Apathy is a highly prevalent and disabling neuropsychiatric syndrome, but its multi-dimensional structure is a challenge for progress towards better identification and treatment. A crucial unresolved question is whether social disengagement reflects a distinct deficit in social motivation or a by-product of diminished initiative or emotional blunting. Previous studies have been constrained by modest sample sizes and limited use of apathy-specific instruments or phenotypically narrow cohorts. Here, we analysed item-level data from 11,243 individuals recruited across multiple centres, including 1154 neurological patients with Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia, autoimmune encephalitis and small vessel disease, alongside people with depression and healthy adults. Across exploratory and confirmatory factor analyses, symptom-level network modelling, and lifespan analyses, social apathy consistently emerged as a coherent and separable dimension. This pattern was preserved across health, psychiatric, and neurocognitive cohorts, from adolescence through late life. Recognising social apathy as an independent domain reframes a central aspect of mental health—the motivation to connect, care, and act for others—and provides a foundation for more precise assessment and for interventions targeting both social and neurobiological mechanisms.

Apathy is a prevalent and persistent neuropsychiatric syndrome across many neurological disorders, significantly impacting both patients and caregivers. We systematically quantified discrepancies between self- and caregiver-reported apathy in 335 patients with a variety of diagnoses, such as frontotemporal dementia (behavioural variant and semantic dementia subtypes), Parkinson's disease, Parkinson's disease dementia, dementia with Lewy bodies, Alzheimer's disease dementia, mild cognitive impairment, small vessel cerebrovascular disease, subjective cognitive decline and autoimmune encephalitis. Using the Apathy Motivation Index (AMI) and its analogous caregiver version (AMI-CG), we found that caregiver-reported apathy consistently exceeded self-reported levels across all conditions. Moreover, self-reported apathy accounted for only 14.1% of the variance in caregiver ratings. This apathy reporting discrepancy was most pronounced in conditions associated with impaired insight, such as behavioural variant frontotemporal dementia, and was significantly correlated with cognitive impairment. Deficits in memory and fluency explained an additional 11.2% of the variance in caregiver-reported apathy. Specifically, executive function deficits (e.g. indexed by fluency) and memory impairments may contribute to behavioural inertia or recall of it. These findings highlight the need to integrate patient and caregiver perspectives in apathy assessments, especially for conditions with prominent cognitive impairment. To improve diagnostic accuracy and deepen our understanding of apathy across neurological disorders, we highlight the need for adapted apathy assessment strategies that account for cognitive impairment particularly in individuals with insight or memory deficits. Understanding the cognitive mechanisms underpinning discordant apathy reporting in dementia might help inform targeted clinical interventions and reduce caregiver burden.

Behavioural changes are a central feature of frontotemporal dementia (FTD); they occur in both behavioural-variant (bvFTD) and semantic dementia (SD)/semantic-variant primary progressive aphasia subtypes. In this study, we addressed two current clinical knowledge gaps: (i) are there qualitative or clear distinctions between behavioural profiles in bvFTD and SD; and (ii) what are the precise roles of the prefrontal cortex and anterior temporal lobes in supporting social behaviour? Resolving these conundrums is crucial for improving diagnostic accuracy and for the development of targeted interventions to treat challenging behaviours in FTD. Informant questionnaires to assess behavioural changes included the Cambridge Behavioural Inventory-Revised and two targeted measures of apathy and impulsivity. Participants completed a detailed neuropsychological battery to permit investigation of the relationship between cognitive status (including social-semantic knowledge, general semantic knowledge and executive function) with behaviour change in FTD. To explore changes in regional grey matter volume, a subset of patients had structural MRI. Diagnosis-based group comparisons were supplemented by a transdiagnostic approach that encompassed the spectrum of bvFTD, SD and 'mixed' or intermediate cases. Such an approach is sensitive to the systematic graded variation in FTD and allows the neurobiological underpinnings of behaviour change to be explored across an FTD spectrum. We found a wide range of behavioural changes across FTD. Although quantitatively more severe on average in bvFTD, as expected, the item-level analyses found no evidence for qualitative differences in behavioural profiles or 'behavioural double dissociations' between bvFTD and SD. Comparisons of self and informant ratings revealed strong discrepancies in the perspective of the caregiver versus the patient. Logistic regression revealed that neuropsychological measures had better discriminative accuracy for bvFTD versus SD than caregiver-reported behavioural measures. A principal component analysis of all informant questionnaire domains extracted three components, interpreted as reflecting: (i) apathy; (ii) challenging behaviours; and (iii) activities of daily living. More severe apathy in both FTD subtypes was associated with: (i) increased levels of impaired executive function; and (ii) anterior cingulate cortex atrophy. Questionnaire ratings of impaired behaviour were not correlated with either anterior temporal lobe atrophy or degraded social-semantic knowledge. Together, these findings highlight the presence of a wide range of behavioural changes in both bvFTD and SD, which vary by degree rather than quality. We recommend a transdiagnostic approach for future studies of the neuropsychological and neuroanatomical underpinnings of behavioural deficits in FTD.

