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Ging-Jehli NR, Kuhn M, Blank JM, Chanthrakumar P, Steinberger DC, Yu Z, Herrington TM, Dillon DG, Pizzagalli DA, Frank MJ. Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:726-736. [PMID: 38401881 PMCID: PMC11227402 DOI: 10.1016/j.bpsc.2024.02.005] [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] [Received: 08/09/2023] [Revised: 02/03/2024] [Accepted: 02/09/2024] [Indexed: 02/26/2024]
Abstract
BACKGROUND Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach. METHODS Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks. RESULTS Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005). CONCLUSIONS We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.
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Affiliation(s)
- Nadja R Ging-Jehli
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island.
| | - Manuel Kuhn
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Jacob M Blank
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Pranavan Chanthrakumar
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island; Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - David C Steinberger
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts
| | - Zeyang Yu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Todd M Herrington
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Daniel G Dillon
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Michael J Frank
- Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island
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Hapakova L, Necpal J, Kosutzka Z. The antisaccadic paradigm: A complementary neuropsychological tool in basal ganglia disorders. Cortex 2024; 178:116-140. [PMID: 38991475 DOI: 10.1016/j.cortex.2024.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 04/20/2024] [Accepted: 06/17/2024] [Indexed: 07/13/2024]
Abstract
This review explores the role of the antisaccadic task in understanding inhibitory mechanisms in basal ganglia disorders. It conducts a comparative analysis of saccadic profiles in conditions such as Parkinson's disease, Tourette syndrome, obsessive-compulsive disorder, Huntington's disease, and dystonia, revealing distinct patterns and proposing mechanisms for impaired performance. The primary focus is on two inhibitory mechanisms: global, pre-emptive inhibition responsible for suppressing prepotent responses, and slower, selective response inhibition. The antisaccadic task demonstrates practicality in clinical applications, aiding in differential diagnoses, treatment monitoring and reflecting gait control. To further enhance its differential diagnostic value, future directions should address issues such as the standardization of eye-tracking protocol and the integration of eye-tracking data with other disease indicators in a comprehensive dataset.
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Affiliation(s)
- Lenka Hapakova
- 2nd Department of Neurology, Comenius University Faculty of Medicine, University Hospital Bratislava, Bratislava, Slovakia.
| | - Jan Necpal
- Neurology Department, Hospital Zvolen, a. s., Zvolen, Slovakia.
| | - Zuzana Kosutzka
- 2nd Department of Neurology, Comenius University Faculty of Medicine, University Hospital Bratislava, Bratislava, Slovakia.
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3
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Le Stanc L, Lunven M, Giavazzi M, Sliwinski A, Youssov K, Bachoud-Lévi AC, Jacquemot C. Cognitive reserve involves decision making and is associated with left parietal and hippocampal hypertrophy in neurodegeneration. Commun Biol 2024; 7:741. [PMID: 38890487 PMCID: PMC11189446 DOI: 10.1038/s42003-024-06416-x] [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: 11/27/2023] [Accepted: 06/05/2024] [Indexed: 06/20/2024] Open
Abstract
Cognitive reserve is the ability to actively cope with brain deterioration and delay cognitive decline in neurodegenerative diseases. It operates by optimizing performance through differential recruitment of brain networks or alternative cognitive strategies. We investigated cognitive reserve using Huntington's disease (HD) as a genetic model of neurodegeneration to compare premanifest HD, manifest HD, and controls. Contrary to manifest HD, premanifest HD behave as controls despite neurodegeneration. By decomposing the cognitive processes underlying decision making, drift diffusion models revealed a response profile that differs progressively from controls to premanifest and manifest HD. Here, we show that cognitive reserve in premanifest HD is supported by an increased rate of evidence accumulation compensating for the abnormal increase in the amount of evidence needed to make a decision. This higher rate is associated with left superior parietal and hippocampal hypertrophy, and exhibits a bell shape over the course of disease progression, characteristic of compensation.
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Affiliation(s)
- Lorna Le Stanc
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France
- Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France
- Université Paris-Est Créteil, Faculté de Santé, Créteil, France
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005 Paris, France
| | - Marine Lunven
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France
- Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France
- Université Paris-Est Créteil, Faculté de Santé, Créteil, France
| | - Maria Giavazzi
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France
- Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France
- Université Paris-Est Créteil, Faculté de Santé, Créteil, France
| | - Agnès Sliwinski
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France
- Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France
- Université Paris-Est Créteil, Faculté de Santé, Créteil, France
- AP-HP, Centre de Référence Maladie de Huntington, Service de Neurologie, Hôpital Henri Mondor-Albert Chenevier, Créteil, France
| | - Katia Youssov
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France
- Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France
- Université Paris-Est Créteil, Faculté de Santé, Créteil, France
- AP-HP, Centre de Référence Maladie de Huntington, Service de Neurologie, Hôpital Henri Mondor-Albert Chenevier, Créteil, France
| | - Anne-Catherine Bachoud-Lévi
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France
- Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France
- Université Paris-Est Créteil, Faculté de Santé, Créteil, France
- AP-HP, Centre de Référence Maladie de Huntington, Service de Neurologie, Hôpital Henri Mondor-Albert Chenevier, Créteil, France
| | - Charlotte Jacquemot
- Département d'Études Cognitives, École Normale Supérieure-PSL, Paris, France.
- Institut Mondor de Recherche Biomédicale, Inserm U955, Equipe E01 Neuropsychologie Interventionnelle, Créteil, France.
- Université Paris-Est Créteil, Faculté de Santé, Créteil, France.
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Shiino S, van Wouwe NC, Wylie SA, Claassen DO, McDonell KE. Huntington disease exacerbates action impulses. Front Psychol 2023; 14:1186465. [PMID: 37397312 PMCID: PMC10312388 DOI: 10.3389/fpsyg.2023.1186465] [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: 03/14/2023] [Accepted: 05/16/2023] [Indexed: 07/04/2023] Open
Abstract
Background Impulsivity is a common clinical feature of Huntington disease (HD), but the underlying cognitive dynamics of impulse control in this population have not been well-studied. Objective To investigate the temporal dynamics of action impulse control in HD patients using an inhibitory action control task. Methods Sixteen motor manifest HD patients and seventeen age-matched healthy controls (HC) completed the action control task. We applied the activation-suppression theoretical model and distributional analytic techniques to differentiate the strength of fast impulses from their top-down suppression. Results Overall, HD patients produced slower and less accurate reactions than HCs. HD patients also exhibited an exacerbated interference effect, as evidenced by a greater slowing of RT on non-corresponding compared to corresponding trials. HD patients made more fast, impulsive errors than HC, evidenced by significantly lower accuracy on their fastest reaction time trials. The slope reduction of interference effects as reactions slowed was similar between HD and controls, indicating preserved impulse suppression. Conclusion Our results indicate that patients with HD show a greater susceptibility to act rapidly on incorrect motor impulses but preserved proficiency of top-down suppression. Further research is needed to determine how these findings relate to clinical behavioral symptoms.
