Neurometabolic Alterations in Motor Neuron Disease: Insights from Magnetic Resonance Spectroscopy.

Magnetic resonance spectroscopy (MRS) has contributed important academic insights in motor neuron diseases (MNDs), particularly in amyotrophic lateral sclerosis (ALS). Over the past three decades momentous methodological advances took place, including the emergence of high-field magnetic resonance imaging (MRI) platforms, multi-voxel techniques, whole-brain protocols, novel head-coil designs, and a multitude of open-source imaging suites. Technological advances in MRS are complemented by important conceptual developments in MND, such as the recognition of the importance of extra-motor brain regions, multi-timepoint longitudinal study designs, assessment of asymptomatic mutation carriers, description of genotype-associated signatures, and the gradual characterisation of non-ALS MND phenotypes. We have conducted a systematic review of published MRS studies in MND to identify important emerging research trends, key lessons from pioneering studies, and stereotyped shortcomings. We also sought to highlight notable gaps in the current literature so that research priorities for future studies can be outlined. While MRS remains relatively underutilised in MND compared to other structural, diffusivity and functional imaging modalities, our review suggests that MRS can not only advance our academic understanding of MND biology, but has a multitude of practical benefits for clinical and pharmaceutical trial applications.

Alterations in somatosensory, visual and auditory pathways in amyotrophic lateral sclerosis: an under-recognised facet of ALS.

BACKGROUND: While amyotrophic lateral sclerosis (ALS) is widely recognised as a multi-network disorder with extensive frontotemporal and cerebellar involvement, sensory dysfunction is relatively under evaluated. Subtle sensory deficits have been sporadically reported, but there is a prevailing notion that sensory pathways may be relatively spared in ALS. METHODS: In a prospective neuroimaging study we have systematically evaluated cerebral grey and white matter structures involved in the processing, relaying and mediation of sensory information. Twenty two C9orf72 positive ALS patients (C9+ ALS), 138 C9orf72 negative ALS patients (C9- ALS) and 127 healthy controls were included. RESULTS: Widespread cortical alterations were observed in C9+ ALS including both primary and secondary somatosensory regions. In C9- ALS, cortical thickness reductions were observed in the postcentral gyrus. Thalamic nuclei relaying somatosensory information as well as the medial and lateral geniculate nuclei exhibited volume reductions. Diffusivity indices revealed posterior thalamic radiation pathology and a trend of left medial lemniscus degeneration was also observed in C9- ALS (p = 0.054). Our radiology data confirm the degeneration of somatosensory, visual and auditory pathways in ALS, which is more marked in GGGGCC hexanucleotide repeat expansion carriers. CONCLUSIONS: In contrast to the overwhelming focus on motor system degeneration and frontotemporal dysfunction in recent research studies, our findings confirm that sensory circuits are also affected in ALS. The involvement of somatosensory, auditory and visual pathways in ALS may have important clinical ramifications which are easily overlooked in the context of unremitting motor decline. Subtle sensory deficits may exacerbate mobility, contribute to fall risk, impair dexterity, and worsen bulbar dysfunction, therefore comprehensive sensory testing should also be performed as part of the clinical assessments in ALS.

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.

Keratosis follicularis spinulosa decalvans-like cicatricial alopecia in a patient with cardiofaciocutaneous syndrome.

We describe a patient with the keratosis pilaris atrophicans variant of cicatricial alopecia in conjunction with cardiofaciocutaneous syndrome.

Cerebellar remodelling decades after spinal cord insult: neuroplasticity in poliomyelitis survivors.

BACKGROUND: The cerebellum integrates a multitude of motor and cognitive processes through ample spinal and supratentorial projections. Despite emerging evidence of adaptive neuroplasticity, cerebellar reorganisation in response to severe spinal insult early in life is poorly characterised. The objective of this study is the systematic characterisation of cerebellar integrity metrics in a cohort of adult poliomyelitis survivors as a template condition for longstanding lower motor neuron injury. METHODS: A total of 143 participants, comprising 43 adult poliomyelitis survivors and 100 age- and sex-matched healthy controls were recruited in a prospective, single-centre neuroimaging study with a uniform structural and diffusion imaging protocol. First, standard voxelwise grey and white matter analyses were performed. Then, the cerebellum was anatomically segmented into lobules, and cortical thickness and grey matter volumes were evaluated in each lobule. The integrity of cerebellar peduncles was also assessed based on their diffusivity profiles. RESULTS: Compared to healthy controls, poliomyelitis survivors exhibited greater cortical thickness in lobules I, II, and III in the right hemisphere and in lobules VIIIA and VIIIB bilaterally. A trend of higher cortical thickness was also detected lobules I, II and III in the left hemisphere. Enhanced cerebellar peduncle organisation was detected, particularly within the middle cerebellar peduncles. CONCLUSIONS: Increased cerebellar integrity measures in poliomyelitis survivors are primarily identified in lobules associated with sensorimotor functions. The identified pattern of cerebellar reorganisation may represent compensatory changes in response to severe lower motor neuron injury in childhood and ensuing motor disability.

