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Apathy is a debilitating but poorly understood disorder characterized by a reduction in motivation. As well as being associated with several brain disorders, apathy is also prevalent in varying degrees in healthy people. Whilst many tools have been developed to assess levels of apathy in clinical disorders, surprisingly there are no measures of apathy suitable for healthy people. Moreover, although apathy is commonly comorbid with symptoms of depression, anhedonia and fatigue, how and why these symptoms are associated is unclear. Here we developed the Apathy-Motivation Index (AMI), a brief self-report index of apathy and motivation. Using exploratory factor analysis (in a sample of 505 people), and then confirmatory analysis (in a different set of 479 individuals), we identified subtypes of apathy in behavioural, social and emotional domains. Latent profile analyses showed four different profiles of apathy that were associated with varying levels of depression, anhedonia and fatigue. The AMI is a novel and reliable measure of individual differences in apathy and might provide a useful means of probing different mechanisms underlying sub-clinical lack of motivation in otherwise healthy individuals. Moreover, associations between apathy and comorbid states may be reflective of problems in different emotional, social and behavioural domains.

Apathy is one of the most prevalent and disabling non-cognitive symptoms of dementia. This loss of motivation and pervasive decline in goal-directed behaviour represents a core diagnostic feature of behavioural-variant frontotemporal dementia (bvFTD) and is also common in Alzheimer’s disease (AD). However, despite growing recognition of a multidimensional model, apathy has typically been examined as a unitary symptom. Here, we employed a cross-sectional design to characterise the multidimensional nature of apathy across syndromes and disease course. 92 participants (44 bvFTD, 20 AD, 28 controls) completed the Dimensional Apathy Scale (DAS) to quantify emotional, executive, and initiation apathy. Patients were divided into early and late stages based on time since symptom onset. All participants underwent structural MRI and voxel-based morphometry was used to identify neural correlates of apathy dimensions. In the early stage of the disease (< 5 years since onset), emotional apathy was greater in bvFTD than AD. In contrast, in the late stage (> 5 years since onset), executive apathy was greater in AD than bvFTD, although apathy was elevated across all dimensions compared to controls. Notably, apathy was observed in the absence of self-reported depression in 46.2% of patients, with no patients classified as depressed only. Neuroimaging analyses revealed both common and divergent prefrontal and subcortical neural correlates associated with apathy dimensions. Our results reveal differing profiles of apathy across the disease course, in AD and bvFTD, with distinct brain regions mediating these dimensions. These findings will inform the development of appropriate treatment targets to ameliorate the impact of apathy in dementia.

Background: Neuropsychiatric symptoms (NPS) affect almost all patients with dementia and are a major focus of study and treatment. Accurate assessment of NPS through valid, sensitive and reliable measures is crucial. Although current NPS measures have many strengths, they also have some limitations (e.g. acquisition of data is limited to informants or caregivers as respondents, limited depth of items specific to moderate dementia). Therefore, we developed a revised version of the NPI, known as the NPI-C. The NPI-C includes expanded domains and items, and a clinician-rating methodology. This study evaluated the reliability and convergent validity of the NPI-C at ten international sites (seven languages). Methods: Face validity for 78 new items was obtained through a Delphi panel. A total of 128 dyads (caregivers/patients) from three severity categories of dementia (mild = 58, moderate = 49, severe = 21) were interviewed separately by two trained raters using two rating methods: the original NPI interview and a clinician-rated method. Rater 1 also administered four additional, established measures: the Apathy Evaluation Scale, the Brief Psychiatric Rating Scale, the Cohen-Mansfield Agitation Index, and the Cornell Scale for Depression in Dementia. Intraclass correlations were used to determine inter-rater reliability. Pearson correlations between the four relevant NPI-C domains and their corresponding outside measures were used for convergent validity. Results: Inter-rater reliability was strong for most items. Convergent validity was moderate (apathy and agitation) to strong (hallucinations and delusions; agitation and aberrant vocalization; and depression) for clinician ratings in NPI-C domains. Conclusion: Overall, the NPI-C shows promise as a versatile tool which can accurately measure NPS and which uses a uniform scale system to facilitate data comparisons across studies.

Apathy is a disease characterized by diminished motivation not attributable to a diminished level of consciousness, cognitive impairment, or emotional distress. It is a serious problem facing the elderly in today's society. The diagnosis of apathy needs to be done at a clinic, which is particularly inconvenient and difficult for elderly patients. In this work, we examine the possibility of using doppler radar imaging for the classification of apathy in the elderly. We recruited 178 elderly participants to help create a dataset by having them fill out a questionnaire and submit to doppler radar imaging while performing a walking action. We selected walking because it is one of the most common actions in daily life and potentially contains a variety of useful health information. We used radar imaging rather than an RGB camera due to the greater privacy protection it affords. Seven machine learning models, including our proposed one, which uses a neural network, were applied to apathy classification using the walking doppler radar images of the elderly. Before classification, we perform a simple image pre-processing for feature extraction. This pre-processing separates every walking doppler radar image into four parts on the vertical and horizontal axes and the number of feature points is then counted in every separated part after binarization to create eight features. In this binarization, the optimized threshold is obtained by experimentally sliding the threshold. We found that our proposed neural network achieved an accuracy of more than 75% in apathy classification. This accuracy is not as high as that of other object classification methods in current use, but as an initial research in this area, it demonstrates the potential of apathy classification using doppler radar images for the elderly. We will examine ways of increasing the accuracy in future work.

