Frontotemporal Dementia Patients Research Articles

Genome-Wide DNA Methylation in Early-Onset-Dementia Patients Brain Tissue and Lymphoblastoid Cell Lines.

Epigenetics, a potential underlying pathogenic mechanism of neurodegenerative diseases, has been in the scope of several studies performed so far. However, there is a gap in regard to analyzing different forms of early-onset dementia and the use of Lymphoblastoid cell lines (LCLs). We performed a genome-wide DNA methylation analysis on sixty-four samples (from the prefrontal cortex and LCLs) including those taken from patients with early-onset forms of Alzheimer's disease (AD) and frontotemporal dementia (FTD) and healthy controls. A beta regression model and adjusted p-values were used to obtain differentially methylated positions (DMPs) via pairwise comparisons. A correlation analysis of DMP levels with Clariom D array gene expression data from the same cohort was also performed. The results showed hypermethylation as the most frequent finding in both tissues studied in the patient groups. Biological significance analysis revealed common pathways altered in AD and FTD patients, affecting neuron development, metabolism, signal transduction, and immune system pathways. These alterations were also found in LCL samples, suggesting the epigenetic changes might not be limited to the central nervous system. In the brain, CpG methylation presented an inverse correlation with gene expression, while in LCLs, we observed mainly a positive correlation. This study enhances our understanding of the biological pathways that are associated with neurodegeneration, describes differential methylation patterns, and suggests LCLs are a potential cell model for studying neurodegenerative diseases in earlier clinical phases than brain tissue.

Combined in vivo MRI assessment of locus coeruleus and nucleus basalis of Meynert integrity in amnestic Alzheimer’s disease, suspected-LATE and frontotemporal dementia

BackgroundThe locus coeruleus (LC) and the nucleus basalis of Meynert (NBM) are altered in early stages of Alzheimer’s disease (AD). Little is known about LC and NBM alteration in limbic-predominant age-related TDP-43 encephalopathy (LATE) and frontotemporal dementia (FTD). The aim of the present study is to investigate in vivo LC and NBM integrity in patients with suspected-LATE, early-amnestic AD and FTD in comparison with controls.MethodsSeventy-two participants (23 early amnestic-AD patients, 17 suspected-LATE, 17 FTD patients, defined by a clinical-biological diagnosis reinforced by amyloid and tau PET imaging, and 15 controls) underwent neuropsychological assessment and 3T brain MRI. We analyzed the locus coeruleus signal intensity (LC-I) and the NBM volume as well as their relation with cognition and with medial temporal/cortical atrophy.ResultsWe found significantly lower LC-I and NBM volume in amnestic-AD and suspected-LATE in comparison with controls. In FTD, we also observed lower NBM volume but a slightly less marked alteration of the LC-I, independently of the temporal or frontal phenotype. NBM volume was correlated with the global cognitive efficiency in AD patients. Strong correlations were found between NBM volume and that of medial temporal structures, particularly the amygdala in both AD and FTD patients.ConclusionsThe alteration of LC and NBM in amnestic-AD, presumed-LATE and FTD suggests a common vulnerability of these structures to different proteinopathies. Targeting the noradrenergic and cholinergic systems could be effective therapeutic strategies in LATE and FTD.

Osteopontin drives neuroinflammation and cell loss in MAPT-N279K frontotemporal dementia patient neurons

Frontotemporal dementia (FTD) is an incurable group of early-onset dementias that can be caused by the deposition of hyperphosphorylated tau in patient brains. However, the mechanisms leading to neurodegeneration remain largely unknown. Here, we combined single-cell analyses of FTD patient brains with a stem cell culture and transplantation model of FTD. We identified disease phenotypes in FTD neurons carrying the MAPT-N279K mutation, which were related to oxidative stress, oxidative phosphorylation, and neuroinflammation with an upregulation of the inflammation-associated protein osteopontin (OPN). Human FTD neurons survived less and elicited an increased microglial response after transplantation into the mouse forebrain, which we further characterized by single nucleus RNA sequencing of microdissected grafts. Notably, downregulation of OPN in engrafted FTD neurons resulted in improved engraftment and reduced microglial infiltration, indicating an immune-modulatory role of OPN in patient neurons, which may represent a potential therapeutic target in FTD.

