Nocturnal hypoglycemia is associated with next day cognitive performance in adults with type 1 diabetes: Pilot data from the GluCog study.

OBJECTIVE:  Individuals with type 1 diabetes (T1D) have increased risk for cognitive dysfunction and high rates of sleep disturbance. Despite associations between glycemia and cognitive performance using cross-sectional and experimental methods few studies have evaluated this relationship in a naturalistic setting, or the impact of nocturnal versus daytime hypoglycemia. Ecological Momentary Assessment (EMA) may provide insight into the dynamic associations between cognition, affective, and physiological states. The current study couples EMA data with continuous glucose monitoring (CGM) to examine the within-person impact of nocturnal glycemia on next day cognitive performance in adults with T1D. Due to high rates of sleep disturbance and emotional distress in people with T1D, the potential impacts of sleep characteristics and negative affect were also evaluated. METHODS:  This pilot study utilized EMA in 18 adults with T1D to examine the impact of glycemic excursions, measured using CGM, on cognitive performance, measured via mobile cognitive assessment using the TestMyBrain platform. Multilevel modeling was used to test the within-person effects of nocturnal hypoglycemia and hyperglycemia on next day cognition. RESULTS:  Results indicated that increases in nocturnal hypoglycemia were associated with slower next day processing speed. This association was not significantly attenuated by negative affect, sleepiness, or sleep quality. CONCLUSIONS:  These results, while preliminary due to small sample size, showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive fluctuation in real world settings, an approach that may be utilized in other populations. Findings suggest reducing nocturnal hypoglycemia may improve cognition in adults with T1D.

Virtual histology of Alzheimer's disease: Biometal entrapment within amyloid-ß plaques allows for detection via X-ray phase-contrast imaging.

Amyloid-ß (Aß) plaques from Alzheimer's Disease (AD) can be visualized ex vivo in label-free brain samples using synchrotron X-ray phase-contrast tomography (XPCT). However, for XPCT to be useful as a screening method for amyloid pathology, it is essential to understand which factors drive the detection of Aß plaques. The current study was designed to test the hypothesis that Aß-related contrast in XPCT could be caused by Aß fibrils and/or by metals trapped in the plaques. Fibrillar and elemental compositions of Aß plaques were probed in brain samples from different types of AD patients and AD models to establish a relationship between XPCT contrast and Aß plaque characteristics. XPCT, micro-Fourier-Transform Infrared spectroscopy and micro-X-Ray Fluorescence spectroscopy were conducted on human samples (one genetic and one sporadic case) and on four transgenic rodent strains (mouse: APPPS1, ArcAß, J20; rat: TgF344). Aß plaques from the genetic AD patient were visible using XPCT, and had higher ß-sheet content and higher metal levels than those from the sporadic AD patient, which remained undetected by XPCT. Aß plaques in J20 mice and TgF344 rats appeared hyperdense on XPCT images, while they were hypodense with a hyperdense core in the case of APPPS1 and ArcAß mice. In all four transgenic strains, ß-sheet content was similar, while metal levels were highly variable: J20 (zinc and iron) and TgF344 (copper) strains showed greater metal accumulation than APPPS1 and ArcAß mice. Hence, a hyperdense contrast formation of Aß plaques in XPCT images was associated with biometal entrapment within plaques. STATEMENT OF SIGNIFICANCE: The role of metals in Alzheimer's disease (AD) has been a subject of continuous interest. It was already known that amyloid-ß plaques (Aß), the earliest hallmark of AD, tend to trap endogenous biometals like zinc, iron and copper. Here we show that this metal accumulation is the main reason why Aß plaques are detected with a new technique called X-ray phase contrast tomography (XPCT). XPCT enables to map the distribution of Aß plaques in the whole excised brain without labeling. In this work we describe a unique collection of four transgenic models of AD, together with a human sporadic and a rare genetic case of AD, thus exploring the full spectrum of amyloid contrast in XPCT.

Ecological Momentary Assessment of Cognition in Clinical and Community Samples: Reliability and Validity Study.

