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First published online November 5, 2019

Action observation training promotes motor improvement and modulates functional network dynamic connectivity in multiple sclerosis

Claudio Cordani https://orcid.org/0000-0002-9014-7887, Paola Valsasina, Paolo Preziosa https://orcid.org/0000-0002-7826-0019, Alessandro Meani, Massimo Filippi https://orcid.org/0000-0002-5485-0479, and Maria A Rocca https://orcid.org/0000-0003-2358-4320 [email protected]View all authors and affiliations
Volume 27, Issue 1
https://doi.org/10.1177/1352458519887332

Abstract

We investigated modifications of resting state dynamic functional network connectivity (dFNC) following a 2-week action observation training (AOT) in 46 right-handed healthy controls (HC) and 41 patients with multiple sclerosis (pwMS) and dominant-hand motor impairment, who were randomized to AOT or control (C) training. PwMS had decreased baseline dFNC versus HC. After training, MS groups improved in right upper limb functions, mainly in AOT, and showed dFNC increase (larger in MS-AOT vs MS-C) in sensorimotor and cognitive networks (p range, ⩽0.001–0.01). Both HC groups showed decreased dFNC over time (p range, ⩽0.001–0.01). Clinical improvements following interventions correlated with sensorimotor and cognitive dFNC changes, suggesting their possible role in motor recovery.

Introduction

Upper limb motor impairment is frequent in patients with multiple sclerosis (pwMS), with a strong impact on quality of life. Action observation training (AOT) is promising for upper limb function, by acting through a modulation of the mirror neuron system (MNS).1 Recently, we proved the efficacy of 2-week AOT in pwMS,2 who improved their upper limb clinical scores and showed increased resting state (RS) functional connectivity (FC) of the left-hand motor cortex and left inferior frontal gyrus (IFG) with the cerebellum.2 In that study,2 static RS FC was assessed, that is, functional connections were analyzed over the whole RS fMRI acquisition.
Dynamic functional network connectivity (dFNC) is a novel technique investigating recurring FC patterns between networks, by analyzing time-varying correlations within small RS fMRI temporal segments.3 The dFNC recently helped to characterize functional reorganization occurring in pwMS.4–6 Since MS-related clinical impairment is mainly due to an hampered functional interaction between brain areas due to delayed communication, we hypothesized that studying time-varying FC following AOT could add novel insights into training-induced brain reorganization occurring in pwMS.2 To test this, we performed a post hoc dFNC analysis of RS fMRI data from pwMS patients undergoing AOT.2 Aims were to explore how AOT modifies time-varying RS FC and how dFNC modifications relate to concomitant clinical improvements.

Methods

Subjects, training, and clinical assessment

After approval from the local ethical standards committee, 41 pwMS and 46 healthy controls (HC) were enrolled.2 As previously described, subjects underwent a daily AOT or control (C) training.2 Clinical scores (Expanded Disability Status Scale (EDSS), hand muscle strength, manual dexterity, Paced Auditory Serial Addition Test (PASAT))2 and MRI scans were acquired at baseline (t0) and after 2 weeks (w2).2

