Altered resting cerebral blood flow specific to patients with diabetic retinopathy revealed by arterial spin labeling perfusion magnetic resonance imaging

Abstract

Background

Previous neuroimaging studies have shown that patients with diabetic retinopathy (DR) were accompanied by abnormalities in cerebral functional and structural architecture, whereas the resting cerebral blood flow (CBF) alterations in patients with DR are not well understood.

Purpose

To explore CBF alterations in patients with DR using pseudo-continuous arterial spin labeling (pCASL) imaging.

Material and Methods

Thirty-one individuals with DR (15 men, 16 women; mean age = 53.38 ± 9.12 years) and 33 healthy controls (HC) (12 men, 21 women; mean age = 51.61 ± 9.84 years) closely matched for age, sex, and education, underwent pCASL imaging scans. Two-sample T test was conducted to compare different CBF values between two groups.

Results

Patients with DR exhibited significantly increased CBF values in the left middle temporal gyrus (Brodmann’s area, BA 22) and the bilateral supplementary motor area (BA3) and decreased CBF values in the bilateral calcarine (BA17,18) and bilateral caudate relative to HC group (two-tailed, voxel level at P < 0.01, Gaussian random field (GRF), cluster level at P < 0.05). Moreover, the HbA1c (%) level showed a positive correlation with CBF values in the bilateral caudate (r = 0.473, P = 0.007) in patients with DR.

Conclusion

Our results highlighted that patients with DR had abnormal CBF values in the visual cortices, caudate, middle temporal gyrus, and supplementary motor area, which might reflect vision and sensorimotor and cognition dysfunction in patients with DR. These findings might help us to understanding the neural mechanism of patients with DR.

Keywords

Diabetic retinopathy cerebral blood flow arterial spin labeling magnetic resonance imaging

Introduction

Diabetic retinopathy (DR) is a serious retinal microvascular complication of diabetes and one of the major causes of blindness in middle-aged people worldwide (1). There are several risk factors for the progression of DR including hypertension (2), hyperglycemia (3), and prolonged duration of diabetes (4). Worldwide, the number of patients with DR is expected to increase from 126.6 million in 2010 to 191.0 million by 2030 (5). Thus, DR is a worldwide major global health issue. Furthermore, DR is accompanied by retinal microvascular dysfunction, characterized by retinal capillary non-perfusion, vascular leakage, and degeneration (6). Importantly, the retinal vasculature shares similar anatomic, physiological, and embryological characteristics with cerebral vessels. There is increasing evidence that patients with DR are at higher risk of cerebrovascular disease, compared with normal individuals (7,8). Recent studies have shown that patients with DR are also at increased risk of cognitive decline and dementia (9,10). However, the etiology of this increased risk remains unclear.

Thus far, resting-state functional magnetic resonance imaging (fMRI) has provided a powerful framework for the characterization of functional and structural architecture changes related to cognitive decline in patients with DR. Wang et al. (11) demonstrated that patients with DR exhibit abnormal spontaneous neural activity in visual cortices, as well as in the cerebellar network and default mode network (DMN). Moreover, van Duinkerken et al. (12) reported that patients with DR showed higher eigenvector centrality mapping in the bilateral lateral occipital cortex, right cuneus, and occipital fusiform gyrus, relative to healthy controls (HCs); these areas are related to altered visual, sensorimotor, auditory, and language networks. Patients with DR also exhibited specific cerebral structural changes. Using a voxel-based morphometry method, Wessels et al. (13) found that patients with DR had lower gray matter density (GMD) in the right inferior frontal gyrus and right occipital lobe, relative to healthy controls. In another study, Patients with DR were found to exhibit lower local path length and lower local clustering in the middle frontal, postcentral, and occipital areas in the gray-matter network, relative to HCs (14). However, the abovementioned studies focused on altered cerebral functional and structural changes in patients with DR. It remains largely unknown whether and how cerebral blood flow (CBF) changes in patients with DR.

