The impact of bilateral versus unilateral anterior temporal lobe damage on face recognition, person knowledge and semantic memory.

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.

Identifying the presence and severity of dementia by applying interpretable machine learning techniques on structured clinical records.

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.

Perioperative Cerebral Microbleeds After Adult Cardiac Surgery.

Background and Purpose- Cerebral microbleeds (CMBs) have been observed using magnetic resonance imaging in patients with cardiovascular risk factors, cognitive deterioration, small vessel disease, and dementia. They are a well-known consequence of cerebral amyloid angiopathy, chronic hypertension, and diffuse axonal injury, among other causes. However, the frequency and location of new CMBs postadult cardiac surgery, in association with cognition and perioperative risk factors, have yet to be studied. Methods- Pre- and postsurgery magnetic resonance susceptibility-weighted images and neuropsychological tests were analyzed from a total of 75 patients undergoing cardiac surgery (70 men; mean age, 63±10 years). CMBs were identified by a neuroradiologist blinded to clinical details who independently assessed the presence and location of CMBs using standardized criteria. Results- New CMBs were identified in 76% of patients after cardiac surgery. The majority of new CMBs were located in the frontal lobe (46%) followed by the parietal lobe (15%), cerebellum (13%), occipital lobe (12%), and temporal lobe (8%). Patients with new CMBs typically began with a higher prevalence of preexisting CMBs ( P=0.02). New CMBs were associated with longer cardiopulmonary bypass times ( P=0.003), and there was a borderline association with lower percentage hematocrit ( P=0.04). Logistic regression analysis suggested a ≈2% increase in the odds of acquiring new CMBs during cardiac surgery for every minute of bypass time (odds ratio, 1.02; 95% CI, 1.00-1.05; P=0.04). Postoperative neuropsychological decline was observed in 44% of patients and seemed to be unrelated to new CMBs. Conclusions- New CMBs identified using susceptibility-weighted images were found in 76% of patients who underwent cardiac surgery. CMBs were globally distributed with the highest numbers in the frontal and parietal lobes. Our regression analysis indicated that length of cardiopulmonary bypass time and lowered hematocrit may be significant predictors for new CMBs after cardiac surgery. Clinical Trial Registration- URL: http://www.isrctn.com . Unique identifier: 66022965.

Screening for Aphasia in NeuroDegeneration for the Diagnosis of Patients with Primary Progressive Aphasia: Clinical Validity and Psychometric Properties.

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.

SAND: a Screening for Aphasia in NeuroDegeneration. Development and normative data.

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.

Innovative diagnostic tools for early detection of Alzheimer's disease.

Current state-of-the-art diagnostic measures of Alzheimer's disease (AD) are invasive (cerebrospinal fluid analysis), expensive (neuroimaging) and time-consuming (neuropsychological assessment) and thus have limited accessibility as frontline screening and diagnostic tools for AD. Thus, there is an increasing need for additional noninvasive and/or cost-effective tools, allowing identification of subjects in the preclinical or early clinical stages of AD who could be suitable for further cognitive evaluation and dementia diagnostics. Implementation of such tests may facilitate early and potentially more effective therapeutic and preventative strategies for AD. Before applying them in clinical practice, these tools should be examined in ongoing large clinical trials. This review will summarize and highlight the most promising screening tools including neuropsychometric, clinical, blood, and neurophysiological tests.

Connected speech as a marker of disease progression in autopsy-proven Alzheimer's disease.

Although an insidious history of episodic memory difficulty is a typical presenting symptom of Alzheimer's disease, detailed neuropsychological profiling frequently demonstrates deficits in other cognitive domains, including language. Previous studies from our group have shown that language changes may be reflected in connected speech production in the earliest stages of typical Alzheimer's disease. The aim of the present study was to identify features of connected speech that could be used to examine longitudinal profiles of impairment in Alzheimer's disease. Samples of connected speech were obtained from 15 former participants in a longitudinal cohort study of ageing and dementia, in whom Alzheimer's disease was diagnosed during life and confirmed at post-mortem. All patients met clinical and neuropsychological criteria for mild cognitive impairment between 6 and 18 months before converting to a status of probable Alzheimer's disease. In a subset of these patients neuropsychological data were available, both at the point of conversion to Alzheimer's disease, and after disease severity had progressed from the mild to moderate stage. Connected speech samples from these patients were examined at later disease stages. Spoken language samples were obtained using the Cookie Theft picture description task. Samples were analysed using measures of syntactic complexity, lexical content, speech production, fluency and semantic content. Individual case analysis revealed that subtle changes in language were evident during the prodromal stages of Alzheimer's disease, with two-thirds of patients with mild cognitive impairment showing significant but heterogeneous changes in connected speech. However, impairments at the mild cognitive impairment stage did not necessarily entail deficits at mild or moderate stages of disease, suggesting non-language influences on some aspects of performance. Subsequent examination of these measures revealed significant linear trends over the three stages of disease in syntactic complexity, semantic and lexical content. The findings suggest, first, that there is a progressive disruption in language integrity, detectable from the prodromal stage in a subset of patients with Alzheimer's disease, and secondly that measures of semantic and lexical content and syntactic complexity best capture the global progression of linguistic impairment through the successive clinical stages of disease. The identification of disease-specific language impairment in prodromal Alzheimer's disease could enhance clinicians' ability to distinguish probable Alzheimer's disease from changes attributable to ageing, while longitudinal assessment could provide a simple approach to disease monitoring in therapeutic trials.

