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All Publications


  • Attention Matters: How Orchestrating Attention May Relate to Classroom Learning. CBE life sciences education Keller, A. S., Davidesco, I., Tanner, K. D. 2020; 19 (3): fe5

    Abstract

    Attention is thought to be the gateway between information and learning, yet there is much we do not understand about how students pay attention in the classroom. Leveraging ideas from cognitive neuroscience and psychology, we explore a framework for understanding attention in the classroom, organized along two key dimensions: internal/external attention and on-topic/off-topic attention. This framework helps us to build new theories for why active-learning strategies are effective teaching tools and how synchronized brain activity across students in a classroom may support learning. These ideas suggest new ways of thinking about how attention functions in the classroom and how different approaches to the same active-learning strategy may vary in how effectively they direct students' attention. We hypothesize that some teaching approaches are more effective than others because they leverage natural fluctuations in students' attention. We conclude by discussing implications for teaching and opportunities for future research.

    View details for DOI 10.1187/cbe.20-05-0106

    View details for PubMedID 32870089

  • Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial. JAMA network open Rajpurkar, P., Yang, J., Dass, N., Vale, V., Keller, A. S., Irvin, J., Taylor, Z., Basu, S., Ng, A., Williams, L. M. 2020; 3 (6): e206653

    Abstract

    Importance: Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted.Objective: To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures.Design, Setting, and Participants: This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019.Exposures: Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n=162), sertraline hydrochloride (n=176), or extended-release venlafaxine hydrochloride (n=180).Main Outcomes and Measures: The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index.Results: The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]).Conclusions and Relevance: This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings.Trial Registration: ClinicalTrials.gov Identifier: NCT00693849.

    View details for DOI 10.1001/jamanetworkopen.2020.6653

    View details for PubMedID 32568399

  • Electroencephalography profiles as a biomarker of wellbeing: A twin study. Journal of psychiatric research Chilver, M. R., Keller, A. S., Park, H. R., Jamshidi, J., Montalto, A., Schofield, P. R., Clark, C. R., Harmon-Jones, E., Williams, L. M., Gatt, J. M. 2020; 126: 114–21

    Abstract

    Alterations to electroencephalography (EEG) power have been reported for psychiatric conditions such as depression and anxiety, but not for mental wellbeing in a healthy population. This study examined the resting EEG profiles associated with mental wellbeing, and how genetics and environment contribute to these associations using twin modelling. Mental wellbeing was assessed using the COMPAS-W Wellbeing Scale which measures both subjective and psychological wellbeing. In 422 healthy adult monozygotic and dizygotic twins aged 18-61 years, we examined the association between mental wellbeing and EEG power (alpha, beta, theta, delta) using linear mixed models. This was followed by univariate and multivariate twin modelling to assess the heritability of wellbeing and EEG power, and whether the association was driven by shared genetics or environment. A significant association between wellbeing and an interaction of alpha, beta, and delta (ABD) power was found (beta=-0.33, p<0.001) whereby a profile of high alpha and delta and low beta was associated with higher wellbeing, independent of depression and anxiety symptoms. This finding was supported by a five-fold cross-validation analysis. A significant genetic correlation (rG=-0.43) was found to account for 94% of the association between wellbeing and the EEG power interaction. Together, this study has identified a novel EEG profile with a common genetic component that may be a potential biomarker of mental wellbeing. Future studies need to clarify the causal direction of this association.

    View details for DOI 10.1016/j.jpsychires.2020.04.010

    View details for PubMedID 32450375

  • Mechanistic Trial Evaluating the Effect of Repetitive Transcranial Magnetic Stimulation on RDoC Constructs in Treatment-Resistant Depression Hack, L. M., Keller, A. S., Whicker, C., Williams, L. M. ELSEVIER SCIENCE INC. 2020: S412–S413
  • Beyond "Concentration Difficulties": Probing Attention Impairments in Depression and Anxiety Across Multiple Units of Analysis Keller, A., Holt-Gosselin, B., Ling, R., Williams, L. M. ELSEVIER SCIENCE INC. 2020: S124
  • Attention Impairments and the "Inattention Biotype" in Major Depressive Disorder Keller, A., Ball, T., Williams, L. ELSEVIER SCIENCE INC. 2019: S244
  • Deep phenotyping of attention impairments and the 'Inattention Biotype' in Major Depressive Disorder. Psychological medicine Keller, A. S., Ball, T. M., Williams, L. M. 2019: 1–10

    Abstract

    Attention impairment is an under-investigated feature and diagnostic criterion of Major Depressive Disorder (MDD) that is associated with poorer outcomes. Despite increasing knowledge regarding mechanisms of attention in healthy adults, we lack a detailed characterization of attention impairments and their neural signatures in MDD.Here, we focus on selective attention and advance a deep multi-modal characterization of these impairments in MDD, using data acquired from n = 1008 patients and n = 336 age- and sex-matched healthy controls. Selective attention impairments were operationalized and anchored in a behavioral performance measure, assessed within a battery of cognitive tests. We sought to establish the accompanying neural signature using independent measures of functional magnetic resonance imaging (15% of the sample) and electroencephalographic recordings of oscillatory neural activity.Greater impairment on the behavioral measure of selective attention was associated with intrinsic hypo-connectivity of the fronto-parietal attention network. Not only was this relationship specific to the fronto-parietal network unlike other large-scale networks; this hypo-connectivity was also specific to selective attention performance unlike other measures of cognition. Selective attention impairment was also associated with lower posterior alpha (8-13 Hz) power at rest and was related to more severe negative bias (frequent misidentifications of neutral faces as sad and lingering attention on sad faces), relevant to clinical features of negative attributions and brooding. Selective attention impairments were independent of overall depression severity and of worrying or sleep problems.These results provide a foundation for the clinical translational development of objective markers and targeted therapeutics for attention impairment in MDD.