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Affiliation(s)
- Shuhei Shiino
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | | | - Scott A. Wylie
- Department of Neurological Surgery, University of Louisville, Louisville, KY, United States
| | - Daniel O. Claassen
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Katherine E. McDonell
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, United States
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Lunven M, Hernandez Dominguez K, Youssov K, Hamet Bagnou J, Fliss R, Vandendriessche H, Bapst B, Morgado G, Remy P, Schubert R, Reilmann R, Busse M, Craufurd D, Massart R, Rosser A, Bachoud-Lévi AC. A new approach to digitized cognitive monitoring: validity of the SelfCog in Huntington's disease. Brain Commun 2023; 5:fcad043. [PMID: 36938527 PMCID: PMC10018460 DOI: 10.1093/braincomms/fcad043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 11/30/2022] [Accepted: 03/03/2023] [Indexed: 03/09/2023] Open
Abstract
Cognitive deficits represent a hallmark of neurodegenerative diseases, but evaluating their progression is complex. Most current evaluations involve lengthy paper-and-pencil tasks which are subject to learning effects dependent on the mode of response (motor or verbal), the countries' language or the examiners. To address these limitations, we hypothesized that applying neuroscience principles may offer a fruitful alternative. We thus developed the SelfCog, a digitized battery that tests motor, executive, visuospatial, language and memory functions in 15 min. All cognitive functions are tested according to the same paradigm, and a randomization algorithm provides a new test at each assessment with a constant level of difficulty. Here, we assessed its validity, reliability and sensitivity to detect decline in early-stage Huntington's disease in a prospective and international multilingual study (France, the UK and Germany). Fifty-one out of 85 participants with Huntington's disease and 40 of 52 healthy controls included at baseline were followed up for 1 year. Assessments included a comprehensive clinical assessment battery including currently standard cognitive assessments alongside the SelfCog. We estimated associations between each of the clinical assessments and SelfCog using Spearman's correlation and proneness to retest effects and sensitivity to decline through linear mixed models. Longitudinal effect sizes were estimated for each cognitive score. Voxel-based morphometry and tract-based spatial statistics analyses were conducted to assess the consistency between performance on the SelfCog and MRI 3D-T1 and diffusion-weighted imaging in a subgroup that underwent MRI at baseline and after 12 months. The SelfCog detected the decline of patients with Huntington's disease in a 1-year follow-up period with satisfactory psychometric properties. Huntington's disease patients are correctly differentiated from controls. The SelfCog showed larger effect sizes than the classical cognitive assessments. Its scores were associated with grey and white matter damage at baseline and over 1 year. Given its good performance in longitudinal analyses of the Huntington's disease cohort, it should likely become a very useful tool for measuring cognition in Huntington's disease in the future. It highlights the value of moving the field along the neuroscience principles and eventually applying them to the evaluation of all neurodegenerative diseases.
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Affiliation(s)
- Marine Lunven
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Karen Hernandez Dominguez
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Katia Youssov
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Jennifer Hamet Bagnou
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Rafika Fliss
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Henri Vandendriessche
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Blanche Bapst
- Department of Neuroradiology, AP-HP, Henri Mondor University Hospital, 94010 Créteil, France
- Faculty of Medicine, Université Paris Est Créteil, F-94010 Créteil, France
| | - Graça Morgado
- Inserm, Centre d’Investigation Clinique 1430, APHP, Hôpital Henri Mondor, 94010 Créteil, France
| | - Philippe Remy
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Robin Schubert
- George Huntington Institute, Technology-Park, 48149 Muenster, Germany
- Department of Neurodegeneration and Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany
| | - Ralf Reilmann
- George Huntington Institute, Technology-Park, 48149 Muenster, Germany
- Department of Neurodegeneration and Hertie Institute for Clinical Brain Research, University of Tuebingen, 72076 Tuebingen, Germany
- Department of Clinical Radiology, University of Muenster, 48149 Muenster, Germany
| | - Monica Busse
- Centre for Trials Research, Cardiff University, Cardiff CF14 4EP, UK
- Wales Brain Research And Intracranial Neurotherapeutics (BRAIN) Biomedical Research Unit, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
| | - David Craufurd
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9PL, UK
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, UK
| | - Renaud Massart
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
| | - Anne Rosser
- Wales Brain Research And Intracranial Neurotherapeutics (BRAIN) Biomedical Research Unit, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF14 4EP, UK
- Cardiff School of Medicine, Neuroscience and Mental Health Institute, Cardiff CF24 4HQ, UK
- School of Biosciences, Cardiff University Brain Repair Group, Cardiff CF10 3AX, UK
| | - Anne-Catherine Bachoud-Lévi
- Département d'Etudes Cognitives, École normale supérieure, PSL University, 75005 Paris, France
- University Paris Est Creteil, INSERM U955, Institut Mondor de Recherche Biomédicale, Equipe NeuroPsychologie Interventionnelle, F-94010 Creteil, France
- AP-HP, Hôpital Henri Mondor-Albert Chenevier, Centre de référence Maladie de Huntington, Service de Neurologie, F-94010 Créteil, France
- NeurATRIS, Hôpital Henri Mondor, 94010 Créteil, France
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Modeling brain dynamics and gaze behavior: Starting point bias and drift rate relate to frontal midline theta oscillations. Neuroimage 2023; 268:119871. [PMID: 36682508 DOI: 10.1016/j.neuroimage.2023.119871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 12/31/2022] [Accepted: 01/10/2023] [Indexed: 01/22/2023] Open
Abstract
Frontal midline theta oscillatory dynamics have been implicated as an important neural signature of inhibitory control. However, most proactive cognitive control studies rely on behavioral tasks where individual differences are inferred through button presses. We applied computational modeling to further refine our understanding of theta dynamics in a cued anti-saccade task with gaze-contingent eye tracking. Using a drift diffusion model, increased frontal midline theta power during high-conflict, relative to low-conflict, trials predicted a more conservative style of responding through the starting point (bias). During both high- and low-conflict trials, increases in frontal midline theta also predicted improvements in response efficiency (drift rate). Regression analyses provided support for the importance of the starting point bias, which was associated with frontal midline theta over the course of the task above-and-beyond both drift rate and mean reaction time. Our findings provide a more thorough understanding of proactive gaze control by linking trial-by-trial increases of frontal midline theta to a shift in starting point bias facilitating a more neutral style of responding.