BACKGROUND: The current methods of evaluating cognitive functioning typically rely on a single time point to assess and characterize an individual's performance. However, cognitive functioning fluctuates within individuals over time in relation to environmental, psychological, and physiological contexts. This limits the generalizability and diagnostic utility of single time point assessments, particularly among individuals who may exhibit large variations in cognition depending on physiological or psychological context (eg, those with type 1 diabetes [T1D], who may have fluctuating glucose concentrations throughout the day). OBJECTIVE: We aimed to report the reliability and validity of cognitive ecological momentary assessment (EMA) as a method for understanding between-person differences and capturing within-person variation in cognition over time in a community sample and sample of adults with T1D. METHODS: Cognitive performance was measured 3 times a day for 15 days in the sample of adults with T1D (n=198, recruited through endocrinology clinics) and for 10 days in the community sample (n=128, recruited from TestMyBrain, a web-based citizen science platform) using ultrabrief cognitive tests developed for cognitive EMA. Our cognitive EMA platform allowed for remote, automated assessment in participants' natural environments, enabling the measurement of within-person cognitive variation without the burden of repeated laboratory or clinic visits. This allowed us to evaluate reliability and validity in samples that differed in their expected degree of cognitive variability as well as the method of recruitment. RESULTS: The results demonstrate excellent between-person reliability (ranging from 0.95 to 0.99) and construct validity of cognitive EMA in both the sample of adults with T1D and community sample. Within-person reliability in both samples (ranging from 0.20 to 0.80) was comparable with that observed in previous studies in healthy older adults. As expected, the full-length baseline and EMA versions of TestMyBrain tests correlated highly with one another and loaded together on the expected cognitive domains when using exploratory factor analysis. Interruptions had higher negative impacts on accuracy-based outcomes (ß=-.34 to -.26; all P values <.001) than on reaction time-based outcomes (ß=-.07 to -.02; P<.001 to P=.40). CONCLUSIONS: We demonstrated that ultrabrief mobile assessments are both reliable and valid across 2 very different clinic versus community samples, despite the conditions in which cognitive EMAs are administered, which are often associated with more noise and variability. The psychometric characteristics described here should be leveraged appropriately depending on the goals of the cognitive assessment (eg, diagnostic vs everyday functioning) and the population being studied.

Accurate Prediction of Momentary Cognition From Intensive Longitudinal Data.

BACKGROUND: Deficits in cognitive performance are implicated in the development and maintenance of psychopathology. Emerging evidence further suggests that within-person fluctuations in cognitive performance may represent sensitive early markers of neuropsychiatric decline. Incorporating routine cognitive assessments into standard clinical care-to identify between-person differences and monitor within-person fluctuations-has the potential to improve diagnostic screening and treatment planning. In support of these goals, it is critical to understand to what extent cognitive performance varies under routine, remote assessment conditions (i.e., momentary cognition) in relation to a wide range of possible predictors. METHODS: Using data-driven, high-dimensional methods, we ranked strong predictors of momentary cognition and evaluated out-of-sample predictive accuracy. Our approach leveraged innovations in digital technology, including ambulatory assessment of cognition and behavior 1) at scale (n = 122 participants, n = 94 females), 2) in naturalistic environments, and 3) within an intensive longitudinal study design (mean = 25.5 assessments/participant). RESULTS: Reaction time (R2 > 0.70) and accuracy (0.56 >R2 > 0.35) were strongly predicted by age, between-person differences in mean performance, and time of day. Effects of self-reported, intraindividual fluctuations in environmental (e.g., noise) and internal (e.g., stress) states were also observed. CONCLUSIONS: Our results provide robust estimates of effect size to characterize sources of cognitive variability, to support the identification of optimal windows for psychosocial interventions, and to possibly inform clinical evaluation under remote neuropsychological assessment conditions.

Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog): Observational Study Using Ecological Momentary Assessment of Cognition.

