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Original Article
Reversible alterations of the neuronal activity in spontaneous intracranial hypotension
Cephalalgia 0(0) 1–10 ! International Headache Society 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0333102415585085 cep.sagepub.com
Shiori Amemiya1, Koichi Takahashi2, Tatsuo Mima2, Naoki Yoshioka3, Soichiro Miki4 and Kuni Ohtomo1 Abstract Aim: The aim of this article is to investigate the pathophysiology underlying the alternation of the cognitive function and neuronal activity in spontaneous intracranial hypotension (SIH). Methods: Fifteen patients with SIH underwent resting-state functional magnetic resonance imaging and working-memory (WM) test one day before and one month after a surgical operation. Alternation of the cognitive function and spontaneous neuronal activity measured as amplitude of the low-frequency fluctuations (ALFF) and the functional connectivity of the default-mode network (DMN) and frontoparietal networks (FPNs) were evaluated. Results: WM performance significantly improved post-operatively. Whole-brain linear regression analysis of the ALFF revealed a positive correlation between cognitive performance change and ALFF change in the precuneus while a negative correlation was found in the bilateral orbitofrontal cortices (OFCs) and right medial frontal cortex (MFC). The ALFF changes normalised with the WM performance improvement post-operatively. The FPN activity in the right OFC was also increased pre-operatively. Partial correlation analysis revealed a significant correlation between WM performance and right OFC activity controlled for right FPN activity. Conclusions: The abnormal activity of the OFCs and MFC that is not originating from the synchronous intrinsic network activity, together with the decreased activity of the central node of the DMN, could lead to cognitive impairment in SIH that is reversible through restoration of the cerebrospinal fluid. Keywords Spontaneous intracranial hypotension, working memory, spontaneous neuronal activity, fMRI Date received: 14 March 2015; revised: 8 April 2015; accepted: 11 April 2015
Introduction Spontaneous intracranial hypotension (SIH) or cerebrospinal fluid (CSF) hypovolaemia is a rare disorder mostly affecting young and middle-aged individuals (1). In SIH, CSF leakage in the spine leads to loss of dynamics of intracranial pressure or the buoyancy of the brain, resulting in various neurologic symptoms characterised by postural headache (1). Traction on intracranial structures is considered to directly cause headache and some visuoauditory symptoms such as diplopia, photophobia, tinnitus, and hypacusia. The impairments, notably headache, could also be attributed to subdural fluid collections, dural thickening, or engorgement of cerebral venous sinuses that occur to compensate for the volume reduction within the rigid skull, as is explained by the modern Monro-Kellie hypothesis (2).
Although frank dementia (3) or coma resulting from diencephalic or brainstem herniation in SIH is not frequent (4), subtle cognitive deficits often unrecognised before treatment are experientially known to be common (1). However, the pathophysiology of the neural subsystems underlying the impairment remains unknown. In contrast to hydrocephalus, in which 1 Department of Radiology, Graduate School of Medicine, University of Tokyo, Japan 2 Department of Neurosurgery, Sanno Hospital, Japan 3 Department of Radiology, Sanno Hospital, Japan 4 22nd Century Medical and Research Center, University of Tokyo, Japan
Corresponding author: Shiori Amemiya, Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. Email: [email protected]
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(CEP)
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2 raised intracranial pressure or ventricular dilatation has been shown to directly or indirectly, e.g. via hypoperfusion, lead to permanent cerebral injury (5), no specific neural pathology has been demonstrated in SIH (6). However, based on the clinical features resembling frontotemporal dementia affecting memory and executive function, as well as imaging characteristics of the sagging brain, mechanical compression of the basal structures of the brain has been hypothesised as a possible cause of the cognitive impairment (3,6). Examination of the patients with SIH was thus considered to provide an opportunity to study a distinctive reversible form of cognitive impairment. High-order cognitive functions such as memory and executive function rely on coordination and interaction between the large-scale brain networks (7). Working memory is the ability to transiently store and simultaneously manipulate information to be used (8). Numerous studies have reported activation of the frontoparietal areas during the execution of n-back tasks, and that the synchronous interactions among these regions comprising frontoparietal networks (FPNs) support working-memory processes (9). Synchronised neuronal activity has been demonstrated not only during the task execution but also in the absence of tasks within both task-positive network and tasknegative or the default-mode network (DMN) (10). Development of the resting-state functional magnetic resonance imaging (fMRI) enabled investigation of these large-scale intrinsic neuronal networks, and recent studies on healthy individuals have indicated that increased synchronisation within the DMN contributes to the facilitation or monitoring of cognitive performance (11). Furthermore, clinical studies on neurodegenerative and psychiatric diseases have consistently revealed dysfunction of the resting-state networks, notably the DMN, emphasising the importance of the large-scale brain networks’ function for cognition (12). While conventional task-based fMRI reveals changes in neuronal activity that is limited in small areas of activation for a specific task, resting-state fMRI can be used to study the whole-brain neural process, even for patients who cannot perform a task (12). In the present study we thus investigated changes in cognitive function and the neuronal activity to test the hypothesis that cognitive impairment in SIH is associated with disruption of normal spontaneous activity of the FPNs and/or DMN nodes.
