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Gui A, Throm E, da Costa PF, Penza F, Aguiló Mayans M, Jordan-Barros A, Haartsen R, Leech R, Jones EJH. Neuroadaptive Bayesian optimisation to study individual differences in infants' engagement with social cues. Dev Cogn Neurosci 2024; 68:101401. [PMID: 38870603 DOI: 10.1016/j.dcn.2024.101401] [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: 02/12/2024] [Revised: 05/31/2024] [Accepted: 06/01/2024] [Indexed: 06/15/2024] Open
Abstract
Infants' motivation to engage with the social world depends on the interplay between individual brain's characteristics and previous exposure to social cues such as the parent's smile or eye contact. Different hypotheses about why specific combinations of emotional expressions and gaze direction engage children have been tested with group-level approaches rather than focusing on individual differences in the social brain development. Here, a novel Artificial Intelligence-enhanced brain-imaging approach, Neuroadaptive Bayesian Optimisation (NBO), was applied to infant electro-encephalography (EEG) to understand how selected neural signals encode social cues in individual infants. EEG data from 42 6- to 9-month-old infants looking at images of their parent's face were analysed in real-time and used by a Bayesian Optimisation algorithm to identify which combination of the parent's gaze/head direction and emotional expression produces the strongest brain activation in the child. This individualised approach supported the theory that the infant's brain is maximally engaged by communicative cues with a negative valence (angry faces with direct gaze). Infants attending preferentially to faces with direct gaze had increased positive affectivity and decreased negative affectivity. This work confirmed that infants' attentional preferences for social cues are heterogeneous and shows the NBO's potential to study diversity in neurodevelopmental trajectories.
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Affiliation(s)
- A Gui
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom; Department of Psychology, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, United Kingdom.
| | - E Throm
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - P F da Costa
- Department of Neuroimaging, Institute of Psychiatry, Psychology and, Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - F Penza
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - M Aguiló Mayans
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - A Jordan-Barros
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - R Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
| | - R Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and, Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - E J H Jones
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Malet Street, London WC1E 7HX, United Kingdom
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2
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Vidaurre D. A generative model of electrophysiological brain responses to stimulation. eLife 2024; 12:RP87729. [PMID: 38231034 PMCID: PMC10945576 DOI: 10.7554/elife.87729] [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] [Indexed: 01/18/2024] Open
Abstract
Each brain response to a stimulus is, to a large extent, unique. However this variability, our perceptual experience feels stable. Standard decoding models, which utilise information across several areas to tap into stimuli representation and processing, are fundamentally based on averages. Therefore, they can focus precisely on the features that are most stable across stimulus presentations. But which are these features exactly is difficult to address in the absence of a generative model of the signal. Here, I introduce genephys, a generative model of brain responses to stimulation publicly available as a Python package that, when confronted with a decoding algorithm, can reproduce the structured patterns of decoding accuracy that we observe in real data. Using this approach, I characterise how these patterns may be brought about by the different aspects of the signal, which in turn may translate into distinct putative neural mechanisms. In particular, the model shows that the features in the data that support successful decoding-and, therefore, likely reflect stable mechanisms of stimulus representation-have an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory component. At the individual trial level, still, responses are found to be highly variable, which can be due to various factors including phase noise and probabilistic activations.
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Affiliation(s)
- Diego Vidaurre
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus UniversityAarhusDenmark
- Department of Psychiatry, Oxford UniversityOxfordUnited Kingdom
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3
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Pattison AJ, Pedroso CCS, Cohen BE, Ondry JC, Alivisatos AP, Theis W, Ercius P. Advanced techniques in automated high-resolution scanning transmission electron microscopy. NANOTECHNOLOGY 2023; 35:015710. [PMID: 37703845 DOI: 10.1088/1361-6528/acf938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 09/12/2023] [Indexed: 09/15/2023]
Abstract
Scanning transmission electron microscopy is a common tool used to study the atomic structure of materials. It is an inherently multimodal tool allowing for the simultaneous acquisition of multiple information channels. Despite its versatility, however, experimental workflows currently rely heavily on experienced human operators and can only acquire data from small regions of a sample at a time. Here, we demonstrate a flexible pipeline-based system for high-throughput acquisition of atomic-resolution structural data using an all-piezo sample stage applied to large-scale imaging of nanoparticles and multimodal data acquisition. The system is available as part of the user program of the Molecular Foundry at Lawrence Berkeley National Laboratory.
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Affiliation(s)
- Alexander J Pattison
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, United States of America
| | - Cassio C S Pedroso
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, United States of America
| | - Bruce E Cohen
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, United States of America
- Division of Molecular Biophysics & Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States of America
| | - Justin C Ondry
- Department of Chemistry, University of California, Berkeley, CA, United States of America
- Kavli Energy NanoScience Institute, Berkeley, CA, United States of America
- Department of Chemistry and Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, United States of America
| | - A Paul Alivisatos
- Department of Chemistry, University of California, Berkeley, CA, United States of America
- Kavli Energy NanoScience Institute, Berkeley, CA, United States of America
- Department of Chemistry and Pritzker School of Molecular Engineering, University of Chicago, Chicago, IL 60637, United States of America
- Material Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
- Department of Materials Science and Engineering, University of California, Berkeley, CA, United States of America
| | - Wolfgang Theis
- School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom
| | - Peter Ercius
- Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, United States of America
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4
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Throm E, Gui A, Haartsen R, da Costa PF, Leech R, Jones EJH. Real-time monitoring of infant theta power during naturalistic social experiences. Dev Cogn Neurosci 2023; 63:101300. [PMID: 37741087 PMCID: PMC10523417 DOI: 10.1016/j.dcn.2023.101300] [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: 03/14/2023] [Revised: 06/30/2023] [Accepted: 09/08/2023] [Indexed: 09/25/2023] Open
Abstract
Infant-directed speech and direct gaze are important social cues that shape infant's attention to their parents. Traditional methods for probing their effect on infant attention involve a small number of pre-selected screen-based stimuli, which do not capture the complexity of real-world interactions. Here, we used neuroadaptive Bayesian Optimization (NBO) to search a large 'space' of different naturalistic social experiences that systematically varied in their visual (gaze direct to averted) and auditory properties (infant directed speech to nonvocal sounds). We measured oscillatory brain responses (relative theta power) during episodes of naturalistic social experiences in 57 typically developing 6- to 12-month-old infants. Relative theta power was used as input to the NBO algorithm to identify the naturalistic social context that maximally elicited attention in each individual infant. Results showed that individual infants were heterogeneous in the stimulus that elicited maximal theta with no overall stronger attention for direct gaze or infant-directed speech; however, individual differences in attention towards averted gaze were related to interpersonal skills and greater likelihood of preferring speech and direct gaze was observed in infants whose parents showed more positive affect. Our work indicates NBO may be a fruitful method for probing the role of distinct social cues in eliciting attention in naturalistic social contexts at the individual level.