MRI acquisition and dFNC analysis

RS fMRI scans for dynamic FC assessment and structural MRI were acquired at 3.0 T (Supplementary Methods).2 As reported in the Supplementary Methods, RS fMRI data were analyzed using the GIFT software and dFNC toolbox.3 Forty-one relevant independent components of interest (rICs) were assigned to the sensorimotor, default-mode network (DMN), attentional, executive control, visual, auditory, basal ganglia, and cerebellar networks (Figure 1). DFNC was computed using sliding-window correlations3 and hard-clustering analysis,3 which identified two average recurring connectivity patterns (states) using the elbow criterion. Average dwell times (the time spent in each FC state) and numbers of transitions between states were also computed.3
Figure 1. (a) Composite map of the 41 identified independent components (ICs) in all study subjects. ICs were sorted into eight subcategories (sensorimotor, default-mode, attentional, executive control, visual, auditory, basal ganglia, and cerebellar networks). Each color in the composite map corresponds to a different IC within a given subcategory. (b) Recurring dynamic functional network connectivity (dFNC) states in healthy controls (HC) at baseline (t0) (left panels) and dFNC comparison between patients with multiple sclerosis (pwMS) and HC (right panels) (p < 0.01, uncorrected for illustrative purposes). Differences are color coded according to their p value (color intensity) and dFNC connectivity strength (red–yellow: higher positive dFNC (or lower negative dFNC) in pwMS patients than in HC; blue: lower positive dFNC (or higher negative dFNC) in pwMS patients than in HC). Purple boxes indicate between-group differences in dFNC (discussed in detail in the text). (c) Differences in dFNC between baseline (t0) and week 2 (w2) in each study group (p < 0.01, uncorrected for illustrative purposes). Results are color coded according to their p value (color intensity) and dFNC connectivity strength (red–yellow: lower positive dFNC (or higher negative dFNC) at w2 than at t0; blue: higher positive dFNC (or lower negative dFNC) at w2 than at t0). Images are represented in neurological convention.
L: left; R: right; AOT: action observation training; C: control group; ATT: attentional network; AUD: auditory network; BG: basal ganglia network; CER: cerebellar network; DMN: default-mode network; EXE: executive control network; SMN: sensorimotor network; VIS: visual network.

Statistical analysis

T0 and w2 comparisons of element-wise dFNC strengths, dwell times, and number of state transitions were assessed using two-sample t tests, while their longitudinal changes were assessed using paired t tests (GIFT dFNC toolbox). Repeated measures 2 × 2 analysis of variance (ANOVA) models were used to test Treatment (AOT vs C) × Group (HC vs MS) dFNC changes and interactions over time. A p ⩽ 0.05 was considered significant. Results were assessed both correcting for the number of tested rICs (n = 41) using the false discovery rate (FDR) approach and, given the exploratory nature of this study, at uncorrected threshold. The Spearman’s rank correlation coefficient was used to perform baseline and longitudinal correlations between dFNC and clinical, neuropsychological, and motor measures.

Results

Clinical/conventional MRI assessment

Twenty-three HC/20 pwMS were randomized to AOT and 23 HC/21 pwMS to C-treatment.2 As previously reported,2 all groups improved at PASAT; both MS-groups and HC-AOT improved at several right upper limb/hand function scales (p range, ⩽0.001–0.03). MS-AOT improved more significantly than MS-C at right Jamar (p = 0.04).

