
==== Front
Biomed J
Biomed J
Biomedical Journal
2319-4170
2320-2890
Chang Gung University

S2319-4170(23)00115-4
10.1016/j.bj.2023.100678
100678
Original Article
A fixel-based analysis of white matter reductions early detects Parkinson disease with mild cognitive impairment
Liao Ting-Wei a1
Wang Jiun-Jie bcdef1
Tsai Chih-Chien bc
Wang Pei-Ning gh
Chen Yao-Liang ei
Wu Yi-Ming bi
Wu Yih-Ru yihruwu@cgmh.org.tw
aj∗
a Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
b Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan
c Healthy Aging Research Center, College of Medicine, Chang Gung University, Taoyuan, Taiwan
d Department of Diagnostic Radiology, Chang Gung Memorial Hospital at Keelung, Keelung, Taiwan
e Department of Chemical Engineering, Ming-Chi University of Technology, New Taipei City, Taiwan
f Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University/Chang Gung Memorial Hospital, Taoyuan, Taiwan
g Division of General Neurology, Department of Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
h Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
i Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
j Department of Neurology, Chang Gung University, College of Medicine, Taoyuan, Taiwan
∗ Corresponding author. Department of Neurology, Linkou Chang Gung Memorial Hospital, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 33305, Taiwan yihruwu@cgmh.org.tw
1 These authors contributed equally to this work and should be considered co-first authors.

08 11 2023
10 2024
08 11 2023
47 5 1006787 6 2023
27 10 2023
30 10 2023
© 2023 The Authors
2023
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

White matter (WM) tract alterations are early signs of cognitive impairment in Parkinson disease (PD) patients. Fixel-based analysis (FBA) has advantages over traditional diffusion tensor imaging in managing complex and crossing fibers. We used FBA to measure fiber-specific changes in patients with PD mild cognitive impairment (PD-MCI) and PD normal cognition (PD-NC).

Methods

Seventy-one patients with PD without dementia were included: 39 PD-MCI and 32 PD-NC. All underwent diffusion-weighted imaging, clinical examinations, and tests to evaluate their cognitive function globally and in five cognitive domains. FBA was used to investigate fiber-tract alterations and compare PD-MCI with PD-NC subjects. Correlations with each cognitive test were analyzed.

Results

Patients with PD-MCI were significantly older (p = 0.044), had a higher male-to-female ratio (P = 0.006) and total Unified Parkinson's Disease Rating Scale score (P = 0.001). All fixel-based metrics were significantly reduced within the body of the corpus callosum and superior corona radiata in PD-MCI patients (family-wise error-corrected P value < 0.05) compared with PD-NC patients. The cingulum, superior longitudinal fasciculi, and thalamocortical circuit exhibited predominantly fiber-bundle cross-section (FC) changes. In regression analysis, reduced FC values in cerebellar circuits were associated with poor motor function in PD-MCI patients and poor picture-naming ability in PD-NC patients.

Conclusions

PD-MCI patients have significant WM alterations compared with PD-NC patients. FBA revealed these changes in various bundle tracts, helping us to better understand specific WM changes that are functionally implicated in PD cognitive decline. FBA is potentially useful in detecting early cognitive decline in PD.

Keywords

Parkinson disease
Mild cognitive impairment
Fixel-based analysis
Fiber density
Fiber density and bundle cross-sectional area
==== Body
pmc1 Introduction

Cognitive decline is a prevalent nonmotor symptom in individuals with Parkinson's disease (PD). It is commonly categorized into mild cognitive impairment (PD-MCI) and Parkinson's disease dementia (PDD), depending on the presence of impaired functional activity [1]. PD-MCI is estimated to occur in 20 %–42 % of individuals at the time of PD diagnosis [2]. While nonamnestic, single-domain impairment is the most frequently observed subtype, there are also multi-domain subtypes characterized by deficits in attention, memory, executive control, and visuospatial abilities [2]. The impact of cognitive impairment on the patient's quality of life and caregiver burden is greater than that of motor symptoms [3]. Therefore, early identification of these patients is crucial.

Previous studies using structural magnetic resonance imaging (MRI) have provided insights into the structural brain changes associated with cognitive impairment in PD. These studies have reported increased brain atrophy in patients with PDD and PD-MCI compared to controls or PD patients with normal cognition (PD-NC) [4]. The observed changes are widespread, affecting regions in the parietal, occipital, temporal, and frontal lobes, as well as structures such as the hippocampus, amygdala, caudate, putamen, thalamus, and substantia innominate.

Diffusion tensor imaging (DTI) studies have shown white matter (WM) abnormalities in the corpus callosum, corona radiata, and inferior and superior longitudinal fasciculi in both PDD and PD-MCI patients [4]. While PDD patients exhibit reduced gray matter (GM) volume and fractional anisotropy (FA), PD-MCI patients primarily show FA abnormalities in major WM tracts, suggesting that tract damage may precede GM atrophy [5]. These findings suggest that WM impairment in PD might serve as a sensitive marker occurring prior to neuronal loss in associated GM regions [6].

However, the application of DTI can be limited when modeling complex and crossing-fiber populations, which can be present in a significant portion of WM voxels, up to 90 % [7]. Consequently, DTI studies may yield inconsistent results due to variations in study methods and imaging settings. To address these limitations, fixel-based analysis (FBA) has been introduced as a promising technique. FBA allows for fiber tract-specific statistical analysis, offering a more precise characterization of multiple fiber orientations within each imaging voxel [8]. FBA utilizes three quantitative metrics: fiber density (FD), fiber-bundle cross-section (FC), and fiber density and bundle cross-sectional area (FDC). These metrics provide a comprehensive assessment of both microstructural and macrostructural WM alterations [8]. By leveraging FBA, researchers can gain a more nuanced understanding of WM changes and their implications.

Our hypothesis centered on the notion that cognitive decline in patients with PD-MCI would be linked to disrupted WM integrity in specific regions. Furthermore, we postulated that deficits in various cognitive domains could be associated with distinct areas of fiber-tract alterations. To address these hypotheses, we conducted a cross-sectional study utilizing FBA to examine WM changes related to cognition in PD-MCI patients.

