# Microstructural changes in the median and ulnar nerve in people with and without diabetic neuropathy in their hands: A cross-sectional diffusion MRI study

Eva Sierra-Silvestre, Robert Smith, Ricardo Andrade, Ben Kennedy, Michel Coppieters

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## ► To cite this version:

Eva Sierra-Silvestre, Robert Smith, Ricardo Andrade, Ben Kennedy, Michel Coppieters. Microstructural changes in the median and ulnar nerve in people with and without diabetic neuropathy in their hands: A cross-sectional diffusion MRI study. European Journal of Radiology, 2024, 230 (5), pp.111721. 10.1016/j.ejrad.2024.111721. hal-04691057

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# Microstructural changes in the median and ulnar nerve in people with and without diabetic neuropathy in their hands: A cross-sectional diffusion MRI study

Eva Sierra-Silvestre<sup>a,b,c</sup>, Robert E. Smith<sup>d,e</sup>, Ricardo J. Andrade<sup>a,f</sup>, Ben Kennedy<sup>g</sup>,
Michel W. Coppieters<sup>a,b,*</sup>

## ARTICLE INFO

**Keywords:**

Diabetes
Neuropathy
Imaging
Diffusion MRI
Upper limb
Hand

## ABSTRACT

**Purpose:** Diffusion weighted imaging (DWI) has revealed microstructural changes in lower limb nerves in people with diabetic neuropathy. Microstructural changes in upper limb nerves using DWI in people with diabetes have not yet been explored.

**Methods:** This cross-sectional study aimed to quantify and compare the microstructure of the median and ulnar nerve in people without diabetes (n = 10), people with diabetes without distal symmetrical polyneuropathy (DSPN; n = 10), people with DSPN in the lower limbs only (DSPN FEET ONLY; n = 12), and people with DSPN in the upper and lower limbs (DSPN HANDS & FEET; n = 9). DSPN diagnosis included electrodiagnosis and corneal confocal microscopy. Tensor metrics, such as fractional anisotropy, radial diffusivity and axial diffusivity, and constrained spherical deconvolution metrics, such as dispersion and complexity, were calculated. Linear mixed-models were used to quantify DWI metrics from multiple models in median and ulnar nerves across the groups, and to evaluate potential differences in metrics at the wrist and elbow based on the principle of a distal-to-proximal disease progression.

**Results:** Tensor metrics revealed microstructural abnormalities in the median and ulnar nerve in people with DSPN HANDS & FEET, and also already in DSPN FEET ONLY. There were significant negative correlations between electrodiagnostic parameters and tensor metrics. A distal-to-proximal pattern was more pronounced in the median nerve. Non-tensor metrics showed early microstructural changes in people with diabetes without DSPN.

**Conclusion:** Compared to people without diabetes, microstructural changes in upper limb nerves can be identified in people with diabetes with and without DSPN, even before symptoms occur.

## 1. Introduction

Distal symmetrical polyneuropathy (DSPN) is the most common complication in people with diabetes, and is responsible for increased morbidity and mortality [1,2]. Despite being the most prevalent

neuropathy worldwide [3,4], its natural course is not fully understood [5]. DSPN is a length-dependent sensorimotor polyneuropathy, which manifests itself as a loss of sensation in the toes that progresses to the lower legs and hands in more severe cases (i.e., in a 'distal-to-proximal' distribution). Symptoms such as numbness, tingling and/or pain are

<footnote><sup>a</sup> School of Health Sciences and Social Work, Griffith University, Brisbane, Australia</footnote>

<footnote><sup>b</sup> Amsterdam Movement Sciences – Program Musculoskeletal Health, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands</footnote>

<footnote><sup>c</sup> Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK</footnote>

<footnote><sup>d</sup> The Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia</footnote>

<footnote><sup>e</sup> The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia</footnote>

<footnote><sup>f</sup> Movement – Interactions – Performance (MIP), Nantes University, Nantes, France</footnote>

<footnote><sup>g</sup> Mermaid Beach Radiology, Gold Coast, Australia</footnote>

<footnote>* Corresponding author at: Building N55 – Room 2.34, Griffith University, 170 Kessels Road, QLD, 4111 Nathan, Australia. Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 9, 1081 BT Amsterdam, The Netherlands.

*E-mail addresses:* m.coppieters@griffith.edu.au, m.coppieters@vu.nl (M.W. Coppieters).
@esiesil (E. Sierra-Silvestre), @jacobofhume (R.J. Andrade), @michelcoppie (M.W. Coppieters)</footnote>
typical, whereas weakness may be reported in more severe cases [6].

The underlying pathophysiology of DSPN is complex and remains under debate [5]. Prolonged elevated blood glucose levels can lead to cellular damage to neurons, glial cells and microvasculature endothelial cells [7,8]. Oxidative stress, mitochondrial dysfunction and long-term inflammatory responses are present, and may be perpetuated by the involvement of macrophages [7,9]. The process of degeneration and the difficulties in neuronal regeneration due to diabetes may induce microstructural changes in peripheral nerves that lead to axonal loss and demyelination in more severe cases [10,11].

Diffusion Magnetic Resonance Imaging (Diffusion MRI) of peripheral nerves is a non-invasive quantitative method to reveal microstructural alterations in the nervous system [12,13]. Diffusion MRI is based on indirect measurements of the motion of the water molecules [14], which is influenced by the characteristics and architecture of the surrounding tissues. In the peripheral nervous system, changes in the water diffusion properties have been observed in motor [15,16] and sensorimotor neuropathies [17-21]. Compared to healthy controls, metrics of the diffusion tensor model [18,22] were decreased in the sciatic and tibial nerve in people with DSPN in feet. Moreover, microstructural nerve changes were already present in people with diabetes without symptoms of DSPN in their feet [22], showing that DWI can detect structural signs before symptoms occur [23,24]. Interestingly, some studies have also demonstrated a correlation between electrodiagnostic parameters and diffusion tensor metrics in entrapment neuropathies [25,26]. Although DSPN is defined as a length-dependent polyneuropathy affecting feet before legs, and hands before arms [27], microstructural changes in the lower limb nerves were more prominent proximally (i.e., sciatic nerve) than distally (i.e., tibial nerve) [18]. Since proximal changes were already detected in the sciatic nerve in people with DSPN in feet, microstructural abnormalities may also already be present in the upper limbs. However, microstructural changes in the upper limb nerves (e.g., median or ulnar nerve) have not yet been explored. Exploring upper limb nerves in people with diabetes is important, as the prevalence of upper limb DSPN is anticipated to increase [28].

The use of diffusion MRI in studies of peripheral nerves has thus far been limited to the diffusion tensor model. In contrast, diffusion MRI studies of the central nervous system regularly utilise higher-order diffusion models, such as spherical deconvolution [29]. Although the applicability of these higher-order diffusion models has not yet been tested in the peripheral nervous system, they may have advantages for the characterisation of the microstructure of peripheral nerves.

The aims of this study were therefore to use diffusion MRI to (1) quantify and compare the microstructure of the median and ulnar nerves at the wrist and elbow in people without diabetes and in people with diabetes with and without DSPN (in lower limbs only, and in upper and lower limbs); (2) evaluate whether changes in nerve microstructure are more pronounced distally (i.e., at the wrist) than more proximally (i.e., at the elbow), consistent with the 'distal-to-proximal' principle; (3) to compare whether there is a correlation between electrodiagnostic and diffusion tensor metrics; and (4) test the utility of quantitative metrics from a higher-order diffusion model to quantify the microstructure of peripheral nerves.

## 2. Methods

In this cross-sectional study, diffusion MRI data were obtained from a subset of people (N = 41) who participated in the DIANE (DIAbetic NEuropathy) Project (N = 125). The overall aim of the DIANE Project was to comprehensively assess upper limb nerve function and morphology to better understand DSPN affecting the hands. Detailed results on quantitative sensory testing [24] and corneal confocal microscopy [30] have been published elsewhere. For the present study focussing on Diffusion MRI findings, we compared the microstructural properties of the median and ulnar nerve across four groups of participants: (1) people without diabetes, (2) people with diabetes but without

DSPN, (3) people with DSPN in the lower limbs but not in the upper limbs (DSPN FEET ONLY), and (4) people with DSPN in upper and lower limbs (DSPN HANDS & FEET). The clinical presentation, electrodiagnostic tests and *in vivo* corneal confocal microscopy were assessed for diagnostic and categorisation purposes. The study was approved by the Human Ethics Committee of Griffith University (2018/669) and Queensland University of Technology (1800001224). All participants provided written consent prior to participating in the study.

### 2.1. Participation criteria

The inclusion and exclusion criteria from the DIANE project [24] also applied to this study. Briefly, inclusion criteria included participants who were 18 years or older, with or without diabetes, and with or without diabetic neuropathy (see 'Groups and allocation criteria' below). Exclusion criteria were self-reported conditions that can mimic DSPN (e.g., hypothyroidism, vitamin B12 deficiency, degenerative disc disease or nerve root compression), unilateral symptoms suggestive of a neuropathy other than DSPN, trauma-related nerve injuries, self-reported psychiatric disorders, fibromyalgia, irritable bowel syndrome, chronic fatigue syndrome, complex regional pain syndrome, a history of malignancy or chemotherapy, cataract surgery in the past year, hard contact lenses, medication for glaucoma, or having had laser eye surgery. In addition to the selection criteria for DIANE, this study required participants to have no contra-indications for MRI and fit comfortably in a 70 cm bore MRI scanner.

### 2.2. Groups and allocation criteria

Participants were allocated to one of the four groups outlined at the start of the Methods section (No diabetes, diabetes without DSPN; DSPN FEET ONLY; DSPN HANDS & FEET). The presence of diabetes (type 1 or 2) was assessed using the level of glycated haemoglobin HbA1c ≥6.0% [31] as classification criterion. The diagnosis of DSPN was confirmed according to: (1) a clinical presentation indicative of DSPN; and (2) an abnormal electrodiagnostic tests and/or reduced corneal nerve fibre length [24,32]. Further details are listed below.

#### 2.2.1. Clinical presentation indicative of DSPN

Bilateral symptoms (such as numbness, tingling, pins and needles, and burning or aching pain) in the distal parts of the limbs in people with diabetes was considered consistent with a clinical presentation suggestive of DSPN. Participants drew their symptoms on a body chart. If DSPN symptoms were restricted to the lower limbs, patients with diabetes met the clinical presentation criterion to be allocated to the DSPN FEET ONLY group. If DSPN symptoms were present in both lower and upper limbs, patients with diabetes met the clinical presentation criterion to be allocated to the DSPN HANDS & FEET group.

#### 2.2.2. Electrodiagnosis

Electrodiagnostic tests were performed with a neurodiagnostic system (Sierra Summit system, Cadwell, WA, USA). Sural sensory, fibular motor, tibial motor, median (sensory and motor), and ulnar motor nerves were evaluated following recommendations by SENIAM [33] and the American Academy of Neurology and American Association of Electrodiagnostic Medicine [34]. The criteria for an electrodiagnostic confirmation of DSPN was a conduction velocity for the fibular motor nerve of less than 42 m/s [32].

#### 2.2.3. Corneal confocal microscopy

*In vivo* corneal confocal microscopy was performed to detect early neuropathy changes [32]. This method is non-invasive. All participants underwent an examination of the sub-basal nerve plexus of the cornea using the Heidelberg Tomograph Rostock Cornea Module III (Heidelberg Engineering GmbH, Heidelberg, Germany and Heidelberg Engineering, Smithfield, RI, USA) according to published methods [35]. Corneal
nerve fibre length (CNFL) was quantified in an automated way using the ACCMetrics software (University of Manchester, Manchester, UK) [36]. An experienced investigator blinded to group allocation selected five to eight images with non-overlapping areas of the centre of the cornea to quantify CNFL. The optimal threshold for confirmation of DSPN was a CNFL of <12.5 mm/mm² in people with type 1 diabetes and <12.3 mm/mm² in people with type 2 diabetes [32].