DFNC analysis

Two recurring connectivity states (state 1, frequency = 69% and state 2, frequency = 31%) were identified. Compared to HC, pwMS had decreased dFNC of the cerebellum and basal ganglia with attentional, DMN, sensory, and motor networks (FDR corrected; Figure 1, Table 1). They also showed circumscribed visual and attentional-sensorimotor dFNC increase.
Table 1. Resting state (RS) dynamic functional network connectivity (dFNC) abnormalities in patients with multiple sclerosis (pwMS) versus healthy controls (HC) at baseline in state 1 and state 2.
 State 1State 2
 rIC (corresponding network)HC connectivity strengthpwMS connectivity strengthparIC (corresponding network)HC connectivity strengthpwMS connectivity strengthpa
pwMS > HCL orbitofrontal cortex (attentional)—postcentral gyrus (sensorimotor)0.2710.4850.001bThalamus (basal ganglia)—Cerebellum crus I (cerebellar)0.2220.3440.008b
R IFG/angular gyrus (attentional)—supramarginal gyrus (attentional)0.2650.4570.003bCerebellum lobule VI (cerebellar)—Temporal pole (auditory)−0.0410.1070.003b
Postcentral gyrus (sensorimotor)—R IFG/angular gyrus (attentional)0.2560.4160.002bHippocampus (DMN)—SMA (executive control)−0.071−0.2000.004
Pre/postcentral gyrus (sensorimotor)—L orbitofrontal cortex (attentional)0.0950.3240.004Precuneus (DMN)—IFG (executive control)−0.147−0.2500.003b
Calcarine cortex (visual)—MOG (visual)0.2580.4140.003bInsula (executive control)—Calcarine cortex (visual)−0.025−0.1590.006
MOG (visual)—lingual gyrus (visual)0.4200.5570.007Pre/postcentral gyrus (sensorimotor)—MTG (auditory)0.0730.2040.009
pwMS < HCInsula (attentional)—Cerebellum crus I (cerebellar)0.4750.3030.002bSTG (attentional)—Cerebellum crus I (cerebellar)0.3890.2470.002b
Insula (attentional)—medial SFG (executive)0.3620.1720.002bSTG (attentional)—MOG (visual)0.002−0.1470.005
R IFG/angular gyrus (attentional)—putamen (basal ganglia)0.3600.1810.008Putamen (basal ganglia)—STG (auditory)0.2510.0960.003b
Cerebellum crus I (cerebellar)—R IFG/angular gyrus (attentional)0.4690.3270.004bPutamen (basal ganglia)—Caudate nucleus (basal ganglia)0.1870.0510.004
Cerebellum lobule VI (cerebellar)—R IFG/angular gyrus (attentional)0.3430.091<0.001bPutamen (basal ganglia)—Cerebellum crus I (cerebellar)0.2390.1210.009b
Cerebellum crus I (cerebellar)—STG (attentional)0.5810.4400.007Thalamus (basal ganglia)—IFG (executive)0.173−0.010<0.001b
Medial SFG (executive)—L IPL (attentional)−0.269−0.0700.008Caudate nucleus (basal ganglia)—Medial SFG (executive control)0.070−0.0530.002b
Putamen (basal ganglia)—STG (auditory)0.4790.2670.003bHippocampus (DMN)—Cerebellum crus I (cerebellar)0.091−0.100.001b
Putamen (basal ganglia)—caudate nucleus (basal ganglia)0.3510.1710.005Cerebellum crus I (cerebellar)—STG (auditory)0.3280.1830.001b
Putamen (basal ganglia)—Cerebellum crus I (cerebellar)0.3750.181<0.001bCerebellum crus I (cerebellar)—Medial SFG (executive)0.2030.0740.004