2 Methods and materials

2.1 Institutional review

The institutional review board of Chang Gung Medical Systems granted approval for this study, which adhered to the ethical guidelines outlined in the Declaration of Helsinki (ethical license No:201701093B0 and No:201700176B0). Prior to participating in the study, each patient provided written informed consent.

2.2 Participants

This study included a total of 71 patients diagnosed with PD, consisting of 39 patients with PD-MCI (26 males and 13 females) and 32 patients with PD-NC (14 males and 18 females). The patients with PD-MCI had a mean age of 65.9 ± 11.4 years (ranging from 30 to 80 years) at the time of recruitment, while the patients with PD-NC had a mean age of 60.5 ± 10.4 years (ranging from 35 to 76 years) upon recruitment. All participants were recruited from the Department of Neurology at Chang Gung Memorial Hospital and were assessed by a movement disorder specialist (YR Wu) using the diagnostic criteria outlined by the UK Parkinson's Disease Society Brain Bank clinical (UKPDSBRC) [9]. The diagnosis of PD-MCI in patients was determined using the Movement Disorders Society (MDS) Task Force PD-MCI level-I criteria, specifically by evaluating their Montreal Cognitive Assessment (MoCA) scores [1,10]. In accordance with a previous study, a MoCA score below 26 was established as the cutoff for PD-MCI [11]. The images from T2-weighted Flow Attenuated Inversion Recovery and T1-weighted 3D Turbo Field Echo (T1-3D-TFE) sequences were independently reviewed by two experienced neuroradiologists (YL Chen and YM Wu).

The inclusion criteria for participants in this study were as follows: (1) being literate adults who met the diagnostic criteria for PD according to the UKPDSBRC guidelines; (2) having Mandarin as their first or primary language; and (3) providing signed informed consent. Participants were excluded if they met any of the following criteria: (1) diagnosis of a specific dementia syndrome; (2) history of neurodegenerative disease or significant medical condition other than PD that may impact cognition or clinical assessment; (3) presence of known structural brain lesions or anomalies; (4) known pathogenic mutation associated with cognitive decline in PD, such as SNCA, GBA, or LRRK2 mutations; or (5) contraindication for undergoing MRI scans.

We gathered essential clinical information from the patients, including their age at recruitment, disease duration, education level (in years), Unified Parkinson's Disease Rating Scale (UPDRS) scores [12], and modified Hoehn-and-Yahr stage [13]. Additionally, we computed gait and postural stability-related subscores from the UPDRS, which encompassed the gait score (item 3.29), postural stability score (item 3.30), the sum of the gait and postural stability scores (sum of items 3.29 and 3.30), the postural instability/gait difficulty (PIGD) score (sum of items 2.13, 2.14, 2.15, 3.29, and 3.30) [14], and the tremor dominant (TD)/PIGD ratio (the mean of items 2.13, 2.14, 2.15, 3.29, and 3.30 divided by the mean of items 2.16, 3.20 (face and 4 extremities), 3.21 (both upper extremities)) [14]. All participants underwent comprehensive neuropsychological assessments that evaluated global cognitive function and performance across five cognitive domains: attention/working memory, executive function, language, visuospatial function, and memory [1]. Global cognition was evaluated using the MoCA [10] and the Mini Mental State Examination (MMSE) [15]. Attention/working memory was assessed using the Digit Span Test (forward and backward) [16], Digit Symbol-Coding [16], and the Trail Making Test (TMT) part A (TMT-A) and part B (TMT-B) [17]. Executive function was measured through categorical Verbal Fluency Test [18] and calculation tasks. Language function was evaluated using the 30-item Boston Naming Test (BNT) [19] and similarity tasks [16]. Visuospatial function was tested through tasks like intersecting Pentagon copying [15], Judgment of Line Orientation [20], and copying a Taylor Complex Figure [21]. Memory function was assessed using a three-item recall test [15] and the Chinese Version Verbal Learning Test (CVVLT) [22]. Visual memory was evaluated by immediate and delayed recall of a Taylor Complex Figure, with the delayed recall occurring after a 10-min interval [21]. A summary of the demographic, clinical, and neuropsychological data of the participants is provided in Table 1.Table 1 Demographic, clinical and neuropsychological data of the enrolled patients.

Table 1	PD-NC	PD-MCI	P value	
Number	32	39		
Sex (male/female)	14/18	26/13	0.006	
Age of recruitment	60.5 ± 10.4	65.9 ± 11.4	0.044	
Disease duration (years)	5.9 ± 4.9	8.2 ± 5.5	0.071	
Education level (years)	13.7 ± 3.8	10.8 ± 4.2	0.003	
UPDRS-total	16.5 (10.75)	25 (15)	0.004	
UPDRS-part III	9 (5.25)	13 (6.5)	0.002	
Modified Hoehn-and-Yahr stage	1 (0.5)	1.5 (1)	0.014	
Global cognition				
MoCA	28 (1.5)	21 (6)	<0.001	
MMSE	30 (1)	28 (2.5)	<0.001	
Attention and working memory	
Digit Span Test				
 Forward	9 (0.25)	8 (1)	0.010	
 Backward	6 (2.25)	4 (2)	<0.001	
Digit Symbol-Coding	58.1 ± 16.7	36.1 ± 13.4	<0.001	
TMT	
 TMT-A (seconds)	11 (8.25)	20 (18.5)	<0.001	
 TMT-A (correct)	7 (0)	7 (0)	0.365	
 TMT-B (seconds)	24.5 (20)	56.5 (59.5)	<0.001	
 TMT-B (correct)	14 (0)	14 (0)	0.067	
Executive function	
Categorical verbal fluency	16.4 ± 4.4	13.9 ± 4.5	0.019	
Calculation	5 (0)	5 (1)	0.007	
Language function	
BNT				
 Spontaneous	27 (3.25)	23 (4.5)	<0.001	
 Semantic cues	1 (1)	1 (2)	0.265	
 Phonemic cues	1.5 (1)	3 (2)	<0.001	
Similarity	8 (0)	8 (1)	0.005	
Visuospatial function	
Intersecting pentagon copying	1 (0)	1 (0)	0.064	
Judgment of Line Orientation	16.5 (5)	14 (4)	0.008	
Taylor Complex Figure				
 Copying	31.0 ± 3.1	28.0 ± 4.8	0.003	
Memory (verbal and visual)	
Three-item recall test	3 (0)	2 (2)	0.001	
CVVLT				
Sum of trials 1–4	25.9 ± 4.2	21.9 ± 5.2	0.001	
 Immediate free recalls (30 s)	8 (2.25)	6 (2)	<0.001	
 Delayed free recalls (10 min)	7 (2)	6 (2)	0.001	
 Cued recall (10 min)	7.5 (3)	6 (2.5)	0.001	
Taylor Complex Figure	
 Immediate recall	19.4 ± 6.7	12.4 ± 8.1	<0.001	
 Delayed recall (10 min)	17.9 ± 7.5	12.6 ± 8.5	0.008	
Values are expressed as mean ± standard deviation or median (interquartile range).