Material and methods Participants From June 2013 to May 2014, 15 patients (eight men; mean age 44 13 years) who fulfilled the diagnostic
Cephalalgia 0(0) criteria for SIH by the Headache Classification Subcommittee of the International Headache Society (2004) (13) were prospectively included in the study. All patients underwent brain MRI and a conventional myelography for diagnosis and identification of the sites of leakage. A single epidural blood patch was performed on the day following the pre-operative evaluation. Informed consent was obtained from all participants before data acquisition. All procedures were in compliance with the Declaration of Helsinki, and the Sanno hospital ethics committee approved the study.
Data acquisition Data were acquired one day before and one month following a blood patch procedure. MRI was performed with a single whole-body 1.5-T MR unit (Achieva, Philips Healthcare, Best, the Netherlands) by using an eight-channel phased-array head coil. In addition to high spatial resolution T1-weighted and T2-weighted imaging, 10-minute resting-state fMRI with singleshot T2*-weighted gradient-echo echo-planar imaging (repetition time/echo time 2000/30 ms, 90-degree flip angle, 64 64 matrix, 256 256 mm2 field of view, 4-mm-thick sections, no gap, 34 sections, and 304 frames) were acquired. All participants were instructed to lie still and remain awake with their eyes open. Behavioural assessment including the Mini-Mental State Examination (MMSE), Headache Impact Test (HIT6) (14), and a working-memory (n-back) test were performed outside the scanner after imaging examinations. The n-back task comprised a one-back and a two-back test. Participants viewed a series of white numbers (0–9) presented in the centre of a black screen. Each number was presented for 1200 ms followed by 1000 ms of interstimulus interval. The target was any number that was identical to the one presented one or two trials back, respectively. Participants responded to the target (33% of 60 trials) by pressing a button on a laptop computer. Each response and response time was recorded and the performance was evaluated using the correct answer rate.
Data analysis fMRI data were pre-processed and analysed using SPM 8 software (www.fil.ion.ucl.ac.uk/spm/software/spm8/) implemented in Matlab 713.0 (MathWorks, Sherborn, MA, USA). The first four volumes were discarded to allow for T1 equilibration effects. Data were realigned to the first volume, corrected for differences in acquisition time between sections, spatially normalised to standard Montreal Neurological Institute (MNI) stereotaxic coordinates using subject-specific
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(CEP)
[1–10] [PREPRINTER stage]
3
Amemiya et al. 1 mm-iso-voxel T1-weighted images aligned to fMRI data, and spatially smoothed with a Gaussian kernel with 8-mm full width at half maximum. Following linear detrending, data were temporally band-pass filtered at 0.01–0.1 Hz. Possible artefacts from motion and physiological noise were modelled as nuisance regressors (rigid-body translations and rotations determined from the realignment stage and signal changes in the white matter and CSF) and removed with linear regression.
Behavioural performance and spontaneous brain activity Spontaneous neuronal activity was measured as amplitude of low-frequency fluctuations (ALFF) by computing the integral of the signal amplitude in the selected frequency domain that was scaled for standardisation by dividing by the global mean ALFF value (15). In order to detect the areas whose ALFF change is significantly correlated with working-memory performance change after the therapeutic intervention, a wholebrain linear regression analysis of changes in ALFF was performed with the correct answer rate change of the two-back test as a regressor. All whole-brain statistics were corrected for multiple comparisons with a voxel-wise threshold of p < 0.001 and a minimum cluster size of 11 voxels to achieve a corrected map-wise false-positive probability of p < 0.05 as determined by 5000 iteration Monte Carlo simulation using the AlphaSim program included in AFNI (http://afni. nimh.nih.gov/afni). While it is not considered to directly affect arterial blood flow, intracranial hypotension leads to dilatation of the venous systems, which can theoretically increase the amplitude of the blood-oxygenation-level dependent (BOLD) signal since the amplitude is dependent on fraction of venous blood within each voxel (16). However, the effect of vasodilation is expected to be cancelled out through scaling unless it affects the BOLD measurement heterogeneously across the brain. In order to assess the possibility, we examined the correlation between ALFF and CSF pressure. The effect of motion artefact was also examined. ALFF of each region was measured as an average within a 5-mm-diameter sphere centred on the peak of each region of interest (ROI).