## 2.3. Clinical parameters

Information about age, sex, BMI, ethnicity, type of diabetes, years with diabetes and years with DSPN, and percentage of HbA1c were recorded for all participants. The HbA1c test was carried out using a point-of-care test system (Afinion Test System, Abbott, Chicago, IL) [31].

## 2.4. Diffusion MRI

### 2.4.1. Data acquisition

MRI was performed on a 3-Tesla MR scanner (Philips Elition X, Best, The Netherlands) with dedicated 16-channel elbow and 8-channel wrist coils (Philips BIU, Best, The Netherlands). Participants were scanned lying on their back ('head first supine' position) with the hand by their side. This position was well tolerated by all participants. Median and ulnar nerves were explored at the wrist and elbow on the right arm (Fig. 1). The MR imaging protocol included: 1) two-dimensional axial T1-weighted fast spin echo images with an in-plane resolution of 2 × 2 mm, slice thickness of 2 mm, and an inter-slice gap of 2 mm; 2) a single-shot echo-planar imaging (ss-EPI) 2D sequence with six repetitions of 12 directions and b values = 0, 1200 s/mm² (Table 1). The phase-encoding direction was Anterior-Posterior (AP). Additionally, a pair of b = 0 images was acquired with phase encoding reversed to enable correction of susceptibility-induced distortion during image processing [37]. Total scan time was ~15 min per location (wrist and elbow), with coil repositioning adding ~5 min.

<table><caption>**Table 1**
Scan parameters.</caption><thead><tr><th></th><th>T1w</th><th>DWI</th></tr></thead><tbody><tr><td>Repetition time</td><td>0.6</td><td>9</td></tr><tr><td>Echo time</td><td>14 ms</td><td>62 ms</td></tr><tr><td>Flip angle</td><td>80 degrees</td><td>90 degrees</td></tr><tr><td>Field of view</td><td>128 mm</td><td>128 mm</td></tr><tr><td>Dimensionality</td><td>2D</td><td>2D</td></tr><tr><td>Slice thickness</td><td>2 mm</td><td>2 mm</td></tr><tr><td>Number of slices</td><td>29</td><td>32</td></tr><tr><td>In-plane resolution</td><td>0.4 × 0.4 mm</td><td>2 × 2 mm</td></tr><tr><td>Echo spacing</td><td>0.21 ms</td><td>0.82 ms</td></tr><tr><td>δ Diffusion time</td><td>N/A</td><td>31</td></tr><tr><td>Δ Diffusion time</td><td>N/A</td><td>18.9</td></tr><tr><td>Fat suppression</td><td>Fat suppression</td><td>Fat saturation</td></tr><tr><td>Acceleration factor</td><td>2</td><td>2</td></tr><tr><td>Bandwidth</td><td>346 Hz/px</td><td>2741 Hz/px</td></tr><tr><td>Number of gradient directions</td><td>N/A</td><td>12</td></tr><tr><td>Number of repetitions</td><td>N/A</td><td>6</td></tr><tr><td>b-value (s/mm²)</td><td>N/A</td><td>[0, 1200]</td></tr><tr><td>Total scan time (min)</td><td>1 min 46 s</td><td>11 min 37 s</td></tr></tbody></table>

T1w: anatomical T1 weighted morphological scans; DWI: diffusion weighted imaging.

### 2.4.2. Diffusion MRI processing

Data were pre-processed using *MRtrix3* [38], FSL (FMRIB's software library tools)[39] and ANTs [40]. Diffusion Tensor Imaging (DTI) sequences were denoised (dwidenoise, *MRtrix3*) [41] before performing motion and eddy current correction (eddy, FSL) with inhomogeneity distortion correction (topup, FSL) [42] and replacement of data within slices deemed as outliers due to signal dropout [43]. Finally, bias field correction was performed based on the *b* = 0 image using the N4 algorithm (N4BiasFieldCorrection, ANTs) to eliminate low frequency intensity inhomogeneities across the image [44]. The output images were visually inspected after pre-processing for quality control.

The diffusion tensor was computed on this pre-processed image using iteratively reweighted least-squares (dwi2tensor, *MRtrix3*) [14,45]. Maps of tensor-derived parameters [14,45] were generated for

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Representation of the image acquisition and processing methods. (A): Arm diagram showing the MRI scanning area at the elbow and wrist (between dashed yellow lines). T1-weighted (T1w) images of the elbow (B) and wrist (F). Inserts show magnified areas with the median and ulnar nerve highlighted in yellow at the elbow and wrist. Diffusion-weighted images (DWI) with the voxel selection from which the metrics were computed (C: elbow; G: wrist). Diffusion tensor model glyphs (D: elbow; H: wrist), coloured according to the direction of the principal eigenvector (red: medial-lateral; green: anterior-posterior; blue: distal-proximal). Constrained spherical deconvolution (CSD) (E: elbow; K: wrist). Magnified windows represent the median nerve (top) and ulnar nerve (bottom). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
Fractional Anisotropy (FA), Radial Diffusivity (RD) and Axial Diffusivity (AD). Apparent Diffusion Coefficient (ADC) was calculated based on a mono-exponential fit to the trace-weighted data (mean image intensity per unique *b*-value); while unlike other approaches this parameter was estimated in a manner agnostic to the tensor model, it is nevertheless reported herein alongside the tensor-derived parameters.

In addition to metrics from the tensor model, we investigated the use of two voxel-wise quantitative metrics utilising the Constrained Spherical Deconvolution (CSD) model [29,46]. 'Dispersion' aims to quantify the extent to which the underlying nerves within a bundle are not all coherently oriented, and was computed as the ratio of the integral to peak amplitude of the dominant fibre orientation within the Fibre Orientation Distribution (FOD) estimated by CSD [47]. 'Complexity' aims to quantify the extent to which the underlying fibrous geometry contains bundles in multiple distinct orientations; its computation relates to the proportion of fibre density that is not attributed to the dominant fibre orientation within the FOD [48]. We investigated the properties of these metrics for different selections of response functions for spherical deconvolution [49]. Results derived from a subset of these are presented in the main text, with a comprehensive description of the full gamut of analyses performed and corresponding data deferred to Supplementary Information.

Quantitative DWI measures were extracted from the median and ulnar nerves at the elbow and wrist. The anatomical images of the elbow and wrist were co-registered to their respective pre-processed diffusion-weighted images to allow for better identification of the median and ulnar nerves. Masks were generated on a per-subject basis by manually selecting within each transverse slice the voxel closest to the centre of the median and ulnar nerve. For each combination of quantitative metrics, nerve, and anatomical location, the mean value of that metric within the corresponding mask was calculated (Fig. 1). For each quantitative metric, the difference at the elbow between each group and the healthy group was calculated (e.g., elbow metric (DSPN FEET ONLY) – elbow metric (no diabetes)). The same group difference was calculated at the wrist level (e.g., wrist metric (DSPN FEET ONLY) – wrist metric (no diabetes)). A possible distal-to-proximal gradient effect of DSPN was calculated per metric, as per prior related work [18]. The difference of the elbow output minus wrist per group was quantified to explore the distal-to-proximal differences (e.g., (DSPN FEET ONLY (elbow metric – wrist metric)) – (No diabetes (elbow metric – wrist metric))).

## 2.5. Statistical analysis

Statistical analyses were performed in R Studio v.3.6 (The R Foundation for Statistical Computing Platform) [50]. Demographic data were compared between groups with a one-way analysis of variance (ANOVA). The 'lme4' [51], 'lmerTest' [52], and 'emmeans' [53] packages in R Studio were used to perform linear mixed effects analysis of the relationship between each metric, pair of groups, and anatomical location (or difference between distal and proximal values). Linear mixed effects models can account for missing data [54].

Assessing changes in metrics between the groups involved, for each combination of nerve and quantitative imaging metric, fitting a linear mixed model using group (no diabetes, diabetes without DSPN, DSPN FEET ONLY and DSPN HANDS & FEET) and anatomical location (elbow, wrist) as fixed effects with a random-intercept model, allowing only for between-subject variance. The assumptions of normality of residuals, homogeneity of variance and linearity were visually checked and revealed no obvious violations. P-values for t-statistics were obtained using the Satterthwaite's degrees of freedom method [55]. We report the estimated marginal means (EMMs) per quantification, with standard errors (SE), t (degrees of freedom) and p values. The correlation between electrodiagnostic parameters (i.e., median and ulnar SNAP, median and ulnar SCV, median CMAP and median MCV) and diffusion tensor metrics (i.e., FA, RD, AD and ADC) was explored using Pearson correlation in the group of people with hand symptoms (DSPN HANDS & FEET).

# 3. Results

## 3.1. Participants and groups

Forty-one participants were included in this study (People without diabetes: N = 10; diabetes without DSPN: N = 10; DSPN FEET ONLY: N = 12; DSPN HANDS & FEET: N = 9). Fig. 2 presents the sequence of application of participation criteria for the study and the larger DIANE Project. Six MRIs from the elbow location had to be excluded due to insufficient image coverage as they presented with slice drop-out (excluded from people without diabetes: n = 1; diabetes without DSPN n = 1; DSPN FEET ONLY n = 3; DSPN HANDS & FEET n = 1; Fig. 1). Participant demographic characteristics along with their electrodiagnostic and corneal confocal test results are summarised in Table 2.

## 3.2. Microstructural changes

### 3.2.1. Tensor-derived metrics

Changes in the tensor metrics were more frequently observed in the median nerve, as shown in Fig. 3. The estimates for these comparisons are presented in Table 3. Raw mean and standard deviations are presented in Supplementary Table 1.

For the median nerve at the wrist, DSPN HANDS & FEET showed reduced FA (EMMs (SE): 0.083 (0.04); t (60.6) = 2.13; p = 0.002), increased RD (-1.74 × 10⁻⁴ (7.47 × 10⁻⁵); t(61.0) = -2.33; p = 0.023), and increased ADC (-1.49 × 10⁻⁴ (6.84 × 10⁻⁵); t(59.6) = -2.18; p = 0.033) compared to people without diabetes. DSPN FEET ONLY showed reduced FA and AD compared to people without diabetes (FA: 0.116 (0.04); t(60.6) = 3.20, p = 0.002; AD: 2.15 × 10⁻⁴ (9.69 × 10⁻⁵); t (62.6) = 2.22; p = 0.030) and people with diabetes without DSPN (FA: 0.073 (0.04); t(60.6) = 2.02, p = 0.047; AD: 2.75 × 10⁻⁴ (9.69 × 10⁻⁵); t (62.6) = 2.84; p = 0.006). Moreover, AD was reduced compared to DSPN HANDS & FEET (-2.53 × 10⁻⁴ (9.98 × 10⁻⁵); t(62.6) = -2.53; p = 0.014).

For the median nerve at the elbow, DSPN HANDS & FEET showed reduced RD (2.03 × 10⁻⁴ (7.87 × 10⁻⁵), t (63.4) = 2.58; p = 0.012), and ADC (1.79 × 10⁻⁴ (7.19 × 10⁻⁵); t(62.4) = 2.49; p = 0.015) compared to people with diabetes without DSPN. DSPN FEET ONLY showed reduced FA and AD compared to people without diabetes (FA: 0.080 (0.04); t (64.6) = 2.01, p = 0.048; AD: 2.84 × 10⁻⁴ (1.06 × 10⁻⁴); t(65.6) = 2.67; p = 0.010); and reduced AD and ADC compared to people with diabetes without DSPN (AD: 2.15 × 10⁻⁴ (1.06 × 10⁻⁴); t(65.6) = 2.03, p = 0.047; ADC: 1.52 × 10⁻⁴ (6.95 × 10⁻⁵); t(64.0) = 2.19; p = 0.032). FA was reduced in people with diabetes without DSPN compared to people without diabetes (0.087 (0.04); t(63.0) = 2.19; p = 0.032).