A P value < 0.05 was considered significant.

Abbreviations: PD: Parkinson disease; PD-NC: Parkinson disease–normal cognition; PD-MCI: Parkinson disease–mild cognitive impairment; UPDRS: Unified Parkinson's Disease Rating Scale; MoCA: Montreal Cognitive Assessment; MMSE: Mini Mental State Examination; CVVLT: Chinese Version Verbal Learning Test; BNT: Boston Naming Test; TMT: Trail Making Test; TMT-A: Trail Making Test part A; TMT-B: Trail Making Test part B.

2.3 Imaging procedure

The images were obtained using a 3T scanner (Ingenia; Philips, Amsterdam, Netherlands) with a 15-channel transmit-receive head coil. To minimize head motion, a fixation pad was utilized. High-resolution images were acquired using a T1-weighted MPRAGE sequence, which was subsequently used for image normalization. The imaging parameters were as follows: repetition time (TR) of 7.84 ms, echo time (TE) of 3.58 ms, flip angle of 8°, 160 slices, a slice thickness of 1 mm, matrix size of 256 × 256, and a field of view of 256 × 256 mm2.

Diffusion-weighted images were obtained using a spin-echo echo-planar-imaging sequence with the following parameters: a repetition time (TR) of 4000 ms, an echo time (TE) of 75.2 ms, a flip angle of 90°, a field of view of 256 × 256 mm2, a slice thickness of 2 mm, and a matrix size of 128 × 128. A total of 64 axial slices were acquired to cover the entire brain. The data acquisition was accelerated with a factor of 2 using a SENSitivity Encoding (SENSE) reconstruction. Diffusion-weighted gradients were applied along 64 non-collinear directions, with a b-value of 1000 s/mm2. An additional non-diffusion weighted image (b = 0 s/mm2) was acquired. The total acquisition time was 17 min and 52 s.

2.4 Image processing

Fixel-based analysis, encompassing both reconstruction and statistical analysis, was conducted using the MRtrix3 freeware, following recommended procedures and parameters [8]. Preprocessing steps involved denoising [23], motion distortion correction [24,25], bias field correction [26], and removal of Gibbs ringing artifacts [27]. Multi-tissue constrained spherical deconvolution was utilized to compute the fiber orientation distribution (FOD) function within each voxel [28]. To enable intergroup comparisons, a study-specific template was generated by employing symmetric diffeomorphic nonlinear transformation registration based on the fiber orientation distribution [29]. Fixel-specific measures, including FD, FC, and FDC, were computed within each voxel of the imaging data [8].

2.5 Statistical analysis

2.5.1 Demographic, clinical, and neuropsychological data

Statistical analysis was conducted using IBM SPSS Statistics, version 26 (IBM Corp., Armonk, NY, USA).

The male-to-female ratio was assessed using the chi-squared test. Age at recruitment, disease duration, and education level (in years) were evaluated using the Student's t-test. Normality of the demographic data was examined using the Shapiro-Wilk test. Differences in clinical parameters and neuropsychological assessments, including Digit Symbol-Coding, Categorical verbal fluency, Taylor Complex Figure (copying, immediate and delayed recall), and CVVLT (sum of trial 1–4), were compared using the Student's t-test. Differences in UPDRS, gait and postural stability-related subscores, modified Hoehn-and-Yahr stage, and the remaining neuropsychological tests were assessed using the Mann-Whitney test.

A threshold of statistical significance was set at p < 0.05 for all tests.

2.5.2 Fixels

To identify significant differences in fixel-based metrics, nonparametric permutation testing and connectivity-based fixel enhancement (CFE) as implemented in MRtrix 3 were employed [30]. A family-wise error (FWE)-corrected P value of <0.05, with a cluster extent-based threshold of 10 or more voxels, was considered as statistically significant [31]. The relationships between fixel-based metrics and clinical parameters were assessed using linear regression separately for each group of patients (PD-NC and PD-MCI), with age and sex treated as confounding factors in the regression analysis. The location of fixels exhibiting significant differences was mapped onto the Johns Hopkins University (JHU) template [32,33]. The involved fiber tracts were visually identified based on their spatial location and fiber orientations. A senior neuroradiologist (YM Wu) further confirmed the identified fiber tracts.

3 Results

3.1 Demographic, clinical, and neuropsychological data by groups

The demographic, clinical, and neuropsychological data are presented in Table 1. The gait and postural stability-related subscores of UPDRS are presented in Supplementary Table 1. The PD-MCI group had a significantly higher mean age upon recruitment (p = 0.044) and a higher male-to-female ratio (p = 0.006). Additionally, the PD-MCI group had a higher mean UPDRS total score (P = 0.001), UPDRS part III score (P = 0.002), and lower education level (P = 0.003) compared to the PD-NC group. The PD-MCI group also had a higher gait score, postural stability score, sum of gait and postural stability score, PIGD score, and lower TD/PIGD ratio compared to the PD-NC group. In terms of neuropsychological performance, patients with PD-MCI performed significantly worse than patients with PD-NC in most of the tests, except for intersecting pentagon copying (p = 0.064), the correctness of TMT-A (P = 0.365), and the TMT-B (P = 0.067).

