifesting in limb spasticity, gait impairment, bulbar symptoms and pseudobulbar affect. Recent studies have described frontotemporal dysfunction in some patients resulting in cognitive manifestations. Cerebellar pathology is much less well characterised despite sporadic reports of cerebellar disease. Methods A multi-timepoint, longitudinal neuroimaging study was conducted to characterise the evolution of both intra-cerebellar disease burden and cerebro-cerebellar connectivity. The volumes of deep cerebellar nuclei, cerebellar cortical volumes, cerebro-cerebellar structural and functional connectivity were assessed longitudinally in a cohort of 43 individuals with PLS. Results Cerebello-frontal, -temporal, -parietal, -occipital and cerebello-thalamic structural disconnection was detected at baseline based on radial diffusivity (RD) and cerebello-frontal decoupling was also evident based on fractional anisotropy (FA) alterations. Functional connectivity changes were also detected in cerebello-frontal, parietal and occipital projections. Volume reductions were identified in the vermis, anterior lobe, posterior lobe, and crura. Among the deep cerebellar nuclei, the dorsal dentate was atrophic. Longitudinal follow-up did not capture statistically significant progressive changes. Significant primary motor cortex atrophy and inter-hemispheric transcallosal degeneration were also captured. Conclusions PLS is not only associated with upper motor neuron dysfunction, but cerebellar cortical volume loss and deep cerebellar nuclear atrophy can also be readily detected. In addition to intra-cerebellar disease burden, cerebro-cerebellar connectivity alterations also take place. Our data add to the evolving evidence of widespread neurodegeneration in PLS beyond the primary motor regions. Cerebellar dysfunction in PLS is likely to exacerbate bulbar, gait and dexterity impairment and contribute to pseudobulbar affect.

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.

Background and Objectives Amyotrophic lateral sclerosis (ALS) is predominantly associated with motor cortex, corticospinal tract (CST), brainstem, and spinal cord degeneration, and cerebellar involvement is much less well characterized. However, some of the cardinal clinical features of ALS, such as dysarthria, dysphagia, gait impairment, falls, and impaired dexterity, are believed to be exacerbated by coexisting cerebellar pathology. Cerebellar pathology may also contribute to cognitive, behavioral, and pseudobulbar manifestations. Our objective was to systematically assess both intracerebellar pathology and cerebrocerebellar connectivity alterations in a genetically stratified cohort of ALS. Methods A prospective, multimodal neuroimaging study was conducted to evaluate the longitudinal evolution of intracerebellar pathology and cerebrocerebellar connectivity, using structural and functional measures. Results A total of 113 healthy controls and 212 genetically stratified individuals with ALS were included: (1) C9orf72 hexanucleotide carriers ("C9POS"), (2) sporadic patients who tested negative for ALS-associated genetic variants, and (3) intermediate-length CAG trinucleotide carriers in ATXN2 ("ATXN2"). Flocculonodular lobule (padj = 0.014, 95% CI -5.06e-5 to -3.98e-6) and crura (p(adj )= 0.031, 95% CI -1.63e-3 to -5.55e-5) volume reductions were detected at baseline in sporadic patients. Cerebellofrontal and cerebelloparietal structural connectivity impairment was observed in both C9POS and sporadic patients at baseline, and both projections deteriorated further over time in sporadic patients (p(adj) = 0.003, t(249) = 3.04 and p(adj) = 0.05, t(249) = 1.93). Functional cerebelloparietal uncoupling was evident in sporadic patients at baseline (p(adj) = 0.004, 95% CI -0.19 to -0.03). ATXN2 patients exhibited decreased cerebello-occipital functional connectivity at baseline (p(adj) = 0.004, 95% CI -0.63 to -0.06), progressive cerebellotemporal functional disconnection (p(adj) = 0.025, t(199) = -2.26), and progressive flocculonodular lobule degeneration (p(adj) = 0.017, t(249) = -2.24). C9POS patients showed progressive ventral dentate atrophy (p(adj) = 0.007, t(249) = -2.75). The CSTs (p(adj) < 0.001, 95% CI 4.89e-5 to 1.14e-4) and transcallosal interhemispheric fibers (p(adj) < 0.001, 95% CI 5.21e-5 to 1.31e-4) were affected at baseline in C9POS and exhibited rapid degeneration over the 4 time points. The rate of decline in CST and corpus callosum integrity was faster than the rate of cerebrocerebellar disconnection (padj = 0.001, t(190) = 6.93). Discussion ALS is associated with accruing intracerebellar disease burden as well as progressive corticocerebellar uncoupling. Contrary to previous suggestions, we have not detected evidence of compensatory structural or functional changes in response to supratentorial degeneration. The contribution of cerebellar disease burden to dysarthria, dysphagia, gait impairment, pseudobulbar affect, and cognitive deficits should be carefully considered in clinical assessments, monitoring, and multidisciplinary interventions.

