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Decreased stimulus-driven connectivity of the primary visual cortex during visual motion stimulation in amnestic mild cognitive impairment: An fMRI study
Neuroscience Letters 711 (2019) 134402
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Research article
Decreased stimulus-driven connectivity of the primary visual cortex during visual motion stimulation in amnestic mild cognitive impairment: An fMRI study
T
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Takao Yamasakia,b, ,1, Toshihiko Asoc,1, Yumiko Kasedad, Yasuyo Mimorie, Hikaru Doif, Naoki Matsuokag, Naomi Takamiyaa,h, Tsuyoshi Toriii, Tetsuya Takahashij, Tomohiko Ohshitak, Hiroshi Yamashital, Hitoka Doif, Saeko Inamizua,m, Hiroshi Chatania,m, Shozo Tobimatsua a
Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan Department of Neurology, Minkodo Minohara Hospital, Fukuoka, Japan Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan d Department of Neurology, Hiroshima City Rehabilitation Hospital, Hiroshima, Japan e Department of Rehabilitation, Faculty of Rehabilitation, Hiroshima International University, Hiroshima, Japan f Doi Clinic Internal Medicine/Neurology, Hiroshima, Japan g Matsuoka Neurology Clinic, Hiroshima, Japan h Department of Physical Therapy, Faculty of Health and Welfare, Prefectural University of Hiroshima, Hiroshima, Japan i Department of Neurology, National Hospital Organization Kure Medical Center, Hiroshima, Japan j Department of Clinical Neuroscience and Therapeutics, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan k Department of Neurology, Suiseikai Kajikawa Hospital, Hiroshima, Japan l Department of Neurology, Hiroshima City Asa Citizens Hospital, Hiroshima, Japan m Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Alzheimer’s disease Amnestic mild cognitive impairment Functional magnetic resonance imaging Resting-state Motion perception Primary visual cortex
Motion perceptual deficits are common in Alzheimer’s disease (AD). Although the posterior parietal cortex is thought to play a critical role in these deficits, it is currently unclear whether the primary visual cortex (V1) contributes to these deficits in AD. To elucidate this issue, we investigated the net activity or connectivity within V1 in 17 amnestic mild cognitive impairment (aMCI) patients, 17 AD patients and 17 normal controls (NC) using functional magnetic resonance imaging (fMRI). fMRI was recorded under two conditions: visual motion stimulation and resting-state. The net activity or connectivity within V1 extracted by independent component analysis (ICA) was significantly increased during visual motion stimuli compared with that of the resting-state condition in NC, but not in aMCI or AD patients. These findings suggest the alteration of the net activity or connectivity within V1, which may contribute to the previously reported motion perceptual deficits in aMCI and AD. Therefore, the decreased net V1 activity measured as the strength of the ICA component may provide a new disease biomarker for early detection of AD.
1. Introduction Alzheimer’s disease (AD) is a progressive, degenerative brain disease, and is the most common cause of dementia [1]. The characteristic symptoms of AD include difficulties with memory, language, problemsolving and other cognitive skills that affect a person’s ability to perform everyday activities [1]. Alongside these characteristics, higher visual dysfunction, particularly impaired motion perception, is a
prominent feature in AD [2–4]. Mild cognitive impairment (MCI) is a transitional stage between age-related memory decline and dementia. Amnestic MCI (aMCI), the most common subtype of MCI, is considered to be a potential precursor to AD [1,5]. Interestingly, impaired motion perception has been identified even in aMCI patients [6]. Therefore, motion perceptual deficits may be key clinical features for detecting aMCI and AD. The human visual system is characterized by parallel and
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Corresponding author at: Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. E-mail address: [email protected] (T. Yamasaki). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.neulet.2019.134402 Received 28 March 2019; Received in revised form 26 June 2019; Accepted 22 July 2019 Available online 26 July 2019 0304-3940/ © 2019 Elsevier B.V. All rights reserved.
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explained to all participants. The experimental procedures were approved by the ethics committee of Hiroshima City Rehabilitation Hospital. Patients with aMCI and AD were recruited from hospitals specializing in neurology and dementia in Hiroshima City. All NC participants were recruited through public advertisements. We recruited subjects who had at least finished compulsory education (i.e., 9 years or more). Inclusion criteria for aMCI, AD and NC participants followed the criteria of the Japanese Alzheimer’s Disease Neuroimaging Initiative [28]. These criteria are based on several neuropsychological tests: the Japanese versions of Mini-Mental State Examination (MMSE-J, full score = 30) and Clinical Dementia Rating (CDR-J, range 0–3, with 0 as the best score), and delayed recall of logical memory in the Wechsler Memory Scale-Revised (delayed LM WMS-R, full score = 25). The inclusion criteria for aMCI patients included a score of 24–30 on the MMSE-J, memory disturbance identified by the study partner with or without a subjective complaint by the participant, a score of 0.5 on the CDR-J, and an education-adjusted score below the cutoff level on the delayed LM WMS-R (education for 0–9 years was ≤ 2, for 10–15 years was ≤ 4, and for > 15 years was ≤ 8). Inclusion criteria for AD patients included a score of 20–26 on the MMSE-J, a score of 0.5 or 1 on the CDR-J, and an education-adjusted score below the cutoff level on the delayed LM WMS-R (same as for aMCI). AD patients were required to meet the criteria of the National Institute of Neurological and Communicative Diseases and Stroke and the Alzheimer’s Disease and Related Disorders Association (the NINCDS-ADRDA) for probable AD [29]. The inclusion criteria for NC participants included the following: a score of 24–30 on the MMSE-J, a global score of 0 on the CDR-J, and an education-adjusted score above the cutoff level on the delayed LM WMS-R (education for 0–9 years was ≥ 3, for 10–15 years was ≥ 5, and for > 15 years was ≥ 9). The exclusion criteria included depression (the Japanese version of Geriatric Depression Scale [GDS-J] ≥ 6), cerebrovascular disorders (Hachinski Ischemic Score ≥ 5) and other neurological or psychiatric disorders [28]. Demographics and neuropsychological findings for each group are summarized in Table 1. Demographic characteristics of patients with aMCI and AD, and NC participants, including age, gender, and years of education, were matched among the three groups. The matching of gender and other variables (age and years of education) were further confirmed by Chi-square test and one-way analysis of variance (ANOVA), respectively.