3.2 Whole-brain fixel-based analysis

Fig. 1 and Supplementary Table 2 demonstrate significant reductions in fixel-based metrics within multiple areas in patients with PD-MCI (FWE-corrected p < 0.05). Specifically, compared to patients with PD-NC, patients with PD-MCI exhibited decreased FD in the body of the corpus callosum, bilateral superior corona radiata, and the right posterior corona radiata. Furthermore, the patients with PD-MCI showed significant reductions in FC in the bilateral superior longitudinal fasciculi, bilateral cingulum, bilateral superior corona radiata, posterior limbs of the internal capsule containing superior thalamic radiation, and the body of the corpus callosum, in comparison to patients with PD-NC. Additionally, significant reductions in FDC were identified in the body of the corpus callosum and bilateral superior corona radiata in patients with PD-MCI.Fig. 1 Whole brain fixel-based analysis comparing patients with PD-NC with patients with PD-MCI. a Significant fixels are colored according to family-wise error-corrected P values. b-g Significant fixels are colored by direction (anterior-to-posterior: green; superior-to-inferior: blue; and left-to-right: red). Abbreviations: FD: fiber density; FC: fiber-bundle cross-section; FDC: fiber density and bundle cross-sectional area; PD: Parkinson disease; PD-NC: Parkinson disease–normal cognition; PD-MCI: Parkinson disease–mild cognitive impairment; bCC: body of corpus callosum; CG: cingulum; PLIC: posterior limb of internal capsule; SCR: superior corona radiata; SLF: superior longitudinal fasciculus; STR: superior thalamic radiation.

Fig. 1

3.3 Regression of fixel-based metrics and clinical parameters

Table 2 provides an overview of the regions exhibiting significant associations between fixel-based metrics and clinical parameters in patients with PD-NC or PD-MCI (FWE-corrected p < 0.05). In patients with PD-NC, the UPDRS part III score displayed a negative correlation with FC in the body of the corpus callosum, genu of the corpus callosum, bilateral anterior corona radiata, and left superior corona radiata (Fig. 2a). Additionally, a negative correlation was observed between the UPDRS part III score and FDC in the body of the corpus callosum and right anterior corona radiata. Furthermore, in patients with PD-NC, the BNT scores demonstrated a positive correlation with FC in the right superior cerebellar peduncle, left central tegmental tract, and left dentatorubrothalamic tract (Fig. 2b), as well as with FDC in the genu of the corpus callosum. Supplementary Fig. 1 provides further details on the regression results in patients with PD-NC.Table 2 Regions with significant association of fixel-based metrics with clinical parameters.

Table 2Within patients with PD-NC	FD	FC	FDC	
UPDRS part III (N)	None	Body of corpus callosum	Body of corpus callosum	
		Genu of corpus callosum	Anterior corona radiata, right	
		Anterior corona radiata, bilateral		
		Superior corona radiata, left		


	
BNT - Spontaneous (P)	None	Superior cerebellar peduncle, right	Genu of corpus callosum	
		Central tegmental tract, left		
		Dentatorubrothalamic tract, left		


	
CVVLT - trial 3 (N)	Body of corpus callosum			
	Posterior corona radiata, bilateral			
	∗Near right postcentral gyrus			
Within patients with PD-MCI	FD	FC	FDC	
UPDRS total (N)	None	Superior longitudinal fasciculus, right	Body of corpus callosum	
		Superior cerebellar peduncle, right		
		Central tegmental tract, bilateral		
		∗Superior cerebellar peduncle, left		


	
UPDRS part II (N)	None	None	∗Near right postcentral gyrus	


	
UPDRS part III (N)	None	Central tegmental tract, bilateral	∗Body of corpus callosum	
		∗Body of corpus callosum		


	
PIGD score (N)	None	∗Superior cerebellar peduncle, right	None	
		∗Central tegmental tract, right		
		∗Dentatorubrothalamic tract, right		


	
Sum of gait and postural stability score (N)	None	Superior cerebellar peduncle, bilateral	None	
	Central tegmental tract, bilateral		
	Dentatorubrothalamic tract, right		
			
TMT-B, seconds (N)	∗Body of corpus callosum	None	Splenium of corpus callosum	
		∗Body of corpus callosum	
		∗Tapetum, right	


	
TMT-B, corrects (P)	Body of corpus callosum	None	Body of corpus callosum	
Superior corona radiata, bilateral		Superior corona radiata, bilateral	
Posterior corona radiata, left		Posterior corona radiata, left	


	
Three-item recall test (N)	Superior longitudinal fasciculus, right	None	Superior longitudinal fasciculus, right	
		∗Superior longitudinal fasciculus, left	


	
CVVLT - trial 2 (P)	Body of corpus callosum	None	None	
∗Superior corona radiata, right			


	
CVVLT - trial 4 (P)	Body of corpus callosum		None	
Superior corona radiata, right			


	
CVVLT - sum of trials 1–4 (P)	Body of corpus callosum			
			
CVVLT - cued recall (P)	Fornix	None	None	
∗Splenium of corpus callosum			
In parentheses following a task name, “P" indicates a positive correlation of fixel-based metrics and the clinical parameter. “N" indicates a negative correlation of fixel-based metrics and the clinical parameter.

A family-wise error-corrected P value < 0.05 with a cluster-extent-based threshold of 10 or more was considered statistically significant.

∗ Statistically significant but less prominently affected area.

Abbreviations: FD: fiber density; FC: fiber-bundle cross-section; FDC: fiber density and bundle cross-sectional area; PD: Parkinson disease; PD-NC: Parkinson disease–normal cognition; PD-MCI: Parkinson disease–mild cognitive impairment; UPDRS: Unified Parkinson's Disease Rating Scale; BNT: Boston Naming Test; CVVLT: Chinese Version Verbal Learning Test; TMT-B: Trail Making Test part B; PIGD: postural.

instability/gait difficulty.

