Metabolic network alterations as a supportive biomarker in dementia with Lewy bodies with preserved dopamine transmission.

PURPOSE: Metabolic network analysis of FDG-PET utilizes an index of inter-regional correlation of resting state glucose metabolism and has been proven to provide complementary information regarding the disease process in parkinsonian syndromes. The goals of this study were (i) to evaluate pattern similarities of glucose metabolism and network connectivity in dementia with Lewy bodies (DLB) subjects with subthreshold dopaminergic loss compared to advanced disease stages and to (ii) investigate metabolic network alterations of FDG-PET for discrimination of patients with early DLB from other neurodegenerative disorders (Alzheimer's disease, Parkinson's disease, multiple system atrophy) at individual patient level via principal component analysis (PCA). METHODS: FDG-PETs of subjects with probable or possible DLB (n = 22) without significant dopamine deficiency (z-score < 2 in putamen binding loss on DaT-SPECT compared to healthy controls (HC)) were scaled by global-mean, prior to volume-of-interest-based analyses of relative glucose metabolism. Single region metabolic changes and network connectivity changes were compared against HC (n = 23) and against DLB subjects with significant dopamine deficiency (n = 86). PCA was applied to test discrimination of patients with DLB from disease controls (n = 101) at individual patient level. RESULTS: Similar patterns of hypo- (parietal- and occipital cortex) and hypermetabolism (basal ganglia, limbic system, motor cortices) were observed in DLB patients with and without significant dopamine deficiency when compared to HC. Metabolic connectivity alterations correlated between DLB patients with and without significant dopamine deficiency (R2 = 0.597, p < 0.01). A PCA trained by DLB patients with dopamine deficiency and HC discriminated DLB patients without significant dopaminergic loss from other neurodegenerative parkinsonian disorders at individual patient level (area-under-the-curve (AUC): 0.912). CONCLUSION: Disease-specific patterns of altered glucose metabolism and altered metabolic networks are present in DLB subjects without significant dopaminergic loss. Metabolic network alterations in FDG-PET can act as a supporting biomarker in the subgroup of DLB patients without significant dopaminergic loss at symptoms onset.

Orienting to different dimensions of word meaning alters the representation of word meaning in early processing regions.

Conscious processing of word meaning can be guided by attention. In this event-related functional magnetic resonance imaging study in 22 healthy young volunteers, we examined in which regions orienting attention to two fundamental and generic dimensions of word meaning, concreteness versus valence, alters the semantic representations coded in activity patterns. The stimuli consisted of 120 nouns in written or spoken modality which varied factorially along the concreteness and valence axis. Participants performed a forced-choice judgement of either concreteness or valence. Rostral and subgenual anterior cingulate were strongly activated during valence judgement, and precuneus and the dorsal attention network during concreteness judgement. Task and stimulus type interacted in right posterior fusiform gyrus, left lingual gyrus, precuneus, and insula. In the right posterior fusiform gyrus and the left lingual gyrus, the correlation between the pairwise similarity in activity patterns evoked by words and the pairwise distance in valence and concreteness was modulated by the direction of attention, word valence or concreteness. The data indicate that orienting attention to basic dimensions of word meaning exerts effects on the representation of word meaning in more peripheral nodes, such as the ventral occipital cortex, rather than the core perisylvian language regions.

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review.

INTRODUCTION: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.

Blood-based Aß42 increases in the earliest pre-pathological stage before decreasing with progressive amyloid pathology in preclinical models and human subjects: opening new avenues for prevention.

