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Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.

Primary progressive aphasia (PPA) is a group of neurodegenerative disorders featuring primary-language impairments and typically classified into three-variants: semantic, non-fluent and logopenic. Yet their language profiles significantly overlap. Language deficits can also occur in other frontotemporal dementias (FTD) like progressive supranuclear palsy and corticobasal syndrome, suggesting that language symptoms are transdiagnostic. Though brain atrophy determines language difficulties, similar atrophy profiles often are related to different language deficits, and different atrophies can cause similar impairments (e.g., logopenic/non-fluent PPA). We apply a transdiagnotic approach to link the language variantions across FTD-PPA spectrum to their underlying neuroanatomical factors. Principal component analysis identified three language dimensions (motor-speech/phonology, semantics and syntax) and thirteen atrophic dimensions, including temporal, frontal, parietal regions. While anterior temporal lobe atrophy predicts semantic impairments, frontoparietal atrophies predict motor-speech/phonology, and syntax. Atrophy and language variations across FTD-PPA syndromes are deconstructed on a brain-behaviour continuum, with only semantic variants being clearly separate.

This paper explores the potential of machine learning for recognizing and analysing linguistic markers of hubris in CEO speech. This research is based on three assumptions: hubris is associated with potentially destructive leader behaviours; linguistic utterances are a way of distinguishing between leaders who are likely to exhibit such behaviours; identifying hubris at‐a‐distance using machine learning techniques provides a reliable, automated and scalable method for the identification and prevention of destructive outcomes emanating from CEO hubris. Using machine learning techniques, we analysed spoken utterances from a sample of hubristic CEOs and compared them with non‐hubristic CEOs. We found that machine learning algorithms have the ability to identify automatically hubristic versus non‐hubristic speech patterns. One of the main implications of this study is building a foundation for future studies that are interested in the application of machine learning in the fields of hubristic and other forms of destructive leadership, and in the study of the role that language plays in management and organizations more generally. We discuss the implications of automated data extraction and analysis for the prediction of CEOs’, and other employees’, category membership, intentions and behaviours. We offer recommendations for how hubristic and destructive leadership in organizations can be managed and curtailed more effectively, thereby obviating their negative consequences.

Conté: Appendix A. Supplementary data Linguistic measures in spontaneous speech have shown promise in the early detection of Alzheimer’s disease (AD), but it remains unknown which specific linguistic variables show sensitivity and how language decline relates to primary memory deficits. We hypothesized that a set of finegrained linguistic variables relating specifically to forms of syntactic complexity involved in referencing objects and events as part of episodes would show sensitivity. We tested this in speech samples obtained from a picture description task, maximally isolating language deficits from the confound of episodic memory (EM) demands. 105 participants were split into Mild Cognitive Impairment (MCI), Mild-to-Moderate AD, and healthy controls (HC). Results showed that groups did not differ on generic linguistic variables such as number or length of utterances. However, AD relative to HC produced fewer embedded adjunct clauses, indefinite noun phrases, and Aspect marking, with moderate-to-large effect sizes. MCI compared to HC produced fewer adjunct clauses as well as fewer adverbial adjuncts. Together, these results confirm language impairment in AD and MCI at the level of specific linguistic variables relating to structures required for endowing narrative with specificity and episodic richness, independently of EM demands. This work was supported by grants MR/N013638/1 (St. Georges University of London, United Kingdom), FI-DGR, and SGR-1265 (both Generalitat de Catalunya), and by PID 2019-105241 GB-I00/AEI/10.13039/501100011033 financed by the Ministerio de Ciencia, Innovacion y Universidades (MCIU) and by the Agencia Estatal de Investigacion (AEI).

Introduction: Genome-wide association studies (GWAS) in late onset Alzheimer's disease (LOAD) provide lists of individual genetic determinants. However, GWAS do not capture the synergistic effects among multiple genetic variants and lack good specificity. Methods: We applied tree-based machine learning algorithms (MLs) to discriminate LOAD (>700 individuals) and age-matched unaffected subjects in UK Biobank with single nucleotide variants (SNVs) from Alzheimer's disease (AD) studies, obtaining specific genomic profiles with the prioritized SNVs. Results: MLs prioritized a set of SNVs located in genes PVRL2, TOMM40, APOE, and APOC1, also influencing gene expression and splicing. The genomic profiles in this region showed interaction patterns involving rs405509 and rs1160985, also present in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. rs405509 located in APOE promoter interacts with rs429358 among others, seemingly neutralizing their predisposing effect. Discussion: Our approach efficiently discriminates LOAD from controls, capturing genomic profiles defined by interactions among SNVs in a hot-spot region. Funding: the research leading to these results was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 727658 (project iASiS), European Research Council ASTRA 855923, and the European Genome‐phenome Archive (EGA). Claudia Giambartolomei has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska‐Curie grant agreement No 754490–MINDED project. This research has been conducted using the UK Biobank Resource under Application Number 35916

