1
|
Mills EP, Bosma RL, Rogachov A, Cheng JC, Osborne NR, Kim JA, Besik A, Bhatia A, Davis KD. Pretreatment Brain White Matter Integrity Associated With Neuropathic Pain Relief and Changes in Temporal Summation of Pain Following Ketamine. THE JOURNAL OF PAIN 2024; 25:104536. [PMID: 38615801 DOI: 10.1016/j.jpain.2024.104536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/07/2024] [Accepted: 04/09/2024] [Indexed: 04/16/2024]
Abstract
Neuropathic pain (NP) is a prevalent condition often associated with heightened pain responsiveness suggestive of central sensitization. Neuroimaging biomarkers of treatment outcomes may help develop personalized treatment strategies, but white matter (WM) properties have been underexplored for this purpose. Here we assessed whether WM pathways of the default mode network (DMN: medial prefrontal cortex [mPFC], posterior cingulate cortex, and precuneus) and descending pain modulation system (periaqueductal gray [PAG]) are associated with ketamine analgesia and attenuated temporal summation of pain (TSP, reflecting central sensitization) in NP. We used a fixel-based analysis of diffusion-weighted imaging data to evaluate WM microstructure (fiber density [FD]) and macrostructure (fiber bundle cross-section) within the DMN and mPFC-PAG pathways in 70 individuals who underwent magnetic resonance imaging and TSP testing; 35 with NP who underwent ketamine treatment and 35 age- and sex-matched pain-free individuals. Individuals with NP were assessed before and 1 month after treatment; those with ≥30% pain relief were considered responders (n = 18), or otherwise as nonresponders (n = 17). We found that WM structure within the DMN and mPFC-PAG pathways did not differentiate responders from nonresponders. However, pretreatment FD in the anterior limb of the internal capsule correlated with pain relief (r=.48). Moreover, pretreatment FD in the DMN (left mPFC-precuneus/posterior cingulate cortex; r=.52) and mPFC-PAG (r=.42) negatively correlated with changes in TSP. This suggests that WM microstructure in the DMN and mPFC-PAG pathway is associated with the degree to which ketamine reduces central sensitization. Thus, fixel metrics of WM structure may hold promise to predict ketamine NP treatment outcomes. PERSPECTIVE: We used advanced fixel-based analyses of MRI diffusion-weighted imaging data to identify pretreatment WM microstructure associated with ketamine outcomes, including analgesia and markers of attenuated central sensitization. Exploring associations between brain structure and treatment outcomes could contribute to a personalized approach to treatment for individuals with NP.
Collapse
Affiliation(s)
- Emily P Mills
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Rachael L Bosma
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Anton Rogachov
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Joshua C Cheng
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Natalie R Osborne
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Junseok A Kim
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
| | - Ariana Besik
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Anuj Bhatia
- Department of Anesthesia and Pain Management, University Health Network, Toronto, Ontario, Canada; Department of Anesthesia, University of Toronto, Toronto, Ontario, Canada
| | - Karen D Davis
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
2
|
Maskell G. How on earth did I miss that? BMJ 2024; 385:q847. [PMID: 38604672 DOI: 10.1136/bmj.q847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
|
3
|
Robinson EA, Gleeson J, Arun AH, Clemente A, Gaillard A, Rossetti MG, Brambilla P, Bellani M, Crisanti C, Curran HV, Lorenzetti V. Measuring white matter microstructure in 1,457 cannabis users and 1,441 controls: A systematic review of diffusion-weighted MRI studies. FRONTIERS IN NEUROIMAGING 2023; 2:1129587. [PMID: 37554654 PMCID: PMC10406316 DOI: 10.3389/fnimg.2023.1129587] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/09/2023] [Indexed: 08/10/2023]
Abstract
INTRODUCTION Cannabis is the most widely used regulated substance by youth and adults. Cannabis use has been associated with psychosocial problems, which have been partly ascribed to neurobiological changes. Emerging evidence to date from diffusion-MRI studies shows that cannabis users compared to controls show poorer integrity of white matter fibre tracts, which structurally connect distinct brain regions to facilitate neural communication. However, the most recent evidence from diffusion-MRI studies thus far has yet to be integrated. Therefore, it is unclear if white matter differences in cannabis users are evident consistently in selected locations, in specific diffusion-MRI metrics, and whether these differences in metrics are associated with cannabis exposure levels. METHODS We systematically reviewed the results from diffusion-MRI imaging studies that compared white matter differences between cannabis users and controls. We also examined the associations between cannabis exposure and other behavioral variables due to changes in white matter. Our review was pre-registered in PROSPERO (ID: 258250; https://www.crd.york.ac.uk/prospero/). RESULTS We identified 30 diffusion-MRI studies including 1,457 cannabis users and 1,441 controls aged 16-to-45 years. All but 6 studies reported group differences in white matter integrity. The most consistent differences between cannabis users and controls were lower fractional anisotropy within the arcuate/superior longitudinal fasciculus (7 studies), and lower fractional anisotropy of the corpus callosum (6 studies) as well as higher mean diffusivity and trace (4 studies). Differences in fractional anisotropy were associated with cannabis use onset (4 studies), especially in the corpus callosum (3 studies). DISCUSSION The mechanisms underscoring white matter differences are unclear, and they may include effects of cannabis use onset during youth, neurotoxic effects or neuro adaptations from regular exposure to tetrahydrocannabinol (THC), which exerts its effects by binding to brain receptors, or a neurobiological vulnerability predating the onset of cannabis use. Future multimodal neuroimaging studies, including recently developed advanced diffusion-MRI metrics, can be used to track cannabis users over time and to define with precision when and which region of the brain the white matter changes commence in youth cannabis users, and whether cessation of use recovers white matter differences. SYSTEMATIC REVIEW REGISTRATION www.crd.york.ac.uk/prospero/, identifier: 258250.