For the ulnar nerve, DSPN HANDS & FEET showed increased AD compared to DSPN FEET ONLY at the wrist (-1.56 × 10⁻⁴ (7.63 × 10⁻⁵); t (52.6) = -2.05; p = 0.046). At the elbow, DSPN FEET ONLY showed reduced FA compared to people without diabetes (0.096 (0.033); t (68.0) = 2.86; p = 0.006) and people with diabetes without DSPN (0.096 (0.033); t(68.0) = 2.87; p = 0.006).

### 3.2.2. Constrained spherical deconvolution metrics

The method of spherical deconvolution can be used to estimate the distribution of fibre orientations present within each imaging voxel [49]. The robustness of the deconvolution method can be greatly enhanced by a non-negativity constraint [29], leading to a robust determination of the fibre orientations within a clinically acceptable time. Changes in the CSD metrics were observed in both the ulnar and median nerve, as shown in Fig. 4. The estimates for these comparisons are presented in Table 4. Raw mean and standard deviations are presented in Supplementary Table 2.

For the median nerve at the wrist, DSPN HANDS & FEET showed a reduction in complexity compared to DSPN FEET ONLY (0.08 (0.04); t (68.0) = 1.99; p = 0.049. DSPN FEET ONLY showed larger complexity compared to people without diabetes (-0.12 (0.04); t(68.0) = -3.01; p = 0.006).
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Flow diagram of participants in the larger DIANE Project (N = 125) and the Diffusion MRI study (N = 41). DSPN: distal symmetrical polyneuropathy; HbA1c: glycated haemoglobin test; CV: conduction velocity; CNFL: corneal nerve fibre length; IVCM: *in vivo* corneal confocal microscopy; MRI: magnetic resonance imaging; DWI: diffusion weighted imaging.</figcaption></figure>

p = 0.004) and people with diabetes without DSPN (−0.08 (0.04); t(68.0) = −2.07; p = 0.047). No differences were detected for the median nerve at the elbow (all p > 0.20).

For the ulnar nerve at the wrist, DSPN HANDS & FEET showed reduced dispersion compared to DSPN FEET ONLY (−0.12 (0.04); t(68.0) = −3.01; p = 0.004). Dispersion was increased in DSPN FEET ONLY compared to people without diabetes (−0.72 (0.17); t(67.4) = −4.30; p < 0.001) and

people with diabetes without DSPN (−0.37 (0.17); t(67.4) = −2.23; p = 0.029).

For the ulnar nerve at the elbow, DSPN HANDS & FEET showed no differences compared to any other group (all p > 0.07). Dispersion and complexity were increased in DSPN FEET ONLY compared to people without diabetes (dispersion: −0.40 (0.18); t(67.8) = −2.17; p = 0.034; complexity: −0.13 (0.06); t(64.4) = −2.32; p = 0.024).
<table><caption>**Table 2**
Overview of demographic characteristics per group.</caption><thead><tr><th></th><th>No diabetes (n = 10)</th><th>Diabetes no DSPN (n = 10)</th><th>DSPN FEET ONLY (n = 12)</th><th>DSPN HANDS & FEET (n = 9)</th><th>ANOVA results</th><th>Pairwise differences</th></tr></thead><tbody><tr><td>Age</td><td>51.8 (14.7)</td><td>44.5 (20.1)</td><td>60.4 (10.4)</td><td>62.9 (7.7)</td><td>0.022</td><td>2-4*</td></tr><tr><td>Female</td><td>3 (30%)</td><td>3 (30%)</td><td>3 (25%)</td><td>2 (22%)</td><td>0.97</td><td></td></tr><tr><td>BMI (kg/m2)</td><td>24.5 (1.7)</td><td>28.5 (6.8)</td><td>35.0 (8.2)</td><td>29.0 (4.5)</td><td>0.028</td><td>1-3**</td></tr><tr><td>Ethnicity:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Asian</td><td>3 (30%)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td></td><td></td></tr><tr><td>Mixed</td><td>1 (10%)</td><td>0 (0%)</td><td>0 (0%)</td><td>1 (11%)</td><td></td><td></td></tr><tr><td>White</td><td>6 (60%)</td><td>10 (100%)</td><td>10 (83%)</td><td>7 (78%)</td><td></td><td></td></tr><tr><td>Indigenous Australian</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>1 (11%)</td><td></td><td></td></tr><tr><td>Not reported</td><td>0 (0%)</td><td>0 (0%)</td><td>2 (17%)</td><td>0 (0%)</td><td></td><td></td></tr><tr><td>Type II diabetes</td><td>0 (0%)</td><td>2 (20%)</td><td>7 (58%)</td><td>9 (100%)</td><td>0.002</td><td>2-4*; 2-3*; 3,4*</td></tr><tr><td>Years diabetes</td><td>N/A</td><td>14.3 (9.1)</td><td>14.7 (9.1)</td><td>8.8 (6.8)</td><td>0.18</td><td></td></tr><tr><td>Years DSPN</td><td>N/A</td><td>N/A</td><td>3.2 (5.1)</td><td>4.9 (6.2)</td><td>0.51</td><td></td></tr><tr><td>HbA1c (%)</td><td>5.4 (0.3)</td><td>6.7 (1.6)</td><td>8.4 (1.4)</td><td>7.8 (2.1)</td><td>0.0019</td><td>1-3**; 1-4*</td></tr><tr><td>Electrodiagnostic tests:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sural SNAP (µV)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>8.1 (3.3)</td><td>10.4 (4.9)</td><td>7.8 (6.9)</td><td>4.6 (2.3)</td><td>0.086</td><td></td></tr><tr><td>Abnormal (&lt;4 µV)</td><td>0 (0%)</td><td>0 (0%)</td><td>1 (8%)</td><td>3 (33%)</td><td></td><td></td></tr><tr><td>Sural SCV (m/s)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>38 (12.7)</td><td>34.7 (10.7)</td><td>31.3 (11.3)</td><td>35.1 (11)</td><td>0.46</td><td></td></tr><tr><td>Abnormal (&lt;37 m/s)</td><td>3 (30%)</td><td>3 (30%)</td><td>1 (8%)</td><td>6 (66%)</td><td></td><td></td></tr><tr><td>Fibular MCV (m/s)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>52.6 (8.4)</td><td>46.1 (12.2)</td><td>38.9 (9.8)</td><td>42.1 (8.2)</td><td>0.0087</td><td>1-3*</td></tr><tr><td>Abnormal (&lt;42 m/s)</td><td>1 (10%)</td><td>3 (30%)</td><td>7 (58%)</td><td>5 (56%)</td><td></td><td></td></tr><tr><td>Tibial CMAP (mV)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>10.1 (4.8)</td><td>6.6 (5.2)</td><td>5.1 (3.4)</td><td>4.6 (4.9)</td><td>0.0068</td><td>1-4*</td></tr><tr><td>Abnormal (&lt;4.4 mV)</td><td>2 (20%)</td><td>5 (50%)</td><td>6 (50%)</td><td>7 (78%)</td><td></td><td></td></tr><tr><td>Tibial MCV (m/s)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>49 (6.5)</td><td>47.6 (9.7)</td><td>42.5 (4.7)</td><td>41.4 (12)</td><td>0.023</td><td>1-4*</td></tr><tr><td>Abnormal (&lt;39 m/s)</td><td>1 (10%)</td><td>2 (20%)</td><td>3 (25%)</td><td>5 (56%)</td><td></td><td></td></tr><tr><td>Median CMAP (mV)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>4.2 (0.9)</td><td>4.6 (2.1)</td><td>5.9 (2.1)</td><td>4.4 (1.9)</td><td>0.80</td><td></td></tr><tr><td>Abnormal (&lt;4.1 mV)</td><td>1 (10%)</td><td>3 (30%)</td><td>2 (17%)</td><td>3 (33%)</td><td></td><td></td></tr><tr><td>Median MCV (m/s)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>64 (1.4)</td><td>71.1 (7.6)</td><td>64.4 (6.2)</td><td>62 (10.1)</td><td>0.12</td><td></td></tr><tr><td>Abnormal (&lt;49 m/s)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td></td><td></td></tr><tr><td>Median SNAP (µV)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>8.5 (6.4)</td><td>19.7 (14.3)</td><td>12.3 (16.6)</td><td>5.8 (1.9)</td><td>0.23</td><td></td></tr><tr><td>Abnormal (&lt;10 µV)</td><td>1 (10%)</td><td>1 (10%)</td><td>8 (67%)</td><td>6 (67%)</td><td></td><td></td></tr><tr><td>Median SCV (m/s)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>44.5 (1)</td><td>46.3 (8)</td><td>38.1 (10.4)</td><td>41.5 (4.8)</td><td>0.25</td><td></td></tr><tr><td>Abnormal (&lt;44 m/s)</td><td>1 (10%)</td><td>2 (20%)</td><td>8 (67%)</td><td>3 (33%)</td><td></td><td></td></tr><tr><td>Ulnar SNAP (µV)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>17 (0)</td><td>28.1 (14.9)</td><td>15.8 (12.1)</td><td>10 (4.3)</td><td>0.40</td><td></td></tr><tr><td>Abnormal (&lt;10.0 mV)</td><td>0 (0%)</td><td>0 (0%)</td><td>2 (17%)</td><td>4 (44%)</td><td></td><td></td></tr><tr><td>Ulnar SCV (m/s)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean (SD)</td><td>48.3 (0)</td><td>48.7 (7.8)</td><td>39.9 (13)</td><td>35.9 (7.4)</td><td>0.022</td><td>1-4*</td></tr><tr><td>Abnormal (&lt;45 m/s)</td><td>0 (0%)</td><td>2 (20%)</td><td>4 (33%)</td><td>6 (67%)</td><td></td><td></td></tr><tr><td>CCM:</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CNFL (mm/mm²)</td><td>14.8 (2.4)</td><td>13.3 (3.8)</td><td>10.8 (2.7)</td><td>12.2 (3.9)</td><td>0.059</td><td></td></tr></tbody></table>

Data shown as mean (standard deviation) and analysed by a one-way ANOVA (ANOVA results), with Bonferroni correction (pairwise differences). Categorical data are reported as frequencies (percentages). Pairwise differences indicate significant *p* values between group pairs: 1: People without diabetes; 2: diabetes without DSPN; 3: DSPN in feet only; 4: DSPN in hands & feet. *p* < 0.05; **p* < 0.01; ***p* < 0.001. DSPN: distal symmetrical polyneuropathy; BMI: body mass index; HbA1c: glycated haemoglobin; SNAP: sensory nerve action potential; µV, microvolts; SCV: sensory conduction velocity; m/s: metres per second; CMAP: compound muscle action potentials; MCV: motor conduction velocity; IVCM: *in vivo* corneal confocal microscopy; CNFL: corneal nerve fibre length; N/A: Not applicable.

### 3.3. Distal-to-proximal gradient

Differences in the distal-to-proximal gradient in the median nerve were observed in the expected direction for DSPN HANDS & FEET for the RD and ADC metrics, suggesting a length-dependent effect of DSPN on nerve microstructure. Estimates for the comparison between elbow and wrist per group and metric can be found in Table 5. In the median nerve, DSPN HANDS & FEET showed a change in the distal-to-proximal ratio in RD (2.68 × 10⁻⁴ (8.83 × 10⁻⁵); t(33.2) = 3.03p < 0.001), and ADC (2.65 × 10⁻⁴ (7.84 × 10⁻⁵); t(33.0) = -3.37; p = 0.002) compared to people

without diabetes. FA, AD, dispersion and complexity were not different in any group compared to people without diabetes (all p > 0.06). No changes in the distal-to-proximal gradient were observed in the ulnar nerve for any metric (all p > 0.09).

