Characterizing Longitudinal Patterns in Cognition, Mood, And Activity in Depression With 6-Week High-Frequency Wearable Assessment: Observational Study.

BACKGROUND: Cognitive symptoms are an underrecognized aspect of depression that are often untreated. High-frequency cognitive assessment holds promise for improving disease and treatment monitoring. Although we have previously found it feasible to remotely assess cognition and mood in this capacity, further work is needed to ascertain the optimal methodology to implement and synthesize these techniques. OBJECTIVE: The objective of this study was to examine (1) longitudinal changes in mood, cognition, activity levels, and heart rate over 6 weeks; (2) diurnal and weekday-related changes; and (3) co-occurrence of fluctuations between mood, cognitive function, and activity. METHODS: A total of 30 adults with current mild-moderate depression stabilized on antidepressant monotherapy responded to testing delivered through an Apple Watch (Apple Inc) for 6 weeks. Outcome measures included cognitive function, assessed with 3 brief n-back tasks daily; self-reported depressed mood, assessed once daily; daily total step count; and average heart rate. Change over a 6-week duration, diurnal and day-of-week variations, and covariation between outcome measures were examined using nonlinear and multilevel models. RESULTS: Participants showed initial improvement in the Cognition Kit N-Back performance, followed by a learning plateau. Performance reached 90% of individual learning levels on average 10 days after study onset. N-back performance was typically better earlier and later in the day, and step counts were lower at the beginning and end of each week. Higher step counts overall were associated with faster n-back learning, and an increased daily step count was associated with better mood on the same (P<.001) and following day (P=.02). Daily n-back performance covaried with self-reported mood after participants reached their learning plateau (P=.01). CONCLUSIONS: The current results support the feasibility and sensitivity of high-frequency cognitive assessments for disease and treatment monitoring in patients with depression. Methods to model the individual plateau in task learning can be used as a sensitive approach to better characterize changes in behavior and improve the clinical relevance of cognitive data. Wearable technology allows assessment of activity levels, which may influence both cognition and mood.

Cognitive functioning in anxiety and depression: results from the ALSPAC cohort.

Anxiety and depression are associated with a range of impairments in cognitive functioning. Understanding the nature of these deficits may identify targets for intervention and prevent functional decline. We used observational and genetic methods to investigate the relationship of anxiety and depression with three cognitive domains: emotion recognition, response inhibition, and working memory, in the Avon Longitudinal Study of Parents and Children (ALSPAC). We examined: (i) cross-sectional associations between anxiety, depression, and cognition at age 24 (n = 2187), (ii) prospective associations between anxiety and depression at age 18 and cognition at age 24 (n = 1855), and (iii) the casual effect of anxiety and depression on cognition using Mendelian randomization (MR). Both disorders were associated with altered emotion recognition; anxiety with decreased happiness recognition (b = -0.27 [-0.54,0.01], p = 0.045), and depression with increased sadness recognition (b = 0.35 [0.07,0.64], p = 0.016). Anxiety was also associated with poorer working memory (b = -0.14 [-0.24,0.04], p = 0.005). There was no evidence for an association with response inhibition. MR provided no clear evidence of causal relationships between mental health and cognition, but these analyses were underpowered. Overall, there was little evidence for impairments in executive functioning, but moderate alterations in emotion recognition. This may inform the development of psychosocial interventions.

Prediction of mental effort derived from an automated vocal biomarker using machine learning in a large-scale remote sample.

Introduction: Biomarkers of mental effort may help to identify subtle cognitive impairments in the absence of task performance deficits. Here, we aim to detect mental effort on a verbal task, using automated voice analysis and machine learning. Methods: Audio data from the digit span backwards task were recorded and scored with automated speech recognition using the online platform NeuroVocalixTM, yielding usable data from 2,764 healthy adults (1,022 male, 1,742 female; mean age 31.4 years). Acoustic features were aggregated across each trial and normalized within each subject. Cognitive load was dichotomized for each trial by categorizing trials at >0.6 of each participants' maximum span as "high load." Data were divided into training (60%), test (20%), and validate (20%) datasets, each containing different participants. Training and test data were used in model building and hyper-parameter tuning. Five classification models (Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, and Gradient Boosting) were trained to predict cognitive load ("high" vs. "low") based on acoustic features. Analyses were limited to correct responses. The model was evaluated using the validation dataset, across all span lengths and within the subset of trials with a four-digit span. Classifier discriminant power was examined with Receiver Operating Curve (ROC) analysis. Results: Participants reached a mean span of 6.34 out of 8 items (SD = 1.38). The Gradient Boosting classifier provided the best performing model on test data (AUC = 0.98) and showed excellent discriminant power for cognitive load on the validation dataset, across all span lengths (AUC = 0.99), and for four-digit only utterances (AUC = 0.95). Discussion: A sensitive biomarker of mental effort can be derived from vocal acoustic features in remotely administered verbal cognitive tests. The use-case of this biomarker for improving sensitivity of cognitive tests to subtle pathology now needs to be examined.

