scholarly journals Using the Disease State Fingerprint Tool for Differential Diagnosis of Frontotemporal Dementia and Alzheimer's Disease

2016 ◽  
Vol 6 (2) ◽  
pp. 313-329 ◽  
Author(s):  
Miguel Ángel Muñoz-Ruiz ◽  
Anette Hall ◽  
Jussi Mattila ◽  
Juha Koikkalainen ◽  
Sanna-Kaisa Herukka ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Frontotemporal Dementia ◽  
Clinical Decision Making ◽  
Apoe Genotype ◽  
Disease State ◽  
Diagnostic Process ◽  
Computer Assisted ◽  
Study Cohort

Background: Disease State Index (DSI) and its visualization, Disease State Fingerprint (DSF), form a computer-assisted clinical decision making tool that combines patient data and compares them with cases with known outcomes. Aims: To investigate the ability of the DSI to diagnose frontotemporal dementia (FTD) and Alzheimer's disease (AD). Methods: The study cohort consisted of 38 patients with FTD, 57 with AD and 22 controls. Autopsy verification of FTD with TDP-43 positive pathology was available for 14 and AD pathology for 12 cases. We utilized data from neuropsychological tests, volumetric magnetic resonance imaging, single-photon emission tomography, cerebrospinal fluid biomarkers and the APOE genotype. The DSI classification results were calculated with a combination of leave-one-out cross-validation and bootstrapping. A DSF visualization of a FTD patient is presented as an example. Results: The DSI distinguishes controls from FTD (area under the receiver-operator curve, AUC = 0.99) and AD (AUC = 1.00) very well and achieves a good differential diagnosis between AD and FTD (AUC = 0.89). In subsamples of autopsy-confirmed cases (AUC = 0.97) and clinically diagnosed cases (AUC = 0.94), differential diagnosis of AD and FTD performs very well. Conclusions: DSI is a promising computer-assisted biomarker approach for aiding in the diagnostic process of dementing diseases. Here, DSI separates controls from dementia and differentiates between AD and FTD.

Brazilian Version of Addenbrooke’s Cognitive Examination—Revised in the Differential Diagnosis of Alzheimer’S Disease and Behavioral Variant Frontotemporal Dementia

2021 ◽  
Author(s):  
Viviane Amaral-Carvalho ◽  
Thais Bento Lima-Silva ◽  
Luciano Inácio Mariano ◽  
Leonardo Cruz de Souza ◽  
Henrique Cerqueira Guimarães ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Diagnostic Accuracy ◽  
Frontotemporal Dementia ◽  
Predictive Value ◽  
Univariate Analysis ◽  
Behavioral Variant Frontotemporal Dementia ◽  
Present Sample ◽  
Addenbrooke’S Cognitive Examination

Abstract Introduction Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are frequent causes of dementia and, therefore, instruments for differential diagnosis between these two conditions are of great relevance. Objective To investigate the diagnostic accuracy of Addenbrooke’s Cognitive Examination-Revised (ACE-R) for differentiating AD from bvFTD in a Brazilian sample. Methods The ACE-R was administered to 102 patients who had been diagnosed with mild dementia due to probable AD, 37 with mild bvFTD and 161 cognitively healthy controls, matched according to age and education. Additionally, all subjects were assessed using the Mattis Dementia Rating Scale and the Neuropsychiatric Inventory. The performance of patients and controls was compared by using univariate analysis, and ROC curves were calculated to investigate the accuracy of ACE-R for differentiating AD from bvFTD and for differentiating AD and bvFTD from controls. The verbal fluency plus language to orientation plus name and address delayed recall memory (VLOM) ratio was also calculated. Results The optimum cutoff scores for ACE-R were <80 for AD, <79 for bvFTD, and <80 for dementia (AD + bvFTD), with area under the receiver operating characteristic curves (ROC) (AUC) >0.85. For the differential diagnosis between AD and bvFTD, a VLOM ratio of 3.05 showed an AUC of 0.816 (Cohen’s d = 1.151; p < .001), with 86.5% sensitivity, 71.4% specificity, 72.7% positive predictive value, and 85.7% negative predictive value. Conclusions The Brazilian ACE-R achieved a good diagnostic accuracy for differentiating AD from bvFTD patients and for differentiating AD and bvFTD from the controls in the present sample.

scholarly journals Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI

2016 ◽  
Vol 27 (8) ◽  
pp. 3372-3382 ◽  
Author(s):  
Esther E. Bron ◽  
Marion Smits ◽  
Janne M. Papma ◽  
Rebecca M. E. Steketee ◽  
Rozanna Meijboom ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Frontotemporal Dementia ◽  
Computer Aided

scholarly journals Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer’s Disease

