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Sastry NC, Banerjee A. Dynamicity of brain network organization & their community architecture as characterizing features for classification of common mental disorders from whole-brain connectome. Transl Psychiatry 2024; 14:268. [PMID: 38951513 PMCID: PMC11217301 DOI: 10.1038/s41398-024-02929-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 07/03/2024] Open
Abstract
The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.
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Affiliation(s)
- Nisha Chetana Sastry
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre, Gurgaon, Haryana, India.
| | - Arpan Banerjee
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre, Gurgaon, Haryana, India.
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Meyhoefer I, Sprenger A, Derad D, Grotegerd D, Leenings R, Leehr EJ, Breuer F, Surmann M, Rolfes K, Arolt V, Romer G, Lappe M, Rehder J, Koutsouleris N, Borgwardt S, Schultze-Lutter F, Meisenzahl E, Kircher TTJ, Keedy SS, Bishop JR, Ivleva EI, McDowell JE, Reilly JL, Hill SK, Pearlson GD, Tamminga CA, Keshavan MS, Gershon ES, Clementz BA, Sweeney JA, Hahn T, Dannlowski U, Lencer R. Evidence from comprehensive independent validation studies for smooth pursuit dysfunction as a sensorimotor biomarker for psychosis. Sci Rep 2024; 14:13859. [PMID: 38879556 PMCID: PMC11180169 DOI: 10.1038/s41598-024-64487-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 06/10/2024] [Indexed: 06/19/2024] Open
Abstract
Smooth pursuit eye movements are considered a well-established and quantifiable biomarker of sensorimotor function in psychosis research. Identifying psychotic syndromes on an individual level based on neurobiological markers is limited by heterogeneity and requires comprehensive external validation to avoid overestimation of prediction models. Here, we studied quantifiable sensorimotor measures derived from smooth pursuit eye movements in a large sample of psychosis probands (N = 674) and healthy controls (N = 305) using multivariate pattern analysis. Balanced accuracies of 64% for the prediction of psychosis status are in line with recent results from other large heterogenous psychiatric samples. They are confirmed by external validation in independent large samples including probands with (1) psychosis (N = 727) versus healthy controls (N = 292), (2) psychotic (N = 49) and non-psychotic bipolar disorder (N = 36), and (3) non-psychotic affective disorders (N = 119) and psychosis (N = 51) yielding accuracies of 65%, 66% and 58%, respectively, albeit slightly different psychosis syndromes. Our findings make a significant contribution to the identification of biologically defined profiles of heterogeneous psychosis syndromes on an individual level underlining the impact of sensorimotor dysfunction in psychosis.
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Affiliation(s)
- Inga Meyhoefer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Andreas Sprenger
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - David Derad
- Department of Neurology, University of Luebeck, Luebeck, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Fabian Breuer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Marian Surmann
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Karen Rolfes
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Volker Arolt
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
| | - Georg Romer
- Department of Child Adolescence Psychiatry and Psychotherapy, University of Muenster, Muenster, Germany
| | - Markus Lappe
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Johanna Rehder
- Institute of Psychology, University of Muenster, Muenster, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University Munich, Munich, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Max-Planck-Institute of Psychiatry Munich, Munich, Germany
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany
- Department of Psychiatry, Psychiatric University Hospital, University of Basel, Basel, Switzerland
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
- Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine University, Duesseldorf/LVR, Duesseldorf, Germany
| | - Tilo T J Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Sarah S Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Jeffrey R Bishop
- Department of Experimental and Clinical Pharmacology and Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, USA
| | - Elena I Ivleva
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jennifer E McDowell
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - James L Reilly
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Scot Kristian Hill
- Department of Psychology, Rosalind Franklin University of Medicine and Science, Chicago, IL, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, and Olin Research Center, Institute of Living/Hartford Hospital, Hartford, CT, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, USA
| | - Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, USA
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Muenster, Albert Schweitzer Campus 1, Build. A9a, 48149, Muenster, Germany.
- Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Muenster, Muenster, Germany.
- Department of Psychiatry and Psychotherapy, University of Luebeck, Luebeck, Germany.
