Marker Of Neurodegenerative Diseases Research Articles

Influence of mild cognitive impairment and body mass index on white matter integrity assessed by diffusion tensor imaging.

Mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease, is characterized by decreased memory and cognition, which are linked to degenerative changes in the brain. To assess whether white matter (WM) integrity is compromised in MCI, we collected diffusion-weighted images from 60 healthy older adults (OA) (69.16 ± 0.7) and 20 older adults with amnestic MCI (72.45 ± 1.9). WM integrity differences were examined using Tract-Based Spatial Statistics (TBSS). We hypothesized that those with MCI would have diminished WM integrity relative to OA. In a whole-brain comparison, those with MCI showed higher axial diffusivity in the splenium (SCC) and body of the corpus callosum (BCC), superior corona radiata (SCR), and the retrolenticular part of the internal capsule (RLIC) (p's < .05 TFCE-corrected). Additionally, significant between-group connectivity differences were observed using probabilistic tractography between the SCC, chosen from the TBSS results, and forceps major and minor (p-value's < .05). To further relate a physical health indicator to WM alterations, linear regression showed significant interactions between cognitive status and body mass index (BMI) on diffusivity outcome measures from probabilistic tractography (p-value-'s < .05). Additionally, we examined the association between relational memory, BMI, and WM integrity. WM integrity was positively associated with relational memory performance. These findings suggest that these regions may be more sensitive to early markers of neurodegenerative disease and health behaviors, suggesting that modifiable lifestyle factors may affect white matter integrity.

Revisiting the effects of exercise on cerebral neurovascular functions in rats using multimodal assessment techniques

Physical activity in the form of aerobic exercise has many beneficial effects on brain function. Here, we aim to revisit the effects of exercise on brain morphology and neurovascular organization using a rat running model. Electrocorticography (ECoG) was integrated with laser speckle contrast imaging (LSCI) and applied to simultaneously detect CSD propagation and the corresponding neurovascular function. In addition, blood oxygenation level-dependent (BOLD) signal in fMRI was used to observe cerebral utilization of oxygen. Results showed significant decrease in somatosensory evoked potentials (SSEPs) and deceleration of CSD propagation in the EXE group. Western blot results in the EXE group showed significant increases in BDNF, GFAP, and NeuN levels and significant decreases in neurodegenerative disease markers. Decreases in SSEP and CSD parameters may result from exercise-induced increases in cerebrovascular system function and increases in the stability and buffering of extracellular ion concentrations and cortical excitability.

Functional connectivity and mild behavioral impairment in dementia-free elderly.

Mild behavioral impairment (MBI) is a syndrome that uses later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a group at high risk for incident dementia. MBI is associated with neurodegenerative disease markers in advance of syndromic dementia. Functional connectivity (FC) correlates of MBI are understudied and could provide further insights into mechanisms early in the disease course. We used resting-state functional magnetic resonance imaging (rs-fMRI) to test the hypothesis that FC within the default mode network (DMN) and salience network (SN) of persons with MBI (MBI+) is reduced, relative to those without (MBI-). From two harmonized dementia-free cohort studies, using a score of ≥6 on the MBI Checklist to define MBI status, 32 MBI+ and 63 MBI- individuals were identified (mean age: 71.7 years; 54.7% female). Seed-based connectivity analysis was implemented in each MBI group using the CONN fMRI toolbox (v20.b), with the posterior cingulate cortex (PCC) as the seed region within the DMN and anterior cingulate cortex (ACC) as the seed within the SN. The average time series from the PCC and ACC were used to determine FC with other regions within the DMN (medial prefrontal cortex, lateral inferior parietal cortex) and SN (anterior insula, supramarginal gyrus, rostral prefrontal cortex), respectively. Age, sex, years of education, and Montreal Cognitive Assessment scores were included as model covariates. The false discovery rate approach was used to correct for multiple comparisons, with a p-value of .05 considered significant. For the DMN, MBI+ individuals exhibited reduced FC between the PCC and the medial prefrontal cortex, compared to MBI-. For the SN, MBI+ individuals exhibited reduced FC between the ACC and left anterior insula. MBI in dementia-free older adults is associated with reduced FC in networks known to be disrupted in dementia. Our results complement the evidence linking MBI with Alzheimer's disease biomarkers. Resting-state functional magnetic resonance imaging was completed in 95 dementia-free persons from FAVR and COMPASS-ND studies.Participants were stratified by informant-rated Mild Behavioral Impairment Checklist (MBI-C) score, ≥6 for MBI+.MBI+ participants showed reduced functional connectivity (FC) within the default mode network and salience network.These FC changes are consistent with those seen in early-stage Alzheimer's disease.MBI may help identify persons with early-stage neurodegenerative disease.

