Cognitive Screening for Mild Cognitive Impairment: Clinician Perspectives on Current Practices and Future Directions

 This study surveyed 51 specialist clinicians for their views on existing cognitive screening tests for mild cognitive impairment and their opinions about a hypothetical remote screener driven by artificial intelligence (AI). Responses revealed significant concerns regarding the sensitivity, specificity, and time taken to administer current tests, along with a general willingness to consider adopting telephone-based screening driven by AI. Findings highlight the need to design screeners that address the challenges of recognizing the earliest stages of cognitive decline and that prioritize not only accuracy but also stakeholder input.

A framework for language technologies in behavioral research and clinical applications: Ethical challenges, implications, and solutions

Technological advances in the assessment and understanding of speech and language within the domains of automatic speech recognition, natural language processing, and machine learning present a remarkable opportunity for psychologists to learn more about human thought and communication, evaluate a variety of clinical conditions, and predict cognitive and psychological states. These innovations can be leveraged to automate traditionally time-intensive assessment tasks (e.g., educational assessment), provide psychological information and care (e.g., chatbots), and when delivered remotely (e.g., by mobile phone or wearable sensors) promise underserved communities greater access to health care. Indeed, the automatic analysis of speech provides a wealth of information that can be used for patient care in a wide range of settings (e.g., mHealth applications) and for diverse purposes (e.g., behavioral and clinical research, medical tools that are implemented into practice) and patient types (e.g., numerous psychological disorders and in psychiatry and neurology). However, automation of speech analysis is a complex task that requires the integration of several different technologies within a large distributed process with numerous stakeholders. Many organizations have raised awareness about the need for robust systems for ensuring transparency, oversight, and regulation of technologies utilizing artificial intelligence. Since there is limited knowledge about the ethical and legal implications of these applications in psychological science, we provide a balanced view of both the optimism that is widely published on and also the challenges and risks of use, including discrimination and exacerbation of structural inequalities. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

An explainable machine learning model of cognitive decline derived from speech

INTRODUCTION

Traditional Alzheimer's disease (AD) and mild cognitive impairment (MCI) screening lacks the sensitivity and timeliness required to detect subtle indicators of cognitive decline. Multimodal artificial intelligence technologies using only speech data promise improved detection of neurodegenerative disorders.

METHODS

Speech collected over the telephone from 91 older participants who were cognitively healthy (n = 29) or had diagnoses of AD (n = 30) or amnestic MCI (aMCI; n = 32) was analyzed with multimodal natural language and speech processing methods. An explainable ensemble decision tree classifier for the multiclass prediction of cognitive decline was created.

RESULTS

This approach was 75% accurate overall-an improvement over traditional speech-based screening tools and a unimodal language-based model. We include a dashboard for the examination of the results, allowing for novel ways of interpreting such data.

DISCUSSION

This work provides a foundation for a meaningful change in medicine as clinical translation, scalability, and user friendliness were core to the methodologies.

Highlights

Remote assessments and artificial intelligence (AI) models allow greater access to cognitive decline screening.Speech impairments differ significantly between mild AD, amnestic mild cognitive impairment (aMCI), and healthy controls.AI predictions of cognitive decline are more accurate than experts and standard tools.The AI model was 75% accurate in classifying mild AD, aMCI, and healthy controls.

Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors

Incoherent speech in schizophrenia has long been described as the mind making "leaps" of large distances between thoughts and ideas. Such a view seems intuitive, and for almost two decades, attempts to operationalize these conceptual "leaps" in spoken word meanings have used language-based embedding spaces. An embedding space represents meaning of words as numerical vectors where a greater proximity between word vectors represents more shared meaning. However, there are limitations with word vector-based operationalizations of coherence which can limit their appeal and utility in clinical practice. First, the use of esoteric word embeddings can be conceptually hard to grasp, and this is complicated by several different operationalizations of incoherent speech. This problem can be overcome by a better visualization of methods. Second, temporal information from the act of speaking has been largely neglected since models have been built using written text, yet speech is spoken in real time. This issue can be resolved by leveraging time stamped transcripts of speech. Third, contextual information - namely the situation of where something is spoken - has often only been inferred and never explicitly modeled. Addressing this situational issue opens up new possibilities for models with increased temporal resolution and contextual relevance. In this paper, direct visualizations of semantic distances are used to enable the inspection of examples of incoherent speech. Some common operationalizations of incoherence are illustrated, and suggestions are made for how temporal and spatial contextual information can be integrated in future implementations of measures of incoherence.

Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function

Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.

Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and Research Agenda From a Cross-Linguistic Workshop

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

Using Automated Speech Processing for Repeated Measurements in a Clinical Setting of the Behavioral Variability in the Stroop Task

The Stroop interference task is indispensable to current neuropsychological practice. Despite this, it is limited in its potential for repeated administration, its sensitivity and its demands on professionals and their clients. We evaluated a digital Stroop deployed using a smart device. Spoken responses were timed using automated speech recognition. Participants included adult nonpatients (N = 113; k = 5 sessions over 5 days) and patients with psychiatric diagnoses (N = 85; k = 3-4 sessions per week over 4 weeks). Traditional interference (difference in response time between color incongruent words vs. color neutral words; M = 0.121 s) and facilitation (neutral vs. color congruent words; M = 0.085 s) effects were robust and temporally stable over testing sessions (ICCs 0.50-0.86). The performance showed little relation to clinical symptoms for a two-week window for either nonpatients or patients but was related to self-reported concentration at the time of testing for both groups. Performance was also related to treatment outcomes in patients. The duration of response word utterances was longer in patients than in nonpatients. Measures of intra-individual variability showed promise for understanding clinical state and treatment outcome but were less temporally stable than measures based solely on average response time latency. This framework of remote assessment using speech processing technology enables the fine-grained longitudinal charting of cognition and verbal behavior. However, at present, there is a problematic lower limit to the absolute size of the effects that can be examined when using voice in such a brief 'out-of-the-laboratory condition' given the temporal resolution of the speech-to-text detection system (in this case, 10 ms). This resolution will limit the parsing of meaningful effect sizes.

What Drives Animal Fluency Performance in Cantonese-Speaking Chinese Patients with Adult-Onset Psychosis?

Among the numerous studies investigating semantic factors associated with functioning in psychotic patients, most have been conducted on western populations. By contrast, the current cross-sectional study involved native Cantonese-speaking Chinese participants. Using the category fluency task, we compared performance between patients and healthy participants and examined clinical and sociodemographic correlates. First-episode psychosis patients (n = 356) and gender- and age-matched healthy participants (n = 35) were asked to generate as many ‘animals’ as they could in a minute. As expected, patients generated fewer correct responses (an average of 15.5 vs. 22.9 words), generated fewer clusters (an average of 3.7 vs. 5.4 thematically grouped nouns), switched less between clusters (on average 8.0 vs. 11.9 switches) and, interestingly, produced a larger percentage of Chinese zodiac animals than healthy participants (an average of 37.7 vs. 24.2). However, these significant group differences in the clusters and switches disappeared when the overall word production was controlled for. Within patients, education was the strongest predictor of category fluency performance (namely the number of correct responses, clusters, and switches). The findings suggest that an overall slowness in patients may account for the group differences in category fluency performance rather than any specific abnormality per se.

Discovering the structure and organization of a free Cantonese emotion-label word association graph to understand mental lexicons of emotions

Emotions are not necessarily universal across different languages and cultures. Mental lexicons of emotions depend strongly on contextual factors, such as language and culture. The Chinese language has unique linguistic properties that are different from other languages. As a main variant of Chinese, Cantonese has some emotional expressions that are only used by Cantonese speakers. Previous work on Chinese emotional vocabularies focused primarily on Mandarin. However, little is known about Cantonese emotion vocabularies. This is important since both language variants might have distinct emotional expressions, despite sharing the same writing system. To explore the structure and organization of Cantonese-label emotion words, we selected 79 highly representative emotion cue words from an ongoing large-scale Cantonese word association study (SWOW-HK). We aimed to identify the categories of these emotion words and non-emotion words that related to emotion concepts. Hierarchical cluster analysis was used to generate word clusters and investigate the underlying emotion dimensions. As the cluster quality was low in hierarchical clustering, we further constructed an emotion graph using a network approach to explore how emotions are organized in the Cantonese mental lexicon. With the support of emotion knowledge, the emotion graph defined more distinct emotion categories. The identified network communities covered basic emotions such as love, happiness, and sadness. Our results demonstrate that mental lexicon graphs constructed from free associations of Cantonese emotion-label words can reveal fine categories of emotions and their relevant concepts.

Celebrating the accomplishments of thought leaders in psychiatry research: Introduction

In academia and related industry, particularly in the medical sciences, some individuals are noticed for their ability to attract others towards their ideas, theories and objectives. They are often referred to as the "thought leaders" of the field. Noticeably, individuals who are labeled as "thought leaders" appear more often to be males than females. Moreover, this is not a racially or ethnically diverse group. In this special issue, we intend to challenge that bias. As we look world-wide at the incredibly important contributions of women in both psychiatry and related neuroscience, it was a logical step to ask these 'thought leaders' to write commentaries on their most important work, how they got there, and what they predict for the future. When compiling a list of "thought leaders" for future academic and industry workshops, these scientists are certain to enrich and advance the discourse.

Using automated syllable counting to detect missing information in speech transcripts from clinical settings

Speech rate and quantity reflect clinical state; thus automated transcription holds potential clinical applications. We describe two datasets where recording quality and speaker characteristics affected transcription accuracy. Transcripts of low-quality recordings omitted significant portions of speech. An automated syllable counter estimated actual speech output and quantified the amount of missing information. The efficacy of this method differed by audio quality: the correlation between missing syllables and word error rate was only significant when quality was low. Automatically counting syllables could be useful to measure and flag transcription omissions in clinical contexts where speaker characteristics and recording quality are problematic.

Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies

OBJECTIVES

Machine learning (ML) and natural language processing have great potential to improve efficiency and accuracy in diagnosis, treatment recommendations, predictive interventions, and scarce resource allocation within psychiatry. Researchers often conceptualize such an approach as operating in isolation without much need for human involvement, yet it remains crucial to harness human-in-the-loop practices when developing and implementing such techniques as their absence may be catastrophic. We advocate for building ML-based technologies that collaborate with experts within psychiatry in all stages of implementation and use to increase model performance while simultaneously increasing the practicality, robustness, and reliability of the process.

METHODS

We showcase pitfalls of the traditional ML framework and explain how it can be improved with human-in-the-loop techniques. Specifically, we applied active learning strategies to the automatic scoring of a story recall task and compared the results to a traditional approach.

RESULTS

Human-in-the-loop methodologies supplied a greater understanding of where the model was least confident or had knowledge gaps during training. As compared to the traditional framework, less than half of the training data were needed to reach a given accuracy.

CONCLUSIONS

Human-in-the-loop ML is an approach to data collection and model creation that harnesses active learning to select the most critical data needed to increase a model's accuracy and generalizability more efficiently than classic random sampling would otherwise allow. Such techniques may additionally operate as safeguards from spurious predictions and can aid in decreasing disparities that artificial intelligence systems otherwise propagate.

Predicting self-injurious thoughts in daily life using ambulatory assessment of state cognition

Self-injurious thoughts (SITs) fluctuate considerably from moment to moment. As such, "static" and temporally stable predictors (e.g., demographic variables, prior history) are suboptimal in predicting imminent SITs. This concern is particularly true for "online" cognitive abilities, which are important for understanding SITs, but are typically measured using tests selected for temporal stability. Advances in ambulatory assessments (i.e., real-time assessment in a naturalistic environment) allow for measuring cognition with improved temporal resolution. The present study measured relationships between "state" cognitive performance, measured using an ambulatory-based Trail Making Test, and SITs. Self-reported state hope and social connectedness was also measured. Data were collected using a specially designed mobile application (administered 4x/week up to 28 days) in substance use inpatients (N = 99). Consistent with prior literature, state hope and social connectedness was significantly associated with state SITs. Importantly, poorer state cognitive performance also significantly predicted state SITs, independent of hallmark static and state self-report risk variables. These findings highlight the potential importance of "online" cognition to predict SITs. Ambulatory recording reflects an efficient, sensitive, and ecological valid methodology for evaluating subjective and objectives predictors of imminent SITs.

Extending the usefulness of the verbal memory test: The promise of machine learning

The evaluation of verbal memory is a core component of neuropsychological assessment in a wide range of clinical and research settings. Leveraging story recall to assay neurocognitive function could be made more useful if it were possible to administer frequently (i.e., would allow for the collection of more patient data over time) and automatically assess the recalls with machine learning methods. In the present study, we evaluated a novel story recall test with 24 parallel forms that was deployed using smart devices in 94 psychiatric inpatients and 80 nonpatient adults. Machine learning and vector-based natural language processing methods were employed to automate test scoring, and performance using these methods was evaluated in their incremental validity, criterion validity (i.e., convergence with trained human raters), and parallel forms reliability. Our results suggest moderate to high consistency across the parallel forms, high convergence with human raters (r values ~ 0.89), and high incremental validity for discriminating between groups. While much work remains, the present findings are critical for implementing an automated, neuropsychological test deployable using remote technologies across multiple and frequent administrations.

Validating Biobehavioral Technologies for Use in Clinical Psychiatry

The last decade has witnessed the development of sophisticated biobehavioral and genetic, ambulatory, and other measures that promise unprecedented insight into psychiatric disorders. As yet, clinical sciences have struggled with implementing these objective measures and they have yet to move beyond "proof of concept." In part, this struggle reflects a traditional, and conceptually flawed, application of traditional psychometrics (i.e., reliability and validity) for evaluating them. This paper focuses on "resolution," concerning the degree to which changes in a signal can be detected and quantified, which is central to measurement evaluation in informatics, engineering, computational and biomedical sciences. We define and discuss resolution in terms of traditional reliability and validity evaluation for psychiatric measures, then highlight its importance in a study using acoustic features to predict self-injurious thoughts/behaviors (SITB). This study involved tracking natural language and self-reported symptoms in 124 psychiatric patients: (a) over 5-14 recording sessions, collected using a smart phone application, and (b) during a clinical interview. Importantly, the scope of these measures varied as a function of time (minutes, weeks) and spatial setting (i.e., smart phone vs. interview). Regarding reliability, acoustic features were temporally unstable until we specified the level of temporal/spatial resolution. Regarding validity, accuracy based on machine learning of acoustic features predicting SITB varied as a function of resolution. High accuracy was achieved (i.e., ~87%), but only when the acoustic and SITB measures were "temporally-matched" in resolution was the model generalizable to new data. Unlocking the potential of biobehavioral technologies for clinical psychiatry will require careful consideration of resolution.

Acceptability of collecting speech samples from the elderly via the telephone

OBJECTIVE

There is a critical need to develop rapid, inexpensive and easily accessible screening tools for mild cognitive impairment (MCI) and Alzheimer's disease (AD). We report on the efficacy of collecting speech via the telephone to subsequently develop sensitive metrics that may be used as potential biomarkers by leveraging natural language processing methods.