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-characterized 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 categorize 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) single-subject MRI classification strategies in symptomatic and pre-symptomatic FTLD, (2) practical clinical implications, and (3) the limitations of current single-subject data interpretation models. EXPERT OPINION: Classification studies in FTLD have demonstrated the feasibility of categorizing 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.

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.

Focal thalamus pathology in frontotemporal dementia: Phenotype-associated thalamic profiles.

BACKGROUND: The clinical phenotypes of frontotemporal dementia (FTD) are defined by distinctive clinical features and associated with unique cortical atrophy patterns. Clinical manifestations in FTD however are not solely driven by cortical pathology, but stem from the selective dysfunction of corticobasal circuits, the majority of which are relayed through thalamic nuclei. The objective of this study is the systematic radiological characterisation of thalamic pathology across the clinical spectrum of FTD to describe phenotype-associated thalamic signatures. METHODS: 170 participants were included in a multimodal, prospective neuroimaging study to evaluate thalamic degeneration at a nuclear, vertex, and morphometric level using a uniform imaging protocol and a multimodal analysis approach. RESULTS: Patients with behavioural variant FTD (bvFTD), non-fluent variant primary progressive aphasia (nfvPPA), semantic variant primary progressive aphasia (svPPA) and amyotrophic lateral sclerosis-FTD (ALS-FTD) exhibit distinctive thalamic disease-burden profiles with the preferential degeneration of specific thalamic nuclei. While vertex analyses reveal largely overlapping thalamic atrophy patterns, morphometric analyses successfully capture focal intra-thalamic degeneration. CONCLUSIONS: Mirroring selective cortical vulnerability, focal rather than global thalamic atrophy characterises the clinical subtypes of FTD. Thalamic degeneration is a likely contributor to the heterogeneity of clinical manifestations observed in FTD. As thalamic imaging techniques capture different facets of pathological change and differ in their sensitivity to detect distinguishing features, future studies should implement a multimodal approach with complementary MRI techniques.

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.

Sitagliptin and Narrow-Band Ultraviolet-B for Moderate Psoriasis (DINUP): A Randomised Controlled Clinical Trial.

BACKGROUND: Sitagliptin, a dipeptidyl peptidase-4 (DPP-4) inhibitor licensed for the treatment of type 2 diabetes mellitus (T2DM), has been reported to improve psoriasis. OBJECTIVE: We compared the effects of sitagliptin treatment, a DPP-4 inhibitor, in combination with narrow-band ultraviolet-B (NB-UVB) phototherapy compared to NB-UVB alone on psoriasis severity, quality of life, cardiovascular disease risk factors and immune parameters in people with moderate psoriasis without T2DM. METHODS: In this 39-week, single-centre, randomised controlled trial, people were allocated randomly to receive sitagliptin for 24 weeks with NB-UVB or NB-UVB alone. The primary endpoint was the change in Psoriasis Area and Severity Index (PASI) from baseline to 24 weeks. We estimated that 120 participants would be needed to have 80% power to find a significant difference between the groups. RESULTS: A total of 118 patients were randomised. The median (IQR) baseline PASI was 8.8 (7.5-11.6). At 24 weeks, the mean difference from baseline in PASI (-1.0 [95% CI -2.0 to 0.0]) was significantly larger in the sitagliptin/NB-UVB arm than in the NB-UVB-alone arm (p = 0.044). There were significant differences in the change in Hospital Anxiety and Depression Scale (-2.5 [95% CI -4.0 to -1.0]; p = 0.002) and EuroQol 5-item questionnaire (0.1 [95% CI 0.0-0.1]; p = 0.036) values from baseline to 24 weeks between the sitagliptin/NB-UVB and the NB-UVB-alone arm. There were no treatment-related serious adverse events. CONCLUSION: Sitagliptin therapy combined with NB-UVB phototherapy significantly improved psoriasis severity, albeit modestly, compared to NB-UVB phototherapy alone in patients with moderate psoriasis without T2DM.

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.

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.

Imaging data reveal divergent longitudinal trajectories in PLS, ALS and poliomyelitis survivors: Group-level and single-subject traits.

Imaging profiles from a longitudinal single-centre motor neuron disease study are presented. A standardized T1-weighted MRI protocol was implemented to characterise cortical disease burden trajectories across the UMN (upper motor neuron) - LMN (lower motor neuron) spectrum of motor neuron diseases (MNDs) (Tahedl et al., 2021). Patients with amyotrophic lateral sclerosis (ALS n = 61), patients with primary lateral sclerosis (PLS n = 23) and poliomyelitis survivors (PMS n = 45) were included. Up to four longitudinal scans were available for each patient, separated by an inter-scan-interval of four months. Individual and group-level cortical thickness profiles were appraised using a normalisation procedure with reference to subject-specific control groups. A z-scoring approach was utilised, where each patients' cortex was first segmented into 1000 cortical regions, and then rated as 'thin', 'thick', or 'comparable' to the corresponding region of a demographically-matched control cohort. Fractions of significantly 'thin' and 'thick' patches were calculated across the entire cerebral vertex as well as in specific brain regions, such as the motor cortex, parietal, frontal and temporal cortices. This approach allows the characterisation of disease burden in individual subjects as well as at a group-level, both cross-sectionally and longitudinally. The presented framework may aid the interpretation of individual cortical disease burden in other patient cohorts.

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.