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This systematic review of 57 studies examines the accuracy of brief cognitive tests (standalone tests; memory, executive function, and language tests; and brief multidomain batteries) for distingui...

This systematic review of 55 trials with low or medium risk of bias examines the benefits and harms of several prescription drugs and supplements for treatment of clinical Alzheimer-type dementia.

We examined whether symptoms of dementia are improved by olfactory nerve stimulation in Alzheimer type dementia patients. First, a stick‐type olfactory identification ability test was performed in patients with Alzheimer type dementia, to select patients without olfactory dysfunctions. Then, these patients were randomly assigned into the intervention (n = 19) and the control groups (n = 17). To evaluate the effects of olfactory nerve stimulation, we exposed the intervention group to a disinfecting ethanol with added aroma extracts from ceder and the control group to the ethanol without the added aroma extracts. Each group underwent the intervention for 8 weeks, cognitive and behavioral functions were evaluated before and after treatments using the Neuropsychiatric Inventory (NPI), the Japanese version of Zarit Caregiver Burden interview (J‐ZBI), and the Alzheimer's Disease Assessment Scale‐cognitive subscale (ADAS‐cog). A significant improvement was observed in the NPI score and J‐ZBI in the intervention group compared to the control group at 4 and 8 weeks. On the other hand, there was no significant difference in the score of ADAS‐cog. Exposure to cedar fragrance improved behavioral and psychological symptoms of dementia (BPSD) in Alzheimer type dementia and may reduce the burden of nursing care. In addition to its effectiveness, the procedure is simple and minimally invasive and would be a valuable non‐pharmaceutical treatment.

Objective: The aim of this study was to identify serum uric acid (UA) concentrations according to disease stage in patients with Alzheimer-type dementia (AD), and to collect data related to the hypothesis that oxidative stress is effective on the etiopathogenesis and progression of disease stage. Materials and Methods: The study was conducted with 180 patients who were categorized into disease stages in accordance with the Clinical Dementia Rating Scale (CDR) and 150 healthy individuals of the same age group. In our retrospective study, patients who showed more than one cognitive deficit and diagnosed with possible AD according to the diagnostic criteria of National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association were administered the CDR, Mini-Mental Test (MMSE), Geriatric Depression Rating Scale (GDRS), and individuals in the control group were administered the MMSE and GDRS. Venous blood samples for biochemical studies were collected after 12-14 hours of fasting in the patient and control groups. Results: In patients diagnosed as having AD, it was found that there was a statistically significant increase in age and duration of disease parallel with the progression of disease stages, and a decrease in UA and albumin concentrations in both males and females (p

Objectives:The present study explored relationships among personality, Alzheimer’s disease (AD) biomarkers, and dementia by addressing the following questions: (1) Does personality discriminate healthy aging and earliest detectable stage of AD? (2) Does personality predict conversion from healthy aging to early-stage AD? (3) Do AD biomarkers mediate any observed relationships between personality and dementia status/conversion?Methods:Both self- and informant ratings of personality were obtained in a large well-characterized longitudinal sample of cognitively normal older adults (N = 436) and individuals with early-stage dementia (N = 74). Biomarkers included amyloid imaging, hippocampal volume, cerebral spinal fluid (CSF) Aβ42, and CSF tau.Results:Higher neuroticism, lower conscientiousness, along with all four biomarkers strongly discriminated cognitively normal controls from early-stage AD individuals. The direct effects of neuroticism and conscientiousness were only mediated by hippocampal volume. Conscientiousness along with all biomarkers predicted conversion from healthy aging to early-stage AD; however, none of the biomarkers mediated the relationship between conscientiousness and conversion. Conscientiousness predicted conversion as strongly as the biomarkers, with the exception of hippocampal volume.Conclusions:Conscientiousness and to a lesser extent neuroticism serve as important independent behavioral markers for AD risk.