hierarchical processing via the ventral and dorsal streams [7–9], and the latter is specialized for motion perception. Motion information is first extracted in the primary visual cortex (V1) at a local level, then projects to the V5/middle temporal area (MT) and the posterior parietal cortex (PPC) for integration and processing at a global level [3,8]. AD pathology is characterized by amyloid β plaques, tau-containing neurofibrillary tangles, and neuronal cell death [1]. This pathology preferentially occurs in parieto-temporal association areas, including the higher level dorsal stream, while the V1 is typically spared until very late stages of the disease [10,11]. In accord with these pathological features, aMCI and AD patients have been found to exhibit selective elevation of motion coherence thresholds for radial optic flow (OF) motion, which is related to self-motion perception [4,12,13]. Note that motion coherence threshold paradigms require the recruitment of the PPC for global motion processing. Similarly, we found that the latency of visual evoked potentials (VEPs) for OF was selectively delayed in aMCI [14,15]. Thus, the PPC is thought to be the most important brain area underlying impaired motion perception in aMCI and AD [14,16,17]. In the last several decades, a number of studies have reported that V1 function is relatively spared in the early stages of AD (i.e., MCI) in contrast to the early impairment of PPC function indicated by the characteristics of AD pathology [3]. For example, most VEP studies using pattern stimuli (gratings and checks, which are preferred for exploring the V1 function) suggested that early VEP components were unaffected in AD [18–20]. However, some studies reported prolonged latencies of pattern-VEPs in AD [21,22]. A steady-state VEP study also demonstrated temporal frequency deficits (particularly for faster temporal frequencies [ > 15 Hz]) [23] suggesting impairment of dorsal stream function at V1 in early AD patients. In addition, recent psychophysical studies revealed that altered motion perception (motion repulsion, motion-induced position shift) is associated with possible dysfunction of V1 (and V5/MT) in early AD patients [24,25]. Functional magnetic resonance imaging (fMRI) studies also showed that various V1 functions were impaired in MCI and AD patients [26,27]. Therefore, it remains unclear whether the functional impairment of V1 exists in early AD or even aMCI patients. Based on these previous findings, the current study sought to investigate whether V1 function was altered in aMCI and AD patients during motion perception. For this purpose, mass V1 activity during two different conditions (visual motion stimulation and resting-state), were extracted using a data-driven approach. By comparing fMRI connectivity between these two conditions, we were able to evaluate the difference in stimulus-driven V1 connectivity relative to restingstate among patients with aMCI and AD, and elderly normal control (NC) participants. We hypothesized that stimulus-driven V1 connectivity, as measured by a difference in independent component strength between visual motion stimulation and resting-state, would reveal a disease effect in these patients.
2.2. Experimental design We performed two experiments, examining the visual motion stimulation and resting-state conditions. Regarding the visual motion stimulation condition, visual stimuli were generated using Presentation software (Neurobehavioral Systems Inc., San Francisco, CA) running on a personal computer. We used coherent motion stimuli as visual stimuli, which consisted of 400 white square dots (RGB, 1,1,1; visual angle, 0.05 × 0.05°) randomly distributed on a black background (RGB, 0,0,0; visual angle, 18 × 10°). The white dots moved at a velocity of 5.0°/s. Two types of motion stimuli (OF and horizontal motion [HO]) were used. OF stimuli contained dots that moved in a radial outward pattern. HO contained dots that moved leftward or rightward. The coherence level was 90% for both stimuli, and both had the same dot density, luminance, contrast and average dot speed. Random motion (RM) was used as a baseline condition. Therefore, the two types of stimuli (OF and HO), each lasting 21 s, were pseudo-randomly presented three times, separated by a RM block lasting 30 s. No baseline rest block was included. The visual stimuli were back-projected onto a screen from an LCD projector and the subject viewed them through a mirror attached to a head coil. Subjects lay on their backs and fixated on a central fixation point on a screen through a mirror attached to the head coil [3,17]. For the resting-state condition, participants were required to remain
2. Material and methods 2.1. Participants Patients were enrolled between January 2010 and March 2017. The period from registration to data collection was less than 1 month. Neuropsychological tests and fMRI recording were performed on the same day. All aMCI patients were re-evaluated using medical records in March 2017, resulting in variable follow-up periods for conversion to AD across patients. Seventeen patients with aMCI (11 females; age: 76.3 ± 3.8 years), 17 patients with AD (nine females; age: 75.9 ± 6.6 years), and 17 NC (11 females; age: 72.9 ± 7.2 years) participated in this study (Table 1). All participants were native speakers of Japanese, right-handed, and had normal or corrected-to-normal visual acuity. Written informed consent was obtained after the nature of the experiment was fully 2
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Table 1 Demographic characteristics and neuropsychological findings of aMCI, AD patients and NC groups. Groups
Age (y)
Female/Male
Education (y)
MMSE-J
aMCI (n = 17) AD (n = 17) NC (n = 17)
76.3 ± 3.8 75.9 ± 6.6 72.9 ± 7.2
11/6 9/8 11/6
12.6 ± 1.5 11.7 ± 2.1 11.9 ± 1.6
26.4 ± 2.1# 22.1 ± 1.5††, 28.6 ± 2.8
**
Delayed LM WMS-R
CDR-J
GDS-J
1.1 ± 1.6## 0.1 ± 0.3†† 9.5 ± 3.8
0.5 0.5-1.0 0.0
2.0 ± 1.3 1.9 ± 1.4 2.4 ± 1.9
Data are expressed as mean ± SD. aMCI vs. NC, ##p < 0.001, #p < 0.01; AD vs. NC, ††p < 0.001; AD vs. aMCI, **p < 0.001. Abbreviations: aMCI, amnestic mild cognitive impairment; AD, Alzheimer’s disease; NC, normal control; MMSE, Mini-Mental State Examination; LM WMS-R, Logical Memory in Wechsler Memory Scale-Revised; CDR, Clinical Dementia Rating; GDS, Geriatric Depression Scale; J, Japanese version; SD, standard deviation.
(0.08–0.07 Hz) global signal as the initial seed. This phase shift in each voxel was tracked up to 7 s both upstream and downstream to create a “phase lag” map and a corresponding timeseries set from each region. This spatiotemporal lag structure was then regressed out from the original data. This process can be considered a global signal regression tailored to each voxel but affecting only the slow signal components.
alert with their eyes closed for 5 min. To avoid the effect of participants employing specific strategies to maintain alertness (e.g., reminiscing or counting scan number), participants were instructed to avoid thinking about anything in particular, as much as possible [30]. 2.3. Data acquisition
2.4.3. Resting-state activity analysis of the visual network The denoised dataset was fed into an ICA pipeline using Group ICA in the fMRI Toolbox (GIFT, http://icatb.sourceforge.net) [38]. With no information about the task structure, the temporally concatenated fMRI data from both conditions were subjected to the ICASSO pipeline in which ICA optimization was repeatedly run 20 times while bootstrapping the data and randomizing the initial conditions. Repeated occurrence of similar components indicated the robustness of the set of estimated components. This process involved a hierarchical agglomerative clustering in a “similarity graph” using the average linkage criterion. We chose the Infomax algorithm with a predetermined number of components, or model order, of 30, as an appropriate compromise between insufficient decomposition and excessive splitting of the major networks. As a result, the visual network comprising medial parts of the occipital lobe was detected as the first component (IC1) to account for the largest variance, presumably due to the usage of strong visual stimuli [39]. From the z-score map of this component, a region of interest (ROI) was created at a threshold of z > 2, from which the mean z score was extracted from the individual IC1 maps created by back projection implemented in GIFT. In statistics, we firstly performed paired t-test in each group to examine whether the strength of the component changed between the resting-state and the visual motion stimulation condition. Next, percent proportional change during the visual motion stimulation session relative to rest was calculated. Finally, one-way ANOVA with a post-hoc Tukey’s honestly significant difference (HSD) test was performed to examine the effect of diagnosis on stimulus-related V1 connectivity in 51 subjects (17 NC, 17 aMCI and 17 AD). For further confirmation, additional ANOVAs were conducted on a subset of the group, after excluding outliers in each group by interquartile range testing and removing aMCI patients who did not convert to AD during the follow-up period (n = 3).