Retinal microvascular and small cerebral vessels share similar morphological and physiological properties. The retinal microvasculature may serve as an indicator of pathological changes in cerebral vasculature (15). Previous studies showed that patients with DR were at higher risk of ischemic stroke (16,17). Thus, we hypothesized that patients with DR might exhibit abnormal CBF. The human brain has a high rate of metabolism and requires continuous CBF; importantly, the CBF reflects the rate of delivery of arterial blood to the capillary bed in brain tissue. Moreover, CBF is closely correlated with neuronal activity and metabolism. The regulation of CBF is a key aspect of various important physiological functions including cognition (18), sleep (19), and memory (20). Arterial spin labeling (ASL) is a non-invasive MRI technique that measures alterations in CBF (21,22). In the ASL method, the water in arterial blood is labeled and used as an endogenous tracer for measurement of CBF values. Unlike other perfusion imaging methods, the advantage of ASL is that it does not require the use of radioactive tracers or long scan times (23). Recently, the ASL method has been successfully applied to investigate neural mechanisms in patients with diabetes. Cui et al. (24) demonstrated that patients with type 2 diabetes mellitus (T2DM) had significantly lower CBF values in the posterior cingulate cortex (PCC), precuneus, and bilateral occipital lobe, relative to HCs; conversely, patients with T2DM had higher CBF values in the anterior cingulate cortex. Bangenet al. (25) found that patients with T2DM exhibited lower CBF in the hippocampus, as well as in the inferior temporal, inferior parietal, and frontal cortices; these changes were related to poor cognitive performance. However, the effect of DR on resting CBF remains unknown. Growing evidences demonstrated that DR might be a potential predictor of cognitive decline progression in diabetes patients (26 –28). In addition, retinal microvascular abnormalities constitute useful clinical biomarkers for cognitive decline (29). Deal et al. (30) demonstrated that the retinal microvasculature might reflect the brain microvasculature and was related to dementia. Thus, we hypothesized that patients with DR would exhibit distinct patterns of changes in CBF, relative to T2DM patients without retinopathy.

Based on this hypothesis, the aim of the present study was to determine whether patients with DR exhibited abnormal CBF patterns using the pseudo-continuous ASL (pCASL) method. Moreover, this study investigated relationships between altered CBF values in brain regions and clinical variables (i.e. visual function and biochemical examination) in patients with DR. The findings might provide new insights into the underlying neural mechanisms involved in DR.

Material and Methods

Participants

Thirty-one patients with DR (15 men, 16 women) and 33 HCs (12 men, 21 women) matched for age, sex, and education participated in the study. The diagnostic criteria of patients with DR were: (i) fasting plasma glucose ≥7.0 mmol/L, random plasma glucose ≥11.1 mmol/L, or 2-h glucose ≥ 11.1 mmol/L; and (ii) the non-proliferative DR group exhibited microaneurysms, hard exudates, and retinal hemorrhages.

The exclusion criteria of individuals with DR in the study were: (i) proliferative DR with retinal detachment; (ii) vitreous hemorrhage; (iii) additional ocular-related complications (e.g. cataract, glaucoma, high myopia, or optic neuritis); and (iv) individuals with DR and diabetic nephropathy, diabetic neuropathy.

All HCs met the following criteria: (i) fasting plasma glucose < 7.0 mmol/L, random plasma glucose < 11.1 mmol/L, and HbA1c < 6.5%; (ii) no ocular diseases (e.g. myopia, cataracts, glaucoma, optic neuritis, or retinal degeneration); (iii) binocular visual acuity ≥ 1.0; (iv) no ocular surgical history; and (v) no mental disorders.

Ethical statement

The research protocol followed the Declaration of Helsinki and was approved by the medical ethics committee of the Hospital. All individuals provided written informed consent to participate in the study.

MRI data acquisition

MRI scanning was performed on a 3-T MRI scanner (Discovery MR 750W system; GE Healthcare, Milwaukee, WI, USA) with an eight-channel head coil. The resting-state perfusion imaging was performed using a pCASL sequence with the following parameters: TR/TE = 4699/11 ms; labeling duration = 1525 ms; labeling pulse flip angle = 18°; bandwidth = 3.3 kHz/pixel; field of view (FOV) = 240 × 240; slice thickness = 3.5 mm; no gap. All participants underwent MRI scanning with their eyes closed.