Automatic classification of AD pathology in FTD phenotypes using natural speech.

Screening for Alzheimer's disease neuropathologic change (ADNC) in individuals with atypical presentations is challenging but essential for clinical management. We trained automatic speech-based classifiers to distinguish frontotemporal dementia (FTD) patients with ADNC from those with frontotemporal lobar degeneration (FTLD). We trained automatic classifiers with 99 speech features from 1 minute speech samples of 179 participants (ADNC=36, FTLD=60, healthy controls [HC]=89). Patients' pathology was assigned based on autopsy or cerebrospinal fluid analytes. Structural network-based magnetic resonance imaging analyses identified anatomical correlates of distinct speech features. Our classifier showed 0.88 0.03 area under the curve (AUC) for ADNC versusFTLD and 0.93 0.04 AUC for patients versusHC. Noun frequency and pause rate correlated with gray matter volume loss in the limbic and salience networks, respectively. Brief naturalistic speech samples can be used for screening FTD patients for underlying ADNC in vivo. This work supports the future development of digital assessment tools for FTD. We trained machine learning classifiers for frontotemporal dementia patients using natural speech. We grouped participants by neuropathological diagnosis (autopsy) or cerebrospinal fluid biomarkers. Classifiers well distinguished underlying pathology (Alzheimer's disease vs. frontotemporal lobar degeneration) in patients. We identified important features through an explainable artificial intelligence approach. This work lays the groundwork for a speech-based neuropathology screening tool.

A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in Resting-State Adults.

Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented.

Neuropsychiatric Symptoms in Frontotemporal Dementia: More Than Just Noise?

Neuropsychiatric symptoms cause significant suffering and poor quality of life for patients and their caregivers. They are not considered specific to frontotemporal dementia (FTD); therefore, their clinical role and impact might be underestimated. The aims of the present study are to: 1) describe the prevalence of neuropsychiatric symptoms in FTD starting from the prodromal stage, 2) define their association with disease severity, 3) identify symptoms which are unrelated to FTD-specific symptoms, and 4) assess their association with clinical features and outcomes. In this retrospective study, we analyzed data of 461 FTD patients, including behavioral variant of FTD (bvFTD, n = 318) and primary progressive aphasia (PPA, n = 143). Neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory, and patients' staging and global disease severity were estimated using the Clinical Dementia Rating plus NACC FTLD. The most common neuropsychiatric symptoms in prodromal FTD were irritability (48%), depression (35%), and anxiety (34%); delusions were reported in 6%of prodromal bvFTD cases. The severity of most neuropsychiatric symptoms increased with global disease severity. Psychosis (delusions and hallucinations) and mood symptoms (depression and anxiety) were mostly independent from FTD-specific symptoms. Psychosis was associated with older age, higher disease severity, shorter survival rate, and was higher in bvFTD than in PPA. Neuropsychiatric symptoms are common in patients with FTD, also in the prodromal phase. Psychosis might be unrelated to FTD pathology, and be associated with worse clinical outcomes. The prompt detection and treatment of these symptoms might improve patient's management and quality of life.

Atrophy network mapping of clinical subtypes and main symptoms in frontotemporal dementia.