BackgroundIn European memory clinics, requests for assessing people with cognitive complaints in a foreign language are becoming frequent. Currently, over one million people living in EU are older migrants presenting with possible cognitive deterioration. The assessments of cognitive functions is the gateway to diagnosis, and the main tool for monitoring progression of neurocognitive disorders. However, the limitations of older migrants in the language of the host country add hurdles to the diagnostic process. This represents an emerging problem for the EU healthcare systems. In several EU countries, the current best approach is to involve a translator, who assists the healthcare personnel with the assessment. This practice is far from perfect.MethodsWe convened a multidisciplinary and international team, including experts in neurocognitive disorders and in digital tools, to develop a user-friendly process aimed at connecting clinicians dealing with the diagnosis of people affected by cognitive deficits in different languages or countries. The initial network included countries with great prevalence of immigrants (DE, F, I, UK) and referents from partner specialists from the main immigrant nationalities. This network is planned to expand.ResultsThis preliminary network convened on a reference procedure to be supported with digital tools. The procedure includes referral of migrants with cognitive complaints to a colleague from the established network, for remote assessment in their mother-tongue using tools appropriate for their cultural background. The outcome of the referral is sent to the clinicians in the adopted country who could take a better informed decision on the diagnosis and handling of the symptoms presented by the migrant.DiscussionThe proposed procedure overcomes several limitations of the current assessment of migrants via a translator, relative to time, languages availability, linguistic and cultural specificity of materials and norms used for the assessment. Assessment would improve thank to greater empathy allowed through the process, and the specific medical background will allow to detect issues that could emerge during the interview or the formal testing. The proposed process solves several problems identified within the current practice, with considerable benefit on detection of neurocognitive disorders in a heterogeneous population.

Mini-Mental State Examination (MMSE) is used as a diagnostic test for dementia to screen a patient’s cognitive assessment and disease severity. However, these examinations are often inaccurate and unreliable either due to human error or due to patients’ physical disability to correctly interpret the questions as well as motor deficit. Erroneous data may lead to a wrong assessment of a specific patient. Therefore, other clinical factors (e.g., gender and comorbidities) existing in electronic health records, can also play a significant role, while reporting her examination results. This work considers various clinical attributes of dementia patients to accurately determine their cognitive status in terms of the Mini-Mental State Examination (MMSE) Score. We employ machine learning models to calibrate MMSE score and classify the correctness of diagnosis among patients, in order to assist clinicians in a better understanding of the progression of cognitive impairment and subsequent treatment. For this purpose, we utilize a curated real-world ageing study data. A random forest prediction model is employed to estimate the Mini-Mental State Examination score, related to the diagnostic classification of patients.This model uses various clinical attributes to provide accurate MMSE predictions, succeeding in correcting an important percentage of cases that contain previously identified miscalculated scores in our dataset. Furthermore, we provide an effective classification mechanism for automatically identifying patient episodes with inaccurate MMSE values with high confidence. These tools can be combined to assist clinicians in automatically finding episodes within patient medical records where the MMSE score is probably miscalculated and estimating what the correct value should be. This provides valuable support in the decision making process for diagnosing potential dementia patients. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Background: Primary progressive aphasia (PPA) is a neurodegenerative syndrome with three main clinical variants: non-fluent, semantic, and logopenic. Clinical diagnosis and accurate classification are challenging and often time-consuming. The Mini-Linguistic State Examination (MLSE) has been recently developed as a short language test to specifically assess language in neurodegenerative disorders. Objective: Our aim was to adapt and validate the Spanish version of MLSE for PPA diagnosis. Methods: Cross-sectional study involving 70 patients with PPA and 42 healthy controls evaluated with the MLSE. Patients were independently diagnosed and classified according to comprehensive cognitive evaluation and advanced neuroimaging. Results: Internal consistency was 0.758. The influence of age and education was very low. The area under the curve for discriminating PPA patients and healthy controls was 0.99. Effect sizes were moderate-large for the discrimination between PPA and healthy controls. Motor speech, phonology, and semantic subscores discriminated between the three clinical variants. A random forest classification model obtained an F1-score of 81%for the three PPA variants. Conclusion: Our study provides a brief and useful language test for PPA diagnosis, with excellent properties for both clinical routine assessment and research purposes.