Functional connectivity analysis For the functional-connectivity analysis, FSL tools (www.fmrib.ox.ac.uk/fsl) were used to carry out dual regression analysis that allows for voxel-wise comparisons of resting functional connectivity (17). Preprocessed data were temporally concatenated across participants to create a single 4D dataset that was
subsequently decomposed using probabilistic independent component analysis (ICA) (MELODIC Ver. 3.13) to identify large-scale patterns of functional connectivity, with the number of components constrained to 25 (18). Using group ICA spatial maps in a linear model fit against the separate fMRI data sets, matrices describing temporal dynamics for each component and individual were obtained (spatial regression) (17). Participant-specific spatial maps were subsequently estimated by applying the time-course matrices in a linear model fit against the associated fMRI data set (temporal regression) (17). Based on the hypothesis as well as the results of the ALFF analysis showing involvement of the main nodes of the networks, namely, precuneus, bilateral orbitofrontal cortices (OFCs), and medial frontal cortex (MFC), the network analysis was focused on the DMN and FPNs. Correlation analysis was performed to test if cognitive performance was modulated with the regions’ connectivity with the network to which each node belongs; i.e. DMN in precuneus, right and left FPN (RFPN, LFPN) in right and left OFC (ROFC, LOFC), respectively, and RFPN in right MFC. In order to examine the contribution of network-level activity and to examine if the source of the abnormal neural activity affecting the cognitive performance is network level or local, linear partial correlations tests were performed. For all the analyses using ROIs, linear relationship was examined with Pearson’s correlation and statistical threshold was set at p < 0.05 (Bonferroni correction for multiple comparisons).
Results Patient characteristics and behavioural performance A synopsis of clinical findings in all 15 patients is provided in Table 1. Although patients had multiple symptoms other than postural headache, they had spontaneously recovered from visual and hearing problems and all had normal visual and auditory acuity at the time of the pre-operative evaluation. Apparent preoperative cognitive impairment was seen in a patient with large subdural haematomas (P12) who underwent evacuation of the haematoma prior to referral to our hospital. Overall, headache immediately began to improve after the blood patch procedure (HIT score: pre- vs. post-operative: 70 11 vs. 44 7.6, p < .0001, t ¼ 8.2), despite a temporary enlargement of subdural haematoma in two patients (P1, P5). All patients have remained free from relapse for 10 to 21 months after the surgery. Two-back working-memory performance significantly improved after surgery (0.66 0.29 vs.
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Cephalalgia 0(0)
Table 1. Synopsis of clinical findings. No
Age/Sex
Days after onset
Symptomsa
Imaging findingsb
Leakagec
Pressure (mmH2O)
HITd (pre-/post-)
MMSE (pre-/post-)
SDHe
History of interventionf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
57/M 75/M 51/M 47/F 38/F 26/F 32/F 45/M 43/M 39/F 41/M 53/M 29/M 46/F 56/F
30 41 79 15 21 14 22 43 87 55 84 82 34 86 30
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 2 1, 2 1, 2 2 1, 2 1, 2 2 1, 2 1, 2 1, 2 1 1 1 1 2
CTL NA TL TL CTL CTL CTL CTL CL CL CTL TL NA CTL CTL
25 41 0 7 10 20 63 53 178 38 9 10 0 5.8 9
