Artificial intelligence classifies primary progressive aphasia from connected speech.

Neurodegenerative dementia syndromes, such as Primary Progressive Aphasias (PPA), have traditionally been diagnosed based in part on verbal and nonverbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and also regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we first harnessed the capabilities of artificial intelligence (AI) and Natural Language Processing (NLP) to perform unsupervised classification of short, connected speech samples from 78 PPA patients. We then used NLP to identify linguistic features that best dissociate the three PPA variants. Large Language Models (LLMs) discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. In the subsequent supervised classification, seventeen distinctive features emerged, including the observation that separating verbs into high and low-frequency types significantly improves classification accuracy. Using these linguistic features derived from the analysis of short, connected speech samples, we developed a classifier that achieved 97.9% accuracy in classifying the four groups (three PPA variants and healthy controls). The data-driven section of this study showcases the ability of LLMs to find natural partitioning in the speech of patients with PPA consistent with conventional variants. In addition, the work identifies a robust set of language features indicative of each PPA variant, emphasizing the significance of dividing verbs into high and low-frequency categories. Beyond improving diagnostic accuracy, these findings enhance our understanding of the neurobiology of language processing.

Measuring Sentence Information via Surprisal: Theoretical and Clinical Implications in Nonfluent Aphasia.

OBJECTIVE: Nonfluent aphasia is characterized by simplified sentence structures and word-level abnormalities, including reduced use of verbs and function words. The predominant belief about the disease mechanism is that a core deficit in syntax processing causes both structural and word-level abnormalities. Here, we propose an alternative view based on information theory to explain the symptoms of nonfluent aphasia. We hypothesize that the word-level features of nonfluency constitute a distinct compensatory process to augment the information content of sentences to the level of healthy speakers. We refer to this process as lexical condensation. METHODS: We use a computational approach based on language models to measure sentence information through surprisal, a metric calculated by the average probability of occurrence of words in a sentence, given their preceding context. We apply this method to the language of patients with nonfluent primary progressive aphasia (nfvPPA; n = 36) and healthy controls (n = 133) as they describe a picture. RESULTS: We found that nfvPPA patients produced sentences with the same sentence surprisal as healthy controls by using richer words in their structurally impoverished sentences. Furthermore, higher surprisal in nfvPPA sentences correlated with the canonical features of agrammatism: a lower function-to-all-word ratio, a lower verb-to-noun ratio, a higher heavy-to-all-verb ratio, and a higher ratio of verbs in -ing forms. INTERPRETATION: Using surprisal enables testing an alternative account of nonfluent aphasia that regards its word-level features as adaptive, rather than defective, symptoms, a finding that would call for revisions in the therapeutic approach to nonfluent language production. ANN NEUROL 2023;94:647-657.

Feasibility of multidisciplinary telehealth evaluations in atypical dementia.

INTRODUCTION: While cognitive assessment by videoconference has become possible over the past decade, the COVID-19 pandemic underscores the critical need for expansion and examination of these methods, their appropriateness for various patient populations, and their benefits and limitations. Validity and reliability studies of tele-neuropsychological testing have been conducted in MCI or mild AD dementia patients (e.g., MMSE=25+); few studies have assessed the feasibility of neurologic examination by video, and none in atypical dementias, assuming that patients with some types (e.g., language, comportment) or greater severity of cognitive-behavioral impairment would be unable to participate. Here we report the feasibility of telehealth services for a multi-disciplinary dementia subspecialty clinic that include cognitive-behavioral and neurologic assessment with patients with atypical neurodegenerative syndromes. METHODS: 104 patient-carepartner (P-C) dyads met with providers in the MGH FTD Unit by videoconference (March-December, 2020) for routine clinical care. P-Cs completed validated questionnaires assessing cognition-mood/behavior/function on REDCap prior to video clinical interview and cognitive assessment, including the MoCA and Boston Cognitive Exam (BCE2.0), a newly revised brief cognitive assessment battery adapted for telehealth. P-Cs met with a neurologist for a basic neurologic examination (including eye-movement examination), review of assessment results, and discussion of care plan. P-Cs completed a satisfaction survey. RESULTS: The 104 P-Cs included a range of atypical neurodegenerative disorders (bvFTD, PCA, PPA, CBS, PSP, eoAD, Multidomain syndrome) mild-to-severe impairment (CDR range: 0-3). 76% completed the MoCA (25% had CDR=2). 36% also completed the BCEv2. Comparison of remote assessment data to previous in-person testing is ongoing. Of P-Cs who completed a satisfaction survey, all reported being "very satisfied" with the appointment, with 93% open to participating in a remote visit again. 87% found the telehealth visit comparable to an in-person visit. 66% preferred a future combination of remote and in-person visits. CONCLUSIONS: Multi-disciplinary telehealth visits appear to be feasible with patients with atypical cognitive-behavioral syndromes of across the severity spectrum. P-Cs report a high degree of satisfaction with the telehealth visit and an openness to ongoing telehealth visits. Results have implications for increasing accessibility of multidisciplinary medical services for patients and families living with complex forms of dementia.