Leveraging speech and artificial intelligence to screen for early Alzheimer's disease and amyloid beta positivity.

Early detection of Alzheimer's disease is required to identify patients suitable for disease-modifying medications and to improve access to non-pharmacological preventative interventions. Prior research shows detectable changes in speech in Alzheimer's dementia and its clinical precursors. The current study assesses whether a fully automated speech-based artificial intelligence system can detect cognitive impairment and amyloid beta positivity, which characterize early stages of Alzheimer's disease. Two hundred participants (age 54-85, mean 70.6; 114 female, 86 male) from sister studies in the UK (NCT04828122) and the USA (NCT04928976), completed the same assessments and were combined in the current analyses. Participants were recruited from prior clinical trials where amyloid beta status (97 amyloid positive, 103 amyloid negative, as established via PET or CSF test) and clinical diagnostic status was known (94 cognitively unimpaired, 106 with mild cognitive impairment or mild Alzheimer's disease). The automatic story recall task was administered during supervised in-person or telemedicine assessments, where participants were asked to recall stories immediately and after a brief delay. An artificial intelligence text-pair evaluation model produced vector-based outputs from the original story text and recorded and transcribed participant recalls, quantifying differences between them. Vector-based representations were fed into logistic regression models, trained with tournament leave-pair-out cross-validation analysis to predict amyloid beta status (primary endpoint), mild cognitive impairment and amyloid beta status in diagnostic subgroups (secondary endpoints). Predictions were assessed by the area under the receiver operating characteristic curve for the test result in comparison with reference standards (diagnostic and amyloid status). Simulation analysis evaluated two potential benefits of speech-based screening: (i) mild cognitive impairment screening in primary care compared with the Mini-Mental State Exam, and (ii) pre-screening prior to PET scanning when identifying an amyloid positive sample. Speech-based screening predicted amyloid beta positivity (area under the curve = 0.77) and mild cognitive impairment or mild Alzheimer's disease (area under the curve = 0.83) in the full sample, and predicted amyloid beta in subsamples (mild cognitive impairment or mild Alzheimer's disease: area under the curve = 0.82; cognitively unimpaired: area under the curve = 0.71). Simulation analyses indicated that in primary care, speech-based screening could modestly improve detection of mild cognitive impairment (+8.5%), while reducing false positives (-59.1%). Furthermore, speech-based amyloid pre-screening was estimated to reduce the number of PET scans required by 35.3% and 35.5% in individuals with mild cognitive impairment and cognitively unimpaired individuals, respectively. Speech-based assessment offers accessible and scalable screening for mild cognitive impairment and amyloid beta positivity.

A remote speech-based AI system to screen for early Alzheimer's disease via smartphones.

Introduction: Artificial intelligence (AI) systems leveraging speech and language changes could support timely detection of Alzheimer's disease (AD). Methods: The AMYPRED study (NCT04828122) recruited 133 subjects with an established amyloid beta (Aß) biomarker (66 Aß+, 67 Aß-) and clinical status (71 cognitively unimpaired [CU], 62 mild cognitive impairment [MCI] or mild AD). Daily story recall tasks were administered via smartphones and analyzed with an AI system to predict MCI/mild AD and Aß positivity. Results: Eighty-six percent of participants (115/133) completed remote assessments. The AI system predicted MCI/mild AD (area under the curve [AUC] = 0.85, ±0.07) but not Aß (AUC = 0.62 ±0.11) in the full sample, and predicted Aß in clinical subsamples (MCI/mild AD: AUC = 0.78 ±0.14; CU: AUC = 0.74 ±0.13) on short story variants (immediate recall). Long stories and delayed retellings delivered broadly similar results. Discussion: Speech-based testing offers simple and accessible screening for early-stage AD.

Mainstreaming adult ADHD into primary care in the UK: guidance, practice, and best practice recommendations.

