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Caporale AS, Chiarelli AM, Biondetti E, Villani A, Lipp I, Di Censo D, Tomassini V, Wise RG. Changes of brain parenchyma free water fraction reflect tissue damage and impaired processing speed in multiple sclerosis. Hum Brain Mapp 2024; 45:e26761. [PMID: 38895882 PMCID: PMC11187860 DOI: 10.1002/hbm.26761] [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: 06/30/2023] [Revised: 05/13/2024] [Accepted: 06/02/2024] [Indexed: 06/21/2024] Open
Abstract
Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS) is related to increased tissue loss. The use of mcDESPOT (multicomponent driven single pulse observation of T1 and T2), a 3D imaging method which exploits both the T1 and T2 contrasts, allows FWF to be derived in clinically feasible times. However, this method has not been used to quantify changes of FWF and their potential clinical impact in MS. The aim of this study is to investigate the changes in FWF in MS patients and their relationship with tissue damage and cognition, under the hypothesis that FWF is a proxy of clinically meaningful tissue loss. To this aim, we tested the relationship between FWF, MS lesion burden and information processing speed, evaluated via the Symbol Digit Modalities Test (SDMT). In addition to standard sequences, used for T1- and T2-weighted lesion delineation, the mcDESPOT sequence with 1.7 mm isotropic resolution and a diffusion weighted imaging protocol (b = 0, 1200 s/mm2, 40 diffusion directions) were employed at 3 T. The fractional anisotropy map derived from diffusion data was used to define a subject-specific white matter (WM) atlas. Brain parenchyma segmentation returned masks of gray matter (GM) and WM, and normal-appearing WM (NAWM), in addition to the T1 and T2 lesion masks (T1L and T2L, respectively). Ninety-nine relapsing-remitting MS patients (age = 43.3 ± 9.9 years, disease duration 12.3 ± 7.7 years) were studied, together with twenty-five healthy controls (HC, age = 38.8 ± 11.0 years). FWF was higher in GM and NAWM of MS patients, compared to GM and WM of HC (both p < .001). In MS patients, FWF was the highest in the T1L and GM, followed by T2L and NAWM, respectively. FWF increased significantly with T1L and T2L volume (ρ ranging from 0.40 to 0.58, p < .001). FWF in T2L was strongly related to both T1L volume and the volume ratio T1L/T2L (ρ = 0.73, p < .001). MS patients performed worse than HC in the processing speed test (mean ± SD: 54.1 ± 10.3 for MS, 63.8 ± 10.8 for HC). FWF in GM, T2L, perilesional tissue and NAWM increased with SDMT score reduction (ρ = -0.30, -0.29, -0.33 respectively and r = -.30 for T2L, all with p < .005). A regional analysis, conducted to determine which NAWM regions were of particular importance to explain the relationship between FWF and cognitive impairment, revealed that FWF spatial variance was negatively related to SDMT score in the corpus callosum and the superior longitudinal fasciculus, WM structures known to be associated with cognitive impairment, in addition to the left corticospinal tract, the sagittal stratum, the right anterior limb of internal capsule. In conclusion, we found excess free water in brain parenchyma of MS patients, an alteration that involved not only MS lesions, but also the GM and NAWM, impinging on brain function and negatively associated with cognitive processing speed. We suggest that the FWF metric, derived from noninvasive, rapid MRI acquisitions and bearing good biological interpretability, may prove valuable as an MRI biomarker of tissue damage and associated cognitive impairment in MS.
