Localizing apraxia in corticobasal syndrome: a morphometric MRI study.

Apraxia localization has relied on voxel-based, lesion-symptom mapping studies in left hemisphere stroke patients. Studies on the neural substrates of different manifestations of apraxia in neurodegenerative disorders are scarce. The primary aim of this study was to look into the neural substrates of different manifestations of apraxia in a cohort of corticobasal syndrome patients (CBS) by use of cortical thickness. Twenty-six CBS patients were included in this cross-sectional study. The Goldenberg apraxia test (GAT) was applied. 3D-T1-weighted images were analyzed via the automated recon-all Freesurfer version 6.0 pipeline. Vertex-based multivariate General Linear Model analysis was applied to correlate GAT scores with cortical thickness. Deficits in imitation of meaningless gestures correlated with bilateral superior parietal atrophy, extending to the angular and supramarginal gyri, particularly on the left. Finger imitation relied predominantly on superior parietal lobes, whereas the left angular and supramarginal gyri, in addition to superior parietal lobes, were critical for hand imitation. The widespread bilateral clusters of atrophy in CBS related to apraxia indicate different pathophysiological mechanisms mediating praxis in neurodegenerative disorders compared to vascular lesions, with implications both for our understanding of praxis and for the rehabilitation approaches of patients with apraxia.

Spatial neurolipidomics-MALDI mass spectrometry imaging of lipids in brain pathologies.

Given the complexity of nervous tissues, understanding neurochemical pathophysiology puts high demands on bioanalytical techniques with respect to specificity and sensitivity. Mass spectrometry imaging (MSI) has evolved to become an important, biochemical imaging technology for spatial biology in biological and translational research. The technique facilitates comprehensive, sensitive elucidation of the spatial distribution patterns of drugs, lipids, peptides, and small proteins in situ. Matrix-assisted laser desorption ionization (MALDI)-based MSI is the dominating modality due to its broad applicability and fair compromise of selectivity, sensitivity price, throughput, and ease of use. This is particularly relevant for the analysis of spatial lipid patterns, where no other comparable spatial profiling tools are available. Understanding spatial lipid biology in nervous tissue is therefore a key and emerging application area of MSI research. The aim of this review is to give a concise guide through the MSI workflow for lipid imaging in central nervous system (CNS) tissues and essential parameters to consider while developing and optimizing MSI assays. Further, this review provides a broad overview of key developments and applications of MALDI MSI-based spatial neurolipidomics to map lipid dynamics in neuronal structures, ultimately contributing to a better understanding of neurodegenerative disease pathology.

Fractal Dimension Studies of the Brain Shape in Aging and Neurodegenerative Diseases.

The fractal dimension is a morphometric measure that has been used to investigate the changes of brain shape complexity in aging and neurodegenerative diseases. This chapter reviews fractal dimension studies in aging and neurodegenerative disorders in the literature. Research has shown that the fractal dimension of the left cerebral hemisphere increases until adolescence and then decreases with aging, while the fractal dimension of the right hemisphere continues to increase until adulthood. Studies in neurodegenerative diseases demonstrated a decline in the fractal dimension of the gray matter and white matter in Alzheimer's disease, amyotrophic lateral sclerosis, and spinocerebellar ataxia. In multiple sclerosis, the white matter fractal dimension decreases, but conversely, the fractal dimension of the gray matter increases at specific stages of disease. There is also a decline in the gray matter fractal dimension in frontotemporal dementia and multiple system atrophy of the cerebellar type and in the white matter fractal dimension in epilepsy and stroke. Region-specific changes in fractal dimension have also been found in Huntington's disease and Parkinson's disease. Associations were found between the fractal dimension and clinical scores, showing the potential of the fractal dimension as a marker to monitor brain shape changes in normal or pathological processes and predict cognitive or motor function.

Preclinical Evaluation of the Reversible Monoacylglycerol Lipase PET Tracer (R)-[11C]YH132: Application in Drug Development and Neurodegenerative Diseases.