Background Caregiver burden in dementia is an important area of research. Providing care for a relative can be a potent source of chronic stress and can have deleterious consequences for both the physical and emotional health of caregivers. This study aims to evaluate the burden of care in caregivers of patients with Alzheimer-type dementia and compare it with elderly psychosis; and to also study the factors that influence burden of care in Alzheimer’s dementia. Methods Thirty-two caregiver-patient dyads of Alzheimer-type dementia were compared with thirty-two caregiver-patient dyads of psychosis. Cognitive assessment, abilities to perform activities of daily living and severity of dementia was assessed in the patients. Zarit Burden Interview was used to study the caregiver burden in both groups. Results The mean burden score in dementia caregivers was high at 47.7, whereas the mean burden score for elderly psychosis caregivers was lesser at 33.6, and this difference in mean burden scores was found to be statistically significant. Spouses had the highest mean burden scores of 53.48. Caregiver burden in dementia was positively correlated with cognitive impairment and inability to carry out ADLs. Presence of psychological distress in caregivers was also an indicator for greater caregiver burden in dementia. Conclusion The study revealed that dementia carries a greater caregiver burden when compared with elderly patients with psychosis. Innovative interventions are needed to remove burden from caregiving, making it a meaningful practice integral to the Indian society.

In this study, the aim was to identify the prevalence of sarcopenia and dynapenia according to disease stage among Alzheimer-type dementia (AD) patients and collect data to suggest precautions related to reducing the disease load.The study was completed with 127 patients separated into stages according to Clinical Dementia Rating Scale (CDR) criteria and 279 healthy volunteers aged 18-39 years and 70-80 years abiding by the exclusion criteria who agreed to participate in the research. Our prospective and cross-sectional study applied the CDR and mini mental test (MMSE) to patients with disorder in more than one cognitive area and possible AD diagnosis according to NINCDS-ADRDA (National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association) diagnostic criteria. The patient and control groups had skeletal muscle mass index (SMMI), muscle strength and physical performance assessed with sarcopenia diagnosis according to European Working Group on Sarcopenia in Older People (EWGSOP) diagnostic criteria.In our study, in parallel with the increase in disease stage of AD patients, the prevalence of sarcopenia (led by severe sarcopenia) and dynapenia was higher compared to a control group of similar age.In chronic, progressive diseases, like AD, identification of changes in parameters, like muscle mass and strength and reductions in physical performance in the early period, is important for identification and to take precautions in the initial stages considering the limitations of the preventive effects of treatment applied after diagnosis of AD.A vizsgálat célja a sarcopenia és a dynapenia prevalenciájának megállapítása a különböző stádiumú Alzheimer-típusú dementiában (AD) szenvedő betegek körében, továbbá adatgyűjtés annak érdekében, hogy javaslatokat tehessünk a betegségteher csökkentése érdekében.A prospektív és keresztmetszeti vizsgálatban 127, különböző stádiumú AD-beteg és 279, a beválasztási kritériumoknak megfelelő, egészséges kontrollszemély vett részt; a stádiumbeosztás a Clinical Dementia Rating Scale/CDR alapján történt, a kontrollszemélyek 18-39 és 70-80 év közöttiek voltak. A beválasztott AD-betegek a NINCDS-ADRDA (National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer’s Disease and Related Disorders Association) diagnosztikai kritériumai alapján nagy valószínűséggel minősültek AD-betegnek, továbbá a mini-mentál teszt (MMSE) szerint több mint egy kognitív területen kórosan teljesítettek. Mértük a betegek és kontrollok vázizomtömeg-indexét (skeletal muscle mass index/SMMI), izomerejét, fizikai teljesítőképességét; a sarcopenia diagnózisát a European Working Group on Sarcopenia in Older People/EWGSOP kritériumai alapján állapítottuk meg.A korban illesztett, egészséges kontrollszemélyekkel összehasonlítva, az AD stádiumának emelkedésével párhuzamosan nőtt a sarcopenia és a dynapenia prevalenciája.A krónikus, progresszív betegségek esetén, amilyen az AD is, fontos a különböző élettani paraméterek - például izomtömeg, izomerő, fizikai teljesítőképesség - romlásának korai stádiumban való azonosítása, és a szekunder prevenció mielőbbi elkezdése.

Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). On the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively. This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders. The present study was conducted as part of the EMIF-AD project, which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies’ in-kind contribution. The DESCRIPA study was funded by the European Commission within the fifth framework program (QLRT-2001-2455). The EDAR study was funded by the European Commission within the fifth framework program (contract no. 37670). The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government (allocation 17.0.1.08.12.0000.2.454.01. 41142.001.H). Kristel Sleegers is supported by the Research Fund of the University of Antwerp. Daniel Stamate is supported by the Alzheimer’s Research UK (ARUK-PRRF2017-012).

