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Accurate processing of emotional information is a critical component of appropriate social interactions and interpersonal relationships. Disturbance of emotion processing is present in frontotemporal dementia (FTD) and is a clinical feature in two of the three subtypes: behavioural-variant FTD and semantic dementia. Emotion processing in progressive nonfluent aphasia, the third FTD subtype, is thought to be mostly preserved, although current evidence is scant. This paper reviews the literature on emotion recognition, reactivity and expression in FTD subtypes, although most studies focus on emotion recognition. The relationship between patterns of emotion processing deficits and patterns of neural atrophy are considered, by integrating evidence from recent neuroimaging studies. The review findings are discussed in the context of three contemporary theories of emotion processing: the limbic system model, the right hemisphere model and a multimodal system of emotion. Results across subtypes of FTD are most consistent with the multimodal system model, and support the presence of somewhat dissociable neural correlates for basic emotions, with strongest evidence for the emotions anger and sadness. Poor emotion processing is evident in all three subtypes, although deficits are more widespread than what would be predicted based on studies in healthy cohorts. Studies that include behavioural and imaging data are limited. Future investigations combining these approaches will help improve the understanding of the neural network underlying emotion processing. Presently, longitudinal investigations of emotion processing in FTD are lacking, and studies investigating emotion processing over time are critical to understand the clinical manifestations of disease progression in FTD.

As a global health emergency, the rapid spread of the novel coronavirus disease (COVID-19) led to the implementation of widespread restrictions (e.g., quarantine, physical/social distancing measures). However, while these restrictions reduce the viral spread of COVID-19, they may exacerbate behavioural and cognitive symptoms in dementia patients and increase pressure on caregiving. Here, we aimed to assess the impact of COVID-19 and related restrictions on both carers and people living with dementia across the world. We conducted an international survey (Australia, Germany, Spain, and the Netherlands) to assess the impact of COVID-19 on carers and people living with dementia. People with dementia experienced worsened neuropsychiatric symptoms since the outbreak of COVID-19, most commonly, depression, apathy, delusions, anxiety, irritability, and agitation. Regression analyses revealed that limited understanding of the COVID-19 situation and not living with the carer was associated with worsened neuropsychiatric symptoms. Carers also reported a decline in their own mental health, increased stress and reduced social networks as a result of COVID-19 and related restrictions. Regression analyses revealed uncertainty about the future and loneliness were associated with worsened carer mental health. Findings from this study will inform strategies for the development of support services and compassionate protocols that meet the evolving needs of those living with dementia and their carers.

Patients with frontotemporal dementia have pervasive changes in emotion recognition and social cognition, yet the neural changes underlying these emotion processing deficits remain unclear. The multimodal system model of emotion proposes that basic emotions are dependent on distinct brain regions, which undergo significant pathological changes in frontotemporal dementia. As such, this syndrome may provide important insight into the impact of neural network degeneration upon the innate ability to recognise emotions. This study used voxel-based morphometry to identify discrete neural correlates involved in the recognition of basic emotions (anger, disgust, fear, sadness, surprise and happiness) in frontotemporal dementia. Forty frontotemporal dementia patients (18 behavioural-variant, 11 semantic dementia, 11 progressive nonfluent aphasia) and 27 healthy controls were tested on two facial emotion recognition tasks: The Ekman 60 and Ekman Caricatures. Although each frontotemporal dementia group showed impaired recognition of negative emotions, distinct associations between emotion-specific task performance and changes in grey matter intensity emerged. Fear recognition was associated with the right amygdala; disgust recognition with the left insula; anger recognition with the left middle and superior temporal gyrus; and sadness recognition with the left subcallosal cingulate, indicating that discrete neural substrates are necessary for emotion recognition in frontotemporal dementia. The erosion of emotion-specific neural networks in neurodegenerative disorders may produce distinct profiles of performance that are relevant to understanding the neurobiological basis of emotion processing.

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.

From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.

Progressive and relatively circumscribed loss of semantic knowledge, referred to as semantic dementia (SD) which falls under the broader umbrella of frontotemporal dementia, was officially identified as a clinical syndrome less than 50 years ago. Here, we review recent neuroimaging, pathological, and genetic research in SD. From a neuroimaging perspective, SD is characterised by hallmark asymmetrical atrophy of the anterior temporal pole and anterior fusiform gyrus, which is usually left lateralised. Functional magnetic resonance imaging (fMRI) studies have revealed widespread changes in connectivity, implicating the anterior temporal regions in semantic deficits in SD. Task-related fMRI have also demonstrated the relative preservation of frontal and parietal regions alongside preserved memory performance. In addition, recent longitudinal studies have demonstrated that, with disease progression, atrophy encroaches into the contralateral temporal pole and medial prefrontal cortices, which reflects emerging changes in behaviour and social cognition. Notably, unlike other frontotemporal dementia subtypes, recent research has demonstrated strong clinicopathological concordance in SD, with TDP43 type C as the most common pathological subtype. Moreover, an underlying genetic cause appears to be relatively rare in SD, with the majority of cases having a sporadic form of the disease. The relatively clear diagnosis, clinical course, and pathological homogeneity of SD make this syndrome a promising target for novel disease-modifying interventions. The development of neuroimaging markers of disease progression at the individual level is an important area of research for future studies to address, in order to assist with this endeavour.