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Affiliation(s)
- Alessandra Stella Caporale
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Alessandro Villani
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Ilona Lipp
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Davide Di Censo
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Valentina Tomassini
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical Neurology‘SS. Annunziata’ University HospitalChietiItaly
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Richard Geoffrey Wise
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
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Biondetti E, Chiarelli AM, Germuska M, Lipp I, Villani A, Caporale AS, Patitucci E, Murphy K, Tomassini V, Wise RG. Breath-hold BOLD fMRI without CO 2 sampling enables estimation of venous cerebral blood volume: potential use in normalization of stimulus-evoked BOLD fMRI data. Neuroimage 2024; 285:120492. [PMID: 38070840 DOI: 10.1016/j.neuroimage.2023.120492] [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: 02/22/2023] [Revised: 10/30/2023] [Accepted: 12/06/2023] [Indexed: 01/13/2024] Open
Abstract
BOLD fMRI signal has been used in conjunction with vasodilatory stimulation as a marker of cerebrovascular reactivity (CVR): the relative change in cerebral blood flow (CBF) arising from a unit change in the vasodilatory stimulus. Using numerical simulations, we demonstrate that the variability in the relative BOLD signal change induced by vasodilation is strongly influenced by the variability in deoxyhemoglobin-containing cerebral blood volume (CBV), as this source of variability is likely to be more prominent than that of CVR. It may, therefore, be more appropriate to describe the relative BOLD signal change induced by an isometabolic vasodilation as a proxy of deoxygenated CBV (CBVdHb) rather than CVR. With this in mind, a new method was implemented to map a marker of CBVdHb, termed BOLD-CBV, based on the normalization of voxel-wise BOLD signal variation by an estimate of the intravascular venous BOLD signal from voxels filled with venous blood. The intravascular venous BOLD signal variation, recorded during repeated breath-holding, was extracted from the superior sagittal sinus in a cohort of 27 healthy volunteers and used as a regressor across the whole brain, yielding maps of BOLD-CBV. In the same cohort, we demonstrated the potential use of BOLD-CBV for the normalization of stimulus-evoked BOLD fMRI by comparing group-level BOLD fMRI responses to a visuomotor learning task with and without the inclusion of voxel-wise vascular covariates of BOLD-CBV and the BOLD signal change per mmHg variation in end-tidal carbon dioxide (BOLD-CVR). The empirical measure of BOLD-CBV accounted for more between-subject variability in the motor task-induced BOLD responses than BOLD-CVR estimated from end-tidal carbon dioxide recordings. The new method can potentially increase the power of group fMRI studies by including a measure of vascular characteristics and has the strong practical advantage of not requiring experimental measurement of end-tidal carbon dioxide, unlike traditional methods to estimate BOLD-CVR. It also more closely represents a specific physiological characteristic of brain vasculature than BOLD-CVR, namely blood volume.
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Affiliation(s)
- Emma Biondetti
- Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy.
| | - Antonio Maria Chiarelli
- Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy
| | - Michael Germuska
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Ilona Lipp
- Department of Neurophysics, Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Alessandro Villani
- Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy
| | - Alessandra S Caporale
- Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy
| | - Eleonora Patitucci
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Valentina Tomassini
- Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK; MS Centre, Neurology Unit, 'SS. Annunziata' University Hospital, Chieti, Italy; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK; Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, UK
| | - Richard G Wise
- Department of Neurosciences, Imaging, and Clinical Sciences, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, 'G. D'Annunzio' University of Chieti-Pescara, Chieti, Italy; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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Patitucci E, Lipp I, Stickland RC, Wise RG, Tomassini V. Changes in brain perfusion with training-related visuomotor improvement in MS. Front Mol Neurosci 2023; 16:1270393. [PMID: 38025268 PMCID: PMC10665528 DOI: 10.3389/fnmol.2023.1270393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/26/2023] [Indexed: 12/01/2023] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system. A better understanding of the mechanisms supporting brain plasticity in MS would help to develop targeted interventions to promote recovery. A total of 29 MS patients and 19 healthy volunteers underwent clinical assessment and multi-modal MRI acquisition [fMRI during serial reaction time task (SRT), DWI, T1w structural scans and ASL of resting perfusion] at baseline and after 4-weeks of SRT training. Reduction of functional hyperactivation was observed in MS patients following the training, shown by the stronger reduction of the BOLD response during task execution compared to healthy volunteers. The functional reorganization was accompanied by a positive correlation between improvements in task accuracy and the change in resting perfusion after 4 weeks' training in right angular and supramarginal gyri in MS patients. No longitudinal changes in WM and GM measures and no correlation between task performance improvements and brain structure were observed in MS patients. Our results highlight a potential role for CBF as an early marker of plasticity, in terms of functional (cortical reorganization) and behavioral (performance improvement) changes in MS patients that may help to guide future interventions that exploit preserved plasticity mechanisms.