 Thalamus (basal ganglia)—STG (auditory)0.5870.4450.009Cerebellum crus I (cerebellar)—Cuneus (visual)0.2860.1210.005b
Thalamus (basal ganglia)—calcarine cortex (visual)0.4070.2490.003bCerebellum crus I (cerebellar)—Calcarine cortex (visual)0.3770.2020.002b
Cerebellum crus I (cerebellar)—STG (auditory)0.5360.3620.002bCerebellum crus I (cerebellar)—Lingual gyrus/MOG (visual)0.3470.106<0.001b
Cerebellum crus I (cerebellar)—Cingulum/precuneus (DMN)0.4700.2900.001bCerebellum lobule VI (cerebellar)—Vermis (cerebellar)0.4070.2460.004
Cerebellum crus I (cerebellar)—Calacarine cortex (visual)0.5000.2800.004bCingulum/precuneus (DMN)—Cuneus (visual)0.121−0.0360.001b
Cerebellum lobule VI (cerebellar)—Angular gyrus (DMN)0.3030.1600.005SMA (executive control)—Thalamus (basal ganglia)0.2740.1610.009
Hippocampus (DMN)—Precuneus (DMN)0.3330.1610.006IFG (executive control)—STG (auditory)0.1400.0130.005b
Angular gyrus (DMN)—Medial SFG (executive)0.5190.325<0.001bPostcentral gyrus (sensorimotor)—Cerebellum crus I (cerebellar)0.2270.0870.006b
L angular gyrus (DMN)—Medial SFG (executive control)0.5940.4410.002bL pre/postcentral gyrus (sensorimotor)—Cerebellum crus I (cerebellar)0.1910.0460.009b
————L pre/postcentral gyrus (sensorimotor)—STG (auditory)0.2000.0730.009b
————MOG (visual)—STG (auditory)0.118−0.0210.004b
rIC: relevant independent component; L: left; R: right; IFG: inferior frontal gyrus; DMN: default-mode network; SMA: supplementary motor area; MOG: middle occipital gyrus; MTG: middle temporal gyrus; STG: superior temporal gyrus; SFG: superior frontal gyrus; IPL: inferior parietal lobule.
a
Two-sample t test.
b
p values surviving the correction for multiple comparisons using a false discovery rate approach.
Detailed longitudinal within-group modifications are reported in the Supplementary Results, while those significant at the Time × Treatment interaction are reported in Table 2. This latter analysis (FDR corrected) showed significant effects of AOT versus C-treatment in MS patients versus HC for the (1) increased state 1 sensorimotor-DMN dFNC (p = 0.03), (2) increased state 1 sensorimotor-visual dFNC (p = 0.03), (3) increased state 2 DMN-visual dFNC (p = 0.009), and (4) decreased state 2 attentional-executive dFNC (p = 0.02). A significant effect of AOT versus C-treatment was also found in HC versus MS patients for the decreased state 1 cerebellar-attentional dFNC (p = 0.01).
Table 2. Significant changes over time of dynamic functional network connectivity (dFNC) strength in action observation training (AOT) and control (C) groups of patients with multiple sclerosis (pwMS) and healthy controls (HC).
   rIC (corresponding network)dFNC strength at t0dFNC strength at w2papb
MS-AOTIncreaseState 1L pre/postcentral gyrus (sensorimotor)—Hippocampus (DMN)0.1200.4420.001c0.03
L pre/postcentral gyrus (sensorimotor)—MOG (visual)0.3350.5810.003c0.03