Accurate early diagnosis of neurodegenerative diseases represents a growing challenge for current clinical practice. Promisingly, current tools can be complemented by computational decision-support methods to objectively analyze multidimensional measures and increase diagnostic confidence. Yet, widespread application of these tools cannot be recommended unless they are proven to perform consistently and reproducibly across samples from different countries. We implemented machine-learning algorithms to evaluate the prediction power of neurocognitive biomarkers (behavioral and imaging measures) for classifying two neurodegenerative conditions –Alzheimer Disease (AD) and behavioral variant frontotemporal dementia (bvFTD)– across three different countries (>200 participants). We use machine-learning tools integrating multimodal measures such as cognitive scores (executive functions and cognitive screening) and brain atrophy volume (voxel based morphometry from fronto-temporo-insular regions in bvFTD, and temporo-parietal regions in AD) to identify the most relevant features in predicting the incidence of the diseases. In the Country-1 cohort, predictions of AD and bvFTD became maximally improved upon inclusion of cognitive screenings outcomes combined with atrophy levels. Multimodal training data from this cohort allowed predicting both AD and bvFTD in the other two novel datasets from other countries with high accuracy (>90%), demonstrating the robustness of the approach as well as the differential specificity and reliability of behavioral and neural markers for each condition. In sum, this is the first study, across centers and countries, to validate the predictive power of cognitive signatures combined with atrophy levels for contrastive neurodegenerative conditions, validating a benchmark for future assessments of reliability and reproducibility.

The terminology used to describe components of social cognition lacks clarity and specificity. Recent studies have tried to reach consensus on definitions of social cognition based on expert opinion. These efforts resulted in semantically well-defined terms and distinct concepts, but it remains unclear whether these terms also align with empirical data and existing theoretical models of social cognition. In this commentary, we examine whether the proposed definitions for social cognition are supported by clinical observations and the extant knowledge base on the underlying neural substrates of these skills. In addition, we consider how the proposed definitions align with existing theoretical models of social cognition. We argue that consensus should not be based solely on expert opinion. Therefore, we propose an updated biopsychosocial model of social cognition that integrates proposed expert definitions with a theoretical model of social cognition based on empirical data: the Hierarchical Interdependent Taxonomy of Social cognition (HITS) model. The HITS model guides future research, helps to address the poor construct validity that has been revealed for several tests of social cognition, and provides a framework for the assessment of social cognition.

Introduction: Growing research has shown the negative impact of social isolation on the health and psychological well-being of individuals with dementia and their carers. This study investigated the effectiveness of a psychosocial intervention for dementia carers during a lockdown period of the COVID-19 pandemic. Methods: Twenty-three family carers of individuals diagnosed with dementia living in the community were recruited and provided with an online psychoeducation toolkit that aims to improve health literacy, management of social and behavioural symptoms in dementia, carer social engagement, and coping skills. Carers were divided into “mild” or “moderate” groups based on the disease severity of the person with dementia they cared for. Outcome measures including distress and severity of neuropsychiatric symptoms, carer self-efficacy and burden, social network, and feelings of loneliness were assessed at baseline and 2 weeks later. Results: Carers in the moderate group reported higher levels of distress (p = 0.001) and severity (p < 0.001) of neuropsychiatric symptoms and greater carer burden (p = 0.003) than carers in the mild group. Following the intervention, both groups reported increased social networks (p = 0.001). In addition, carers in the moderate group reported significantly reduced distress for neuropsychiatric symptoms (p = 0.013), enhanced carer self-efficacy for controlling upsetting thoughts (p = 0.040), and decreased loneliness (p = 0.023). Conclusions: This study demonstrated that psychosocial interventions improve outcomes for carers of individuals with dementia, particularly those caring for individuals with greater disease severity. Findings from this study will inform the development of support services that meet the evolving needs of individuals with dementia and their carers in social isolation, during and in a post-pandemic context.

INTRODUCTION Some people with dementia develop changes in behaviour and cognition that may lead to interactions with police or the legal system. However, large, prospective case–control studies examining these behaviours are lacking. METHODS One hundred and forty‐four people with dementia and 53 controls completed the Misdemeanours and Transgressions Screener. RESULTS Criminal risk behaviours were reported in: 65.6% of behavioural‐variant frontotemporal dementia, 46.2% of right‐lateralised semantic dementia, and 27.0% of Alzheimer's disease patients. In 19.1% of patients these behaviours led to contact with police or authority figures. Compared to controls, people with dementia showed higher rates of physical assault (p = 0.024), financial/professional recklessness (p = 0.009), and inappropriate behaviours (p = 0.052). DISCUSSION Criminal risk behaviours are common across dementia subtypes and may be one of the first clinical signs of frontotemporal dementia. Further research to understand how to balance risk minimisation with an individual's liberties as well as the inappropriate criminalisation of people with dementia is needed. Highlights The Misdemeanours and Transgressions Screener is a new tool to assess criminal risk behaviours. Forty‐seven percent of patients with dementia show criminal risk behaviour after dementia onset. Behaviours included verbal abuse, traffic violations, physical assault. New onset of criminal risk behaviours >50 years is a clinical sign for frontotemporal dementia.