ASL data processing

The ASL preprocessing was performed using Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk) implemented in MATLAB 2013a (MathWorks, Natick, MA, USA). An ASL difference image was calculated using a single-compartment model (31) after subtracting the label image from the control image. The 3D ASL difference images were averaged to calculate the CBF maps in combination with the proton-density-weighted reference images (32). According to previous studies (33,34), the native CBF images normalize to the Montreal Neurological Institute (MNI) space using the following steps: (i) the native CBF images were non-linearly normalized to a PET-perfusion template (35) in the MNI space and then averaged to generate a study-specific MNI-standard CBF template; (ii) the native CBF images were co-registered to the MNI standard CBF template; and (iii) each co-registered CBF map was spatially smoothed with a Gaussian kernel of 6 × 6 × 6 mm FWHM. The CBF of each voxel was normalized by dividing the mean CBF of the whole brain (36).

Statistical analysis

Chi-square (χ²) test and independent-sample t-test was used to investigate the clinical features between two groups using SPSS version 20.0 (SPSS Inc, Chicago, IL, USA) (P < 0.05 significant differences).

One-sample t-test was performed to assess spatial patterns of CBF maps. Two-sample t-test was then used to investigate different CBF values between two groups using the Gaussian random field (GRF) method. It was used to correct for multiple comparisons and regressed covariates of age and sex using the Resting-State fMRI Data Analysis Toolkit plus V1.2 (RESTplus V1.2, http://restfmri.net/forum/RESTplusV1.2) (two-tailed, voxel-level P < 0.01, GRF correction, cluster-level P < 0.05). The visualization of these results was shown using BrainNet Viewer software (https://www.nitrc.org/projects/bnv/).

The relationships between the CBF values of different brain regions and clinical variables in the DR group were investigated with Pearson correlation coefficient using SPSS version 20.0 software (SPSS Inc., Chicago, IL, USA).

Results

Demographics and visual measurements

There were significant differences in best-corrected visual acuity between patients with DR and HCs (P < 0.001). However, there were no significant differences in sex or age between the groups (Table 1).

Table 1.

Demographics and visual measurements between two groups.

	DR group	HC group	T-values	P values
Gender (male/female)	15/16	12/21	N/A	N/A
Age (years)	53.38 ± 9.12	51.61 ± 9.84	0.750	0.456
Handedness	31 R	33 R	N/A	N/A
BCVA-OD	0.49 ± 0.29	1.36 ± 0.15	–15.331	<0.001
BCVA-OS	0.42 ± 0.32	1.14 ± 0.20	–10.835	<0.001
HbA1c (%)	7.34 ± 1.40	N/A	N/A	N/A
Fasting blood glucose (mmol/L)	7.82 ± 2.60	N/A	N/A	N/A

Values are given as n or mean ± SD. χ² test for sex (n). Independent t-test for the other normally distributed continuous data.

BCVA, best corrected visual acuity; DR, diabetic retinopathy; Hb, glycosylated hemoglobin; HC, healthy control; N/A, not applicable; OD, oculus dexter; OS, oculus sinister.

CBF differences

The spatial distribution of CBF maps of the patients with DR and HCs were compared at the group mean level (Fig. 1). Patients with DR exhibited significantly increased CBF values in the left middle temporal gyrus (MTG) and the bilateral supplementary motor area (SMA) and decreased CBF values in the bilateral calcarine and bilateral caudate relative to the HC group (Fig. 2 and Table 2). The mean values of altered CBF between the two groups were shown with a histogram (Fig. 3).

Fig. 1.

Spatial patterns of CBF were observed at the group level in the DR (a) and HC (b) groups. CBF, cerebral blood flow; DR, diabetic retinopathy; HC, healthy control; L, left; R, right.

Fig. 2.

Significant different CBF values between the DR and HC groups. Significant CBF differences were found between the two groups. Cool color indicates decreased CBF, warm color indicates increased CBF. CBF, cerebral blood flow; DR, diabetic retinopathy; GRF, Gaussian random field; HC, healthy control; L, left hemisphere; R, right hemisphere.

Table 2.

Significantly different levels of CBF between the two groups.