BackgroundPrimary lateral sclerosis (PLS) is traditionally regarded as a pure upper motor neuron disorder, but recent cases series have highlighted cognitive deficits in executive and language domains.MethodsA single-centre, prospective neuroimaging study was conducted with comprehensive clinical and genetic profiling. The structural and functional integrity of language-associated brain regions and networks were systematically evaluated in 40 patients with PLS in comparison to 111 healthy controls. The structural integrity of the arcuate fascicle, frontal aslant tract, inferior occipito-frontal fascicle, inferior longitudinal fascicle, superior longitudinal fascicle and uncinate fascicle was evaluated. Functional connectivity between the supplementary motor region and the inferior frontal gyrus and connectivity between Wernicke's and Broca's areas was also assessed.ResultsCortical thickness reductions were observed in both Wernicke's and Broca's areas. Fractional anisotropy reduction was noted in the aslant tract and increased radical diffusivity (RD) identified in the aslant tract, arcuate fascicle and superior longitudinal fascicle in the left hemisphere. Functional connectivity was reduced along the aslant track, i.e. between the supplementary motor region and the inferior frontal gyrus, but unaffected between Wernicke's and Broca's areas. Cortical thickness alterations, structural and functional connectivity changes were also noted in the right hemisphere.ConclusionsDisease-burden in PLS is not confined to motor regions, but there is also a marked involvement of language-associated tracts, networks and cortical regions. Given the considerably longer survival in PLS compared to ALS, the impact of language impairment on the management of PLS needs to be carefully considered.

Although machine-learning (ML) approaches have been extensively utilized in neurodegenerative conditions, they can be challenging to implement in motor neuron diseases (MNDs) due to disease-specific characteristics. The potential of ML algorithms has been explored by academic amyotrophic lateral sclerosis (ALS) studies, but they have not been developed into viable clinical applications to date. ALS studies traditionally conduct "group-level" analyses to describe phenotype- or genotype-associated clinical traits, survival characteristics, progression rates, biomarker profiles, and imaging signatures [1-4]. These, although academically interesting, have limited utility for the interpretation of data from single individuals. The appeal of ML frameworks in a condition with considerable clinical heterogeneity, such as ALS, is the opportunity to categorize individual patients into clinically relevant subgroups. The long-awaited transition from "group-level" descriptive analyses to precision, "individual-subject" data interpretation has been fueled by the emergence of large training datasets, in the form of purpose-designed data repositories, national registries, or leftover data from clinical trials. Harnessing the availability of such data sources, a multitude of promising ML studies have been published demonstrating the prospect of accurately classifying a single individual into relevant diagnostic or prognostic subgroups [5]. There are important lessons to consider from early ML initiatives in ALS. Irrespective of the specific ML model implemented, cohort size for model training is crucial, which is one of the biggest challenges in ALS in contrast to more common neurodegenerative conditions. A considerable shortcoming of single-centre ML studies is the lack of external validation, which coupled with small training datasets, increases the risk of model overfitting to local data. Binary classification studies merely categorizing individuals into "ALS" versus "healthy control" groups have limited practical appeal, as the diagnostic dilemma in the clinical setting is not whether an individual is healthy, but rather whether the constellation of findings represents incipient ALS or an alternative neurodegenerative or neuromuscular condition. Multiclass classification studies mirror real-life clinical scenarios better, especially if multiple MND phenotypes are represented. The