67, 74, 76, 74, 78, 75, 78, 64, 66, 78, 66, 76, 74, 64, 36,
29, 29, 27, 30, 30, 26, 28, 30, 30, 30, 30, 13, 30, 30, 30,
þ þ þ þ þ þ þ þ þ þ þ þ
None Evacuation None None None None None Steroid None Evacuation None Evacuation None None Blood patch
1, 3 2, 1, 1, 1, 1, 1 1, 1, 2, 1, 1, 1, 1,
3, 5 3 3 3, 5 3, 4, 5 3, 4, 5 2, 5 3, 5 5 3, 2,
3, 5 5
4, 5 3
46 46 36 41 60 40 50 54 36 36 48 48 38 52 36
29 29 27 30 29 26 28 30 30 30 30 27 30 30 30
a
0 ¼ postural headache, 1 ¼ neck stiffness, 2 ¼ tinnitus, 3 ¼ hypacusia, 4 ¼ photophobia, 5 ¼ nausea. 1 ¼ evidence of low CSF pressure on MR; 2 ¼ evidence of CSF leakage on conventional myelography or CT myelography. c Site of leakage: C ¼ cervical; T ¼ thoracic; L ¼ lumbar spine; NA ¼ not applicable (not identified in the spine). d Headache inventory score (pre-/post-op). e Presence (þ) or absence () of subdural effusion/haematoma. f Evacuation ¼ evacuation of the subdural haematoma. Data are preoperative findings unless otherwise indicated. M: male; F: female; CSF: cerebrospinal fluid; CT: computed tomography; HIT: Headache Impact Test; MMSE: Mini-Mental State Examination; SDH: subdural haematoma. b
0.79 0.13; p ¼ 0.047, t ¼ 1.8), while one-back performance was at ceiling pre-operatively (0.99 0.04 vs. 1 0; p ¼ 0.17, t ¼ 1). The false alarm rate was small for two-back test (0.04 0.04 vs. 0.02 0.02; p ¼ 0.14, t ¼ 1.11) and zero for one-back test both pre- and postoperatively across patients. Averaged response time for correct answers did not significantly differ between the two conditions (two-back: 1020 183 vs. 943 126 ms; p ¼ 0.07, t ¼ 1.6; one-back: 826 148 vs. 822 136 ms; p ¼ 0.46, t ¼ –0.09). Percentage of correct answers for the two-back test was henceforward used as a cognitive index.
Working-memory performance and spontaneous brain activity Whole-brain linear regression analysis revealed a significant positive correlation between changes in workingmemory performance and ALFF of the precuneus/posterior cingulate cortex (MNI coordinates: 2, 48, 42) as well as a negative correlation between changes in cognitive performance and ALFF of the bilateral OFCs (Brodmann area, BA11; MNI coordinates, ROFC, 34, 60, 6; LOFC 38, 60, 14) and the right MFC (BA8/24; MNI coordinates, 10, 16, 54)
(voxel-wise threshold of p < 0.001, map-wise threshold of p < 0.05), (precuneus, r ¼ 0.89, p < 0.0001; ROFC, r ¼ 0.83, p ¼ 0.0001; MFC, r ¼ 0.83, p ¼ 0.0002; LOFC, r ¼ 0.82, p ¼ 0.0002) (Figure 1). Cognitive performance and ALFF were significantly correlated preoperatively in all four regions (precuneus, r ¼ 0.76, p ¼ 0.001; ROFC, r ¼ 0.89, p < 0.0001; MFC, r ¼ 0.70, p ¼ 0.004; LOFC, r ¼ 0.94, p < 0.0001). However the correlations were attenuated post-operatively as a result of ALFF changes associated with cognitive task performance improvement in all (precuneus, r ¼ 0.52, p ¼ 0.049; ROFC, r ¼ 0.40, p ¼ 0.14; MFC, r ¼ 0.05, p ¼ 0.87; LOFC, r ¼ 0.45, p ¼ 0.089) (Figure 2). Whole-brain linear regression analysis revealed a significant positive correlation between ALFF and CSF pressure in the basal (interpeduncular and prepontine) cisterns, which might be due to low CSF fluctuations under hypovolaemia. However, no area with negative correlation indicating the presence of vascular confounds was found. Average head motion was neither different between prevs. post-operative state (p ¼ 0.28, t ¼ 1.13) nor correlated with task performance (pre-/post-operative: r ¼ 0.0009, p ¼ 0.99; r ¼ 0.21, p ¼ 0.46) or with the ALFF of the
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Amemiya et al.