Blood-based (BB) biomarkers for Aß and tau can indicate pathological processes in the brain, in the early pathological, even pre-symptomatic stages in Alzheimer's disease. However, the relation between BB biomarkers and AD-related processes in the brain in the earliest pre-pathology stage before amyloid pathology develops, and their relation with total brain concentrations of Aß and tau, is poorly understood. This stage presents a critical window for the earliest prevention of AD. Preclinical models with well-defined temporal progression to robust amyloid and tau pathology provide a unique opportunity to study this relation and were used here to study the link between BB biomarkers with AD-related processes in pre- and pathological stages. We performed a cross-sectional study at different ages assessing the link between BB concentrations and AD-related processes in the brain. This was complemented with a longitudinal analysis and with analysis of age-related changes in a small cohort of human subjects. We found that BB-tau concentrations increased in serum, correlating with progressive development of tau pathology and with increasing tau aggregates and p-tau concentrations in brain in TauP301S mice (PS19) developing tauopathy. BB-Aß42 concentrations in serum decreased between 4.5 and 9 months of age, correlating with the progressive development of robust amyloid pathology in APP/PS1 (5xFAD) mice, in line with previous findings. Most importantly, BB-Aß42 concentrations significantly increased between 1.5 and 4.5 months, i.e., in the earliest pre-pathological stage, before robust amyloid pathology develops in the brain, indicating biphasic BB-Aß42 dynamics. Furthermore, increasing BB-Aß42 in the pre-pathological phase, strongly correlated with increasing Aß42 concentrations in brain. Our subsequent longitudinal analysis of BB-Aß42 in 5xFAD mice, confirmed biphasic BB-Aß42, with an initial increase, before decreasing with progressive robust pathology. Furthermore, in human samples, BB-Aß42 concentrations were significantly higher in old (> 60 years) compared to young (< 50 years) subjects, as well as to age-matched AD patients, further supporting age-dependent increase of Aß42 concentrations in the earliest pre-pathological phase, before amyloid pathology. Also BB-Aß40 concentrations were found to increase in the earliest pre-pathological phase both in preclinical models and human subjects, while subsequent significantly decreasing concentrations in the pathological phase were characteristic for BB-Aß42. Together our data indicate that BB biomarkers reflect pathological processes in brain of preclinical models with amyloid and tau pathology, both in the pathological and pre-pathological phase. Our data indicate a biphasic pattern of BB-Aß42 in preclinical models and a human cohort. And most importantly, we here show that BB-Aß increased and correlated with increasing concentrations of Aß in the brain, in the earliest pre-pathological stage in a preclinical model. Our data thereby identify a novel critical window for prevention, using BB-Aß as marker for accumulating Aß in the brain, in the earliest pre-pathological stage, opening new avenues for personalized early preventive strategies against AD, even before amyloid pathology develops.

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.

PURPOSE: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. MATERIALS AND METHODS: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. RESULTS: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6-100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7-100) in AD, 71.4% (51.6-91.2) in MCI-AD, and 94.7% (90-99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. CONCLUSION: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus.

The Dutch Oxford Cognitive Screen (OCS-NL): psychometric properties in Flemish stroke survivors.

BACKGROUND AND PURPOSE: The Oxford Cognitive Screen is a stroke-specific screen to evaluate attention, executive functions, memory, praxis, language, and numeric cognition. It was originally validated in England for acute stroke patients. In this study, we examined the psychometric properties of the Dutch OCS (OCS-NL). METHODS: A total of 193 (99 acute stroke unit, 94 rehabilitation unit) patients were included in our study. A subset of patients (n = 128) completed a retest with the parallel version of the OCS-NL. RESULTS: First, we did not find evidence for a difference in prevalence of impairment between patients in the acute stroke versus rehabilitation unit on all but one of the subtests. For praxis, we observed a 14% lower prevalence of impairment in the rehabilitation than the acute stroke unit. Second, the parallel-form reliability ranged from weak to excellent across subtests. Third, in stroke patients below age 60, the OCS-NL had a 92% sensitivity relative to the MoCA, while the MoCA had a 55% sensitivity relative to the OCS-NL. Last, although left-hemispheric stroke patients performed worse on almost all MoCA subdomains, they performed similarly to right-hemispheric stroke patients on non-language domains on the OCS-NL. CONCLUSIONS: Our results suggest that the OCS-NL is a reliable cognitive screen that can be used in acute stroke and rehabilitation units. The OCS-NL may be more sensitive to detect cognitive impairment in young stroke patients and less likely to underestimate cognitive abilities in left-hemispheric stroke patients than the MoCA.

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones.

European Stroke Organisation and European Academy of Neurology joint guidelines on post-stroke cognitive impairment.