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Affiliation(s)
- Elena Throm
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London WC1E 7HX, United Kingdom
| | - Anna Gui
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London WC1E 7HX, United Kingdom
| | - Rianne Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, TodderLab, Malet Street, London WC1E 7HX, United Kingdom
| | - Pedro F da Costa
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, de Crespigny Road, London SE5 8AB, United Kingdom
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Department of Psychological Science, Birkbeck, University of London, Henry Wellcome Building, Malet Street, London WC1E 7HX, United Kingdom.
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5
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Soleimani G, Nitsche MA, Bergmann TO, Towhidkhah F, Violante IR, Lorenz R, Kuplicki R, Tsuchiyagaito A, Mulyana B, Mayeli A, Ghobadi-Azbari P, Mosayebi-Samani M, Zilverstand A, Paulus MP, Bikson M, Ekhtiari H. Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments. Transl Psychiatry 2023; 13:279. [PMID: 37582922 PMCID: PMC10427701 DOI: 10.1038/s41398-023-02565-5] [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: 07/08/2022] [Revised: 07/06/2023] [Accepted: 07/20/2023] [Indexed: 08/17/2023] Open
Abstract
One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.
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Affiliation(s)
- Ghazaleh Soleimani
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Michael A Nitsche
- Department of Psychology and Neuroscience, Leibniz Research Center for Working Environment and Human Factors, Dortmund, Germany
- Bielefeld University, University Hospital OWL, Protestant Hospital of Bethel Foundation, University Clinic of Psychiatry and Psychotherapy, and University Clinic of Child and Adolescent Psychiatry and Psychotherapy, Bielefeld, Germany
| | - Til Ole Bergmann
- Neuroimaging Center, Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
- Leibniz Institute for Resilience Research, Mainz, Germany
| | - Farzad Towhidkhah
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guilford, UK
| | - Romy Lorenz
- Department of Psychology, Stanford University, Stanford, CA, USA
- MRC CBU, University of Cambridge, Cambridge, UK
- Department of Neurophysics, MPI, Leipzig, Germany
| | | | | | - Beni Mulyana
- Laureate Institute for Brain Research, Tulsa, OK, USA
- School of Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, USA
| | - Ahmad Mayeli
- University of Pittsburgh Medical Center, Pittsburg, PA, USA
| | - Peyman Ghobadi-Azbari
- Department of Biomedical Engineering, Shahed University, Tehran, Iran
- Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohsen Mosayebi-Samani
- Department of Psychology and Neuroscience, Leibniz Research Center for Working Environment and Human Factors, Dortmund, Germany
| | - Anna Zilverstand
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
| | | | | | - Hamed Ekhtiari
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
- Laureate Institute for Brain Research, Tulsa, OK, USA.
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6
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Lam SL, Criaud M, Lukito S, Westwood SJ, Agbedjro D, Kowalczyk OS, Curran S, Barret N, Abbott C, Liang H, Simonoff E, Barker GJ, Giampietro V, Rubia K. Double-Blind, Sham-Controlled Randomized Trial Testing the Efficacy of fMRI Neurofeedback on Clinical and Cognitive Measures in Children With ADHD. Am J Psychiatry 2022; 179:947-958. [PMID: 36349428 PMCID: PMC7614456 DOI: 10.1176/appi.ajp.21100999] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Functional MRI neurofeedback (fMRI-NF) could potentially be a novel, safe nonpharmacological treatment for attention deficit hyperactivity disorder (ADHD). A proof-of-concept randomized controlled trial of fMRI-NF of the right inferior frontal cortex (rIFC), compared to an active control condition, showed promising improvement of ADHD symptoms (albeit in both groups) and in brain function. However, comparison with a placebo condition in a larger trial is required to test efficacy. METHODS This double-blind, sham-controlled randomized controlled trial tested the effectiveness and efficacy of fMRI-NF of the rIFC on symptoms and executive functions in 88 boys with ADHD (44 each in the active and sham arms). To investigate treatment-related changes, groups were compared at the posttreatment and 6-month follow-up assessments, controlling for baseline scores, age, and medication status. The primary outcome measure was posttreatment score on the ADHD Rating Scale (ADHD-RS). RESULTS No significant group differences were found on the ADHD-RS. Both groups showed similar decreases in other clinical and cognitive measures, except for a significantly greater decrease in irritability and improvement in motor inhibition in sham relative to active fMRI-NF at the posttreatment assessment, covarying for baseline. There were no significant side effects or adverse events. The active relative to the sham fMRI-NF group showed enhanced activation in rIFC and other frontal and temporo-occipital-cerebellar self-regulation areas. However, there was no progressive rIFC upregulation, correlation with ADHD-RS scores, or transfer of learning. CONCLUSIONS Contrary to the hypothesis, the study findings do not suggest that fMRI-NF of the rIFC is effective in improving clinical symptoms or cognition in boys with ADHD.
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Affiliation(s)
- Sheut-Ling Lam
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Marion Criaud
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Steve Lukito
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Samuel J Westwood
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Deborah Agbedjro
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Olivia S Kowalczyk
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Sarah Curran
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Nadia Barret
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Chris Abbott
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Holan Liang
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Emily Simonoff
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Gareth J Barker
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Vincent Giampietro
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
| | - Katya Rubia
- Department of Child and Adolescent Psychiatry (Lam, Criaud, Lukito, Westwood, Simonoff, Rubia), Department of Neuroimaging (Kowalczyk, Barker, Giampietro), and Department of Biostatistics (Agbedjro), King's College London; Institute for Globally Distributed Open Research and Education (Criaud); Institute of Human Sciences, University of Wolverhampton, Wolverhampton, U.K. (Westwood); Department of Psychology, School of Social Science, University of Westminster, London (Westwood); Southwest London and St George's Mental Health NHS Trust, London (Curran); South London and Maudsley NHS Foundation Trust, London (Barret, Abbott); Great Ormond Street Hospital for Children NHS Foundation Trust, London (Liang); Department of Child and Adolescent Psychiatry, Technical University Dresden, Germany (Rubia)
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7
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Turnbull A, Seitz A, Tadin D, Lin FV. Unifying framework for cognitive training interventions in brain aging. Ageing Res Rev 2022; 81:101724. [PMID: 36031055 PMCID: PMC10681332 DOI: 10.1016/j.arr.2022.101724] [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: 04/15/2022] [Revised: 07/29/2022] [Accepted: 08/22/2022] [Indexed: 01/31/2023]
Abstract
Cognitive training is a promising tool for slowing or preventing cognitive decline in older adults at-risk for dementia. Its success, however, has been limited by a lack of evidence showing that it reliably causes broad training effects: improvements in cognition across a range of domains that lead to real-world benefits. Here, we propose a framework for enhancing the effect of cognitive training interventions in brain aging. The focus is on (A) developing cognitive training task paradigms that are informed by population-level cognitive characteristics and pathophysiology, and (B) personalizing how these sets are presented to participants during training via feedback loops that aim to optimize "mismatch" between participant capacity and training demands using both adaptation and random variability. In this way, cognitive training can better alter whole-brain topology in a manner that supports broad training effects in the context of brain aging.