Fig. 2 Regions with significant associations of FC and a UPDRS part III, b BNT - Spontaneous in PD-NC group. c-d Regions with significant associations of FC and UPDRS total (c), UPDRS part III (d), sum of gait and postural stability score (e) in PD-MCI group. a-e Significant fixels are colored by direction (anterior-to-posterior: green; superior-to-inferior: blue; and left-to-right: red). Regions with significant associations of FDC and TMT-B (seconds) (f), TMT-B (corrects) (g) in PD-MCI group. f-g Significant fixels are colored according to family-wise error-corrected P values. In parentheses following a task name, “P" indicates a positive correlation of fixel-based metrics and the clinical parameter. “N" indicates a negative correlation of fixel-based metrics and the clinical parameter. Abbreviations: FD: fiber density; FC:fiber-bundle cross-section; FDC: fiber density and bundle cross-sectional area; PD: Parkinson disease; PD-NC: Parkinson disease–normal cognition; PD-MCI: Parkinson disease–mild cognitive impairment; UPDRS:Unified Parkinson's Disease Rating Scale; BNT:Boston Naming Test; TMT:Trail Making Test; ACR:anterior corona radiata; bCC:body of corpus callosum; CTT: central tegmental tract; DRT:dentatorubrothalamic tract; gCC:genu of corpus callosum; PCR:posterior corona radiata; sCC:splenium of corpus callosum; SCP:superior cerebellar peduncle; SCR:superior corona radiata; SLF:superior longitudinal fasciculus; TP: tapetum.

Fig. 2

In patients with PD-MCI, UPDRS total scores displayed a negative correlation with FC in the bilateral superior cerebellar peduncle, bilateral central tegmental tract, and right superior longitudinal fasciculus (Fig. 2c), as well as with FDC in the body of the corpus callosum. UPDRS part III scores showed a negative correlation with FC in the bilateral central tegmental tract and body of the corpus callosum (Fig. 2d), and with FDC in the body of the corpus callosum. Furthermore, the PIGD score exhibited a negative correlation with FC in the right superior cerebellar peduncle, right central tegmental tract, and right dentatorubrothalamic tract. Similarly, the sum of gait and postural stability scores displayed a negative correlation with FC in the bilateral superior cerebellar peduncle, bilateral central tegmental tract, and right dentatorubrothalamic tract (Fig. 2e). The time taken on the TMT-B in patients with PD-MCI exhibited a negative correlation with FD in the body of the corpus callosum, and with FDC in the body of the corpus callosum, the splenium, and the right tapetum (Fig. 2f). Conversely, the correctness of the TMT-B results demonstrated a positive correlation with FD and FDC in the body of the corpus callosum, as well as in the superior corona radiata and left posterior corona radiata (Fig. 2g). Performance on the three-item recall test exhibited a negative correlation with FD in the right superior longitudinal fasciculus, and with FDC in the bilateral superior longitudinal fasciculus. Additionally, FD in the fornix and the splenium of the corpus callosum were associated with the CVVLT-cued recall score. For more regression results in patients with PD-MCI, refer to Supplementary Fig. 2.

4 Discussion

4.1 Major findings

This study utilized FBA to examine fiber-specific changes in patients with PD-MCI compared to patients with PD-NC. Our findings revealed significant WM degeneration in patients with PD-MCI, indicating both macrostructural and microstructural alterations in the body of the corpus callosum and superior corona radiata. Regression analysis demonstrated that the impairment in the corpus callosum was not only associated with motor function but also correlated with multiple cognitive tests. FC changes were predominantly observed in the cingulum, superior longitudinal fasciculi, and thalamocortical circuit, and their impairment was linked to global cognitive decline in PD patients. Furthermore, macrostructural alterations in cerebellar circuits were associated with poor motor function and poor gait and postural stability in patients with PD-MCI, as well as impaired picture-naming ability in patients with PD-NC.

These results reaffirm the significance of WM alterations in the development of cognitive decline in PD and provide insights into brain circuitry changes that contribute to motor and cognitive dysfunction in PD patients.

4.2 Corpus callosum and both motor and cognitive function

In our study, we observed significant WM alterations in patients with PD-MCI within the corpus callosum, as indicated by all fixel-based metrics, when compared to patients with PD-NC (Fig. 1). Importantly, the impairment of the corpus callosum showed correlations not only with motor function but also with multiple cognitive tests (Table 2). Specifically, in both patients with PD-NC and PD-MCI, the FC and FDC of the corpus callosum were correlated with the UPDRS part III score. Additionally, the FD or FDC of the corpus callosum was associated with performance on the TMT-B and CVVLT. These findings suggest that the involvement of the corpus callosum is linked to increased disease severity and advanced stage of PD.

Previous longitudinal FBA studies have provided evidence that degeneration in the splenium of the corpus callosum and other commissural areas is an early characteristic in patients with PD [34]. The decline in FDC in the corpus callosum over time has been found to be associated with increased clinical severity. Another FBA study comparing early- and middle-stage PD patients with controls also demonstrated early reduction in FD in the corpus callosum [35]. While the exact mechanisms underlying callosal atrophy in PD are not fully understood, corpus callosum pathology has been observed in very early stages of the disease [36]. Importantly, corpus callosum involvement is not unique to PD, as atrophy in this region has also been reported in other neurodegenerative disorders like Alzheimer's disease [37]. Due to its status as the largest fiber bundle in the human brain, some researchers have proposed that the corpus callosum may be particularly vulnerable to Wallerian degeneration and myelin damage associated with various neurodegenerative processes [37].

4.3 The cingulum and global cognitive function

Significant reductions in FC values were observed in the cingulum of patients with PD-MCI compared to those with PD-NC (Fig. 1). The involvement of the cingulum is recognized as a significant indicator of disease progression in PD, as the anterior cingulate cortex is among the neocortical regions affected early in the course of the disease, as described in Braak staging [38]. Consistent findings of WM alterations in the cingulum have been reported in previous DTI studies, suggesting its potential as a biomarker for PD-MCI [4].