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7
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Chaki J, Woźniak M. Deep learning for neurodegenerative disorder (2016 to 2022): A systematic review. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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8
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Self-judgment dissected: A computational modeling analysis of self-referential processing and its relationship to trait mindfulness facets and depression symptoms. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2023; 23:171-189. [PMID: 36168080 PMCID: PMC9931629 DOI: 10.3758/s13415-022-01033-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 11/08/2022]
Abstract
Cognitive theories of depression, and mindfulness theories of well-being, converge on the notion that self-judgment plays a critical role in mental health. However, these theories have rarely been tested via tasks and computational modeling analyses that can disentangle the information processes operative in self-judgments. We applied a drift-diffusion computational model to the self-referential encoding task (SRET) collected before and after an 8-week mindfulness intervention (n = 96). A drift-rate regression parameter representing positive-relative to negative-self-referential judgment strength positively related to mindful awareness and inversely related to depression, both at baseline and over time; however, this parameter did not significantly relate to the interaction between mindful awareness and nonjudgmentalness. At the level of individual depression symptoms, at baseline, a spectrum of symptoms (inversely) correlated with the drift-rate regression parameter, suggesting that many distinct depression symptoms relate to valenced self-judgment between subjects. By contrast, over the intervention, changes in only a smaller subset of anhedonia-related depression symptoms showed substantial relationships with this parameter. Both behavioral and model-derived measures showed modest split-half and test-retest correlations. Results support cognitive theories that implicate self-judgment in depression and mindfulness theories, which imply that mindful awareness should lead to more positive self-views.
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9
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Benjamin AS, Powell G, Bompas A, Sumner P. Strategy and processing speed eclipse individual differences in control ability in conflict tasks. J Exp Psychol Learn Mem Cogn 2022; 48:1448-1469. [PMID: 34591554 PMCID: PMC9899369 DOI: 10.1037/xlm0001028] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Response control or inhibition is one of the cornerstones of modern cognitive psychology, featuring prominently in theories of executive functioning and impulsive behavior. However, repeated failures to observe correlations between commonly applied tasks have led some theorists to question whether common response conflict processes even exist. A challenge to answering this question is that behavior is multifaceted, with both conflict and nonconflict processes (e.g., strategy, processing speed) contributing to individual differences. Here, we use a cognitive model to dissociate these processes; the diffusion model for conflict tasks (Ulrich et al., 2015). In a meta-analysis of fits to seven empirical datasets containing combinations of the flanker, Simon, color-word Stroop, and spatial Stroop tasks, we observed weak (r < .05) zero-order correlations between tasks in parameters reflecting conflict processing, seemingly challenging a general control construct. However, our meta-analysis showed consistent positive correlations in parameters representing processing speed and strategy. We then use model simulations to evaluate whether correlations in behavioral costs are diagnostic of the presence or absence of common mechanisms of conflict processing. We use the model to impose known correlations for conflict mechanisms across tasks, and we compare the simulated behavior to simulations when there is no conflict correlation across tasks. We find that correlations in strategy and processing speed can produce behavioral correlations equal to, or larger than, those produced by correlated conflict mechanisms. We conclude that correlations between conflict tasks are only weakly informative about common conflict mechanisms if researchers do not control for strategy and processing speed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2022; 20:26-36. [PMID: 35078946 PMCID: PMC8813324 DOI: 10.9758/cpn.2022.20.1.26] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/09/2021] [Accepted: 06/14/2021] [Indexed: 11/21/2022]
Abstract
Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.
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Affiliation(s)
- Ali Khaleghi
- Psychiatry and Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Mohammadi
- Psychiatry and Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kian Shahi
- Psychiatry and Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran
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Abstract
Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Peter F Hitchcock
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912, USA; ,
| | - Eiko I Fried
- Department of Clinical Psychology, Leiden University, 2333 AK Leiden, The Netherlands;
| | - Michael J Frank
- Department of Cognitive, Linguistic, and Psychological Sciences, Brown University, Providence, Rhode Island 02912, USA; , .,Carney Institute for Brain Science, Brown University, Providence, Rhode Island 02192
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12
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Weigard AS, Brislin SJ, Cope LM, Hardee JE, Martz ME, Ly A, Zucker RA, Sripada C, Heitzeg MM. Evidence accumulation and associated error-related brain activity as computationally-informed prospective predictors of substance use in emerging adulthood. Psychopharmacology (Berl) 2021; 238:2629-2644. [PMID: 34173032 PMCID: PMC8452274 DOI: 10.1007/s00213-021-05885-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/27/2021] [Indexed: 01/05/2023]
Abstract
RATIONALE Substance use peaks during the developmental period known as emerging adulthood (ages 18-25), but not every individual who uses substances during this period engages in frequent or problematic use. Although individual differences in neurocognition appear to predict use severity, mechanistic neurocognitive risk factors with clear links to both behavior and neural circuitry have yet to be identified. Here, we aim to do so with an approach rooted in computational psychiatry, an emerging field in which formal models are used to identify candidate biobehavioral dimensions that confer risk for psychopathology. OBJECTIVES We test whether lower efficiency of evidence accumulation (EEA), a computationally characterized individual difference variable that drives performance on the go/no-go and other neurocognitive tasks, is a risk factor for substance use in emerging adults. METHODS AND RESULTS In an fMRI substudy within a sociobehavioral longitudinal study (n = 106), we find that lower EEA and reductions in a robust neural-level correlate of EEA (error-related activations in salience network structures) measured at ages 18-21 are both prospectively related to greater substance use during ages 22-26, even after adjusting for other well-known risk factors. Results from Bayesian model comparisons corroborated inferences from conventional hypothesis testing and provided evidence that both EEA and its neuroimaging correlates contain unique predictive information about substance use involvement. CONCLUSIONS These findings highlight EEA as a computationally characterized neurocognitive risk factor for substance use during a critical developmental period, with clear links to both neuroimaging measures and well-established formal theories of brain function.