BACKGROUND: ADHD in adults is a common and debilitating neurodevelopmental mental health condition. Yet, diagnosis, clinical management and monitoring are frequently constrained by scarce resources, low capacity in specialist services and limited awareness or training in both primary and secondary care. As a result, many people with ADHD experience serious barriers in accessing the care they need. METHODS: Professionals across primary, secondary, and tertiary care met to discuss adult ADHD clinical care in the United Kingdom. Discussions identified constraints in service provision, and service delivery models with potential to improve healthcare access and delivery. The group aimed to provide a roadmap for improving access to ADHD treatment, identifying avenues for improving provision under current constraints, and innovating provision in the longer-term. National Institute for Health and Care Excellence (NICE) guidelines were used as a benchmark in discussions. RESULTS: The group identified three interrelated constraints. First, inconsistent interpretation of what constitutes a 'specialist' in the context of delivering ADHD care. Second, restriction of service delivery to limited capacity secondary or tertiary care services. Third, financial limitations or conflicts which reduce capacity and render transfer of care between healthcare sectors difficult. The group recommended the development of ADHD specialism within primary care, along with the transfer of routine and straightforward treatment monitoring to primary care services. Longer term, ADHD care pathways should be brought into line with those for other common mental health disorders, including treatment initiation by appropriately qualified clinicians in primary care, and referral to secondary mental health or tertiary services for more complex cases. Long-term plans in the NHS for more joined up and flexible provision, using a primary care network approach, could invest in developing shared ADHD specialist resources. CONCLUSIONS: The relegation of adult ADHD diagnosis, treatment and monitoring to specialist tertiary and secondary services is at odds with its high prevalence and chronic course. To enable the cost-effective and at-scale access to ADHD treatment that is needed, general adult mental health and primary care must be empowered to play a key role in the delivery of quality services for adults with ADHD.

Validation of a Remote and Fully Automated Story Recall Task to Assess for Early Cognitive Impairment in Older Adults: Longitudinal Case-Control Observational Study.

BACKGROUND: Story recall is a simple and sensitive cognitive test that is commonly used to measure changes in episodic memory function in early Alzheimer disease (AD). Recent advances in digital technology and natural language processing methods make this test a candidate for automated administration and scoring. Multiple parallel test stimuli are required for higher-frequency disease monitoring. OBJECTIVE: This study aims to develop and validate a remote and fully automated story recall task, suitable for longitudinal assessment, in a population of older adults with and without mild cognitive impairment (MCI) or mild AD. METHODS: The "Amyloid Prediction in Early Stage Alzheimer's disease" (AMYPRED) studies recruited participants in the United Kingdom (AMYPRED-UK: NCT04828122) and the United States (AMYPRED-US: NCT04928976). Participants were asked to complete optional daily self-administered assessments remotely on their smart devices over 7 to 8 days. Assessments included immediate and delayed recall of 3 stories from the Automatic Story Recall Task (ASRT), a test with multiple parallel stimuli (18 short stories and 18 long stories) balanced for key linguistic and discourse metrics. Verbal responses were recorded and securely transferred from participants' personal devices and automatically transcribed and scored using text similarity metrics between the source text and retelling to derive a generalized match score. Group differences in adherence and task performance were examined using logistic and linear mixed models, respectively. Correlational analysis examined parallel-forms reliability of ASRTs and convergent validity with cognitive tests (Logical Memory Test and Preclinical Alzheimer's Cognitive Composite with semantic processing). Acceptability and usability data were obtained using a remotely administered questionnaire. RESULTS: Of the 200 participants recruited in the AMYPRED studies, 151 (75.5%)-78 cognitively unimpaired (CU) and 73 MCI or mild AD-engaged in optional remote assessments. Adherence to daily assessment was moderate and did not decline over time but was higher in CU participants (ASRTs were completed each day by 73/106, 68.9% participants with MCI or mild AD and 78/94, 83% CU participants). Participants reported favorable task usability: infrequent technical problems, easy use of the app, and a broad interest in the tasks. Task performance improved modestly across the week and was better for immediate recall. The generalized match scores were lower in participants with MCI or mild AD (Cohen d=1.54). Parallel-forms reliability of ASRT stories was moderate to strong for immediate recall (mean rho 0.73, range 0.56-0.88) and delayed recall (mean rho=0.73, range=0.54-0.86). The ASRTs showed moderate convergent validity with established cognitive tests. CONCLUSIONS: The unsupervised, self-administered ASRT task is sensitive to cognitive impairments in MCI and mild AD. The task showed good usability, high parallel-forms reliability, and high convergent validity with established cognitive tests. Remote, low-cost, low-burden, and automatically scored speech assessments could support diagnostic screening, health care, and treatment monitoring.

