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Connectivity within the brainstem is impaired in chronic fatigue syndrome
Presented During: Poster Session
Thursday, June 13, 2019: 12:45 PM - 02:45 PM
Authors:
Leighton Barnden1, Zack SHAN1, Donald Staines1, Sonya Marshall-Gradisnik1, Kevin Finegan2, Timothy Ireland2, Sandeep Bhuta2
Institutions:
1Griffith University, Gold Coast, QLD, Australia, 2Gold Coast University Hospital, Gold Coast, QLD, Australia
Introduction:
Chronic fatigue syndrome or myalgic encephalomyelitis (CFS) is a common, debilitating, multisystem disorder of uncertain pathogenesis, for which there exists evidence of dysregulation of the central nervous system (Carruthers. 2011). Quantitative T1wSE and T2wSE MRI studies of CFS have reported abnormal correlations with severity and autonomic measures which implied brainstem conduction deficits (Barnden. 2015, Barnden. 2016). The aim of this study was to directly measure connectivity within the brainstem (midbrain, pons and medulla) to confirm these implied deficits.
Methods:
Resting-state and Stroop-task functional MRI (rsfMRI and tfMRI) studies with companion hemodynamic and respiratory parameters were acquired for 27 healthy control (HC) and 45 chronic fatigue syndrome (CFS) subjects who met Fukuda criteria.
Each fMRI consisted of 1100 sagittal images and was acquired for 15 minutes on a 3T Siemens Skyra with a simultaneous multi-slice echo-planar imaging (EPI) pulse sequence(Auerbach. 2013) with 72 slices, multiband factor = 8, TR = 798 ms, TE = 30 ms, flip angle = 40°, acquisition matrix = 106 x 106, and voxel size 2 x 2 x 2 mm. Given that this study was focused on connectivity within the brain stem whereas routine spatial normalization of fMRI is focused on the cerebral cortex, we optimized spatial normalization for alignment of the brain stem.
A two-stage normalization strategy was performed on the single band reference (SBRef) images which were acquired before the 1100 fMRI volumes. Stage I was a 9DF deformation to the MNI T2 template constrained to 20° rotation for all 3 axes using FSL's 'flirt'. The 9DF accounted for individual magnification differences and angular constraints prevented some grossly inaccurate deformations.
For stage II, a mask was manually created using FSL's 'fslview' to isolate the brainstem and thalamus (Fig 1A). This mask excluded the ventral half of the pons and medulla where variable signal dropout/distortion was seen (Fig 1C). The deformed images from stage I were then further deformed with 6DF (rigid) to the T2 template within this thalamus-brainstem mask, again with 20° angular constraints.
The two deformations were then merged and applied to the 1100 EPI images. ROIs (Fig 1B) were constructed for the brainstem left and right medulla (Mdul_L, Mdul_R) and midbrain cuneiform nucleus (CnF_L, CnF_R) based on locations where abnormal CFS autonomic regressions were detected earlier (Barnden. 2016); and for the left and right hippocampus subiculum (subic_L and subic_R) which have medulla connections (Edlow. 2016). BOLD fMRI time series were computed for these ROIs and de-noised using 15 RETROICOR covariates derived from the physiological measures with the PhysIO toolbox (Kasper. 2017) and QA and principle components of white matter and CSF BOLD variations from the CONN toolbox (Whitfield-Gabrieli. 2012). Second-level testing for group difference in connectivities were then performed with CONN.
Fig 1A: Brainstem+thalamus mask to exclude ventral medulla & pons dropout (see C). B: Mdul_L&R, CnF_L&R and subic_L&R ROIs. C: Stem normalised tfMRI images of 6 subjects with Mdul_R, CnF_R ROIs.
Results:
For the task fMRI, group connectivity differences were found between Mdul_R and CnF_L (FDR-corrected P = 0.003) – see Fig 2; Mdul_L and CnF_L (0.004); and Mdul_R and subic_L (0.007). No connectivitiy differences were found in the resting state fMRI.
Fig 2 Connectivity between 3 pairs of nodes was significantly different for CFS and HC groups. Mdul_R right medulla; CnF_L left cuneiform nucleus; subic_L subiculum of left hippocampus.