Postcentral gyrus (sensorimotor)—IFG (executive control)0.2040.3610.0060.05
MOG (visual)—L orbitofrontal cortex (attentional)0.2040.3480.0080.42
State 2Insula (attentional)—L angular gyrus (DMN)0.0810.1980.0070.21
Hippocampus (DMN)—Cuneus (visual)0.0160.1940.002c0.009
Pre/postcentral gyrus (sensorimotor)—Postcentral gyrus (sensorimotor)0.0680.2970.002c0.13
Pre/postcentral gyrus (sensorimotor)—L pre/postcentral gyrus (sensorimotor)0.1490.3520.0060.20
DecreaseState 1—————
State 2Insula (attentional)—IFG (executive control)0.131−0.0100.001c0.02
Medial SFG (executive control)—Lingual gyrus/MOG (visual)0.1660.0210.0050.002
MS-CIncreaseState 1Precuneus (DMN)—Thalamus (basal ganglia)−0.0810.1760.0060.19
ACC (DMN)—STG (auditory)0.4470.5640.0070.44
State 2—————
DecreaseState 1—————
State 2L angular gyrus (DMN)—SMA (executive control)0.3470.2040.0090.18
HC-AOTIncreaseState 1—————
State 2Temporal pole (auditory)—Insula (attentional)0.0630.2010.0020.16
Hippocampus (DMN)—Insula (attentional)0.0350.1290.0020.76
DecreaseState 1Putamen (basal ganglia)—Cuneus (visual)0.287−0.0020.003c0.42
Putamen (basal ganglia)—Lingual gyrus/MOG (visual)0.3930.0580.006c0.20
Thalamus (basal ganglia)—Precuneus (DMN)0.1560.0010.0030.19
Cerebellum crus I (cerebellar)—Supramarginal gyrus (attentional)0.197−0.0190.002c0.01
Cerebellum crus I (cerebellar)—Postcentral gyrus (sensorimotor)0.3970.0630.005c0.63
Cerebellum crus I (cerebellar)—L pre/postcentral gyrus (sensorimotor)0.4560.1270.001c0.95
State 2—————
HC-CIncreaseState 1—————
State 2Lingual gyrus (visual)—L orbitofrontal cortex (attentional)−0.0340.1390.0010.08
DecreaseState 1Putamen (basal ganglia)—R angular gyrus (attentional)0.2540.0990.0030.07
R lingual gyrus (visual)—Precuneus (DMN)0.2800.1180.0080.50
  State 2Hippocampus (DMN)—L orbitofrontal cortex (attentional)−0.0260.1120.001c0.36
Cuneus (visual)—Cerebellum lobule VI (cerebellar)0.2610.086<0.001c0.74
Lingual gyrus/MOG (visual)—STG (auditory)0.2700.1470.010.52
MOG (visual)—R angular gyrus (DMN)0.2350.0670.002c0.21
MOG (visual)—R lingual gyrus (visual)0.2090.0290.002c0.07
Lingual gyrus (visual)—Precuneus (DMN)0.4050.2820.010.08
The dFNC changes over time significant at the interaction analysis are highlighted in bold (see main text for details). rIC: relevant independent component; t0: baseline; w2: week 2; MS: multiple sclerosis; L: left; R: right; DMN: default-mode network; MOG: middle occipital gyrus; IFG: inferior frontal gyrus; SFG: superior frontal gyrus; ACC: anterior cingulate cortex; STG: superior temporal gyrus; SMA: supplementary motor area.
a
Paired-sample t test.
b
Time × Treatment (AOT vs C) × Group (HC vs MS) interaction.
c
p values surviving the correction for multiple comparisons using a false discovery rate approach.
No cross-sectional differences in dwell times and number of state transitions were observed between HC and pwMS, nor within any group over time (p range = 0.09–0.93).