Protocol for Rhapsody: a longitudinal observational study examining the feasibility of speech phenotyping for remote assessment of neurodegenerative and psychiatric disorders.

INTRODUCTION: Neurodegenerative and psychiatric disorders (NPDs) confer a huge health burden, which is set to increase as populations age. New, remotely delivered diagnostic assessments that can detect early stage NPDs by profiling speech could enable earlier intervention and fewer missed diagnoses. The feasibility of collecting speech data remotely in those with NPDs should be established. METHODS AND ANALYSIS: The present study will assess the feasibility of obtaining speech data, collected remotely using a smartphone app, from individuals across three NPD cohorts: neurodegenerative cognitive diseases (n=50), other neurodegenerative diseases (n=50) and affective disorders (n=50), in addition to matched controls (n=75). Participants will complete audio-recorded speech tasks and both general and cohort-specific symptom scales. The battery of speech tasks will serve several purposes, such as measuring various elements of executive control (eg, attention and short-term memory), as well as measures of voice quality. Participants will then remotely self-administer speech tasks and follow-up symptom scales over a 4-week period. The primary objective is to assess the feasibility of remote collection of continuous narrative speech across a wide range of NPDs using self-administered speech tasks. Additionally, the study evaluates if acoustic and linguistic patterns can predict diagnostic group, as measured by the sensitivity, specificity, Cohen's kappa and area under the receiver operating characteristic curve of the binary classifiers distinguishing each diagnostic group from each other. Acoustic features analysed include mel-frequency cepstrum coefficients, formant frequencies, intensity and loudness, whereas text-based features such as number of words, noun and pronoun rate and idea density will also be used. ETHICS AND DISSEMINATION: The study received ethical approval from the Health Research Authority and Health and Care Research Wales (REC reference: 21/PR/0070). Results will be disseminated through open access publication in academic journals, relevant conferences and other publicly accessible channels. Results will be made available to participants on request. TRIAL REGISTRATION NUMBER: NCT04939818.

Test-retest reliability on the Cambridge Neuropsychological Test Automated Battery: Comment on Karlsen et al. (2020).

Test-retest reliability is essential to the development and validation of psychometric tools. Here we respond to the article by Karlsen et al. (Applied Neuropsychology: Adult, 2020), reporting test-retest reliability on the Cambridge Neuropsychological Test Automated Battery (CANTAB), with results that are in keeping with prior research on CANTAB and the broader cognitive assessment literature. However, after adopting a high threshold for adequate test-retest reliability, the authors report inadequate reliability for many measures. In this commentary we provide examples of stable, trait-like constructs which we would expect to remain highly consistent across longer time periods, and contrast these with measures which show acute within-subject change in response to contextual or psychological factors. Measures characterized by greater true within-subject variability typically have lower test-retest reliability, requiring adequate powering in research examining group differences and longitudinal change. However, these measures remain sensitive to important clinical and functional outcomes. Setting arbitrarily elevated test-retest reliability thresholds for test adoption in cognitive research limits the pool of available tools and precludes the adoption of many well-established tests showing consistent contextual, diagnostic, and treatment sensitivity. Overall, test-retest reliability must be balanced with other theoretical and practical considerations in study design, including test relevance and sensitivity.

Developing Digital Tools for Remote Clinical Research: How to Evaluate the Validity and Practicality of Active Assessments in Field Settings.

The ability of remote research tools to collect granular, high-frequency data on symptoms and digital biomarkers is an important strength because it circumvents many limitations of traditional clinical trials and improves the ability to capture clinically relevant data. This approach allows researchers to capture more robust baselines and derive novel phenotypes for improved precision in diagnosis and accuracy in outcomes. The process for developing these tools however is complex because data need to be collected at a frequency that is meaningful but not burdensome for the participant or patient. Furthermore, traditional techniques, which rely on fixed conditions to validate assessments, may be inappropriate for validating tools that are designed to capture data under flexible conditions. This paper discusses the process for determining whether a digital assessment is suitable for remote research and offers suggestions on how to validate these novel tools.

Using e-diaries to investigate ADHD - State-of-the-art and the promising feature of just-in-time-adaptive interventions.

