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"Dementia - diagnosis"
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Plasma NfL levels and longitudinal change rates in C9orf72 and GRN-associated diseases: from tailored references to clinical applications
ObjectiveNeurofilament light chain (NfL) is a promising biomarker in genetic frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). We evaluated plasma neurofilament light chain (pNfL) levels in controls, and their longitudinal trajectories in C9orf72 and GRN cohorts from presymptomatic to clinical stages.MethodsWe analysed pNfL using Single Molecule Array (SiMoA) in 668 samples (352 baseline and 316 follow-up) of C9orf72 and GRN patients, presymptomatic carriers (PS) and controls aged between 21 and 83. They were longitudinally evaluated over a period of >2 years, during which four PS became prodromal/symptomatic. Associations between pNfL and clinical–genetic variables, and longitudinal NfL changes, were investigated using generalised and linear mixed-effects models. Optimal cut-offs were determined using the Youden Index.ResultspNfL levels increased with age in controls, from ~5 to~18 pg/mL (p<0.0001), progressing over time (mean annualised rate of change (ARC): +3.9%/year, p<0.0001). Patients displayed higher levels and greater longitudinal progression (ARC: +26.7%, p<0.0001), with gene-specific trajectories. GRN patients had higher levels than C9orf72 (86.21 vs 39.49 pg/mL, p=0.014), and greater progression rates (ARC:+29.3% vs +24.7%; p=0.016). In C9orf72 patients, levels were associated with the phenotype (ALS: 71.76 pg/mL, FTD: 37.16, psychiatric: 15.3; p=0.003) and remarkably lower in slowly progressive patients (24.11, ARC: +2.5%; p=0.05). Mean ARC was +3.2% in PS and +7.3% in prodromal carriers. We proposed gene-specific cut-offs differentiating patients from controls by decades.ConclusionsThis study highlights the importance of gene-specific and age-specific references for clinical and therapeutic trials in genetic FTD/ALS. It supports the usefulness of repeating pNfL measurements and considering ARC as a prognostic marker of disease progression.Trial registration numbers NCT02590276 and NCT04014673.
Journal Article
Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics
Dementia spectrum disorders, characterized by progressive cognitive decline, pose a significant global health burden. Early screening and diagnosis are essential for timely and accurate treatment, improving patient outcomes and quality of life. This study investigated dynamic features of resting-state electroencephalography (EEG) functional connectivity to identify characteristic patterns of dementia subtypes, such as Alzheimer’s disease (AD) and frontotemporal dementia (FD), and to evaluate their potential as biomarkers. We extracted distinctive statistical features, including mean, variance, skewness, and Shannon entropy, from brain connectivity measures, revealing common alterations in dementia, specifically a generalized disruption of Alpha-band connectivity. Distinctive characteristics were found, including generalized Delta-band hyperconnectivity with increased complexity in AD and disrupted phase-based connectivity in Theta, Beta, and Gamma bands for FD. We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. Our classification models achieved a multiclass classification accuracy of 93.6% across AD, FD, and healthy control groups. Furthermore, the model demonstrated 97.8% and 96.7% accuracy in differentiating AD and FD from healthy controls, respectively, and 97.4% accuracy in classifying AD and FD in pairwise classification. These establish a high-performance deep learning framework utilizing dynamic EEG connectivity patterns as potential biomarkers, offering a promising approach for early screening and diagnosis of dementia spectrum disorders using EEG. Our findings suggest that analyzing brain connectivity dynamics as a network and during cognitive tasks could offer more valuable information for diagnosis, assessing disease severity, and potentially identifying personalized neurological deficits.