Background: There are few available methods for qualitatively evaluating patients with primary progressive aphasia (PPA). Commonly adopted approaches are time-consuming, of limited accuracy, or designed to assess different patient populations. This paper introduces a new clinical test - the Mini Linguistic State Examination (MLSE) - which was designed uniquely to enable a clinician to assess and subclassify both classical and mixed presentations of PPA. The adoption of a novel assessment method (error classification) greatly amplifies the clinical information that can be derived from a set of standard linguistic tasks and allows a five-dimensional profile to be defined. Methods: Fifty-four patients and 30 matched controls were recruited. Five domains of language competence (motor speech, phonology, semantics, syntax, and working memory) were assessed using a sequence of 11 distinct linguistic assays. A random forest classification was used to assess the diagnostic accuracy for predicting PPA subtypes and create a decision tree as a guide to clinical classification. Findings: The random forest prediction model was 96% accurate overall (92% for logopenic variant, 93% for semantic variant, and 98% for non-fluent variant PPA). The derived decision tree produced a correct classification of 91% of participants whose data were not included in the training set. Interpretation: The MLSE is a new cognitive test incorporating a novel and powerful, yet straightforward, approach to scoring. Rigorous assessment of its diagnostic accuracy confirmed excellent matching of PPA syndromes to clinical gold standard diagnoses. Adoption of the MLSE by clinicians will have a decisive impact on the consistency and uniformity with which patients can be described clinically. It will also facilitate screening for cohort-based research, including future therapeutic trials, and is suitable for describing, quantifying and monitoring language deficits in other brain disorders. Funding Information: The research was funded by a Medical Research Council Research Grant award (Ref MR/N025881/1) to PG, MLR JR, KP and SC. Additional support was provided through grants from the MRC (UAG051 and G101400), Wellcome Trust (103838) and ERC (GAP: 670428), and through funding awarded to the National Institute for Health Research Cambridge Biomedical Research Centre and to the MRC CBU (MC_UU_00005/18). Declaration of Interests: JBR reports consultancy unrelated to the work with Biogen, UCB, Asceneuron and Althira; and receipt of research grants, unrelated to this work, from Janssen, AZ-Medimmune, and Lilly. The other authors declare no conflicts of interest. Ethics Approval Statement: Written informed consent was provided by all participants. The study protocol was reviewed and approved by the London (Chelsea) Research Ethics Committee [Ref. 16/LO/1735].

Background: The Mini Linguistic State Examination (MLSE) was developed in British English as a 20-minutes Primary Progressive Aphasia (PPA) screening test (Garrard, P. et al, 2012). Its tasks are: picture naming; listening comprehension of sentence; comprehension of single word; word repetition; sentence repetition; reading; writing; semantic association; and figure description. The MLSE was later translated to Italian and Spanish (Patel, N. et al, 2020) due to the following features: applicability by clinicians without expertise in language; sensitivity for diagnosis, distinction and progression of PPA subtypes; ability to identify features of atypical forms of PPA. Objective: Translation, cultural adaptation, standardization and validation of the MLSE English version to Brazilian Portuguese, allowing its application for PPA screening in the Brazilian population. Methods: Five controls were evalu- ated in order to verify tasks intelligibility of the first Brazilian Portuguese version of the MLSE. A control performed the test via telemedicine. The highest possible score was 100. Results: All controls were female. One subject was left-handed. Mean age was 50,4 (+- 12,94) years with mean schooling of 17 (+- 3,28) years. Mean Mini Mental State Examination score was 28,6 (+- 1,52), while mean MLSE score was 98,2 (+- 1,40). Conclusion: All MLSE tasks were intelligible to five individuals without aphasia. Subsequently more controls with different ranges of age and schooling will be evaluated for standardization.

