scholarly journals The Effects of Bipolar Disorder Risk on a Mobile Phone Keystroke Dynamics Based Biomarker of Brain Age

2021 ◽  
Vol 12 ◽  
Author(s):  
John Zulueta ◽  
Alexander Pantelis Demos ◽  
Claudia Vesel ◽  
Mindy Ross ◽  
Andrea Piscitello ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mobile Phone ◽  
Prediction Error ◽  
Chronological Age ◽  
Prediction Errors ◽  
Age Related ◽  
Brain Changes ◽  
Mood And Cognition ◽  
Study Participants ◽  
Brain Age

Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.

scholarly journals Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

Brain ◽  
2019 ◽  
Vol 142 (3) ◽  
pp. 662-673 ◽  
Author(s):  
Aaron L Wong ◽  
Cherie L Marvel ◽  
Jordan A Taylor ◽  
John W Krakauer
Keyword(s):  
Prediction Error ◽  
Poor Performance ◽  
Visuomotor Adaptation ◽  
Cerebellar Degeneration ◽  
Prediction Errors ◽  
Cerebellar Disease ◽  
Learning Mechanisms ◽  
Visuomotor Rotation ◽  
Age Related ◽  
Sensory Prediction

Abstract Systematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed—by allowing vision of the hand, which eliminates sensory-prediction errors—patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction error signal to zero. We demonstrated in younger healthy control subjects that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with cerebellar degeneration could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed (although they could not improve beyond what they retrieved), and retain it for at least 1 year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age 18–33 years), likely due to age-related reductions in spatial and working memory. Patients also failed to generalize, i.e. they were unable to adopt analogous aiming strategies in response to novel rotations. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction error-based learning—even when this system is impaired in patients with cerebellar disease. The persistence of sensory-prediction error-based learning effectively suppresses a switch to target error-based learning, which perhaps explains the unexpectedly poor performance by patients with cerebellar degeneration in visuomotor adaptation tasks.

Brain age in bipolar disorders: Effects of lithium treatment

2019 ◽  
Vol 53 (12) ◽  
pp. 1179-1188 ◽  
Author(s):  
Holly Van Gestel ◽  
Katja Franke ◽  
Joanne Petite ◽  
Claire Slaney ◽  
Julie Garnham ◽  
...  
Keyword(s):  
Lithium Treatment ◽  
Accelerated Aging ◽  
Bipolar Disorders ◽  
Treated Group ◽  
Chronological Age ◽  
Neuroprotective Effects ◽  
The Individual ◽  
Study Participants ◽  
And Control ◽  
Brain Age

Objective:Bipolar disorders increase the risk of dementia and show biological and brain alterations, which resemble accelerated aging. Lithium may counter some of these processes and lower the risk of dementia. However, until now no study has specifically investigated the effects of Li on brain age.Methods:We acquired structural magnetic resonance imaging scans from 84 participants with bipolar disorders (41 with and 43 without Li treatment) and 45 controls. We used a machine learning model trained on an independent sample of 504 controls to estimate the individual brain ages of study participants, and calculated BrainAGE by subtracting chronological from the estimated brain age.Results:BrainAGE was significantly greater in non-Li relative to Li or control participants, F(2, 125) = 10.22, p < 0.001, with no differences between the Li treated and control groups. The estimated brain age was significantly higher than the chronological age in the non-Li (4.28 ± 6.33 years, matched t(42) = 4.43, p < 0.001), but not the Li-treated group (0.48 ± 7.60 years, not significant). Even Li-treated participants with partial prophylactic treatment response showed lower BrainAGE than the non-Li group, F(1, 64) = 4.80, p = 0.03.Conclusions:Bipolar disorders were associated with greater, whereas Li treatment with lower discrepancy between brain and chronological age. These findings support the neuroprotective effects of Li, which were sufficiently pronounced to affect a complex, multivariate measure of brain structure. The association between Li treatment and BrainAGE was independent of long-term thymoprophylactic response and thus may generalize beyond bipolar disorders, to neurodegenerative disorders.