Late-life Onset Psychosis-like Symptoms Assessed in the Mild Behavioral Impairment Framework are Associated Impaired Performance on the Stroop Task

Late-life onset psychosis and milder delusion-like ideation are known risk factors for cognitive decline and dementia. The Mild Behavioural Impairment (MBI) framework was developed to capture specific psychotic-like symptoms relevant to dementia prognosis in older adults. This study aims to investigate the cognitive deficits associated with MBI-psychosis and their implications for understanding the underlying mechanisms and potential treatment targets. The study recruited participants between November 2021 and July 2022 from the PROTECT study registry. Participants completed the Cambridge Gambling Task, Stroop, Trail Making, Paired Associates Learning, Verbal Reasoning, Digit Span and Self-Ordered Search. Psychotic symptom status was assessed using the Mild Behavioural Impairment Checklist (MBI-C), with participants categorized as MBI-psychosis if they or their study partner reported any psychotic symptoms. Out of 2,111 eligible participants invited, 417 consented to participate. There were no significant differences in age, sex, education level, or mental health history between the MBI-psychosis and No Psychosis groups. Participants with MBI-psychosis exhibited significantly worse performance on the Stroop task (p=0.0002, Cohen’s d=0.37) compared to those without psychosis. There was also some evidence of impairment in verbal reasoning, though it did not reach significance after Bonferroni correction. No significant differences were found on other cognitive measures. This cross-sectional study provides insight into the cognitive deficits associated with MBI-psychosis. The finding of impaired Stroop task performance in individuals with MBI-psychosis is noteworthy, as this deficit is commonly observed in earlier-life major psychotic disorders. Further research is needed to explore the neural underpinnings of these deficits and to determine whether they represent early markers of neurodegenerative disease or other factors.

Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics.

Neurodegenerative disease often affects speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to delineate healthy speech from speech of people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We subjected 74 spectral and temporal prosodic features from the speech recordings to machine learning. Results showed that Friedreich ataxia, multiple sclerosis and healthy controls were all identified with high accuracy (over 82%). Twenty-one acoustic features were strong markers of neurodegenerative diseases, falling under the categories of spectral qualia, spectral power, and speech rate. We demonstrated that speech markers can delineate neurodegenerative diseases and distinguish healthy speech from pathological speech with high accuracy. Findings emphasize the importance of examining speech outcomes when assessing indicators of neurodegenerative disease. We propose large-scale initiatives to broaden the scope for differentiating other neurological diseases and affective disorders.

A novel episodic memory test optimized for smart phone high‐frequency assessments

AbstractBackgroundDecline in episodic memory is a strong marker of neurodegenerative diseases, making it an important target for cognitive assessment. We present a new remote, repeatable, and brief assessment of episodic memory and visual short‐term memory (VSTM) for smart phone deployment. We evaluated this task for sensitivity to age and established cognitive measures of memory and attention.MethodThe task consists of a learning phase where participants see a sequence of items, then are asked to replicate the item order and location by dragging and dropping these items on‐screen. A recall phase follows a minimum 12‐hour delay, with replication of the earlier response. Each session takes less than 2 minutes. The first phase supplies metrics of spatial and order precision VSTM, with the second phase characterising long‐term episodic memory.Experiment 1 (n = 133) compares the task with CANTAB assessments of visuospatial memory. In Experiment 2 (n = 80) the novel task was repeated twice daily for a week, alongside momentary mood ratings, and a more comprehensive battery of CANTAB tasks at baseline.In Experiment 1, multiple regression and dominance analyses explore the independent and shared variance of measures in explaining age. In Experiment 2, clustering and multivariate autoregressive models outline differences in timeseries relationships between mood and memory, and age.ResultExperiment 1 revealed that CANTAB measures and novel spatial and order metrics were correlated with age (r = .20 to r = .37, adjusted P = &lt;.05). Multiple regression showed models with CANTAB Paired Associate Learning (PAL) score, and delayed novel spatial metrics were significantly predictive of age (t = ‐4.08, 3.17, p &lt;.005). The dominance analysis revealed novel immediate precision overlapped with existing measures in predicting Age, while delayed precision had more unique variance (4.6% vs 0.62% unique sample variance explained in Age, accounting for PAL).Experiment 2 characterised variance in individual learning curves over time and differing autoregressive processes associated with age and CANTAB measures.ConclusionWe present a brief novel episodic memory task that can be deployed remotely, with delayed spatial precision significantly predictive of healthy Ageing. Additionally, we show this test’s suitability as a repeated assessment, compatible with high‐frequency study designs in older populations.