### 3.4. Correlation between electrodiagnostic and diffusion parameters

The amplitude of the ulnar and median SNAP was negatively correlated with RD (r = -0.53; *p* = 0.043; 95% CI: -0.82, -0.02) and to ADC (r = -0.55; *p* = 0.033, 95% CI: -0.83, -0.05). No other
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Estimated marginal means (95 % confidence intervals) for each tensor derived metric, per location (i.e., wrist and elbow) and nerve (i.e., median and ulnar nerve). P-values for t-statistics were obtained using the Satterthwaite's degrees of freedom method indicating *: p < 0.05; **: p < 0.01; ***: p < 0.001. DSPN: distal symmetrical polyneuropathy; FA: fractional anisotropy; AD: axial diffusivity; RD: radial diffusivity; ADC: apparent diffusion coefficient.</figcaption></figure>

correlations were significant. The estimates for these comparisons are presented in Table 6.

## 4. Discussion

Compared to people without diabetes and people with diabetes without DSPN, microstructural abnormalities were revealed in the median nerve and to a lesser extent in the ulnar nerve in people with DSPN HANDS & FEET and DSPN FEET ONLY. In DSPN HANDS & FEET, the microstructural abnormalities in the median nerve were in line with the distal-to-proximal principle, whereas this pattern was not observed for the ulnar nerve. In DSPN FEET ONLY, early signs of microstructural abnormalities were detected at the wrist and elbow but did not follow a distal-to-proximal pattern. These results confirm that changes in the microstructure of upper limb nerves occur in DSPN, even before symptoms are reported in the upper limbs. Diffusion metrics from higher-order models may have a role in unravelling early changes in the microstructure of

peripheral nerves, given that changes were detected in the median and ulnar nerve in people with diabetes without DSPN and in people with DSPN FEET ONLY. Differences were detected in CSD-based metrics in some group comparisons where there was no difference observed in tensor metrics.

In DSPN HANDS & FEET, microstructural abnormalities were detected mainly in the median nerve at the wrist. Moreover, the distal-to-proximal gradient of RD and ADC was altered. These findings are in line with the pattern of structural changes previously observed in DSPN in lower limbs [56]. In the lower limbs, a distal-to-proximal gradient of ADC was detected in people with severe symptoms of DSPN. The absence of a distal-to-proximal gradient for the ulnar nerve could be related to a larger vulnerability of the ulnar nerve at the elbow compared to the wrist [57]. DWI tensor metrics have been reported to detect the degenerative process or inefficient regeneration of peripheral nerves as observed in histology [58]. Moreover, these microstructural changes could explain the deterioration in function previously observed
<caption>**Table 3**</caption>

Estimates of linear-mixed models for tensor-derived metrics in the median and ulnar nerves, for the elbow and wrist locations.

<table><thead><tr><th rowspan="2">Predictors</th><th colspan="4">FA</th><th colspan="4">AD</th><th colspan="4">RD</th><th colspan="4">ADC</th></tr><tr><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th></tr></thead><tbody><tr><td colspan="16"><strong>Median nerve</strong></td></tr><tr><td colspan="16"><strong>Wrist:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>0.043 (0.038)</td><td>60.6</td><td>1.13</td><td>0.27</td><td>-5.98 × 10<sup>-5</sup> (1.01 × 10<sup>-4</sup>)</td><td>62.6</td><td>-0.59</td><td>0.56</td><td>-9.02 × 10<sup>-5</sup> (7.28 × 10<sup>-5</sup>)</td><td>61.0</td><td>-1.24</td><td>0.22</td><td>-8.42 × 10<sup>-5</sup> (6.66 × 10<sup>-5</sup>)</td><td>59.6</td><td>-1.27</td><td>0.21</td></tr><tr><td>No diabetes vs DSPN FEET</td><td>0.116 (0.036)</td><td>60.6</td><td>3.20</td><td><strong>0.002</strong></td><td>2.15 × 10<sup>-4</sup> (9.69 × 10<sup>-5</sup>)</td><td>62.6</td><td>2.22</td><td><strong>0.03</strong></td><td>-8.89 × 10<sup>-5</sup> (6.97 × 10<sup>-5</sup>)</td><td>61.0</td><td>-1.28</td><td>0.21</td><td>-2.38 × 10<sup>-5</sup> (6.37 × 10<sup>-5</sup>)</td><td>59.6</td><td>-0.37</td><td>0.71</td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>0.083 (0.039)</td><td>60.6</td><td>2.13</td><td><strong>0.038</strong></td><td>-3.71 × 10<sup>-5</sup> (1.04 × 10<sup>-4</sup>)</td><td>62.6</td><td>-0.36</td><td>0.72</td><td>-1.74 × 10<sup>-4</sup> (7.47 × 10<sup>-5</sup>)</td><td>61.0</td><td>-2.33</td><td><strong>0.023</strong></td><td>-1.49 × 10<sup>-4</sup> (6.84 × 10<sup>-5</sup>)</td><td>59.6</td><td>-2.18</td><td><strong>0.033</strong></td></tr><tr><td>Diabetes no DSPN vs DSPN FEET ONLY</td><td>0.073 (0.036)</td><td>60.6</td><td>2.02</td><td><strong>0.047</strong></td><td>2.75 × 10<sup>-4</sup> (9.69 × 10<sup>-5</sup>)</td><td>62.6</td><td>2.84</td><td><strong>0.006</strong></td><td>1.25 × 10<sup>-6</sup> (6.97 × 10<sup>-5</sup>)</td><td>61.0</td><td>0.020</td><td>0.99</td><td>6.04 × 10<sup>-5</sup> (6.37 × 10<sup>-5</sup>)</td><td>59.6</td><td>0.95</td><td>0.35</td></tr><tr><td>Diabetes no DSPN vs DSPN HANDS & FEET</td><td>0.040 (0.039)</td><td>60.6</td><td>1.03</td><td>0.31</td><td>2.26 × 10<sup>-5</sup> (1.04 × 10<sup>-4</sup>)</td><td>62.6</td><td>0.22</td><td>0.83</td><td>-8.42 × 10<sup>-5</sup> (7.47 × 10<sup>-5</sup>)</td><td>61.0</td><td>-1.13</td><td>0.26</td><td>-6.50 × 10<sup>-5</sup> (6.84 × 10<sup>-5</sup>)</td><td>59.6</td><td>-0.95</td><td>0.35</td></tr><tr><td>DSPN FEET ONLY vs DSPN HANDS & FEET</td><td>-0.033 (0.037)</td><td>60.6</td><td>-0.89</td><td>0.38</td><td>-2.53 × 10<sup>-4</sup> (9.98 × 10<sup>-5</sup>)</td><td>62.6</td><td>-2.53</td><td><strong>0.014</strong></td><td>-8.55 × 10<sup>-5</sup> (7.17 × 10<sup>-5</sup>)</td><td>61.0</td><td>-1.19</td><td>0.24</td><td>-1.25 × 10<sup>-4</sup> (6.56 × 10<sup>-5</sup>)</td><td>59.6</td><td>-1.91</td><td>0.06</td></tr><tr><td colspan="16"><strong>Elbow:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>0.087 (0.040)</td><td>63.0</td><td>2.19</td><td><strong>0.032</strong></td><td>6.87 × 10<sup>-5</sup> (1.06 × 10<sup>-4</sup>)</td><td>64.4</td><td>0.65</td><td>0.52</td><td>-1.10 × 10<sup>-4</sup> (7.64 × 10<sup>-5</sup>)</td><td>63.3</td><td>-1.44</td><td>0.15</td><td>-6.39 × 10<sup>-5</sup> (6.98 × 10<sup>-5</sup>)</td><td>62.2</td><td>-0.92</td><td>0.36</td></tr><tr><td>No diabetes vs DSPN FEET</td><td>0.080 (0.040)</td><td>64.6</td><td>2.01</td><td><strong>0.048</strong></td><td>2.84 × 10<sup>-4</sup> (1.06 × 10<sup>-4</sup>)</td><td>65.6</td><td>2.67</td><td><strong>0.01</strong></td><td>3.80 × 10<sup>-5</sup> (7.62 × 10<sup>-5</sup>)</td><td>64.8</td><td>0.50</td><td>0.62</td><td>8.83 × 10<sup>-5</sup> (6.95 × 10<sup>-5</sup>)</td><td>64.0</td><td>1.27</td><td>0.21</td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>0.033 (0.041)</td><td>63.1</td><td>0.80</td><td>0.43</td><td>2.15 × 10<sup>-4</sup> (1.10 × 10<sup>-4</sup>)</td><td>64.5</td><td>1.96</td><td>0.055</td><td>9.32 × 10<sup>-5</sup> (7.87 × 10<sup>-5</sup>)</td><td>63.4</td><td>1.18</td><td>0.24</td><td>1.15 × 10<sup>-4</sup> (7.19 × 10<sup>-5</sup>)</td><td>62.4</td><td>1.60</td><td>0.11</td></tr><tr><td>Diabetes no DSPN vs DSPN FEET ONLY</td><td>-0.007 (0.040)</td><td>64.6</td><td>-0.19</td><td>0.85</td><td>2.15 × 10<sup>-4</sup> (1.06 × 10<sup>-4</sup>)</td><td>65.6</td><td>2.03</td><td><strong>0.047</strong></td><td>1.48 × 10<sup>-4</sup> (7.62 × 10<sup>-5</sup>)</td><td>64.8</td><td>1.95</td><td>0.06</td><td>1.52 × 10<sup>-4</sup> (6.95 × 10<sup>-5</sup>)</td><td>64.0</td><td>2.19</td><td><strong>0.032</strong></td></tr><tr><td>Diabetes no DSPN vs DSPN HANDS & FEET</td><td>-0.054 (0.041)</td><td>63.1</td><td>-1.33</td><td>0.19</td><td>1.46 × 10<sup>-4</sup> (1.10 × 10<sup>-4</sup>)</td><td>64.5</td><td>1.33</td><td>0.19</td><td>2.03 × 10<sup>-4</sup> (7.87 × 10<sup>-5</sup>)</td><td>63.4</td><td>2.58</td><td><strong>0.012</strong></td><td>1.79 × 10<sup>-4</sup> (7.19 × 10<sup>-5</sup>)</td><td>62.4</td><td>2.49</td><td><strong>0.015</strong></td></tr><tr><td>DSPN FEET ONLY vs DSPN HANDS & FEET</td><td>-0.047 (0.041)</td><td>64.6</td><td>-1.15</td><td>0.25</td><td>-6.93 × 10<sup>-5</sup> (1.10 × 10<sup>-4</sup>)</td><td>65.6</td><td>-0.63</td><td>0.53</td><td>5.52 × 10<sup>-5</sup> (7.85 × 10<sup>-5</sup>)</td><td>64.8</td><td>0.70</td><td>0.48</td><td>2.70 × 10<sup>-5</sup> (7.16 × 10<sup>-5</sup>)</td><td>64.0</td><td>0.38</td><td>0.71</td></tr><tr><td colspan="16"><strong>Ulnar nerve</strong></td></tr><tr><td colspan="16"><strong>Wrist:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>-0.010 (0.032)</td><td>68.0</td><td>-0.33</td><td>0.74</td><td>7.15 × 10<sup>-5</sup> (7.74 × 10<sup>-5</sup>)</td><td>52.6</td><td>0.93</td><td>0.36</td><td>5.38 × 10<sup>-5</sup> (6.41 × 10<sup>-5</sup>)</td><td>66.1</td><td>0.84</td><td>0.40</td><td>5.68 × 10<sup>-5</sup> (6.03 × 10<sup>-5</sup>)</td><td>62.0</td><td>0.94</td><td>0.35</td></tr><tr><td>No diabetes vs DSPN FEET</td><td>0.016 (0.030)</td><td>68.0</td><td>0.54</td><td>0.59</td><td>1.19 × 10<sup>-4</sup> (7.41 × 10<sup>-5</sup>)</td><td>52.6</td><td>1.61</td><td>0.11</td><td>3.45 × 10<sup>-5</sup> (6.14 × 10<sup>-5</sup>)</td><td>66.1</td><td>0.56</td><td>0.58</td><td>4.64 × 10<sup>-5</sup> (5.77 × 10<sup>-5</sup>)</td><td>62.0</td><td>0.81</td><td>0.42</td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>0.002 (0.032)</td><td>68.0</td><td>0.06</td><td>0.95</td><td>-3.68 × 10<sup>-5</sup> (7.95 × 10<sup>-5</sup>)</td><td>52.6</td><td>-0.46</td><td>0.65</td><td>-2.90 × 10<sup>-5</sup> (6.58 × 10<sup>-5</sup>)</td><td>66.1</td><td>-0.44</td><td>0.66</td><td>-2.32 × 10<sup>-5</sup> (6.19 × 10<sup>-5</sup>)</td><td>62.0</td><td>-0.38</td><td>0.71</td></tr><tr><td>Diabetes no DSPN vs DSPN FEET ONLY</td><td>0.027 (0.030)</td><td>68.0</td><td>0.89</td><td>0.38</td><td>4.77 × 10<sup>-5</sup> (7.41 × 10<sup>-5</sup>)</td><td>52.6</td><td>0.64</td><td>0.52</td><td>-1.94 × 10<sup>-5</sup> (6.14 × 10<sup>-5</sup>)</td><td>66.1</td><td>-0.32</td><td>0.75</td><td>-1.04 × 10<sup>-5</sup> (5.77 × 10<sup>-5</sup>)</td><td>62.0</td><td>-0.18</td><td>0.86</td></tr><tr><td>Diabetes no DSPN vs DSPN HANDS & FEET</td><td>0.012 (0.032)</td><td>68.0</td><td>0.38</td><td>0.70</td><td>-1.08 × 10<sup>-4</sup> (7.95 × 10<sup>-5</sup>)</td><td>52.6</td><td>-1.36</td><td>0.18</td><td>-8.29 × 10<sup>-5</sup> (6.58 × 10<sup>-5</sup>)</td><td>66.1</td><td>-1.26</td><td>0.21</td><td>-8.00 × 10<sup>-5</sup> (6.19 × 10<sup>-5</sup>)</td><td>62.0</td><td>-1.29</td><td>0.20</td></tr><tr><td>DSPN FEET ONLY vs DSPN HANDS & FEET</td><td>-0.014 (0.031)</td><td>68.0</td><td>-0.46</td><td>0.64</td><td>-1.56 × 10<sup>-4</sup> (7.63 × 10<sup>-5</sup>)</td><td>52.6</td><td>-2.05</td><td><strong>0.046</strong></td><td>-6.35 × 10<sup>-5</sup> (6.32 × 10<sup>-5</sup>)</td><td>66.1</td><td>-1.01</td><td>0.32</td><td>-6.97 × 10<sup>-5</sup> (5.94 × 10<sup>-5</sup>)</td><td>62.0</td><td>-1.17</td><td>0.25</td></tr><tr><td colspan="16"><strong>Elbow:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>-0.0003 (0.033)</td><td>68.0</td><td>-0.01</td><td>0.99</td><td>3.21 × 10<sup>-6</sup> (8.03 × 10<sup>-5</sup>)</td><td>56.1</td><td>0.04</td><td>0.97</td><td>-2.87 × 10<sup>-6</sup> (6.76 × 10<sup>-5</sup>)</td><td>66.8</td><td>-0.04</td><td>1</td><td>4.65 × 10<sup>-5</sup> (6.33 × 10<sup>-5</sup>)</td><td>64.0</td><td>0.73</td><td>0.47</td></tr><tr><td>No diabetes vs DSPN FEET</td><td>0.096 (0.033)</td><td>68.0</td><td>2.86</td><td><strong>0.006</strong></td><td>1.29 × 10<sup>-4</sup> (7.94 × 10<sup>-5</sup>)</td><td>58.9</td><td>1.63</td><td>0.11</td><td>-1.11 × 10<sup>-4</sup> (6.77 × 10<sup>-5</sup>)</td><td>67.2</td><td>-1.64</td><td>0.11</td><td>-1.04 × 10<sup>-5</sup> (6.32 × 10<sup>-5</sup>)</td><td>65.3</td><td>-0.17</td><td>0.87</td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>0.033 (0.034)</td><td>68.0</td><td>0.96</td><td>0.34</td><td>-2.30 × 10<sup>-5</sup> (8.27 × 10<sup>-5</sup>)</td><td>56.4</td><td>-0.28</td><td>0.78</td><td>-7.82 × 10<sup>-5</sup> (6.97 × 10<sup>-5</sup>)</td><td>66.8</td><td>-1.12</td><td>0.27</td><td>-3.13 × 10<sup>-5</sup> (6.53 × 10<sup>-5</sup>)</td><td>64.1</td><td>-0.48</td><td>0.63</td></tr></tbody></table>