BACKGROUND: Individuals with type 1 diabetes represent a population with important vulnerabilities to dynamic physiological, behavioral, and psychological interactions, as well as cognitive processes. Ecological momentary assessment (EMA), a methodological approach used to study intraindividual variation over time, has only recently been used to deliver cognitive assessments in daily life, and many methodological questions remain. The Glycemic Variability and Fluctuations in Cognitive Status in Adults with Type 1 Diabetes (GluCog) study uses EMA to deliver cognitive and self-report measures while simultaneously collecting passive interstitial glucose in adults with type 1 diabetes. OBJECTIVE: We aimed to report the results of an EMA optimization pilot and how these data were used to refine the study design of the GluCog study. An optimization pilot was designed to determine whether low-frequency EMA (3 EMAs per day) over more days or high-frequency EMA (6 EMAs per day) for fewer days would result in a better EMA completion rate and capture more hypoglycemia episodes. The secondary aim was to reduce the number of cognitive EMA tasks from 6 to 3. METHODS: Baseline cognitive tasks and psychological questionnaires were completed by all the participants (N=20), followed by EMA delivery of brief cognitive and self-report measures for 15 days while wearing a blinded continuous glucose monitor. These data were coded for the presence of hypoglycemia (<70 mg/dL) within 60 minutes of each EMA. The participants were randomized into group A (n=10 for group A and B; starting with 3 EMAs per day for 10 days and then switching to 6 EMAs per day for an additional 5 days) or group B (N=10; starting with 6 EMAs per day for 5 days and then switching to 3 EMAs per day for an additional 10 days). RESULTS: A paired samples 2-tailed t test found no significant difference in the completion rate between the 2 schedules (t17=1.16; P=.26; Cohen dz=0.27), with both schedules producing >80% EMA completion. However, more hypoglycemia episodes were captured during the schedule with the 3 EMAs per day than during the schedule with 6 EMAs per day. CONCLUSIONS: The results from this EMA optimization pilot guided key design decisions regarding the EMA frequency and study duration for the main GluCog study. The present report responds to the urgent need for systematic and detailed information on EMA study designs, particularly those using cognitive assessments coupled with physiological measures. Given the complexity of EMA studies, choosing the right instruments and assessment schedules is an important aspect of study design and subsequent data interpretation.

The TestMyBrain Digital Neuropsychology Toolkit: Development and Psychometric Characteristics.

INTRODUCTION: To allow continued administration of neuropsychological evaluations remotely during the pandemic, tests from the not-for-profit platform, TestMyBrain.org (TMB), were used to develop the TMB Digital Neuropsychology Toolkit (DNT). This study details the psychometric characteristics of the DNT, as well as the infrastructure and development of the DNT. METHOD: The DNT was primarily distributed for clinical use, with (72.8%) of individuals requesting access for clinical purposes. To assess reliability and validity of the DNT, anonymous data from DNT test administrations were analyzed and compared to a large, non-clinical normative sample from TMB. RESULTS: DNT test scores showed acceptable to very good split-half reliability (.68-.99). Factor analysis revealed three latent factors, corresponding to processing speed, working memory, and a broader general cognitive ability factor that included perceptual reasoning and episodic memory. Average test scores were slightly poorer for the DNT sample than for the TMB comparison sample, as expected given the clinical use of the DNT. CONCLUSIONS: Initial estimates of reliability and validity of DNT tests support their use as digital measures of neuropsychological functioning. Tests within cognitive domains correlated highly with each other and demonstrated good reliability and validity. Future work will seek to validate DNT tests in specific clinical populations and determine best practices for using DNT outcome measures to assess engagement and psychological symptomatology.

Technology meets tradition: a hybrid model for implementing digital tools in neuropsychology.

The COVID-19 pandemic has significantly impacted the provision of mental health care services and the ability to provide neuropsychological evaluations. The inability to conduct traditional evaluations has left neuropsychologists with the unprecedented task of determining how to modify existing paradigms while balancing the need to provide services and adhere to safety parameters. The COVID-19 literature suggests clinicians are modifying their evaluations based on the following models: (1) continuing to administer in-person evaluations; (2) discontinuing all evaluations due to issues related to standardization, test security, and patient-specific characteristics; (3) conducting virtual evaluations; and/or (4) adopting a hybrid model incorporating both traditional and technology-based modalities. Given the challenges with models 1-3, along with the modifications in telehealth guidelines and insurance reimbursement rates, neuropsychologists are more poised than ever to solidify the implementation of a hybrid model that lasts beyond COVID-19. We introduce the term Hybrid Neuropsychology, a model for the future of neuropsychological evaluations that includes three Action Items: (1) building a technology-based practice; (2) integrating data science; and (3) engaging with innovators in other fields. Hybrid Neuropsychology will enable clinicians to effectively modernize their practice, improve health care equity, and ensure neuropsychology secures its place in a technology-based world.