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Hu ◽  
Zhao Qing ◽  
Renyuan Liu ◽  
Xin Zhang ◽  
Pin Lv ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Frontotemporal Dementia ◽  
Image Data ◽  
Rapid Acquisition ◽  
Magnetic Resonance Imaging Mri ◽  
The One ◽  
The Right

Frontotemporal dementia (FTD) and Alzheimer’s disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing. On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively. In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes (n = 4,099) was collected from two publicly available databases, i.e., the ADNI and the NIFD. We trained a DL-based network directly based on raw T1 images to classify FTD, AD and corresponding NCs. And we evaluated the convergence speed, differential diagnosis ability, robustness and generalizability under nine scenarios. The proposed network yielded an accuracy of 91.83% based on the most common T1-weighted sequence [magnetization-prepared rapid acquisition with gradient echo (MPRAGE)]. The knowledge learned by the DL network through multiple classification tasks can also be used to solve subproblems, and the knowledge is generalizable and not limited to a specified dataset. Furthermore, we applied a gradient visualization algorithm based on guided backpropagation to calculate the contribution graph, which tells us intuitively why the DL-based networks make each decision. The regions making valuable contributions to FTD were more widespread in the right frontal white matter regions, while the left temporal, bilateral inferior frontal and parahippocampal regions were contributors to the classification of AD. Our results demonstrated that DL-based networks have the ability to solve the enigma of differential diagnosis of diseases without any hypothesis-based preprocessing. Moreover, they may mine the potential patterns that may be different from human clinicians, which may provide new insight into the understanding of FTD and AD.

scholarly journals Subcortical atrophy in frontotemporal dementia and Alzheimer's disease: Significance for differential diagnosis and correlation with clinical manifestations

2008 ◽  
Vol 2 (4) ◽  
pp. 284-288 ◽  
Author(s):  
Renata Teles Vieira ◽  
Leonardo Caixeta
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Frontotemporal Dementia ◽  
Daily Living ◽  
Independent Living ◽  
Clinical Manifestations ◽  
Linear Measurement ◽  
Clinical Dementia Rating ◽  
Significant Difference

Abstract Cerebral subcortical atrophy occurs in both Alzheimer's disease (AD) and frontotemporal dementia (FTD) but its significance for clinical manifestations and differential diagnosis between these common types of dementia has not been extensively investigated. Objectives: To compare the severity of cerebral subcortical atrophy in FTD and AD and to analyze the correlations between cerebral subcortical atrophy and demographics and clinical characteristics. Methods: Twenty three patients with FTD and 21 with AD formed the sample, which comprised 22 men and 22 women, aged 33 to 89, with mean age (±SD) of 68.52±12.08 years, with schooling ranging from 1 to 20 years, with a mean (±SD) of 7.35±5.54 years, and disease duration with a mean (±SD) of 3.66±3.44 years. The degree of cerebral subcortical atrophy was measured indirectly with a linear measurement of subcortical atrophy, the Bifrontal Index (BFI), using magnetic resonance imaging. We evaluated cognition, activities of daily living and dementia severity with the Mini-Mental State Examination, Functional Activities Questionnaire and the Clinical Dementia Rating, respectively. Results: There was no significant difference (p>0.05) in BFI between FTD and AD. The severity of cognitive deficits (for both FTD and AD groups) and level of daily living activities (only for AD group) were correlated with BFI. Conclusions: A linear measurement of cerebral subcortical atrophy did not differentiate AD from FTD in this sample. Cognitive function (in both FTD and AD groups) and capacity for independent living (only in AD group) were inversely correlated with cerebral subcortical atrophy.

scholarly journals An MRI-based strategy for differentiation of frontotemporal dementia and Alzheimer’s disease

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Qun Yu ◽  
◽  
Yingren Mai ◽  
Yuting Ruan ◽  
Yishan Luo ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differential Diagnosis ◽  
Frontotemporal Dementia ◽  
Brain Atrophy ◽  
Clinical Symptoms ◽  
Threshold Value ◽  
Imaging Biomarker ◽  
Independent Data ◽  
Significant Difference