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<table><caption>Table 3 (continued)</caption><thead><tr><th rowspan="2">Predictors</th><th colspan="3">FA</th><th colspan="3">AD</th><th colspan="3">RD</th><th colspan="3">ADC</th></tr><tr><th>Estimate (SE)</th><th>df</th><th>t</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th></tr></thead><tbody><tr><td>Diabetes no DSPN vs DSPN</td><td>0.096 (0.033)</td><td>68.0</td><td>2.87</td><td><strong>0.006</strong></td><td>1.26 × 10<sup>-4</sup> (7.94 × 10<sup>-5</sup>)</td><td>58.9</td><td>1.59</td><td>0.12</td><td>-1.08 × 10<sup>-4</sup> (6.77 × 10<sup>-5</sup>)</td><td>67.2</td><td>-1.59</td><td>0.12</td><td>-5.69 × 10<sup>-5</sup> (6.32×10<sup>-5</sup>)</td><td>65.3</td><td>-0.90</td><td>0.37</td></tr><tr><td>FEET ONLY</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Diabetes no DSPN vs DSPN</td><td>0.033 (0.034)</td><td>68.0</td><td>0.97</td><td>0.33</td><td>-2.62 × 10<sup>-5</sup> (8.27 × 10<sup>-5</sup>)</td><td>56.4</td><td>-0.32</td><td>0.75</td><td>-7.53 × 10<sup>-5</sup> (6.97 × 10<sup>-5</sup>)</td><td>66.8</td><td>-1.08</td><td>0.28</td><td>-7.77 × 10<sup>-5</sup> (6.53 × 10<sup>-5</sup>)</td><td>64.1</td><td>-1.19</td><td>0.24</td></tr><tr><td>HANDS & FEET</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DSPN FEET ONLY vs DSPN</td><td>-0.062 (0.034)</td><td>68.0</td><td>-1.81</td><td>0.07</td><td>-1.52 × 10<sup>-4</sup> (8.18 × 10<sup>-5</sup>)</td><td>58.9</td><td>-1.86</td><td>0.07</td><td>3.26 × 10<sup>-5</sup> (6.98 × 10<sup>-5</sup>)</td><td>67.2</td><td>0.47</td><td>0.64</td><td>-2.09 × 10<sup>-5</sup> (6.52 × 10<sup>-5</sup>)</td><td>65.3</td><td>-0.32</td><td>0.75</td></tr><tr><td>HANDS & FEET</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Estimates are estimated-marginal means (standard errors) from linear mixed models with a random-intercept model, allowing only for between-subject variance, controlling for the interaction between group and location (fixed effects). Degrees of freedom are calculated using the Kenward-Roger method, which offers a more precise small-sample estimator for the variance-covariance of the fixed effects parameters and the approximate denominator degrees of freedom in t-tests. Values that are significant (p < 0.05) are marked in bold. DSPN: distal symmetrical polyneuropathy; FA: fractional anisotropy; AD: axial diffusivity; RD: radial diffusivity; ADC: apparent diffusion coefficient; SE: standard error; df: degrees of freedom; t: t-values; p: p-values.

in DSPN HANDS & FEET. [24].

In DSPN FEET ONLY, microstructural changes were detected in the median and ulnar nerves at distal and proximal locations. A reduction of AD and FA was observed in the median nerve at the wrist and elbow, and FA was reduced only at the elbow for the ulnar nerve. However, these abnormalities did not follow a distal-to-proximal pattern. Our findings using DWI are in line with a previous DTI study [22] that showed widespread microstructural abnormalities in people with DSPN that were more prominent proximally in the sciatic nerve compared to the tibial nerve. In that study [22], the precise location of symptoms in people with DSPN was not reported, so the proximal changes observed in DWI metrics for the sciatic nerve could be influenced by a larger, more wide-spread extension of DSPN in the lower limbs affecting more proximal areas. Our study complements the findings in the lower limbs, suggesting that DSPN microstructural changes occur in early stages in the upper limbs before symptoms are reported.

The relation between electrodiagnostic parameters and diffusion tensor metrics seems to reflect the axonal loss and demyelination in the sensory nerves in people with DSPN in their hands. The SNAP of ulnar and median nerve were lower, while the RD and ADC values were higher. In electrodiagnostic studies, a reduction in SNAP indicates axonal loss [59]. In diffusion MRI, a reduction in ADC reflects increased water content between axons and can therefore identify peripheral nerve oedema, whereas an increase in RD suggests the presence of damage or loss of myelin integrity in peripheral nerves [60,61]. Our results suggest that changes in sensory nerve function in electro-diagnostic studies could be detected and correlated to diffusion metrics, as previous studies in entrapment neuropathies [25]. However, these results should be interpreted with caution due to the small sample size in this group.

While there are multiple quantitative parameters that can be extracted from the well-known diffusion tensor model, and each has some degree of interpretability with respect to the underlying micro-structure, these interpretations all have caveats and limitations, some of which arise from the limited complexity of the diffusion tensor model itself. Here we chose to additionally explore the utility of a higher-order diffusion model for peripheral nerve characterisation, specifically spherical deconvolution and two voxel-wise parameters that are derived from the resulting fibre orientation distributions. Due to the novelty of this application, a range of configurations of response function(s) for spherical deconvolution were explored (more information in Supplementary Material). We found that there were circumstances in which the presence or absence of statistically significant effects were in line with the more conventionally utilised tensor metrics, whereas in other instances CSD-based metrics exhibited unique sensitivity to detect significant effects. While care should be taken in interpretation of these findings given the large number of statistical analyses performed for different spherical deconvolution configurations (Supplementary Material) and our DWI acquisition was not ideal for this model (a larger number of unique diffusion sensitisation directions and greater maximal b-value is preferable for spherical deconvolution), we have nevertheless shown evidence for the sensitivity of these metrics to the pathology present in DSPN. Future studies utilising more advanced acquisitions (in particular, multiple b-values and greater maximal b-value) would facilitate more robust application of both CSD and other higher-order diffusion models, enabling exploration of applicability of the technological advancements in diffusion MRI in neuroimaging to study peripheral nerves.

It is important to note that only one voxel per slice was used to calculate the different quantitative metrics, and that those voxels were selected manually to identify the best located voxel in the nerve. An automated selection method would be preferable in future studies. We suggest that future studies aim for a higher spatial resolution in their acquisition to reduce partial volume and allow for use of a greater number of samples. Additionally, a template registration based method would be preferable to manual voxel selection.
<figure><img src="image_8.png" /><figcaption>Fig. 4. Estimated marginal means (95 % confidence interval) for the Constrained Spherical Deconvolution-based metrics, per location (i.e., wrist and elbow) and nerve (i.e., median and ulnar). P-values for t-statistics were obtained using the Satterthwaite's degrees of freedom method indicating *: p < 0.05; DSPN: distal symmetrical polyneuropathy; a. u.: arbitrary units.</figcaption></figure>

<caption>Table 4</caption>

Estimates of the linear-mixed model for Constrained Spherical Deconvolution-derived metrics in the median and ulnar nerves, for the elbow and wrist locations.