Degraded semantic memory is a prominent feature of frontotemporal dementia (FTD). It is classically associated with semantic dementia and anterior temporal lobe (ATL) atrophy, but semantic knowledge can also be compromised in behavioural variant FTD. Motivated by understanding behavioural change in FTD, recent research has focused selectively on social-semantic knowledge, with proposals that the right ATL is specialized for social concepts. Previous studies have assessed very different types of social concepts and have not compared performance with that of matched non-social concepts. Consequently, it remains unclear to what extent various social concepts are (i) concurrently impaired in FTD, (ii) distinct from general semantic memory and (iii) differentially supported by the left and right ATL. This study assessed multiple aspects of social-semantic knowledge and general conceptual knowledge across cohorts with ATL damage arising from either neurodegeneration or resection. We assembled a test battery measuring knowledge of multiple types of social concept. Performance was compared with non-social general conceptual knowledge, measured using the Cambridge Semantic Memory Test Battery and other matched non-social-semantic tests. Our trans-diagnostic approach included behavioural variant FTD, semantic dementia and 'mixed' intermediate cases to capture the FTD clinical spectrum, as well as age- matched healthy controls. People with unilateral left or right ATL resection for temporal lobe epilepsy were also recruited to assess how selective damage to the left or right ATL impacts social- and non-social-semantic knowledge. Social- and non-social-semantic deficits were severe and highly correlated in FTD. Much milder impairments were found after unilateral ATL resection, with no left versus right differences in social-semantic knowledge or general semantic processing and with only naming showing a greater deficit following left versus right damage. A principal component analysis of all behavioural measures in the FTD cohort extracted three components, interpreted as capturing (i) FTD severity, (ii) semantic memory and (iii) executive function. Social and non-social measures both loaded heavily on the same semantic memory component, and scores on this factor were uniquely associated with bilateral ATL grey matter volume but not with the degree of ATL asymmetry. Together, these findings demonstrate that both social- and non-socialsemantic knowledge degrade in FTD (semantic dementia and behavioural variant FTD) following bilateral ATL atrophy. We propose that social-semantic knowledge is part of a broader conceptual system underpinned by a bilaterally implemented, functionally unitary semantic hub in the ATLs. Our results also highlight the value of a trans-diagnostic approach for investigating the neuroanatomical underpinnings of cognitive deficits in FTD.

The functional importance of the anterior temporal lobes (ATLs) has come to prominence in two active, albeit unconnected literatures-(i) face recognition and (ii) semantic memory. To generate a unified account of the ATLs, we tested the predictions from each literature and examined the effects of bilateral versus unilateral ATL damage on face recognition, person knowledge, and semantic memory. Sixteen people with bilateral ATL atrophy from semantic dementia (SD), 17 people with unilateral ATL resection for temporal lobe epilepsy (TLE; left = 10, right = 7), and 14 controls completed tasks assessing perceptual face matching, person knowledge and general semantic memory. People with SD were impaired across all semantic tasks, including person knowledge. Despite commensurate total ATL damage, unilateral resection generated mild impairments, with minimal differences between left- and right-ATL resection. Face matching performance was largely preserved but slightly reduced in SD and right TLE. All groups displayed the familiarity effect in face matching; however, it was reduced in SD and right TLE and was aligned with the level of item-specific semantic knowledge in all participants. We propose a neurocognitive framework whereby the ATLs underpin a resilient bilateral representation system that supports semantic memory, person knowledge and face recognition.

