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Rezaii N, Hochberg D, Quimby M, Wong B, Brickhouse M, Touroutoglou A, Dickerson BC, Wolff P. Artificial intelligence classifies primary progressive aphasia from connected speech. Brain 2024; 147:3070-3082. [PMID: 38912855 PMCID: PMC11370793 DOI: 10.1093/brain/awae196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/08/2024] [Accepted: 05/21/2024] [Indexed: 06/25/2024] Open
Abstract
Neurodegenerative dementia syndromes, such as primary progressive aphasias (PPA), have traditionally been diagnosed based, in part, on verbal and non-verbal cognitive profiles. Debate continues about whether PPA is best divided into three variants and regarding the most distinctive linguistic features for classifying PPA variants. In this cross-sectional study, we initially harnessed the capabilities of artificial intelligence and natural language processing to perform unsupervised classification of short, connected speech samples from 78 pateints with PPA. We then used natural language processing to identify linguistic features that best dissociate the three PPA variants. 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. In the subsequent supervised classification, 17 distinctive features emerged, including the observation that separating verbs into high- and low-frequency types significantly improved 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 large language models 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.
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Affiliation(s)
- Neguine Rezaii
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Daisy Hochberg
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Megan Quimby
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Michael Brickhouse
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129, USA
- Massachusetts Alzheimer's Disease Research Center, Harvard Medical School, Boston, MA 02114, USA
| | - Phillip Wolff
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
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Sanati M, Bayat S, Panahi MM, Khodadadi A, Rezaee S, Ghasimi M, Besharat S, Fooladi ZM, Dooghaee MA, Taheri MS, Dickerson BC, Goldberg A, Rezaii N. Impaired language in Alzheimer's disease: A comparison between English and Persian implicates content-word frequency rather than the noun-verb distinction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.09.24305534. [PMID: 38645255 PMCID: PMC11030473 DOI: 10.1101/2024.04.09.24305534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
This study challenges the conventional psycholinguistic view that the distinction between nouns and verbs is pivotal in understanding language impairments in neurological disorders. Traditional views link frontal brain region damage with verb processing deficits and posterior temporoparietal damage with noun difficulties. However, this perspective is contested by findings from patients with Alzheimer's disease (pwAD), who show impairments in both word classes despite their typical temporoparietal atrophy. Notably, pwAD tend to use semantically lighter verbs in their speech than healthy individuals. By examining English-speaking pwAD and comparing them with Persian-speaking pwAD, this research aims to demonstrate that language impairments in Alzheimer's disease (AD) stem from the distributional properties of words within a language rather than distinct neural processing networks for nouns and verbs. We propose that the primary deficit in AD language production is an overreliance on high-frequency words. English has a set of particularly high-frequency verbs that surpass most nouns in usage frequency. Since pwAD tend to use high-frequency words, the byproduct of this word distribution in the English language would be an over-usage of high-frequency verbs. In contrast, Persian features complex verbs with an overall distribution lacking extremely high-frequency verbs like those found in English. As a result, we hypothesize that Persian-speaking pwAD would not have a bias toward the overuse of high-frequency verbs. We analyzed language samples from 95 English-speaking pwAD and 91 healthy controls, along with 27 Persian-speaking pwAD and 27 healthy controls. Employing uniform automated natural language processing methods, we measured the usage rates of nouns, verbs, and word frequencies across both cohorts. Our findings showed that English-speaking pwAD use higher-frequency verbs than healthy individuals, a pattern not mirrored by Persian-speaking pwAD. Crucially, we found a significant interaction between the frequencies of verbs used by English and Persian speakers with and without AD. Moreover, regression models that treated noun and verb frequencies as separate predictors did not outperform models that considered overall word frequency alone in classifying AD. In conclusion, this study suggests that language abnormalities among English-speaking pwAD reflect the unique distributional properties of words in English rather than a universal noun-verb class distinction. Beyond offering a new understanding of language abnormalities in AD, the study highlights the critical need for further investigation across diverse languages to deepen our insight into the mechanisms of language impairments in neurological disorders.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Mostafa Almasi Dooghaee
- Abrar Institute of Higher Education
- Azad University Science and Research Branch
- Institute for Cognitive Science Studies
- Mashhad University of Medical Science
- Shahid Beheshti University of Medical Sciences
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School
- Princeton University
| | | | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School
| | | | - Neguine Rezaii