Condition	Brain regions	BA	Peak T-scores	MNI coordinates (x, y, z)	Cluster size (voxels)
DR > HC	L – MTG	22	4.5317	–48, –32, –14	792
DR < HC	B – Calcarine	17,18	–4.6637	–14, –92, –6	2506
DR < HC	B – Caudate	–	–3.7999	–12, 8, 22	914
DR > HC	B – SMA	3	4.114	0, –12, 72	1151

The statistical threshold was set at the voxel level with P < 0.01 for multiple comparisons using Gaussian random-field theory.

B, bilateral; BA, Brodmann area; CBF, cerebral blood flow; DR, diabetic retinopathy; GRF, Gaussian random field; HC, healthy control; L, left; MNI, Montreal Neurologic Institute; MTG, middle temporal gyrus; SMA, supplementary motor area.

Fig. 3.

The mean values of altered CBF values between the DR and HC groups. B, bilateral; CAL, calcarine; CAU, caudate; CBF, cerebral blood flow; DR, diabetic retinopathy; HC, healthy control; L, left; MTG, middle temporal gyrus; SMA, supplementary motor area.

Correlations between CBF values and clinical variables

The HbA1c (%) level of patients with DR showed a positive correlation with CBF values in the B-CAU (r = 0.473, P = 0.007) (Fig. 4).

Fig. 4.

Significant positive correlation between the CBF values in the B-CAU and HbA1c (%) level in DR patients. The HbA1c (%) level of patients with DR showed a positive correlation with CBF values in the B-CAU (r = 0.473, P = 0.007). B, bilateral; CAU, caudate; CBF, cerebral blood flow; DR, diabetic retinopathy; Hb, glycosylated hemoglobin.

Discussion

To the best of our knowledge, this study is the first to investigate the presence of abnormal CBF patterns in patients with DR using the ASL method. The present study revealed that patients with DR exhibited significantly lesser perfusion in the bilateral calcarine (BA17 and BA18) and bilateral caudate, as well as greater perfusion in the left MTG (BA22) and bilateral SMA (BA3), relative to the HC group.

The present study showed that patients with DR had significantly lower CBF values in the bilateral calcarine. Notably, the calcarine is the location of the visual cortex, which is involved in visual information processing (37). The main pathological changes in DR are retinal leukostasis (38) and retinal capillary non-perfusion due to endothelial cell damage (39). Retinal ischemia triggers pathological neovascularization, which leads to proliferative DR (40) and tractional retinal detachment (41). Furthermore, recent studies have shown that DR leads to retinal neurodegeneration (42,43). Vision loss is the main feature of DR. Previous neuroimaging studies demonstrated that some patients who had eye diseases with visual loss exhibited lower CBF in the visual cortex (44,45). Thus, the reduced retinal input might contribute to the lower CBF values in the bilateral calcarine in patients with DR. Furthermore, patients with DR had abnormalities in the visual pathway and visual cortex. Wang et al. (46) reported that patients with proliferative and non-proliferative DR both had an increased apparent diffusion coefficient in the visual cortex, relative to HCs. Another study demonstrated that patients with DR had lower GMD in the right inferior frontal gyrus and right occipital lobe, relative to HCs (13). Thus, we speculated that trans-synaptic retrograde degeneration of the visual pathway might lead to lower CBF values in the visual cortex in patients with DR. We concluded that the reduced retinal input and trans-synaptic retrograde degeneration of the visual pathway might contribute to the lower CBF values observed in the bilateral calcarine. In addition, the present study revealed that patients with DR had significantly lower CBF values in the bilateral caudate. The caudate is an important area of dorsal striatum, which plays an important role in motor behavior formulation (47) and cognitive functions (48). Moreover, the caudate is involved in spatial working memory (49). Previous neuroimaging studies demonstrated that patients with T2DM exhibited lower gray matter volume in the caudate nucleus (50 –52). Prior studies showed that dysfunction of the caudate might lead to movement disorders, such as Parkinson’s disease and Huntington’s disease (53,54). Moreover, patients with T2DM reportedly exhibited dysfunctional motor activity (55,56). Notably, there was a significant positive correlation between CBF values in the bilateral caudate and HbA_1c levels in patients with DR in the present study. Thus, we speculate that lower CBF values of the bilateral caudate might indicate motor disorder in patients with DR.