MRI scans were performed using a 3.0 T MRI scanner (GE Signa EXCITE, Milwaukee, WI, USA). The imaging parameters were determined based on our previous fMRI studies of healthy young subjects in visual motion stimulation and resting-state conditions [3,17,30]. For the visual motion stimulation condition, functional images were acquired using a gradient-echo echo-planar imaging (EPI) sequence (repetition time [TR], 3000 ms; echo time [TE], 30 ms; flip angle, 79°; field of view [FOV], 19.2 × 19.2 cm; matrix, 64 × 64; transverse slices covering the whole brain; slice thickness, 3.0 mm; no gap). Each of two conditions (OF and HO) lasted for seven volumes, and each condition occurred three times intermittently, followed by the RM-baseline condition for 10 volumes. Thus, a total of 102 volumes were acquired for each subject (306 s). Regarding the resting-state condition, functional images were acquired using another gradient-echo EPI sequence (TR, 2500 ms; TE, 30 ms; flip angle, 79°; FOV, 19.2 × 19.2 cm; matrix, 64 × 64; transverse slices covering the whole brain; slice thickness, 3.0 mm; no gap). Thus, a total of 120 volumes were acquired for each subject (300 s). High-resolution sagittal T1-weighted images were also obtained using a three-dimensional (3D) spoiled gradient-recalled echo sequence (SPGR) sequence (TR, 7.9 ms; TE, 3.0 ms; flip angle, 12°; FOV, 23.0 cm × 23.0 cm; matrix, 256 × 256; slice thickness, 1.0 mm). 2.4. Independent component analysis (ICA) for V1 connectivity 2.4.1. Data processing Both fMRI datasets with visual motion stimulation and resting-state conditions were fed into an analysis pipeline. FSL5 (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl) [31] was used in combination with SPM12 (Wellcome Department of Cognitive Neurology, London, UK) and in-house Matlab scripts. After inter-scan slice timing correction, head motion was compensated by 3D motion correction and data repair [32]. The data repair procedure is designed to remove motion-related signal dropout, and involves searching for time points presenting abrupt global signal change exceeding 1% and framewise displacement exceeding a Euclidian distance of ± 1 mm or ± 1° rotation per TR. The data were further cleaned by regressing out 24 head motion-related parameters. Images were spatially normalized using a T1 anatomical image and resliced to 4-mm isotropic voxels.
3. Results 3.1. Progression from aMCI to AD During the follow-up period (mean: 18.6 ± 23.7 months), 13 of 17 (76.5%) aMCI patients converted to clinical diagnosis of probable AD on the basis of medical records, neuropsychological evaluation (i.e., MMSE) or interviews. No NC participants were converted to aMCI or AD within the same period.
2.4.2. Removal of perfusion information Contamination of non-neural signal components is a critical issue in functional connectivity analysis that does not involve trial averaging [33,34]. To cope with this problem, a recently proposed denoising approach was applied to isolate neural activity [35–37]. This procedure involves tracking the regional phase variation of the low-frequency oscillation of systemic origin by using the bandpass-filtered
3.2. Neuropsychological tests Regarding the MMSE-J scores, a significant main effect was found (F 3
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Fig. 1. The visual network detected by the group ICA. This component accounted for the most variance, possibly due to the inclusion of visual motion stimulation condition of OF, HO and RM. z coordinate in template MNI space is shown for each slice. Abbreviations: ICA, independent component analysis; OF, optic flow; HO, horizontal motion; RM, random motion; MNI, Montreal Neurological Institute.
group failed to show either significant pairwise differences or a significant main effect of diagnosis in ANOVA, even after excluding the three aMCI patients who did not convert to AD during the follow-up period.
[2, 48] = 54.45, p < 0.001). The following groups were significantly different: aMCI vs. NC, p = 0.003; AD vs. NC, p < 0.001; AD vs. aMCI, p < 0.001. Similarly, for delayed LM WMS-R, there was a significant main effect (F[2, 48] = 79.65, p < 0.001). Significant differences were observed between the groups as follows: aMCI vs. NC, p < 0.001; AD vs. NC, p < 0.001 (one-way ANOVA with Bonferroni correction). In contrast, no significant main effect was obtained for GDS-J scores (Table 1).
4. Discussion In the current study, we investigated alteration of the net activity or connectivity within V1 during visual motion stimulation in aMCI and AD patients using fMRI under visual motion stimulation and restingstate conditions. Our fMRI results based on ICA suggested alteration of V1 connectivity that may underlie previously reported motion perceptual deficits in aMCI and AD patients [4,6,14]. To the best of our knowledge, this is the first fMRI study to reveal decreased stimulusdriven V1 connectivity in aMCI and AD by comparing visual motion stimulation with the resting-state condition. Therefore, the current study provides new insight into the research fields of visual perception in aMCI and AD.
3.3. ICA for V1 connectivity The first group ICA component representing the early visual cortices, or the visual network is shown in Fig. 1. The strength of the component changed between the resting-state and the visual motion stimulation condition (Fig. 2A), which was significant in the NC group (p = 0.002), but not in the aMCI (p = 0.12) and AD (p = 0.57) groups. The proportional change relative to the resting-state was calculated for each individual to evaluate the effect of diagnosis (Fig. 2B). Oneway ANOVA on all 51 subjects failed to identify significant effect of diagnosis. An additional ANOVA on a subset of the group (47 subjects: 17 NC, 14 aMCI and 16 AD) revealed a significant effect of diagnosis (F [2,46] = 4.99, p = 0.031), with a significant difference between the NC and AD groups (p < 0.05 by Tukey’s HSD). However, the aMCI
4.1. Decreased stimulus-driven V1 connectivity during visual motion stimulation in aMCI and AD The block-design analysis is not suitable for the examination of V1 function because it cannot detect subtle differences in V1 activity
Fig. 2. First independent component of V1. Summed Z scores over the ROI created from the group component map in Fig. 1 are shown. The back-projected individual maps for restingstate and visual motion stimulation conditions were used to quantify V1 connectivity during each condition. (A) V1 connectivity significantly increased during the visual motion stimulation compared with the resting-state condition only in the NC group (*p = 0.002, paired t test). (B) The increase in stimulusdriven V1 connectivity was significantly stronger in the NC group compared with the AD group (**p = 0.031, Tukey’s HSD test). Data are expressed as mean (colored thick line) ± SEM (colored area). Multiple thin black lines show individual data. There were three outliers further than 1.5 interquartile ranges in the aMCI group and one in the AD group, respectively. Abbreviations: NC: normal control; aMCI: amnestic mild cognitive impairment; AD: Alzheimer’s disease; ROI: region of interest; V1: primary visual cortex; SEM: standard error of the mean. 4
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early detection of aMCI and AD. However, it remains unclear whether the alteration in stimulus-driven connectivity of V1 is specific to visual motion stimulation, or whether similar phenomena can be observed with other visual stimuli (e.g., static pattern). Thus, further research examining stimulus-driven V1 connectivity in response to various visual stimuli compared with the resting-state in aMCI and AD patients would be useful for elucidating this issue.