Atrophy in Distinct Corticolimbic Networks Subserving Socioaffective Behavior in Semantic Variant Primary Progressive Aphasia.

BACKGROUND: Although traditionally conceptualized as a language disorder, semantic variant primary progressive aphasia (svPPA) is often accompanied by significant behavioral and affective symptoms which considerably increase disease morbidity. Specifically, these neuropsychiatric symptoms are characterized by breaches in normative socioaffective function, for example, an inability to read social cues, excessive trusting of others, and decreased empathy. Our prior neuroimaging work identified 3 corticolimbic networks anchored in the amygdala, temporal pole, and frontoinsular cortex: an affiliation network, theorized to mediate social approach behavior; an aversion network, theorized to subserve the appraisal of social threat; and a perception network, theorized to mediate the detection of social cues. We hy-pothesized that degeneration of these networks could provide neuroanatomical substrates for socioaffective deficits in svPPA. METHODS: We examined hypothesized relationships between subscores on the Social Impairment Rating Scale (SIRS) and atrophy in each of these 3 networks in a group of 16 svPPA patients (using matched cognitively normal controls as a reference). RESULTS: Consistent with our predictions, the magnitude of atrophy in the affiliation network in svPPA patients correlated with the SIRS subscore of socioemotional detachment, while the magnitude of atrophy in the aversion network in svPPA patients correlated with the SIRS subscore of inappropriate trusting. We did not find the predicted association between perception network atrophy and the SIRS subscore of lack of attention to social cues. CONCLUSION: These findings highlight specific socioaffective deficits in svPPA and provide a neuroanatomical basis for these impairments by linking them to networks commonly targeted in this disorder.

Focal temporal pole atrophy and network degeneration in semantic variant primary progressive aphasia.

A wealth of neuroimaging research has associated semantic variant primary progressive aphasia with distributed cortical atrophy that is most prominent in the left anterior temporal cortex; however, there is little consensus regarding which region within the anterior temporal cortex is most prominently damaged, which may indicate the putative origin of neurodegeneration. In this study, we localized the most prominent and consistent region of atrophy in semantic variant primary progressive aphasia using cortical thickness analysis in two independent patient samples (n = 16 and 28, respectively) relative to age-matched controls (n = 30). Across both samples the point of maximal atrophy was located in the same region of the left temporal pole. This same region was the point of maximal atrophy in 100% of individual patients in both semantic variant primary progressive aphasia samples. Using resting state functional connectivity in healthy young adults (n = 89), we showed that the seed region derived from the semantic variant primary progressive aphasia analysis was strongly connected with a large-scale network that closely resembled the distributed atrophy pattern in semantic variant primary progressive aphasia. In both patient samples, the magnitude of atrophy within a brain region was predicted by that region's strength of functional connectivity to the temporopolar seed region in healthy adults. These findings suggest that cortical atrophy in semantic variant primary progressive aphasia may follow connectional pathways within a large-scale network that converges on the temporal pole.

Language uncovers visuospatial dysfunction in posterior cortical atrophy: a natural language processing approach.

