Automatic Subtyping of Individuals with Primary Progressive Aphasia

The use of low-density EEG for the classification of PPA and MCI

Objective

Dissociating Primary Progressive Aphasia (PPA) from Mild Cognitive Impairment (MCI) is an important, yet challenging task. Given the need for low-cost and time-efficient classification, we used low-density electroencephalography (EEG) recordings to automatically classify PPA, MCI and healthy control (HC) individuals. To the best of our knowledge, this is the first attempt to classify individuals from these three populations at the same time.

Methods

We collected three-minute EEG recordings with an 8-channel system from eight MCI, fourteen PPA and eight HC individuals. Utilizing the Relative Wavelet Entropy method, we derived (i) functional connectivity, (ii) graph theory metrics and extracted (iii) various energy rhythms. Features from all three sources were used for classification. The k-Nearest Neighbor and Support Vector Machines classifiers were used.

Results

A 100% individual classification accuracy was achieved in the HC-MCI, HC-PPA, and MCI-PPA comparisons, and a 77.78% accuracy in the HC-MCI-PPA comparison.

Conclusion

We showed for the first time that successful automatic classification between HC, MCI and PPA is possible with short, low-density EEG recordings. Despite methodological limitations of the current study, these results have important implications for clinical practice since they show that fast, low-cost and accurate disease diagnosis of these disorders is possible. Future studies need to establish the generalizability of the current findings with larger sample sizes and the efficient use of this methodology in a clinical setting.

Primary Progressive Aphasia in Italian and English: A Cross-Linguistic Cohort Study

BACKGROUND AND OBJECTIVES

Current formulations of primary progressive aphasia (PPA) derive largely from English-speaking patients. We hypothesized that language-specific characteristics influence PPA phenotypes in 2 contrasting languages: Italian and English.

METHODS

We undertook a retrospective, cross-sectional, observational comparison of 2 patient cohorts representing all major PPA syndromes, in London and Florence. Neuropsychological scores in a range of linguistic and general cognitive domains were normalized to native speaker controls and dichotomized as impaired/unimpaired. Proportions were compared using χ2 tests and adjusted for symptom duration and severity of cognitive impairment using logistic regression.

RESULTS

The cohorts comprised 106 (48.1% female) Italian speakers (14 nonfluent/agrammatic [nfvPPA], 20 semantic [svPPA], 41 logopenic variant [lvPPA], 31 mixed PPA [mPPA]) and 166 (45.2% female) English speakers (70 nfvPPA, 45 svPPA, 42 lvPPA, 9 mPPA). Comparing cohorts, the English cohort was younger (mean 62.7 [SD = 8.4] vs 65.9 [7.8] years; p = 0.003, Cohen d = 0.39), with longer symptom duration (4.6 [4.3] vs 3.1 [2.5] years; p = 0.048, d = 1.08), a higher proportion of nfvPPA cases (42% vs 13%, χ2 = 25.4, p < 0.001), and lower proportions of lvPPA (25% vs 38%, χ2 = 5.46, p = 0.019) and mPPA (5% vs 29%, χ2 = 29.3, p < 0.001). English-speaking nfvPPA patients had less frequent expressive agrammatism (46% vs 93%, p = 0.015, odds ratio [OR] 16.05, 95% CI 1.70-151.13) but more frequently impaired single-word comprehension (60% vs 8%, p = 0.013, OR 0.06, 95% CI 0.00-0.56). English svPPA patients had more frequent surface dyslexia (68% vs 30%, p = 0.046, OR 0.24, 95% CI 0.06-0.97) and dysgraphia (38% vs 10%, p = 0.021, OR 0.09, 95% CI 0.01-0.70) while English lvPPA patients had more frequently impaired single-word comprehension (89% vs 29%, p < 0.001, OR 0.05, 95% CI 0.01-0.28), word repetition (61% vs 26%, p = 0.020, OR 0.24, 95% CI 0.07-0.80), nonword repetition (78% vs 30%, p = 0.010, OR 0.18, 95% CI 0.05-0.06), nonverbal working memory (69% vs 36%, p = 0.005, OR 0.10, 95% CI 0.02-0.51), and visuomotor function (89% vs 25%, p < 0.001, OR 0.02, 95% CI 0.01-0.20).