Frontotemporal Dementia (FTD) is a disease of high heterogeneity, apathy and disinhibition present in all subtypes of FTD and imposes a significant burden on families/society. Traditional neuroimaging analysis has limitations in elucidating the network localization due to individual clinical and neuroanatomical variability. The study aims to identify the atrophy network map associated with different FTD clinical subtypes and determine the specific localization of the network for apathy and disinhibition. Eighty FTD patients [45 behavioral variant FTD (bvFTD) and 35 semantic variant progressive primary aphasia (svPPA)] and 58 healthy controls (HCs) at Xuanwu Hospital were enrolled as Dataset 1; 112 FTD patients including 50 bvFTD, 32 svPPA, and 30 non-fluent variant PPA (nfvPPA) cases, and 110 HCs from Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) dataset were included as Dataset 2. Initially, single-subject atrophy maps were defined by comparing cortical thickness in each FTD patient versus HCs. Next, the network of brain regions functionally connected to each FTD patient's location of atrophy was determined using seed-based functional connectivity in a large (n = 1000) normative connectome. Finally, we used atrophy network mapping to define clinical subtype-specific network (45 bvFTD, 35 svPPA and 58 HCs in Dataset 1; 50 bvFTD, 32 svPPA, 30 nfvPPA and 110 HCs in Dataset 2) and symptom-specific networks [combined dataset 1 and 2, apathy without depression Vs non-apathy without depression (80:26), disinhibition Vs non-disinhibition (88:68)]. We compare the result with matched symptom networks derived from patients with focal brain lesions or conjunction analysis. Through the analysis of two datasets, we identified heterogeneity in atrophy patterns among FTD patients. However, these atrophy patterns are connected to a common brain network. The primary regions affected by atrophy in FTD included the frontal and temporal lobes, particularly the anterior temporal lobe. bvFTD connects to frontal and temporal cortical areas, svPPA mainly impacts the anterior temporal region, and nfvPPA targets the inferior frontal gyrus and precentral cortex regions. The apathy-specific network was localized in the orbital frontal cortex and ventral striatum, while the disinhibition-specific network was localized in the bilateral orbital frontal gyrus and right temporal lobe. Apathy and disinhibition atrophy networks resemble known motivational and criminal lesion networks respectively. A significant correlation was found between the apathy/disinhibition scores and functional connectivity between atrophy maps and the peak of the networks. This study localizes the common network of clinical subtypes and main symptoms in FTD, guiding future FTD neuromodulation interventions.

TDP-43-M323K causes abnormal brain development and progressive cognitive and motor deficits associated with mislocalised and increased levels of TDP-43

TDP-43 pathology is found in several neurodegenerative disorders, collectively referred to as “TDP-43 proteinopathies”. Aggregates of TDP-43 are present in the brains and spinal cords of >97% of amyotrophic lateral sclerosis (ALS), and in brains of ∼50% of frontotemporal dementia (FTD) patients. While mutations in the TDP-43 gene (TARDBP) are usually associated with ALS, many clinical reports have linked these mutations to cognitive impairments and/or FTD, but also to other neurodegenerative disorders including Parkinsonism (PD) or progressive supranuclear palsy (PSP). TDP-43 is a ubiquitously expressed, highly conserved RNA-binding protein that is involved in many cellular processes, mainly RNA metabolism. To investigate systemic pathological mechanisms in TDP-43 proteinopathies, aiming to capture the pleiotropic effects of TDP-43 mutations, we have further characterised a mouse model carrying a point mutation (M323K) within the endogenous Tardbp gene. Homozygous mutant mice developed cognitive and behavioural deficits as early as 3 months of age. This was coupled with significant brain structural abnormalities, mainly in the cortex, hippocampus, and white matter fibres, together with progressive cortical interneuron degeneration and neuroinflammation. At the motor level, progressive phenotypes appeared around 6 months of age. Thus, cognitive phenotypes appeared to be of a developmental origin with a mild associated progressive neurodegeneration, while the motor and neuromuscular phenotypes seemed neurodegenerative, underlined by a progressive loss of upper and lower motor neurons as well as distal denervation. This is accompanied by progressive elevated TDP-43 protein and mRNA levels in cortex and spinal cord of homozygous mutant mice from 3 months of age, together with increased cytoplasmic TDP-43 mislocalisation in cortex, hippocampus, hypothalamus, and spinal cord at 12 months of age. In conclusion, we find that Tardbp M323K homozygous mutant mice model many aspects of human TDP-43 proteinopathies, evidencing a dual role for TDP-43 in brain morphogenesis as well as in the maintenance of the motor system, making them an ideal in vivo model system to study the complex biology of TDP-43.