A novel episodic memory test optimized for smart phone high-frequency assessments.

Decline in episodic memory is a strong marker of neurodegenerative diseases, making it an important target for cognitive assessment. We present a new remote, repeatable, and brief assessment of episodic memory and visual short-term memory (VSTM) for smart phone deployment. We evaluated this task for sensitivity to age and established cognitive measures of memory and attention. The task consists of a learning phase where participants see a sequence of items, then are asked to replicate the item order and location by dragging and dropping these items on-screen. A recall phase follows a minimum 12-hour delay, with replication of the earlier response. Each session takes less than 2 minutes. The first phase supplies metrics of spatial and order precision VSTM, with the second phase characterising long-term episodic memory. Experiment 1 (n=133) compares the task with CANTAB assessments of visuospatial memory. In Experiment 2 (n=80) the novel task was repeated twice daily for a week, alongside momentary mood ratings, and a more comprehensive battery of CANTAB tasks at baseline. In Experiment 1, multiple regression and dominance analyses explore the independent and shared variance of measures in explaining age. In Experiment 2, clustering and multivariate autoregressive models outline differences in timeseries relationships between mood and memory, and age. Experiment 1 revealed that CANTAB measures and novel spatial and order metrics were correlated with age (r=.20 to r=.37, adjusted P= <.05). Multiple regression showed models with CANTAB Paired Associate Learning (PAL) score, and delayed novel spatial metrics were significantly predictive of age (t= -4.08, 3.17, p <.005). The dominance analysis revealed novel immediate precision overlapped with existing measures in predicting Age, while delayed precision had more unique variance (4.6% vs 0.62% unique sample variance explained in Age, accounting for PAL). Experiment 2 characterised variance in individual learning curves over time and differing autoregressive processes associated with age and CANTAB measures. We present a brief novel episodic memory task that can be deployed remotely, with delayed spatial precision significantly predictive of healthy Ageing. Additionally, we show this test's suitability as a repeated assessment, compatible with high-frequency study designs in older populations.

Accounting for retest effects in cognitive testing with the Bayesian double exponential model via intensive measurement burst designs.

Monitoring early changes in cognitive performance is useful for studying cognitive aging as well as for detecting early markers of neurodegenerative diseases. Repeated evaluation of cognition via a measurement burst design can accomplish this goal. In such design participants complete brief evaluations of cognition, multiple times per day for several days, and ideally, repeat the process once or twice a year. However, long-term cognitive change in such repeated assessments can be masked by short-term within-person variability and retest learning (practice) effects. In this paper, we show how a Bayesian double exponential model can account for retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance. We also highlight how this approach allows for the inclusion of person-level predictors and draw intuitive inferences on cognitive change with Bayesian posterior probabilities. We use older adults’ performance on cognitive tasks of processing speed and spatial working memory to demonstrate how individual differences in peak performance and change can be related to predictors of aging such as biological age and mild cognitive impairment status.

Transfreq: A Python package for computing the theta-to-alpha transition frequency from resting state electroencephalographic data.

A classic approach to estimate individual theta-to-alpha transition frequency (TF) requires two electroencephalographic (EEG) recordings, one acquired in a resting state condition and one showing alpha desynchronisation due, for example, to task execution. This translates into long recording sessions that may be cumbersome in studies involving patients. Moreover, an incomplete desynchronisation of the alpha rhythm may compromise TF estimates. Here we present transfreq, a publicly available Python library that allows TF computation from resting state data by clustering the spectral profiles associated to the EEG channels based on their content in alpha and theta bands. A detailed overview of transfreq core algorithm and software architecture is provided. Its effectiveness and robustness across different experimental setups are demonstrated on a publicly available EEG data set and on in-house recordings, including scenarios where the classic approach fails to estimate TF. We conclude with a proof of concept of the predictive power of transfreq TF as a clinical marker. Specifically, we present a scenario where transfreq TF shows a stronger correlation with the mini mental state examination score than other widely used EEG features, including individual alpha peak and median/mean frequency. The documentation of transfreq and the codes for reproducing the analysis of the article with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/. Motivated by the results showed in this article, we believe our method will provide a robust tool for discovering markers of neurodegenerative diseases.