Monoacylglycerol lipase (MAGL) plays a crucial role in the degradation of 2-arachidonoylglycerol (2-AG), one of the major endocannabinoids in the brain. Inhibiting MAGL could lead to increased levels of 2-AG, which showed beneficial effects on pain management, anxiety, inflammation, and neuroprotection. In the current study, we report the characterization of an enantiomerically pure (R)-[11C]YH132 as a novel MAGL PET tracer. It demonstrates an improved pharmacokinetic profile compared to its racemate. High in vitro MAGL specificity of (R)-[11C]YH132 was confirmed by autoradiography studies using mouse and rat brain sections. In vivo, (R)-[11C]YH132 displayed a high brain penetration, and high specificity and selectivity toward MAGL by dynamic PET imaging using MAGL knockout and wild-type mice. Pretreatment with a MAGL drug candidate revealed a dose-dependent reduction of (R)-[11C]YH132 accumulation in WT mouse brains. This result validates its utility as a PET probe to assist drug development. Moreover, its potential application in neurodegenerative diseases was explored by in vitro autoradiography using brain sections from animal models of Alzheimer's disease and Parkinson's disease.

Glymphatic system dysfunction in neurodegenerative diseases.

PURPOSE OF REVIEW: Purpose of this review is to update the ongoing work in the field of glymphatic and neurodegenerative research and to highlight focus areas that are particularly promising. RECENT FINDINGS: Multiple reports have over the past decade documented that glymphatic fluid transport is broadly suppressed in neurodegenerative diseases. Most studies have focused on Alzheimer's disease using a variety of preclinical disease models, whereas the clinical work is based on various neuroimaging approaches. It has consistently been reported that brain fluid transport is impaired in patients suffering from Alzheimer's disease compared with age-matched control subjects. SUMMARY: An open question in the field is to define the mechanistic underpinning of why glymphatic function is suppressed. Other questions include the opportunities for using glymphatic imaging for diagnostic purposes and in treatment intended to prevent or slow Alzheimer disease progression.

Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models.

Purpose: Retinal images contain rich biomarker information for neurodegenerative disease. Recently, deep learning models have been used for automated neurodegenerative disease diagnosis and risk prediction using retinal images with good results. Methods: In this review, we systematically report studies with datasets of retinal images from patients with neurodegenerative diseases, including Alzheimer's disease, Huntington's disease, Parkinson's disease, amyotrophic lateral sclerosis, and others. We also review and characterize the models in the current literature which have been used for classification, regression, or segmentation problems using retinal images in patients with neurodegenerative diseases. Results: Our review found several existing datasets and models with various imaging modalities primarily in patients with Alzheimer's disease, with most datasets on the order of tens to a few hundred images. We found limited data available for the other neurodegenerative diseases. Although cross-sectional imaging data for Alzheimer's disease is becoming more abundant, datasets with longitudinal imaging of any disease are lacking. Conclusions: The use of bilateral and multimodal imaging together with metadata seems to improve model performance, thus multimodal bilateral image datasets with patient metadata are needed. We identified several deep learning tools that have been useful in this context including feature extraction algorithms specifically for retinal images, retinal image preprocessing techniques, transfer learning, feature fusion, and attention mapping. Importantly, we also consider the limitations common to these models in real-world clinical applications. Translational Relevance: This systematic review evaluates the deep learning models and retinal features relevant in the evaluation of retinal images of patients with neurodegenerative disease.

Graph kernel of brain networks considering functional similarity measures.

As a tool of brain network analysis, the graph kernel is often used to assist the diagnosis of neurodegenerative diseases. It is used to judge whether the subject is sick by measuring the similarity between brain networks. Most of the existing graph kernels calculate the similarity of brain networks based on structural similarity, which can better capture the topology of brain networks, but all ignore the functional information including the lobe, centers, left and right brain to which the brain region belongs and functions of brain regions in brain networks. The functional similarities can help more accurately locate the specific brain regions affected by diseases so that we can focus on measuring the similarity of brain networks. Therefore, a multi-attribute graph kernel for the brain network is proposed, which assigns multiple attributes to nodes in the brain network, and computes the graph kernel of the brain network according to Weisfeiler-Lehman color refinement algorithm. In addition, in order to capture the interaction between multiple brain regions, a multi-attribute hypergraph kernel is proposed, which takes into account the functional and structural similarities as well as the higher-order correlation between the nodes of the brain network. Finally, the experiments are conducted on real data sets and the experimental results show that the proposed methods can significantly improve the performance of neurodegenerative disease diagnosis. Besides, the statistical test shows that the proposed methods are significantly different from compared methods.