Connected speech samples elicited by a picture description task are widely used in the assessment of aphasias, but it is not clear what their interpretation should focus on. Although such samples are easy to collect, analyses of them tend to be time-consuming, inconsistently conducted and impractical for non-specialist settings. Here, we analysed connected speech samples from patients with the three variants of primary progressive aphasia (semantic, svPPA N = 9; logopenic, lvPPA N = 9; and non-fluent, nfvPPA N = 9), progressive supranuclear palsy (PSP Richardson's syndrome N = 10), corticobasal syndrome (CBS N = 13) and age-matched healthy controls (N = 24). There were three principal aims: (i) to determine the differences in quantitative language output and psycholinguistic properties of words produced by patients and controls, (ii) to identify the neural correlates of connected speech measures and (iii) to develop a simple clinical measurement tool. Using data-driven methods, we optimized a 15-word checklist for use with the Boston Diagnostic Aphasia Examination 'cookie theft' and Mini Linguistic State Examination 'beach scene' pictures and tested the predictive validity of outputs from least absolute shrinkage and selection operator (LASSO) models using an independent clinical sample from a second site. The total language output was significantly reduced in patients with nfvPPA, PSP and CBS relative to those with svPPA and controls. The speech of patients with lvPPA and svPPA contained a disproportionately greater number of words of both high frequency and high semantic diversity. Results from our exploratory voxel-based morphometry analyses across the whole group revealed correlations between grey matter volume in (i) bilateral frontal lobes with overall language output, (ii) the left frontal and superior temporal regions with speech complexity, (iii) bilateral frontotemporal regions with phonology and (iv) bilateral cingulate and subcortical regions with age of acquisition. With the 15-word checklists, the LASSO models showed excellent accuracy for within-sample k-fold classification (over 93%) and out-of-sample validation (over 90%) between patients and controls. Between the motor disorders (nfvPPA, PSP and CBS) and lexico-semantic groups (svPPA and lvPPA), the LASSO models showed excellent accuracy for within-sample k-fold classification (88-92%) and moderately good (59-74%) differentiation for out-of-sample validation. In conclusion, we propose that a simple 15-word checklist provides a suitable screening test to identify people with progressive aphasia, while further specialist assessment is needed to differentiate accurately some groups (e.g. svPPA versus lvPPA and PSP versus nfvPPA). Henderson et al. analysed connected speech samples from patients with primary progressive aphasia, progressive supranuclear palsy and corticobasal syndrome and optimized simple, easy-to-use and practical word checklists for two widely used picture narratives.

Professor Peter Garrard is an expert consultant neurologist and active clinician scientist in London, with a focus on linguistic profiles of disorders of the nervous system. He has more than 25 years of clinical experience, including 15 years as an accredited specialist in neurology. His specialist interests include neurological disorders, cognitive disorders, progressive language disorders, frontotemporal dementia, and early-onset dementia. In 1990, Professor Garrard received his primary medical qualification from the University of Bristol and subsequently undertook his higher medical training in Edinburgh for surgery and Yeovil for medicine. Once in London, he completed his general medical training before taking on specialist training in neurology in 2000. During this time, Professor Garrard completed his PhD at Cambridge University on language abnormalities in Alzheimer’s and other dementias as a Medical Research Council clinical training fellow. Professor Garrard has completed extensive neuroscience research, and currently holds the position of Professor of Neurology at St George’s University of London, where his primary research interest is in the early language changes associated with neurodegenerative dementians, such as Alzheimer’s. He is deputy director of the Molecular and Clinical Sciences Research Institute at St George’s, and also leads an active dementia research laboratory. Additionally, Professor Garrard has continuously taken on educational roles in his field since becoming a member of the General Medical Council. Abstract In this talk I will briefly review the origins and development of the idea that the neurodegenerative dementias may have a presymptomatic signature detectable in spontaneous speech, and the implications of the diagnostic potential that this idea may hold. I will move on to discussing the special case of neurodegenerative syndromes that selectively impair language (the primary progressive aphasias), and describe the principles by which clinical information relevant to these conditions can be obtained using the newly developed mini linguistic state examination (MLSE). Finally, the diagnostic properties of the MLSE and its further development will be discussed.