Adamantinomatous craniopharyngioma cyst fluid can trigger inflammatory activation of microglia to damage the hypothalamic neurons by inducing the production of β-amyloid

IntroductionThe mechanism by which adamantinomatous craniopharyngioma (ACP) damages the hypothalamus is still unclear. Cyst fluid rich in lipids and inflammatory factors is a characteristic pathological manifestation of ACP and may play a very important role in hypothalamic injury caused by tumors.ObjectiveThe objective of this study was to construct a reliable animal model of ACP cyst fluid-induced hypothalamic injury and explore the specific mechanism of hypothalamic injury caused by cyst fluid.MethodsAn animal model was established by injecting human ACP cyst fluid into the bilateral hypothalamus of mice. ScRNA-seq was performed on the mice hypothalamus and on an ACP sample to obtain a complete gene expression profile for analysis. Data verification was performed through pathological means.ResultsACP cystic fluid caused growth retardation and an increased obesity index in mice, affected the expression of the Npy, Fgfr2, Rnpc3, Sst, and Pcsk1n genes that regulate growth and energy metabolism in hypothalamic neurons, and enhanced the cellular interaction of Agrp–Mc3r. ACP cystic fluid significantly caused inflammatory activation of hypothalamic microglia. The cellular interaction of CD74–APP is significantly strengthened between inflammatory activated microglia and hypothalamic neurons. Beta-amyloid, a marker of neurodegenerative diseases, was deposited in the ACP tumor tissues and in the hypothalamus of mice injected with ACP cyst fluid.ConclusionIn this study, a novel animal model of ACP cystic fluid-hypothalamic injury was established. For the first time, it was found that ACP cystic fluid can trigger inflammatory activation of microglia to damage the hypothalamus, which may be related to the upregulation of the CD74–APP interaction and deposition of β-amyloid, implying that there may be a similar mechanism between ACP cystic fluid damage to the hypothalamus and neurodegenerative diseases.

Detection of Pathological Markers of Neurodegenerative Diseases following Microfluidic Direct Conversion of Patient Fibroblasts into Neurons.

Neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are clinically diagnosed using neuropsychological and cognitive tests, expensive neuroimaging-based approaches (MRI and PET) and invasive and time-consuming lumbar puncture for cerebrospinal fluid (CSF) sample collection to detect biomarkers. Thus, a rapid, simple and cost-effective approach to more easily access fluids and tissues is in great need. Here, we exploit the chemical direct reprogramming of patient skin fibroblasts into neurons (chemically induced neurons, ciNs) as a novel strategy for the rapid detection of different pathological markers of neurodegenerative diseases. We found that FAD fibroblasts have a reduced efficiency of reprogramming, and converted ciNs show a less complex neuronal network. In addition, ciNs from patients show misfolded protein accumulation and mitochondria ultrastructural abnormalities, biomarkers commonly associated with neurodegeneration. Moreover, for the first time, we show that microfluidic technology, in combination with chemical reprogramming, enables on-chip examination of disease pathological processes and may have important applications in diagnosis. In conclusion, ciNs on microfluidic devices represent a small-scale, non-invasive and cost-effective high-throughput tool for protein misfolding disease diagnosis and may be useful for new biomarker discovery, disease mechanism studies and design of personalised therapies.

Analysis of the Biomarkers for Neurodegenerative Diseases in Aged Progranulin Deficient Mice.

Neurodegenerative diseases are debilitating impairments that affect millions of people worldwide and are characterized by progressive degeneration of structure and function of the central or peripheral nervous system. Effective biomarkers for neurodegenerative diseases can be used to improve the diagnostic workup in the clinic as well as facilitate the development of effective disease-modifying therapies. Progranulin (PGRN) has been reported to be involved in various neurodegenerative disorders. Hence, in the current study we systematically compared the inflammation and accumulation of typical neurodegenerative disease markers in the brain tissue between PGRN knockout (PGRN KO) and wildtype (WT) mice. We found that PGRN deficiency led to significant neuron loss as well as activation of microglia and astrocytes in aged mice. Several characteristic neurodegenerative markers, including α-synuclein, TAR DNA-binding protein 43 (TDP-43), Tau, and β-amyloid, were all accumulated in the brain of PGRN-deficient mice as compared to WT mice. Moreover, higher aggregation of lipofuscin was observed in the brain tissue of PGRN-deficient mice compared with WT mice. In addition, the autophagy was also defective in the brain of PGRN-deficient mice, indicated by the abnormal expression level of autophagy marker LC3-II. Collectively, comprehensive assays support the idea that PGRN plays an important role during the development of neurodegenerative disease, indicating that PGRN might be a useful biomarker for neurodegenerative diseases in clinical settings.