The decline in cognitive function in patients with PD has been associated with the dopaminergic afferent and cholinergic systems within the cingulum [[39], [40], [41]]. In a study involving patients with PD-MCI, memory impairment and executive performance deficits were linked to decreased D2-receptor binding in the anterior cingulate cortex and insula [39]. Additionally, a reduction in α4β2∗-nicotinic acetylcholine receptor availability in various brain regions, including the midbrain, pons, posterior anterior cingulate cortex, frontoparietal cortex, and cerebellum, has been correlated with the severity of cognitive and depressive symptoms in PD patients [40]. Furthermore, the maintenance of cholinergic activity in the default mode networks and frontoparietal networks has been identified as a prerequisite for cognitive improvement following cholinergic treatment in PDD [41].

No significant correlation was observed between the integrity of the cingulum and cognitive performance in our study, possibly due to the inclusion of patients without evident dementia. Further investigations involving patients with PDD, who are expected to have more pronounced WM degeneration, are warranted to elucidate the specific cognitive domains affected by cingulum impairment.

4.4 The thalamocortical circuit and global cognitive function

We found that patients with PD-MCI exhibited decreased FC values in the thalamocortical circuit, which includes the pathway originating from the thalamus, passing through the posterior limbs of the internal capsule and superior corona radiata, and ultimately reaching the premotor area (Fig. 1c–f). This trajectory aligns with the course of the superior thalamic radiation.

Past research has highlighted increased atrophy in the thalamus among individuals with PD-MCI or PDD [4]. Additionally, deficits in the superior thalamic radiation have been associated with cognitive decline in PD patients [42]. However, the exact mechanisms underlying thalamocortical circuit impairment in cognitive decline among PD patients remain unclear based on our current study. Further investigations, including neuroimaging and clinicopathological studies, are necessary to validate the involvement of the thalamocortical circuit in PD-related cognitive impairment.

4.5 Cerebellar circuits and motor function

We observed that cerebellum-related tracts exhibited macrostructural changes, which were associated with poor clinical status and motor function in patients with PD-MCI (Fig. 2c and d). Specifically, reduced FC values in the superior cerebellar peduncle and central tegmental tract were correlated with higher UPDRS total scores, while reduced FC values in the central tegmental tract were correlated with higher UPDRS part III scores. Furthermore, reduced FC values in the superior cerebellar peduncle, central tegmental tract, and dentatorubrothalamic tract were correlated with PIGD scores and the sum of gait and postural stability scores (Fig. 2e). These findings align with previous evidence highlighting the significant role of the cerebellum in the pathophysiology of PD [43].

Previous studies investigating cerebellum-related circuits in PD have yielded inconsistent results. For instance, a study utilizing FBA reported higher mean log-FC values in the superior cerebellar peduncle in early- and middle-stage PD patients compared to healthy controls [35]. Conversely, a DTI study focusing on PD patients with freezing of gait found lower FA values in the superior cerebellar peduncles compared to other PD patients [44]. Increased and decreased volumes of the red nucleus have both been associated with clinical severity in PD [45,46].

The contradictory findings regarding cerebellum-related circuits in PD can potentially be explained by a counterbalance between pathological and compensatory effects within the cerebellum. Pathological changes in the cerebellum in PD involve degeneration of nigrostriatal dopaminergic neurons, disinhibition from the basal ganglia, direct α-synuclein pathology, and altered dopaminergic neurotransmission [43]. Despite these pathological changes, the cerebellum plays a role in maintaining motor and nonmotor functions in patients with PD, indicating the presence of compensatory mechanisms [43]. However, the underlying pathogenesis and the interplay between pathological and compensatory effects remain poorly understood. In our study, we hypothesized that patients with PD-MCI had more pronounced pathological changes in cerebellum-related circuits, leading to more significant WM degeneration. These structural alterations may have further impaired the compensatory capacity of the cerebellum, resulting in more severe motor symptoms in patients with PD-MCI.

4.6 Cerebellar circuits and linguistic function

Furthermore, we observed a correlation between the performance on the BNT and FC values in the superior cerebellar peduncle, central tegmental tract, and dentatorubrothalamic tract in patients with PD-NC (Fig. 2b). The emerging evidence suggests that the cerebellum plays a role in the modulation of linguistic function [47]. For instance, focal hypoperfusion in the left middle and inferior frontal gyrus resulting in transcortical motor aphasia can be seen in cases of acute ischemic infarct in the right superior cerebellar artery [48]. Lesion-symptom mapping studies have also indicated that damage to the right crus in patients with isolated cerebellar stroke is associated with poorer performance on the BNT [49]. Moreover, in our study, we observed more prominent FC changes in the right superior cerebellar peduncle and more prominent involvement of the left dentatorubrothalamic tract. This finding aligns with the concept of “crossed cerebral diaschisis” [50].

While the role of cerebro-cerebellar pathways in language modulation has received significant attention, there is a relative scarcity of reports specifically addressing the dentatorubro-olivary pathway [47]. The observed WM alterations within the central tegmental tract could potentially be attributed to concurrent pathological changes occurring in other cerebellar circuits involved in language function. To gain a better understanding of the precise role of the dentatorubro-olivary pathway in language function, further studies are warranted.

5 Limitations and future directions

The first limitation of our study is the use of level I PD-MCI criteria instead of level II criteria [1]. Although the level II criteria provide a more comprehensive assessment, they can be challenging to apply in routine clinical practice for every patient with PD.

The second limitation is the utilization of raw performance scores from neuropsychological tests for both group comparisons and regression analysis. This approach may introduce additional fluctuations related to age. To address this issue, we included age as a confounding factor in our fixel analysis. However, future research can further mitigate this potential error by considering patients with similar age ranges in the analysis.

Thirdly, we recognize the presence of potential confounding variables associated with systemic conditions that were not accounted for in our analysis. Conditions such as diabetes and hypertension have the capacity to induce microstructural disturbances in central neural structures, potentially leading to adverse effects on cognitive functions.

6 Conclusion

Our study has revealed significant WM alterations in patients with PD-MCI compared to those with PD-NC, using FBA analysis of various bundle tracts. These findings contribute to our understanding of the specific WM changes that are associated with cognitive decline in PD patients. However, further longitudinal studies with larger sample sizes and comprehensive neuropsychological assessments are needed to fully elucidate the impact of these fixel changes on impairment in different cognitive domains in PD-MCI.

Data availability statement

The datasets generated for this study are available on request to the corresponding author.

