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Le Cunff AL, Dommett E, Giampietro V. Neurophysiological measures and correlates of cognitive load in attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia: A scoping review and research recommendations. Eur J Neurosci 2024; 59:256-282. [PMID: 38109476 DOI: 10.1111/ejn.16201] [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: 03/28/2023] [Revised: 10/27/2023] [Accepted: 11/06/2023] [Indexed: 12/20/2023]
Abstract
Working memory is integral to a range of critical cognitive functions such as reasoning and decision-making. Although alterations in working memory have been observed in neurodivergent populations, there has been no review mapping how cognitive load is measured in common neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD) and dyslexia. This scoping review explores the neurophysiological measures used to study cognitive load in these specific populations. Our findings highlight that electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are the most frequently used methods, with a limited number of studies employing functional near-infrared spectroscopy (fNIRs), magnetoencephalography (MEG) or eye-tracking. Notably, eye-related measures are less commonly used, despite their prominence in cognitive load research among neurotypical individuals. The review also highlights potential correlates of cognitive load, such as neural oscillations in the theta and alpha ranges for EEG studies, blood oxygenation level-dependent (BOLD) responses in lateral and medial frontal brain regions for fMRI and fNIRS studies and eye-related measures such as pupil dilation and blink rate. Finally, critical issues for future studies are discussed, including the technical challenges associated with multimodal approaches, the possible impact of atypical features on cognitive load measures and balancing data richness with participant well-being. These insights contribute to a more nuanced understanding of cognitive load measurement in neurodivergent populations and point to important methodological considerations for future neuroscientific research in this area.
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Affiliation(s)
- Anne-Laure Le Cunff
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Eleanor Dommett
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Vincent Giampietro
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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Biabani N, Birdseye A, Higgins S, Delogu A, Rosenzweig J, Cvetkovic Z, Nesbitt A, Drakatos P, Steier J, Kumari V, O’Regan D, Rosenzweig I. The neurophysiologic landscape of the sleep onset: a systematic review. J Thorac Dis 2023; 15:4530-4543. [PMID: 37691675 PMCID: PMC10482638 DOI: 10.21037/jtd-23-325] [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: 03/03/2023] [Accepted: 07/21/2023] [Indexed: 09/12/2023]
Abstract
Background The sleep onset process is an ill-defined complex process of transition from wakefulness to sleep, characterized by progressive modifications at the subjective, behavioural, cognitive, and physiological levels. To this date, there is no international consensus which could aid a principled characterisation of this process for clinical research purposes. The current review aims to systemise the current knowledge about the underlying mechanisms of the natural heterogeneity of this process. Methods In this systematic review, studies investigating the process of the sleep onset from 1970 to 2022 were identified using electronic database searches of PsychINFO, MEDLINE, and Embase. Results A total of 139 studies were included; 110 studies in healthy participants and 29 studies in participants with sleep disorders. Overall, there is a limited consensus across a body of research about what distinct biomarkers of the sleep onset constitute. Only sparse data exists on the physiology, neurophysiology and behavioural mechanisms of the sleep onset, with majority of studies concentrating on the non-rapid eye movement stage 2 (NREM 2) as a potentially better defined and a more reliable time point that separates sleep from the wake, on the sleep wake continuum. Conclusions The neurophysiologic landscape of sleep onset bears a complex pattern associated with a multitude of behavioural and physiological markers and remains poorly understood. The methodological variation and a heterogenous definition of the wake-sleep transition in various studies to date is understandable, given that sleep onset is a process that has fluctuating and ill-defined boundaries. Nonetheless, the principled characterisation of the sleep onset process is needed which will allow for a greater conceptualisation of the mechanisms underlying this process, further influencing the efficacy of current treatments for sleep disorders.
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Affiliation(s)
- Nazanin Biabani
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
| | - Adam Birdseye
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Sean Higgins
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
| | - Alessio Delogu
- James Black Centre, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
| | - Jan Rosenzweig
- Department of Engineering, King’s College London, London, UK
| | - Zoran Cvetkovic
- Department of Engineering, King’s College London, London, UK
| | - Alexander Nesbitt
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
- Department of Neurology, Guy’s Hospital, Guy’s and St. Thomas’ NHS Foundation Trust, London, UK
| | - Panagis Drakatos
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
- School of Basic and Medical Biosciences, Faculty of Life Science and Medicine, King’s College London, London, UK
| | - Joerg Steier
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
- School of Basic and Medical Biosciences, Faculty of Life Science and Medicine, King’s College London, London, UK
| | - Veena Kumari
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Centre for Cognitive Neuroscience (CCN), College of Health, Medicine and Life Sciences, Brunel University London, London, UK
| | - David O’Regan
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
- School of Basic and Medical Biosciences, Faculty of Life Science and Medicine, King’s College London, London, UK
| | - Ivana Rosenzweig
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, UK
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, UK
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Ljungberg E, Wood TC, Solana AB, Williams SCR, Barker GJ, Wiesinger F. Motion corrected silent ZTE neuroimaging. Magn Reson Med 2022; 88:195-210. [PMID: 35381110 PMCID: PMC9321117 DOI: 10.1002/mrm.29201] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/16/2022] [Accepted: 01/28/2022] [Indexed: 11/11/2022]
Abstract
Purpose To develop self‐navigated motion correction for 3D silent zero echo time (ZTE) based neuroimaging and characterize its performance for different types of head motion. Methods The proposed method termed MERLIN (Motion Estimation & Retrospective correction Leveraging Interleaved Navigators) achieves self‐navigation by using interleaved 3D phyllotaxis k‐space sampling. Low resolution navigator images are reconstructed continuously throughout the ZTE acquisition using a sliding window and co‐registered in image space relative to a fixed reference position. Rigid body motion corrections are then applied retrospectively to the k‐space trajectory and raw data and reconstructed into a final, high‐resolution ZTE image. Results MERLIN demonstrated successful and consistent motion correction for magnetization prepared ZTE images for a range of different instructed motion paradigms. The acoustic noise response of the self‐navigated phyllotaxis trajectory was found to be only slightly above ambient noise levels (<4 dBA). Conclusion Silent ZTE imaging combined with MERLIN addresses two major challenges intrinsic to MRI (i.e., subject motion and acoustic noise) in a synergistic and integrated manner without increase in scan time and thereby forms a versatile and powerful framework for clinical and research MR neuroimaging applications.
