Differential Diagnosis Of Dementia Research Articles

Validation of the Addenbrooke’s Cognitive Examination-III for detecting vascular dementia in Iranian patients with stroke: A secondary data analysis

The current study was conducted with the aim of evaluating the third version of Addenbrooke’s Cognitive Examination (ACE-III), and exploring its diagnostic power for a sample of stroke patients in the Iranian population. This was a cross-sectional analytical study, in which 206 patients with stroke were compared with 200 normal individuals as the control group. The patients were diagnosed based on the findings of neuroimaging and clinical examination by a neurologist. ACE-III, Montreal Cognitive Assessment (MoCA), Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE), and The Structured Clinical Interview for DSM-5 (SCID-5) were used to gather the data and assess the vascular dementia in the patients. Furthermore, Cronbach’s alpha, Pearson correlation coefficient, discriminant function analysis, and the receiver operating characteristic (ROC) curve were used to respectively measure internal consistency, convergent validity, discriminant validity, sensitivity, specificity, and the cutoff point of ACE-III. Internal consistency of ACE-III was excellent (α = 0.92 − 0.95), and convergent validity was measured through calculating the correlation between the scores of ACE-III and MoCA, which was very high (r = 0.957, P < 0.0001). Moreover, overall classification accuracy of ACE-III revealed that it is able to differentiate 87% of patients with vascular dementia from other patients. The area under the ROC curve was found to be 0.84, and cutoff point was 45/46, at which sensitivity and specificity were obtained as 0.72 and 0.90, respectively. ACE-III is a rapid, inexpensive, and efficient tool for evaluating cognitive deficits in specialized neurology clinics to provide a clinical and differential diagnosis of vascular dementia after stroke.

Functional connectivity and cerebral cortex activation during the resting state and verbal fluency tasks for patients with mild cognitive impairment, Lewy body dementia, and Alzheimer's disease: A multi-channel fNIRS study

ObjectiveTo explore changes in cerebral cortex activation and functional connectivity during resting-state and verbal fluency tasks in patients with different types of dementia. MethodsWe recorded oxygenated hemoglobin concentration (HbO) signals detected by functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex, partial parietal cortex, and cortex of the temporal lobe in four groups of participants: mild cognitive impairment (MCI), Lewy body dementia (LBD), Alzheimer's disease (AD), and cognitively normal (CN). ResultsThe study recruited 120 older adults with MCI (n = 30), LBD (n = 28), AD (n = 30), or CN (n = 32). The mean functional connectivity of the frontal and temporal lobe in resting state was significantly less in the AD (0.19 ± 0.11) group than in the MCI (0.23 ± 0.11), LBD (0.29 ± 0.12), and CN (0.40 ± 0.11) groups (p < 0.001). Further, the mean HbO concentrations in the brain regions and channels were significantly lower in the AD group than in the LBD and MCI groups (p < 0.001). Cognitive levels correlated significantly with the mean HbO concentrations in the resting state and verbal fluency task conditions. ConclusionThe fNIRS HbO signals significantly differed in the cerebral cortex regions in participants with different types of dementia. These findings suggest that fNIRS can effectively enhance the differential diagnosis and assessment of dementia.

Beyond group classification: Probabilistic differential diagnosis of frontotemporal dementia and Alzheimer’s disease with MRI and CSF biomarkers

Neuroimaging and fluid biomarkers are used to differentiate frontotemporal dementia (FTD) from Alzheimer’s disease (AD). We implemented a machine learning algorithm that provides individual probabilistic scores based on magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. We investigated whether combining MRI and CSF levels could improve the diagnosis confidence. 215 AD patients, 103 FTD patients, and 173 healthy controls (CTR) were studied. With MRI data, we obtained an accuracy of 82 % for AD vs. FTD. A total of 74 % of FTD and 73 % of AD participants have a high probability of accurate diagnosis. Adding CSF-NfL and 14–3–3 levels improved the accuracy and the number of patients in the confidence group for differentiating FTD from AD. We obtain individual diagnostic probabilities with high precision to address the problem of confidence in the diagnosis. We suggest when MRI, CSF, or the combination are necessary to improve the FTD and AD diagnosis. This algorithm holds promise towards clinical applications as support to clinical findings or in settings with limited access to expert diagnoses.

Artificial intelligence for differential diagnosis of dementia.

Artificial intelligence for differential diagnosis of dementia.