Positive
Precuneus
LOFC
1
1
0.8
0.8
0.6
R
Δ%correct
Δ%correct
0.6
0.4
0.2
0.4
0.2
0
0 -0.2
r = 0.89 p < 0.0001
-0.2
r = -0.82 p = 0.0002
Negative
-0.4 -0.4
0
0.2
0.4
0.6
0.8
1
-0.8
-0.6
-0.4
1
0.8
0.8
0.6
0.6
0.4
0.2
7
0
0.2
0.4
0.6
0.4
0.2
0 0
r = -0.83 p = 0.0002
-0.2
-0.4
T-value
-0.2
ROFC
1
Δ%correct
Δ%correct
MFC
Whole Brain analysis P
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Original Article
Reversible alterations of the neuronal activity in spontaneous intracranial hypotension
Cephalalgia 0(0) 1–10 ! International Headache Society 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/0333102415585085 cep.sagepub.com
Shiori Amemiya1, Koichi Takahashi2, Tatsuo Mima2, Naoki Yoshioka3, Soichiro Miki4 and Kuni Ohtomo1 Abstract Aim: The aim of this article is to investigate the pathophysiology underlying the alternation of the cognitive function and neuronal activity in spontaneous intracranial hypotension (SIH). Methods: Fifteen patients with SIH underwent resting-state functional magnetic resonance imaging and working-memory (WM) test one day before and one month after a surgical operation. Alternation of the cognitive function and spontaneous neuronal activity measured as amplitude of the low-frequency fluctuations (ALFF) and the functional connectivity of the default-mode network (DMN) and frontoparietal networks (FPNs) were evaluated. Results: WM performance significantly improved post-operatively. Whole-brain linear regression analysis of the ALFF revealed a positive correlation between cognitive performance change and ALFF change in the precuneus while a negative correlation was found in the bilateral orbitofrontal cortices (OFCs) and right medial frontal cortex (MFC). The ALFF changes normalised with the WM performance improvement post-operatively. The FPN activity in the right OFC was also increased pre-operatively. Partial correlation analysis revealed a significant correlation between WM performance and right OFC activity controlled for right FPN activity. Conclusions: The abnormal activity of the OFCs and MFC that is not originating from the synchronous intrinsic network activity, together with the decreased activity of the central node of the DMN, could lead to cognitive impairment in SIH that is reversible through restoration of the cerebrospinal fluid. Keywords Spontaneous intracranial hypotension, working memory, spontaneous neuronal activity, fMRI Date received: 14 March 2015; revised: 8 April 2015; accepted: 11 April 2015
Introduction Spontaneous intracranial hypotension (SIH) or cerebrospinal fluid (CSF) hypovolaemia is a rare disorder mostly affecting young and middle-aged individuals (1). In SIH, CSF leakage in the spine leads to loss of dynamics of intracranial pressure or the buoyancy of the brain, resulting in various neurologic symptoms characterised by postural headache (1). Traction on intracranial structures is considered to directly cause headache and some visuoauditory symptoms such as diplopia, photophobia, tinnitus, and hypacusia. The impairments, notably headache, could also be attributed to subdural fluid collections, dural thickening, or engorgement of cerebral venous sinuses that occur to compensate for the volume reduction within the rigid skull, as is explained by the modern Monro-Kellie hypothesis (2).
Although frank dementia (3) or coma resulting from diencephalic or brainstem herniation in SIH is not frequent (4), subtle cognitive deficits often unrecognised before treatment are experientially known to be common (1). However, the pathophysiology of the neural subsystems underlying the impairment remains unknown. In contrast to hydrocephalus, in which 1 Department of Radiology, Graduate School of Medicine, University of Tokyo, Japan 2 Department of Neurosurgery, Sanno Hospital, Japan 3 Department of Radiology, Sanno Hospital, Japan 4 22nd Century Medical and Research Center, University of Tokyo, Japan
Corresponding author: Shiori Amemiya, Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. Email: [email protected]
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2 raised intracranial pressure or ventricular dilatation has been shown to directly or indirectly, e.g. via hypoperfusion, lead to permanent cerebral injury (5), no specific neural pathology has been demonstrated in SIH (6). However, based on the clinical features resembling frontotemporal dementia affecting memory and executive function, as well as imaging characteristics of the sagging brain, mechanical compression of the basal structures of the brain has been hypothesised as a possible cause of the cognitive impairment (3,6). Examination of the patients with SIH was thus considered to provide an opportunity to study a distinctive reversible form of cognitive impairment. High-order cognitive functions such as memory and executive function rely on coordination and interaction between the large-scale brain networks (7). Working memory is the ability to transiently store and simultaneously manipulate information to be used (8). Numerous studies have reported activation of the frontoparietal areas during the execution of n-back tasks, and that the synchronous interactions among these regions comprising frontoparietal networks (FPNs) support working-memory processes (9). Synchronised neuronal activity has been demonstrated not only during the task execution but also in the absence of tasks within both task-positive network and tasknegative or the default-mode network (DMN) (10). Development of the resting-state functional magnetic resonance imaging (fMRI) enabled investigation of these large-scale intrinsic neuronal networks, and recent studies on healthy individuals have indicated that increased synchronisation within the DMN contributes to the facilitation or monitoring of cognitive performance (11). Furthermore, clinical studies on neurodegenerative and psychiatric diseases have consistently revealed dysfunction of the resting-state networks, notably the DMN, emphasising the importance of the large-scale brain networks’ function for cognition (12). While conventional task-based fMRI reveals changes in neuronal activity that is limited in small areas of activation for a specific task, resting-state fMRI can be used to study the whole-brain neural process, even for patients who cannot perform a task (12). In the present study we thus investigated changes in cognitive function and the neuronal activity to test the hypothesis that cognitive impairment in SIH is associated with disruption of normal spontaneous activity of the FPNs and/or DMN nodes.