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Affiliation(s)
- Eleonora Patitucci
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, United Kingdom
| | - Ilona Lipp
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, United Kingdom
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Rachael Cecilia Stickland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, United Kingdom
| | - Richard G. Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, United Kingdom
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara “G. d’Annunzio,”Chieti, Italy
- Department of Neurosciences, Imaging and Clinical Sciences, University of Chieti-Pescara “G. d’Annunzio,”Chieti, Italy
| | - Valentina Tomassini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff, United Kingdom
- Institute for Advanced Biomedical Technologies, University of Chieti-Pescara “G. d’Annunzio,”Chieti, Italy
- Department of Neurosciences, Imaging and Clinical Sciences, University of Chieti-Pescara “G. d’Annunzio,”Chieti, Italy
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom
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Mascali D, Villani A, Chiarelli AM, Biondetti E, Lipp I, Digiovanni A, Pozzilli V, Caporale AS, Rispoli MG, Ajdinaj P, D'Apolito M, Grasso E, Sensi SL, Murphy K, Tomassini V, Wise RG. Pathophysiology of multiple sclerosis damage and repair: Linking cerebral hypoperfusion to the development of irreversible tissue loss in multiple sclerosis using magnetic resonance imaging. Eur J Neurol 2023; 30:2348-2356. [PMID: 37154298 PMCID: PMC7615142 DOI: 10.1111/ene.15827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 03/10/2023] [Accepted: 05/01/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND AND PURPOSE Reduced cerebral perfusion has been observed in multiple sclerosis (MS) and may contribute to tissue loss both acutely and chronically. Here, we test the hypothesis that hypoperfusion occurs in MS and relates to the presence of irreversible tissue damage. METHODS In 91 patients with relapsing MS and 26 healthy controls (HC), gray matter (GM) cerebral blood flow (CBF) was assessed using pulsed arterial spin labeling. GM volume, T1 hypointense and T2 hyperintense lesion volumes (T1LV and T2LV, respectively), and the proportion of T2-hyperintense lesion volume that appears hypointense on T1-weighted magnetic resonance imaging (T1LV/T2LV) were quantified. GM CBF and GM volume were evaluated globally, as well as regionally, using an atlas-based approach. RESULTS Global GM CBF was lower in patients (56.9 ± 12.3 mL/100 g/min) than in HC (67.7 ± 10.0 mL/100 g/min; p < 0.001), a difference that was widespread across brain regions. Although total GM volume was comparable between groups, significant reductions were observed in a subset of subcortical structures. GM CBF negatively correlated with T1LV (r = -0.43, p = 0.0002) and T1LV/T2LV (r = -0.37, p = 0.0004), but not with T2LV. CONCLUSIONS GM hypoperfusion occurs in MS and is associated with irreversible white matter damage, thus suggesting that cerebral hypoperfusion may actively contribute and possibly precede neurodegeneration by hampering tissue repair abilities in MS.