Collapse
Affiliation(s)
- Emily Anne Robinson
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
| | - John Gleeson
- Digital Innovation in Mental Health and Well-Being Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
| | - Arush Honnedevasthana Arun
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
| | - Adam Clemente
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
| | - Alexandra Gaillard
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
- Centre for Mental Health and Brain Sciences, Swinburne University of Technology, Melbourne, VIC, Australia
| | - Maria Gloria Rossetti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Camilla Crisanti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - H. Valerie Curran
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
- Clinical Psychopharmacology Unit, University College London, London, United Kingdom
| | - Valentina Lorenzetti
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural and Health Sciences, Australian Catholic University, Melbourne, VIC, Australia
| |
Collapse
|
4
|
Thomson P, Vijayakumar N, Fuelscher I, Malpas CB, Hazell P, Silk TJ. White matter and sustained attention in children with attention/deficit-hyperactivity disorder: A longitudinal fixel-based analysis. Cortex 2022; 157:129-141. [PMID: 36283135 DOI: 10.1016/j.cortex.2022.09.006] [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: 12/23/2021] [Revised: 05/29/2022] [Accepted: 09/20/2022] [Indexed: 12/15/2022]
Abstract
Sustained attention is a cognitive function with known links to academic success and mental health disorders such as attention/deficit-hyperactivity disorder (ADHD). Several functional networks are critical to sustained attention, however the association between white matter maturation in tracts linking functional nodes and sustained attention in typical and atypical development is unknown. 309 diffusion-weighted imaging scans were acquired from 161 children and adolescents (80 ADHD, 81 control) at up to three timepoints over ages 9-14. A fixel-based analysis approach was used to calculate mean fiber density and fiber-bundle cross section in tracts of interest. Sustained attention was measured using omission errors and response time variability on the out-of-scanner sustained attention to response task. Linear mixed effects models examined associations of age, group and white matter metrics with sustained attention. Greater fiber density in the bilateral superior longitudinal fasciculus (SLF) I and right SLF II was associated with fewer attention errors in the control group only. In ADHD and control groups, greater fiber density in the left ILF and right thalamo-premotor pathway, as well as greater fiber cross-section in the left SLF I and II and right SLF III, was associated with better sustained attention. Relationships were consistent across the age span. Results suggest that greater axon diameter or number in the dorsal and middle SLF may facilitate sustained attention in neurotypical children but does not assist those with ADHD potentially due to disorder-related alterations in this region. Greater capacity for information transfer across the SLF was associated with attention maintenance in 9-14-year-olds regardless of diagnostic status, suggesting white matter macrostructure may also be important for attention maintenance. White matter and sustained attention associations were consistent across the longitudinal study, according with the stability of structural organization over this time. Future studies can investigate modifiability of white matter properties through ADHD medications.
Collapse
Affiliation(s)
- Phoebe Thomson
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Murdoch Children's Research Institute, Melbourne, Australia.
| | | | - Ian Fuelscher
- School of Psychology, Deakin University, Melbourne, Australia
| | - Charles B Malpas
- Murdoch Children's Research Institute, Melbourne, Australia; Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia; Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Australia
| | - Philip Hazell
- Discipline of Psychiatry, The University of Sydney, Sydney, Australia
| | - Timothy J Silk
- Department of Paediatrics, The University of Melbourne, Melbourne, Australia; Murdoch Children's Research Institute, Melbourne, Australia; School of Psychology, Deakin University, Melbourne, Australia
| |
Collapse
|
5
|
Poudel GR, Barnett A, Akram M, Martino E, Knibbs LD, Anstey KJ, Shaw JE, Cerin E. Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10977. [PMID: 36078704 PMCID: PMC9517821 DOI: 10.3390/ijerph191710977] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/26/2022] [Accepted: 08/31/2022] [Indexed: 06/02/2023]
Abstract
The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34-97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data.
Collapse
Affiliation(s)
- Govinda R. Poudel
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
| | - Anthony Barnett
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
| | - Muhammad Akram
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
| | - Erika Martino
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Luke D. Knibbs
- School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia
- Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia
| | - Kaarin J. Anstey
- School of Psychology, University of New South Wales, Sydney, NSW 2052, Australia
- UNSW Ageing Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia
- Neuroscience Research Australia, Sydney, NSW 2031, Australia
| | - Jonathan E. Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
| | - Ester Cerin
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
| |
Collapse
|