Attention-deficit/hyperactive disorder (ADHD) is characterized by symptoms which are dynamic in nature: states of hyperactivity, inattention and impulsivity as core symptoms, and emotion dysregulation as associated feature. Although tremendous work has been done to investigate between-subject differences (how patients with ADHD differ from healthy controls or patients with other disorders), little is known about the relationship between symptoms with triggers and contexts, that may allow us to better understand their causes and consequences. Understanding the temporal associations between symptoms and environmental triggers in an ecologically valid manner may be the basis to developing just-in-time adaptive interventions. Fortunately, recent years have seen advances in methodology, hardware and innovative statistical approaches to study dynamic processes in daily life. In this narrative review, we provide a description of the methodology (ambulatory assessment), summarize the existing literature in ADHD, and discuss future prospects for these methods, namely mobile sensing to assess contextual information, real-time analyses and just-in-time adaptive interventions.

Failure of Healthcare Provision for Attention-Deficit/Hyperactivity Disorder in the United Kingdom: A Consensus Statement.

Background: Despite evidence-based national guidelines for ADHD in the United Kingdom (UK), ADHD is under-identified, under-diagnosed, and under-treated. Many seeking help for ADHD face prejudice, long waiting lists, and patchy or unavailable services, and are turning to service-user support groups and/or private healthcare for help. Methods: A group of UK experts representing clinical and healthcare providers from public and private healthcare, academia, ADHD patient groups, educational, and occupational specialists, met to discuss shortfalls in ADHD service provision in the UK. Discussions explored causes of under-diagnosis, examined biases operating across referral, diagnosis and treatment, together with recommendations for resolving these matters. Results: Cultural and structural barriers operate at all levels of the healthcare system, resulting in a de-prioritization of ADHD. Services for ADHD are insufficient in many regions, and problems with service provision have intensified as a result of the response to the COVID-19 pandemic. Research has established a range of adverse outcomes of untreated ADHD, and associated long-term personal, social, health and economic costs are high. The consensus group called for training of professionals who come into contact with people with ADHD, increased funding, commissioning and monitoring to improve service provision, and streamlined communication between health services to support better outcomes for people with ADHD. Conclusions: Evidence-based national clinical guidelines for ADHD are not being met. People with ADHD should have access to healthcare free from discrimination, and in line with their legal rights. UK Governments and clinical and regulatory bodies must act urgently on this important public health issue.

Comparable emotional dynamics in women with ADHD and borderline personality disorder.

BACKGROUND: Emotional dysregulation (ED) is a core diagnostic symptom in borderline personality disorder (BPD) and an associated feature of attention-deficit/hyperactivity disorder (ADHD). We aimed to investigate differences in dynamical indices of ED in daily life in ADHD and BPD. METHODS: We used experience sampling method (ESM) and multilevel modelling to assess momentary changes in reports of affective symptoms, and retrospective questionnaire measures of ED in a sample of 98 adult females with ADHD, BPD, comorbid ADHD+BPD and healthy controls. RESULTS: We found marked differences between the clinical groups and healthy controls. However, the ESM assessments did not show differences in the intensity of feeling angry and irritable, and the instability of feeling sad, irritable and angry, findings paralleled by data from retrospective questionnaires. The heightened intensity in negative emotions in the clinical groups compared to controls was only partially explained by bad events at the time of reporting negative emotions, suggesting both reactive and endogenous influences on ED in both ADHD and BPD. CONCLUSIONS: This study supports the view that ED is a valuable trans-diagnostic aspect of psychopathology in both ADHD and BPD, with similar levels of intensity and instability. These findings suggest that the presence or severity of ED should not be used in clinical practice to distinguish between the two disorders.

Testing the association between tobacco and cannabis use and cognitive functioning: Findings from an observational and Mendelian randomization study.