Journal Article
Differentiating between right-lateralised semantic dementia and behavioural-variant frontotemporal dementia: an examination of clinical characteristics and emotion processing
Background and purposeRight-lateralised semantic dementia (right SD) and behavioural-variant frontotemporal dementia (bvFTD) appear clinically similar, despite different patterns of underlying brain changes. This study aimed to elucidate distinguishing clinical and cognitive features in right SD versus bvFTD, emphasising emotion processing and its associated neural correlates.Methods12 patients with right SD and 19 patients with bvFTD were recruited. Clinical features were documented. All patients were assessed on standardised neuropsychological tests and a facial emotion processing battery. Performance was compared to 20 age-matched and education-matched controls. Grey matter intensity was related to emotion processing performance using whole-brain voxel-based morphometry analysis.ResultsPatients with right SD exhibited disproportionate language dysfunction, prosopagnosia and a suggestion of increased obsessive personality/behavioural changes versus patients with bvFTD. In contrast, patients with bvFTD demonstrated pronounced deficits in attention/working memory, increased apathy and greater executive dysfunction, compared to patients with right SD. Decreased empathy, disinhibition and diet changes were common to both dementia subtypes. Emotion processing deficits were present in both FTD syndromes but were associated with divergent patterns of brain atrophy. In right SD, emotion processing dysfunction was associated with predominantly right medial and lateral temporal integrity, compared to mainly left temporal, inferior frontal and orbitofrontal and right frontal gyrus integrity in bvFTD.ConclusionsThis study demonstrates comparable deficits in facial emotion processing in right SD and bvFTD, in keeping with their similar clinical profiles. These deficits are attributable to divergent neural substrates in each patient group, namely, right lateralised regions in right SD, versus predominantly left lateralised regions in bvFTD.
Journal Article
Contactin proteins in cerebrospinal fluid show different alterations in dementias
Background
The proteins contactin (CNTN) 1–6 are synaptic proteins for which there is evidence that they are dysregulated in neurodegenerative dementias. Less is known about CNTN changes and differences in cerebrospinal fluid (CSF) of dementias, which can provide important information about alterations of the CNTN network and be of value for differential diagnosis.
Methods
We developed a mass spectrometry-based multiple reaction monitoring (MRM) method to simultaneously determine all six CNTNs in CSF samples using stable isotope-labeled standard peptides. The analytical performance of the method was evaluated for peptide stability, dilution linearity and precision. CNTNs were measured in 82 CSF samples from patients with Alzheimer’s disease (AD,
n
= 19), behavioural variant frontotemporal dementia (bvFTD,
n
= 18), Parkinson’s disease dementia/dementia with Lewy bodies (PDD/DLB,
n
= 18) and non-neurodegenerative controls (
n
= 27) and compared with core AD biomarkers.
Results
The MRM analysis revealed down-regulation of CNTN2 (fold change (FC) = 0.77), CNTN4 (FC = 0.75) and CNTN5 (FC = 0.67) in bvFTD and CNTN3 (FC = 0.72), CNTN4 (FC = 0.75) and CNTN5 (FC = 0.73) in PDD/DLB compared to AD. CNTN levels strongly correlated with each other in controls (
r
= 0.73), bvFTD (
r
= 0.86) and PDD/DLB (
r
= 0.70), but the correlation was significantly lower in AD (
r
= 0.41). CNTNs in AD did not show correlation even with core AD biomarkers. Combined use of CNTN1-6 levels increased diagnostic performance of AD core biomarkers.
Conclusions
Our data show CNTNs differentially altered in dementias and indicate CNTN homeostasis being selectively dysregulated in AD. The combined use of CNTNs with AD core biomarkers might help to improve differential diagnosis.
Journal Article
Clinical Manifestations
Transcultural adaptations of brief cognitive screening tools (BCTs) involve the development of alternate versions that are psychometrically equivalent to the original, while being linguistically and culturally adapted to a new sociodemographic context. The RUDAS (Rowland Universal Dementia Assessment Scale) is less affected by culture, language, and education compared with other BCTs. However, several studies have reported an effect of education on RUDAS scores. In Peru, performance on visuospatial construction and judgement items of the RUDAS may be particularly low amongst rural communities. The main aim of this study was to evaluate the performance of a culturally-refined version of the Peruvian version of RUDAS (RUDAS-PE), the RUDAS-PEm. We tested how well the RUDAS-PEm distinguished between controls and patients with Alzheimer's disease (AD), behavioral variant frontotemporal dementia (bvFTD), and other types of dementia, compared to the RUDAS-PE.