Colony Stimulating Factor-1 Receptor: An emerging target for neuroinflammation PET imaging and AD therapy.

Although neuroinflammation is a significant pathogenic feature of many neurologic disorders, its precise function in-vivo is still not completely known. PET imaging enables the longitudinal examination, quantification, and tracking of different neuroinflammation biomarkers in living subjects. Particularly, PET imaging of Microglia, specialised dynamic immune cells crucial for maintaining brain homeostasis in central nervous system (CNS), is crucial for staging the neuroinflammation. Colony Stimulating Factor- 1 Receptor (CSF-1R) PET imaging is a novel method for the quantification of neuroinflammation. CSF-1R is mainly expressed on microglia, and neurodegenerative disorders greatly up-regulate its expression. The present review primarily focuses on the development, pros and cons of all the CSF-1R PET tracers reported for neuroinflammation imaging. Apart from neuroinflammation imaging, CSF-1R inhibitors are also reported for the therapy of neurodegenerative diseases such as Alzheimer's disease (AD). AD is a prevalent, advancing, and fatal neurodegenerative condition that have the characteristic feature of persistent neuroinflammation and primarily affects the elderly. The aetiology of AD is profoundly influenced by amyloid-beta (Aß) plaques, intracellular neurofibrillary tangles, and microglial dysfunction. Increasing evidence suggests that CSF-1R inhibitors (CSF-1Ri) can be helpful in preclinical models of neurodegenerative diseases. This review article also summarises the most recent developments of CSF-1Ri-based therapy for AD.

Early Diagnosis of Neurodegenerative Diseases: What Has Been Undertaken to Promote the Transition from PET to Fluorescence Tracers.

Alzheimer's Disease (AD) and Parkinson's Disease (PD) represent two among the most frequent neurodegenerative diseases worldwide. A common hallmark of these pathologies is the misfolding and consequent aggregation of amyloid proteins into soluble oligomers and insoluble ß-sheet-rich fibrils, which ultimately lead to neurotoxicity and cell death. After a hundred years of research on the subject, this is the only reliable histopathological feature in our hands. Since AD and PD are diagnosed only once neuronal death and the first symptoms have appeared, the early detection of these diseases is currently impossible. At present, there is no effective drug available, and patients are left with symptomatic and inconclusive therapies. Several reasons could be associated with the lack of effective therapeutic treatments. One of the most important factors is the lack of selective probes capable of detecting, as early as possible, the most toxic amyloid species involved in the onset of these pathologies. In this regard, chemical probes able to detect and distinguish among different amyloid aggregates are urgently needed. In this article, we will review and put into perspective results from ex vivo and in vivo studies performed on compounds specifically interacting with such early species. Following a general overview on the three different amyloid proteins leading to insoluble ß-sheet-rich amyloid deposits (amyloid ß1-42 peptide, Tau, and α-synuclein), a list of the advantages and disadvantages of the approaches employed to date is discussed, with particular attention paid to the translation of fluorescence imaging into clinical applications. Furthermore, we also discuss how the progress achieved in detecting the amyloids of one neurodegenerative disease could be leveraged for research into another amyloidosis. As evidenced by a critical analysis of the state of the art, substantial work still needs to be conducted. Indeed, the early diagnosis of neurodegenerative diseases is a priority, and we believe that this review could be a useful tool for better investigating this field.

Retinal Alterations Predict Early Prodromal Signs of Neurodegenerative Disease.