This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The first proposed methodology is based on the selection of those voxels which present a greater difference between Controls and Alzheimer type dementia patients. The mean value of the intensities of the selected voxels is used as features for different classifiers. The proposed methodology reaches an accuracy of 89% in the classification task. A second criterion is chosen to select voxels based on considering those which present not only greater overall difference between both modalities (Controls and Alzheimer) but also present lower dispersion. The classification accuracy using this second condition increases to 94%.

We intend to construct an image diagnosis support system for Alzheimer-type Dementia (ATD) that extracts temporal lobe regions and an intracranial region as interest regions from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the regions of interest. In this paper, we specifically discuss extraction of regions of interest. The proposed method consists of three steps. First, we emphasis features of an obscure T2-weighted brain image. Second, we set a first contour that approximates a shape of the temporal lobe region to a triangle and apply Balloon models with the added presser force that push the initial contour outside in order to extract a temporal lobe region. Finally, we extract an intracranial region using extracted temporal lobe regions. Our proposed method can extract regions of interest along individual brain features by only a few interactions with three points. We demonstrate the potential of our method using actual diagnosis images. Moreover, we show a possibility to use of atrophy rate at the regions of interest for diagnosis support of ATD.

Eleven patients with Alzheimer type dementia were investigated by single-photon-emission computed tomography (SPECT) using 99mTc-hexamethylpropylene-aminoxim (HM-PAO). Nine had a typical, symmetrically reduced enrichment in the parieto-temporo-occipital area. In three of these nine patients a similar pattern was recorded additionally in the frontal, in the remaining two in the temporal or parietal region. In a semi-quantitative evaluation radioactivity concentrations in the cerebrum and cerebellum were compared. It demonstrated that there was in relation to the cerebellum a reduced concentration in seven of the eight studied cerebral regions. The relative blood flow of the parieto-temporo-occipital region was most markedly affected and in patients with Alzheimer type dementia was reduced by 25% below that of normal subjects. The results indicate that cerebral SPECT with HM-PAO is a reasonable method of investigation to confirm the diagnosis of "Alzheimer type dementia" and exclude other cerebral disease, especially if pathognomonic findings are to be obtained.

It is well known that temporal lobe in MR brain image is in use for estimating the grade of Alzheimer-type dementia. It is difficult to use only region of temporal lobe for estimating the grade of Alzheimer-type dementia. From the standpoint for supporting the medical specialists, this paper proposes a data processing approach on the automatic extraction of the intracranial region from the MR brain image. The method is able to eliminate the cranium region with the laplacian histogram method and the brainstem with the feature points which are related to the observations given by a medical specialist.In order to examine the usefulness of the proposed approach, the percentage of the temporal lobe in the intracranial region was calculated. As a result, the percentage of temporal lobe in the intracranial region on the process of the grade was in agreement with the visual sense standards of temporal lobe atrophy given by the medical specialist. It became clear that intracranial region extracted by the proposed method was good for estimating the grade of Alzheimer-type dementia.

A system for evaluating dementia of the Alzheimer type (DAT) based on magnetic resonance (MR) imaging by means of fuzzy neural networks (FNNs) was investigated. The T1-weighted head MR transverse section images were obtained by a routinely performed examination. Nine slices including the thalamus were analyzed for each subject. Each MR image (MRI) was divided into four parts. The ratio of the brain area to the intracranial area was defined as the atrophy ratio. DAT severity was assessed by the Mini-Mental State (MMS) examination administered to each patient, and the results were used as teaching values for the FNN models. To construct the FNN model with high accuracy, MRI-based input variables were examined. Using atrophy ratios of 9 MRIs based on thalamically fiducial images and the corresponding areal or volumetric data as input variables, highly accurate FNN models were constructed that gave an average error of 1.29 points out of 30 in the MMS scores.

The occurrence of mania in the geriatric population is rare. Furthermore, there were only six case reports of elderly patients with secondary mania resulting from treatment with cholinesterase inhibitors. In all cases, patients had a prior psychiatric history. We report the case of an elderly patient with no prior history of psychiatric or other organic disorders who experienced first episode mania following treatment with rivastigmine. We discuss the possible mechanism of mania in this patient.

