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Simon AJ, Gallen CL, Ziegler DA, Mishra J, Marco EJ, Anguera JA, Gazzaley A. Quantifying attention span across the lifespan. FRONTIERS IN COGNITION 2023; 2:1207428. [PMID: 37920687 PMCID: PMC10621754 DOI: 10.3389/fcogn.2023.1207428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Introduction Studies examining sustained attention abilities typically utilize metrics that quantify performance on vigilance tasks, such as response time and response time variability. However, approaches that assess the duration that an individual can maintain their attention over time are lacking. Methods Here we developed an objective attention span metric that quantified the maximum amount of time that a participant continuously maintained an optimal "in the zone" sustained attention state while performing a continuous performance task. Results In a population of 262 individuals aged 7-85, we showed that attention span was longer in young adults than in children and older adults. Furthermore, declines in attention span over time during task engagement were related to clinical symptoms of inattention in children. Discussion These results suggest that quantifying attention span is a unique and meaningful method of assessing sustained attention across the lifespan and in populations with inattention symptoms.
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Affiliation(s)
- Alexander J. Simon
- Neuroscape Center, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences & Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Courtney L. Gallen
- Neuroscape Center, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences & Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - David A. Ziegler
- Neuroscape Center, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences & Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Jyoti Mishra
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Elysa J. Marco
- Department of Neurodevelopmental Medicine, Cortica Healthcare, San Rafael, CA, United States
- Department of Radiology, University of California, San Francisco, San Francisco, CA, United States
| | - Joaquin A. Anguera
- Neuroscape Center, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences & Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Adam Gazzaley
- Neuroscape Center, University of California, San Francisco, San Francisco, CA, United States
- Department of Neurology, University of California, San Francisco, San Francisco, CA, United States
- Weill Institute for Neurosciences & Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
- Department of Physiology, University of California, San Francisco, San Francisco, CA, United States
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Mulholland MM, Prinsloo S, Kvale E, Dula AN, Palesh O, Kesler SR. Behavioral and biologic characteristics of cancer-related cognitive impairment biotypes. Brain Imaging Behav 2023; 17:320-328. [PMID: 37127832 PMCID: PMC10195718 DOI: 10.1007/s11682-023-00774-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2023] [Indexed: 05/03/2023]
Abstract
Psychiatric diagnosis is moving away from symptom-based classification and towards multi-dimensional, biologically-based characterization, or biotyping. We previously identified three biotypes of chemotherapy-related cognitive impairment based on functional brain connectivity. In this follow-up study of 80 chemotherapy-treated breast cancer survivors and 80 non-cancer controls, we evaluated additional factors to help explain biotype expression: neurofunctional stability, brain age, apolipoprotein (APOE) genotype, and psychoneurologic symptoms. We also compared the discriminative ability of a traditional, symptom-based cognitive impairment definition with that of biotypes. We found significant differences in cortical brain age (F = 10.50, p < 0.001), neurofunctional stability (F = 2.83, p = 0.041), APOE e4 genotype (X2 = 7.68, p = 0.050), and psychoneurological symptoms (Pillai = 0.378, p < 0.001) across the three biotypes. The more resilient Biotype 2 demonstrated significantly higher neurofunctional stability compared to the other biotypes. Symptom-based classification of cognitive impairment did not differentiate biologic or other behavioral variables, suggesting that traditional categorization of cancer-related cognitive effects may miss important characteristics which could inform targeted treatment strategies. Additionally, biotyping, but not symptom-typing, was able to distinguish survivors with cognitive versus psychological effects. Our results suggest that Biotype 1 survivors might benefit from first addressing symptoms of anxiety and fatigue, Biotype 3 might benefit from a treatment plan which includes sleep hygiene, and Biotype 2 might benefit most from cognitive skills training or rehabilitation. Future research should include additional demographic and clinical information to further investigate biotype expression related to risk and resilience and examine integration of more clinically feasible imaging approaches.