To create the RUDAS-PEm, we modified two domains of RUDAS-PE, visuo-spatial construction and judgement, to psychometrically-congruent alternatives that are more appropriate for the Peruvian context. We changed the 'cube drawing' task (visuospatial construction) to drawing a circle, rhombus, and rectangle. The 'crossing the street in transit' task (judgement) was changed to one asking patients to tell the difference between sugar and vinegar and to explain how to locate a new friend in a new city.
We included 110 controls and 87 patients with dementia: 54 with AD, 20 with bvFTD, and 13 with other dementias. Mean age was 70 (SD 7.8) and mean education was 14.2 years (SD 4.1). For visuospatial construction in RUDAS-PE, there were significant differences between the scores of controls and AD patients (<0.001), bvFTD patients (0.02), and other dementias (p<0.001) (figure 1). For RUDAS-PEm, no significant differences were found between scores of controls and bvFTD patients. For the judgment item both versions had good discrimination, but RUDAS-PEm showed better performance (table 1). The times to complete both versions and the AUC for diagnosis of AD versus controls, bvFTD versus controls, and all dementias versus controls, were similar in both versions.
The RUDAS-PEm including modification of the judgment item shows modest improvement in detecting dementia.
Journal Article
Validation of Addenbrooke’s cognitive examination III for detecting mild cognitive impairment and dementia in Japan
Background
Early detection of mild cognitive impairment (MCI) and dementia is very important to begin appropriate treatment promptly and to prevent disease exacerbation. We investigated the screening accuracy of the Japanese version of Addenbrooke’s Cognitive Examination III (ACE-III) to diagnose MCI and dementia.
Methods
The original ACE-III was translated and adapted to Japanese. It was then administered to a Japanese population. The Hasegawa Dementia Scale-revised (HDS-R) and Mini-mental State Examination (MMSE) were also applied to evaluate cognitive dysfunction. In total, 389 subjects (dementia = 178, MCI = 137, controls = 73) took part in our study.
Results
The optimal ACE-III cut-off scores to detect MCI and dementia were 88/89 (sensitivity 0.77, specificity 0.92) and 75/76 (sensitivity 0.82, specificity 0.90), respectively. ACE-III was superior to HDS-R and MMSE in the detection of MCI or dementia. The internal consistency, test-retest reliability, and inter-rater reliability of ACE-III were excellent.
Conclusions
ACE-III is a useful cognitive test to detect MCI and dementia. ACE-III may be widely useful in clinical practice.
Journal Article
Unravelling the plasma proteome: Pioneering biomarkers for differential dementia diagnosis
INTRODUCTION
Diagnosing Alzheimer's disease (AD) is challenging due to overlapping symptoms with other dementias and the invasiveness of current biomarkers. This study introduces the NULISA platform, a novel proteomics technology, to evaluate diagnostic accuracy of known biomarkers and uncover novel biomarkers underlying different dementias.
METHODS
We analyzed plasma and cerebrospinal fluid (CSF) samples from 248 participants diagnosed with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), frontotemporal dementia (FTD), and mild cognitive impairment (MCI). Plasma biomarkers were evaluated using regression models, receiver operating characteristics curve (ROC) analysis, and pathway enrichment.
RESULTS
Plasma phosphorylated Tau217 (pTau217) demonstrated the highest diagnostic accuracy for AD, DLB, and FTD (area under the curve [AUCs]: 0.9, 0.84, and 0.79, respectively). CXCL1 (fractalkine), synaptosomal‐associated protein 25 (SNAP25), triggering receptor expressed on myeloid cells 1 (TREM1), β‐synuclein, and tyrosine kinase (TEK) are expressed differently in DLB and FTD than AD. Ingenuity pathway analyses revealed astrocytic, synaptic, and inflammatory pathways as shared and distinct mechanisms across these dementia types.