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Affiliation(s)
- Adam Turnbull
- University of Rochester, USA; Stanford University, USA
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8
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Abstract
For most neuroimaging questions the range of possible analytic choices makes it unclear how to evaluate conclusions from any single analytic method. One possible way to address this issue is to evaluate all possible analyses using a multiverse approach, however, this can be computationally challenging and sequential analyses on the same data can compromise predictive power. Here, we establish how active learning on a low-dimensional space capturing the inter-relationships between pipelines can efficiently approximate the full spectrum of analyses. This approach balances the benefits of a multiverse analysis without incurring the cost on computational and predictive power. We illustrate this approach with two functional MRI datasets (predicting brain age and autism diagnosis) demonstrating how a multiverse of analyses can be efficiently navigated and mapped out using active learning. Furthermore, our presented approach not only identifies the subset of analysis techniques that are best able to predict age or classify individuals with autism spectrum disorder and healthy controls, but it also allows the relationships between analyses to be quantified.
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9
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Liu M, Amey RC, Backer RA, Simon JP, Forbes CE. Behavioral Studies Using Large-Scale Brain Networks – Methods and Validations. Front Hum Neurosci 2022; 16:875201. [PMID: 35782044 PMCID: PMC9244405 DOI: 10.3389/fnhum.2022.875201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/17/2022] [Indexed: 11/13/2022] Open
Abstract
Mapping human behaviors to brain activity has become a key focus in modern cognitive neuroscience. As methods such as functional MRI (fMRI) advance cognitive scientists show an increasing interest in investigating neural activity in terms of functional connectivity and brain networks, rather than activation in a single brain region. Due to the noisy nature of neural activity, determining how behaviors are associated with specific neural signals is not well-established. Previous research has suggested graph theory techniques as a solution. Graph theory provides an opportunity to interpret human behaviors in terms of the topological organization of brain network architecture. Graph theory-based approaches, however, only scratch the surface of what neural connections relate to human behavior. Recently, the development of data-driven methods, e.g., machine learning and deep learning approaches, provide a new perspective to study the relationship between brain networks and human behaviors across the whole brain, expanding upon past literatures. In this review, we sought to revisit these data-driven approaches to facilitate our understanding of neural mechanisms and build models of human behaviors. We start with the popular graph theory approach and then discuss other data-driven approaches such as connectome-based predictive modeling, multivariate pattern analysis, network dynamic modeling, and deep learning techniques that quantify meaningful networks and connectivity related to cognition and behaviors. Importantly, for each topic, we discuss the pros and cons of the methods in addition to providing examples using our own data for each technique to describe how these methods can be applied to real-world neuroimaging data.
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Affiliation(s)
- Mengting Liu
- School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, China
- Mengting Liu,
| | - Rachel C. Amey
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
- *Correspondence: Rachel C. Amey,
| | - Robert A. Backer
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, United States
| | - Julia P. Simon
- Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Chad E. Forbes
- Department of Psychology, Florida Atlantic University, Boca Raton, FL, United States
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10
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Gui A, Throm EV, da Costa PF, Haartsen R, Leech R, Jones EJH. Proving and improving the reliability of infant research with neuroadaptive Bayesian optimization. INFANT AND CHILD DEVELOPMENT 2022. [DOI: 10.1002/icd.2323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Anna Gui
- Centre for Brain and Cognitive Development, Department of Psychological Science Birkbeck, University of London, Henry Wellcome Building London UK
| | - Elena V. Throm
- Centre for Brain and Cognitive Development, Department of Psychological Science Birkbeck, University of London, Henry Wellcome Building London UK
| | - Pedro F. da Costa
- Centre for Brain and Cognitive Development, Department of Psychological Science Birkbeck, University of London, Henry Wellcome Building London UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | - Rianne Haartsen
- Centre for Brain and Cognitive Development, Department of Psychological Science Birkbeck, University of London, Henry Wellcome Building London UK
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience King's College London London UK
| | - Emily J. H. Jones
- Centre for Brain and Cognitive Development, Department of Psychological Science Birkbeck, University of London, Henry Wellcome Building London UK
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11
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Chen Q, Turnbull A, Cole M, Zhang Z, Lin FV. Enhancing Cortical Network-level Participation Coefficient as a Potential Mechanism for Transfer in Cognitive Training in aMCI. Neuroimage 2022; 254:119124. [PMID: 35331866 PMCID: PMC9199485 DOI: 10.1016/j.neuroimage.2022.119124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 03/19/2022] [Indexed: 02/06/2023] Open
Abstract
Effective cognitive training must improve cognition beyond the trained domain (show a transfer effect) and be applicable to dementia-risk populations, e.g., amnesic mild cognitive impairment (aMCI). Theories suggest training should target processes that 1) show robust engagement, 2) are domain-general, and 3) reflect long-lasting changes in brain organization. Brain regions that connect to many different networks (i.e., show high participation coefficient; PC) are known to support integration. This capacity is 1) relatively preserved in aMCI, 2) required across a wide range of cognitive domains, and 3) trait-like. In 49 individuals with aMCI that completed a 6-week visual speed of processing training (VSOP) and 28 active controls, enhancement in PC was significantly more related to transfer to working memory at global and network levels in VSOP compared to controls, particularly in networks with many high-PC nodes. This suggests that enhancing brain integration may provide a target for developing effective cognitive training.