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Affiliation(s)
- Daniele Mascali
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Alessandro Villani
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Antonio M. Chiarelli
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Emma Biondetti
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Ilona Lipp
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Anna Digiovanni
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Valeria Pozzilli
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Alessandra S. Caporale
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Marianna G. Rispoli
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Paola Ajdinaj
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Maria D'Apolito
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Eleonora Grasso
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Department of PaediatricsSS. Annunziata University HospitalChietiItaly
| | - Stefano L. Sensi
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Behavioral Neurology and Molecular Neurology Units, Centre for Advanced Studies and TechnologyG. d'Annunzio University of Chieti‐PescaraChietiItaly
| | - Kevin Murphy
- Cardiff University Brain Research Imaging Centre, School of Physics and AstronomyCardiff UniversityCardiffUK
| | - Valentina Tomassini
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- MS Centre, Department of Clinical NeurologySS. Annunziata University HospitalChietiItaly
| | - Richard G. Wise
- Department of Neurosciences, Imaging, and Clinical SciencesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical TechnologiesG. d'Annunzio University of Chieti‐PescaraChietiItaly
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
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5
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Rispoli MG, D'Apolito M, Pozzilli V, Tomassini V. Lessons from immunotherapies in multiple sclerosis. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:293-311. [PMID: 36803817 DOI: 10.1016/b978-0-323-85555-6.00013-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The improved understanding of multiple sclerosis (MS) neurobiology alongside the development of novel markers of disease will allow precision medicine to be applied to MS patients, bringing the promise of improved care. Combinations of clinical and paraclinical data are currently used for diagnosis and prognosis. The addition of advanced magnetic resonance imaging and biofluid markers has been strongly encouraged, since classifying patients according to the underlying biology will improve monitoring and treatment strategies. For example, silent progression seems to contribute significantly more than relapses to overall disability accumulation, but currently approved treatments for MS act mainly on neuroinflammation and offer only a partial protection against neurodegeneration. Further research, involving traditional and adaptive trial designs, should strive to halt, repair or protect against central nervous system damage. To personalize new treatments, their selectivity, tolerability, ease of administration, and safety must be considered, while to personalize treatment approaches, patient preferences, risk-aversion, and lifestyle must be factored in, and patient feedback used to indicate real-world treatment efficacy. The use of biosensors and machine-learning approaches to integrate biological, anatomical, and physiological parameters will take personalized medicine a step closer toward the patient's virtual twin, in which treatments can be tried before they are applied.
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Affiliation(s)
- Marianna G Rispoli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Maria D'Apolito
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valeria Pozzilli
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy
| | - Valentina Tomassini
- Institute for Advanced Biomedical Technologies (ITAB) and Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; MS Centre, SS. Annunziata University Hospital, Chieti, Italy.
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6
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Lingo VanGilder J, Hooyman A, Bosch PR, Schaefer SY. Generalizing the predictive relationship between 1-month motor skill retention and Rey-Osterrieth Delayed Recall scores from nondemented older adults to individuals with chronic stroke: a short report. J Neuroeng Rehabil 2021; 18:94. [PMID: 34082761 PMCID: PMC8173502 DOI: 10.1186/s12984-021-00886-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/25/2021] [Indexed: 11/24/2022] Open
Abstract
Motor learning is fundamental to motor rehabilitation outcomes. There is growing evidence from non-neurological populations supporting the role of visuospatial memory function in motor learning, but current predictive models of motor recovery of individuals with stroke generally exclude cognitive measures, thereby overlooking the potential link between motor learning and visuospatial memory. Recent work has demonstrated that a clinical test of visuospatial memory (Rey-Osterrieth Complex Figure Delayed Recall) may predict 1-month skill learning in older adults; however, whether this relationship persists in individuals with chronic stroke remains unknown. The purpose of this short report was to validate previous findings using Rey-Osterrieth Complex Figure Delayed Recall test scores to predict motor learning and determine if this relationship generalized to a set of individuals post-stroke. Two regression models (one including Delayed Recall scores and one without) were trained using data from non-stroke older adults. To determine the extent to which Delayed Recall test scores impacted prediction accuracy of 1-month skill learning in older adults, we used leave-one-out cross-validation to evaluate the prediction error between models. To test if this predictive relationship generalized to individuals with chronic ischemic stroke, we then tested each trained model on an independent stroke dataset. Results indicated that in both stroke and older adult datasets, inclusion of Delayed Recall scores explained significantly more variance of 1-month skill performance than models that included age, education, and baseline motor performance alone. This proof-of-concept suggests that the relationship between delayed visuospatial memory and 1-month motor skill performance generalizes to individuals with chronic stroke, and supports the idea that visuospatial testing may provide prognostic insight into clinical motor rehabilitation outcomes.