Neurodegenerative diseases are an increasingly common group of diseases that occur late in life with a significant impact on personal, family, and economic life. Among these, Alzheimer's disease (AD) and Parkinson's disease (PD) are the major disorders that lead to mild to severe cognitive and physical impairment and dementia. Interestingly, those diseases may show onset of prodromal symptoms early after middle age. Commonly, the evaluation of these neurodegenerative diseases is based on the detection of biomarkers, where functional and structural magnetic resonance imaging (MRI) have shown a central role in revealing early or prodromal phases, although it can be expensive, time-consuming, and not always available. The aforementioned diseases have a common impact on the visual system due to the pathophysiological mechanisms shared between the eye and the brain. In Parkinson's disease, α-synuclein deposition in the retinal cells, as well as in dopaminergic neurons of the substantia nigra, alters the visual cortex and retinal function, resulting in modifications to the visual field. Similarly, the visual cortex is modified by the neurofibrillary tangles and neuritic amyloid ß plaques typically seen in the Alzheimer's disease brain, and this may reflect the accumulation of these biomarkers in the retina during the early stages of the disease, as seen in postmortem retinas of AD patients. In this light, the ophthalmic evaluation of retinal neurodegeneration could become a cost-effective method for the early diagnosis of those diseases, overcoming the limitations of functional and structural imaging of the deep brain. This analysis is commonly used in ophthalmic practice, and interest in it has risen in recent years. This review will discuss the relationship between Alzheimer's disease and Parkinson's disease with retinal degeneration, highlighting how retinal analysis may represent a noninvasive and straightforward method for the early diagnosis of these neurodegenerative diseases.

Optical Imaging Demonstrates Tissue-Specific Metabolic Perturbations in Mblac1 Knockout Mice.

OBJECTIVE: Metabolic changes have been extensively documented in neurodegenerative brain disorders, including Parkinson's disease and Alzheimer's disease (AD). Mutations in the C. elegans swip-10 gene result in dopamine (DA) dependent motor dysfunction accompanied by DA neuron degeneration. Recently, the putative human ortholog of swip-10 (MBLAC1) was implicated as a risk factor in AD, a disorder that, like PD, has been associated with mitochondrial dysfunction. Interestingly, the AD risk associated with MBLAC1 arises in subjects with cardiovascular morbidity, suggesting a broader functional insult arising from reduced MBLAC1 protein expression and one possibly linked to metabolic alterations. METHODS: Our current studies, utilizing Mblac1 knockout (KO) mice, seek to determine whether mitochondrial respiration is affected in the peripheral tissues of these mice. We quantified the levels of mitochondrial coenzymes, NADH, FAD, and their redox ratio (NADH/FAD, RR) in livers and kidneys of wild-type (WT) mice and their homozygous KO littermates of males and females, using 3D optical cryo-imaging. RESULTS: Compared to WT, the RR of livers from KO mice was significantly reduced, without an apparent sex effect, driven predominantly by significantly lower NADH levels. In contrast, no genotype and sex differences were observed in kidney samples. Serum analyses of WT and KO mice revealed significantly elevated glucose levels in young and aged KO adults and diminished cholesterol levels in the aged KOs, consistent with liver dysfunction. DISCUSSION/CONCLUSION: As seen with C. elegans swip-10 mutants, loss of MBLAC1 protein results in metabolic changes that are not restricted to neural cells and are consistent with the presence of peripheral comorbidities accompanying neurodegenerative disease in cases where MBLAC1 expression changes impact risk.

In vivo imaging of synaptic density in neurodegenerative disorders with positron emission tomography: A systematic review.

Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2 A (SV2A) enables quantification of synaptic density in the living human brain. Assessing the regional distribution and severity of synaptic density loss will contribute to our understanding of the pathological processes that precede atrophy in neurodegeneration. In this systematic review, we provide a discussion of in vivo SV2A PET imaging research for quantitative assessment of synaptic density in various dementia conditions: amnestic Mild Cognitive Impairment and Alzheimer's disease, Frontotemporal dementia, Progressive supranuclear palsy and Corticobasal degeneration, Parkinson's disease and Dementia with Lewy bodies, Huntington's disease, and Spinocerebellar Ataxia. We discuss the main findings concerning group differences and clinical-cognitive correlations, and explore relations between SV2A PET and other markers of pathology. Additionally, we touch upon synaptic density in healthy ageing and outcomes of radiotracer validation studies. Studies were identified on PubMed and Embase between 2018 and 2023; last searched on the 3rd of July 2023. A total of 36 studies were included, comprising 5 on normal ageing, 21 clinical studies, and 10 validation studies. Extracted study characteristics were participant details, methodological aspects, and critical findings. In summary, the small but growing literature on in vivo SV2A PET has revealed different spatial patterns of synaptic density loss among various neurodegenerative disorders that correlate with cognitive functioning, supporting the potential role of SV2A PET imaging for differential diagnosis. SV2A PET imaging shows tremendous capability to provide novel insights into the aetiology of neurodegenerative disorders and great promise as a biomarker for synaptic density reduction. Novel directions for future synaptic density research are proposed, including (a) longitudinal imaging in larger patient cohorts of preclinical dementias, (b) multi-modal mapping of synaptic density loss onto other pathological processes, and (c) monitoring therapeutic responses and assessing drug efficacy in clinical trials.