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Affiliation(s)
- Quanjing Chen
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, United States
| | - Adam Turnbull
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, United States; School of Nursing, University of Rochester, United States.
| | - Martin Cole
- Department of Biostatics and Computational Biology, University of Rochester, United States
| | - Zhengwu Zhang
- Department of Statistics and Operations Research, UNC-Chapel Hill, United States
| | - Feng V Lin
- CogT Lab, Department of Psychiatry and Behavioral Sciences, Stanford University, United States; The Wu Tsai Neuroscience Institute, Stanford University, University of Rochester, United States
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12
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Ouyang G, Dien J, Lorenz R. Handling EEG artifacts and searching individually optimal experimental parameter in real time: a system development and demonstration. J Neural Eng 2022; 19. [PMID: 34902847 DOI: 10.1088/1741-2552/ac42b6] [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: 07/30/2021] [Accepted: 12/13/2021] [Indexed: 02/02/2023]
Abstract
Objective.Neuroadaptive paradigms that systematically assess event-related potential (ERP) features across many different experimental parameters have the potential to improve the generalizability of ERP findings and may help to accelerate ERP-based biomarker discovery by identifying the exact experimental conditions for which ERPs differ most for a certain clinical population. Obtaining robust and reliable ERPs online is a prerequisite for ERP-based neuroadaptive research. One of the key steps involved is to correctly isolate electroencephalography artifacts in real time because they contribute a large amount of variance that, if not removed, will greatly distort the ERP obtained. Another key factor of concern is the computational cost of the online artifact handling method. This work aims to develop and validate a cost-efficient system to support ERP-based neuroadaptive research.Approach.We developed a simple online artifact handling method, single trial PCA-based artifact removal (SPA), based on variance distribution dichotomies to distinguish between artifacts and neural activity. We then applied this method in an ERP-based neuroadaptive paradigm in which Bayesian optimization was used to search individually optimal inter-stimulus-interval (ISI) that generates ERP with the highest signal-to-noise ratio.Main results.SPA was compared to other offline and online algorithms. The results showed that SPA exhibited good performance in both computational efficiency and preservation of ERP pattern. Based on SPA, the Bayesian optimization procedure was able to quickly find individually optimal ISI.Significance.The current work presents a simple yet highly cost-efficient method that has been validated in its ability to extract ERP, preserve ERP effects, and better support ERP-based neuroadaptive paradigm.
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Affiliation(s)
- Guang Ouyang
- Faculty of Education, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Joseph Dien
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States of America
| | - Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Department of Psychology, Stanford University, Stanford, CA, United States of America
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13
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Váša F, Hobday H, Stanyard RA, Daws RE, Giampietro V, O'Daly O, Lythgoe DJ, Seidlitz J, Skare S, Williams SCR, Marquand AF, Leech R, Cole JH. Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging. Hum Brain Mapp 2021; 43:1749-1765. [PMID: 34953014 PMCID: PMC8886661 DOI: 10.1002/hbm.25755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/02/2021] [Accepted: 11/21/2021] [Indexed: 12/17/2022] Open
Abstract
Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1 -weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1 -FLAIR, T2 , T2 *, T2 -FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single-contrast T1 -weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T1 -FLAIR and single-contrast T1 -weighted scans, using correlations between voxels and regions of interest across participants, measures of within- and between-participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix-derived data using test-retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.
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Affiliation(s)
- František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Harriet Hobday
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Ryan A Stanyard
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Forensic & Developmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Richard E Daws
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London, UK
| | - Vincent Giampietro
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Owen O'Daly
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - David J Lythgoe
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stefan Skare
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andre F Marquand
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands.,Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Robert Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - James H Cole
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
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14
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Lorenz R, Johal M, Dick F, Hampshire A, Leech R, Geranmayeh F. A Bayesian optimization approach for rapidly mapping residual network function in stroke. Brain 2021; 144:2120-2134. [PMID: 33725125 PMCID: PMC8370405 DOI: 10.1093/brain/awab109] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 11/16/2022] Open
Abstract
Post-stroke cognitive and linguistic impairments are debilitating conditions, with limited therapeutic options. Domain-general brain networks play an important role in stroke recovery and characterizing their residual function with functional MRI has the potential to yield biomarkers capable of guiding patient-specific rehabilitation. However, this is challenging as such detailed characterization requires testing patients on multitudes of cognitive tasks in the scanner, rendering experimental sessions unfeasibly lengthy. Thus, the current status quo in clinical neuroimaging research involves testing patients on a very limited number of tasks, in the hope that it will reveal a useful neuroimaging biomarker for the whole cohort. Given the great heterogeneity among stroke patients and the volume of possible tasks this approach is unsustainable. Advancing task-based functional MRI biomarker discovery requires a paradigm shift in order to be able to swiftly characterize residual network activity in individual patients using a diverse range of cognitive tasks. Here, we overcome this problem by leveraging neuroadaptive Bayesian optimization, an approach combining real-time functional MRI with machine-learning, by intelligently searching across many tasks, this approach rapidly maps out patient-specific profiles of residual domain-general network function. We used this technique in a cross-sectional study with 11 left-hemispheric stroke patients with chronic aphasia (four female, age ± standard deviation: 59 ± 10.9 years) and 14 healthy, age-matched control subjects (eight female, age ± standard deviation: 55.6 ± 6.8 years). To assess intra-subject reliability of the functional profiles obtained, we conducted two independent runs per subject, for which the algorithm was entirely reinitialized. Our results demonstrate that this technique is both feasible and robust, yielding reliable patient-specific functional profiles. Moreover, we show that group-level results are not representative of patient-specific results. Whereas controls have highly similar profiles, patients show idiosyncratic profiles of network abnormalities that are associated with behavioural performance. In summary, our study highlights the importance of moving beyond traditional 'one-size-fits-all' approaches where patients are treated as one group and single tasks are used. Our approach can be extended to diverse brain networks and combined with brain stimulation or other therapeutics, thereby opening new avenues for precision medicine targeting a diverse range of neurological and psychiatric conditions.
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Affiliation(s)
- Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
- Stanford University, Stanford, CA 94305, USA
- Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04303, Germany
| | - Michelle Johal
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London W12 0NN, UK
| | - Frederic Dick
- Birkbeck/UCL Centre for Neuroimaging, Birkbeck University, London WC1H 0AP, UK
| | - Adam Hampshire
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London W12 0NN, UK
| | - Robert Leech
- Centre for Neuroimaging Science, King’s College London, London SE5 8AF, UK
| | - Fatemeh Geranmayeh
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain Sciences, Imperial College London, London W12 0NN, UK
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15
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Steinley D, Vilidaite G, Lygo FA, Smith AK, Flack TR, Gouws AD, Andrews TJ. Power contours: Optimising sample size and precision in experimental psychology and human neuroscience. Psychol Methods 2021; 26:295-314. [PMID: 32673043 PMCID: PMC8329985 DOI: 10.1037/met0000337] [Citation(s) in RCA: 75] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
When designing experimental studies with human participants, experimenters must decide how many trials each participant will complete, as well as how many participants to test. Most discussion of statistical power (the ability of a study design to detect an effect) has focused on sample size, and assumed sufficient trials. Here we explore the influence of both factors on statistical power, represented as a 2-dimensional plot on which iso-power contours can be visualized. We demonstrate the conditions under which the number of trials is particularly important, that is, when the within-participant variance is large relative to the between-participants variance. We then derive power contour plots using existing data sets for 8 experimental paradigms and methodologies (including reaction times, sensory thresholds, fMRI, MEG, and EEG), and provide example code to calculate estimates of the within- and between-participants variance for each method. In all cases, the within-participant variance was larger than the between-participants variance, meaning that the number of trials has a meaningful influence on statistical power in commonly used paradigms. An online tool is provided (https://shiny.york.ac.uk/powercontours/) for generating power contours, from which the optimal combination of trials and participants can be calculated when designing future studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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16
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Coveney PV, Highfield RR. When we can trust computers (and when we can't). PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200067. [PMID: 33775149 PMCID: PMC8059589 DOI: 10.1098/rsta.2020.0067] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.