To examine the possibility of diagnosing Alzheimer-type dementia, we studied this condition using the run length matrix, on head MR images of 29 Alzheimer-type dementia patients (8 men, 21 women, 78.7 +/- 6.7 years) and healthy elderly controls (10 men, 19 women, 72.3 +/- 8.7 years) . The results showed that differences in GLN (gray level nonuniformity) and RLN (run length nonuniformity) were statistically significant. Furthermore, discriminant analysis based on GLN and RLN showed a rate of sensitivity of 69.0%, specificity 86.2%, and correct classification 77.6%. Although this rate of correct classification is inferior to the planimetric and volumetric methods, run length matrix is only one method of texture analysis. The results of this study indicate the possibility of MR imaging-based diagnosis of Alzheimer-type dementia with texture analysis including a run length matrix.

Light reflex and oculogyration analysing system is proposed in order to diagnose the Alzheimer type dementia (DAT) objectively. 19 patients are studied, which shows that the maximum miosis ratio and miosis velocity are sensitive parameters to evaluate the severity of dementia and the changing time of internal and external rectus eye muscles is the effective index for screening of the dementia. The threshold of the changing time over 0.35 s can discriminate DAT clearly from the normal. Biofeedback trainings of the demented are executed by the objective diagnosing method, which shows improvement in MMSE, ADL score and the eye reflex parameters. The proposed method may become a safe non-pharmacological treatment of the dementia.

Summary In patients with different forms of dementia we compared serum and cerebrospinal fluid concentrations of α-tocopherol (vitamin-E), the major circulating lipid-soluble antioxidant, and neopterin, a product released from monocytic cells upon stimulation with interferon-γ. A significant inverse correlation was found between the concentrations of both compounds in the cerebrospinal fluid. The data point to a role of oxidative stress to reduce antioxidant levels in elderly demented patients with neurodegenerative diseases.

In a previous article, recent reports by Japanese researchers on non-Alzheimer-type degenerative dementias were reviewed. In the present article, recent Japanese reports on Alzheimer-type dementia (ATD) are reviewed. Alzheimer-type dementia has received great attention and has been studied from various viewpoints in Japan as well as in Europe and the Americas. In Japan, although it was believed that vascular dementia was the most frequent dementia in the elderly, ATD has recently been shown to be the most predominant type of dementia. Such a great number of papers on ATD have been reported in Japan that mainly the clinical, neuropathological, biochemical and molecular biological research papers alone are reviewed here.

The effects of chronic combined treatment with alpha7-nicotinic cholinergic receptor agonist RJR-2403 (1.0 mg/kg intraperitoneally) or alpha7-nicotinic cholinergic receptor antagonist mecamylamine (1.0 mg/kg intraperitoneally) and 17beta-estradiol (0.5 microg per rat intramuscularly) for 10 days on passive avoidance retention were studied in middle-aged (15 months) ovariectomized rats with experimental Alzheimer type dementia. Chronic treatment with RJR-2403 and 17beta-estradiol had a pronounced antiamnestic effect under conditions of Alzheimer type dementia in middle-aged ovariectomized rats.

Die Magnetresonanztomographie (MRT) des Gehirns kommt als strukturelles bildgebendes Verfahren in der Psychiatrie und Neurologie zur breiten Anwendung. Bei der Demenz vom Alzheimer-Typ (AD) tragt die MRT zur Verbesserung der klinischen Diagnosestellung im Rahmen der Mehrebenendiagnostik [1] und zur differentialdiagnostischen Abgrenzung anderer mit einem dementiellen Syndrom einhergehender Erkrankungen bei. Neu entwickelte Verfahren der MRT-gestutzten Volumetrie kortikaler und subkortikaler Strukturen erwiesen sich als nutzlich in der Differenzierung zwischen kognitiv gesunden alteren Probanden und AD-Patienten. Daruber hinaus versprechen sie neue Einsichten in den zeitlichen und strukturellen Verlauf der zerebralen Degeneration. Zusatzlich kann die systematische quantitative Erfassung von Signalveranderungen bei AD-Patienten zur Verbesserung der diagnostischen Genauigkeit beitragen. Es ist die Absicht dieser Arbeit, den Beitrag der MRT zur Diagnostik der AD und zum Verstandnis ihrer Pathogenese darzustellen, aber auch die Grenzen dieser Methodik aufzuzeigen. Dabei werden die Veranderungen des gesunden Alterungsprozesses miteinbezogen, die mit Hilfe der MRT-Bildgebung fasbar sind. Zukunftige Perspektiven liegen insbesondere in der Weiterentwicklung der mikroskopischen MRT, der genauen Differenzierung und Klassifikation von Signalveranderungen der weisen Substanz und im kombinierten Einsatz der MRT mit funktionellen bildgebenden Methoden.