Ethics statement

This study was approved by the institutional review board of Chang Gung Medical Systems and conducted following the Declaration of Helsinki (ethical license No:201701093B0 and No:201700176B0). The patients/participants provided their written informed consent to participate in this study.

Author contributions

Conceptualization/Methodology: Jiun-Jie Wang, Yih-Ru Wu. Neuropsychological test programing: Pei-Ning Wang. MRI reading: Yao-Liang Chen, Yi-Ming Wu. Software (MRtrix3): Chih-Chien Tsai. Validation: Jiun-Jie Wang. Statistics: Chih-Chien Tsai, Ting-Wei Liao. Formal analysis: Jiun-Jie Wang, Yih-Ru Wu, Chih-Chien Tsai, Ting-Wei Liao. Resources: Jiun-Jie Wang, Yih-Ru Wu. Writing—original draft preparation: Ting-Wei Liao. Writing—review and editing: Jiun-Jie Wang, Yih-Ru Wu. Supervision: Jiun-Jie Wang, Yih-Ru Wu. Project administration: Yih-Ru Wu. Funding acquisition: Jiun-Jie Wang, Yih-Ru Wu. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by the Chang Gung Memorial Hospital (CMRPD1L0141 ), National Science and Technology Council, Taiwan (NSTC 109-2221-E-182-009-MY3 , NSTC 109-2314-B-182-021-MY3 , NSTC 112-2314-B-182-052-MY3 ), and Healthy Aging Research Center (grant EMRPD1M0451 , EMRPD1M0431 , EMRPD1N0151 ).

Declaration of competing interest

The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

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Acknowledgements

This work was supported by Chang Gung Memorial Hospital, Taipei, Taiwan (CMRPG3H0312 ) and Department of Neurology, Chang Gung University, College of Medicine, Taoyuan, Taiwan. The authors thank the Neuroscience Research Center (Chang Gung Memorial Hospital) and the Healthy Aging Research Center (Chang Gung University) for their invaluable support.

Peer review under responsibility of Chang Gung University.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bj.2023.100678.
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References