<table><thead><tr><th rowspan="2">Predictors</th><th colspan="4">Dispersion (aDF & CSF-like)</th><th colspan="4">Complexity (WM-like & CSF-like)</th></tr><tr><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th></tr></thead><tbody><tr><td colspan="9"><strong>Median nerve</strong></td></tr><tr><td colspan="9"><strong>Wrist:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>0.10 (0.19)</td><td>64.5</td><td>0.53</td><td>0.60</td><td>-0.04 (0.04)</td><td>68.0</td><td>-0.90</td><td>0.37</td></tr><tr><td>No diabetes vs DSPN FEET ONLY</td><td>-0.16 (0.19)</td><td>64.5</td><td>-0.86</td><td>0.39</td><td>-0.12 (0.04)</td><td>68.0</td><td>-3.01</td><td><strong>0.004</strong></td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>0.11 (0.20)</td><td>64.5</td><td>0.53</td><td>0.60</td><td>-0.04 (0.04)</td><td>68.0</td><td>-0.89</td><td>0.37</td></tr><tr><td>Diabetes no DSPN vs DSPN FEET ONLY</td><td>-0.26 (0.19)</td><td>64.5</td><td>-1.41</td><td>0.16</td><td>-0.08 (0.04)</td><td>68.0</td><td>-2.07</td><td><strong>0.047</strong></td></tr><tr><td>Diabetes no DSPN vs DSPN HANDS & FEET</td><td>0.00 (0.20)</td><td>64.5</td><td>0.02</td><td>0.99</td><td>0.00 (0.04)</td><td>68.0</td><td>-0.02</td><td>0.99</td></tr><tr><td>DSPN FEET ONLY vs DSPN HANDS & FEET</td><td>0.26 (0.19)</td><td>64.5</td><td>1.39</td><td>0.17</td><td>0.08 (0.04)</td><td>68.0</td><td>1.99</td><td><strong>0.05</strong></td></tr><tr><td colspan="9"><strong>Elbow:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>0.04 (0.20)</td><td>65.8</td><td>0.20</td><td>0.84</td><td>0.06 (0.05)</td><td>68.0</td><td>1.29</td><td>0.20</td></tr><tr><td>No diabetes vs DSPN FEET ONLY</td><td>0.09 (0.20)</td><td>66.5</td><td>0.45</td><td>0.65</td><td>0.04 (0.05)</td><td>68.0</td><td>0.81</td><td>0.42</td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>0.17 (0.21)</td><td>65.8</td><td>0.70</td><td>0.43</td><td>0.06 (0.05)</td><td>68.0</td><td>1.33</td><td>0.19</td></tr><tr><td>Diabetes no DSPN vs DSPN FEET ONLY</td><td>0.05 (0.20)</td><td>66.5</td><td>0.25</td><td>0.80</td><td>-0.02 (0.05)</td><td>68.0</td><td>-0.48</td><td>0.63</td></tr><tr><td>Diabetes no DSPN vs DSPN HANDS & FEET</td><td>0.13 (0.21)</td><td>65.8</td><td>0.60</td><td>0.55</td><td>0.00 (0.05)</td><td>68.0</td><td>0.08</td><td>0.94</td></tr><tr><td>DSPN FEET ONLY vs DSPN HANDS & FEET</td><td>0.07 (0.21)</td><td>66.6</td><td>0.35</td><td>0.72</td><td>0.03 (0.05)</td><td>68.0</td><td>0.55</td><td>0.59</td></tr><tr><td colspan="9"><strong>Ulnar nerve</strong></td></tr><tr><td colspan="9"><strong>Wrist:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>-0.35 (0.17)</td><td>67.4</td><td>-1.99</td><td>0.05</td><td>-0.09 (0.06)</td><td>60.3</td><td>-1.55</td><td>0.13</td></tr><tr><td>No diabetes vs DSPN FEET ONLY</td><td>-0.72 (0.17)</td><td>67.4</td><td>-4.30</td><td><strong>&lt;0.001</strong></td><td>-0.02 (0.05)</td><td>60.3</td><td>-0.39</td><td>0.70</td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>-0.33 (0.18)</td><td>67.4</td><td>-1.86</td><td>0.07</td><td>-0.08 (0.06)</td><td>60.3</td><td>-1.35</td><td>0.18</td></tr><tr><td>Diabetes no DSPN vs DSPN FEET ONLY</td><td>-0.37 (0.17)</td><td>67.4</td><td>-2.23</td><td><strong>0.029</strong></td><td>0.06 (0.05)</td><td>60.3</td><td>1.23</td><td>0.22</td></tr><tr><td>Diabetes no DSPN vs DSPN HANDS & FEET</td><td>0.01 (0.18)</td><td>67.4</td><td>0.08</td><td>0.94</td><td>0.01 (0.06)</td><td>60.3</td><td>0.15</td><td>0.88</td></tr><tr><td>DSPN FEET ONLY vs DSPN HANDS & FEET</td><td>0.38 (0.17)</td><td>67.4</td><td>2.24</td><td><strong>0.03</strong></td><td>-0.06 (0.05)</td><td>60.3</td><td>-1.04</td><td>0.30</td></tr><tr><td colspan="9"><strong>Elbow:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>-0.05 (0.18)</td><td>67.6</td><td>-0.26</td><td>0.79</td><td>-0.02 (0.06)</td><td>62.7</td><td>-0.37</td><td>0.71</td></tr><tr><td>No diabetes vs DSPN FEET ONLY</td><td>-0.40 (0.18)</td><td>67.8</td><td>-2.17</td><td><strong>0.034</strong></td><td>-0.13 (0.06)</td><td>64.4</td><td>-2.32</td><td><strong>0.024</strong></td></tr><tr><td>No diabetes vs DSPN HANDS & FEET</td><td>-0.28 (0.19)</td><td>67.6</td><td>-1.50</td><td>0.14</td><td>-0.03 (0.06)</td><td>62.9</td><td>-0.42</td><td>0.67</td></tr><tr><td>Diabetes no DSPN vs DSPN FEET ONLY</td><td>-0.35 (0.18)</td><td>67.8</td><td>-1.91</td><td>0.06</td><td>-0.11 (0.06)</td><td>64.4</td><td>-1.95</td><td>0.06</td></tr><tr><td>Diabetes no DSPN vs DSPN HANDS & FEET</td><td>-0.24 (0.19)</td><td>67.6</td><td>-1.25</td><td>0.22</td><td>0.00 (0.06)</td><td>62.9</td><td>-0.07</td><td>0.95</td></tr><tr><td>DSPN FEET ONLY vs DSPN HANDS & FEET</td><td>0.11 (0.19)</td><td>67.8</td><td>0.60</td><td>0.55</td><td>0.11 (0.06)</td><td>64.4</td><td>1.83</td><td>0.07</td></tr></tbody></table>

Data shown as estimated marginal means (standard error) from linear mixed models with a random-intercept model, allowing only for between-subject variance, controlling for the interaction between group and location (fixed effects). Values that are significant (p < 0.05) are marked in bold. DSPN, distal symmetrical polyneuropathy; aDF: apodized disc function; CSF: cerebro-spinal fluid; WM: white-matter; SE: standard error; df: degrees of freedom; t: t-values; p: p-values.
<table><caption>**Table 5**
Estimates of the linear mixed-model for distal-to-proximal differences in tensor and constrained spherical deconvolution-derived metrics.</caption><thead><tr><th rowspan="2"></th><th colspan="3">FA</th><th colspan="3">AD</th><th colspan="3">RD</th><th colspan="3">ADC</th><th colspan="3">Dispersion</th><th colspan="3">Complexity</th></tr><tr><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th><th>Estimate (SE)</th><th>df</th><th>t</th><th>p</th></tr></thead><tbody><tr><td colspan="17"><strong>Median nerve:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>0.044 (0.044)</td><td>33.0</td><td>1.01</td><td>0.32</td><td>1.28 × 10<sup>-4</sup> (1.23 × 10<sup>-4</sup>)</td><td>33.2</td><td>1.04</td><td>0.31</td><td>-2.00 × 10<sup>-5</sup> (8.57 × 10<sup>-5</sup>)</td><td>33.1</td><td>-0.23</td><td>0.82</td><td>2.03 × 10<sup>-5</sup> (7.61 × 10<sup>-5</sup>)</td><td>33.0</td><td>0.27</td><td>0.79</td><td>33.6</td><td>0.14</td><td>0.9</td><td>0.028 (0.054)</td><td>33</td><td>0.52</td><td>0.6</td></tr><tr><td>No diabetes vs DSPN FEET ONLY</td><td>-0.036 (0.044)</td><td>34.3</td><td>-0.83</td><td>0.41</td><td>6.86 × 10<sup>-5</sup> (1.22 × 10<sup>-4</sup>)</td><td>34.6</td><td>0.56</td><td>0.58</td><td>1.27 × 10<sup>-4</sup> (8.48 × 10<sup>-5</sup>)</td><td>34.3</td><td>1.50</td><td>0.14</td><td>1.12 × 10<sup>-4</sup> (7.53 × 10<sup>-5</sup>)</td><td>34.2</td><td>1.49</td><td>0.15</td><td>35.1</td><td>-1.6</td><td>0.1</td><td>-0.053 (0.054)</td><td>34.3</td><td>-1.0</td><td>0.3</td></tr><tr><td>No diabetes vs DSPN HANDS &amp; FEET</td><td>-0.050 (0.046)</td><td>33.1</td><td>-1.1</td><td>0.28</td><td>2.52 × 10<sup>-4</sup> (1.27 × 10<sup>-4</sup>)</td><td>33.3</td><td>1.98</td><td>0.06</td><td>2.68 × 10<sup>-4</sup> (8.83 × 10<sup>-5</sup>)</td><td>33.2</td><td>3.03</td><td><strong>0.0047</strong></td><td>2.65 × 10<sup>-4</sup> (7.84 × 10<sup>-5</sup>)</td><td>33.0</td><td>3.37</td><td><strong>0.002</strong></td><td>33.7</td><td>-0.4</td><td>0.7</td><td>-0.022 (0.055)</td><td>33.1</td><td>-0.4</td><td>0.7</td></tr><tr><td colspan="17"><strong>Ulnar nerve:</strong></td></tr><tr><td>No diabetes vs Diabetes no DSPN</td><td>0.010 (0.046)</td><td>34.2</td><td>0.22</td><td>0.82</td><td>-6.83 × 10<sup>-5</sup> (7.45 × 10<sup>-5</sup>)</td><td>32.4</td><td>-0.92</td><td>0.37</td><td>-5.67 × 10<sup>-5</sup> (8.49 × 10<sup>-5</sup>)</td><td>33.7</td><td>-0.67</td><td>0.51</td><td>-1.04 × 10<sup>-5</sup> (7.25 × 10<sup>-5</sup>)</td><td>33.2</td><td>-0.14</td><td>0.89</td><td>32.5</td><td>-0.1</td><td>0.9</td><td>0.0088 (0.056)</td><td>32.4</td><td>0.16</td><td>0.9</td></tr><tr><td>No diabetes vs DSPN FEET ONLY</td><td>0.079 (0.045)</td><td>35.8</td><td>1.77</td><td>0.09</td><td>9.86 × 10<sup>-6</sup> (7.40 × 10<sup>-5</sup>)</td><td>33.3</td><td>0.13</td><td>0.89</td><td>-1.45 × 10<sup>-4</sup> (8.36 × 10<sup>-5</sup>)</td><td>35.1</td><td>-1.74</td><td>0.09</td><td>-5.68 × 10<sup>-5</sup> (7.17 × 10<sup>-5</sup>)</td><td>34.5</td><td>-0.79</td><td>0.43</td><td>33.5</td><td>-0.2</td><td>0.9</td><td>0.0058 (0.055)</td><td>33.4</td><td>0.11</td><td>0.9</td></tr><tr><td>No diabetes vs DSPN HANDS &amp; FEET</td><td>0.031 (0.047)</td><td>34.3</td><td>0.66</td><td>0.51</td><td>1.38 × 10<sup>-5</sup> (7.67 × 10<sup>-5</sup>)</td><td>32.4</td><td>0.18</td><td>0.86</td><td>-4.92 × 10<sup>-5</sup> (8.74 × 10<sup>-5</sup>)</td><td>33.8</td><td>-0.56</td><td>0.58</td><td>-8.05 × 10<sup>-6</sup> (7.47 × 10<sup>-5</sup>)</td><td>33.3</td><td>-0.11</td><td>0.91</td><td>32.6</td><td>0.38</td><td>0.7</td><td>0.052 (0.057)</td><td>32.5</td><td>0.90</td><td>0.4</td></tr></tbody></table>

Estimated marginal means (standard error) from linear mixed models with a random-intercept model, allowing only for between-subject variance, controlling for the interaction between group and location (fixed effects). The distal-to-proximal ratio was calculated with the difference elbow vs wrist in the no diabetes group compared to the same difference in the other groups. Values that are significant (p < 0.05) are marked in bold. DSPN: distal symmetrical polyneuropathy; FA: fractional anisotropy; AD: axial diffusivity; RD: radial diffusivity; ADC: apparent diffusion coefficient; SNAP: sensory nerve action potential; CV: conduction velocity; CMAP: compound muscle action potential; DSPN: distal symmetrical polyneuropathy.