Introduction: Posterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher-order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease. Methods: We compared the language samples of 25 patients with PCA with age-matched cognitively normal (CN) individuals across two distinct tasks: a visually-dependent picture description and a visually-independent job description task. We extracted word frequency, word utterance latency, and spatial relational words for this comparison. We then conducted an in-depth analysis of the language used in the picture description task to identify specific linguistic indicators that reflect the visuospatial processing deficits of PCA. Results: Patients with PCA showed significant language deficits in the visually-dependent task, characterized by higher word frequency, prolonged utterance latency, and fewer spatial relational words, but not in the visually-independent task. An in-depth analysis of the picture description task further showed that PCA patients struggled to identify certain visual elements as well as the overall theme of the picture. A predictive model based on these language features distinguished PCA patients from CN individuals with high classification accuracy. Discussion: The findings indicate that language is a sensitive behavioral construct to detect visuospatial processing abnormalities of PCA. These insights offer theoretical and clinical avenues for understanding and managing PCA, underscoring language as a crucial marker for the visuospatial deficits of this atypical variant of Alzheimer's disease.

Using explainable artificial intelligence to identify linguistic biomarkers of amyloid pathology in primary progressive aphasia.

Introduction: Recent success has been achieved in Alzheimer's disease (AD) clinical trials targeting amyloid beta (ß), demonstrating a reduction in the rate of cognitive decline. However, testing methods for amyloid-ß positivity are currently costly or invasive, motivating the development of accessible screening approaches to steer patients toward appropriate diagnostic tests. Here, we employ a pre-trained language model (Distil-RoBERTa) to identify amyloid-ß positivity from a short, connected speech sample. We further use explainable AI (XAI) methods to extract interpretable linguistic features that can be employed in clinical practice. Methods: We obtained language samples from 74 patients with primary progressive aphasia (PPA) across its three variants. Amyloid-ß positivity was established through the analysis of cerebrospinal fluid, amyloid PET, or autopsy. 51% of the sample was amyloid-positive. We trained Distil-RoBERTa for 16 epochs with a batch size of 6 and a learning rate of 5e-5, and used the LIME algorithm to train interpretation models to interpret the trained classifier's inference conditions. Results: Over ten runs of 10-fold cross-validation, the classifier achieved a mean accuracy of 92%, SD = 0.01. Interpretation models were able to capture the classifier's behavior well, achieving an accuracy of 97% against classifier predictions, and uncovering several novel speech patterns that may characterize amyloid-ß positivity. Discussion: Our work improves previous research which indicates connected speech is a useful diagnostic input for prediction of the presence of amyloid-ß in patients with PPA. Further, we leverage XAI techniques to reveal novel linguistic features that can be tested in clinical practice in the appropriate subspecialty setting. Computational linguistic analysis of connected speech shows great promise as a novel assessment method in patients with AD and related disorders.

Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease.

Identifying individuals with early stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would allow professionals and loved ones to make better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. In this study, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A+/T+/N+ patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes (Posterior Cortical Atrophy, logopenic variant Primary Progressive Aphasia, and amnestic syndrome with multi-domain impairment and age of onset < 65 years). All patients underwent structural magnetic resonance imaging (MRI), tau (18F-Flortaucipir) PET, and amyloid (either 18F-Florbetaben or 11C-Pittsburgh Compound B) PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Our sample of early atypical AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t(47) = 6.56, p < .001, d = 0.95. These AD patients showed prominent baseline tau burden in posterior cortical regions including the major nodes of the default mode network, including the angular gyrus, posterior cingulate cortex/precuneus, and lateral temporal cortex. Greater baseline tau in the broader default mode network predicted faster clinical decline. Tau in the default mode network was the strongest predictor of clinical decline, outperforming baseline clinical impairment, tau in other functional networks, and the magnitude of cortical atrophy and amyloid burden in the default mode network. Overall, these findings point to the contribution of baseline tau burden within the default mode network of the cerebral cortex to predicting the magnitude of clinical decline in a sample of atypical early AD patients one year later. This simple measure based on a tau PET scan could aid the development of a personalized prognostic, monitoring, and treatment plan tailored to each individual patient, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.

Less is more in language production: an information-theoretic analysis of agrammatism in primary progressive aphasia.