scholarly journals Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

2018 ◽  
Author(s):  
Aaron L. Wong ◽  
Cherie L. Marvel ◽  
Jordan A. Taylor ◽  
John W. Krakauer
Keyword(s):  
Spinocerebellar Ataxia ◽  
Prediction Error ◽  
Visuomotor Adaptation ◽  
Cerebellar Degeneration ◽  
Prediction Errors ◽  
Healthy Controls ◽  
Learning Mechanisms ◽  
Visuomotor Rotation ◽  
Age Related ◽  
Sensory Prediction

ABSTRACTSystematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction-error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed – by allowing vision of the hand, which eliminates sensory-prediction errors – patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction-error signal to zero. We demonstrated in younger healthy controls that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with spinocerebellar ataxia could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed, and retain it for at least one year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age, 18-33), likely due to age-related reductions in spatial and working memory. Moreover, patients failed to generalize, i.e., they were unable to adopt analogous aiming strategies in response to novel rotations, nor could they further improve their performance without vision of the hand. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction-error-based learning – even when this system is impaired in patients with cerebellar degeneration. The persistence of sensory-prediction-error-based learning effectively suppresses a switch to target-error-based learning, which perhaps explains the unexpectedly poor performance by patients with spinocerebellar ataxia in visuomotor adaptation tasks.

scholarly journals Learning Patterns of the Ageing Brain in MRI using Deep Convolutional Networks

2020 ◽  
Author(s):  
Nicola K. Dinsdale ◽  
Emma Bluemke ◽  
Stephen M Smith ◽  
Zobair Arya ◽  
Diego Vidaurre ◽  
...  
Keyword(s):  
Morphological Changes ◽  
Imaging Modality ◽  
Training Dataset ◽  
List Type ◽  
Prediction Errors ◽  
Uk Biobank ◽  
Age Related ◽  
3D Cnn ◽  
The Uk ◽  
Brain Age

AbstractBoth normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning models are particularly suited to capture these patterns and can produce a model that is sensitive to changes of interest, despite the large variety in healthy brain appearance. In this paper, the power of convolutional neural networks (CNNs) and the rich UK Biobank dataset, the largest database currently available, are harnessed to address the problem of predicting brain age. We developed a 3D CNN architecture to predict chronological age, using a training dataset of 12, 802 T1-weighted MRI images and a further 6, 885 images for testing. The proposed method shows competitive performance on age prediction, but, most importantly, the CNN prediction errors ΔBrainAge = AgePredicted − AgeTrue correlated significantly with many clinical measurements from the UK Biobank in the female and male groups. In addition, having used images from only one imaging modality in this experiment, we examined the relationship between ΔBrainAge and the image-derived phenotypes (IDPs) from all other imaging modalities in the UK Biobank, showing correlations consistent with known patterns of ageing. Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance. Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the ΔBrainAge from models such as this network were predictive of any health outcomes.HighlightsBrain age is estimated using a 3D CNN from 12,802 full T1-weighted images.Regions used to drive predictions are different for linearly and nonlinearly registered data.Linear registrations utilise a greater diversity of biologically meaningful areas.Correlations with IDPs and non-imaging variables are consistent with other publications.Excluding subjects with various health conditions had minimal impact on main correlations.

scholarly journals Improved prediction of chronological age from DNA methylation limits it as a biomarker of ageing

2018 ◽  
Author(s):  
Qian Zhang ◽  
Costanza L. Vallerga ◽  
Rosie M Walker ◽  
Tian Lin ◽  
Anjali K. Henders ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Sample Size ◽  
Prediction Error ◽  
Small Samples ◽  
Chronological Age ◽  
Prediction Errors ◽  
Biological Ageing ◽  
Cell Composition ◽  
Illumina Humanmethylation450 ◽  
Age Range