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Affiliation(s)
- Alexander S Weigard
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA.
| | - Sarah J Brislin
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Lora M Cope
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Jillian E Hardee
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Meghan E Martz
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Alexander Ly
- Department of Psychological Methods, University of Amsterdam, Amsterdam, The Netherlands
- Machine Learning Group, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Robert A Zucker
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
| | - Mary M Heitzeg
- Department of Psychiatry, University of Michigan, Rachel Upjohn Building, 4250 Plymouth Road, Ann Arbor, MI, 48109, USA
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13
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Pedersen ML, Ironside M, Amemori KI, McGrath CL, Kang MS, Graybiel AM, Pizzagalli DA, Frank MJ. Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder. PLoS Comput Biol 2021; 17:e1008955. [PMID: 33970903 PMCID: PMC8136861 DOI: 10.1371/journal.pcbi.1008955] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 05/20/2021] [Accepted: 04/09/2021] [Indexed: 11/19/2022] Open
Abstract
Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders.
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Affiliation(s)
- Mads L. Pedersen
- Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, Rhode Island, United States of America
- Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Maria Ironside
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Boston, Massachusetts, United States of America
| | - Ken-ichi Amemori
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Massachusetts, United States of America
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Hakubi Center for Advanced Research, Kyoto University, Kyoto, Japan
- Primate Research Institute, Kyoto University, Aichi, Japan
| | - Callie L. McGrath
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Boston, Massachusetts, United States of America
| | - Min S. Kang
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Boston, Massachusetts, United States of America
| | - Ann M. Graybiel
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Massachusetts, United States of America
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Diego A. Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, United States of America
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Boston, Massachusetts, United States of America
- McLean Imaging Center, McLean Hospital, Boston, Massachusetts, United States of America
| | - Michael J. Frank
- Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, Rhode Island, United States of America
- Carney Institute for Brain Science, Brown University, Providence, Rhode Island, United States of America
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14
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Quantifying mechanisms of cognition with an experiment and modeling ecosystem. Behav Res Methods 2021; 53:1833-1856. [PMID: 33604839 DOI: 10.3758/s13428-020-01534-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2020] [Indexed: 11/08/2022]
Abstract
Although there have been major strides toward uncovering the neurobehavioral mechanisms involved in cognitive functions like memory and decision making, methods for measuring behavior and accessing latent processes through computational means remain limited. To this end, we have created SUPREME (Sensing to Understanding and Prediction Realized via an Experiment and Modeling Ecosystem): a toolbox for comprehensive cognitive assessment, provided by a combination of construct-targeted tasks and corresponding computational models. SUPREME includes four tasks, each developed symbiotically with a mechanistic model, which together provide quantified assessments of perception, cognitive control, declarative memory, reward valuation, and frustrative nonreward. In this study, we provide validation analyses for each task using two sessions of data from a cohort of cognitively normal participants (N = 65). Measures of test-retest reliability (r: 0.58-0.75), stability of individual differences (ρ: 0.56-0.70), and internal consistency (α: 0.80-0.86) support the validity of our tasks. After fitting the models to data from individual subjects, we demonstrate each model's ability to capture observed patterns of behavioral results across task conditions. Our computational approaches allow us to decompose behavior into cognitively interpretable subprocesses, which we can compare both within and between participants. We discuss potential future applications of SUPREME, including clinical assessments, longitudinal tracking of cognitive functions, and insight into compensatory mechanisms.
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15
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Cutsuridis V, Jiang S, Dunn MJ, Rosser A, Brawn J, Erichsen JT. Neural modeling of antisaccade performance of healthy controls and early Huntington's disease patients. CHAOS (WOODBURY, N.Y.) 2021; 31:013121. [PMID: 33754760 DOI: 10.1063/5.0021584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 12/14/2020] [Indexed: 06/12/2023]
Abstract
Huntington's disease (HD), a genetically determined neurodegenerative disease, is positively correlated with eye movement abnormalities in decision making. The antisaccade conflict paradigm has been widely used to study response inhibition in eye movements, and reliable performance deficits in HD subjects have been observed, including a greater number and timing of direction errors. We recorded the error rates and response latencies of early HD patients and healthy age-matched controls performing the mirror antisaccade task. HD participants displayed slower and more variable antisaccade latencies and increased error rates relative to healthy controls. A competitive accumulator-to-threshold neural model was then employed to quantitatively simulate the controls' and patients' reaction latencies and error rates and uncover the mechanisms giving rise to the observed HD antisaccade deficits. Our simulations showed that (1) a more gradual and noisy rate of accumulation of evidence by HD patients is responsible for the observed prolonged and more variable antisaccade latencies in early HD; (2) the confidence level of early HD patients making a decision is unaffected by the disease; and (3) the antisaccade performance of healthy controls and early HD patients is the end product of a neural lateral competition (inhibition) between a correct and an erroneous decision process, and not the end product of a third top-down stop signal suppressing the erroneous decision process as many have speculated.
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Affiliation(s)
- Vassilis Cutsuridis
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, United Kingdom
| | - Shouyong Jiang
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, United Kingdom
| | - Matt J Dunn
- School of Optometry and Vision Sciences, Cardiff University, Cardiff CF24 4HQ, United Kingdom
| | - Anne Rosser
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff CF14 4XN, United Kingdom
| | - James Brawn
- School of Optometry and Vision Sciences, Cardiff University, Cardiff CF24 4HQ, United Kingdom
| | - Jonathan T Erichsen
- School of Optometry and Vision Sciences, Cardiff University, Cardiff CF24 4HQ, United Kingdom
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16
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Browning M, Carter CS, Chatham C, Den Ouden H, Gillan CM, Baker JT, Chekroud AM, Cools R, Dayan P, Gold J, Goldstein RZ, Hartley CA, Kepecs A, Lawson RP, Mourao-Miranda J, Phillips ML, Pizzagalli DA, Powers A, Rindskopf D, Roiser JP, Schmack K, Schiller D, Sebold M, Stephan KE, Frank MJ, Huys Q, Paulus M. Realizing the Clinical Potential of Computational Psychiatry: Report From the Banbury Center Meeting, February 2019. Biol Psychiatry 2020; 88:e5-e10. [PMID: 32113656 DOI: 10.1016/j.biopsych.2019.12.026] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 12/30/2019] [Accepted: 12/30/2019] [Indexed: 12/31/2022]
Affiliation(s)
- Michael Browning
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom; Oxford Health National Health Service Foundation Trust, Warneford Hospital, Oxford, United Kingdom.