AI-based differential diagnosis of dementia etiologies on multimodal data

Differential diagnosis of dementia remains a challenge in neurology due to symptom overlap across etiologies, yet it is crucial for formulating early, personalized management strategies. Here, we present an artificial intelligence (AI) model that harnesses a broad array of data, including demographics, individual and family medical history, medication use, neuropsychological assessments, functional evaluations and multimodal neuroimaging, to identify the etiologies contributing to dementia in individuals. The study, drawing on 51,269 participants across 9 independent, geographically diverse datasets, facilitated the identification of 10 distinct dementia etiologies. It aligns diagnoses with similar management strategies, ensuring robust predictions even with incomplete data. Our model achieved a microaveraged area under the receiver operating characteristic curve (AUROC) of 0.94 in classifying individuals with normal cognition, mild cognitive impairment and dementia. Also, the microaveraged AUROC was 0.96 in differentiating the dementia etiologies. Our model demonstrated proficiency in addressing mixed dementia cases, with a mean AUROC of 0.78 for two co-occurring pathologies. In a randomly selected subset of 100 cases, the AUROC of neurologist assessments augmented by our AI model exceeded neurologist-only evaluations by 26.25%. Furthermore, our model predictions aligned with biomarker evidence and its associations with different proteinopathies were substantiated through postmortem findings. Our framework has the potential to be integrated as a screening tool for dementia in clinical settings and drug trials. Further prospective studies are needed to confirm its ability to improve patient care.

Plasma biomarkers of amyloid, tau, axonal, and neuroinflammation pathologies in dementia with Lewy bodies

BackgroundIncreasing evidence supports the use of plasma biomarkers of amyloid, tau, neurodegeneration, and neuroinflammation for diagnosis of dementia. However, their performance for positive and differential diagnosis of dementia with Lewy bodies (DLB) in clinical settings is still uncertain.MethodsWe conducted a retrospective biomarker study in two tertiary memory centers, Paris Lariboisière and CM2RR Strasbourg, France, enrolling patients with DLB (n = 104), Alzheimer’s disease (AD, n = 76), and neurological controls (NC, n = 27). Measured biomarkers included plasma Aβ40/Aβ42 ratio, p-tau181, NfL, and GFAP using SIMOA and plasma YKL-40 and sTREM2 using ELISA. DLB patients with available CSF analysis (n = 90) were stratified according to their CSF Aβ profile.ResultsDLB patients displayed modified plasma Aβ ratio, p-tau181, and GFAP levels compared with NC and modified plasma Aβ ratio, p-tau181, GFAP, NfL, and sTREM2 levels compared with AD patients. Plasma p-tau181 best differentiated DLB from AD patients (ROC analysis, area under the curve [AUC] = 0.80) and NC (AUC = 0.78), and combining biomarkers did not improve diagnosis performance. Plasma p-tau181 was the best standalone biomarker to differentiate amyloid-positive from amyloid-negative DLB cases (AUC = 0.75) and was associated with cognitive status in the DLB group. Combining plasma Aβ ratio, p-tau181 and NfL increased performance to identify amyloid copathology (AUC = 0.79). Principal component analysis identified different segregation patterns of biomarkers in the DLB and AD groups.ConclusionsAmyloid, tau, neurodegeneration and neuroinflammation plasma biomarkers are modified in DLB, albeit with moderate diagnosis performance. Plasma p-tau181 can contribute to identify Aβ copathology in DLB.

The role of biomarkers in differential diagnosis of neurodegenerative dementias: A case report

The role of biomarkers in differential diagnosis of neurodegenerative dementias: A case report

Limbic encephalitis – A case report of atypical dementia syndrome with potentially therapeutic consequence