Patients with dementia may be unable to describe their symptoms, and caregivers frequently suffer emotional burden that can interfere with judgment of the patient's behavior. The Neuropsychiatric Inventory-Clinician rating scale (NPI-C) was therefore developed as a comprehensive and versatile instrument to assess and accurately measure neuropsychiatric symptoms (NPS) in dementia, thereby using information from caregiver and patient interviews, and any other relevant available data. The present study is a follow-up to the original, cross-national NPI-C validation, evaluating the reliability and concurrent validity of the NPI-C in quantifying psychopathological symptoms in dementia in a large Brazilian cohort. Two blinded raters evaluated 312 participants (156 patient-knowledgeable informant dyads) using the NPI-C for a total of 624 observations in five Brazilian centers. Inter-rater reliability was determined through intraclass correlation coefficients for the NPI-C domains and the traditional NPI. Convergent validity included correlations of specific domains of the NPI-C with the Brief Psychiatric Rating Scale (BPRS), the Cohen-Mansfield Agitation Index (CMAI), the Cornell Scale for Depression in Dementia (CSDD), and the Apathy Inventory (AI). Inter-rater reliability was strong for all NPI-C domains. There were high correlations between NPI-C/delusions and BPRS, NPI-C/apathy-indifference with the AI, NPI-C/depression-dysphoria with the CSDD, NPI-C/agitation with the CMAI, and NPI-C/aggression with the CMAI. There was moderate correlation between the NPI-C/aberrant vocalizations and CMAI and the NPI-C/hallucinations with the BPRS. The NPI-C is a comprehensive tool that provides accurate measurement of NPS in dementia with high concurrent validity and inter-rater reliability in the Brazilian setting. In addition to universal assessment, the NPI-C can be completed by individual domains.

ObjectiveInconsistent results exist regarding the cognitive profile in patients with Parkinson's disease with mild cognitive impairment (PD-MCI). We aimed at providing data on this topic from a large cohort of patients with PD-MCI.MethodsSociodemographic, clinical and neuropsychological baseline data from patients with PD-MCI recruited in the multicentre, prospective, observational DEMPARK/LANDSCAPE study were analysed.Results269 patients with PD-MCI (age 67.8±7.4, Unified Parkinson's Disease Rating Scale (UPDRS-III) scores 23.2±11.6) were included. PD-MCI subtypes were 39.4% non-amnestic single domain, 30.5% amnestic multiple domain, 23.4% non-amnestic multiple domain and 6.7% amnestic single domain. Executive functions were most frequently impaired. The most sensitive tests to detect cognitive dysfunctions were the Modified Card Sorting Test, digit span backwards and word list learning direct recall. Multiple stepwise regression analyses showed that global cognition, gender and age, but not education or disease-related parameters predicted PD-MCI subtypes.ConclusionsThis study with the so far largest number of prospectively recruited patients with PD-MCI indicates that non-amnestic PD-MCI is more frequent than amnestic PD-MCI; executive dysfunctions are the most typical cognitive symptom in PD-MCI; and age, gender and global cognition predict the PD-MCI subtype. Longitudinal data are needed to test the hypothesis that patients with PD-MCI with specific cognitive profiles have different risks to develop dementia.

Despite the ubiquitous nature of boredom, the definition, function, and correlates of boredom are still poorly understood. In this review, we summarize the “known” (consistent evidence) and “unknown” (inconsistent evidence) correlates of boredom. We show that boredom is consistently related to negative affect, task-unrelated thought, over-estimation of elapsed time, reduced agency, as well as to over- and under-stimulation. Activation of the default mode network was consistent across the few available fMRI studies, while the recruitment of other brain areas such as the hippocampus and anterior insular cortex, was a notable but less consistent correlate of boredom. Other less consistent correlates of boredom are also reviewed, such as the level of arousal and the mental attributions given to fluctuations of attention. Finally, we identify two critical factors that may contribute to current inconsistencies in the literature and may hamper further progress in the field. First, there is relatively little consistency in the way in which boredom has been operationalized across studies to date, with operationalizations of boredom ranging from negative affect paired with under-stimulation, over-stimulation, to negative affect paired with a lack of goal-directed actions. Second, preliminary evidence suggests the existence of distinct types of boredom (e.g., searching vs. apathetic) that may have different and sometimes even opposing correlates. Adopting a more precise and consistent way of operationalizing boredom, and arriving at an empirically validated taxonomy of different types of boredom, could serve to overcome the current roadblocks to facilitate further progress in our scientific understanding of boredom.