METHODS

Ninety-one older individuals who were cognitively unimpaired or diagnosed with MCI or AD participated from home in an audio-recorded telephone interview, which included a standard cognitive screening tool, and the collection of speech samples. In this paper we address six questions of interest: (1) Will elderly people agree to participate in a recorded telephone interview? (2) Will they complete it? (3) Will they judge it an acceptable approach? (4) Will the speech that is collected over the telephone be of a good quality? (5) Will the speech be intelligible to human raters? (6) Will transcriptions produced by automated speech recognition accurately reflect the speech produced?

RESULTS

Participants readily agreed to participate in the telephone interview, completed it in its entirety, and rated the approach as acceptable. Good quality speech was produced for further analyses to be applied, and almost all recorded words were intelligible for human transcription. Not surprisingly, human transcription outperformed off the shelf automated speech recognition software, but further investigation into automated speech recognition shows promise for its usability in future work.

CONCLUSION

Our findings demonstrate that collecting speech samples from elderly individuals via the telephone is well tolerated, practical, and inexpensive, and produces good quality data for uses such as natural language processing.

Applying speech technologies to assess verbal memory in patients with serious mental illness

Verbal memory deficits are some of the most profound neurocognitive deficits associated with schizophrenia and serious mental illness in general. As yet, their measurement in clinical settings is limited to traditional tests that allow for limited administrations and require substantial resources to deploy and score. Therefore, we developed a digital ambulatory verbal memory test with automated scoring, and repeated self-administration via smart devices. One hundred and four adults participated, comprising 25 patients with serious mental illness and 79 healthy volunteers. The study design was successful with high quality speech recordings produced to 92% of prompts (Patients: 86%, Healthy: 96%). The story recalls were both transcribed and scored by humans, and scores generated using natural language processing on transcriptions were comparable to human ratings (R = 0.83, within the range of human-to-human correlations of R = 0.73-0.89). A fully automated approach that scored transcripts generated by automatic speech recognition produced comparable and accurate scores (R = 0.82), with very high correlation to scores derived from human transcripts (R = 0.99). This study demonstrates the viability of leveraging speech technologies to facilitate the frequent assessment of verbal memory for clinical monitoring purposes in psychiatry.

Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness

The rapid embracing of artificial intelligence in psychiatry has a flavor of being the current "wild west"; a multidisciplinary approach that is very technical and complex, yet seems to produce findings that resonate. These studies are hard to review as the methods are often opaque and it is tricky to find the suitable combination of reviewers. This issue will only get more complex in the absence of a rigorous framework to evaluate such studies and thus nurture trustworthiness. Therefore, our paper discusses the urgency of the field to develop a framework with which to evaluate the complex methodology such that the process is done honestly, fairly, scientifically, and accurately. However, evaluation is a complicated process and so we focus on three issues, namely explainability, transparency, and generalizability, that are critical for establishing the viability of using artificial intelligence in psychiatry. We discuss how defining these three issues helps towards building a framework to ensure trustworthiness, but show how difficult definition can be, as the terms have different meanings in medicine, computer science, and law. We conclude that it is important to start the discussion such that there can be a call for policy on this and that the community takes extra care when reviewing clinical applications of such models..

The importance of loneliness in psychotic-like symptoms: Data from three studies

Poor social connection or loneliness is a prominent feature of schizotypy and may exacerbate psychosis risk. Previous studies have examined the inter-relationships between loneliness and psychosis risk, but critically, they have largely been conducted in non-clinical samples or exclusively used laboratory questionnaires with limited consideration of the heterogeneity within schizotypy (i.e., positive, negative, disorganized factors). The present study examined links between loneliness and psychotic-like symptoms across the dimensions of schizotypy through cross-sectional, laboratory-based questionnaires (Study 1; N = 160), ambulatory assessment (Study 2; N = 118) in undergraduates, and ambulatory assessment in inpatients in a substance abuse treatment program (Study 3; N = 48). Trait positive schizotypy consistently predicted cross-sectional and state psychotic-like symptoms. Loneliness, assessed via cross-sectional and ambulatory means, was largely linked with psychotic-like symptoms. Importantly, psychotic-like symptoms were dynamic: psychotic-like symptoms largely increased with loneliness in individuals with elevated positive and disorganized schizotypal traits, though there were some inconsistency related to disorganized schizotypy and state psychotic-like symptoms. Negative schizotypy and loneliness did not significantly interact to predict psychotic-like symptoms, suggesting specificity to positive schizotypy. Ambulatory approaches provide the opportunity for ecologically valid identification of risk states across psychopathology, thus informing early intervention.