Real world data analysis of frontotemporal dementia: Implications for future clinical research

Frontotemporal dementia (FTD) is a progressive decline of cognitive abilities associated with other neuropsychiatric comorbidities. A real-world data (RWD) analysis of a large electronic healthcare records (EHR) database identified the comorbidities of FTD. Deidentified EHRs in the TriNetX Network database from >155,000,000 individuals in the United States established an FTD Cohort (ICD-10 Code G31.0) of adult patients who visited a healthcare provider in 2022. The non-FTD cohort were age-matched individuals who had not received a diagnosis of ICD-10 Code G31.0, and who had visited a healthcare provider in 2022. The median age of both cohorts was 73 years. A comparative analysis was performed between the FTD and non-FTD cohorts. There were 6660 individuals (aged ≥18) with FTD and 11,810,060 individuals (aged ≥63) without a diagnosis of FTD, with healthcare visits in 2022. There were 25 ICD-10 Codes for disorders that were present in >10% of FTD patients, with a Relative Risk (RR) of ≥2.0 compared the non-FTD cohort. Multiple neuropsychiatric disorders had RRs ≥ 2.0, with minimal evidence for significant involvement of other organ systems. These data document that FTD, as known previously, is associated with multiple neuropsychiatric comorbidities. There was minimal evidence of comorbid involvement of other organ systems. These data provide a baseline of general FTD symptoms for the rapidly evolving analysis of genetic subvariants of FTD. These data also provide insights into the clinical management of FTD, as well as recommendations for specific endpoints in clinical trials.

Entorhinal cortex astrocytic atrophy in human frontotemporal dementia.

The pathophysiology of Fronto Temporal Dementia (FTD) remains poorly understood, specifically the role of astroglia. Our aim was to explore the hypothesis of astrocytic alterations as a component for FTD pathophysiology. We performed an in-depth tri-dimensional (3-D) anatomical and morphometric study of glial fibrillary acidic protein (GFAP)-positive and glutamine synthetase (GS)-positive astrocytes in the human entorhinal cortex (EC) of FTD patients. The studies at this level in the different types of human dementia are scarce. We observed a prominent astrocyte atrophy of GFAP-positive astrocytes and co-expressing GFAP/GS astrocytes, characterised by a decrease in area and volume, whilst minor changes in GS-positive astrocytes in FTD compared to non-dementia controls (ND) samples. This study evidences the importance of astrocyte atrophy and dysfunction in human EC. We hypothesise that FTD is not only a neuropathological disease, but also a gliopathological disease having a major relevance in the understanding the astrocyte role in FTD pathological processes and development.

Quantitative Analysis of Retinal Perfusion in Patients with Frontotemporal Dementia Using Optical Coherence Tomography Angiography.

Optical coherence tomography angiography (OCT-A) provides detailed visualization of the perfusion of the vascular network of the eye. While in other forms of dementia, such as Alzheimer's disease and mild cognitive impairment, reduced retinal perfusion was frequently reported, data of patients with frontotemporal dementia (FTD) are lacking. Retinal and optic nerve head perfusion was evaluated in patients with FTD with OCT-A. Quantitative OCT-A metrics were analyzed and correlated with clinical markers and vascular cerebral lesions in FTD patients. OCT-A was performed in 18 eyes of 18 patients with FTD and 18 eyes of 18 healthy participants using RTVue XR Avanti with AngioVue. In addition, patients underwent a detailed ophthalmological, neurological, and neuropsychological examination, cerebral magnetic resonance imaging (MRI), and lumbar puncture. The flow density in the optic nerve head (ONH) and in the superficial capillary plexus (SCP) of the macula of patients was significantly lower compared to that of healthy controls (p < 0.001). Similarly, the VD in the deep capillary plexus (DCP) of the macula of patients was significantly lower compared to that of healthy controls (p < 0.001). There was no significant correlation between the flow density data, white matter lesions in brain MRI, cognitive deficits, and cerebrospinal fluid markers of dementia. Patients with FTD showed a reduced flow density in the ONH, and in the superficial and deep retinal capillary plexus of the macula, when compared with that of healthy controls. Quantitative analyses of retinal perfusion using OCT-A may therefore help in the diagnosis and monitoring of FTD. Larger and longitudinal studies are necessary to evaluate if OCT-A is a suitable biomarker for patients with FTD.

Autophagy Markers Are Altered in Alzheimer's Disease, Dementia with Lewy Bodies and Frontotemporal Dementia.