BACKGROUND: Although studies have examined the association between tobacco and cannabis use in adolescence with subsequent cognitive functioning, study designs are usually not able to distinguish correlation from causation. METHODS: Separate patterns of tobacco and cannabis use were derived using longitudinal latent class analysis based on measures assessed on five occasions from age 13-18 in a large UK population cohort (Avon Longitudinal Study of Parents and Children). Cognitive functioning measures comprised of working memory, response inhibition, and emotion recognition assessed at 24 years of age. Mendelian randomization was used to examine the possible causal relationship between smoking initiation, lifetime cannabis use and cognitive functioning. RESULTS: We found evidence of a relationship between tobacco and cannabis use and diminished cognitive functioning for each of the outcomes in the observational analyses. There was evidence to suggest that late-onset regular tobacco smokers (b=-0.29, 95 %CI=-0.45 to -0.13), early-onset regular tobacco smokers (b=-0.45, 95 %CI=-0.84 to -0.05), and early-onset regular cannabis users (b=-0.62, 95 %CI=-0.93 to -0.31) showed poorer working memory. Early-onset regular tobacco smokers (b = 0.18, 95 %CI = 0.07 to 0.28), and early-onset regular cannabis users (b = 0.30, 95 %CI = 0.08 to 0.52) displayed poorer ability to inhibit responses. Late-onset regular (b=-0.02, 95 %CI=-0.03 to - 0.00), and early-onset regular tobacco smokers (b=-0.04, 95 %CI=-0.08 to -0.01) showed poorer ability to recognise emotions. Mendelian randomization analyses were imprecise and did not provide additional support for the observational results. CONCLUSION: There was some evidence to suggest that adolescent tobacco and cannabis use were associated with deficits in working memory, response inhibition and emotion recognition. Better powered genetic studies are required to determine whether these associations are causal.

Alcohol use and cognitive functioning in young adults: improving causal inference.

BACKGROUND AND AIMS: There have been few longitudinal studies of association between alcohol use and cognitive functioning in young people. We aimed to examine whether alcohol use is a causal risk factor for deficient cognitive functioning in young adults. DESIGN: Linear regression was used to examine the relationship between longitudinal latent class patterns of binge drinking and subsequent cognitive functioning. Two-sample Mendelian randomization (MR) tested evidence for the causal relationship between alcohol use and cognitive functioning. SETTING: South West England. PARTICIPANTS: The observational study included 3155 adolescents and their parents (fully adjusted models) from the Avon Longitudinal Study of Parents and Children (ALSPAC). Genetic instruments for alcohol use were based on almost 1 000 000 individuals from the genome-wide association studies (GWAS) and Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Genome-wide association studies for cognitive outcomes were based on 2500 individuals from ALSPAC. MEASUREMENTS: Binge drinking was assessed at approximately 16, 17, 18, 21 and 23 years. Cognitive functioning comprised working memory, response inhibition and emotion recognition assessed at 24 years of age. Ninety-nine independent genome-wide significant single nucleotide polymorphisms (SNPs) associated with 'number of drinks per week' were used as the genetic instrument for alcohol consumption. Potential confounders were included in the observational analyses. FINDINGS: Four binge drinking classes were identified: 'low-risk' (41.3%), 'early-onset monthly' (19.1%), 'adult frequent' (22.5%) and 'early-onset frequent' (17.0%). The association between early-onset frequent binge drinking and cognitive functioning: working memory (b = -0.42, 95% confidence interval (CI) = -1.24 to 0.41), response inhibition (b = 31.9, 95% CI = -25.3 to 89.2), and emotion recognition (b = 0.02, 95% CI = -0.07 to 0.10) in comparison to low-risk drinkers were inconclusive as to whether a difference was present. Two-sample MR analyses similarly provided little evidence that alcohol use is associated with deficits in working memory using the inverse variance weight (b = 0.29, 95% CI = -0.42 to 0.99), response inhibition (b = -0.32, 95% CI = -1.04 to 0.39) and emotion recognition (b = 0.03, 95% CI = -0.55 to 0.61). CONCLUSIONS: Binge drinking in adolescence and early adulthood may not be causally related to deficiencies in working memory, response inhibition or emotion recognition in youths.

Females with ADHD: An expert consensus statement taking a lifespan approach providing guidance for the identification and treatment of attention-deficit/ hyperactivity disorder in girls and women.