| | - Cameron S Carter
- Department of Psychiatry, University of California, Davis, Davis, California; Department of Psychology, University of California, Davis, Davis, California
| | - Christopher Chatham
- Department of Neuroscience and Rare Diseases, Roche Pharma Research and Early Development, Roche Innovation Center, Basel, Switzerland
| | - Hanneke Den Ouden
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Claire M Gillan
- School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Justin T Baker
- McLean Institute for Technology in Psychiatry, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | | | - Roshan Cools
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter Dayan
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - James Gold
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, Maryland
| | - Rita Z Goldstein
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, New York
| | - Rebecca P Lawson
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Janaina Mourao-Miranda
- Centre for Medical Image Computing, University College London, London, United Kingdom; Department of Computer Science, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Diego A Pizzagalli
- Department of Psychiatry, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Albert Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
| | - David Rindskopf
- Educational Psychology, Graduate School and University Center of the City University of New York, New York, New York
| | - Jonathan P Roiser
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
| | - Katharina Schmack
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, New York
| | - Daniela Schiller
- Department of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Miriam Sebold
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Klaas Enno Stephan
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Michael J Frank
- J. & Nancy D. Carney Institute for Brain Science, Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, Rhode Island
| | - Quentin Huys
- Department of Computer Science, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom; Division of Psychiatry, University College London, London, United Kingdom; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich, Zurich, Switzerland; Eidgenössische Technische Hochschule Zürich, Zurich, Switzerland
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, Oklahoma
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17
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Frässle S, Marquand AF, Schmaal L, Dinga R, Veltman DJ, van der Wee NJA, van Tol MJ, Schöbi D, Penninx BWJH, Stephan KE. Predicting individual clinical trajectories of depression with generative embedding. NEUROIMAGE-CLINICAL 2020; 26:102213. [PMID: 32197140 PMCID: PMC7082217 DOI: 10.1016/j.nicl.2020.102213] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/27/2020] [Accepted: 02/13/2020] [Indexed: 12/11/2022]
Abstract
Patients with major depressive disorder (MDD) show variable clinical trajectories. Generative embedding (GE) is used to predict clinical trajectories in MDD patients. GE classifies patients with chronic depression vs. fast remission with 79% accuracy. GE provides mechanistic interpretability and outperforms conventional measures. Proof-of-concept that illustrates the potential of GE for clinical prediction.
Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly variable clinical trajectories: while some patients experience swift recovery, others show relapsing-remitting or chronic courses. Predicting individual clinical trajectories at an early stage is a key challenge for psychiatry and might facilitate individually tailored interventions. So far, however, reliable predictors at the single-patient level are absent. Here, we evaluated the utility of a machine learning strategy – generative embedding (GE) – which combines interpretable generative models with discriminative classifiers. Specifically, we used functional magnetic resonance imaging (fMRI) data of emotional face perception in 85 MDD patients from the NEtherlands Study of Depression and Anxiety (NESDA) who had been followed up over two years and classified into three subgroups with distinct clinical trajectories. Combining a generative model of effective (directed) connectivity with support vector machines (SVMs), we could predict whether a given patient would experience chronic depression vs. fast remission with a balanced accuracy of 79%. Gradual improvement vs. fast remission could still be predicted above-chance, but less convincingly, with a balanced accuracy of 61%. Generative embedding outperformed classification based on conventional (descriptive) features, such as functional connectivity or local activation estimates, which were obtained from the same data and did not allow for above-chance classification accuracy. Furthermore, predictive performance of GE could be assigned to a specific network property: the trial-by-trial modulation of connections by emotional content. Given the limited sample size of our study, the present results are preliminary but may serve as proof-of-concept, illustrating the potential of GE for obtaining clinical predictions that are interpretable in terms of network mechanisms. Our findings suggest that abnormal dynamic changes of connections involved in emotional face processing might be associated with higher risk of developing a less favorable clinical course.
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Affiliation(s)
- Stefan Frässle
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich 8032, Switzerland.
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom
| | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Richard Dinga
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Dick J Veltman
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands
| | - Nic J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden University, Leiden, The Netherlands
| | - Marie-José van Tol
- Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Dario Schöbi
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich 8032, Switzerland
| | - Brenda W J H Penninx
- Department of Psychiatry and Neuroscience Campus Amsterdam, VU University Medical Center Amsterdam, Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam UMC, VU University, and Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Klaas E Stephan
- Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich 8032, Switzerland; Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3BG, United Kingdom; Max Planck Institute for Metabolism Research, Cologne, Germany
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18
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García-Gutiérrez MS, Navarrete F, Sala F, Gasparyan A, Austrich-Olivares A, Manzanares J. Biomarkers in Psychiatry: Concept, Definition, Types and Relevance to the Clinical Reality. Front Psychiatry 2020; 11:432. [PMID: 32499729 PMCID: PMC7243207 DOI: 10.3389/fpsyt.2020.00432] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/28/2020] [Indexed: 12/12/2022] Open
Abstract
During the last years, an extraordinary effort has been made to identify biomarkers as potential tools for improving prevention, diagnosis, drug response and drug development in psychiatric disorders. Contrary to other diseases, mental illnesses are classified by diagnostic categories with a broad variety list of symptoms. Consequently, patients diagnosed from the same psychiatric illness present a great heterogeneity in their clinical presentation. This fact together with the incomplete knowledge of the neurochemical alterations underlying mental disorders, contribute to the limited efficacy of current pharmacological options. In this respect, the identification of biomarkers in psychiatry is becoming essential to facilitate diagnosis through the developing of markers that allow to stratify groups within the syndrome, which in turn may lead to more focused treatment options. In order to shed light on this issue, this review summarizes the concept and types of biomarkers including an operational definition for therapeutic development. Besides, the advances in this field were summarized and sorted into five categories, which include genetics, transcriptomics, proteomics, metabolomics, and epigenetics. While promising results were achieved, there is a lack of biomarker investigations especially related to treatment response to psychiatric conditions. This review includes a final conclusion remarking the future challenges required to reach the goal of developing valid, reliable and broadly-usable biomarkers for psychiatric disorders and their treatment. The identification of factors predicting treatment response will reduce trial-and-error switches of medications facilitating the discovery of new effective treatments, being a crucial step towards the establishment of greater personalized medicine.