Interestingly, we found that patients with DR had significantly higher CBF values in the left MTG and bilateral SMA. The left MTG plays important roles in language comprehension (57,58) and social cognition (59). Previous neuroimaging studies demonstrated that patients with T2DM had abnormal structural changes in the MTG, which were related to cognitive decline (60,61). Furthermore, Xia et al. (62) reported that patients with T2DM had significantly lower amplitude of low-frequency fluctuations in the MTG, which were closely correlated with cognitive decline (62). Based on these findings, we speculate that the higher CBF values in the left MTG might reflect impaired cognitive function in patients with DR. The SMA is well-known to play an important role in the control of action (63,64) and finger movement (65). Diabetic polyneuropathy is a complication of T2DM, which leads to sensory deficits in the motor system (66). Souayah et al. (67) found that type 1 diabetes mice exhibited motor activity decline. Picconiet al. (68) also demonstrated a relationship between neuroretinal dysfunction and motor function decline in patients with type 1 diabetes. Thus, greater cerebral perfusion in the SMA might indicate functional reorganization in this region in patients with DR.

Compared with previous CBF studies in diabetic patients, patients with DR showed distinct patterns of changes in CBF. The association of T2DM with CBF has been investigated in several studies. Dai W et al demonstrated that the T2DM patients had decreased CBF in the default mode, visual, and cerebellum networks relative to health controls (69). Cui et al. (24) demonstrated that patients with T2DM showed decreased CBF in the posterior cingulate cortex, precuneus, and bilateral occipital lobe, and increased in the anterior cingulate cortex (24). Xia et al. (70) also reported that patients with T2DM exhibited decreased CBF in the visual area and the default mode network. Thus, the altered CBF values of diabetic patients are mainly located in the visual area and the default mode network-related brain regions. In the present study, we found that patients with DR also showed significantly lesser perfusion in the bilateral calcarine (BA17 and BA18). However, patients with DR had decreased CBF values in the bilateral caudate and increased CBF values in the left MTG (BA22) and bilateral SMA (BA3), which were different from the CBF pattern of diabetic patients. Previous studies demonstrated that diabetic patients who developed proliferative retinopathy show the most severe decrements and are most at risk of developing cerebral complications (71). van Duinkerken et al. (72) demonstrated that diabetic patients with proliferative retinopathy showed lower functional connectivity in the auditory and language, ventral attention, and left frontal-parietal networks, where those without retinopathy had intermediate connectivity levels. Our results revealed that the patients with DR might be associated with more widespread abnormal CBF brain regions (temporal gyrus and SMA) than diabetic patients.

In conclusion, we found that patients with DR had abnormal CBF values in the visual cortices, caudate, MTG, and SMA, which might reflect visual, sensorimotor, and cognitive dysfunction in patients with DR. These findings might aid in greater understanding of the neural mechanisms involved in DR.

Footnotes

Declaration of conflicting interests

The author(s) declare no potential competing interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received the following financial support for the research, authorship, and/or publication of this article: This work was supported by: National Key R&D Program of China (Grant No. 294 2017YFE0103400) and The National Nature Science Foundation of China (Grant No. 81470628).

References

Wong

Cheung

Larsen

, et al. Diabetic retinopathy. Nat Rev Dis Primers 2016; 2:16012.

Saif

Karawya

Abdelhamid

Blood pressure is a risk factor for progression of diabetic retinopathy in normotensive patients with type 2 diabetes: correlation with carotid intima-media thickness. Endocr Regul 2014; 48:189–194.

Shiraiwa

Kaneto

Miyatsuka

, et al. Postprandial hyperglycemia is a better predictor of the progression of diabetic retinopathy than HbA1c in Japanese type 2 diabetic patients. Diabetes Care 2005; 28:2806–2807.

Yau

Rogers

Kawasaki

, et al. Meta-Analysis for Eye Disease (META-EYE) Study Group. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 2012; 35:556–564.

Zheng

Congdon

The worldwide epidemic of diabetic retinopathy. Indian J Ophthalmol 2012; 60:428–431.

Ciulla

Amador

Zinman

Diabetic retinopathy and diabetic macular edema: pathophysiology, screening, and novel therapies. Diabetes Care 2003; 26:2653–2664.