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Affiliation(s)
- Peter V. Coveney
- Centre for Computational Science, University College London, Gordon Street, London WC1H 0AJ, UK
- Institute for Informatics, Science Park 904, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
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17
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Fehér KD, Wunderlin M, Maier JG, Hertenstein E, Schneider CL, Mikutta C, Züst MA, Klöppel S, Nissen C. Shaping the slow waves of sleep: A systematic and integrative review of sleep slow wave modulation in humans using non-invasive brain stimulation. Sleep Med Rev 2021; 58:101438. [PMID: 33582581 DOI: 10.1016/j.smrv.2021.101438] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 10/14/2020] [Accepted: 11/10/2020] [Indexed: 01/19/2023]
Abstract
The experimental study of electroencephalographic slow wave sleep (SWS) stretches over more than half a century and has corroborated its importance for basic physiological processes, such as brain plasticity, metabolism and immune system functioning. Alterations of SWS in aging or pathological conditions suggest that modulating SWS might constitute a window for clinically relevant interventions. This work provides a systematic and integrative review of SWS modulation through non-invasive brain stimulation in humans. A literature search using PubMed, conducted in May 2020, identified 3220 studies, of which 82 fulfilled inclusion criteria. Three approaches have been adopted to modulate the macro- and microstructure of SWS, namely auditory, transcranial electrical and transcranial magnetic stimulation. Our current knowledge about the modulatory mechanisms, the space of stimulation parameters and the physiological and behavioral effects are reported and evaluated. The integration of findings suggests that sleep slow wave modulation bears the potential to promote our understanding of the functions of SWS and to develop new treatments for conditions of disrupted SWS.
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Affiliation(s)
- Kristoffer D Fehér
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Marina Wunderlin
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Jonathan G Maier
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Elisabeth Hertenstein
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Carlotta L Schneider
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Christian Mikutta
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland; Privatklinik Meiringen, Meiringen, Switzerland
| | - Marc A Züst
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Christoph Nissen
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland.
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18
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Lipka R, Ahlers E, Reed TL, Karstens MI, Nguyen V, Bajbouj M, Cohen Kadosh R. Resolving heterogeneity in transcranial electrical stimulation efficacy for attention deficit hyperactivity disorder. Exp Neurol 2020; 337:113586. [PMID: 33382986 DOI: 10.1016/j.expneurol.2020.113586] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/15/2020] [Accepted: 12/23/2020] [Indexed: 10/22/2022]
Abstract
While the treatment of Attention Deficit Hyperactivity Disorder (ADHD) is dominated by pharmacological agents, transcranial electrical stimulation (tES) is gaining attention as an alternative method for treatment. Most current meta-analyses have suggested that tES can improve cognitive functions that are otherwise impaired in ADHD, such as inhibition and working memory, as well as alleviated clinical symptoms. Here we review some of the promising findings in the field of tES. At the same time, we highlight two factors, which hinder the effective application of tES in treating ADHD: 1) the heterogeneity of tES protocols used in different studies; 2) patient profiles influencing responses to tES. We highlight potential solutions for overcoming such limitations, including the use of active machine learning, and provide simulated data to demonstrate how these solutions could also improve the understanding, diagnosis, and treatment of ADHD.
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Affiliation(s)
- Renée Lipka
- Department of Psychiatry, Charité Universitätsmedizin, Campus Benjamin Franklin, Hindenburgdamm 30, Berlin 12203, Germany
| | - Eike Ahlers
- Department of Psychiatry, Charité Universitätsmedizin, Campus Benjamin Franklin, Hindenburgdamm 30, Berlin 12203, Germany
| | - Thomas L Reed
- Department of Experimental Psychology, University of Oxford, Radcliffe Observatory, Anna Watts Building, Woodstock Rd, Oxford OX2 6GG, United Kingdom
| | - Malin I Karstens
- Department of Experimental Psychology, University of Oxford, Radcliffe Observatory, Anna Watts Building, Woodstock Rd, Oxford OX2 6GG, United Kingdom
| | - Vu Nguyen
- Department of Materials, University of Oxford, Oxford OX2 6HT, United Kingdom
| | - Malek Bajbouj
- Department of Psychiatry, Charité Universitätsmedizin, Campus Benjamin Franklin, Hindenburgdamm 30, Berlin 12203, Germany
| | - Roi Cohen Kadosh
- Department of Experimental Psychology, University of Oxford, Radcliffe Observatory, Anna Watts Building, Woodstock Rd, Oxford OX2 6GG, United Kingdom.
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19
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Dzieżyc M, Gjoreski M, Kazienko P, Saganowski S, Gams M. Can We Ditch Feature Engineering? End-to-End Deep Learning for Affect Recognition from Physiological Sensor Data. SENSORS 2020; 20:s20226535. [PMID: 33207564 PMCID: PMC7697590 DOI: 10.3390/s20226535] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/01/2020] [Accepted: 11/06/2020] [Indexed: 01/18/2023]
Abstract
To further extend the applicability of wearable sensors in various domains such as mobile health systems and the automotive industry, new methods for accurately extracting subtle physiological information from these wearable sensors are required. However, the extraction of valuable information from physiological signals is still challenging—smartphones can count steps and compute heart rate, but they cannot recognize emotions and related affective states. This study analyzes the possibility of using end-to-end multimodal deep learning (DL) methods for affect recognition. Ten end-to-end DL architectures are compared on four different datasets with diverse raw physiological signals used for affect recognition, including emotional and stress states. The DL architectures specialized for time-series classification were enhanced to simultaneously facilitate learning from multiple sensors, each having their own sampling frequency. To enable fair comparison among the different DL architectures, Bayesian optimization was used for hyperparameter tuning. The experimental results showed that the performance of the models depends on the intensity of the physiological response induced by the affective stimuli, i.e., the DL models recognize stress induced by the Trier Social Stress Test more successfully than they recognize emotional changes induced by watching affective content, e.g., funny videos. Additionally, the results showed that the CNN-based architectures might be more suitable than LSTM-based architectures for affect recognition from physiological sensors.
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Affiliation(s)
- Maciej Dzieżyc
- Department of Computational Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland; (P.K.); (S.S.)
- Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
- Correspondence:
| | - Martin Gjoreski
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (M.G.); (M.G.)