Natural Language Processing (NLP) is an ever-growing field of computational science that aims to model natural human language. Combined with advances in machine learning, which learns patterns in data, it offers practical capabilities including automated language analysis. These approaches have garnered interest from clinical researchers seeking to understand the breakdown of language due to pathological changes in the brain, offering fast, replicable and objective methods. The study of Alzheimer's disease (AD), and preclinical Mild Cognitive Impairment (MCI), suggests that changes in discourse (connected speech or writing) may be key to early detection of disease. There is currently no disease-modifying treatment for AD, the leading cause of dementia in people over the age of 65, but detection of those at risk of developing the disease could help with the identification and testing of medications which can take effect before the underlying pathology has irreversibly spread. We outline important components of natural language, as well as NLP tools and approaches with which they can be extracted, analysed and used for disease identification and risk prediction. We review literature using these tools to model discourse across the spectrum of AD, including the contribution of machine learning approaches and Automatic Speech Recognition (ASR). We conclude that NLP and machine learning techniques are starting to greatly enhance research in the field, with measurable and quantifiable language components showing promise for early detection of disease, but there remain research and practical challenges for clinical implementation of these approaches. Challenges discussed include the availability of large and diverse datasets, ethics of data collection and sharing, diagnostic specificity and clinical acceptability.

BackgroundThis paper introduces a new clinical test, the Mini Linguistic State Examination (MLSE), as a short assessment for screening and classification of the different manifestations of primary progressive aphasia (PPA). Differentiation and monitoring of PPA variants are vital for management, planning and development of new treatments. The MLSE is designed to improve the uniformity of testing, screening for recruitment to clinical trials, and consistency of research results. It is a brief but effective test which can be adapted to the world’s major languages.MethodsFifty-four patients and 30 age-, sex- and education-matched controls completed testing with the MLSE and components of the Boston Diagnostic Aphasia Examination in addition to their standard clinical diagnostic assessment. The MLSE includes five domains (motor speech, phonology, semantics, syntax and working memory) that were compared across groups. A random forest classification was used to learn the relationship between these five domains and assess the power of the diagnostic accuracy for predicting PPA subtypes. The final machine learning model was used to create a decision tree to guide the optimal manual classification of patients.ResultsOn average, the test took less than 20 minutes to administer. Significant group differences were found across all five domains, in terms of the distributions of error-types. These differences mirror the well-known language profiles for the three main PPA variants, which typically require an extended neuropsychology and speech pathology assessment. The random forest prediction model had an overall classification accuracy of 96% (92% for logopenic variant PPA, 93% for semantic variant PPA and 98% for non-fluent variant PPA). The derived decision tree for manual classification produced correct classification of 91% of participants whose data were not included in the training set.ConclusionsThe MLSE is a new short cognitive test, with a scoring system that is easy to learn and apply. It is accurate for classifying PPA syndromes, and has potential to screen and monitor language deficits that occur in other focal and neurodegenerative brain disorders associated with language impairment. With increasing importance of language assessment in clinical research, the MLSE’s linguistic assessment tool enables the essential profiling of language deficits in a wide clinical community.

Language assessment has a critical role in the clinical diagnosis of neurodegenerative diseases, in particular, in the case of Primary Progressive Aphasia (PPA). The current diagnostic criteria (Gorno-Tempini et al., 2011) identify three main variants on the basis of clinical features and patterns of brain atrophy. Widely accepted tools to diagnose, clinically classify, and follow up the heterogeneous language profiles of PPA are still lacking. In this study, we develop a screening battery, composed of nine tests (picture naming, word and sentence comprehension, word and sentence repetition, reading, semantic association, writing and picture description), following the recommendations of current diagnostic guidelines and taking into account recent research on the topic. All tasks were developed with consideration of the psycholinguistic factors that can affect performance, with the aim of achieving sensitivity to the language deficit to which each task was relevant, and to allow identification of the selective characteristic impairments of each PPA variant. Normative data on 134 Italian subjects pooled across homogeneous subgroups for age, sex, and education are reported. Although further work is still needed, this battery represents a first step towards a concise multilingual standard language examination, a fast and simple tool to help clinicians and researchers in the diagnosis of PPA.

A progressive speech/language disorder, such as the non fluent/agrammatic variant of primary progressive aphasia and progressive apraxia of speech, can be due to neuropathologically verified Progressive Supranuclear Palsy (PSP). The prevalence of linguistic deficits and the linguistic profile in PSP patients who present primarily with a movement disorder is unknown. In the present study, we investigated speech and language performance in a sample of clinically diagnosed PSP patients using a comprehensive language battery, including, besides traditional language tests, a detailed analysis of connected speech (picture description task assessing 26 linguistic features). The aim was to identify the most affected linguistic levels in seventeen PSP with a movement disorder presentation, compared to 21 patients with Parkinson's disease and 27 healthy controls. Machine learning methods were used to detect the most relevant language tests and linguistic features characterizing the language profile of PSP patients. Our results indicate that even non-clinically aphasic PSP patients have subtle language deficits, in particular involving the lexical-semantic and discourse levels. Patients with the Richardson's syndrome showed a lower performance in the word comprehension task with respect to the other PSP phenotypes with predominant frontal presentation, parkinsonism and progressive gait freezing. The present findings support the usefulness of a detailed language assessment in all patients in the PSP spectrum.