The accumulation of protein aggregates defines distinct, yet overlapping pathologies such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). In this study, we investigated ATG5, UBQLN2, ULK1, and LC3 concentrations in 66 brain specimens and 120 plasma samples from AD, DLB, FTD, and control subjects (CTRL). Protein concentration was measured with ELISA kits in temporal, frontal, and occipital cortex specimens of 32 AD, 10 DLB, 10 FTD, and 14 CTRL, and in plasma samples of 30 AD, 30 DLB, 30 FTD, and 30 CTRL. We found alterations in ATG5, UBQLN2, ULK1, and LC3 levels in patients; ATG5 and UBQLN2 levels were decreased in both brain specimens and plasma samples of patients compared to those of the CTRL, while LC3 levels were increased in the frontal cortex of DLB and FTD patients. In this study, we demonstrate alterations in different steps related to ATG5, UBQLN2, and LC3 autophagy pathways in DLB and FTD patients. Molecular alterations in the autophagic processes could play a role in a shared pathway involved in the pathogenesis of neurodegeneration, supporting the hypothesis of a common molecular mechanism underlying major neurodegenerative dementias and suggesting different potential therapeutic targets in the autophagy pathway for these disorders.

A comprehensive review on frontotemporal dementia: its impact on language, speech and behavior.

Frontotemporal dementia (FTD) is a progressive neurodegenerative disorder defined by the selective deterioration of the frontal and temporal lobes of the brain. This comprehensive review explored the effect of FTD on language, speech, and behavior. Early symptoms include difficulty in word finding, reduced speech output, and comprehension deficits, often leading to aphasia. The study discussed the profound behavioral changes observed in FTD patients, including apathy, disinhibition, compulsive behaviors, and loss of empathy, the importance of an accurate and early diagnosis, and its challenges. We even reviewed the potential for targeted therapies and the essential role of multidisciplinary care in managing the language, speech, and behavioral aspects of FTD. By examining objective data and comprehensive research on the subject, this study offers valuable insights into FTD's profound effects on language, speech, and behavior, assisting in improved clinical management and potential therapeutic strategies for this devastating condition.

Characterization of Progranulin Gene Mutations in Portuguese Patients with Frontotemporal Dementia.

In Portugal, heterozygous loss-of-function mutations in the progranulin (GRN) gene account for approximately half of the genetic mediated forms of frontotemporal dementia (FTD). GRN mutations reported thus far cause FTD through a haploinsufficiency disease mechanism. Herein, we aim to unveil the GRN mutation spectrum, investigated in 257 FTD patients and 19 family members from the central/north region of Portugal using sequencing methods. Seven different pathogenic variants were identified in 46 subjects including 40 patients (16%) and 6 relatives (32%). bvFTD was the most common clinical presentation among the GRN mutation patients, who showed a global pattern of moderate-to-severe frontotemporoparietal deficits in the neuropsychological evaluation. Interestingly, two mutations were novel (p.Thr238Profs*18, p.Leu354Profs*16), and five were previously described, although three of them only in the Portuguese population, suggesting a population-specific GRN mutational spectrum. The subjects harboring a GRN mutation showed a significant reduction in serum PGRN levels, supporting the pathogenic nature of these variants. This work broadens the mutation spectrum of GRN and the identification of the underlying GRN mutations provided an accurate genetic counselling and allowed the enrolment of subjects with GRN mutations (both asymptomatic and symptomatic) in ongoing clinical trials, which is essential to test new drugs for the disease.