Psychiatric Risk Assessment from the Clinician's Perspective: Lessons for the Future

Accurate prediction of risk-states in Serious Mental Illnesses (SMIs) is critical for reducing their massive societal burden. Risk-state assessments are notably inaccurate. Recent innovations, including widely available and inexpensive mobile technologies for ambulatory "biobehavioral" data, can reshape risk assessment. To help understand and accelerate clinician involvement, we surveyed 90 multi-disciplinary clinicians serving SMI populations in various settings to evaluate how risk assessment is conducted and can improve. Clinicians reported considerable variability in conducting risk assessment, and few clinicians explicated their procedures beyond tying it to broader mental status examinations or interviews. Very few clinicians endorsed using currently-available standardized risk measures, and most reported low confidence in their utility. Clinicians also reported spending approximately half the time conducting individual risk assessments than optimally needed. When asked about improvement, virtually no clinicians acknowledged biobehavioral, objective technologies, or ambulatory recording. Overall, clinicians seemed unaware of meaningful ways to improve risk assessment.

Moving psychological assessment out of the controlled laboratory setting: Practical challenges

Behavioral assessment using smart devices affords novel methods, notably remote self-administration by the individuals themselves. However, this new approach requires navigating complex legal and technical terrain. Given the limited empirical data that currently exists, we provide and discuss anecdotes of the methodological, technical, legal, and cultural issues associated with an implementation in both U.S. and European settings of a mobile software application for regular psychological monitoring purposes. The tasks required participants to listen, watch, speak, and touch to interact with the smart device, thus assessing cognition, motor skill, and language. Four major findings merit mention: First, moving assessment out of the hands of a trained investigator necessitates excellent usability engineering, such that the tool is easily usable by the participant and the resulting data relevant to the investigator. Second, remote assessment requires that the data are transferred safely back to the investigator, and that risk of compromising participant confidentiality is minimized. Third, frequent data collection over long periods of time is associated with a possibility that participants may choose to withdraw consent for participation thus requiring data retraction. Fourth, data collection and analysis across international borders creates new challenges and new opportunities because of important cultural and language issues that may inform the underlying behavioral constructs of interest. In conclusion, the new technological frameworks provide unprecedented opportunities for remote self-administered behavioral assessments but will be most productive in multidisciplinary teams to ensure the highest level of user satisfaction and data quality, and to guarantee the highest level of data protection. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Comparing static and dynamic predictors of risk for hostility in serious mental illness: Preliminary findings

This study compared static predictors of hostility (e.g. demographics, clinician ratings) to subjective (i.e., self-reported affect on slider scales in response to written questions) and objective (i.e., vocal indicators of arousal from speech samples in a story-retelling task) dynamic predictors using ambulatory assessment over five days in a sample of 25 stable outpatients with diagnoses of a serious mental illness. Multilevel modeling showed that both subjective and objective dynamic predictors were significant, but none of the static predictors were. These results suggest that, in predicting hostility, it is more important to account for state variation than static traits.

Can RDoC Help Find Order in Thought Disorder?

Thought disorder is a pernicious and nonspecific aspect of numerous serious mental illnesses (SMIs) and related conditions. Despite decades of empirical research on thought disorder, our present understanding of it is poor, our clinical assessments focus on a limited set of extreme behaviors, and treatments are palliative at best. Applying a Research Domain Criteria (RDoC) framework to thought disorder research offers advantages to explicate its phenotype; isolate its mechanisms; and develop more effective assessments, treatments, and potential cures. In this commentary, we discuss ways in which thought disorder can be understood within the RDoC framework. We propose operationalizing thought disorder within the RDoC construct of language using psycholinguistic sciences, to help objectify and quantify language within individuals; technologically sophisticated paradigms, to allow naturalistic behavioral sampling techniques with unprecedented ecological validity; and computational modeling, to account for a network of interconnected and dynamic linguistic, cognitive, affective, and social functions. We also highlight challenges for understanding thought disorder within an RDoC framework. Thought disorder likely does not occur as an isomorphic dysfunction in a single RDoC construct, but rather, as multiple potential dysfunctions in a network of RDoC constructs. Moreover, thought disorder is dynamic over time and context within individuals. In sum, RDoC is a useful framework to integrate multidisciplinary research efforts aimed at operationalizing, understanding, and ameliorating thought disorder.

An examination of the language construct in NIMH's research domain criteria: Time for reconceptualization!

The National Institute of Mental Health's Research Domain Criteria (RDoC) Initiative "calls for the development of new ways of classifying psychopathology based on dimensions of observable behavior." As a result of this ambitious initiative, language has been identified as an independent construct in the RDoC matrix. In this article, we frame language within an evolutionary and neuropsychological context and discuss some of the limitations to the current measurements of language. Findings from genomics and the neuroimaging of performance during language tasks are discussed in relation to serious mental illness and within the context of caveats regarding measuring language. Indeed, the data collection and analysis methods employed to assay language have been both aided and constrained by the available technologies, methodologies, and conceptual definitions. Consequently, different fields of language research show inconsistent definitions of language that have become increasingly broad over time. Individually, they have also shown significant improvements in conceptual resolution, as well as in experimental and analytic techniques. More recently, language research has embraced collaborations across disciplines, notably neuroscience, cognitive science, and computational linguistics and has ultimately re-defined classical ideas of language. As we move forward, the new models of language with their remarkably multifaceted constructs force a re-examination of the NIMH RDoC conceptualization of language and thus the neuroscience and genetics underlying this concept. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.