Clinical variants of primary progressive aphasia (PPA) are diagnosed on unique patterns of language dysfunction and corresponding brain changes. Mounting evidence indicates (i) within-/between-category clinical heterogeneity; (ii) overlapping language profiles between variants; and (iii) the presence of co-occurring non-linguistic cognitive deficits in some patients that may be independent of aphasia magnitude and disease severity. The neurobiological bases of such cognitive-linguistic heterogeneity further remain unclear. An understanding of the relationship between these variables is important for improved PPA characterisation. Here, we bridge these knowledge gaps using a data-driven transdiagnostic approach to capture language, cognitive changes and their associations with grey and white matter degeneration across PPA variants, irrespective of diagnostic labels. Forty-seven PPA patients (13 semantic, 15 non-fluent and 19 logopenic variant) underwent assessments of general cognition (Addenbrooke’s Cognitive Examination – III), errors on language performance (Mini Linguistic Status Examination), and structural and diffusion magnetic resonance imaging to capture whole-brain grey and white matter changes, respectively. Behavioural data for all patients were entered into varimax-rotated principal component analyses to derive statistically independent dimensions explaining the majority of performance variation. To uncover neural correlates of cognitive heterogeneity in PPA, emergent components were used as covariates in neuroimaging analyses of grey matter (voxel-based morphometry) and white matter (network-based statistics of structural connectomes). Four behavioural principal components emerged: general cognition, semantics, working memory, and motor-speech/phonology (Figure 1). Performance patterns on the latter three principal components were in keeping with each variant’s characteristic profile (Figure 2). General cognitive changes were most marked in logopenic PPA. Regardless of clinical diagnosis, general cognitive performance was associated with inferior/posterior parietal grey and white matter involvement, semantic dysfunction with bilateral temporal grey and white matter, working memory deficits with temporoparietal and frontostriatal involvement, and motor-speech/phonology impairment with inferior/middle frontal regions (Figure 3). Pervasive cognitive and linguistic heterogeneity in PPA closely relates to individual-level variations on multiple dimensions of behavioural changes and grey and white matter degeneration of regions within and beyond the language network. The employment of such transdiagnostic approaches may help expand clinical boundaries by showing symptom clusters shared across distinct variants.

Este trabajo presenta la adaptación lingüística y cultural de la versión en español argentino de una prueba breve de evaluación del lenguaje, el Minilinguistic State Examination (MLSE) diseñada para diagnosticar, clasificar y monitorear las variantes de la Afasia Progresiva Primaria. Se siguieron los lineamientos de la International Test Commission. El criterio principal fue la equivalencia de propiedades psicolingüísticas con los ítems de la versión original en inglés. Se administró a 20 participantes una primera versión (v1) con el doble de los ítems requeridos. De allí se seleccionaron aquellos con tasas de precisión entre el 80% y 95% y se elaboró una segunda versión (v2) con el número definitivo de estímulos. Esta versión se administró en 31 voluntarios. Como producto de este proceso se obtuvo la versión argentina del MLSE, la cual busca ser equivalente a las otras versiones en desarrollo (inglés, italiano y español peninsular).

Humans use predictions to improve speech perception, especially in noisy environments. Here we use 7-T functional MRI (fMRI) to decode brain representations of written phonological predictions and degraded speech signals in healthy humans and people with selective frontal neurodegeneration (non-fluent variant pri-mary progressive aphasia [nfvPPA]). Multivariate analyses of item-specific patterns of neural activation indi-cate dissimilar representations of verified and violated predictions in left inferior frontal gyrus, suggestive of processing by distinct neural populations. In contrast, precentral gyrus represents a combination of phono-logical information and weighted prediction error. In the presence of intact temporal cortex, frontal neurode-generation results in inflexible predictions. This manifests neurally as a failure to suppress incorrect predic-tions in anterior superior temporal gyrus and reduced stability of phonological representations in precentral gyrus. We propose a tripartite speech perception network in which inferior frontal gyrus supports prediction reconciliation in echoic memory, and precentral gyrus invokes a motor model to instantiate and refine perceptual predictions for speech.

This paper presents the linguistic and cultural adaptation of the Argentinian Spanish version of a brief language assessment test, the Minilinguistic State Examination (MLSE) designed to classify variants of Primary Progressive Aphasia. The guidelines of the International Test Commission were followed. The main criterion was the equivalence of psycholinguistic properties with the items of the original English version. To verify the performance of the adaptation, a first version (v1) with twice the number of items required was administered to 20 participants. Based on an analysis of the items' performance, those with accuracy rates between 80% and 95% were selected to form a second version with the definitive number of items (v2). This final version was tested on 31 participants. The result of this process was the Argentinian Spanish version of the MLSE, which is intended to be equivalent to the other versions under development.