A dearth of population-based epidemiological research examines neuropsychiatric symptom (NPS) in sub-clinical populations across the spectrum from normal aging to mild cognitive impairment (MCI). The construct of mild behavioral impairment (MBI) describes the emergence of sustained and impactful NPS in advance of or in combination with MCI. This is the first epidemiological study to operationalize the recently published diagnostic criteria for MBI and determine prevalence estimates across the spectrum from cognitively normal to MCI. MBI was assessed in 1,377 older (age range 72–79 years; 52% male; MCI ;= 133; cognitively normal, but-at-risk = 397; cognitively healthy = 847). MBI was assessed in accordance with the ISTAART-AA diagnostic criteria for MBI using the neuropsychiatric inventory. 34.1% of participants met the criteria for MBI. High prevalence of MBI across the cognitive spectrum was reported (48.9% vs. 43.1% vs. 27.6%). Irrespective of level of cognitive impairment, impulse dyscontrol (33.8% vs. 28.7% vs. 17.2%) and decreased motivation (32.3% vs. 26.2% vs. 16.3%) were the most frequently met MBI domains. MBI was more prevalent in men (χ2 = 4.98, p = 0.026), especially the domains of decreased motivation and impulse dyscontrol. This study presents the first population-based prevalence estimates for MBI using the recently published ISTAART-AA diagnostic criteria. Findings indicate relatively high prevalence of MBI in pre-dementia clinical states and amongst cognitively healthy older adults. Findings were gender-specific, with MBI affecting more men than women. Knowing the estimates of these symptoms in the population is essential for understanding and differentiating the very early development of clinical disorders.

ObjectiveIn 2017, the diagnostic criteria for cognitive and behavioural impairment in amyotrophic lateral sclerosis (ALS) with frontotemporal dementia (ALSFTD-1) have been modified (ALSFTD-2) with the inclusion of a novel category (ALS with combined cognitive and behavioural impairment, ALScbi) and with changes of operational criteria of the other categories (ALS with cognitive impairment (ALSci), ALS with behavioural impairment (ALSbi) and ALS with frontotemporal dementia (ALS-FTD)). We compared the two sets of criteria to assess the effect of the revised criteria on the cognitive classification of patients with ALS.MethodsTwo cohorts of patients with ALS were included in this study: a population-based cohort including patients identified through the Piemonte/Valle d’Aosta register for ALS in the 2014–2017 period (n=321), and a referral cohort recruited at the Turin ALS centre and at the ALS centre of the Maugeri Institute in Milan in the same period (n=205). Cognitive function was classified in blind by two neuropsychologists expert in ALS.ResultsALSFTD-2 criteria determined a shift of about 15% of patients from their original category to a new one. In both cohorts, about 9% of patients were reclassified to the novel category ALScbi. Among patients previously classified as cognitively normal, 14 (4.3%, population-based cohort) and 19 (9.3%, referral cohort) were reclassified as ALSbi or ALSci. The median survival of the different categories was significantly different with both with sets of criteria.ConclusionsThe new ALSFTD-2 criteria, compared with the old ones, have positive effects on the clinical practice being more sensitive to the early cognitive impairment and having a better prognostic yield.

Abstract Background and Hypothesis Quantitative models of psychopathology can empirically guide subclassification of heterogeneous clinical presentations such as psychosis; they are particularly well-equipped to capture the nuanced symptomatology observed in first-episode psychosis. As well, components may be better aligned with biological variables. The current study sought to confirm and extend knowledge of the hierarchical structure of psychosis symptoms in first-episode psychosis. Based on past hierarchical work, we hypothesized that a 4 component level would be most closely associated with longitudinal disability. Study Design Participants with early-stage psychosis (N = 370) underwent clinical assessment with the scale for the assessment of positive symptoms (SAPS), scale for assessment of negative symptoms (SANS), and global assessment scale(GAS). A subset was assessed at 6 months (N = 221) and 1 year (N = 207). Hierarchical symptom components were extracted at 12 levels. The predictive utility of the components for global functioning was tested. Study Results As predicted, the 4-component model (reality distortion, thought disorder, inexpressivity, apathy/asociality) provided a superior prediction of functioning over other levels of the hierarchy. Baseline apathy/asociality longitudinally predicted functioning beyond the shared variance of the components at 6 months (b = −4.83, t(216) = −5.37, p < .001, R2adj = 0.12) and 1-year (b = −4.49, t(202) = −4.38, p < .001, R2adj = 0.09). Conclusions The hierarchical structure of psychotic symptomatology and its external validity have been robustly established in independent, longitudinal first-episode psychosis samples. The established model incorporates multiple levels of granularity that can be flexibly applied based on the level that offers the greatest predictive utility for external validators.