<caption>**Table 6**</caption>

Correlations between electrodiagnostic parameters and diffusion tensor metrics with confidence intervals in DSPN HANDS & FEET.

<table><thead><tr><th>Variable</th><th>FA</th><th>RD</th><th>AD</th><th>ADC</th></tr></thead><tbody><tr><td>SNAP amplitude (µV)</td><td>0.45</td><td>-0.53*</td><td>-0.24</td><td>-0.55*</td></tr><tr><td>Sensory CV (m/s)</td><td>(-0.08, 0.78)</td><td>(-0.82, -0.02)</td><td>(-0.67, 0.31)</td><td>(-0.83, -0.05)</td></tr><tr><td>Motor CMAP (mV)</td><td>0.15 (-0.39, 0.62)</td><td>-0.13 (-0.60, 0.41)</td><td>-0.13 (-0.60, 0.41)</td><td>-0.05 (-0.55, 0.47)</td></tr><tr><td>Motor CV (m/s)</td><td>(-0.33, 0.85)</td><td>(-0.91, 0.04)</td><td>(-0.84, 0.36)</td><td>(-0.88, 0.20)</td></tr><tr><td></td><td>-0.19 (-0.76, 0.54)</td><td>0.16 (-0.57, 0.74)</td><td>0.15 (-0.57, 0.74)</td><td>0.16 (-0.56, 0.75)</td></tr></tbody></table>

Values in brackets indicate the 95 % confidence interval for each correlation. *: *p* < 0.05; **: *p* < 0.01. FA: fractional anisotropy; AD: axial diffusivity; RD: radial diffusivity; ADC: apparent diffusion coefficient; SNAP: sensory nerve action potential; CV: conduction velocity; CMAP: compound muscle action potential; DSPN: distal symmetrical polyneuropathy.

Changes in the microstructure of the median nerve can be detected in people with DSPN with and without symptoms in hands with traditional diffusion tensor metrics. DWI and the use of higher-order diffusion models require further exploration to unravel their potential application in understanding the effects of diabetes and DSPN on the microstructure of peripheral nerves.

## Funding

ESS received funding from Griffith University. RES receives fellowship funding from the National Imaging Facility (NIF), an initiative from the Australian Government National Collaborative Research Infrastructure Strategy (NCRIS).

## CRediT authorship contribution statement

**Eva Sierra-Silvestre:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Robert E. Smith:** Writing – review & editing, Visualization, Validation, Supervision, Methodology, Formal analysis. **Ricardo J. Andrade:** Writing – review & editing, Supervision, Resources, Methodology, Investigation, Data curation. **Ben Kennedy:** Writing – review & editing, Methodology, Investigation, Formal analysis, Data curation. **Michel W. Coppieters:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Robert E Smith reports a relationship with Australian National Imaging Facility that includes: employment. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ejrad.2024.111721.
## References

[1] P. Saeedi, P. Salpea, S. Karuranga, I. Petersohn, B. Malanda, E.W. Gregg, N. Unwin, S.H. Wild, R. Williams, Mortality attributable to diabetes in 20-79 years old adults, 2019 estimates: Results from the International Diabetes Federation Diabetes Atlas, 9th edition, Diabetes Res Clin Pract 162 (2020). 10.1016/j.diabres.2020.108086.

[2] International Diabetes Federation, IDF Diabetes Atlas 9th edition: key messages, (2019). https://diabetesatlas.org/en/resources/.

[3] S. Tesfaye, A.J.M. Boulton, A.H. Dickenson, Mechanisms and management of diabetic painful distal symmetrical polyneuropathy, Diabetes Care 36 (2013) 2456-2465, https://doi.org/10.2337/dc12-1964.

[4] L. Litwak, S.-Y. Goh, Z. Hussein, R. Malek, V. Prusty, M.E. Khamseh, Prevalence of diabetes complications in people with type 2 diabetes mellitus and its association with baseline characteristics in the multinational A1 achieve study, Diabetol. Metab. Syndr. 5 (2013) 57, https://doi.org/10.1186/1758-5996-5-57*.

[5] E.L. Feldman, B.C. Callaghan, R. Pop-Busui, D.W. Zochodne, D.E. Wright, D. L. Bennett, V. Bril, J.W. Russell, V. Viswanathan, Diabetic neuropathy, Nat. Rev. Dis. Primers 5 (2019), https://doi.org/10.1038/s41572-019-0092-1.

[6] B.C. Callaghan, H.T. Cheng, C.L. Stables, A.L. Smith, E.L. Feldman, Diabetic neuropathy: clinical manifestations and current treatments, Lancet Neurol 11 (2012) 521-534, https://doi.org/10.1016/S1474-4422(12)0065-0.

[7] A.M. Vincent, B.C. Callaghan, A.L. Smith, E.L. Feldman, Diabetic neuropathy: cellular mechanisms as therapeutic targets, Nat. Rev. Neurol 7 (2011) 573-583, https://doi.org/10.1038/nrn neurol.2011.137.

[8] I.G. Obroensa, Increased sorbitol pathway activity generates oxidative stress in tissue sites for diabetic complications, Antioxid. Redox Signal 7 (2005) 1543-1552, https://doi.org/10.1089/ars.2005.7.1543.

[9] G.H. Tesch, Role of macrophages in complications of Type 2 diabetes, in: Clin Exp Pharmacol Physiol, Clin Exp Pharmacol Physiol, 2007: pp. 1016-1019. 10.1111/j.1440-1681.2007.04729.x.

[10] D. Ziegler, W. Rathmann, T. Dickhaus, C. Meisinger, A. Mielck, KORA study group, neuropathic pain in diabetes, prediabetes and normal glucose tolerance: the MONICA/KORA augsburg surveys S2 and S3, Pain Med 10 (2009) 393-400, https://doi.org/10.1111/j.1526-4637.2008.00555.x.

[11] R.A. Malik, S. Tesfaye, P.G. Newrick, D. Walker, S.M. Rajbhandari, I. Siddique, A. K. Sharma, A.J.M. Boulton, R.H.M. King, P.K. Thomas, J.D. Ward, Sural nerve pathology in diabetic patients with minimal but progressive neuropathy, Diabetologia 48 (2005) 578-585, https://doi.org/10.1007/s00125-004-1663-5.

[12] A. Heckel, M. Weiler, A. Xia, M. Ruetters, M. Pham, M. Bendszus, S. Heiland, P. Baeumer, Peripheral Nerve Diffusion Tensor Imaging: Assessment of Axon and Myelin Sheath Integrity, PLoS One 10 (2015) e0130833. 10.1371/journal.pone.0130833.

[13] P. Bäumer, M. Pham, M. Ruetters, S. Heiland, A. Heckel, A. Radbruch, M. Bendszus, M. Weiler, Peripheral neuropathy: detection with diffusion-tensor imaging, Radiology 273 (2014) 185-193, https://doi.org/10.1148/radiol.14132837.

[14] P.J. Basser, J. Mattiello, D. Lebihan, Estimation of the effective self-diffusion tensor from the NMR spin echo, J. Magn. Reson. B 103 (1994) 247-254, https://doi.org/10.1006/jmrb.1994.1037.

[15] J. Oudemans, F. Eftimov, G.J. Strijkers, J.J. Schneiders, S.D. Roosendaal, M. P. Engbersen, M. Froeling, H.S. Goedee, P.A. Van Doorn, M.W.A. Caan, I.N. Van Schaik, M. Maas, A.J. Nederveen, M. De Visser, C. Verhamme, Diagnostic accuracy of MRI and ultrasound in chronic immune-mediated neuropathies, Neurology 94 (2020) e62-e74, https://doi.org/10.1212/WNL.00000000000008697.

[16] W. Haakma, B.A. Jongbloed, M. Froeling, H.S. Goedee, C. Bos, A. Leemans, L. H. van den Berg, H. Hendrikse, W.L. van der Pol, MRI shows thickening and altered diffusion in the median and ulnar nerves in multifocal motor neuropathy, Eur. Radiol. 27 (2017) 2216-2224, https://doi.org/10.1007/s00330-016-4575-0.

[17] P.L. Cheah, T. Krisnan, J.H.D. Wong, F.I. Rozalli, F. Fadzli, K. Rahmat, N. Shahrizaila, L.K. Tan, O. Nawawi, N. Ramli, Microstructural integrity of peripheral nerves in charcot-marie-tooth disease: an MRI evaluation study, J. Magn. Reson. Imaging 53 (2021) 437-444, https://doi.org/10.1002/jmri.27354.

[18] M. Vaeggemose, M. Pham, S. Ringgaard, H. Tankisi, N. Ejskjaer, S. Heiland, P. L. Poulsen, H. Andersen, Magnetic resonance neurography visualizes abnormalities in sciatic and tibial nerves in patients with type 1 diabetes and neuropathy, Diabetes 66 (2017) 1779-1788, https://doi.org/10.2337/db16-1049.

[19] A.S. Klauser, M. Abd Ellah, C. Kremser, M. Taljanovic, G. Schmidle, M. Gabl, F. Cartes-Zumelzu, R. Steiger, E.R. Gizewski, Carpal tunnel syndrome assessment with diffusion tensor imaging: value of fractional anisotropy and apparent diffusion coefficient, Eur. Radiol. 28 (2018) 1111-1117, https://doi.org/10.1007/s00330-017-5046-y.

[20] W. Wu, J. Liang, N. Ru, C. Zhou, J. Chen, Y. Wu, Z. Yang, Microstructural changes in compressed nerve roots are consistent with clinical symptoms and symptom duration in patients with lumbar disc herniation, Spine (Phila Pa 1976) 41 (2016) E661-E666. 10.1097/BRS.0000000000001354.

[21] S.W. Hughes, P.J. Hellyer, D.J. Sharp, R.D. Newbould, M.C. Patel, P.H. Strutton, Diffusion tensor imaging reveals changes in microstructural integrity along compressed nerve roots that correlate with chronic pain along motor deficiencies in elderly stenosis patients, Neuroimage. Clin. 23 (2019), https://doi.org/10.1016/j.ncl.2019.101880.