Conclusions:
Significant differences were found between CFS and HC for connectivity within the brainstem between the rostral medulla and midbrain cuneiform nucleus and between the rostral medulla and subiculum of the left hippocampus. Impaired brainstem connectivity can explain reported autonomic and compensatory structural changes in CFS (Barnden. 2015, 2016), and may also explain the impaired cognitive performance, sleep quality and pain of CFS. The fast 0.798s multiband fMRI used here yielded suboptimal brainstem image quality although it will have provided superior physiological artefact correction. Studies with improved image quality may yield deeper insights into the nature and location of brainstem conduction deficits in CFS.
Disorders of the Nervous System:
Disorders of the Nervous System Other 1
Imaging Methods:
BOLD fMRI
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Brainstem
Cognition
Data Registration
Degenerative Disease
FUNCTIONAL MRI
Sleep
Sub-Cortical
1|2Indicates the priority used for review
My abstract is being submitted as a Software Demonstration.
No Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state Task-activation Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable Please indicate which methods were used in your research:
Functional MRI For human MRI, what field strength scanner do you use?
3.0T Provide references using author date format
Auerbach, E. J., J. Xu, E. Yacoub, S. Moeller and K. Ugurbil (2013). ‘Multiband accelerated spin-echo echo planar imaging with reduced peak RF power using time-shifted RF pulses.’ Magnetic Resonance in Medicine vol 69, no. 5, pp. 1261-1267.
Barnden, L. (2015). ‘Evidence in Chronic Fatigue Syndrome for severity-dependent upregulation of prefrontal myelination that is independent of anxiety and depression.’ NMR in Biomedicine, 28, no.3, pp. 404-413.
Barnden, L. (2016). ‘Autonomic correlations with MRI are abnormal in the brainstem vasomotor centre in Chronic Fatigue Syndrome.’ NeuroImage: Clinical, vol 11, pp. 530-537.
Carruthers, B. (2011). ‘Myalgic encephalomyelitis: International Consensus Criteria.’ Journal of Internal Medicine, vol 270, pp 327-338.
Edlow, B. L. (2016). ‘The Structural Connectome of the Human Central Homeostatic Network.’ Brain Connectivity, vol 6, no. 3, pp. 187-200.
Kasper, L. (2017). ‘The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.’ Journal of Neuroscience Methods, vol 276, pp. 56-72.
Whitfield-Gabrieli, S. (2012). ‘Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.’ Brain Connectivity vol 2, no. 3, pp. 125-141.
Presented During: Poster Session
Thursday, June 13, 2019: 12:45 PM - 02:45 PM
Authors:
Leighton Barnden1, Zack SHAN1, Donald Staines1, Sonya Marshall-Gradisnik1, Kevin Finegan2, Timothy Ireland2, Sandeep Bhuta2
Institutions:
1Griffith University, Gold Coast, QLD, Australia, 2Gold Coast University Hospital, Gold Coast, QLD, Australia
Introduction:
Chronic fatigue syndrome or myalgic encephalomyelitis (CFS) is a common, debilitating, multisystem disorder of uncertain pathogenesis, for which there exists evidence of dysregulation of the central nervous system (Carruthers. 2011). Quantitative T1wSE and T2wSE MRI studies of CFS have reported abnormal correlations with severity and autonomic measures which implied brainstem conduction deficits (Barnden. 2015, Barnden. 2016). The aim of this study was to directly measure connectivity within the brainstem (midbrain, pons and medulla) to confirm these implied deficits.
Methods:
Resting-state and Stroop-task functional MRI (rsfMRI and tfMRI) studies with companion hemodynamic and respiratory parameters were acquired for 27 healthy control (HC) and 45 chronic fatigue syndrome (CFS) subjects who met Fukuda criteria.
Each fMRI consisted of 1100 sagittal images and was acquired for 15 minutes on a 3T Siemens Skyra with a simultaneous multi-slice echo-planar imaging (EPI) pulse sequence(Auerbach. 2013) with 72 slices, multiband factor = 8, TR = 798 ms, TE = 30 ms, flip angle = 40°, acquisition matrix = 106 x 106, and voxel size 2 x 2 x 2 mm. Given that this study was focused on connectivity within the brain stem whereas routine spatial normalization of fMRI is focused on the cerebral cortex, we optimized spatial normalization for alignment of the brain stem.
A two-stage normalization strategy was performed on the single band reference (SBRef) images which were acquired before the 1100 fMRI volumes. Stage I was a 9DF deformation to the MNI T2 template constrained to 20° rotation for all 3 axes using FSL's 'flirt'. The 9DF accounted for individual magnification differences and angular constraints prevented some grossly inaccurate deformations.