The concentrations of pancreastatin-like immunoreactivity (PST-LI) of the cerebrospinal fluid (CSF) were measured in the patients with Alzheimer type dementia (ATD) and in age-matched normal subjects. The mean PST-LI concentration in the CSF of ATD patients was significantly lower than that of normal subjects. Gel chromatographic analysis revealed that the main PST-LI peak of ATD's CSF eluted at molecular weight (MW) 13.5 kDa. However, the age-related change of the molecular forms of PST-LI in CSF was observed in normal subjects as following; PST-LI in neonatal CSF showed one peak at MW 13.5 kDa, that of 16-64-year-old showed two peaks at MW 13.5 and 5.4 kDa, however, only one main peak was shown at MW 5.4 kDa in the CSFs of 72-85-year-old. These findings suggest that the production of PST-LI was decreased and the proteolytic cleavage, which should process big PST to PST (1-52) in normal subjects, was altered to that of neonatal type in the CNS of the patients with ATD.

We proposed a segmentation method of brain tissues on T2-weighted MR frontal image. In our previous work, we showed an image diagnosis support system for Alzheimer-type Dementia that extracts temporal lobe regions and an intracranial region as regions of interest (ROI) from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the ROI. In this paper, we specifically discuss segmentation of brain tissues which are used for calculation of atrophy rate. We proposed a brain tissue segmentation method using two kinds of unsupervised neural networks: Self-Organizing Maps (SOMs) and Fuzzy Adaptive Resonance Theory (ART). The performance of proposed method was tested in two brain MR images used in daily diagnosis. Proposed method could segment CSF accurately with continuity of brain tissues.

Amyloid deposits (AmD) and neurofibrillary tangles (NFT) were quantitatively examined in methenamine-silver stained hemisphere sections from 98 non-demented elderly individuals and 30 patients with Alzheimer-type dementia (ATD). Of the non-demented cases, 35 (35.7%) had some AmD, and 72 (73.5%) had some NFT, whereas all the ATD cases had both AmD and NFT. AmD-and NFT-positive cases were classified into developmental stages, based on the distribution and incidence of each of these senile changes (AmD: A1, A2, B1, B2, C′ and C; NFT: Ia, Ib, IIa, IIb, III, and IV). When the AmD-positive cases were divided into stages A-B and stages C′-C and the NFT-positive cases into stages I-III and stage IV, all the non-demented individuals were found to belong to NFT stages I-III and all the ATD cases to AmD stages C′-C. This study provides evidence that an approximate dividing line can be drawn between non-demented individuals and ATD patients by using quantitative neuropathological criteria, and that the distinction between the two groups can usually be made on the basis of the difference in the incidence of AmD in the precentral motor area and the incidence of NFT in the temporal isocortex.

We constructed an image diagnosis support system for Alzheimer-type Dementia that extracts temporal lobe regions and an intracranial region as regions of interest (ROI) from a T2-weighted MR frontal image and uses the cerebral atrophy rates at the ROI. In this paper, we specifically discuss extraction of brain regions and diagnosis support information based on atrophy speeds of the ROI. The proposed method comprises of three steps. Step 1: A first contour that approximates a shape of the temporal lobe region to a triangle is set and Balloon model is applied. Step 2: A temporal lobe AAM is constructed and it is applied to the result obtained in Step 1. Step 3: Intracranial region is extracted using temporal lobe regions detected in Step 2. We demonstrate the potential of our method using actual diagnosis images. Additionally, we calculate atrophy rates of the ROI to consider a possibility for diagnosis of ATD using atrophy progress speeds of the ROI.