between coherent (OF, HO) and RM, even in healthy young adults, possibly due to the limitations of the temporal resolution of fMRI [3,17]. In contrast, examining differences between the task and restingstate conditions can reveal how the brain responds to a given task relative to resting brain activity [40]. Importantly, the strength of the ICA component evaluated in the current study represents the proportional contribution of this component to the local signal variance (including that from noise or other overlapping components) and can thus be considered to represent the net neural activity in V1. Accordingly, comparing connectivity changes in V1 between the visual motion stimulation and resting-state conditions is an appropriate method for evaluating V1 function. This analysis can be used to identify stimulusdriven V1 connectivity caused by a series of visual motion stimuli (i.e., OF, HO and RM) compared with the resting-state condition. In the NC group, V1 connectivity was significantly increased by a series of visual motion stimuli, compared with the resting-state condition. Conversely, the augmentation of V1 connectivity was not observed in either the aMCI or AD groups. In the between-groups comparison, stimulus-driven V1 connectivity was significantly higher in the NC group compared with the AD group. These findings indicate that stimulus-driven V1 connectivity elicited by visual motion stimulation relative to the resting-state was attenuated in the patients. The aMCI group failed to show a significant difference from the NC group despite the intermediate value between the NC and AD groups. This is possibly due to the insufficient statistical power caused by the weaker disease effect of aMCI compared with AD. In addition, extra inter-individual variation related to the disease stage, final diagnosis, and compensatory neural processes may exist in aMCI patients [41]. Unfortunately, the present data also failed to support this possibility by showing a significant effect after excluding the three aMCI patients who did not convert to AD. Further studies with larger cohorts are needed to determine whether there is a difference in stimulus-driven V1 connectivity between the aMCI and NC groups. AD pathology, including amyloid β (senile plaques) and hyperphosphorylated tau (neurofibrillary tangles) preferentially involves parietotemporal association areas (including the higher level dorsal stream), but V1 function is typically spared until the very late stages of the disease [10,11]. Contrary to these pathological characteristics, the present results suggest the existence of V1 dysfunction even in the early stage of the disease (i.e., MCI). Thus, functional changes of V1 may occur before AD pathology in V1 becomes apparent, even in aMCI patients. To date, amyloid β oligomers are widely regarded as the most toxic and pathogenic form of amyloid β, and their buildup occurs prior to the formation of amyloid β plaques [42,43]. Therefore, we speculate that the present results reflect synaptic dysfunction at V1 due to amyloid β oligomers. A possible alternative interpretation is that, in AD, there is a paradoxical increase of V1 connectivity at rest, resulting in a smaller effect of visual motion stimulation. In contrast to the current fMRI results suggesting alteration of V1 activity in aMCI and AD, our previous VEP study [14] revealed no significant differences in early VEP components (V1 origin) for both OF and HO stimuli among the three groups (aMCI, AD and NC). These results suggest that the V1 response to these motion stimuli is preserved in aMCI and AD patients [14]. This seemingly contradictory finding may be attributable to a disease-related change in resting-state activity [14] because the fluctuation during resting-state was measured and compared with that during sequential presentation of visual motion stimuli in the current fMRI analysis. In other words, we compared the brain state changes by the switching visual motion stimuli to resting-state fluctuation. Thus, the differential increase ratio of V1 connectivity may be due to paradoxically increased V1 recruitment in aMCI and AD at rest, as part of the compensatory mechanisms. Taken together, these findings indicate that the investigation of stimulus-driven and resting-state V1 connectivity may be useful for the evaluation of subtle changes of V1 function, and decreased stimulusdriven V1 connectivity change can provide a novel biomarker for the
4.2. Limitations and future directions The current study involved several potential limitations that should be considered. First, the sample size was relatively small. This may partially explain why some results did not reach statistical significance. However, because the main results of this study were statistically verified, the observed differences between the three groups can be considered meaningful. Second, we used the task of passive perception of moving dots during the visual motion stimulation condition. However, eye tracking data were not recorded. Thus, spurious effects (effects of alertness or fixation) were not fully excluded. A further study using a very simple task (i.e., the discrimination of motion direction) could provide an adequate control for these potential confounds. Third, we used a different TR of EPI sequences between the visual motion stimulation (TR = 3000 ms) and resting-state (TR = 2500 ms) conditions, based on our previous fMRI studies in healthy young subjects [3,17,30]. The difference in TR may induce a differential aliasing effect between the two conditions [44]. The different sampling rate of 1/TR may also confound the direct comparison of correlation coefficients. Thus, we propose that the uniform TR should be applied to evaluate our fMRI data. However, the present findings regarding the disease effect were unaffected by this non-uniformity because the experimental conditions were uniform across the three groups. At the same time, a future study using the same TR between both conditions would be helpful for testing whether the difference in TR affected the present results. Finally, the current study used a cross-sectional design. Further prospective and longitudinal fMRI studies with large numbers of subjects, including other subtypes of MCI patients [5], will be necessary to confirm whether our results are specific to MCI caused by AD [45]. In particular, future studies should compare alterations of stimulus-driven V1 connectivity between MCI caused by AD and MCI caused by Lewy body disease (LBD) because patients with LBD are characterized by V1 impairment [46]. This approach could be used to confirm that the fMRI changes observed in this study provide a useful approach for differentiating between MCI due to AD and healthy individuals, or between MCI caused by AD and MCI caused by other major forms of degenerative dementia, including LBD.
5. Conclusions The present results indicate that altered V1 connectivity may be responsible for previously reported impaired motion perception in aMCI and AD. Therefore, decreases in stimulus-driven V1 connectivity in response to visual motion stimulation relative to the resting-state may be useful as a new fMRI biomarker for early detection of aMCI and AD.
Financial disclosure This study was partly supported by the following grants: JSPS KAKENHI Grant Number JP17K09801 to TY, and Grant from the Research on Innovative Areas (No. 15H05875) from the Ministry of Education, Culture, Sports, Science, and Technology to ST. 5
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Declaration of Competing Interest