DISCUSSION

Language-specific characteristics influenced PPA phenotypes, with more frequent expressive agrammatism in Italian (reflecting its morphologic complexity), more frequently impaired word processing in English (reflecting its articulatory, acoustic, and orthographic complexity), and increased prevalence of mPPA in Italian. These findings have implications for PPA diagnosis and management. Limitations of test heterogeneity and cohort size should be addressed in future, prospective, multicenter initiatives using cross-linguistic tools.

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 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.

Open Brain AI and language assessment

Neurolinguistic assessments play a vital role in neurological examinations, revealing a wide range of language and communication impairments associated with developmental disorders and acquired neurological conditions. Yet, a thorough neurolinguistic assessment is time-consuming and laborious and takes valuable resources from other tasks. To empower clinicians, healthcare providers, and researchers, we have developed Open Brain AI (OBAI). The aim of this computational platform is twofold. First, it aims to provide advanced AI tools to facilitate spoken and written language analysis, automate the analysis process, and reduce the workload associated with time-consuming tasks. The platform currently incorporates multilingual tools for English, Danish, Dutch, Finnish, French, German, Greek, Italian, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, and Swedish. The tools involve models for (i) audio transcription, (ii) automatic translation, (iii) grammar error correction, (iv) transcription to the International Phonetic Alphabet, (v) readability scoring, (vi) phonology, morphology, syntax, semantic measures (e.g., counts and proportions), and lexical measures. Second, it aims to support clinicians in conducting their research and automating everyday tasks with "OBAI Companion," an AI language assistant that facilitates language processing, such as structuring, summarizing, and editing texts. OBAI also provides tools for automating spelling and phonology scoring. This paper reviews OBAI's underlying architectures and applications and shows how OBAI can help professionals focus on higher-value activities, such as therapeutic interventions.

Advances in semantic dementia: Neuropsychology, pathology & neuroimaging

Semantic dementia is a kind of neurodegenerative disorder, characterized by prominent semantic impairments and anterior temporal lobe atrophy. Since 2010, more studies have devoted to this rare disorder, revealing that it is more complex than we think. Clinical advances include more specific findings of semantic impairments and other higher order cognitive deficits. Neuroimaging techniques can help revealing the different brain networks affected (both structurally and functionally) in this condition. Pathological and genetic studies have also found more complex situations of semantic dementia, which might explain the huge variance existing in semantic dementia. Moreover, the current diagnosis criteria mainly focus on semantic dementia's classical prototype. We further delineated the features of three subtypes of semantic dementia based on atrophy lateralization with three severity stages. In a broader background, as a part of the continuum of neurodegenerative disorders, semantic dementia is commonly compared with other resembling conditions. Therefore, we summarized the differential diagnosis between semantic dementia and them. Finally, we introduced the challenges and achievements of its diagnosis, treatment, care and cross cultural comparison. By providing a comprehensive picture of semantic dementia on different aspects of advances, we hope to deepen the understanding of semantic dementia and promote more inspirations on both clinical and theoretical studies about it.

Connected Speech Fluency in Poststroke and Progressive Aphasia: A Scoping Review of Quantitative Approaches and Features

PURPOSE

Speech fluency has important diagnostic implications for individuals with poststroke aphasia (PSA) as well as primary progressive aphasia (PPA), and quantitative assessment of connected speech has emerged as a widely used approach across both etiologies. The purpose of this review was to provide a clearer picture on the range, nature, and utility of individual quantitative speech/language measures and methods used to assess connected speech fluency in PSA and PPA, and to compare approaches across etiologies.

METHOD

We conducted a scoping review of literature published between 2012 and 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines. Forty-five studies were included in the review. Literature was charted and summarized by etiology and characteristics of included patient populations and method(s) used for derivation and analysis of speech/language features. For a subset of included articles, we also charted the individual quantitative speech/language features reported and the level of significance of reported results.

RESULTS

Results showed that similar methodological approaches have been used to quantify connected speech fluency in both PSA and PPA. Two hundred nine individual speech-language features were analyzed in total, with low levels of convergence across etiology on specific features but greater agreement on the most salient features. The most useful features for differentiating fluent from nonfluent aphasia in both PSA and PPA were features related to overall speech quantity, speech rate, or grammatical competence.

CONCLUSIONS

Data from this review demonstrate the feasibility and utility of quantitative approaches to index connected speech fluency in PSA and PPA. We identified emergent trends toward automated analysis methods and data-driven approaches, which offer promising avenues for clinical translation of quantitative approaches. There is a further need for improved consensus on which subset of individual features might be most clinically useful for assessment and monitoring of fluency.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.25537237.