BACKGROUND AND PURPOSE: The optimal management of post-stroke cognitive impairment (PSCI) remains controversial. These joint European Stroke Organisation (ESO) and European Academy of Neurology (EAN) guidelines provide evidence-based recommendations to assist clinicians in decision making regarding prevention, diagnosis, treatment and prognosis. METHODS: Guidelines were developed according to the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The working group identified relevant clinical questions, performed systematic reviews, assessed the quality of the available evidence, and made specific recommendations. Expert consensus statements were provided where insufficient evidence was available to provide recommendations. RESULTS: There was limited randomized controlled trial (RCT) evidence regarding single or multicomponent interventions to prevent post-stroke cognitive decline. Lifestyle interventions and treating vascular risk factors have many health benefits, but a cognitive effect is not proven. We found no evidence regarding routine cognitive screening following stroke, but recognize the importance of targeted cognitive assessment. We describe the accuracy of various cognitive screening tests, but found no clearly superior approach to testing. There was insufficient evidence to make a recommendation for use of cholinesterase inhibitors, memantine nootropics or cognitive rehabilitation. There was limited evidence on the use of prediction tools for post-stroke cognition. The association between PSCI and acute structural brain imaging features was unclear, although the presence of substantial white matter hyperintensities of presumed vascular origin on brain magnetic resonance imaging may help predict cognitive outcomes. CONCLUSIONS: These guidelines highlight fundamental areas where robust evidence is lacking. Further definitive RCTs are needed, and we suggest priority areas for future research.

Associations among education, age, and the dementia with Lewy bodies (DLB) metabolic pattern: A European-DLB consortium project.

INTRODUCTION: We assessed the influence of education as a proxy of cognitive reserve and age on the dementia with Lewy bodies (DLB) metabolic pattern. METHODS: Brain 18F-fluorodeoxyglucose positron emission tomography and clinical/demographic information were available in 169 probable DLB patients included in the European DLB-consortium database. Principal component analysis identified brain regions relevant to local data variance. A linear regression model was applied to generate age- and education-sensitive maps corrected for Mini-Mental State Examination score, sex (and either education or age). RESULTS: Age negatively covaried with metabolism in bilateral middle and superior frontal cortex, anterior and posterior cingulate, reducing the expression of the DLB-typical cingulate island sign (CIS). Education negatively covaried with metabolism in the left inferior parietal cortex and precuneus (making the CIS more prominent). DISCUSSION: These findings point out the importance of tailoring interpretation of DLB biomarkers considering the concomitant effect of individual, non-disease-related variables such as age and cognitive reserve.

Representation of associative and affective semantic similarity of abstract words in the lateral temporal perisylvian language regions.

The examination of semantic cognition has traditionally identified word concreteness as well as valence as two of the principal dimensions in the representation of conceptual knowledge. More recently, corpus-based vector space models as well as graph-theoretical analysis of large-scale task-related behavioural responses have revolutionized our insight into how the meaning of words is structured. In this fMRI study, we apply representational similarity analysis to investigate the conceptual representation of abstract words. Brain activity patterns were related to a cued-association based graph as well as to a vector-based co-occurrence model of word meaning. Twenty-six subjects (19 females and 7 males) performed an overt repetition task during fMRI. First, we performed a searchlight classification procedure to identify regions where activity is discriminable between abstract and concrete words. These regions were left inferior frontal gyrus, the upper and lower bank of the superior temporal sulcus bilaterally, posterior middle temporal gyrus and left fusiform gyrus. Representational Similarity Analysis demonstrated that for abstract words, the similarity of activity patterns in the cortex surrounding the superior temporal sulcus bilaterally and in the left anterior superior temporal gyrus reflects the similarity in word meaning. These effects were strongest for semantic similarity derived from the cued association-based graph and for affective similarity derived from either of the two models. The latter effect was mainly driven by positive valence words. This research highlights the close neurobiological link between the information structure of abstract and affective word content and the similarity in activity pattern in the lateral and anterior temporal language system.

Metabolic patterns across core features in dementia with lewy bodies.