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The authors declare that they have no conflict of interest. Acknowledgment We wish to thank Drs. Hiroyuki Muranaka, Toshiaki Terao, Hiroshi Itahara and Takeharu Yukitomo (Department of Radiology, Hiroshima City Rehabilitation Hospital) for their research assistance. We thank Benjamin Knight, MSc., from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript. References [1] Alzhemier’s Association, Alzheimer’s disease facts and figures, Alzheimers Dement. 14 (2018) 367–429. [2] M.F. Mendez, M.A. Mendez, R. Martin, K.A. Smyth, P.J. Whitehouse, Complex visual disturbances in Alzheimer’s disease, Neurology 40 (1990) 439–443. [3] T. Yamasaki, S. Horie, H. Muranaka, Y. Kaseda, Y. Mimori, S. Tobimatsu, Relevance of in vivo neurophysiological biomarkers for mild cognitive impairment and Alzheimer’s disease, J. Alzheimers Dis. 31 (2012) S137–S154. [4] S.J. Tetewsky, C.J. Duffy, Visual loss and getting lost in Alzheimer’s disease, Neurology 52 (1999) 958–965. [5] R.C. Petersen, R. Doody, A. Kurz, R.C. Mohs, J.C. Morris, P.V. Rabins, et al., Current concepts in mild cognitive impairment, Arch. Neurol. 58 (2001) 1985–1992. [6] M. Mapstone, T.M. Steffenella, C.J. Duffy, A visuospatial variant of mild cognitive impairment: getting lost between aging and AD, Neurology 60 (2003) 802–808. [7] M. Livingstone, D. Hubel, Segregation of form, color, movement, and depth: anatomy, physiology, and perception, Science 240 (1988) 740–749. [8] S. Tobimatsu, G.G. Celesia, Studies of human visual pathophysiology with visual evoked potentials, Clin. Neurophysiol. 117 (2006) 1414–1433. [9] G. Rizzolatti, M. Matelli, Two different streams form the dorsal visual system: anatomy and functions, Exp. Brain Res. 153 (2003) 146–157. [10] A.C. McKee, R. Au, H.J. Cabral, N.W. Kowall, S. Seshadri, C.A. Kubilus, et al., Visual association pathology in preclinical Alzheimer disease, J. Neuropathol. Exp. Neurol. 65 (2006) 621–630. [11] D.A. Lewis, M.J. Campbell, R.D. Terry, J.H. Morrison, Laminar and regional distributions of neurofibrillary tangles and neuritic plaques in Alzheimer’s disease: a quantitative study of visual and auditory cortices, J. Neurosci. 7 (1987) 1799–1808. [12] J.J. Gibson, The Perception of the Visual World, Houghton Mifflin, Boston MA, 1950. [13] W.H. Warren, D.J. Hannon, Direction of self-motion is perceived from optic flow, Nature 336 (1988) 162–163. [14] T. Yamasaki, Y. Goto, Y. Ohyagi, A. Monji, S. Munetsuna, M. Minohara, et al., Selective impairment of optic flow perception in amnestic mild cognitive impairment: evidence from event-related potentials, J. Alzheimers Dis. 28 (2012) 695–708. [15] T. Yamasaki, S. Horie, Y. Ohyagi, E. Tanaka, N. Nakamura, Y. Goto, et al., A potential VEP biomarker for mild cognitive impairment: evidence from selective visual deficit of higher-level dorsal pathway, J. Alzheimers Dis. 53 (2016) 661–676. [16] T. Yamasaki, S. Tobimatsu, Driving ability in Alzheimer disease spectrum: neural basis, assessment, and potential use of optic flow event-related potentials, Front. Neurol. 9 (2018) 750. [17] T. Yamasaki, H. Muranaka, Y. Kaseda, Y. Mimori, S. Tobimatsu, Understanding the pathophysiology of Alzheimer’s disease and mild cognitive impairment: a mini review on fMRI and ERP studies, Neurol. Res. Int. 2012 (2012) 719056. [18] C.E. Wright, N. Drasdo, G.F. Harding, Pathology of the optic nerve and visual association areas. Information given by the flash and pattern visual evoked potential, and the temporal and spatial contrast sensitivity function, Brain 110 (1987) 107–120. [19] Z. Kubovά, J. Kremlάček, M. Vališ, J. Langrovά, J. Szanyi, F. Vít, et al., Visual evoked potentials to pattern, motion and cognitive stimuli in Alzheimer’s disease, Doc. Ophtalmol. 121 (2010) 37–49. [20] P.K. Iseri, Ӧ. Altinaş, T. Tokay, N. Yüksel, Relationship between cognitive impairment and retinal morphological and visual functional abnormalities in Alzheimer disease, J. Neuroophthalmol. 26 (2006) 18–24. [21] K. Krasodomska, W. Lubiński, A. Potemkowski, K. Honczarenko, Pattern electroretinogram (PERG) and pattern visual evoked potential (PVEP) in the early stages of Alzheimer’s disease, Doc. Ophthalmol. 121 (2010) 111–121.
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Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Research article
Decreased stimulus-driven connectivity of the primary visual cortex during visual motion stimulation in amnestic mild cognitive impairment: An fMRI study
T
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Takao Yamasakia,b, ,1, Toshihiko Asoc,1, Yumiko Kasedad, Yasuyo Mimorie, Hikaru Doif, Naoki Matsuokag, Naomi Takamiyaa,h, Tsuyoshi Toriii, Tetsuya Takahashij, Tomohiko Ohshitak, Hiroshi Yamashital, Hitoka Doif, Saeko Inamizua,m, Hiroshi Chatania,m, Shozo Tobimatsua a
Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan Department of Neurology, Minkodo Minohara Hospital, Fukuoka, Japan Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan d Department of Neurology, Hiroshima City Rehabilitation Hospital, Hiroshima, Japan e Department of Rehabilitation, Faculty of Rehabilitation, Hiroshima International University, Hiroshima, Japan f Doi Clinic Internal Medicine/Neurology, Hiroshima, Japan g Matsuoka Neurology Clinic, Hiroshima, Japan h Department of Physical Therapy, Faculty of Health and Welfare, Prefectural University of Hiroshima, Hiroshima, Japan i Department of Neurology, National Hospital Organization Kure Medical Center, Hiroshima, Japan j Department of Clinical Neuroscience and Therapeutics, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, Japan k Department of Neurology, Suiseikai Kajikawa Hospital, Hiroshima, Japan l Department of Neurology, Hiroshima City Asa Citizens Hospital, Hiroshima, Japan m Department of Neurology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan b c
A R T I C LE I N FO
A B S T R A C T
Keywords: Alzheimer’s disease Amnestic mild cognitive impairment Functional magnetic resonance imaging Resting-state Motion perception Primary visual cortex
Motion perceptual deficits are common in Alzheimer’s disease (AD). Although the posterior parietal cortex is thought to play a critical role in these deficits, it is currently unclear whether the primary visual cortex (V1) contributes to these deficits in AD. To elucidate this issue, we investigated the net activity or connectivity within V1 in 17 amnestic mild cognitive impairment (aMCI) patients, 17 AD patients and 17 normal controls (NC) using functional magnetic resonance imaging (fMRI). fMRI was recorded under two conditions: visual motion stimulation and resting-state. The net activity or connectivity within V1 extracted by independent component analysis (ICA) was significantly increased during visual motion stimuli compared with that of the resting-state condition in NC, but not in aMCI or AD patients. These findings suggest the alteration of the net activity or connectivity within V1, which may contribute to the previously reported motion perceptual deficits in aMCI and AD. Therefore, the decreased net V1 activity measured as the strength of the ICA component may provide a new disease biomarker for early detection of AD.
1. Introduction Alzheimer’s disease (AD) is a progressive, degenerative brain disease, and is the most common cause of dementia [1]. The characteristic symptoms of AD include difficulties with memory, language, problemsolving and other cognitive skills that affect a person’s ability to perform everyday activities [1]. Alongside these characteristics, higher visual dysfunction, particularly impaired motion perception, is a
prominent feature in AD [2–4]. Mild cognitive impairment (MCI) is a transitional stage between age-related memory decline and dementia. Amnestic MCI (aMCI), the most common subtype of MCI, is considered to be a potential precursor to AD [1,5]. Interestingly, impaired motion perception has been identified even in aMCI patients [6]. Therefore, motion perceptual deficits may be key clinical features for detecting aMCI and AD. The human visual system is characterized by parallel and
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Corresponding author at: Department of Clinical Neurophysiology, Neurological Institute, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan. E-mail address: [email protected] (T. Yamasaki). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.neulet.2019.134402 Received 28 March 2019; Received in revised form 26 June 2019; Accepted 22 July 2019 Available online 26 July 2019 0304-3940/ © 2019 Elsevier B.V. All rights reserved.