Agrammatism is a disorder of language production characterized by short, simplified sentences, the omission of function words, an increased use of nouns over verbs and a higher use of heavy verbs. Despite observing these phenomena for decades, the accounts of agrammatism have not converged. Here, we propose and test the hypothesis that the lexical profile of agrammatism results from a process that opts for words with a lower frequency of occurrence to increase lexical information. Furthermore, we hypothesize that this process is a compensatory response to patients' core deficit in producing long, complex sentences. In this cross-sectional study, we analysed speech samples of patients with primary progressive aphasia (n = 100) and healthy speakers (n = 65) as they described a picture. The patient cohort included 34 individuals with the non-fluent variant, 41 with the logopenic variant and 25 with the semantic variant of primary progressive aphasia. We first analysed a large corpus of spoken language and found that the word types preferred by patients with agrammatism tend to have lower frequencies of occurrence than less preferred words. We then conducted a computational simulation to examine the impact of word frequency on lexical information as measured by entropy. We found that strings of words that exclude highly frequent words have a more uniform word distribution, thereby increasing lexical entropy. To test whether the lexical profile of agrammatism results from their inability to produce long sentences, we asked healthy speakers to produce short sentences during the picture description task. We found that, under this constrained condition, a similar lexical profile of agrammatism emerged in the short sentences of healthy individuals, including fewer function words, more nouns than verbs and more heavy verbs than light verbs. This lexical profile of short sentences resulted in their lower average word frequency than unconstrained sentences. We extended this finding by showing that, in general, shorter sentences get packaged with lower-frequency words as a basic property of efficient language production, evident in the language of healthy speakers and all primary progressive aphasia variants.

Language Uncovers Visuospatial Dysfunction in Posterior Cortical Atrophy: A Natural Language Processing Approach.

Introduction: Posterior Cortical Atrophy (PCA) is a syndrome characterized by a progressive decline in higher-order visuospatial processing, leading to symptoms such as space perception deficit, simultanagnosia, and object perception impairment. While PCA is primarily known for its impact on visuospatial abilities, recent studies have documented language abnormalities in PCA patients. This study aims to delineate the nature and origin of language impairments in PCA, hypothesizing that language deficits reflect the visuospatial processing impairments of the disease. Methods: We compared the language samples of 25 patients with PCA with age-matched cognitively normal (CN) individuals across two distinct tasks: a visually-dependent picture description and a visually-independent job description task. We extracted word frequency, word utterance latency, and spatial relational words for this comparison. We then conducted an in-depth analysis of the language used in the picture description task to identify specific linguistic indicators that reflect the visuospatial processing deficits of PCA. Results: Patients with PCA showed significant language deficits in the visually-dependent task, characterized by higher word frequency, prolonged utterance latency, and fewer spatial relational words, but not in the visually-independent task. An in-depth analysis of the picture description task further showed that PCA patients struggled to identify certain visual elements as well as the overall theme of the picture. A predictive model based on these language features distinguished PCA patients from CN individuals with high classification accuracy. Discussion: The findings indicate that language is a sensitive behavioral construct to detect visuospatial processing abnormalities of PCA. These insights offer theoretical and clinical avenues for understanding and managing PCA, underscoring language as a crucial marker for the visuospatial deficits of this atypical variant of Alzheimer's disease.

Automated analysis of written language in the three variants of primary progressive aphasia.

Despite the important role of written language in everyday life, abnormalities in functional written communication have been sparsely investigated in primary progressive aphasia. Prior studies have analysed written language separately in each of the three variants of primary progressive aphasia-but have rarely compared them to each other or to spoken language. Manual analysis of written language can be a time-consuming process. We therefore developed a program that quantifies content units and total units in written or transcribed language samples. We analysed written and spoken descriptions of the Western Aphasia Battery picnic scene, based on a predefined content unit corpus. We calculated the ratio of content units to units as a measure of content density. Our cohort included 115 participants (20 controls for written, 20 controls for spoken, 28 participants with nonfluent variant primary progressive aphasia, 30 for logopenic variant and 17 for semantic variant). Our program identified content units with a validity of 99.7% (95%CI 99.5-99.8). All patients wrote fewer units than controls (P < 0.001). Patients with the logopenic variant (P = 0.013) and the semantic variant (0.004) wrote fewer content units than controls. The content unit-to-unit ratio was higher in the nonfluent and semantic variants than controls (P = 0.019), but no difference in the logopenic variant (P = 0.962). Participants with the logopenic (P < 0.001) and semantic (P = 0.04) variants produced fewer content units in written compared to spoken descriptions. All variants produced fewer units in written samples compared to spoken (P < 0.001). However, due to a relatively smaller decrease in written content units, we observed a larger content unit-to-unit ratio in writing over speech (P < 0.001). Written and spoken content units (r = 0.5, P = 0.009) and total units (r = 0.64, P < 0.001) were significantly correlated in patients with nonfluent variant, but this was not the case for logopenic or semantic. Considering all patients with primary progressive aphasia, fewer content units were produced in those with greater aphasia severity (Progressive Aphasia Severity Scale Sum of Boxes, r = -0.24, P = 0.04) and dementia severity (Clinical Dementia Rating scale Sum of Boxes, r = -0.34, P = 0.004). In conclusion, we observed reduced written content in patients with primary progressive aphasia compared to controls, with a preference for content over non-content units in patients with the nonfluent and semantic variants. We observed a similar 'telegraphic' style in both language modalities in patients with the nonfluent variant. Lastly, we show how our program provides a time-efficient tool, which could enable feedback and tracking of writing as an important feature of language and cognition.