AbstractDNA methylation is associated with age. The deviation of age predicted from DNA methylation from actual age has been proposed as a biomarker for ageing. However, a better prediction of chronological age implies less opportunity for biological age. Here we used 13,661 samples (from blood and saliva) in the age range of 2 to 104 years from 14 cohorts measured on Illumina HumanMethylation450/EPIC arrays to perform prediction analyses. We show that increasing the sample size achieves a smaller prediction error and higher correlations in test datasets. We demonstrate that smaller prediction errors provide a limit to how much variation in biological ageing can be captured by methylation and provide evidence that age predictors from small samples are prone to confounding by cell composition. Our predictor shows a similar or better performance in non-blood tissues including saliva, endometrium, breast, liver, adipose and muscle, compared with Horvath’s across-tissue age predictor.

scholarly journals “Virus and Epidemic”: Causal Knowledge Activates Prediction Error Circuitry

2010 ◽  
Vol 22 (10) ◽  
pp. 2151-2163 ◽  
Author(s):  
Daniela B. Fenker ◽  
Mircea A. Schoenfeld ◽  
Michael R. Waldmann ◽  
Hartmut Schuetze ◽  
Hans-Jochen Heinze ◽  
...  
Keyword(s):  
Substantia Nigra ◽  
Ventral Tegmental Area ◽  
Semantic Memory ◽  
Mental Representation ◽  
Prediction Error ◽  
Error Processing ◽  
Prediction Errors ◽  
Causal Knowledge ◽  
Fmri Study ◽  
Study Participants

Knowledge about cause and effect relationships (e.g., virus–epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events and one's own actions. Although there is evidence that predictions and learning from prediction errors are instrumental in acquiring causal knowledge, it is unclear whether prediction error circuitry remains involved in the mental representation and evaluation of causal knowledge already stored in semantic memory. In an fMRI study, participants assessed whether pairs of words were causally related (e.g., virus–epidemic) or noncausally associated (e.g., emerald–ring). In a second fMRI study, a task cue prompted the participants to evaluate either the causal or the noncausal associative relationship between pairs of words. Causally related pairs elicited higher activity in OFC, amygdala, striatum, and substantia nigra/ventral tegmental area than noncausally associated pairs. These regions were also more activated by the causal than by the associative task cue. This network overlaps with the mesolimbic and mesocortical dopaminergic network known to code prediction errors, suggesting that prediction error processing might participate in assessments of causality even under conditions when it is not explicitly required to make predictions.

scholarly journals No association between age-related hearing loss and accelerated brain aging derived from structural neuroimaging data

2020 ◽  
Author(s):  
Stephanie Rosemann ◽  
Christiane Thiel
Keyword(s):  
Hearing Loss ◽  
Brain Aging ◽  
Normal Hearing ◽  
Chronological Age ◽  
Listening Effort ◽  
Structural Neuroimaging ◽  
Age Related ◽  
Neuroimaging Data ◽  
Age Related Hearing Loss ◽  
Brain Age

Aging affects the brain’s underlying biophysical structure as well as its’ cellular and molecular functioning. Brain aging varies largely across individuals and is accelerated in a variety of disease states. Age-related hearing loss affects a large part of the older population and has been shown to impact cognition, brain structure and function. The main aim of this study was to investigate whether age-related hearing loss accelerates brain aging and is related to a decrease in cognitive function and increase in daily listening effort. We used structural neuroimaging data from a large sample of elderly subjects (n=163) with mild to moderate uncompensated age-related hearing loss or normal hearing. An established machine learning approach was applied to predict brain age from grey and white matter maps. Predicted brain age and chronological age significantly correlated across all participants. However, the difference between the predicted brain age and chronological age was neither significantly different between hard of hearing and normal-hearing participants, nor was this age difference significantly associated with general cognitive status or daily life listening effort. We conclude that uncompensated mild to moderate age-related hearing loss has negligible effects on brain age derived from structural neuroimaging data.