Study of microRNAs and their targets in exosome from Alzheimer’s and Frontotemporal Dementia patients

AbstractBackgroundA central role of RNA metabolism and non‐coding RNAs biogenesis has emerged in Alzheimer’s disease (AD) and Frontotemporal Dementia (FTD). We investigated miRNA cargo of Small Extracellular Vesicles (SEVs) derived from plasma of FTD and AD patients, and Healthy Controls (HC). The purpose was to evaluate deregulated miRNAs in patients to identify new peripheral biomarkers and to determine mRNA targets involved in FTD and AD pathogenesis.MethodSEVs were isolated from plasma of 13 FTD patients, 20 AD and 20 HC by differential centrifugation and characterized by Nanosight and Western Blot. MicroRNA libraries were generated using Small RNA Seq Library Prep Kit (Lexogen) and sequenced on a NextSeq 500 (Illumina). Interaction prediction was carried out on TarBase v.8 database.ResultWe found a total of 339 Differentially Expressed (DE) microRNAs in FTD, and 291 DE microRNAs in AD compared to controls. Interestingly, 74 miRNAs were commonly deregulated: 38 had the same trend and 36 had an opposite trend. In order to find a miRNA biomarker able to discern FTD and AD, we selected 8 DE miRNAs with opposite trend based on fold changes difference and targets. In fact these miRNAs were found to have validated mRNAs target that may be of interest for molecular study of dementia, such as APP and PTEN.ConclusionOur data highlight the importance of miRNAs cargo examination in EVs of FTD and AD patients. In fact, their potential is exploitable both for biomarkers discovery and for study of gene expression alteration in dementia pathogenesis.

Data‐driven analysis of most common neurodegenerative dementias reveals atrophy patterns associated with overlapping cognitive symptoms

AbstractBackgroundA precise understanding of the neuroanatomical basis of clinical manifestation of dementia is an important step for timely diagnosis. Dementia due to proteinopathies, such as Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and Frontotemporal dementia (FTD), have different yet overlapping atrophy patterns which could account for their overlapping clinical presentations. To disentangle these relationships and facilitate timely differential diagnosis, we estimated distinct and overlapping atrophy patterns and associated them with cognitive deficits.MethodWe included 1702 AD (age = 66.1±8.3), 319 FTD (age = 63.3±7.7), 213 DLB (age = 69.7±7.0), and 1330 subjective cognitive decline (SCD) (age = 59.8±9.0) participants from the Amsterdam Dementia Cohort. MRIs were processed with Freesurfer and outputs were manually checked. Scanner‐related bias in outputs was corrected using NeuroHarmony pipeline. Participants’ cognition was evaluated using standardized neuropsychological tests. Z‐scores for cognitive domains of memory, executive functioning, attention, language, and visuospatial functioning were derived using SCD group as reference. We estimated distinct and overlapping atrophy patterns in each disease by computing odds ratio in each region, using SCD group as reference. Furthermore, we computed group‐wise Pearson partial correlation between the (sub‐)cortical measures and cognition while correcting for confounding effects of age, sex, and education level.ResultAtrophy in bilateral inferior temporal, and right hippocampus was more prevalent in AD and FTD patients than in DLB patients (Figures 1‐3). Temporal pole atrophy was more prevalent in FTD than in AD or DLB patients. Superior parietal and supra marginal atrophy was more prevalent in AD than in FTD or DLB patients. Bilateral hippocampus and amygdala atrophy associated with memory impairment in AD, whereas left hemisphere atrophy associated with memory impairment in FTD. Frontal atrophy associated with worse executive functioning and attention in FTD, but not in AD. Language deficit associated with left temporal lobe atrophy in both FTD and AD. Visuospatial dysfunction associated with parietal lobe atrophy in both AD and FTD. In DLB, we observed no significant correlation between atrophy and cognitive deficits.ConclusionWe identified distinct and overlapping atrophy patterns and associations of atrophy patterns with cognitive deficits in AD, FTD, and DLB. These data‐driven insights are crucial for accurate diagnosis and prognosis.

Statistical analysis to identify MRI markers for Alzheimer’s Disease: why moving towards AI?