Woerdeman

van Duinkerken

Wattjes

, et al. Proliferative retinopathy in type 1 diabetes is associated with cerebral microbleeds, which is part of generalized microangiopathy. Diabetes Care 2014; 37:1165–1168.

Hägg

Thorn

Putaala

, et al. Incidence of stroke according to presence of diabetic nephropathy and severe diabetic retinopathy in patients with type 1 diabetes. Diabetes Care 2013; 36:4140–4146.

Crosby-Nwaobi

Sivaprasad

Amiel

, et al. The relationship between diabetic retinopathy and cognitive impairment. Diabetes Care 2013; 36:3177–3186.

10.

Exalto

Biessels

Karter

, et al. Severe diabetic retinal disease and dementia risk in type 2 diabetes. J Alzheimers Dis 2014; 42:S109–S117.

11.

Wang

Zou

, et al. Abnormal spontaneous brain activity in type 2 diabetic retinopathy revealed by amplitude of low-frequency fluctuations: a resting-state fMRI study. Clin Radiol 2017; 72:340.e1–340.e7.

12.

van Duinkerken

Schoonheim

IJzerman

, et al. Altered eigenvector centrality is related to local resting-state network functional connectivity in patients with longstanding type 1 diabetes mellitus. Hum Brain Mapp 2017; 38:3623–3636.

13.

Wessels

Simsek

Remijnse

, et al. Voxel-based morphometry demonstrates reduced grey matter density on brain MRI in patients with diabetic retinopathy. Diabetologia 2006; 49:2474–2480.

14.

van Duinkerken

Ijzerman

Klein

, et al

Disrupted subject-specific gray matter network properties and cognitive dysfunction in type 1 diabetes patients with and without proliferative retinopathy.

Hum Brain Mapp 2016;37:1194–1208.

15.

Patton

Aslam

Macgillivray

, et al. Retinal vascular image analysis as a potential screening tool for cerebrovascular disease: a rationale based on homology between cerebral and retinal microvasculatures. J Anat 2005; 206:319–348.

16.

Chou

Liu

Chao

, et al. Presence of diabetic microvascular complications does not incrementally increase risk of ischemic stroke in diabetic patients with atrial fibrillation: A nationwide cohort study. Medicine (Baltimore) 2016; 95:e3992.

17.

Cheung

Rogers

Couper

, et al. Is diabetic retinopathy an independent risk factor for ischemic stroke? Stroke 2007; 38:398–401.

18.

Ogoh

Relationship between cognitive function and regulation of cerebral blood flow. J Physiol Sci 2017; 67:345–351.

19.

Elvsåshagen

Mutsaerts

Zak

, et al. Cerebral blood flow changes after a day of wake, sleep, and sleep deprivation. Neuroimage 2019; 186:497–509.

20.

Liang

Zou

, et al. Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc Natl Acad Sci U S A 2013; 110:1929–1934.

21.

Detre

Leigh

Williams

, et al. Perfusion imaging. Magn Reson Med 1992; 23:37–45.

22.

Detre

Wang

Technical aspects and utility of fMRI using BOLD and ASL. Clin Neurophysiol 2002; 113:621–634.

23.

Williams

Detre

Leigh

, et al. Magnetic resonance imaging of perfusion using spin inversion of arterial water. Proc Natl Acad Sci U S A 1992; 89:212–216.

24.

Cui

Liang

, et al. Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction. Brain Imaging Behav 2017; 11:1248–1257.

25.

Bangen

Werhane

Weigand

, et al. Reduced regional cerebral blood flow relates to poorer cognition in older adults with type 2 diabetes. Front Aging Neurosci 2018; 10:270.

26.

Ding

Strachan

Reynolds

, et al. Diabetic retinopathy and cognitive decline in older people with type 2 diabetes: the Edinburgh Type 2 Diabetes Study. Diabetes 2010; 59:2883–2889.

27.

Liao

Xiong

Yang

, et al. An association of cognitive impairment with diabetes and retinopathy in end stage renal disease patients under peritoneal dialysis. PLoS One 2017; 12:e0183965.

28.

Ding

Patton

Deary

, et al. Retinal microvascular abnormalities and cognitive dysfunction: a systematic review. Br J Ophthalmol 2008; 92:1017–1025.

29.