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Affiliation(s)
- Michele M Mulholland
- Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Bastrop, TX, USA
| | - Sarah Prinsloo
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Elizabeth Kvale
- Department of Geriatrics and Palliative Care, Baylor College of Medicine, Houston, TX, USA
| | - Adrienne N Dula
- Department of Neurology, Dell School of Medicine, The University of Texas at Austin, Austin, TX, USA
| | - Oxana Palesh
- Department of Psychiatry, Massey Cancer Center, Virginia Commonwealth University School of Medicine, Richmond,, VA, USA
| | - Shelli R Kesler
- Department of Geriatrics and Palliative Care, Baylor College of Medicine, Houston, TX, USA.
- Department of Adult Health, School of Nursing, The University of Texas at Austin, 1710 Red River St, D0100, Austin, TX, 78712, USA.
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Hüpen P, Kumar H, Shymanskaya A, Swaminathan R, Habel U. Impulsivity Classification Using EEG Power and Explainable Machine Learning. Int J Neural Syst 2023; 33:2350006. [PMID: 36632032 DOI: 10.1142/s0129065723500065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Impulsivity is a multidimensional construct often associated with unfavorable outcomes. Previous studies have implicated several electroencephalography (EEG) indices to impulsiveness, but results are heterogeneous and inconsistent. Using a data-driven approach, we identified EEG power features for the prediction of self-reported impulsiveness. To this end, EEG signals of 56 individuals (18 low impulsive, 20 intermediate impulsive, 18 high impulsive) were recorded during a risk-taking task. Extracted EEG power features from 62 electrodes were fed into various machine learning classifiers to identify the most relevant band. Robustness of the classifier was varied by stratified [Formula: see text]-fold cross validation. Alpha and beta band power showed best performance in the classification of impulsiveness (accuracy = 95.18% and 95.11%, respectively) using a random forest classifier. Subsequently, a sequential bidirectional feature selection algorithm was used to estimate the most relevant electrode sites. Results show that as little as 10 electrodes are sufficient to reliably classify impulsiveness using alpha band power ([Formula: see text]-measure = 94.50%). Finally, the Shapley Additive exPlanations (SHAP) analysis approach was employed to reveal the individual EEG features that contributed most to the model's output. Results indicate that frontal as well as posterior midline alpha power seems to be of most importance for the classification of impulsiveness.
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Affiliation(s)
- Philippa Hüpen
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany.,JARA - Translational Brain Medicine, Aachen, Germany
| | - Himanshu Kumar
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India
| | - Aliaksandra Shymanskaya
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany
| | - Ramakrishnan Swaminathan
- Biomedical Engineering Group, Department of Applied Mechanics, Indian Institute of Technology Madras, 600036 Chennai, India
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany.,Institute of Neuroscience and Medicine, JARA-Institute Brain Structure Function Relationship (INM 10), Research Center Jülich, Jülich, Germany
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Buitelaar J, Bölte S, Brandeis D, Caye A, Christmann N, Cortese S, Coghill D, Faraone SV, Franke B, Gleitz M, Greven CU, Kooij S, Leffa DT, Rommelse N, Newcorn JH, Polanczyk GV, Rohde LA, Simonoff E, Stein M, Vitiello B, Yazgan Y, Roesler M, Doepfner M, Banaschewski T. Toward Precision Medicine in ADHD. Front Behav Neurosci 2022; 16:900981. [PMID: 35874653 PMCID: PMC9299434 DOI: 10.3389/fnbeh.2022.900981] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022] Open
Abstract
Attention-Deficit Hyperactivity Disorder (ADHD) is a complex and heterogeneous neurodevelopmental condition for which curative treatments are lacking. Whilst pharmacological treatments are generally effective and safe, there is considerable inter-individual variability among patients regarding treatment response, required dose, and tolerability. Many of the non-pharmacological treatments, which are preferred to drug-treatment by some patients, either lack efficacy for core symptoms or are associated with small effect sizes. No evidence-based decision tools are currently available to allocate pharmacological or psychosocial treatments based on the patient's clinical, environmental, cognitive, genetic, or biological characteristics. We systematically reviewed potential biomarkers that may help in diagnosing ADHD and/or stratifying ADHD into more homogeneous subgroups and/or predict clinical course, treatment response, and long-term outcome across the lifespan. Most work involved exploratory studies with cognitive, actigraphic and EEG diagnostic markers to predict ADHD, along with relatively few studies exploring markers to subtype ADHD and predict response to treatment. There is a critical need for multisite prospective carefully designed experimentally controlled or observational studies to identify biomarkers that index inter-individual variability and/or predict treatment response.