Association of Regional Cortical Network Atrophy With Progression to Dementia in Patients With Primary Progressive Aphasia.

BACKGROUND AND OBJECTIVES: Patients with primary progressive aphasia (PPA) have gradually progressive language deficits during the initial phase of the illness. As the underlying neurodegenerative disease progresses, patients with PPA start losing independent functioning due to the development of nonlanguage cognitive or behavioral symptoms. The timeline of this progression from the mild cognitive impairment stage to the dementia stage of PPA is variable across patients. In this study, in a sample of patients with PPA, we measured the magnitude of cortical atrophy within functional networks believed to subserve diverse cognitive and affective functions. The objective of the study was to evaluate the utility of this measure as a predictor of time to subsequent progression to dementia in PPA. METHODS: Patients with PPA with largely independent daily function were recruited through the Massachusetts General Hospital Frontotemporal Disorders Unit. All patients underwent an MRI scan at baseline. Cortical atrophy was then estimated relative to a group of amyloid-negative cognitively normal control participants. For each patient, we measured the time between the baseline visit and the subsequent visit at which dementia progression was documented or last observation. Simple and multivariable Cox regression models were used to examine the relationship between cortical atrophy and the likelihood of progression to dementia. RESULTS: Forty-nine patients with PPA (mean age = 66.39 ± 8.36 years, 59.2% females) and 25 controls (mean age = 67.43 ± 4.84 years, 48% females) were included in the data analysis. Greater baseline atrophy in not only the left language network (hazard ratio = 1.47, 95% CI = 1.17-1.84) but also in the frontoparietal control (1.75, 1.25-2.44), salience (1.63, 1.25-2.13), default mode (1.55, 1.19-2.01), and ventral frontotemporal (1.41, 1.16-1.71) networks was associated with a higher risk of progression to dementia. A multivariable model identified contributions of the left frontoparietal control (1.94, 1.09-3.48) and ventral frontotemporal (1.61, 1.09-2.39) networks in predicting dementia progression, with no additional variance explained by the language network (0.75, 0.43-1.31). DISCUSSION: These results suggest that baseline atrophy in cortical networks subserving nonlanguage cognitive and affective functions is an important predictor of progression to dementia in PPA. This measure should be included in precision medicine models of prognosis in PPA.

Using Generative Artificial Intelligence to Classify Primary Progressive Aphasia from Connected Speech.

Neurodegenerative dementia syndromes, such as Primary Progressive Aphasias (PPA), have traditionally been diagnosed based in part on verbal and nonverbal cognitive profiles. Debate continues about whether PPA is best subdivided into three variants and also regarding the most distinctive linguistic features for classifying PPA variants. In this study, we harnessed the capabilities of artificial intelligence (AI) and natural language processing (NLP) to first perform unsupervised classification of concise, connected speech samples from 78 PPA patients. Large Language Models discerned three distinct PPA clusters, with 88.5% agreement with independent clinical diagnoses. Patterns of cortical atrophy of three data-driven clusters corresponded to the localization in the clinical diagnostic criteria. We then used NLP to identify linguistic features that best dissociate the three PPA variants. Seventeen features emerged as most valuable for this purpose, including the observation that separating verbs into high and low-frequency types significantly improves classification accuracy. Using these linguistic features derived from the analysis of brief connected speech samples, we developed a classifier that achieved 97.9% accuracy in predicting PPA subtypes and healthy controls. Our findings provide pivotal insights for refining early-stage dementia diagnosis, deepening our understanding of the characteristics of these neurodegenerative phenotypes and the neurobiology of language processing, and enhancing diagnostic evaluation accuracy.