Autism Detection in Children: Integrating Machine Learning and Natural Language Processing in Narrative Analysis

Despite the consensus that early identification leads to better outcomes for individuals with autism spectrum disorder (ASD), recent research reveals that the average age of diagnosis in the Greek population is approximately six years. However, this age of diagnosis is delayed by an additional two years for families from lower-income or minority backgrounds. These disparities result in adverse impacts on intervention outcomes, which are further burdened by the often time-consuming and labor-intensive language assessments for children with ASD. There is a crucial need for tools that increase access to early assessment and diagnosis that will be rigorous and objective. The current study leverages the capabilities of artificial intelligence to develop a reliable and practical model for distinguishing children with ASD from typically-developing peers based on their narrative and vocabulary skills. We applied natural language processing-based extraction techniques to automatically acquire language features (narrative and vocabulary skills) from storytelling in 68 children with ASD and 52 typically-developing children, and then trained machine learning models on the children's combined narrative and expressive vocabulary data to generate behavioral targets that effectively differentiate ASD from typically-developing children. According to the findings, the model could distinguish ASD from typically-developing children, achieving an accuracy of 96%. Specifically, out of the models used, hist gradient boosting and XGBoost showed slightly superior performance compared to the decision trees and gradient boosting models, particularly regarding accuracy and F1 score. These results bode well for the deployment of machine learning technology for children with ASD, especially those with limited access to early identification services.

Discriminating nonfluent/agrammatic and logopenic PPA variants with automatically extracted morphosyntactic measures from connected speech

Morphosyntactic assessments are important for characterizing individuals with nonfluent/agrammatic variant primary progressive aphasia (nfvPPA). Yet, standard tests are subject to examiner bias and often fail to differentiate between nfvPPA and logopenic variant PPA (lvPPA). Moreover, relevant neural signatures remain underexplored. Here, we leverage natural language processing tools to automatically capture morphosyntactic disturbances and their neuroanatomical correlates in 35 individuals with nfvPPA relative to 10 healthy controls (HC) and 26 individuals with lvPPA. Participants described a picture, and ensuing transcripts were analyzed via part-of-speech tagging to extract sentence-related features (e.g., subordinating and coordinating conjunctions), verbal-related features (e.g., tense markers), and nominal-related features (e.g., subjective and possessive pronouns). Gradient boosting machines were used to classify between groups using all features. We identified the most discriminant morphosyntactic marker via a feature importance algorithm and examined its neural correlates via voxel-based morphometry. Individuals with nfvPPA produced fewer morphosyntactic elements than the other two groups. Such features robustly discriminated them from both individuals with lvPPA and HCs with an AUC of .95 and .82, respectively. The most discriminatory feature corresponded to subordinating conjunctions was correlated with cortical atrophy within the left posterior inferior frontal gyrus across groups (pFWE < .05). Automated morphosyntactic analysis can efficiently differentiate nfvPPA from lvPPA. Also, the most sensitive morphosyntactic markers correlate with a core atrophy region of nfvPPA. Our approach, thus, can contribute to a key challenge in PPA diagnosis.

Progress in Primary Progressive Aphasia: A Review

We present a review of the definition, classification, and epidemiology of primary progressive aphasia (PPA); an update of the taxonomy of the clinical syndrome of PPA; and recent advances in the neuroanatomy, pathology, and genetics of PPA, as well as the search for biomarkers and treatment. PPA studies that have contributed to concepts of language organization and disease propagation in neurodegeneration are also reviewed. In addition, the issues of heterogeneity versus the relationships of the clinical phenotypes and their relationship to biological, pathological, and genetic advances are discussed, as is PPA's relationship to other conditions such as frontotemporal dementia, corticobasal degeneration, progressive supranuclear palsy, Pick disease, and amyotrophic lateral sclerosis. Arguments are presented in favor of considering these conditions as one entity versus many.

Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI

Background

Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN).

Methods

Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach.

Results

The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping.

Conclusion

In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.

Natural language processing techniques for studying language in pathological ageing: A scoping review

BACKGROUND

In the past few years there has been a growing interest in the employment of verbal productions as digital biomarkers, namely objective, quantifiable behavioural data that can be collected and measured by means of digital devices, allowing for a low-cost pathology detection, classification and monitoring. Numerous research papers have been published on the automatic detection of subtle verbal alteration, starting from written texts, raw speech recordings and transcripts, and such linguistic analysis has been singled out as a cost-effective method for diagnosing dementia and other medical conditions common among elderly patients (e.g., cognitive dysfunctions associated with metabolic disorders, dysarthria).