Harnessing two-dimensional nanomaterials for diagnosis and therapy in neurodegenerative diseases: Advances, challenges and prospects.

Neurodegenerative diseases (NDDs) are specific brain disorders characterized by the progressive deterioration of different motor activities as well as several cognitive functions. Current conventional therapeutic options for NDDs are limited in addressing underlying causes, delivering drugs to specific neuronal targets, and promoting tissue repair following brain injury. Due to the paucity of plausible theranostic options for NDDs, nanobiotechnology has emerged as a promising field, offering an interdisciplinary approach to create nanomaterials with high diagnostic and therapeutic efficacy for these diseases. Recently, two-dimensional nanomaterials (2D-NMs) have gained significant attention in biomedical and pharmaceutical applications due to their precise drug-loading capabilities, controlled release mechanisms, enhanced stability, improved biodegradability, and reduced cell toxicity. Although various studies have explored the diagnostic and therapeutic potential of different nanomaterials in NDDs, there is a lack of comprehensive review addressing the theranostic applications of 2D-NMs in these neuronal disorders. Therefore, this concise review aims to provide a state-of-the-art understanding of the need for these ultrathin 2D-NMs and their potential applications in biosensing and bioimaging, targeted drug delivery, tissue engineering, and regenerative medicine for NDDs.

Recovery after Prolonged Disturbance of Consciousness and Repeated Cerebral Perfusion Changes in Neuronal Intranuclear Inclusion Disease.

Encephalitic episodes are a clinical manifestation of neuronal intranuclear inclusion disease (NIID) and often show transient disturbance of consciousness. We herein report a genetically confirmed patient with NIID who initially presented progressive dementia and showed prolonged disturbance of consciousness preceded by an acute-onset headache. During that time, we performed N-isopropyl-p-[123I] iodoamphetamine single-photon-emission computed tomography twice and found that the blood flow increased in different regions. Prolonged disturbance of consciousness following an encephalitic episode may be associated with repeated hyperperfusion in various regions resulting from mitochondrial dysfunction. NIID patients presenting with encephalitic episodes can recover gradually and spontaneously even after prolonged disturbances of consciousness.

Polypathologic Associations with Gray Matter Atrophy in Neurodegenerative Disease.

Mixed pathologies are common in neurodegenerative disease; however, antemortem imaging rarely captures copathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer's pathologies. We leveraged a dataset comprising antemortem MRI and postmortem histopathology to assess polypathologic associations with atrophy in a clinically heterogeneous sample of 125 human dementia patients (41 female, 84 male) with T1-weighted MRI ≤ 5 years before death and postmortem ordinal ratings of amyloid-[Formula: see text], tau, TDP-43, and [Formula: see text]-synuclein. Regional volumes were related to pathology using linear mixed-effects models; approximately 25% of data were held out for testing. We contrasted a polypathologic model comprising independent factors for each proteinopathy with two alternatives: a model that attributed atrophy entirely to the protein(s) associated with the patient's primary diagnosis and a protein-agnostic model based on the sum of ordinal scores for all pathology types. Model fits were evaluated using log-likelihood and correlations between observed and fitted volume scores. Additionally, we performed exploratory analyses relating atrophy to gliosis, neuronal loss, and angiopathy. The polypathologic model provided superior fits in the training and testing datasets. Tau, TDP-43, and [Formula: see text]-synuclein burden were inversely associated with regional volumes, but amyloid-[Formula: see text] was not. Gliosis and neuronal loss explained residual variance in and mediated the effects of tau, TDP-43, and [Formula: see text]-synuclein on atrophy. Regional brain atrophy reflects not only the primary molecular pathology but also co-occurring proteinopathies; inflammatory immune responses may independently contribute to degeneration. Our findings underscore the importance of antemortem biomarkers for detecting mixed pathology.