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Sadvandi G, Kianfar AE, Becker K, Heinzel A, Wolf M, Said‐Yekta Michael S. Systematic review on effects of experimental orthodontic tooth displacement on brain activation assessed by fMRI. Clin Exp Dent Res 2024; 10:e879. [PMID: 38558512 PMCID: PMC10982672 DOI: 10.1002/cre2.879] [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] [Received: 12/28/2023] [Revised: 03/04/2024] [Accepted: 03/09/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Orthodontic treatment is often accompanied by discomfort and pain in patients, which are believed to be a result of orthodontic tooth displacement caused by the mechanical forces exerted by the orthodontic appliances on the periodontal tissues. These lead to change blood oxygen level dependent response in related brain regions. OBJECTIVE This systematic review aims to assess the impact of experimental orthodontic tooth displacement on alterations in central nervous system activation assessed by tasked based and resting state fMRI. MATERIALS AND METHODS A literature search was conducted using online databases, following PRISMA guidelines and the PICO framework. Selected studies utilized magnetic resonance imaging to examine the brain activity changes in healthy participants after the insertion of orthodontic appliances. RESULTS The initial database screening resulted in 791 studies. Of these, 234 were duplicates and 547 were deemed irrelevant considering the inclusion and exclusion criteria. Of the ten remaining potential relevant studies, two were excluded during full-text screening. Eight prospective articles were eligible for further analysis. The included studies provided evidence of the intricate interplay between orthodontic treatment, pain perception, and brain function. All of the participants in the included studies employed orthodontic separators in short-term experiments to induce tooth displacement during the early stage of orthodontic treatment. Alterations in brain activation were observed in brain regions, functional connectivity and brain networks, predominantly affecting regions implicated in nociception (thalamus, insula), emotion (insula, frontal areas), and cognition (frontal areas, cerebellum, default mode network). CONCLUSIONS The results suggest that orthodontic treatment influences beyond the pain matrix and affects other brain regions including the limbic system. Furthermore, understanding the orthodontically induced brain activation can aid in development of targeted pain management strategies that do not adversely affect orthodontic tooth movement. Due to the moderate to serious risk of bias and the heterogeneity among the included studies, further clinical trials on this subject are recommended.
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Affiliation(s)
- Gelareh Sadvandi
- Department of OrthodonticsRWTH Aachen University HospitalGermany
| | | | - Kathrin Becker
- Department of Dentofacial Orthopedics and OrthodonticsCharité Universitätsmedizin BerlinBerlinCC03Germany
| | - Alexander Heinzel
- Department of Nuclear MedicineMartin‐Luther‐University Halle‐WittenbergHalleGermany
| | - Michael Wolf
- Department of OrthodonticsRWTH Aachen University HospitalGermany
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van der Veer A, Madern T, van Lenthe FJ. Tunneling, cognitive load and time orientation and their relations with dietary behavior of people experiencing financial scarcity - an AI-assisted scoping review elaborating on scarcity theory. Int J Behav Nutr Phys Act 2024; 21:26. [PMID: 38439067 PMCID: PMC10910771 DOI: 10.1186/s12966-024-01576-9] [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] [Received: 09/14/2023] [Accepted: 02/14/2024] [Indexed: 03/06/2024] Open
Abstract
BACKGROUND The concept of a financial scarcity mindset has raised much attention as an explanation for poor decision-making and dysfunctional behavior. It has been suggested that financial scarcity could also impair dietary behavior, through a decline in self-control. Underlying cognitive mechanisms of tunneling (directing attention to financial issues and neglecting other demands), cognitive load (a tax on mental bandwidth interfering with executive functioning) and time orientation (a shift towards a present time horizon, versus a future time horizon) may explain the association between financial scarcity and self-control related dietary behavior. The current scoping review gathers recent evidence on how these mechanisms affect dietary behavior of people experiencing financial scarcity. It builds on a theoretical framework based on insights from behavioral economics and health psychology. METHODS A literature search was executed in six online databases, which resulted in 9.975 papers. Search terms were tunneling, cognitive load and time orientation, financial scarcity, and dietary behavior. Screening was performed with ASReview, an AI-ranking tool. In total, 14 papers were included in the scoping review. We used PRISMA-ScR guidelines for reporting. RESULTS Limited evidence indicates that a scarcity mindset could increase tunneling, through attentional narrowing on costs of food, which then directly impacts dietary behavior. A scarcity mindset involves experiencing financial stress, which can be understood as cognitive load. Cognitive load decreases attentional capacity, which could impair self-control in dietary choices. Financial scarcity is related to a present time orientation, which affects dietary choices by shifting priorities and decreasing motivation for healthy dietary behavior. CONCLUSIONS A scarcity mindset affects dietary behavior in different ways. Tunneling and a shift in time orientation are indicative of an attentional redirection, which can be seen as more adaptive to the situation. These may be processes indirectly affecting self-control capacity. Cognitive load could decrease self-control capacity needed for healthy dietary behavior because it consumes mental bandwidth. How a changing time orientation when experiencing financial scarcity relates to motivation for self-control in dietary behavior is a promising theme for further inquiry.
