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.

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.

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

Background and purpose Primary lateral sclerosis (PLS) is a progressive upper motor neuron disorder associated with considerable clinical disability. Symptoms are typically exclusively linked to primary motor cortex degeneration and the contribution of pre‐motor, supplementary motor, cortico‐medullary and inter‐hemispheric connectivity alterations are less well characterized. Methods In a single‐centre, prospective, longitudinal neuroimaging study 41 patients with PLS were investigated. Patients underwent standardized neuroimaging, genetic profiling with whole exome sequencing, and comprehensive clinical assessments including upper motor neuron scores, tapping rates, mirror movements, spasticity assessment, cognitive screening and evaluation for pseudobulbar affect. Longitudinal neuroimaging data from 108 healthy controls were used for image interpretation. A standardized imaging protocol was implemented including 3D T1‐weighted structural, diffusion tensor imaging and resting‐state functional magnetic resonance imaging. Following somatotopic segmentation, cortical thickness analyses, probabilistic tractography, blood oxygenation level dependent signal analyses and brainstem volumetry were conducted to evaluate cortical, brainstem, cortico‐medullary and inter‐hemispheric connectivity alterations both cross‐sectionally and longitudinally. Results Our data confirm progressive primary motor cortex degeneration, considerable supplementary motor and pre‐motor area involvement, progressive brainstem atrophy, cortico‐medullary and inter‐hemispheric disconnection, and close associations between clinical upper motor neuron scores and somatotopic connectivity indices in PLS. Discussion Primary lateral sclerosis is associated with relentlessly progressive motor connectome degeneration. Clinical disability in PLS is likely to stem from a combination of intra‐ and inter‐hemispheric connectivity decline and primary, pre‐ and supplementary motor cortex degeneration. Simple ‘bedside’ clinical tools, such as tapping rates, are excellent proxies of the integrity of the relevant fibres of the contralateral corticospinal tract.

Computational imaging and quantitative biomarkers offer invaluable insights in the pre-symptomatic phase of neurodegenerative conditions several years before clinical manifestation. In recent years, there has been a focused effort to characterize pre-symptomatic cerebral changes in familial frontotemporal dementias using computational imaging. Accordingly, a systematic literature review was conducted of original articles investigating pre-symptomatic imaging changes in frontotemporal dementia focusing on study design, imaging modalities, data interpretation, control cohorts and key findings. The review is limited to the most common genotypes: chromosome 9 open reading frame 72 (C9orf72), progranulin (GRN), or microtubule-associated protein tau (MAPT) genotypes. Sixty-eight studies were identified with a median sample size of 15 (3–141) per genotype. Only a minority of studies were longitudinal (28%; 19/68) with a median follow-up of 2 (1–8) years. MRI (97%; 66/68) was the most common imaging modality, and primarily grey matter analyses were conducted (75%; 19/68). Some studies used multimodal analyses 44% (30/68). Genotype-associated imaging signatures are presented, innovative study designs are highlighted, common methodological shortcomings are discussed and lessons for future studies are outlined. Emerging academic observations have potential clinical implications for expediting the diagnosis, tracking disease progression and optimising the timing of pharmaceutical trials.