- Jožef Stefan Postgraduate School, 1000 Ljubljana, Slovenia
| | - Przemysław Kazienko
- Department of Computational Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland; (P.K.); (S.S.)
- Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Stanisław Saganowski
- Department of Computational Intelligence, Wrocław University of Science and Technology, 50-370 Wrocław, Poland; (P.K.); (S.S.)
- Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Matjaž Gams
- Jožef Stefan Institute, 1000 Ljubljana, Slovenia; (M.G.); (M.G.)
- Jožef Stefan Postgraduate School, 1000 Ljubljana, Slovenia
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20
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Kasten FH, Herrmann CS. Discrete sampling in perception via neuronal oscillations-Evidence from rhythmic, non-invasive brain stimulation. Eur J Neurosci 2020; 55:3402-3417. [PMID: 33048382 DOI: 10.1111/ejn.15006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 10/07/2020] [Accepted: 10/08/2020] [Indexed: 11/26/2022]
Abstract
A variety of perceptual phenomena suggest that, in contrast to our everyday experience, our perception may be discrete rather than continuous. The possibility of such discrete sampling processes inevitably prompts the question of how such discretization is implemented in the brain. Evidence from neurophysiological measurements suggest that neural oscillations, particularly in the lower frequencies, may provide a mechanism by which such discretization can be implemented. It is hypothesized that cortical excitability is rhythmically enhanced or reduced along the positive and negative half-cycle of such oscillations. In recent years, rhythmic non-invasive brain stimulation approaches such as rhythmic transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS) are increasingly used to test this hypothesis. Both methods are thought to entrain endogenous brain oscillations, allowing them to alter their power, frequency, and phase in order to study their roles in perception. After a brief introduction to the core mechanisms of both methods, we will provide an overview of rTMS and tACS studies probing the role of brain oscillations for discretized perception in different domains and will contrast these results with unsuccessful attempts. Further, we will discuss methodological pitfalls and challenges associated with the methods.
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Affiliation(s)
- Florian H Kasten
- Experimental Psychology Lab, Department of Psychology, Cluster of Excellence "Hearing for All", European Medical School, Carl von Ossietzky University, Oldenburg, Germany.,Neuroimaging Unit, European Medical School, Carl von Ossietzky University, Oldenburg, Germany
| | - Christoph S Herrmann
- Experimental Psychology Lab, Department of Psychology, Cluster of Excellence "Hearing for All", European Medical School, Carl von Ossietzky University, Oldenburg, Germany.,Neuroimaging Unit, European Medical School, Carl von Ossietzky University, Oldenburg, Germany.,Research Center Neurosensory Science, Carl von Ossietzky University, Oldenburg, Germany
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21
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Braithwaite EK, Jones EJH, Johnson MH, Holmboe K. Dynamic modulation of frontal theta power predicts cognitive ability in infancy. Dev Cogn Neurosci 2020; 45:100818. [PMID: 32741754 PMCID: PMC7393453 DOI: 10.1016/j.dcn.2020.100818] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 05/18/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022] Open
Abstract
Cognitive ability is a key factor that contributes to individual differences in life trajectories. Identifying early neural indicators of later cognitive ability may enable us to better elucidate the mechanisms that shape individual differences, eventually aiding identification of infants with an elevated likelihood of less optimal outcomes. A previous study associated a measure of neural activity (theta EEG) recorded at 12-months with non-verbal cognitive ability at ages two, three and seven in individuals with older siblings with autism (Jones et al., 2020). In a pre-registered study (https://osf.io/v5xrw/), we replicate and extend this finding in a younger, low-risk infant sample. EEG was recorded during presentation of a non-social video to a cohort of 6-month-old infants and behavioural data was collected at 6- and 9-months-old. Initial analyses replicated the finding that frontal theta power increases over the course of video viewing, extending this to 6-month-olds. Further, individual differences in the magnitude of this change significantly predicted non-verbal cognitive ability measured at 9-months, but not early executive function. Theta change at 6-months-old may therefore be an early indicator of later cognitive ability. This could have important implications for identification of, and interventions for, children at risk of poor cognitive outcomes.
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Affiliation(s)
- Eleanor K Braithwaite
- Centre for Brain and Cognitive Development, Birkbeck, University of London, United Kingdom
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck, University of London, United Kingdom
| | - Mark H Johnson
- Centre for Brain and Cognitive Development, Birkbeck, University of London, United Kingdom; Department of Psychology, University of Cambridge, United Kingdom
| | - Karla Holmboe
- Department of Experimental Psychology, University of Oxford, United Kingdom.
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22
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Braithwaite EK, Gui A, Jones EJH. Social attention: What is it, how can we measure it, and what can it tell us about autism and ADHD? PROGRESS IN BRAIN RESEARCH 2020; 254:271-303. [PMID: 32859292 DOI: 10.1016/bs.pbr.2020.05.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Neurodevelopmental disorders like autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) affect 2-10% of children worldwide but are still poorly understood. Prospective studies of infants with an elevated familial likelihood of ASD or ADHD can provide insight into early mechanisms that canalize development down a typical or atypical course. Such work holds potential for earlier identification and intervention to support optimal outcomes in individuals with neurodevelopmental disorders. Disrupted attention may be involved in developmental trajectories to ASD and ADHD. Specifically, altered attention to social stimuli has been suggested as a possible endophenotype of ASD, lying between genetic factors impacting brain development and later symptoms. Similarly, changes in domain-general aspects of attention are commonly seen in ADHD and emerging evidence suggests these may begin in infancy. Could these patterns point to a common risk factor for both disorders? Or does social attention reflect the activity of a particular network of brain systems that is distinct to those underpinning general attention skills? One challenge to addressing such questions is our lack of understanding of the relation between social and general attention. In this chapter we review evidence from infants with later ASD and ADHD that illuminates this question.
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Affiliation(s)
- Eleanor K Braithwaite
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom
| | - Anna Gui
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck, University of London, London, United Kingdom.
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23
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Krol LR, Haselager P, Zander TO. Cognitive and affective probing: a tutorial and review of active learning for neuroadaptive technology. J Neural Eng 2020; 17:012001. [DOI: 10.1088/1741-2552/ab5bb5] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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24
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Kumar M, Ellis CT, Lu Q, Zhang H, Capotă M, Willke TL, Ramadge PJ, Turk-Browne NB, Norman KA. BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis. PLoS Comput Biol 2020; 16:e1007549. [PMID: 31940340 PMCID: PMC6961866 DOI: 10.1371/journal.pcbi.1007549] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 11/17/2019] [Indexed: 12/15/2022] Open
Abstract
Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery. The analysis of brain activity, as measured using functional magnetic resonance imaging (fMRI), has led to significant discoveries about how the brain processes information and how this is affected by disease. However, exhaustive multivariate analyses in space and time, run across a large number of subjects, can be complex and computationally intensive, creating a high barrier for entry into this field. Furthermore, the materials available to learn these methods do not encompass all the methods used, work is often published with no publicly available code, and the analyses are often difficult to run on large datasets without cluster computing. We have created interactive software tutorials that make it easy to understand and execute advanced analyses on fMRI data using the BrainIAK package—an open-source package built in Python. We have released these tutorials freely to the public and have significantly reduced computational roadblocks for users by making it possible to run the tutorials with a web browser and internet connection. We hope that this facilitated access and the usability of the underlying code—a compendium for how to program and optimize the latest fMRI analyses—will accelerate training, reproducibility, and discovery in cognitive neuroscience.