Breakdowns in Informativeness of Naturalistic Speech Production in Primary Progressive Aphasia.

"Functional communication" refers to an individual's ability to communicate effectively in his or her everyday environment, and thus is a paramount skill to monitor and target therapeutically in people with aphasia. However, traditional controlled-paradigm assessments commonly used in both research and clinical settings often fail to adequately capture this ability. In the current study, facets of functional communication were measured from picture-elicited speech samples from 70 individuals with mild primary progressive aphasia (PPA), including the three variants, and 31 age-matched controls. Building upon methods recently used by Berube et al. (2019), we measured the informativeness of speech by quantifying the content of each patient's description that was relevant to a picture relative to the total amount of speech they produced. Importantly, form-based errors, such as mispronunciations of words, unusual word choices, or grammatical mistakes are not penalized in this approach. We found that the relative informativeness, or efficiency, of speech was preserved in non-fluent variant PPA patients as compared with controls, whereas the logopenic and semantic variant PPA patients produced significantly less informative output. Furthermore, reduced informativeness in the semantic variant is attributable to a lower production of content units and a propensity for self-referential tangents, whereas for the logopenic variant, a lower production of content units and relatively "empty" speech and false starts contribute to this reduction. These findings demonstrate that functional communication impairment does not uniformly affect all the PPA variants and highlight the utility of naturalistic speech analysis for measuring the breakdown of functional communication in PPA.

Regional prefrontal cortical atrophy predicts specific cognitive-behavioral symptoms in ALS-FTD.

Amyotrophic Lateral Sclerosis-Frontotemporal Dementia (ALS-FTD) may present typical behavioral variant FTD symptoms. This study aims to determine whether profile and severity of cognitive-behavioral symptoms in ALS/ALS-FTD are predicted by regional cortical atrophy. The hypothesis is that executive dysfunction can be predicted by dorsolateral prefrontal cortical (dlPFC) atrophy, apathy by dorsomedial PFC (dmPFC) and anterior cingulate cortical (ACC) atrophy, disinhibition by orbitofrontal cortical (OFC) atrophy. 3.0 Tesla MRI scans were acquired from 22 people with ALS or ALS-FTD. Quantitative cortical thickness analysis was performed with FreeSurfer. A priori-defined regions of interest (ROI) were used to measure cortical thickness in each participant and calculate magnitude of atrophy in comparison to 115 healthy controls. Spearman correlations were used to evaluate associations between frontal ROI cortical thickness and cognitive-behavioral symptoms, measured by Neuropsychiatric Inventory Questionnaire (NPI-Q) and Clinical Dementia Rating (CDR) scale. ALS-FTD participants exhibited variable degrees of apathy (NPI-Q/apathy: 1.6 ± 1.2), disinhibition (NPI-Q/disinhibition: 1.2 ± 1.2), executive dysfunction (CDR/judgment-problem solving: 1.7 ± 0.8). Within the ALS-FTD group, executive dysfunction correlated with dlPFC atrophy (ρ:-0.65;p < 0.05); similar trends were seen for apathy with ACC (ρ:-0.53;p < 0.10) and dmPFC (ρ:-0.47;p < 0.10) atrophy, for disinhibition with OFC atrophy (ρ:-0.51;p < 0.10). Compared to people with ALS, those with ALS-FTD showed more diffuse atrophy involving precentral gyrus, prefrontal, temporal regions. Profile and severity of cognitive-behavioral symptoms in ALS-FTD are predicted by regional prefrontal atrophy. These findings are consistent with established brain-behavior models and support the role of quantitative MRI in diagnosis, management, counseling, monitoring and prognostication for a neurodegenerative disorder with diverse phenotypes.

Neural substrates of verbal repetition deficits in primary progressive aphasia.

