Distinctive neuropsychological profiles differentiate patients with functional memory disorder from patients with amnestic-mild cognitive impairment

OBJECTIVES: Patients with functional memory disorder (FMD) report significant memory failures in everyday life. Differentiating these patients from those with memory difficulties due to early stage neurodegenerative conditions is clinically challenging. The current study explored whether distinctive neuropsychological profiles could be established, suitable to differentiate patients with FMD from healthy individuals and those experiencing amnestic mild cognitive impairment (a-MCI). METHODS: Patients with a clinical diagnosis of FMD were compared with patients with a-MCI, and healthy matched controls on several tests assessing different cognitive functions. Patients with clinically established mood disorders were excluded. Patients with FMD and a-MCI were broadly comparable on the level of their subjective memory complaints as assessed by clinical interview. RESULTS: The neuropsychological profile of the FMD patients, although they expressed subjective memory and attention concerns during their clinical interview was distinct from patients with a-MCI on tests of memory [semantic fluency, age of acquisition (AoA) analysis of semantic fluency, verbal and non-verbal memory]. FMD patients did not differ significantly from healthy controls, but their scores on the letter fluency and digit cancellation tasks were not significantly different from those of the a-MCI patients indicating a possible sub-threshold deficit on these tasks. CONCLUSION: Whilst subjective complaints are common within the FMD population, no objective impairment could be detected, even on a sensitive battery of tasks designed to detect subtle deficits caused by an early neurodegenerative brain disease. This study indicates that FMD patients can be successfully differentiated from patients with neurodegenerative memory decline by characterising their neuropsychological profile

Why We Read Wikipedia

Wikipedia is one of the most popular sites on the Web, with millions of users relying on it to satisfy a broad range of information needs every day. Although it is crucial to understand what exactly these needs are in order to be able to meet them, little is currently known about why users visit Wikipedia. The goal of this paper is to fill this gap by combining a survey of Wikipedia readers with a log-based analysis of user activity. Based on an initial series of user surveys, we build a taxonomy of Wikipedia use cases along several dimensions, capturing users' motivations to visit Wikipedia, the depth of knowledge they are seeking, and their knowledge of the topic of interest prior to visiting Wikipedia. Then, we quantify the prevalence of these use cases via a large-scale user survey conducted on live Wikipedia with almost 30,000 responses. Our analyses highlight the variety of factors driving users to Wikipedia, such as current events, media coverage of a topic, personal curiosity, work or school assignments, or boredom. Finally, we match survey responses to the respondents' digital traces in Wikipedia's server logs, enabling the discovery of behavioral patterns associated with specific use cases. For instance, we observe long and fast-paced page sequences across topics for users who are bored or exploring randomly, whereas those using Wikipedia for work or school spend more time on individual articles focused on topics such as science. Our findings advance our understanding of reader motivations and behavior on Wikipedia and can have implications for developers aiming to improve Wikipedia's user experience, editors striving to cater to their readers' needs, third-party services (such as search engines) providing access to Wikipedia content, and researchers aiming to build tools such as recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table

An avatar-based system for identifying individuals likely to develop dementia

This paper presents work on developing an automatic dementia screening test based on patients’ ability to interact and communicate — a highly cognitively demanding process where early signs of dementia can often be detected. Such a test would help general practitioners, with no specialist knowledge, make better diagnostic decisions as current tests lack specificity and sensitivity. We investigate the feasibility of basing the test on conversations between a ‘talking head’ (avatar) and a patient and we present a system for analysing such conversations for signs of dementia in the patient’s speech and language. Previously we proposed a semi-automatic system that transcribed conversations between patients and neurologists and extracted conversation analysis style features in order to differentiate between patients with progressive neurodegenerative dementia (ND) and functional memory disorders (FMD). Determining who talks when in the conversations was performed manually. In this study, we investigate a fully automatic system including speaker diarisation, and the use of additional acoustic and lexical features. Initial results from a pilot study are presented which shows that the avatar conversations can successfully classify ND/FMD with around 91% accuracy, which is in line with previous results for conversations that were led by a neurologist

Toward the Automation of Diagnostic Conversation Analysis in Patients with Memory Complaints.