OBJECTIVE: To identify brain regions whose metabolic impairment contributes to dementia with Lewy bodies (DLB) clinical core features expression and to assess the influence of severity of global cognitive impairment on the DLB hypometabolic pattern. METHODS: Brain fluorodeoxyglucose positron emission tomography and information on core features were available in 171 patients belonging to the imaging repository of the European DLB Consortium. Principal component analysis was applied to identify brain regions relevant to the local data variance. A linear regression model was applied to generate core-feature-specific patterns controlling for the main confounding variables (Mini-Mental State Examination [MMSE], age, education, gender, and center). Regression analysis to the locally normalized intensities was performed to generate an MMSE-sensitive map. RESULTS: Parkinsonism negatively covaried with bilateral parietal, precuneus, and anterior cingulate metabolism; visual hallucinations (VH) with bilateral dorsolateral-frontal cortex, posterior cingulate, and parietal metabolism; and rapid eye movement sleep behavior disorder (RBD) with bilateral parieto-occipital cortex, precuneus, and ventrolateral-frontal metabolism. VH and RBD shared a positive covariance with metabolism in the medial temporal lobe, cerebellum, brainstem, basal ganglia, thalami, and orbitofrontal and sensorimotor cortex. Cognitive fluctuations negatively covaried with occipital metabolism and positively with parietal lobe metabolism. MMSE positively covaried with metabolism in the left superior frontal gyrus, bilateral-parietal cortex, and left precuneus, and negatively with metabolism in the insula, medial frontal gyrus, hippocampus in the left hemisphere, and right cerebellum. INTERPRETATION: Regions of more preserved metabolism are relatively consistent across the variegate DLB spectrum. By contrast, core features were associated with more prominent hypometabolism in specific regions, thus suggesting a close clinical-imaging correlation, reflecting the interplay between topography of neurodegeneration and clinical presentation in DLB patients. Ann Neurol 2019;85:715-725.

Metabolic Correlates of Dopaminergic Loss in Dementia with Lewy Bodies.

BACKGROUND: Striatal dopamine deficiency and metabolic changes are well-known phenomena in dementia with Lewy bodies and can be quantified in vivo by 123 I-Ioflupane brain single-photon emission computed tomography of dopamine transporter and 18 F-fluorodesoxyglucose PET. However, the linkage between both biomarkers is ill-understood. OBJECTIVE: We used the hitherto largest study cohort of combined imaging from the European consortium to elucidate the role of both biomarkers in the pathophysiological course of dementia with Lewy bodies. METHODS: We compared striatal dopamine deficiency and glucose metabolism of 84 dementia with Lewy body patients and comparable healthy controls. After normalization of data, we tested their correlation by region-of-interest-based and voxel-based methods, controlled for study center, age, sex, education, and current cognitive impairment. Metabolic connectivity was analyzed by inter-region coefficients stratified by dopamine deficiency and compared to healthy controls. RESULTS: There was an inverse relationship between striatal dopamine availability and relative glucose hypermetabolism, pronounced in the basal ganglia and in limbic regions. With increasing dopamine deficiency, metabolic connectivity showed strong deteriorations in distinct brain regions implicated in disease symptoms, with greatest disruptions in the basal ganglia and limbic system, coincident with the pattern of relative hypermetabolism. CONCLUSIONS: Relative glucose hypermetabolism and disturbed metabolic connectivity of limbic and basal ganglia circuits are metabolic correlates of dopamine deficiency in dementia with Lewy bodies. Identification of specific metabolic network alterations in patients with early dopamine deficiency may serve as an additional supporting biomarker for timely diagnosis of dementia with Lewy bodies. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Left perirhinal cortex codes for semantic similarity between written words defined from cued word association.