Perspective on the relationship between microbiota dysbiosis and neuroinflammation: probiotics as a treatment option.

Perspective on the relationship between microbiota dysbiosis and neuroinflammation: probiotics as a treatment option.

Association between Delirium and Cognitive Impairment: Is there a Link?

Delirium and dementia are both disorders involving global cognitive impairment that can occur separately or at the same time in the elderly. The aim of this study was to examine the frequency, correlation, and relative risk between delirium and cognitive impairment in a prospective population study starting at the basal line (onset of delirium) over a period of five years. The secondary aim was to determine any possible correlation between the kind of delirium and a specific type of dementia. We studied 325 patients diagnosed according to the DSM-IV. The neuropsychological, moods and delirium disorders were evaluated with Hamilton Depression Rating Scale, Delirium Rating Scale-Revised-98, MMSE, Rey auditory-verbal learning test, Digit Span, Symbol Digit Modalities Test, Raven Progressive Matrices, ADL, and IADL. The prevalence of delirium in our population was 89 cases (27.4%): 78 patients (48 women and 30 men) showed evolution toward dementia (mean age was 67.9 ± 6.1 years for men and 68.4 ± 9.1 for women), and 11 patients (5 men and 6 women) presented only isolated delirium without evolution toward cognitive impairment (mean age of men was 68.1 ± 5.1 years and of women 66.4 ± 7.1). The neuropsychological study of the patients with delirium with dementia evolution revealed statistically significant differences over time with a statistically significant intergroup difference and predisposition toward depression. The association between delirium and cognitive impairment and the possible role of delirium as an early marker of neurodegenerative diseases need to be investigated in the future.

Possible association between PM2.5 and neurodegenerative diseases: A systematic review

Air pollution is one of the most serious environmental problems that afflict our planet and one of the greatest risk factors for human health. In particular, PM2.5 is able to cross the blood-alveolar and blood-brain barriers, thus increasing the onset of respiratory, cardiovascular and neurodegenerative diseases. Neurodegenerative disease is a progressive neuronal dysfunction that leads to neuronal lesions in both structure and function, and includes several diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), vascular dementia (VaD), multiple sclerosis (MS), and others. We carried out a systematic review using PRISMA approach to investigate on the possible association between exposure to PM2.5 and neurodegenerative diseases.The international databases (PubMed, Science Direct, Web of Sciences) were used to find published studies on the topic. The search period was between January 2011 and June 2021. About 2000 full research articles were selected, and finally, we included 20 full-research articles.Selected studies have highlighted how PM2.5 exposure can be associated with the onset of neurodegenerative diseases (AD, PD, MS, VaD). This association depends not only on age, PM2.5 levels and exposure time, but also on exposure to other air pollutants, proximity to areas with high vehicular traffic, and the presence of comorbidities. Exposure to PM2.5 promotes neuroinflammation processes, because through breathing the particles can reach the nasal epithelial mucosa and transferred to the brain through the olfactory bulb. Furthermore, exposure to PM2.5 has been associated with an increased expression of markers of neurodegenerative diseases (e.g. alpha-synuclein or beta-amyloid), which can contribute to the etiopathogenesis of neurodegenerative diseases.Although many studies have revealed the pathological relationship between PM2.5 exposure and cognitive impairment, the potential cellular and molecular mechanisms of PM2.5 leading to neurodegenerative disease remain not entirely clear, and then, further studies need to be carried out on the topic.

Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases.