Wei

Raaijmakers

Zhang

, et al. Association between cognition and the retinal microvasculature in 11-year old children born preterm or at term. Early Hum Dev 2018; 118:1–7.

30.

Deal

Sharrett

Albert

, et al. Retinal signs and risk of incident dementia in the Atherosclerosis Risk in Communities study. Alzheimers Dement 2019; 15:477–486.

31.

Buxton

Frank

Wong

, et al. A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med 1998; 40:383–396.

32.

Rowley

, et al. Reliability and precision of pseudo-continuous arterial spin labeling perfusion MRI on 3.0 T and comparison with 15O-water PET in elderly subjects at risk for Alzheimer’s disease. NMR Biomed 2010 ;23:286–293.

33.

Zhu

Zhuo

Qin

, et al. Altered resting-state cerebral blood flow and its connectivity in schizophrenia. J Psychiatr Res 2015; 63:28–35.

34.

Zhuo

Zhu

Qin

, et al. Cerebral blood flow alterations specific to auditory verbal hallucinations in schizophrenia. Br J Psychiatry 2017; 210:209–215.

35.

Gispert

Pascau

Reig

, et al. Influence of the normalization template on the outcome of statistical parametric mapping of PET scans. Neuroimage 2003; 19:601–612.

36.

Aslan

On the sensitivity of ASL MRI in detecting regional differences in cerebral blood flow. Magn Reson Imaging 2010; 28:928–935.

37.

Lalli

Hussain

Ayub

, et al. Role of the calcarine cortex (V1) in perception of visual cues for saccades. Clin Neurophysiol 2006; 117:2030–2038.

38.

Antcliff

Marshall

The pathogenesis of edema in diabetic maculopathy. Semin Ophthalmol 1999; 14:223–232.

39.

Miyamoto

Ogura

Pathogenetic potential of leukocytes in diabetic retinopathy. Semin Ophthalmol 1999; 14:233–239.

40.

Takagi

Molecular mechanisms of retinal neovascularization in diabetic retinopathy. Intern Med 2003; 42:299–301.

41.

Muraoka

Murakami

Nishijima

, et al. Association between retinal venular dilation and serous retinal detachment in diabetic macular edema. Retina 2014; 34:725–731.

42.

Simó

Stitt

Gardner

TW.

Neurodegeneration in diabetic retinopathy: does it really matter?

Diabetologia 2018; 61:1902–1912.

43.

Sohn

van Dijk

Jiao

, et al. Retinal neurodegeneration may precede microvascular changes characteristic of diabetic retinopathy in diabetes mellitus. Proc Natl Acad Sci U S A 2016; 113:E2655–E2664.

44.

Wang

Chen

, et al. Reduced cerebral blood flow in the visual cortex and its correlation with glaucomatous structural damage to the retina in patients with mild to moderate primary open-angle glaucoma. J Glaucoma 2018; 27:816–822.

45.

Dan

Zhou

Xing

Arterial spin labeling perfusion magnetic resonance imaging reveals resting cerebral blood flow alterations specific to retinitis pigmentosa patients. Curr Eye Res 2019; 44:1353–1359.

46.

Wang

, et al. Evaluation of apparent diffusion coefficient measurements of brain injury in type 2 diabetics with retinopathy by diffusion-weighted MRI at 3.0 T. Neuroreport 2017; 28:69–74.

47.

Postle

D’Esposito

. Dissociation of human caudate nucleus activity in spatial and nonspatial working memory: an event-related fMRI study. Brain Res Cogn Brain Res 1999; 8:107–115.

48.

Grahn

Parkinson

Owen

AM.

The cognitive functions of the caudate nucleus. Prog Neurobiol 2008; 86:141–155.

49.

Postle

D’Esposito

Spatial working memory activity of the caudate nucleus is sensitive to frame of reference. Cogn Affect Behav Neurosci 2003; 3:133–144.

50.

Nouwen

Chambers

Chechlacz

, et al. Microstructural abnormalities in white and gray matter in obese adolescents with and without type 2 diabetes. Neuroimage Clin 2017; 16:43–51.

51.

Rofey

Arslanian

El Nokali

, et al. Brain volume and white matter in youth with type 2 diabetes compared to obese and normal weight, non-diabetic peers: A pilot study. Int J Dev Neurosci 2015; 46:88–91.