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Affiliation(s)
- Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Medical Radiation Physics, Lund University, Lund, Sweden
| | - Tobias C Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | | | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Florian Wiesinger
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,GE Healthcare, Munich, Germany
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Naysmith LF, Kumari V, Williams SCR. Neural mapping of prepulse-induced startle reflex modulation as indices of sensory information processing in healthy and clinical populations: A systematic review. Hum Brain Mapp 2021; 42:5495-5518. [PMID: 34414633 PMCID: PMC8519869 DOI: 10.1002/hbm.25631] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 07/30/2021] [Accepted: 08/06/2021] [Indexed: 12/18/2022] Open
Abstract
Startle reflex is modulated when a weaker sensory stimulus ("prepulse") precedes a startling stimulus ("pulse"). Prepulse Inhibition (PPI) is the attenuation of the startle reflex (prepulse precedes pulse by 30-500 ms), whereas Prepulse Facilitation (PPF) is the enhancement of the startle reflex (prepulse precedes pulse by 500-6000 ms). Here, we critically appraise human studies using functional neuroimaging to establish brain regions associated with PPI and PPF. Of 10 studies, nine studies revealed thalamic, striatal and frontal lobe activation during PPI in healthy groups, and activation deficits in the cortico-striato-pallido-thalamic circuitry in schizophrenia (three studies) and Tourette Syndrome (two studies). One study revealed a shared network for PPI and PPF in frontal regions and cerebellum, with PPF networks recruiting superior medial gyrus and cingulate cortex. The main gaps in the literature are (i) limited PPF research and whether PPI and PPF operate on separate/shared networks, (ii) no data on sex differences in neural underpinnings of PPI and PPF, and (iii) no data on neural underpinnings of PPI and PPF in other clinical disorders.
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Affiliation(s)
- Laura F. Naysmith
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Veena Kumari
- Department of Psychology, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
- Centre for Cognitive Neuroscience, College of HealthMedicine and Life Sciences, Brunel University LondonUK
| | - Steven C. R. Williams
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
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Damestani NL, O'Daly O, Solana AB, Wiesinger F, Lythgoe DJ, Hill S, de Lara Rubio A, Makovac E, Williams SCR, Zelaya F. Revealing the mechanisms behind novel auditory stimuli discrimination: An evaluation of silent functional MRI using looping star. Hum Brain Mapp 2021; 42:2833-2850. [PMID: 33729637 PMCID: PMC8127154 DOI: 10.1002/hbm.25407] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 02/12/2021] [Accepted: 03/02/2021] [Indexed: 12/20/2022] Open
Abstract
Looping Star is a near‐silent, multi‐echo, 3D functional magnetic resonance imaging (fMRI) technique. It reduces acoustic noise by at least 25dBA, with respect to gradient‐recalled echo echo‐planar imaging (GRE‐EPI)‐based fMRI. Looping Star has successfully demonstrated sensitivity to the cerebral blood‐oxygen‐level‐dependent (BOLD) response during block design paradigms but has not been applied to event‐related auditory perception tasks. Demonstrating Looping Star's sensitivity to such tasks could (a) provide new insights into auditory processing studies, (b) minimise the need for invasive ear protection, and (c) facilitate the translation of numerous fMRI studies to investigations in sound‐averse patients. We aimed to demonstrate, for the first time, that multi‐echo Looping Star has sufficient sensitivity to the BOLD response, compared to that of GRE‐EPI, during a well‐established event‐related auditory discrimination paradigm: the “oddball” task. We also present the first quantitative evaluation of Looping Star's test–retest reliability using the intra‐class correlation coefficient. Twelve participants were scanned using single‐echo GRE‐EPI and multi‐echo Looping Star fMRI in two sessions. Random‐effects analyses were performed, evaluating the overall response to tones and differential tone recognition, and intermodality analyses were computed. We found that multi‐echo Looping Star exhibited consistent sensitivity to auditory stimulation relative to GRE‐EPI. However, Looping Star demonstrated lower test–retest reliability in comparison with GRE‐EPI. This could reflect differences in functional sensitivity between the techniques, though further study is necessary with additional cognitive paradigms as varying cognitive strategies between sessions may arise from elimination of acoustic scanner noise.
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Affiliation(s)
| | - Owen O'Daly
- Department of Neuroimaging, King's College London, London, UK
| | | | - Florian Wiesinger
- Department of Neuroimaging, King's College London, London, UK.,ASL Europe, GE Healthcare, Munich, Germany
| | - David J Lythgoe
- Department of Neuroimaging, King's College London, London, UK
| | - Simon Hill
- Department of Neuroimaging, King's College London, London, UK
| | | | - Elena Makovac
- Department of Neuroimaging, King's College London, London, UK
| | | | - Fernando Zelaya
- Department of Neuroimaging, King's College London, London, UK
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