Knowledge of visual and nonvisual attributes of concrete entities is distributed over neocortical uni- and polymodal association cortex. Here we investigated the role of left perirhinal cortex in explicit knowledge retrieval from written words. We examined whether it extended across visual and nonvisual properties, animate and inanimate entities, how this differed from picture input and how specific it was for perirhinal cortex compared to surrounding structures. The semantic similarity between stimuli was determined on the basis of a word association-based model. Eighteen participants participated in this event-related fMRI experiment. During property verification, the left perirhinal cortex coded for the similarity in meaning between written words. No differences were found between visual and nonvisual properties or between animate and inanimate entities. Among the surrounding regions, a semantic similarity effect for written words was also present in the left parahippocampal gyrus, but not in the hippocampus nor in the right perirhinal cortex. Univariate analysis revealed higher activity for visual property verification in visual processing regions and for nonvisual property verification in an extended system encompassing the superior temporal sulcus along its anterior-posterior axis, the inferior and the superior frontal gyrus. The association strength between the concept and the property correlated positively with fMRI response amplitude in visual processing regions, and negatively with response amplitude in left inferior and superior frontal gyrus. The current findings establish that input-modality determines the semantic similarity effect in left perirhinal cortex more than the content of the knowledge retrieved or the semantic control demand do. We propose that left perirhinal cortex codes for the association between a concrete written word and the object it refers to and operates as a connector hub linking written word input to the distributed cortical representation of word meaning.

Cerebrospinal fluid levels of synaptic and neuronal integrity correlate with gray matter volume and amyloid load in the precuneus of cognitively intact older adults.

The main pathophysiological alterations of Alzheimer's disease (AD) include loss of neuronal and synaptic integrity, amyloidogenic processing, and neuroinflammation. Similar alterations can, however, also be observed in cognitively intact older subjects and may prelude the clinical manifestation of AD. The objectives of this prospective cross-sectional study in a cohort of 38 cognitively intact older adults were twofold: (i) to investigate the latent relationship among cerebrospinal fluid (CSF) biomarkers reflecting the main pathophysiological processes of AD, and (ii) to assess the correlation between these biomarkers and gray matter volume as well as amyloid load. All subjects underwent extensive neuropsychological examinations, CSF sampling, [18 F]-flutemetamol amyloid positron emission tomography, and T1 -weighted magnetic resonance imaging. A factor analysis revealed one factor that explained most of the variance in the CSF biomarker dataset clustering t-tau, α-synuclein, p-tau181 , neurogranin, BACE1, visinin-like protein 1, chitinase-3-like protein 1 (YKL-40), Aß1-40 and Aß1-38 . Higher scores on this factor correlated with lower gray matter volume and with higher amyloid load in the precuneus. At the level of individual CSF biomarkers, levels of visinin-like protein 1, neurogranin, BACE1, Aß1-40 , Aß1-38, and YKL-40 all correlated inversely with gray matter volume of the precuneus. These findings demonstrate that in cognitively intact older subjects, CSF levels of synaptic and neuronal integrity biomarkers, amyloidogenic processing and measures of innate immunity (YKL-40) display a latent structure of common variance, which is associated with loss of structural integrity of brain regions implicated in the earliest stages of AD. OPEN SCIENCE BADGES: This article has received a badge for *Open Materials* because it provided all relevant information to reproduce the study in the manuscript, and for *Preregistration* because the study was pre-registered at https://osf.io/7qm9t/. The complete Open Science Disclosure form for this article can be found at the end of the article. More information about the Open Practices badges can be found at https://cos.io/our-services/open-science-badges/.

Distinct [18F]THK5351 binding patterns in primary progressive aphasia variants.

PURPOSE: To assess the binding of the PET tracer [18F]THK5351 in patients with different primary progressive aphasia (PPA) variants and its correlation with clinical deficits. The majority of patients with nonfluent variant (NFV) and logopenic variant (LV) PPA have underlying tauopathy of the frontotemporal lobar or Alzheimer disease type, respectively, while patients with the semantic variant (SV) have predominantly transactive response DNA binding protein 43-kDa pathology. METHODS: The study included 20 PPA patients consecutively recruited through a memory clinic (12 NFV, 5 SV, 3 LV), and 20 healthy controls. All participants received an extensive neurolinguistic assessment, magnetic resonance imaging and amyloid biomarker tests. [18F]THK5351 binding patterns were assessed on standardized uptake value ratio (SUVR) images with the cerebellar grey matter as the reference using statistical parametric mapping. Whole-brain voxel-wise regression analysis was performed to evaluate the association between [18F]THK5351 SUVR images and neurolinguistic scores. Analyses were performed with and without partial volume correction. RESULTS: Patients with NFV showed increased binding in the supplementary motor area, left premotor cortex, thalamus, basal ganglia and midbrain compared with controls and patients with SV. Patients with SV had increased binding in the temporal lobes bilaterally and in the right ventromedial frontal cortex compared with controls and patients with NFV. The whole-brain voxel-wise regression analysis revealed a correlation between agrammatism and motor speech impairment, and [18F]THK5351 binding in the left supplementary motor area and left postcentral gyrus. Analysis of [18F]THK5351 scans without partial volume correction revealed similar results. CONCLUSION: [18F]THK5351 imaging shows a topography closely matching the anatomical distribution of predicted underlying pathology characteristic of NFV and SV PPA. [18F]THK5351 binding correlates with the severity of clinical impairment.