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explained to all participants. The experimental procedures were approved by the ethics committee of Hiroshima City Rehabilitation Hospital. Patients with aMCI and AD were recruited from hospitals specializing in neurology and dementia in Hiroshima City. All NC participants were recruited through public advertisements. We recruited subjects who had at least finished compulsory education (i.e., 9 years or more). Inclusion criteria for aMCI, AD and NC participants followed the criteria of the Japanese Alzheimer’s Disease Neuroimaging Initiative [28]. These criteria are based on several neuropsychological tests: the Japanese versions of Mini-Mental State Examination (MMSE-J, full score = 30) and Clinical Dementia Rating (CDR-J, range 0–3, with 0 as the best score), and delayed recall of logical memory in the Wechsler Memory Scale-Revised (delayed LM WMS-R, full score = 25). The inclusion criteria for aMCI patients included a score of 24–30 on the MMSE-J, memory disturbance identified by the study partner with or without a subjective complaint by the participant, a score of 0.5 on the CDR-J, and an education-adjusted score below the cutoff level on the delayed LM WMS-R (education for 0–9 years was ≤ 2, for 10–15 years was ≤ 4, and for > 15 years was ≤ 8). Inclusion criteria for AD patients included a score of 20–26 on the MMSE-J, a score of 0.5 or 1 on the CDR-J, and an education-adjusted score below the cutoff level on the delayed LM WMS-R (same as for aMCI). AD patients were required to meet the criteria of the National Institute of Neurological and Communicative Diseases and Stroke and the Alzheimer’s Disease and Related Disorders Association (the NINCDS-ADRDA) for probable AD [29]. The inclusion criteria for NC participants included the following: a score of 24–30 on the MMSE-J, a global score of 0 on the CDR-J, and an education-adjusted score above the cutoff level on the delayed LM WMS-R (education for 0–9 years was ≥ 3, for 10–15 years was ≥ 5, and for > 15 years was ≥ 9). The exclusion criteria included depression (the Japanese version of Geriatric Depression Scale [GDS-J] ≥ 6), cerebrovascular disorders (Hachinski Ischemic Score ≥ 5) and other neurological or psychiatric disorders [28]. Demographics and neuropsychological findings for each group are summarized in Table 1. Demographic characteristics of patients with aMCI and AD, and NC participants, including age, gender, and years of education, were matched among the three groups. The matching of gender and other variables (age and years of education) were further confirmed by Chi-square test and one-way analysis of variance (ANOVA), respectively.
hierarchical processing via the ventral and dorsal streams [7–9], and the latter is specialized for motion perception. Motion information is first extracted in the primary visual cortex (V1) at a local level, then projects to the V5/middle temporal area (MT) and the posterior parietal cortex (PPC) for integration and processing at a global level [3,8]. AD pathology is characterized by amyloid β plaques, tau-containing neurofibrillary tangles, and neuronal cell death [1]. This pathology preferentially occurs in parieto-temporal association areas, including the higher level dorsal stream, while the V1 is typically spared until very late stages of the disease [10,11]. In accord with these pathological features, aMCI and AD patients have been found to exhibit selective elevation of motion coherence thresholds for radial optic flow (OF) motion, which is related to self-motion perception [4,12,13]. Note that motion coherence threshold paradigms require the recruitment of the PPC for global motion processing. Similarly, we found that the latency of visual evoked potentials (VEPs) for OF was selectively delayed in aMCI [14,15]. Thus, the PPC is thought to be the most important brain area underlying impaired motion perception in aMCI and AD [14,16,17]. In the last several decades, a number of studies have reported that V1 function is relatively spared in the early stages of AD (i.e., MCI) in contrast to the early impairment of PPC function indicated by the characteristics of AD pathology [3]. For example, most VEP studies using pattern stimuli (gratings and checks, which are preferred for exploring the V1 function) suggested that early VEP components were unaffected in AD [18–20]. However, some studies reported prolonged latencies of pattern-VEPs in AD [21,22]. A steady-state VEP study also demonstrated temporal frequency deficits (particularly for faster temporal frequencies [ > 15 Hz]) [23] suggesting impairment of dorsal stream function at V1 in early AD patients. In addition, recent psychophysical studies revealed that altered motion perception (motion repulsion, motion-induced position shift) is associated with possible dysfunction of V1 (and V5/MT) in early AD patients [24,25]. Functional magnetic resonance imaging (fMRI) studies also showed that various V1 functions were impaired in MCI and AD patients [26,27]. Therefore, it remains unclear whether the functional impairment of V1 exists in early AD or even aMCI patients. Based on these previous findings, the current study sought to investigate whether V1 function was altered in aMCI and AD patients during motion perception. For this purpose, mass V1 activity during two different conditions (visual motion stimulation and resting-state), were extracted using a data-driven approach. By comparing fMRI connectivity between these two conditions, we were able to evaluate the difference in stimulus-driven V1 connectivity relative to restingstate among patients with aMCI and AD, and elderly normal control (NC) participants. We hypothesized that stimulus-driven V1 connectivity, as measured by a difference in independent component strength between visual motion stimulation and resting-state, would reveal a disease effect in these patients.
2.2. Experimental design We performed two experiments, examining the visual motion stimulation and resting-state conditions. Regarding the visual motion stimulation condition, visual stimuli were generated using Presentation software (Neurobehavioral Systems Inc., San Francisco, CA) running on a personal computer. We used coherent motion stimuli as visual stimuli, which consisted of 400 white square dots (RGB, 1,1,1; visual angle, 0.05 × 0.05°) randomly distributed on a black background (RGB, 0,0,0; visual angle, 18 × 10°). The white dots moved at a velocity of 5.0°/s. Two types of motion stimuli (OF and horizontal motion [HO]) were used. OF stimuli contained dots that moved in a radial outward pattern. HO contained dots that moved leftward or rightward. The coherence level was 90% for both stimuli, and both had the same dot density, luminance, contrast and average dot speed. Random motion (RM) was used as a baseline condition. Therefore, the two types of stimuli (OF and HO), each lasting 21 s, were pseudo-randomly presented three times, separated by a RM block lasting 30 s. No baseline rest block was included. The visual stimuli were back-projected onto a screen from an LCD projector and the subject viewed them through a mirror attached to a head coil. Subjects lay on their backs and fixated on a central fixation point on a screen through a mirror attached to the head coil [3,17]. For the resting-state condition, participants were required to remain
2. Material and methods 2.1. Participants Patients were enrolled between January 2010 and March 2017. The period from registration to data collection was less than 1 month. Neuropsychological tests and fMRI recording were performed on the same day. All aMCI patients were re-evaluated using medical records in March 2017, resulting in variable follow-up periods for conversion to AD across patients. Seventeen patients with aMCI (11 females; age: 76.3 ± 3.8 years), 17 patients with AD (nine females; age: 75.9 ± 6.6 years), and 17 NC (11 females; age: 72.9 ± 7.2 years) participated in this study (Table 1). All participants were native speakers of Japanese, right-handed, and had normal or corrected-to-normal visual acuity. Written informed consent was obtained after the nature of the experiment was fully 2
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Table 1 Demographic characteristics and neuropsychological findings of aMCI, AD patients and NC groups. Groups
Age (y)
Female/Male
Education (y)
MMSE-J
aMCI (n = 17) AD (n = 17) NC (n = 17)
76.3 ± 3.8 75.9 ± 6.6 72.9 ± 7.2
11/6 9/8 11/6
12.6 ± 1.5 11.7 ± 2.1 11.9 ± 1.6
26.4 ± 2.1# 22.1 ± 1.5††, 28.6 ± 2.8
**
Delayed LM WMS-R
CDR-J
GDS-J
1.1 ± 1.6## 0.1 ± 0.3†† 9.5 ± 3.8
0.5 0.5-1.0 0.0
2.0 ± 1.3 1.9 ± 1.4 2.4 ± 1.9
Data are expressed as mean ± SD. aMCI vs. NC, ##p < 0.001, #p < 0.01; AD vs. NC, ††p < 0.001; AD vs. aMCI, **p < 0.001. Abbreviations: aMCI, amnestic mild cognitive impairment; AD, Alzheimer’s disease; NC, normal control; MMSE, Mini-Mental State Examination; LM WMS-R, Logical Memory in Wechsler Memory Scale-Revised; CDR, Clinical Dementia Rating; GDS, Geriatric Depression Scale; J, Japanese version; SD, standard deviation.