AbstractBackgroundAlzheimer’s disease (AD) is characterized by progressive neurodegeneration of some brain structures and cognitive function decline. To evaluate the anatomical changes, structural Magnetic Resonance Imaging (sMRI) is a non‐invasive examination and allows the quantification of brain atrophy. Some of the affected structures could be common with other dementia types such as frontotemporal dementia (FTD) and dementia with Lewy bodies (DLB). The objective of this work is to identify specific affected structures in patients with AD to healthy individuals, and to patients suffering from FTD and DLB. This is a part of a larger effort to develop methods to better diagnose AD and to find specific markers of this dementia type.MethodThe ADDIA clinical study (EU‐H2020 project led by Firalis company) enrolled 800 participants and was designed to obtain high‐quality samples for the discovery of novel biomarkers for early and differential diagnosis of AD. The sMRI of 623 participants with the above‐mentioned pathologies and healthy individuals were analyzed and the volume of different areas measured with the software FreeSurfer. Statistics were performed to define significant differences in brain volumes between groups.ResultOne hundred and twelve brain areas were analyzed for volume by study participant. Compared with a healthy population, the atrophy of structures observed in AD patients almost completely overlaps with the one observed in DLB and FTD patients. When the DLB and FTD groups are compared to a healthy population, one brain area is specific in DLB patients and fifty‐eight in FTD patients. However, on these fifty‐eight affected brain areas, forty‐seven overlap with the mild AD patient group.ConclusionThe measurement from imaging modality is not enough precise to report biomarkers as affected areas specific to the type of dementia. However, a promising framework lies in the application of machine and deep learning algorithms on a set of modalities: imaging, clinical and omics. These new tools allow for a finer understanding of data and a personalized approach to diagnosis. In preparation for this multimodal integration framework, the above categories of algorithms will be applied on MRI images. This will permit to discriminate AD from other dementia types at the individual level.

Diagnostic performance RT‐QuIC based detection of alpha‐synuclein seeds in a clinical cohort with cognitive impairment

AbstractBackgroundLewy Body Dementia (LBD) is the second most common neurodegenerative dementia. To date, no validated biochemical marker is available to support clinical diagnosis. The development of the Real‐Time Quaking‐Induced Conversion (RT‐QuIC) assay for detecting alpha‐synuclein (aSyn) seeds in biological samples can be a sensitive biomarker specific for the diagnosis easily applicable in a clinical setting. We aimed to describe the diagnostic performance of RT‐QuIC aSyn assay in cerebrospinal fluid (CSF) to diagnose LBD in a clinical cohort with cognitive impairment.MethodA cohort of subjects with cognitive impairment (neurodegenerative and non‐neurodegenerative) with available CSF sample at the moment of first evaluation was selected by convenience sample in our database. Subjects had clinical follow‐ups ranging from 6 months to 12 years and current clinical diagnosis according to established consensus criteria. The diagnostic performance of RT‐QuIC aSyn for the diagnosis of LBD were evaluated.ResultThe test was evaluated in 155 subjects (age 65 years (SD 11), MMSE 24 (SD 4.5), 56% male) with the following clinical diagnoses: LBD (n = 40), Alzheimer’s disease (AD) (n = 73)(all with compatible CSF profile), frontotemporal dementia (FTD) (n = 7), non‐neurodegenerative mild cognitive impairment (MCI) (n = 21), other neurodegenerative diagnoses (n = 12) and healthy controls (n = 2). aSyn was detected in 33/40 (83%) LBD patients, 7/73 (10%) AD, 0/7 FTD patients, 0/21 MCI patients, 0/12 patients with other neurodegenerative diseases and 0/2 healthy controls.Only 3/7 (42%) LBD subjects with a negative RT‐QuIC asyn fulfilled criteria for LBD (established or prodromal) at the moment of first evaluation as compared with 23/33 (69%) of LBD with positive RTQuIC suggesting a more initial disease in the negative group at the moment of CSF sampling.The sensitivity and specificity of the assay were 83% and 94% respectively for the diagnosis of LBD, with a PPV of 83% and a NPV of 94%.ConclusionDetection of αSyn seeds by RT‐QuIC has a good performance in identifying patients with LBD in a clinical cohort of cognitively impaired subjects. The test also identifies aSyn co‐pathology in a subgroup of subjects diagnosed with AD.