Cross-modal representation of spoken and written word meaning in left pars triangularis.

The correspondence in meaning extracted from written versus spoken input remains to be fully understood neurobiologically. Here, in a total of 38 subjects, the functional anatomy of cross-modal semantic similarity for concrete words was determined based on a dual criterion: First, a voxelwise univariate analysis had to show significant activation during a semantic task (property verification) performed with written and spoken concrete words compared to the perceptually matched control condition. Second, in an independent dataset, in these clusters, the similarity in fMRI response pattern to two distinct entities, one presented as a written and the other as a spoken word, had to correlate with the similarity in meaning between these entities. The left ventral occipitotemporal transition zone and ventromedial temporal cortex, retrosplenial cortex, pars orbitalis bilaterally, and the left pars triangularis were all activated in the univariate contrast. Only the left pars triangularis showed a cross-modal semantic similarity effect. There was no effect of phonological nor orthographic similarity in this region. The cross-modal semantic similarity effect was confirmed by a secondary analysis in the cytoarchitectonically defined BA45. A semantic similarity effect was also present in the ventral occipital regions but only within the visual modality, and in the anterior superior temporal cortex only within the auditory modality. This study provides direct evidence for the coding of word meaning in BA45 and positions its contribution to semantic processing at the confluence of input-modality specific pathways that code for meaning within the respective input modalities.

Core auditory processing deficits in primary progressive aphasia.

The extent to which non-linguistic auditory processing deficits may contribute to the phenomenology of primary progressive aphasia is not established. Using non-linguistic stimuli devoid of meaning we assessed three key domains of auditory processing (pitch, timing and timbre) in a consecutive series of 18 patients with primary progressive aphasia (eight with semantic variant, six with non-fluent/agrammatic variant, and four with logopenic variant), as well as 28 age-matched healthy controls. We further examined whether performance on the psychoacoustic tasks in the three domains related to the patients' speech and language and neuropsychological profile. At the group level, patients were significantly impaired in the three domains. Patients had the most marked deficits within the rhythm domain for the processing of short sequences of up to seven tones. Patients with the non-fluent variant showed the most pronounced deficits at the group and the individual level. A subset of patients with the semantic variant were also impaired, though less severely. The patients with the logopenic variant did not show any significant impairments. Significant deficits in the non-fluent and the semantic variant remained after partialling out effects of executive dysfunction. Performance on a subset of the psychoacoustic tests correlated with conventional verbal repetition tests. In sum, a core central auditory impairment exists in primary progressive aphasia for non-linguistic stimuli. While the non-fluent variant is clinically characterized by a motor speech deficit (output problem), perceptual processing of tone sequences is clearly deficient. This may indicate the co-occurrence in the non-fluent variant of a deficit in working memory for auditory objects. Parsimoniously we propose that auditory timing pathways are altered, which are used in common for processing acoustic sequence structure in both speech output and acoustic input.

3D Shape Perception in Posterior Cortical Atrophy: A Visual Neuroscience Perspective.