(0.08–0.07 Hz) global signal as the initial seed. This phase shift in each voxel was tracked up to 7 s both upstream and downstream to create a “phase lag” map and a corresponding timeseries set from each region. This spatiotemporal lag structure was then regressed out from the original data. This process can be considered a global signal regression tailored to each voxel but affecting only the slow signal components.
alert with their eyes closed for 5 min. To avoid the effect of participants employing specific strategies to maintain alertness (e.g., reminiscing or counting scan number), participants were instructed to avoid thinking about anything in particular, as much as possible [30]. 2.3. Data acquisition
2.4.3. Resting-state activity analysis of the visual network The denoised dataset was fed into an ICA pipeline using Group ICA in the fMRI Toolbox (GIFT, http://icatb.sourceforge.net) [38]. With no information about the task structure, the temporally concatenated fMRI data from both conditions were subjected to the ICASSO pipeline in which ICA optimization was repeatedly run 20 times while bootstrapping the data and randomizing the initial conditions. Repeated occurrence of similar components indicated the robustness of the set of estimated components. This process involved a hierarchical agglomerative clustering in a “similarity graph” using the average linkage criterion. We chose the Infomax algorithm with a predetermined number of components, or model order, of 30, as an appropriate compromise between insufficient decomposition and excessive splitting of the major networks. As a result, the visual network comprising medial parts of the occipital lobe was detected as the first component (IC1) to account for the largest variance, presumably due to the usage of strong visual stimuli [39]. From the z-score map of this component, a region of interest (ROI) was created at a threshold of z > 2, from which the mean z score was extracted from the individual IC1 maps created by back projection implemented in GIFT. In statistics, we firstly performed paired t-test in each group to examine whether the strength of the component changed between the resting-state and the visual motion stimulation condition. Next, percent proportional change during the visual motion stimulation session relative to rest was calculated. Finally, one-way ANOVA with a post-hoc Tukey’s honestly significant difference (HSD) test was performed to examine the effect of diagnosis on stimulus-related V1 connectivity in 51 subjects (17 NC, 17 aMCI and 17 AD). For further confirmation, additional ANOVAs were conducted on a subset of the group, after excluding outliers in each group by interquartile range testing and removing aMCI patients who did not convert to AD during the follow-up period (n = 3).
MRI scans were performed using a 3.0 T MRI scanner (GE Signa EXCITE, Milwaukee, WI, USA). The imaging parameters were determined based on our previous fMRI studies of healthy young subjects in visual motion stimulation and resting-state conditions [3,17,30]. For the visual motion stimulation condition, functional images were acquired using a gradient-echo echo-planar imaging (EPI) sequence (repetition time [TR], 3000 ms; echo time [TE], 30 ms; flip angle, 79°; field of view [FOV], 19.2 × 19.2 cm; matrix, 64 × 64; transverse slices covering the whole brain; slice thickness, 3.0 mm; no gap). Each of two conditions (OF and HO) lasted for seven volumes, and each condition occurred three times intermittently, followed by the RM-baseline condition for 10 volumes. Thus, a total of 102 volumes were acquired for each subject (306 s). Regarding the resting-state condition, functional images were acquired using another gradient-echo EPI sequence (TR, 2500 ms; TE, 30 ms; flip angle, 79°; FOV, 19.2 × 19.2 cm; matrix, 64 × 64; transverse slices covering the whole brain; slice thickness, 3.0 mm; no gap). Thus, a total of 120 volumes were acquired for each subject (300 s). High-resolution sagittal T1-weighted images were also obtained using a three-dimensional (3D) spoiled gradient-recalled echo sequence (SPGR) sequence (TR, 7.9 ms; TE, 3.0 ms; flip angle, 12°; FOV, 23.0 cm × 23.0 cm; matrix, 256 × 256; slice thickness, 1.0 mm). 2.4. Independent component analysis (ICA) for V1 connectivity 2.4.1. Data processing Both fMRI datasets with visual motion stimulation and resting-state conditions were fed into an analysis pipeline. FSL5 (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl) [31] was used in combination with SPM12 (Wellcome Department of Cognitive Neurology, London, UK) and in-house Matlab scripts. After inter-scan slice timing correction, head motion was compensated by 3D motion correction and data repair [32]. The data repair procedure is designed to remove motion-related signal dropout, and involves searching for time points presenting abrupt global signal change exceeding 1% and framewise displacement exceeding a Euclidian distance of ± 1 mm or ± 1° rotation per TR. The data were further cleaned by regressing out 24 head motion-related parameters. Images were spatially normalized using a T1 anatomical image and resliced to 4-mm isotropic voxels.
3. Results 3.1. Progression from aMCI to AD During the follow-up period (mean: 18.6 ± 23.7 months), 13 of 17 (76.5%) aMCI patients converted to clinical diagnosis of probable AD on the basis of medical records, neuropsychological evaluation (i.e., MMSE) or interviews. No NC participants were converted to aMCI or AD within the same period.
2.4.2. Removal of perfusion information Contamination of non-neural signal components is a critical issue in functional connectivity analysis that does not involve trial averaging [33,34]. To cope with this problem, a recently proposed denoising approach was applied to isolate neural activity [35–37]. This procedure involves tracking the regional phase variation of the low-frequency oscillation of systemic origin by using the bandpass-filtered
3.2. Neuropsychological tests Regarding the MMSE-J scores, a significant main effect was found (F 3
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Fig. 1. The visual network detected by the group ICA. This component accounted for the most variance, possibly due to the inclusion of visual motion stimulation condition of OF, HO and RM. z coordinate in template MNI space is shown for each slice. Abbreviations: ICA, independent component analysis; OF, optic flow; HO, horizontal motion; RM, random motion; MNI, Montreal Neurological Institute.
group failed to show either significant pairwise differences or a significant main effect of diagnosis in ANOVA, even after excluding the three aMCI patients who did not convert to AD during the follow-up period.
[2, 48] = 54.45, p < 0.001). The following groups were significantly different: aMCI vs. NC, p = 0.003; AD vs. NC, p < 0.001; AD vs. aMCI, p < 0.001. Similarly, for delayed LM WMS-R, there was a significant main effect (F[2, 48] = 79.65, p < 0.001). Significant differences were observed between the groups as follows: aMCI vs. NC, p < 0.001; AD vs. NC, p < 0.001 (one-way ANOVA with Bonferroni correction). In contrast, no significant main effect was obtained for GDS-J scores (Table 1).
4. Discussion In the current study, we investigated alteration of the net activity or connectivity within V1 during visual motion stimulation in aMCI and AD patients using fMRI under visual motion stimulation and restingstate conditions. Our fMRI results based on ICA suggested alteration of V1 connectivity that may underlie previously reported motion perceptual deficits in aMCI and AD patients [4,6,14]. To the best of our knowledge, this is the first fMRI study to reveal decreased stimulusdriven V1 connectivity in aMCI and AD by comparing visual motion stimulation with the resting-state condition. Therefore, the current study provides new insight into the research fields of visual perception in aMCI and AD.