accurate categorization of an early stage upper motor neuron-predominant case into "ALS" versus "probable PLS," for example, is hugely important due to the survival ramifications of the correct diagnosis [6, 7]. Another stereotyped caveat of ML studies in ALS is model validation on cohorts with long symptom duration. The categorization of patients with long symptom duration with considerable disability and marked biomarker changes is not ideal to test model accuracy. A more compelling validation of a model is whether early stage patients or asymptomatic gene-carriers are accurately categorized into prospective diagnostic and prognostic groups based on peridiagnostic or presymptomatic biomarker profiles [8]. The critical appraisal of published ML studies in MND helps to outline desirable future study designs. Models should ideally be validated on external datasets; the choice of ML model should be determined by inherent data characteristics (missing data, number of features, etc.); multiclass classification models should be implemented preferably with disease-mimics, disease-controls, and several MND phenotypes; categorization beyond diagnostic groups into prognostic categories has additional clinical utility; and the implementation of several ML models on the same dataset may help to juxtapose the comparative efficiency of proposed models. The interrogation of quantitative biomarker panels (serum, cerebrospinal fluid [CSF], imaging) may support clinical decision-making independently [9, 10]. An alternative strategy is the interpretation of demographic and clinical variables in ML models [11], which has a number of practical advantages compared to relying on instrumental metrics (magnetic resonance, positron emission tomography, CSF); data collection is easily harmonized across multiple sites, data acquisition is relatively cheap, data transfer is logistically simple, et cetera. Core clinical variables are typically already recorded, so with the appropriate approvals in place, extant data may potentially be used retrospectively for model training. In this issue of European Journal of Neurology, Gromicho et al. from the University of Lisbon, Portugal present a particularly innovative ML study [12]. The authors implement dynamic Bayesian networks (DBNs) to evaluate the influence of the most commonly recorded clinical variables on disease progression in ALS. DBNs model variable dependencies that evolve over time and are trained upon multi-time point observations. The five key determinants of disease progression according to the authors are symptom duration at first consultation, body mass index at diagnosis, subscores 1 (speech) and 9 (stairs) of the revised Amyotrophic Lateral Sclerosis Functional Rating Scale, and maximum expiratory pressure. The pragmatic relevance of identifying key determinants of progression rate is that patients entering clinical trials may be informedly stratified so that ensuing "slow progression" is not intuitively attributed to a putative drug effect, and that "fast progression" is not automatically regarded as failure to respond to therapy. Despite its practical pitfalls, ML is one of the most exciting frontiers of ALS research, and it is gaining considerable momentum thanks to the increased availability of large datasets, multicentre data harmonization efforts, and dedicated international consortia. These developments offer unparalleled opportunities for model optimization and validation, paving the way for viable clinical applications. Peter Bede: Conceptualization (equal); writing – original draft (equal). Kai Ming Chang: Conceptualization (equal); writing – original draft (equal). Ee Ling Tan: Conceptualization (equal); writing – original draft (equal). P.B. is supported by the Health Research Board (HRB EIA-2017-019 & JPND-Cofund-2-2019-1), the Irish Institute of Clinical Neuroscience, the Spastic Paraplegia Foundation, the EU Joint Programme–Neurodegenerative Disease Research, the Andrew Lydon scholarship, and the Iris O'Brien Foundation. Open access funding provided by IReL. None of the authors has any conflict of interest to disclose. This is an editorial commentary and not based on a specific dataset.