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Affiliation(s)
- Annemarieke van der Veer
- Research Group of Debt and Debt Collection, University of Applied Sciences Utrecht, Utrecht, PO Box 85397, 3508 AJ, The Netherlands.
| | - Tamara Madern
- Research Group of Debt and Debt Collection, University of Applied Sciences Utrecht, Utrecht, PO Box 85397, 3508 AJ, The Netherlands
| | - Frank J van Lenthe
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, PO Box 2040, Rotterdam, 3000 CA, The Netherlands
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Kaiser RH, Moser AD, Neilson C, Jones J, Peterson EC, Ruzic L, Rosenberg BM, Hough CM, Sandman C, Schneck CD, Miklowitz DJ. Neurocognitive risk phenotyping to predict mood symptoms in adolescence. JOURNAL OF PSYCHOPATHOLOGY AND CLINICAL SCIENCE 2024; 133:90-102. [PMID: 38059934 PMCID: PMC10752243 DOI: 10.1037/abn0000866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
Predicting mood disorders in adolescence is a challenge that motivates research to identify neurocognitive predictors of symptom expression and clinical profiles. This study used machine learning to test whether neurocognitive variables predicted future manic or anhedonic symptoms in two adolescent samples risk-enriched for lifetime mood disorders (Sample 1, n = 73, ages = 13-25, M [SD] = 19.22 [2.49] years, 68% lifetime mood disorder) or familial mood disorders (Sample 2, n = 154, ages = 13-21, M [SD] = 16.46 [1.95] years, 62% first-degree family history of mood disorder). Participants completed cognitive testing and functional magnetic resonance imaging at baseline, for behavioral and neural measures of reward processing and executive functioning. Next, participants completed a daily diary procedure for 8-16 weeks. Penalized mixed-effects models identified neurocognitive predictors of future mood symptoms and stress-reactive changes in mood symptoms. Results included the following. In both samples, adolescents showing ventral corticostriatal reward hyposensitivity and lower reward performance reported more severe stress-reactive anhedonia. Poorer executive functioning behavior was associated with heightened anhedonia overall in Sample 1, but lower stress-reactive anhedonia in both samples. In Sample 1, adolescents showing ventral corticostriatal reward hypersensitivity and poorer executive functioning reported more severe stress-reactive manic symptoms. Clustering analyses identified, and replicated, five neurocognitive subgroups. Adolescents characterized by neural or behavioral reward hyposensitivities together with average-to-poor executive functioning reported unipolar symptom profiles. Adolescents showing neural reward hypersensitivity together with poor behavioral executive functioning reported a bipolar symptom profile (Sample 1 only). Together, neurocognitive phenotypes may hold value for predicting symptom expression and profiles of mood pathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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Affiliation(s)
- Roselinde H Kaiser
- Research on Affective Disorders and Development (RADD) Lab, Department of Psychology and Neuroscience, University of Colorado Boulder
| | - Amelia D Moser
- Research on Affective Disorders and Development (RADD) Lab, Department of Psychology and Neuroscience, University of Colorado Boulder
| | - Chiara Neilson
- Institute of Cognitive Science, University of Colorado Boulder
| | - Jenna Jones
- Institute of Cognitive Science, University of Colorado Boulder
| | - Elena C Peterson
- Research on Affective Disorders and Development (RADD) Lab, Department of Psychology and Neuroscience, University of Colorado Boulder
| | - Luke Ruzic
- Institute of Cognitive Science, University of Colorado Boulder
| | | | | | | | | | - David J Miklowitz
- Department of Psychiatry, Semel Institute, University of California, Los Angeles
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Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior KJ, Poirrier JE, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Rev Pharmacoecon Outcomes Res 2024; 24:63-115. [PMID: 37955147 DOI: 10.1080/14737167.2023.2279107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/31/2023] [Indexed: 11/14/2023]
Abstract
INTRODUCTION The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery. METHODS A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted. RESULTS In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively). EXPERT OPINION The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.