52.

Chen

Zhang

Liu

, et al. Abnormal subcortical nuclei shapes in patients with type 2 diabetes mellitus. Eur Radiol 2017; 27:4247–4256.

53.

McColgan

Seunarine

Razi

, et al. Selective vulnerability of Rich Club brain regions is an organizational principle of structural connectivity loss in Huntington’s disease. Brain 2015; 138:3327–3344.

54.

Liu

, et al. Functional Connectivity of the Corticobasal Ganglia-Thalamocortical Network in Parkinson Disease: A Systematic Review and Meta-Analysis with Cross-Validation. Radiology 2018; 287:973–982.

55.

Ochoa

Gogola

Gorniak

SL.

Contribution of tactile dysfunction to manual motor dysfunction in type II diabetes. Muscle Nerve 2016; 54:895–902.

56.

Gorniak

Khan

Ochoa

, et al. Detecting subtle fingertip sensory and motor dysfunction in adults with type II diabetes. Exp Brain Res 2014; 232:1283–1291.

57.

Ashtari

Lencz

Zuffante

, et al. Left middle temporal gyrus activation during a phonemic discrimination task. Neuroreport 2004; 15:389–393.

58.

Tesink

Petersson

van Berkum

, et al. Unification of speaker and meaning in language comprehension: an FMRI study. J Cogn Neurosci 2009; 21:2085–2099.

59.

Labek

Viviani

Gizewski

, et al. Neural correlates of the appraisal of attachment scenes in healthy controls and social cognition-an fMRI study. Front Hum Neurosci 2016; 10:345.

60.

Tan

Fang

, et al. Micro-structural white matter abnormalities in type 2 diabetic patients: a DTI study using TBSS analysis. Neuroradiology 2016; 58:1209–1216.

61.

Zhang

, et al. Gray matter volume abnormalities in type 2 diabetes mellitus with and without mild cognitive impairment. Neurosci Lett 2014; 562:1–6.

62.

Xia

Wang

Sun

, et al. Altered baseline brain activity in type 2 diabetes: a resting-state fMRI study. Psychoneuroendocrinology 2013; 38:2493–2501.

63.

Nachev

Wydell

O’Neill

, et al. The role of the pre-supplementary motor area in the control of action. Neuroimage 2007; 36 Suppl 2:T155–T163.

64.

Tanji

Shima

Role for supplementary motor area cells in planning several movements ahead. Nature 1994; 371:413–416.

65.

Shibasaki

Sadato

Lyshkow

, et al. Both primary motor cortex and supplementary motor area play an important role in complex finger movement. Brain 1993; 116:1387–1398.

66.

Allen

Doherty

Rice

, et al. Physiology in Medicine: neuromuscular consequences of diabetic neuropathy. J Appl Physiol (1985) 2016; 121:1–6.

67.

Souayah

Potian

Garcia

, et al. Motor unit number estimate as a predictor of motor dysfunction in an animal model of type 1 diabetes. Am J Physiol Endocrinol Metab 2009; 297:E602–E608.

68.

Picconi

Mataluni

Ziccardi

, et al. Association between early neuroretinal dysfunction and peripheral motor unit loss in patients with type 1 diabetes mellitus. J Diabetes Res 2018; 2018:9763507.

69.

Dai

Duan

Alfaro

, et al. The resting perfusion pattern associates with functional decline in type 2 diabetes. Neurobiol Aging 2017; 60:192–202.

70.

Xia

Rao

Spaeth

, et al. Blood pressure is associated with cerebral blood flow alterations in patients with T2DM as revealed by perfusion functional MRI. Medicine (Baltimore) 2015; 94:e2231.

71.

Jacobson

Ryan

Cleary

, et al. Biomedical risk factors for decreased cognitive functioning in type 1 diabetes: an 18 year follow-up of the Diabetes Control and Complications Trial (DCCT) cohort. Diabetologia 2011; 54:245–255.

72.

van Duinkerken

Schoonheim

Sanz-Arigita

, et al. Resting-state brain networks in type 1 diabetic patients with and without microangiopathy and their relation to cognitive functions and disease variables. Diabetes 2012; 61:1814–1821.