Toward dynamic phenotypes and the scalable measurement of human behavior.

Precision psychiatry demands the rapid, efficient, and temporally dense collection of large scale and multi-omic data across diverse samples, for better diagnosis and treatment of dynamic clinical phenomena. To achieve this, we need approaches for measuring behavior that are readily scalable, both across participants and over time. Efforts to quantify behavior at scale are impeded by the fact that our methods for measuring human behavior are typically developed and validated for single time-point assessment, in highly controlled settings, and with relatively homogeneous samples. As a result, when taken to scale, these measures often suffer from poor reliability, generalizability, and participant engagement. In this review, we attempt to bridge the gap between gold standard behavioral measurements in the lab or clinic and the large-scale, high frequency assessments needed for precision psychiatry. To do this, we introduce and integrate two frameworks for the translation and validation of behavioral measurements. First, borrowing principles from computer science, we lay out an approach for iterative task development that can optimize behavioral measures based on psychometric, accessibility, and engagement criteria. Second, we advocate for a participatory research framework (e.g., citizen science) that can accelerate task development as well as make large-scale behavioral research more equitable and feasible. Finally, we suggest opportunities enabled by scalable behavioral research to move beyond single time-point assessment and toward dynamic models of behavior that more closely match clinical phenomena.

The differential influence of computerized neuropsychological assessment across psychopathology.

Objective: Novel technologies have transformed neuropsychological test administration so that research examining the equivalency of computerized versions of traditional tests is needed. This study examined the relationship between psychological symptom severity and performance on the Wisconsin Card Sorting Test (WCST) using the manual versus computerized administration.Method: Eighty-five participants were randomly assigned to the manual or computerized WCST administration and also completed the Dot Counting Test, Depression Anxiety Stress Scales, and Short UPPS-P Impulsive Behavior Scale. Moderation analyses examined the effects of depressive, anxiety, and impulsivity symptom severity on WCST performance.Results: For Perseverative Responses (PR), the methods of administration (MOAs) were equivalent regardless of psychological symptom severity. For failures to maintain set (FMS), MOA itself influenced performance, with participants making at least twice as many FMS on the computerized WCST. MOA also significantly moderated the relationship between FMS and impulsivity severity, including Lack of Perseverance, Sensation Seeking, and Positive Urgency. Individuals with greater Positive Urgency made more FMS on the manual WCST, and individuals with greater Lack of Perseverance made more FMS on the computerized WCST.Conclusions: Findings suggest that equivalence between the manual and computerized versions of the WCST depends on the WCST subscale and the type of psychological symptom. New normative data need to be developed for the computerized WCST, along with a more consistent method of scoring and interpreting WCST subscales.

Effective psychotherapeutic approaches to treatment for ethnic minorities

Members of the Sikh Khalsa who make their residence in the United States represent a psychologically underserved and understudied population. A lack of awareness of psychological services contributes to this status; however, the challenges inherent in reconciling cultural norms within the United States with the native cultures of immigrant populations should not be neglected. As a consequence of the paucity of ethnically Sikh psychotherapists, the number of therapists with a competent cultural understanding of this population is limited. By sharing the insights and observations culled from dialogue with members of a Sikh community recovering in the wake of a national tragedy, we present our insights and an approach to therapeutic intervention developed to facilitate future psychotherapeutic endeavors both in Sikh communities and other ethnic minorities at large. The model developed in this study identifies demographic issues, therapeutic approach, gender bias, language, confidentiality, peer support, and immigrant status as the most important factors when treating this population. Ultimately, it is our intention to elevate awareness of some of the idiosyncratic complexities involved in treatment and research of this underserved minority group, particularly as our population continues to diversify

No disponible