1 Litvan I Goldman JG Troster AI Schmand BA Weintraub D Petersen RC Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: movement Disorder Society Task Force guidelines Mov Disord 27 3 2012 349 356 22275317
2 Weil RS Costantini AA Schrag AE. Mild cognitive impairment in Parkinson’s disease-what is it? Curr Neurol Neurosci Rep 18 4 2018 17 29525906
3 Leroi I McDonald K Pantula H Harbishettar V Cognitive impairment in Parkinson disease: impact on quality of life, disability, and caregiver burden J Geriatr Psychiatr Neurol 25 4 2012 208 214
4 Delgado-Alvarado M Gago B Navalpotro-Gomez I Jimenez-Urbieta H Rodriguez-Oroz MC. Biomarkers for dementia and mild cognitive impairment in Parkinson’s disease Mov Disord 31 6 2016 861 881 27193487
5 Hattori T Orimo S Aoki S Ito K Abe O Amano A Cognitive status correlates with white matter alteration in Parkinson’s disease Hum Brain Mapp 33 3 2012 727 739 21495116
6 Rektor I Svatkova A Vojtisek L Zikmundova I Vanicek J Kiraly A White matter alterations in Parkinson’s disease with normal cognition precede grey matter atrophy PLoS One 13 1 2018 e0187939
7 Jeurissen B Leemans A Tournier JD Jones DK Sijbers J. Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging Hum Brain Mapp 34 11 2013 2747 2766 22611035
8 Raffelt DA Tournier JD Smith RE Vaughan DN Jackson G Ridgway GR Investigating white matter fibre density and morphology using fixel-based analysis Neuroimage 144 Pt A 2017 58 73 27639350
9 Hughes AJ Daniel SE Kilford L Lees AJ. Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases J Neurol Neurosurg Psychiatry 55 3 1992 181 184 1564476
10 Tsai CF Lee WJ Wang SJ Shia BC Nasreddine Z Fuh JL. Psychometrics of the Montreal Cognitive Assessment (MoCA) and its subscales: validation of the Taiwanese version of the MoCA and an item response theory analysis Int Psychogeriatr 24 4 2012 651 658 22152127
11 Dalrymple-Alford JC MacAskill MR Nakas CT Livingston L Graham C Crucian GP The MoCA: well-suited screen for cognitive impairment in Parkinson disease Neurology 75 19 2010 1717 1725 21060094
12 Fahn S Elton R. UPDRS program members. Unified Parkinsons disease rating scale Recent developments in Parkinson’s disease 2 1987 153 163
13 Hoehn MM Yahr MD. Parkinsonism: onset, progression and mortality Neurology 17 5 1967 427 442 6067254
14 Stebbins GT Goetz CG Burn DJ Jankovic J Khoo TK Tilley BC. How to identify tremor dominant and postural instability/gait difficulty groups with the movement disorder society unified Parkinson’s disease rating scale: comparison with the unified Parkinson’s disease rating scale Mov Disord 28 5 2013 668 670 23408503
15 Guo N Liu H Wong P Liao K Yan S Lin K Chinese version and norms of the mini-mental state examination Journal of Rehabilitation Medicine Association 16 52 1988 5
16 Wechsler D. Wechsler adult intelligence scale-(WAIS-III) manual for Taiwan 2002 The Chinese Behavioral Science Corporation In: Chen Y, Chen H, editors. Taipei
17 Wei M Shi J Li T Ni J Zhang X Li Y Diagnostic accuracy of the Chinese version of the trail-making test for screening cognitive impairment J Am Geriatr Soc 66 1 2018 92 99 29135021
18 Tsai PH Liu JL Lin KN Chang CC Pai MC Wang WF Development and validation of a dementia screening tool for primary care in Taiwan: brain Health Test PLoS One 13 4 2018 e0196214
19 Chen TB Lin CY Lin KN Yeh YC Chen WT Wang KS Culture qualitatively but not quantitatively influences performance in the Boston naming test in a Chinese-speaking population Dement Geriatr Cogn Dis Extra 4 1 2014 86 94 24847347
20 Moses BJWBLYJA Faustman Jr ALBWO. Development of an optimally reliable short form for judgment of line orientation Clin Neuropsychol 12 3 1998 311 314
21 Taylor LB. Localisation of cerebral lesions by psychological testing Clin Neurosurg 16 1969 269 287 5811709
22 Chang CC Kramer JH Lin KN Chang WN Wang YL Huang CW Validating the Chinese version of the verbal learning test for screening Alzheimer’s disease J Int Neuropsychol Soc 16 2 2010 244 251 20003579
23 Veraart J Novikov DS Christiaens D Ades-Aron B Sijbers J Fieremans E. Denoising of diffusion MRI using random matrix theory Neuroimage 142 2016 394 406 27523449
24 Andersson JL Sotiropoulos SN. Non-parametric representation and prediction of single- and multi-shell diffusion-weighted MRI data using Gaussian processes Neuroimage 122 2015 166 176 26236030
25 Andersson JLR Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging Neuroimage 125 2016 1063 1078 26481672
26 Tustison NJ Avants BB Cook PA Zheng Y Egan A Yushkevich PA N4ITK: improved N3 bias correction IEEE Trans Med Imag 29 6 2010 1310 1320
27 Kellner E Dhital B Kiselev VG Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts Magn Reson Med 76 5 2016 1574 1581 26745823
28 Jeurissen B Tournier JD Dhollander T Connelly A Sijbers J. Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data Neuroimage 103 2014 411 426 25109526
29 Raffelt D Tournier JD Fripp J Crozier S Connelly A Salvado O. Symmetric diffeomorphic registration of fibre orientation distributions Neuroimage 56 3 2011 1171 1180 21316463
30 Raffelt DA Smith RE Ridgway GR Tournier JD Vaughan DN Rose S Connectivity-based fixel enhancement: whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres Neuroimage 117 2015 40 55 26004503
31 Nichols TE Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples Hum Brain Mapp 15 1 2002 1 25 11747097
32 Mori S Oishi K Jiang H Jiang L Li X Akhter K Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template Neuroimage 40 2 2008 570 582 18255316
33 Lo CY Wang PN Chou KH Wang J He Y Lin CP. Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer’s disease J Neurosci 30 50 2010 16876 16885 21159959
34 Rau YA Wang SM Tournier JD Lin SH Lu CS Weng YH A longitudinal fixel-based analysis of white matter alterations in patients with Parkinson’s disease Neuroimage Clin 24 2019 102098
35 Li Y Guo T Guan X Gao T. Sheng W Zhou C Fixel-based analysis reveals fiber-specific alterations during the progression of Parkinson’s disease Neuroimage Clin 27 2020 102355
36 Roberts RF Wade-Martins R Alegre-Abarrategui J. Direct visualization of alpha-synuclein oligomers reveals previously undetected pathology in Parkinson’s disease brain Brain 138 Pt 6 2015 1642 1657 25732184
37 Di Paola M Luders E Di Iulio F Cherubini A Passafiume D Thompson PM Callosal atrophy in mild cognitive impairment and Alzheimer’s disease: different effects in different stages Neuroimage 49 1 2010 141 149 19643188
38 Braak H Del Tredici K Rub U de Vos RA Jansen Steur EN Braak E. Staging of brain pathology related to sporadic Parkinson’s disease Neurobiol Aging 24 2 2003 197 211 12498954
39 Christopher L Duff-Canning S Koshimori Y Segura B Boileau I Chen R Salience network and parahippocampal dopamine dysfunction in memory-impaired Parkinson disease Ann Neurol 77 2 2015 269 280 25448687
40 Meyer PM Strecker K Kendziorra K Becker G Hesse S Woelpl D Reduced alpha4beta2∗-nicotinic acetylcholine receptor binding and its relationship to mild cognitive and depressive symptoms in Parkinson disease Arch Gen Psychiatr 66 8 2009 866 877 19652126
41 Colloby SJ McKeith IG Burn DJ Wyper DJ O’Brien JT Taylor JP. Cholinergic and perfusion brain networks in Parkinson disease dementia Neurology 87 2 2016 178 185 27306636
42 Pu W Shen X Huang M Li Z Zeng X Wang R Assessment of white matter lesions in Parkinson’s disease: voxel-based analysis and tract-based spatial Statistics analysis of Parkinson’s disease with mild cognitive impairment Curr Neurovascular Res 17 4 2020 480 486
43 Wu T Hallett M. The cerebellum in Parkinson’s disease Brain 136 Pt 3 2013 696 709 23404337
44 Bharti K Suppa A Pietracupa S Upadhyay N Gianni C Leodori G Abnormal cerebellar connectivity patterns in patients with Parkinson’s disease and freezing of gait Cerebellum 18 3 2019 298 308 30392037
45 Bonnet A Sanford R Riou A Drapier S Le Jeune F Verin M Parkinson’s disease motor symptoms are linked to red nucleus volume and cerebellar metabolism International Congress Abstracts of Parkinson’s Disease and Movement Disorders 2018
46 Philippens I Wubben JA Franke SK Hofman S Langermans JAM. Involvement of the red nucleus in the compensation of parkinsonism may explain why primates can develop stable Parkinson’s disease Sci Rep 9 1 2019 880 30696912
47 Marien P Ackermann H Adamaszek M Barwood CH Beaton A Desmond J Consensus paper: language and the cerebellum: an ongoing enigma Cerebellum 13 3 2014 386 410 24318484
48 Marien P Saerens J Nanhoe R Moens E Nagels G Pickut BA Cerebellar induced aphasia: case report of cerebellar induced prefrontal aphasic language phenomena supported by SPECT findings J Neurol Sci 144 1–2 1996 34 43 8994102
49 Stoodley CJ MacMore JP Makris N Sherman JC Schmahmann JD Location of lesion determines motor vs. cognitive consequences in patients with cerebellar stroke Neuroimage Clin 12 2016 765 775 27812503
50 Sonmezoglu K Sperling B Henriksen T Tfelt-Hansen P Lassen NA. Reduced contralateral hemispheric flow measured by SPECT in cerebellar lesions: crossed cerebral diaschisis Acta Neurol Scand 87 4 1993 275 280 8503255