[22] M. Vaeggemose, W. Haakma, M. Pham, S. Ringgaard, H. Tankisi, N. Ejskjaer, S. Heiland, P.L. Poulsen, H. Andersen, Diffusion tensor imaging MR Neurography detects polyneuropathy in type 2 diabetes, J. Diabetes Complications 34 (2020), https://doi.org/10.1016/j.jdiacomp.2019.107439.

[23] R. Pop-Busui, A.J.M. Boulton, E.L. Feldman, V. Bril, R. Freeman, R.A. Malik, J. M. Sosenko, D. Ziegler, Diabetic neuropathy: a position statement by the American

diabetes association, Diabetes Care 40 (2017) 136-154, https://doi.org/10.2337/dc16-2042.

[24] E. Sierra-Silvestre, R.J. Andrade, A.C. Schroeter, L. Basset, M.W. Coppieters, Diabetic neuropathy in hands: an endemic complication waiting to unfold? Diabetes 71 (2022) 1785-1794, https://doi.org/10.2337/DB21-1147.

[25] A.A.K.A. Razek, A.A.E. Shabana, T.O. El Saied, N. Alrefey, Diffusion tensor imaging of mild-moderate carpal tunnel syndrome: correlation with nerve conduction study and clinical tests, Clin Rheumatol 36 (2017) 2319-2324, https://doi.org/10.1007/s10067-016-3463-Y.

[26] C.K. Wang, I.M. Jou, H.W. Huang, P.Y. Chen, H.M. Tsai, Y.S. Liu, C.C.K. Lin, Carpal tunnel syndrome assessed with diffusion tensor imaging: comparison with electrophysiological studies of patients and healthy volunteers, Eur. J. Radiol. 81 (2012) 3378-3383, https://doi.org/10.1016/j.ejrad.2012.01.008.

[27] S. Tesfaye, A.J.M. Boulton, P.J. Dyck, R. Freeman, M. Horowitz, P. Kempler, G. Lauria, R.A. Malik, V. Spallone, A. Vinik, L. Bernardi, P. Valensi, Diabetic neuropathies: update on definitions, diagnostic criteria, estimation of severity, and treatments, Diabetes Care 33 (2010) 2285-2293. http://care.diabetesjournals.org/content/33/10/2285 abstract.

[28] Z. Zhang, X. Hu, X. Qi, G. Di, Y. Zhang, Q. Wang, Q. Zhou, Resolvin D1 promotes corneal epithelial wound healing and restoration of mechanical sensation in diabetic mice, Mol. Vis. 24 (2018) 274-285.

[29] J.D. Tournier, F. Calamante, A. Connelly, Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution, Neuroimage 35 (2007) 1459-1472, https://doi.org/10.1016/j.neuroimage.2007.02.016.

[30] E. Sierra-Silvestre, R.J. Andrade, L. Holguín-Colorado, K. Edwards, M. W. Coppieters, Occurrence of corneal sub-epithelial microneumomas and axonal swelling in people with diabetes with and without (painful) diabetic neuropathy, Diabetologia 66 (2023), https://doi.org/10.1007/s00125-023-05945-0.

[31] Alere Technologies AS, Hemoglobin A1c Test For use with the Alere Afinion™ AS100 Analyzer and AFINION™ 2 Analyzer Afinion™ HbA1c, Oslo, 2018.

[32] B.A. Perkins, L.E. Lovblom, V. Bril, D. Scarr, I. Ostrovski, A. Orszag, K. Edwards, N. Pritchard, A. Russell, C. Dehghani, D. Pacaud, K. Romanchuk, J.K. Mah, M. Jeziorska, A. Marshall, R.M. Shtein, R. Pop-Busui, S.I. Lentz, N.A.J. Boulton, M. Tavakoli, N. Efron, R.A. Malik, Corneal confocal microscopy for identification of diabetic sensorimotor polyneuropathy: a pooled multinational consortium study, Diabetologia 61 (2018) 1856-1861, https://doi.org/10.1007/s00125-018-4653-8.

[33] H.J. Hermens, B. Freriks, C. Disselhorst-Klug, G. Rau, Development of recommendations for SEMG sensors and sensor placement procedures, accessed September 13, 2018, J. Electromyogr. Kinesiol. 10 (2000) 361-374, http://www.ncbi.nlm.nih.gov/pubmed/11018445.

[34] J.D. England, G.S. Gronseth, G. Franklin, R.G. Miller, A.K. Asbury, G.T. Carter, J.A. Cohen, M.A. Fisher, J.F. Howard, L.J. Kinsella, N. Latov, R.A. Lewis, P.A. Low, A.J. Sumner, American Academy of Neurology, American Association of Electrodiagnostic Medicine, American Academy of Physical Medicine and Rehabilitation, Distal symmetric polyneuropathy: A definition for clinical research: Report of the American Academy of Neurology, the American Association of Electrodiagnostic Medicine, and the American Academy of Physical Medicine and Rehabilitation, Neurology 64 (2005) 199-207. 10.1212/01.WNL.0000149522.32823.EA.

[35] D. Vagenas, N. Pritchard, K. Edwards, A.M. Shahidi, G.P. Sampson, A.W. Russell, R. A. Malik, N. Efron, Optimal image sample size for corneal nerve morphometry, Optom. Vis. Sci. 89 (2012) 812-817, https://doi.org/10.1097/OPX.0b013e31824ee8c9.

[36] X. Chen, J. Graham, M.A. Dabbah, I.N. Petropoulos, G. Ponirakis, O. Asghar, U. Alam, A. Marshall, H. Fadavi, M. Ferdousi, S. Azmi, M. Tavakoli, N. Efron, M. Jeziorska, R.A. Malik, Small nerve fiber quantification in the diagnosis of diabetic sensorimotor polyneuropathy: comparing corneal confocal microscopy with intraepidermal nerve fiber density, Diabetes Care 38 (2015) 1138-1144, https://doi.org/10.2337/dc14-2422.

[37] J.L.R. Andersson, S. Skare, J. Ashburner, How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging, Neuroimage 20 (2003) 870-888, https://doi.org/10.1016/S1053-8119(03)00336-7.

[38] J.D. Tournier, R.E. Smith, R. Tabbara, T. Dhollander, M. Pietsch, D. Christianens, B. Jeurissen, C.-H. Yeh, A. Connelly, MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation, Neuroimage 202 (2019) 116-137.

[39] M. Jenkinson, C.F. Beckmann, T.E.J. Behrens, M.W. Woolrich, S.M. Smith, FSL, Neuroimage 62 (2012) 782-90. 10.1016/j.neuroimage.2011.09.015.

[40] B.B. Avants, N.J. Tustison, G. Song, P.A. Cook, A. Klein, J.C. Gee, A reproducible evaluation of ANTs similarity metric performance in brain image registration, Neuroimage 54 (2011) 2033-2044, https://doi.org/10.1016/j.neuroimage.2010.09.025.

[41] J. Veraart, D.S. Novikov, D. Christiaens, B. Ades-aron, J. Sijbers, E. Fieremans, Denoising of diffusion MRI using random matrix theory, Neuroimage 142 (2016) 394-406, https://doi.org/10.1016/j.neuroimage.2016.08.016.

[42] J.L.R. Andersson, S.N. Sotiropoulos, An integrated approach to correction for off-resonance effects and subject movement in diffusion MRI imaging, Neuroimage 125 (2016) 1063-1078, https://doi.org/10.1016/j.neuroimage.2015.10.019.

[43] J.L.R. Andersson, M.S. Graham, E. Zsoldos, S.N. Sotiropoulos, Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images, Neuroimage 141 (2016) 556-572, https://doi.org/10.1016/j.neuroimage.2016.06.058.
[44] N.J. Tustison, B.B. Avants, P.A. Cook, Y. Zheng, A. Egan, P.A. Yushkevich, J.C. Gee, N4ITK: Improved N3 bias correction, IEEE Trans. Med. Imaging 29 (2010) 1310–1320, https://doi.org/10.1109/TMI.2010.2046908.

[45] J. Veraart, J. Sijbers, S. Sunaert, A. Leemans, B. Jeurissen, Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls, Neuroimage 81 (2013) 335–346, https://doi.org/10.1016/j.neuroimage.2013.05.028.

[46] B. Jeurissen, J.D. Tournier, T. Dhollander, A. Connelly, J. Sijbers, Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data, Neuroimage 103 (2014) 411–426, https://doi.org/10.1016/j.neuroimage.2014.07.061.

[47] H. Zhang, T. Schneider, C.A. Wheeler-Kingshott, D.C. Alexander, NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain, Neuroimage 61 (2012) 1000–1016, https://doi.org/10.1016/j.neuroimage.2012.03.072.

[48] T.W. Riffert, J. Schreiber, A. Anwander, T.R. Knösche, Beyond fractional anisotropy: extraction of bundle-specific structural metrics from crossing fiber models, Neuroimage 100 (2014) 176–191, https://doi.org/10.1016/j.neuroimage.2014.06.015.

[49] J. Tournier, F. Calamante, D. Gadian, A. Connelly, Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution, Neuroimage 23 (2004) 1176–1185, https://doi.org/10.1016/J.NEIROIMAGE.2004.07.037.

[50] R Core Team, R: A Language and Environment for Statistical Computing, (2021). https://www.r-project.org/.

[51] D. Bates, M. Maechler, B. Bolker, S. Walker, lme4: Linear mixed-effects models using Eigen and S4, (2014). http://cran.r-project.org/package=lme4 (accessed June 24, 2021).

[52] A. Kuznetsova, P.B. Brockhoff, R.H.B. Christensen, lmerTest Package: Tests in Linear Mixed Effects Models, J Stat Softw 82 (2017). 10.18637/jss.v082.i13.

[53] R. V Lenth, emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.6.1., (2021). https://cran.r-project.org/package=emmeans.

[54] C. Krueger, L. Tian, A comparison of the general linear mixed model and repeated measures ANOVA using a dataset with multiple missing data points, Biol. Res Nurs 6 (2004) 151–157, https://doi.org/10.1177/1099800404267682.

[55] A.H.T. Fai, P.L. Cornelius, Approximate f-tests of multiple degree of freedom hypotheses in generalized least squares analyses of unbalanced split-plot experiments, J. Stat. Comput. Simul. 54 (1996) 363–378, https://doi.org/10.1080/00949659608811740.

[56] T. Frank, P. Nawroth, R. Kuner, Structure-function relationships in peripheral nerve contributions to diabetic peripheral neuropathy, Pain 160 (2019) S29–S36, https://doi.org/10.1097/j.pain.0000000000001530.

[57] E. Rota, N. Morelli, Entrapment neuropathies in diabetes mellitus, World J. Diabetes 7 (2016) 342, https://doi.org/10.4239/wjd.v7.i17.342.

[58] G.J. Bönhof, A. Strom, S. Püttgen, B. Ringel, J. Brüggemann, K. Bódis, K. Müssig, J. Szendroedi, M. Roden, D. Ziegler, Patterns of cutaneous nerve fibre loss and regeneration in type 2 diabetes with painful and painless polyneuropathy, Diabetologia 60 (2017) 2495–2503, https://doi.org/10.1007/s00125-017-4438-5.

[59] T. Chung, K. Prasad, T.E. Lloyd, Peripheral neuropathy – clinical and electrophysiological considerations, Neuroimaging Clin. N. Am. 24 (2014) 49, https://doi.org/10.1016/J.NIC.2013.03.023.

[60] S. Ahlawat, A. Chhabra, J. Blakely, Magnetic resonance neurography of peripheral nerve tumors and tumorlike conditions, Neuroimaging Clin. N. Am. 24 (2014) 171–192, https://doi.org/10.1016/J.NIC.2013.03.035.

[61] T.M. Noguerol, R. Barousse, M. Socolovsky, A. Luna, Quantitative magnetic resonance (MR) neurography for evaluation of peripheral nerves and plexus injuries, Quant. Imaging Med. Surg. 7 (2017) 398–421. 10.21037/QIMS.2017.08.01.