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Affiliation(s)
- Manoj Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
| | - Cameron T. Ellis
- Department of Psychology, Yale University, New Haven, Connecticut, United States of America
| | - Qihong Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
| | - Hejia Zhang
- Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, United States of America
| | - Mihai Capotă
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, Oregon, United States of America
| | - Theodore L. Willke
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, Oregon, United States of America
| | - Peter J. Ramadge
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey, United States of America
| | | | - Kenneth A. Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- Department of Psychology, Princeton University, Princeton, New Jersey, United States of America
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25
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Cole JH, Lorenz R, Geranmayeh F, Wood T, Hellyer P, Williams S, Turkheimer F, Leech R. Active Acquisition for multimodal neuroimaging. Wellcome Open Res 2019; 3:145. [PMID: 31667357 PMCID: PMC6807153 DOI: 10.12688/wellcomeopenres.14918.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/17/2019] [Indexed: 02/02/2023] Open
Abstract
In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field -of -view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.
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Affiliation(s)
- James H. Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Romy Lorenz
- MRC Centre for Cognition and Brain Sciences, University of Cambridge, Cambridge, UK
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Fatemeh Geranmayeh
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Tobias Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Peter Hellyer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Steven Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Rob Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
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26
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Lorenz R, Simmons LE, Monti RP, Arthur JL, Limal S, Laakso I, Leech R, Violante IR. Efficiently searching through large tACS parameter spaces using closed-loop Bayesian optimization. Brain Stimul 2019; 12:1484-1489. [PMID: 31289013 PMCID: PMC6879005 DOI: 10.1016/j.brs.2019.07.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 06/26/2019] [Accepted: 07/01/2019] [Indexed: 11/25/2022] Open
Abstract
Background Selecting optimal stimulation parameters from numerous possibilities is a major obstacle for assessing the efficacy of non-invasive brain stimulation. Objective We demonstrate that Bayesian optimization can rapidly search through large parameter spaces and identify subject-level stimulation parameters in real-time. Methods To validate the method, Bayesian optimization was employed using participants’ binary judgements about the intensity of phosphenes elicited through tACS. Results We demonstrate the efficiency of Bayesian optimization in identifying parameters that maximize phosphene intensity in a short timeframe (5 min for >190 possibilities). Our results replicate frequency-dependent effects across three montages and show phase-dependent effects of phosphene perception. Computational modelling explains that these phase effects result from constructive/destructive interference of the current reaching the retinas. Simulation analyses demonstrate the method's versatility for complex response functions, even when accounting for noisy observations. Conclusion Alongside subjective ratings, this method can be used to optimize tACS parameters based on behavioral and neural measures and has the potential to be used for tailoring stimulation protocols to individuals.
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Affiliation(s)
- Romy Lorenz
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK; Max-Planck Institute for Human Cognitive and Brain Sciences, Leipzig, 04303, Germany.
| | - Laura E Simmons
- Computational, Cognitive and Clinical Neuroscience Laboratory, Department of Medicine, Imperial College London, London, W12 0NN, UK
| | - Ricardo P Monti
- Gatsby Computational Neuroscience Unit, University College London, London, W1T 4JG, UK
| | - Joy L Arthur
- Computational, Cognitive and Clinical Neuroscience Laboratory, Department of Medicine, Imperial College London, London, W12 0NN, UK
| | - Severin Limal
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - Ilkka Laakso
- Department of Electrical Engineering and Automation, Aalto University, Espoo, 02150, Finland
| | - Robert Leech
- Centre for Neuroimaging Science, King's College London, London, SE5 8AF, UK
| | - Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK.
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27
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Loth E, Evans DW. Converting tests of fundamental social, cognitive, and affective processes into clinically useful bio‐behavioral markers for neurodevelopmental conditions. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2019; 10:e1499. [DOI: 10.1002/wcs.1499] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 01/27/2019] [Accepted: 02/12/2019] [Indexed: 12/27/2022]
Affiliation(s)
- Eva Loth
- Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment Institute of Psychiatry, Psychology and Neuroscience, King's College London London UK
| | - David W. Evans
- Department of Psychology Bucknell University Lewisburg Pennsylvania
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28
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Power and temporal dynamics of alpha oscillations at rest differentiate cognitive performance involving sustained and phasic cognitive control. Neuroimage 2019; 188:135-144. [DOI: 10.1016/j.neuroimage.2018.12.001] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2018] [Revised: 11/09/2018] [Accepted: 12/01/2018] [Indexed: 11/18/2022] Open
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29
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Chung MH, Martins B, Privratsky A, James GA, Kilts CD, Bush KA. Individual differences in rate of acquiring stable neural representations of tasks in fMRI. PLoS One 2018; 13:e0207352. [PMID: 30475812 PMCID: PMC6261022 DOI: 10.1371/journal.pone.0207352] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 10/24/2018] [Indexed: 11/18/2022] Open
Abstract
Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in both healthy and clinical populations. There is growing interest in mapping individual differences in fMRI task behavior and neural responses. By utilizing neuroadaptive task designs accounting for such individual differences, task durations can be personalized to potentially optimize neuroimaging study outcomes (e.g., classification of task-related brain states). To test this hypothesis, we first retrospectively tracked the volume-by-volume changes of beta weights generated from general linear models (GLM) for 67 adult subjects performing a stop-signal task (SST). We then modeled the convergence of the volume-by-volume changes of beta weights according to their exponential decay (ED) in units of half-life. Our results showed significant differences in beta weight convergence estimates of optimal stopping times (OSTs) between go following successful stop trials and failed stop trials for both cocaine dependent (CD) and control group (Con), and between go following successful stop trials and go following failed stop trials for Con group. Further, we implemented support vector machine (SVM) classification for 67 CD/Con labeled subjects and compared the classification accuracies of fMRI-based features derived from (1) the full fMRI task versus (2) the fMRI task truncated to multiples of the unit of half-life. Among the computed binary classification accuracies, two types of task durations based on 2 half-lives significantly outperformed the accuracies using fully acquired trials, supporting this length as the OST for the SST. In conclusion, we demonstrate the potential of a neuroadaptive task design that can be widely applied to personalizing other task-based fMRI experiments in either dynamic real-time fMRI applications or within fMRI preprocessing pipelines.