Material and methods Participants From June 2013 to May 2014, 15 patients (eight men; mean age 44 13 years) who fulfilled the diagnostic
Cephalalgia 0(0) criteria for SIH by the Headache Classification Subcommittee of the International Headache Society (2004) (13) were prospectively included in the study. All patients underwent brain MRI and a conventional myelography for diagnosis and identification of the sites of leakage. A single epidural blood patch was performed on the day following the pre-operative evaluation. Informed consent was obtained from all participants before data acquisition. All procedures were in compliance with the Declaration of Helsinki, and the Sanno hospital ethics committee approved the study.
Data acquisition Data were acquired one day before and one month following a blood patch procedure. MRI was performed with a single whole-body 1.5-T MR unit (Achieva, Philips Healthcare, Best, the Netherlands) by using an eight-channel phased-array head coil. In addition to high spatial resolution T1-weighted and T2-weighted imaging, 10-minute resting-state fMRI with singleshot T2*-weighted gradient-echo echo-planar imaging (repetition time/echo time 2000/30 ms, 90-degree flip angle, 64 64 matrix, 256 256 mm2 field of view, 4-mm-thick sections, no gap, 34 sections, and 304 frames) were acquired. All participants were instructed to lie still and remain awake with their eyes open. Behavioural assessment including the Mini-Mental State Examination (MMSE), Headache Impact Test (HIT6) (14), and a working-memory (n-back) test were performed outside the scanner after imaging examinations. The n-back task comprised a one-back and a two-back test. Participants viewed a series of white numbers (0–9) presented in the centre of a black screen. Each number was presented for 1200 ms followed by 1000 ms of interstimulus interval. The target was any number that was identical to the one presented one or two trials back, respectively. Participants responded to the target (33% of 60 trials) by pressing a button on a laptop computer. Each response and response time was recorded and the performance was evaluated using the correct answer rate.
Data analysis fMRI data were pre-processed and analysed using SPM 8 software (www.fil.ion.ucl.ac.uk/spm/software/spm8/) implemented in Matlab 713.0 (MathWorks, Sherborn, MA, USA). The first four volumes were discarded to allow for T1 equilibration effects. Data were realigned to the first volume, corrected for differences in acquisition time between sections, spatially normalised to standard Montreal Neurological Institute (MNI) stereotaxic coordinates using subject-specific
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(CEP)
[1–10] [PREPRINTER stage]
3
Amemiya et al. 1 mm-iso-voxel T1-weighted images aligned to fMRI data, and spatially smoothed with a Gaussian kernel with 8-mm full width at half maximum. Following linear detrending, data were temporally band-pass filtered at 0.01–0.1 Hz. Possible artefacts from motion and physiological noise were modelled as nuisance regressors (rigid-body translations and rotations determined from the realignment stage and signal changes in the white matter and CSF) and removed with linear regression.
Behavioural performance and spontaneous brain activity Spontaneous neuronal activity was measured as amplitude of low-frequency fluctuations (ALFF) by computing the integral of the signal amplitude in the selected frequency domain that was scaled for standardisation by dividing by the global mean ALFF value (15). In order to detect the areas whose ALFF change is significantly correlated with working-memory performance change after the therapeutic intervention, a wholebrain linear regression analysis of changes in ALFF was performed with the correct answer rate change of the two-back test as a regressor. All whole-brain statistics were corrected for multiple comparisons with a voxel-wise threshold of p < 0.001 and a minimum cluster size of 11 voxels to achieve a corrected map-wise false-positive probability of p < 0.05 as determined by 5000 iteration Monte Carlo simulation using the AlphaSim program included in AFNI (http://afni. nimh.nih.gov/afni). While it is not considered to directly affect arterial blood flow, intracranial hypotension leads to dilatation of the venous systems, which can theoretically increase the amplitude of the blood-oxygenation-level dependent (BOLD) signal since the amplitude is dependent on fraction of venous blood within each voxel (16). However, the effect of vasodilation is expected to be cancelled out through scaling unless it affects the BOLD measurement heterogeneously across the brain. In order to assess the possibility, we examined the correlation between ALFF and CSF pressure. The effect of motion artefact was also examined. ALFF of each region was measured as an average within a 5-mm-diameter sphere centred on the peak of each region of interest (ROI).