AIMS

To provide a critical appraisal and synthesis of evidence concerning the application of natural language processing (NLP) techniques for clinical purposes in the geriatric population. In particular, we discuss the state of the art on studying language in healthy and pathological ageing, focusing on the latest research efforts to build non-intrusive language-based tools for the early identification of cognitive frailty due to dementia. We also discuss some challenges and open problems raised by this approach.

METHODS & PROCEDURES

We performed a scoping review to examine emerging evidence about this novel domain. Potentially relevant studies published up to November 2021 were identified from the databases of MEDLINE, Cochrane and Web of Science. We also browsed the proceedings of leading international conferences (e.g., ACL, COLING, Interspeech, LREC) from 2017 to 2021, and checked the reference lists of relevant studies and reviews.

MAIN CONTRIBUTION

The paper provides an introductory, but complete, overview of the application of NLP techniques for studying language disruption due to dementia. We also suggest that this technique can be fruitfully applied to other medical conditions (e.g., cognitive dysfunctions associated with dysarthria, cerebrovascular disease and mood disorders).

CONCLUSIONS & IMPLICATIONS

Despite several critical points need to be addressed by the scientific community, a growing body of empirical evidence shows that NLP techniques can represent a promising tool for studying language changes in pathological aging, with a high potential to lead a significant shift in clinical practice.

WHAT THIS PAPER ADDS

What is already known on this subject Speech and languages abilities change due to non-pathological neurocognitive ageing and neurodegenerative processes. These subtle verbal modifications can be measured through NLP techniques and used as biomarkers for screening/diagnostic purposes in the geriatric population (i.e., digital linguistic biomarkers-DLBs). What this paper adds to existing knowledge The review shows that DLBs can represent a promising clinical tool, with a high potential to spark a major shift to dementia assessment in the elderly. Some challenges and open problems are also discussed. What are the potential or actual clinical implications of this work? This methodological review represents a starting point for clinicians approaching the DLB research field for studying language in healthy and pathological ageing. It summarizes the state of the art and future research directions of this novel approach.

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.

Automatic Assessment of Aphasic Speech Sensed by Audio Sensors for Classification into Aphasia Severity Levels to Recommend Speech Therapies

Aphasia is a type of speech disorder that can cause speech defects in a person. Identifying the severity level of the aphasia patient is critical for the rehabilitation process. In this research, we identify ten aphasia severity levels motivated by specific speech therapies based on the presence or absence of identified characteristics in aphasic speech in order to give more specific treatment to the patient. In the aphasia severity level classification process, we experiment on different speech feature extraction techniques, lengths of input audio samples, and machine learning classifiers toward classification performance. Aphasic speech is required to be sensed by an audio sensor and then recorded and divided into audio frames and passed through an audio feature extractor before feeding into the machine learning classifier. According to the results, the mel frequency cepstral coefficient (MFCC) is the most suitable audio feature extraction method for the aphasic speech level classification process, as it outperformed the classification performance of all mel-spectrogram, chroma, and zero crossing rates by a large margin. Furthermore, the classification performance is higher when 20 s audio samples are used compared with 10 s chunks, even though the performance gap is narrow. Finally, the deep neural network approach resulted in the best classification performance, which was slightly better than both K-nearest neighbor (KNN) and random forest classifiers, and it was significantly better than decision tree algorithms. Therefore, the study shows that aphasia level classification can be completed with accuracy, precision, recall, and F1-score values of 0.99 using MFCC for 20 s audio samples using the deep neural network approach in order to recommend corresponding speech therapy for the identified level. A web application was developed for English-speaking aphasia patients to self-diagnose the severity level and engage in speech therapies.

Clinical and neuroimaging characteristics of primary progressive aphasia

The chapter covers the clinical syndrome of a primary progressive aphasia (PPA), the demographics of this rare neurodegenerative disease, defining clinical and neuroanatomic characteristics of each PPA variant, disease progression, and behavioral features. The chapter begins with a brief introduction that includes references to seminal papers that defined this clinical syndrome and its three variants. The classic PPA subtypes discussed in the chapter are semantic variant PPA (svPPA), nonfluent/agrammatic PPA (nfaPPA), and logopenic variant PPA (lvPPA). The key language and cognitive characteristics, and language tasks that can elicit these language impairments, are detailed. Overlap in the clinical profiles of the PPA variants, which make differential diagnosis challenging, are explained. Disease progression is described, revealing that the PPA variants become more similar over time. Although PPA is language-predominant dementia, there are behavioral manifestations, particularly in svPPA. Changes in behavior in this variant are addressed as well as behavioral changes in nfaPPA and lvPPA that are less well recognized. The patterns of atrophy in the left temporal, parietal, and/or frontal cortices unique to each PPA variant are described. The underlying neuropathologies of the PPA variants are discussed, specifically tauopathies and non-tauopathies associated with svPPA and nfaPPA and Alzheimer's disease pathology in lvPPA.