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Affiliation(s)
- Ming-Hua Chung
- Brain Imaging Research Center, Psychiatric Research Institute, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- * E-mail:
| | - Bradford Martins
- Brain Imaging Research Center, Psychiatric Research Institute, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Anthony Privratsky
- Brain Imaging Research Center, Psychiatric Research Institute, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - G. Andrew James
- Brain Imaging Research Center, Psychiatric Research Institute, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Clint D. Kilts
- Brain Imaging Research Center, Psychiatric Research Institute, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Keith A. Bush
- Brain Imaging Research Center, Psychiatric Research Institute, Department of Psychiatry, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
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30
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Cole JH, Lorenz R, Geranmayeh F, Wood T, Hellyer P, Williams S, Turkheimer F, Leech R. Active Acquisition for multimodal neuroimaging. Wellcome Open Res 2018; 3:145. [PMID: 31667357 PMCID: PMC6807153 DOI: 10.12688/wellcomeopenres.14918.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/08/2018] [Indexed: 02/02/2023] Open
Abstract
In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field -of -view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery.
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Affiliation(s)
- James H. Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Romy Lorenz
- MRC Centre for Cognition and Brain Sciences, University of Cambridge, Cambridge, UK
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Fatemeh Geranmayeh
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Tobias Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Peter Hellyer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Steven Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Rob Leech
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
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31
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Algermissen J, Mehler DMA. May the power be with you: are there highly powered studies in neuroscience, and how can we get more of them? J Neurophysiol 2018; 119:2114-2117. [DOI: 10.1152/jn.00765.2017] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Statistical power is essential for robust science and replicability, but a meta-analysis by Button et al. in 2013 diagnosed a “power failure” for neuroscience. In contrast, Nord et al. ( J Neurosci 37: 8051–8061, 2017) reanalyzed these data and suggested that some studies feature high power. We illustrate how publication and researcher bias might have inflated power estimates, and review recently introduced techniques that can improve analysis pipelines and increase power in neuroscience studies.
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Affiliation(s)
- Johannes Algermissen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - David M. A. Mehler
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, United Kingdom
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32
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Lorenz R, Violante IR, Monti RP, Montana G, Hampshire A, Leech R. Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization. Nat Commun 2018; 9:1227. [PMID: 29581425 PMCID: PMC5964320 DOI: 10.1038/s41467-018-03657-3] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Accepted: 02/28/2018] [Indexed: 11/08/2022] Open
Abstract
Understanding the unique contributions of frontoparietal networks (FPN) in cognition is challenging because they overlap spatially and are co-activated by diverse tasks. Characterizing these networks therefore involves studying their activation across many different cognitive tasks, which previously was only possible with meta-analyses. Here, we use neuroadaptive Bayesian optimization, an approach combining real-time analysis of functional neuroimaging data with machine-learning, to discover cognitive tasks that segregate ventral and dorsal FPN activity. We identify and subsequently refine two cognitive tasks, Deductive Reasoning and Tower of London, which maximally dissociate the dorsal from ventral FPN. We subsequently investigate these two FPNs in the context of a wider range of FPNs and demonstrate the importance of studying the whole activity profile across tasks to uniquely differentiate any FPN. Our findings deviate from previous meta-analyses and hypothesized functional labels for these FPNs. Taken together the results form the starting point for a neurobiologically-derived cognitive taxonomy.
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Affiliation(s)
- Romy Lorenz
- Department of Medicine, Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Imperial College London, London, W12 0NN, UK.
| | - Ines R Violante
- School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK
| | - Ricardo Pio Monti
- Gatsby Computational Neuroscience Unit, University College London, London, W1T 4JG, UK
| | - Giovanni Montana
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK
- Department of Biomedical Engineering, King's College London, London, SE1 7EH, UK
| | - Adam Hampshire
- Department of Medicine, Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Imperial College London, London, W12 0NN, UK
| | - Robert Leech
- Centre for Neuroimaging Science, King's College London, London, SE5 8AF, UK.
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33
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Lancaster J, Lorenz R, Leech R, Cole JH. Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction. Front Aging Neurosci 2018; 10:28. [PMID: 29483870 PMCID: PMC5816033 DOI: 10.3389/fnagi.2018.00028] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 01/23/2018] [Indexed: 02/02/2023] Open
Abstract
Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16-90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18-88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias.
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Affiliation(s)
- Jenessa Lancaster
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom
| | - Romy Lorenz
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom
| | - Rob Leech
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom
| | - James H. Cole
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Department of Medicine, Imperial College London, London, United Kingdom,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom,*Correspondence: James H. Cole, ;
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Szucs D, Ioannidis JPA. When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment. Front Hum Neurosci 2017; 11:390. [PMID: 28824397 PMCID: PMC5540883 DOI: 10.3389/fnhum.2017.00390] [Citation(s) in RCA: 127] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 07/13/2017] [Indexed: 11/24/2022] Open
Abstract
Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of (cognitive) neuroscience, psychology, and biomedical science in general. We review these shortcomings and suggest that, after sustained negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. If theoretical predictions are weak we should not rely on all or nothing hypothesis tests. Different inferential methods may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Hypothesis-testing studies should be pre-registered and optimally raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out.
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Affiliation(s)
- Denes Szucs
- Department of Psychology, University of CambridgeCambridge, United Kingdom
| | - John P. A. Ioannidis
- Meta-Research Innovation Center at Stanford and Department of Medicine, Department of Health Research and Policy, and Department of Statistics, Stanford UniversityStanford, CA, United States
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35
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Monti RP, Lorenz R, Hellyer P, Leech R, Anagnostopoulos C, Montana G. Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods. Front Comput Neurosci 2017; 11:14. [PMID: 28373838 PMCID: PMC5357637 DOI: 10.3389/fncom.2017.00014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2016] [Accepted: 02/27/2017] [Indexed: 01/24/2023] Open
Abstract
An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project.
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Affiliation(s)
- Ricardo P Monti
- Department of Mathematics, Imperial College London London, UK
| | - Romy Lorenz
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, The Hammersmith HospitalLondon, UK; Department of Bioengineering, Imperial College LondonLondon, UK
| | - Peter Hellyer
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, The Hammersmith HospitalLondon, UK; Department of Bioengineering, Imperial College LondonLondon, UK; Center for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College LondonLondon, UK
| | - Robert Leech
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, The Hammersmith Hospital London, UK
| | | | - Giovanni Montana
- Department of Mathematics, Imperial College LondonLondon, UK; Department of Biomedical Engineering, King's College London, St Thomas' HospitalLondon, UK
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