Objectives: To write a new macro programme for the use of the non-parametric 3-D Receiver Operating Characteristic (ROC) surface method in the discrimination of three or more groups, to study the usefulness of electroencephalography (EEG) frequency in the diagnosis of Alzheimer type dementia (AD), non-Alzheimer type dementia (NAD) and healthy subjects, and to determine cut-off values of EEG frequency. Patients and Methods: The mean EEG frequencies in the left fontal region EEG records of subjects in the present study were calculated and the ROC surface method was used in discrimination of the groups. Results: The volume under the ROC surface was calculated as 0.464±0.150. Accuracy of the mean frequency in differentiation of groups was statistically significant (p=0.024). 8 Hz was found to be convenient for differentiation of AD from NAD patients and 10 Hz for the differentiation of NAD patients and healthy subjects. The sensitivity was 87.5% for Alzheimer's dementia diagnosis and total accuracy was 65% using this cut-off pair. Conclusion: We suggest that the mean EEG frequency may be used only as a pre-diagnostic tool for the differential diagnosis of these groups. Amaç: İlk olarak en az üç grubun ayırıcı tanısını yapmak için kullanılabilecek parametrik olmayan ROC yüzeyi metodunun yaygınlaşmasını sağlamak için yeni bir makro programı yazmayı; ikinci olarak bu metotla Alzheimer tipi demans (AD), non-Alzheimer tipi demans (NAD) ve sağlıklı bireylerin ayırıcı tanısında ortalama EEG frekansının kullanılabilirliğini araştırmayı ve üçüncü olarak ortalama EEG frekansına ait cut-off değerleri belirlemeyi amaçladık. Hastalar ve Yöntemler: Çalışmadaki bireylerin sol frontal bölgesinden kaydedilen EEG örneklerinin ortalama frekansı hesaplanmış ve ROC yüzeyi metodu ile grupların ayırımı planlanmıştır. Bulgular: Ortalama EEG frekansına göre ROC yüzeyi altındaki hacim 0.464, bootstrap standart sapması ise 0.150 olarak hesaplanmıştır. Grupları ayırmada ortalama frekansın başarısı istatistiksel olarak anlamlı bulunmuştur (Z=1.99 ve p=0.024). Muhtemel bütün değerler arasından en uygun cutoff değerleri sensitivite ve spesifite değerlerine göre belirlenmiştir. Alzheimer tipi demans hastalarını bireylerinden en iyi ayıran değer 8 Hz iken NAD bireylerini sağlıklı bireylerden en iyi ayıran değer 10 Hz olarak belirlenmiştir. Bu cut-off çiftinin kullanılması ile AD tanısı için sensitivite %87.5 ve toplam doğru tanı başarısı %65 olarak bulunmuştur. Sonuç: Bulgularımıza dayanarak ortalama EEG frekansının AD diğer demanslılar ve sağlıklı bireylerden ayırmada bir ön tanı aracı olarak kullanılabileceğini önerebiliriz., Amaç: İlk olarak en az üç grubun ayırıcı tanısını yapmak için kullanılabilecek parametrik olmayan ROC yüzeyi metodunun yaygınlaşmasını sağlamak için yeni bir makro programı yazmayı; ikinci olarak bu metotla Alzheimer tipi demans (AD), non-Alzheimer tipi demans (NAD) ve sağlıklı bireylerin ayırıcı tanısında ortalama EEG frekansının kullanılabilirliğini araştırmayı ve üçüncü olarak ortalama EEG frekansına ait cut-off değerleri belirlemeyi amaçladık. Hastalar ve Yöntemler: Çalışmadaki bireylerin sol frontal bölgesinden kaydedilen EEG örneklerinin ortalama frekansı hesaplanmış ve ROC yüzeyi metodu ile grupların ayırımı planlanmıştır. Bulgular: Ortalama EEG frekansına göre ROC yüzeyi altındaki hacim 0.464, bootstrap standart sapması ise 0.150 olarak hesaplanmıştır. Grupları ayırmada ortalama frekansın başarısı istatistiksel olarak anlamlı bulunmuştur (Z=1.99 ve p=0.024). Muhtemel bütün değerler arasından en uygun cutoff değerleri sensitivite ve spesifite değerlerine göre belirlenmiştir. Alzheimer tipi demans hastalarını bireylerinden en iyi ayıran değer 8 Hz iken NAD bireylerini sağlıklı bireylerden en iyi ayıran değer 10 Hz olarak belirlenmiştir. Bu cut-off çiftinin kullanılması ile AD tanısı için sensitivite %87.5 ve toplam doğru tanı başarısı %65 olarak bulunmuştur. Sonuç: Bulgularımıza dayanarak ortalama EEG frekansının AD diğer demanslılar ve sağlıklı bireylerden ayırmada bir ön tanı aracı olarak kullanılabileceğini önerebiliriz.