Background: We evaluated the psychometric proprieties of the Screening for Aphasia in NeuroDegeneration (SAND) battery in Italian primary progressive aphasia (PPA) and movement disorder (MD) patients. Methods: The sample included 30 consecutive PPA and 45 MD patients who completed the SAND battery together with a clinical interview and a neurological/neuropsychological examination and 130 healthy controls (HC). Results: The SAND battery showed good internal consistency and good convergent and divergent validity. receiver operating characteristic analysis revealed an area under the curve of 0.978 for PPA versus HC and of 0.786 for PPA versus MD. A cutoff ≥3 gave a sensitivity of 0.933% and a specificity of 0.946% for discriminating PPA versus HC, whereas a cutoff ≥5 gave a sensitivity of 0.767% and a specificity of 0.667% for discriminating PPA versus MD. Conclusion: These results indicate that the SAND battery is an adequate, reliable, and valid diagnostic tool for PPA.

Retrieval of autobiographical memories (AMs) is important for "sense of self". Previous research and theoretical accounts suggest that working memory (WM) and semantic and phonemic fluency abilities facilitate the hierarchical search for, and reliving of past, personal events in the mind's eye. However, there remains a lack of consensus as to the nature of the relationships between these cognitive functions and the truly episodic aspects of AM. The present study therefore aimed to explore the associations between these variables in a sample with a wide range of cognitive abilities. The study incorporated a between-groups component, and a correlational component with multiple regression. Participants with Alzheimer's disease (n=10) and matched healthy controls (n=10) were assessed on measures of semantic and episodic AM search and retrieval, auditory and spatial WM, and semantic and phonemic fluency. The AD group produced less episodic AM content compared to controls. Semantic fluency predicted episodic AM retrieval independent of age effects but there were no significant relationships between measures of phonemic fluency, WM and episodic AM. The results suggest that the ability to maintain hierarchical search of the semantic knowledge-base is important for truly episodic reliving, and interventions for people with AM impairment might therefore benefit from incorporating structured, individualised external memory-aids to facilitate AM search and retrieval.

A range of theories have been put forward to account for the distinctive personality change that is sometimes observed after individuals in positions of power have enjoyed an extended period of successful and/or unchallenged authority. In this chapter, Peter Garrard explores the similarities between this phenomenon (which has come to be known as the Hubris Syndrome) and the development of grotesquely exaggerated, often self-destructive personality traits in response to damage in frontal brain regions. The idea that personality can be altered by focal physical changes to the brain has its roots in early clinical descriptions beginning with the celebrated case of Phineas Gage. In modern medical practice, however, the most dramatic cases emerge not following traumatic brain injury but in response to a slowly progressive degenerative process that, over years to decades, erodes the neuronal circuits of the brain’s frontal lobes, on which judgement, socialisation, moral reasoning, theory of mind, and other manifestations of high-level abstract cognitive processes, are dependent. Unravelling the complex combination of personality changes that are seen in the context of frontotemporal dementia is difficult, but description has been facilitated by the use of structured and systematic symptom questionnaires such as the Neuropsychiatric Inventory (NPI). The symptoms elicited by the NPI overlap with some of the changes of HS.

Background: Perseveration – repetition of words, phrases or questions in speech – is commonly described in Alzheimer’s disease (AD). Measuring perseveration is difficult, but may index cognitive performance, aiding diagnosis and disease monitoring. Continuous recording of speech would produce a large quantity of data requiring painstaking manual analysis, and risk violating patients’ and others’ privacy. A secure record and an automated approach to analysis are required.Objectives: To record bone-conducted acoustic energy fluctuations from a subject’s vocal apparatus using an accelerometer, to describe the recording and analysis stages in detail, and demonstrate that the approach is feasible in AD.Methods: Speech-related vibration was captured by an accelerometer, affixed above the temporo-mandibular joint. Healthy subjects read a script with embedded repetitions. Features were extracted from recorded signals and combined using Principal Component Analysis to obtain a one-dimensional representation of the feature vector. Motif discovery techniques were used to detect repeated segments. The equipment was tested in AD patients to determine device acceptability and recording quality. Results: Comparison with the known location of embedded motifs suggests that, with appropriate parameter tuning, the motif discovery method can detect repetitions. The device was acceptable to patients and produced adequate signal quality in their home environments.Conclusions: We established that continuously recording bone-conducted speech and detecting perseverative patterns were both possible. In future studies we plan to associate the frequency of verbal repetitions with stage, progression and type of dementia. It is possible that the method could contribute to the assessment of disease-modifying treatments.