BACKGROUND: There is evidence to suggest that the broad discrepancy in the ratio of males to females with diagnosed ADHD is due, at least in part, to lack of recognition and/or referral bias in females. Studies suggest that females with ADHD present with differences in their profile of symptoms, comorbidity and associated functioning compared with males. This consensus aims to provide a better understanding of females with ADHD in order to improve recognition and referral. Comprehensive assessment and appropriate treatment is hoped to enhance longer-term clinical outcomes and patient wellbeing for females with ADHD. METHODS: The United Kingdom ADHD Partnership hosted a meeting of experts to discuss symptom presentation, triggers for referral, assessment, treatment and multi-agency liaison for females with ADHD across the lifespan. RESULTS: A consensus was reached offering practical guidance to support medical and mental health practitioners working with females with ADHD. The potential challenges of working with this patient group were identified, as well as specific barriers that may hinder recognition. These included symptomatic differences, gender biases, comorbidities and the compensatory strategies that may mask or overshadow underlying symptoms of ADHD. Furthermore, we determined the broader needs of these patients and considered how multi-agency liaison may provide the support to meet them. CONCLUSIONS: This practical approach based upon expert consensus will inform effective identification, treatment and support of girls and women with ADHD. It is important to move away from the prevalent perspective that ADHD is a behavioural disorder and attend to the more subtle and/or internalised presentation that is common in females. It is essential to adopt a lifespan model of care to support the complex transitions experienced by females that occur in parallel to change in clinical presentation and social circumstances. Treatment with pharmacological and psychological interventions is expected to have a positive impact leading to increased productivity, decreased resource utilization and most importantly, improved long-term outcomes for girls and women.

Comparing Web-Based and Lab-Based Cognitive Assessment Using the Cambridge Neuropsychological Test Automated Battery: A Within-Subjects Counterbalanced Study.

BACKGROUND: Computerized assessments are already used to derive accurate and reliable measures of cognitive function. Web-based cognitive assessment could improve the accessibility and flexibility of research and clinical assessment, widen participation, and promote research recruitment while simultaneously reducing costs. However, differences in context may influence task performance. OBJECTIVE: This study aims to determine the comparability of an unsupervised, web-based administration of the Cambridge Neuropsychological Test Automated Battery (CANTAB) against a typical in-person lab-based assessment, using a within-subjects counterbalanced design. The study aims to test (1) reliability, quantifying the relationship between measurements across settings using correlational approaches; (2) equivalence, the extent to which test results in different settings produce similar overall results; and (3) agreement, by quantifying acceptable limits to bias and differences between measurement environments. METHODS: A total of 51 healthy adults (32 women and 19 men; mean age 36.8, SD 15.6 years) completed 2 testing sessions, which were completed on average 1 week apart (SD 4.5 days). Assessments included equivalent tests of emotion recognition (emotion recognition task [ERT]), visual recognition (pattern recognition memory [PRM]), episodic memory (paired associate learning [PAL]), working memory and spatial planning (spatial working memory [SWM] and one touch stockings of Cambridge), and sustained attention (rapid visual information processing [RVP]). Participants were randomly allocated to one of the two groups, either assessed in-person in the laboratory first (n=33) or with unsupervised web-based assessments on their personal computing systems first (n=18). Performance indices (errors, correct trials, and response sensitivity) and median reaction times were extracted. Intraclass and bivariate correlations examined intersetting reliability, linear mixed models and Bayesian paired sample t tests tested for equivalence, and Bland-Altman plots examined agreement. RESULTS: Intraclass correlation (ICC) coefficients ranged from ρ=0.23-0.67, with high correlations in 3 performance indices (from PAL, SWM, and RVP tasks; ρ≥0.60). High ICC values were also seen for reaction time measures from 2 tasks (PRM and ERT tasks; ρ≥0.60). However, reaction times were slower during web-based assessments, which undermined both equivalence and agreement for reaction time measures. Performance indices did not differ between assessment settings and generally showed satisfactory agreement. CONCLUSIONS: Our findings support the comparability of CANTAB performance indices (errors, correct trials, and response sensitivity) in unsupervised, web-based assessments with in-person and laboratory tests. Reaction times are not as easily translatable from in-person to web-based testing, likely due to variations in computer hardware. The results underline the importance of examining more than one index to ascertain comparability, as high correlations can present in the context of systematic differences, which are a product of differences between measurement environments. Further work is now needed to examine web-based assessments in clinical populations and in larger samples to improve sensitivity for detecting subtler differences between test settings.

Wearable Technology for High-Frequency Cognitive and Mood Assessment in Major Depressive Disorder: Longitudinal Observational Study.