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Affiliation(s)
- Maria Salud García-Gutiérrez
- Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Alicante, Spain.,Red Temática de Investigación Cooperativa en Salud (RETICS), Red de Trastornos Adictivos, Instituto de Salud Carlos III, MICINN and FEDER, Madrid, Spain
| | - Francisco Navarrete
- Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Alicante, Spain.,Red Temática de Investigación Cooperativa en Salud (RETICS), Red de Trastornos Adictivos, Instituto de Salud Carlos III, MICINN and FEDER, Madrid, Spain
| | - Francisco Sala
- Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Alicante, Spain
| | - Ani Gasparyan
- Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Alicante, Spain.,Red Temática de Investigación Cooperativa en Salud (RETICS), Red de Trastornos Adictivos, Instituto de Salud Carlos III, MICINN and FEDER, Madrid, Spain
| | | | - Jorge Manzanares
- Instituto de Neurociencias, Universidad Miguel Hernández-CSIC, Alicante, Spain.,Red Temática de Investigación Cooperativa en Salud (RETICS), Red de Trastornos Adictivos, Instituto de Salud Carlos III, MICINN and FEDER, Madrid, Spain
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19
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Tovar A, Garí Soler A, Ruiz-Idiago J, Mareca Viladrich C, Pomarol-Clotet E, Rosselló J, Hinzen W. Language disintegration in spontaneous speech in Huntington's disease: a more fine-grained analysis. JOURNAL OF COMMUNICATION DISORDERS 2020; 83:105970. [PMID: 32062158 DOI: 10.1016/j.jcomdis.2019.105970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 11/26/2019] [Accepted: 12/01/2019] [Indexed: 06/10/2023]
Abstract
Huntington's disease (HD) is a neurodegenerative disease causing motor symptoms along with cognitive and affective problems. Recent evidence suggests that HD also affects language across core levels of linguistic organization, including at stages of the disease when standardized neuropsychological test profiles are still normal and motor symptoms do not yet reach clinical thresholds ('pre-manifest HD'). The present study aimed to subject spontaneous speech to a more fine-grained linguistic analysis in a sample of 20 identified HD gene-carriers, 10 with pre-manifest and 10 with early manifest HD. We further explored how language performance related to non-linguistic cognitive impairment, using standardized neuropsychological measures. A distinctive pattern of linguistic impairments marked off participants with both pre-manifest and manifest HD from healthy controls and each other. Fluency patterns in premanifest HD were marked by prolongations, filled pauses, and repetitions, which shifted to a pattern marked by empty (unfilled) pauses, re-phrasings, and truncations in manifest HD. Both HD groups also significantly differed from controls and each other in how they grammatically connected clauses and used noun phrases referentially. Functional deficits in language occurred in pre-manifest HD in the absence of any non-linguistic neuropsychological impairment and did largely not correlate with standardized neuropsychological measures in manifest HD. These results further corroborate that language can act as a fine-grained clinical marker in HD, which can track disease progression from the pre-manifest stage, define critical remediation targets, and inform the role of the basal ganglia in language processing.
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Affiliation(s)
- Antonia Tovar
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Jesús Ruiz-Idiago
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain; Neuropsychiatry Unit, Hospital Mare de Déu de la Mercè, Barcelona, Spain; FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
| | - Celia Mareca Viladrich
- Neuropsychiatry Unit, Hospital Mare de Déu de la Mercè, Barcelona, Spain; FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
| | | | - Joana Rosselló
- Department of Catalan Philology and General Linguistics, Universitat de Barcelona, Barcelona, Spain
| | - Wolfram Hinzen
- Department of Translation and Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain; FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain; ICREA (Catalan Institution for Research and Advanced Studies), Barcelona, Spain.
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20
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Weigard AS, Sathian K, Hampstead BM. Model-based assessment and neural correlates of spatial memory deficits in mild cognitive impairment. Neuropsychologia 2020; 136:107251. [PMID: 31698011 PMCID: PMC7218757 DOI: 10.1016/j.neuropsychologia.2019.107251] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 10/28/2019] [Accepted: 10/30/2019] [Indexed: 01/13/2023]
Abstract
Mild cognitive impairment (MCI) is characterized by subjective and objective memory impairments within the context of generally intact everyday functioning. Such memory deficits are typically thought to arise from medial temporal lobe dysfunction; however, differences in memory task performance can arise from a variety of altered processes (e.g., strategy adjustments) rather than, or in addition to, "pure" memory deficits. To address this problem, we applied the linear ballistic accumulator (LBA: Brown and Heathcote, 2008) model to data from individuals with MCI (n = 18) and healthy older adults (HOA; n = 16) who performed an object-location association memory retrieval task during functional magnetic resonance imaging (fMRI). The primary goals were to 1) assess between-group differences in model parameters indexing processes of interest (memory sensitivity, accumulation speed, caution and time spent on peripheral perceptual and motor processes) and 2) determine whether differences in model-based metrics were consistent with fMRI data. The LBA provided evidence that, relative to the HOA group, those with MCI displayed lower sensitivity (i.e., difficulty discriminating targets from lures), suggestive of memory impairment, and displayed higher evidence accumulation speed and greater caution, suggestive of increased arousal and strategic changes in this group, although these changes had little impact on MCI-related accuracy differences. Consistent with these findings, fMRI revealed reduced activation in brain regions previously linked to evidence accumulation and to the implementation of caution reductions in the MCI group. Findings suggest that multiple cognitive mechanisms differ during memory retrieval in MCI, and that these mechanisms may explain neuroimaging alterations outside of the medial temporal lobes.
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Affiliation(s)
- Alexander S Weigard
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; Addiction Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - K Sathian
- Department of Neurology, Penn State College of Medicine, Hershey, PA, USA; Department of Neural and Behavioral Sciences, Penn State College of Medicine, Hershey, PA, USA; Psychology Department, Penn State University, University Park, PA, USA
| | - Benjamin M Hampstead
- Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA; Neuropsychology Section, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
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21
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The effect of impulsivity and inhibitory control deficits in the saccadic behavior of premanifest Huntington's disease individuals. Orphanet J Rare Dis 2019; 14:246. [PMID: 31703597 PMCID: PMC6839196 DOI: 10.1186/s13023-019-1218-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 10/09/2019] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND This study aims to test response inhibition in premanifest Huntington's disease individuals (Pre-HD), in the context of a saccadic paradigm with working memory demands and fronto-executive load as a way to measure inhibitory control deficits and impulsive behavior in Huntington's disease (HD). METHODS The oculomotor function of 15 Pre-HD and 22 Control individuals was assessed using an experimental paradigm comprising four horizontal saccadic tasks: prosaccade (PS), antisaccade (AS), 1- or 2-back memory prosaccade (MPS), and 1- or 2-back memory antisaccade (MAS). Success rate, latency, directional and timing errors were calculated for each task. A comprehensive battery of neuropsychological tests was also used to assess the overall cognitive functioning of study participants. Statistical correlations between oculomotor, clinical and cognitive measures were computed for the Pre-HD group. RESULTS Pre-HD participants showed reduced success rate in the AS task, increased direction errors in the AS and MAS tasks and decreased latency in the MAS task when compared to Controls, despite presenting similar executive and memory scores in the conventional neuropsychological tests applied. Significant associations were identified between specific AS and MAS parameters and disease-related measures, cognitive skills and other oculomotor results of Pre-HD participants. CONCLUSIONS Our results show that oculomotor performance in premanifest Huntington's disease deteriorates once inhibitory control, working memory and/or fronto-executive load are added to the task. A more automatic pattern of performance, including a faster response time and directionally erroneous eye movements were detected in the oculomotor behavior of the Pre-HD group-these alterations were significantly correlated with disease stage and cognitive status. Our saccadic paradigm was able to capture impulsivity and inhibitory control deficits in a group of Pre-HD individuals on average far from symptom onset, thus holding the potential to identify the earliest disease-related changes.