The impact of Val108/158Met polymorphism of catechol-O-methyltransferase on brain oscillations during working memory

This study investigated whether catechol-O-methyltransferase (COMT) Val/Met polymorphism was associated with variation in event-related desynchronization/synchronization (ERD/ERS) of responses during working memory (WM). 11 Val/Val and 11 Met/Met homozygous participants underwent magnetoencephalography (MEG) while performing a WM task. In contrast to small effects behaviourally, during the delay period Val/Val individuals showed lower ERS in the gamma band (Hz 30-50) in frontal regions, increased ERS in the alpha band (Hz 8-12) in the right frontal and parietal regions and increased ERD in the beta band (Hz 14-30) in the left fronto-temporal regions as compared with Met/Met homozygous individuals. During the response period Val/Val participants showed greater beta ERD in the prefrontal and parietotemporal regions. These results demonstrate that COMT genotype has a strong impact on brain responses (oscillatory activity) during WM performance likely a consequence of compensatory activity during the delay and response periods.

What do we really know about blunted vocal affect and alogia? A meta-analysis of objective assessments

Deficits in nonverbal vocal expression (e.g., blunted vocal affect, alogia) are a hallmark of schizophrenia and are a focus of the Research Domain Criteria initiative from the National Institute of Mental Health. Results from studies using symptom rating scales suggest that these deficits are profound; on the order of four to six standard deviations. To complement this endeavor, we conducted a meta-analysis of studies employing objective analysis of natural speech in patients with schizophrenia and nonpsychiatric controls. Thirteen studies, collectively including 480 patients with schizophrenia and 326 nonpsychiatric controls, were identified. There was considerable variability across studies in which aspects of vocal communication were examined and in the magnitudes of deficit. Overall, speech production (reflecting alogia) was impaired at a large effects size level (d=-.80; k=13), whereas speech variability (reflecting blunted affect) was much more modest (d=-.36; k=2). Regarding the former, this was largely driven by measures of pause behavior, as opposed to other aspects of speech (e.g., number of words/utterances). On the other hand, ratings of negative symptoms across these studies suggested profound group differences (d=3.54; k=4). These data suggest that only certain aspects of vocal expression are affected in schizophrenia, and highlight major discrepancies between symptom rating and objective-based measures. The discussion centers on advancing objective analysis for understanding vocal expression in schizophrenia and for identifying and defining more homogenous patient subsets for study.

A computational language approach to modeling prose recall in schizophrenia

Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes. We employ computational language approaches to assess time-varying semantic and sequential properties of prose recall at various retrieval intervals (immediate, 30 min and 24 h later) in patients with schizophrenia, unaffected siblings and healthy unrelated control participants. First, we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis (i.e., group membership) on performance. Next we model the human scoring of recall performance using an n-gram language sequence technique, and then with a semantic feature based on Latent Semantic Analysis. These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring. The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features. Taken individually, the semantic feature is most predictive, while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach. We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall.

Category fluency, latent semantic analysis and schizophrenia: a candidate gene approach

BACKGROUND

Category fluency is a widely used task that relies on multiple neurocognitive processes and is a sensitive assay of cortical dysfunction, including in schizophrenia. The test requires naming of as many words belonging to a certain category (e.g., animals) as possible within a short period of time. The core metrics are the overall number of words produced and the number of errors, namely non-members generated for a target category. We combine a computational linguistic approach with a candidate gene approach to examine the genetic architecture of this traditional fluency measure.

METHODS

In addition to the standard metric of overall word count, we applied a computational approach to semantics, Latent Semantic Analysis (LSA), to analyse the clustering pattern of the categories generated, as it likely reflects the search in memory for meanings. Also, since fluency performance probably also recruits verbal learning and recall processes, we included two standard measures of this cognitive process: the Wechsler Memory Scale and California Verbal Learning Test (CVLT). To explore the genetic architecture of traditional and LSA-derived fluency measures we employed a candidate gene approach focused on SNPs with known function that were available from a recent genome-wide association study (GWAS) of schizophrenia. The selected candidate genes were associated with language and speech, verbal learning and recall processes, and processing speed. A total of 39 coding SNPs were included for analysis in 665 subjects.

RESULTS AND DISCUSSION

Given the modest sample size, the results should be regarded as exploratory and preliminary. Nevertheless, the data clearly illustrate how extracting the meaning from participants' responses, by analysing the actual content of words, generates useful and neurocognitively viable metrics. We discuss three replicated SNPs in the genes ZNF804A, DISC1 and KIAA0319, as well as the potential for computational analyses of linguistic and textual data in other genomics tasks.