Abstract Background The differential diagnosis of frontotemporal dementia (FTD) and Alzheimer’s disease (AD) is difficult due to the overlaps of clinical symptoms. Structural magnetic resonance imaging (sMRI) presents distinct brain atrophy and potentially helps in their differentiation. In this study, we aim at deriving a novel integrated index by leveraging the volumetric measures in brain regions with significant difference between AD and FTD and developing an MRI-based strategy for the differentiation of FTD and AD. Methods In this study, the data were acquired from three different databases, including 47 subjects with FTD, 47 subjects with AD, and 47 normal controls in the NACC database; 50 subjects with AD in the ADNI database; and 50 subjects with FTD in the FTLDNI database. The MR images of all subjects were automatically segmented, and the brain atrophy, including the AD resemblance atrophy index (AD-RAI), was quantified using AccuBrain®. A novel MRI index, named the frontotemporal dementia index (FTDI), was derived as the ratio between the weighted sum of the volumetric indexes in “FTD dominant” structures over that obtained from “AD dominant” structures. The weights and the identification of “FTD/AD dominant” structures were acquired from the statistical analysis of NACC data. The differentiation performance of FTDI was validated using independent data from ADNI and FTLDNI databases. Results AD-RAI is a proven imaging biomarker to identify AD and FTD from NC with significantly higher values (p < 0.001 and AUC = 0.88) as we reported before, while no significant difference was found between AD and FTD (p = 0.647). FTDI showed excellent accuracy in identifying FTD from AD (AUC = 0.90; SEN = 89%, SPE = 75% with threshold value = 1.08). The validation using independent data from ADNI and FTLDNI datasets also confirmed the efficacy of FTDI (AUC = 0.93; SEN = 96%, SPE = 70% with threshold value = 1.08). Conclusions Brain atrophy in AD, FTD, and normal elderly shows distinct patterns. In addition to AD-RAI that is designed to detect abnormal brain atrophy in dementia, a novel index specific to FTD is proposed and validated. By combining AD-RAI and FTDI, an MRI-based decision strategy was further proposed as a promising solution for the differential diagnosis of AD and FTD in clinical practice.

scholarly journals Plasma Lipocalin 2 in Alzheimer’s disease: potential utility in the differential diagnosis and relationship with other biomarkers

2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Peter Hermann ◽  
Anna Villar-Piqué ◽  
Matthias Schmitz ◽  
Christian Schmidt ◽  
Daniela Varges ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vascular Dementia ◽  
Apoe Genotype ◽  
Cognitive Status ◽  
Differential Diagnoses ◽  
Study Cohort ◽  
Healthy Controls ◽  
Lipocalin 2 ◽  
Reactive Microglia

Abstract Background Lipocalin-2 is a glycoprotein that is involved in various physiological and pathophysiological processes. In the brain, it is expressed in response to vascular and other brain injury, as well as in Alzheimer’s disease in reactive microglia and astrocytes. Plasma Lipocalin-2 has been proposed as a biomarker for Alzheimer’s disease but available data is scarce and inconsistent. Thus, we evaluated plasma Lipocalin-2 in the context of Alzheimer’s disease, differential diagnoses, other biomarkers, and clinical data. Methods For this two-center case-control study, we analyzed Lipocalin-2 concentrations in plasma samples from a cohort of n = 407 individuals. The diagnostic groups comprised Alzheimer’s disease (n = 74), vascular dementia (n = 28), other important differential diagnoses (n = 221), and healthy controls (n = 84). Main results were validated in an independent cohort with patients with Alzheimer’s disease (n = 19), mild cognitive impairment (n = 27), and healthy individuals (n = 28). Results Plasma Lipocalin-2 was significantly lower in Alzheimer’s disease compared to healthy controls (p < 0.001) and all other groups (p < 0.01) except for mixed dementia (vascular and Alzheimer’s pathologic changes). Areas under the curve from receiver operation characteristics for the discrimination of Alzheimer’s disease and healthy controls were 0.783 (95%CI: 0.712–0.855) in the study cohort and 0.766 (95%CI: 0.627–0.905) in the validation cohort. The area under the curve for Alzheimer’s disease versus vascular dementia was 0.778 (95%CI: 0.667–0.890) in the study cohort. In Alzheimer’s disease patients, plasma Lipocalin2 did not show significant correlation with cerebrospinal fluid biomarkers of neurodegeneration and AD-related pathology (total-tau, phosphorylated tau protein, and beta-amyloid 1-42), cognitive status (Mini Mental Status Examination scores), APOE genotype, or presence of white matter hyperintensities. Interestingly, Lipocalin 2 was lower in patients with rapid disease course compared to patients with non-rapidly progressive Alzheimer’s disease (p = 0.013). Conclusions Plasma Lipocalin-2 has potential as a diagnostic biomarker for Alzheimer’s disease and seems to be independent from currently employed biomarkers.

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