In this cross-sectional study, we examined the relationship between cortical thickness and performance on several verbal repetition tasks in a cohort of patients with primary progressive aphasia in order to test predictions generated by theoretical accounts of phonological working memory that predict phonological content buffers in left posterior inferior frontal sulcus and supramarginal gyrus. Cortical surfaces were reconstructed from magnetic resonance imaging scans from 42 participants diagnosed with primary progressive aphasia. Cortical thickness was measured in a set of anatomical regions spanning the entire cerebral cortex. Correlation analyses were performed between cortical thickness and average score across three phonological working memory-related tasks: the Repetition sub-test from the Western Aphasia Battery, a forward digit span task, and a backward digit span task. Significant correlations were found between average working memory score across tasks and cortical thickness in left supramarginal gyrus and left posterior inferior frontal sulcus, in support of prior theoretical accounts of phonological working memory. Exploratory whole-brain correlation analyses performed for each of the three behavioural tasks individually revealed a distinct set of positively correlated regions for each task. Comparison of cortical thickness measures from different primary progressive aphasia sub-types to cortical thickness in age-matched controls further revealed unique patterns of atrophy in the different subtypes.

A category-selective semantic memory deficit for animate objects in semantic variant primary progressive aphasia.

Data are mixed on whether patients with semantic variant primary progressive aphasia exhibit a category-selective semantic deficit for animate objects. Moreover, there is little consensus regarding the neural substrates of this category-selective semantic deficit, though prior literature has suggested that the perirhinal cortex and the lateral posterior fusiform gyrus may support semantic memory functions important for processing animate objects. In this study, we investigated whether patients with semantic variant primary progressive aphasia exhibited a category-selective semantic deficit for animate objects in a word-picture matching task, controlling for psycholinguistic features of the stimuli, including frequency, familiarity, typicality and age of acquisition. We investigated the neural bases of this category selectivity by examining its relationship with cortical atrophy in two primary regions of interest: bilateral perirhinal cortex and lateral posterior fusiform gyri. We analysed data from 20 patients with semantic variant primary progressive aphasia (mean age = 64 years, S.D. = 6.94). For each participant, we calculated an animacy index score to denote the magnitude of the category-selective semantic deficit for animate objects. Multivariate regression analysis revealed a main effect of animacy (ß = 0.52, t = 4.03, P < 0.001) even after including all psycholinguistic variables in the model, such that animate objects were less likely to be identified correctly relative to inanimate objects. Inspection of each individual patient's data indicated the presence of a disproportionate impairment in animate objects in most patients. A linear regression analysis revealed a relationship between the right perirhinal cortex thickness and animacy index scores (ß = -0.57, t = -2.74, P = 0.015) such that patients who were more disproportionally impaired for animate relative to inanimate objects exhibited thinner right perirhinal cortex. A vertex-wise general linear model analysis restricted to the temporal lobes revealed additional associations between positive animacy index scores (i.e. a disproportionately poorer performance on animate objects) and cortical atrophy in the right perirhinal and entorhinal cortex, superior, middle, and inferior temporal gyri, and the anterior fusiform gyrus, as well as the left anterior fusiform gyrus. Taken together, our results indicate that a category-selective semantic deficit for animate objects is a characteristic feature of semantic variant primary progressive aphasia that is detectable in most individuals. Our imaging findings provide further support for the role of the right perirhinal cortex and other temporal lobe regions in the semantic processing of animate objects.

Altered functional connectivity of cortical networks in semantic variant Primary Progressive Aphasia.

As their illness progresses, patients with the semantic variant of Primary Progressive Aphasia (svPPA) frequently exhibit peculiar behaviors indicative of altered visual attention or an increased interest in artistic endeavors. In the present study, we examined changes within and between large-scale functional brain networks that may explain this altered visual behavior. We first examined the connectivity of the visual association network, the dorsal attention network, and the default mode network in healthy young adults (n = 89) to understand the typical architecture of these networks in the healthy brain. We then compared the large-scale functional connectivity of these networks in a group of svPPA patients (n = 12) to a group of age-matched cognitively normal controls (n = 30). Our results showed that the between-network connectivity of the dorsal attention and visual association networks was elevated in svPPA patients relative to controls. We further showed that this heightened between-network connectivity was associated with a decrease in the within-network connectivity of the default mode network, possibly due to progressive degeneration of the anterior temporal lobes in svPPA. These results suggest that focal neurodegeneration can lead to the reorganization of large-scale cognitive networks beyond the primarily affected network(s), possibly contributing to cognitive or behavioral changes that are commonly present as part of the clinical phenotype of svPPA.