Cerebral microbleeds (CMB) are increasingly present with aging and can reveal vascular pathologies associated with neurodegeneration. Deep learning-based classifiers can detect and quantify CMB from MRI, such as susceptibility imaging, but are challenging to train because of the limited availability of ground truth and many confounding imaging features, such as vessels or infarcts. In this study, we present a novel generative adversarial network (GAN) that has been trained to generate three-dimensional lesions, conditioned by volume and location. This allows one to investigate CMB characteristics and create large training datasets for deep learning-based detectors. We demonstrate the benefit of this approach by achieving state-of-the-art CMB detection of real CMB using a convolutional neural network classifier trained on synthetic CMB. Moreover, we showed that our proposed 3D lesion GAN model can be applied on unseen dataset, with different MRI parameters and diseases, to generate synthetic lesions with high diversity and without needing laboriously marked ground truth.

Implication of type 4 NADPH oxidase (NOX4) in tauopathy

Aggregates of the microtubule-associated protein tau are a common marker of neurodegenerative diseases collectively termed as tauopathies, such as Alzheimer's disease (AD) and frontotemporal dementia. Therapeutic strategies based on tau have failed in late stage clinical trials, suggesting that tauopathy may be the consequence of upstream causal mechanisms. As increasing levels of reactive oxygen species (ROS) may trigger protein aggregation or modulate protein degradation and, we had previously shown that the ROS producing enzyme NADPH oxidase 4 (NOX4) is a major contributor to cellular autotoxicity, this study was designed to evaluate if NOX4 is implicated in tauopathy. Our results show that NOX4 is upregulated in patients with frontotemporal lobar degeneration and AD patients and, in a humanized mouse model of tauopathy induced by AVV-TauP301L brain delivery. Both, global knockout and neuronal knockdown of the Nox4 gene in mice, diminished the accumulation of pathological tau and positively modified established tauopathy by a mechanism that implicates modulation of the autophagy-lysosomal pathway (ALP) and, consequently, improving the macroautophagy flux. Moreover, neuronal-targeted NOX4 knockdown was sufficient to reduce neurotoxicity and prevent cognitive decline, even after induction of tauopathy, suggesting a direct and causal role for neuronal NOX4 in tauopathy. Thus, NOX4 is a previously unrecognized causative, mechanism-based target in tauopathies and blood-brain barrier permeable specific NOX4 inhibitors could have therapeutic potential even in established disease.

Addressing dementia challenges through international networks: Evidence from the Latin American and Caribbean Consortium on Dementia (LAC-CD).

LAC-CD has recently reported that challenges faced by LAC countries are very similar to those experienced by HIC, and that regional networks will be needed to bridge gaps. ReDLat is a US-LAC multi-partner consortium aimed at expanding dementia research in LAC. The UK-Latin America Brain Connectivity Research Network (UL-BCRN) focuses on developing new affordable EEG-based biomarkers for dementia. The Language and Brain Health Network (LBHN) integrate international multidisciplinary efforts to reveal linguistic markers of neurodegenerative diseases. ReDLat is collecting genomic, neuroimaging, clinical, cognitive, and socioeconomic data from a first-in-class cohort anchored in six LAC (Argentina, Chile, Colombia, Brazil, Mexico, and Peru) and will compare these to US data (> 4200 participants, including 2100 controls, 1050 AD patients, and 1050 FTD patients). The 5-year project will develop innovative, harmonized, and cross-nation approaches on two of the most prevalent neurodegenerative diseases, Alzheimer's disease (AD) and Frontotemporal Dementia (FTD). The UL-BCRN currently links seven EEG labs from Colombia, Argentina, Chile, Brazil and the UK. The network's vision is to forge strong relationships between labs through which new methodologies, EEG plus multidimensional datasets, and experience will be developed and shared. The LBHN will analyze speech samples from massive AD, FTD, and control cohorts across LAC, Spain, and the US. This effort aims to identify low-cost disease-specific markers in diverse Latino and English-speaking patients. ReDLat will aims to reveal unique risk factors for AD and FTD in LAC compared to US populations. Data collection strategies have been adapted to face the challenges brought by the COVID-19 pandemic. The UL-BCRN has now standardised recording protocols across LAC and UK labs and is collecting data from longitudinal cohorts of patients at risk of dementia relying on novel culture-free cognitive paradigms. Preliminary LBHN data shows that, automated speech analyses can identify Latinos with AD and FTD, with good generalization across socio-biological profiles, dialects, and languages. By broadening our understanding of dementia phenotypes, risk factors, and affordable diagnostic approaches in LAC, adding new evidence on variability across HIC and LMIC, the above networks will contribute unique knowledge that will help enhance future global dementia strategies.