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Affiliation(s)
- Katarzyna Kolasa
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
| | - Bisrat Admassu
- Division of Health Economics and Healthcare Management, Kozminski University, Warsaw, Poland
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Giansanti D. An Umbrella Review of the Fusion of fMRI and AI in Autism. Diagnostics (Basel) 2023; 13:3552. [PMID: 38066793 PMCID: PMC10706112 DOI: 10.3390/diagnostics13233552] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/22/2023] [Accepted: 11/25/2023] [Indexed: 04/05/2024] Open
Abstract
The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central role in autism diagnosis. The integration of Artificial Intelligence (AI) into the realm of applications further contributes to its development. This study's objective is to analyze emerging themes in this domain through an umbrella review, encompassing systematic reviews. The research methodology was based on a structured process for conducting a literature narrative review, using an umbrella review in PubMed and Scopus. Rigorous criteria, a standard checklist, and a qualification process were meticulously applied. The findings include 20 systematic reviews that underscore key themes in autism research, particularly emphasizing the significance of technological integration, including the pivotal roles of fMRI and AI. This study also highlights the enigmatic role of oxytocin. While acknowledging the immense potential in this field, the outcome does not evade acknowledging the significant challenges and limitations. Intriguingly, there is a growing emphasis on research and innovation in AI, whereas aspects related to the integration of healthcare processes, such as regulation, acceptance, informed consent, and data security, receive comparatively less attention. Additionally, the integration of these findings into Personalized Medicine (PM) represents a promising yet relatively unexplored area within autism research. This study concludes by encouraging scholars to focus on the critical themes of health domain integration, vital for the routine implementation of these applications.
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Affiliation(s)
- Daniele Giansanti
- Centro Nazionale TISP, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Roma, Italy
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Kesler SR, Henneghan AM, Prinsloo S, Palesh O, Wintermark M. Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment. Front Med (Lausanne) 2023; 10:1199605. [PMID: 37720513 PMCID: PMC10499624 DOI: 10.3389/fmed.2023.1199605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/15/2023] [Indexed: 09/19/2023] Open
Abstract
Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.