BACKGROUND: Cognitive symptoms are common in major depressive disorder and may help to identify patients who need treatment or who are not experiencing adequate treatment response. Digital tools providing real-time data assessing cognitive function could help support patient treatment and remediation of cognitive and mood symptoms. OBJECTIVE: The aim of this study was to examine feasibility and validity of a wearable high-frequency cognitive and mood assessment app over 6 weeks, corresponding to when antidepressant pharmacotherapy begins to show efficacy. METHODS: A total of 30 patients (aged 19-63 years; 19 women) with mild-to-moderate depression participated in the study. The new Cognition Kit app was delivered via the Apple Watch, providing a high-resolution touch screen display for task presentation and logging responses. Cognition was assessed by the n-back task up to 3 times daily and depressed mood by 3 short questions once daily. Adherence was defined as participants completing at least 1 assessment daily. Selected tests sensitive to depression from the Cambridge Neuropsychological Test Automated Battery and validated questionnaires of depression symptom severity were administered on 3 occasions (weeks 1, 3, and 6). Exploratory analyses examined the relationship between mood and cognitive measures acquired in low- and high-frequency assessment. RESULTS: Adherence was excellent for mood and cognitive assessments (95% and 96%, respectively), did not deteriorate over time, and was not influenced by depression symptom severity or cognitive function at study onset. Analyses examining the relationship between high-frequency cognitive and mood assessment and validated measures showed good correspondence. Daily mood assessments correlated moderately with validated depression questionnaires (r=0.45-0.69 for total daily mood score), and daily cognitive assessments correlated moderately with validated cognitive tests sensitive to depression (r=0.37-0.50 for mean n-back). CONCLUSIONS: This study supports the feasibility and validity of high-frequency assessment of cognition and mood using wearable devices over an extended period in patients with major depressive disorder.

Determinants of IQ outcome after focal epilepsy surgery in childhood: A longitudinal case-control neuroimaging study.

OBJECTIVE: Intelligence quotient (IQ) outcomes after pediatric epilepsy surgery show significant individual variation. Clinical factors such as seizure cessation or antiepileptic medication discontinuation have been implicated, but do not fully account for the heterogeneity seen. Less is known about the impact of neurobiological factors, such as brain development and resection location. This study examines clinical and neuroimaging factors associated with cognitive outcome after epilepsy surgery in childhood. METHODS: Fifty-two children (28 boys, 24 girls) were evaluated for epilepsy surgery and reassessed on average 7.7 years later. In the intervening time, 13 were treated pharmacologically and 39 underwent focal surgery (17 temporal, 16 extratemporal, six multilobar; mean age at surgery = 14.0 years). Pre- and postsurgical assessments included IQ tests and T1-weighted brain images. Predictors of IQ change were investigated, including voxel-based analyses of resection location, and gray and white matter volume change. RESULTS: Overall modest IQ improvement was seen in children treated surgically, but not in those treated pharmacologically only. Applying a ≥10-point change threshold, 39% of the surgically treated children improved, whereas 10% declined. Clinical factors associated with IQ increases were lower preoperative IQ and longer follow-up duration, whereas seizure and antiepileptic medication cessation were not predictive. Among neuroimaging factors, we observed that left anterior temporal resections impacted negatively on verbal reasoning, linked to full-scale IQ decline. In contrast, gray matter volume change in ipsi- and contralesional hemispheres was positively correlated with IQ change. Voxel-based morphometry identified the gray matter volume change in the contralesional dorsolateral frontal cortex as most strongly associated with IQ improvement. SIGNIFICANCE: We show that a variety of factors are likely to contribute to patterns of postsurgical change in IQ. Neuroimaging results indicate that left anterior temporal resections constrain development of verbal cognition, whereas simultaneously cortical growth after surgical treatment can support improvements in IQ.

Normative data from linear and nonlinear quantile regression in CANTAB: Cognition in mid-to-late life in an epidemiological sample.

INTRODUCTION: Normative cognitive data can help to distinguish pathological decline from normal aging. This study presents normative data from the Cambridge Neuropsychological Test Automated Battery, using linear regression and nonlinear quantile regression approaches. METHODS: Heinz Nixdorf Recall study participants completed Cambridge Neuropsychological Test Automated Battery tests: paired-associate learning, spatial working memory, and reaction time. Data were available for 1349-1529 healthy adults aged 57-84 years. Linear and nonlinear quantile regression analyses examined age-related changes, adjusting for sex and education. Quantile regression differentiated seven performance bands (percentiles: 97.7, 93.3, 84.1, 50, 15.9, 6.7, and 2.3). RESULTS: Normative data show age-related cognitive decline across all tests, but with quantile regression revealing heterogeneous trajectories of cognitive aging, particularly for the test of episodic memory function (paired-associate learning). DISCUSSION: This study presents normative data from Cambridge Neuropsychological Test Automated Battery in mid-to-late life. Quantile regression can model heterogeneity in age-related cognitive trajectories as seen in the paired-associate learning episodic memory measure.