scholarly journals Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth-cohort

2019 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Daniel W. Belsky ◽  
Annchen R. Knodt ◽  
David Ireland ◽  
Tracy R. Melzer ◽  
...  
Keyword(s):  
Cognitive Decline ◽  
Birth Cohort ◽  
Brain Aging ◽  
Chronological Age ◽  
Biological Aging ◽  
Imaging Data ◽  
Cohort Differences ◽  
Age Related ◽  
Brain Age ◽  
The Brain

AbstractAn individual’s brain-age is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. Brain-age has been proposed as a biomarker of age-related deterioration of the brain. Having an older brain-age has been linked to Alzheimer’s, dementia and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brain-age a biomarker of accelerated brain aging, a study is needed of a large cohort all born the same year who nevertheless vary on brain-age. In a population-representative 1972-73 birth cohort we measured brain-age at age 45, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N=869). In this cohort, all chronological age 45 years, brain-age was measured reliably (ICC=.81) and ranged from 24 to 72 years. Those with older midlife brain-ages tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brain-ages had an accelerated pace of biological aging, older facial appearance and early signs of cognitive decline from childhood to midlife. These findings help to validate brain-age as a potential surrogate biomarker for midlife intervention studies that seek to measure treatment response to dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brain-age scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

scholarly journals A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

2021 ◽  
Author(s):  
Mei Sum Chan ◽  
Matthew Arnold ◽  
Alison Offer ◽  
Imen Hammami ◽  
Marion Mafham ◽  
...  
Keyword(s):  
Principal Components ◽  
Hospital Admissions ◽  
Later Life ◽  
Chronological Age ◽  
Uk Biobank ◽  
Hand Grip ◽  
Cox Proportional Hazard ◽  
Prediction Of Mortality ◽  
Age Related ◽  
Cox Proportional Hazard Models

Abstract Background Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention. Methods A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40–70 recruited in 2006–2010, and followed up for 6–12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models. Results Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for &gt;65% and &gt;84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p &lt; .001) and hospital admissions (p &lt; 1 × 10−10) over chronological age alone. Conclusions This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

scholarly journals Bidirectional Relationship Between Subjective Age and Frailty: Findings From the NHATS

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 271-271
Author(s):  
Yuxiao Li ◽  
Minhui Liu ◽  
Christina Miyawaki ◽  
Xiaocao Sun ◽  
Tianxue Hou ◽  
...  
Keyword(s):  
Older Adults ◽  
Clinical Syndrome ◽  
Community Dwelling ◽  
Chronological Age ◽  
Subjective Age ◽  
Bidirectional Relationship ◽  
Study Participants ◽  
Community Dwelling Older Adults ◽  
The Relationship ◽  
Hispanic White

Abstract Frailty is a clinical syndrome that becomes increasingly common as people age. Subjective age refers to how young or old individuals experience themselves to be. It is associated with many risk factors of frailty, such as increased depression, worse cognitive function, and poorer psychological wellbeing. In this study, we examined the relationship between subjective age and frailty using the 2011-2015 waves of the National Health and Aging Trends Study. Participants were community-dwelling older adults without frailty in the initial wave (N=1,165). Subjective age was measured by asking participants, “What age do you feel most of the time?” Based on the Fried five phenotypic criteria: exhaustion, unintentional weight loss, low physical activity, slow gait, and weak grip strength, frailty was categorized into robust=0, pre-frail=1 or 2; frail=3 or more criteria met. Participants were, on average, 74.1±6.5 years old, female (52%), and non-Hispanic White (81%). Eighty-five percent of the participants felt younger, and 3% felt older than their chronological age, but 41% of them were pre-frail/frail. Generalized estimating equations revealed that an “older” subjective age predicted a higher likelihood of pre-frailty and frailty (OR, 95%CI= 1.01, 1.01-1.02). In contrast, frailty predicted an “older” subjective age (OR, 95%CI= 2.97, 1.65-5.35) adjusting for demographics and health conditions. These findings suggest a bidirectional relationship between subjective age and frailty. Older people who feel younger than their chronological age are at reduced risk of becoming pre-frail/frail. Intervention programs to delay frailty progression should include strategies that may help older adults perceive a younger subjective age.

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