IntroductionLimbic encephalitis (LE) is a subacute or chronic, non-infectious inflammation of the brain, usually occurring in adulthood, with predominant involvement of mesiotemporal structures and a clinical manifestation consisting mainly of new memory impairment, affective disorder, temporal lobe epilepsy, psychoses, etc.ObjectivesTo point out the importance of knowledge of potentially treatable dementia syndromes such as atypical manifestation of probably LE.MethodsWe present a clinical case of a 47-years-old woman with an atypical dementia syndrome and typical radiological findings corresponding to a LE, among others, but without the previously known immunological antibodies.ResultsAccording to the literature, the diverse subsyndromes of LE can be subsumed under the two main categories of “paraneoplastic” and “non-paraneoplastic”. In addition to the acute and subacute courses, there is increasing evidence for chronic, slowly progressive courses, which expand the spectrum of potentially treatable dementia syndromes. Understanding and knowledge of the broad, clinical syndrome of LE have increased dramatically in recent years. Both nosological classification through differentiated diagnosis and specific therapeutic protocols have become increasingly developed and established. Nevertheless, there are rare clinical cases with a clinical phenotype and radiological findings that correspond to LE, but are both non-paraneoplastic in origin and seronegative with respect to the previously known immunological typing by autoantibodies. This gray area of nosological entity represents a diagnostic and therapeutic challenge.ConclusionsThe authors would like to point out the importance of an adequate diagnosis of the forms of LE that have been nosologically classified so far and are partly well treatable. Limbic encephalitis is an important differential diagnosis in dementia, especially in young patients with atypical courses. There is a need for further research regarding better diagnosis and therapy of the so far immunologically unidentifiable forms of clinical LE.Literature:Bazir Ahmad et al., Practical Neurology 2011Guidelines of the German Neurological Society (DGN), 2008Leypoldt et al., Akt Neurol 2012Prüss et al., Neurology 2012Disclosure of InterestNone Declared

On the road to biological blood diagnosis of Alzheimer's disease?

The growing number of people suffering from Alzheimer's disease (AD) represents a major public health problem. The diagnosis of AD is multidisciplinary and involves the use of amyloid and tau biomarkers measured in cerebrospinal fluid. Recent advances in analytical techniques now allow us to measure these biomarkers in blood. Blood biomarkers offer particularly promising potential for early, minimally invasive detection of AD, as well as for differential diagnosis of dementia and patient follow-up. The aim of this review is to provide an overview of current and candidate blood biomarkers for AD, their informative value, and their potential to be integrated into clinical practice in the near future.

Evolution of Concepts of Differential Diagnostics in Frontotemporal Dementia

Background: frontotemporal dementia (FTD) is a group of neurodegenerative diseases, with onset usually in presenile age, the clinical picture is manifested by behavioral disorders and relatively intact cognitive features in the initial disease. In the early stages of FTD, it is difficult to differentiate this type from other dementias or other mental diseases. The aim was to analyse recent scientific publications on the problem of differential diagnostics of frontotemporal dementia. Material and methods: using the keywords “frontotemporal dementia”, “frontotemporal lobar degeneration”, “differential diagnosis of frontotemporal dementia”, “behavioral variant of frontotemporal dementia”, selected and analyze publications for the last two decades. Results: the behavioral variant of FTD (bv-FTD) is the most common form of FTD, accounting for 50% of all cases of FTD, and especially in cases with early onset. Predominantly, this variant of FTD presents diagnostic difficulties, due to the limited accuracy of neuroimaging examinations and the lack of specific biomarkers. The clinical symptoms of bv-FTD are characterized by considerable overlap with symptoms of neurodegenerative diseases and mental diseases, such as schizophrenia, bipolar affective disorder, obsessive-compulsive disorder, and personality disorders. Conclusion: the diagnosis of FTD at the initial stage of the disease is problematic and difficult, the sensitivity and specificity of almost all diagnostic methods increase as the disease progresses. This literature review highlights some of the diagnostic methods that can be used in suspected cases of FTD and informs about the differential diagnostics recommendations that have been developed to improve the accuracy of FTD diagnosis.

Potential Utility of Plasma Biomarker Panels in Differential Diagnosis of Alzheimer's Disease.

Blood tests are in need, in the clinical diagnosis of Alzheimer's disease (AD) as minimally invasive and less expensive alternatives to cerebrospinal fluid and neuroimaging methods. On these lines, single molecule array (Simoa) analysis of amyloid-β (Aβ42), total tau (t-tau), phospho-tau (p-tau 181), and neurofilament L (NfL) in the plasma samples of AD subjects, healthy controls (HC), and non-AD subjects was conducted. Findings from this study suggest that a panel of multiple plasma biomarkers (NfL, Aβ42, t-tau, and p-tau 181) combined with the clinical assessments could support differential diagnosis of AD and other dementias from healthy controls.