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Affiliation(s)
- Shelli R. Kesler
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
- Department of Diagnostic Medicine, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
| | - Ashley M. Henneghan
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
| | - Sarah Prinsloo
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Oxana Palesh
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer, Houston, TX, United States
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van Dijk SHB, Brusse-Keizer MGJ, Bucsán CC, van der Palen J, Doggen CJM, Lenferink A. Artificial intelligence in systematic reviews: promising when appropriately used. BMJ Open 2023; 13:e072254. [PMID: 37419641 PMCID: PMC10335470 DOI: 10.1136/bmjopen-2023-072254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 06/26/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND Systematic reviews provide a structured overview of the available evidence in medical-scientific research. However, due to the increasing medical-scientific research output, it is a time-consuming task to conduct systematic reviews. To accelerate this process, artificial intelligence (AI) can be used in the review process. In this communication paper, we suggest how to conduct a transparent and reliable systematic review using the AI tool 'ASReview' in the title and abstract screening. METHODS Use of the AI tool consisted of several steps. First, the tool required training of its algorithm with several prelabelled articles prior to screening. Next, using a researcher-in-the-loop algorithm, the AI tool proposed the article with the highest probability of being relevant. The reviewer then decided on relevancy of each article proposed. This process was continued until the stopping criterion was reached. All articles labelled relevant by the reviewer were screened on full text. RESULTS Considerations to ensure methodological quality when using AI in systematic reviews included: the choice of whether to use AI, the need of both deduplication and checking for inter-reviewer agreement, how to choose a stopping criterion and the quality of reporting. Using the tool in our review resulted in much time saved: only 23% of the articles were assessed by the reviewer. CONCLUSION The AI tool is a promising innovation for the current systematic reviewing practice, as long as it is appropriately used and methodological quality can be assured. PROSPERO REGISTRATION NUMBER CRD42022283952.
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Affiliation(s)
- Sanne H B van Dijk
- Health Technology & Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
- Pulmonary Medicine, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Marjolein G J Brusse-Keizer
- Health Technology & Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
- Medical School Twente, Medisch Spectrum Twente, Enschede, The Netherlands
| | - Charlotte C Bucsán
- Pulmonary Medicine, Medisch Spectrum Twente, Enschede, The Netherlands
- Cognition, Data & Education, Faculty of Behavioural, Management & Social Sciences, University of Twente, Enschede, The Netherlands
| | - Job van der Palen
- Medical School Twente, Medisch Spectrum Twente, Enschede, The Netherlands
- Cognition, Data & Education, Faculty of Behavioural, Management & Social Sciences, University of Twente, Enschede, The Netherlands
| | - Carine J M Doggen
- Health Technology & Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
- Clinical Research Centre, Rijnstate Hospital, Arnhem, The Netherlands
| | - Anke Lenferink
- Health Technology & Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands
- Pulmonary Medicine, Medisch Spectrum Twente, Enschede, The Netherlands
- Clinical Research Centre, Rijnstate Hospital, Arnhem, The Netherlands
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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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11
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Fisher ZF, Parsons J, Gates KM, Hopfinger JB. Blind Subgrouping of Task-based fMRI. PSYCHOMETRIKA 2023; 88:434-455. [PMID: 36892726 DOI: 10.1007/s11336-023-09907-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Indexed: 05/17/2023]
Abstract
Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender). This makes it difficult to make inferences regarding these predefined subgroups. For this reason, researchers sometimes wish to identify subsets of individuals who have similarities in their dynamic processes regardless of any predefined category. This requires unsupervised classification of individuals based on similarities in their dynamic processes, or equivalently, in this case, similarities in their network structures of edges. The present paper tests a recently developed algorithm, S-GIMME, that takes into account heterogeneity across individuals with the aim of providing subgroup membership and precise information about the specific network structures that differentiate subgroups. The algorithm has previously provided robust and accurate classification when evaluated with large-scale simulation studies but has not yet been validated on empirical data. Here, we investigate S-GIMME's ability to differentiate, in a purely data-driven manner, between brain states explicitly induced through different tasks in a new fMRI dataset. The results provide new evidence that the algorithm was able to resolve, in an unsupervised data-driven manner, the differences between different active brain states in empirical fMRI data to segregate individuals and arrive at subgroup-specific network structures of edges. The ability to arrive at subgroups that correspond to empirically designed fMRI task conditions, with no biasing or priors, suggests this data-driven approach can be a powerful addition to existing methods for unsupervised classification of individuals based on their dynamic processes.
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Affiliation(s)
- Zachary F Fisher
- Quantitative Developmental Systems Methodology Core, Department of Human Development and Family Studies, The Pennsylvania State University, Health and Human Development Building, University Park, PA, 16802, USA.
| | | | - Kathleen M Gates
- The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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12
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Brucar LR, Feczko E, Fair DA, Zilverstand A. Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes. Biol Psychiatry 2023; 93:704-716. [PMID: 36841702 PMCID: PMC10038896 DOI: 10.1016/j.biopsych.2022.12.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 12/15/2022] [Accepted: 12/19/2022] [Indexed: 12/31/2022]
Abstract
The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.