Background Clinical variants of primary progressive aphasia (PPA) are diagnosed based on characteristic patterns of language deficits, supported by corresponding neural changes on brain imaging. However, there is (i) considerable phenotypic variability within and between each diagnostic category with partially overlapping profiles of language performance between variants and (ii) accompanying non-linguistic cognitive impairments that may be independent of aphasia magnitude and disease severity. The neurobiological basis of this cognitive-linguistic heterogeneity remains unclear. Understanding the relationship between these variables would improve PPA clinical/research characterisation and strengthen clinical trial and symptomatic treatment design. We address these knowledge gaps using a data-driven transdiagnostic approach to chart cognitive-linguistic differences and their associations with grey/white matter degeneration across multiple PPA variants. Methods Forty-seven patients (13 semantic, 15 non-fluent, and 19 logopenic variant PPA) underwent assessment of general cognition, errors on language performance, and structural and diffusion magnetic resonance imaging to index whole-brain grey and white matter changes. Behavioural data were entered into varimax-rotated principal component analyses to derive orthogonal dimensions explaining the majority of cognitive variance. To uncover neural correlates of cognitive heterogeneity, derived components were used as covariates in neuroimaging analyses of grey matter (voxel-based morphometry) and white matter (network-based statistics of structural connectomes). Results Four behavioural components emerged: general cognition, semantic memory, working memory, and motor speech/phonology. Performance patterns on the latter three principal components were in keeping with each variant’s characteristic profile, but with a spectrum rather than categorical distribution across the cohort. General cognitive changes were most marked in logopenic variant PPA. Regardless of clinical diagnosis, general cognitive impairment was associated with inferior/posterior parietal grey/white matter involvement, semantic memory deficits with bilateral anterior temporal grey/white matter changes, working memory impairment with temporoparietal and frontostriatal grey/white matter involvement, and motor speech/phonology deficits with inferior/middle frontal grey matter alterations. Conclusions Cognitive-linguistic heterogeneity in PPA closely relates to individual-level variations on multiple behavioural dimensions and grey/white matter degeneration of regions within and beyond the language network. We further show that employment of transdiagnostic approaches may help to understand clinical symptom boundaries and reveal clinical and neural profiles that are shared across categorically defined variants of PPA.

Background Dementia develops as cognitive abilities deteriorate, and early detection is critical for effective preventive interventions. However, mainstream diagnostic tests and screening tools, such as CAMCOG and MMSE, often fail to detect dementia accurately. Various graph-based or feature-dependent prediction and progression models have been proposed. Whenever these models exploit information in the patients’ Electronic Medical Records, they represent promising options to identify the presence and severity of dementia more precisely. Methods The methods presented in this paper aim to address two problems related to dementia: (a) Basic diagnosis: identifying the presence of dementia in individuals, and (b) Severity diagnosis: predicting the presence of dementia, as well as the severity of the disease. We formulate these two tasks as classification problems and address them using machine learning models based on random forests and decision tree, analysing structured clinical data from an elderly population cohort. We perform a hybrid data curation strategy in which a dementia expert is involved to verify that curation decisions are meaningful. We then employ the machine learning algorithms that classify individual episodes into a specific dementia class. Decision trees are also used for enhancing the explainability of decisions made by prediction models, allowing medical experts to identify the most crucial patient features and their threshold values for the classification of dementia. Results Our experiment results prove that baseline arithmetic or cognitive tests, along with demographic features, can predict dementia and its severity with high accuracy. In specific, our prediction models have reached an average f1-score of 0.93 and 0.81 for problems (a) and (b), respectively. Moreover, the decision trees produced for the two issues empower the interpretability of the prediction models. Conclusions This study proves that there can be an accurate estimation of the existence and severity of dementia disease by analysing various electronic medical record features and cognitive tests from the episodes of the elderly population. Moreover, a set of decision rules may comprise the building blocks for an efficient patient classification. Relevant clinical and screening test features (e.g. simple arithmetic or animal fluency tasks) represent precise predictors without calculating the scores of mainstream cognitive tests such as MMSE and CAMCOG. Such predictive model can identify not only meaningful features, but also justifications of classification. As a result, the predictive power of machine learning models over curated clinical data is proved, paving the path for a more accurate diagnosis of dementia.

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.

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 fine-grained 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 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.