Functional connectivity analysis For the functional-connectivity analysis, FSL tools (www.fmrib.ox.ac.uk/fsl) were used to carry out dual regression analysis that allows for voxel-wise comparisons of resting functional connectivity (17). Preprocessed data were temporally concatenated across participants to create a single 4D dataset that was
subsequently decomposed using probabilistic independent component analysis (ICA) (MELODIC Ver. 3.13) to identify large-scale patterns of functional connectivity, with the number of components constrained to 25 (18). Using group ICA spatial maps in a linear model fit against the separate fMRI data sets, matrices describing temporal dynamics for each component and individual were obtained (spatial regression) (17). Participant-specific spatial maps were subsequently estimated by applying the time-course matrices in a linear model fit against the associated fMRI data set (temporal regression) (17). Based on the hypothesis as well as the results of the ALFF analysis showing involvement of the main nodes of the networks, namely, precuneus, bilateral orbitofrontal cortices (OFCs), and medial frontal cortex (MFC), the network analysis was focused on the DMN and FPNs. Correlation analysis was performed to test if cognitive performance was modulated with the regions’ connectivity with the network to which each node belongs; i.e. DMN in precuneus, right and left FPN (RFPN, LFPN) in right and left OFC (ROFC, LOFC), respectively, and RFPN in right MFC. In order to examine the contribution of network-level activity and to examine if the source of the abnormal neural activity affecting the cognitive performance is network level or local, linear partial correlations tests were performed. For all the analyses using ROIs, linear relationship was examined with Pearson’s correlation and statistical threshold was set at p < 0.05 (Bonferroni correction for multiple comparisons).
Results Patient characteristics and behavioural performance A synopsis of clinical findings in all 15 patients is provided in Table 1. Although patients had multiple symptoms other than postural headache, they had spontaneously recovered from visual and hearing problems and all had normal visual and auditory acuity at the time of the pre-operative evaluation. Apparent preoperative cognitive impairment was seen in a patient with large subdural haematomas (P12) who underwent evacuation of the haematoma prior to referral to our hospital. Overall, headache immediately began to improve after the blood patch procedure (HIT score: pre- vs. post-operative: 70 11 vs. 44 7.6, p < .0001, t ¼ 8.2), despite a temporary enlargement of subdural haematoma in two patients (P1, P5). All patients have remained free from relapse for 10 to 21 months after the surgery. Two-back working-memory performance significantly improved after surgery (0.66 0.29 vs.
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(CEP)
[1–10] [PREPRINTER stage]
4
Cephalalgia 0(0)
Table 1. Synopsis of clinical findings. No
Age/Sex
Days after onset
Symptomsa
Imaging findingsb
Leakagec
Pressure (mmH2O)
HITd (pre-/post-)
MMSE (pre-/post-)
SDHe
History of interventionf
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
57/M 75/M 51/M 47/F 38/F 26/F 32/F 45/M 43/M 39/F 41/M 53/M 29/M 46/F 56/F
30 41 79 15 21 14 22 43 87 55 84 82 34 86 30
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 2 1, 2 1, 2 2 1, 2 1, 2 2 1, 2 1, 2 1, 2 1 1 1 1 2
CTL NA TL TL CTL CTL CTL CTL CL CL CTL TL NA CTL CTL
25 41 0 7 10 20 63 53 178 38 9 10 0 5.8 9
67, 74, 76, 74, 78, 75, 78, 64, 66, 78, 66, 76, 74, 64, 36,
29, 29, 27, 30, 30, 26, 28, 30, 30, 30, 30, 13, 30, 30, 30,
þ þ þ þ þ þ þ þ þ þ þ þ
None Evacuation None None None None None Steroid None Evacuation None Evacuation None None Blood patch