Deriving semantic structure from category fluency: clustering techniques and their pitfalls

Assessing verbal output in category fluency tasks provides a sensitive indicator of cortical dysfunction. The most common metrics are the overall number of words produced and the number of errors. Two main observations have been made about the structure of the output, first that there is a temporal component to it with words being generated in spurts, and second that the clustering pattern may reflect a search for meanings such that the 'clustering' is attributable to the activation of a specific semantic field in memory. A number of sophisticated approaches to examining the structure of this clustering have been developed, and a core theme is that the similarity relations between category members will reveal the mental semantic structure of the category underlying an individual's responses, which can then be visualized by a number of algorithms, such as MDS, hierarchical clustering, ADDTREE, ADCLUS or SVD. Such approaches have been applied to a variety of neurological and psychiatric populations, and the general conclusion has been that the clinical condition systematically distorts the semantic structure in the patients, as compared to the healthy controls. In the present paper we explore this approach to understanding semantic structure using category fluency data. On the basis of a large pool of patients with schizophrenia (n = 204) and healthy control participants (n = 204), we find that the methods are problematic and unreliable to the extent that it is not possible to conclude that any putative difference reflects a systematic difference between the semantic representations in patients and controls. Moreover, taking into account the unreliability of the methods, we find that the most probable conclusion to be made is that no difference in underlying semantic representation exists. The consequences of these findings to understanding semantic structure, and the use of category fluency data, in cortical dysfunction are discussed.

Dynamic cortical involvement in implicit anticipation during statistical learning

The prediction of future events is fundamental in a large number of critical neurobehavioral contexts including implicit motor learning. This learning process relies on the probabilities with which events occur, and is a dynamic phenomenon. The aim of present study was to investigate the development of anticipatory processes during implicit learning. A decision making task was employed in which the frequency of trial types was manipulated such that one trial type was disproportionately prevalent as compared to the remaining three trial types. A 275 channel whole-head magnetoencephalography (MEG) system was used to investigate the spatiotemporal distribution of event-related desynchronization (ERD) and synchronization (ERS). The results revealed that oscillations within the alpha (10-12 Hz) and beta (14-30 Hz) frequencies were associated with anticipatory processes in distinct networks in the course of learning. During early phases of learning the contralateral motor cortex, the anterior cingulate, the caudate and the inferior frontal gyrus showed ERDs within beta and alpha frequencies, putatively reflecting preparation of next motor response. As the task progressed, alpha ERSs in occipitotemporal regions and putamen likely reflect perceptual anticipation of the forthcoming stimuli.

Neural correlates of the relationship between discourse coherence and sensory monitoring in schizophrenia

Unusual language use is a core feature of psychosis, but the nature and significance of this are not understood. In particular, thought disorder in schizophrenia (SZ) is characterized by markedly bizarre speech, but the cognitive components that contribute to this and the brain correlates of these components are unknown. A number of studies have demonstrated language abnormalities in single word processing, but few have examined speech in SZ at the discourse level. This has been at least partly due to the difficulty in quantifying content of discourse. Recently, methods in computational linguistics have been found to be useful for detecting differences in semantic coherence during discourse between different clinical groups. We build on this work by demonstrating how these methods can be combined with funtional magnetic resonance imaging (fMRI) in order to tease apart factors that underlie free discourse and its deviations, and how they relate to brain activity. Eleven volunteers with SZ and eleven controls participated in an interview during which they were asked to talk as much as they could about 'religious belief'. These same participants underwent fMRI during a word monitoring task, during which modality of monitoring was manipulated by varying the congruence of auditory and visual stimuli. Semantic coherence scores, measured from free discourse, were examined for their relationship to brain activations during fMRI. In healthy controls, regions associated with executive function were related to coherence. In persons with SZ, coherence was mainly related to auditory and visual regions, depending on the modality of monitoring, but superior/middle temporal cortex related to coherence regardless of task. These findings are consistent with existing evidence for a role of superior temporal cortex in thought disorder, and demonstrate that computational measures of semantic content capture objective measures of coherence in speech that can be usefully related to underlying neurophysiological processes.

Ergotism in Norway. Part 2: The symptoms and their interpretation from the eighteenth century onwards

Ergotism, the disease caused by consuming Claviceps purpurea, a highly poisonous, grain-infecting fungus, occurred at various places scattered throughout Norway during the eighteenth and nineteenth centuries. By focusing on these cases we chart the changing interpretations of the peculiar disease, frequently understood within a religious context or considered as a supernatural (e.g. ghostly) experience. However, there was a growing awareness of the disease ergotism, and from the late eighteenth century onwards it was often correctly interpreted as being due to a fungus consumed via bread or porridge. Also, nineteenth-century fairy-tales and regional legends reveal that people were increasingly aware and fearful of the effects of consuming infected grain.

The evolution of the exponent of Zipf's law in language ontogeny

It is well-known that word frequencies arrange themselves according to Zipf's law. However, little is known about the dependency of the parameters of the law and the complexity of a communication system. Many models of the evolution of language assume that the exponent of the law remains constant as the complexity of a communication systems increases. Using longitudinal studies of child language, we analysed the word rank distribution for the speech of children and adults participating in conversations. The adults typically included family members (e.g., parents) or the investigators conducting the research. Our analysis of the evolution of Zipf's law yields two main unexpected results. First, in children the exponent of the law tends to decrease over time while this tendency is weaker in adults, thus suggesting this is not a mere mirror effect of adult speech. Second, although the exponent of the law is more stable in adults, their exponents fall below 1 which is the typical value of the exponent assumed in both children and adults. Our analysis also shows a tendency of the mean length of utterances (MLU), a simple estimate of syntactic complexity, to increase as the exponent decreases. The parallel evolution of the exponent and a simple indicator of syntactic complexity (MLU) supports the hypothesis that the exponent of Zipf's law and linguistic complexity are inter-related. The assumption that Zipf's law for word ranks is a power-law with a constant exponent of one in both adults and children needs to be revised.