Posterior cortical atrophy (PCA) is a rare focal neurodegenerative syndrome characterized by progressive visuoperceptual and visuospatial deficits, most often due to atypical Alzheimer's disease (AD). We applied insights from basic visual neuroscience to analyze 3D shape perception in humans affected by PCA. Thirteen PCA patients and 30 matched healthy controls participated, together with two patient control groups with diffuse Lewy body dementia (DLBD) and an amnestic-dominant phenotype of AD, respectively. The hierarchical study design consisted of 3D shape processing for 4 cues (shading, motion, texture, and binocular disparity) with corresponding 2D and elementary feature extraction control conditions. PCA and DLBD exhibited severe 3D shape-processing deficits and AD to a lesser degree. In PCA, deficient 3D shape-from-shading was associated with volume loss in the right posterior inferior temporal cortex. This region coincided with a region of functional activation during 3D shape-from-shading in healthy controls. In PCA patients who performed the same fMRI paradigm, response amplitude during 3D shape-from-shading was reduced in this region. Gray matter volume in this region also correlated with 3D shape-from-shading in AD. 3D shape-from-disparity in PCA was associated with volume loss slightly more anteriorly in posterior inferior temporal cortex as well as in ventral premotor cortex. The findings in right posterior inferior temporal cortex and right premotor cortex are consistent with neurophysiologically based models of the functional anatomy of 3D shape processing. However, in DLBD, 3D shape deficits rely on mechanisms distinct from inferior temporal structural integrity. SIGNIFICANCE STATEMENT: Posterior cortical atrophy (PCA) is a neurodegenerative syndrome characterized by progressive visuoperceptual dysfunction and most often an atypical presentation of Alzheimer's disease (AD) affecting the ventral and dorsal visual streams rather than the medial temporal system. We applied insights from fundamental visual neuroscience to analyze 3D shape perception in PCA. 3D shape-processing deficits were affected beyond what could be accounted for by lower-order processing deficits. For shading and disparity, this was related to volume loss in regions previously implicated in 3D shape processing in the intact human and nonhuman primate brain. Typical amnestic-dominant AD patients also exhibited 3D shape deficits. Advanced visual neuroscience provides insight into the pathogenesis of PCA that also bears relevance for vision in typical AD.

Similarity of fMRI activity patterns in left perirhinal cortex reflects semantic similarity between words.

How verbal and nonverbal visuoperceptual input connects to semantic knowledge is a core question in visual and cognitive neuroscience, with significant clinical ramifications. In an event-related functional magnetic resonance imaging (fMRI) experiment we determined how cosine similarity between fMRI response patterns to concrete words and pictures reflects semantic clustering and semantic distances between the represented entities within a single category. Semantic clustering and semantic distances between 24 animate entities were derived from a concept-feature matrix based on feature generation by >1000 subjects. In the main fMRI study, 19 human subjects performed a property verification task with written words and pictures and a low-level control task. The univariate contrast between the semantic and the control task yielded extensive bilateral occipitotemporal activation from posterior cingulate to anteromedial temporal cortex. Entities belonging to a same semantic cluster elicited more similar fMRI activity patterns in left occipitotemporal cortex. When words and pictures were analyzed separately, the effect reached significance only for words. The semantic similarity effect for words was localized to left perirhinal cortex. According to a representational similarity analysis of left perirhinal responses, semantic distances between entities correlated inversely with cosine similarities between fMRI response patterns to written words. An independent replication study in 16 novel subjects confirmed these novel findings. Semantic similarity is reflected by similarity of functional topography at a fine-grained level in left perirhinal cortex. The word specificity excludes perceptually driven confounds as an explanation and is likely to be task dependent.

A systematic review of the quantitative markers of speech and language of the frontotemporal degeneration spectrum and their potential for cross-linguistic implementation.

Frontotemporal dementia (FTD) is a neurodegenerative disease spectrum with an urgent need for reliable biomarkers for early diagnosis and monitoring. Speech and language changes occur in the early stages of FTD and offer a potential non-invasive, early, and accessible diagnostic tool. The use of speech and language markers in this disease spectrum is limited by the fact that most studies investigate English-speaking patients. This systematic review examines the literature on psychoacoustic and linguistic features of speech that occur across the FTD spectrum across as many different languages as possible. 76 papers were identified that investigate psychoacoustic and linguistic markers in discursive speech. 75 % of these papers studied English-speaking patients. The most generalizable features found across different languages, are speech rate, articulation rate, pause frequency, total pause duration, noun-verb ratio, and total number of nouns. While there are clear interlinguistic differences across patient groups, the results show promise for implementation of cross-linguistic markers of speech and language across the FTD spectrum particularly for psychoacoustic features.