Neural Correlates of Letter and Semantic Fluency in Primary Progressive Aphasia

Verbal fluency (VF) is an informative cognitive task. Lesion and functional imaging studies implicate distinct cerebral areas that support letter versus semantic fluency and the understanding of neural and cognitive mechanisms underlying task performance. Most lesion studies include chronic stroke patients. People with primary progressive aphasia (PPA) provide complementary evidence for lesion-deficit associations, as different brain areas are affected in stroke versus PPA. In the present study we sought to determine imaging, clinical and demographic correlates of VF in PPA. Thirty-five patients with PPA underwent an assessment with letter and category VF tasks, evaluation of clinical features and an MRI scan for volumetric analysis. We used stepwise regression models to determine which brain areas are associated with VF performance while acknowledging the independent contribution of clinical and demographic factors. Letter fluency was predominantly associated with language severity (R2 = 38%), and correlated with the volume of the left superior temporal regions (R2 = 12%) and the right dorsolateral prefrontal area (R2 = 5%). Semantic fluency was predominantly associated with dementia severity (R2 = 47%) and correlated with the volume of the left inferior temporal gyrus (R2 = 7%). No other variables were significantly associated with performance in the two VF tasks. We concluded that, independently of disease severity, letter fluency is significantly associated with the volume of frontal and temporal areas whereas semantic fluency is associated mainly with the volume of temporal areas. Furthermore, our findings indicated that clinical severity plays a critical role in explaining VF performance in PPA, compared to the other clinical and demographic factors.

Learning the Use of Artificial Intelligence in Heterogeneous Catalysis

We describe the use of artificial intelligence techniques in heterogeneous catalysis. This description is intended to give readers some clues for the use of these techniques in their research or industrial processes related to hydrodesulfurization. Since the description corresponds to supervised learning, first of all, we give a brief introduction to this type of learning, emphasizing the variables X and Y that define it. For each description, there is a particular emphasis on highlighting these variables. This emphasis will help define them when one works on a new application. The descriptions that we present relate to the construction of learning machines that infer adsorption energies, surface areas, adsorption isotherms of nanoporous materials, novel catalysts, and the sulfur content after hydrodesulfurization. These learning machines can predict adsorption energies with mean absolute errors of 0.15 eV for a diverse chemical space. They predict more precise surface areas of porous materials than the BET technique and can calculate their isotherms much faster than the Monte Carlo method. These machines can also predict new catalysts by learning from the catalytic behavior of materials generated through atomic substitutions. When the machines learn from the variables associated with a hydrodesulfurization process, they can predict the sulfur content in the final product.

Effects of tDCS on Sound Duration in Patients with Apraxia of Speech in Primary Progressive Aphasia

Transcranial direct current stimulation (tDCS) over the left inferior frontal gyrus (IFG) was found to improve oral and written naming in post-stroke and primary progressive aphasia (PPA), speech fluency in stuttering, a developmental speech-motor disorder, and apraxia of speech (AOS) symptoms in post-stroke aphasia. This paper addressed the question of whether tDCS over the left IFG coupled with speech therapy may improve sound duration in patients with apraxia of speech (AOS) symptoms in non-fluent PPA (nfvPPA/AOS) more than sham. Eight patients with non-fluent PPA/AOS received either active or sham tDCS, along with speech therapy for 15 sessions. Speech therapy involved repeating words of increasing syllable-length. Evaluations took place before, immediately after, and two months post-intervention. Words were segmented into vowels and consonants and the duration of each vowel and consonant was measured. Segmental duration was significantly shorter after tDCS compared to sham and tDCS gains generalized to untrained words. The effects of tDCS sustained over two months post-treatment in trained and untrained sounds. Taken together, these results demonstrate that tDCS over the left IFG may facilitate speech production by reducing segmental duration. The results provide preliminary evidence that tDCS may maximize efficacy of speech therapy in patients with nfvPPA/AOS.