    View details for DOI 10.1017/S0033291719002290

    View details for PubMedID 31477195

  • Paying attention to attention in depression. Translational psychiatry Keller, A. S., Leikauf, J. E., Holt-Gosselin, B., Staveland, B. R., Williams, L. M. 2019; 9 (1): 279

    Abstract

    Attention is the gate through which sensory information enters our conscious experiences. Oftentimes, patients with major depressive disorder (MDD) complain of concentration difficulties that negatively impact their day-to-day function, and these attention problems are not alleviated by current first-line treatments. In spite of attention's influence on many aspects of cognitive and emotional functioning, and the inclusion of concentration difficulties in the diagnostic criteria for MDD, the focus of depression as a disease is typically on mood features, with attentional features considered less of an imperative for investigation. Here, we summarize the breadth and depth of findings from the cognitive neurosciences regarding the neural mechanisms supporting goal-directed attention in order to better understand how these might go awry in depression. First, we characterize behavioral impairments in selective, sustained, and divided attention in depressed individuals. We then discuss interactions between goal-directed attention and other aspects of cognition (cognitive control, perception, and decision-making) and emotional functioning (negative biases, internally-focused attention, and interactions of mood and attention). We then review evidence for neurobiological mechanisms supporting attention, including the organization of large-scale neural networks and electrophysiological synchrony. Finally, we discuss the failure of current first-line treatments to alleviate attention impairments in MDD and review evidence for more targeted pharmacological, brain stimulation, and behavioral interventions. By synthesizing findings across disciplines and delineating avenues for future research, we aim to provide a clearer outline of how attention impairments may arise in the context of MDD and how, mechanistically, they may negatively impact daily functioning across various domains.

    View details for DOI 10.1038/s41398-019-0616-1

    View details for PubMedID 31699968

  • Feature-Based Selective Attention as a Biomarker of Impaired Cognition in Depression Keller, A., Korgaonkar, M., Williams, L. ELSEVIER SCIENCE INC. 2018: S281–S282
  • Characterizing the roles of alpha and theta oscillations in multisensory attention NEUROPSYCHOLOGIA Keller, A. S., Payne, L., Sekuler, R. 2017; 99: 48–63

    Abstract

    Cortical alpha oscillations (8-13Hz) appear to play a role in suppressing distractions when just one sensory modality is being attended, but do they also contribute when attention is distributed over multiple sensory modalities? For an answer, we examined cortical oscillations in human subjects who were dividing attention between auditory and visual sequences. In Experiment 1, subjects performed an oddball task with auditory, visual, or simultaneous audiovisual sequences in separate blocks, while the electroencephalogram was recorded using high-density scalp electrodes. Alpha oscillations were present continuously over posterior regions while subjects were attending to auditory sequences. This supports the idea that the brain suppresses processing of visual input in order to advantage auditory processing. During a divided-attention audiovisual condition, an oddball (a rare, unusual stimulus) occurred in either the auditory or the visual domain, requiring that attention be divided between the two modalities. Fronto-central theta band (4-7Hz) activity was strongest in this audiovisual condition, when subjects monitored auditory and visual sequences simultaneously. Theta oscillations have been associated with both attention and with short-term memory. Experiment 2 sought to distinguish these possible roles of fronto-central theta activity during multisensory divided attention. Using a modified version of the oddball task from Experiment 1, Experiment 2 showed that differences in theta power among conditions were independent of short-term memory load. Ruling out theta's association with short-term memory, we conclude that fronto-central theta activity is likely a marker of multisensory divided attention.

    View details for DOI 10.1016/j.neuropsychologia.2017.02.021

    View details for Web of Science ID 000401202600006

    View details for PubMedID 28259771

    View details for PubMedCentralID PMC5410970

  • Distinct Phases of Tau, Amyloid, and Functional Connectivity in Healthy Older Adults. The Journal of neuroscience : the official journal of the Society for Neuroscience Keller, A. S., Christopher, L. 2017; 37 (37): 8857–59

    View details for PubMedID 28904212

  • Memory and learning with rapid audiovisual sequences JOURNAL OF VISION Keller, A. S., Sekuler, R. 2015; 15 (15): 7

    Abstract

    We examined short-term memory for sequences of visual stimuli embedded in varying multisensory contexts. In two experiments, subjects judged the structure of the visual sequences while disregarding concurrent, but task-irrelevant auditory sequences. Stimuli were eight-item sequences in which varying luminances and frequencies were presented concurrently and rapidly (at 8 Hz). Subjects judged whether the final four items in a visual sequence identically replicated the first four items. Luminances and frequencies in each sequence were either perceptually correlated (Congruent) or were unrelated to one another (Incongruent). Experiment 1 showed that, despite encouragement to ignore the auditory stream, subjects' categorization of visual sequences was strongly influenced by the accompanying auditory sequences. Moreover, this influence tracked the similarity between a stimulus's separate audio and visual sequences, demonstrating that task-irrelevant auditory sequences underwent a considerable degree of processing. Using a variant of Hebb's repetition design, Experiment 2 compared musically trained subjects and subjects who had little or no musical training on the same task as used in Experiment 1. Test sequences included some that intermittently and randomly recurred, which produced better performance than sequences that were generated anew for each trial. The auditory component of a recurring audiovisual sequence influenced musically trained subjects more than it did other subjects. This result demonstrates that stimulus-selective, task-irrelevant learning of sequences can occur even when such learning is an incidental by-product of the task being performed.

    View details for DOI 10.1167/15.15.7

    View details for Web of Science ID 000368252600007

    View details for PubMedID 26575193

    View details for PubMedCentralID PMC4666275