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Affiliation(s)
- Jan Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands.,Karakter Child and Adolescent Psychiatry University Center, Nijmegen, Netherlands
| | - Sven Bölte
- Center of Neurodevelopmental Disorders (KIND), Centre for Psychiatry Research, Department of Women's and Children's Health, Karolinska Institutet, Solna, Sweden.,Child and Adolescent Psychiatry, Stockholm Health Care Services, Stockholm, Sweden.,Curtin Autism Research Group, School of Occupational Therapy, Social Work and Speech Pathology, Curtin University, Perth, WA, Australia
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.,Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Arthur Caye
- Department of Psychiatry, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, São Paulo, Brazil
| | - Nina Christmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Samuele Cortese
- Centre for Innovation in Mental Health, Academic Unit of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, United Kingdom.,Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, United Kingdom.,Solent National Health System Trust, Southampton, United Kingdom.,Hassenfeld Children's Hospital at NYU Langone, New York University Child Study Center, New York, NY, United States.,Division of Psychiatry and Applied Psychology, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - David Coghill
- Departments of Paediatrics and Psychiatry, Royal Children's Hospital, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen V Faraone
- Departments of Psychiatry, Neuroscience and Physiology, SUNY Upstate Medical University, Syracuse, New York, NY, United States
| | - Barbara Franke
- Departments of Human Genetics and Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Markus Gleitz
- Medice Arzneimittel Pütter GmbH & Co. KG, Iserlohn, Germany
| | - Corina U Greven
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, Netherlands.,Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands.,King's College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Sandra Kooij
- Amsterdam University Medical Center, Location VUMc, Amsterdam, Netherlands.,PsyQ, Expertise Center Adult ADHD, The Hague, Netherlands
| | - Douglas Teixeira Leffa
- Department of Psychiatry, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.,National Institute of Developmental Psychiatry for Children and Adolescents, São Paulo, Brazil
| | - Nanda Rommelse
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, Netherlands.,Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jeffrey H Newcorn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Guilherme V Polanczyk
- Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Luis Augusto Rohde
- National Institute of Developmental Psychiatry for Children and Adolescents, São Paulo, Brazil.,ADHD Outpatient Program and Developmental Psychiatry Program, Hospital de Clinica de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Emily Simonoff
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, United Kingdom
| | - Mark Stein
- Department of Psychiatry and Behavioral Sciences, Seattle, WA, United States
| | - Benedetto Vitiello
- Department of Public Health and Pediatric Sciences, Section of Child and Adolescent Neuropsychiatry, University of Turin, Turin, Italy.,Department of Public Health, Johns Hopkins University, Baltimore, MA, United States
| | - Yanki Yazgan
- GuzelGunler Clinic, Istanbul, Turkey.,Yale Child Study Center, New Haven, CT, United States
| | - Michael Roesler
- Institute for Forensic Psychology and Psychiatry, Neurocenter, Saarland, Germany
| | - Manfred Doepfner
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Medical Faculty of the University of Cologne, Cologne, Germany.,School for Child and Adolescent Cognitive Behavioural Therapy, University Hospital of Cologne, Cologne, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
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