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Affiliation(s)
| | - Andrew Hooyman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, USA
| | - Pamela R Bosch
- Department of Physical Therapy, Northern Arizona University, Phoenix Campus, Phoenix, USA
| | - Sydney Y Schaefer
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, USA.
- Department of Physical Therapy and Athletic Training, University of Utah, Salt Lake City, USA.
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7
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Winter M, Tallantyre EC, Brice TAW, Robertson NP, Jones DK, Chamberland M. Tract-specific MRI measures explain learning and recall differences in multiple sclerosis. Brain Commun 2021; 3:fcab065. [PMID: 33959710 PMCID: PMC8088789 DOI: 10.1093/braincomms/fcab065] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/07/2021] [Accepted: 03/01/2021] [Indexed: 12/19/2022] Open
Abstract
Cognitive difficulties are common and a key concern for people with multiple sclerosis. Advancing knowledge of the role of white matter pathology in multiple sclerosis-related cognitive impairment is essential as both occur early in the disease with implications for early intervention. Consequently, this cross-sectional study asked whether quantifying the relationships between lesions and specific white matter structures could better explain co-existing cognitive differences than whole brain imaging measures. Forty participants with relapse-onset multiple sclerosis underwent cognitive testing and MRI at 3 Tesla. They were classified as cognitively impaired (n = 24) or unimpaired (n = 16) and differed across verbal fluency, learning and recall tasks corrected for intelligence and education (corrected P-values = 0.007-0.04). The relationships between lesions and white matter were characterized across six measures: conventional voxel-based T2 lesion load, whole brain tractogram load (lesioned volume/whole tractogram volume), whole bundle volume, bundle load (lesioned volume/whole bundle volume), Tractometry (diffusion-tensor and high angular resolution diffusion measures sampled from all bundle streamlines) and lesionometry (diffusion measures sampled from streamlines traversing lesions only). The tract-specific measures were extracted from corpus callosum segments (genu and isthmus), striato-prefrontal and -parietal pathways, and the superior longitudinal fasciculi (sections I, II and III). White matter measure-task associations demonstrating at least moderate evidence against the null hypothesis (Bayes Factor threshold < 0.2) were examined using independent t-tests and covariate analyses (significance level P < 0.05). Tract-specific measures were significant predictors (all P-values < 0.05) of task-specific clinical scores and diminished the significant effect of group as a categorical predictor in Story Recall (isthmus bundle load), Figure Recall (right striato-parietal lesionometry) and Design Learning (left superior longitudinal fasciculus III volume). Lesion load explained the difference in List Learning, whereas Letter Fluency was not associated with any of the imaging measures. Overall, tract-specific measures outperformed the global lesion and tractogram load measures. Variation in regional lesion burden translated to group differences in tract-specific measures, which in turn, attenuated differences in individual cognitive tasks. The structural differences converged in temporo-parietal regions with particular influence on tasks requiring visuospatial-constructional processing. We highlight that measures quantifying the relationships between tract-specific structure and multiple sclerosis lesions uncovered associations with cognition masked by overall tract volumes and global lesion and tractogram loads. These tract-specific white matter quantifications show promise for elucidating the relationships between neuropathology and cognition in multiple sclerosis.
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Affiliation(s)
- Mia Winter
- Department of Clinical Neuropsychology, University Hospital of Wales, Cardiff, CF14 4XW, UK
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Emma C Tallantyre
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
- Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, CF14 4XW, UK
| | - Thomas A W Brice
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
| | - Neil P Robertson
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
- Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, CF14 4XW, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria 3000, Australia
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
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