For stage II, a mask was manually created using FSL's 'fslview' to isolate the brainstem and thalamus (Fig 1A). This mask excluded the ventral half of the pons and medulla where variable signal dropout/distortion was seen (Fig 1C). The deformed images from stage I were then further deformed with 6DF (rigid) to the T2 template within this thalamus-brainstem mask, again with 20° angular constraints.
The two deformations were then merged and applied to the 1100 EPI images. ROIs (Fig 1B) were constructed for the brainstem left and right medulla (Mdul_L, Mdul_R) and midbrain cuneiform nucleus (CnF_L, CnF_R) based on locations where abnormal CFS autonomic regressions were detected earlier (Barnden. 2016); and for the left and right hippocampus subiculum (subic_L and subic_R) which have medulla connections (Edlow. 2016). BOLD fMRI time series were computed for these ROIs and de-noised using 15 RETROICOR covariates derived from the physiological measures with the PhysIO toolbox (Kasper. 2017) and QA and principle components of white matter and CSF BOLD variations from the CONN toolbox (Whitfield-Gabrieli. 2012). Second-level testing for group difference in connectivities were then performed with CONN.
Fig 1A: Brainstem+thalamus mask to exclude ventral medulla & pons dropout (see C). B: Mdul_L&R, CnF_L&R and subic_L&R ROIs. C: Stem normalised tfMRI images of 6 subjects with Mdul_R, CnF_R ROIs.
Results:
For the task fMRI, group connectivity differences were found between Mdul_R and CnF_L (FDR-corrected P = 0.003) – see Fig 2; Mdul_L and CnF_L (0.004); and Mdul_R and subic_L (0.007). No connectivitiy differences were found in the resting state fMRI.
Fig 2 Connectivity between 3 pairs of nodes was significantly different for CFS and HC groups. Mdul_R right medulla; CnF_L left cuneiform nucleus; subic_L subiculum of left hippocampus.
Conclusions:
Significant differences were found between CFS and HC for connectivity within the brainstem between the rostral medulla and midbrain cuneiform nucleus and between the rostral medulla and subiculum of the left hippocampus. Impaired brainstem connectivity can explain reported autonomic and compensatory structural changes in CFS (Barnden. 2015, 2016), and may also explain the impaired cognitive performance, sleep quality and pain of CFS. The fast 0.798s multiband fMRI used here yielded suboptimal brainstem image quality although it will have provided superior physiological artefact correction. Studies with improved image quality may yield deeper insights into the nature and location of brainstem conduction deficits in CFS.
Disorders of the Nervous System:
Disorders of the Nervous System Other 1
Imaging Methods:
BOLD fMRI
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Brainstem
Cognition
Data Registration
Degenerative Disease
FUNCTIONAL MRI
Sleep
Sub-Cortical
1|2Indicates the priority used for review
My abstract is being submitted as a Software Demonstration.
No Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state Task-activation Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable Please indicate which methods were used in your research:
Functional MRI For human MRI, what field strength scanner do you use?
3.0T Provide references using author date format
Auerbach, E. J., J. Xu, E. Yacoub, S. Moeller and K. Ugurbil (2013). ‘Multiband accelerated spin-echo echo planar imaging with reduced peak RF power using time-shifted RF pulses.’ Magnetic Resonance in Medicine vol 69, no. 5, pp. 1261-1267.
Barnden, L. (2015). ‘Evidence in Chronic Fatigue Syndrome for severity-dependent upregulation of prefrontal myelination that is independent of anxiety and depression.’ NMR in Biomedicine, 28, no.3, pp. 404-413.
Barnden, L. (2016). ‘Autonomic correlations with MRI are abnormal in the brainstem vasomotor centre in Chronic Fatigue Syndrome.’ NeuroImage: Clinical, vol 11, pp. 530-537.
Carruthers, B. (2011). ‘Myalgic encephalomyelitis: International Consensus Criteria.’ Journal of Internal Medicine, vol 270, pp 327-338.
Edlow, B. L. (2016). ‘The Structural Connectome of the Human Central Homeostatic Network.’ Brain Connectivity, vol 6, no. 3, pp. 187-200.
Kasper, L. (2017). ‘The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data.’ Journal of Neuroscience Methods, vol 276, pp. 56-72.
Whitfield-Gabrieli, S. (2012). ‘Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.’ Brain Connectivity vol 2, no. 3, pp. 125-141.