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School
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Bayat S, Santai M, Panahi MM, Khodadadi A, Ghassimi M, Rezaei S, Besharat S, Mahboubi Z, Almasi M, Sanei Taheri M, Dickerson BC, Rezaii N. Language Abnormalities in Alzheimer's Disease Arise from Reduced Informativeness: A Cross-Linguistic Study in English and Persian. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304407. [PMID: 38562858 PMCID: PMC10984049 DOI: 10.1101/2024.03.19.24304407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
INTRODUCTION This research investigates the psycholinguistic origins of language impairments in Alzheimer's Disease (AD), questioning if these impairments result from language-specific structural disruptions or from a universal deficit in generating meaningful content. METHODS Cross-linguistic analysis was conducted on language samples from 184 English and 52 Persian speakers, comprising both AD patients and healthy controls, to extract various language features. Furthermore, we introduced a machine learning-based metric, Language Informativeness Index (LII), to quantify informativeness. RESULTS Indicators of AD in English were found to be highly predictive of AD in Persian, with a 92.3% classification accuracy. Additionally, we found robust correlations between the typical linguistic abnormalities of AD and language emptiness (low LII) across both languages. DISCUSSION Findings suggest AD linguistics impairments are attributed to a core universal difficulty in generating informative messages. Our approach underscores the importance of incorporating biocultural diversity into research, fostering the development of inclusive diagnostic tools.
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Rezaii N, Hochberg D, Quimby M, Wong B, McGinnis S, Dickerson BC, Putcha D. Language uncovers visuospatial dysfunction in posterior cortical atrophy: a natural language processing approach. Front Neurosci 2024; 18:1342909. [PMID: 38379764 PMCID: PMC10876777 DOI: 10.3389/fnins.2024.1342909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/18/2024] [Indexed: 02/22/2024] Open
Abstract
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.
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Affiliation(s)
- Neguine Rezaii
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Daisy Hochberg
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Megan Quimby
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Scott McGinnis
- Center for Brain Mind Medicine, Department of Neurology, Brigham and Women’s Hospital, Boston, MA, United States
| | - Bradford C. Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Alzheimer’s Disease Research Center, Massachusetts General Hospital, Charlestown, MA, United States
| | - Deepti Putcha
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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Rezaii N, Quimby M, Wong B, Hochberg D, Brickhouse M, Touroutoglou A, Dickerson BC, Wolff P. Using Generative Artificial Intelligence to Classify Primary Progressive Aphasia from Connected Speech. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.22.23300470. [PMID: 38234853 PMCID: PMC10793520 DOI: 10.1101/2023.12.22.23300470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
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.
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Affiliation(s)
- Neguine Rezaii
- Frontotemporal Disorders Unit, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
| | - Megan Quimby
- Frontotemporal Disorders Unit, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
| | - Daisy Hochberg
- Frontotemporal Disorders Unit, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
| | - Michael Brickhouse
- Frontotemporal Disorders Unit, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
| | - Alexandra Touroutoglou
- Frontotemporal Disorders Unit, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Massachusetts Alzheimer’s Disease Research Center, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
| | - Bradford C. Dickerson
- Frontotemporal Disorders Unit, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Department of Neurology, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Department of Psychiatry, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
- Massachusetts Alzheimer’s Disease Research Center, Massachusetts General Hospital & Harvard Medical School, Boston MA, USA
| | - Phillip Wolff
- Department of Psychology, Emory University, Atlanta, GA, USA
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Rezaii N, Hochberg D, Quimby M, Wong B, McGinnis S, Dickerson BC, Putcha D. Language Uncovers Visuospatial Dysfunction in Posterior Cortical Atrophy: A Natural Language Processing Approach. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.21.23298864. [PMID: 38045263 PMCID: PMC10690359 DOI: 10.1101/2023.11.21.23298864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
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.
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Affiliation(s)
- Neguine Rezaii
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Daisy Hochberg
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Megan Quimby
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Bonnie Wong
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
| | - Scott McGinnis
- Center for Brain Mind Medicine, Department of Neurology, Brigham & Women’s Hospital, Boston, MA 02115
| | - Bradford C Dickerson
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129
- Alzheimer’s Disease Research Center, Massachusetts General Hospital, Charlestown, MA 02129
| | - Deepti Putcha
- Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114
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