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Affiliation(s)
- Leyla R Brucar
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, Minnesota; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota Medical School, Minneapolis, Minnesota; Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota; Institute of Child Development, University of Minnesota Medical School, Minneapolis, Minnesota
| | - Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota; Medical Discovery Team on Addiction, University of Minnesota Medical School, Minneapolis, Minnesota.
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13
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Miranda L, Bordes J, Gasperoni S, Lopez JP. Increasing resolution in stress neurobiology: from single cells to complex group behaviors. Stress 2023; 26:2186141. [PMID: 36855966 DOI: 10.1080/10253890.2023.2186141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
Stress can have severe psychological and physiological consequences. Thus, inappropriate regulation of the stress response is linked to the etiology of mood and anxiety disorders. The generation and implementation of preclinical animal models represent valuable tools to explore and characterize the mechanisms underlying the pathophysiology of stress-related psychiatric disorders and the development of novel pharmacological strategies. In this commentary, we discuss the strengths and limitations of state-of-the-art molecular and computational advances employed in stress neurobiology research, with a focus on the ever-increasing spatiotemporal resolution in cell biology and behavioral science. Finally, we share our perspective on future directions in the fields of preclinical and human stress research.
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Affiliation(s)
- Lucas Miranda
- Department of Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- International Max Planck Research School for Translational Psychiatry (IMPRS-TP), Munich, Germany
| | - Joeri Bordes
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Serena Gasperoni
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Juan Pablo Lopez
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
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14
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Dunsmoor JE, Cisler JM, Fonzo GA, Creech SK, Nemeroff CB. Laboratory models of post-traumatic stress disorder: The elusive bridge to translation. Neuron 2022; 110:1754-1776. [PMID: 35325617 PMCID: PMC9167267 DOI: 10.1016/j.neuron.2022.03.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/18/2022] [Accepted: 02/28/2022] [Indexed: 12/14/2022]
Abstract
Post-traumatic stress disorder (PTSD) is a debilitating mental illness composed of a heterogeneous collection of symptom clusters. The unique nature of PTSD as arising from a precipitating traumatic event helps simplify cross-species translational research modeling the neurobehavioral effects of stress and fear. However, the neurobiological progress on these complex neural circuits informed by animal models has yet to produce novel, evidence-based clinical treatment for PTSD. Here, we provide a comprehensive overview of popular laboratory models of PTSD and provide concrete ideas for improving the validity and clinical translational value of basic research efforts in humans. We detail modifications to simplified animal paradigms to account for myriad cognitive factors affected in PTSD, which may contribute to abnormalities in regulating fear. We further describe new avenues for integrating different areas of psychological research underserved by animal models of PTSD. This includes incorporating emerging trends in the cognitive neuroscience of episodic memory, emotion regulation, social-emotional processes, and PTSD subtyping to provide a more comprehensive recapitulation of the human experience to trauma in laboratory research.
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Affiliation(s)
- Joseph E Dunsmoor
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin Dell Medical School, Austin, TX, USA; Center for Psychedelic Research and Therapy, University of Texas at Austin Dell Medical School, Austin, TX, USA.
| | - Josh M Cisler
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin Dell Medical School, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA; Center for Psychedelic Research and Therapy, University of Texas at Austin Dell Medical School, Austin, TX, USA
| | - Gregory A Fonzo
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin Dell Medical School, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA; Center for Psychedelic Research and Therapy, University of Texas at Austin Dell Medical School, Austin, TX, USA
| | - Suzannah K Creech
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin Dell Medical School, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA
| | - Charles B Nemeroff
- Department of Psychiatry and Behavioral Sciences, University of Texas at Austin Dell Medical School, Austin, TX, USA; Institute for Early Life Adversity Research, University of Texas at Austin, Austin, TX, USA; Center for Psychedelic Research and Therapy, University of Texas at Austin Dell Medical School, Austin, TX, USA.
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