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.

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.

Advances in amyotrophic lateral sclerosis (ALS) research: Research in ALS has gained unprecedented momentum in recent years fueled by important conceptual developments, establ ishment of internat ional consort ia , breakthrough genetic discoveries and relentless technological advances. The first genotypespecific pharmaceutical trials signal the paradigm shift from the notion of ‘one-drug-for-all’ to precision, individualized therapies. The once arcane presymptomatic phase of the disease is gradually unraveled by seminal studies of asymptomatic mutation carriers (Geevasinga et al., 2015; Querin et al., 2019). The meticulous analysis of data from large population-based registries has contributed to the identification of etiological factors, genetic risk profiles, epigenetic and environmental modifiers. Progression patterns have been characterized in vivo by robust clinical, neurophysiology and neuroimaging studies and led to the development of clinical staging systems and biomarkers with practical utility in clinical trials (Chipika et al., 2019). While ALS was once considered a ‘pure’ motor system disorder, it is now widely regarded as multisystem condition with frontotemporal, cerebellar, and subcortical grey matter involvement and a range of extrapyramidal, cognitive, and behavioral manifestations (Elamin et al., 2017). Disease-specific functional rating scales are now routinely used and screening instruments have been developed to assess the most commonly affected cognitive and behavioral domains in ALS. Advances in genetics paved the way for the first large presymptomatic studies which confirmed considerable cerebral and spinal cord alterations decades before symptom manifestation (Vucic et al., 2008; Querin et al., 2019). The characterization of genotype-associated molecular cascades, pathological signatures and clinical features were important milestones for the development of novel therapies, and the first antisense oligonucleotide trials are now underway. The datasets generated by multicenter initiatives offer unprecedented data mining opportunities; clustering patterns, prognostic determinants, and reliable diagnostic indicators were identified using machine-learning approaches that could not have previously been applied to smaller datasets. Technological advances in electrophysiology and the emergence of magnetoencephalography generated important functional insights (Bede et al., 2018). Novel imaging modalities, such as multivoxel spectroscopy, spinal cord imaging, diffusion kurtosis imaging captured pathological changes that were previously impossible to ascertain in vivo (Bede et al., 2017; Huang et al., 2020). Advanced neurophysiology techniques, such as transcranial magnetic stimulation or motor unit number estimation are now widely used in both clinical and academic settings and contribute to diagnostic clarification and the monitoring of individual patients. In response to the inevitable sample size limitations of single-center studies (Schuster et al., 2016), ambitious international initiatives such as Project MinE established large biobanks to conduct genetic studies with sufficient statistical power. Societies such as NISALS provide pioneering frameworks to conduct large multicenter neuroimaging studies. Barriers to drug development: Despite the coordinated work of large research centers, research consortia, patient charities, advocacy groups and pharmaco log ica l companies , relatively limited progress has been made in the development of effective disease-modifying therapies. The barriers to successful drug development in ALS include the marked clinical heterogeneity of the condition, the relatively late inclusion of patients into clinical trials, and an inadvertent selection bias to patients with limited cognitive impairment, who may live closer to research centers and who may have been diagnosed relatively early. Clinical heterogeneity in ALS is multidimensional and encompasses considerable di fferences in age of onset, progression rates, extra-motor manifestations, bulbar versus limb disability, lowerversus upper motor neuron predominance. The considerable differences in cl inical profi les necessitate individualized management and a series of welltimed multidisciplinary interventions such as feeding-tube placement, initiation of non-invasive ventilation and ultimately palliative measures tailored to the patient’s specific medical needs and care preferences. While the benefits of individualized clinical care in contrast to a blanket strategy are widely accepted, the ill-conceived expectation that a single drug may be useful for all patients with ALS prevails. It is increasingly clear that unique genotype-associated clinical profiles exist and patients with specific mutations may have relatively distinct disease trajectories. It is also apparent that considerable phenotypic differences exist in survival, progression rates and disability profiles. It is therefore likely that patients may benefit from individualized pharmacological intervent ions ta i lored to their genotype, phenotype and disease-stage as opposed to the notion of ‘one-drug-for-all’. Despite the enthusiasm generated by the first antisense oligonucleotide studies, it is noteworthy that the vast majority of patients with ALS are seemingly sporadic and test negative for large panels of mutations linked to ALS such as SOD1, ALS2, SETX, SPG11, FUS, VAPB, ANG, TARDBP, FIG4, OPTN, ATXN2, VCP, C9orf72, UBQLN2, SQSTM1, NEK1, FUS, TBK1 etc. Accordingly, the majority of patients with ALS are not candidates for genotypespecific interventions, cannot be included in presymptomatic studies and the risk of their relatives developing neurodegenerative change is unclear. Another barrier to successful clinical trial is the relatively late inclusion of patients into clinical trials due to stringent inclusion criteria. Large epidemiology studies in ALS have repeatedly demonstrated that the interval between symptom onset and diagnosis is in the range of 12–14 months which is a considerable delay with a multitude of adverse ramifications. Quantitative radiology studies have shown that by the time the diagnosis is confirmed, patients already exhibit considerable motor cortex, corticospinal tract and corpus callosum degeneration which are unlikely to be ameliorated by pharmacological intervention. The observation that significant pathological changes have already taken place by the time a patient fulfills diagnostic criteria suggests that the optimal therapeutic window is Perspective

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.

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.

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 ALSor 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.

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 de-formations 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.

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.

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.

The confluence of droplet-compartmentalised chemical systems and architectures composed of interacting droplets points towards a novel technology mimicking core features of the cellular architecture that dominates biology. A key challenge to achieve such a droplet technology is long-term stability in conjunction with interdroplet communication. Here, we probed the parameter space of the Belousov-Zhabotinsky (BZ) medium, an extensively studied model for non-equilibrium chemical reactions, pipetted as 2.5 mm droplets in hexadecane oil. The presence of asolectin lipids enabled the formation of arrays of contacted BZ droplets, of which the wave patterns were characterised over time. We utilised laser-cut acrylic templates with over 40 linear oil-filled slots in which arrays are formed by pipetting droplets of the desired BZ composition, enabling parallel experiments and automated image analysis. Using variations of conventional malonic acid BZ medium, wave propagation over droplet-droplet interfaces was not observed. However, a BZ medium containing both malonic acid and 1,4-cyclohexanedione was found to enable inter-droplet wave propagation. We anticipate that the chemical excitation properties of this mixed-substrate BZ medium, in combination with the droplet stability of the networks demonstrated here for nearly 400 droplets in a template-defined topology, will facilitate the development of scalable functional droplet networks.