Plasma Neurofilament Light Chain predicts neuroimaging alterations and functional decline in frontotemporal dementia

AbstractBackgroundPlasma Neurofilament light chain (pNfL) is a promising biomarker to discriminate Frontotemporal dementia (FTD) from other diagnoses such as Alzheimer’s disease (AD) or psychiatric disorders. Currently, the diagnostic criteria for FTD syndromes are structured hierarchically into “Possible”, “Probable” and “Definite” levels depending on the degree of confidence set by biomarkers. We aim to study the diagnostic and prognostic values of pNfL in the different confidence levels of FTD diagnosis.MethodPatients fulfilling criteria for FTD were classified as “Possible”, “Probable” or “Definite” FTD based on the current diagnostic criteria. pNfL levels were determined with SiMoA, and then compared to Control and AD groups, as well as within the different levels of confidence in the FTD diagnosis.Result187 subjects were included: 37 controls, 79 AD and 71 FTD (11 Possible, 49 Probable and 11 Definite). The FTD group showed higher concentrations of pNfL than the Control (p &lt; 0.001, AUC: 0.817) and the AD groups (p &lt; 0.001, AUC: 0.677) (Figure 1A and 1B).Within the FTD group, pNfL levels differed across the different diagnostic confidence levels (Figure 1C). Subjects with Probable and Definite FTD showed higher levels of pNfL than subjects with Possible FTD (p &lt; 0.001 and p &lt; 0.01 respectively). By contrast, the Possible FTD group did not show differences in pNfL concentrations with the control group. In that sense, the diagnostic value of pNfL varied according to the different grades of diagnostic confidence (Figure 1D), with cases with probable or definite FTD exhibiting an excellent AUC to distinguish from controls (AUC 0.860 and 0.908 respectively), but with no discrimination between possible FTD and controls (AUC 0.583).pNfL presented an excellent performance in the differentiation of structural or functional impairment in the neuroimage (AUC 0.94, and AUC 0.86 respectively) (Figure 2). Finally, higher baseline pNfL levels were associated with worse clinical progression in FTD cases (Figure 3).ConclusionpNfL were higher in FTD patients compared to controls or AD patients. However, we found significant variation across the different confidence levels of FTD diagnosis. Finally, pNfL predicted neuroimage alterations and a worse clinical progression in FTD patients.

Probabilistic computer‐aided diagnosis of Alzheimer’s disease and frontotemporal dementia based on MRI and biochemical markers

AbstractBackgroundOne of the clinical problems for biomarkers' clinical use is the ability to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), and healthy subjects (CTR). This clearly challenges diagnosis and prognosis. We implemented a ML algorithm that provides individual probabilistic diagnoses for these dementias based on magnetic resonance imaging (MRI) and we correlated the results with biochemical markers.MethodWe studied 3T‐T1w MRI of 432 subjects. A subset of these participants had cerebrospinal fluid (CSF) and plasma biomarkers (Table 1). We obtained regional subcortical gray matter volumes and cortical thickness measures using Freesurfer. We implemented a calibrated classifier with a Support Vector Machine with only the MRI data. We tested paired‐wise classification and classification across the 3 groups. We obtained individual probabilities associated with group correspondence. We studied the correlation between these probabilities and CSF and plasma biomarkers. For this, we subdivided the groups into true‐group (subjects with classification according to clinical diagnosis) and false‐group (subjects which did not coincide with clinical diagnosis). Finally, we implemented a permutation test to find the importance of each region in the classification.ResultWe obtained accuracies of 90.7 ± 6.7% in the CTR vs AD classification, 88.6 ± 4.5% for CTR vs FTD, 79.3 ± 8.8% for AD vs FTD, and 79.9 ± 5.1% when discriminating the 3 groups. We obtained a significant positive correlation for plasma p‐tau181 for the false‐AD in the comparison AD vs CTR (Figure 1). The correlation of the false‐CTR was significantly positive for the CSF and plasma NfL. Finally, in the AD vs FTD, the true‐FTD had a significant negative correlation with CSF NfL. The other biochemical biomarkers did not provide additional information. The most important regions for classification are shown in Figure 2.ConclusionThe ML algorithm gave high accuracies. Within wrongly classified AD patients, probabilities correlated positively with plasma p‐tau181, suggesting hidden pathological processes associated with subjects clinically classified as CTR. Finally, the group probability within well‐classified FTD patients in comparison with AD negatively correlated with CSF NfL. We suggest that this approach can be used as a tool to try to develop personalized diagnoses.