3.3. ICA for V1 connectivity The first group ICA component representing the early visual cortices, or the visual network is shown in Fig. 1. The strength of the component changed between the resting-state and the visual motion stimulation condition (Fig. 2A), which was significant in the NC group (p = 0.002), but not in the aMCI (p = 0.12) and AD (p = 0.57) groups. The proportional change relative to the resting-state was calculated for each individual to evaluate the effect of diagnosis (Fig. 2B). Oneway ANOVA on all 51 subjects failed to identify significant effect of diagnosis. An additional ANOVA on a subset of the group (47 subjects: 17 NC, 14 aMCI and 16 AD) revealed a significant effect of diagnosis (F [2,46] = 4.99, p = 0.031), with a significant difference between the NC and AD groups (p < 0.05 by Tukey’s HSD). However, the aMCI
4.1. Decreased stimulus-driven V1 connectivity during visual motion stimulation in aMCI and AD The block-design analysis is not suitable for the examination of V1 function because it cannot detect subtle differences in V1 activity
Fig. 2. First independent component of V1. Summed Z scores over the ROI created from the group component map in Fig. 1 are shown. The back-projected individual maps for restingstate and visual motion stimulation conditions were used to quantify V1 connectivity during each condition. (A) V1 connectivity significantly increased during the visual motion stimulation compared with the resting-state condition only in the NC group (*p = 0.002, paired t test). (B) The increase in stimulusdriven V1 connectivity was significantly stronger in the NC group compared with the AD group (**p = 0.031, Tukey’s HSD test). Data are expressed as mean (colored thick line) ± SEM (colored area). Multiple thin black lines show individual data. There were three outliers further than 1.5 interquartile ranges in the aMCI group and one in the AD group, respectively. Abbreviations: NC: normal control; aMCI: amnestic mild cognitive impairment; AD: Alzheimer’s disease; ROI: region of interest; V1: primary visual cortex; SEM: standard error of the mean. 4
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early detection of aMCI and AD. However, it remains unclear whether the alteration in stimulus-driven connectivity of V1 is specific to visual motion stimulation, or whether similar phenomena can be observed with other visual stimuli (e.g., static pattern). Thus, further research examining stimulus-driven V1 connectivity in response to various visual stimuli compared with the resting-state in aMCI and AD patients would be useful for elucidating this issue.
between coherent (OF, HO) and RM, even in healthy young adults, possibly due to the limitations of the temporal resolution of fMRI [3,17]. In contrast, examining differences between the task and restingstate conditions can reveal how the brain responds to a given task relative to resting brain activity [40]. Importantly, the strength of the ICA component evaluated in the current study represents the proportional contribution of this component to the local signal variance (including that from noise or other overlapping components) and can thus be considered to represent the net neural activity in V1. Accordingly, comparing connectivity changes in V1 between the visual motion stimulation and resting-state conditions is an appropriate method for evaluating V1 function. This analysis can be used to identify stimulusdriven V1 connectivity caused by a series of visual motion stimuli (i.e., OF, HO and RM) compared with the resting-state condition. In the NC group, V1 connectivity was significantly increased by a series of visual motion stimuli, compared with the resting-state condition. Conversely, the augmentation of V1 connectivity was not observed in either the aMCI or AD groups. In the between-groups comparison, stimulus-driven V1 connectivity was significantly higher in the NC group compared with the AD group. These findings indicate that stimulus-driven V1 connectivity elicited by visual motion stimulation relative to the resting-state was attenuated in the patients. The aMCI group failed to show a significant difference from the NC group despite the intermediate value between the NC and AD groups. This is possibly due to the insufficient statistical power caused by the weaker disease effect of aMCI compared with AD. In addition, extra inter-individual variation related to the disease stage, final diagnosis, and compensatory neural processes may exist in aMCI patients [41]. Unfortunately, the present data also failed to support this possibility by showing a significant effect after excluding the three aMCI patients who did not convert to AD. Further studies with larger cohorts are needed to determine whether there is a difference in stimulus-driven V1 connectivity between the aMCI and NC groups. AD pathology, including amyloid β (senile plaques) and hyperphosphorylated tau (neurofibrillary tangles) preferentially involves parietotemporal association areas (including the higher level dorsal stream), but V1 function is typically spared until the very late stages of the disease [10,11]. Contrary to these pathological characteristics, the present results suggest the existence of V1 dysfunction even in the early stage of the disease (i.e., MCI). Thus, functional changes of V1 may occur before AD pathology in V1 becomes apparent, even in aMCI patients. To date, amyloid β oligomers are widely regarded as the most toxic and pathogenic form of amyloid β, and their buildup occurs prior to the formation of amyloid β plaques [42,43]. Therefore, we speculate that the present results reflect synaptic dysfunction at V1 due to amyloid β oligomers. A possible alternative interpretation is that, in AD, there is a paradoxical increase of V1 connectivity at rest, resulting in a smaller effect of visual motion stimulation. In contrast to the current fMRI results suggesting alteration of V1 activity in aMCI and AD, our previous VEP study [14] revealed no significant differences in early VEP components (V1 origin) for both OF and HO stimuli among the three groups (aMCI, AD and NC). These results suggest that the V1 response to these motion stimuli is preserved in aMCI and AD patients [14]. This seemingly contradictory finding may be attributable to a disease-related change in resting-state activity [14] because the fluctuation during resting-state was measured and compared with that during sequential presentation of visual motion stimuli in the current fMRI analysis. In other words, we compared the brain state changes by the switching visual motion stimuli to resting-state fluctuation. Thus, the differential increase ratio of V1 connectivity may be due to paradoxically increased V1 recruitment in aMCI and AD at rest, as part of the compensatory mechanisms. Taken together, these findings indicate that the investigation of stimulus-driven and resting-state V1 connectivity may be useful for the evaluation of subtle changes of V1 function, and decreased stimulusdriven V1 connectivity change can provide a novel biomarker for the
4.2. Limitations and future directions The current study involved several potential limitations that should be considered. First, the sample size was relatively small. This may partially explain why some results did not reach statistical significance. However, because the main results of this study were statistically verified, the observed differences between the three groups can be considered meaningful. Second, we used the task of passive perception of moving dots during the visual motion stimulation condition. However, eye tracking data were not recorded. Thus, spurious effects (effects of alertness or fixation) were not fully excluded. A further study using a very simple task (i.e., the discrimination of motion direction) could provide an adequate control for these potential confounds. Third, we used a different TR of EPI sequences between the visual motion stimulation (TR = 3000 ms) and resting-state (TR = 2500 ms) conditions, based on our previous fMRI studies in healthy young subjects [3,17,30]. The difference in TR may induce a differential aliasing effect between the two conditions [44]. The different sampling rate of 1/TR may also confound the direct comparison of correlation coefficients. Thus, we propose that the uniform TR should be applied to evaluate our fMRI data. However, the present findings regarding the disease effect were unaffected by this non-uniformity because the experimental conditions were uniform across the three groups. At the same time, a future study using the same TR between both conditions would be helpful for testing whether the difference in TR affected the present results. Finally, the current study used a cross-sectional design. Further prospective and longitudinal fMRI studies with large numbers of subjects, including other subtypes of MCI patients [5], will be necessary to confirm whether our results are specific to MCI caused by AD [45]. In particular, future studies should compare alterations of stimulus-driven V1 connectivity between MCI caused by AD and MCI caused by Lewy body disease (LBD) because patients with LBD are characterized by V1 impairment [46]. This approach could be used to confirm that the fMRI changes observed in this study provide a useful approach for differentiating between MCI due to AD and healthy individuals, or between MCI caused by AD and MCI caused by other major forms of degenerative dementia, including LBD.
5. Conclusions The present results indicate that altered V1 connectivity may be responsible for previously reported impaired motion perception in aMCI and AD. Therefore, decreases in stimulus-driven V1 connectivity in response to visual motion stimulation relative to the resting-state may be useful as a new fMRI biomarker for early detection of aMCI and AD.
Financial disclosure This study was partly supported by the following grants: JSPS KAKENHI Grant Number JP17K09801 to TY, and Grant from the Research on Innovative Areas (No. 15H05875) from the Ministry of Education, Culture, Sports, Science, and Technology to ST. 5
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Declaration of Competing Interest
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