CONCLUSION
Our findings establish plasma pTau217 as a robust diagnostic marker. This study provides new plasma biomarkers for differential diagnosis of dementias with a noninvasive method.
Highlights
Plasma pTau217 showed high diagnostic accuracy for AD, DLB, and FTD.
CXCL1, SNAP25, TREM1, β‐synuclein, and TEK are novel markers distinguishing other dementias from AD.
Noninvasive plasma biomarkers enable diagnosis and differentiation of dementias.
Journal Article
Everyday functioning in young onset dementia: differences between diagnostic groups
BACKGROUND
The aim of this study was to examine differences in Instrumental Activities of Daily Living (IADL) among young‐onset dementia (YOD) diagnoses.
METHODS
Participants were included from Amsterdam Dementia and Longitudinal Early‐Onset Alzheimer's Disease (LEADS) cohorts, with diagnoses of typical Alzheimer's disease (AD), behavioral variant frontotemporal dementia (bvFTD), primary progressive aphasia (PPA), posterior cortical atrophy (PCA), or dementia with Lewy bodies (DLB) established in multidisciplinary meetings. We compared overall IADL scores and item level scores between groups using multiple regression analyses, adjusted for cohort, demographics, and disease severity.
RESULTS
We included 582 YOD patients (58.4 ± 4.2 years; 59%F), with overall moderate IADL problems (47.5 ± 8.57). DLB patients showed the most IADL difficulties (41.8 ± 7.8) compared to PCA, typical AD, bvFTD, and PPA (adjusted β range 4.62 to 14.14, all p < 0.01), whereas PPA patients showed the least IADL difficulties (55.8 ± 9.83), with item‐specific differences.
CONCLUSION
We found differences in everyday functioning between YOD types. Understanding IADL in YOD types will assist in care planning.
Highlights
Patients with DLB showed the most IADL difficulties compared to PCA, typical AD, bvFTD, and PPA
Patients with PPA showed the least IADL difficulties compared to DLB, PCA, typical AD, and bvFTD
We identified diagnostic group‐specific activity challenges. While ‘working’ was among the most commonly impaired activities across al groups, distinct functional challenges emerged per diagnosis: for example, DLB had high impairment in financial tasks, PCA patients in visual‐spatial tasks, and bvFTD with planning and organizational activities (e.g. making appointments).
Journal Article
Deep learning-based classification of dementia using image representation of subcortical signals
Background
Dementia is a neurological syndrome marked by cognitive decline. Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early and accurate diagnosis of dementia cases (AD and FTD) is crucial for effective medical care, as both conditions have similar early-symptoms. EEG, a non-invasive tool for recording brain activity, has shown potential in distinguishing AD from FTD and mild cognitive impairment (MCI).
Methods
This study aims to develop a deep learning-based classification system for dementia by analyzing EEG derived scout time-series signals from deep brain regions, specifically the hippocampus, amygdala, and thalamus. Scout time series extracted via the standardized low-resolution brain electromagnetic tomography (sLORETA) technique are utilized. The time series is converted to image representations using continuous wavelet transform (CWT) and fed as input to deep learning models. Two high-density EEG datasets are utilized to validate the efficacy of the proposed method: the online BrainLat dataset (128 channels, comprising 16 AD, 13 FTD, and 19 healthy controls (HC)) and the in-house IITD-AIIA dataset (64 channels, including subjects with 10 AD, 9 MCI, and 8 HC). Different classification strategies and classifier combinations have been utilized for the accurate mapping of classes in both data sets.
Results
The best results were achieved using a product of probabilities from classifiers for left and right subcortical regions in conjunction with the DenseNet model architecture. It yield accuracies of 94.17
%
and 77.72
%
on the BrainLat and IITD-AIIA datasets, respectively.
Conclusions
The results highlight that the image representation-based deep learning approach has the potential to differentiate various stages of dementia. It pave the way for more accurate and early diagnosis, which is crucial for the effective treatment and management of debilitating conditions.
Journal Article