The neural substrates of transdiagnostic cognitive-linguistic heterogeneity in primary progressive aphasia.

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.

Temporal lobe perceptual predictions for speech are instantiated in motor cortex and reconciled by inferior frontal cortex.

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 primary progressive aphasia [nfvPPA]). Multivariate analyses of item-specific patterns of neural activation indicate 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 phonological information and weighted prediction error. In the presence of intact temporal cortex, frontal neurodegeneration results in inflexible predictions. This manifests neurally as a failure to suppress incorrect predictions 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.

Logopenic aphasia in Alzheimer's disease: clinical variant or clinical feature?

BACKGROUND: Primary progressive aphasia (PPA) is a clinical syndrome characterised by progressive decline in components of the language system. Recent evidence suggests that the logopenic/phonological (LPA) variant is a reliable in vivo marker of Alzheimer related pathology. The aim of this study was to determine if patients with clinically typical early stage Alzheimer's disease (AD) display a characteristic language disorder that resembles LPA, or if LPA is a clinical manifestation of an atypical form of AD. METHODS: Spoken language samples were obtained using the Cookie Theft picture description task from 18 post mortem confirmed cases of AD, where speech samples were taken at the first point of clinical diagnosis, and 18 post mortem confirmed healthy controls. Spoken samples were transcribed from tape recordings and analysed using the scoring system described by Wilson et al. RESULTS: Group comparisons between normal controls and AD patients showed no significant overall differences. Individual review of the linguistic variables compared with the PPA variants showed that a third of patients had normal language (n=6). The remainder showed varied patterns of linguistic impairment. In the majority of the affected group, the most salient feature was a reduction in one or more measures of syntactic complexity. One patient's deficit was comparable to that found in LPA. CONCLUSIONS: The impairment found in clinically typical early stage AD did not correspond consistently to the linguistic profiles described in any of the sub-syndromes of PPA. The only reliably distinguishing feature was a reduction across a range of syntactic complexity measures. The findings suggest that LPA represents an atypical clinical presentation of AD rather than a common clinical feature of typical AD.

Machine learning methods applied to genotyping data capture interactions between single nucleotide variants in late onset Alzheimer's disease.

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.

A 'Mini Linguistic State Examination' to classify primary progressive aphasia.

There are few available methods for qualitatively evaluating patients with primary progressive aphasia. 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-which was designed uniquely to enable a clinician to assess and subclassify both classical and mixed presentations of primary progressive aphasia. 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. 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 primary progressive aphasia subtypes and create a decision tree as a guide to clinical classification. The random forest prediction model was 96% accurate overall (92% for the logopenic variant, 93% for the semantic variant and 98% for the non-fluent variant). The derived decision tree produced a correct classification of 91% of participants whose data were not included in the training set. The Mini Linguistic State Examination 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 primary progressive aphasia syndromes to clinical gold standard diagnoses. Adoption of the Mini Linguistic State Examination 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.

Spanish Version of the Mini-Linguistic State Examination for the Diagnosis of Primary Progressive Aphasia.

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.

Language Disorder in Progressive Supranuclear Palsy and Corticobasal Syndrome: Neural Correlates and Detection by the MLSE Screening Tool.

Background: Progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS) affect speech and language as well as motor functions. Clinical and neuropathological data indicate a close relationship between these two disorders and the non-fluent variant of primary progressive aphasia (nfvPPA). We use the recently developed Mini Linguistic State Examination tool (MLSE) to study speech and language disorders in patients with PSP, CBS, and nfvPPA, in combination with structural magnetic resonance imaging (MRI). Methods: Fifty-one patients (PSP N = 13, CBS N = 19, nfvPPA N = 19) and 30 age-matched controls completed the MLSE, the short form of the Boston Diagnostic Aphasia Examination (BDAE), and the Addenbrooke's Cognitive Examination III. Thirty-eight patients and all controls underwent structural MRI at 3 Tesla, with T1 and T2-weighted images processed by surface-based and subcortical segmentation within FreeSurfer 6.0.0 to extract cortical thickness and subcortical volumes. Morphometric differences were compared between groups and correlated with the severity of speech and language impairment. Results: CBS and PSP patients showed impaired MLSE performance, compared to controls, with a similar language profile to nfvPPA, albeit less severe. All patient groups showed reduced cortical thickness in bilateral frontal regions and striatal volume. PSP and nfvPPA patients also showed reduced superior temporal cortical thickness, with additional thalamic and amygdalo-hippocampal volume reductions in nfvPPA. Multivariate analysis of brain-wide cortical thickness and subcortical volumes with MLSE domain scores revealed associations between performance on multiple speech and language domains with atrophy of left-lateralised fronto-temporal cortex, amygdala, hippocampus, putamen, and caudate. Conclusions: The effect of PSP and CBS on speech and language overlaps with nfvPPA. These three disorders cause a common anatomical pattern of atrophy in the left frontotemporal language network and striatum. The MLSE is a short clinical screening tool that can identify the language disorder of PSP and CBS, facilitating clinical management and patient access to future clinical trials.