Ergotism in Norway. Part 1: The symptoms and their interpretation from the late Iron Age to the seventeenth century

Ergotism is a horrendous disease with grotesque symptoms caused by ingesting specific ergot alkaloids. Mass poisoning episodes are attributable to consumption of grain - usually rye - infected with the fungus Claviceps purpurea. By focusing on possible cases of ergotism, we re-examine Norwegian history from the sagas through to the end of the seventeenth century. Our review - not intended to be exhaustive, or ex post facto to assign medical or psychiatric labels - draws attention to the very real possibility that many remarkable medical cases may have been the result of the ingestion of highly poisonous and psychoactive food substances. Where possible we highlight explanations given at the time - often rooted in religion or demonology - to explain the disease.

Latent semantic variables are associated with formal thought disorder and adaptive behavior in older inpatients with schizophrenia

INTRODUCTION

Formal thought disorder is a hallmark feature of schizophrenia in which disorganized thoughts manifest as disordered speech. A dysfunctional semantic system and a disruption in executive functioning have been proposed as possible mechanisms for formal thought disorder and verbal fluency impairment. Traditional rating scales and neuropsychological test scores might not be sensitive enough to distinguish among types of semantic impairments. This has lead to the proposed used of a natural language processing technique, Latent Semantic Analysis (LSA), which offers improved semantic sensitivity.

METHOD

In this study, LSA, a computational, vector-based text analysis technique to examine the contribution of vector length, an LSA measure related to word unusualness and cosines between word vectors, an LSA measure of semantic coherence to semantic and phonological fluency, disconnectedness of speech, and adaptive functioning in 165 older inpatients with schizophrenia.

RESULTS

In stepwise regressions word unusualness was significantly associated with semantic fluency and phonological fluency, disconnectedness in speech, and impaired functioning, even after considering the contribution of premorbid cognition, positive and negative symptoms, and demographic variables.

CONCLUSIONS

These findings support the utility of LSA in examining the contribution of coherence to thought disorder and the its relationship with daily functioning. Deficits in verbal fluency may be an expression of underlying disorganization in thought processes.

A modular approach to language production: models and facts

Numerous cortical disorders affect language. We explore the connection between the observed language behavior and the underlying substrates by adopting a neurocomputational approach. To represent the observed trajectories of the discourse in patients with disorganized speech and in healthy participants, we design a graphical representation for the discourse as a trajectory that allows us to visualize and measure the degree of order in the discourse as a function of the disorder of the trajectories. Our work assumes that many of the properties of language production and comprehension can be understood in terms of the dynamics of modular networks of neural associative memories. Based upon this assumption, we connect three theoretical and empirical domains: (1) neural models of language processing and production, (2) statistical methods used in the construction of functional brain images, and (3) corpus linguistic tools, such as Latent Semantic Analysis (henceforth LSA), that are used to discover the topic organization of language. We show how the neurocomputational models intertwine with LSA and the mathematical basis of functional neuroimaging. Within this framework we describe the properties of a context-dependent neural model, based on matrix associative memories, that performs goal-oriented linguistic behavior. We link these matrix associative memory models with the mathematics that underlie functional neuroimaging techniques and present the "functional brain images" emerging from the model. This provides us with a completely "transparent box" with which to analyze the implication of some statistical images. Finally, we use these models to explore the possibility that functional synaptic disconnection can lead to an increase in connectivity between the representations of concepts that could explain some of the alterations in discourse displayed by patients with schizophrenia.

A feasibility study of a telephone-based screening service for mild cognitive impairment and its uptake by elderly people

The risk of developing mild cognitive impairment (MCI), and subsequently dementia, increases with age. Early detection requires a comprehensive clinical examination, which is time consuming and expensive; a face-to-face examination can also be problematic for people living in rural areas which may result in unequal access to services. Telephone-based screening may provide a feasible method of identifying people who would benefit from a full diagnostic workup. We conducted a pilot study in which we offered telephone screening to all patients aged over 60 years at a health clinic in rural northern Norway (n = 259). Fifteen percent of them volunteered (n = 39). Screening identified a number of suspicious cases and we recommended to their general practitioner that 7 patients (18%) be offered a follow-up appointment. Surveys showed that the volunteers were generally positive towards the service, as was the general practitioner who found it helpful to be provided with such information about the elderly patients in his care. In addition, we surveyed the opinions of all general practitioners (n = 480) in the three northernmost counties of Norway concerning a potential service. There was a response rate of 40% (n = 190). Almost half of respondents (45%) would like to make use of such a service if it existed, and 34% believed that their patients would make use of it if available. The pilot study demonstrates the feasibility of telephone screening for clinically significant memory decline, and that users (general practitioners and the elderly) are positive towards such a service.