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22
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Abstract
Recent applications of eye tracking for diagnosis, prognosis and follow-up of therapy in age-related neurological or psychological deficits have been reviewed. The review is focused on active aging, neurodegeneration and cognitive impairments. The potential impacts and current limitations of using characterizing features of eye movements and pupillary responses (oculometrics) as objective biomarkers in the context of aging are discussed. A closer look into the findings, especially with respect to cognitive impairments, suggests that eye tracking is an invaluable technique to study hidden aspects of aging that have not been revealed using any other noninvasive tool. Future research should involve a wider variety of oculometrics, in addition to saccadic metrics and pupillary responses, including nonlinear and combinatorial features as well as blink- and fixation-related metrics to develop biomarkers to trace age-related irregularities associated with cognitive and neural deficits.
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Affiliation(s)
- Ramtin Z Marandi
- Department of Health Science & Technology, Aalborg University, Aalborg E 9220, Denmark
| | - Parisa Gazerani
- Department of Health Science & Technology, Aalborg University, Aalborg E 9220, Denmark
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23
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Salinas E, Steinberg BR, Sussman LA, Fry SM, Hauser CK, Anderson DD, Stanford TR. Voluntary and involuntary contributions to perceptually guided saccadic choices resolved with millisecond precision. eLife 2019; 8:46359. [PMID: 31225794 PMCID: PMC6645714 DOI: 10.7554/elife.46359] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Accepted: 06/20/2019] [Indexed: 11/13/2022] Open
Abstract
In the antisaccade task, which is considered a sensitive assay of cognitive function, a salient visual cue appears and the participant must look away from it. This requires sensory, motor-planning, and cognitive neural mechanisms, but what are their unique contributions to performance, and when exactly are they engaged? Here, by manipulating task urgency, we generate a psychophysical curve that tracks the evolution of the saccadic choice process with millisecond precision, and resolve the distinct contributions of reflexive (exogenous) and voluntary (endogenous) perceptual mechanisms to antisaccade performance over time. Both progress extremely rapidly, the former driving the eyes toward the cue early on (∼100 ms after cue onset) and the latter directing them away from the cue ∼40 ms later. The behavioral and modeling results provide a detailed, dynamical characterization of attentional and oculomotor capture that is not only qualitatively consistent across participants, but also indicative of their individual perceptual capacities.
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Affiliation(s)
- Emilio Salinas
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Benjamin R Steinberg
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Lauren A Sussman
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Sophia M Fry
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Christopher K Hauser
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Denise D Anderson
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
| | - Terrence R Stanford
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston-Salem, United States
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24
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Cabanski C, Gilbert H, Mosesova S. Can Graphics Tell Lies? A Tutorial on How To Visualize Your Data. Clin Transl Sci 2018; 11:371-377. [PMID: 29603646 PMCID: PMC6039197 DOI: 10.1111/cts.12554] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2017] [Accepted: 03/12/2017] [Indexed: 11/30/2022] Open
Abstract
Visualizations are a powerful tool for telling a story about a data set or analysis. If done correctly, visualizations not only display data but also help the audience digest key information. However, if done haphazardly, visualization has the potential to confuse the audience and, in the most extreme circumstances, deceive. In this tutorial, we provide a set of general principles for creating informative visualizations that tell a complete and accurate story of the data.
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Affiliation(s)
| | | | - Sofia Mosesova
- Denali Therapeutics Inc, South San Francisco, California, USA
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25
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FitzGerald JJ, Lu Z, Jareonsettasin P, Antoniades CA. Quantifying Motor Impairment in Movement Disorders. Front Neurosci 2018; 12:202. [PMID: 29695949 PMCID: PMC5904266 DOI: 10.3389/fnins.2018.00202] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2017] [Accepted: 03/14/2018] [Indexed: 02/05/2023] Open
Abstract
Until recently the assessment of many movement disorders has relied on clinical rating scales that despite careful design are inherently subjective and non-linear. This makes accurate and truly observer-independent quantification difficult and limits the use of sensitive parametric statistical methods. At last, devices capable of measuring neurological problems quantitatively are becoming readily available. Examples include the use of oculometers to measure eye movements and accelerometers to measure tremor. Many applications are being developed for use on smartphones. The benefits include not just more accurate disease quantification, but also consistency of data for longitudinal studies, accurate stratification of patients for entry into trials, and the possibility of automated data capture for remote follow-up. In this mini review, we will look at movement disorders with a particular focus on Parkinson's disease, describe some of the limitations of existing clinical evaluation tools, and illustrate the ways in which objective metrics have already been successful.
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Affiliation(s)
- James J FitzGerald
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom
| | - Zhongjiao Lu
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Department of Neurology, West China Hospital of Medicine, Sichuan University, Chengdu, China
| | - Prem Jareonsettasin
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.,Exeter College, University of Oxford, Oxford, United Kingdom
| | - Chrystalina A Antoniades
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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26
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ANN and Fuzzy Logic Based Model to Evaluate Huntington Disease Symptoms. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:4581272. [PMID: 29713439 PMCID: PMC5866873 DOI: 10.1155/2018/4581272] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 01/24/2018] [Indexed: 12/03/2022]
Abstract
We introduce an approach to predict deterioration of reaction state for people having neurological movement disorders such as hand tremors and nonvoluntary movements. These involuntary motor features are closely related to the symptoms occurring in patients suffering from Huntington's disease (HD). We propose a hybrid (neurofuzzy) model that combines an artificial neural network (ANN) to predict the functional capacity level (FCL) of a person and a fuzzy logic system (FLS) to determine a stage of reaction. We analyzed our own dataset of 3032 records collected from 20 test subjects (both healthy and HD patients) using smart phones or tablets by asking a patient to locate circular objects on the device's screen. We describe the preparation and labelling of data for the neural network, selection of training algorithms, modelling of the fuzzy logic controller, and construction and implementation of the hybrid model. The feed-forward backpropagation (FFBP) neural network achieved the regression R value of 0.98 and mean squared error (MSE) values of 0.08, while the FLS provides a final evaluation of subject's reaction condition in terms of FCL.