Experimental models of vascular dementia and vascular cognitive impairment: a systematic review.

Vascular cognitive impairment (VCI) encompasses vascular dementia and is the second most common cause of dementing illness after Alzheimer's disease. The main causes of VCI are: cerebral small vessel disease; multi-infarct dementia; strategic infarct (i.e. located in a functionally-critical brain area); haemorrhage/microbleed; angiopathy (including cerebral amyloid angiopathy); severe hypoperfusion (e.g. cardiac arrhythmia); and hereditary vasculopathy (e.g. cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, CADASIL). In this systematic analysis, we aimed to relate cognitive and neuropathological features of experimental models to clinical VCI. We extracted data from 107 studies covering 16 models. These included: brief global ischaemic insults (in rats, mice or gerbils); chronic global hypoperfusion (rats, mice, gerbils); chronic hypertension (in primates or stroke-prone, spontaneously-hypertensive rats); multiple ischaemic lesions because of intra-vascular emboli (in rodents, rabbits or primates); strategic ischaemic lesions (in rats or mini-pigs); generalised vasculopathies, because of mutant Notch3, hyperhomocysteinaemia, experimental diabetes mellitus or lack of cerebral vasodilator M(5) receptors (rats or mice). Most cognitive testing showed deficits in working and reference memory. The lesions observed were microinfarcts, diffuse white matter lesions, hippocampal neuronal death, focal ischaemic lesions and micro-haemorrhages. The most-used model was bilateral carotid artery occlusion in rats, leading to chronic hypoperfusion and white matter injury.

Abnormal discourse in semantic dementia: a data-driven approach.

Structural and content-related deficits occur in connected discourse of patients with semantic dementia (SD). We used principal components analysis (PCA) to characterise the sources of variation in word usage during picture description by controls and SD patients. This data-driven approach allowed: comparison of the distance between individuals in the two-dimensional space; correlational analyses between principal component (PC) values and performance on other tests; identification of words whose variance contributed most to the definition of the PCs.Transcripts of Cookie Theft picture descriptions from 21 patients with SD and 21 controls were used to generate frequencies of all word types (n = 557) across participants. Frequency values of words with ≥10 occurrences (n= 81) were entered into a PCA. Values of emergent dimensions were correlated with performance on tests of single word meaning. The first PC accounted for 59% of the variance, the second for a further 10%. Patients and controls showed good separation within the resulting space. Factor loading scores indicated that control performance was characterised by function (factor 1) and content (factor 2) word usage, while patients showed a greater tendency to use pronouns, deictic and generic words. Knowledge of single word meaning correlated with factor 1 but not with factor 2. Differences in word usage can differentiate connected speech of SD patients from controls using a rapid, automated, data-driven algorithm. The distinction between groups, loadings on the two components, and their differential correlations with semantic tasks raise the possibility of independent differences in syntax and lexical content.

How to do things with (thousands of) words: Computational approaches to discourse analysis in Alzheimer's disease.

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.

Neurological impact of emboli during adult cardiac surgery.

OBJECTIVES: This study draws on advances in Doppler ultrasound bubble sizing to investigate whether high volumes of macro-bubbles entering the brain during cardiac surgery increase the risk of new cerebral microbleeds (CMBs), ischemic MR lesions, or post-operative cognitive decline (POCD). METHODS: Transcranial Doppler (TCD) ultrasound recordings were analysed to estimate numbers of emboli and macrobubbles (>100 µm) entering the brain during cardiac surgery. Logistic regression was used to explore the hypothesis that emboli characteristics affect the incidence of new brain injuries identified through pre- and post-operative MRI and neuropsychological testing. RESULTS: TCD, MRI, and neuropsychological test data were compared between 28 valve and 18 CABG patients. Although valve patients received over twice as many emboli per procedure [median: 1995 vs. 859, p = .004], and seven times as many macro-bubbles [median: 218 vs. 28, p = .001], high volumes of macrobubbles were not found to be significantly associated with new CMBs, new ischaemic lesions, or POCD. The odds of acquiring new CMBs increased by approximately 5% [95% CI: 1 to 10%] for every embolus detected in the first minute after the release of the aortic cross-clamp (AxC). Logistic regression models also confirmed previous findings that cardiopulmonary bypass time and valve surgery were significant predictors for new CMBs (both p = .03). Logistic regression analysis estimated an increase in the odds of acquiring new CMBs of 6% [95% CI: 1 to 12%] for every minute of bypass time over 91 mins. CONCLUSIONS: This small study provides new information about the properties and numbers of bubbles entering the brain during surgery, but found no evidence to substantiate a direct link between large numbers of macrobubbles and adverse cognitive or MR outcome. Clinical Trial Registration URL - http://www.isrctn.com. Unique identifier: 66022965.

The language profile of progressive supranuclear palsy.

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.