アルツハイマー型痴呆により意欲や自発性が低下し, 筋廃用性萎縮が進み, 寝たきりとなった一例に対し, 塩酸ドネペジルを内服させながらリハビリテーションを行ったところ痴呆レベル及び日常生活動作の著しい改善を認めた.精神活動性が低下した症例はリハビリテーションの動機づけが困難であるが, 塩酸ドネペジルとリハビリテーションの組み合わせは痴呆と日常生活動作の改善に有効であると考えられた.

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. However, currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician's diagnosis. However, conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on feature selection and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach attaining a 90% AD diagnosis accuracy.

This paper presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of the Alzheimer type dementia. The proposed methodology is based on the selection of those voxels which present a greater difference between normals and Alzheimer's type dementia patients. The mean value of the intensities of the selected voxels are used as features for different classifiers. The proposed methodology reaches an accuracy of 89% in the classification task.

An oculogyration analyzing system is proposed in order to diagnose Alzheimer type dementia (DAT) objectively. 19 patients are studied, which shows that the changing time of internal and external rectus eye muscles is the most effective index for screening of the demented people. The threshold of the changing time to discriminate DAT is considered to be 0.35 second.

Functional brain imaging with PET (Positron Emission Tomography) and SPECT (Single Photon Emission Computed Tomography) has a definitive and well established role in the investigation of a variety of conditions such as Alzheimer’s Disease (AD). Nowadays the inspection of PET and SPECT images is performed by expert clinicians, but usually entails time consuming and subjective steps. This work aims at providing an automatic tool to assist the interpretation of SPECT and PET images for the diagnosis of AD. The main problem to be handled is the so-called small size sample, which consists in having a small number of available images compared to the large number of features. This problem is faced up by reducing intensively the dimension of the feature space by means of Principal Component Analysis (PCA). Our approach is based on bayesian classifiers, which uses the a posteriori information to determine to which class the subject belongs, yielding 88.6% and 98.3% accuracy for SPECT and PET images respectively.

The early detection of subjects with probable Alzheimer Type Dementia (ATD) is crucial for effective appliance of treatment strategies. Functional brain imaging including SPECT (Single-Photon Emission Computed Tomography) and PET (Positron Emission Tomography) are commonly used to guide the clinician's diagnosis. Nowadays, no automatic tool has been developed to aid the experts to diagnose the ATD. Instead, conventional evaluation of these scans often relies on subjective, time consuming and prone to error steps. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the ATD. The proposed approach is based on the majority voting cast by an ensemble of Support Vector Machine (SVM) classifiers, trained on a component-based feature extraction technique which searches the most discriminant regions over the brain volume.

Alzheimer disease (AD) is a progressive neurodegenerative disorder first affecting memory functions and then gradually affecting all cognitive functions with behavioral impairments. As the number of patients with AD has increased, early diagnosis has received more attention for both social and medical reasons. Functional brain imaging including single-photon emission computed tomography (SPECT) is commonly used to guide the clinician's diagnosis. Conventional evaluation of SPECT scans often relies on manual reorientation, visual reading and semiquantitative analysis of certain regions of the brain. These steps are time consuming, subjective and prone to error. Currently, accuracy in the early diagnosis of certain neurodegenerative diseases such as the Alzheimer type dementia is below 70% and, frequently, these do not receive the suitable treatment. This paper shows a fully automatic computer-aided diagnosis (CAD) system for improving the accuracy in the early diagnosis of the AD. The proposed approach is based on a feature extraction process based on the normalized mean square error (NMSE) features of several coronal slices of interest (SOI) and support vector machine (SVM) classification. The proposed system yields clear improvements over existing techniques such as the voxel as features (VAF) approach yielding a 97.5% AD diagnosis accuracy.