Association of deep‐learning–derived brain computed tomography measures with cognition and blood‐based biomarkers of neurodegenerative diseases

AbstractBackgroundExisting imaging biomarkers for neurodegenerative diseases such as magnetic resonance imaging (MRI) and positron emission tomography are costly or have limited accessibility. Computed tomography (CT) and novel blood tests have greater potential as screening modalities for suspected neurodegenerative diseases and to potentially exclude other treatable causes of neurodegeneration.ObjectiveTo evaluate the association of volumetric brain measures, derived using a novel CT‐based deep‐learning algorithm with cognition and blood levels of neurodegeneration markers.MethodWe previously developed deep‐learning‐based models using 734 datasets (722 cognitively normals (CN)) from Gothenburg H70 Birth Cohort to segment head CTs by learning from MRI‐derived labels. We used these models to segment, without further training, 204 novel CT datasets (73 Alzheimer's disease (AD), 49 mild cognitive impairment (MCI), 20 vascular dementia (VAD), 40 vascular cognitive impairment (VCI), 22 CN) recruited from the Singaporean Memory Clinic cohort (mean age= 74.0±8.2 years, 50.5% female), without MRI intervention. We tested associations between six CT‐derived volumetric measures (grey matter (GM), white matter (WM), GM/CSF, GM/ventricular CSF (VCSF), brain volume (BV)/CSF, BV/VCSF) with diagnosis, plasma biomarkers, and cognition. The plasma biomarkers were measured using a Simoa platform (Quanterix, Bilerica, MA,USA).ResultThe novel CT segmentations correlated significantly with their corresponding MRI‐segmentations. Lower CT‐derived tissue volumes correlated with higher plasma neurofilament (NFL) concentrations in the H70 cohort. In the MCC NUH cohort, lower CT‐derived volumes correlated with lower MMSE scores and higher plasma NFL. CT‐derived volumes yielded high diagnostic accuracy for AD vs CN (AUC ROC 0.87). Mean CT‐based volumetric measures were lower in AD and VAD compared with CN. Kruskal‐Wallis tests showed a statistically significant difference in CT‐based volumetric measures predominantly for GM/CSF and BV/CSF across all diagnostic groups, notably between AD vs CN and CN vs early stages of dementia.ConclusionDeep‐learning‐derived CT‐based volumetric measures correlate with relevant other imaging, cognitive and biochemical markers of neurodegenerative diseases. This supports the potential application of CT‐derived volumetric measures in aiding diagnostics and early detection of dementia. Further explorations of the association between CT‐based measures with visual assessments and other clinical and imaging biomarkers will be undertaken.

Prodromal markers of neurodegenerative disease: Insights from the UK Biobank dataset

AbstractBackgroundAbnormal quantitative motor testing and subtle cognitive deficits are associated with increased neurodegenerative disease risk although assessment methods and outcomes vary. Understanding how and when these deficits develop will improve early detection and clarify prodromal disease course. We set out to determine markers predictive of two common neurodegenerative diseases, Alzheimer’s Disease (AD) and Parkinson’s Disease (PD), in a prospective cohort study.MethodThe UK Biobank dataset includes demographics, medical history, cognitive function tests and physical measures collected from 500,000 adults aged 40‐69 between 2006‐2010. Ongoing follow‐up includes repeat testing, death registry and hospital diagnostic coding. Cases were identified from hospital coding data and death registry and were matched with age and gender‐similar controls (2:1). Those with a known diagnosis at baseline were excluded.Result2248 AD cases (male=48%, mean age 65.3±4.2 years, mean time to diagnosis 8.4±2.5 years) and 2212 PD cases (male=62%, mean age 63.8±5.2 years, mean time to diagnosis 7.7±2.7 years) were identified. 537/2248 (male=51%) AD cases had died by June 2020 (mean assessment to death 9.8±2.0 years) and 357/2212 (male=70%) PD cases (mean assessment to death 9.4±2.2 years). The AD group had shown poorer performance on tasks of fluid intelligence (p&lt;0.0001) and numeric memory (p&lt;0.0001) at baseline than both controls and the PD group. Both AD and PD groups had had slower reaction times (p&lt;0.0001) and weaker hand grip strength at baseline (p&lt;0.0001) than controls. There were no significant effects of age or sex.ConclusionPoor performance on tasks of cognitive function are established features of AD at diagnosis, however it is unknown when during the prodromal disease course they begin. Our data demonstrate subtle changes in markers of these features many years prior to diagnosis in a large, prospective cohort.