Correlation analysis

All correlations are reported at uncorrected threshold. In MS patients, lower baseline state 1 cerebellar-auditory dFNC correlated with worse left finger tapping (FT) (r = 0.4, p = 0.04), while lower state 1 DMN-executive dFNC was associated with worse PASAT (r = 0.43, p = 0.03).
The longitudinal analysis showed, in MS-AOT, a significant association of increased state 1 dFNC between the postcentral gyrus (sensorimotor network) and IFG (executive control network) with improved right Pinch (r = 0.77, p = 0.05). Increased state 2 dFNC between the DMN, attentional and visual networks were associated with improved right FT (r = 0.52, p = 0.01) and Pinch (r = 0.49, p = 0.04). In MS-C, better right FT performance correlated with increased state 2 auditory-cerebellar dFNC (r = 0.59, p = 0.01). In HC-AOT, improved right Pinch and PASAT correlated with reduced state 1 dFNC between the basal ganglia and executive control/sensorimotor networks (r =−0.67, p = 0.01 and r =−0.70, p = 0.01) and cerebellar-sensorimotor dFNC (r =−0.51, p = 0.04).

Discussion

In this post hoc analysis, we assessed the ability of dFNC in detecting the effects of a 2-week AOT in pwMS with upper limb motor deficits. Compared to previous static FC results, which highlighted RS FC changes mainly in the MNS and IFG,2 here we found AOT-related functional reorganization between a larger number of functional networks, and distinct correlations between clinical improvements and dFNC changes in each study group. This is probably due to the fact that dFNC specifically investigates delayed temporal interactions between networks, a main cause of clinical impairment in pwMS.
Before training, pwMS showed circumscribed dFNC increase versus HC, probably linked to a compensatory response to brain disease-related structural damage.7 However, baseline results mainly highlighted a widespread reduction of dFNC in pwMS versus HC, associated with worse clinical disability. These findings suggest that RS FC decreases in patients with longer disease duration and more severe clinical disability.
At w2, significant dFNC changes involving a considerable amount of networks were detected in all groups, probably due to the complexity of exercised gestures requiring multi-system integration.
In MS-AOT, an increased dFNC, which correlated with clinical improvements, was found at w2 versus t0 between attentional, DMN, executive control, and sensorimotor networks. Modifications occurred in the IFG, part of the attentional and executive control networks. The IFG is important for complex objects manipulation and is also active during observation and imitation of movements.8 In MS-AOT, motor function improvements were also correlated with modifications of dFNC with the DMN. This network plays a central role in brain functioning, contributing to spatial attention.9 This may explain why a specific effect of AOT (significant at the interaction analysis) in pwMS was found in modulating dFNC of hippocampal DMN regions with other networks. Huiskamp et al.10 also described a correlation between improved visuospatial functions and the functional embedding of the hippocampus in the DMN after a motor training.
In MS-C, complex dFNC longitudinal changes were found at the level of DMN regions. The different type of stimulation (inanimate natural landscapes for C group, motor task observation for AOT group) may have resulted in a different DMN engagement.
Notably, in both HC-groups a widespread dFNC decrease involving the basal ganglia, sensorimotor, and executive control networks was detected, which also correlated with motor and cognitive improvements in AOT group. This is in line with previous literature in HC showing that an amelioration of motor ability is associated with reduced neuronal activity in regions subserving the trained functions.11 Conversely, in pwMS, increased activity could play a compensatory role and may help to limit clinical deficits.2,7
Both in pwMS and HC, the AOT approach showed a greater effect on dynamic interactions of the attentional network compared to the control treatment. This phenomenon is probably due to the key role of regions like the IFG in the action observation/action execution matching system.
While these results must be considered exploratory, given the training limitations and the potential dFNC pitfalls described elsewhere2,4,12 and the presence of results not surviving to FDR correction, this study suggests that 2-week AOT might be able to promote clinical improvements and modulate dFNC of sensorimotor and cognitive networks.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: P.V. received speakers honoraria from ExceMED. P.P. received speakers honoraria from Biogen Idec, Novartis, and ExceMED. M.F. is editor-in-chief of the Journal of Neurology; received compensation for consulting services and/or speaking activities from Biogen Idec, Merck Serono, Novartis, and Teva Pharmaceutical Industries; and receives research support from Biogen Idec, Merck Serono, Novartis, Teva Pharmaceutical Industries, Roche, Italian Ministry of Health, Fondazione Italiana Sclerosi Multipla, and ARiSLA (Fondazione Italiana di Ricerca per la SLA). M.A.R. received speakers honoraria from Biogen Idec, Novartis, Teva Neurosciences, Merck Serono, Genzyme, Roche, Bayer, and Celgene and receives research support from the Italian Ministry of Health and Fondazione Italiana Sclerosi Multipla. C.C. and A.M. have nothing to disclose.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Study partially supported by Fondazione Italiana Sclerosi Multipla (FISM2012/R/15) and Italian Ministry of Health (RF-2011-02350374).

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Published In

Volume 27, Issue 1
Pages: 139 - 146
Article first published online: November 5, 2019
Issue published: January 2021

Keywords

  1. Multiple sclerosis
  2. training
  3. hand motor impairment
  4. action observation
  5. time-varying functional connectivity

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© The Author(s), 2019.
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PubMed: 31686577

Authors

Affiliations

Claudio Cordani
Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
https://orcid.org/0000-0002-9014-7887
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Paola Valsasina
Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Paolo Preziosa
Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
https://orcid.org/0000-0002-7826-0019
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Alessandro Meani
Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
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Massimo Filippi
Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy/Vita-Salute San Raffaele University, Milan, Italy
https://orcid.org/0000-0002-5485-0479
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Maria A Rocca
Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy/Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
https://orcid.org/0000-0003-2358-4320
[email protected]
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Notes

Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132 Milan, Italy. [email protected]

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