Neuropsychological differential diagnosis of Alzheimer's disease and Lewy body dementia: A systematic review

Neuropsychological differential diagnosis of Alzheimer's disease and Lewy body dementia: A systematic review

Differential diagnosis of Lewy body dementias using multivariate EEG classifiers

AbstractBackgroundLewy body dementia (LBD) results from pathology in ɑ‐synuclein and is the second most common cause of dementia. LBD has two manifestations: Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB). There is currently no non‐invasive, fast, reliable way to distinguish between early‐stage PDD, DLB, and Alzheimer’s disease (AD). Although previous traditional EEG visual rating methods have not been useful in delineating dementia subtypes, resting‐state electroencephalography (rsEEG) is a promising method for differential diagnosis of dementia, because it is inexpensive, non‐invasive, and widely available for clinical use. Here, we demonstrate how a multivariate classifier using rsEEG as input can differentiate PDD, DLB, and Alzheimer’s disease.MethodWe analyzed rsEEG data from older adults (N = 133, age 59‐89) with AD (n = 33), DLB (n = 27) or PDD (n = 45). We computed the aperiodic and periodic parameterization of brain activity, along with metrics of variation in fractal dimension within specific frequency bands. We then used these features, along with age and sex, to develop XGBoost machine‐learning models to predict patient diagnoses. Models were evaluated using cross‐validation with balanced accuracy, weighted sensitivity, and weighted specificity.ResultThe first task was a two‐way classification: Control vs Dementia (accuracy = 86%, sensitivity = 93%, specificity = 79%, p &lt; .001). The second task was a three‐way classification: AD vs DLB vs PDD (accuracy = 66%, sensitivity = 70%, specificity = 82%, p &lt; .001). We also evaluated two‐way models distinguishing between AD vs DLB (accuracy = 86%, sensitivity = 87%, specificity = 86%, p &lt; .001), AD vs PDD (accuracy = 84%, sensitivity = 85%, specificity = 83%, p &lt; .001), AD vs LBD (combined DLB and PDD; accuracy = 82%, sensitivity = 85%, specificity = 78%, p &lt; .01), and DLB vs PDD (accuracy = 68%, sensitivity = 71%, specificity = 65%, p &lt; .05).ConclusionMachine‐learning and rsEEG are capable of distinguishing between AD and LBD. Moreover, machine‐learning combined with spectral and fractal features can distinguish between DLB and PDD, despite both forms of dementia arising from ɑ‐synuclein pathology.

Divergent brain regional atrophy and associated fiber disruption in amnestic and non-amnestic MCI

BackgroundUnderstanding the pathological characteristics of various mild cognitive impairment (MCI) subtypes is crucial for the differential diagnosis of dementia. The purpose of this study was to feature divergent symptom-deficit profiles in amnestic MCI (aMCI) and non-amnestic MCI (naMCI).MethodsT1 and DTI MRI data from a total of 158 older adults with 50 normal controls, 56 aMCI, and 52 naMCI were included. The voxel-wise gray matter volumes and the number of seed-based white matter fiber bundles were compared among these three groups. Furthermore, correlation and mediation analyses between the neuroimaging indices and cognitive measures were performed.ResultsThe aMCI with specific memory abnormalities was characterized by volumetric atrophy of the left hippocampus but not by damage in the linked white matter fiber bundles. Conversely, naMCI was characterized by both the altered volume of the right inferior frontal gyrus and the significant damage to fiber bundles traversing the region in all three directions, not only affecting fibers around the atrophied area but also distant fibers. Mediation analyses of gray matter-white matter-cognition showed that gray matter atrophy affects the number of fiber bundles and further affects attention and executive function. Meanwhile, fiber bundle damage also affects gray matter volume, which further affects visual processing and language.ConclusionsThe divergent structural damage patterns of the MCI subtypes and cognitive dysfunctions highlight the importance of detailed differential diagnoses in the early stages of pathological neurodegenerative diseases to deepen the understanding of dementia subtypes and inform targeted early clinical interventions.

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis.