BACKGROUND: The early diagnosis of dementia is of great clinical and social importance. A recent study using the qualitative methodology of conversation analysis (CA) demonstrated that language and communication problems are evident during interactions between patients and neurologists, and that interactional observations can be used to differentiate between cognitive difficulties due to neurodegenerative disorders (ND) or functional memory disorders (FMD). OBJECTIVE: This study explores whether the differential diagnostic analysis of doctor-patient interactions in a memory clinic can be automated. METHODS: Verbatim transcripts of conversations between neurologists and patients initially presenting with memory problems to a specialist clinic were produced manually (15 with FMD, and 15 with ND). A range of automatically detectable features focusing on acoustic, lexical, semantic, and visual information contained in the transcripts were defined aiming to replicate the diagnostic qualitative observations. The features were used to train a set of five machine learning classifiers to distinguish between ND and FMD. RESULTS: The mean rate of correct classification between ND and FMD was 93% ranging from 97% by the Perceptron classifier to 90% by the Random Forest classifier.Using only the ten best features, the mean correct classification score increased to 95%. CONCLUSION: This pilot study provides proof-of-principle that a machine learning approach to analyzing transcripts of interactions between neurologists and patients describing memory problems can distinguish people with neurodegenerative dementia from people with FMD

Memory difficulties are not always a sign of incipient dementia: a review of the possible causes of loss of memory efficiency

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Towards diagnostic conversational profiles of patients presenting with dementia or functional memory disorders to memory clinics

Objective: This study explores whether the profile of patients' interactional behaviour in memory clinic conversations with a doctor can contribute to the clinical differentiation between functional memory disorders (FMD) and memory problems related to neurodegenerative diseases. Methods: Conversation Analysis of video recordings of neurologists' interactions with patients attending a specialist memory clinic. "Gold standard" diagnoses were made independently of CA findings by a multi-disciplinary team based on clinical assessment, neuropsychological testing and brain imaging. Results: Two discrete conversational profiles for patients with memory complaints emerged, including (i) who attends the clinic (i.e., whether or not patients are accompanied), and (ii) patients' responses to neurologists' questions about memory problems, such as difficulties with compound questions and providing specific and elaborated examples and frequent "I don't know" responses. Conclusion: Specific communicative difficulties are characteristic of the interaction patterns of patients with a neurodegenerative pathology. Those difficulties are manifest in memory clinic interactions with neurologists, thereby helping to differentiate patients with dementia from those with FMD. Practical implications: Our findings demonstrate that conversational profiles based on patients' contributions to memory clinic encounters have diagnostic potential to assist the screening and referral process from primary care, and the diagnostic service in secondary care

A new technique for elucidating β\beta-decay schemes which involve daughter nuclei with very low energy excited states

A new technique of elucidating $\beta$-decay schemes of isotopes with large density of states at low excitation energies has been developed, in which a Broad Energy Germanium (BEGe) detector is used in conjunction with coaxial hyper-pure germanium detectors. The power of this technique has been demonstrated on the example of 183Hg decay. Mass-separated samples of 183Hg were produced by a deposition of the low-energy radioactive-ion beam delivered by the ISOLDE facility at CERN. The excellent energy resolution of the BEGe detector allowed $\gamma$ rays energies to be determined with a precision of a few tens of electronvolts, which was sufficient for the analysis of the Rydberg-Ritz combinations in the level scheme. The timestamped structure of the data was used for unambiguous separation of $\gamma$ rays arising from the decay of 183Hg from those due to the daughter decays

Band offsets of metal oxide contacts on TlBr radiation detectors

Metal oxides are investigated as an alternative to metal contacts on thallium bromide (TlBr) radiation detectors. X-ray photoelectron spectroscopy studies of SnO 2/TlBr and ITO/TlBr devices indicate that a type-II staggered heterojunction forms between TlBr and metal oxides upon contacting. By using the Kraut method of valence band offset (VBO) determination, the VBOs of SnO 2/TlBr and ITO/TlBr heterojunctions are determined to be 1.05 ± 0.17 and 0.70 ± 0.17 eV, respectively. The corresponding conduction band offsets are then found to be 0.13 ± 0.17 and 0.45 ± 0.17 eV, respectively. The I-V response of symmetric In/SnO 2/TlBr and In/ITO/TlBr planar devices is almost Ohmic with a leakage current of less than 2.5 nA at 100 V