BackgroundThere is currently no disease-modifying treatment available to halt or delay the progression of the disease pathology in dementia. An agreed core set of the best-available and most appropriate outcomes for disease modification would facilitate the design of trials and ensure consistency across disease modification trials, as well as making results comparable and meta-analysable in future trials.ObjectivesTo agree a set of core outcomes for disease modification trials for mild to moderate dementia with the UK dementia research community and patient and public involvement (PPI).Data sourcesWe included disease modification trials with quantitative outcomes of efficacy from (1) references from related systematic reviews in workstream 1; (2) searches of the Cochrane Dementia and Cognitive Improvement Group study register, Cochrane Central Register of Controlled Trials, Cumulative Index to Nursing and Allied Health Literature, EMBASE, Latin American and Caribbean Health Sciences Literature and PsycINFO on 11 December 2015, and clinical trial registries [International Standard Randomised Controlled Trial Number (ISRCTN) and clinicaltrials.gov] on 22 and 29 January 2016; and (3) hand-searches of reference lists of relevant systematic reviews from database searches.Review methodsThe project consisted of four workstreams. (1) We obtained related core outcome sets and work from co-applicants. (2) We systematically reviewed published and ongoing disease modification trials to identify the outcomes used in different domains. We extracted outcomes used in each trial, recording how many used each outcome and with how many participants. We divided outcomes into the domains measured and searched for validation data. (3) We consulted with PPI participants about recommended outcomes. (4) We presented all the synthesised information at a conference attended by the wider body of National Institute for Health Research (NIHR) dementia researchers to reach consensus on a core set of outcomes.ResultsWe included 149 papers from the 22,918 papers screened, referring to 125 individual trials. Eighty-one outcomes were used across trials, including 72 scales [31 cognitive, 12 activities of daily living (ADLs), 10 global, 16 neuropsychiatric and three quality of life] and nine biological techniques. We consulted with 18 people for PPI. The conference decided that only cognition and biological markers are core measures of disease modification. Cognition should be measured by the Mini Mental State Examination (MMSE) or the Alzheimer’s Disease Assessment Scale – Cognitive subscale (ADAS-Cog), and brain changes through structural magnetic resonance imaging (MRI) in a subset of participants. All other domains are important but not core. We recommend using the Neuropsychiatric Inventory for neuropsychiatric symptoms: the Disability Assessment for Dementia for ADLs, the Dementia Quality of Life Measure for quality of life and the Clinical Dementia Rating scale to measure dementia globally.LimitationsMost of the trials included participants with Alzheimer’s disease, so recommendations may not apply to other types of dementia. We did not conduct economic analyses. The PPI consultation was limited to members of the Alzheimer’s Society Research Network.ConclusionsCognitive outcomes and biological markers form the core outcome set for future disease modification trials, measured by the MMSE or ADAS-Cog, and structural MRI in a subset of participants.Future workWe envisage that the core set may be superseded in the future, particularly for other types of dementia. There is a need to develop an algorithm to compare scores on the MMSE and ADAS-Cog.Study registrationThe project was registered with Core Outcome Measures in Effectiveness Trials [www.comet-initiative.org/studies/details/819?result=true(accessed 7 April 2016)]. The systematic review protocol is registered as PROSPERO CRD42015027346.FundingThe National Institute for Health Research Health Technology Assessment programme.

The retrospective diagnosis of illnesses by medical historians can often be difficult and prone to bias, although knowledge of the medical disorders of historical figures is key to the understanding of their behavior and reactions. The recent application of computer diagnostics to historical figures allows an objective differential diagnosis to be accomplished. Taking an example from clinical neurology, we analyzed the earliest reported cases of Devic’s disease (neuromyelitis optica) that commonly affects the optic nerve and spinal cord and was previously often confused with multiple sclerosis. We conclude that in most identified cases the software concurred with the contemporary physicians’ interpretation, but some claimed cases either had insufficient data to provide a diagnosis or other possible diagnoses were suggested that had not been considered. Computational methods may, therefore, help historians to diagnose the ailments of historical figures with greater objectivity.