Application of the modified qualitative scoring of MMSE pentagon test in the differential diagnosis of lewy body dementia and Alzheimer’s disease

AbstractBackgroundTo explore the value of the modified qualitative scoring of MMSE pentagon test (mQSPT) in the differential diagnosis of Lewy body dementia (DLB) and Alzheimer’s disease (AD).MethodStudy the patients who met the inclusion criteria in the Department of Neurology, Xuanwu Hospital, Capital Medical University from January 2018 to August 2021. The baseline data of gender, age and education, Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment Scale (MoCA) and Clinical Dementia Assessment Scale (CDR) scores of 61 DLB patients and 71 AD patients were analyzed retrospectively. At the same time, the scores of sub items in the scale that can reflect visualspatial impairment were recorded,: i.e. connection test, cube copy, clock drawing test in MoCA, and pentagon copy test in MMSE. The images in MMSE pentagon copy test of these patients were further scored according to QSPT and mQSPT, and compared between the two groups. Multivariate stepwise logistic regression was used to analyze the differential efficacy of QSPT, mQSPT combined with other clinical psychological evaluation between DLB and AD.ResultThere were significant differences between DLB and AD patients in connection test, cube copy, clock drawing test, pentagon copy test, QSPT, mQSPT and gender, but there were no significant differences in MMSE, MoCA, age and education. The sensitivity of QSPT in differentiating DLB and AD was 71.8%, the specificity was 67.2%, the area under ROC curve was 0.682 (95%CI: 0.584‐0.772), and the cut‐off value was 9.5. The sensitivity of mQSPT in differentiating DLB and AD was 68.9%, the specificity was 84.5%, the area under ROC curve was 0.78 (95%CI: 0.696‐0.862), and the cut‐off value was 8.5. Multivariate stepwise logistic regression showed that while QSPT failed to establish any suitable modeling, mQSPT and cube copy were related to the distinction between the two diseases. Moreover, the efficacy of these two indexes to distinguish AD and DLB: the sensitivity was 70.5%, and the specificity was 84.1% and the area under the ROC curve was 0.817(95%CI: 0.743‐0.891).ConclusionThe mQSPT can be used for the screening tool in differential diagnosis between AD and DLB patients.

Functional near-infrared spectroscopy in elderly patients with four types of dementia.

Functional near-infrared spectroscopy (fNIRS) is commonly used to study human brain function by measuring the hemodynamic signals originating from cortical activation and provides a new noninvasive detection method for identifying dementia. To investigate the fNIRS imaging technique and its clinical application in differential diagnosis of subtype dementias including frontotemporal lobe dementia, Lewy body dementia, Parkinson's disease dementia (PDD) and Alzheimer's disease (AD). Four patients with different types of dementia were examined with fNIRS during two tasks and a resting state. We adopted the verbal fluency task, working memory task and resting state task. Each patient was compared on the same task. We conducted and analyzed the fNIRS data using a general linear model and Pearson's correlation analysis. Compared with other types of dementias, fNIRS showed the left frontotemporal and prefrontal lobes to be poorly activated during the verbal fluency task in frontotemporal dementia. In Lewy body dementia, severe asymmetry of prefrontal lobes appeared during both verbal fluency and working memory tasks, and the patient had low functional connectivity during a resting state. In PDD, the patient's prefrontal cortex showed lower excitability than the temporal lobe during the verbal fluency task, while the prefrontal cortex showed higher excitability during the working memory task. The patient with AD showed poor prefrontal and temporal activation during the working memory task, and more activation of frontopolar instead of the dorsolateral prefrontal cortex. Different hemodynamic characteristics of four types of dementia (as seen by fNIRS imaging) provides evidence that fNIRS can serve as a potential tool for the diagnosis between dementia subtypes.

CERAD (Consortium to Establish a Registry for Alzheimer's Disease) Neuropsychology Assessment Battery: 35 Years and Counting.

In 1986, the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) was mandated to develop a brief neuropsychological assessment battery (CERAD-NAB) for AD, for uniform neuropsychological assessment, and information aggregation. Initially used across the National Institutes of Aging-funded Alzheimer's Disease Research Centers, it has become widely adopted wherever information is desired on cognitive status and change therein, particularly in older populations. Our purpose is to provide information on the multiple uses of the CERAD-NAB since its inception, and possible further developments. Since searching on "CERAD neuropsychological assessment battery" or similar terms missed important information, "CERAD" alone was entered into PubMed and SCOPUS, and CERAD-NAB use identified from the resulting studies. Use was sorted into major categories, e.g., psychometric information, norms, dementia/differential dementia diagnosis, epidemiology, intervention evaluation, genetics, etc., also translations, country of use, and alternative data gathering approaches. CERAD-NAB is available in ∼20 languages. In addition to its initial purpose assessing AD severity, CERAD-NAB can identify mild cognitive impairment, facilitate differential dementia diagnosis, determine cognitive effects of naturally occurring and experimental interventions (e.g., air pollution, selenium in soil, exercise), has helped to clarify cognition/brain physiology-neuroanatomy, and assess cognitive status in dementia-risk conditions. Surveys of primary and tertiary care patients, and of population-based samples in multiple countries have provided information on prevalent and incident dementia, and cross-sectional and longitudinal norms for ages 35-100 years. CERAD-NAB has fulfilled its original mandate, while its uses have expanded, keeping up with advances in the area of dementia.