Cerebellar peduncle damage in Langerhans cell histiocytosis-associated neurodegenerative disease revealed by diffusion tensor imaging.

PURPOSE: To confirm the hypothesis that brain white matter damage is involved in the pathogenesis and disease progression of Langerhans cell histiocytosis (LCH)-associated neurodegenerative disease (ND), we aimed to analyze pediatric patients with LCH using diffusion tensor imaging (DTI). METHODS: We enrolled 33 patients with LCH and obtained 33 DTI datasets. Using DTI-based tractography, fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD) were measured in the cerebral and cerebellar white matter tracts. The participants were divided into three groups-non-ND, ND without clinical symptoms (r-ND), and ND with clinical symptoms (c-ND)-according to their clinical status during the examination with DTI. We compared the DTI parameters in white matter tracts were compared among the three groups. RESULTS: In the order of non-ND, r-ND, and c-ND groups, the FA in superior cerebellar peduncle (SCP) and middle cerebellar peduncle (MCP) significantly decreased, the ADC, AD, and RD of MCP, and the RD of SCP were significantly elevated (FA-SCP; p < 0.001, FA-MCP; p = 0.026, ADC-MCP; p < 0.001, AD-MCP; p = 0.002, RD-MCP; p = 0.003, and RD-SCP; p = 0.018). Furthermore, in the simple linear regression analysis, the FA, ADC, AD, and RD values in the MCP and the FA value in the SCP were significantly influenced by the presence of neurological symptoms and ND findings on MRI (all p < 0.001). CONCLUSION: In LCH-ND, we identified microstructural damage in the SCP and MCP. DTI parameters in these tracts may help monitor LCH-ND; therefore, future studies are required to validate these results in a large cohort.

Changes in iron load in specific brain areas lead to neurodegenerative diseases of the central nervous system.

The causes of neurodegenerative diseases remain largely elusive, increasing their personal and societal impacts. To reveal the causal effects of iron load on Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis and multiple sclerosis, we used Mendelian randomisation and brain imaging data from a UK Biobank genome-wide association study of 39,691 brain imaging samples (predominantly of European origin). Using susceptibility-weighted images, which reflect iron load, we analysed genetically significant brain regions. Inverse variance weighting was used as the main estimate, while MR Egger and weighted median were used to detect heterogeneity and pleiotropy. Nine clear associations were obtained. For AD and PD, an increased iron load was causative: the right pallidum for AD and the right caudate, left caudate and right accumbens for PD. However, a reduced iron load was identified in the right and left caudate for multiple sclerosis, the bilateral hippocampus for mixed vascular dementia and the left thalamus and bilateral accumbens for subcortical vascular dementia. Thus, changes in iron load in different brain regions have causal effects on neurodegenerative diseases. Our results are crucial for understanding the pathogenesis and investigating the treatment of these diseases.

Robustly uncovering the heterogeneity of neurodegenerative disease by using data-driven subtyping in neuroimaging: A review.

Neurodegenerative diseases are associated with heterogeneity in genetics, pathology, and clinical manifestation. Understanding this heterogeneity is particularly relevant for clinical prognosis and stratifying patients for disease modifying treatments. Recently, data-driven methods based on neuroimaging have been applied to investigate the subtyping of neurodegenerative disease, helping to disentangle this heterogeneity. We reviewed brain-based subtyping studies in aging and representative neurodegenerative diseases, including Alzheimer's disease, mild cognitive impairment, frontotemporal dementia, and Lewy body dementia, from January 2000 to November 2022. We summarized clustering methods, validation, robustness, reproducibility, and clinical relevance of 71 eligible studies in the present study. We found vast variations in approaches between studies, including ten neuroimaging modalities, 24 cluster algorithms, and 41 methods of cluster number determination. The clinical relevance of subtyping studies was evaluated by summarizing the analysis method of clinical measurements, showing a relatively low clinical utility in the current studies. Finally, we conclude that future studies of heterogeneity in neurodegenerative disease should focus on validation, comparison between subtyping approaches, and prioritise clinical utility.

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration.

INTRODUCTION: Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification. MATERIALS AND METHODS: We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition. RESULTS: CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration. DISCUSSION: These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation. HIGHLIGHTS: Computed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.