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27
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Bayesian Approaches to Learning and Decision-Making. COMPUTATIONAL PSYCHIATRY 2018. [DOI: 10.1016/b978-0-12-809825-7.00010-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
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28
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Hassanzadeh P, Atyabi F, Dinarvand R. Application of modelling and nanotechnology-based approaches: The emergence of breakthroughs in theranostics of central nervous system disorders. Life Sci 2017; 182:93-103. [DOI: 10.1016/j.lfs.2017.06.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Revised: 05/30/2017] [Accepted: 06/01/2017] [Indexed: 01/28/2023]
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29
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Knolle F, McBride SD, Stewart JE, Goncalves RP, Morton AJ. A stop-signal task for sheep: introduction and validation of a direct measure for the stop-signal reaction time. Anim Cogn 2017; 20:615-626. [PMID: 28389761 PMCID: PMC5486475 DOI: 10.1007/s10071-017-1085-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2016] [Revised: 01/11/2017] [Accepted: 03/28/2017] [Indexed: 12/02/2022]
Abstract
Huntington's disease (HD) patients show reduced flexibility in inhibiting an already-started response. This can be quantified by the stop-signal task. The aim of this study was to develop and validate a sheep version of the stop-signal task that would be suitable for monitoring the progression of cognitive decline in a transgenic sheep model of HD. Using a semi-automated operant system, sheep were trained to perform in a two-choice discrimination task. In 22% of the trials, a stop-signal was presented. Upon the stop-signal presentation, the sheep had to inhibit their already-started response. The stopping behaviour was captured using an accelerometer mounted on the back of the sheep. This set-up provided a direct read-out of the individual stop-signal reaction time (SSRT). We also estimated the SSRT using the conventional approach of subtracting the stop-signal delay (i.e., time after which the stop-signal is presented) from the ranked reaction time during a trial without a stop-signal. We found that all sheep could inhibit an already-started response in 91% of the stop-trials. The directly measured SSRT (0.974 ± 0.04 s) was not significantly different from the estimated SSRT (0.938 ± 0.04 s). The sheep version of the stop-signal task adds to the repertoire of tests suitable for investigating both cognitive dysfunction and efficacy of therapeutic agents in sheep models of neurodegenerative disease such as HD, as well as neurological conditions such as attention deficit hyperactivity disorder.
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Affiliation(s)
- Franziska Knolle
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK
| | - Sebastian D McBride
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Penglais, Aberystwyth, Ceredigion, SY23 4SD, UK
| | - James E Stewart
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK
| | - Rita P Goncalves
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK
| | - A Jennifer Morton
- Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3DY, UK.
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30
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Hassani SA, Oemisch M, Balcarras M, Westendorff S, Ardid S, van der Meer MA, Tiesinga P, Womelsdorf T. A computational psychiatry approach identifies how alpha-2A noradrenergic agonist Guanfacine affects feature-based reinforcement learning in the macaque. Sci Rep 2017; 7:40606. [PMID: 28091572 PMCID: PMC5238510 DOI: 10.1038/srep40606] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Accepted: 12/08/2016] [Indexed: 01/05/2023] Open
Abstract
Noradrenaline is believed to support cognitive flexibility through the alpha 2A noradrenergic receptor (a2A-NAR) acting in prefrontal cortex. Enhanced flexibility has been inferred from improved working memory with the a2A-NA agonist Guanfacine. But it has been unclear whether Guanfacine improves specific attention and learning mechanisms beyond working memory, and whether the drug effects can be formalized computationally to allow single subject predictions. We tested and confirmed these suggestions in a case study with a healthy nonhuman primate performing a feature-based reversal learning task evaluating performance using Bayesian and Reinforcement learning models. In an initial dose-testing phase we found a Guanfacine dose that increased performance accuracy, decreased distractibility and improved learning. In a second experimental phase using only that dose we examined the faster feature-based reversal learning with Guanfacine with single-subject computational modeling. Parameter estimation suggested that improved learning is not accounted for by varying a single reinforcement learning mechanism, but by changing the set of parameter values to higher learning rates and stronger suppression of non-chosen over chosen feature information. These findings provide an important starting point for developing nonhuman primate models to discern the synaptic mechanisms of attention and learning functions within the context of a computational neuropsychiatry framework.
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Affiliation(s)
- S. A. Hassani
- Department of Biology, Centre for Vision Research, York University, Toronto, Ontario M6J 1P3, Canada
| | - M. Oemisch
- Department of Biology, Centre for Vision Research, York University, Toronto, Ontario M6J 1P3, Canada
| | - M. Balcarras
- Department of Biology, Centre for Vision Research, York University, Toronto, Ontario M6J 1P3, Canada
| | - S. Westendorff
- Department of Biology, Centre for Vision Research, York University, Toronto, Ontario M6J 1P3, Canada
| | - S. Ardid
- Department of Mathematics, Boston University, Boston, MA 02215, USA
| | - M. A. van der Meer
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA
| | - P. Tiesinga
- Department of Neuroinformatics, Donders Centre for Neuroscience, Radboud University Nijmegen, Nijmegen, AJ 6525, The Netherlands
| | - T. Womelsdorf
- Department of Biology, Centre for Vision Research, York University, Toronto, Ontario M6J 1P3, Canada
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31
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Huys QJM, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci 2016; 19:404-13. [PMID: 26906507 DOI: 10.1038/nn.4238] [Citation(s) in RCA: 496] [Impact Index Per Article: 62.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 01/04/2016] [Indexed: 12/12/2022]
Abstract
Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.
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Affiliation(s)
- Quentin J M Huys
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zürich and Swiss Federal Institute of Technology (ETH) Zürich, Zürich, Switzerland.,Centre for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zürich, Zürich, Switzerland
| | - Tiago V Maia
- School of Medicine and Institute for Molecular Medicine, University of Lisbon, Lisbon, Portugal
| | - Michael J Frank
- Computation in Brain and Mind, Brown Institute for Brain Science, Psychiatry and Human Behavior, Brown University, Providence, USA
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