Quantitative EEG for early differential diagnosis of dementia with Lewy bodies.

Differentiating between the two most common forms of dementia, Alzheimer's dementia and dementia with Lewy bodies (DLB) remains difficult and requires the use of invasive, expensive, and resource-intensive techniques. We aimed to investigate the sensitivity and specificity of electroencephalography quantified using the statistical pattern recognition method (qEEG-SPR) for identifying dementia and DLB. Thirty-two outpatients and 16 controls underwent clinical assessment (by two blinded neurologists), EEG recording, and a 6-month follow-up clinical assessment. EEG data were processed using a qEEG-SPR protocol to derive a Dementia Index (positive or negative) and DLB index (positive or negative) for each participant which was compared against the diagnosis given at clinical assessment. Confusion matrices were used to calculate sensitivity, specificity, and predictive values for identifying dementia and DLB specifically. Clinical assessment identified 30 cases of dementia, 2 of which were diagnosed clinically with possible DLB, 14 with probable DLB and DLB was excluded in 14 patients. qEEG-SPR confirmed the dementia diagnosis in 26 out of the 32 patients and led to 6.3% of false positives (FP) and 9.4% of false negatives (FN). qEEG-SPR was used to provide a DLB diagnosis among patients who received a positive or inconclusive result of Dementia index and led to 13.6% of FP and 13.6% of FN. Confusion matrices indicated a sensitivity of 80%, a specificity of 89%, a positive predictive value of 92%, a negative predictive value of 72%, and an accuracy of 83% to diagnose dementia. The DLB index showed a sensitivity of 60%, a specificity of 90%, a positive predictive value of 75%, a negative predictive value of 81%, and an accuracy of 75%. Neuropsychological scores did not differ significantly between DLB and non- DLB patients. Head trauma or story of stroke were identified as possible causes of FP results for DLB diagnosis. qEEG-SPR is a sensitive and specific tool for diagnosing dementia and differentiating DLB from other forms of dementia in the initial state. This non-invasive, low-cost, and environmentally friendly method is a promising diagnostic tool for dementia diagnosis which could be implemented in local care settings.

A Pilot Study on Cerebral Blood Flow and Mini-Mental State Examination to Predict Amyloid Deposition in Preclinical Alzheimer's Disease.

Earlier differential diagnosis of dementia remains a major challenge. Although amyloid deposition by positron emission tomography is an emerging standard for the diagnosis of Alzheimer's disease, it is too expensive for routine use in clinical settings. We conducted a pilot study on the potential usefulness of single-photon emission computed tomography and the Mini-Mental State Examination to predict amyloid positron emission tomography positivity in preclinical Alzheimer's disease. Eighteen subjects, including 11 with mild cognitive impairment and 7 with subjective cognitive decline, underwent 18F-florbetapir positron emission tomography, 99mTc-ethylcysteinate dimer cerebral perfusion single-photon emission computed tomography, and the Mini-Mental State Examination. For the assessment of amyloid deposition, visual judgment as a qualitative method and a semiautomatic software analysis as a quantitative method were used. Six subjects were judged as amyloid positive, including 4 mild cognitive impairment and 2 subjective cognitive decline subjects. Compared to the amyloid positron emission tomography-negative group, this group showed a statistically significant difference in the Mini-Mental State Examination recall score [2 (1 : 3) vs. 3 (2 : 3), P = .041] and single-photon emission computed tomography findings from the amyloid-negative group. In the mild cognitive impairment subgroup, correlations were found between amyloid deposition and single-photon emission computed tomography indicators, while in the subjective cognitive decline subgroup, only the Mini-Mental State Examination recall score correlated with amyloid deposition. The Mini-Mental State Examination recall score and single-photon emission computed tomography indicators may be worthwhile for further evaluation as predictors of amyloid deposition in the preclinical stage.