1, 3 2, 1, 1, 1, 1, 1 1, 1, 2, 1, 1, 1, 1,
3, 5 3 3 3, 5 3, 4, 5 3, 4, 5 2, 5 3, 5 5 3, 2,
3, 5 5
4, 5 3
46 46 36 41 60 40 50 54 36 36 48 48 38 52 36
29 29 27 30 29 26 28 30 30 30 30 27 30 30 30
a
0 ¼ postural headache, 1 ¼ neck stiffness, 2 ¼ tinnitus, 3 ¼ hypacusia, 4 ¼ photophobia, 5 ¼ nausea. 1 ¼ evidence of low CSF pressure on MR; 2 ¼ evidence of CSF leakage on conventional myelography or CT myelography. c Site of leakage: C ¼ cervical; T ¼ thoracic; L ¼ lumbar spine; NA ¼ not applicable (not identified in the spine). d Headache inventory score (pre-/post-op). e Presence (þ) or absence () of subdural effusion/haematoma. f Evacuation ¼ evacuation of the subdural haematoma. Data are preoperative findings unless otherwise indicated. M: male; F: female; CSF: cerebrospinal fluid; CT: computed tomography; HIT: Headache Impact Test; MMSE: Mini-Mental State Examination; SDH: subdural haematoma. b
0.79 0.13; p ¼ 0.047, t ¼ 1.8), while one-back performance was at ceiling pre-operatively (0.99 0.04 vs. 1 0; p ¼ 0.17, t ¼ 1). The false alarm rate was small for two-back test (0.04 0.04 vs. 0.02 0.02; p ¼ 0.14, t ¼ 1.11) and zero for one-back test both pre- and postoperatively across patients. Averaged response time for correct answers did not significantly differ between the two conditions (two-back: 1020 183 vs. 943 126 ms; p ¼ 0.07, t ¼ 1.6; one-back: 826 148 vs. 822 136 ms; p ¼ 0.46, t ¼ –0.09). Percentage of correct answers for the two-back test was henceforward used as a cognitive index.
Working-memory performance and spontaneous brain activity Whole-brain linear regression analysis revealed a significant positive correlation between changes in workingmemory performance and ALFF of the precuneus/posterior cingulate cortex (MNI coordinates: 2, 48, 42) as well as a negative correlation between changes in cognitive performance and ALFF of the bilateral OFCs (Brodmann area, BA11; MNI coordinates, ROFC, 34, 60, 6; LOFC 38, 60, 14) and the right MFC (BA8/24; MNI coordinates, 10, 16, 54)
(voxel-wise threshold of p < 0.001, map-wise threshold of p < 0.05), (precuneus, r ¼ 0.89, p < 0.0001; ROFC, r ¼ 0.83, p ¼ 0.0001; MFC, r ¼ 0.83, p ¼ 0.0002; LOFC, r ¼ 0.82, p ¼ 0.0002) (Figure 1). Cognitive performance and ALFF were significantly correlated preoperatively in all four regions (precuneus, r ¼ 0.76, p ¼ 0.001; ROFC, r ¼ 0.89, p < 0.0001; MFC, r ¼ 0.70, p ¼ 0.004; LOFC, r ¼ 0.94, p < 0.0001). However the correlations were attenuated post-operatively as a result of ALFF changes associated with cognitive task performance improvement in all (precuneus, r ¼ 0.52, p ¼ 0.049; ROFC, r ¼ 0.40, p ¼ 0.14; MFC, r ¼ 0.05, p ¼ 0.87; LOFC, r ¼ 0.45, p ¼ 0.089) (Figure 2). Whole-brain linear regression analysis revealed a significant positive correlation between ALFF and CSF pressure in the basal (interpeduncular and prepontine) cisterns, which might be due to low CSF fluctuations under hypovolaemia. However, no area with negative correlation indicating the presence of vascular confounds was found. Average head motion was neither different between prevs. post-operative state (p ¼ 0.28, t ¼ 1.13) nor correlated with task performance (pre-/post-operative: r ¼ 0.0009, p ¼ 0.99; r ¼ 0.21, p ¼ 0.46) or with the ALFF of the
XML Template (2015) [28.4.2015–8:17am] //blrnas3.glyph.com/cenpro/ApplicationFiles/Journals/SAGE/3B2/CEPJ/Vol00000/150064/APPFile/SG-CEPJ150064.3d
[1–10] [PREPRINTER stage]
(CEP)
5
Amemiya et al.
Positive
Precuneus
LOFC
1
1
0.8
0.8
0.6
R
Δ%correct
Δ%correct
0.6
0.4
0.2
0.4
0.2
0
0 -0.2
r = 0.89 p < 0.0001
-0.2
r = -0.82 p = 0.0002
Negative
-0.4 -0.4
0
0.2
0.4
0.6
0.8
1
-0.8
-0.6
-0.4
1
0.8
0.8
0.6
0.6
0.4
0.2
7
0
0.2
0.4
0.6
0.4
0.2
0 0
r = -0.83 p = 0.0002
-0.2
-0.4
T-value
-0.2
ROFC
1
Δ%correct
Δ%correct
MFC
Whole Brain analysis P
